Midgaard

Free to Choose

2010-05-05T11:08:00.002-04:00

Today's New York Times features a bizarre op-ed by Charles Murray of the American Enterprise Institute, a think tank that describes its scholars as "committed to expanding liberty, increasing individual opportunity, and strengthening free enterprise."

Murray summarizes the results of head-to-head comparison of standardized-test performance for students in public and charter schools. The charter-school students, he admits, "generally had 'achievement growth rates that are comparable' to similar Milwaukee public-school students. This is just one of several evaluations of school choice programs that have failed to show major improvements in test scores, but the size and age of the Milwaukee program, combined with the rigor of the study, make these results hard to explain away."

"So let's not try to explain them away," Murray says.

This makes good sense. This kind of experiment has been done many times, as described by Diane Ravitch in her thought-provoking 2010 book, The Death and Life of the Great American School System: How Testing and Choice Are Undermining Education. The results are clear in their lack of clarity: some charter schools are excellent, others are terrible. The data don't support the vision that non-public schools are automatically superior, although some no doubt are. In addition, the data show no indication that public schools faced with competition respond by cleaning up their act, another frequent argument for school choice.

So do these conclusions shake the confidence of a school-choice advocate like Murray? Hardly: "Why not instead finally acknowledge that standardized test scores are a terrible way to decide whether one school is better than another?" he says.

Murray is correct, of course, that tests scores have serious flaws. Ravitch, who admits that she was once a strong proponent of both choice and testing, spends much of her book describing the problems with standardized tests. Especially troublesome are the tests that states devise to show that they are meeting the goals of the "No Child Left Behind" act. Ravitch laments both the limited range of skills being tested--essentially basic math and reading--as well as the distortions that inevitably occur when tools meant to monitor progress start to be used to enforce it.

Ravitch forcefully argues that school improvement is a hard slog, not achieved by silver bullets like charter schools or by extensive data collection like that promoted by the Obama administration's "Race to the Top." Instead of statistical analyses modeled on business practices, she advocates a rigorous (voluntary) national curriculum and on-the-ground assessments by professional educators, not business managers.

Murray acknowledges the failures of previous silver bullets: "whether the reform in question is vouchers, charter schools, increased school accountability, smaller class sizes, better pay for all teachers, bonuses for good teachers, firing of bad teachers — measured by changes in test scores, each has failed to live up to its hype." But he concludes is that the problem lies with testing, and that choice is still a social good, because it allows parents to choose schools whose teaching styles the parents find appropriate.

It will be interesting to see whether Murray's fellow school-choice advocates follow his recommendation to admit that there is no measurable benefit of charter schools, but policy should support them anyway on ideological grounds. Somehow I doubt it.

Changing the Rules

2010-04-05T22:03:00.002-04:00

Is the Large Hadron Collider a time machine?

Although I usually like Dennis Overbye's physics writing for the New York Times, I thought he misfired in answering this question yesterday, in the general-audience "Week in Review" section.

In a Q&A entitled A Primer on the Great Proton Smashup that discussed the scientific ideas that underlie research at the LHC, Overbye addressed the question:

"What does it mean to say that the collider will allow physicists to go back to the Big Bang? Is the collider a time machine?"

It may seem silly, but it's actually a good question, since I'd bet a lot of people get confused by the metaphors that writers use to motivate the research. These metaphors get repeated often enough that they are almost cliché, but, as with all metaphors, it's important to know which parts to take seriously and which parts are more poetic or even misleading. Not everybody will know which is which, and it's good to explain it every so often.

Here's Overbye's complete answer:

"Physicists suspect that the laws of physics evolved as the universe cooled from billions or trillions of degrees in the first moments of the Big Bang to superfrigid temperatures today (3 degrees Kelvin) — the way water changes from steam to liquid to ice as temperatures decline. As the universe cooled, physicists suspect, everything became more complicated. Particles and forces once indistinguishable developed their own identities, the way Spanish, French and Italian diverged from the original Latin.
By crashing together subatomic particles — protons — physicists create little fireballs that revisit the conditions of these earlier times and see what might have gone on back then, sort of like the scientists in Jurassic Park reincarnating dinosaurs."

I'll discuss in a moment what I think Overbye means by "the laws of physics evolved," but this notion is awfully subtle for a general reader. More importantly, it completely undercuts the whole thrust of the question: physicists believe they are learning about the early universe in high-energy particle collisions precisely because the laws of physics are the same. If the laws are the same, we can create the same conditions (mostly temperature) to learn about what might have happened in the early universe. (He eventually does say that.)

The confusion comes because the phrase "the laws of physics" can be mean quite different things.

In the context of LHC, it seems clear to me that we refer to the behavior at the deepest levels of the universe. These rules don't get repealed overnight.

In fact, as I understand the phrase, it refers not to the current human description of events, which changes as we learn more, but to the "truth," which doesn't. Otherwise, it wouldn't make sense to say that we want to learn about the laws of physics from the collider (since we already know the laws, even if they're wrong).

Still, we often say the laws of physics say that something is impossible. In that context, the phrase can only refer to our current understanding of the laws, as best as we can discern them.

In fact, when we talk about the laws of physics we're frequently not talking about the deep levels probed by the LHC. Instead, we're referring to laws that describe the more mundane behavior of objects in our cold everyday reality.

In one sense, these "laws" are just a manifestation of the deeper laws. Describing the world in terms of protons, or nuclei, or atoms, or molecules, or cells, or organs, or organisms, or societies, is often vastly more useful than describing it with quarks or strings.

In some cases, the higher-level description can be mathematically related to the deeper description, for example by "coarse graining" the description to smooth out fine details.

This is the sense in which we can say that the "laws of physics" evolve: when the universe was very hot, the description had to include a lot of ingredients that are no longer important now that the universe is much cooler. We can now accurately describe things using a simplified description that doesn't have to include the messier details. The "laws" are different now.

This is Overbye's answer. But I think it will confuse people, since the goal of the LHC is to learn about the immutable laws, not the simpler descriptions or approximations.

One further, mind-blowing complication. Many cosmologists are exploring the possibility that our universe is just one of an infinite number of universes that formed, like bubbles, out of a larger multiverse. According to this view, the "laws of physics" --perhaps even the dimension of space--may be entirely different in each of these universes.

Even if we will always see the laws of physics as unchanging, they may be not be the same everywhere.

Picturing Quantum Mechanics

2010-03-31T16:59:00.002-04:00

They say a picture is worth a thousand words. But what if those words are wrong?

Very cool recent experiments demonstrated a chemical reaction between molecules below a millionth of a degree (in Science, subscription required). My latest story for Physical Review Focus describes theoretical modeling of this reaction. We accompanied the story with this picture from the news release issued by the Joint Quantum Institute (a partnership between the National Institute of Standards and Technology and the University of Maryland), where the work was done.

It's a pretty picture, with its superhero color scheme and all, and it satisfies our need to avoid a solid block of text. But although it might not be a bad illustration of a room-temperature chemical reaction, it distorts much of what makes these ultra-low-temperature reactions special.

It's clear in the picture that two diatomic molecules are approaching each other, with dramatic consequences in store. The details of how the artist represents the bonds connecting a potassium and a rubidium atom in each molecule don't bother me too much. It doesn't match either of the customary representations, which are ball-and-stick models and the more accurate space-filling models, but there's no perfect way to represent something that can never be seen with visible light. Of course everyone knows that potassium atoms are green, but we'll let that slide, too.

The really problematic part of this picture is very difficult to avoid: the molecules really aren't anywhere, in the sense the picture conveys.

As first shown by experiments at Bell Labs in 1927, matter acts as waves as well as particles. At temperatures below a millionth of a degree, the relevant wavelength for these molecules is hundreds of nanometers, which is much, much larger than the separation of molecules shown in the experiment. There is no meaning to saying that these molecules are separated by such a short distance. They are simultaneously close and far away.

One way to think about this is to invoke Heisenberg's uncertainty principle. According to this principle, if you know an object's momentum with very high precision, you can't, even in principle, know its position very accurately. For these ultracold molecules, the momentum is almost zero, with very high precision, so you can only know where it is to the nearest hundreds of nanometers.

There's a second problem, too. The picture shows the molecules with particular orientations in space. That may not seem strange, but the molecules in the experiment were prepared in the rotational "ground state," with the lowest possible energy. Like the s-orbitals of electrons in a hydrogen atom, this state is spherically symmetrical. This means that the molecule is equally likely to be pointing in any direction. This isn't the same thing as saying we don't know what direction it's pointing (even though it does). Quantum mechanics says that it has no direction, at least until an experiment requires it to.

So the reacting molecules really aren't at any particular distance from one another, and they don't have any particular orientation relative to each other. That's one of the things that makes this chemical reaction--and the theoretical description of it--so interesting.

But good luck drawing that.

Flyfire

2010-03-25T23:34:00.003-04:00

When I was an undergraduate at MIT in the late 1970s, one of the most impressive people I knew was my roommate Walter.

Walter was a mechanical engineer, and knew an incredible amount of just-plain-practical stuff about how to make things work, from types of stainless steel to the ins and outs of convective cooling.

The rest of us vicariously enjoyed his entry in Woodie Flowers' design completion, the 2.70 contest (named for the number of the design course). The idea was to take a box of stuff--tongue depressors, rubber bands, and so forth--and turn it into a machine that would beat other students in a glorified "king-of-the-hill" like task. This was way before I heard the phrase KISS, "Keep It Simple, Stupid," but we all learned that successful designs took this minimalist idea as a core principle.

This sort of contest has been picked up by others, even bringing high-school teams into the game, for example in the FIRST competitions. It's a great way to inspire imaginative people in ways that go way beyond the ordinary classroom experience. And it's a hands-on demonstration of the creativity that underlies true design.

Walter also worked with Otto Piene, the artist who headed MIT's Center for Advanced Visual Studies. Among other things Walter helped sew enormous inflatable anemones for Piene's Centerbeam project on the mall in Washington, D.C., as well as designing and building beam-steering mechanisms for an early laser show.

I'd forgotten all of this until reporting my latest story for CACM on a new project from MIT, which brings together two teams. One group comes from the Department of Urban Studies and Planning, which is exploring ways that distributed technology can illuminate or improve urban life, such as tracking trash disposal or using cell phones to monitor commuting. The second group is from the "Aero and Astro" department, where researchers have been exploring autonomous vehicles for military and other applications.

The Flyfire project aims to do something even stranger: to use LED-bearing helicopters to make a giant display. Although there may be some practical need for such a display, the most compelling vision for now is to create giant public art projects, of the kind pioneered by Piene and others.

Maybe it will never be particularly useful. Maybe it will never even work. But these grand visions tap into something essential in the human spirit.

Mining Evolution

2010-03-22T15:21:00.002-04:00

Can a worm get breast cancer? And how would you know if it did (since it doesn't have breasts)?

Biomedical research has made great use of "disease models": conditions in lab organisms that resemble the human diseases that the researchers really want to learn about. By seeing how a model condition develops and how it responds to drugs or other changes, researchers can make better guesses about what might help people. But finding such disease models usually requires some obvious similarity between the outward manifestations of the disease in humans and the animal subjects.

In the Proceedings of the National Academy of Sciences, a team from the University of Texas in Austin led by Edward Marcotte use the underlying molecular relationships to find connections between disorders with no such obvious relationship. In addition to a worm analog of breast cancer, they found an amazing connection between plant and human disorders. The analysis of plants' failure to respond to gravity led them to human genes related to Waardenburg syndrome. This syndrome includes an odd constellation of syndromes resulting from defects in the development of neural crest cells.

I saw Marcotte speak about this fascinating work at the conference I attended last December in Cambridge, Massachusetts. My writeup should be posted soon by the New York Academy of Sciences.

Biologists have repeatedly found that the networks of interacting molecules are organized into modules. Over the course of evolution, these modules can be re-used, often for purposes quite different from their original function. This much is well known, although seeming the persistence of modules over the vast evolutionary separation of plants and people is very dramatic.

What the Austin team did was to devise a methodology to identify related molecular modules in different species even without relying on similar outward manifestations, or phenotypes. They combed the known molecular networks of different species for modules that had a lot of "orthologous" genes: those that had retained similarity--and similar relationships--through evolution. They call the particular traits associated with these genes "orthologous phenotypes," or "phenologs." "We're identifying ancient systems of genes that predate the split of these organisms, that in each case retain their functional coherence," Marcotte said at the conference.

The importance of this scheme is that many molecular networks are poorly mapped, especially in humans. But if a particular gene is part of the network underlying a phenotype in another species--such as poor response of a plant to gravity--it's a good guess that the corresponding gene may be active in the orthologous phenotype in people. The researchers in fact confirmed many of these predicted relationships. Some of these genes were previously known to relate to disease, while others were new. The researchers created a list of hundreds more that they still hope to check.

These genes could give researchers many potential new targets for drugs or other interventions in diseases. So evolution is not just helping us to understand the biologic world we live in, but helping us devise ways to improve human health.

The Language of Life

2010-03-19T15:09:00.002-04:00

Ten years after the announcement of the draft human genome, the world of human health seems in many ways unchanged. But it is changing, in many profound ways, says Francis Collins, who led the government-funded part of the genome project and is now the director of the National Institutes of Health.

Collin's new book, The Language of Life: DNA and the Revolution in Personalized Medicine, aims to help the public to understand the changes so far, and those that are still to come. He covers a wide range of topics, but the guiding theme is the promise of "personalized medicine" that tailors treatment for each individual based on their genetic information.

As he shows in his occasional columns in Parade magazine, Collins is a skilled communicator of complex medical topics, including their ethical and personal dimensions. He steers authoritatively but caringly through challenging topics like race-based medicine. On the pros and cons of genetic screening, for example, he describes the desirability of genetic tests as a product of not just the relative and absolute changes in risk associate with a gene, but the seriousness of the disease and the availability of effective intervention.

I confess that I was worried that Collins might let his well-publicized Christian beliefs color this book (his previous book is called The Language of God). They did not. His beliefs arise a few times, for example in the context of stem cell research, but he deals with serious ethical questions with great respect for different points of view. In addition, as should be expected for any modern biomedical researcher, he repeatedly and matter-of-factly draws important insights from evolution.

On the whole, the writing is accessible to general readers, even as Collins discusses complex scientific topics. On occasion, however, he shows an academic's tolerance for complex, caveat-filled verbiage, as when he writes, "Therefore, at the time of this writing, the effort to utilize genetic analysis to optimize the treatment of depression has not yet reached the point of effective implementation." This stilted language is the exception, but he also slips into occasional jargon that might leave some readers temporarily stranded.

A trickier issue is Collins' frequent use of patient anecdotes to illustrate how genetic information can lead to better decisions. These human stories, drawn from his long research and clinical experience, certainly succeed at Collins' goal of inspiring hope for the potential of personalized medicine, as well as showing clearly what it mean to people. But the succession of optimistic stories begins to seem skewed to draw attention away from structural challenges in American medicine that could seriously undermine this potential. When Collins mentions these issues, it tends to be in careful euphemisms: "A recent study estimated that in the United States each year, more than 2 million hospitalized patients suffer serious adverse drug reactions, with more than 100,000 of those resulting in a fatal outcome."

In a similar vein, Collins describes the successful identifications of gene variants associated with macular degeneration. The fact that similar studies for other diseases have been rather disappointing doesn't seem to bother him much. Perhaps his decades in research, including the identification of the cystic fibrosis gene, have made him confident that these problems too will pass. But he comes across as a very optimistic person.

In spite of my quibbles, I think The Language of Life succeeds well at putting the omnipresent news stories about genetic advances in a useful context of individual medical choices. As a writer who covers these areas of science, I didn't learn an awful lot of new things from the book, but I think most people will, and will enjoy themselves in the process.

Mathophobia

2010-03-16T21:03:00.002-04:00

In many fields of science and engineering, any technical argument must be formulated mathematically if it is to be taken seriously. In contrast, in popular writing--even about science and engineering--including a single equation is a known recipe for getting many readers to click on to the next story. Understanding this discordant reaction to math reveals a lot about how popular and technical writing differ.

Back in 2003, when I was thinking about morphing from a practicing scientist into a science writer, I seriously questioned how I could possible to explain scientific arguments without variables and equations. Without the precision of a mathematical description, I wondered, how could I really know whether readers interpreted ordinary English phrases the way I intended? Moreover, without a algebraic description, how could readers judge how well a model matches observations?

Interestingly, in the years since, I've almost never felt hobbled by not being able to explain things with equations.

A lot of the difference arises from the different goals of journal articles and popular stories, and their very different sources of authority.

In a journal article, the goal is to convince other experts. In other words, the article should ideally be self contained, assembling all the relevant details so that an independent observer can make up their own mind.

In contrast, a popular science story aims merely to describe the conclusions, not prove them. As David Ehrenstein, the editor at Physical Review Focus, once told me, the goal is to present a plausibility argument for the conclusions: to give enough context and explanation that readers can appreciate what's being claimed and who's claiming it.

The last point is also critical: by and large, popular writing gains its authority from the quoted judgments of experts, not directly from the model or observations. Indirectly, of course, this authority comes from the reputation of the writer and the publication (that is, the editors), because they are the ones who decide which commentators are worth quoting. (Of course, those commentators must also respond to emails or phone calls!)

Since the goal is plausibility and a qualitative understanding, the limited precision of ordinary writing is usually good enough to convey the message.

Compartments

2010-03-10T19:38:00.002-05:00

The development of a single cell into a complex creature such as you or me is almost miraculous. Early scientists were so baffled by this process that some supposed that the fertilized egg might contain a complete specification of the final organism.

This is just silly.

Still, it has only been in the last few decades that researchers have worked out how a smooth starting pattern in the concentrations of a few molecules develops into a complex, highly structured pattern. These molecules act as transcription factors that modify the activity of genes making dozens of other transcription factors, which spontaneously form patterns that serve as the invisible framework driving all subsequent development. The essence of this understanding is described in the charming 2006 book Coming to Life: How Genes Drive Development by Christiane Nüsslein-Volhard, who shared a Nobel Prize for her germinal work on the development of the fruit fly, Drosophila melanogaster.

A central part of this view is that different regions of the developing embryo are uniquely identified by the particular combination of transcription factor concentrations they contain. Each combination (influenced in part by its neighbors) stimulates the cells in that local region, or "compartment," to develop toward a particular final structure, such as the hindmost edge of a wing. Mutated animals that are missing the genes for particular factors develop characteristic problems, like extra wings or legs sprouting where their antennae should be. Researchers have also learned how to label the molecules with fluorescent dies that directly reveals the invisible patterns of factors that drive later development.

In their book The Plausibility of Life, Marc Kirschner and John Gerhart include developmental compartments as one of the key elements, along with weakly-linked modules and exploratory behavior, of "facilitated variation": the ability of organisms to respond to small genetic changes with large but viable changes in their structure.

Compartmentalization is in some ways similar to exploratory behavior, in which developing body structures such as blood vessels respond to local stimuli such as chemicals emitted by oxygen-starved cells. In both cases, individual cells respond to nearby cues, without needing to refer to some master plan. In the case of compartmentalization, however, both the cues and responses are more general chemical changes, in contrast to the more apparent structural changes seen in exploratory development.

What the two processes have in common is that they allow development that is flexible enough to succeed in diverse situations, for example when nutrients are scarce or the embryo is damaged. This sort of robustness presents a clear evolutionary advantage, since it makes it more likely that a complex organism will grow up and survive to reproduce.

But in addition, robust development lets organisms deal with genetic changes. Although many mutations are fatal, some cause dramatic changes in body organization or other features, while the flexible development process adapts to the new situation. As a result of this facilitated variation, evolution is able to explore a wider variety of strategies and move quickly to new solutions.

For Kirschner and Gerhart, this flexibility is key to understanding the nature and rapidity of evolution. A population can explore the potential advantage of a longer hindlimb, for example, without the need to separately coordinate changes in bone, muscle, blood vessels, nerves, and so forth. Adaptive development takes care of all that.

In fact, the pattern of compartmentalization appears to have been much more stable over the course of evolution than the details of body structure have been. The appearance of compartmentalization and the other features that allow facilitated variation look like the crucial revolutionary events that made rapid evolutionary change possible.

Poles Apart

2010-03-09T22:06:00.002-05:00

Over the past few months, scientists studying global warming have been rocked by a series of awkward revelations. In November, someone made public more than 1000 emails, many quite damning, from climate researchers at the University of East Anglia (UEA), in what skeptics successfully branded as "Climategate." December and January saw the authoritative Intergovernmental Panel on Climate Change (IPCC) admitting that their published claim that Himalayan Glaciers would disappear by 2035 was improperly sourced and wrong, even as critics pounced on other instances in which the panel violated their own procedures for insuring that science was properly represented in their influential reports. (The IPCC had shared the 2007 Nobel Peace Prize with Al Gore.)

Not surprisingly, different people responded completely differently to these events. Critics see the revelations as confirming that global warming, and especially its human source, is just a hoax. Others counter that the revelations that scientists are fallible human beings do not in the least affect the overwhelming evidence for man-made climate change. Unsurprisingly, both extremes find confirmation for what they already believed.

They are also both wrong.

First let's examine the hoax theory. Although we don't know who released the emails, they look to have been culled from more than a decade of exchanges and chosen to best incriminate the mainstream climate researchers, both those at UEA and their correspondents. What is striking is that the emails do not
reveal any serious evidence that the researchers are fabricating the global temperature rise.

To be sure, the emails show serious misbehavior, including a request from UEA researcher Phil Jones that others delete emails to avoid a freedom-of-information inquiry. Other emails suggest that the researchers hoped to tweak the IPCC process to exclude legitimate, peer-reviewed papers that they didn't regard as credible.

But if the emails showed evidence of a true hoax, the critics poring over them haven't found it--and not for lack of trying. Instead, they have highlighted examples of ambiguous or unfortunate wording, which are conveniently picked up by the likes of Sarah Palin. But although Palin may not know any better, the critics understand that when Jones referred in 1999 to Penn State researcher Michael Mann's "trick" to "hide the decline," he was describing a technique to de-emphasize the inconvenient truth that tree-ring data don't match measured temperatures, which were clearly rising.

This is a serious matter: if the tree-ring data are not good proxies for temperature in cases where we know the temperature, how can we trust them in cases where we don't know the temperature? But as far as I know, the published papers acknowledge this manipulation, which is an acceptable way to deal with omissions of questionable data. Nothing is being hidden. In fact, this whole issue was addressed by the U.S. National Academy of Sciences in 1996, who confirmed the unprecedented nature of the current warming trend.

The critics know this. Their use of this and similar examples shows that they are less interested in the overall truth than in scoring points and discrediting mainstream climate science.

But the disingenuous actions of the critics do not excuse the behavior of the mainstream scientists.

It is tricky to interpret what must have been seen as private correspondence between colleagues. Nonetheless, the emails seem to show that the East Anglia scientists did not really trust the processes of science, or at least the political decisions based on the science. The researchers in the emails are acutely aware of the political context of their results, and present the data to make their case. For example, UEA tree-ring expert Keith Briffa says "I know there is pressure to present a nice tidy story as regards 'apparent unprecedented warming in a thousand years or more in the proxy data' but in reality the situation is not quite so simple." Briffa, whose data contains the "decline," commendably goes on to argue for a more honest and nuanced description of the data.

It's not unusual for researchers to choose data to tell a particular story. And there really is no evidence that the researchers suspected the story they were telling--of unprecedented, industrially-caused warming--was wrong. But it is clear that the handful of teams around the world who evaluate historical climate trends displayed their data to support a clear end goal. Subsequent revelations show that this mindset also affected the choice of references in the IPCC report, especially with regard to the impacts of climate changes.

One of my correspondents, a biologist who uses complex biological models, likens the climate-science consensus to Lysenkoism--the Soviet era anti-Darwinist biological agenda. I take this person to mean that dissent from the reigning paradigm is not accepted in mainstream scientific circles: anyone questioning the "consensus" is quickly branded a "denier." This is not the way to do science, especially for something as important as climate change. And what it means is that the "overwhelming consensus" is not as convincing as it seemed a few months ago.

But it doesn't mean that the consensus is wrong.

Survival of the Most Entangled

2010-03-01T21:03:00.003-05:00

Most people think quantum-mechanics affects only atomic-sized objects, but many experiments have shown that it applies over many miles. In an experiment last year, for example, researchers sent pairs of light particles, or photons, between two of the Canary Islands off the coast of Africa, a distance of 144 kilometers.

http://www.flickr.com/photos/tenerife/ / CC BY 2.0
This picture dimly shows La Palma, where the photons started, as seen from Tenerife, where they were detected.

Although the Austrian team, led by Anton Zeilinger, only detected one in a million of the pairs they sent, they found that these pairs retained the critical property of entanglement. This means that results of measurements on the two particles are related in ways that can't be explained if each particle responds to the measurement independently: the pair acts like a single quantum-mechanical entity. Such pairs can be used to securely transmit information over long distances.

My latest story at Physical Review Focus describes a theoretical analysis of this experiment by researchers in Ukraine and Germany. They suggest that the pairs that survive the half-millisecond trip must have had unusually smooth sailing through the turbulent atmosphere, and that this is part of the reason why they are still entangled. (The motion of the air, like the shimmering of a mirage in the desert, generally disrupts the light transmission, but there are short moments of clarity.) This is a pretty comprehensible idea, so in the story I was able to sidestep a lot of interesting issues about how the entanglement was measured and what it means.

For example, the experimenters delayed one photon by about 50 ns by passing it through a fiber before sending it after the other one. That's not a long time, so the atmospheric conditions probably looked pretty similar to the two photons. Since they were subjected to much the same conditions, it doesn't seem so surprising that they would remain entangled. In fact, the original experimenters were pretty pleased that it all worked, but clearly they were hoping it might or they wouldn't have gone to the trouble.

Sending the two photons on the same path certainly isn't the most demanding task, either. More impressive would be sending them on different routes to a final destination where they were compared. But sending an entangled pair is good enough for some quantum communication schemes.

What made the paper particularly interesting was the conclusion that the turbulent atmosphere would be better than, say, an optical fiber that had the same average loss, because the fiber's properties wouldn't change with time. Zeilinger expressed pleasant surprise that the rare moments of exceptional clarity would more than make up for the times when the atmosphere was worse than usual. Still, having no turbulence at all (or a very clear fiber) would be even better.

Exploiting quantum mechanics in secure long-distance communication, for example via satellites, looks more realistic than ever.

Targeting Cancer

2010-02-25T20:43:00.002-05:00

Amy Harmon of The New York Times had an excellent three-part series this week called "Target Cancer." She follows one clinician/researcher as he pursues a "targeted" treatment for melanoma, which aims at the protein produced by a gene called B-Raf that is mutated more than half of the time in this skin cancer.

The series does a great job in following the emotional roller-coaster ride of the doctor, and of course his patients. One early targeted drug doesn't work at all, perhaps because it also attacks normal cells and the side effects become intolerable before the dose is high enough to affect the cancer. A new drug seems not to do anything, but then the team decides to wait for the drug company to reformulate it to deliver higher effective doses.

The results are spectacular: the new formulation causes a virtually unheard of remission in the cancer, and raises hopes in formerly hopeless patients and in the doctors. The excitement and the potential are palpable as some patients dare to hope and others can't bear to. But within a few months, the patients are dying again.

The new drug is an example of personalized medicine, since it is effective only for patients with a particular mutation. There are a few other examples of therapy tuned to patients with a particular genetic profile, such as the breast-cancer drug erbitux and the anticoagulant warfarin (Coumadin).

But this treatment is actually for cancers with a particular mutation--a mutation the normal cells of the patient don't have. Cancers cells generally have more and more of mutations as the disease progresses, because it disrupts the normal quality-control mechanisms in the cell. A study announced last week (registration required) showed that the specific pattern of mutations could be used to monitor the ebb and flow during treatment, although it doesn't look practical yet for tailoring treatment.

