CycSim—an online tool for exploring and experimenting with genome-scale metabolic models

Summary: CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deleti...

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Detalles Bibliográficos
Autores principales: Le Fèvre, F., Smidtas, S., Combe, C., Durot, M., d'Alché-Buc, Florence, Schachter, V.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712333/
https://www.ncbi.nlm.nih.gov/pubmed/19420054
http://dx.doi.org/10.1093/bioinformatics/btp268
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author Le Fèvre, F.
Smidtas, S.
Combe, C.
Durot, M.
d'Alché-Buc, Florence
Schachter, V.
author_facet Le Fèvre, F.
Smidtas, S.
Combe, C.
Durot, M.
d'Alché-Buc, Florence
Schachter, V.
author_sort Le Fèvre, F.
collection PubMed
description Summary: CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models. Availability: http://www.genoscope.cns.fr/cycsim Contact: cycsim@genoscope.cns.fr
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spelling pubmed-27123332009-07-21 CycSim—an online tool for exploring and experimenting with genome-scale metabolic models Le Fèvre, F. Smidtas, S. Combe, C. Durot, M. d'Alché-Buc, Florence Schachter, V. Bioinformatics Applications Note Summary: CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models. Availability: http://www.genoscope.cns.fr/cycsim Contact: cycsim@genoscope.cns.fr Oxford University Press 2009-08-01 2009-05-06 /pmc/articles/PMC2712333/ /pubmed/19420054 http://dx.doi.org/10.1093/bioinformatics/btp268 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Le Fèvre, F.
Smidtas, S.
Combe, C.
Durot, M.
d'Alché-Buc, Florence
Schachter, V.
CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title_full CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title_fullStr CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title_full_unstemmed CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title_short CycSim—an online tool for exploring and experimenting with genome-scale metabolic models
title_sort cycsim—an online tool for exploring and experimenting with genome-scale metabolic models
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712333/
https://www.ncbi.nlm.nih.gov/pubmed/19420054
http://dx.doi.org/10.1093/bioinformatics/btp268
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