A Shrinking Target

Last week, Business Week published a lament from business consultant Adrian Slywotzky about declining industrial research in the U.S., and what we could do about it. The decline is not a revelation, but he's right to wonder where future technological innovations will come from.

I do think he exaggerates in describing Bell Labs as "essentially gone." For example, he claims that Bell Labs had 30,000 employees in 2001 and has only 1000 today (disclosure: my wife is one of the latter). Certainly the labs shrank after the dot-com bubble--as did the company that pays for them. But the 30,000 number included tens of thousands of people doing advanced development aligned with business--certainly not the sort of basic researchers Slywotzky is worried about replacing. Even when I went there in 1985, the research area at Bell Labs was not much over 1000.

Still, no honest observer could deny that Bell Labs is not what it used to be. But I think Slywotzky neglects a critical cause for changes at Bell and other research labs: the moves of their corporate parents away from vertical integration.

Like Ford, which in the 1920s bought Amazon rubber plantations to assure a supply for tires, the AT&T that spawned Bell Labs did everything. At the low end, they drew their own copper wire and developed durable plastic materials for telephones. At the high end, they provided both local and long-distance service to millions of Americans. With this vertically integrated structure, research didn't have to worry too much about what part of the company their innovations might affect. If it affected technology, AT&T could take advantage of it.

Even the 1984 breakup of local and long-distance didn't change this integration. It did remove the monopoly subsidy for Bell Labs, a name that AT&T kept. But to the surprise of many, it was the part of Bell Labs funded by the still-monopoly local phone companies, renamed Bellcore, that quickly ran into trouble. Bell Labs, although it became more focused, continued to pursue speculative long-term--or infinite-term--research.

When AT&T spun off Lucent Technologies in 1995, it kept many math and computer researchers, but physical science research was left largely intact as part of Bell Labs in the new company. But the company no longer provided phone services. When Lucent decided to spin off Optical Fiber Solutions and Agere Systems in 2000, they no longer made fiber, lasers, or integrated circuits. Bell Labs physical scientists were discouraged from research that supported these technologies (making Hendrik Schön's orthogonal work seem much more attractive). These spinoffs transformed the formerly vertically integrated company.

With the increasingly narrow vertical scope of Lucent's business (now merged with Alcatel but covering much the same product space) Slywotzky's description of material physics and semiconductor research as having been Bell Labs' "last remaining areas of basic science," seems misguided. For one thing, Bell Labs is still researching such things as using semiconductor chips to process light, which is certainly as basic as the introduction of the graphical user interface by Xerox PARC that he cites. But it is hardly surprising that a lab would shift its focus over time toward things the company actually does.

Although the shift is not surprising, the lack of vertical integration is a bigger challenge for long-term research. By definition, revolutionary innovations don't match well with existing businesses, so they face huge barriers in an established company. But in a smaller company that is focused on only one tier of activity, it's even easier for research to miss the mark, raising the pressure to shorten the time horizon. No innovation in semiconductor device physics, however profound, is going to help the bottom line of a telecommunications equipment manufacturer. The narrower the company's focus, the harder it is to argue that research has a chance of helping it.

I suspect IBM's much more vertically integrated model--they still manufacture integrated circuits as well as providing services--has helped IBM research to survive after a near-death experience in the early 1990s. Just last week, they revealed a very cool image of the organic molecule pentacene (ironically, one that was also studied by Hendrik Schön).

But although Slywotzky overlooks an important contributor to the decline, I agree with him that the U.S. and the world need to explore new ways to encourage long-term research.

 

One Little Word

Accuracy is paramount in journalism, including science journalism. But so is independence.

To ensure that sources (the people interviewed) don't have undue influence over the story, journalistic tradition demands that they never be shown a story before it's in print. Unsurprisingly, this practice leads to some real bloopers.

Tradition does let a reporter read back quotes to a source to check for accuracy. But the significance of a quote changes completely depending on its context, and other facts matter too, so this has limited value.

In my experience, science editors aren't always as strict about this rule, since they know how easy it is to mangle complex scientific issues. Physical Review Focus, for example, regularly runs an early draft by the author of the original paper. This has uncovered important goofs before publication, which is much better than getting a nasty note later!

The review also raises problems, of course. In their work, scientists often frame issues in complex ways, with lots of caveats and conditions. They also think in terms of jargon that may be quite precise, but unfamiliar to general readers. I find that most scientists appreciate the simple descriptions that a broader audience needs, but every so often a draft comes back from review with extensive, unreadably complex revisions.

Sometimes you just have to say no.

Meeting reports, like the eBriefings I do for the New York Academy of Sciences, also raise different expectations than pure journalism. The primary purpose is to convey the speaker's ideas and to help nonspecialists to get up to speed, rather than to provide complete and balanced view. So NYAS routinely asks speakers to vet their eBriefings before posting. I have found this process quite instructive for using words precisely.

There are some interesting surprises, too. One of the biggest hazards is dual-use words. Although many jargon words are blatantly technical, others have an everyday use as well, which may differ significantly. In physics, for example, words like "work" and "force" have precise mathematical meanings that their ordinary usage doesn't convey.

In biology, in a synopsis of one symposium at NYAS, I had written that a gene was "linked" to a trait. The speaker found this language unacceptable. Of course, much of science is the study of correlations between disparate phenomena, and English tends to express this clumsily, often in the dreaded passive voice. So I tried to mix it up a bit. But in genetics, linkage describes a specific protocol for establishing correlation, and is definitely not the same as "association." Writers must continually walk the line between smooth language and precision.

The most startling suggestion I've gotten, though, was to replace "the" with "an"! I had covered a very interesting symposium on the surprisingly rich interactions between the immune and nervous systems. The problem was that I had written that a specific nervous activity "activates the immune response."

See the problem? The speaker knew many ways that the immune system responds to stimuli. Saying "the" immune response implied that there was only one. The more accurate description, using "an," acknowledges that the response is one of many.

