Gene networks accelerate evolution by fitness landscape learning

TL;DR

This paper introduces a gene regulation model with feedback that enables organisms to learn fitness landscapes, significantly speeding up evolution and increasing robustness, but also raising the risk of errors during adaptation.

Contribution

The paper presents a novel gene network model with feedback control that allows organisms to learn fitness landscapes, accelerating evolution and enhancing robustness.

Findings

Learning reduces mutations needed for adaptation.

Accelerated evolution increases robustness to mutations.

Learning can produce errors, risking maladaptation.

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. 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.