Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools and Cross-Omics>Agent-Based Modeling/Simulation/Tools

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

It is based on Performance Evaluation Process Algebra (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.

One of the Main features of Bio-PEPA is the ability to support different kinds of analysis, including stochastic simulation, analysis based on ordinary differential equations (ODEs) and model checking in PRISM.

PRISM -- PRISM is a probabilistic model checker, a tool for the formal modeling and analysis of systems which exhibit random or probabilistic behavior.

It supports three (3) types of probabilistic models: discrete-time Markov chains (DTMCs), continuous-time Markov chains (CTMCs) and Markov decision processes (MDPs), plus extensions of these models with costs and rewards.

PRISM has been used to analyze systems from a wide range of application domains, including communication and multimedia protocols, randomized distributed algorithms, security protocols, biological systems and many others.

The Main features/capabilities of Bio-PEPA are:

1) Bio-PEPA offers a ‘formal abstraction’ of biochemical networks such as signaling, metabolic or genetic pathways. These networks are composed of a set of ‘biochemical species’, such as genes or proteins that interact with each other through some reactions.

2) Bio-PEPA supports ‘general kinds of kinetic laws’ and expresses them by means of functional rates.

3) Bio-PEPA supports the definition of stoichiometry and the information about the role of the species (reactant, product, enzyme ...) with respect to a given reaction.

4) Bio-PEPA is defined in terms of syntax and (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.

5) A stochastic labeled 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.

The manufacturer assumes a finite maximum concentration and, given a concentration step size H, the manufacturer obtains 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.

6) The view in terms of levels is reflected in the CTMC derived from the stochastic labeled transition system. The manufacturer calls these Markov Chains CTMC with levels.

7) A Bio-PEPA system is a formal, intermediate and compositional representation of biochemical systems, on which different kinds of analysis, such as ODEs, Stochastic simulation (Gillespie's algorithm), CTMC with levels, and PRISM (Model Checking) can be carried out.

Note: 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 with each other in order to have a better understanding of the system.

Bio-PEPA Extensions --

1) Bio-PEPA with SBML-events - This extension has been defined in order to handle events, constructs that represent changes in the system due to some trigger conditions.

This allows the manufacturer to represent the possible change to the system, due, for instance, to the introduction of some reagents or the interruption of some external stimuli.

The language is mapped to Hybrid Automata (HA), a formalism that considers both continuous and discrete changes.

2) Bio-PEPA with biological compartments - The language is extended with some features in order to represent more details about locations of species and reactions. Locations can represent either compartments or membranes.

A ‘hierarchy of locations’ is considered to represent the relation between them and the transition labels are enriched with information about locations. Compartments have a fixed structure, but their size can depend on time.

Bio-PEPA Tools --

1) Bio-PEPA Workbench - The Bio-PEPA Workbench is a modeling and analysis tool for models of reaction networks expressed in the Bio-PEPA process algebra.

Given a Bio-PEPA model the Workbench generates a simulation model ready for execution using the StochKit implementation of Gillespie's Stochastic Simulation Algorithm (SSA).

It additionally generates a Markov chain model suitable for analyzing with PRISM and an ODE model suitable for analysis with the ‘Sundials ODE’ suite or MATLAB.

Thus, this tool enables the modeler to switch between analysis via simulation or model-checking and analysis via differential equations while maintaining only a single source model in the Bio-PEPA language.

2) The Bio-PEPA Eclipse Plug-in - Currently the plug-in enables the Eclipse Workbench to edit and analyze Bio-PEPA models.

Key features/capabilities of the Bio-PEPA Eclipse Plug-in include:

3) SBGNtext2BioPEPA - From SBGN to quantitative analysis in Bio-PEPA --

SBGNtext2BioPEPA is a tool that automatically translates biochemical networks from a textual representation of Systems Biology Graphical Notation (SBGN) Process Diagrams to the ‘process algebra’ Bio-PEPA to facilitate quantitative analysis.

While the ready-to-download code is tailored towards generating Bio-PEPA code, the underlying principles are general and can also help you to translate other quantitative analysis formalisms.

This work has also led to the definition of a textual representation for ‘SBGN Process Diagrams’, which is formally defined by a BNF grammar and might also be useful in other contexts.

System Requirements

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Manufacturer Web Site Bio-PEPA

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G6G Abstract Number 20601

G6G Manufacturer Number 104202