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WIREs Syst Biol Med
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Metabolic network modeling of microbial communities

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Abstract 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
There are many aspects of life in a microbial community that would be useful to capture using mathematical models. Techniques utilizing constraint‐based metabolic models (sometimes in conjunction with other modeling approaches) are capable of capturing all of these scenarios.
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Community modeling frameworks that feature GENREs. (A) The compartmentalization approach unites all species‐level GENREs into a unified stoichiometric matrix with a shared compartment. The objective function is generally assumed to be a linear combination of the individual biomass functions from each species. The community objectives approach (OptCom) is an extension of the simple compartmentalization approach that utilizes a nested, bi‐level optimization framework. The bi‐level optimization enables the representation of more classes of interactions between species, but comes at the cost of increased computational complexity. (B) Dynamic analysis simulates changes in metabolites and biomass over time, which requires constraints on uptake reaction kinetics. (C) ‘Enzyme soup’ FBA ignores species boundaries and assumes that all reactions can interact in a community‐level meta‐GENRE. Other methods include: (D) network expansion, which has been used to identify potential emergent biosynthetic capacity between species by comparing species‐specific ‘reachable’ metabolites to the result of pooling reactions from both species; (E) graph‐based methods, which can be used to quantify general characteristics of an interaction between species, such as the level of expected competition or cooperation; (F) comparative analyses, which are used to assess the differences in gene essentiality, biosynthetic capacity, and resource utilization between species. Note that mets signifies metabolites, Bm stands for biomass, s.t. means subject to, ub and lb signify upper and lower bounds, respectively.
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A simple workflow for genome‐scale metabolic network reconstruction and accompanying constraint‐based analysis. The process begins with an annotated genome. The metabolic network is derived from this genome annotation by searching databases for homologous proteins with known enzymatic activity. The corresponding metabolic reactions are collected into a draft network reconstruction. This simple procedure can be augmented through gap filling, and often manual curation. A metabolic objective is defined, which for microbes is often assumed to be a biomass equation (i.e., it is assumed that cells are configured to grow as fast as possible). Exchange reactions are defined to allow metabolites to enter and leave the network. All reactions are compiled into a stoichiometric (S) matrix. FBA is a common analytical approach that searches for a flux distribution through the network that optimizes the metabolic objective subject to steady‐state constraints and flux bounds.
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Biological Mechanisms > Metabolism
Analytical and Computational Methods > Computational Methods

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