Abstract
Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.
Highlights
Gene expression in living cells is a complex stochastic process characterized by various probabilistic chemical reactions, giving rise to spontaneous fluctuations in the abundances of proteins and mRNAs1–4
The biochemical state of the gene of interest can be described by three variables: the activity i of its promoter with i = 1 and i = 0 corresponding to the active and inactive forms, respectively, the copy number m of the mRNA transcript, and the copy number n of the protein product
We present a comprehensive analysis of the three-stage model of stochastic gene expression with nonlinear feedback regulation
Summary
Gene expression in living cells is a complex stochastic process characterized by various probabilistic chemical reactions, giving rise to spontaneous fluctuations in the abundances of proteins and mRNAs1–4. If all other rate constants remain unchanged, positive (negative) feedback will lead to an increase (decrease) in the protein mean 〈n〉10 and lead to a decrease (increase) in the feedback-free noise 1/q〈n〉.
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