In fairness, neo-Darwinian biologists have mathematical models of their own—models indicating to them that nearly unlimited evolutionary change can occur under the right conditions. The assumption that these models, which are based on the equations of population genetics, accurately represent how much evolution can occur has left many evolutionary biologists confident in the creative power of various mutational mechanisms. But should they be?
In the next chapter, I will take up this question. As I do, I’ll explain why evolutionary biologists have been, heretofore, untroubled by mathematical challenges to neo-Darwinism. I’ll also show why that has begun to change as new developments in molecular genetics have introduced another formidable mathematical challenge to the creative power of the neo-Darwinian mechanism—a challenge that arises from within the neo-Darwinian framework and raises yet new questions about the causal adequacy of the neo-Darwinian mechanism.
12
Complex Adaptations and the Neo-Darwinian Math
University of Illinois biologist Tom Frazzetta knew the textbook story as well as anyone. According to neo-Darwinian theory, organisms with all their complex systems came into existence via natural selection acting on randomly arising, small-scale variations and mutations. As Frazzetta understood, this evolutionary mechanism necessarily transforms organisms gradually, with modifications parceled into increments “as a sort of continuous change, where one structural condition melts gradually into another.”1
Frazzetta had his doubts, however. As an expert in functional biomechanics—studying how animals actually work—he had dissected the skulls of rare snakes found only on the island of Mauritius, in the Indian Ocean. These snakes, called bolyerines, are boa-like but have an anatomical specialization found in no other vertebrate. Their maxilla, the tooth-bearing bone of the upper jaw, is divided into two segments, linked by a flexible joint and serviced by many specialized nerves, extra bones, tissues, and differently arranged ligaments. This unique trait allows the snakes to bend the front half of their upper jaw backwards when they attack prey (see Fig. 12.1).
Could this complex system of bones, joints, tissues, and ligaments have evolved gradually? “A movable joint dividing the maxilla into two segments,” observed Frazzetta, “seems to have either a presence or absence, with no intermediate to connect the two conditions.”2 That is, either the maxilla occurs as one bone (as it does in every other vertebrate) or as two segments with all the accompanying joints, bones, ligaments, and tissues necessary to make it work, as it does in the bolyerine snakes. No intermediate condition—a broken maxilla with two pieces of bone lacking the necessary joints, tissues, and ligaments, for example—appears viable. As Stephen Jay Gould asked of the same system, “How can a jawbone be half broken?”3 Or as Frazzetta himself observed, “I thus find it difficult to envision a smooth transition from a single maxilla to the divided condition seen in bolyerines.”4 Yet because the intermediate forms would not be viable, building a bolyerine jaw would require all the necessary parts—the jointed maxilla, the adjoining ligaments, and the necessary muscles and tissues—arising together.
Yet the problem for neo-Darwinian theory, Frazzetta realized, extended well beyond the anatomical peculiarities of rare snakes. As a young evolutionary biology professor, he had studied complex features in a wide variety of species. He knew that almost any biological structure of interest—the inner ear, the amniotic egg, eyes, olfactory organs, gills, lungs, feathers, the reproductive, circulatory, and respiratory systems—possesses multiple necessary components. To change such systems requires altering each of the many independent parts upon which their functions are based. This cannot be done willy-nilly. For example, changing any of the three bones of the mammalian inner ear—the incus, stapes, or malleus—will perforce require corresponding changes in the other bones and in other parts of the ear as well, such as the tympanic membrane or the cochlea. Complex biological systems depend for their functions on tens or hundreds of such independent, yet jointly necessary parts. As the number of necessary components increases, the requisite number of coordinated changes increases too, rapidly driving up the difficulty of maintaining the functional integrity of the system while modifying any of its parts.
FIGURE 12.1
A complex adaptation: the jointed upper jaw of the bolyerine snake, made possible by its accompanying tendons, ligaments, and musculature. The other skull shows the single-boned jaw, found in other related snakes.
And that was the problem, as Frazzetta understood it. Any system that depends for its function on the coordinated action of many parts could not be changed gradually without losing function. But in the neo-Darwinian scheme of things, natural selection acts to preserve only functional advantages. Changes that result in death or reduced function will not be preserved. The integrated complexity of many biological systems thus imposes limitations on the evolutionary process—limitations that human engineers do not face when they design complex integrated systems. In 1975, Frazzetta wrote a minor classic entitled Complex Adaptations in Evolving Populations explaining this concern. He wrote:
When modifying the design of a machine, an engineer is not bound by the need to maintain a real continuity between the first machine and the modification… . But in evolution, transitions from one type to the next presumably involve a greater continuity by means of a vast number of intermediate types. Not only must the end product—the final machine—be feasible, but so must be all the intermediates. The evolutionary problem is, in a real sense, the gradual improvement of a machine while it is running!5
Historically, evolutionary biologists tried to solve this problem one advantageous variation or mutation at a time. Starting with Darwin himself, they have attempted to explain how natural selection and random variation could build complex systems as the result of a series of incremental changes, each of which might confer some selectable advantage. Darwin famously employed this strategy to explain the origin of the eye, asking his readers to imagine a series of incremental, advantageous changes to a simple “nerve sensitive to light.”6
As Frazzetta thought about the problem of explaining the origin of complex systems, he came to doubt both the classical and the modern Darwinian accounts of such systems. Frazzetta acknowledged that he was influenced in part by the skepticism expressed by the Wistar “outsiders” (see Chapter 9). He admitted “revealing some hideous personalia” in confessing that he was attracted to the worries about neo-Darwinism expressed by Murray Eden and other Wistar skeptics.7
Frazzetta’s concerns about the adequacy of the neo-Darwinian mechanism, like Eden’s, turned on the growing appreciation of the nature and importance of genetic information. Though biologists then (as now) didn’t fully understand how genetic information in DNA correlates or “maps” to these higher-level complex morphological structures, by 1975 they did know that many hundreds of genes can be involved in coding for a single complex integrated structure. Thus, altering the anatomical structure of the mammalian ear or the vertebrate eye, for example, would involve altering the genes that code for its constituents, which implies, most implausibly, that multiple coordinated mutations would occur virtually simultaneously.
