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 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.
Highlights
For testing the proposed approach, we considered a binary consortium composed of a Thermosynechococcus elongatus BP-1 and Meiothermus ruber Strain A as a model photoautotroph–heterotroph consortium
As it is known that T. elongatus is autotrophic, we only generated two versions of this model (Table I): (i) an ungapfilled model and (ii) a model gapfilled on autotrophic media
We provided a new approach that uses community-level data for microbial community network reconstruction, and we demonstrated tools that implement this approach in a userfriendly manner in the Department of Energy (DOE) Systems Biology Knowledgebase
Summary
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. This conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. A conventional practice to build community metabolic networks focuses on the reconstruction of high-quality individual networks so that their combination provides quantitative predictions of metabolic interactions and community behaviors (Shoaie et al, 2015) This approach becomes ineffective; if sufficient data required for curating individual networks are not available. We describe reconstruction workflows for single species and extension of such protocols to the building of models of microbial communities, using our case study for illustration
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