Most of you don’t understand evolution. I mean this in the most charitable way; there’s a common conceptual model of how evolution occurs that I find everywhere, and that I particularly find common among bright young students who are just getting enthusiastic about biology. Let me give you the Standard Story, the one that I get all the time from supporters of biology.
Evolution proceeds by mutation and selection. A novel mutation occurs in a gene that gives the individual inheriting it an advantage, and that person passes it on to their children who also gets the advantage and do better than their peers, and leave more offspring. Given time, the advantageous mutation spreads through the population so the entire species has it.
One example is the human brain. An ape man millions of years ago acquired a mutation that made his or her brain slightly larger, and since those individuals were slightly smarter than other ape men, it spread through the population. Then later, other mutations occured and were selected for and so human brains gradually got larger and larger.
You either know what’s wrong here or you’re feeling a little uneasy—I gave you enough hints that you know I’m going to complain about that story, but if your knowledge is at the Evolutionary Biology 101 level, you may not be sure what it is.
Just to make you even more queasy, the misunderstanding here is one that creationists have, too. If you’ve ever encountered the cryptic phrase “RM+NS” (“random mutation + natural selection”) used as a pejorative on a creationist site, you’ve found someone with this affliction. They’ve got it completely wrong.
Here’s the problem, and also a brief introduction to Evolutionary Biology 201.
First, it’s not exactly wrong — it’s more like taking one good explanation of certain kinds of evolution and making it a sweeping claim that that is how all evolution works. By reducing it to this one scheme, though, it makes evolution far too plodding and linear, and reduces it all to a sort of personal narrative. It isn’t any of those things. What’s left out in the 101 story, and in creationist tales, is that: evolution is about populations, so many changes go on in parallel; selectable traits are usually the product of networks of genes, so there are rarely single alleles that can be categorized as the effector of change; and genes and gene networks are plastic or responsive to the environment. All of these complications make the actual story more complicated and interesting, and also, perhaps to your surprise, make evolutionary change faster and more powerful.
Think populations
Mutations are the root of biological variation, of course, but we often have a naive view of their consequences. Most mutations are neutral. Even advantageous mutations are subject to laws of chance in their propagation, and a positive selection coefficient does not mean there will be an inexorable march to fixation, where every individual has the allele. This is also true of deleterious mutations: chance often dominates, and unless it is a strongly negative allele, like an embryonic lethal mutation, there’s also a chance it can spread through the population.
Stop thinking of mutations as unitary events that either get swiftly culled, because they’re deleterious, or get swiftly hauled into prominence by the uplifting crane of natural selection. Mutations are usually negligible changes that get tossed into the stewpot of the gene pool, where they simmer mostly unnoticed and invisible to selection. Look at human faces, for instance: they’re all different, and unless you’re looking at the extremes of beauty or ugliness, the variations simply don’t make much difference. Yet all those different faces really are the result of subtly different combinations of mutant forms of genes.
“Combinations” is the magic word. A single mutation rarely has a significant effect on a feature, but the combination of multiple mutations may have a detectable or even novel effect that can be seen by natural selection. And that’s what’s going on all the time: the population is a huge reservoir of genetic variation, and what we do when we reproduce is sort and mix and generate new combinations that are then tested in the environment.
Compare it to a game of poker. A two of hearts in itself seems to be a pathetic little card, but if it’s part of a flush or a straight or three of a kind, it can produce a winning hand. In the game, it’s not the card itself that has power, it’s its utility in a pattern or combination of other cards. A large population like ours is a great shuffler that is producing millions of new hands every day.
We know that this recombination is essential to the rapid acquisition of new phenotypes. Here are some results from a classic experiment by Waddington. Waddington noted that fruit flies expressed the odd trait of developing four wings (the bithorax phenotype) instead of two if they were exposed to ether early in development. This is not a mutation! This is called a phenocopy, where an environmental factor induces an effect similar to a genetic mutation.
What Waddington did next was to select for individuals that expressed the bithorax phenotype most robustly, or that were better at resisting the ether, and found that he could get a progressive strengthening of the response.
The progress of selection for or against a bithorax-like response to ether treatment in two wild-type populations. Experiments 1 and 2 initially showed about 25 and 48% of the bithorax (He) phenotype.
