Adi Livnat

A mixability theory for the role of sex in evolution

The question of what role sex plays in evolution is still open despite decades of research. It has often been assumed that sex should facilitate the increase in fitness. Hence, the fact that it may break down highly favorable genetic combinations has been seen as a problem. Here, we consider an alternative approach. We define a measure that represents the ability of alleles to perform well across different combinations and, using numerical iterations within a classical population-genetic framework, show that selection in the presence of sex favors this ability in a highly robust manner. We also show that the mechanism responsible for this effect has been out of the purview of previous theory, because it operates during the evolutionary transient, and that the breaking down of favorable genetic combinations is an integral part of it. Implications of these results and more to evolutionary theory are discussed.

Algorithms, games, and evolution

Significance Theoretical biology was founded on the mathematical tools of statistics and physics. We believe there are productive connections to be made with the younger field of theoretical computer science, which shares with it an interest in complexity and functionality. In this paper, we find that the mathematical description of evolution in the presence of sexual recombination and weak selection is equivalent to a repeated game between genes played according to the multiplicative weight updates algorithm, an algorithm that has surprised computer scientists time and again in its usefulness. This equivalence is informative for the pursuit of two key problems in evolution: the role of sex and the maintenance of variation. Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: “What algorithm could possibly achieve all this in a mere three and a half billion years?” In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution.

The Evolution of Intergenerational Discounting in Offspring Quality

Intergenerational effects occur when an individual’s actions affect not only its own survivorship and reproduction but also those of its offspring and possibly later descendants. In the presence of intergenerational effects, short‐term and long‐term measures of success (such as the expected numbers of surviving offspring and of farther descendants, respectively) may be in conflict. When such conflicts occur, life‐history theory normally takes long‐term measures to predict the outcome of selection. This ignores the fact that, because traits change in time—through mutation, sex, and recombination—long‐term relations disintegrate. We study this issue with numerical simulations and analytical models combining intergenerational effects and evolutionary change. In the models, the parental investment per offspring, as well as the total reproductive effort, stand for investments in future generations. The models show that the rate of evolutionary change determines the level of those investments. Higher rates of mutation and of sexual as opposed to parthenogenetic reproduction favor lower parental investment per offspring and lower total reproductive effort. It follows that the level of investment of ancestors in descendants responds to the genetic relatedness between the generations of the lineage, in a manner unaccounted for by preexisting theory.

Sex as an algorithm: the theory of evolution under the lens of computation

Looking at the mysteries of evolution from a computer science point of view yields some unexpected insights.

Interaction-based evolution: how natural selection and nonrandom mutation work together

BackgroundThe modern evolutionary synthesis leaves unresolved some of the mostfundamental, long-standing questions in evolutionary biology: What is therole of sex in evolution? How does complex adaptation evolve? How canselection operate effectively on genetic interactions? More recently, themolecular biology and genomics revolutions have raised a host of criticalnew questions, through empirical findings that the modern synthesis fails toexplain: for example, the discovery of de novo genes; the immenseconstructive role of transposable elements in evolution; genetic varianceand biochemical activity that go far beyond what traditional naturalselection can maintain; perplexing cases of molecular parallelism; andmore.Presentation of the hypothesisHere I address these questions from a unified perspective, by means of a newmechanistic view of evolution that offers a novel connection betweenselection on the phenotype and genetic evolutionary change (while relying,like the traditional theory, on natural selection as the only source offeedback on the fit between an organism and its environment). I hypothesizethat the mutation that is of relevance for the evolution of complexadaptation—while not Lamarckian, or “directed” to increasefitness—is not random, but is instead the outcome of a complex andcontinually evolving biological process that combines information frommultiple loci into one. This allows selection on a fleeting combination ofinteracting alleles at different loci to have a hereditary effect accordingto the combination’s fitness.Testing and implications of the hypothesisThis proposed mechanism addresses the problem of how beneficial geneticinteractions can evolve under selection, and also offers an intuitiveexplanation for the role of sex in evolution, which focuses on sex as thegenerator of genetic combinations. Importantly, it also implies that geneticvariation that has appeared neutral through the lens of traditional theorycan actually experience selection on interactions and thus has a muchgreater adaptive potential than previously considered. Empirical evidencefor the proposed mechanism from both molecular evolution and evolution atthe organismal level is discussed, and multiple predictions are offered bywhich it may be tested.ReviewersThis article was reviewed by Nigel Goldenfeld (nominated by Eugene V.Koonin), Jürgen Brosius and W. Ford Doolittle.

Evolution and Learning: Used Together, Fused Together. A Response to Watson and Szathmáry.

In their recent article in TREE [1], Watson and Szathmary make an invaluable contribution to the study of evolution by opening it to powerful new ideas and, in particular, bringing to wider attention the potent three-way connection between evolution, machine learning, and the brain. We are strong believers in the importance of this particular interdisciplinary mix, and we have contributed to it in the recent past (e.g., [2–7]). Evidently, our point of view has much in common with that of Watson and Szathmary; here, we flesh out certain differences in emphasis, pertaining to mutational mechanisms and their importance for simplification [4,7,8] and to the centrality of innateness and automatization [5,7,9].

Satisfiability and Evolution

We show that, if truth assignments on n variables reproduce through recombination so that satisfaction of a particular Boolean function confers a small evolutionary advantage, then a polynomially large population over polynomially many generations (polynomial in n and the inverse of the initial satisfaction probability) will end up almost certainly consisting exclusively of satisfying truth assignments. We argue that this theorem sheds light on the problem of the evolution of complex adaptations.

Simplification, Innateness, and the Absorption of Meaning from Context: How Novelty Arises from Gradual Network Evolution

How does new genetic information arise? Traditional thinking holds that mutation happens by accident and then spreads in the population by either natural selection or random genetic drift. There have been at least two fundamental conceptual problems with imagining an alternative. First, it seemed that the only alternative is a mutation that responds “smartly” to the immediate environment; but in complex multicellulars, it is hard to imagine how this could be implemented. Second, if there were mechanisms of mutation that “knew” what genetic changes would be favored in a given environment, this would have only begged the question of how they acquired that particular knowledge to begin with. This paper offers an alternative that avoids these problems. It holds that mutational mechanisms act on information that is in the genome, based on considerations of simplicity, parsimony, elegance, etc. (which are different than fitness considerations). This simplification process, under the performance pressure exerted by selection, not only leads to the improvement of adaptations but also creates elements that have the capacity to serve in new contexts they were not originally selected for. Novelty, then, arises at the system level from emergent interactions between such elements. Thus, mechanistically driven mutation neither requires Lamarckian transmission nor closes the door on novelty, because the changes it implements interact with one another globally in surprising and beneficial ways. Finally, I argue, for example, that genes used together are fused together; that simplification leads to complexity; and that evolution and learning are conceptually linked.