Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition

Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition

Abstract Background The dynamics of many gene regulatory networks (GRNs) is characterized by the occurrence of metastable phenotypes and stochastic phenotype switches. The chemical master equation (CME) is the most accurate description to model such stochastic dynamics, whereby the long-time dynamic...

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Bibliographic Details
Main Authors: Maryam Yousefian, Anna-Simone Frank, Marcus Weber, Susanna Röblitz
Format: Article
Language:English
Published: BMC 2025-06-01
Series:BMC Bioinformatics
Subjects:
Gene regulatory networks
Stochastic phenotype switching
Chemical master equation
Markov state modelling
Robust Perron cluster analysis
Voronoi tessellation
Online Access:https://doi.org/10.1186/s12859-025-06174-5
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https://doi.org/10.1186/s12859-025-06174-5

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