A Computational Pipeline for Identifying Gene Regulatory Networks: A Case Study of Response to Exercise

Editor

Yan, Q.

Department

Research

Document Type

Book Chapter

Publication Title

Psychoneuroimmunology. Methods in Molecular Biology

Abstract

Gene regulatory networks are foundational in the control of virtually all biological processes. These networks orchestrate a myriad of cell functions ranging from metabolic rate to the response to a drug or other intervention. The data required to accurately identify these control networks remains very cost and labor intensive typically leading to relatively sparse time course data that is largely incompatible with conventional data-driven model identification techniques. In this work, we combine empirical identification of gene-gene interactions with constraints describing the expected dynamic behavior of the network to infer regulatory dynamics from under-sampled data. We apply this to the identification of gene regulatory subnetworks recruited in groups of subjects participating in several different exercise interventions. Intervention-specific response networks are compared to one another and control actions driving differences are identified. We propose that this approach can extract statistically robust and biologically meaningful insights into gene regulatory dynamics from a dataset consisting of a small number of participants with very limited longitudinal sampling, for example pre- and post- intervention only.

First Page

247

Last Page

264

DOI

10.1007/978-1-0716-4200-9_13

Volume

2868

Publication Date

11-16-2024

Publisher

Humana, New York, NY

PubMed ID

39546234

Link to Full Text

Share

COinS