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State-dependent doubly weighted stochastic simulation algorithm for automatic characterization of stochastic
Min K Roh1, Bernie J Daigle, Dan T Gillespie
1Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA. min@cs.ucsb.edu
The Journal of Chemical Physics
|December 24, 2011
Summary
We developed a new algorithm, the state-dependent doubly weighted SSA (sdwSSA), to efficiently characterize rare events in biochemical systems. This method improves accuracy and efficiency over existing simulation techniques.
Area of Science:
- Computational biology
- Biochemical systems analysis
- Algorithm development
Background:
- Stochastic biochemical systems often involve rare events that are challenging to simulate.
- Existing algorithms like swSSA and dwSSA have limitations in parameter identification or efficiency for dynamic systems.
Purpose of the Study:
- To introduce a novel algorithm, the state-dependent doubly weighted SSA (sdwSSA), that overcomes the limitations of previous methods.
- To combine the strengths of state-dependent and automatic parameter identification for rare event simulation.
Main Methods:
- The state-dependent doubly weighted SSA (sdwSSA) algorithm was developed.
- It utilizes the multilevel cross-entropy method for automatic computation of state-dependent importance sampling parameters.
- The algorithm was tested on a reversible isomerization process, a yeast polarization model, and a lac operon model.
Main Results:
- The sdwSSA demonstrated substantial improvements in both accuracy and efficiency compared to existing algorithms.
- It effectively handles systems with widely varying states by automatically computing state-dependent parameters.
- Successful application to diverse biological models validates its utility.
Conclusions:
- The sdwSSA provides a more accurate and efficient approach for simulating rare events in stochastic biochemical systems.
- This novel method enhances the characterization of complex biological processes.
- The integration of automatic parameter computation with state-dependency marks a significant advancement in simulation techniques.
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