It was an easy change to make (hey, it even shortened the text by a character!) but it forcefully reminded me that no word is too small to be important.

New Roles for RNA

The 2006 Nobel Prize in Physiology or Medicine went to Andrew Fire and Craig Mello for work that had been published only eight years earlier, in 1998. This rapid award reflected the enormous impact of their discovery that adding double-stranded RNA to cells can alter their expression of genes that were already transcribed into messenger RNA, an effect called RNA interference, or RNAi.

Manipulating the activity of specific genes, without needing to breed new organisms with altered DNA, has proven to be an extremely powerful laboratory tool for revealing the functions of those genes. Naturally, researchers also hope they can use it medically, pulling off the equivalent of gene therapy without the risks of messing with people's DNA. This is still a work in progress, because it's not trivial to deliver a largish RNA molecule intact to the right tissues and to avoid side effects.

In addition to the promise for manipulating gene expression for research or therapy, however, researchers have begun to realize that RNAi-like phenomena are critical to gene regulation in normal organisms (as well as being disrupted in some diseases). In this case, gene expression is altered, not by RNA added to the cells, but by naturally occurring RNA transcribed from parts of the genome that don't code for proteins.

I suspect that we will look back with smug superiority at the primitive era when people thought they might explain most gene expression changes to the action of protein transcription factors. For now, though, both the understanding and the terminology of the field are evolving rapidly.

It's important to understand that RNA doesn't act alone. Instead, it joins together in complexes with proteins. In particular, proteins from one family, the Argonautes, link up with one strand of the interfering RNA in a way that lets the combination recognize messenger RNA that contains a more-or-less complementary sequence. This lets the complex selectively target the messenger RNA for a particular protein, for example, out of all of the RNA in the cell.

Different protein-RNA complexes affect their target RNA in different ways. One type of complex slices up the matching messenger RNA. By decreasing the amount of the target RNA in the cell, this reduces the rate at which protein is translated from it. In a second important mechanism, a different RNA/protein complex directly slows (or speeds) translation of the target messenger RNA, without changing how much of it there is.

Researchers are only beginning to explore the networks of molecular interactions that involve these RNA-based processes, and how these molecular patterns change during normal cellular activities and in diseases like cancer.

Transcription Factors

As I blogged previously, biological systems often do one thing in many ways. Nowhere is this clearer than genetic regulation, the vital process--er, processes--by which the activity of genes changes in response to conditions.

By far the best-known way that gene activity is regulated is by binding of a protein, called a transcription factor, to a nearby region of DNA. Its presence speeds or slows the transcription of the DNA sequence into the complementary sequence in messenger RNA, which is then translated into biologically active protein.

For example, the bound transcription factor can slow transcription by blocking access to the DNA by the RNA polymerase enzyme that does the transcription. Alternatively, it can attract the enzyme, make transcription more likely.

The first known example of genetic regulation, which controls digestion of lactose by the bacterium E. coli, uses these basic ingredients. The details have been clarified on many levels, ranging from the atomic interaction of the proteins with the DNA double helix to the differential equations that describe it. Mark Ptashne described these levels in his dense but wonderful 1986 book A Genetic Switch, last updated in 2004 (Amazon, B&N).

The transcription-factor mechanism for gene regulation is easy to grasp because it doesn't challenge the simplest view of gene activity: DNA is transcribed to RNA, which is translated to protein. In this mechanism, the control arises when the last step of this chain, a protein, modifies the beginning of the chain, transcription. Moreover, this mechanism has all the ingredients needed for positive or negative feedback, leading to homeostasis, oscillations, hysteresis, and other, more complex dynamical behavior.

But human cells have several thousand transcription factors, perhaps 10% of the total number of proteins. Also, some transcription factors affect many different genes. One of the many computation-intensive tasks for modern biologists is finding all possible target genes of a transcription factor by combing through gigabytes of genome data.

A real cell, then, contains a complex network of tens of thousands of genes, which code for proteins, many of which go on to modify the activity of their own or other genes. Building and exploring mathematical models of these complex networks is the continuing goal of systems biology, which I've written about extensively, for example at the New York Academy of Sciences (see my Clips for details). A critical tool for learning about the networks is microarrays, which simultaneously measure thousands of messenger RNA levels.

But wait, there's more!

In eukaryotes like ourselves, DNA must be unpacked from tightly-wound spools for transcription, which is done by a complex of many proteins that interact with RNA polymerase and the transcription factors. The resulting RNA is processed to splice out some sections, and is chemically labeled and exported from the nucleus. Once in the body of the cell, the RNA needs to escape degradation and get to a ribosome where it may be translated. Finally, the resulting protein is chemically modified both during production and afterwards, and must be imported into the nucleus if it is to affect transcription.

Essentially every one of these steps--not just transcription --is regulated. In particular, over the past decade or so, researchers have found that other short RNA segments in the cell act (in conjunction with proteins) to modify the production and translation of messenger RNA. Many of these mechanisms are the subject of active research, and I will describe some of them in future posts.

Acoustic Diode?

My latest story at Physical Review Focus describes a layered structure that would transmit acoustic energy one way but not the other.

The authors call it an "acoustic diode," by analogy with an electric diode that passes electrical current one way but not the other. But I'd like to think that someone reading my story will have a more accurate understanding of what it is--and what it isn't--than they would get by skimming the article in Physical Review Letters.

The authors are upfront about a couple of issues. First, it's just a simulation. That's fine. Lots of publications don't like to cover simulations until they're backed up by experiment, but Focus has no strict rule, other than that it be clear to readers. Second, it works only in a narrow range of frequencies. Acoustics is hard in part because it often deals with frequencies that vary by orders of magnitude. A concert hall, for example, might need to gracefully handle sound frequencies from tens of hertz, with a wavelength of many meters, to above 10kHz, with a wavelength of a tens of millimeters. Getting both the geometry of the hall and its acoustic properties right over that large range is challenging, to say the least. A narrow-band solution like this one isn't likely to help, so it will only be relevant in applications using one frequency, like medical ultrasound.

