Background: The Hierarchy of Evolutionary Apologetics
Scientific papers document important and perfectly legitimate research. The results are usually presented carefully and rarely are they exaggerated. And it is difficult to find such papers claiming that evolution is a fact.
These papers do, however, discuss the results strictly in terms of evolution. No matter how unlikely evolution is given the results, they are interpreted as though evolution were the only explanation. Problematic results, and they are common, are not allowed to suggest epistemological challenges. They may only pose theoretical problems.
The authors cannot suggest that we may not, after all, know evolution to be a fact. They may only point out our ignorance of how it is supposed to have occurred. In all of this it is difficult to avoid misrepresentations of the research results, but such blunders are limited by the narrow scope of such papers.
Review and survey papers, on the other hand, do make more expansive evolutionary claims. These review papers draw on the vast body of research literature to organize and summarize the state of the research. They draw broader conclusions, and when discussing the subject of evolution these papers are more likely to make obviously unsupportable evolutionary claims.
Finally the popular literature is the next step beyond the review papers. If the review papers are summaries for scientists, the popular literature provides summaries for the non scientific audience. It is here that the dogmatic, sweeping evolutionary claims are most prevalent. Evolution must be a fact, all the scientific evidence unquestionably supports and proves evolution, doubters have nothing but nefarious motivations, there is war between science and religion, and so forth.
In terms of sheer magnitude, the first category—the narrowly focused research papers—dominates the literature. And here there is far less speculation and far more technical detail. Problems do arise in the evolutionary interpretations of the results, but such speculation is a minor part of the paper.
But like toxic pollutants that accumulate and reach increasing concentrations at higher levels of the food chain, the speculation becomes amplified in the review papers, and then even more so in the popular literature. Like a morphing rumor, what begins as tentative and unlikely speculation of how evolution might account for problematic findings ultimately becomes yet another evolutionary proof text in the popular literature.
So when evolutionists argue that those scientific papers and reports cannot all be wrong, we might agree with them. The technical details of the scientific research are certainly not wrong. True, there are misrepresentations of science when the results are force fit into evolution, but the far more egregious misrepresentations of science are found as one moves up the hierarchy of the evolution literature.
Case study: Protein evolution
Imagine a choppy ocean filled with waves for as far as the eye can see. Meanwhile the sky is dotted with an occasional jet airliner flying far above. This scene gives an idea of what science is telling us about protein evolution. A protein consists of hundreds of amino acids glued together in a long sequence. Because nature uses 20 different amino acids, the total number of possible sequences is astronomically huge. For a protein with 300 amino acids in all, for instance, there are 20^300 (roughly equal to a one followed by 390 zeros) different possible sequences.
It is an ocean of possibilities. But the vast majority of these possible amino acid sequences are worthless to evolution. Not only are they essentially dysfunctional themselves, but even as evolutionary starting points they don’t lead to much better designs. If you evolve a typical randomly selected amino acid sequence, you can improve the design a bit, but the search quickly stagnates.
Like the choppy ocean, the protein function landscape seems to be filled with a great many swells. Evolution can move up from the bottom of a swell to the top of a nearby wave, but this is only a minor improvement in the protein function. Go any further and evolution would fall into the neighboring swell, leaving it no better off than when it started.
Very rarely, in this seemingly endless sea of minor ups and downs, an extremely efficient, functional protein punctuates the protein function landscape. Here the landscape rapidly shoots up into the sky. This is not a gradual rise leading to these lofty and tiny regions of functional proteins. Rather, the landscape abruptly rises to heights far above the ocean’s surface. Like the jet airliners flying far above the ocean, proteins appear to be rare events in an otherwise non descript landscape.
Several types of protein studies point to this conclusion. Some of these studies begin with random amino acid sequences and attempt to evolve them toward nature’s proteins or something like them. These studies show that only minor functionality can be evolved from random starting points. Nature’s marvels, or anything like them, are so astronomically rare evolution would never find them using its blind, adaptive walk. (This is to say nothing of how the machinery for such a search could have evolved in the first place.)
Other studies begin not with a random amino acid sequence, but rather work backwards from a known protein. These studies show that proteins are quite sensitive. The function of a typical protein exponentially degrades as random mutations are introduced.
