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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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FERN design. The figure illustrates the overall design of FERN into three layers. Each layer is represented by one interface or abstract class: Interface Network and abstract classes Simulator and Observer.
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Figure 2: FERN design. The figure illustrates the overall design of FERN into three layers. Each layer is represented by one interface or abstract class: Interface Network and abstract classes Simulator and Observer.

Mentions: In this article, we present FERN, a Java framework for modeling and simulating biological systems which provides accurate and state-of-the-art simulation algorithms (exact, approximate and hybrid) and has been designed to be easily extendable to new ones (see Figure 2). With the help of observers, the simulation progress can be monitored on every level and modifications to the systems can be introduced during simulations in an intuitive way. Even with these additional functionalities, the implemented algorithms are faster than the ISBJava implementations. Results can be visualized easily and networks can be loaded from different sources. Contrary to ISBJava, FERN supports the most current version of SBML and allows arbitrary rate law definitions. FERN can be used as an integral part of other Java applications or as a standalone program in the form of a command-line tool and plugins to Cytoscape and CellDesigner.


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

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

FERN design. The figure illustrates the overall design of FERN into three layers. Each layer is represented by one interface or abstract class: Interface Network and abstract classes Simulator and Observer.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: FERN design. The figure illustrates the overall design of FERN into three layers. Each layer is represented by one interface or abstract class: Interface Network and abstract classes Simulator and Observer.
Mentions: In this article, we present FERN, a Java framework for modeling and simulating biological systems which provides accurate and state-of-the-art simulation algorithms (exact, approximate and hybrid) and has been designed to be easily extendable to new ones (see Figure 2). With the help of observers, the simulation progress can be monitored on every level and modifications to the systems can be introduced during simulations in an intuitive way. Even with these additional functionalities, the implemented algorithms are faster than the ISBJava implementations. Results can be visualized easily and networks can be loaded from different sources. Contrary to ISBJava, FERN supports the most current version of SBML and allows arbitrary rate law definitions. FERN can be used as an integral part of other Java applications or as a standalone program in the form of a command-line tool and plugins to Cytoscape and CellDesigner.

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.

Show MeSH