Abstract
We consider evolution of a large population, where fitness of each organism is defined by many phenotypical traits. These traits result from expression of many genes. Under some assumptions on fitness we prove that such model organisms are capable, to some extent, to recognize the fitness landscape.That fitness landscape learning sharply reduces the number of mutations needed for adaptation.Moreover, this learning increases phenotype robustness with respect to mutations, i.e., canalizes the phenotype. We show that learning and canalization work only when evolution is gradual. Organisms can be adapted to many constraints associated with a hard environment, if that environment becomes harder step by step.Our results explain why evolution can involve genetic changes of a relatively large effect and why the total number of changes are surprisingly small.
Highlights
A central idea of modern biology is that evolution proceeds by mutation and selection
There is a limited amount of experimental support for this idea2 and some experimental evidence that evolution can involve genetic changes of a relatively large effect and that the total number of changes are surprisingly small3
5 Discussion In this paper, we proposed a model for fitness landscape learning, which extends earlier work by 7–9 in two ways
Summary
1. Eors Szathmary , MTA Centre for Ecological Research, Tihany, Hungary Parmenides Center for the Conceptual Foundations of Science, Pullach, Germany Eötvös University, Budapest, Hungary. Any reports and responses or comments on the article can be found at the end of the article. To respond to the reviewers’ comments, in this revision, we extended the discussion. In order to explain in more detail the regulation we added a new Section 2.5 on “Gene regulation networks”. Two new figures (Figure 1 and Figure 2) show results of numerical simulations based on the`strong selection weak mutation’’ (SSWM) algorithm. In the discussion of Theorem 3.2 we added a paragraph on rare mutants and giving some intuitions towards the proof of Theorem 3.2 as it is based on estimates of the accuracy of the Nagylaki equations. For the main Lemma 4.5 for the proof of Theorem 3.2. Additional references pointed out by the reviewers are incorporated into version 2 of the paper
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