The authors are less forthcoming about a third aspect. Their abstract refers to a "rectifying effect on the acoustic energy flux." Now, rectifying--which is what diodes do--usually means allowing only one direction of steady flow, for example of electrical charge. In other words, it affects constant (dc) signals. Its effect on an oscillating wave (such as turning an AM radio signal into sound) is a byproduct of this dc action. Earlier papers described "acoustic diodes" that would indeed work at zero frequency. In the present case, however, one-way transmission occurs only for a wave with a high frequency, which strikes me as rather different. The analogous device for electromagnetic waves such as light or microwaves is called an isolator.

More importantly, I think most casual readers would interpret "rectifying effect on the acoustic energy flux" to mean that if a wave has the right frequency and is going in the right direction, it will pass through unhindered, or maybe a bit weaker. That's not what will happen here. Instead, before acoustic energy can pass through, it is doubled in frequency using a nonlinear film. This doubled wave can pass through the filter part of the device to the other side. A wave with the original frequency couldn't get back through the filter, hence the "rectifying effect." But if the sound wave that is actually emitted--that is, the doubled wave--were reflected, it could come right back through the filter. Maybe it won't cause a problem, but the acoustic diode won't stop it coming back. All of this makes it harder to understand how the device could be used.

To be sure, this would all be clear to someone reading the paper carefully, but I suspect many casual readers would come away with a different picture. The structure still appears to be novel and interesting, and both of the independent researchers I interviewed liked it a lot. It may turn out to be interesting in itself, or it may provide inspiration for other researchers to do something even more interesting. But analogies have the power to both inform and mislead, and we need to be careful when we use a familiar word in a new way. In my story I tried to highlight the positive without getting carried away with the analogy.

The Responsibilities of Authorship

One of the most difficult parts of our investigation of scientific misconduct by Hendrik Schön was assessing the role of Bertram Batlogg. There were no accusations that he, or any of Hendrik's several other co-authors, participated in the fraud. But Bertram's role was special, and our report commented on that.

Partially it was Bertram's position, at first, as Hendrik's advisor. But more importantly, Bertram, as the senior member of the team and an established and respected experimentalist, brought to the work a sense of authority that Hendrik alone could not have hoped for. Many members of the community and journal editors felt that, by putting his name on the work, Bertram was standing behind the validity of the results.

After the errors in those results became clear, Bertram did not lose his job at ETH Zurich or his grants. Nonetheless, his dream of completing his career as a respected authority for physics in Europe was irrevocably damaged, and he suffered great personal anguish. Some of his colleagues still blame him for failing to live up to the expectations of a co-author.

This background makes the recent revelations about "ghostwriting" of medical articles particularly shocking. People have complained about the practice before, but recent PLoS Medicine and The New York Times recently joined in a lawsuit that documents how pharmaceutical companies have covertly choreographed multiple journal articles supporting their products. (On Friday, PLoS Medicine made this discovered material available online.) As part of this process, for example, companies selected to manage the publications sometimes wrote review articles even before the authors were determined! These authors were later paid to put their authority behind an article that they had not written and may not have even carefully reviewed.

A blog entry from PLoS Medicine summarizes how one of those companies described it to potential clients:

"The first step is to choose the target journal best suited to the manuscript's content. …We will then analyze the data and write the manuscript, recruit a suitable well-recognized expert to lend his/her name as author of the document, and secure his/her approval of its content."

This is not ghostwriting. It is fraud.

Typically these articles are not summaries of ongoing research by the investigator in question. Instead, they are review articles, for which the experience and judgment of the author is ostensibly invoked to help sort through the complicated and possibly conflicting results that have previously published. The tedious process of reviewing of the literature is the content of the article, and cannot be outsourced to an someone hired by a drug company.

Moreover, unlike Schön's work or cases of "honorary co-authorship," in these medical reviews there is often no other author who holds primary responsibility.

Most troubling, the reason the paper even exists is specifically to skew treatment choices in favor a company's products, rather than the medical needs of patients.

There must be zero tolerance of this practice by the universities and teaching hospitals that employ these "authors," by the agencies that fund them, and by the journals that publish their work. Even in the absence of formal censure, though, they should be treated by their colleagues as disgraces to the profession. Which they are.

Zero Net Energy Buildings

Over the last year, I covered four separate symposia at the New York Academy of Sciences on "Zero Net Energy Buildings." The final installment was just posted. (If you're not an NYAS member, you can see my story and view the presentations by going through the NYAS link, under the "Clips" tab, at my website, http://www.DonMonroe.info.)

Something like 2/5 of the energy in the U.S. is used for buildings--more than for transportation. The zero net energy building vision is that by combining serious energy conservation measures with on-site generation (often photovoltaic, but also including wind and other sources) an individual building can put just as much energy back onto the grid as it takes off, over the course of a year.

One critical aspect of the definition is determining what's included in "the building." Does it include photovoltaics over the parking lot? Firewood grown in the back? I did not understand why the building should be the right level of granularity, rather than, say, the block or the city. (Obviously zero-net energy rooms would be too fine-grained.)

Part of the reasoning is that transmission is expensive, and that cost can be avoided with local generation. But most of the buildings under discussion meet the zero-net-energy goal only over time, achieving the goal only because of easy transfer of electricity to and from the grid. And some generation methods clearly have economies of scale (take nuclear, for the sake of argument) that make them most effective at a larger scale than a single building. Perhaps the most compelling reason to focus on the building level is that this is where a single team of architects, engineers, owners, and so forth can work together to make the needed decisions.

