Relating continuous and discrete PEPA models of signalling pathways

PEPA and its semantics have recently been extended to model biological systems. In order to cope with massive quantities of processes (as is usually the case when considering biological reactions) the model is interpreted in terms of a small set of coupled ordinary differential equations (ODEs) instead of a large state space continuous time Markov chain (CTMC). So far the relationship between these two semantics of PEPA had not been established. This is the goal of the present paper. After introducing a new extension of PEPA, denoted [email protected], that allows models to capture both mass action law and bounded capacity law cooperations, the relationship between these two semantics is demonstrated. The result relies on Kurtz's Theorem that expresses that a set of ODEs can be, in some sense, considered as the limit of pure jump Markov processes.

[1]  P J Goss,et al.  Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  S. Gilmore,et al.  Automatically deriving ODEs from process algebra models of signalling pathways , 2005 .

[3]  Paola Lecca,et al.  A Biospi Model of Lymphocyte-Endothelial Interactions in Inflamed Brain Venules , 2004, Pacific Symposium on Biocomputing.

[4]  T. Kurtz The Relationship between Stochastic and Deterministic Models for Chemical Reactions , 1972 .

[5]  J. Hillston The nature of synchronisation , 1994 .

[6]  Alberto Policriti,et al.  Stochastic Concurrent Constraint Programming and Differential Equations , 2007, QAPL.

[7]  Adam Duguid,et al.  Stronger Computational Modelling of Signalling Pathways Using Both Continuous and Discrete-State Methods , 2006, CMSB.

[8]  Nil Geisweiller An attempt to give a clear semantics of the extention of PEPA for massively parallel processes and biological modelling , 2006 .

[9]  Vipul Periwal,et al.  Numerical Simulation for Biochemical Kinetics , 2006 .

[10]  Pierpaolo Degano,et al.  VICE: A VIrtual CEll , 2004, CMSB.

[11]  Jane Hillston,et al.  A compositional approach to performance modelling , 1996 .

[12]  Jane Hillston,et al.  Fluid flow approximation of PEPA models , 2005, Second International Conference on the Quantitative Evaluation of Systems (QEST'05).

[13]  J E Ferrell,et al.  What Do Scaffold Proteins Really Do? , 2000, Science's STKE.

[14]  Ian Stark,et al.  The Continuous pi-Calculus: A Process Algebra for Biochemical Modelling , 2008, CMSB.

[15]  T. Kurtz Solutions of ordinary differential equations as limits of pure jump markov processes , 1970, Journal of Applied Probability.

[16]  Luca Cardelli,et al.  From Processes to ODEs by Chemistry , 2008, IFIP TCS.

[17]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[18]  Joachim Niehren,et al.  Gene Regulation in the Pi Calculus: Simulating Cooperativity at the Lambda Switch , 2006, Trans. Comp. Sys. Biology.

[19]  Muffy Calder What do scaffold proteins really do , 2006 .

[20]  David R. Gilbert,et al.  Analysis of Signalling Pathways Using Continuous Time Markov Chains , 2006, Trans. Comp. Sys. Biology.

[21]  Corrado Priami,et al.  Application of a stochastic name-passing calculus to representation and simulation of molecular processes , 2001, Inf. Process. Lett..

[22]  Stephen Gilmore,et al.  Modelling the Influence of RKIP on the ERK Signalling Pathway Using the Stochastic Process Algebra PEPA , 2006, Trans. Comp. Sys. Biology.