If it's not natural selection, then it must be...

The folks at the Discovery Institute (DI) are engaged in an extensive attempt to rebut my friend Dennis Venema's critiques of Stephen Meyer's surprisingly lame ID manifesto, Signature in the Cell. There are several aspects of this conversation that I hope to address in the coming days and weeks, but one jumped out at me today: the consistent confusion about natural selection in depictions of evolutionary theory by design advocates.

Consider this excerpt from a recent blog post by a writer at the Discovery Institute:

...we need a brief primer in fundamental evolutionary theory. Natural selection preserves randomly arising variations only if those variations cause functional differences affecting reproductive output.

A few sentences later, the same claim is repeated:

Indeed, given that natural selection favors only functionally advantageous variations, ...

Those claims were first made in a piece written by unnamed DI "fellows" mocking the work and conclusions of Joe Thornton, an evolutionary biologist at the University of Oregon and the University of Chicago. And the claims are badly misleading.

"The stamp of one defect": an endless series on harmful mutations

Not surprisingly, Hamlet weighed in on the nature vs. nurture question, at least once.

So, oft it chances in particular men,
That for some vicious mole of nature in them,
As, in their birth,―wherein they are not guilty,
Since nature cannot choose his origin,―
By the o’ergrowth of some complexion,
Oft breaking down the pales and forts of reason,
Or by some habit that too much o’er-leavens
The form of plausive manners; that these men,
Carrying, I say, the stamp of one defect,
Being nature’s livery, or fortune’s star,
Their virtues else, be they as pure as grace,
As infinite as man may undergo,
Shall in the general censure take corruption
From that particular fault: the dram of eale
Doth all the noble substance of a doubt,
To his own scandal.

– Hamlet, Act I, Scene IV, The Oxford Shakespeare

It is certainly true that "the stamp of one defect" can wreak havoc on the scale that Hamlet describes, and whether the result is a debilitating physical limitation or damage to "the pales and forts of reason," the outcome is tragic by any measure.

Reflecting on the reality of inherited dysfunction, we might be tempted to assume that a "vicious mole of nature" is something seen only "in particular men," and that those who are not so characterized (let's call them "normal people") have been dealt a genetic hand that lacks such devilish cards. Normal people don't have bad genes.

Okay, so in the real world I suspect that most people are not so naïve; if you're reading this blog, then you probably know that bad genes can be carried by normal, healthy people. Nevertheless, when we think about bad genes – or more technically, deleterious mutations – we are likely to think that they are not very common.

Molecular evolution: improve a protein by weakening it

In the cartoon version of evolution that is often employed by critics of the theory, a new protein (B) can arise from an ancestral version (A) by stepwise evolution only if each of the intermediates between A and B are functional in some way (or at least not harmful). This sounds reasonable enough, and it's a good starting point for basic evolutionary reasoning.

But that simple version can lead one to believe that only those mutations that help a protein, or leave it mostly the same, can be proposed as intermediates in some postulated evolutionary trajectory. There are several reasons why that is a misleading simplification – there are in fact many ways in which a mutant gene or protein that seems to be partially disabled might nevertheless persist in a population or lineage. Here are two possibilities:

1. The partially disabled protein might be beneficial precisely because it's partially disabled. In other words, sometimes it can be valuable to turn down a protein's function.

2. The effects of the disabling mutations might be masked, partially or completely, by other mutations in the protein or its functional partners. In other words, some mutations can be crippling in one setting but not in another.

In work just published by Joe Thornton's lab at the University of Oregon*, reconstruction of the likely evolutionary trajectory of a protein family (i.e., the steps that were probably followed during an evolutionary change) points to both of those explanations, and illustrates the increasing power of experimental analyses in molecular evolution.

Genetics, evolution, and sexual orientation: the gay extinction hypothesis

Three weeks ago, I went to the Cornerstone Music Festival with my two oldest kids. For the second year, I was an invited speaker in the festival's excellent seminar program. This year, my two series were entitled "Alien Worlds" and "Zombies on Jeopardy" – exploring extreme biology and human nature, respectively. It was fun, if a little too hot for a day or so.

At one point, I was discussing human intelligence and its genetic underpinnings. And I got a loaded question, paraphrased thus: "What happens when you substitute 'sexual orientation' for intelligence? Is homosexuality 'genetic' and if so, what does that mean for Christian views of sexuality?" (The Cornerstone Festival is a Christian music festival, known for embracing music at the 'fringes' while remaining consistent with most mainstream evangelical sensibilities, including a typically evangelical view of homosexuality.) I answered that sexual orientation also has a fairly significant heritable component, meaning that some of the variation in sexual orientation is accounted for by genetics. Then I got a followup question/comment, delivered with intriguing smugness, and paraphrased as follows: "Homosexuality can't be genetic, because homosexuals don't have kids and so the trait will be eliminated from the population." Without going into the complexity of sexual orientation as a biological phenomenon, I will critique this person's claim, since I hear it from Christians with disheartening frequency.

Mapping fitness: ribozymes, landscapes, and Seattle

A few months ago, we were looking at the concept of a fitness landscape and how new technologies are creating opportunities for biologists to look in detail at relationships between genetics and fitness. The first post discussed the concepts of a fitness landscapes and adaptive walks, with some focus on the limitations of the metaphor. The second post summarized some recent work on bacterial fitness and mutation rates, with the concept of a fitness landscape as a theme, and the third post reviewed another recent paper, one that described techniques for studying fitness landscapes in detail by linking protein function (which can be screened and/or selected) and genetic information. Here we'll look at yet another approach to the problem, in which the subject of the analysis is not an organism (as in the first paper) or a protein (as in the second paper) but an RNA molecule.

Mapping fitness: protein display, fitness, and Seattle

A couple of months ago we started looking at the concept of fitness landscapes and at some new papers that have significantly expanded our knowledge of the maps of these hypothetical spaces. Recall that a fitness landscape, basically speaking, is a representation of the relative fitness of a biological entity, mapped with respect to some measure of genetic change or diversity. The entity in question could be a protein or an organism or a population, mapped onto specific genetic sequences (a DNA or protein sequence) or onto genetic makeup of whole organisms. The purpose of the map is to depict the effects of genetic variation on fitness.

Suppose we want to examine the fitness landscape represented by the structure of a single protein. Our map would show the fitness of the protein (its function, measured somehow) and how fitness is affected by variations in the structure of the protein (its sequence, varied somehow). It's hard enough to explain or read such a map. Even more daunting is the task of creating a detailed map of such a widely-varying space. Two particular sets of challenges come to mind.

How bad genes can escape the Grim Reaper (and why this is good)

Last month, PZ Myers wrote an interesting piece at the Panda's Thumb in which he discussed some problems with very simple models of evolutionary genetics. One of his main themes was the idea that many genetic changes are neutral with regard to fitness; instead of immediately triggering selection (positive or negative), they're just "tossed into the stewpot." I recently joined the crew at PT, so for my first post I picked up on PZ's point and discussed a very recent article that describes one way in which organisms can carry seemingly deleterious genes around with them.

The concept of interest is called "developmental buffering," and it's just another fascinating biological phenomenon that creationists and ID propagandists are wise to ignore.

Read the article at the Panda's Thumb.

