| Title |
MONALISA for stochastic simulations of Petri net models of biochemical systems
|
|---|---|
| Published in |
BMC Bioinformatics, July 2015
|
| DOI | 10.1186/s12859-015-0596-y |
| Pubmed ID | |
| Authors |
Pavel Balazki, Klaus Lindauer, Jens Einloft, Jörg Ackermann, Ina Koch |
| Abstract |
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. 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. 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Italy | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 4% |
| Unknown | 26 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 19% |
| Student > Ph. D. Student | 5 | 19% |
| Researcher | 5 | 19% |
| Professor | 2 | 7% |
| Student > Master | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 5 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 22% |
| Biochemistry, Genetics and Molecular Biology | 4 | 15% |
| Agricultural and Biological Sciences | 3 | 11% |
| Engineering | 3 | 11% |
| Medicine and Dentistry | 2 | 7% |
| Other | 4 | 15% |
| Unknown | 5 | 19% |
Attention Score in Context
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#15,339,713
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Outputs from BMC Bioinformatics
#5,374
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Outputs of similar age from BMC Bioinformatics
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