Document Type

Article

Publication Date

2-17-2023

Keywords

JMG

JAX Source

iScience. 2023;26(2):105927.

ISSN

2589-0042

PMID

36698721

DOI

https://doi.org/10.1016/j.isci.2023.105927

Grant

This work is supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM128717, and by startup funds from Northeastern University.

Abstract

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.

Comments

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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