Weasels, clouds and biomorphs, part III

The Blind Watchmaker is a superb book by a masterful science writer. It's not just a book about evolution, or even about how evolution works. It's a book about how evolution explains design, and more specifically how natural selection accounts for design. As I wrote before, I consider chapter 3 to be the most important chapter of the book. The chapter is called "Accumulating small change" and it features two different computer programs that Dawkins uses to teach readers about the effectiveness of selection in evolution. Before we play with the Biomorph program in the next post, allow me to set us up by discussing the importance of the program in Dawkins' argument, and by outlining the logic of the program's design.

First let me try to convince you that chapter 3 really is the heart and soul of the book. The chapter is about gradual, step-by-step evolution resulting from natural selection. And as you might already know, natural selection is what Dawkins considers to be The Big Idea, the idea that answered Paley's seemingly insurmountable challenge. In chapter 2, Dawkins makes this clear. Here's how he starts.

Natural selection is the blind watchmaker, blind because it does not see ahead, does not plan consequences, has no purpose in view. Yet the living results of natural selection overwhelmingly impress us with the appearance of design as if by a master watchmaker, impress us with the illusion of design and planning. The purpose of this book is to resolve this paradox to the satisfaction of the reader....

–The Blind Watchmaker, page 21

Chapter 2 famously focuses on echolocation in bats, and I would buy the book just to read Dawkins' description of the engineering feat that is the little brown bat. (He gleefully recounts the utter incredulity of an audience of biologists when the mere existence of such biological phenomena was first described.) And here's his conclusion.

I hope that the reader is as awestruck as I am, and as William Paley would have been, by these bat stories. My aim has been in one respect identical to Paley's aim. I do not want the reader to underestimate the prodigious works of nature and the problems we face in explaining them. Echolocation in bats, although unknown in Paley's time, would have served his purpose just as well as any of his examples. Paley rammed home his argument by multiplying up his examples. He went right through the body, from head to toe, showing how every part, every last detail, was like the interior of a beautifully fashioned watch. In many ways I should like to do the same, for there are wonderful stories to be told, and I love storytelling. But there is really no need to multiply examples. One or two will do. The hypothesis that can explain bat navigation is a good candidate for explaining anything in the world of life, and if Paley's explanation for any one of his examples was wrong we can't make it right by multiplying up examples. His hypothesis was that living watches were literally designed and built by a master watchmaker. Our modern hypothesis is that the job was done in gradual evolutionary stages by natural selection.

–The Blind Watchmaker, page 37

Chapter 4 builds on chapter 3, and the rest of the book deals with how it might all work. Chapter 3 is Dawkins' attempt to show us the power of cumulative selection, and cumulative selection is The Blind Watchmaker. This is the heart of the matter, and Dawkins' argument (and his world) hinges on the success of this idea.

And so Dawkins tackles the concept of cumulative selection in chapter 3, and as we've already seen, he immediately faces a serious problem: the end result of an evolutionary process is the generation of design, of biological machines that are complex and, more importantly, wildly improbable. In other words, such things "can't just happen." The human mind is prone to a serious error when faced with this challenge. The error is to envision complexity arising spontaneously from chaos, in a single step, and thus to conclude that such things cannot be explained naturally. The error is in bold, and Dawkins addresses it first with the simple and effective Weasel illustration. The illustration is highly effective as a corrective for that error, but it fails as a model of evolution, as I explained in the previous post.

The Biomorph program was Dawkins' more serious attempt at modeling the development of complex structures by cumulative selection. It's important to understand just how central the program really is, and thus why it's so silly to make a big deal out of the Weasel exercise. Chapter 3 is the heart of the book, and the Biomorph program is the soul of chapter 3. The Biomorph program improves on Weasel in two very important ways:

1. It models evolutionary unfolding without a specific goal. The Weasel program "homed in" on a particular goal; the Biomorph program has no such constraint.

2. The entities that evolve in the Biomorph program, called biomorphs, "develop," and their development is controlled by a number of factors ("genes") which change (i.e., mutate) in each generation, so that mutations result in alterations to development and thus to new forms.

The biomorphs are tree-like structures, and they are drawn according to simple rules. (This post is decorated with a few that I made using a nice Java applet.) The rules control the branching of the trees (branch at a certain angle or at a certain point on the existing branch, or branch of a certain length, or whatever). The drawing of a biomorph, then, is a representation of embryonic development. And the rules represent the various processes in development.

It should be fairly easy to see how to model the effects of genes: a gene will influence a rule, by assigning a number to the rule (e.g., branch at a bigger or smaller angle). Reproduction is simple: the biomorphs are redrawn, based on the parent's structure, using the same rules influenced by the same genes. Boring? No: mutation acts to change the numeric value of the genes, randomly changing the value by either +1 or -1. The result is a set of offspring, each differing slightly from the parent by virtue of a single mutation.

So it goes like this. A parent is selected. The subroutine REPRODUCTION runs, and generates random mutations in each of the genes of the parent (there are 9 genes); the new genes are passed to the subroutine DEVELOPMENT, which draws new biomorphs based on the new genes. The result is a set of 9 offspring, each with a different version of one of the parent's genes. One is selected to be the parent of the next generation. Keep doing this, over and over and over, and you get the program EVOLUTION.

But how does selection work in this program? Recall that the major problem with the Weasel illustration was its goal-directed nature. In the Biomorph program, things are different:

...the selection criterion is not survival, but the ability to appeal to human whim. Not necessarily idle, casual whim, for we can resolve to select consistently for some quality such as 'resemblance to a weeping willow'. In my experience, however, the human selector is more often capricious and opportunistic. This, too, is not unlike certain kinds of natural selection.

–The Blind Watchmaker, page 57

Selection, in other words, is done by you, the human who is "playing God."

That's the Biomorph program. Next time: what it demonstrates about evolution.

Weasels, clouds and biomorphs, part II

Back in September I wrote about the silly preoccupation on the part of various anti-evolutionists with the so-called Weasel program, a simple exercise created more than 20 years ago by Richard Dawkins to illustrate the efficacy of cumulative selection in evolutionary scenarios. My main point was that the Weasel program had one very simple purpose (comparing "single-step selection" – which is purely random – to cumulative selection) and constitutes a trivial fraction of the argument in Dawkins' The Blind Watchmaker.

One might think that Dawkins' basic message – that random one-step flying-together of a Shakespearean phrase or a hemoglobin molecule is impossibly unlikely compared to cumulative selection of intermediate stages – is so elementary that no intelligent person would need to consider it more than once. (Once seems like a lot to me in this case, but never mind.) And yet the error (if that's what it is) is shockingly common. (It forms one pillar of poor Cornelius Hunter's whole enterprise, for example.)

Dawkins understood this problem when he wrote The Blind Watchmaker, so before he unveiled the fascinating program that forms the heart of his case for the power of selection, he took one last stab at making the basic outline clear by going back to clouds. Wait...clouds? (Fans of Hamlet are already sighing blissfully; those who don't get the connection between weasels and clouds should read either Act III, Scene II of Hamlet or Chapter 3 of The Blind Watchmaker.) Yep. Dawkins pointed back at what makes cumulative selection work: the things that are evolving must be able to generate related offspring. And that's what clouds can't do.

Clouds are not capable of entering into cumulative selection. There is no mechanism whereby clouds of particular shapes can spawn daughter clouds resembling themselves. If there were such a mechanism, if a cloud resembling a weasel or a camel could give rise to a lineage of clouds of roughly the same shape, cumulative selection would have the opportunity to get going. Of course, clouds do break up and form 'daughter' clouds sometimes, but this isn't enough for cumulative selection. It is also necessary that the 'progeny' of any given cloud should resemble its 'parent' more than it resemble any old 'parent' in the 'population'... It is further necessary that the chances of a given cloud's surviving and spawning copies should depend on its shape.

