Two Qualitative Dynamics Semantics for SBGN Process Description Maps
Abstract
Qualitative dynamics semantics allow to model large reaction networks with un-
known kinetic parameters. In this poster, we present two qualitative dynamics seman-
tics for reaction networks formalized into the SBGN Process Description language
(SBGN-PD). These two semantics, namely the general semantics and the stories se-
mantics, allow to model any SBGN-PD map into an automata network, that can then
be simulated to catch the main dynamical features of the network. While the general
semantics refines the standard Boolean semantics of reaction networks by taking into
account all the main features of SBGN-PD, the stories semantics allows to model
several molecules of a network by a unique variable, reducing in this way the size of
the models. We present those two semantics and compare them on two biological
network examples.
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