Stochastic modeling of auto-regulatory genetic feedback loops: a review and comparative study

TL;DR

This paper reviews various stochastic models of auto-regulatory genetic feedback loops, comparing their assumptions, methodologies, and analytical solutions to enhance understanding of protein fluctuation dynamics.

Contribution

It provides a comprehensive comparison of existing models, clarifies their differences, and discusses insights gained from stochastic modeling of genetic feedback loops.

Findings

Models differ in process inclusion and methodology

Analytical solutions vary across models

Insights into protein fluctuation mechanisms

Abstract

Auto-regulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic parameters of the main biochemical steps. These models differ according to (i) which sub-cellular processes are explicitly modelled; (ii) the modelling methodology employed (discrete, continuous or hybrid); (iii) whether they can be analytically solved for the steady-state distribution of protein numbers. We discuss the assumptions and properties of the main models in the literature, summarize our current understanding of the relationship between them and highlight some of the insights gained through modelling.