Get ready for a tale stranger than "Complexity by Subtraction." According to Nietzsche’s famous maxim, "What does not kill me makes me stronger." That might be a good motto for a guy training for the Tough Mudder, but will it work in a bacterial cell or in a dinosaur trying to evolve flight? A paper in PNAS by Richard Lenski and his colleagues Arthur Covert III, Claus Wilke and Charles Ofria, "Experiments on the role of deleterious mutations as stepping stones in adaptive evolution," seeks to cast evolution as kind of a trampoline: organisms can bounce higher by jumping off the fitness peak (sometimes). Could it be true?
Deleterious mutations are just that: random changes that cause harm. Remember, the bacterium or other organism is not a Navy Seal running an obstacle course to get stronger by will and determination. No; its genome just got hit by a bullet. It’s going down in flames. What must happen for Lenski’s idea to gain traction is that a beneficial mutation, like a midair rescue helicopter, has to show up just in time — and not only that, but take the creature up to a higher fitness peak. We read:
It might seem obvious that deleterious mutations must impede evolution. However, a later mutation may interact with a deleterious predecessor, facilitating otherwise inaccessible adaptations. Although such interactions have been reported before, it is unclear whether they are rare and inconsequential or, alternatively, are important for sustaining adaptation. We studied digital organisms� — computer programs that replicate and evolve� — to compare adaptation in populations where deleterious mutations were disallowed with unrestricted controls. Control populations achieved higher fitness values because some deleterious mutations acted as stepping stones across otherwise impassable fitness valleys. Deleterious mutations can thus sometimes play a constructive role in adaptive evolution. (Emphasis added.)
It might seem obvious, indeed, to anyone except a Darwinian evolutionist. Another maxim (not from Nietzsche) applies here: "If something sounds too good to be true, it probably is." There’s good reason to be suspicious. This magic trick only worked in the computer. Computers are notoriously guided by programmers who would be happy to see a favored notion confirmed. In this case, there would be no PNAS paper, or funding, if it didn’t work.
But Covert et al. already knew evolution is in a bind. In Sewall Wright’s old "fitness landscape" scenario, organisms can get stuck on a "fitness peak" and never evolve higher. Crossing to a higher peak requires losing fitness in the valley between, where the organism will likely die. For years, evolutionists have worried about the ability of relaxed selection, neutral mutations or other mechanisms to get organisms across the valleys. These evolutionists from Michigan State and University of Texas were basically looking for new ways to help them get down so they could go up higher.
We know that humans, with intelligent planning, can go down to go up. They will risk running through fire or diving deep in water to get out of a bind. Sometimes the way out is through. Humans are very good at hurting themselves to achieve greater good in the long run: rigorous training, tearing down muscle to build it up stronger. We know all about that; but what does a bacterium, yeast cell or plant know? It knows nothing. It has no goals in mind. A deleterious mutation, if not outright lethal, will take its toll. What are the chances a beneficial "rescuing" mutation will happen along before purifying selection weeds out the unfit? If that is rare, how much rarer to get a beneficial mutation that joins forces with the deleterious mutation to make things better!
If your research goal is to prove this "could" happen, you can certainly program a computer to get it to happen. That’s what these scientists did. They had a lot of faith in the potential of deleterious mutations:
Although such mutations are expected to be rare, new detrimental mutations are constantly generated, thus providing a multitude of potential stepping stones.
Ah, "potential" stepping-stones. They’re all over the place! Throw rocks randomly into the lake; some of them could become potential stepping-stones. This is already sounding crazy.
It’s unsurprising to learn that Lenski & Co. have resorted to a favorite magic kit, the evolutionary Avida program, written by philosopher Robert T. Pennock, author of the anti-ID book Tower of Babel. We’ve discussed this and similar algorithms many times.
Whether the "digital organisms" generated by such programs have any connection to the real world is highly unlikely, especially when human beings are rewarding them by design. The digital organisms live as long as the programmers let them. They don’t have to find food. They don’t have to endure the slings and arrows of outrageous fortune. Anyway, what does a logic routine in a computer have to do with building a trilobite?
A reading of the paper reveals investigator interference all over the place: e.g., "we disallowed double mutations to facilitate classifying each mutation as beneficial, neutral, deleterious, or lethal," they say in one place. Continuing:
Before a mutant offspring was placed into the population, its fitness was evaluated in an isolated test environment, thus taking advantage of an opportunity that exists in a computational realm but not in a biological one, namely, to measure the effect of a mutation before it impacts a population’s evolution.
Convenient as this might have been for setting up this team’s research, things don’t work that way in the real world. Organisms respond holistically to all the influences acting them at the time. Fitness (whatever that slippery word means) in an "isolated test environment" might have nothing to do with fitness in the real world.
