Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data
1
Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA
2
Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA
3
Facultad de Ingeniería Química, Campus de Ciencias Exactas e Ingenierías, Universidad Autónoma de Yucatán, Merida 97203, Yucatan, Mexico
4
Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA
5
Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Hunter N. B. Moseley
Metabolites 2022, 12(1), 14; https://doi.org/10.3390/metabo12010014
Received: 15 November 2021
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Revised: 18 December 2021
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Accepted: 20 December 2021
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Published: 24 December 2021
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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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Keywords:
genome-scale metabolic models; big data; computational tools; phenotypes; flux balance analysis; machine learning; reconstruction; ME-models
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MDPI and ACS Style
Passi, A.; Tibocha-Bonilla, J.D.; Kumar, M.; Tec-Campos, D.; Zengler, K.; Zuniga, C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites 2022, 12, 14. https://doi.org/10.3390/metabo12010014
AMA Style
Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites. 2022; 12(1):14. https://doi.org/10.3390/metabo12010014
Chicago/Turabian StylePassi, Anurag, Juan D. Tibocha-Bonilla, Manish Kumar, Diego Tec-Campos, Karsten Zengler, and Cristal Zuniga. 2022. "Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data" Metabolites 12, no. 1: 14. https://doi.org/10.3390/metabo12010014
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