The challenges are great, not least because there are so many inefficient buildings out there already. Many retrofits that will pay for themselves over time, even discounting future savings. Unfortunately, decision makers are often short-sighted, and need to learn how to value sustainable investments, including the growing desirability of "green" properties for tenants.

Nonetheless, stabilizing global carbon levels could require reductions in the ballpark of 75%, and simulations by The World Business Council for Sustainable Development indicate that market forces alone won't get there, even with onerous carbon taxes. This coalition of sustainability-oriented corporations advocates serious government intervention, on top of carbon pricing, as critical to reducing carbon fast enough to lessen global warming.

Over the Wall

The Bell Labs I came to in 1985 was a mammoth enterprise, spanning activities from truly basic research to advanced development, while other parts of the company (then AT&T) made actual products.

Each of these activities was centered in distinct parts of the organization. One of the amusing consequences was the idea that once basic researchers found something useful, they figured they just had to "throw it over the wall." Ideally, it would hit some developer on the head. After rubbing the bruise, he (rarely she) would notice the shiny new object and figure out to make tons of money.

That was the theory.

Toward the end of my career, I spent a lot more time with developers, for example at the factory in Orlando where they perfected the processes that in a few years would create the next generation of integrated circuits. It was rather embarrassing to realize that these folks were not hanging out next to the wall, waiting to be hit on the head. In fact, they were far across the field, barely visible, working on the problems that really mattered to manufacturing. Back at the wall lay hundreds of rusting ideas, with no one paying the least attention.

I'm afraid this is a common problem among academic scientists, as well. They get grants for research related to some economically important field, but often they never take the time to learn the real issues, and talk to the people that understand them. So they devise elegant solutions for exciting, unimportant problems, often the same problems over and over, while engineers do the real work. (The analogous problem in biomedicine is the need for "translational" research.)

A perennial example is ultrasmall transistors. Of course Moore's law demands that transistors get ever smaller. But smallness is not the end goal. Smallness is related to low power and to high speed, but these don't come for free; they must be directly addressed. Moreover, a small transistor is useful only if it can be packed very densely with other small transistors. A single, really small transistor every millimeter or so is not solving any important problem. Finally, making anything like a modern chip requires billions of transistors with virtually none failing. For all these reasons, making a single, isolated, slow, wasteful transistor is just irrelevant.

Of course, every so often one of those crazy, disconnected ideas changes the world.

Where is the FDA?

In a previous post, I fretted that grand aspirations for personalized medicine don't gibe with drug companies' histories of doing whatever they can to expand sales, even to people who won't benefit. You would hope that this would not include clearly deceptive marketing, though, since in the U.S. this is supposed to be monitored for accuracy by the Food and Drug Administration.

You be the judge.

To set the stage, here's a current ad for Aricept, a drug that is approved and widely prescribed for Alzheimer's Disease:

Here's the critical quote:

"Studies showed Aricept slows the progression of Alzheimer's symptoms."

That statement has a clear meaning: if you take this drug you'll decline less than you otherwise would have. Of course this claim (which I've heard repeated by a doctor) is critical, because it implies that you're losing ground every month you don't take this drug, so you'd better get on it soon and stay on it (for the rest of your life). It is not the same thing as saying "Aricept relieves Alzheimer's symptoms," which would mean you could try it for a while and see if it helps.

Fortunately, those ultra-fine-print sheets that come with prescription drugs are now available online. Here's a pdf of the prescribing information for Aricept, from the Aricept.com website, which I'm confident was checked by the FDA. Go ahead and follow the link; it's tough reading, but a good habit, as you'll see.

At the top of the first page, in Figure 1, is a plot of the average cognitive scores over time, with and without the drug. (The shifts are small compared to the spread of individual responses, though, so many people who try it may not notice any effect). Here's the key: after patients are taken off the drug, their scores rapidly decline until they are indistinguishable from those of patients who never took it. But don't take my word for it, here's the conclusion in the fine print:

"This suggests that the beneficial effects of ARICEPT® abate over 6 weeks following discontinuation of treatment and do not represent a change in the underlying disease."

Am I missing something?

A Program for Aging?

In the science section of today's New York Times, Nicholas Wade skillfully reviews progress toward drugs to extend human lifespan. As he notes, researchers have known for decades that severe caloric restriction extends the life of many animals. Beginning in the 1990s they realized that changing the action of even one gene, for example by mutation or drugs, can have a similar effect. I've written on incremental advances for Scientific American in 2004 and for The Scientistin 2005, but there has been steady progress before and since.

I do, however, question Wade's assertion that "Evolutionary biologists, the experts on the theory of aging, have strong reasons to suppose that human life span cannot be altered in any quick and easy way. But they have been confounded by experiments with small laboratory animals, like roundworms, fruit flies and mice." Although I'm sure some evolutionary biologists would be skeptical, others have devised coherent ways to understand how a reprogrammable lifespan could evolve.

The first thing to recognize is that immortality is perfectly normal, in the following sense: your cells are the surviving members of an unbroken line of succession going back billions of years. Sure, along the way, many individual animals died without offspring. Even in the animals that reproduced to pass on the torch, most cells died along with their bodies. But their sacrifice doesn't alter the overall continuity of life, any more than the billions of skin cells you lose each day.

So practically speaking, cellular processes are quite capable of self-renewal that makes organisms immortal. There's nothing inherent about death, in a properly self-maintained body. The idea that we just "wear out" after a while is an oversimplification, partly based on analogy with the flawed machines that we build. There is accumulating damage to the telomeres at the end of chromosomes and oxidative damage resulting from metabolism, but clearly there are ways to evade them.

Why then do our bodies die at all, to be replaced by new generations? This is where it gets interesting, and speculative. One reason could be that periodic sexual scrambling of our genetic material is important for evolutionary innovation. No doubt there's some truth to that, but asexual species also have a fixed, relatively short lifespan. Whatever the reason, it seems that there are benefits to building a new body from scratch. As Thomas Jefferson said, "a little rebellion, now and then, is a good thing."

