MEMOSys: Bioinformatics platform for genome-scale metabolic models
Journal article, 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.

annotation

prediction

systems biology

reconstruction

network

sbml

database

validation

pathways

saccharomyces-cerevisiae

Author

S. Pabinger

Technische Universität Graz

Christian Doppler Laboratory for Genomics and Bioinformatics

Medical University of Innsbruck

R. Rader

Technische Universität Graz

Rasmus Ågren

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Z. Trajanoski

Christian Doppler Laboratory for Genomics and Bioinformatics

Technische Universität Graz

Medical University of Innsbruck

BMC Systems Biology

1752-0509 (ISSN)

Vol. 5 20

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Biological Sciences

DOI

10.1186/1752-0509-5-20

More information

Latest update

3/19/2018