As Frazzetta explained, “Phenotypic alteration of integrated systems requires an improbable coincidence of genetic (and hence, heritable phenotypic) modifications of a tightly specified kind.”8 Yet the extreme specificity of the fit of the components and the functional dependence of the whole system on this fit imply limits to allowable genetic change. Genetic change affecting any one of the necessary components, unless matched by many corresponding—and vastly improbable—genetic changes, will result in functional loss and often death. For this reason, as Frazzetta concluded, “We are still left with the unabating need to explain evolutionary changes in systems that have the operational integration characteristic of things we recognize as ‘machines.’ ”9 At the time, the doubts he expressed gained little traction in the evolutionary biology community, because neo-Darwinian evolutionary biologist
s assumed that mutation and selection had nearly unlimited creative power, enough to generate even complex systems of the kind described in Frazzetta’s book.
The mathematical expression of neo-Darwinian theory, as represented in the equations of a subdiscipline of biology known as population genetics, seemed to confirm this conviction. Population genetics models how gene frequencies change as the result of processes such as mutation, genetic drift (neutral changes in the genome that natural selection neither favors nor eliminates), and natural selection. On the assumption that advantageous variations or traits will arise as the result of even single mutations, the mathematical models of population genetics describe how much evolutionary change can occur in a given period of time. These estimates are based upon, among others, three primary factors: mutation rates, effective population sizes, and generation times. When evolutionary biologists plug estimates for these factors into the equations of population genetics, their calculations seem to imply that standard evolutionary mechanisms could generate significant amounts of evolutionary change in many groups of organisms—even enough to build complex systems. As long as mutations generate a continuous supply of new traits, any system, however complex, can be built one trait at a time—trait upon trait—via the creative power of natural selection. Or so the story goes.
Confidence in these mathematical models (and their underlying assumptions) led many neo-Darwinists to disregard the need to give detailed accounts of the specific evolutionary pathways by which complex systems might have arisen. For example, in an evolutionary biology text widely used about the time Frazzetta first posed this challenge, evolutionary biologists Paul Ehrlich and Richard Holm advised:
One need not go into the details of the evolution of the bird’s wing, the giraffe’s neck, the vertebrate eye, the nest building of some fish, etc., as the selective origins of these and other structures and of behavioral patterns may be assumed to be basically the same in outline as those, such as industrial melanism, which have already been discussed. Even a slight advantage or disadvantage in a particular genetic change provides a sufficient differential for the operation of natural selection.10
The phrase “sufficient differential for the operation of natural selection” refers to the equations of population genetics and one of the factors (the so-called selection coefficient) that determines how rapidly particular traits would be likely to disseminate through a population. The message was clear: the math tells the story; the biological details of the origin of complex systems don’t matter.
The neo-Darwinian focus on mathematical modeling helps to explain why mainstream evolutionary biologists haven’t worried about the problem of the origin of new genes and proteins or the problem of combinatorial inflation, discussed in Chapters 9 and 10. Many contemporary evolutionary biologists, like the founders of population genetics, assumed that some mechanism for building new genes already existed. Indeed, they assumed that new traits (and the genes for building them) can arise as the result of even single mutations (or a series of such mutations that each confer a small, incremental, selectable advantage). Thus, the mathematical expression of neo-Darwinian theory seemed to certify the plausibility of even large-scale evolutionary changes—again, provided these changes could occur one mutation at a time.
But what if there are systems in living organisms that cannot be built one mutation at a time, and instead must be built by simultaneous coordinated changes? What if building just a single new gene or protein requires such coordinated mutational changes? What if individual genes turned out to be complex adaptations?
Mathematical challenges of the kind first advanced at Wistar, and that Douglas Axe’s experimental findings have exacerbated, initially did not dent confidence in the adequacy of neo-Darwinian explanations. Many evolutionary biologists have simply regarded mathematical challenges to the creative power of the mechanism, coming as they mostly do from scientists and engineers in other fields, as exotic or irrelevant.
