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Cellular processes such as building and breaking down complex molecules occur through stepwise chemical reactions. Some of these chemical reactions are spontaneous and release energy, whereas others require energy to proceed. Cells often couple the energy-releasing reaction with the energy-requiring one to carry out important cell functions. 
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FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

Florian Erhard1, Caroline C Friedel, Ralf Zimmer

  • 1LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, München, Germany. erhardf@cip.ifi.lmu.de

BMC Bioinformatics
|August 30, 2008
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Summary

FERN (Framework for Evaluation of Reaction Networks) is a Java framework that offers efficient and extensible stochastic simulation for biological systems. It overcomes limitations of existing tools by providing advanced algorithms and integration capabilities for systems biology applications.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Stochastic simulation models biological system development and inherent randomness.
  • Existing simulation tools lack efficiency, extensibility, and user-friendly integration.
  • Need for flexible tools for advanced modeling and analysis in systems biology.

Purpose of the Study:

  • Introduce FERN (Framework for Evaluation of Reaction Networks), a Java framework for efficient chemical reaction network simulation.
  • Address limitations of current stochastic simulation software.
  • Facilitate integration into systems biology applications.

Main Methods:

  • Developed a layered Java framework (network representation, simulation, visualization).
  • Implemented efficient and accurate state-of-the-art stochastic simulation algorithms.
  • Integrated a powerful observer system for real-time simulation monitoring and control.
  • Created plugins for Cytoscape and CellDesigner for seamless integration.

Main Results:

  • FERN provides a broad range of efficient and accurate stochastic simulation algorithms.
  • FERN implementations demonstrate superior performance compared to existing tools (gillespie2, ISBJava).
  • The framework supports easy extension with new algorithms.
  • Plugins enable real-time simulation and observation within Cytoscape and CellDesigner.

Conclusions:

  • FERN overcomes limitations of existing stochastic simulation programs.
  • Offers efficient, extensible, and easily integrable solutions for systems biology.
  • Facilitates complex scenario modeling with intervention capabilities.
  • Enhances the usability and applicability of stochastic simulation in biological research.