TY - JOUR A1 - Yousefian, Maryam A1 - Frank, Anne-Simone A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition T2 - BMC Bioinformatics N2 - 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 dynamics of the system is encoded in the spectral properties of the CME operator. Markov State Models (MSMs) provide a general framework for analyzing and visualizing stochastic multistability and state transitions based on these spectral properties. Until now, however, this approach is either limited to low-dimensional systems or requires the use of high-performance computing facilities, thus limiting its usability. Y1 - 2025 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10042 VL - 26 IS - 147 ER -