Markov State Models of gene regulatory networks. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Chu, Brian; Tse, Margaret; Sato, Royce; Read, Elizabeth
• Is Part Of: BMC systems biology. Volume 11:Issue 1(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-12
• Subjects: Multistable systems -- Stochastic processes -- Gene regulatory networks -- Markov State Models -- Cluster analysis; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-017-0394-4
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. Results The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. Conclusions In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework—adopted from the field of atomistic Molecular Dynamics—can be a useful tool for quantitative Systems Biology at the network scale.