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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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Runtime Comparisons. The EGF signaling pathway described by Lee et al. [38] was simulated with the three exact methods provided by FERN (original and enhanced Gillespie algorithm and the next reaction method by Gibson and Bruck) for a simulated time of 800 seconds both with an SBML network using expression trees to represent MathML expressions and a FernML network. For each combination of network type and stochastic simulation algorithm, 1,000 simulations were performed and the average runtime in milliseconds was calculated. The same simulations were performed with the Gillespie and Gibson-Bruck algorithms of ISBJava. All results were obtained on one processor of an Intel Core2Duo with 2.4 GHz. Standard errors in all cases were < 1.5 milliseconds.
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Figure 5: Runtime Comparisons. The EGF signaling pathway described by Lee et al. [38] was simulated with the three exact methods provided by FERN (original and enhanced Gillespie algorithm and the next reaction method by Gibson and Bruck) for a simulated time of 800 seconds both with an SBML network using expression trees to represent MathML expressions and a FernML network. For each combination of network type and stochastic simulation algorithm, 1,000 simulations were performed and the average runtime in milliseconds was calculated. The same simulations were performed with the Gillespie and Gibson-Bruck algorithms of ISBJava. All results were obtained on one processor of an Intel Core2Duo with 2.4 GHz. Standard errors in all cases were < 1.5 milliseconds.

Mentions: Since FernML supports only the reaction rate equations used by Gillespie [5], the propensities can be recalculated at each step efficiently by a few arithmetic operations. SBML uses MathML to store the kinetics of a reaction. This allows for more complex reaction mechanisms and is particularly useful if the model cannot be formulated exclusively with first or higher order rate equations. To evaluate MathML expressions, FERN creates expression trees from them which have to be evaluated every time a propensity is calculated. Since this is one of the essential steps of SSAs, the simulation of an SBML network in FERN can be significantly slower than the simulation of the same network as a FernML network (see Figure 5). Thus, if only simple reaction rate equations are used, an SBML network should be converted to a FernML network using the provided conversion methods before performing the simulation.


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

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

Runtime Comparisons. The EGF signaling pathway described by Lee et al. [38] was simulated with the three exact methods provided by FERN (original and enhanced Gillespie algorithm and the next reaction method by Gibson and Bruck) for a simulated time of 800 seconds both with an SBML network using expression trees to represent MathML expressions and a FernML network. For each combination of network type and stochastic simulation algorithm, 1,000 simulations were performed and the average runtime in milliseconds was calculated. The same simulations were performed with the Gillespie and Gibson-Bruck algorithms of ISBJava. All results were obtained on one processor of an Intel Core2Duo with 2.4 GHz. Standard errors in all cases were < 1.5 milliseconds.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Runtime Comparisons. The EGF signaling pathway described by Lee et al. [38] was simulated with the three exact methods provided by FERN (original and enhanced Gillespie algorithm and the next reaction method by Gibson and Bruck) for a simulated time of 800 seconds both with an SBML network using expression trees to represent MathML expressions and a FernML network. For each combination of network type and stochastic simulation algorithm, 1,000 simulations were performed and the average runtime in milliseconds was calculated. The same simulations were performed with the Gillespie and Gibson-Bruck algorithms of ISBJava. All results were obtained on one processor of an Intel Core2Duo with 2.4 GHz. Standard errors in all cases were < 1.5 milliseconds.
Mentions: Since FernML supports only the reaction rate equations used by Gillespie [5], the propensities can be recalculated at each step efficiently by a few arithmetic operations. SBML uses MathML to store the kinetics of a reaction. This allows for more complex reaction mechanisms and is particularly useful if the model cannot be formulated exclusively with first or higher order rate equations. To evaluate MathML expressions, FERN creates expression trees from them which have to be evaluated every time a propensity is calculated. Since this is one of the essential steps of SSAs, the simulation of an SBML network in FERN can be significantly slower than the simulation of the same network as a FernML network (see Figure 5). Thus, if only simple reaction rate equations are used, an SBML network should be converted to a FernML network using the provided conversion methods before performing the simulation.

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