Promoting coordinated development of community-based information standards for modeling in biology: the COMBINE initiative
The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
Additional Information© 2015 Hucka, Nickerson, Bader, Bergmann, Cooper, Demir, Garny, Golebiewski, Myers, Schreiber, Waltemath and Le Novère. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 10 November 2014; Accepted: 08 February 2015; Published online: 24 February 2015. Edited by: Steve McKeever, Uppsala University, Sweden. AG would like to thank the Asclepios team at Inria, France, for kindly hosting him. The authors also thank the reviewers for their helpful suggestions. Funding : DN and AG are supported by The Virtual Physiological Rat Project (NIHP50-GM094503). DN is also supported by the Maurice Wilkins Centre for Molecular Biodiversity. JC gratefully acknowledges research support from the "2020 Science" program funded through the EPSRC Cross-Disciplinary Interface Programme (EP/I017909/1) and supported by Microsoft Research. DW is funded through the Junior Research Group SEMS, BMBF e:Bio program, grant no. FKZ0316194. CM and SBOL are supported by the National Science Foundation under Grant Nos. DBI-1356041 and DBI-1355909. FB and MH are supported by NIH grant R01GM070923. FS is supported by BMBF grant FKZ0316181 and ARC grant DP140100077. NN is supported by the BBSRC Signalling Institute Strategic Programme (BBS/E/B/000C0419). Gis supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) through the NormSys project (FKZ01FS14019) and by the German Federal Ministry of Education and Research (BMBF) through the Virtual Liver Network. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation or the US National Institutes of Health.
Published - fbioe-03-00019.pdf