Open AccessMetabolic network modeling of microbial communities
Author(s) -
Biggs Matthew B.,
Medlock Gregory L.,
Kolling Glynis L.,
Papin Jason A.
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: systems biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.087
H-Index - 51
eISSN - 1939-005X
pISSN - 1939-5094
DOI - 10.1002/wsbm.1308
Subject(s) - microbial population biology , data science , metabolic engineering , computer science , metabolic network , field (mathematics) , systems biology , compartmentalization (fire protection) , microbial ecology , scale (ratio) , computational biology , biochemical engineering , ecology , biology , engineering , geography , mathematics , biochemistry , genetics , cartography , bacteria , pure mathematics , enzyme
Genome‐scale metabolic network reconstructions and constraint‐based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome‐scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species‐level and community‐level objectives, dynamic analysis, the ‘enzyme‐soup’ approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high‐level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering. WIREs Syst Biol Med 2015, 7:317–334. doi: 10.1002/wsbm.1308 This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Metabolism