Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.

ABSTRACT: Boolean networks provide robust, explainable, and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of the transcriptome, potentially relating thousands of genes. We present a general methodology for integrating transcriptome data and prior knowledge on the underlying gene regulatory network in order to generate automatically ensembles of Boolean networks able to reproduce the modeled qualitative behavior. Our methodology builds on the software BoNesis, which implements the automatic construction of Boolean networks from a specification of their expected structural and dynamical properties. We show

SUBMITTER: Chevalier S 

PROVIDER: S-EPMC12475257 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature