Gene expression is a complex stochastic process composed of numerous enzymatic reactions with rates coupled to cell state variables, the majority of which are hidden and uncontrollable. Despite advances in single-cell technologies, the lack of an accurate theory describing gene expression has restricted a robust, quantitative understanding of gene expression variability among cells. Here we present the Chemical Fluctuation Theorem (CFT), providing an accurate relationship between the environment-coupled chemical dynamics of gene expression and gene expression variability. With factorization of the transcription rate into the control variable dependent part, modelled explicitly, and the environmental variable dependent part, modelled in a formal but accurate manner, the CFT provides a unified, quantitative explanation of mRNA variability for various experimental systems. From this analysis, we construct a quantitative model of transcription dynamics, with which we make predictions for the dependence of the mRNA noise on the mRNA lifetime distribution, whose correctness is confirmed against stochastic simulation. This work suggests promising, new directions for quantitative investigation into cellular control over biological functions by making the complex dynamics of intracellular reactions accessible to rigorous mathematical deductions .
: Park et al., Nature Communications 9, 297 (2018).