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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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Stochastic simulation. This figure shows the flow of one simulation step. On the left-hand side the flow for the original Gillespie algorithm can be seen. On the right-hand side, we illustrate how the different steps are modified by the Gibson-Bruck, enhanced Gillespie and tau-leaping algorithms. Here, U(0, 1) denotes the uniform distribution on the range of 0 to 1 and aμ the reaction propensity for reaction μ.
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Figure 1: Stochastic simulation. This figure shows the flow of one simulation step. On the left-hand side the flow for the original Gillespie algorithm can be seen. On the right-hand side, we illustrate how the different steps are modified by the Gibson-Bruck, enhanced Gillespie and tau-leaping algorithms. Here, U(0, 1) denotes the uniform distribution on the range of 0 to 1 and aμ the reaction propensity for reaction μ.

Mentions: The stochastic nature of biological systems can be simulated using numerical simulation algorithms such as the stochastic simulation algorithm (SSA) of Gillespie [5]. The Gillespie algorithm simulates the system reaction by reaction. A reaction step in this case consists of two parts (see Figure 1). First, the time interval τ until the next reaction is drawn from the exponential distribution P(τ) = a exp(-aτ) using the inversion method. Here, a is the sum over all reaction propensities aμ. Second, the reaction μ which is to occur in this time interval is drawn with propability P(μ/τ) = aμ/a. At the end of each step, molecule numbers and reaction propensities are updated. Both simulations via ODEs and SSAs assume a well-mixed system with a homogeneous distribution of molecules.


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

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

Stochastic simulation. This figure shows the flow of one simulation step. On the left-hand side the flow for the original Gillespie algorithm can be seen. On the right-hand side, we illustrate how the different steps are modified by the Gibson-Bruck, enhanced Gillespie and tau-leaping algorithms. Here, U(0, 1) denotes the uniform distribution on the range of 0 to 1 and aμ the reaction propensity for reaction μ.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Stochastic simulation. This figure shows the flow of one simulation step. On the left-hand side the flow for the original Gillespie algorithm can be seen. On the right-hand side, we illustrate how the different steps are modified by the Gibson-Bruck, enhanced Gillespie and tau-leaping algorithms. Here, U(0, 1) denotes the uniform distribution on the range of 0 to 1 and aμ the reaction propensity for reaction μ.
Mentions: The stochastic nature of biological systems can be simulated using numerical simulation algorithms such as the stochastic simulation algorithm (SSA) of Gillespie [5]. The Gillespie algorithm simulates the system reaction by reaction. A reaction step in this case consists of two parts (see Figure 1). First, the time interval τ until the next reaction is drawn from the exponential distribution P(τ) = a exp(-aτ) using the inversion method. Here, a is the sum over all reaction propensities aμ. Second, the reaction μ which is to occur in this time interval is drawn with propability P(μ/τ) = aμ/a. At the end of each step, molecule numbers and reaction propensities are updated. Both simulations via ODEs and SSAs assume a well-mixed system with a homogeneous distribution of molecules.

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