Unfortunately, as described in this series, even when a drug targets a mutation in a particular patient's cancer, cancers often develop alternate routes to proliferation. Harmon alludes to one approach to this problem: a multi-pronged "cocktail" that attacks many possible mutations at once. Such cocktails are standard, for example, in treating HIV/AIDS.

Without vilifying the drug companies, she explains some challenges for these profit-oriented companies in pursuing this approach. In particular, even if the cocktail may ultimately be more effective, getting approval might delay or threaten their profits from the drug they have in hand, even if it only extends life for a few months. This is especially true if other drugs in the cocktail are owned by competing companies. In any case, the difficulties in testing multiple drugs make it much harder to know what is effective and what side effects may appear.

The idea of analyzing molecular networks and attacking them at many points simultaneously is a recurring theme in systems biology. But sometimes it seems very far in the future.

Stoner Magnetism

2010-02-22T17:38:00.002-05:00

My latest story at Physical Review Focus describes experimental evidence that a missing atom in a chicken-wire-like sheet of carbon can hold a single extra electron.

Theorists have long expected this to be the case, and that unpaired electrons on such vacancies might join up to make an entire single-atom-thick graphene sheet magnetic at relatively high temperatures. Many researchers are excited about the rapid and unusual motion of electrons in these sheets, and IBM researchers recently described a graphene field-effect transistor, grown on silicon carbide, whose expected frequency (f_T) exceeds 100GHz. If the layers are also magnetic at normal temperatures, this material could be fun and potentially practical for spintronics, which manipulates both the charge and magnetic properties of electrons.

The actually experiment didn't directly show magnetism, though, just a state that looked like it should hold only one electron. The researchers used scanning-tunneling microscopy to look at a clean, cold graphite surface, which includes many stacked graphene-like layers. In fact, the authors suggest that magnetism may exist in graphite, but not in graphene, because in the latter the effects of two equivalent carbon positions for a vacancy may cancel each other out.

It turns out to be a little bit tricky to explain the connection between local spins, which naturally carry a magnetic moment, and magnetism in a bulk material.

The usual story is straightforward: some types of atoms (or vacancies) naturally have a magnetic moment, "like a tiny bar magnet." Nearby moments exert forces that tend to align their neighbors, either the same way or oppositely. If it's the same, then the moments on many different atoms can all line up to form a net magnetization in a large sample, if the temperature is not so high that they get jostled out of position.

This description is correct--but only for some magnets.

For other magnets, it's just not accurate to say that the atoms each have magnetic moments that line up with each other. In these so-called "itinerant" magnets, the magnetization comes from the metallic electrons washing over all of the atoms. In this case, preference for one direction or another at a particular atom develops only as a part of the magnetization of the whole sample.

Mathematically, itinerant magnetism takes the form of an instability, in which the energy benefit of aligning the moments of the electrons overcomes the energy cost of doing so. A simple description was developed back in the 1940s by Edmund Stoner at the University of Leeds, and his name is still used to convey the ideas. (I apologize to anyone who expected this post to be about the natural charisma of pot-smokers.)

Of course, the distinction between the "local-moment" and "itinerant" magnetism is often somewhat fuzzy, and for the purpose of explanation to the general public it may not seem that important. But to people who understand the issues, getting it wrong is unforgivable, as I found out to my chagrin after using the above simple local picture in my Focus story on the 2007 Physics Nobel on Giant Magnetoresistance (GMR).

GMR read heads in disc drives can be seen as a simple type of spintronics device. In more sophisticated devices that people dream about, electrons will carry their magnetization to new locations, so it's important to be clear on the nature of that magnetism.

Trailblazing

2010-02-17T17:40:00.002-05:00

What a piece of work is a man! For that matter, what an awesomely complex apparatus is any large organism, from a dog to dogwood!

But as we learn more about biology, our awe shifts from the intricate cellular arrangements in mature multicellular life to the ways these structures arise during development from simpler (but not simple) rules. Even if we can accept simple examples of self-organization, like the spontaneous arrangement of wind-blown sand into regular dunes, the self-assembly of living creatures seems to be a different scale of miracle. But researchers have repeatedly found that simple rules, in which cells respond to local cues like chemical concentrations and mechanical stresses, suffice to describe how various aspects of our complex bodies develop.

In The Plausibility of Life, Marc Kirschner and John Gerhart describe this rule-based strategy, which they call "exploratory behavior," as a very effective way for organisms to develop dependably in the face of unpredictable changes in their environment. But they go further, stressing that flexible, adaptive development speeds evolution by letting small genetic changes give rise to vastly different--but still viable--organisms. Exploratory behavior is thus a critical component of their concept of "facilitated variation."

As an illustration of rule-based organization, Kirschner and Gerhart review the foraging of ants. Steven Johnson described this and other examples in his thought-provoking 2002 book, Emergence. Simply by following local rules and responding to the scent trails left behind by their predecessors, individual ants join to form major thoroughfares between a food source and their nest. No master planner guides their motions.

Similarly, in a developing animal, some cells may find themselves far from the nearest blood vessel. In response to the lack of oxygen, they secrete chemicals that encourage the growth of new capillaries nearby. And in the brain, the intricate wiring of nerve cells is guided in part by signals that they receive and transmit during certain periods of development.

It really has to be this way. Although it's true--and amazing--that the 558 cells of the roundworm C. elegans take up pre-ordained positions in the final creature, the cells in much bigger creatures like us simply can't all have designated roles in the final organism. For one thing, there's just not enough information in our 20,000 or so genes to tell every cell where to go on some genetic master plan. Instead, each cell has to have a degree of autonomy in dealing with new situations. For example, if one of your legs is stunted early on, the muscles, nerves, blood vessels, and skin will all adapt to its new size, rather than blindly proceeding with some idealized plan. Even in C. elegans, the fixed cellular arrangement mostly results from such adaptive behavior of individual cells.

If you're still not convinced, think of the offspring of a bulldog and a Great Dane, which will have a facial and body structure unlike either of its parents. But we are not even surprised that the blood vessels and muscles will successfully adapt themselves to this completely novel shape.

It makes perfect sense that creatures that use this adaptive process in their development would be more successful during evolution.

But the reverse is also true: this flexibility makes evolutionary innovations much easier. The repurposing of mammalian digits for a dolphin's flipper, a horse's hoof, or a bat's wing is much faster if only a few genes have to change to determine the new shape, and the others adapt in parallel. In concert with modular organization, development that is built on exploratory principles is critical to letting evolution explore radically new architectures in response to small genetic changes.

Brain-Machine Interfaces

2010-02-10T22:28:00.002-05:00

I have a short news story about exchanging information between machines and people's brains, now online at the Communications of the Association for Computing Machinery. This is a difficult field to capture in a few hundred words. There's a lot of progress, but people are trying a lot of different approaches, and they're not all addressing the same problem.

For example, some people are hoping to provide much needed help to people with disabilities, while others see the opportunity for new user interfaces for games.

Naturally, people will be willing to spend a lot more for the rehabilitation. In addition, recreational use pretty much rules out (I hope!) any approach that requires surgically implanting something in the skull. Even the researchers who are exploring rehabilitation don't yet feel confident exposing people to the risk, because they can't be sure of any benefits. As a result, these studies mostly involve patients who have received implants for other reasons.

If surgery is ruled out, there are fairly few ways to get at what's going on in the brain. With huge, expensive machines, you can do functional MRI, but that doesn't look particularly practical. Both Honda and Hitachi are using infrared monitoring of blood flow, with impressive results. But the best established measurement is EEG, which measures electrical signals with electrodes pasted to the surface of the head.

One up-and-coming technique that I mention in the story is called ECoG, or electrocorticography. Like EEG, it measures the "field potentials" that result from the combined actions of many neurons. However, the electrodes are in an array that is draped over the surface of the brain (yes, under the skull), so the signal is much cleaner.

Finally there are approaches like Braingate that put an array of a few dozen electrodes right into the cortex, where they can monitor the spikes from individual neurons. 60 Minutes did a story a while ago that showed people using this technology to move a computer mouse.

If the implants are to be practical, they will need to be powered and interrogated remotely, not through a bundle of wires snaking through the skull. Many people are exploring wireless interfaces for this purpose, as described by my NYU classmate Prachi Patel in IEEE Spectrum.

Brain-machine interfaces can also run in either direction. My story dealt mostly with trying to tap the output of the brain, for example letting paralyzed people control a wheelchair or computer mouse. But input devices, such as artificial cochleas or retinas, are also proceeding quite rapidly. To my surprise, Rahul Sarpeshkar, who works on both directions, told me the issues are not that different.

My guess would have been that input to the brain can take advantage of the brain's natural plasticity, which will adapt it to a crude incoming signal. To usefully interpret the output of haphazardly placed electrodes, people need to do an awful lot of sophisticated processing of the signal, which can slow things down.

The toughest thing about this sort of story, though, is time. There's a lot of progress, but there's a long way to go. Once the proof of principle is in hand, there's still a lot of hard work to do, some of which may involve major decisions about basic aspects of the system. It's hard to communicate the progress that's being made without getting into a lot of details that are only interesting to specialists.

Even when it lets the blind see or the lame walk, writing about engineering is a hard sell for the general public.

Blinded Science

2010-02-08T22:52:00.002-05:00

Medical researchers systematically shield their results from their own biases. Other scientists, not so much.

Medical science includes many types of studies, but the universally accepted "gold standard" is the randomized, controlled, double-blind, prospective, clinical trial. In this kind of study, patients are randomly assigned to either receive the treatment being tested or an ineffective substitute and then watched for their response, according to predefined criteria.

"Blind" means that the patients don't know whether they are getting the "real thing" or not. This is important because some will respond pretty well even to a sugar pill, and a truly effective drug must at least do better than that. This feature may not be so important in other fields, assuming that the rats or superconductors they study don't respond to this "placebo effect."

But the "double" part of "double blind" is also critical: in a proper trial, the doctors, nurses, and the researchers themselves don't know which patients are getting the real treatment until after the results are in. Without this provision, experience shows, they might treat the subjects differently, or evaluate their responses differently, and thus skew the conclusions. The researchers have a lot invested in the outcome, they have expectations, and they are human.

So are other scientists, I'm afraid.

It is surprising that most fields don't expect similar protection against self-deception. Sure, it's not always easy. In my Ph.D. work, for example, I made the samples, measured them, and interpreted the results. Being at the center of all aspects of the research helped keep me engaged and excited in spite of the long hours and modest pay, and was good training. But I also felt the pressure to get more compelling results.

These days, many fields already involve multidisciplinary collaborations that leverage the distinct skills of different specialists. Would it be so hard for the person who prepares a sample to withhold its detailed provenance from the person doing the measurement until the measurement is finished? With some effort, people could even hide information from themselves, for example by randomly labeling samples. No doubt it takes some of the immediate gratification out of the measuring process, but the results would be more trustworthy.

As recent events have made clear, trust is especially important in climate change. In October, Seth Borenstein, a journalist with the Associated Press, gave a series of measurements to four statisticians, without telling them what the data represented. In each case the experts found the same thing. When the data were revealed to be temperature measurements, each expert had found a long-term upward trend in global temperatures, with no convincing sign of the supposed cooling of the last ten years.

But why did it take a journalist to ask the question this way? Shouldn't this sort of self-doubt be built into all science at a structural level, not just assumed as an ethical obligation?

Experimental science will always be a human endeavor, I hope, so the results can never be completely divorced from expectations. But there are ways of making it more trustworthy.

Fractal Biology

2010-02-04T15:16:00.012-05:00

I first learned about the intermediate-dimension objects called fractals in the late 1970s, from Marvin Gardner's wonderful "Mathematical Games" column in Scientific American. One of the cool and compelling things they can do is explain is how highly branched circulatory system, with an effective dimension between two and three, could have an effectively infinite surface area, abutting every cell in the body, while taking up only a fraction of the body volume.

Twenty years later, Geoffrey West and his collaborators used this fractal model to explain the well known "3/4" law of metabolism, in which different organisms' resting metabolic rate varies as the 3/4 power of their body mass. West, an erstwhile theoretical physicist from Los Alamos who recently stepped down as the head of the delightfully eclectic Santa Fe Institute (for which I've done some writing), used similar scaling analyses of things for other aspects of biology as well as resource usage in cities.

Unfortunately, according to Peter Dodds at the University of Vermont, the well known 3/4 law is also wrong. In my latest story for Physical Review Focus, I briefly describe how Dodds uses a model of the branched network to derive an exponent of 2/3.

Interestingly, this 2/3 exponent is precisely what you'd expect from a simple computation of the surface to volume ration of any simple object. A 2/3 law for metabolism was first proposed in the mid 1800s, Dodds said, at "a tobacco factory in France, trying to figure out how much to feed their workers, based on their size. They asked some scientists and they said 'we think this 2/3 rule would make sense.'" Experimental data on dogs seemed to fit this idea.

But later experiments hinted at a slightly higher exponent. "At some point it became more concretely ¾," Dodds said, based on the work of Max Kleiber published in 1932. "He'd measured some things that looked like 0.75 to him. You know, he had nine or ten organisms, and it was easier on a slide rule." At a conference in the 1960s, scientists even voted to make 3/4 the official exponent.

But in the wake of the fractal ideas, Dodds and his collaborators re-examined the data in 2001. "What really amazed me was I went back and looked at the original data and it's not what people thought. People had sort of forgotten about it by that point." Instead, Dodds, found, the data really matched 2/3 better. At the very least, the 3/4 law was not definitive. This doesn't mean that the fractal description is not useful, only that it has a different connection to the metabolic rate.

From C.R. White and R.S. Seymour, Allometric scaling of mammalian metabolism, Journal of Experimental Biology 208, 1611-1619 (2005). BMR is resting metabolic rate. The best fit line has a slope (exponent) of 0.686±0.014 (95% CI), much more consistent with 2/3 than with 3/4.

Other authors have since supported this conclusion, especially if they omit big herbivores like kangaroos, rabbits, and shrews, whose resting metabolism is hard to measure. The real biological data is messy, and perhaps it is silly to expect a simple mathematical law to apply to diverse biological systems. In any case, the difference between the two exponents is modest, amounting to a factor of about 2.5 in metabolism over the range of experimental data in the plot.

Still, some experts, such as the commentators that I interviewed for the Focus story, still think that the 3/4 law is correct. But it seems plausible that many decades of experimental observations have been colored by researchers' expectations. Science remains a human endeavor.

Beautiful Data

2010-02-03T18:10:00.002-05:00

There's an old joke where a scientist presents "representative data," when everyone knows it's really their best data. Like many jokes, there's a large measure of truth in it. (And like many science jokes, it's not actually funny unless you're a scientist.)

Audiences, whether in person or in print, like a good story. And it's best when data tell a story on their own, without further words of explanation. Scientists can handle tables of numbers better than most people, but, even for them, pictures tell the most compelling stories.

In fact, many experienced researchers begin preparing a new manuscript by deciding what figures to include. In part this is because figures take a lot of space in journals, but in addition many readers will go from the title straight to the figures, bypassing even the short abstract. If the pictures don't tell a good story, readers may just move on.

This is what it means when scientists say data are "beautiful": not that they have some intrinsic aesthetic appeal, but that they tell a good story about what's happening. Ideally, the story is compelling because the experiments have been done very well. But that's not always the reason.

Some of the most beautiful data I ever saw, in this sense, were presented at Bell Labs by Hendrik Schön in early 2002, at a seminar honoring Bob Willett and the other winners of that year's Buckley Prize. In contrast to Willett's painstaking work over many years elucidating the properties of the even-denominator fractional quantum Hall effect, Schön presented one slide after another demonstrating a wide variety of phenomena in high-mobility organic semiconductors.

The problem was that the story the beautiful data were telling was a lie. Schön's rise and fall were expertly described in Eugenie Reich's 2009 book Plastic Fantastic, and I was later on the committee that concluded that he had committed scientific misconduct.

But honest scientists also must be careful when they choose data that supports the story they want to tell. There is an intrinsic conflict of interest between telling the most compelling story and facing honestly what the data are saying. Decisions about which data to omit, and how to process the remainder, must be handled with great care.

As a rule, scientists overestimate their objectivity in selecting and processing data. As individuals, they are more swayed by expectations than they would like to admit. Collaborators can help keep each other honest, but only to a degree.

One thing that keeps science on track in these situations is that other people may make the same sort of measurements. Often different experimenters have a different idea of what's right, and the back-and-forth helps the field as a whole converge toward the truth.

But what happens when a whole field expects the same thing? There's a real danger that the usual checks and balances of scientific competition will break down, and all the slop and subjectivity of experiments will be enlisted in service of the common expectation.

Just because data is beautiful--that is, tells a good story--doesn't mean that story is right.

Tunable Flexibility

2010-02-02T22:01:00.003-05:00

Some genes can't be substantially changed without fatal consequences, while tweaking other genes lets organisms gracefully adapt to new situations.

This modular organization, in which some groups of components maintain a fixed relationship to one another even as the relationship between groups changes, is common in biology. In their book, The Plausibility of Life, Marc Kirschner and John Gerhart argue that the weak linkages between unchanging modules are a critical ingredient of "facilitated variation," which in turn makes rapid, dramatic evolution possible. But this long-term adaptability may be, in part, a fortunate side effect of a system that lets individual organisms respond to changes during their lifetime.

Natural selection is not based on compassion. It would be reasonable if both essential genes--those within modules--and nonessential genes--including those forming the weak linkages between modules--mutated equally readily. A mutation within a module might kill its host, but that's the way of progress. Mutations of the linkages could survive, and, by altering the relationship between modules, allow a population to explore new innovations.

Evolution would be more efficient if genes within modules didn't mutate as quickly as those between them. But it's not required.

But what if the evolvability is just one facet of a more general flexibility? At a December 2009 meeting in Cambridge, MA, which I'm covering for the New York Academy of Sciences, Naama Barkai of the Weizmann Institute in Israel showed evidence that genes that evolve rapidly also show greater variability in expression.

To measure the rate of evolution, Barkai's postdoc Itay Tirosh compared different yeast species to see which genes had the most differences. Some genes differed a lot, while others were quite similar.

Tirosh also looked at two measures of the intrinsic variability of gene expression (measured by messenger RNA levels): the degree of change in response to changed conditions, like stress, and the time-dependent variation in expression, or noise. Again, some genes varied a lot, while others stayed quite steady. Moreover, the variable genes were likely to be the same ones that evolved rapidly.

These three correlated measures of gene flexibility were connected with differences in the structure of the promoter, which is the region of DNA near where its transcription into RNA begins. Flexible genes tended to include the well-known "TATA" sequence of alternating tyrosine and adenosine bases, as well as different arrangements of the nucleosomes.

A complete understanding of the role of flexibility in both short- and long-term variation will require a lot more research. But these results support the notion that arrangements that let organisms adapt to the slings and arrows of everyday life also give them the tools to rapidly evolve dramatically new ways of life.

Fusion on the Horizon?

2010-01-29T17:51:00.004-05:00

When I arrived at MIT in 1976, fresh off the bus from Oklahoma, nuclear fusion looked like an exciting scientific career. The country was still reeling from "the" energy crisis (oil was over $50/barrel in today's prices!), and fusion was the energy source of the future.

It still is.

The promise has always been compelling, and is often described as "unlimited pollution-free energy from seawater." The fusing of two hydrogen nuclei to form a helium nucleus, releasing abundant energy without the radioactive products of nuclear fission, certainly seems cheap and clean. Indeed, this kind of process is the ultimate source of all solar energy as well, and the H-bomb showed that we can create it on earth.

So the challenges for fusion are not fundamental. They're "just engineering."

Foremost among these challenges is keeping the hydrogen nuclei together when they're heated to millions of degrees. This temperature is needed so they can overcome their natural electrical repulsion, but when they have a lot of energy they're just as likely to go in other directions. Sadly, techniques to confine these tiny nuclei seem to require tons and tons of expensive, high-tech equipment. Of course, advocates of cold fusion, now called "Low Energy Nuclear Reactions," think they don't have to solve this problem, but most scientists are unconvinced.

The traditional approach to fusion, then being pursued at MIT, involves confining a donut-shaped plasma of ultra-hot charged particles by using an enormous magnetic field. One problem is that the plasma finds all sorts of ways to wiggle out of the confinement. Over the decades, researchers have made steady progress in controlling these "instabilities." Recent research, still done at MIT and published online in Nature Physics this week, used a surprising technique of levitating a half-ton magnet in mid-air.

The other mainstream approach is to squeeze and heat hydrogen-containing materials by blasting a pellet with powerful lasers from all sides. Research at Lawrence Livermore's National Ignition Facility, published online in Science this week, showed promising results for this approach.

I've always found the idea of milking a steady stream of power out of occasional explosions inside of a horrendously expensive, delicate laser apparatus confusing. In fact, the long-defunct radical magazine Science for the Peoplepublished an article in 1981 claiming that "inertial confinement" fusion was just a plot by the military to test fusion explosions in the lab. That at least made sense.

The new results seem like steps forward for both approaches, but there's a long way to go. For one thing, neither group actually fused anything. They just set up conditions that seemed promising.

The reality is that no researchers want to actually use fusion-capable fuel in their machines, because it would make them radioactive (the machines, not the researchers). This may sound surprising, since fusion is supposed to be so clean. But although fusion doesn't produce radioactive nuclei, it does make a whole lot of high-speed neutrons. To generate power, researchers would need schemes to extract the energy from these neutrons. But the neutrons also irradiate everything in sight, turning much of the apparatus into hazardous waste, which would make experiments much harder.

But if the researchers keep making progress, they're going to have to use the real stuff soon. They'll look for any fusion at all, and eventually for "scientific breakeven," where they get more energy out than they use to power all the equipment. "Commercial breakeven," where the whole endeavor makes money, is much further down the road.

I wish the researchers good luck; they may yet save our planet. But I'm also glad I didn't decide to spend the last third of a century working on fusion.

Mix and Match

2010-01-28T20:01:00.003-05:00

It's easier to reconfigure a complex system to do new things if it is built from simpler, independent modules. But in biology, modules may be useful for more immediate reasons.

Biological systems, ranging from communities to molecular networks, often feature a modular organization, which for one thing makes it easier for a species to evolve in response to changes in the environment. In some cases, this flexibility might have been selected during prior changes. But modularity can also make life easier for a single organism during its lifetime, and be selected for this reason.

In their book The Plausibility of Life, Marc Kirschner and John Gerhart include modularity as one aspect of "facilitated variation." In particular, they say, genetic changes that affect the "weak linkages" between modules can cause major changes in the resulting phenotype. As long as the modules, the "conserved core processes," are not disrupted, the resulting organism is likely to be viable, and possibly an improvement on its predecessors.

In describing facilitated variation, Kirschner and Gerhart defer the question of whether facilitating rapid future evolution alone causes these features to be selected. Perhaps it does, in some circumstances. But in any case, we can regard the presence of features that enable rapid reconfiguration as an observational fact.

Moreover, the practical challenges of development demand the same sort of robust flexibility that encourages rapid evolutionary change. Over the development of a complex organism, various cells are exposed to drastically different local environments. In addition, genetic or other changes pose unpredictable challenges to the molecular and other systems of the cells. Throughout these changes, critical processes, like metabolism and DNA replication, need to keep working reliably.

To survive and reproduce in the face of these variations organisms need a robust and flexible organization. Features that allow such flexibility should be selected, if only because they improve individual fitness. These same features may then increase evolutionary adaptability, whether or not that adaptability is, by itself, evolutionarily favored.

Whether adaptability is selected for its evolutionary potential or only for helping organisms thrive in a chaotic environment, it has a profound effect on subsequent evolution. A flexible organization including modularity and other features allows small genetic alterations to be leveraged into large but nonfatal changes in the developing creature, so the population can rapidly explore possible innovations.

Changing Times

2010-01-27T15:57:00.005-05:00

One way to explain the modularity that is seen in biology is that it helps species to evolve quickly as their environment changes.

But the notion that "evolvability" can be selectively favored is tricky intellectual territory, and people can get drawn in to sloppy thinking. Just as group selection must favor more than just "the good of the species," selection for flexibility cannot be grounded in future advantages to the species. To be effective, evolutionary pressure must influence the survival of individuals in the present.

Whether this happens in practice depends on a lot of specific details. Some simulations of the effect of changing environments have not shown any effect. But at a meeting I covered last year for the New York Academy of Sciences, Uri Alon showed one model system that evolves modularity in response to a changing environment.

Alon is well known for describing of "motifs" in networks of molecular interactions. A motif is a regulatory relationship between a few molecules, for example a feed-forward loop, that is seen more frequently in real networks than would be expected by chance. It can be regarded as a building block for the network, but it is not necessarily a module because its action may depend on how it connects with other motifs.

Alon's postdoc Nadav Kashtan simulated a computational system consisting of a set of NAND gates, which perform a primitive logic function. He used an evolutionary algorithm to explore different ways to wire the gates. Wiring configurations that came closest to a chosen overall computation result were rewarded by giving making future generations more likely to resemble them. "The generic thing you see when you evolve something on the computer", Alon said, "is that you get a good solution to the problem, but if you open the box, you see that it's not modular." In general, modules cannot achieve the absolute best performance.

Kashtan then periodically changed the goal, rewarding the system for a different computational output. Over time, the structure of the surviving systems came to have a modular structure. One interesting surprise was that in response to changing goals, the simulated systems evolved much more rapidly than those exposed to a single goal.

But Alon emphasized that this was not a general feature. Instead, the different goals needed to have sub-problems in common. Evolution would then favor the development of dedicated modules to deal with these problems. It is easy to imagine the challenges facing organisms in nature also contain many recurrent tasks, such as the famous "four Fs" of behavior: feeding, fighting, fleeing, and reproducing.

So some biological modularity may reflect the evolutionary response to persistent tasks within a changing environment. But does this explain the wide prevalence of modules? In a future post, I will examine another explanation: that modularity is one of the tools that helps individual organisms adapt to the changing conditions of development and survival during their own lifetimes.

Modules

2010-01-26T23:20:00.005-05:00

When people design a complex system, they use a modular approach. But why should biology?

For us, modularity is a way to limit complexity. Breaking a big problem into a series or hierarchy of smaller ones makes it more manageable and comprehensible, which is especially important if it is assembled by many people--or one person over an extended time.

The key to a successful module is that its "guts"--the way its parts work together--doesn't depend on how it connects to other stuff. The module can be thought of as a "black box," that just does its job. You don't have to think about it again. For this to work, the connections between modules must be weak, limited to well-defined inputs and outputs that don't directly affect its internal workings.

When it's done right, a module can be easily re-used in new situations. For example, the part of a computer operating system that offers a help menu is tapped by lots of programs without worrying about how it works.

But biology is not designed. Biological systems emerge from an evolutionary process that rewards only survival and reproduction, with no regard for elegance or comprehensibility. Re-usability sounds like a good thing in the long run, but doesn't help an individual survive in the here and now.

Nonetheless, modularity seems to be widespread feature of biological systems. Your gall bladder, for example, is a well-defined blob that receives and releases fluids like blood and bile, but otherwise keeps its own counsel. It can even be removed if necessary. And it does much the same thing in other people and animals.

At a smaller scale, many of the basic components of cells are the same for all eukaryotes. They have the discrete nuclei that define them, and they also have other organelles that perform essential functions, like mitochondria that generate energy. These modules work the same way, whether they happen to appear in a brain cell or a skin cell.

Even at the molecular level, re-usable modules abound. For example, although ribosomes don't have a membrane delimiting them, they consist of very similar bundles of proteins and RNA for all eukaryotic cells, and only modestly different bundles for bacteria. In addition to such complexes, many "pathways," or chains of molecular interactions, recur in many different species.

