Relevance of phenotypic noise to adaptation and evolution.
Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness.
Kaneko
K
K
University of Tokyo, Department of Basic Science, Tokyo, Japan.
Furusawa
C
C
D000222:Adaptation, Physiological / Q000235:genetics*; D000818:Animals; D003198:Computer Simulation; D019143:Evolution, Molecular; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D010641:Phenotype; D020543:Proteome / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*; D013269:Stochastic Processes
D020543:Proteome