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/Contact: zurhadas@post.tau.ac.il; ruppin@post.tau.ac.il; tomersh@cs.technion.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
%0 Journal Article
%1 Zur2010IMAT
%A Zur, Hadas
%A Ruppin, Eytan
%A Shlomi, Tomer
%D 2010
%J Bioinformatics
%K metabolic-networks software tool
%N 24
%P 3140--3142
%R 10.1093/bioinformatics/btq602
%T iMAT: an integrative metabolic analysis tool
%U http://dx.doi.org/10.1093/bioinformatics/btq602
%V 26
%X 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/Contact: zurhadas@post.tau.ac.il; ruppin@post.tau.ac.il; tomersh@cs.technion.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
@article{Zur2010IMAT,
abstract = {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/Contact}: zurhadas@post.tau.ac.il; ruppin@post.tau.ac.il; {tomersh@cs.technion.ac.ilSupplementary} information: Supplementary data are available at Bioinformatics online.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Zur, Hadas and Ruppin, Eytan and Shlomi, Tomer},
biburl = {https://www.bibsonomy.org/bibtex/21e5d10b8f0d51303634cd1fa33ed6ba9/karthikraman},
citeulike-article-id = {8299639},
citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btq602},
citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/early/2010/11/15/bioinformatics.btq602.abstract},
citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/early/2010/11/15/bioinformatics.btq602.full.pdf},
citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/21081510},
citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=21081510},
day = 15,
doi = {10.1093/bioinformatics/btq602},
interhash = {1beef0b5e54c1f209350b0288b81780e},
intrahash = {1e5d10b8f0d51303634cd1fa33ed6ba9},
journal = {Bioinformatics},
keywords = {metabolic-networks software tool},
month = dec,
number = 24,
pages = {3140--3142},
pmid = {21081510},
posted-at = {2010-11-24 14:41:37},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {{iMAT}: an integrative metabolic analysis tool},
url = {http://dx.doi.org/10.1093/bioinformatics/btq602},
volume = 26,
year = 2010
}