We have to be careful, of course: simply because we represent complex biological systems as modules doesn't mean they are there. The modules we think we see could simply reflect our limited capacity to understand messy reality. But when researchers have looked at this question carefully, they found that modules really exist in biology, much more than they would in a random system of similar complexity.

But why should evolution favor modular arrangements? And how does a modular structure change the way organisms evolve?

Don't Fear the Hyphen

2010-01-23T23:30:00.003-05:00

Hyphens come up a lot in scientific writing. Or at least they should. Unfortunately, small as they are, many people are afraid of them.

One problem is that hyphens get used for some very different purposes, although all of them tend to bind words or fragments together. I'm only going to talk here about making compound words with them.

Another problem is that you don't have to use a hyphen if you don't have to: if the meaning is clear without it (semantics), you're allowed to omit it (punctuation). This makes it very hard to figure out the real punctuation rules, since they don't arise from syntax alone.

Compound words are hard to predict. Sometimes two words are locked together to form one new word, as in German: eyewitness. Sometimes the two words keep their distance even as they form a single, new concept: eye shadow. But some pairs are bound with the medium-strength hyphen: eye-opener. This mostly happens when both words are nouns, so that the first noun is acting as an adjective, making the second more specialized.

There's really no perfect way to know which form is favored, and it varies over time. Novel pairings generally start out separated and become hyphenated when they seem to represent a unique combined identity. When that combined form becomes so familiar that it is easily recognized, the hyphen disappears, (unless the result would be confusing, as in a recent example, "muppethugging," or the less novel "cowriting.") You just have to look in an up-to-date dictionary.

For the single compound words and those that are always hyphenated that's the end of the story. The problem is that the isolated pairs of words sometimes should be hyphenated, too. This hyphenation is not a property of the pair, and it can't be found in the dictionary. It's a real punctuation mark, and depends on the details of the sentence.

The hyphen belongs when the pair is used as an adjective, known as a compound modifier, as in the previous "medium-strength hyphen." But generally the hyphen is omitted when the adjective occurs later on: "The hyphen has a medium strength." But the AP Stylebook (not hyphenated!) says that when that "after a form of to be, the hyphen usually must be retained to avoid confusion: The man is well-known." AP also has a rule that when for an adverb-adjective pair, the hyphen is not used if the adverb is "very" or ends in "-ly."

OK, this is getting confusing, so let's regroup: The important principle is that the hyphen is there to make clear when there is a link that might otherwise be missed. If we talk about a "highly important person," it's clear that it's the importance that's high, not the person. But if we talk about a "little known person," it's not so obvious whether it's the knowledge or the person that's diminutive, because
"little" can be an adjective or an adverb. If it's an adjective, you might have said "little, known person, " but "little-known person" avoids any chance of confusion.

The problem is worse when the first word is a noun, because it doesn't really give you any syntax clues about whether it's acting as an adjective or adverb. This issue comes up frequently in science writing. I imagine most people will realize that a "surface area calculation," refers to a calculation of surface area, and not a surface calculation of the area. But sometimes it's hard to know what will be confusing. I prefer to assume as little possible about what my readers are getting, so I would use "surface-area calculation." But many editors correct this (and I generally defer to them).

Unfortunately, there is a compelling to reason to be sparing with hyphens, which is that they can't be nested. This also comes up frequently in science writing, when a compound modifier is constructed from another compound modifier, as in "surface area calculation results." If we used parentheses to tie together related words, this would be rendered "((surface area) calculation) results." But there's no way to indicate priority with hyphens.

The right thing to do is to decide what level of compounding needs to be made explicit, and retain hyphens at all levels up to that, for example "surface-area-calculation results." Sadly, I frequently see something like "surface area-calculation results." That's just wrong, since the hyphen ties together "area" and "calculation" more strongly than "surface" and "area." In this case you'd be better off leaving out all hyphens and hoping for the best.

Of course, as in most cases of confusing writing, the best alternative is "none of the above": recast the sentence so that it doesn't have compound-compound modifiers. "The results of the surface-area calculation" leaves nothing to chance. But it's clunkier.

Innovation

2010-01-21T23:52:00.003-05:00

About 540 million years ago, virtually all the basic types of animals appeared in a geographic eyeblink known as the Cambrian explosion.

The late Stephen Jay Gould used this amazing event (if a period of millions of years can be called an event) in his 1989 bestseller Wonderful Life: The Burgess Shale and the Nature of History to debunk two popular myths about evolution. First, evolution is not a steady march toward more and more advanced forms (presumably culminating in us). Second, diversity does not steadily increase. The Cambrian was populated by many types of creature no longer around today, some so exotic as to be worthy of a science fiction film. Rather than growing steadily bushier, the tree of life was later brutally pruned, and we grew from the remaining twigs.

In The Plausibility of Life, Marc Kirschner and John Gerhart highlight another critical facet of this amazing period: since that period of innovation, no more than one new animal type has appeared. The diversity we enjoy today is built from basic parts that were "invented" in the Cambrian explosion.

Rather than get into how and why this happened, for now let's just regard it as an observational fact from the fossil record:

True innovation is rare.

We know this is true in human affairs. Producers of movies and TV shows, for example, often play the odds by re-using and recombining proven concepts. The same goes for technology, where many innovations combine familiar elements in new ways. It's faster, it's cheaper, and it's safer.

For whatever reason, the evolutionary history of life is a series of one-time innovations. After they are adopted, these "core processes" change very little, even though they have eons of time to do so. That doesn't mean that the organisms themselves stay the same--far from it. But they use the core processes in different ways, just as a bat wing is built in the same way as the human hand.

Kirschner and Gerhart discuss several examples of conserved core process, starting with the fundamental chemistry of DNA, RNA, proteins and the genetic code that connects them. Every living thing on earth uses the same chemistry. The appearance of the eukaryotic cell is defined by the presence of the nucleus, but a host of other innovations occurred at the same time. All eukaryotes from amoebae to people share these features even now. The joining of cells into multicellular organisms was also accompanied by innovations that helped the cells stick together and cooperate. Every plant and animal has retained these features largely intact.

Like the animal body plans of the Cambrian explosion, these burst of innovation occurred over relatively short periods, and were permanently added to the toolkit. All of the animals we know, from cockroaches to cockatoos, from squirrels to squids, arose by applying those tools in new ways.

But what is it about the core processes that makes them so resistant to change? What makes them so useful? And are the answers to these questions related?

Monday Morning Quarterbacks

2010-01-20T15:20:00.002-05:00

Many news stories simply report the facts; the better ones put the facts in the context and explore their likely impact.

Then there are stories that explain "why."

I have a simple assessment for these explanation stories that saves me a lot of time. This is it:

"What's new?"

More specifically, is there anything about the "explanation" that wasn't known before the event actually happened? If not, click on.

A classic example is election-night analysis. Even before all the votes are counted, pundits materialize to explain what message the voters were trying to send, and which campaign screwed up.

Unfortunately, most of what they say was just as true the day before. Sure, they now have more precise results, and maybe a geographic breakdown and some exit polls. But most of the commentators' facts were already known to everyone. Somehow the surrounding story seems more profound once it aligns with actual events--and once the arguments for the other side have been conveniently forgotten.

Stories about stocks can also be amusing, or pathetic. It's hard to find any report on a market move that doesn't attribute it to concern about Chinese exports, or some such. When the market is truly uncooperative, analysts resort to saying that it "shrugged off" some really dire economic news. Sorry, guys. If you knew how the market would react, you'd be rich.

Unfortunately, the challenge of explanation applies to the real economy as well. I'm a regular reader of Paul Krugman, who warned a year ago that the stimulus package would likely not generate enough jobs. Sure enough, we now have an unemployment rate that once would have been thought unacceptable. So was Krugman right? Or were the conservatives who said the stimulus just wouldn't work?

The sad fact is we hardly know any better now than we did a year ago. We know what happened, of course. But we still don't know what would have happened if we had done something different. Evaluating such past hypothetical situations requires the same kind of modeling, and relies on the same ideological assumptions, as predictions do. Unsurprisingly, the experts mostly see the past the same way they saw the future.

The same problem may apply to climate change. I don't usually think of the existence of global warming as a political question (as opposed the policy response). In a hundred years, after all, liberals and conservatives will both suffer the same heat and drought and see the same sea level rise, or not. But even then, they probably won't agree on what we did, or didn't do, to get them there. We don't have a duplicate world to do control experiments on.

As Yogi Berra is quoted, "Predictions are hard, especially about the future." Sadly, they are almost as hard about the past.

The Plausibility of Life

2010-01-19T22:01:00.005-05:00

When creationists, or, as they would have it, advocates of "intelligent design," talk about the "weaknesses" of evolutionary theory, knowledgeable people generally roll their eyes and ignore them. This is appropriate, as these advocates only raise the questions in a disingenuous attempt to promote a religious agenda, under the pretense of open-mindedness and "teaching the controversy." In truth, there is no controversy in the scientific community about the dominant role of natural selection (evolution, the theory) in shaping the observed billions of years of change (evolution, the fact) of life on this planet.

But this response obscures the fact that very interesting issues in evolution remain poorly understood.

I'm not referring to the direct exchange of genetic material between single-cell organisms, although that does call into question the tree-like structure of relationships between these simple species. But at the level of complex, multi-cellular creatures like ourselves, this "horizontal gene transfer" is unimportant compared the "vertical" transfer from parents to offspring. The tree metaphor is still intact.

But even for complex creatures-- especially for complex creatures--there are important open questions about how evolution works in detail. The insightful (and cheap!) 2006 book, The Plausibility of Life, by Marc Kirschner and John Gerhart, began to frame some answers to these questions.

The fundamental ingredients of evolution by natural selection were laid out by Darwin: heritable natural variations lead some individuals to be more likely to survive and thus to pass on these variations.

We now know in great detail how cells use some genes in the DNA as a blueprint for proteins, and how these proteins and other parts of the DNA in turn regulate when various genes are active. And we know, as Darwin could only imagine, how that DNA is copied and mixed between generations, only occasionally developing mutations at single positions or in larger chunks. We understand heritable variation.

We also understand the arithmetic of natural selection, which confirms Darwin's intuition: a mutation that improves the chances that its host will survive to be reproduced will spread through a population, while a deleterious mutation will die out (although evolution is indifferent to most mutations). This all takes many generations, but the history of life on earth is long.

But there is something missing, what Kirschner and Gerhart call the third leg of the stool: how does the variability at the DNA level translate into variability at the level of the organism? Selection must occur at this higher level, the level of phenotype, but can only be passed on at the level of the genotype. How do we close this loop?

It would be easy if a creature's fitness were some average of the fitness of each of the three billion bases in the DNA, but it's not that simple. For example, if two proteins work together as a critical team, a mutation in one can kill the organism, even if they could be an even better team if they both mutated in a coordinated way.

This sounds disturbingly reminiscent of the neo-creationist argument that life is so "irreducibly complex" that there must have been a creator--er, designer. But Kirschner and Gerhart don't believe that for a second. What they argue instead is that organisms are constructed so that genetic change can dramatically alter phenotype without sacrificing key functions--in a process they call facilitated variation.

In future posts I will discuss clues that this construction--I'm avoiding the word "design"-- is present in organisms today, and some of the principles it follows.

A man who knew how to inspire

2010-01-18T13:19:00.001-05:00

This speech was given the day before MLK was assassinated.

Sys Devo

2010-01-15T19:36:00.002-05:00

Lawrence Berkeley Labs

My latest eBriefing for the New York Academy of Sciences, Growth Networks: Systems Biology Meets Developmental Biology, is now up (the direct link should work only for Academy members; others may get to it through the NYAS page of my website.)

The symposium was very interesting, but, as often happens, it was challenging to present the three talks as a coherent unit. In this case, the overall message (provided by the visionary organizer, Andrea Califano) is that the sweeping and irreversible changes that occur during early development, which are often driven by a relatively few molecular events (perhaps dozens), can provide stringent and useful tests for understanding molecular regulation. This is quite a different way to learn about networks than by gently poking ("perturbing," for example with stress or drugs or RNA interference) a mature animal, in which various molecules are generally cooperating to keep things stable.

The hope is that the overlap between development and systems biology, can have the sort of powerful synergy that have enriched evolutionary and developmental biology in Evo Devo, as popularized by Sean B. Carroll and others. But I suspect the final synthesis will be more of a three way combination, SysEvoDevo.

Angela DePace of Harvard, for example, described her nascent efforts to exploit evolutionary comparisons between related species from the fly genus Drosophila, which have been a playground for development (once called embryology) for nearly a century. In the past couple of decades researchers have learned how to modify particular genes so they produce fluorescent molecules of various colors along with their normal protein products. The results have shown in living color how various transcription factors interact to generate that spatial patterns and compartments that ultimately shape the segmented body of the fly. DePace and her former colleagues at Lawrence Berkeley Labs refined the technique to let them measure the quantitative changes in gene expression at thousands of individual cells in the early embryo (see the figure), which let them test the models of gene activity (and the differences between species) in fascinating detail.

Stanislav Shvartsman of Princeton also looked at early Drosophila development, but he showed that the transcription factors alone don't explain everything. Instead, some of the patterning depends on protein phosphorylation, which is a half-century old process that among other things carries signals from a cell's outer membrane to its nucleus, but is rarely considered in development. Antonio Iavarone of Columbia studies the development of the early nervous system in mice from stem cells to differentiated neurons. This is a process that is subverted by brain cancers, which re-activate this cellular program to grow and nourish themselves.

Pulling these three diverse talks together was a bit of a shoe-horning exercise, but they were all fascinating.

Outliers

2010-01-14T22:39:00.002-05:00

One of the most subtle and perilous questions in science is when to omit data.

Sometimes there are really good reasons to leave something out. After all, there are lots of ways to screw up data.

In the old days, people could read instruments wrong, or write or copy measurements incorrectly. Even with data acquired and processed by computers, instruments can overload or malfunction and produce incorrect readings. More frequently, even if a measurement itself is correct, changes in the apparatus or the external context can destroy its apparent significance. And it's almost always possible to save data in the wrong place or with the wrong description.

And it matters. Wrong data can cause a lot of headaches. Many analyses reflect a statistical representation of the complete data set, such as the average value, for example, and curve fitting typically penalizes large deviations even more than small ones. So even a single errant measurement can distract from many good ones.

All this means that scientists have good and powerful reasons to eliminate "outliers" that fall outside the normal range of variation, since there's a good chance they are wrong and could skew the results away from the "real" answer.

The problem is that eliminating points requires a subjective judgment by a human experimenter. Often this person is testing a hypothesis, and so has a working expectation of what the data "should" look like. The experimenter will be strongly motivated to toss out points that "don't look right"--even if that just means they are unexpected. That temptation must be avoided.

Distinguishing truly nonsensical measurements from those that simply don't accord with a researcher's expectations requires a level of objectivity and humility that is rare in most people, and difficult even for well-trained scientists.

But it is one of a scientist's most important tasks.

I once heard someone say that it's OK to throw out one data point out of seven. I think that's ridiculously general, and also dangerous. Human nature being what it is, I think the standards need to be higher.

What I learned in my undergraduate laboratory class is that you should check the data as you go along (plotting it by hand in your ever-present lab notebook, if you must know how old I am), to be ready for any measurement problems that arise. If a measurement looks funny, repeat it. If the repeat is what you originally expected, it may be OK toss out the funny one. The repeat might be an individual point, or an entire series. Even better is to do the new measurement twice, and use the majority rule.

Unfortunately, it's not always possible to repeat the measurement exactly. Another alternative is to make a similar measurement, for example with a similar sample. Whenever possible, replication should be part of normal quality control anyway, so this may not be too hard.

But what about when no repeat is possible at all, as happens in historical sciences? You could just throw out all the measurements as unreliable and find a new line of work. But if you opt to toss some of it and not the rest, you really need a very good argument about why that data has a problem. This is a really slippery slope, if there is no way to double-check your argument. People--including scientists--are notoriously good at coming up with post-hoc "just-so" stories for why things are the way they are.

If you really think the data is wrong, but you can't be sure everyone would agree with your logic, scientific tradition still gives you an option: say what you did. Whenever a data is chosen or processed according to a questionable procedure, proper conduct requires that you declare the procedure, certainly in any journal article.

Unfortunately, I have the feeling that, in the era where hot results are sent to general-interest journals like Scienceandnature, this sort of documentation is relegated to the supplementary material or never stated at all. This is a dangerous trend.

Incidentally, many definitions of scientific misconduct include errors of omission. For example, here is the relevant definition from the National Science Foundation's policy:

Falsification means manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record.

In other words, if your deliberate omission distorts the conclusion, you are guilty of fraud. Don't do it.

Anniversary of Hopping Paper

2010-01-12T22:28:00.002-05:00

Thursday, January 14, 2010 is the 25^th anniversary of one of my first scientific papers, Hopping in Exponential Band Tails, in Physical Review Letters. It came out just as I arrived at Bell Labs.

It still surprises me that this paper has gotten nearly 200 citations, and that they continue to dribble in even now. Most papers are surpassed by new developments within a few years of publication. In this case, I stumbled on a useful but very accessible concept that people can easily wrap their head around. But I'd guess that most people that cite it have never read it.

The paper concerns motion of electrons in amorphous semiconductors, that is, semiconductors without a crystalline lattice. The best known example is the amorphous silicon alloys that are used for cheap solar cells.

Until the 1960s, some physicists questioned whether amorphous semiconductors could even exist (although they clearly did), because the quantum-mechanical understanding of semiconductors depended on the mathematical properties of wavelike electrons moving among the regularly-spaced atoms in a crystal. For electrons in some range of energies, the electron waves that diffract from the atoms destructively interfere, creating a bandgap with no electron states. At other energies, where there are electron states, they extend through the entire crystal. None of this mathematical framework for understanding semiconductors seemed to work unless the atoms were arranged in a regular crystal.

Phil Anderson, then at Bell Labs, showed in 1958 that if atoms were arranged in an irregular pattern, electronic states could exist, but be localized near particular atoms. Neville Mott and others suggested that in an amorphous semiconductors, electrons would be localized over some range of energies but extend infinite distances at higher or lower energies. The energies that separated the localized and extended states, which have the character of a phase transition, were called "mobility edges." If one conceptually replaces the band gap of crystalline semiconductors with the gap between mobility edges, then the mathematical treatment of amorphous semiconductors looks very familiar. Anderson and Mott shared the 1977 Physics Nobel with John van Vleck for their discoveries. Instead of being denigrated as "dirt physics," disorder is now a perennial topic in condensed matter physics

In Mark Kastner's group at MIT, we were studying what happened to optically generated electrons in the "band tails": the localized states near the mobility edge, whose number decreases exponentially into the gap. Based on some experiments I had done, I proposed that, at low temperatures, electrons would at first simply "hop" from one localized state to another, avoiding the extended states at the mobility edge altogether. Later on, as they moved to energies where the states where farther and farther apart, they would find it faster to hop up to where there were more states--but not all the way back to the mobility edge.

I called the energy to which electrons hopped--and where they could move easily--the "transport energy," and used a simple model to calculate how this energy varies with temperature.

If once conceptually replaces the band gap of crystalline semiconductors with the gap between transport energies, then the mathematical treatment of amorphous semiconductors looks very familiar. There are some important differences, though. For example, a magnetic field has a different effect on hopping electrons than on those that are freely moving. But although some details are different, the overall picture of amorphous semiconductors looks much like the pictures used by electrical engineers.

At the time, I was concerned that people would only remember Marc Kastner's name, so he graciously agreed to let me be sole author. I later regretted that selfishness, because anyone who knew Mark could see his style in it, and he certainly helped me to frame the ideas. Such are the follies of youth.

"World's First Molecular Transistor"

2010-01-11T23:10:00.003-05:00

Overall electrode geometry, probably not for a device that was actually measured. Inset: Molecule-coated gold nanowire between the electrodes has developed a nanometer-scale gap because of electromigration, in which current pushes atoms away. White rectangle is 100nm long, for scale. Inset of inset: complete fantasy of what might happen in a small fraction of cases.

An interesting article in Nature, Observation of molecular orbital gating, got somewhat lost over the holidays, in spite of the breathless Yale University press release, Scientists create world's first molecular transistor.

Mark Reed of Yale and his colleagues made hundreds of small gold wires between large electrodes on a nominally 3nm-thick alumina insulator on a substrate and coated it with organic molecules. They then applied an electric current that pushed enough atoms out of the way to make a small gap in the wire. Near absolute zero, in a few of the wires, they then measured a current variation with source-drain voltage that looked like what they expected if the current was passing through a molecule close to the surface, whose energy they could change by applying a voltage to the substrate, or gate. In addition, to test whether the current was really going through the extra organic molecules, the researchers found sharp features in the current trace when the source-drain voltage brought the energy levels into alignment, in agreement with the molecules' "signature."

So far, so good. As the authors note, there have been previous observations of gated conduction in molecules before, but it's not easy to get two electrodes connected to a tiny molecule, let alone three.

But the press release says this shows that "a benzene molecule attached to gold contacts could behave just like a silicon transistor."

How does this fall short of that description? Let me count the ways.

No saturation. The current doesn't look at all like a normal silicon field-effect transistor (FET), where the gate voltage changes the channel resistance. Instead, the authors describe the conduction as tunneling between the two remaining pieces of the wire, while the gate voltage changes the precise energy levels in the molecule. The current is low near zero source-drain voltage and rises dramatically as the voltage increases. In contrast, the current in an ordinary FET rises linearly with voltage, like a resistor, and then saturates. In ordinary circuits, this saturation, corresponding to a voltage-independent current, is central to the transistor's gain, which gives it the ability to amplify power or to drive other transistors.

Low current. Tunneling conduction gives inherently low current levels. The currents observed are in the nanoamp range, a million or so times smaller than those in transistors on integrated circuits. This small current would take correspondingly longer to charge up any capacitor, so the circuits would be slow.

Large parasitic capacitance. Because the source and drain electrodes lie right on top of the gate, the actual capacitance is even bigger. Modern transistors are built with self-aligned processes that minimize the overlap capacitance.

Low gate coupling. The authors estimate that they need to put 4V on the gate to change the electron energies by 1V (25%). This is actually surprisingly good for a device like this, where multiplies of 0.1% are not unheard of. But it's still a problem. Silicon technologists work very hard to get perhaps 80-90% of the gate voltage to show up on the channel, and if it doesn't the device is very hard to turn off, resulting in excessive power. Moreover, if the energy isn't being controlled by the gate, it should be controlled by the drain, which means that it will never be possible to saturate the current to isolate the input from the output.

Packing Density. The entire device is much bigger than the molecule. From the micrograph, the electrodes are many microns in size. No doubt the electrodes could be made more compact, but to compete with integrated circuits they would have to be packed to separations comparable to their size, and this technique doesn't look like it could ever do that. No one really cares whether transistors are small. They care if they can be packed densely (and are cheap and fast and use little power).

Low Yield. The authors measured 35 devices that did what they hoped, out of 418 attempts, so about 8% of them worked. In contrast, in an integrated circuit only about 0.000001% of the transistors fail. (Or something like that--I don't have access to real numbers these days, but you get the idea.) Building a large circuit from occasionally-functional devices would require a completely new type of circuit design, and probably wouldn't be worth it.

This low yield is not surprising (or easily avoided), since the fabrication seems to demand that most of the current run through a molecule that is positioned right just at the gap and right at the corner where the wire meets the substrate, and that it not get blown away during the electromigration. Still, it is a matter of concern that devices are defined to be working if they do what the experimenters think they ought to do. This is a problem with many molecular fabrications schemes, and I give the team credit for doing the "inelastic tunneling spectroscopy" to verify the molecules have something to do with the current. But I would feel better if they gave an indication of how representative the devices they showed are.

The authors did a couple of other tests that I'm guessing didn't work as dramatically as they had hoped. First, they saw rather small differences between "insulating" molecules--fully saturated alkanes sandwiched between sulfur groups-- and "metallic" molecules--in which the organic "meat" of the sandwich is an aromatic benzene ring. Second, the voltage "fingerprints" of the molecules didn't shift when they applied the gate voltage, as one would naively have expected. The shapes and sizes of the peaks changed, but not their positions.

Overall, this is a nice research result, with some strong observations and some puzzling features. Some of my quibbles could be addressed in time, but it's not clear that these molecules will ever behave "just like a silicon transistor."

In 1997, Mark Reed was quoted to the effect that silicon technologists were "shaking in their boots" over his team's results. Those of us working in silicon technology at the time got a good laugh out of that claim, and went back to work. The new press release says that "Reed stressed that this is strictly a scientific breakthrough and that practical applications such as smaller and faster 'molecular computers'—if possible at all—are many decades away. 'We're not about to create the next generation of integrated circuits,' he said."

He's got that right.

Delayed Re-entry

2010-01-04T22:08:00.003-05:00

The new year finds me working through the summary for the RECOMBsat/DREAM conference last month in Cambridge, MA (more than 10,000 words on over 20 separate subjects), so daily blogging will have to wait until the week of January 11.

Check back then--upcoming posts will include molecular transistors, the 25^th anniversary of one of my first scientific papers, issues raised by the "climategate" emails, a series on evolutionary biology inspired by the book, The Plausibility of Life, new stories I've written, and more.

In the meantime, check out this video (Click the "Technology" tab) explaining how modern "deep sequencing" technology (here from Illumina) can simultaneously determine the base sequences for something like a million short snippets of DNA simultaneously. In contrast to the "traditional" (decade-old) microarray method of matching to preselected targets, this method allows new sequences to be quickly found, for example in the human gut or other natural environments. In addition, by matching these sequences to previously mapped genomes, this technology has revolutionized the identification of short regulatory RNAs, alternative splicing of proteins, DNA changes in cancer, DNA binding sites for transcription factors, fractal DNA structure, and many other areas of biology.

And it's just beginning.

Salting out and in

2009-12-29T00:02:00.002-05:00

My latest story for Physical Review Focus concerns calculations of the tendency of various ions dissolved in water to accumulate at its surface.

This is a really old problem, discussed by some of the giants of physical chemistry. In the 1930s, for example, later Nobel winner Lars Onsager and others suggested that the termination of the electrical polarization at the surface of the water would give rise to an "image charge"--a surface charge of the same sign as the ion that creates an electric field just like that of a point charge at the mirror-image location on the other side of the interface. The repulsion from this image charge, they suggested, would keep ions away from the surface.

People have apparently suspected for decades that things can't be that simple, because different ions alter the surface tension to different degrees, indicating that they are changing the energy of the surface, presumably by being part of it. But only in the past decade or so have new experiments and simulations shown that some simple negative ions like halogens can be stable at the surface. Such ions at the surface of atmospheric droplets could be important catalysts, for example for breaking down ozone.

The two closely related Physical Review Letters that motivated the Focus story attribute the attractiveness of the surface position of a large negative ion to its internal polarizability. The internal rearrangement of charge, they say, allows the ion to retain much of the electrostatic attraction to nearby water molecules without creating a big hole in the water. However, I talked to another researcher who attributes the stabilization of the surface ion to a distortion it induces in the shape of the nearby surface. These both seem like potentially important effects, and both may play a role in the ultimate understanding.

The difference between the two could be important, though, for a related and even older phenomenon: the effect of various added salts on dissolved proteins. In 1888, Hofmeister ranked a series of ions in terms of their effectiveness in precipitating the proteins, and the order of the series mirrors that which was later found for the effects of ions on surface tension.

"Salting out" occurs when an added salt reduces the solubility of a protein, presumably by tying up water molecules and raising its effective concentration. This effect has been used for decades to create the protein crystals needed for structural studies like x-ray crystallography.