Whether we start at the beginning or the end, the science tells us that the protein function landscape is not one of smooth funnels leading to fantastic molecular machines, as evolution would expect.
And as it seems to be with so much of biology, when scientists work with nature’s designs some fascinating engineering can be done. Just as the design of a jet aircraft can be adjusted and augmented to meet a new performance requirement, so too proteins can be adapted to meet desired properties. And just as engines or other components can sometimes be swapped between aircraft, so too proteins are marvelously modular, allowing for new designs to be created by mixing and matching.
Unfortunately evolutionists routinely conflate such design engineering with evolutionary possibilities. Proteins are adaptable, so can’t they gradually evolve? Proteins are module, so can’t they just swap designs when gradualism fails? The science contradicts such conclusions, but evolutionists are driven more by their theory than by the data. Here are two examples.
An example research paper
In this research paper, evolutionists investigated how proteins might have evolved. They attempted to demonstrate the evolution of a virus—a molecular machine consisting of several proteins—in the laboratory. To simplify the problem they started with all but a small part of the virus intact. They randomized the amino acid sequence of one part of one of the viral proteins, and they repeatedly evolved that randomized segment in hopes of reconstructing the entire virus.
What they discovered was that the evolutionary process could produce only tiny levels of functionality (in this case the virus’ ability to infect a host). Their evolved sequences showed no similarity to the native sequence which is supposed to have evolved. And the best virus they could produce, even with the vast majority of the virus already intact, was several orders of magnitude weaker than nature’s virus.
The reason their evolutionary process failed was that the search for better amino acid sequences, that would improve the virus’ ability to infect the host, became too difficult. A possible evolutionary explanation for these disappointing results is that in such a limited laboratory study, the evolutionists were simply unable to reproduce what the vast resources of nature could produce. Perhaps in the course of time evolution could evolve what the evolutionists could not do in the laboratory.
But the results refuted even this fall back explanation. In fact, the evolutionists would not merely need an expanded study with more time in the laboratory, they would need more time than evolution ever had—many times over. The number of experiments they would need to conduct in order to have any hope of evolving a virus that rivals nature’s version is difficult to compute. But it is at least 10^70 (a one followed by 70 zeros).
And yet, there it is. This relatively short sequence of amino acids exists as part of of the virus, with its fantastically high infection capabilities. And of course this is not merely a problem for a part of one protein, in one virus. It is a problem for all life, for proteins are crucial molecular machines throughout biology.
But not surprisingly the evolutionists interpreted their results according to their theory. The majority of the paper presents the detailed scientific results. There is no misinterpretation or exaggeration, until that is, the discussion of the implications for evolution. The evolutionists write:
Such a huge search is impractical and implies that evolution of the wild-type phage must have involved not only random substitutions but also other mechanisms, such as homologous recombination.
Homologous recombination? It would be difficult to imagine a more unlikely explanation. Homologous recombination is a complex genetic mechanism assisted by finely-tuned proteins. It is circular to recruit such a mechanism for the initial evolution of proteins—for no such mechanism is likely to have existed. And that is putting it mildly.
And even if homologous recombination could somehow have been in play, it wouldn’t help anyway. For while this is a clever mechanism for the swapping of nature’s protein modules, it does not help when used with sequences that are nowhere close to solving the problem. Jumping from one ocean wave to another doesn’t improve the odds in finding the astronomical, one-in-10^70, longshot.
The evolutionists found that it is impossible for evolution’s gradual search to solve the problem, even for the single module they were experimenting with (and all the other modules in the virus already at their native sequences). But if repeated attempts by evolution are going to fail, then the mixing and matching of those errant attempts will not help either. They merely represent another blind attempt. Unfortunately, it is unscientific conclusions such as these that inform the next level up in the apologetics hierarchy.
