Bio-PEPA at a glance

Bio-PEPA is a language for the modelling and the analysis of biochemical networks.

It is based on PEPA, a process algebra originally defined for the performance analysis of computer systems, and extends it in order to handle some features of biochemical networks, such as stoichiometry and different kinds of kinetic laws. A main feature of Bio-PEPA is the possibility to support different kinds of analysis, including stochastic simulation, analysis based on ordinary differential equations (ODEs) and model checking in PRISM.

You can find a detailed description of the language in the following paper:

Bio-PEPA: a Framework for the Modelling and Analysis of Biochemical Networks.
F. Ciocchetta and J. Hillston.
Theoretical Computer Science 410 (33-34), pp. 3065-3084, 2009.
Preprint version.

Main features

The main features of Bio-PEPA are:
• it offers a formal abstraction of biochemical networks such as signalling, metabolic or genetic pathways. These networks are composed of a set of biochemical species, such as genes or proteins, that interact each other through some reactions.

• It supports general kinds of kinetic laws and expresses them by means of functional rates.

• It supports the definition of stoichiometry and the information about the role of the species (reactant, product, enzyme, ...) with respect to a given reaction.

• It is defined in terms of a syntax and a (structural operational) semantics. There are species components, to represent biochemical species, and model components, to express how species components cooperate with each other. The model component contains the initial/current concentration of each species. In addition to these components, a Bio-PEPA system is composed of (the set of) compartments, (the set of) functional rates, (the set of) constant parameters and auxiliary information for the analysis. For details see here.

• A stochastic labelled transition system can be derived from the Bio-PEPA system. Differently from other process algebras, each (species) component is associated with a discrete level of concentration. We assume a finite maximum concentration and, given a concentration step size H, we obtain a number of concentration levels for the species. The step size is assumed equal for all the species in a given compartment and represents the granularity of the system. The smaller H is, the finer the granularity. For details see here.

• The view in terms of levels is reflected in the CTMC derived from the stochastic labelled transition system. We call these Markov Chains CTMC with levels. For details see here.

• A Bio-PEPA system is a formal, intermediate and compositional representation of biochemical systems, on which different kinds of analysis can be carried out. The idea underlying Bio-PEPA is represented in the following schema:

Each of these kinds of analysis can be of help for studying different aspects of the biological model. Moreover, they can be used in conjunction in order to have a better understanding of the system.

Acknowledgements

The Bio-PEPA project has been supported by:

• the CODA project (Process Algebra Approaches for Collective Dynamics), founded by the EPSRC, reference EP/c54370x/0.

• the Signal Project (Stochastic process algebra for biochemical signalling pathway analysis), funded by EPSRC, reference EP/E031439/1

• the Centre for Systems Biology at Edinburgh (CSBE), a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/D019621/1.