– The Blind Watchmaker, pages 50-51, italics in the original

Hence the Weasel program.

But I noted last time that Dawkins spent a tiny amount of time and text on the Weasel program, and that he declared it to be "misleading in important ways." The most important, by far, is this: the selection that drove the Weasel program was goal-directed. A better simulation of evolution would be one in which selection is more capricious, more "in the moment." (Survival can be capricious; reproduction happens rather decisively "in the moment.")

Dawkins came up with just such a program, and I mentioned it in the previous post. It's a wonderfully simple simulation of the basic aspects of selection-driven evolution: it includes development, reproduction, genes, and selection, and generates "organisms" with shapes instead of a phrase in all caps. We'll look at that program in the next and final post. But if you want to play with a modern version, you'll find plenty of nice implementations out there. So much more fun than studying...or grading.

Weasels, clouds and biomorphs, part I

There's usually no point in piling on when the minions of the ID movement get their just deserts after some typically brainless culture-war test launch. Consider the responses (by, most notably, Ian Musgrave at the Panda's Thumb) to the most recent rendition of the ID movement's hilariously idiotic fixation on a particular computer program written by Richard Dawkins. It seems there is little to add. But I think something important is being lost in this conversation, probably because the level of the "conversation" is the level of the ID movement. So let's start with a little quiz.

1. True or false: Richard Dawkins' 1986 classic The Blind Watchmaker used a computer model (a simulation) as a key teaching device while explaining the effectiveness of cumulative selection in evolution. The program is the main focus of chapter 3 ("Accumulating small change") of the book.

Answer: True.

2. True or false: the computer program used for this purpose was made available to the public and has since been adapted for free use on the web.

Answer: True.

3. True or false: the computer program in question is called WEASEL (or similar) and it demonstrates the stepwise generation of a famous phrase from Hamlet.

Answer: False.

Now if this surprises you, then either you haven't read The Blind Watchmaker or you haven't read it in a long time. Because even if you've been influenced by the hysterical antics of the ID crowd, you could not long believe its claims about the Weasel program if you had recently read the book. If you haven't recently read The Blind Watchmaker, you might consider a stroll through some representative ID musings on WEASEL followed by a visit to Chapter 3 of the book (and, if you have a copy from 1989 or later, a visit to the two appendices.) The experience could be jarring for those who have a positive view of these arguments by ID apologists.

But if you don't have a copy of The Blind Watchmaker handy, I can help. First, in this post, I'll discuss the Weasel program and its place in the thesis of The Blind Watchmaker – in the context of the current ID fixation on the program. Then I'll introduce the program that Dawkins really did emphasize in the book, a program called EVOLUTION (or later, when it was expanded and made commercially available, The Blind Watchmaker Evolution Simulation program) but commonly known as the biomorph(s) program. In the second post I'll talk more about the biomorph program and its usefulness.

Chapter 3 of The Blind Watchmaker is a tour de force of expository scientific writing. Called "Accumulating small change", the chapter has a single and simple thesis, laid out in the first paragraph:

We have seen that living things are too improbable and too beautifully 'designed' to have come into existence by chance. How, then, did they come into existence? The answer, Darwin's answer, is by gradual, step-by-step transformations from simple beginnings, from primordial entities sufficiently simple to have come into existence by chance. Each successive change in the gradual evolutionary process was simple enough, relative to its predecessor, to have arisen by chance. But the whole sequence of cumulative steps constitutes anything but a chance process, when you consider the complexity of the final end-product relative to the original survival. The purpose of this chapter is to demonstrate the power of this cumulative selection as a fundamentally nonrandom process.

– The Blind Watchmaker, page 43, italics in the original

Dawkins immediately tackles the crazy misconception of evolution as a process that is akin to the impossibly improbable flying-together of the parts of a machine in a single step. Echoing Isaac Asimov, he calculates the probability of the spontaneous assembly of a hemoglobin molecule in a single step, and arrives at a number of predictably indescribable magnitude. It can't just happen.

In a 1987 BBC television show , he uses a much better metaphor: the opening of a safe by entering a combination. To open the safe, a banker (or thief) must correctly enter all of the correct numbers, in order, at the same time. His point is: of course it can't "just happen." The concept that Dawkins aims to communicate is this: from an evolutionary perspective, "success" doesn't happen all at once; it is accumulated. Evolutionary change is cumulative change; it's as though the safe opens a little when one correct number is entered, and allows the banker to reach in and get a little money. ("Small change" is the topic, remember.)

This is a very basic and very important aspect of the Darwinian mechanism, and yet it is maddeningly common to see it ignored or completely misunderstood. So in the first few pages of Chapter 3, Dawkins looks for an illustration of the difference between "randomly getting the whole thing right in one fell swoop" and "accumulating random improvements till the whole thing is assembled." He starts with the old saw about monkeys, typewriters and Shakespeare. Choosing a single phrase from Hamlet, "Methinks it is like a weasel," he first calculates the probability of a random character generator (a monkey) spontaneously banging out Hamlet's phrase. The calculation charmingly indicates that there isn't enough time in the universe for such a thing to occur. Yeah, yeah, yeah. Now, it's easy enough to get a computer (even a 1986-vintage machine) to churn out 28-character strings randomly, and so Dawkins describes a program that can do this. Then he introduces the occurrence of cumulative selection in the program, to illustrate its profound effectiveness compared to mere randomness.

We again use our computer monkey, but with a crucial difference in its program. It again begins by choosing a random sequence of 28 letters... It now 'breeds from' this random phrase. It duplicates it repeatedly, but with a certain chance of random error – 'mutation' – in the copying. The computer examines the mutant nonsense phrases, the 'progeny' of the original phrase, and chooses the one which, however slightly, most resembles the target phrase... the procedure is repeated, again mutant 'progeny' are 'bred from' the phrase, and a new 'winner' is chosen.

– The Blind Watchmaker, pages 47-48, italics in the original

Dawkins shows that this procedure can get from a random monkey-phrase to "Methinks it is like a weasel" in mere seconds. And the point is simply this: cumulative selection is more effective than mere "randomness" by incomprehensibly gigantic magnitudes. Dawkins makes it very clear that the Weasel program is meant to demonstrate nothing more than that. After pointing out that single-step selection would take a near eternity to type the phrase, he reiterates the simple purpose of the comparison, and the whole weasel exercise:

Actually it would be fairer just to say that, in comparison with the time it would take either a monkey or a randomly programmed computer to type our target phrase, the total age of the universe so far is a negligibly small quantity, so small as to be well within the margin of error for this sort of back-of-an-envelope calculation. Whereas the time taken for a computer working randomly but with the constraint of cumulative selection to perform the same task is of the same order as humans ordinarily can understand, between 11 seconds and the time it takes to have lunch.

– The Blind Watchmaker, page 49, italics in the original

Folks, that's all the silly weasel thing was ever about. So what's all the fuss then?

Well, some ID partisans are all agitated about whether Dawkins' program allowed mutations in positions of the string where the correct letter had been hit upon. They wonder: if cumulative selection had gotten us "Methinks it is like a measel", could 'measel' mutate back to 'measer' and thus take the program a step away from the target? And why does this matter? Well, for Dawkins' purposes it really doesn't matter, but the ID scholars seem to think there's a big speed difference. If you're interested you can read some nice work by Wesley Elsberry or Anders Pedersen that shows clearly that it simply doesn't matter.