In contrast, "salting in" makes the protein more soluble, but may denature it. Salts that have this effect may alter the repulsion between water and the hydrophobic regions of the protein. This repulsion is critical for maintaining the shape of proteins that naturally occur in the bulk of the cell, since that shape generally presents hydrophilic regions to the solution and shelters hydrophobic regions inside. (Proteins that naturally occur in membranes, by contrast, generally expose a hydrophobic stripe where they are embedded in the non-aqueous center of the membrane sheet.)

The polarizability of ions at the protein-water interface could have an important effect on this repulsion. In contrast, since the water-protein interface is entirely within the liquid, changing the shape of the interface wouldn't seem to be an option.

It is true that many proteins take on their final shapes only in the presences of "chaperone" proteins, which can also help fix them up if they become denatured. Nonetheless, any insight into the interactions between water and proteins could be very important to understanding why they fold the way they do, and how circumstances might change that folding.

Rules to Design By

2009-12-14T23:17:00.002-05:00

Once something gets really too complicated, it's almost certain to fail. So how can computer chips, with their billions of components, work at all?

We know lots of other complicated systems, like the world economy or our own bodies. And we know those systems fail often dramatically or tragically.

Of course, computers fail, too, as you know if you've seen a "blue screen of death" recently. But although it won't make you feel any better, those crashes almost always arise from problems with the software, not the hardware it runs on.

So how do engineers ensure that integrated circuits, diced by the score from semiconductor wafers, have a very good chance of working?

Design rules.

Simply put, design rules are a contract between the engineers designing the process for making chips and the engineers designing circuits to put on them. The process engineers guarantee that, if the circuit designers follow these rules in the geometry of their circuits, the chips will work (most of the time).

You may have heard "minimum design rule" used as a shorthand to describe a particular "generation" of computer chips, such as the "32nm" technology recently introduced by Intel. But that is shorthand is somewhat misleading.

For one thing, the true gate length of the transistor--which is critical to their speed and power--is generally about half the generation name. In addition, the "coded" gate length--the length in computer-aided-design files--is not usually the smallest design rule. And this is just one of hundreds of rules that are required to define a technology.

Rather than dive into the details of transistor geometry, consider a simpler design rule: the minimum width of a metal wire connecting the transistors. Together with the spacing between the wires, this dimension determines how tightly the wiring can be packed, which for some circuits determines how many transistors can be used in a parcel of semiconductor real estate.

The minimum safe design width of a wire depends on how fine it can be made and still assure that it will conduct electricity. This has to be guaranteed even under variations over time of the process used to make it, as well as the variation in that process across a, say, 12-inch diameter wafer.

To test what number is safe, the process engineers will make a whole series of test patterns, each consisting of very long wires with various design widths. After measuring hundreds of these test structures, they have a good idea what they can reliably make.

In developing the process technology, they have hundreds of test structures, each aimed at testing one or more design rules. The structures are automatically measured on different positions on different wafers made in different processing runs. Only then will the engineers have the confidence to guarantee that any circuit that follows those rules will work.

After a long process, a set of design rules will be given to designers to use for their circuit layouts. None of this would work without computers to check whether a particular chip layout meets the rules, since the job is beyond human capacity. Therefore a key feature of the design rules is that they can be embodied in an efficient algorithm.

The design-rule paradigm has been extraordinarily successful. But its success depends on a characteristic of the failures it is intended to prevent: they are all dependent on the local properties of the circuit. Some of the more complex rules involve quantities like the area of the metal "antenna" that is connected to a particular device at some point during processing. And frequently the engineers will play it safe by crafting the rules to cover the worst possible situation. But if the rules are chosen and followed properly, there is no chance for a combination of small choices that satisfy the rules to join together to cause a problem in the larger circuit. That's what makes a chip with a billion transistors possible.

Chromatin Compartments

2009-12-11T18:18:00.002-05:00

The fractal packing of some DNA is just one of the interesting results from the recent Science paper by Lieberman-Aiden and colleagues. Of greater practical importance is the ability of their experimental technique to assign each region of DNA to one of two compartments.

The fact that some DNA regions, called heterochromatin, are packed more densely than other regions, called euchromatin, was discovered 80 years ago, by observing darker and lighter regions of stained nuclei under the optical microscope. Researchers have since learned that the heterochromatin is more densely packed, and that the genes it contains are transcriptionally silent. Heterochromatin also tends to segregate to the periphery of the nucleus, but to avoid the nuclear pores through which gene products are exported.

The Science authors did not mention this well-known classification. However, when they measured which regions of the genome were close together in the clumped DNA, they found that they could divide the mappable regions of the genome into two distinct "compartments." Regions from compartment A were more likely to lie close to other regions from compartment A, and similarly for compartment B. Importantly, they could make this assignment even for regions on different chromosomes, suggesting that the compartments represent regions of the nucleus in which segments of different chromosomes mingle.

The researchers also found that regions in compartment B were much more likely to be in close contact, so they designated that compartment "closed," and the other one "open." But Erez Lieberman-Aiden told me that "it seemed best to use terminology attached to things that we can probe and which clearly correspond to our compartments." Indeed, the regions they call "open" correspond well to the regions that can are accessible to DNA-digesting enzymes, but do not correspond to the light and dark bands that appear on the chromosomes during cell division.

Although the relationship to microscopically-observed partitioning may need clarification, the ability to globally map closed and open regions of the genome could be a very powerful tool. Looking at different cell types, for example, could reveal overall "signatures" in the chromosome arrangements. Such cell-type-specific patterns are already known to exist in the arrangement of histone modifications, which affect the nucleosome arrangement.

In addition, the chromatin structure enters into regulation of individual genes. Enhancer elements in the DNA sequence, for example, can affect the expression of quite distant genes, while an intervening insulator region can block that effect. Models of these influences generally involve large loops of DNA, but some also include the notion of a densely-packed and transcriptionally silent "scaffold" region that is reminiscent of the closed compartment. Determining which sections of the sequence are in the closed or open arrangements, especially in cells with different types of activity, could add some much-needed experimental visibility into the regulatory activity of these critically important elements.

[For physicist readers: as I was wrapping up this entry, the latest Physics Today arrived with a news story on this subject.]

Fractal DNA

2009-12-10T23:47:00.003-05:00

Packing meters of DNA into a nucleus with a diameter a million times smaller is quite a challenge. Wrapping the DNA around nucleosomes, and arranging these nucleosomes into 30nm fibers, both help, but these structures must themselves be packed densely. Beautiful new research, reported in Science in October, supports a 20-year old idea that some DNA is arranged in an exotic knot-free fractal structure that is particularly easy to unpack.

Alexander Grosberg, now at New York University, predicted (1M pdf) in 1988 that a polymer would initially collapse into a "crumpled globule," in which nearby segments of the chain would be closer to each other than they would be in the final, equilibrium globule. Creating the equilibrium structure requires "reptation," in which the polymer chain threads its way through its own loops, forming knots. This gets very slow for a long chain like DNA. Grosberg also applied (1M pdf) these ideas to DNA, and explored whether fractal patterns in the sequence could stabilize it. But experimental evidence was limited.

Now Erez Lieberman-Aiden and his coworkers at MIT and Harvard have devised a clever way to probe the large-scale folding structure of DNA, and found strong support for this picture.

The experiment is similar to chromatin immunoprecipitation techniques that look for DNA regions that are paired to target proteins by crosslinking and precipitating the pairs and then sequencing the DNA. In this case, however, the researchers crosslink nearby sections of the collapsed DNA to each other. To sequence both sections of DNA, they first splice the ends of the pairs to each other to form a loop, and then break them apart at a different position in the loop. The result is a set of sequence pairs that were physically adjacent in the cell; their positions along the DNA are found by matching them to the known genome.

The researchers found that the number of neighboring sequences decreases as a power law of their sequence separation, with an exponent very close to -1, for sequence distances in the range of 0.5 - 7 million bases. This is precisely the expected exponent for the crumpled--or fractal--globule. This structure is reminiscent of the space-filling Peano curve with its folds, folds of folds, and folds of folds of folds forming a hierarchy. In contrast, the equilibrium globule has an exponent of -3/2.

As a rule, I don't put a lot of stock in claims that a structure is fractal simply by seeing a power law, or a straight line on a double-logarithmic plot, unless the data cover at least a couple of orders of magnitude. After all, a true fractal is self-similar, meaning that the picture looks exactly the same at low resolution at high resolution, and in many cases there's no reason to think that fine structure resembles the coarse structure at all.

But when there's a good theoretical argument for similar behavior at different scales, I relax my standards of evidence a bit. For example, there's a good argument that rate the random walk of a diffusing molecule looks into neighboring volumes looks similar, whatever the size of the volume you consider--this is a known fractal. The standard polymer model is just a self-avoiding random walk, which adds the constraint that two parts of the chain can't occupy the same space. The DNA data are different in detail, but the mathematical motivation is similar.

At the conference I covered last week in Cambridge, MA, Lieberman-Aiden noted that the fractal structure has precisely the features you would want for a DNA library: it is compact, organized, and accessible. The densely packed structure keeps nearby sequence regions close in space, and parts of it can easily be unfolded to allow the transcription machinery to get access to it. Co-author Maxim Imakaev has verified all of these features with simulations of the collapsing DNA.

These experiments and simulations are fantastic, and the fractal globule structure makes a lot of sense. But this dense structure makes it all the more amazing what must happen when cells divide, making a complete copy of each segment of DNA (except the telomeres), and ensuring that the epigenetic markers on the DNA and histones of one copy are replicated on the other. It's still an awesome process.

Short RNAs to the Rescue

2009-12-07T23:10:00.002-05:00

Ever since scientists realized, just over a decade ago, that exposing cells to short snippets of RNA could affect the activity of matching genes, they have dreamed if harnessing this RNA interference, or RNAi, to fight diseases. In the past week, two groups have announced progress toward that goal, treating chimpanzees with hepatitis C and mice with lung cancer.

RNAi, which rapidly earned a 2006 Nobel Prize, is just one facet of the many ways in which short RNAs regulate gene activity. Researchers have since found numerous types of naturally occurring short RNA that play important roles in development, stem cells, cancer, and other biological processes. These RNA-based mechanisms could seriously revise the emerging understanding of how cellular processes are controlled.

Over the same period, manipulating genetic activity with short RNAs has become an essential tool in biology labs. Cells process various forms of short RNA, such as short-hairpin RNA (shRNA) and small interfering RNA (siRNA) into RNA-protein complexes that reduce (usually) how much protein is made from a messenger RNA that include a complementary (or nearly complementary) sequence.

This technique gives researchers a quick way to learn about what a particular gene does, at least in culture dishes, sidestepping the laborious creation and breeding of genetically-modified critters. (Or if they do put in the time, they can insert genes that allow them to controllably trigger RNAi to knock down a gene only in particular cells or after it has completed an indispensible task in helping an organism to grow.)

But affecting genetic regulation in patients faces the challenges of "delivery" that are well-known in the pharmaceutical industry: To have a beneficial effect, the short RNA must survive in the body, get inside the right cells in large quantities, and not cause too many other effects in other cells. The New York Academy of Sciences has a regular series on the challenges of using RNA for treatment, and I covered one very interesting meeting in 2008.

Molecular survival is the first challenge. Researchers have developed various chemical modifications that help RNA (or a lookalikes) withstand assaults by enzymes that degrade rogue nucleic acids. Santaris, for example, which helped in the hepatitis project, has developed proprietary modifications it calls "locked nucleic acids," or LNA. Other researchers and companies are exploring similar techniques.

Getting the protected RNA to the right tissue is another challenge. Foreign chemicals are naturally cycled to the liver for processing, so it's fairly easy to target this organ. For this reason, the hepatitis results don't really prove that the technique is useful for other tissues. The Santaris release also neglects to mention any publication associated with the research.

The mouse lung cancer result appears in Oncogene. The lead Yale researcher, Frank Slack, regularly studies short RNAs in the worm C. elegans, as I described in a recent report from the New York Academy of Sciences. In this work, he teamed with Mirna Therapeutics, which aims to use the short-RNA-delivery vehicle to replace naturally occurring microRNA that are depleted in cancer, like the let-7 they used for this study. The mouse cancers did not disappear, but they regressed to about a third of their previous size, according to the release. Mirna says that since they are replacing natural microRNAs, their technique shouldn't induce many side effects in other tissues.

A further risk for small-RNA delivery is immune responses. The field of gene therapy is only now recovering from the 1998 death of Jesse Gelsinger in what looks like a massive immune response to the virus used to insert new genes in his cells. Although the short-RNA response will be different, some cellular systems are primed to respond to the foreign nucleic acids brought in by viruses.

It's likely that there will be many twists and turns along the way, and I haven't solicited expert opinions on these studies, but they seem to be intriguing steps toward the goal of using RNA not just to study biology, but to change people's lives.

Massachusetts Dreaming

2009-12-02T16:48:00.002-05:00

Today I'm taking Amtrak to Cambridge--our fair city--MA, for an exciting back-to-back-to-back trio of conferences at the MIT/Harvard Broad (rhymes with "road") Center.

Two of the conferences are described as satellites to RECOMB (Research in Computational Molecular Biology), even though that meeting was in Tucson in May. One of these is on regulatory genomics and the other on systems biology. The third is the fourth meeting of the DREAM assessment of methods for modeling biological networks, a series I've covered since its organizational meeting at the New York Academy of Sciences in 2006.

There's a lot in common between these conferences, so it's not always easy to notice the boundaries. The most tightly focused is DREAM--Dialog on Reverse-Engineering Assessment and Methods. The goal is simple to state: what are the best ways to construct networks that mimic real biological networks, and how much confidence should we have in the results. In practice, things are not so straightforward, and border on the philosophical question of how to distinguish models and "reality." The core activity of DREAM is a competition to build networks based on diverse challenges.

The Regulatory Genomics meeting covers detailed mechanisms of gene regulation, often focusing on more formal and algorithmic aspects than would be expected in a pure biology meeting. The Systems Biology meeting addresses techniques, usually based on high-throughput experimental tools, for attacking large networks head on, rather than taking the more traditional pathway-by-pathway approach.

I'll be writing synopses of the invited talks and the DREAM challenges for an eBriefing at NYAS, but I'll be free to relax and enjoy the contributed talks and posters. This promises to be a rich and exhausting five days.

Packing DNA Beads

2009-12-01T23:12:00.002-05:00

The dense packing of DNA in the nucleus of eukaryotes strongly affects how genes within it are expressed, with some regions much more accessible to the transcription machinery than others. At the shortest scales, the accessibility of the DNA double helix is reduced where it is wound around groups of eight histone proteins to form nucleosomes, and the precise position of the nucleosomes in the sequence affects which genes are active.

At a slightly larger scale, the nucleosomes are rather closely packed along the DNA. They can remain floppy, like beads on a string, or they can fold into rods of densely packed beads, which further reduces the accessibility of their DNA. Other proteins in the nucleus, notably the histone H1, help to bind together this dense packing. These rods can pack further, with the help of other proteins.

The histone proteins that form the core of the nucleosome, two copies each of H2A, H2B, H3, and H4, have stray "tails" extending from the core. Small chemical changes at particular positions along these tails can have surprisingly large influence on the expression of the associated DNA. For example, the modification H3K27me3 (three methyl groups attached to the lysine at position 27 on the tail of histone H3) represses expression, while acetylation of the same amino acid, H3K27ac activates expression. There is also a more substantial modification, in which histone H2A is replaced by a variant called H2A.Z also modifies expression.

The detailed mechanisms by which the modifications affect expression, such as changing the wrapping of nucleosomes, the packing of nucleosomes, or recruiting of other proteins in the nucleus, are areas of active research.

Since there are dozens of possible histone tail modifications, there are vast numbers of possible combinations of modifications. Some researchers have proposed that these combinations could each prescribe different expression patterns, for example during development. However, the evidence for a combinatorial "histone code" analogous to the three-base codons of the genetic code remains weak.

Nonetheless, proteins that can modify the tails, either adding or removing a chemical group, can have lasting effects on the activity of the underlying genes. The sirtuin proteins that are candidates for longevity-extending drugs, for example, are best known for their role as histone deacetylases.

Some histone modifications can be passed down through cell division or reproduction, so they qualify as epigenetic changes. In contrast to the natural replication of the mirror-image DNA sequence, replicating histone modifications requires a much more complicated process.

Changes in the pattern of histone modifications are found in many basic biological processes, including development, stem-cell maintenance, and cancer. Particular modification patterns have been used to find specific functional sequences within the DNA, such as transcription start sites and enhancers. For these reasons, the ENCODE project mapped modifications as part of their survey of a select part of the human genome for intense study.

Understanding the mechanisms and roles of DNA organization and how it is changed will be essential to a complete picture of gene regulation.

The Honest Broker

2009-11-30T21:00:00.002-05:00

In case you hadn't noticed, discussion of global warming has become somewhat polarized. Amid accusations, on the one hand, that industry-financed non-experts deliberately sow confusion, and on the other that a leftist cabal exaggerates the risks and threatens our economy, Roger A. Pielke, Jr. is something of an anomaly.

A professor of environmental studies at the University of Colorado, Pielke is an expert who endorses the broad consensus that humans are causing dangerous changes. But he also criticizes scientists like those on the Intergovernmental Panel on Climate Change for stifling legitimate dissent in the service of narrow policy options. In his 2007 book, The Honest Broker: Making sense of science in policy and politics, Pielke touches on climate change only tangentially as he outlines how scientists can more constructively contribute to contentious policy decisions.

Reading the title, I thought at first that I understood what Pielke meant by an "Honest Broker." As an undergraduate thirty years ago I dabbled in the still-young academic field of Science, Technology, and Society. Books like Advice and Dissent: Scientists in the political arena, by Joel Primack and Frank Von Hippel illustrated how scientists who step outside their specialized knowledge to advocate particular policies risk both their own credibility and that of science. To preserve the authority of expertise, scientists should be careful and clear when they spoke outside of their specialty.

But the intervening years, Pielke says, have shown that the whole notion that science provides objective information that is then handed over to inform policy makers, the so-called linear model, is naïve and unrealistic. Only rarely, when people share goals and the relation between causes and effects is simple, can scientists meaningfully contribute by sticking to their fields of expertise as a "Pure Scientist" or by providing focused answers to policy questions as a "Science Arbiter."

More frequently, people do not share goals and the causal relationships are more complicated. Scientists who wish to contribute to these policy debates are naturally pulled into the role of "Issue Advocate," marshalling the science in support of a narrowed range of politically-supported options. Although this is a useful role, Pielke warns, scientists often drift into it unwittingly. As they deny any political influence on their scientific judgments, these "stealth issue advocates" can damage the authority of science even as they obscure the true nature of the political decision.

To address this problem, Pielke pleads for more scientists to act as "Honest Brokers of Policy Alternatives," to give his complete description. Such scientists, presumably as part of multi-disciplinary committees like the now-defunct Congressional Office of Technology Assessment, would act to expand the available policy alternatives rather than restrict them. Unlike the science arbiter, Pielke's honest broker recognizes an inseparability of policy issues from the corresponding scientific issues, but nonetheless provides a palette of options that are grounded in evidence.

In my technology research, I've had my own complaints about the analogous linear model. I've found that pure research often leads to more research, rather than to the promised applied research and products that make everyone's lives better. But Pielke's criticism of the linear model is more fundamental. He correctly notes that in many complex situations, scientific knowledge does not, on its own, determine a policy outcome. But he then seems to conclude that there is no legitimate role for objectively valid science that can narrow policy options.

In discussing Bjørn Lomborg's The Skeptical Environmentalist, for example, Pielke says "Followers of the linear model would likely argue that it really does matter for policy whether or not the information presented in TSE is 'junk' or 'sound' science." He then shows that for some criticisms of the book, the validity of the science was irrelevant to policy. But many of the standard talking points raised by global-warming skeptics are well within the bounds of science, so clarifying them is a useful narrowing of options, even if it doesn't lead to a single, unanimously correct policy.

Nonetheless, Pielke's short, readable book provides a helpful guide for what we can hope for in policy debates involving science, and how scientists can most productively contribute. What we can't hope for is a single, science-endorsed answer to complex issues that trade off competing interests and conflicting values. For that, we have politics.

Green Computing

2009-11-25T20:17:00.003-05:00

Supercomputers run the vast simulations that help us to better predict climate change--but they also contribute to it through their energy consumption.

The lifetime cost of powering supercomputers and data centers is now surpassing the cost of buying the machines in the first place. Computers, small, medium, and large, have become a significant fraction of energy consumption in developed countries. And although the authors of Superfreakonomics may not understand it, the carbon dioxide used to supply this energy will absorb, during its time in the atmosphere, some 100,000 times more heat energy than that.

To draw attention to this issue, for the last two years researchers at Virginia Tech have been reordering the Top500 list of the fastest supercomputers, ranking them according their energy efficiency in the Top Green500 list. I have a wee news story out today on the newest list, released last Thursday, on the web site of the Communications of the Association for Computing Machinery. The top-ranked systems are from the QPACE project in Germany, and are designed for quantum chromodynamics calculations.

Calculating efficiency isn't as straightforward as it sounds. The most obvious metric is the number of operations you get for a certain amount of energy. This is essentially what Green500 measures in its MFLOPS/W, since MFLOPS is millions of floating-point operations per second and watts is joules per second.

As a rule, however, this metric favors smaller systems. It also favors slower operation, which is not what people want from their supercomputers. Some of the performance lost by running slowly can be recovered by doing many operations in parallel, but this requires more hardware. For these reasons, the most efficient systems aren't supercomputers at all. The Green500 list works because they only include the powerhouse machines from the Top500 list, which puts a floor on how slowly the competing machines can go.

Over the years, researchers have explored a family of other metrics, where the energy per operation is multiplied by some power of the delay per operation: EDⁿ. But although these measures may approximately capture the real tradeoffs that systems designers make, none has the compelling simplicity of the MFLOPS/W metric. This measure also leverages the fact that supercomputer makers already measure the computational power to get on the Top500 list, so all they need to do extra is measure the electrical power in a prescribed way.

These systems derive much of their energy efficiency from the processor chips they use. The top systems in the current list all use a special version of IBM's cell processor, for example. I worked on power reduction in integrated circuits more than a decade ago--an eternity in an industry governed by Moore's Law--and some of my work appeared in a talk at the 1995 International Electron Devices Meeting. I also served for several years on the organizing committee of the International Symposium on Low Power Electronics and Design, but I'm sure the issues have advanced a lot since then.

In addition to the chips, the overall system design makes a big difference. The QPACE machine, for example, serves up as about half again as many MFLOPS/W as its closest competitor by using novel water-cooling techniques and fine-tuning the processor voltages, among other things. These improvements aren't driven just by ecological awareness, but by economics.

There's still lots of room for improvement in the energy efficiency of computers. I expect that the techniques developed for these Cadillac systems will end up helping much more common servers to do their job with less energy.

Happy Anniversary

2009-11-24T20:40:00.002-05:00

One hundred fifty years ago today, the first edition of Charles Darwin's masterpiece On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life was published.

I picked up a copy of the book a few years ago for about $10, when the American Museum of Natural History in Manhattan had a Darwin exhibit. The most memorable display for me was the handwritten notebook entry where he first speculated about the tree-like connectivity between different species. I was humbled to be within a few feet of this tangible record of his world-changing inspiration, written with his own pen.

The book itself was very readable, intended as it was for an audience far beyond specialists. Starting with what would then have been familiar techniques of plant and animal breeding--"artificial selection"-- Darwin proposes conceptually extending that process to nature. Combining natural variation with its heritability, a Malthusian appreciation of the struggle to survive and an awareness of the immensity of geographic time, this extension seems eminently reasonable.

And yet there are challenges. Rather than bluster through them, Darwin addresses them head on, conveying an honesty and openmindededness that is bracingly refreshing in our argumentative times.

He confronted head-on, for example, the intellectual challenges of the intricate structure of the eye, fully admitting that the theory demanded that at every step of evolution there be some function for the intermediate forms. Even today, intelligent design proponents profess to be flummoxed by the very challenges that Darwin faced--and faced down.

Darwin also clearly described the tradeoffs needed for the evolution of traits like altruism, avoiding the temptation to invoke the "good of the species." To persist, such traits must provide an advantage to the group that exceeds the cost to individuals. This clear statement of the constraints of group selection needs wider appreciation today.

In these and many other areas, Darwin anticipated and addressed the confusing aspects of his explanation for evolution. And he did it all without even the benefit of Mendel's laws of genetics.

Time and again in the intervening decades, newly uncovered evidence from biology and paleontology has reinforced the essential correctness of Darwin's framework. The laws of genetics and of their DNA mechanism, the fossil record of transitional forms, and mathematical models have all confirmed and clarified the power of undirected selection of random variation for driving innovative new possibilities.

There are caveats, of course. Non-inherited mechanisms of genetic transfer change the story in important ways, especially near the single-celled trunk of the tree of life. Such revisions are hardly surprising after 150 years of scientific advancement.

What is humbling is the persistent soundness of the essence of Darwin's vision, and of this amazing book.

Rules or Consequences

2009-11-22T22:38:00.002-05:00

Can we learn about one phenomenon by studying a completely different one?

Putative "laboratory versions" of exotic phenomena appear regularly in the news, such as microwave analogs of "rogue" ocean waves, optical-fiber analogs of rogue waves and black holes, and, as I've discussed here, magnetic-crystal analogs of magnetic monopoles.

But not all of these experiments are equally illuminating. Researchers, and journalists who write about them, need to think clearly about how the two systems are related, and what's missing. Experiments on a model system can show what behavior arises from shared underlying rules, and how that behavior changes as conditions change. But only experiments on the original system can test whether those rules are relevant.

The results of known mathematical rules aren't always obvious. Even Newton's second law, which relates the force on an object to its acceleration, only stipulates a differential equation that researchers must solve to find how an objects position changes with time. When the force is constant, this is easy: the position follows a parabolic course in time.

For more complicated situations, scientists often can't relate the rules to the end result. In some cases they turn to simulations, which can be regarded as a model system that, ideally, embodies the mathematical rules perfectly. But simulations are often restricted to unrealistically small systems that could behave differently than the real McCoy.

In these cases, researchers can learn from actual systems that--they think--follow similar rules. For one thing, this may make precision measurements easier. Placing a block on an inclined plane, for example, slows down its acceleration due to gravity, making it possible to test the parabolic law more precisely.

Unfortunately, the model system may introduce complications of its own. The friction on a sliding block is significantly different than that air friction on a falling body--for example it's much larger before the block starts to move. Even though the rules of gravitational force are the same, the differences may completely obscure the relationship between the two systems. Researchers must then spend a lot of energy tracking down these differences.

But to draw any parallel between two systems, researchers must establish that both are governed by similar rules. Unless they know that, seeing a particular behavior in a model system, by itself, is irrelevant for deciding if the original system follows the same rules. The way to test that--but not prove it--is to do experiments on that system, and see if the behavior is similar.

In our example, if an object follows a parabolic time course, it might well be that it is responding to a constant force. (Of course, it may just be moving through curved spacetime.) With luck, the model system--the inclined plane--would have demonstrated something close to this parabolic result, even if the equations had been unsolvable. The model system then hints at a similarity of the governing rules.

Similarly, a chaotic microwave cavity or an optical fiber might show a "long tail" in the distribution of wave heights that mathematically resembles that which is experimentally measured on the ocean, and which occasionally spawns mammoth rogue waves. Because it's easier to vary the conditions in the laboratory, these experiments might also show what aspects of wave propagation are relevant to rogue-wave formation. In these systems, researchers already understand the basic features of wave propagation--the question is what happens when they combine the ingredients in various ways.

