RT Journal Article
SR Electronic
T1 A Data-Driven Optimization Method for Coarse-Graining Gene Regulatory Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.10.503498
DO 10.1101/2022.08.10.503498
A1 Cristian Caranica
A1 Mingyang Lu
YR 2022
UL http://biorxiv.org/content/early/2022/08/13/2022.08.10.503498.abstract
AB One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.Competing Interest StatementThe authors have declared no competing interest.