Petri nets: an introduction
Communicating and mobile systems: the &pgr;-calculus
Communicating and mobile systems: the &pgr;-calculus
Theoretical Computer Science
Information Processing Letters
Communication and Concurrency
Graphs for Core Molecular Biology
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Flow logic: a multi-paradigmatic approach to static analysis
The essence of computation
BioAmbients: an abstraction for biological compartments
Theoretical Computer Science - Special issue: Computational systems biology
Checking security policies through an enhanced control flow analysis
Journal of Computer Security - Special issue on WITS'03
CONCUR 2005 - Concurrency Theory
Feasible reactivity in a synchronous Π-calculus
Proceedings of the 9th ACM SIGPLAN international conference on Principles and practice of declarative programming
The Expressive Power of Synchronizations
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Petri net representation of multi-valued logical regulatory graphs
Natural Computing: an international journal
Modelling and analysing genetic networks: from boolean networks to petri nets
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Beta binders for biological interactions
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
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Generalised Boolean Networks are a well known qualitative model used to analyse the evolution of genetic networks as well as generic biological pathways. Despite the qualitative abstraction due to the few threshold concentration values considered for each biological element in the model, the complexity of the execution of a Generalised Boolean model could be non trivial. In this paper, we propose a tailored process algebra, called Sim-@p"n, reminiscent of the @p-calculus to model GBNs. We further apply the Control Flow Analysis methodology to the resulting computational model for making static (and therefore less computationally expensive) predictions on the dynamical evolution of the investigated networks. The scope is twofold: helping in the setting up of the model, for checking its completeness, and checking the evolution of the model, in terms of the possibility to reach particular threshold values of the biological elements in the model, when varying the initial conditions.