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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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Results for the LacZ model. Average results of 1,000 simulations are shown for the LacZ protein over ten bacterial generations (red). After each generation (2100 s) the number molecules for each species was divided by 2 to simulate cell division. The blue line shows a linear fit to the increasing LacZ concentration during the first generation. This yields a rate of protein synthesis of 21s-1.
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Figure 6: Results for the LacZ model. Average results of 1,000 simulations are shown for the LacZ protein over ten bacterial generations (red). After each generation (2100 s) the number molecules for each species was divided by 2 to simulate cell division. The blue line shows a linear fit to the increasing LacZ concentration during the first generation. This yields a rate of protein synthesis of 21s-1.

Mentions: This approximation was also used by Kierzek et al. and assumes that cell volume does not change during a simulation step. To perform an exact simulation of volume change, propensity functions would have to be defined which handle the cell volume as a function of time. However, since the volume change during one reaction is extremely small, the differences between the approximate and exact results should be negligible. Using the cell growth observer, we simulated the LacZ model with the enhanced Gillespie algorithm. Our results for the concentration of the LacZ protein (see Figure 6) show clearly the periodic oscillation in the protein numbers due to cell growth and division. From these results, we can estimate the rate of LacZ protein synthesis by a linear fit to the increasing LacZ concentrations during the first generation. Here, we obtained a rate of protein synthesis of 21s-1 which is close to the 22s-1 obtained by Kierzek et al. [30] and the 20s-1 reported by Kennell and Riezman [37].


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

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

Results for the LacZ model. Average results of 1,000 simulations are shown for the LacZ protein over ten bacterial generations (red). After each generation (2100 s) the number molecules for each species was divided by 2 to simulate cell division. The blue line shows a linear fit to the increasing LacZ concentration during the first generation. This yields a rate of protein synthesis of 21s-1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Results for the LacZ model. Average results of 1,000 simulations are shown for the LacZ protein over ten bacterial generations (red). After each generation (2100 s) the number molecules for each species was divided by 2 to simulate cell division. The blue line shows a linear fit to the increasing LacZ concentration during the first generation. This yields a rate of protein synthesis of 21s-1.
Mentions: This approximation was also used by Kierzek et al. and assumes that cell volume does not change during a simulation step. To perform an exact simulation of volume change, propensity functions would have to be defined which handle the cell volume as a function of time. However, since the volume change during one reaction is extremely small, the differences between the approximate and exact results should be negligible. Using the cell growth observer, we simulated the LacZ model with the enhanced Gillespie algorithm. Our results for the concentration of the LacZ protein (see Figure 6) show clearly the periodic oscillation in the protein numbers due to cell growth and division. From these results, we can estimate the rate of LacZ protein synthesis by a linear fit to the increasing LacZ concentrations during the first generation. Here, we obtained a rate of protein synthesis of 21s-1 which is close to the 22s-1 obtained by Kierzek et al. [30] and the 20s-1 reported by Kennell and Riezman [37].

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