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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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Example program. This figure shows a small example on how to use FERN for running a simulation on a network. First, a network is loaded from an SBML file and then a simulator is created. In the next step, an observer is created and registered with the simulator. In this example, the observer records the current amount of molecule X every second of simulated time. The SBML events are registered with the simulator and the simulation is started to run for 50 seconds. Finally, the recorded results for X are printed.
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Figure 3: Example program. This figure shows a small example on how to use FERN for running a simulation on a network. First, a network is loaded from an SBML file and then a simulator is created. In the next step, an observer is created and registered with the simulator. In this example, the observer records the current amount of molecule X every second of simulated time. The SBML events are registered with the simulator and the simulation is started to run for 50 seconds. Finally, the recorded results for X are printed.

Mentions: A simple simulation can be performed in only five lines of code, one line for each of: loading a network file, creating a simulator, creating and registering an observer, running the simulations and printing the results (see Figure 3). More complex examples for using FERN can be found in the FERN distribution. In the following the three layers of FERN are described in more detail.


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

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

Example program. This figure shows a small example on how to use FERN for running a simulation on a network. First, a network is loaded from an SBML file and then a simulator is created. In the next step, an observer is created and registered with the simulator. In this example, the observer records the current amount of molecule X every second of simulated time. The SBML events are registered with the simulator and the simulation is started to run for 50 seconds. Finally, the recorded results for X are printed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Example program. This figure shows a small example on how to use FERN for running a simulation on a network. First, a network is loaded from an SBML file and then a simulator is created. In the next step, an observer is created and registered with the simulator. In this example, the observer records the current amount of molecule X every second of simulated time. The SBML events are registered with the simulator and the simulation is started to run for 50 seconds. Finally, the recorded results for X are printed.
Mentions: A simple simulation can be performed in only five lines of code, one line for each of: loading a network file, creating a simulator, creating and registering an observer, running the simulations and printing the results (see Figure 3). More complex examples for using FERN can be found in the FERN distribution. In the following the three layers of FERN are described in more detail.

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