From quantitative SBML models to Boolean networks

Abstract

Abstract Modelling complex biological systems is necessary for their study and understanding. Biomodels is a repository of peer-reviewed models represented in the Systems Biology Markup Language (SBML). Most of these models are quantitative, but in some cases, qualitative models—such as Boolean networks (BNs)—are better suited. This paper focuses on the automatic transformation of quantitative SBML models to Boolean networks. We propose SBML2BN, a pipeline dedicated to this task. Our approach takes advantage of several SBML elements (reactions, rules, events) as well as a numerical simulation of the concentration of the species over time to constrain both the structure and the dynamics of the Boolean networks to synthesise. Finding all the BNs complying with the given structure and dynamics was formalised as an optimisation problem solved in the answer-set programming framework. We run SBML2BN on more than 200 quantitative SBML models, and we provide evidence that one can automatically construct Boolean networks which are compatible with the structure and the dynamics of an SBML model. In case the SBML model includes rules or events, we also show how the evaluation criteria are impacted when taking these elements into account.

Keywords

• Boolean networks
• Model transformation
• SBML
• Systems Biology