But how much? How often should generations turn over? The optimum must be a tradeoff between the benefits of new bodies (whatever they may be) and the cost of making them, such as energy and nutrients. The growing offspring are also vulnerable to predators and harsh living conditions.

From this point of view, it hardly seems shocking that we would have ways of recalculating the tradeoff between reproduction and longevity, and there's lots of experimental support for this view. Think of the calorie-reduction effect: when resources are short, all sorts of animals live longer. It makes sense to conserve scarce resources and reproduce when times get better. The widely studied roundworm C. elegans even goes into a long-lived dormant state, something like the spores of some plants, when conditions are bad. Researchers have also found direct that reproducing causes changes that reduce lifespan. (It's not entirely a myth that your kids make you age prematurely--although they also make you young.)

What's particularly interesting is that the biochemical pathways that underlie these responses--the specific molecules and the ways they influence one another--have many common elements in creatures ranging from humans to worms and flies and even to single-celled yeast. Each of these species lives longer when food is drastically reduced, even though the specific ways in which they eventually die are completely different. (Few yeast die of heart attacks, for example.) This suggests to many researchers that there may be a "program" that modifies lifespan in response to environmental and reproductive conditions, and that that program has been adopted and adapted over hundreds of millions of years of evolution.

If so, the question is whether we can hack into this ancient program. And should we?

Microarrays

The regulation of genes, which renders only some of them active in a particular cell, is critical for controlling life processes. But for many years it was tedious to measure which genes were active. That all changed, beginning about 15 years ago, with the microarray revolution, which lets researchers simultaneously monitor the activity of thousands or more genes.

The technique uses methods similar to those in microelectronics to create a huge, two-dimensional array of spots, each containing a short segment of DNA. Typically, this array is bathed with a sample containing a mixture of DNA or RNA molecules, which bind to any complementary sequences in the array. These sample molecules were duplicated from the original DNA or RNA to be tested, and in the process they were labeled with a fluorescent die. The pattern of glowing spots shows which sequences are present in the sample, and their brightness indicates how much is present.

The best-known use of these tools is to measure levels of messenger RNA (mRNA) that have been transcribed from known genes. Since mRNA is the template for making proteins, this level gives an indication of how active the gene is, and is sometimes referred to as "gene expression." However, this terminology is oversimplified, because many effects change the amounts and activity of the final protein, as well as the amount of mRNA in the cell.

The cleanest experiments compare the amounts under two conditions, for example in normal cells and after they are exposed to some perturbation, such as a drug. If the mRNA levels increase or decrease, the corresponding gene is said to be "upregulated" or "downregulated," respectively. Frequently the result for a single perturbation is presented as a column of red or green spots, each representing one gene. The overall pattern shows fingerprint of expression changes corresponding to various changes in the cell. For example, different types of stress tend to activate the same sets of genes and to de-activate others.

The technology is expensive, costing hundreds of dollars for a single commercial array, which can make it difficult for less well funded labs. On the other hand, researchers are expected to deposit their array data into publicly accessible databases that anyone can analyze. Naturally these databases are huge, so exploring them is a bioinformatics challenge that has spawned numerous software tools.

Array technology is most useful for identifying well known sequences, such as those derived from protein-coding genes. Indeed, arrays are available for the complete genomes of many model organisms such as E. coli, Drosophila, and mice, as well as humans. For exploring the large non-coding parts of the genome or the metagenomics of entire ecosystems, however, the need to pre-specify the sequences to be matched is a limitation.

For these and other reasons, researchers are increasingly turning to sequence-based technology, which simultaneously determines the base sequence of millions of short segments of DNA or RNA. Software then looks for matches between each sample and known genome sequences. This technology isn't cheap either, and the millions of sequences are an even larger bioinformatics challenge than the array data.

Gene Regulation

Here's a question that every high-school biology student ought to ask: "if every cell in our body has the same DNA [which is almost true], why are they so different?" Understanding how different cells employ different parts of the common genome, turning some genes "on" and others "off," for example, is a long-standing problem in biology. As is typical for biology, there are many correct answers, some only now being elucidated.

Different cells have characteristic "signatures" of genetic activity. A neuron is a neuron because certain sets of genes, like those that specify proteins used in synapses, are active, while genes involved in synthesizing bile, for example, are not. The pattern also changes though the various stages of the cell cycle that lead to cell division. But such differences are only the tip of the gene-regulation iceberg.

A single-cell zygote develops into a complex multi-cellular organism like you or me through an intricate choreography that activates and represses genes in the appropriate cells at the appropriate times. This gene regulation is coordinated in time, as the activity of one gene triggers event that turn another on or off. It is also coordinated in space, as gene activity in one cell or region creates chemical signals that affect nearby cells. Perhaps it's not too surprising that the complex "network" of genes turning each other on and off can give rise to complicated patterns and time and space. But it is still close to miraculous that this complex process produces such complex creatures--with fingernails, bile ducts, and neural circuits that specialize in speech perception--with such astonishing robustness. (It fails frequently, but it's amazing that it works at all.)

Normally developing body cells change their patterns of expression irreversibly: once they have differentiated from a more general-purpose cell to a specialized cell, they don't go back again. This irreversibility is a somewhat surprising property of the interaction network, and presumably helps to assure proper development. Until recently, therefore, researchers looking to generate new cells for either research or therapeutic purposes had to isolate "stem cells" that had not yet gone through this irreversible differentiation. The most versatile such cells come from embryonic tissue only a few days after fertilization. These embryonic stem cells are still very important, but in the past few years researchers have successfully turned back the clock on fully differentiated skin cells, for example, apparently giving them all the versatility of embryonic stem cells.

