MEMOSys: Bioinformatics platform for genome-scale metabolic models
Artikel i vetenskaplig tidskrift, 2011

Background: Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models. Results: MEMOSys (MEtabolic MOdel research and development System) is a versatile platform for the management, storage, and development of genome-scale metabolic models. It supports the development of new models by providing a built-in version control system which offers access to the complete developmental history. Moreover, the integrated web board, the authorization system, and the definition of user roles allow collaborations across departments and institutions. Research on existing models is facilitated by a search system, references to external databases, and a feature-rich comparison mechanism. MEMOSys provides customizable data exchange mechanisms using the SBML format to enable analysis in external tools. The web application is based on the Java EE framework and offers an intuitive user interface. It currently contains six annotated microbial metabolic models. Conclusions: We have developed a web-based system designed to provide researchers a novel application facilitating the management and development of metabolic models. The system is freely available at http://www.icbi.at/MEMOSys.

saccharomyces-cerevisiae

network

pathways

prediction

reconstruction

annotation

validation

database

sbml

systems biology

Författare

S. Pabinger

Medizinische Universitat Innsbruck

Technische Universitat Graz

Christian Doppler Laboratory for Genomics and Bioinformatics

R. Rader

Technische Universitat Graz

Rasmus Ågren

Kemi- och bioteknik, Livsvetenskaper, Systembiologi

Jens B Nielsen

Kemi- och bioteknik, Livsvetenskaper, Systembiologi

Z. Trajanoski

Christian Doppler Laboratory for Genomics and Bioinformatics

Medizinische Universitat Innsbruck

Technische Universitat Graz

BMC Systems Biology

1752-0509 (ISSN)

Vol. 5 20

Styrkeområden

Informations- och kommunikationsteknik

Livsvetenskaper och teknik

Ämneskategorier

Biologiska vetenskaper

DOI

10.1186/1752-0509-5-20