In contrast, physicists do not know whether the basic equations of physics allow magnetic monopoles. Some grand unified theories predict them, but they've never been seen in free space, despite extensive experiments. The observation of monopole excitations at low temperatures in magnetic materials called spin ices has absolutely no implications for the nature of the fundamental equations. It may be that it helps to understand how "real" monopoles would behave, if they exist. But it says nothing about whether they do.

Model systems can reveal important relationships between models and behavior. They can also uncover real-world complications that need to be included to make models more relevant. But to find out whether a model applies to a particular system in the first place, researchers need experiments on that system. Experiments on a model system aren't enough.

Denialism

2009-11-19T21:24:00.002-05:00

As someone who communicates science for a living, I frequently struggle to understand the widespread distrust of scientific evidence in public and private decisions. I was looking forward to some enlightenment in The New Yorker writer Michael Specter's new book, Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives.

I was disappointed. The book scarcely addresses the origins of denialism, or even, as the subtitle advertises, its consequences. Instead, it reads as a cobbled-together series of feature articles, all too long to be called vignettes. The pieces are mostly interesting, well researched and well written, but they include a lot of background material that is peripheral to denialism. As to where the attitude comes from, Specter offers only speculation.

Specter is unlikely to make many converts to "rational thinking," since he frequently comes across as a cheerleader for progress, even as he acknowledges its risks and uncertainties. For example, near the close of his 21-page introduction, he shares a letter from a New Yorker reader: "…the question remains, will this generation of scientists be labeled the great minds of the amazing genetic-engineering era, or the most irresponsible scientists in the history of the world? With the present posture of the scientific community, my money, unfortunately, is on the latter." I regard this is a valid question, but Specter dismisses it: "Those words might as well have been torn from a denialist instruction manual: change is dangerous; authorities are not to be trusted; the present 'posture' of the scientific community has to be one of collusion and conspiracy." He doesn't seem to allow for reckless overconfidence.

Specter doesn't address climate change, which is the only big issue where denialism (as opposed to progress) threatens to "harm the planet." Cynics will note that it's also the issue where denialism promotes corporate interests, rather than opposing them. But the various chapters cover a wide range of topics.

In Vioxx and the Fear of Science, Specter reviews Merck's coverup of the heart risks of their pain medication, Vioxx. This sorry episode has been discussed elsewhere, for example in Melody Peterson's Our Daily Meds, but on its face it has little to do with irrational denial. In fact, in this case, distrust of pharmaceutical companies and the FDA are quite well founded. But in the final section of the chapter that reads like an afterthought, Specter blames much of the public's disregard for scientific evidence such betrayals of trust, although he gives little evidence for this connection.

Specter also uses the Vioxx case to illustrate a common problem: undue attention to acute harms rather than small, distributed benefits. He even argues that the thousands of deaths from Vioxx might have been a reasonable price to pay for its pain relief benefits to millions. Such weaknesses in risk assessment certainly skew many policy and private decisions. But our oft-lamented poor balancing of accepted risks and benefits strikes me as somewhat distinct from denialism, in which scientific evidence for benefit or harm is dismissed entirely.

Both denialism and poor weighing of pros and cons also come to play into the second chapter, Vaccines and the Great Denial. Specter makes it clear there is virtually no science supporting the anti-vaccine movement, and documents the highly misleading selective quotation of a government report in Robert Kennedy's famous Rolling Stone story. This is an easy case to make, but he does it convincingly.

In The Organic Fetish, Specter combines two distinct food-related issues. He shows convincingly that the benefits of "organic" foods are less clear-cut than advocates would like to believe, although I prefer Michael Pollan's wonderful book, The omnivore's dilemma: a natural history of four meals. But Specter's denialism them lets him zig-zag erratically between organic and genetically modified (GM) foods. He compelling despairs over African nations' rejecting GM foods for their starving populations, but he is too willing to accept the standard, long disproven reassurances about the limited spread of modified foods. Still, Specter resoundingly dispels the mythical distinction between modern modifications and those that have been accepted for decades or millennia.

Specter's chapter on food supplements and alternative medicine, The Era of Echinacea, also has an easy target, although he notably includes multivitamin supplements among the snake oils. But again, his discussion lacks a clear explanation of why many people trust these uncontrolled additives more than they do the tightly-regulated products of the pharmaceutical industry.

Race and the Language of Life combines two disparate topics. Specter's discussion of the complex role of genetics in disease is impressively thorough and accurate, and he gives it a human touch with his own genetic testing. But he also invokes the importance of genetics to support the use of race in medicine. Although Specter is no doubt correct that race is often avoided for political reasons, there is a legitimate scientific question that he fails to clarify: how much of the genetic variation in medical response can be explained with traditional notions of race? If within-group variation is large and the differences between groups are largely statistical, the divisive introduction of race may bring little benefit. The messy story behind the heart drug BiDil, approved by the FDA for African Americans, for example, makes it unconvincing as his poster child for race-based medicine.

Surfing the Exponential delves into the nascent field of synthetic biology, covering much the same ground as Specter's recent story in The New Yorker. This chapter is rich in technical detail on the promise of the technology, and to a lesser degree with the risks of making new, self-replicating life forms. Ultimately, though, Specter advocates "a new and genuinely natural environmental movement--one that doesn't fear what science can accomplish, but only what we might do to prevent it."

Such denialism is no more defensible for assessing risks than for judging benefits--both should to be analyzed thoroughly. Fear of the unknown is not always irrational.

New Guidelines for Breast-Cancer Screening

2009-11-17T20:28:00.003-05:00

A few weeks ago, as I reported here, Gina Kolata at the New York Times reported that the American Cancer Society was planning to scale back their recommendations on routine screening for prostate and breast cancers.

As discussed by the Knight Tracker, she got a lot of grief for this story, and the next day the Times published a more reserved follow-up story by Tara Parker-Pope, also discussed by the Tracker. In fact, her primary source at the society, Dr. Otis Brawley, later wrote a letter to the editor denying any intention to change the guidelines (although he is on record cautioning about overscreening).

This isn't the first time that a page-one story by Kolata has gotten into trouble. Her 1998 story on cancer drugs was cited as a cautionary tale in my medical-writing course at NYU. That story quoted James Watson as saying (privately, at a banquet) that Judah Folkman was "going to cure cancer in two years" with his amniogenesis inhibitors. Watson later denied saying any such thing.

Nonetheless, Kolata accurately conveyed a painful dilemma of cancer screening: more isn't necessarily better. Not for all cancers, and not for all patients.

The U.S Preventive Services Task Force has now issued revised recommendations for breast-cancer screening for patients who have no indications of high risk. In part, they moved the earliest age for mammography back up from 40 to 50, at which point they recommend a scan every two years rather than every year.

These recommendations were based not primarily on financial costs, but on health risks to patients:

"The harms resulting from screening for breast cancer include psychological harms, unnecessary imaging tests and biopsies in women without cancer, and inconvenience due to false-positive screening results. Furthermore, one must also consider the harms associated with treatment of cancer that would not become clinically apparent during a woman's lifetime (overdiagnosis), as well as the harms of unnecessary earlier treatment of breast cancer that would have become clinically apparent but would not have shortened a woman's life. Radiation exposure (from radiologic tests), although a minor concern, is also a consideration."

The blog, Science-Based Medicine, has a thoughtful and thorough discussion of the issue, written before the recent recommendations. I highly recommend it.

In a rather odd move, the Times published a balancing article by Roni Caryn Rabin on the same day (at least in the paper edition), although it was buried in the "Health" section, not on the front page with Kolata's.

Rabin's story empathetically interviews screening advocates, including people who have been treated for breast cancer. But in its empathy, story misses the opportunity to clarify the issues. Or perhaps in the extended quotes, the author is deliberately allowing the sources to reveal themselves? It's hard to tell.

For example, one woman calls screening her "security blanket." "'If someone ran a computer analysis that determined that wearing a seat belt is not going to protect you from being killed during a crash, would you stop using a seat belt?' Ms. Young-Levi asked."

Although it's hard to imagine, I certainly would stop using a seat belt if the best evidence indicated it, whatever psychological security I might ascribe to it.

The story later quotes another survivor: "'You're going to start losing a lot of women,' said Sylvia Moritz, 54, of Manhattan, who learned she had breast cancer at 48 after an annual mammogram. 'I have two friends in their 40s who were just diagnosed with breast cancer. One of them just turned 41. If they had waited until she was 50 to do a routine mammogram, they wouldn't have to bother on her part — she'd be dead.'"

The author negligently lets this quote stand: the whole point of the recommendations is that if those friends had not been diagnosed, they might be doing just fine now, without the risk of the tests and procedures they underwent because of the diagnosis.

But the unfortunate reality is that we need tests that better predict cancer progression, rather than merely signaling its presence. Without such tests, the recommendations can only trade off lives lost (and other damage) because treatment was unnecessarily aggressive with other lives lost because it wasn't aggressive enough.

That's a Wrap

2009-11-16T17:32:00.008-05:00

Almost every human cell contains DNA that, if stretched out, would extend several meters. Packing that length into a nucleus perhaps 1/100,000 of a meter in diameter--and unpacking it so it can be duplicated when the cell divides--is an almost miraculous feat.

In addition, although the details of the packing are not fully understood, it is clear that different parts of the DNA are packed differently, and this packing strongly influences how readily its sequence is transcribed. The packing provides yet another mechanism for regulating gene expression.

To form a nucleosome, DNA wraps almost twice around a group of eight histone proteins, two copies each of H2A (yellow), H2B (red), H3 (blue), and H4 (green). Chemical modifications of the stray tails of the histones affect the packing of nucleosomes with one another and modify transcription of the neighboring DNA. (Higher resolution at the Wikipedia Description Page.)

At the smallest level of organization, the DNA double helix winds around an octet of histone proteins to form what's collectively called a nucleosome. Usually the nucleosomes are quite densely arrayed along the DNA, connected by short linking segments of DNA like "beads on a string."

The nucleosomes tend to jam together as closely as possible. But they form at some DNA sequences more easily than others, due to details of the attraction of different bases to the histones and the need to bend the double helix. They also compete for binding with DNA-binding proteins like transcription factors and the proteins that initiate transcription.

Using the known sequence to model the competition among nucleosomes and between nucleosomes and proteins is a complex statistical-mechanics challenge that has attracted the attention of biophysicists, as discussed in a symposium I covered for the New York Academy of Sciences a couple of years ago.

The exact position matters. The enzyme that transcribes genes, RNA polymerase II, needs access to the DNA, whose the two strands must be temporarily separated to get access to the sequence to copy it. The wrapping of DNA around histones slows both the initiation of transcription and the continued elongation of the RNA transcript.

Still, wrapping around histones is only the first level of DNA packing. The way that the resulting nucleosomes assemble into larger structures is even more important in determining which DNA sections are actively transcribed.

Lies, Damn Lies, and…

2009-11-13T15:32:00.003-05:00

Statistics don't lie. People do.

I have the greatest respect for statisticians, who methodically sift through messy data to determine what can confidently and honestly be said about them. But even the most sophisticated analysis depends on how the data were obtained. The miniscule false-positive rate for DNA tests, for example, is not going to protect you if the police swap the tissue samples.

One of the core principles in clinical trials is that researchers specify what they're looking for before they see the data. Another is that they don't get to keep trying until they get it right.

But that's just the sort of behavior that some drug companies have engaged in.

In the Pipeline informs us this week of a disturbing article in the New England Journal of Medicine. The authors analyzed twenty different trials conducted by Pfizer and Parke-Davis evaluating possible off-label (non-FDA-approved) uses for their epilepsy drug Neurontin (gabapentin).

If that name sounds familiar, it may be because Pfizer paid a $0.43 billion dollar fine in 2004 for illegally promoting just these off-label uses. As Melody Peterson reported for The New York Times and in her chilling book, "Our Daily Meds," company reps methodically "informed" doctors of unapproved uses, for example by giving them journal articles on company-funded studies. The law then allows the doctors to prescribe the drug for whatever they wish.

But the distortion doesn't stop with the marketing division.

The NEJM article draws on internal company documents that were discovered for the trial. Of 20 clinical trials, only 12 were published. Of these, eight reported a statistically significant outcome that was not the one that was described in the original experimental design. The authors say "…trials with findings that were not statistically significant (P≥0.05) for the protocol-defined primary outcome, according to the internal documents, either were not published in full or were published with a changed primary outcome."

A critical reason to specify the goals, or primary outcome, ahead of time is that the likelihood of getting a statistically significant result by chance increases as more possible outcomes are considered. In genome studies, for example, the criterion for significance is typically reduced by a factor that is the number of genes tested, or equivalently the number of possible outcomes.

None of this would be surprising to Peterson. She described a related practice in which drug companies keep doing trials until they get two positive outcomes, which is what the FDA requires for approval.

By arbitrary tradition, the numerical threshold for statistical significance is taken as a 5% or less chance that an outcome arose by chance (P-value). This means that if you do 20 trials you'll have a very good chance of getting one or more that are "significant," even if there is no effect.

A related issue arose for the recent, highly publicized results of an HIV/AIDS vaccine test in Thailand. Among three different analysis methods, one came up with a P-value of 4%, making it barely significant.

This means is that only one in twenty-five trials like this would get such a result by chance. That makes the trial a success, by the usual measures.

But this trial is just one of many trials for potential vaccines, most of which have shown no effect. The chances that any one of these trials gave a positive result is much larger, presumably more than 5%.

In addition, the Thai vaccine was expected to work by slowing down existing infection. Instead, the data show reduced rates of initial infection. Measured in terms of final outcome (death), it was a success. But in some sense the researchers moved the goalposts.

Sometimes, of course, a large trial can uncover a real but anticipated effect. It makes sense to follow up on these cases, recognizing that a single result is only a hint.

Because of the subtleties in defining the outcome of a complex study, there seems to be no substitute for repeating a trial, stating a clearly defined outcome. Good science writers understand this. It would be nice to think that the FDA did, too, and established procedures to ensure reliable conclusions.

Guilt by Association

2009-11-12T22:52:00.003-05:00

Many of the molecular transformations in cells occur inside of complexes, each containing many protein molecules and often other molecules like RNA.

Determining which molecules are in each complex is a critical experimental challenge for unraveling their function.

Ideally, biologists would identify not just the components, but the way they intertwine at an atomic level, for example using x-ray crystallography. The Nobel-prize-winning analysis of the ribosome showed that this detailed structural information also illuminates how the pieces of the molecular machine interact to carry out its biochemical task.

But growing and analyzing crystals takes years of effort. In many cases researchers are happy just to know which molecules are in which complexes. As a first step, biologists have developed several clever techniques to survey thousands of proteins to see which pairs interact, and to confirm whether those interactions really happen in cells.

Identifying additional protein members of complexes requires chemical analysis like chromatography and increasingly powerful mass spectrometry techniques. In contrast, to explore how DNA and RNA act in complexes, researchers can take advantage of the sequence information available for humans and most lab organisms.

To find out which DNA regions bind with a particular protein transcription factor, for example, biologists use Chromatin ImmunoPrecipitation, or ChIP. Bound proteins from a batch of cells are chemically locked to the DNA with a cross-linker like formaldehyde.

This technique then requires an antibody that binds only to the protein (and its bound DNA), and which is sooner or later tethered to a particle. After breaking apart the DNA, the particle precipitates to the bottom of the solution carrying ("pulling down") its bound molecules, which are then separated and analyzed.

A related technique identifies proteins bound to an antibody-targeted partner. Ideally, the methods identify components that were already bound just before the cells are broken up to begin the experiment, rather than all possible binding sites, so they flag only biologically relevant pairings.

For DNA, the state of the art until recently was "ChIP-chip," which uses microarrays to try to match the pulled down DNA to one of perhaps a million complementary test fragments on an analysis "chip." The advent of high-throughput sequencing has allowed "ChIP-seq," in which the sequence of the bound DNA is directly measured and compared by software to the known genome to look for a match. This was the method used recently to find enhancer sequences by their association with a known enhancer-complex protein.

A similar method can identify the RNA targets of RNA-binding proteins, as discussed by Scott Tenenbaum of the University at Albany at a 2007 meeting that I covered for the New York Academy of Sciences (available through the "Going for the Code" link on my website's NYAS page).

Once the fragments are identified, researchers can try to dissect the elements of the sequence that make them prone to binding by a particular protein. When successful, this procedure allows them to identify other targets for interaction with proteins using only computer analysis of sequence information. These bioinformatics techniques are a critical time saver, because the experiments show that each protein can bind to many different molecules in the cell.

Experiments like these are revealing many of the intricate details of cellular regulation, but also how much more there is to learn.

Enhancers, Insulators, and Chromatin

2009-11-11T20:03:00.002-05:00

Some DNA regions affect the activity of genes that are amazingly far away in the linear sequence of the molecule.

The best known way that genes turn on and off--and thus determine a cell's fate--is when special proteins bind to target DNA sequences right next to different genes--within a few tens of bases. Together with other DNA-binding proteins, these sequence-specific transcription factors promote or discourage transcription of the sequence into RNA. This mechanism is an example of what's called cisregulatory action, because the gene and the target sequence are on the same molecule.

There's another type of sequence that affects genes on the same DNA molecule, but these can be so far away--tens of thousands of bases--that it seems odd to call them cis-regulatory elements. These "enhancers" can be upstream or downstream of the gene they regulate, or even inside of it, in an intron that doesn't code for amino acids. In fact, some of them affect genes on entirely different chromosomes.

Enhancers have important roles in regulating the activity of genes during development, "waking up" in specific tissues at specific times.

The flexible location makes it hard to find enhancers in the genome, and researchers have also struggled to find clear sequence signatures for them. In ongoing work that I described last year for the New York Academy of Sciences, Eddy Rubin and his team at Lawrence Berkeley Labs instead looked for sequences that were extremely conserved during evolution. Although evolutionary conservation is not a perfect indicator, when they attached a dye near these sequences in mice, they often found telltale coloration appearing in particular tissues as the mouse embryos developed.

Years of effort have uncovered many important clues about how enhancers exert their long-distance effects, but still no complete picture. Most researchers envision that the DNA folds into a loop, bringing the enhancer physically close to the promoter region at the start of a gene. Proteins bound to the enhancer region of the DNA, including sequence-specific proteins that can also be called transcription factors, can then directly interact with the proteins bound near the gene, and enhance transcription of the DNA.

But the enhancement effect can be turned off, for example, when researchers insert certain sequences in the DNA sequence between the gene and the enhancer. These "insulator" sequences seem to restrict the influence of the enhancer to specific territories of the genome. Naturally occurring insulators serve the same restrictive function, but they can also be turned off, for example by chemical modification, providing yet another way to regulate gene activity.

If enhancers work by looping, it seems surprising that an intermediate sequence could have such a profound effect. Researchers have proposed various other explanations as well.

In addition to stopping the influence of enhancers, many insulators restrict the influence of chromatin organization. Biologists have long recognized that the histone "spools" that carry DNA can pack in different ways that affect their genetic activity. In a simplistic view, tight packing makes it hard for the transcription machinery to get at the DNA. This chromatin packing can be modified in the cell, and is one important mechanism of epigenetic effects that persistently affect gene expression even through cell division.

As a further confirmation of the close relationship, Bing Ren of the Ludwig Institute and the University of California at San Diego has successfully used known chromatin-modifying proteins to guide him to enhancers, in work that I summarized from the same meeting last year.

One model that combines some of these ingredients says that loops of active DNA are tethered to some fixed component of the nucleus, and that enhancers can only affect genes on the same loop. If insulators act as tethers, this naturally explains how it limits interactions to particular regions (which are then lops). There is still much to be learned, but enhancers and the chromatin packing appear to be tightly coupled.

I suspect that enhancers have been somewhat neglected both because their action mechanism is so confusing and because definitive experiments have been difficult. But recent experiments done in a collaboration between Rubin's and Ren's teams have used a protein called p300, which binds to the enhancer complex, to identify new enhancers with very high accuracy. Moreover, the binding changes with tissue and development just as the enhancer activity does. These and other experiments are opening new windows into these important regulatory elements.

Free Will and Quantum Mechanics

2009-11-10T16:09:00.002-05:00

[NOTE: This piece is modified from one written in the spring of 2005, but never published because it was too demanding, so be warned.]

In 2004, two mathematics professors from Princeton University devised the simplest proof yet that the world really is unpredictable at a microscopic level.

Quantum mechanics has passed many experimental tests, but it generally predicts only the probabilities of various outcomes. Over the decades, many physicists, notably Einstein, have longed for a description that doesn't involve "throwing dice."

The Princeton "Free-Will Theorem" concludes that, if experimenters can make choices freely, then this unpredictable behavior of elementary particles is unavoidable. But other experts suspect that the result is another manifestation of the "spooky action at a distance," that dominates the quantum world.

"Physicists usually are not impressed much," admitted John Horton Conway, the inventor of the 1970 cellular-automaton game he called Life. "They actually believe quantum mechanics." Indeed, few dispute that quantum mechanics gives correct predictions. But Conway and his colleague Simon Kochen said that although their conclusions are familiar, they start with three axioms, called SPIN, TWIN, and FIN, that are much simpler than previous theorems, and avoid "counterfactual" experiments that can't be done.

The first axiom, called SPIN, is based on an unusual property of a "spin-one" elementary particle: Measuring the square of its angular momentum, or spin, as projected along three perpendicular directions will always yield two ones and one zero. This bizarre property is usually derived from quantum mechanics, but it could have been observed independently.

"We don't have to know what 'the square of the spin' means," Conway said. "It's really rather important that we don't, because the concept 'squared spin' that we're asking about doesn't exist-- that's one of the things that's proved."

If such a squared spin existed, then experimenters could, in principle, choose a direction to measure it along and know in advance whether it would be one or zero. But in a groundbreaking theorem published in 1967, Kochen and E.P. Specker showed that it is impossible to prepare a list beforehand that gives the required two ones and a zero for all possible sets of measurement directions. They concluded that there are no "hidden variables" that describe the "real" spin.

Later researchers, however, realized that such hidden variables could logically exist, but only if their values changed depending on which measurements were chosen, a property known as "contextuality."

To avoid this problem, Conway and Kochen analyzed pairs of particles with matched properties. Their second axiom, which they call TWIN, is that experimenters can make and separate such pairs. This ability is well established, and experiments on the pairs have confirmed the quantum prediction that measurements of their properties remain correlated long after they separate. (I described one recent experiment for Technology Review.)

In 1964, John Bell showed that the some measurements on the two particles can only be explained if each particle somehow continues to be affected by the other, even though they are far apart. This surprising "nonlocality" has since been confirmed in numerous experiments, which find that the correlation, averaged over many pairs, exceeds the maximum for any conceivable local theory.

To avoid the need for statistical averages, Conway and Kochen applied the Kochen-Specker theorem to a single, matched, spin-one pair. If researchers measure the same component of the squared spin for both particles, they should always find either both zeroes or both ones. (Rutgers student Douglas Hemmick also derived this result in his 1996 doctoral thesis.) A similar "Bell's theorem without inequalities" was described in 1989 by Daniel Greenberger, Michael Horne, and Anton Zeilinger, using three "spin-1/2" particles.

Conway and Kochen's third axiom, FIN, is grounded in special relativity, and says that information travels no faster than, say, the speed of light. In spite of appearances, they say, nonlocal effects do not exceed this speed limit, because they describe only coincidence between two measurements, not causation. In fact, special relativity makes it meaningless to say that either of two widely separated measurements occurred "first," so it makes no sense to talk of information passing between the two.

Combining these ingredients, Conway and Kochen imagine that the squared spin is measured in all three directions for one member of a pair. If an experimenter measures the spin of the other member along any of these directions, the result must agree with the one for the first member.

But if the experimenter is free to choose which direction to measure, then because of FIN, that information is not available to the first particle. The result of the first measurement can't depend on her choice, but since there is no way to consistently anticipate all possible measurements, Conway and Kochen conclude that no hidden variable could have predicted the outcome. The only way to avoid this unpredictability, they say, is if the experimenter wasn't really free to choose which experiment to do.

Tim Maudlin, who heard Conway present the work in a colloquium in November 2004, disputes that conclusion. A philosophy professor at Rutgers University and author of "Quantum Non-Locality & Relativity," Maudlin remarked that saying the behavior of a particle cannot be determined by information in its own past "is just what we mean by non-locality," which is already clearly established. "You've taken the contextuality and stretched it out" to include both members of the pair, he asserts.

Conway and Kochen published their Free Will Theorem in 2008, and the Princeton Alumni Weekly has posted videos of lectures by Conway. But it appears that other scientists are free to choose whether to believe it.

The Wisdom of Ignorance

2009-11-09T21:51:00.003-05:00

Physics and math demand a certain mode of thought. Lots of people think that doing well in those classes takes intelligence, and that's part of it. But they also require something else that is not always a good thing: comfort with abstraction, or stripping problems down to an idealized cartoon.

Those of us who excelled in these subjects can be a bit smug towards those who didn't, but replacing real life with a cartoon isn't always a good thing. In addition to hindering social relations, it can obscure important truths.

It's interesting to contrast Aristotle's view, for example--that objects in motion naturally come to rest--with Newton's--that they naturally keep moving. Thinking about familiar objects, you have to grant that Aristotle had a good point. Of course, he'll leave you flat: if you figure out how to include friction, Newton is going to get you a lot further--even to the moon. But beginning students are asked to commit to an abstract formalism that has a stylized and flawed relationship to the world they know.

Probability has a similar problem.

Most normal people, for example, expect that after a flipped coin shows a string of heads, tails is "due." Probability theory says otherwise: the coin doesn't "know" what happened before, so the chances on the next flip are still 50/50. Abstraction wins, intuition loses.

[Actually, Stanford researchers showed in 2007 (pdf here) showing that unless a coin is flipped perfectly, the results will not be 50/50: if the coin is spinning in its plane at all, its angular momentum will tend to keep it pointed the way it started out. But that's a minor issue.]

On the other hand, there are lots of cases where common intuition is "directionally correct." Take another staple of introductory probability courses: a bag full of different-colored balls. In this case, the probability won't stay the same unless you put each ball back in the bag after you choose it. If you keep it, choosing a ball of one color will increase the chances of a different color on the next pick, in keeping with intuition.

Of course, intuition doesn't get the answer with any precision, and it gets it completely wrong for the coin flip. To do it right, you need the abstract formalism. Still, it's easy to imagine that our brains are hard-wired with an estimating procedure that gets many real-world cases about right.

In other cases, our intuition is more flexible than slavish devotion to calculation. Suppose I start flipping a coin. It's not surprising to see heads the first time, and the second time. How about the third time? The tenth? If it keeps coming up heads, you will quickly suspect that there's a problem with you original assumption that the probability is 50%. Your natural thought processes will make this shift naturally, even if you might be hard pressed to calculate why. Probability theory is not going to help much when the assumptions are wrong.

It's true that people are notoriously bad at probability. ScienceBlogger Jason Rosenhouse has just devoted an entire book to one example, the "The Monty Hall Problem: The Remarkable Story of Math's Most Contentious Brain Teaser. (It was also discussed in 2008's The Drunkard's Walk, by Leonard Mlodinow, and in The Power of Logical Thinking, by Marilyn Vos Savant (1997), the Parade columnist who popularized it.)

The Daily Show's John Olliver amusingly explored how simple probability estimates (especially at around minute 3:20) help us misunderstand the chances that the Large Hadron Collider will destroy the world.

Still, our innate estimation skills developed to deal tolerably well with a wide variety of situations in which we had only a vague notion of the underlying principles. Highly contrived, predictable situations like the coin flip would have been the exception. Even though our intuition frequently fails in detail, it helped us survive a complex, murky world.