Cancer cells also turn back the clock, activating genes that normally function only in early development. Some cancers, for example, turn on genes that induce nearby tissue to build blood vessels to supply the growing cancer. In ordinary cells, the genetic networks that enable this activity are present but inactive after they do their work in the growing embryo. The ability of cancer cells to re-activate these dormant cellular programs is a key to their success, but also gives researchers critical insight into the ways genes are regulated.

In future posts I will discuss various molecular mechanisms of gene regulation.

Too Much Information

When I was mulling over a career change to science writing in 2003, I found myself in an electronics store near a couple of twenty-something guys looking over some new gadget or another. The way they tossed around words like "gigahertz" and "megabytes," it was distressingly clear that they had no clue of the awesome intellectual content that was embedded in the device they would spend a few tens of bucks for. My desire to reveal to them what they held so casually in their hands helped to push me onto my new path.

Ironically, I've hardly written since on what allows people to design and build gadgets in which billions of transistors to work reliably together at a cost of maybe $0.000001 each. I have written a few stories on isolated experiments on one-off devices that the researchers hope could someday transorm technology, even though most of those speculative devices will never find their way to your pocket. But the reality is, most people are happy to use the real technology with no understanding of how it works.

You might think it would be different in biology. After all, everybody has (or is?) a body. Everybody gets sick, or knows someone who has. But the sad truth is that the detailed mechanisms of biology, as intricate and wonderful as they are, are no more interesting to most readers than are the details of their iPod. Sure, something complicated is going on in there, but as long as it works, who cares? And if it doesn't work, how likely is an average person to be able to figure out what to do about it?

So the intricacies of signaling networks or RNA interference, like those of electron velocity saturation or speculative execution, are likely to be forever consigned to a nerdy backwater. But there are still technical issues that people should understand, and that they will want to understand when they have proper motivation and context.

At the highest levels, for example, technical details are directly relevant. You don't have to know anything about discrete cosine transforms to know that an over-compressed jpeg image develops square blocks and ghosts of sharp edges. You don't have to know about cross-linking in cell walls to know that antibiotics are effective against bacteria but not viruses. And more accurate understanding can have important consequences. Fewer people using antibiotics when they are useless would delay the development of resistance. The way we view the biological mechanisms of drug abuse or homosexuality changes how we regard our fellow human beings.

Even without practical importance, though, even fine-grained technical details can be philosophically profound. The DNA sequences that we share speak to a deep connection and common history among all living things. The hive mind of social insects gives us new ways to think about our societies--and our brains. These insights deserve to be part of our common culture. The challenge for a science writer is to communicate their essential significance without overwhelming readers with unnecessary details.

 

RSS Feeds

I'm always surprised, as I was again this weekend, when an otherwise web-savvy friend doesn't know about RSS feeds. I've been using them for more than five years to keep track of things on the web, and when a computer problem recently derailed them for a few days, I felt like I'd been blinded. (Okay, it was a bit of a vacation, too.) So here's my advertisement for RSS (which stands for something that is less important than the initials, like DNA).

I track over 200 feeds, including various blogs, local and national news sites, science magazines, news release sites, university labs, scientific journals, economics sites, and a couple just for fun. For me the key advantages over visiting the web page are first that it looks for new items automatically, but equally important that I don't have to read anything twice (with a few exceptions). Just as in an email program, items that haven't yet been viewed are bold, so it's easy to see what's new. The presentation also gives an easy drill-down capability, progressing from a headline, to a description, to a complete web page, which is a very efficient way to browse through items that are not all equally interesting.

I use the free aggregator FeedDemon, although there are others, and many browsers have RSS capability built in. FeedReader lets you organize feeds in folders, which I find useful because it helps me to predict how much time it's going to take by grouping similar sites. Sites like CNN that have lightweight news items can be scanned quickly, for example, whereas if you're going to take the trouble to look at a journal table contents you'd better be ready to spend some time. If I'm in a hurry, I flag items like journal articles or longer stories to return to when I have the time to spend.

If you monitor any web sites on a regular basis, you owe it to yourself to them with RSS. Download a reader or use a browser, and when you give it the URL for a site, it will most likely auto-discover the feed. You won't look back.

My Regret

I spent much of the summer of 2002 looking into allegations of scientific misconduct against Jan Hendrik Schön, who had until then been a golden boy of organic semiconductor research at Bell Labs. I was part of a five-person "blue ribbon" committee headed by Mac Beasley of Stanford, which also included Supriyo Datta of Purdue, Herwig Kogelnik of Bell Labs, and Nobel-prizewinner Herb Kroemer of UCSB. In the end, we found the evidence compelling that many of Hendrik's results were fictitious or deceptive, putting a rather definitive asterisk on his meteoric career. The investigation was at times dispiriting, but in the end highly satisfying, in that we were able to help purge the scientific literature and community of some highly erroneous results. I certainly don't regret the time and effort I put into the investigation.

As described in Eugenie Samuel Reich's recent book, Plastic Fantastic (Amazon, B&N), however, I was involved in allegations earlier, in the fall of 2001. Specifically, when Hendrik moved from organic crystals to molecular-gate length transistors, his claims began to violate known laws of transistor physics, drawing a great deal of critical attention. My colleagues and I at Agere Systems, who had recently been spun out from Bell Labs, had many serious questions about the results, as did researchers within Bell Labs and elsewhere. I arranged a special seminar by Hendrik, at which Ashraf Alam noticed that some data were unreasonably smooth. Further investigation (described in technical detail in our final committee report, p. E-39) showed that there it was almost inconceivable that the data could come from real samples: it had to have been made up (not merely massaged so it looked better). This wasn't the only problem, just the hardest one to explain away as sloppiness or ignorance.

