KADE: A Tool to Compile Kappa Rules
into (Reduced) ODE ModelsFerdinanda Camporesi1,2,J ́erˆome Feret1,2(B), and Kim Quyˆen L ́y1,2(^1) INRIA,Ecole normale sup ́ ́ erieure, CNRS, PSL Research University,
75005 Paris, France
[email protected]
(^2) D ́epartement d’informatique de l’ENS, ́
Ecole normale sup ́ ́ erieure, CNRS, PSL Research University, 75005 Paris, France
[email protected], [email protected]
Abstract.Kappa is a formal language that can be used to model sys-
tems of biochemical interactions among proteins. It offers several seman-
tics to describe the behaviour of Kappa models at different levels of
abstraction. Each Kappa model is a set of context-free rewrite rules.
One way to understand the semantics of a Kappa model is to read its
rules as an implicit description of a (potentially infinite) reaction net-
work.KaDEis interpreting this definition to compile Kappa models
into reaction networks (or equivalently into sets of ordinary differential
equations).KaDEuses a static analysis that identifies pairs of sites that
are indistinguishable from the rules point of view, to infer backward and
forward bisimulations, hence reducing the size of the underlying reaction
networks without having to generate them explicitly. In this paper, we
describe the main current functionalities ofKaDEand we give some
benchmarks on case studies.
1 The Differential Semantics of Kappa
Kappa [ 1 ] is a rule-based language which describes the behaviour of some agents
that may be bound together on interaction sites. In applications to Systems Biol-
ogy, agents usually abstract proteins and interaction sites specific regions on their
amino acid chains. Mechanistic interactions among proteins are described by the
means of rewriting rules. For instance, the rule on the left in Fig. 1 stipulates
that two proteins may bind via their respective right and left sites. Graphically
(we have usedGKappa[ 2 ] to draw the rules), the shape of a protein implicitly
denotes its type. The same way, sites in proteins are identified by their positions
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, and by the ITMO Plan Cancer 2014. 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, the U.S. Department of Defense, or ITMO.
©cSpringer International Publishing AG 2017
J. Feret and H. Koeppl (Eds.): CMSB 2017, LNBI 10545, pp. 291–299, 2017.
DOI: 10.1007/978-3-319-67471-1 18