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Mechanistic models of microbial community metabolism.
Dillard, Lillian R; Payne, Dawson D; Papin, Jason A.
Affiliation
  • Dillard LR; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA. papin@virginia.edu.
Mol Omics ; 17(3): 365-375, 2021 06 14.
Article in En | MEDLINE | ID: mdl-34125127
ABSTRACT
Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Bacteria Type of study: Prognostic_studies Limits: Humans Language: En Journal: Mol Omics Year: 2021 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Bacteria Type of study: Prognostic_studies Limits: Humans Language: En Journal: Mol Omics Year: 2021 Document type: Article Affiliation country: Estados Unidos