At this point I started heavy discussions with managers at Bell Labs, as described in Reich's book. Why did I not make it public, as others did with the copied data that started the formal investigation in April? Two main reasons: First, I thought there was a small chance I was wrong, and that a public allegation would still have lasting consequences and be hard to withdraw, so it would be best to check first if there was an innocent explanation. Second, the consequences for Bell Labs looked to be so severe that I thought the managers (whom I knew personally from my days at Bell Labs) deserved a chance to do some damage control (I didn't intend to help in any cover-up, of course).

I don't regret the decision to first raise the issue privately. What I regret was my response when Hendrik concocted an "explanation" for the smoothness, claiming that the histogram bars were so wide as to obscure data that were less smooth. At that point I emailed the managers saying that I considered the issue "CLOSED." That was a mistake. In reality, I still felt strongly that there was something horribly wrong, although there was a tiny patch of daylight in an otherwise airtight case for intentional fraud. (And as discovered by a Bell Labs manager months later, Hendrik's explanation was itself provably fraudulent.) I had seriously underestimated how willing the Bell managers would be to threaten the goose that had laid so many golden eggs for them. By removing my charge from the table, I gave them the excuse to delay a serious inquiry for another five months, during which Hendrik's false claims would continue to divert good researchers as well as job offers and prizes. That is my regret.

In 2006 I gave an hour-plus-long talk on this subject at Cornell's Center for Nanoscale Systems. Video is here.

Who Will Personalize Medicine?

There's a lot of excitement in the biomedical community about the potential for "personalized medicine"--the tailoring of a patient's treatment to reflect his or her individual biology. The best known prospect is selecting between drug or dosing alternatives based on a DNA tests that may predict how a person will respond, but there are other ways to improve individual outcomes as well. There have been a few specific commercial drugs with genetic tests so far, notably erbitux and warfarin, with mixed results. Nonetheless, I have no doubt that the researchers are sincere in their hopes for improved treatment.

Unfortunately, the history of the pharmaceutical industry offers less basis for optimism. I've been reading the disturbing book Our Daily Meds, by former New York Times reporter Melody Peterson (Amazon, B&N). This is just one of several recent books documenting the cynical manipulation of the prescription-drug process by Big Pharma in service of their own profits. The list of manipulation techniques is long, including inventing new diseases to be treated by their newest drugs, evading requirements for full disclosure of side effects by advertising by hiring celebrity promoters and funding patient groups focused on individual diseases, flooding the scientific literature with ghost-written articles that favor their drug, encouraging prescriptions for off-label uses, creation and marketing of useless "me too" drugs, and much more.

The take-home message is that the pharmaceutical companies rarely limit their sales to patients who would truly benefit from them, which is what personalized medicine really requires. In fact, the companies have taken many opportunities to extend their drugs to diseases in which studies have shown little benefit, to downplay or deny side effects, and to open their markets to include new. unstudied populations. They also tolerate the fact that many recipients don't benefit from the drug they pay for.

Personalized medicine would demand the opposite, shrinking the market for each drug to those who actually respond. At the same time, the complexity and expense of clinical trials that subdivide the patients into subgroups will be much higher. It seems unrealistic to expect our current commercial and regulatory system to rise to this challenge without some major changes.

I Give Up: Why does Pluto Matter?

I was going to write about how baffling I found the opening to Unscientific America, the much-discussed book by Chris Mooney and Sheril Kirshenbaum, but Jason Rosenhouse said almost exactly what I had in mind. Neither of us can understand what Mooney and Kirshenbaum think astronomers should have done differently. Decided Pluto's status on the basis of a poll? As Jason points out, the only logical choices were either to demote Pluto or to promote many similar objects to planethood. Neither would satisfy the "nine pickles" crowd. And would we really want to decide other scientific questions with a popularity contest?

I haven't read Neil deGrasse Tyson's book (Amazon, B&N), but I found Is Pluto A Planet? (Amazon, B&N), by David A. Weintraub, to be very illuminating, (especially considering that the answer should contain exactly one bit of information!). He describes both the rise and fall in the number of "planets" through history--which make the number nine look quite arbitrary--and the problems with various objective definitions of "planet." Definitions that include Pluto also include numerous other smallish, irregular objects with inclined eccentric orbits, and in one view Pluto is the founding member of athis new class. Weintraub also reviews how Pluto was discovered in a search for the orbital anomalies of Neptune that turned out to be erroneous and in any case much too big to be explained by the pull of tiny Pluto, so that it should never have been promoted. But I won't give away which of the two "answers" he comes to. It's a measure of how good the book is that I still respect it although I reach the opposite conclusion.

But none of this explains why Mooney and Kirshenbaum think Pluto matters.

 

 

Why Midgaard?

The title of this blog comes from the Norse for "middle yard," referring to Earth, the realm of men, as contrasted than realm of the Aesir gods (Odin and his gang) and the underworld. I toyed with Yggdrasil, the world tree that spans all realms, Mimisbrunnr, the well of all wisdom, and Ginnungagap, the primordial void out of which the world formed, but they seemed a bit pretentious. I think the world we live in is plenty interesting as it is, even without gods, supernatural wisdom, or naive origin stories. I chose the Swedish spelling (with two a's to represent that funny "a" with the circle on top) to reflect my one-quarter Swedish heritage.

Flavors of Science

A good friend in graduate school, who was much better schooled in the classics than I, once remarked that "everyone is either a Platonist or an Aristotelian." Plato, as I understand it, sought ideal forms underlying the imperfect reality that we see, but was prone to imposing philosophical prejudices on uncooperative facts. Aristotle was happy to catalog the diversity of Nature, with less need for organizing principles, but could be accused of "stamp collecting." Even after a couple of millennia, differences as to what constitutes an interesting or beautiful science continue cause mutual incomprehension between scientists from different specialties.

Some of the differences are inherent in the types of experiments available. For example, "historical" sciences like astronomy and archaeology are pretty much limited to learning what happened in the past. They can invent better ways to learn about it, and they can devise new explanations, but they can't change it. In contrast, physics and chemistry--and to a growing extent biology-- pose hypothetical questions, and test their understanding by predicting the responses to new, previously untested situations. Experiments make it harder for researchers to fool themselves by making up plausible, but untestable, "Just So Stories."

