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Title: Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

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.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [4] ;  [4] ;  [4]
  1. Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Computation Institute, University of Chicago, Chicago Illinois
  2. Biodetection Sciences, National Security Directorate, Pacific Northwest National Laboratory Richland Washington; Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman Washington
  3. Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Division of Biosciences, Argonne National Laboratory, Argonne Illinois
  4. Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington
Publication Date:
OSTI Identifier:
1324889
Report Number(s):
PNNL-SA-118336
Journal ID: ISSN 0021-9541; KP1601010
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Cellular Physiology; Journal Volume: 231; Journal Issue: 11
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
Mixed-bag metabolic network; compartmentalized metabolic network; photoautotroph-heterotroph consortia; metabolic dependencies