Group Selection

2009-11-06T20:18:00.003-05:00

Can evolution select attributes "for the good of the group," even if they're bad for individuals? That is the essential notion of group selection, which has been a highly controversial area of evolutionary theory. In fact, some evolutionary biologists insist it never happens.

David Sloan Wilson of Binghamton University disagrees, in an interesting series that he calls Truth and Reconciliation in Group Selection. Originally at Huffington Post, he's re-posting the series in his new blog location at ScienceBlogs.

The author of the very readable Evolution for Everyone: How Darwin's Theory Can Change the Way We Think About Our Lives, Wilson is a long-standing proponent of group selection in some circumstances. But his chosen title evokes his contention that opposition to even the possibility of group selection has become a kind of dogma, in stark contrast to the ideal of scientific hypothesis testing.

From the time Darwin introduced it until the 1960s, Wilson says, group selection was taken seriously. But then W.D. Hamilton devised a model that explained why evolution would favor acts that benefit close relatives. As long as the benefit to the relatives, multiplied by the fraction of shared genes, exceeds the cost to the individual, he argued, an action would make propagation of the genes more likely.

But a successful model based on kinship doesn't rule out the possibility of other successful explanations.

Clearly, group selection requires that the group advantage mathematically outweigh the cost to the individual. So the details matter; the existence of one model in which individual needs triumph, such as John Maynard Smith's influential "haystack model," doesn't prove that group needs would not triumph under other circumstances. Nonetheless, Wilson says, the field adopted the kinship model as the only way that evolution could produce altruistic behavior.

Wilson and others have since created models in which group selection works. A theory that encompasses both kinship and other drivers of group selection was developed by the science writer George Price, as described in this 2008 article from Current Biology. Hamilton himself accepted this more general framework for balancing group and individual needs.

Experiments have also demonstrated the effect. Wilson recalls an experiment where researchers artificially selected chickens for greater egg production, according to two different criteria. They found that choosing the individual hens in a cage that laid the most eggs resulted, over generations, in less egg production than selecting cages that together laid the most eggs.

There is no question that selection "for the good of the group" has been carelessly used to motivate "just-so stories" for all sorts of traits. But it seems equally clear that there are examples of altruistic group behavior that does not derive from shared genes. Even the special teamwork that characterizes social insects, for example, still occurs in species like termites that don't have the highly-shared genes that we know from bees.

A comprehensive understanding of how evolution works in complex situations is critical to many areas of biology, such as the inference of biological function from evolutionary constraint. It is troubling to read Wilson's description of a field so mired in prejudice and tradition that it fails to fairly evaluate and incorporate valid new evidence.

Evolution of a Hairball

2009-11-05T18:16:00.004-05:00

Do DNA sequences evolve more slowly if they play important biological roles?

For many genomics researchers, the answer is so self-evidently "yes" that the question is hardly worth asking. Indeed, they often regard sequence conservation between species, or the evolutionary constraint that it implies, as a clear indication of biological function.

And sometimes it is a good indication. But in general, as I described in my story this summer in Science, this connection is only weakly supported by experiments, such as the exhaustive exploration of 1% of the human genome in the pilot phase of the ENCODE project. That work found that roughly 40% of constrained sequences had no obvious biochemical function, while only half of biochemically active sequences seemed to be constrained.

One reason for this is that many important functions, such as markers for alternative splicing, 3D folding of transcribed RNA, or DNA structure that affects binding by proteins, may have an ambiguous signature in the DNA base sequence.

But another reason (there's always more than one!) is that evolutionary pressure depends on biological context.

Several of my sources for the Science story emphasized that redundancy can obscure the importance of a particular region of DNA. For example, deleting one region may not kill an animal, if another region does the same thing. By the same token, redundant sequences may be less visible to evolution, and therefore freer to change over time. Biologists know many cases of important new functions that have evolved from a duplicate copy of a gene.

But redundancy, in which two sequences play interchangeable roles, is only one of many ways that genetic regions affect each other, and a very simple one at that. As systems biologists have been revealing, the full set of interactions between different molecular species forms a rich, complex network, affectionately known as "the hairball."

For some biologists, the importance of context on evolution is obvious. When I spoke on this subject on Tuesday at The Stowers Institute, for example, Rong Li pointed to the work of Harvard systems biologist Mark Kirschner. Kirschner, notably in the 2005 book The Plausibility of Life that he coauthored with John Gerhart, describes biological systems as comprising rigid core components controlled by flexible regulatory linkages.

The conserved core processes generally consist of many complex, precisely interacting pieces. They may be physical structures, like ribosome components, or systems of interactions like signaling pathways. Their structure and relationships are so finely tuned that any change is likely to disrupt their function, so their evolution will be highly constrained.

In contrast, the flexible regulatory processes that link these core components can fine-tune the timing, location, or degree of activity of the conserved core processes. Such changes are at the heart of much biological innovation. For example, the core components that lead to the segmented body plan of insects are broadly similar, at a genetic level, to those that govern our own development. Our obvious differences arise from the way these components are arranged in time and space during development.

The weak predictive power of conservation is particularly relevant as researchers comb the non-protein-coding 98.5% of the genome for new functions. Many of these non-coding DNA sequences are regulatory, so they may evolve faster. Indeed, Mike Snyder of Yale University observed a rapid loss of similarity in regulatory RNA between even closely-related species in deep sequencing studies he described at a symposium at the New York Academy of Sciences (nonmembers can get to my write-up by following the "Go Deep" link at the NYAS section of my website).

Quantifying how evolutionary pressures depend on the way genes interact is likely to keep theorists busy for years to come. But it is clear that the significance of evolutionary constraint in a DNA sequence--or its absence--depends very much on where it fits in the larger biological picture.

The Permanent Record

2009-11-04T21:55:00.002-05:00

The history of science is documented in carefully crafted publications, which contain data the authors have selected and analyzed to best support their claims. But how should day-to-day scientific research be documented?

Once upon a time, measurements were taken by an experimenter reading an instrument, and transcribing the result by hand into a lab notebook. Written in pen, numbered sequentially, and dated, this notebook provided a time-stamped record of the raw data of the research.

Those days are gone. Modern instruments take massive amounts of data under computer control and often save it in files with inscrutable names that can be later modified at will and leaving no trace.

When I served on a committee investigating possible fraud by investigation of Hendrik Schön in 2002, we found numerous discrepancies in his published data. The committee hoped that we might resolve the problems by examining Schön's raw data. We were sorely disappointed.

We requested supporting information for only six of the eventual 25 papers under investigation. Schön (who was still working at Bell Labs) gave us a two-inch-thick stack of printouts. Essentially none of them met the traditional notion of an archival record of the raw data with document provenance. All were processed data--and some of these were clearly manipulated. He said that storage limitations on the computer forced him to delete some of the data.

Schön also gave us a CD-ROM or two full of "raw" data files. Many of these proved to be files produced by the plotting program, Origin--also not raw data. These files were at least time-stamped, with dates corresponding to the original acquisition of the data. However, they had a curious property that, although the dates on the files varied over a couple of years, the creation times on the files showed a steady progression, one every few minutes, even as the dates changed. Real raw data could have saved Schön, but what he produced only made things worse.

Bill Neaves, the Chief Executive Officers at the Stowers Institute for Medical Research in Kansas City, understands the need for an archival record, from investigations scientific misconduct cases in his former position at the Southwestern Medical Center in Dallas. When he was helping to set up the Stowers Institute almost a decade ago, he says, he decided to take advantage of the lack of institutional history to require that researchers maintain lab notebooks. Moreover, every week the notebooks are scanned and stored in an unalterable, time-stamped format.

Would this procedure have stopped Schön? Maybe not. During the investigation he showed that he could fabricate "raw" data as well as he could publishable data. But it would have slowed him down a lot, and it would have ensured that he knew that people were watching. And if he had been honest, the archive would have absolved him, as Neaves says the Stowers system has already done for one falsely-accused researcher.

But these days, the written record is only a shadow of the activities of science. I'd also like to see all data-acquisition software unalterably configured to create read-only files with clear time stamps. With the low price of digital memory today, there is no excuse for not archiving all measurements.

Unfortunately, even this is not enough. Many of the great biology frauds have involved researchers altering samples to get the expected readout--for example spiking them with radioactive compounds that will show up at the right place on a gel. Without mechanisms to track laboratory materials and their manipulation, and connect them to the eventual measurements, there will always be room for chicanery.

But the Stowers efforts are step in the right direction. They send a strong message to researchers that the integrity of the scientific process is paramount, and that ensuring integrity will not be left to chance or to the reputation of any individual.

Spoiler Alert

2009-11-03T00:30:00.003-05:00

On this election day, many New Jersey voters faced a common, unsatisfying, and ultimately unnecessary choice: vote for the best candidate, or vote for a candidate that the polls suggest could win?

Why unnecessary? In a reasonable electoral system, voting for the person you prefer would not increase the chances for someone you don't. But that's not how we do it.

A typical vote today awards the election to the candidate with the most votes--a plurality, not necessarily a majority. If a third candidate enters a two-person race, he or she is more likely to draw votes from the more similar candidate. The result is that an extra candidate with a particular viewpoint can reduce the chances of that viewpoint prevailing. This is the "spoiler effect."

The best known example is Ralph Nader's entry in the 2000 election in Florida. Although Nader got only a few percent of the votes, if everyone who voted for him had voted for Gore, Gore would have beaten Bush--in Florida, and in the country.

But there's nothing liberal or conservative about the problem. Any candidate can lose because of a strong third-party candidate with similar views.

This is just wrong.

It should be changed.

And there is a simple way to change it.

In instant runoff voting, voters rank all the candidates, rather than just voting for their first choice. Not too hard, right?

If no one gets a majority of first-choice votes, the candidate with the fewest is eliminated. Those ballots are redistributed to the second choice on each ballot. No votes are "wasted," but voters get to express their true preference.

The results are exactly to what would happen in an ideal runoff election, except that there's no need for the expense and low turnout of a second election.

In 2000 Florida, for example, Nader would have been eliminated, and the ballots that listed him as first choice would have been allocated between Bush and Gore, depending on who people listed as their second choice. In 2009 New Jersey, independent Chris Daggett was expected to draw more votes from the Republican candidate, which could tip the balance to the Democrat.

Many individual cities, like Oakland and Memphis as well as some national elections use instant runoff voting now. There's nothing particularly difficult about it, except that the two dominant parties may see it as a threat to their exclusive right to power.

Instant runoff voting isn't perfect. Pretty much all voting systems have some quirks that sometimes give results that seem obviously wrong.

But it's not nearly as bad as what we have now.

Kansas City Here I Come

2009-11-02T16:33:00.003-05:00

I'm going to Kansas City.

I've been invited to give a talk Tuesday at the Stowers Institute for Medical Research, based on my story this summer in Science about the surprisingly weak connection between the apparent biological importance of a DNA sequence and the preservation of it sequence through evolution.

I thought at first that Stowers had mistaken me for a real researcher. But they assured me that a writer can sometimes do a better job of providing perspective than a research who is immersed in day-to-day technical details. Since hosting Matt Ridley in 2001, the Institute has periodically included science writers among their speakers.

Preparing the slides for the talk has been a lot of work, but it has reminded me of some big differences between communicating science as a spectator and as a participant.

The most obvious difference is the thoroughness of the discussion. For one researcher to convince another requires data, covering the entire logical chain as well as possible alternative explanations. In contrast, a journalist rarely gives a complete description of the evidence. Instead, as David Ehrenstein, the Physical Review Focus editor, likes to say, we are happy to convince the reader of the plausibility of a conclusion.

This breezier discussion of the evidence gives journalists a freedom to convey the big picture. Ordinary researchers rarely get this opportunity, until their reputations reach a level where others are happy to hear their opinions for their own sake. This freedom is a terrific luxury for science writers.

Ideally, however, a journalist is not expressing a single opinion, however wise, but synthesizing or contrasting the range of opinions in a field. When this is done well, it conveys the entirety of a field more accurately than any single view. Unfortunately, in active, contentious fields, it's easy to get bogged down in the disagreements, obscuring instead of illuminating the big picture. The common journalistic focus on conflict doesn't help.

Especially when covering disagreements, the journalist needs to convey a sense of authority about the key issues are, if not their ultimate resolution. Without being able to rely on the detailed technical results, this authority often comes from the researchers ("sources") interviewed for the story. Of course, a well-written story can suggest authority even when these sources are not representative of the field, which is one reasons science writers are not created equal.

To convey this authority, journalists often use direct quotes. Again, the reader is dependent on the writer to choose truly representative quotes from a much longer interview with each scientist. Still, this appeal to authority (other than the speaker's) rarely happens in scientific presentations.

Finally, the nature of visuals is strikingly different in technical talks and science writing. In a talk, a scientist might use a cartoon or other light material as a diversion, but the meat will be data: descriptions of procedures, photographs of representative results, graphs summarizing the results, and perhaps an abstract representation or cartoon to convey the concept. In a journal article, in fact, many researchers reading a journal article will skip the text and go straight to the figures.

For much science writing, the only one of these elements that is at all likely to survive is the cartoon conveying the concept. The supporting details get short shrift. For many outlets, the figures won't have any meat at all, and may have only a tangential relation to the subject. As a result, the heavy lifting for science journalism is all done by the written word. Clearly this approach does not translate to a presentation, unless it is a classic speech without visuals.

The PowerPoint presentation I'll be presenting at the Stowers Institute will be a kind of mutant hybrid. I don't plan to use any direct quotes on the slides, for example. But I expect I'll do some name dropping and invoke my interviews for authority, especially to convince the audience that there is a puzzle to be solved in the relationship between evolutionary constraint and biological function. Hopefully I'll be able to get that bigger puzzle across, as well as the intriguing possibilities that may arise by solving it.

As Isaac Asimov said, "the most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' (I've found it!), but 'That's funny....'"

Conductance is Transmission

2009-10-30T19:18:00.003-04:00

My latest story in Physical Review Focus describes measurements of electrical conduction between two "buckyballs," or C₆₀molecules. This sort of characterization is a prerequisite for the sort of understanding and control that would be needed for future "molecular electronics."

The electrical conductance (the inverse of the resistance) in such tiny systems is limited to values of the order of 2e²/h, where e is the electron charge and h is Planck's constant, which sets the scale for quantum phenomena. This combination goes by the name of "conductance quantum," or G₀.

Unlike other quanta like photons, however, the conductance is often not generally required to come in discrete packets. Under special experimental circumstances, however, such as in "quantum point contacts," the conductance can take on reasonably stable values that are simple multiples of G₀.

Still, the idea that conductance has special value was quite jarring when it became popular in the 1980s. Most materials have a well-defined conductivity determined by number of electrons and how frequently they scatter from imperfections of atomic motion. The conductance, which is just the total current divided by the voltage, is then calculated from the conductivity by multiplying by the cross-sectional area of a piece of material, and dividing by its length.

In very small devices, however, electrons move as a wave from one end to the other. The conductance is then determined by the likelihood that they propagate to the far end. The visionary IBM researcher Rolf Landauer laid the groundwork for this view in a 1957 article in the IBM Journal of Research and Development.

Only a quarter-century later in the 1980s, however, did experiments start to catch up. Researchers had been doing experiments at very low temperatures in clean semiconductor systems, where the electrons propagate cleanly as waves over many microns. Lithographic patterning can easily create structures that are smaller than this distance, and comparable to the wavelength of the electrons themselves (typically a few hundred angstroms, or a few hundredths of a micron).

In the semiconductor samples, electrons are free to flow only in a thin sheet near the surface. Researchers can apply a voltage a metal film on top of a semiconductor so that the electrons have to avoid the region under the metal. If there is a small gap in a line of metal, electrons can squeeze through this quantum point contact between them. This is the situation where the quantum effects become important.

The usual derivation goes like this (feel free to skip over this long paragraph): on the two sides, electrons fill up the available states equally, so filled states on one side face filled states on the other and have no way to move across. Applying a voltage V raises the energy of electrons on one side, so the top ones now face empty states on the other. The number of such exposed states is the energy change, eV, times the number of states in each energy interval. Here's the magic: the number of states, for the special case where they are one dimensional waves, is determined by how their energy E varies with wave vector k: dE/dk. Their group velocity--the rate at which they impinge on the contact--is (1/h) times dk/dE. Each carries a charge of e, and there are two electrons in each state because there are two spin states. Presto: the total current is eV(dE/dk)(1/h)(dk/dE)2e = 2e²/h x V.

To me it's rather unsatisfying to go through these shenanigans to get a simple answer like 2e²/h. It doesn't seem right that we have to introduce all these extra quantities just to have them cancel out. Is there an easier way to get to this answer?

In any case, it is now clearly established that each quantum state has an overall conductance of G₀=2e²/h, multiplied by the transmission coefficient, which is the probability of a particular wavelike electron making it to the other side. This result applies to any quantum transmission, whether it's in an engineered semiconductor or a single C₆₀ molecule.

Junk or No Junk?

2009-10-29T22:11:00.003-04:00

The phrase "junk DNA" is a hot button. Authors of press releases, news stories, and even some journal articles seem powerless to resist casting any new discovery of function in non-protein-coding DNA as overthrowing a cherished belief that most of the DNA is junk.

In contrast, bloggers including T. Ryan Gregory and Larry Moran regularly gripe that this framing, like many "people used to think x, but now…" stories, is misleading: biologists have known for decades that non-coding DNA contains important regulatory and other functional sequences. Nobody seriously thought it was all junk: that's just a myth that makes the story seem more exciting.

Still, most biologists agree that DNA is mostly junk.

John Mattick is not so sure.

In a 2007 paper in Genome Research, Mattick and his co-author Michael Pheasant, both of the University of Queensland in Australia, suggested that evolution could be sheltering much more than the 5% of the genome estimated by the ENCODE project and others. Those researchers estimate the background rate of neutral evolution by looking at sequences that they assume to be useless, such as "ancient repeats" left behind from long-ago genomic invasions.

Instead, if these sequences are a little bit useful, perhaps because they are occasionally drafted by the cell for other uses, then they would be slightly preserved during evolution. If this is the case, then other sequences that are also slightly preserved may be useful, too.

I discussed this issue with Mattick for my story for Science (subscribers only), but it was hard enough to capture the issues for strongly selected sequences, so this subject didn't make the cut. Mattick didn't claim that the issue is settled, only that the logic was in danger of being circular: "It is basically an open question. We have no good idea how much of the genome is conserved, except for that which is dependent on questionable assumptions about the nonconservation of reference sequence."

For him, the extensive transcription of the genome seen by ENCODE may not be a sign that RNA production is unselective, but that a large fraction of the DNA is serving some useful, if so far unknown, function.

Mattick described himself as a "minor author" among the scores on the ENCODE project. Ewan Birney, of the European Bioinformatics Institute in the U.K., played a coordinating role. But he doesn't strongly dispute Mattick's observations. "Ancient repeats provide a marker of evolution. They may very well be under some selection," Birney said. But "the striking thing," he stressed, "regardless about where the line is between selection and no selection, is that a lot of the functional regions are at the absolute lowest end of what we see across the human genome." They may be important, but not very important.

Or maybe the biochemical assays don't measure biological importance at all. "Many people instinctively feel," Birney said, "that all the functional elements really must be selected for in some sense. But there's alternative view, which is that there's just a big set of cases which are generated randomly, are perfectly assayable, when you assay them they're always there in that species, but in fact evolution doesn't select either for or against them. They are truly neutral elements: they are selected neither for nor against."

More philosophically, Birney doesn't find the evolutionary question to be central to short-term concerns about human health. "For disease biology we're interested in understanding the disease. We're not so interested in proving whether they're weakly under selection or something like that."

In fact, for both Birney and Mattick, the extensive biochemical activity of the weakly selected 95% of the DNA suggests its potential as a reservoir of "spare parts." Whether or not the long-term potential of that reservoir puts evolutionary pressure on its components, so that they are conserved, may not be the key issue. The important thing is that those partially-assembled genetic tools are ready to be called into action for future innovations.

ENCODE

2009-10-28T19:40:00.002-04:00

The mapping of the human genome in draft form in 2000 was a turning point in biology. But the announcement really marked a start, rather than an end, of the practical use of genomic information.

Several large-scale projects in the intervening years have mined particular aspects of the genome and combined it with other sources of information. The HAPMAP project, for example, looked at how common single-nucleotide polymorphisms, or SNPs--changes in a single base--varied among a few selected populations. These studies formed the basis for genome-wide association studies to identify DNA regions associated with various diseases.

Another project, called ENCODE, for ENCyclopedia Of DNA Elements, focused on cataloguing the various types of protein-coding and regulatory structures in the genome. By correlating these with biochemical measures of functional activity and the way the DNA is organized in the nucleus, the researchers got a broad view of how the expression of genes is regulated. They also compared the DNA sequences with those of closely and distantly related organisms, to illuminate how the function of the DNA is related to its evolution.

With some 200 co-authors from 80 different institutions, the ENCODE project rivals some big particle-physics experiments for scope and complexity. In fact, the pilot phase of ENCODE selected "only" 1% of the genome--around 3 million bases--for detailed study. An overview of the results appeared in Nature in June 2007. The data are publicly available, and researchers continue to publish papers on aspects of the work. In addition, follow-up work is aimed at analyzing the entire genome.

Among the profound conclusions from the pilot phase that most of the genome is transcribed into RNA, even though only 1.5% or so codes for protein and only about 5% seems is clearly functional. In other words, much of the regulation of genetic activity may be occurring, not at the level of transcription, but at the level of RNA.

The researchers also found that the organization of the chromosomes in the nucleus, in particular the wrapping of the DNA around histones to form nucleosomes, predicts the locations where transcription begins. These results emphasize the known importance of the positions of nucleosomes in regulating genetic activity at different positions.

Some of the researchers looked at various measures of biochemical activity along the DNA, such as binding to proteins that are known to be active in regulation. Their hope was that these assays would serve to identify regions with a biological function in the cell.

Other ENCODE researchers compared the sequences with corresponding sequences from other organisms--both close relatives like mice and distant eukaryotic relatives like yeast. According to a longstanding assumption, the degree of similarity of these sequences, showing how resistant they are to changes from neutral evolution, should also reflect their biological importance.

These studies revealed two surprises. First, not all biochemically active sequences are evolutionarily constrained. This might mean that the biochemical tests don't measure things that are important to the cell after all. Second, and more puzzling, not all of the constrained sequences had any obvious function.

I wrote a story for Science this summer (subscribers only, sorry) discussing possible reasons why evolution and importance don't always track one another.

ENCODE and other large-scale studies will continue to supply us with extensive, detailed information about the genome. The story is only just beginning.

Climate Cover-Up

2009-10-27T20:53:00.002-04:00

In their new book, Climate Cover-Up: The Crusade to Deny Global Warming, James Hoggan and Richard Littlemore waste little time wringing their hands about the reality or seriousness of the global warming threat. They dispense with this question quickly, showing that the essential features of carbon-dioxide induce warming have been known for over a century.

Although a devil might lurk in the details, the recent state of the science is captured by Naomi Oreskes' 2004 literature study in Science, which found zero dissenters from the consensus among 928 journal articles referencing "global climate change." Similarly, the 2007 Fourth Report of the Intergovernmental Panel on Climate Change, whose political charter leads it to avoid poorly understood possibilities like collapsing ice sheets, nonetheless states that "most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic [greenhouse gas] concentrations."

In contrast, a Pew Survey released last week concludes that only 36% of Americans think there is solid evidence that the Earth is warming because of human activity, down from an already low 47% over the past few years. Climate Cover-Up explores how it has come to pass that the public still thinks that this is an open scientific question. Hoggan and Littlemore describe the extensive, organized efforts to make it appear open, largely funded by corporations with much to lose from effective climate actions.

Hoggan is a public-relations professional who says that PR people have a duty to serve the public good. In 2005 he founded DeSmogBlog to highlight just the sorts of systematic distortions that the book catalogs, and Littlemore is editor at the site. Their highly readable book describes these efforts, and the funding behind them, with journalistic precision and documentation.

Their laundry list of deception includes "astroturf" groups that use sanitized corporate funds to present a "grass roots" appearance; "think tanks" that increasingly eschew analysis for promotion of policies that favor their sponsors, and petitions signed by scientists who are not or who have little or no expertise in climate. Hoggan and Littlemore systematically discuss these and other programs to frame the "debate" as one in which huge uncertainties remain--as if that should be a source of comfort.

Sowing doubt is a tried-and-true strategy for delaying government response. David Michaels' excellent 2008 book, Doubt Is Their Product, for example, and Devra Davis' 2007 The Secret War on Cancer related how the tobacco industry perfected this technique to delay serious government action against their product for decades. Some of the same firms are coordinating the skeptical response to climate change, and some of the same scientists, like Frederick Seitz and S. Fred Singer, have played roles in both controversies (despite having expertise in neither cancer nor climate).

Hoggan and Littlemore describe how these omnipresent figures benefit financially from their support of corporate needs, and they reveal the irrelevance of many of the "thousands" of signatories on some highly publicized petitions. But they don't address why other scientists--many intelligent and sincere--sign on to such statements. Why are researchers who have no expertise in climate, as well as members of the public, so willing to question those who do, when they would never presume to second guess articles about cancer treatments or particle theory?

In the end, though, these efforts have achieved their goal: keeping the journalistic treatment of global warming "balanced," unlike the clear trend in expert opinion. This problem was captured in a 2004 study by Maxwell T. Boykoff and Jules M. Boykoff, "Balance as bias: global warming and the US prestige press" which was published in 2004 in the journal Global Environmental Change, and in Chris Mooney's story, "Blinded by science: how 'balanced' coverage lets the scientific fringe hijack reality" in Columbia Journalism Review later that year.

This book is not likely to convince true skeptics of the seriousness of global warming. For those who understand the stakes, however, the book is a powerful inoculation to help recognize the conspiracy-theory talking points, most recently regurgitated by the authors of SuperFreakonomics, for the misinformation it is.

Where there's smoke, there's not always fire. Sometimes, it's a massive corporate-sponsored campaign to blow smoke.

Reliability

2009-10-25T14:36:00.002-04:00

A light bulb, floating over someone's head, has become a universal icon for a flash of insight. But the history of the incandescent bulb also shows that a good idea is not enough. The success of a new gadget often hinges on its ability to survive the rigors of the real world.

An electric current causes many materials to glow…momentarily. Glowing is a natural consequence of the "red-hot" temperatures produced by the current. But another consequence is reaction with oxygen in the air that promptly burns up the would-be filament. Protecting it in an air-free glass bulb is a key step toward practical electric light.

The filament material is also critical. The recently reopened Thomas Edison National Historical Site in West Orange, New Jersey recounts Edison's 1870s search through thousands of candidates, many involving carbonized threads of various sorts, including exotic materials like bamboo. Much of this selection process aimed at increasing the lifetime beyond the 15 or so hours of the first "successes." (British inventor Joseph Swan devised a similar device in Britain around the same time.)

Some 25 years later, Hungarian and Croatian inventors introduced the tungsten filaments like those we we use today, which last longer while providing more light.

The tungsten-halogen lamp extends the improvement. Its white-hot filament delivers much more light in the visible part of the spectrum, but over time even tungsten evaporates at these temperatures. Small amounts of halogens like iodine or bromine in the bulb react with the evaporated tungsten. The resulting halide migrates back to the filament where the heat decomposes it, leaving the tungsten back where it started, while the halogen goes on to pick up other stray atoms of tungsten. The result is a brighter, more efficient bulb.

Situations like this, where performance is directly improved by increasing the lifetime, occur frequently, for example in semiconductor electronics. In one example that I encountered while working in integrated-circuit technology a decade ago, making a transistor shorter improves its speed, both by increasing the electric field and by decreasing the distance electrons have to traverse. But a few of the more-energetic electrons cause atomic rearrangements that build up over time to change the transistor's properties and render it useless. The shortness of many transistors, and thus their performance, was directly limited by the need to avoid these "hot-electron effects."

