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GillesPy: A Python Package for Stochastic Model Building and Simulation
John H Abel1, Brian Drawert2, Andreas Hellander3
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA and the Department of Chemical Engineering, University of California, Santa Barbara, CA 93106 USA.
IEEE Life Sciences Letters
|June 21, 2017
Summary
GillesPy is a new open-source Python package for building and simulating stochastic biochemical models. It uses the Gillespie stochastic simulation algorithm (SSA) for efficient computations in computational biology.
Area of Science:
- Computational Biology
- Biochemistry
- Software Engineering
Background:
- Stochasticity is crucial in biochemical systems.
- Efficient simulation tools are needed for modeling these systems.
- Existing tools may lack user-friendliness or integration.
Purpose of the Study:
- Introduce GillesPy, an open-source Python package.
- Provide a user-friendly interface for model construction.
- Enable efficient simulation of stochastic biochemical systems.
Main Methods:
- Developed a Python framework for biochemical model building.
- Integrated an interface to the StochKit2 simulation suite.
- Utilized Gillespie stochastic simulation algorithms (SSA).
Main Results:
- GillesPy offers an intuitive, action-oriented programming interface.
- The package facilitates seamless integration with the scientific Python stack.
- A detailed example demonstrates its relevance to computational biology.
Conclusions:
- GillesPy simplifies the construction and simulation of stochastic biochemical models.
- The package enhances accessibility and efficiency for computational biologists.
- It represents a valuable addition to the scientific Python ecosystem.
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