Translating BNGL models into Kappa Our experience

So as to test the Kappa development tools on more examples, we translated the models provided with the BNGL distribution, into Kappa. In this talk, we report about our experience. The translation was quite straight-forward except for few interesting issues that we detail here. Firstly the use of static analysis has exposed some glitches in the modelling of some pathways in the models of the BNGL distribution. We explain how static analysis has helped us to detect, locate, and correct these flaws. Secondly, expanding BNGL rules using equivalent sites into rules with uniquely identified sites is not so easy when one wants to preserve faithfully the kinetics of interactions. We recall the semantics of BNGL for equivalent sites, and explain how to perform such translation. Rule-based languages propose a formal setting to describe and assemble large systems of mechanistic interactions between proteins. Site-graph rewriting languages, such as BNGL [1,10] and Kappa [8,9], focus on the bio-chemical structure of protein compounds, described as graphs, where nodes may denote some proteins and edges may denote some bindings between specific loci in their amino acid chains. Interactions are induced by context-free rewriting rules that model transformations in the biochemical structure of bio-molecular species. Rules offer a local perspective on bio-chemical interactions. There is no need to specify the whole conformation of bio-molecular species. Instead, rules focus only on the information that matters for the interaction to happen (or to define its kinetics). Although they look very similar, BNGL and Kappa have some differences. From a language point of view, apart from the capability to attach global properties to connected components, each BNGL feature may be encoded in Kappa and conversely. In particular, both Kappa and BNGL allow for giving different rate kinetics depending on whether a rule is applied in a unary or in a binary context (i.e. if 1 This material is based upon works partially sponsored by the Defense Advanced Research Projects Agency (DARPA) and the U. S. Army Research Office under grant number W911NF-14-1-0367. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of DARPA, or the U. S. Department of Defense. 2 Email: [email protected]