MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Our previous work revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. Through this work, which consists of a protocol, a toolbox, and tutorials of two use cases, we make our methods available to the broader scientific community. 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, this protocol constitutes a comprehensive guide to the intra-model analysis of extracellular metabolomic data and a resource offering a broad set of computational analysis tools for a wide biomedical and non-biomedical research community

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life

Inputs and outputs covered by generic (ihsTLRv2) and monocyte specific (hMonoTLR & hMonoTLR_LPS) TLR signaling models.

<p>Inputs and outputs covered by generic (ihsTLRv2) and monocyte specific (hMonoTLR & hMonoTLR_LPS) TLR signaling models.</p

Distribution of absent genes.

<p>Distribution of absent genes.</p

Comparison of (chemical compound) connectivity in the LPS stimulation specific versus the up-regulated sub-network.

<p>We report the connectivity as a ratio of compound and (chemical compounds) in the respective model (ihsMonoTLR_LPS subnetwork and ihsMonoTLR_LPS).</p

Maximum possible flux values for output reactions in the different TLR signaling models.

<p>Maximum possible flux values for output reactions in the different TLR signaling models.</p

Network resulting from mapping of the up-regulated genes onto the LPS stimulation specific monocyte model.

<p>We extracted a sub-network from LPS stimulation specific monocyte model (ihsMonoTLR_LPS) consisting of all reactions associated with the 28 up-regulated genes, which included 19% of the reactions and 39% of the chemical compounds of ihsMonoTLR_LPS. The visualization revealed a comprehensively connected network. Details can be viewed using the file provided in the supporting information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049978#pone.0049978.s004" target="_blank">File S4</a>). Network illustration was generated using software Paint4Net <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049978#pone.0049978-Kostromins1" target="_blank">[65]</a>.</p