That has begun to change. And it has begun to change in a way that has not only introduced a new mathematical challenge to the creative power of the neo-Darwinian mechanism, but also in a way that indirectly confirms Axe’s key insight about the rarity of genes and proteins. In the last decade, developments in molecular genetics and population genetics have exposed a connection between the problem of the origin of new genes and proteins and the origin of complex adaptations, a connection first perceived by Tom Frazzetta back in 1975. As more biologists have recognized that connection, they too have begun to share Frazzetta’s doubt.
Population Genetics and the Origin of Genetic Information
The neo-Darwinian synthesis was formulated during the 1930s before the elucidation of the structure of DNA. Biologists at that time did not yet understand the nature, structure, or precise location of genetic information.11 They did not associate genes with long strings of nucleotide bases along the spine of the DNA molecule. They did not think of genes as long sections of digital code stored in complex biomacromolecules. Instead, after Mendel, but before Watson and Crick, genes were defined operationally as those entities, associated with chromosomes, that produced specific visible or selectable anatomical traits, such as eye color or beak shape.
The architects of neo-Darwinism working in the 1930s reformulated evolutionary theory to emphasize the importance of mutations as the source of genetic variation. It followed, therefore, that the mutations—which they regarded as the source of heritable variation—must operate on genes. Not knowing the nature of genes, they also assumed that a single mutation could alter a gene in such a way as to produce a new trait.
The equations of population genetics are predicated upon this assumption. The rate of mutation thus emerges as an important factor in computing the amount of evolutionary change that can occur in any given population. If every individual mutation can produce a new, potentially selectable trait, then the rate at which such variation accumulates partially determines how much change can occur in a given time.
After 1953, biologists no longer conceived of the gene as an abstract entity. Watson and Crick showed that the gene had a definite locus and structure and that individual genes contain hundreds or thousands of precisely sequenced nucleotide bases, each functioning as digital characters in a larger instruction set. Consequently, biologists changed their understanding of mutations as well. Biologists came to understand mutations as something like typographic errors in long strings of digital code. As a result, many scientists began to realize that individual mutations were unlikely by themselves to produce new beneficial traits. Some scientists realized that mutations were instead overwhelmingly more likely to degrade the information contained in a gene than to produce a new function or trait, and that the accumulation of mutations would eventually and typically result in the loss of function.
This change of perspective called for an explanation of how mutations could generate new genes—an explanation that was provided beginning in the 1970s with the ideas of gene duplication, subsequent neutral evolution, and positive selection.
Though the theory of gene duplication played no formal role in the mathematical structure of population genetics, it did serve to buttress a critical assumption of the whole enterprise. After the 1950s, evolutionary biologists no longer assumed that single mutations would necessarily generate whole new traits. That left a critical assumption of population genetics essentially undefended. For many evolutionary biologists, the theory of gene duplication closed that conceptual gap. After the theory was formulated, many evolutionary biologists thought that a mechanism had been discovered by which sections of genetic text could accumulate multiple changes without compromising the fitness of an organism, thus ensuring the eventual production of new genes and a steady supply of new traits.
So when Frazzetta confronted evolutionary biology with the problem of complex adaptations in the mid-1970s, most neo-Darwinian biologists responded with a collective yawn, if they noticed it at all. His challenge was no challenge after all.
So implied the math of population genetics—provided its assumptions about the ease with which new mutations could generate new traits were valid.
But were they valid? Could a series of separate mutations generate the new genes necessary to build new proteins and new traits, or did building genes require multiple coordinated mutations?
Are Genes Complex Adaptations?
Classically, Darwinian biologists have assumed that small, separate step-by-step changes could produce all biological structures and features, provided each change confers some survival or reproductive advantage. In his chapter in the 1909 anthology Darwin and Modern Science, the British geneticist William Bateson wryly described how this widespread assumption prevented evolutionary biologists from confronting the real difficulty of explaining the origin of complex adaptations:
By suggesting that the steps through which an adaptive mechanism arises are indefinite and insensible, all further trouble is spared. While it could be said that species arise by an insensible and imperceptible process of variation, there was clearly no use in tiring ourselves by trying to perceive that process. This labour-saving counsel found great favor.12
One of the first prominent evolutionary biologists to consider the possibility that building new genes and proteins might require multiple coordinated mutations was John Maynard Smith. Maynard Smith worked as an aeronautical engineer during World War II, but then took up the formal study of evolutionary biology after the war. He eventually helped to found the University of Sussex, where he also served as a distinguished professor of biology until the mid-1980s.13
In 1970, Maynard Smith wrote an article in Nature responding to an earlier article by Frank Salisbury, a biologist from Utah State University. Salisbury had raised questions about whether random mutations could explain the specificity of the arrangement of nucleotide bases necessary to produce functional proteins. Salisbury worried, following discussions at Wistar, that the probability of random mutations generating functional arrangements of bases or amino acids was prohibitively low. According to Salisbury’s calculations, “The mutational mechanism as presently imagined could fall short by hundreds of orders of magnitude of producing, in a mere four billion years, even a single required gene.”14
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