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Efficient parametric analysis of the chemical master equation through model order reduction
Steffen Waldherr1, Bernard Haasdonk
1Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, Stuttgart, Germany. waldherr@ist.uni-stuttgart.de
BMC Systems Biology
|July 4, 2012
Summary
Parametric model order reduction creates efficient models for stochastic biochemical reaction networks. These reduced models accelerate systems biology analyses, including parameter estimation and sensitivity analysis.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemical Engineering
Background:
- Stochastic biochemical reaction networks are often modeled using the chemical master equation.
- High-dimensional state spaces in these models lead to computationally expensive simulations.
- This expense hinders parametric analysis tasks requiring repeated simulations.
Purpose of the Study:
- To apply parametric model order reduction techniques to create accurate, low-dimensional models of the chemical master equation.
- To enable efficient parametric analysis for biochemical reaction networks.
Main Methods:
- Parametric model order reduction techniques were applied.
- Low-dimensional parametric surrogate models were constructed.
- Gene regulation network models (bistable switch, oscillatory network) were used as biological examples.
Main Results:
- The study successfully generated accurate, low-dimensional parametric models.
- These surrogate models are suitable for various parametric analysis tasks.
- Demonstrated utility in analyzing gene regulation networks.
Conclusions:
- Parametric model reduction provides efficient models for stochastic biochemical reaction networks.
- These models are valuable for systems biology applications.
- Facilitates parametric analysis tasks like parameter exploration, optimization, estimation, and sensitivity analysis.
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