Speaker
Description
Within a cell, gene expression levels are governed by molecular regulators interacting with each other in gene regulatory networks (GRNs). In this talk, we present an efficient computational framework for analyzing stochastic GRNs, in which cellular behavior is characterized by multiple metastable phenotypes and rare transitions between them. The dynamics of stochastic GRNs is described by the chemical master equation (CME), whose high dimensionality makes direct analysis computationally prohibitive. To address this, a domain decomposition approach (DDA) has been introduced \cite{yousefian2025efficient}, in which the state space is discretized using Voronoi tessellations and reduced to a Markov state model (MSM) \cite{chu2017markov}. In this setting, adaptive stochastic sampling combined with spectral clustering in terms of PCCA+ \cite{frank2024spectral} enables the identification of metastable states and their transition dynamics without relying on high-performance computing. Evaluation on two biological models, one for the genetic toggle switch and one for macrophage polarization, shows that the method accurately detects metastable phenotypes, estimates transition probabilities, and provides uncertainty quantification. Our findings highlight that accuracy is mainly driven by the number of Voronoi cells, while uncertainty is controlled by the sampling effort. The approach is computationally efficient and easily parallelizable, offering a practical tool for studying complex stochastic cellular dynamics and advancing the understanding of gene regulation and phenotype switching.
Bibliography
@article{chu2017markov,
title={Markov state models of gene regulatory networks},
author={Chu, Brian K and Tse, Margaret J and Sato, Royce R and Read, Elizabeth L},
journal={{BMC Systems Biology}},
volume={11},
number={1},
pages={14},
year={2017},
publisher={Springer}
}
@inproceedings{yousefian2025exploring,
title={Exploring Metastable Dynamics of Gene Regulatory Networks with {ISOKANN}},
author={Yousefian, Maryam and Donati, Luca and Sikorski, Alexander and Weber, Marcus and R{\"o}blitz, Susanna},
booktitle={International Conference on Computational Methods in Systems Biology},
pages={126--149},
year={2025},
organization={Springer}
}
@article{frank2024spectral,
title={Spectral clustering of {M}arkov chain transition matrices with complex eigenvalues},
author={Frank, Anna-Simone and Sikorski, Alexander and R{\"o}blitz, Susanna},
journal={{Journal of Computational and Applied Mathematics}},
volume={444},
pages={115791},
year={2024}
}
