iMAT: an integrative metabolic analysis tool

SUMMARY iMAT is an Integrative Metabolic Analysis Tool, enabling the integration of transcriptomic and proteomic data with genome-scale metabolic network models to predict enzymes' metabolic flux, based on the method previously described by Shlomi et al. The prediction of metabolic fluxes based on high-throughput molecular data sources could help to advance our understanding of cellular metabolism, since current experimental approaches are limited to measuring fluxes through merely a few dozen enzymes. AVAILABILITY AND IMPLEMENTATION http://imat.cs.tau.ac.il/.

[1]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[2]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[3]  Matthew DeJongh,et al.  High-throughput generation and optimization of genome-scale metabolic models , 2009 .

[4]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[5]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[6]  Ioannis P. Androulakis,et al.  On the Potential for Integrating Gene Expression and Metabolic Flux Data , 2008 .

[7]  J. Pronk,et al.  Role of Transcriptional Regulation in Controlling Fluxes in Central Carbon Metabolism of Saccharomyces cerevisiae , 2004, Journal of Biological Chemistry.

[8]  Georgios Palaiologos,et al.  Metabolic flux analysis as a tool for the elucidation of the metabolism of neurotransmitter glutamate. , 2003, Metabolic engineering.

[9]  B. Palsson,et al.  Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[11]  J. Nielsen,et al.  Integration of gene expression data into genome-scale metabolic models. , 2004, Metabolic engineering.

[12]  Rick L. Stevens,et al.  High-throughput generation, optimization and analysis of genome-scale metabolic models , 2010, Nature Biotechnology.

[13]  Barbara M. Bakker,et al.  Unraveling the complexity of flux regulation: A new method demonstrated for nutrient starvation in Saccharomyces cerevisiae , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.