A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle.

Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia. BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia. Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia. Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia. Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia. JSC "Sites-Tsentr", 123182 Moscow, Russia. Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia.

International journal of molecular sciences. 2021;(19)
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Abstract

Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling-metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause-effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.