The Effect of Execution Policies on the Semantics and Analysis of Stochastic Petri Nets
IEEE Transactions on Software Engineering
Communicating and mobile systems: the &pgr;-calculus
Communicating and mobile systems: the &pgr;-calculus
Language-based performance prediction for distributed and mobile systems
Information and Computation
Towards Performance Evaluation with General Distributions in Process Algebras
CONCUR '98 Proceedings of the 9th International Conference on Concurrency Theory
Modelling biochemical pathways through enhanced π-calculus
Theoretical Computer Science - Special issue: Computational systems biology
The BlenX language: a tutorial
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Beta binders for biological interactions
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
A generic abstract machine for stochastic process calculi
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Bio-PEPAd: A non-Markovian extension of Bio-PEPA
Theoretical Computer Science
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Electronic Notes in Theoretical Computer Science (ENTCS)
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The Stochastic Simulation Algorithm (SSA) is a milestone in the realm of stochastic modeling of biological systems, as it inspires all the current algorithms for stochastic simulation. Essentially, the SSA shows that under certain hypothesis the time to the next occurrence of a biochemical reaction is a random variable following a negative exponential distribution. Unfortunately, the hypothesis underlying SSA are difficult to meet, and modelers have to face the impact of assuming exponentially distributed reactions besides the prescribed scope of applicability. An opportunity of investigation is offered by the use of generally distributed reaction times. In this paper, we describe how general distributions are introduced into BlenX, a programming language designed for specifying biological models. We then experiment the new extension on few examples of increasing complexity and discuss how the quantitative behaviour of a model is affected by the choice of the reaction time distribution.