But. Here's what's lost in all this. Dawkins never intended the silly little weasel exercise to be a persuasive argument for evolution as it actually occurs in the world. In fact, he is quick to point out why it's deficient (remember Bohr models of the atom in grade school?), noting that it is "misleading in important ways." And at that point, he abandons the Weasel program in favor of a simulation that is far better. That simulation, the Biomorph program, is the topic of the entirety of the rest of Chapter 3 of The Blind Watchmaker (and of my next post).

The absurdity of the ID fixation on the Weasel program is hard to capture with mere words. Perhaps this table will help put the two programs into better perspective.

	Weasel	Biomorph
Number of times program is mentioned* on Uncommon Descent	444	2
Number of pages devoted to program in The Blind Watchmaker	less than 5	45, including two appendices

*All I did was Google the word 'weasel' or 'biomorph' at uncommondescent.com. The two uses of 'biomorph' were in comments by ID critics (one being Wes Elsberry). The uses of 'weasel' surely include insults that aren't references to the program.

The real focus of Chapter 3 of The Blind Watchmaker is the biomorph program. Seven figures, 23 pages, 22 more pages in two appendices which include a small user's manual for the program. The biomorph program constitutes the heart of Dawkins' book and his argument, so much so that the program is named The Blind Watchmaker. The Weasel program was a tiny stepping-stone for Richard Dawkins, a simplistic teaching tool meant to illustrate a single simple point. It's a hill to die on for the ID movement, and that says a lot about the state of that confused community.

Carnival of Evolution 14

Welcome to Quintessence of Dust and to the 14th Edition of the monthly Carnival of Evolution. Thanks for stopping by, and for supporting scientific carnivalia, members of a taxon that seems to be flirting with extinction.

One good reason to visit a carnival: brain stimulation. Brain Stimulant offers some thoughts and speculations on Free Will and the Brain, touching briefly on themes of selection and adaptation, and he doesn't charge as much as the clinic would.

Another good reason: you can bump into real scientists, the kind who actually work on evolution. Ryan Gregory has a day job as an expert on genome evolution, but somehow finds the time to blog at Genomicron. Recent entries there include fascinating pictures of ongoing field work. For this month's carnival, be sure to read two reviews of the ideas of Stephen Jay Gould, focusing on controversial papers by Gould published in 1980 and 1982. You may find that you have been misinformed about Gould's positions, and you'll surely learn more about evolution.

Michael White at Adaptive Complexity is another blogging scientist, and he writes very clearly about parasitic DNA in Selfish Gene Confusion.

David Basanta is a biologist who runs a cool blog called Cancerevo: Evolution and cancer, which is subtitled "Studying cancer as an evolutionary disease." Check it out, and don't miss his recent piece on Stem cells and ecosystems.

Zen Faulkes is a biologist who blogs at Neurodojo. That's cool enough, but the subtitle of that blog is "Train your brain." Hey, this could be a theme for the whole carnival! He recently wrote about a walking bat in New Zealand. Bat evolution...we can't get enough of that. I've written about it myself.

Brains and their origins come up in an extensive discussion of early animal evolution at AK's Rambling Thoughts. The post is The Earliest Eumetazoan Progression.

At The Loom, the peerless Carl Zimmer discusses AIDS in chimps and the relevance of the story to conceptions of scientific progress. AIDS and The Virtues of Slow-Cooked Science is engrossing and important. And John Wilkins discusses some new fossil apes in an excellent recent post at Evolving Thoughts.

John Lynch reviews a new book on Alfred Russell Wallace. Caveat lector. Brian at Laelaps takes us on a historical tour of the work of Florentino Ameghino. Are those elephants or not? Brian's discussion is typically excellent.

At The Spittoon, AnneH discusses new findings concerning both the past and the future of the mammalian Y chromosome.

Hoxful Monsters is a future host of this carnival; Nagraj recently reviewed some recent work on pattern formation in the development of spiders. Wonderful evo-devo stuff.

Someone at Wired wrote some swill about the "10 Worst Evolutionary Designs" which annoyed a few smart bloggers. At Deep-Sea News, Dr. M sets the record straight. The title is self-explanatory: Worst Evolutionary Designs? No! Brilliant Solutions to the Complexity of Nature and Constraints.

Larry Moran at Sandwalk is attending a conference entitled Perspectives on the Tree of Life. He's posted reviews of days one and two so far.

And that's our carnival. Thanks for reading, and on the way out I hope you'll look at my nearly-complete series on Notch and deep homology.

Next month's edition will appear at Southern Fried Science. To submit posts, use the submission form found at the Carnival of Evolution site. And if you like the carnival, help us promote it with a link, and/or consider hosting. More info at the carnival site.

Clone wars, or how evolution got a speed limit

The standard simplified narrative of evolutionary adaptation goes something like this. A population of organisms is exposed to a challenge of some kind. Perhaps a new predator has appeared on the scene, or the temperature of the environment has ticked up a degree or two, or the warm little pond is slowly accumulating a toxic chemical. Some of the organisms in the population harbor (or acquire) mutations – so-called beneficial mutations – and these individuals are more successful in the face of the challenge. The population evolves, then, as these beneficial mutations become more common until they are the new status quo. The change is brought about by selection, and the process is called adaptation.

These beneficial mutations, as one might suppose, are quite rare. Most mutations are either harmful to some degree or have little or no effect. Since the good stuff is so hard to come by, it follows that huge populations will be better able to adapt, and will do it faster, because they contain more of the good stuff.

It's a straightforward conclusion, and it's the basis of some recent challenges to evolutionary theory coming from the Intelligent Design movement. But it's mostly wrong. Here's the problem with the simple story.

In a very large population, many beneficial mutations will be present at the same time, in different individuals. When the challenge is presented, these beneficial mutants will compete against each other, and typically one will win. This means that most beneficial mutations – specifically those with small effects – will be erased from the population as it adapts. So, seemingly paradoxically, a very large population doesn't benefit from its bounty of beneficial mutations when it is subjected to an evolutionary challenge. It's as though adaptation has a built-in speed limit in large populations, and the effect has been clearly demonstrated experimentally. It's called clonal interference.

As geneticists examined this phenomenon, it became clear that any attempt to measure beneficial mutation rates would have been influenced, perhaps dramatically, by clonal interference. Such experiments were often done in bacteria, in the huge populations that can be so easily generated in the lab. Analyses in bacteria, published 6 or 7 years ago, had estimated the beneficial mutation rate to be about 10^-8 per organism per generation. (That's 1 per 100 million genomes per generation.) Since the overall mutation rate is estimated to be about 10^-3 per organism (a few per thousand genomes per generation), it was concluded that beneficial mutations are fantastically rare compared to harmful or irrelevant mutations.

Creationists have long emphasized the rarity of beneficial mutations, for obvious reasons. For their part, geneticists knew that clonal interference was obscuring the true rate, but no one knew just what that rate might be. That changed in the summer of 2007, when a group in Portugal (Lília Perfeito and colleagues) published the results of a study [abstract/full-text DOI] designed to directly address the effect of clonal interference on estimates of the beneficial mutation rate. Their cool bacterial system (based on good old E. coli) enabled them to genetically analyze the results of an evolutionary experiment, using techniques similar to those made famous by Richard Lenski and his colleagues at Michigan State University.

In short, Perfeito et al. took populations of bacteria and allowed them to adapt to a new environment for 1000 generations. Then they looked for evidence of a "selective sweep" in which one particular genetic variant (i.e., mutant) has taken over the population (their system was set up to facilitate the identification of these adaptive phenomena). The same system had been used before to estimate the beneficial mutation rate, and had arrived at the minuscule number I mentioned before.

