Reinitz John, Vakulenko Sergey, Grigoriev Dmitri, Weber Andreas
Departments of Statistics, Ecology and Evolution, Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, USA.
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Federation.
F1000Res. 2019 Apr 1;8:358. doi: 10.12688/f1000research.18575.2. eCollection 2019.
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.
我们考虑一个大种群的进化,其中每个生物体的适应性由许多表型特征定义。这些特征源于许多基因的表达。在关于适应性的一些假设下,我们证明这样的模型生物体在一定程度上能够识别适应度景观。这种适应度景观学习大幅减少了适应所需的突变数量。此外,这种学习增加了表型对突变的稳健性,即使表型趋于稳定。我们表明,只有当进化是渐进的时,学习和表型稳定才会起作用。如果环境逐步变得更恶劣,生物体可以适应与恶劣环境相关的许多限制。我们的结果解释了为什么进化可能涉及具有相对较大影响的基因变化,以及为什么变化的总数出奇地少。