The Plausibility of Life

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

This speech was given the day before MLK was assassinated.

Sys Devo

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

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

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"

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

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.