The Portuguese group introduced one simple novelty: they studied adaptation in the typical large populations, but also in moderately-sized populations, and then compared the results. The difference was profound: the beneficial mutation rate in the smaller populations was 1000-fold greater than that in the very large populations. This means that clonal interference in the large populations led to the loss of 99.9% of the beneficial mutations that arose during experimental evolution. And that means that the actual beneficial mutation rate, at least in bacteria, is 1000 times greater than the typically-cited estimates.

Perfeito et al. further exploited their system to measure the fitness of all of the mutant clones that they recovered. They found that evolution in very large populations generally resulted in beneficial mutations with larger beneficial effects. This makes sense: the slightly-beneficial clones were eliminated by competition, so at the end of the process of adaptation, we're mostly left with the more-beneficial mutations.

Now some comments.

1. It might seem at first that the large populations are still better off during adaptation, since they do generate beneficial mutations, and selectively retain the more-beneficial ones. But the claim is not that large populations don't adapt; the point is that the vast majority of possible adaptive trajectories are lost due to competition, such that only the trajectories that begin with a relatively large first step are explored. That's a significant limitation, and quite the opposite of the simplistic models of design proponents like Michael Behe and Hugh Ross. Genetic models have shown that the only way for an asexual population to get around the barrier is to do what Michael Behe claims is almost impossible: to generate multiple mutations in the same organism. And recent experimental results show that this does indeed occur.

2. Since the early days of evolutionary genetics, the genetic benefits of sex have been postulated to include the bringing together of beneficial mutations to create more-fit genetic combinations expeditiously. In 2002, an experimental study validated this conjecture, showing that sexual reproduction circumvents the "speed limit" imposed by clonal interference in large populations, and in 2005 another experimental analysis showed that sex speeds up adaptation in yeast but confers no other obvious advantage. Perfeito et al. identified this connection as a major implication of their own work:

...if there is a chance for recombination, clonal interference will be much lower and organisms will adapt faster. [...] Given our results, we anticipate that clonal interference is important in maintaining sexual reproduction in eukaryotes.

(One of the hallmarks of sexual reproduction, besides fun, is recombination – the active shuffling of genetic material that generates offspring with wholly unique mixtures of genes from mom and dad.) In other words, one of the most important benefits of sexual reproduction – and especially of genetic recombination – is negation of the evolutionary drag of clonal interference.

3. All of the examples I've mentioned here are bacterial or viral. If clonal interference arises merely as a result of large population sizes, then it should be an issue for other populations too. And it is: in last month's issue of Nature Genetics, Kao and Sherlock present a tour de force of experimental evolution in a eukaryote, demonstrating the importance of clonal interference and multiple mutations in yeast cells growing asexually. In their study, they identified each beneficial mutation by sequencing the affected gene. Wow.

Why does all of this matter? Well, because it's cool, that's why. And it does mean that our biological enemies have a lot more adaptive resources than we used to think. Here are the closing comments of Perfeito and colleagues:

...our estimate of U_a implies that 1 in 150 newly arising mutations is beneficial and that 1 in 10 fitness-affecting mutations increases the fitness of the individual carrying it. Hence, an enterobacterium has an enormous potential for adaptation and [this] may help explain how antibiotic resistance and virulence evolve so quickly.

But also: keep clonal interference in mind when you encounter any simple story about evolution and genetics. Evolution isn't impossibly difficult to comprehend, but getting it straight requires just a little more effort (and a whole lot more integrity) than has been demonstrated in recent work by those who just can't believe that it could be true.

Article(s) discussed in this post:
L. Perfeito, L. Fernandes, C. Mota, I. Gordo (2007). Adaptive Mutations in Bacteria: High Rate and Small Effects. Science, 317 (5839), 813-815 DOI: 10.1126/science.1142284
K.C. Kao and G. Sherlock (2008). Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genetics, 40(12), 1499-1504. DOI: 10.1038/ng.280

Why I'm not a Behe fan, Part IIB: abusing genetics

In a previous post, I started to explain a fact that some people (who don't know me) seem to find surprising or noteworthy. Michael Behe is a Christian who accepts common ancestry and an ancient cosmos, so you'd think I would be excited about the work of a fellow "theistic evolutionist." But I'm not. Two overall problems come to mind. (Basically, I find his conduct as a scientist to be unacceptable, and I find his proposals to be laughable failures.) I'm addressing the second one here. The discussion is quite long, so I divided it into two sections, Part A and this post, Part B, which will have to be split up. I'm sorry about the length; it would really take a whole book to carefully explain how Behe has misused genetics and probability.

1. Behe's fans say that he's a nice guy, and that the evolutionists are "crucifying" him. Both claims seem to be true, but they can't hide some serious problems with his conduct as a scientist.

Those issues are the subject of the first post.

2. Some of Behe's defenders think that he has effectively answered his critics. He has not, nor has he understood or acknowledged the most important criticisms of his crude claims.

Behe's recent book The Edge of Evolution (henceforth EoE) is the focus of this series, and as I exlained in Part A:

EoE makes exactly one specific scientific claim, accompanied by simplistic genetic assumptions and supported by a "case study." The scientific claim is that the mutations that drive large-scale evolution, and that are thought to underlie all evolutionary change (past and present), are non-random. And the "case study" is a long-winded account of the adaptation of the malaria parasite in the face of drugs intended for its destruction.

Part A dealt with the laughable case study. But the heart of EoE is the claim that random mutation rates are insufficient – spectacularly insufficient – to support step-by-step evolution of complex features. The implication, then, is that the mutations that underlie major evolutionary change did not occur randomly.

First, some important points of clarification:

• Behe is not denying that common descent is true, or that evolutionary change results from mutation. He acknowledges both. He is saying that the most important mutations – those that led to, say, new cell types – could not have been random.
• Behe is not saying that the combination of random mutation and natural selection (the "darwinian" mechanism) is not a driving force in evolutionary change. He acknowledges the efficacy of the process in explaining "a number of important details of life," such as drug resistance in bacteria or pesticide resistance in insects, and is willing to attribute the differences between widely divergent organisms to the workings of "randomness." Specifically, he writes that "explicit design appears to reach into biology to a certain level, to the level of the vertebrate class, but not necessarily further." (p. 220) This means that Behe claims to be certain that the major distinctions between goldfish and bats are non-random, but that the major distinctions between bats and people could be accounted for by random mechanisms. (He asserts the "edge of evolution" to lie somewhere between the species level and the class level. [p. 201])
  
• Behe does not commit himself to a particular mode of divine intervention whereby the supposedly non-random mutations came about, and in fact he seems to favor a front-loading scenario in which God "was able to specify from the start not only laws, but much more." (p. 231)

These clarifications are important, because much of the criticism of EoE has been botched significantly. The book is bad, really bad, but it can't be honestly characterized as an anti-evolution argument. Ultimately, Behe seeks to prove that evolution had to be guided. That's the way to understand EoE, and as Joan Roughgarden wisely noted in her review, there are some "constructive" aspects of the book, including the abandonment of opposition to – or even ambivalence about – common descent.

