Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

Zhang, Cheng Cheng; Hua, Qiang

doi:10.3389/fphys.2015.00413

Cited by 129 publications

(119 citation statements)

References 83 publications

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“…Genome-scale metabolic models (GEMs) are reconstructed metabolic networks based on complete annotated genome sequences [41]. GEMs can be used to calculate reaction fluxes for a specific target function—e.g., growth—by optimizing metabolite flow through a metabolic network [42].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

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Section: Introductionmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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“…An essential prerequisite for a successful application of this modeling framework is the careful reconstruction of the metabolic network model to be investigated. Whereas smaller metabolic models, for instance, of the central metabolism, are often compiled from established biochemical “text book” knowledge, the reconstruction of genome-scale models (covering the entire metabolism of small metabolites) was greatly facilitated due to the availability of sequenced genomes for many organisms4567. Several tools have been developed to support the reconstruction and gap filling process to eventually obtain high-quality genome-scale models89101112.…”

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confidence: 99%

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Hädicke

Klamt

2017

Sci Rep

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Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

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“…On the other side, engineering of a metabolic system can produce higher yields of biomass or valuable by-products. Thus, a mechanistic understanding of metabolism is crucial for various disciplines, from biomedical to biofuels research [1]. …”

Section: Introductionmentioning

confidence: 99%

Metabolic network modeling with model organisms

Yılmaz

Walhout

2017

Current Opinion in Chemical Biology

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Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

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Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

Cited by 129 publications

References 83 publications

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Metabolic network modeling with model organisms

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