Literature DB >> 27536246

MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models.

Maike K Aurich1, Ronan M T Fleming1, Ines Thiele1.   

Abstract

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

Entities:  

Keywords:  constraint-based reconstruction and analysis (COBRA); metabolic modeling; metabolic phenotypes; metabolism; metabolomics; model analysis; protocol

Year:  2016        PMID: 27536246      PMCID: PMC4971542          DOI: 10.3389/fphys.2016.00327

Source DB:  PubMed          Journal:  Front Physiol        ISSN: 1664-042X            Impact factor:   4.566


  56 in total

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8.  A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

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