Markov State Models of gene regulatory networks.
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.



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.



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.
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, CA, USA.
Brian K
BK
Chu
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, CA, USA.
Margaret J
MJ
Tse
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, CA, USA.
Royce R
RR
Sato
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, CA, USA. elread@uci.edu.|Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA, USA. elread@uci.edu.
Elizabeth L
EL
Read
D053263:Gene Regulatory Networks; D007700:Kinetics; D008390:Markov Chains; D008957:Models, Genetic
Cluster analysis; Gene regulatory networks; Markov State Models; Multistable systems; Stochastic processes