Work

pbn-STAC: Deep reinforcement learning-based framework for cellular reprogramming

Year: 2025

Type: article

Abstract: Cellular reprogramming can be used for both the prevention and cure of complex diseases. However, the efficiency of discovering reprogramming strategies with classical wet-lab experiments is hindered by lengthy time commitments and high costs. In this study, we leverage deep reinforcement learning to develop a novel computational framework that facilitates the identification of reprogramming strategies. To this end, we formulate a control problem in the context of cellular reprogramming for the Boolean and probabilistic Boolean network models of gene regulatory networks under the asynchronous update mode. Furthermore, to facilitate scalability, we introduce the notion of a pseudo-attractor and a procedure for the identification of pseudo-attractor states. Finally, we devise a computational framework for solving the control problem, which we test on a number of biological networks.

Source: Theoretical Computer Science

DOI: 10.1016/j.tcs.2025.115382

Authors: Andrzej Mizera, Jakub Zarzycki

Institution: University of Warsaw

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $2,880

Funder: Narodowe Centrum Nauki