So what's so wrong with Behe's argument in EoE? Well, first, here's the argument summarized:

1. Evolutionary changes in the features of organisms require changes in genomes, changes which occur by mutation.
2. Many of the most interesting evolutionary changes require multiple changes in the same genome, often in the same gene.
3. Mutation rates, in terms of number of mutations per generation, are known to be on the order of 1 in 100 million.
4. Based on this mutation rate, the probability of occurrence of an evolutionary change requiring several mutations is vanishingly small, such that the whole of life's history is not nearly long enough for the change to occur via random mutation.

And here are some ways in which Behe's argument is wrong and/or misleading.

I. Behe's assumption of a particular mutation rate is both absurdly oversimplified and inappropriately extrapolated into the entire tree of life.

The basis of all of Behe's calculations is a mutation rate of 1 in 100 million. This is the estimated rate at which misspelling-type mutation occurs in each generation, averaged over the entire genome, in humans. (The number doesn't consider other types of mutation, now known to be more common than previously thought.) Behe uses this number in all of his (flawed) probability calculations. Even if we knew nothing about mutation rates, the notion of extrapolating from an human (or even mammalian) characteristic to the whole of the biosphere (past and present) is ludicrous enough that it would by itself cast doubt on the credibility of the author.

Rates and characteristics of mutation are the focus of active current research, and many important questions remain unanswered. But we know that there is no such thing as "the mutation rate," in the biosphere or even in particular species. In fact, mutation rates can vary significantly, between types of organisms, between organisms in different states of health, in individual subpopulations of organisms, even between regions of the genome of a particular organism.

More importantly, it is ridiculous to assume that "the mutation rate" has always been the same. Consider a flowchart outlining mutation and its effects, taken from a recent review of the evolution of mutation rates:

Image from "Mutation rate variation in multicellular eukaryotes: causes and consequences," by C.F. Baer et al., Nature Reviews Genetics, August 2007. Click to enlarge (opens in new window/tab).

The idea is that mutations are created in at least two ways: 1) damage to DNA from external influences such as radiation; and 2) errors in the replication process. During the evolution of early life, neither of these influences would be expected to be the same as – or even comparable to – similar influences today. And that's just the beginning of the flowchart. There are error-correction systems that erase mutations before they can be passed on to the cell's descendants; again, only a fool would suppose that these systems have been present throughout life's history; indeed, bursts of mutation that occur today are usually caused by deficiencies in DNA repair and the appearance of "mutator lines" is thought to be an accelerating force in adaptation.

My point is not that we know what the genetic landscape was like during the early evolution of life's toolkit, nor am I claiming that we know whether or not certain mutations were "nonrandom." My point is that the extrapolation of estimated mutation rates in modern humans into the deep past is clearly unjustified, a move so foolish that it can only be the product of folk science.

II. Behe's treatment of adaptation always ignores existing genetic variation, and his arguments seem to assume that multiple mutations must occur simultaneously.

I've mentioned these problems before, and they constitute some of Behe's biggest errors. When he envisions the process of adaptation, in which several genetic changes separate one state from another, he automatically assumes that none of the changes exists at the beginning. Yet even Darwin knew that populations of organisms harbor huge amounts of genetic variation, as evidenced by the profound success of domestication (of plants and animals) by human selection. Most of Behe's critics have noted this, and Behe's response was a lame dodge. But perhaps the critics haven't been clear about why superfast evolution under human selection is such a problem for his ideas. Here's why: since organisms are so profoundly diverse genetically, many of the genetic changes that could be exploited by selection already exist. In fact, current theory predicts that rapid evolution, such as that required after significant environmental change, is much more likely in populations with significant standing variation.

With his simplistic view of genetics and variation in mind, Behe then describes how an adaptation that requires two different changes will be extraordinary unlikely, because the probability of each change is one in 100 million, and the probability of each occurring together is one in 100 million times 100 million. His critics argue, correctly, that his calculations assume that the mutations must occur simultaneously, and that is indeed very improbable. (Although maybe not nearly as improbable as we used to think.) In some of the discussions in EoE, he describes sequential acquisition of mutations (e.g., p. 111), but he calculates probabilities according to simultaneous occurrence (e.g., p. 63). Jerry Coyne explains why this is a gigantic error, and Behe seems unable to understand why.

I've written a separate post about Behe's mishandling of probability. It shows that he is not someone to consult when the subject is population genetics.

III. Behe claims that huge population sizes automatically generate more evolutionary opportunity than smaller ones do. This is incorrect.

It seems so obvious. More organisms means more mutations means more beneficial mutations means more and faster evolution. It's the kind of obvious, simplistic, intuitive claim that forms the bedrock of any folk science. But it's wrong.

On the contrary, very large population sizes lead to a so-called "speed limit" on adaptation that results from competition among beneficial mutations. The phenomenon is called clonal interference and it's particularly well understood in asexual organisms such as bacteria. The basic idea has been around for decades, but measurement and modeling of the phenomenon has been increasing in the last ten years. A very recent report, the subject of an upcoming post here, showed that the beneficial mutation rate in bacteria is 1000 times higher than previously thought – and the underestimation is due entirely to clonal interference.

The effect is not limited to asexual organisms; in fact, the problem of clonal interference is thought to constitute one of the major driving forces behind the evolutionary development and maintenance of sexual reproduction. The idea is that the genetic shuffling that accompanies sexual reproduction can bring beneficial mutations together and increase the effectiveness of selection. One of the few studies to examine this experimentally led to the conclusion that clonal interference is a problem for sexual organisms, and that sex reduces the impact of clonal interference and lowers the evolutionary "speed limit." (Interestingly, the malaria parasite is partly asexual, and reproduction inside a human is completely asexual, so clonal interference is probably a very significant "speed limit" on the evolution of P. falciparum – another reason not to use malaria as a benchmark "case study" for the understanding of all of evolutionary genetics.)

In summary, I find Behe's handling of genetics in EoE to be unacceptable. He seems ignorant of basic evolutionary genetics, and is clearly content to create a folk science alternative to modern evolutionary biology. No one has proven that random mutation generated the wonders of biology, to be sure, and so I'm not saying that Behe's conclusion is known to be false. I'm saying that his attempts to establish his conclusion have failed miserably, as have his responses to his critics, and the result is that he cannot be trusted as a careful, thoughtful, knowledgeable critic of evolutionary science. EoE is folk science, nothing more.

My final post in the series will have closing comments and some ideas for how we might go about posing questions about the processes that yield biological design.

Mutations, selection, and bacteria

Several weeks ago, a commenter (Donald) asked an interesting question about natural selection and genetic variation, and I promised to address it because I want the issue to be a theme on QoD in the coming months. Here's Donald:

The link below is to a NYT blog where it says that E coli studies have found that there are 100,000 harmful mutations for each single beneficial one. I'm no population geneticist, but this kind of thing does make you wonder how selection could work with that much noise to overcome.

That aside, I have read a little of Ronald Fisher and I recall his mathematical argument that for mutations of very small effect, there was a 50 percent chance that the net effect would be beneficial. This is in "The Genetical Theory of Natural Selection". The mutations with large effects, on the other hand, are almost certainly going to be deleterious.

So are these studies only detecting mutations with large effects, or was Fisher wrong?

The blog article that Donald is citing is at The Wild Side by Olivia Judson, and the figure of 100,000 deleterious mutants for every helpful one is widely referenced.

Donald raises two questions, which I'll rephrase somewhat.

1. How can natural selection lead to adaptation when there is so much interference from harmful mutations?

I think there are at least three misconceptions that are acting together to create this common misunderstanding. First, that widely-cited ratio of harmful to helpful mutations is apparently an overestimate, off by three orders of magnitude, or a factor of 1000. The study that reported this dramatic correction in our understanding of bacterial mutations was published in Science last August, and represents a wonderful case study of the difference between real scientific thinking and the thinking of most design advocates. (Subject of an upcoming post.)

Second, the existence of harmful mutations doesn't necessarily "interfere" with adaptation. Many deleterious mutations will just kill the organism, and that's that. Natural selection does that all the time, and it doesn't get in the way of life in general, so there's no special reason to worry that it will get in the way of adaptation.

But most importantly, I think Donald is a little confused about the material on which natural selection acts, and understandably so. (This error is the centerpiece of Michael Behe's ludicrous recent book The Edge of Evolution.) The mistake seems subtle, but it's gigantic, and I think it arises in part from a semantic shortcut that is often used when explaining selection and adaptation. To see the problem, consider these two alternative descriptions of the process of adaptation.

• Adaptive evolution occurs when natural selection favors certain mutations which are beneficial as opposed to harmful. When new challenges arise, new adaptations arise as new beneficial mutations are generated and selection favors these mutations.
• Adaptive evolution occurs when natural selection favors previously-existing genetic combinations that are more fit than others. When new challenges arise, new adaptations arise as selection favors individuals whose genetic endowments are best suited to the new challenges.

The first description probably sounds more familiar to you than the second one does, but they're quite different, and the second description is far more accurate than the first. The distinction between these two scenarios lies in the implication of the first scenario that new mutations must arise "on demand" or "just in time." Michael Behe's whole silly book is based on calculations that assume that new mutations must be generated, simultaneously, after the introduction of the new challenge. (His main example is the adaptation of the malaria parasite in the face of drugs intended for its destruction.) Those who promulgate this error (intentionally or not) tend to emphasize natural selection acting on mutations, and consequently it's easy to picture a species "mutating around" a challenge or obstacle. (Behe, for example, uses such language repeatedly.)

But that's a mistaken view of the process, and the way to avoid the trap is to picture selection acting on variation, specifically on variation that is always present in any population of organisms. (Populations without significant genetic variation, when confronted with serious challenges, are more likely to illustrate extinction than evolution.) Such variation is continuously generated and therefore continuously present. This is the lesson from studies of the effects of human selection on domesticated species of all kinds: when selection is applied, such populations typically reveal a remarkable propensity for rapid and dramatic change, because they harbor vast resources in the form of genetic diversity. If you carefully attend to this distinction, you will understand Darwinian evolution far better than any ID advocate.

2. Are most large-effect mutations harmful, and many small-effect mutations beneficial, as predicted by Fisher?

Well, first of all, kudos to Donald for reading Fisher. I've been browsing The Genetical Theory of Natural Selection, and it's demanding (but comprehensible). Michael Behe either hasn't read it, or didn't understand it, and in either case is therefore unqualified to write on evolutionary genetics.

Fisher was certainly right that large-effect mutations are almost never beneficial, but it is largely unknown whether very small-effect mutations are frequently beneficial, as he postulated. Theoretical and experimental work in this field has recently accelerated, and the current model is that effects of beneficial mutations are exponentially distributed, such that beneficial mutations are far more likely to be of very small effect than of large effect. This was Allen Orr's proposal, and it has been borne out in some very recent experimental analyses. The most recent, and significant, was the Science paper I mentioned above, in which the authors found that beneficial mutations in bacteria are far more common than previously estimated, but have relatively small effects (individually). Here's an excerpt from their last paragraph:

...our estimate of [the beneficial mutation rate] implies that 1 in 150 newly arising mutations is beneficial and that 1 in 10 fitness-affecting mutations increases the fitness of the individual carrying it. Hence, an enterobacterium has an enormous potential for adaptation and may help explain how antibiotic resistance and virulence evolve so quickly.

That's enough for now. Start with papers by Allen Orr when reading on the genetics of adaption; his historical overview in Nature Reviews Genetics in 2005 is particularly helpful.

Gene duplication: "Not making worse what nature made so clear"

But he that writes of you, if he can tell
That you are you, so dignifies his story,
Let him but copy what in you is writ,
Not making worse what nature made so clear,
And such a counterpart shall fame his wit,
Making his style admired every where.
--Sonnet 84, The Oxford Shakespeare

One of the most common refrains of anti-evolutionists is the claim that evolutionary mechanisms can only degrade what has already come to be. All together now: "No new information!" It's a sad little mantra, an almost religious pronouncement that is made even more annoying by its religious underpinnings, hidden or overt.

But it's a good question: how do new genes come about?

One major source of new genes is gene duplication, which is as conceptually simple as it sounds. It might seem a little odd, and it's not that easy to picture, but the duplication of discrete sections of genetic material is commonplace in genomes. In fact, a significant amount of the genetic variation among individual humans is due to copy number variation, which is variation in the number of copies of particular genes or chunks of genetic material from individual to individual. Genes can be duplicated within a genome via various mechanisms, one of which includes the rare but fascinating occurrence of whole-genome duplication. In any case, it is very clear that gene duplication and subsequent evolution explains the existence of thousands of the most interesting genes in animal genomes.

It should be obvious that gene duplication gives you more genes, but perhaps it's not so clear how this can yield something truly new. For many years, new genes were thought to arise after duplication by a process called neofunctionalization. The basic idea is this: consider a gene A, with a set of functions we'll call F1 and F2. Now suppose the gene is duplicated, so that we now have genes A and B, both capable of carrying out F1 and F2. In neofunctionalization, gene B is free to vary and (potentially) acquire new functions, because gene A is still making sure that F1 and F2 are covered. So the duplication has created an opportunity for a little "experimentation." Most of the time, gene B will be mutated into another piece of genomic debris, a pseudogene with no evident function. (The human genome is riddled with pseudogenes, and that's a story all its own.) Occasionally, though, the tinkering will yield a gene with a new evolutionary trajectory. This model makes good sense and surely accounts for numerous genetic innovations during evolution.

But another model has come to the fore in the last several years, in which the two duplicates seem to "divide and conquer." The process is called subfunctionalization, and the idea is straightforward: gene A covers F1, while gene B covers F2. Straightforward perhaps, but this scenario creates some interesting evolutionary opportunities that aren't immediately obvious. Here in this newest Journal Club, I'll look at another example of the experimental analysis of evolutionary principles and hypotheses, summarizing some recent work that examines subfunctionalization in the laboratory.

In the 11 October issue of Nature, Chris Todd Hittinger and Sean B. Carroll examine an actual example of subfunctionalization in an elegant set of experiments that seeks to re-create the evolutionary changes that occurred after a gene duplication. Specifically, they looked at the events that led to the formation of a new pair of functionally-intertwined genes in yeast. The genes are GAL1 and GAL3, and there are several aspects of this story that make it an ideal system in which to experimentally explore the creation of new genes.

1. GAL1 and GAL3 arose following a whole-genome duplication in an ancestral yeast species about 100 million years ago. The ancestral form of the gene (see Note 1 at the end of this article) is still present in other species of yeast (namely, those that branched off before the duplication event). This means that the authors were able to compare the new genes (meaning GAL1 and GAL3) and their functions to the single ancestral gene and its functions.
2. The genomes of these yeast species have been completely decoded, so that the authors had ready access to the sequences of the genes of interest and any DNA sequences in the neighborhood.
3. Decades of research on yeast have yielded superb tools for the manipulation of the yeast genome. Using these resources, the authors were able to create custom-designed yeast strains in which genes of interest were altered to suit experimental purposes. (Those of us who work in mammalian systems can only dream of being able to do this kind of genetic modification with such ease.)
4. The biochemical functions of GAL1 and GAL3 were already well known.

