MONALISA for stochastic simulations of Petri net models of biochemical systems

BMC Bioinformatics

Pavel Balazki²  Klaus Lindauer²  Jens Einloft¹  Jörg Ackermann¹  Ina Koch¹ 

[1] Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence “Macromolecular Complexes”, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main 60325, Germany

[2] Sanofi Aventis Deutschland GmbH, Industriepark Höchst H831, Frankfurt am Main 65926, Germany

关键词: Insulin receptor recycling;    MonaLisa;    Petri net;    Gillespie’s algorithm;    Stochastic simulation algorithm;   

Others  :  1230993
DOI  :  10.1186/s12859-015-0596-y

 received in 2014-04-30, accepted in 2015-03-10,  发布年份 2015

【 摘 要 】

Background

The concept of Petri nets (PN) is widely used in systems biology and allows modeling of complex biochemical systems like metabolic systems, signal transduction pathways, and gene expression networks. In particular, PN allows the topological analysis based on structural properties, which is important and useful when quantitative (kinetic) data are incomplete or unknown. Knowing the kinetic parameters, the simulation of time evolution of such models can help to study the dynamic behavior of the underlying system. If the number of involved entities (molecules) is low, a stochastic simulation should be preferred against the classical deterministic approach of solving ordinary differential equations. The Stochastic Simulation Algorithm (SSA) is a common method for such simulations. The combination of the qualitative and semi-quantitative PN modeling and stochastic analysis techniques provides a valuable approach in the field of systems biology.

Results

Here, we describe the implementation of stochastic analysis in a PN environment. We extended MONALISA - an open-source software for creation, visualization and analysis of PN - by several stochastic simulation methods. The simulation module offers four simulation modes, among them the stochastic mode with constant firing rates and Gillespie’s algorithm as exact and approximate versions. The simulator is operated by a user-friendly graphical interface and accepts input data such as concentrations and reaction rate constants that are common parameters in the biological context. The key features of the simulation module are visualization of simulation, interactive plotting, export of results into a text file, mathematical expressions for describing simulation parameters, and up to 500 parallel simulations of the same parameter sets. To illustrate the method we discuss a model for insulin receptor recycling as case study.

Conclusions

We present a software that combines the modeling power of Petri nets with stochastic simulation of dynamic processes in a user-friendly environment supported by an intuitive graphical interface. The program offers a valuable alternative to modeling, using ordinary differential equations, especially when simulating single-cell experiments with low molecule counts. The ability to use mathematical expressions provides an additional flexibility in describing the simulation parameters. The open-source distribution allows further extensions by third-party developers. The software is cross-platform and is licensed under the Artistic License 2.0.

【 授权许可】

   
2015 Balazki et al.

【 图 表 】

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