ArticleBMC systems biology2017
Markov State Models of gene regulatory networks.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A tunable, ultrasensitive threshold in enzymatic activity governs the DNA methylation landscape.Biophysical journal · 2026Article
- Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition.BMC bioinformatics · 2025Article
- Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty.Journal of mathematical biology · 2021Article
- Drug Research Meets Network Science: Where Are We?Journal of medicinal chemistry · 2020Article
- Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation.The Journal of chemical physics · 2020Article
- Automatic error control during forward flux sampling of rare events in master equation models.The Journal of chemical physics · 2020Article
- Learning dynamical information from static protein and sequencing data.Nature communications · 2019Article
- Multi-modality in gene regulatory networks with slow promoter kinetics.PLoS computational biology · 2019Article
- Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development.Frontiers in genetics · 2019Article
- Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making.Frontiers in genetics · 2019Article
- Rare-event sampling of epigenetic landscapes and phenotype transitions.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
backgroundGene 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.
resultsThe 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.
conclusionsIn 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.
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