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

Passi, Anurag (ORCID:000000020448794X); Tibocha-Bonilla, Juan D.; Kumar, Manish (ORCID:0000000180353399); Tec-Campos, Diego; Zengler, Karsten (ORCID:0000000280623296); Zuniga, Cristal (ORCID:0000000201357429)

doi:10.3390/metabo12010014

DOE OSTI · 1837451

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

Passi, Anurag (ORCID:000000020448794X)·Tibocha-Bonilla, Juan D.·Kumar, Manish (ORCID:0000000180353399)·Tec-Campos, Diego·Zengler, Karsten (ORCID:0000000280623296)·Zuniga, Cristal (ORCID:0000000201357429)

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Abstract

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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59 BASIC BIOLOGICAL SCIENCES 97 MATHEMATICS AND COMPUTING ME-models big data computational tools flux balance analysis genome-scale metabolic models machine learning phenotypes reconstruction

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Passi, Anurag (ORCID:000000020448794X), Tibocha-Bonilla, Juan D., Kumar, Manish (ORCID:0000000180353399), Tec-Campos, Diego, Zengler, Karsten (ORCID:0000000280623296), Zuniga, Cristal (ORCID:0000000201357429). 2021-12-24. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. https://doi.org/10.3390/metabo12010014

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