The study of processes that lead to eventual failure goes by the somewhat misleading name of reliability. Typically, after an initially high failure rate, called "infant mortality," a batch of devices settles into a steady, low failure rate over time until some accumulating damage leads to eventual wearing out of all of the remaining devices.

Experiments on many similar devices are required to get a complete picture of the different ways they fail. Researchers need to know not just the median lifetime but its statistical distribution, to place strict limits on the number of possible failures. The statistics are also needed to guarantee that a complex circuit with many devices will continue to function.

Reliability engineers also need to quickly measure degradation with accelerated testing, for example at elevated temperature. They then extrapolate those results back to the milder conditions of ordinary wear and tear. For example, if you've owned your computer for a few years, its transistors have already been around much longer than the prototypes that were used to vet the latest manufacturing changes.

Confidently extrapolating wear-out times requires deep and accurate models of subtle, microscopic degradation mechanisms. As a result, reliability involves many fascinating physical phenomena, as well as an appreciation of statistics and of the ways that devices are put to practical use.

And as it does for the light bulb, this understanding can improve performance just as profoundly as a new invention can.

Fictitious Forces

2009-10-22T17:35:00.002-04:00

Gravity is a myth.

Not because, as bumper stickers tell you, "The Earth sucks." Rather, the "force" we call gravity is an illusion that arises from our own motion through space.

Physicists have known this for almost a century, but for some reason this simple and beautiful reality is withheld from everyone except select graduate students. I'm going to let you in on the secret.

You feel a force when your back presses against the seatback of an accelerating car. But you probably no longer perceive it as a force, knowing that it is really an artifact of the car's motion: your body is just trying to stay put, or at whatever speed it was already moving. The push of the seatback nudges you to a higher speed so you can keep up with the car.

Another fictitious force is the so-called centrifugal force that throws you outward in a turning car. If you looked down on this scene, you'd see that your body is just trying to keep moving in a straight line. The real force is the centripetal force that pulls you back toward the center of rotation, staying with the car.

These forces are fictitious. They only appear because an object's motion is compared with an accelerating reference (the car). The clue is that, if left alone, all objects accelerate the same way, independent of their mass.

Normal forces produce smaller acceleration for "heavier" objects--those with greater mass. The introductory physics equation F=ma captures this relationship between Force, mass, and acceleration.

Because the acceleration from a fictitious force is independent of mass, the apparent force must grow in proportion to the mass. Holding a toddler on your lap during a sharp turn is harder than holding a less massive soda can. This proportionality to mass is a clear signal that a force is fictitious: the objects are really just trying to move at a constant speed. It's the car that's accelerating. The equation for centrifugal "force," for example, includes the mass of the object, as it should.

Perhaps you can see where this is going: according to Newton, gravity is also proportional to mass in precisely this way. Unless this is a coincidence, this means gravity is a fictitious force. Over the years physicists have tested the coincidence idea by comparing the "gravitational" and "inertial" mass. They're always exactly the same.

It was Einstein who took the fictitious nature of gravitation seriously. He developed his theory of general relativity by starting with this equivalence principle: inside a small box, like an elevator car, there's no way to distinguish the force of gravity from acceleration of the car.

In this view, the natural state of all objects around you, as well as your own body, is to constantly accelerate downward. The reason you don't do that is that your chair is constantly pushing up on you, accelerating you upward relative to this natural state of motion.

The idea of "free fall" is easier to accept for an orbiting spacecraft or NASA's "vomit comet" on its parabolic arc. But once you accept the idea that you and everything around you are constantly accelerating upward, it's pretty simple, right?

One reason this isn't common knowledge is that pretending gravity is a force makes it easier to connect the falling of an apple on earth to what keeps the planets in their orbits, which is pretty profound, too.

For orbits, acceleration is in different directions, for example, on opposite sides of the earth. It took Einstein more than a decade to figure out how to stitch together different accelerations at different places, using the sophisticated mathematics of curved spaces that had only been developed in the 19^th century. He also had to figure describe how massive (or energetic, thanks to E=mc²) objects warp space to create the curvature, without screwing up the interconnection between space and time that he had found in his theory of special relativity.

For most purposes, regarding gravity as a force gives the same, correct answer. But not always: small timing corrections from general relativity are critical for global positioning systems (GPS).

And isn't it interesting to imagine your chair accelerating you upwards?

Freedom (From Cancer) Is Not Free

2009-10-21T17:47:00.004-04:00

For decades, the American Cancer Society has been a stalwart advocate of steps to reduce cancer risk, including early testing. In a fine story in today's New York Times (registration required), Gina Kolata reports that they are about to back off on those guidelines, for breast and prostate cancers.

The essential issue is that early screening can find small tumors that might never become a problem, or might even disappear on their own. For these tumors, biopsies, further tests, or treatments are an unnecessary financial burden and also a health risk. On the other hand, many rapidly growing tumors may become serious problems in the time between tests.

In a related article (subscription required) published tomorrow [sic] in the Journal of the American Medical Association, entitled "Rethinking Screening for Breast Cancer and Prostate Cancer," three doctors review the disappointing results of twenty years of early detection, and conclude:

"One possible explanation is that screening may be increasing the burden of low-risk cancers without significantly reducing the burden of more aggressively growing cancers and therefore not resulting in the anticipated reduction in cancer mortality."

These results underline the need for measuring the comparative effectiveness of all medical procedures. Not everything that seems like a good idea really is. For every patient whose aggressive early cancer is stopped in its tracks (and whose doctors will vividly remember the events), there are others for whom the trauma, health risk, and expense were unnecessary--and avoidable.

In his book, The Healing of America (which I reviewed here), T.R. Reid notes that the PSA (prostate-specific antigen) test that is routinely given to older men in the U.S. is not paid for by the Public Health Service in the U.K. (p. 120). No doubt this is partially a matter of cost effectiveness. But as his British doctor explained, it is also a matter of medical effectiveness. It may seem brutal to trade off the few lives saved by early testing with lives lost by unnecessary intervention, but such statistical comparisons are, for now, our only option.

Ultimately, though, we need better tests: tests that can identify the molecular or other markers that distinguish between aggressive tumors that people will die from and more passive cancers that people will die with. As the JAMA authors conclude, "To reduce morbidity and mortality from prostate cancerand breast cancer, new approaches for screening, early detection,and prevention for both diseases should be considered."

[Update: Paul Raeburn at the Knight Science Journalism Tracker notes that although other outlets covered this issue, Kolata is unique in projecting a revision from the American Chemical Society.]

[Update (11/7/09): Science-Based Medicine has a fantastic, detailed discussion of the science behind this issue. Short message: keep screening.]

Alternative Splicing

2009-10-20T19:59:00.005-04:00

In 1977, researchers were surprised to learn that the protein-coding sequence of messenger RNA doesn't arise from a continuous section of DNA.

Instead, work that earned Phil Sharp and Richard Roberts a Nobel in 1993 found that the as-transcribed pre-mRNA includes sections called introns that are then cut out of the sequence while the remaining exons are spliced back together (the words can apply to either DNA or RNA).

The final protein-coding section is straddled on both ends, called 5' and 3', by untranslated regions (UTRs). These noncoding regions are also transcribed from the DNA, but aren't usually described as exons. But their sequence still matters: out in the cell, the 3' UTR is a favorite target for complementary microRNAs that affect the stability or translation of the messenger RNA.

Additional processing steps in the nucleus add to the spliced-together RNA a trademark chemical cap at its 5' end and a tail of repeated adenylenes at its 3' end. Both the cap and the polyadenylated tail are important for the later translation of the mature mRNA at ribosomes, once it has been exported from the nucleus.

A further wrinkle was the realization that the splicing can happen in different ways, as illustrated in the figure (from Wikipedia), which connects by blue lines the pieces that can be neighbors in the final RNA. The multiplicity of possible proteins resulting from this alternative splicing significantly increases the number of protein products available from a given stretch of DNA. Most human proteins occur in more than one splicing arrangement, called an isoform.

The splicing is done by a large complex of RNA and proteins called the spliceosome. The choice of isoform depends in part on special RNA sequences, either within an exon or an intron, that bind proteins that promote or inhibit splicing at a particular point. This binding is sensitive to sequence changes that don't change the coded amino acid and are therefore called "synonymous." Because of splicing, these changes aren't always synonymous: they change the final protein.

In addition, the relative amounts of the alternatively spliced isoforms can change, for example, during development of an organism in different tissues, notably the brain. The regulation of this process provides yet another tool for controlling gene expression, but scientists are still clarifying what determines the splice configuration.

To get a global view of alternative splicing, Chris Burge of MIT, at a conference that I covered last year, described a technique called mRNA-seq that preferentially sequences short RNA segments that contain the polyadenylated tail, and are therefore proper mRNA candidates for later translation. (This eliminates the confusing background of transcribed RNA that is useless or acts in other ways.) Using this technique, he and his colleagues identified more than 10,000 sequences that coded for multiple isoforms. Of these, Burge estimated that more than 2/3 were present in different amounts in different tissues, so they different forms seem likely to be doing important things.

Burge also found a surprising connection between the processes that add the polyadenylene tail and those that do splicing. The former process occurs at the end of transcription, but it looks as if the RNA is already being handed off to the splicing machinery before transcription is finished.

Although it is still poorly understood, alternative splicing is an important and widespread mechanisms for regulating genes, as well as for getting multiple proteins out of a single region of DNA.

Blogstorm Warning: SuperFreakonomics

2009-10-19T12:17:00.003-04:00

Is all publicity good publicity? Stephen Dubner and Steven Levitt, the authors of the bestselling book Freakonomics and the like-named blog, may find out. I, for one, have shelved any inclination to buy their new book, SuperFreakonomics: Global Cooling, Patriotic Prostitutes, and Why Suicide Bombers Should Buy Life Insurance, after seeing the blogosphere's reaction to its climate-change chapter.

The battle was joined in this post by the vocal climate-change activist (and fellow MIT physics PhD) Joe Romm. Romm followed up here, here, here, and here, giving some credence to the idea that he has an axe to grind. But in light of the wide audience the book is likely to get, a preemptive takedown might be justified.

Among Romm's claims is that the Steves misrepresent the views of Ken Caldeira, the Stanford ecologist who has been promoting research into geoengineering approaches to global warming. Romm quotes the book as saying of Caldeira, "his research tells him that carbon dioxide is not the right villain in this fight." Meanwhile, Caldeira's web page prominently features this statement: "Carbon dioxide is the right villain," says Caldeira, "insofar as inanimate objects can be villains." Sounds pretty clear.

Romm may not be entirely clean, though. Dubner claims that Romm goaded Caldeira into disavowing the book's characterization. Roger Pielke Jr., who has had his own battles with Romm, regards him as a liar. It does look as though Romm may have compromised his credibility, although his latest post makes a good defense.

But even if Romm made mistakes, it doesn't make those in SuperFreakonomics any more excusable. It is simply disingenuous to claim, as Dubner does, that the "Global Cooling" in the subtitle is supposed to refer to geoengineering solutions, not to the canard that there was a scientific consensus in the 1970s that climate was in danger of cooling. As painstakingly tabulated by Brad DeLong, this is just one of a whole host of misleading or mistaken statements in the book. Paul Krugman also takes issue with the Steves' take on a particular economics case for early action.

Krugman sums up the problem with Freakonomics' trademark contrarianism:

Clever snark like this can get you a long way in career terms — but the trick is knowing when to stop. It's one thing to do this on relatively inconsequential media or cultural issues. But if you're going to get into issues that are both important and the subject of serious study, like the fate of the planet, you'd better be very careful not to stray over the line between being counterintuitive and being just plain, unforgivably wrong.

What's In a Name?

2009-10-17T15:23:00.004-04:00

I was surprised to see a fresh flurry of news stories in the last few days, more than a month and a half after two papers about ostensible magnetic monopoles in spin ices were posted online (although they just came out in print.)

I want to say one word to you. Just one word.

Are you listening?

Magnetricity.

Apparently one of the teams behind the monopole experiments has a new letter to Nature (with an accompanying News and Views article) measuring the total magnetic "charge" in a model-independent way using muons. (See the lifted graphic for a little explanation.)

The researchers adapted a venerable technique for measuring the charges of ions in electrolyte solutions. In a magnetic field, opposite monopoles drift in opposite directions, and the muons sense the field that results from their separation. Seems like a nice experiment.

But by calling the effect "magnetricity," the scientists insured themselves breathless coverage. It's great marketing, although as far as I can tell they did not measure the magnetic analog of an electric current as claimed by some news stories. They measured the separation that results from the current, but not the current itself.

According to the news release,

Dr Sean Giblin, instrument scientist at ISIS and co-author of the paper, added: "The results were astounding, using muons at ISIS we are finally able to confirm that magnetic charge really is conducted through certain materials at certain temperatures – just like the way ions conduct electricity in water."

Now you might not think that the lethargic drifting of magnetic "charges" that only exist in a very special crystal would form the basis of a new information technology, especially when you realize that "certain temperatures" are about a degree above absolute zero. After all, drifting electric charges in electrolytes (like batteries) are only important because they liberate truly mobile electrons in attached wires that do the real work.

But for researchers who only last month claimed to discover a fundamental particle predicted by Dirac, is revolutionizing electronics too much to ask? According to New Scientist,

Bramwell speculates that monopoles could one day be used as a much more compact form of memory than anything available today, given that the monopoles are only about the size of an atom.
"It is in the early stages, but who knows what the applications of magnetricity could be in 100 years time," he says.

I think I might be able to guess.

[Other stories at Physics World(the best one I saw), The Times, BBC, (did I mention the researchers were from the U.K.?), Popular Science, and Next Big Future.]

Neutral Evolution

2009-10-16T17:34:00.003-04:00

We all know the power of natural selection to drive evolution toward more successful characteristics, or phenotypes. At the level of DNA, however, most changes convey neither advantages nor disadvantages--they are neutral. Random drift can happen at the phenotype level as well, but at the genotype level it predominates.

The indifference of evolution to the vast majority of molecular changes was described by Japanese geneticist Mooto Kimura in 1968. Mutations that improve fitness are very rare, he argued. Those that make an organism worse off are more common, but if they are truly detrimental they will never be passed on. Most mutations, however, will make little or no difference to survival. These neutral mutations will be free to accumulate at random.

In the simplest mathematical model, mutations arise in each nucleotide of DNA with constant probability each generation. A larger population will include proportionally more mutations at each position. For a mutation to become "fixed," however, it must spread through the entire population, rather than dying out. The larger the population, the less likely this is; the fixation probability that is inversely proportional to the population size. Because the product is a constant, the rate at which mutations become fixed at any position doesn't depend on population size. (For a very clear recent introduction to this model in the context of geographically heterogeneous populations, see the July 2009 article in Physics Today by Oskar Hallatschek and David R. Nelson.)

The constant accumulation of fixed mutations underlies the powerful "molecular clock" technique, which allows researchers to estimate how recently two species diverged from one another by counting the number of differences that have accumulated in corresponding DNA sequences. Although the actual rates are more variable than would be expected from this model, molecular clocks provide a powerful quantitative window into our evolutionary past and into relationships between living species.

The background accumulation of mutations over evolutionary time also makes it possible to discern sequences that don't change. As a rule, this happens when changes in those sequences prevent survival or reproduction, so they never accumulate: they are constrained during evolution. Researchers often regard constraint (or the related property of sequence conservation) as a sign that a particular section of DNA has a critical function, even if they don't yet know what that function is.

The best known examples of the two types of mutation are those in sequences that code for amino acids in proteins. Mutations that change the amino acid can destroy the function of the protein, so they will be constrained if the protein itself is important. Researchers like Arend Sidow at Stanford have shown that the sequence is highly constrained in the active sites of proteins but less so in less critical regions.

By contrast, because the genetic code is redundant, some mutated groups of three bases still specify the same amino acid, so the protein chain will be unchanged. These "synonymous" mutations are often used to calibrate the background mutation rate. However, the exact base sequence can still have an effect, for example changing the sequences preferences among different alternative splicing arrangements of the final RNA.

Although constraint and function often go together, there are exceptions, as I discuss in my July 10 story in Science (subscribers only, but I have a pdf in the Clips section of my website, encrypted with the password "monroe"). Sometimes important sequences don't seem to be constrained, and sometimes constrained sequences don't seem to be important. Understanding when and why this happens is important as researchers look for new functions in the 98.5% or so of the human genome that doesn't code for proteins, which includes microRNAs and other regulatory regions.

A Missing Layer

2009-10-15T23:03:00.002-04:00

Models of biological networks have always had gaps, but they are bigger than most researchers realized.

Only in the past few years have biologists begun to recognize the extensive regulatory role of naturally occurring small RNAs. The best known of these endogenous RNAs are chains of 21-23 nucleotides with the rather unfortunate designation of "microRNA" (whose abbreviation, miRNA, is awkwardly similar to the mRNA used for messenger RNA). MicroRNAs arise from sections of DNA whose RNA transcripts contain nearly complementary mirror-image sequences, and so naturally fold back on themselves to form a "stem-loop" structure. Processing by a series of protein complexes liberates one strand from the overlapping section and incorporates it into specialized RNA-protein complexes in the cytoplasm that modify protein production.

Traditionally, systems biologists aiming to unravel gene-regulation networks have relied on the wealth of data from microarrays that measure the mRNA precursors of proteins. By regarding the mRNA abundance as a proxy for the corresponding protein, and looking at how various mRNA levels change with cellular conditions, the researchers construct hypothetical networks of interacting genes. In the graphical representations of these networks, genes are connected by a line or "edge" if the protein product of one seems to act as a transcription factor to change the activity of the other.

MicroRNAs complicate this picture dramatically, although many researchers don't yet incorporate them. Improved tools, often directly sequencing of millions of fragments rather than matching pre-chosen sequences in microarrays, let researchers survey the small RNAs in the cell. In many cases, as in the work of Frank Slack of Yale University described in my latest eBriefing for the New York Academy of Sciences, microRNAs control cellular processes in much the same way as traditional protein transcription factors--in Slack's case extending the lifespan of worms. These regulatory RNAs are a previously unsuspected layer of genetic regulation.

Some of the RNA-protein complexes promote degradation of messenger RNA that is complementary to the bound miRNA. In this case the remaining mRNA could still be a good indicator of a gene's activity, although not of its original transcription. Sometimes accounting for the miRNA might require only a change in the mathematical relationship between genes.

Many miRNAs, however, as well as some transcription factors, act as "master regulators," generating coordinated activity among scores of genes. As a result, these master regulators can effectively change one genetic network into an entirely different one--adding or removing edges. For example, researchers have constructed networks for cancerous cells in which the connections differ markedly from those for their healthy counterparts. Such context-dependent networks may be simple and accurate in specific situations, but they obviously lack important ingredients.

In other cases, a miRNA can continuously vary the activity of genes, rather than being a simple on/off switch. Again, such coordinated response will be hard to capture unless the hidden factor is explicitly identified.

A second type of RNA-protein complex slows (or less often speeds) the translation of complementary messenger RNA. One profound implication is that the measured levels of mRNA may no longer be a good proxy for the levels of the protein produced from it. Indeed, in the few cases where researchers have done the experiments, they have found only weak correlations between the levels of mRNA and the corresponding proteins. Any procedure that depends on these levels being equivalent is on thin ice.

In addition to these quantitative effects, qualitatively new behavior appears when molecules are connected in feedback loops. Systems biologists have catalogued the action of many interesting "motifs." Even two molecules, for example, can act to stabilize concentration--if the feedback around the loop is negative--or act as a bistable switch--if the loop feedback is positive. Clearly, if such motifs are acting in the hidden mRNA layer, no tweaking of the gene-gene interactions will replicate their effects.

All of this reinforces the need for researchers to develop and use as many high-throughput techniques as possible to measure different types of RNA as well as the different states of proteins in cells. The "reverse engineering" of networks never seemed easy. Now it's clear that it's even harder than it seemed.

Short RNAs in Stress and Longevity

2009-10-14T22:44:00.002-04:00

My latest eBriefing for the New York Academy of Sciences covers a day-long May 12 meeting on "Short RNAs in Stress and Longevity." (The web publication was delayed by redesign and reorganization disruptions at the academy).

The sponsor, the Non-coding RNA Biology Discussion Group, used to call itself the RNA Interference Discussion Group. Its new name more accurately reflects the diverse regulatory and other roles of short RNAs. Many of these roles are far from being completely understood, and this meeting touched on several of them.

The combination of stress and longevity may seem like an odd pairing. But to the extent that many organisms have a built-in, switchable longevity program, stresses like starvation, which make living longer more attractive than reproducing, can activate it. The stress response is an entire field of its own, and stresses like heat shock are well known to produce major shifts in gene expression, inducing production of proteins like chaperones that help cells cope.

Frank Slack of Yale University and Ramanjulu Sunkar of Oklahoma State University explored two fields where researchers have extensively studied gene regulation by traditional protein-based mechanisms. For longevity in the worm C. elegans and for stress responses in plants, respectively, they saw the effects of naturally-occurring short RNAs (microRNAs and their plant relatives), and changed these responses by manipulating the short RNA levels. As in other fields, these studies are revealing a critical layer of gene regulation that has been overlooked until quite recently.

Germano Cecere works in the lab of Alla Grishok of Columbia University, who had found that short RNAs can regulate not just messenger RNA translation and degradation, but also its initial transcription from DNA. To find new examples, Cecere searched for short RNAs that are involved with chromatin remodeling, and identified many that play a role in stress and longevity.

Cells under stress often develop specialized complexes of proteins and RNA, known as stress granules, which may host some of the regulatory reactions or store low-priority messenger RNA. Anthony Leung of Phil Sharp's lab at MIT described how a particular polymer known for its role in DNA processes might act as a scaffold for these granules or even help regulate their activity.

Irina Groisman of the André Lwoff Institute dissected protein complexes that associate with the poly-adenylated tails found in mature messenger RNA. These complexes enforce the tradeoff between cellular senescence, which is related to longevity, and cancer.

Beyond the regulatory sequences that typically contain twenty-something bases, larger RNA can also process information on its own. Evgeny Nudler of NYU, who had identified riboswitches that respond to metabolites, described a different 600-nucleotide-long RNA that is the temperature-sensing element in the heat-shock response. This RNA sensor binds with the translational elongation factor eEF1A (which can also interact with non-heat stresses) to generate the response.

As this breathless summary hints, it's a challenge to combine such disparate topics into a coherent writeup. In this case, with just six talks, I gave up on aligning the talks into common themes and simply summarized each one separately. This diversity shows how wide open the field of RNA remains, going far beyond its traditional functions as messenger RNA, transfer RNA, and ribosomal RNA.

Aharonov-Bohm Effect

2009-10-13T22:18:00.002-04:00

Thomson Reuters speculated that the 2009 Nobel Prize for Physics might go in part to Yakir Aharonov of Chapman University, in Orange County, California. Fifty years ago, Yakir Aharonov and his thesis adviser David Bohm devised an astonishing experiment that showed that electrons can sense a magnetic field without passing through it (which had, unknown to them, been described a decade earlier by Werner Ehrenberg and Raymond Siday).

The experiment--since demonstrated experimentally--requires a long coil or solenoid. The results depend on the magnetic field threading along the axis of the coil, not on any fields leaking out the sides or ends.

When electrons are shined at the coil, they reveal their wave nature by passing on both sides simultaneously. On the far side, the chances of an electron appearing at a particular position depends on the relative "phase," which is the difference in the number of wavelike oscillations for electrons going on either side. If the peaks from one side match the troughs from the other, few electrons will be seen, even if the wave from each side alone is strong. This interference effect is well known for other waves, like light.

What quantum mechanics predicted, and experiments confirmed in great detail, is that the relative phase is directly proportional to the magnetic field passing through the solenoid. The surprising thing is that this is true even though on neither side do the electrons pass through any magnetic field. They respond to the field between the paths, at a place where they never go.

Now a mathematical digression: It is customary to describe this result using the "vector potential." As a vector field, this quantity has both a magnitude and a direction at each point in space. The vector potential is related to the magnetic field, while its "scalar" counterpart is related to the electric field. But the exact values of the potentials are somewhat arbitrary, so in classical physics they are regarded as poor cousins of the fields. That's less true in quantum mechanics.

The arbitrariness is easiest to describe for the scalar, or electrostatic, potential, which is closely related to voltage. The electric field is the negative of the gradient, or slope, of this potential. The field is large where the potential varies rapidly with position and points in the direction where the potential decreases fastest. But there are an infinite number of potentials that give the same field, because shifting the potential by the same constant everywhere doesn't change how rapidly it varies in space.

The arbitrariness of the vector potential that determines the magnetic field is more subtle, because their relationship is more subtle. The magnetic field is the "curl" of the vector potential, which is how much it swirls around in a circle(the field points in the direction perpendicular to the swirling). This means that you can add to the vector potential any vector field that has no curl, in what is called a gauge transformation, and the magnetic field will be the same.

Quantum mechanics stipulates that the momentum of the electron (and thus its inverse wavelength) should be corrected by the addition of the vector potential (times a constant). A convenient form (gauge) for the vector potential outside a solenoid is one that everywhere points tangentially along concentric circles. For electrons on one side, this adds to the phase, and on the other side it subtracts, causing the experimentally measured phase shift of the Aharonov-Bohm effect. The size of the relative phase shift depends on the vector potential.

It is common to conclude that in quantum mechanics, in contrast to classical physics, the vector potential is "more real" than the magnetic field. I regard this conclusion as misguided. The phase shift can only be observed by interference between complete paths that pass on opposite sides of the solenoid, which reflect the total phase shift around a loop (one that encloses the solenoid). Because the magnetic field represents the swirliness of the vector potential, this change around a loop is just equal to the total magnetic field passing through the loop, or in this case the solenoid.

Another clue that the vector potential is not really important is that its value changes with different choices of gauge, but the results do not.

So what is a better way to think about the Aharonov-Bohm effect? I like to think about that wonderful print by M.C. Escher called Ascending and Descending. As one passes around the path, there is no clue that anything unusual is happening. But on completing a circuit, it is apparent that something is different. Similarly, a magnetic field changes something subtle (the quantum-mechanical phase) of any electron that passes around it. But that hardly makes the effect less mysterious.

[Note: the September 2009 Physics Today has a story on Aharonov-Bohm effects.]

Deadly Mutant Bugs from Space!

2009-10-09T17:55:00.003-04:00

Do we have a destiny to explore space, or should we leave it to expendable but increasingly capable robots? This perennial debate was inflamed by the recent conclusions of the Augustine Commission that the current budget was woefully inadequate for getting people to Mars.

But what about the science? Last month, NASA released a report describing more than 100 science experiments done on the International Space Station over the past eight years. I was surprised to see that "advances in the fight against food poisoning" were listed first among the accomplishments:

"One of the most compelling results reported is the confirmation that the ability of common germs to cause disease increases during spaceflight, but that changing the growth environment of the bacteria can control this virulence."

I wrote about the original research for Scientific American (subscribers only) in 2007. If this is the poster child for space research, maybe we should stay home.

Don't get me wrong, this is interesting and surprising research. But several aspects of the work deflate its global significance.

First, note the word "confirmation": The researchers had already demonstrated, in labs on Earth, the increased virulence of Salmonella Typhimurium, which causes food poisoning. They did this by building a special chamber to simulate the microgravity environment. It's important to confirm the results in real space flight, but it didn't really show any surprises.

You may wonder why there would be any effects at all from gravity, which is a very weak force. The electrostatic force between two electrons, for example, is more than 10⁴² times stronger than the gravitational force at the same distance.

