# 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/E031439/1the

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