| Title | SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. |
| Year of Publication | 2013 |
| Authors | C. Chaouiya; D. Bérenguier; S.M. Keating; A. Naldi; M.P. van Iersel; N. Rodriguez; A. Dräger; F. Büchel; T. Cokelaer; B. Kowal; B. Wicks; E. Gonçalves; J. Dorier; M. Page; P.T. Monteiro; A. von Kamp; I. Xenarios; H. de Jong; M. Hucka; S. Klamt; D. Thieffry; N. Le Novère; J. Saez-Rodriguez; T. Helikar |
| Journal | PLoS Comput Biol |
| Abstract | BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks. |
| URL | http://www.ncbi.nlm.nih.gov/pubmed/24321545?dopt=Abstract |
| PubMed ID | 24321545 |
