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Representation and simulation of biochemical processes using the π-calculus process algebra

Regev, Aviv, Silverman, William, Shapiro, Ehud·2000·Biocomputing 2001

Abstract

Despite the rapidly accumulating body of knowledge about protein networks, there is currently no convenient way of sharing and manipulation of such information. We suggest that a formal computer language for describing the biomolecular processes underlying protein networks is essential for rapid advancement in this field. We propose to model biomolecular processes by using the pi-Calculus, a process algebra, originally developed for describing computer processes. Our model for biochemical processes is mathematically well-defined, while remaining biologically faithful and transparent. It is amenable to computer simulation, analysis and formal verification. We have developed a computer simulation system, the PiFCP, for execution and analysis of pi-calculus programs. The system allows us to trace, debug and monitor the behavior of biochemical networks under various manipulations. We present a pi-calculus model for the RTK-MAPK signal transduction pathway, formally represent detailed molecular and biochemical information, and study it by various PiFCP simulations.

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Metadata

Authors
Regev, A., Silverman, W., & Shapiro, E.
Venue
Biocomputing 2001
Published
2000-12
Pages
459-470
OpenAlex
W2007006124
Cited by
422
References
0
FWCI
21.47
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Representation and simulation of biochemical processes using the π-calculus process algebra. https://app.cochat.ai/papers/10.1142/9789814447362_0045
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Regev, A., Silverman, W., & Shapiro, E. (2000). Representation and simulation of biochemical processes using the π-calculus process algebra. Biocomputing 2001. https://doi.org/10.1142/9789814447362_0045

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.