Feedback between stochastic gene networks and population dynamics enables cellular decision making

Abstract

Phenotypic selection is the process where genetically identical cells are subject to different re-productive abilities due to cellular noise. Such noise arises from fluctuations in the rates by which proteins are synthesised inside living cells and is hypothesised to play a crucial role in how cells make decisions and respond to stress or drugs. Nevertheless, the state-of-the-art modelling tools for quantifying cellular noise, such as the Gillespie algorithm and the chemical master equation, simply ignore selection. Here, we propose a general stochastic agent-based model for growing populations where the feedback of gene expression dynamics and cell cycle progression directly contribute to cell division dynamics. We devise a finite state projection method as a quantitative analysis tool and inference method for single-cell data obtained from mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the interplay between gene expression and phenotypic selection enables robust decision-making essential for developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage, and adaption during antibiotic tolerance inEscherichia coli.