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arXiv 1609.08784q-bio.PE
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基因网络通过适应度景观学习加速进化

Gene networks accelerate evolution by fitness landscape learning

John Reinitz, Sergey Vakulenko, Dmitri Grigoriev, Andreas Weber

摘要:

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。这些特征是由许多基因表达的结果。我们提出了一个新的基因调控模型,其中基因表达是由一个具有阈值机制的基因网络控制的,并且在该阈值和基因表达之间存在反馈。我们表明,这种调节是非常强大的:根据参数,我们可以获得阈值和基因之间的任何功能连接。在一般假设的健身,我们证明,这种模式的生物体是有能力的,在一定程度上,认识到的适应度景观。这种适应度景观学习大大减少了适应所需的突变数量,从而加速了进化。此外,这种学习增加了关于突变的表型鲁棒性。然而,这种加速导致额外的风险,因为学习过程可能产生错误。最后,进化加速提醒在崎岖公路上的比赛:当你加速时,你有更多的机会撞车。这些结果解释了最近的实验数据预测环境变化的一些生物。

英文摘要:

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. We propose a new model of gene regulation, where gene expression is controlled by a gene network with a threshold mechanism and there is a feedback between that threshold and gene expression. We show that this regulation is very powerful: depending on parameters we can obtain any functional connection between thresholds and genes. Under general 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 necessary for adaptation and thus accelerates of evolution. Moreover, this learning increases phenotype robustness with respect to mutations. However, this acceleration leads to an additional risk since learning procedure can produce errors. Finally evolution acceleration reminds races on a rugged highway: when you speed up, you have more chances to crash. These results explain recent experimental data on anticipation of environment changes by some organisms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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