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FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

Erhard F, Friedel CC, Zimmer R - BMC Bioinformatics (2008)

Bottom Line: Currently 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.FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended.Finally, complex scenarios requiring intervention during the simulation progress can be modelled easily with FERN.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

Background: Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently 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.

Results: In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, 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.

Conclusion: FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for 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 modelled easily with FERN.

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UML diagram of Network related classes. This figure shows an UML diagram for the Network interface and related interfaces and classes.
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Figure 4: UML diagram of Network related classes. This figure shows an UML diagram for the Network interface and related interfaces and classes.

Mentions: The interface Network describes the network's structure, i.e. the reactions and species in the networks. For this purpose, each reaction and each species is described by an integer value. Furthermore, the network stores basic information like species names and their initial molecule numbers. For the simulation more information is necessary which is stored in three additional classes (see Figure 4):


FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

Erhard F, Friedel CC, Zimmer R - BMC Bioinformatics (2008)

UML diagram of Network related classes. This figure shows an UML diagram for the Network interface and related interfaces and classes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553347&req=5

Figure 4: UML diagram of Network related classes. This figure shows an UML diagram for the Network interface and related interfaces and classes.
Mentions: The interface Network describes the network's structure, i.e. the reactions and species in the networks. For this purpose, each reaction and each species is described by an integer value. Furthermore, the network stores basic information like species names and their initial molecule numbers. For the simulation more information is necessary which is stored in three additional classes (see Figure 4):

Bottom Line: Currently 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.FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended.Finally, complex scenarios requiring intervention during the simulation progress can be modelled easily with FERN.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

Background: Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently 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.

Results: In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, 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.

Conclusion: FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for 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 modelled easily with FERN.

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