An example survey paper
This survey paper is entitled “Exploring protein fitness landscapes by directed evolution.” The paper discusses both the engineering problem of creating new proteins and its implications for how proteins evolved in the first place. For the most part this survey paper is a helpful and accurate summary of the relevant scientific findings at the time. The enormous complexity of the problem, and even the challenges for evolution are clearly stated. Here are several examples from the paper:
Notwithstanding significant advances, a molecular-level understanding of why one protein performs a certain task better than another remains elusive. This state of affairs is perhaps not surprising when we remember that a protein often undergoes conformational changes during function and exists as a dynamic ensemble of conformers that are only slightly more stable than their unfolded and nonfunctional states and that might themselves be functionally diverse. Mutations far from active sites can influence protein function. Engineering enzymatic activity is particularly difficult, because very small changes in structure or chemical properties can have very significant effects on catalysis. Thus predicting the amino acid sequence, or changes to an amino acid sequence, that would generate a specific behavior remains a challenge, particularly for applications requiring high performance (such as an industrial enzyme or a therapeutic protein). Unfortunately, where function is concerned, details matter, and we just don't understand the details. …
Although the distance between any two sequences is small (that is, equals the number of mutations required to interconvert them and is therefore ≤ L), this high-dimensional space contains an incomprehensibly large number of possible proteins. For even a small protein of 100 amino acids there are 20^100 (~10^130) possible sequences, or more than the number of atoms in the universe. Searching in this space for billions of years for solutions to survival, nature has explored only an infinitesimal fraction of the possible proteins. …
The vast size of sequence space makes it impossible to characterize (or even model) more than a minute fraction of this fitness surface. Despite this, several important features have emerged from accumulated experimental studies. The first is the low overall density of functional sequences: the vast majority do not code for any functional protein, much less the desired protein. …
Because most mutations are deleterious, the probability that a variant retains its fold and function declines exponentially with the number of random substitutions, and random jumps in sequence space uncover mostly inactive proteins. Thus new functions are extremely difficult to obtain without altering some aspect of the search. One approach is to create a new starting point, a parent protein with at least some minimal function, and improve that by directed evolution. …
An approach to making multiple mutations that is used extensively in nature is recombination. Naturally-occurring homologous proteins can be recombined to create genetic diversity within protein sequence libraries. …
Furthermore, natural evolution works on a different fitness landscape, and it is unclear how the protein fitness assayed during directed evolution is related to the organismal fitness that natural evolution optimizes.
These passages discuss some of the difficulties in using protein engineering as evidence for evolution, and some of the contradictory evidence protein engineering has produced. Unfortunately, none of this is interpreted outside of the evolutionary framework, and in fact the paper goes well beyond, and against, the scientific data in elaborating the evolutionary narrative:
Millions of years of life's struggle for survival in different environments have led proteins to provide diverse, creative and efficient solutions to a wide range of problems, from extracting energy from the environment to repairing and replicating their own code. …
Evolution, however, had no difficulty generating these impressive molecules. …
Evolution is unique because it works at all scales, from molecules to ecosystems — no other engineering design algorithm can make that claim. A simple algorithm of mutation and artificial selection has proved effective for everything from the selective breeding of plants and animals to discovering self-replicating nucleic acid sequences. …
Among the large number of mutational trajectories between a starting point and a solution, smooth uphill paths can often be found. …
Despite the vast size of sequence space and the complex nature of protein function, the Darwinian algorithm of mutation and selection provides a powerful method to generate proteins with altered functions.
This is the apologetics message of the paper that informs the popular literature. Whereas the research paper’s undefendable, non scientific statements were limited, now in the survey paper they frame the narrative from beginning to end. Evolution, one way or another, must have happened, so the evidence must support it. There can be no contradictory evidence.
To make this story sound scientific, the paper equivocates on evolution. It conflates the protein engineering findings that nature’s proteins can adapt with the evolutionary narrative:
Despite their complexity and finely-tuned nature, proteins are remarkably evolvable: they can adapt under the pressure of selection, changing behavior, function and even fold. …
Biological components and systems have shown a remarkable ability to adapt under the pressure of artificial selection, an evolvability that very likely reflects their own history of natural selection. …
Even the earliest directed evolution experiments noted how rapidly proteins could adapt to new selective pressures, indicating the ready availability of smooth uphill paths in the fitness landscapes. …
This simple uphill walk on a fitness landscape in sequence space works because proteins are wonderfully evolvable and can adapt to new conditions or even take on new functions with only a few mutations.
Proteins are remarkably evolvable along smooth uphill paths because, after all, their adaptation under artificial selection reflects their own history of natural selection? Of course the adaptation of native proteins proves no such thing. It is yet another rehearsing of Darwin’s flawed logic that animal husbandry and breeding provide a peek into the mechanisms of change that, by the way, created all of biology. Religion drives science and it matters.