Hittinger and Carroll capitalized on this excellent set of tools, and added a key component of their own. They needed a way to measure fitness of different strains of yeast, namely strains that had been modified to resemble various ancestral forms. But most typical methods for testing gene function are unsuitable for estimating fitness, which is the relevant issue. The question, in other words, is focused not on the ability of a particular protein to perform a particular function, but on the ability of a particular protein to change the fitness of the organism that expressed it. The authors' solution can only be described as elegant: they assessed fitness of various yeast strains by measuring the outcomes of head-to-head competitions between strains. Their experimental approach, developed by a colleague (see Note 2) employed some very nice genetic tricks and a sophisticated analytical tool called flow cytometry. (Take some time to read about Abbie Smith's research at ERV if you haven't already done so; in her work on HIV, she asks similar questions regarding fitness and uses a very similar approach in seeking answers.)

Why did the authors choose the GAL1-GAL3 system for close scrutiny? The two genes are critical components of a system in yeast that controls the utilization of galactose (a certain sugar) as an energy source. The GAL1 protein is an enzyme that begins the breakdown of galactose; the GAL3 protein controls the induction of the GAL1 protein. When galactose is present, the GAL3 gene is induced, such that GAL3 protein amounts increase by a few fold. The GAL3 protein is in turn a potent inducer of the GAL1 gene: when galactose is present, GAL1 protein levels increase 1000-fold or so. The two proteins are very similar to each other, and both are very similar to the single protein that is found in the genomes of yeasts that never underwent the genome duplication. So this means that the ancestral protein is bifunctional: it must carry out the very different processes of induction and of galactose metabolism. Not surprisingly, situations like this are thought to involve trade-offs which resolve "adaptive conflicts" between the two different functions of the protein. The reasoning is straightforward: mutations that would improve function A might degrade function B, and vice versa. So the protein is not optimized for either function. There is an adaptive conflict between the two functions. The GAL1-GAL3 system clearly involves subfunctionalization following duplication, and because the ancestral gene is available for comparison, the story invites exploration of the notion of adaptive conflict.

Hittinger and Carroll found that there is indeed an adaptive conflict that was resolved by the evolution of GAL1 and GAL3 following the duplication. But the nature of that conflict is not what some might have predicted. Look again at my description of adaptive conflict above. I focused exclusively on the proteins themselves, claiming that the conflict would arise during attempts to optimize two functions in a single protein. But there's another possibility (that need not exclude the first): perhaps the conflict occurs in the regulation of the expression of those proteins. In the case of GAL1 and GAL3, the two different genes can be turned on and off by two different signaling systems. But in the ancestral situation, there's only one gene and therefore fewer opportunities for diversity in the signaling that leads to expression.

The data presented by Hittinger and Carroll suggest that there is not strong adaptive conflict between the two functions of the ancestral protein. If such a conflict existed, we would expect that changes in GAL1 that make it look more like GAL3 (and vice versa) would cause significant decreases in fitness. But that's not what the fitness analysis showed, and the authors inferred that the adaptive conflict must occur in the arena of regulation, and not in the context of actual protein function. The story is complicated, and I'm not convinced that the authors have ruled out adaptive conflict at the level of the structure of the proteins. Nevertheless, their subsequent experiments demonstrate a clear adaptive conflict in the regulation of expression of the different proteins, and an efficient resolution of that conflict in the subfunctionalization of the two genes following duplication. Those results are strengthened by some detailed structural analysis that seems to account for the physical basis of the optimization that occurred during evolution of the GAL1 and GAL3 genes, optimization that occurred in DNA sequences that control the levels of expression of protein.

If you're a little dizzy at this point, relax and let's zoom out to reflect on this article's significance in evolutionary biology, and its relevance for those who are influenced by the claims of anti-evolution commentators.

First, take note that this article is another example of a sophisticated, hypothesis-driven experimental analysis of a central evolutionary concept. Research like this is reported almost daily, though you'd never learn this by reading the work of Reasons To Believe or the fellows of the Discovery Institute. The mis-characterization of evolutionary biology by the creationists of those organizations is a scandal, and as you might already know, my blog's main purpose is to give evangelical Christians an opportunity to explore the science that is being so carefully avoided by those critics. You don't need to understand sign epistasis or the structure of transcription factors to get this take-home message: evolutionary biologists are hard at work solving the problems that some prominent Christian apologists can't or won't even acknowledge. How does gene duplication lead to the formation of genes with new functions? The folks at the Discovery Institute can't even admit that it happens. Over at Reasons To Believe, they don't mention gene duplication all, despite their fascination with "junk DNA." That's from a ministry that claims to have developed a "testable model" to explain scores of questions regarding origins.

This makes me mad. No matter what you think of the age of the earth or the need for creation miracles, you should be upset by Christians who mangle science to serve apologetic ends.

Second, it's important to note that Hittinger and Carroll's paper is not merely a significant contribution to our understanding of subfunctionalization. It's also a salvo, in an apparently intensifying debate within evolutionary biology regarding the kinds of genetic changes that are more likely to drive evolutionary change. Sean Carroll is one of the leading lights in the new field of evolutionary developmental biology, or evo-devo, and one of the tenets of this upstart school is the claim that most of the genetic changes that lead to adaptation -- and especially to changes in form -- occur in regulatory regions of the genome and not in the genes themselves. (More technically: evo-devo advocates like Carroll postulate that changes in form are more likely to arise from mutations in cis-regulatory regions than in protein-coding sequences within genes.) This assertion is hotly contested, as are many of the other basic views of the evo-devo school. The antagonists include some serious evolutionary biologists, Michael Lynch and Jerry Coyne among them. (Lynch is the guy who took the time to explain why Michael Behe's paper on gene duplication was a joke. Coyne co-wrote the book on speciation, literally.)

I'm a developmental biologist, and therefore partial to many of the arguments of evo-devo thinkers. I'm excited about the union of evolutionary and developmental biology, and I do think that many of the new evo-devo ideas are thought-provoking and potentially fruitful. But the debate is riveting and informative, and I find Lynch and Coyne and their talented colleagues to be alarmingly convincing. I'm worried about some of those cool ideas, but I do take some comfort in this thought: any idea that can survive the onslaught of Lynch and Coyne is a hell of a good idea.

It's easy to see how the disputes spawned by the brash (and perhaps rash) evo-devo folks can lead to innovation and discovery, even if many of their proposals are diminished or destroyed in the process. The disagreement is pretty clear-cut, and both sides seem to agree on how to figure out who's right. They'll go to the lab; they'll perform hypothesis-driven experiments; they'll analyze their data; they'll write up their findings; their work will be subjected to peer review. In other words, they'll do real science.
---
Note 1: The ancestral gene itself, of course, isn't available for analysis. The authors are studying the ancestral form of the gene, using a yeast species that never experienced the whole-genome duplication.
Note 2: As Hittinger and Carroll indicate in the acknowledgments, the experimental design was developed by Barry L. Williams, who was a postdoctoral fellow in Carroll's lab and is now on the faculty at Michigan State. And by the way, this little state of Michigan doesn't have much of an economy, but boy are we crawling with gifted evolutionary biologists.

Article(s) discussed in this post: Hittinger, C.T. and Carroll, S.B. (2007) Gene duplication and the adaptive evolution of a classic genetic switch. Nature 449:677-681.