Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction. Issue 11 (2nd June 2016)

• Record Type: Journal Article
• Authors: Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela; Taylor, Ronald C.; Lee, Joon‐Yong; Zucker, Jeremy; Song, Hyun‐Seob
• Is Part Of: Journal of cellular physiology. Volume 231:Issue 11(2016:Nov.)
• Page Start: 2339
• Page End: 2345
• Publication Date: 2016-06-02
• Subjects: Physiology -- Periodicals; Cell physiology -- Periodicals; 571.6
• DOI: 10.1002/jcp.25428
• Languages: English
• ISSNs: 0021-9541

Abstract

Abstract : Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 Wiley Periodicals, Inc. Abstract : We tested a new community metabolic network reconstruction that uses community‐level data as a critical input. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium.