SBML Qualitative Models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

TL;DR

This paper introduces SBML qual, a standardized format for exchanging qualitative biological network models, enabling interoperability among tools and fostering collaborative development of analysis methods.

Contribution

The paper presents SBML qual, an extension of SBML Level 3, to standardize qualitative model representation and demonstrates its interoperability and potential for collaborative research.

Findings

SBML qual enables model exchange among different software tools.

The format supports analysis of signaling networks.

Open-source library LogicalModel facilitates adoption.

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 System 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 cooperative development of 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 analyze qualitative models. Conclusion: 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.