Of course, if you fall off a building, gravity is plenty strong. But if a bacterium fell off a building, it would just float away. The strength of gravity is proportional to the mass of an object, and thus to its volume. As nanotechnologists know from painful experience, other forces like surface tension and fluid viscosity--which depend on surface area, not volume--become much more important as things get smaller. For a micron-sized bacterium, these forces are perhaps a million times larger, relative to gravity, than in a meter-sized person. So the bacterium simply can't directly detect the difference between a really tiny gravity force and none at all.

What the bacterium can detect is the flow of the surrounding fluid. Gravity (through convection) is one of many things that helps stir things up. (Growing crystals in this quiescent environment has often been invoked as another reason to do science in space.) So if you construct a special chamber where the other stirring is absent (as the researchers did), then a little gravity makes a difference.

What kind of difference? Some news stories at the time talked about microgravity causing mutations. This is just wrong. What happened was that the new, ultrastill environment switched the bacteria into a new way of expressing the genes they already had, turning some on and some off. This made them more virulent, by a factor of three, to chickens.

Why would this happen? Lead researcher Cheryl Nickersen speculated to me that the ultrastill microgravity environment might resemble the sheltered environment the bacteria ordinarily encounter, for example, in remote nooks and crannies of the digestive tract. The new expression profile could reflect the ordinary switch they make as they move from the rough-and-tumble of the outside world and the churn of the stomach and prepare to do their dirty work.

The researchers found some active genes that are normally associated with formation of the dense mats known as biofilms. Microgravity could help jumpstart this process by switching their expression ahead of time--although that might also make the critters less successful getting to the intestine in the first place.

So low gravity creates a quiescent fluid (which can also be recreated in the laboratory) that mimics normal conditions that cause salmonella to activate its natural program to settle in for the long haul as a biofilm. This is all interesting, and could be useful. In fact, a company called Astrogenix is now touting the space research as a route to a salmonella vaccine, and has sent further missions on the shuttle to test it.

But doesn't it seem like there might be more direct (and cheaper) ways to learn these things?

Are the Nobel Categories Obsolete?

2009-10-08T08:38:00.002-04:00

Considering that they've been around for more than a century, the Nobel science categories of "physics," "chemistry," and "physiology or medicine" have held up pretty well. But in truth, much of their durability reflects the fact that the committee hasn't worried much about their precise definitions. Nor have they paid much attention to Alfred Nobel's requirement that the prizes go to "those who, during the preceding year, shall have conferred the greatest benefit on mankind." (Care to make a case for the cosmic microwave background, anyone, other than that understanding the universe is inherently "beneficial" to mankind?)

Still, as noted by Doug Natelson, some people will regard this year's physics prize as an injustice, because both the fiber and CCD achievements are primarily engineering, not physics. In fact, both Kao (with processing experts from Corning and Bell Labs) and Boyle and Smith had previously gotten the Draper Prize, which is often described as the "Nobel Prize of Engineering," as had 2000 Physics Nobel Laureate Jack Kilby, one of the inventors of the integrated circuit (William Noyce had died by the time of the Nobel).

The conflation of physics and engineering raises two issues. On the one hand, some physicists will justifiably ask what right the Nobel Committee has to give their prize to people who aren't even doing real physics. I partially agree with Doug that this is an elitist attitude that devalues the real intellectual contributions of engineers. But what Kao did was engineering and materials science, and what Boyle and Smith did was electrical engineering. That doesn't mean it's inferior. It just doesn't happen to be physics.

On the other hand, some engineers will justifiably ask what right the Nobel Committee has to give credit to physics for accomplishments that were made by engineers. This sort of award reinforces the conceit that any transformative technologies must derive from basic science. The reality is that many of the technologies that are changing our world, like google or Wikipedia or eBay or iPhones or even cell-phone cameras, have little need for fundamentally new science--they rest on clever, imaginative, solid engineering.

The other science prizes have an even bigger mismatch, but for them it reflects the excitement and importance of biology, which has no prize of its own. "Physiology or Medicine," for example, has long been dominated by fundamental biology, which more and more relies on molecular biology. This trend leads to a collision with the "Chemistry" prize, which has also been increasingly dominated by molecular biology (not even biochemistry, really). Many prizes might fit equally well in either category, while other exciting fields are left out entirely.

The growing number of category-defying prizes reflects the reality that many exciting discoveries today lie between disciplines or in collaborations between disciplines, as the chemists, physicists, electrical engineers, materials scientists, biologists, and others who contribute to "nanotechnology" can attest. At the same time, some mature areas of physics and chemistry have really solved most of their interesting problems. Their practitioners have valuable skills and insights, but their traditional topics may not be offering the interesting challenges they did in the past. The Nobel categories are only a symptom of a larger issue in academic research that rewards research that fits with centuries-old disciplines.

The Last Ten Physics Nobels (Bold indicates those that are arguably engineering)

2009 - Charles K. Kao, Willard S. Boyle, George E. Smith -- "for groundbreaking achievements concerning the transmission of light in fibers for optical communication" and "for the invention of an imaging semiconductor circuit – the CCD sensor"
2008 - Yoichiro Nambu, Makoto Kobayashi, Toshihide Maskawa -- "for the discovery of the mechanism of spontaneous broken symmetry in subatomic physics" and "for the discovery of the origin of the broken symmetry which predicts the existence of at least three families of quarks in nature"
2007 - Albert Fert, Peter Grünberg -- "for the discovery of Giant Magnetoresistance" [To my mind, this doesn't count as engineering, especially since it omitted Stuart Parkin of IBM, who was critical in making GMR read heads a reality. In fact, as I described in my story on that prize for Physical Review Focus, Fert and Grünberg really were going after fundamental physics.]
2006 - John C. Mather, George F. Smoot -- "for their discovery of the blackbody form and anisotropy of the cosmic microwave background radiation"
2005 - Roy J. Glauber, John L. Hall, Theodor W. Hänsch -- "for his contribution to the quantum theory of optical coherence" and "for their contributions to the development of laser-based precision spectroscopy, including the optical frequency comb technique" [Again, I count this work as fundamental.]
2004 - David J. Gross, H. David Politzer, Frank Wilczek -- "for the discovery of asymptotic freedom in the theory of the strong interaction"
2003 - Alexei A. Abrikosov, Vitaly L. Ginzburg, Anthony J. Leggett -- "for pioneering contributions to the theory of superconductors and superfluids"
2002 - Raymond Davis Jr., Masatoshi Koshiba, Riccardo Giacconi -- "for pioneering contributions to astrophysics, in particular for the detection of cosmic neutrinos" and "for pioneering contributions to astrophysics, which have led to the discovery of cosmic X-ray sources"
2001 - Eric A. Cornell, Wolfgang Ketterle, Carl E. Wieman -- "for the achievement of Bose-Einstein condensation in dilute gases of alkali atoms, and for early fundamental studies of the properties of the condensates"
2000 - Zhores I. Alferov, Herbert Kroemer, Jack S. Kilby -- "for basic work on information and communication technology"

The Last Ten Chemistry Nobels (Bold indicates those that are arguably biology)

2009 - Venkatraman Ramakrishnan, Thomas A. Steitz, Ada E. Yonath -- "for studies of the structure and function of the ribosome"
2008 - Osamu Shimomura, Martin Chalfie, Roger Y. Tsien -- "for the discovery and development of the green fluorescent protein, GFP"
2007 - Gerhard Ertl -- "for his studies of chemical processes on solid surfaces"
2006 - Roger D. Kornberg -- "for his studies of the molecular basis of eukaryotic transcription"
2005 - Yves Chauvin, Robert H. Grubbs, Richard R. Schrock -- "for the development of the metathesis method in organic synthesis"
2004 - Aaron Ciechanover, Avram Hershko, Irwin Rose -- "for the discovery of ubiquitin-mediated protein degradation"
2003 - Peter Agre, Roderick MacKinnon -- "for discoveries concerning channels in cell membranes"
2002 - John B. Fenn, Koichi Tanaka, Kurt Wüthrich -- "for the development of methods for identification and structure analyses of biological molecules"
2001 - William S. Knowles, Ryoji Noyori, K. Barry Sharpless -- "for work on chirally catalysed hydrogenation and oxygenation reactions"
2000 - Alan Heeger, Alan G. MacDiarmid, Hideki Shirakawa -- "for the discovery and development of conductive polymers"

Ribosomes

2009-10-07T11:13:00.004-04:00

The 2009 Nobel Prize in Chemistry was awarded to Venkatraman Ramakrishnan, Thomas Steitz, and Ada Yonath for their elucidation of the structure of ribosomes and how that structure promotes accurate translation of messenger RNA sequences into the amino acid sequences of proteins.

Ribosomes are the granddaddies of the ribonucleoprotein machines in the cell--big enough to be customarily granted organelle status even though they don't have a membrane. The bacterial version of the complex, for example consists of two large parts, denoted 30S and 50S to represent how fast they separate out of a suspension. Each of these subunits contains many proteins (20 and 33, respectively), together with large "ribosomal" RNA chains.

With the assistance of transfer RNA, ribosomes translate messenger RNA sequences (previously transcribed from DNA in the nucleus) into a corresponding amino-acid sequence or polypeptide, which will be folded and processed into a mature, functioning protein. In light of this critical and intricate task, it should probably not be surprising that the ribosome is very similar in widely different species. However, bacterial, archaeal, and eukaryotic ribosomes are rather different, and the differences in the ribosomal RNA were used by Carl Woese in the 1970s as the evidence for the highest-level classifying of life forms into these three broad domains.

Streptomycin, tetracycline, and about half of current antibiotics preferentially disrupt bacterial protein synthesis by attacking the bacteria-specific versions of the ribosome. The details of the ribosome structure can guide researchers who hope to develop new types of antibiotic molecules.

The three researchers and their collaborators all studied the structure of ribosomes by x-ray crystallography. This structure revealed specific details about how transfer RNA, with its individual matching amino acid cargo, nestles into the ribosome, and how the amino acid forms a covalent bond with the growing polypeptide. (The Nobel Committee notes that the charge-coupled devices that garnered this year's physics prize have made such studies much more productive.) Researchers have used these and other studies to clarify the entropy and energy that drives this synthesis.

A critical aspect of quality control in protein synthesis is the "proofreading" that ensures that the RNA sequence in the transfer RNA is indeed complementary to that of the messenger RNA. In 1974, John Hopfield (then at Princeton and Bell Labs) proposed that a multi-step ratchet that repeatedly checks the match while expending energy could be more selective than depending on the rather weak thermodynamic preference for a match. The increasingly refined structures revealed by the prize winners, together with other experiments, have confirmed how this proofreading works in the real ribosome, achieving an amazingly low error rate of about one error in 10,000 amino acids.

Optical Fiber

2009-10-06T11:58:00.002-04:00

One half of the 2009 Nobel Prize in Physics goes to Charles Kao for his contributions to the development of low-loss optical fiber.

Although free-space transmission had been proposed by Alexander Graham Bell (and such optical techniques as smoke signals and mirrors were used for communication even earlier) optical signals did not become technologically important until the 1970s. The laser provided the needed bright light source, but sending light beams through the air or evacuated tubes never became widespread. Optical fiber, by avoiding the natural spreading of the beams and by letting them be routed like electrical signals in a wire, made widespread optical communication, including undersea transmission, possible.

Many researchers contributed to the development of practical fiber. Early studies had demonstrated the principles of total internal reflection that allowed light to be guided down gently curving paths, and the usefulness of an outer cladding to keep light from leaking out into the surroundings. But in the late 1960s, the attenuation in optical fibers would have prevented signals from being sent more than a few tens of meters.

Charles Kao helped to elucidate intrinsic loss mechanisms in silica (SiO₂) fibers. Inherent density fluctuations in the fiber cause Rayleigh scattering, which increases for higher-frequency light (which is why the sky looks blue and the setting sun looks red). On the other side, low-frequency light is directly absorbed by atomic vibrations in the material. The best transmission occurs for intermediate frequencies where each of these processes is relatively unimportant, which for silica is in the near infrared region of the spectrum. If impurities could be removed from silica fibers, Kao showed, this material could be much clearer.

A major advance came from researchers at Corning, who in 1970 made very clear fibers using chemical-vapor deposition from very pure ingredients. This technique lowered the loss to a few dB per kilometer, making long-distance transmission feasible. Bell Labs later developed a modified deposition process that further reduced the loss from tiny amounts of residual hydrogen in the fibers. Previous prizes, like the Draper Prize, have often included the Corning and Bell Labs contributions to making fiber communication practical. Modern fibers have losses of around 0.2dB per kilometer, meaning that a very useful 1% of the original light power will travel 500km down the fiber, an astonishing degree of clarity for a solid material.

Researchers have explored many variations on the silica fiber over the years. For example, a single crystal core might reduce density fluctuations, while avoiding a material without oxygen would have fewer high-energy vibrations and less absorption. Cheaper materials like plastic can make useful fibers for carrying light over a few meters. But for long-distance transmission, no material has displaced the silica that was championed by Charles Kao.

Charge-Coupled Image Sensors

2009-10-06T11:17:00.002-04:00

One half of the 2009 Nobel Prize in Physics is shared by Willard Boyle and George Smith for the charge-coupled device (CCD) image sensor. Smith says they invented the CCD in 1969 at Bell Labs in Murray Hill, New Jersey, to give their semiconductor device organization leverage against a competing organization's magnetic bubble technology. (In a bit of Bell Labs dirty laundry, Gene Gordon says in this 2000 interview that he was originally listed with Boyle and Smith on the patent, and that he "never could understand why Bill Boyle was listed.") Although the magnetic technology never became important, the CCD became the mainstay of digital imaging for decades.

Electronic devices detect light when it liberates electrons, either into vacuum (as in a photomultiplier) or within a semiconductor. A key challenge is that these photogenerated electrons must outnumber the background "dark current." This becomes more difficult when light levels are low, as in many scientific experiments, or when individual pixels are made small so that they receive few photons. The CCD addresses this problem by electrically isolating the light-detecting region of the semiconductor from the detection circuitry, so it can accumulate electrons for seconds or longer if necessary. To read out the accumulated charge, the device uses a "bucket brigade" that efficiently passes the charge from detector to detector along a row. Circuitry at the end of the row measures the charge from each "bucket" in sequence as it arrives.

Since its invention, CCD technology has been used in thousands of scientific experiments, and it helped to establish the consumer digital camera industry. In the past decade or so, however, technologists have developed imagers based on the mainstream CMOS (complementary metal-oxide-semiconductor) technology by improving both the device design and the manufacturing process. These CMOS imagers are cheaper and can be integrated onto a single chip with the processing electronics, for which CMOS is standard. CCD technology is still preferred for the highest quality images and the lowest light levels, such as those in the Hubble space telescope.

Over the years, researchers have considered CCD technology as a replacement for CMOS in low-power electronics, but it has not had wide impact in that application.

Medicine Nobel

2009-10-05T09:05:00.003-04:00

The 2009 Nobel Prize in Physiology or Medicine was awarded to Elizabeth Blackburn, Carol Greider and Jack Szostak, for their unraveling the special role of the ends of chromosomes, and how they are maintained. The tips of the chromosomes, called telomeres, help ensure the eventual senescence of cells--a process that breaks down in cancers.

In the early days of DNA, as scientists clarified the molecular mechanisms of its replication, they realized that the ordinary step-by-step copying process would not function all the way to the end of the chain. Over time, it seemed, the DNA would get shorter and shorter, progressively infringing on coding sequences near the end of the chain. Szostak and Blackburn discovered that the ends contained a repeated six-base sequence, CCCCAA. A cap of proteins bind to this sequence and protects the tips--Blackburn has likened them to the plastic tips that keep the ends of shoelaces from fraying. Blackburn and her then student Greider discovered the enzyme, telomerase, that recognizes and extends this sequence to prevent continued erosion of the genetic information during division.

Only a few cells normally produce telomerase, however. It had been discovered by Leonard Hayflick that many cells, grown in culture, only divide a fixed number of times--now known as the "Hayflick limit," after which they enter an extended period of senescence. This observation suggested a built-in program for aging that might limit longevity even in multicellular organisms. Researchers quickly realized that the shortening of the telomere during DNA replication provided a natural mechanism for this limit to the number of cell divisions.

Some ordinary cells, like those that lead to sperm and eggs, naturally make the telomerase that restores the telomeres. The enzyme is also produced by many cancer cells, which is one of the reasons that they are able to evade the usual limitations on cell division.

The role of telomerase in preventing cellular senescence suggested to many researchers that the enzyme might also arrest aging in complex creatures like ourselves. The biotech company, Geron, for example, was founded in 1992 in the hopes of exploiting telomerase against aging. Of course one of the dangers of such an approach would be that it might remove protections against cancers. On the other hand, researchers have sought drugs that suppress telomerase as potential anti-cancer agents.

The reality of aging is, not surprisingly, more complicated, and telomerase has not proven to act like the mythical fountain of youth. Geron has moved on to other pursuits, notably stem cells. The current wave of excitement about anti-aging centers on completely different drugs aimed at activating the same molecular pathways as severe caloric restriction, which has long been known to extend life even in mammals.

Even though telomeres and telomerase have not released us from aging, however, their discovery has clarified important aspects of cellular division and programmed senescence, and stimulated new approaches to drug development.

A very good book that discusses the Hayflick limit, telomeres, Geron, caloric restriction and much more is Stephen S. Hall's 2003 Merchants of Immortality.

Turn, Turn

2009-10-01T19:55:00.002-04:00

Everyone knows that a 360° rotation brings an object back where it started.

But sometimes it doesn't.

Here's a demonstration you can do right now--and you should, because this trick is too strange to believe unless you do it yourself.

Take a small object. It doesn't much matter what it is--a business card, a pen, a water glass--just something you can keep vertical as you rotate it in the horizontal plane.

Hold out your right hand, palm up. Use your left hand to lower the object into the grasp of the fingers and thumb of the right hand. This hand and the object will move together from here on.

First rotate the object counterclockwise (looking down) by moving your elbow to your right and your hand to your left. Keep going as the object passes under your arm. To finish a full rotation, if you're joints are like mine, you'll have lift the object up to face level.

At this point, the object has completed a full rotation, and is oriented the way it started. But your contorted arm is telling you that not everything is the same!

Obviously one way to return to the starting point is to reverse the motion that got you here. But there's another way: keep going.

Your arm may object to the idea of becoming even more twisted, but bear with me. Holding the object over your head, keep rotating it in the same direction you were turning it before. This time, though, instead of your arm passing over it, your arm will pass under it.

If you did this right, when you completed the second full rotation, both the object and your arm were now back in their comfortable starting position. Cool, huh? Once you get good at it, you can do it with a partially-filled glass of water, which should convince you that you aren't flipping it over at some point in the motion.

The trickiest part of this trick is that it's not a trick.

This is a little-known property of three-dimensional space: although one full rotation leaves a disconnected object unchanged, two full rotations leave objects unchanged even if they are connected to the (non-rotating) rest of the world. It's not a property of your arm. You can connect the object to its surroundings with as many rubber bands as you like, and you will always be able to untangle them after two full rotations.

Is this just a strange factoid? Maybe. But consider that many elementary particles, notably protons, neutrons, and electrons, have this same property: it takes two full rotations to bring them back where they started. Not only that, but swapping any two of these particles leaves a clear quantum-mechanical signature. (I once saw the mathematical proof that the rotation and swapping properties are intimately connected in an advanced physics class. I was very proud to understand it. For a few hours.)

Not to get all new-agey, but these properties make a lot more sense if you envision electrons as embedded in the larger universe, rather than as independent particles floating freely in space.

Never Fold Alone

2009-09-30T15:11:00.003-04:00

Predicting the structure of a protein--the three dimensional pattern that a particular sequence of amino acids folds into to become biologically active--is a perennial challenge of biology.

Researchers have long recognized major drivers of the final shape, such as exposing hydrophilic amino acids to the aqueous environment, while keeping hydrophobic amino-acids tucked away safely inside or in regions that will lie inside of membranes. Chemists back to Linus Pauling have also recognized recurring structural motifs such as alpha helices and beta sheets that allow somewhat regular packing. But even with these constraints, a chain of hundreds of amino acids can arrange in an astronomical number of ways. Exploring these configurations one by one would take virtually forever, so how do real proteins find the few configurations that will let them do their biological job?

One answer is that they don't always succeed. Generally tens of percent of the molecules get mangled along the way and have to be disposed of. But this just reduces the astronomical challenge by a small factor.

Another important fact is that proteins don't fold in a vacuum--or even in a water environment. Even as it is being translated from messenger RNA by the ribosome, a growing polypeptide is joined by proteins called chaperones. These key proteins help to ensure that the new chain folds properly, and also keeps it from aggregating with others (which is another way to tuck away hydrophobic amino acids).

These molecular chaperones are the best-known members of the family of heat-shock proteins (denoted hsp), which are produced in large quantities by cells that have been stressed. Heat, for example, tends to disrupt protein folding, and the chaperones can help put them back together again. In addition to the small hsp70 chaperone that binds to the growing protein, another protein called hsp60 forms a kind of dressing room where the still-folding protein can assemble itself in privacy.

This activity of these chaperones is driven by ATP, the cell's energy currency. Here is a movie of both processes. I'm afraid it didn't help me much, though.

The important point is that protein folding in a cell, like the processing of DNA and RNA, involves the close coordination of other biological macromolecules. This may be part of the reason that, although researches have made a lot of progress in structure prediction from sequence, in part by draw analogies with similar sequences in proteins with known structure, they still struggle with completely novel sequences.

Folding is only one step in the processing of proteins. They also may be acetylated or phosphorylated, crosslinked with sulfur, and combined with metals like iron, zinc, or manganese. They will be decorated with sugars that can, for example, serve as address labels for their final destinations. Those proteins headed for membranes will not be sent out into the cell to fend for themselves, but will bound with membrane and directly handed off. Much of this activity happens in the endoplasmic reticulum, where proteins that have been mangled are identified and recycled.

Even after processing, many proteins will be further modified chemically, for example by adding or removing phosphate groups to modify their activity. Moreover, many proteins do their work as part of complexes with other proteins, either in pairs or other small groups or in larger complexes that may include RNA.

Biology (the reality, as well as the science) is a team sport.

Science/Journalism

2009-09-29T23:13:00.003-04:00

I've gotten many good insights from Chris Mooney. In a 2004 story in the Columbia Journalism Review called Blinded by Science, for example (~~oddly unlinkable~~posted here), he criticized the journalistic tradition of "balance," as it applied to climate change. He explained that although including diverse points of view gives an impression of objectivity, this habit was giving undeserved credibility to the rare deniers of the consensus on climate. In the intervening years, journalists have become more aware of this problem and more frank in distinguishing the mainstream from the fringe (supported by the increasingly dire predictions of the mainstream view).

In one small section of their recent book, Unscientific America: How Scientific Illiteracy Threatens Our Future, Chris and his coblogger at The Intersection, Sheril Kirshenbaum, expand on this and other ways that journalistic traditions obscure scientific realities. Chief among the disconnects is the news focus on, well, news: what's happening now that we didn't know yesterday? Such event-driven coverage serves poorly many ongoing trends in science (as well as in other areas) that develop continuously or incrementally. The need for a "hook" drives reporters to focus on specific articles in the big journals, rather than the accumulating evidence that they are merely an example of.

Journalists are also prone to framing stories around human elements, especially conflict. There are good reasons for this: people read these stories. But the focus on personalities or revolutions often distracts from the real issues. Biobloggers Larry Moran and T. Ryan Gregory, for example, routinely complain about the misleading narrative that "scientists used to think most of the genome was 'junk," but now they've realized it's good for something." (Scientists have long known that much of it was good for something. Much of it is still junk.)

These differences--driven largely by the business of journalism--are important. Scientists who can't follow Mooney and Kirshenbaum's dictum to transform into public communicators would do well to appreciate what happens to their message when it leaves their hands.

Nonetheless, as someone who has morphed from one to the other, I think the similarities between scientists and journalists are greater than the differences. At a fundamental level, both are professionals dedicated to uncovering reality, wherever it lies. Both groups rely on evidence, and treat personal opinions and popular fads with suspicion, as much as they can recognize them. In each profession, there is a strong social obligation that transcends any loyalty to one's employer or even to one's own prejudices. It is an obligation, as best one can, to speak the truth.

We Did All We Could

2009-09-28T22:34:00.003-04:00

As you read this on your computer screen, it's easy to take for granted the billions of transistors--driving the screen, running the programs, storing the data, and bringing it to you over the internet--that make it all possible.

This embarrassment of transistors is affordable because they're made in a parallel process that produces vast numbers of similar devices at once, combined into integrated circuits (ICs). Making sure that they each behave the way they're supposed to demands extraordinarily clean and reproducible manufacturing processes. In fact, after inventing the transistor, Bell Labs was late to the IC party because they didn't think anybody could get them all to work at once.

Later, Bell Labs' parent, AT&T, did get good at ICs. Towards the end of my time in semiconductor device research at Bell Labs I worked with the excellent developers of the upcoming integrated circuit generations for what was then AT&T Microelectronics, who had moved to Orlando, Florida.

One benefit of visiting Orlando and learning about their challenges was that I managed to design some test structures that they included on the photomasks they used to develop their process. It took some convincing for them give up even a tiny piece (about 0.002 square centimeters!) of their very precious real estate. They also need to be sure that my devices wouldn't flake off and mess up other structures that they needed to do their real work.

Months later, it was a real rush to get the first silicon wafers with my devices on them.

First, the structures looked exactly like what I designed. Instead of looking at multicolored rectangles in a CAD program on a computer screen, though, I was looking at multicolored rectangles in a microscope: real semiconductor devices.

Second, there were lots of them. Even though the entire array of test structures was over a square centimeter in area, there were dozens of repetitions on each eight-inch-diameter silicon wafer.

Third, they were all the same. They didn't just look the same: on the unfortunate occasions when I blew one up with too much voltage, I learned that its repeated version would have very much the same electrical behavior.

I also made friends with people who did testing, robotically stepping across the wafer to measure each repetition. So for simple measurements, after a lot of up-front planning, I could sit back and let the data roll in. Whenever development ran a lot of 25 wafers through the several hundred steps it took to get finished ICs, they also made me hundreds of test structures, and measured them, too.

Compared to what I was used to in the physics labs, where you might work weeks to get a sample or two, this was heaven.

With lots of people helping out, we also did something more challenging, which was to explore new ways to process the wafers. For example, my research colleague Joze Bevk devised a scheme to improve the addition of electrical dopants into the narrow poly-crystalline-silicon ribs that formed the gates of the transistors. Our development colleagues helped track the wafers through step after step of the modified process.

One day, when Joze and I were visiting Orlando, our colleague Steve Kuehne approached us. In the matter of a surgeon telling waiting relatives "I'm sorry. We did all we could," Steve gave us the bad news: "The gates are falling off." Joze and I were very disappointed at this failure, since from Steve's grave expression it was clear that the result was a disaster.

Over the next hour or so, as we discussed what sort of stresses in the materials might cause these terrible problems, an interesting fact emerged. Out of many millions of gates on the test wafer, perhaps 20 had fallen off! Only the high-throughput measurement tools in the development line, which scan the entire wafer looking for anomalies, could even detect them. This is what Steve meant when he said the gates were falling off. For him, a process with even that many broken devices was a non-starter.

I don't doubt that the developers could have devised modifications of the process that reduced the problem, it if had seemed worthwhile--or if they had invented it themselves. Nonetheless, it was a powerful reminder of the degree of reproducibility that IC manufacturing demands.

When I see a news story about some new technique that's going to change the way ICs are made (like this one or this one--not to pick on IBM), I remember how few failures are deadly. If you can see variation in a handful of devices, then someone is going to have to do an awful lot of work before they can be made by the billions.

Now go back to taking them for granted.