Gene expression noise accelerates the evolution of a biological oscillator

Nicolas E. Buchler, Yen Ting Lin

Publication date: 21 March 2022

Abstract: Gene expression is a biochemical process, where stochastic binding and un-binding events naturally generate fluctuations and cell-to-cell variability in gene dynamics. These fluctuations typically have destructive consequences for proper biological dynamics and function (e.g., loss of timing and synchrony in biological oscillators). Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms. We used computer simulations to evolve two mechanistic models of a biological oscillator at different levels of gene expression noise. We first show that gene expression noise induces oscillatory-like dynamics in regions of parameter space that cannot oscillate in the absence of noise. We then demonstrate that these noise-induced oscillations generate a fitness landscape whose gradient robustly and quickly guides evolution by mutation towards robust and self-sustaining oscillation. These results suggest that noise can help dynamical systems evolve or learn new behavior by revealing cryptic dynamic phenotypes outside the bifurcation point.

Has companion code repository: https://github.com/lanl/in-silico-evolution-of-oscillatory-gene-dynamics

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) (92C45) Dynamical systems and their relations with probability theory and stochastic processes (37A50) Biological rhythms and synchronization (92B25)

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