Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Passi, Anurag; Tibocha-Bonilla, Juan D.; Kumar, Manish; Tec-Campos, Diego; Zengler, Karsten; Zuniga, Cristal

doi:10.3390/metabo12010014

Title: Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Journal Article · 24 December 2021 · Metabolites

DOI: https://doi.org/10.3390/metabo12010014 · OSTI ID:1837451

; Tibocha-Bonilla, Juan D.;

; Tec-Campos, Diego;

;

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); University of California San Diego, La Jolla, CA (United States); University of California, Oakland, CA (United States); University of California, San Diego, La Jolla, CA (United States)

Sponsoring Organization:: Mexican National Research Council, CONACYT; Multicampus Research Programs and Initiatives; U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture; US Department of Agriculture (USDA); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; AC36-08GO28308; EE0009275; SC0019388; SC0021234; SC0022137

OSTI ID:: 1837451

Journal Information:: Metabolites, Journal Name: Metabolites Journal Issue: 1 Vol. 12; ISSN 2218-1989; ISSN METALU

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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