Other differences reflect the nature of the subject matter. Physicists are fond of invoking "Occam's razor," which states that among possible explanations, the simplest is probably the best. This works pretty well in physics, but many physicists seem to regard Occam's razor as a law of nature itself, rather than a heuristic or guide for further tests.

In biology the situation is quite different. Frequently, if more than one mechanism might explain a biological phenomenon, they're all true, with maybe a couple of others that no one thought of yet! Perhaps the mechanisms act in different species or in different situations, or they may act cooperatively or competitively in a single organism, as part of a complex web of interactions. It's still important to avoid unnecessary complications in the explanations, but Occam's razor is a lot less useful for biology, and interpreting the experiments, and devising new ones that really test assumptions, can be a lot harder.

Quantitative mathematical experiments are increasingly important in biology, and genetic and other tools let researchers test the response to ever more perturbations of biology, rather than merely observing what evolution has produced. But the starting point is still what evolution has produced: complex interacting systems with many components playing multiple roles. For these systems, trying to cast everything in the simplest possible terms doesn't always work.

Other Customers Who Bought This Item…

My news story on recommender systems (subscription required) is in the August issue of Communications of the Association of Computing Machinery.

Even if you don't recognize the phrase, if you do much of anything on the web you've dealt with "recommender systems": they're the programs that let Amazon, L.L. Bean, YouTube, and pretty much everyone else offer "suggestions" about other items that might interest you.

The timing of this story turned out to be very good. A major driver for this field in the past couple of years has been the Netflix Prize, which offered a million dollar reward to a team that could beat, by 10%, Netflix's algorithm for predicting movie preferences. To lure researchers, the company offered access to its enormous database of customer preferences, but they stipulated that the winners must make their techniques publicly available. The openness of the competition has attracted thousands of competitors, and stories in Wired and the New York Times Magazine, and IEEE Spectrum. It's taken a while, and some researchers were even speculating that Netflix had some secret knowledge that 10% was unreachable, but in the last month a couple of different teams have finally inched past the goal. (As of this writing, the official winner hasn't yet been announced, though.)

What makes my story gratifying, though, is that it goes beyond the prize to put these systems in a larger context. The 10% goal is based on the typical (root-mean-square) discrepancy between the predictions and the actual preferences reported by customers in a secret test batch. But predicting things that people will like is only a beginning. What people really need is pleasant surprises--items that they wouldn't have found on their own. In many cases, this means that the most useful predictions must make mistakes. This is a different goal from that for traditional "classifiers" that trade off false positives with false negatives. (In October in New York, the ACM is sponsoring a conference devoted entirely to recommender systems.)

Another key issue is the user interface, including how data is gathered and how recommendations are presented. If Amazon tells you that customers who bought the glass tumbler you ordered "frequently bought" a pet nail grooming rotary tool (as described recently in Consumer Reports), it makes a funny story. If they told you that the pet tool was especially selected for you by their highly tuned software, you'd likely conclude they were delusional.

One of the fun applications is in music. As part of my "research" I tried out the internet radio station Pandora. I seeded the station with some of my quirkier art-rock music from the early 70s, like Gentle Giant, and was really blown away when it played other music from the distant corners of my collection that I didn't think anyone else new about. Interestingly, the Pandora team uses a large team of musicologists to classify tunes, and does not rely exclusively on user's preferences.

The million dollars that Netflix ponied up is a hint of how commercially important these systems are. Even when we're not aware of it, they will shape more and more of our technological experience, both on the web and with mobile devices. Stay tuned.

Disorder through Order

My latest story went up at Physical Review Focus on Friday. The most obvious hook to this story is that a highly simplified system has more entropy if it forms a crystal than as a random, liquid-like arrangement. This is surprising because randomness usually means higher entropy.

As often happens when looking deeper into a single journal article, though, this finding really isn't all that new. In fact, the first simulations of this effect, for collections of spheres (known as "hard spheres" by the cognoscenti), were done some 50 years ago. A decade or so ago, theoretical and experimental research confirmed that the higher entropy arises because all of the atoms in the crystal have some room to rattle around. Other things being equal, entropy is higher if any extra wiggle room is distributed democratically among all degrees of freedom. (We're not talking about a close-packed crystal here, where all of the spheres are butted together like cannonballs or oranges, but one where they jiggle around in a looser arrangement, each one on average in a crystalline position.) In contrast, a random arrangement of spheres with the same density as this loose crystal becomes "jammed" into a rigid network, with only a few "rattlers" free to move.

Moreover, it's extraordinarily common for order to develop in complex systems, in spite of the apparently irresistible demand of the Second Law of Thermodynamics that entropy never decrease. The catch is that the total entropy increases by slightly increasing the jiggling of a large number of degrees of freedom to pay for a large-scale ordering. Such spontaneous pattern generation occurs everywhere from chemical reactions to thermal convection to--most important for us--life itself.

The thing that actually made this story new was that the spheres were connected together to form a kind of idealized polymer, but could otherwise move freely. The polymer molecules themselves don't arrange regularly, but the spheres do. In some ways the simulation stacked the deck in favor of the crystallization by making them as similar to the bare spheres as possible, but it's a kind of existence proof that even the polymer can crystallize due to entropy alone. Connecting the spheres in a polymer didn't stop it.

The journalistic challenge is to convey the exciting, surprising part without leaving a misleading idea about what was new. Sometimes this can be quite hard or even impossible, if the advance involves a highly technical issue. In this case, the basic ingredients were not that hard to get across (other than entropy itself), so it wasn't too hard to get both the history and the novelty into a short format. The alternative is to neglect the history and let readers think that this new article has created a completely new understanding. Unfortunately, news stories are told this way much more often than they should be.