Sometimes when wading through a paper like this it’s helpful to jump ahead to the "Materials and Methods" section. If there’s a procedural or logical flaw there, the paper is only going to arrive at a credible conclusion by sheer dumb luck.
How the did this research measure fitness? By the ability to survive and reproduce. How were the fake organisms able to survive and reproduce? Well, obviously, if they were the fit ones. Whenever fitness is defined in terms of survival, it collapses into a tautology: survivors survive because of the fitness of the fit. Here it is, under "Measuring the Fitness of Digital Organisms."
In the isolated test environment, and with additional mutations prevented, we evaluated each candidate mutant’s fitness by allowing it to execute its genome. We measured two aspects of its performance: the rate at which it acquired SIPs (single-instruction processing units) and the number of instructions executed to replicate itself. The ratio of these two numbers is a close approximation to the organism’s absolute fitness. If digital organism A has twice the fitness of organism B, then A will, on average, produce twice as many offspring as B in the same amount of time.
Moreover, the fit ones only showed up because the investigators prevented their death. Look at this from the Materials and Methods:
We disallowed mutations that produced unstable genotypes in all treatments, including the control treatment, because our analyses were predicated on single mutations occurring in genomes of constant length.
Too Little, Too Slow
In Chapter 10 of Darwin’s Doubt, citing the work of Douglas Axe, Stephen Meyer shows that a probabilistic abyss lies between the zone of function of one protein and that of another. Degrading a functional protein leads to catastrophic loss of function. Repair systems quickly degrade nonfunctional proteins. Those that escape are eliminated by natural selection.
Initially glad to see that some deleterious mutations were rescued by subsequent beneficial ones, Lenski & Co. thought that if a little is good, more must be better. Look at this astonishing paragraph from the paper: they were "surprised" that adding more mutations didn’t produce more fitness!
We were more surprised, however, that, at higher mutation rates, the use of stepping stones did not produce a measurable increase in final fitness. Previous theoretical studies have implicitly assumed that the rate of compensatory adaptation increases with the mutation rate. In that case, one would expect that deleterious mutations would be more important at higher mutation rate than at lower rates. One possible explanation for this unexpected result is that higher mutation rates drive populations toward flatter areas of the fitness landscape, with a greater proportion of mutations being neutral and thus fewer opportunities for compensatory changes. Further work will be necessary to determine whether this explanation is correct.
Only a Darwinian evolutionist could expect that shooting more random bullets into a complex system would improve it.
The Analogy Breaks Down
In Avida, the fitness of digital organisms is measured in terms of logic functions. Life, though, revolves around genes and proteins. There’s no comparison. For one thing, the information content of the typical gene or protein is vastly greater than the information content of an "AND" or "NAND" or "EQU" logic gate. Proteins typically include hundreds of precisely sequenced amino acids, coded for by equal numbers of genetic alphabet letters (150 amino acids is a relatively small protein).
Moreover, a polypeptide sequence has to fold into a functional shape. A precisely ordered sequence is not enough. Without a stable fold, the polypeptide is useless, if not harmful. Just a few mutations to a working protein are often enough to destabilize the fold. An unstable protein will be targeted by proteases and destroyed; those that are not will be eliminated by "purifying selection." This has the effect of making the walls of a fitness peak steeper, and the valley deeper — more like a cliff on the edge of an ocean filled with sharks.
In one of their experiments, the team tried to model sexual reproduction by having offspring garner bits from two "parents." Unfortunately, there was no benefit:
Given the role of deleterious mutations as stepping stones in long-term adaptation, one might then imagine that sexual populations should evolve higher fitness than asexual ones because sexual populations experience more of these potential stepping stones. However, we found no evidence of such an effect; sexual and asexual populations in the control treatments achieved comparable final fitness values (P = 0.6124, Mann-Whitney test). Thus, any potential benefits of sexual reproduction were offset by costs, including the disruption of beneficial interactions between mutations.
So even the most generous review of their work could only say it might have some applicability to microbes, but not to Cambrian animals, orchids, or humans.
How Lenski’s contrived world of digital organisms inside a computer can have any relevance to living organisms is beyond imagining. Darwinian evolution only works when designers program a computer to make it work. Naturally, Lenski’s lab ignored the critique of Avida published in an IEEE paper in 2009 by Ewert, Dembski and Marks.
Readers of Darwin’s Doubt know that the problem for evolutionists lies not in finding isolated beneficial mutations, but in building whole new cell types, tissues, organs, body plans, and epigenetic information in one fell swoop, as seems to have happened in the Cambrian explosion. Meyer compares the unguided search for functional "stepping-stones" across the abyss with trying to traverse an ocean the size of our galaxy (p. 204).
Taxpayers, in case you were curious, can thank the National Science Foundation for funding Lenski’s decade-long computer game.