Automatic identification of model reductions for discrete stochastic simulation
Abstract
Multiple time scales in cellular chemical reaction systems present a challenge for the efficiency of stochastic simulation. Numerous model reductions have been proposed to accelerate the simulation of chemically reacting systems by exploiting time scale separation. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming, prone to error, and opportunities for model reduction may be missed, particularly for large models. We propose an automatic model analysis algorithm using an adaptively weighted Petri net to dynamically identify opportunities for model reductions for both the stochastic simulation algorithm and tau-leaping simulation, with no requirement of expert knowledge input. Results are presented to demonstrate the utility and effectiveness of this approach.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 17, 2012
- Source ID
- 10.1063/1.4733563
Entities
People
- Hong Li
- Jin Fu
- Linda Petzold
- Sheng Wu
Organizations
- Army Research Office
- United States Department of Energy
- University of California, Santa Barbara