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This paper investigates Bio-PEPA, the stochastic process algebra for biological modelling developed by Ciocchetta and Hillston. It focuses on Bio-PEPA with levels where molecular counts are grouped or concentrations are discretised into a finite number of levels. Basic properties of well-defined Bio-PEPA systems are established after which equivalences used for the stochastic process algebra PEPA are considered for Bio-PEPA, and are shown to be identical for well-defined Bio-PEPA systems. Two new semantic equivalences parameterised by functions, called g-bisimilarity and weak g-bisimilarity are introduced. Different functions lead to different equivalences for Bio-PEPA. Congruence is shown for both forms of g-bisimilarity under certain reasonable conditions on the function and the use of these equivalences are demonstrated with a biologically-motivated example where two similar species are treated as a single species, and modelling of alternative pathways in the MAPK kinase signalling cascade.
|Number of pages||25|
|Journal||Theoretical Computer Science|
|Publication status||Published - Oct 2011|
- Process algebra
- Biological modelling
- Semantic equivalence
- Parameterised bisimulation
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- 1 Finished
SIGNAL: SIGNAL -Stochastic process algebra for biochemical signaling pathway analysis
1/09/07 → 31/01/11