Continued Support and Development of SBML

California Institute Of Technology, Pasadena CA

Investigators

Linked publications & trials

Abstract

[unreadable] DESCRIPTION (provided by applicant): The Systems Biology Markup Language (SBML) is a popular storage and exchange format for computational models of biochemical networks in biological systems. The grant to which this application is a supplement (1 R01 GM070923-01) funds basic research and development on the SBML language and some associated software tools. In response to frequent requests from the user community, we propose to develop a new, related resource: an annotated model database. The database (which we call "biomodels.net", after the website name we have reserved for its use) will be a freely available resource and will feature browsing, searching, visualization and exporting capabilities, accessible from both a web browser interface and remote API (application programming interface). It will be oriented specifically to computational models of biochemical networks in biological systems. The system will provide the ability to export models in a number of formats besides SBML, maximizing its utility to modelers using a variety of software tools. [unreadable] [unreadable] There are three main aspects to the work proposed here: (a) developing the software infrastructure of the database/web system, (b) developing SBML extensions for supporting better categorization of models and their components in a database context, and (c) performing manual examination and annotation (curation) of models submitted to the database. In order to achieve these aims, we will expand the size of our team at our current work sites and also add collaborators at the European Bioinformatics Institute (EBI) in the UK. [unreadable] [unreadable] Computational modeling is becoming increasingly important in biology for helping us cope with the vast size and complexity of natural organisms. The use of quantitative models by experimental scientists promises to pave the way for more rigorous analyses of biological functions, leading to deeper understanding and ultimately to new and better treatments for diseases. But for this to happen, the computational models themselves must reach a wider audience. We believe biomodels.net will act as a crucial community resource enabling greater exchange of models, ideas and research results, accelerating the uptake of computational modeling in biology. [unreadable] [unreadable] [unreadable]