Chapter

Systems Biology for Signaling Networks

Part of the series Systems Biology pp 751-775

Date:

FERN – Stochastic Simulation and Evaluation of Reaction Networks

  • Florian ErhardAffiliated withLFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München Email author 
  • , Caroline C. FriedelAffiliated withLFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München
  • , Ralf ZimmerAffiliated withLFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München

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Abstract

Stochastic simulation can be used to analyze the development of biological systems over time and the stochastic nature of these processes. Most available programs for stochastic simulation, however, are limited in that they either (a) do not provide the most efficient simulation algorithms and are difficult to extend, (b) cannot be easily integrated into other applications, or (c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. FERN (Framework for Evaluation of Reaction Networks) is a Java framework for the efficient simulation of chemical reaction networks. It is subdivided into three layers for network representation, stochastic simulation, and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms for both exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN’s implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new systems biology applications. Finally, complex scenarios requiring intervention during the simulation progress can be modeled easily with FERN.

Keywords

Stochastic simulation Gillespie algorithm Java SBML