This occurred over 10s of generations — far, far too fast for this to be a consequence of the generation of new mutations. What Waddington was doing was selecting for more potent combinations of alleles already extant in the gene pool.
This was confirmed in a cool way with a simple experiment: the results in the graph above were obtained from wild-caught populations. Using highly inbred laboratory strains that have greatly reduced genetic variation abolishes the outcome.
Jonathan Bard sees this as a powerful potential factor in evolution.
Waddington’s results have excited considerable controversy over the years, for example as to whether they reflect threshold effects or hidden variation. In my view, these arguments are irrelevant to the key point: within a population of organisms, there is enough intrinsic variability that, given strong selection pressures, minor but existing variants in a trait that are not normally noticeable can rapidly become the majority phenotype without new mutations. The implications for evolution are obvious: normally silent mutations in a population can lead to adaptation if selection pressures are high enough. This view provides a sensible explanation of the relatively rapid origins of the different beak morphologies of Darwin’s various finches and of species flocks.
Think networks
One question you might have at this point is that the model above suggests that mutations are constantly being thrown into the population’s gene pool and are steadily accumulating — it means that there must be a remarkable amount of genetic variation between individuals (and there is! It’s been measured), yet we generally don’t see most people as weird and obvious mutants. That variation is largely invisible, or represents mere minor variations that we don’t regard as at all remarkable. How can that be?
One important reason is that most traits are not the product of single genes, but of combinations of genes working together in complex ways. The unit producing the phenotype is most often a network of genes and gene products, such at this lovely example of the network supporting expression and regulation of the epidermal growth factor (EGF) pathway.
That is awesomely complex, and yes, if you’re a creationist you’re probably wrongly thinking there is no way that can evolve. The curious thing is, though, that the more elaborate the network, the more pieces tangled into the pathway, the smaller the effect of any individual component (in general, of course). What we find over and over again is that many mutations to any one component may have a completely indetectable effect on the output. The system is buffered to produce a reliable yield.
This is the way networks often work. Consider the internet, for example: a complex network with many components and many different routes to get a single from Point A to Point B. What happens if you take out a single node, or even a set of nodes? The system routes automatically around any damage, without any intelligent agency required to consciously reroute messages.
But further, consider the nature of most mutations in a biological network. Simple knockouts of a whole component are possible, but often what will happen are smaller effects. These gene products are typically enzymes; what happens is a shift in kinetics that will more subtly modify expression. The challenge is to measure and compute these effects.
Graph analysis is showing how networks can be partitioned and analysed, while work on the kinetics of networks has shown first that it is possible to simplify the mathematics of the differential equation models and, second, that the detailed output of a network is relatively insensitive to changes in most of the reaction parameters. What this latter work means is that most gene mutations will have relatively minor effects on the networks in which their proteins are involved, and some will have none, perhaps because they are part of secondary pathways and so redundant under normal circumstances. Indirect evidence for this comes from the surprising observation that many gene knockouts in mice result in an apparently normal phenotype. Within an evolutionary context, it would thus be expected that, across a population of organisms, most
mutations in a network would effectively be silent, in that they would give no selective advantage under normal conditions. It is one of the tasks of systems biologists to understand how and where mutations can lead to sufficient variation in networks properties for selection to have something on which to act.
Combine this with population effects. The population can accumulate many of these sneaky variants that have no significant effect on most individuals, but under conditions of strong selection, combinations of these variants, that together can have detectable effects, can be exposed to selection.
Think flexible genes
Another factor in this process (one that Bard does not touch on) is that the individual genes themselves are not invariant units. Mutations can affect how genes contribute to the network, but in addition, the same allele can have different consequences in different genetic backgrounds — it is affected by the other genes in the network — and also has different consquences in different external environments.
Everything is fluid. Biology isn’t about fixed and rigidly invariant processes — it’s about squishy, dynamic, and interactive stuff making do.
Now do you see what’s wrong with the simplistic caricature of evolution at the top of this article? It’s superficial; it ignores the richness of real biology; it limits and constrains the potential of evolution unrealistically. The concept of evolution as a change in allele frequencies over time is one small part of the whole of evolutionary processes. You’ve got to include network theory and gene and environmental interactions to really understand the phenomena. And the cool thing is that all of these perspectives make evolution an even more powerful force.
Bard J (2010) A systems biology view of evolutionary genetics. Bioessays 32: 559-563.
