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.
Highlights
Omics data are used to determine comprehensively qualitatively or quantitatively cellular components and how they change across different conditions (Mo et al, 2009; Bordbar et al, 2010, 2012; Cuperlovic-Culf et al, 2010)
We provide the data that are needed for the different integration steps (Table 1) and we discuss traits of metabolomic data sets to provide for a basis for their successful integration into the metabolic model (Supplementary Table 1)
After individual uptake and secretion profiles have been generated for each sample, i.e., cell types or conditions, these can be integrated with the metabolic model using the function setQuantConstraints
Summary
Omics data are used to determine comprehensively qualitatively or quantitatively cellular components and how they change across different conditions (Mo et al, 2009; Bordbar et al, 2010, 2012; Cuperlovic-Culf et al, 2010). Based on the input model and user-defined minimal and maximal values, the function prepIntegrationQuant tests whether the uptake and/or secretion of each individual exchange in the input data matrix is feasible, using flux balance analysis (Orth et al, 2010).
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