Abstract
We present in this paper a new exact algorithm for improving performance of exact stochastic simulation algorithm. The algorithm is developed on concepts of the partial-propensity and the rejection-based approaches. It factorizes the propensity bounds of reactions and groups factors by common reactant species for selecting next reaction firings. Our algorithm provides favorable computational advantages for simulating of biochemical reaction networks by reducing the cost for selecting the next reaction firing to scale with the number of chemical species and avoiding expensive propensity updates during the simulation. We present the details of our new algorithm and benchmark it on concrete biological models to demonstrate its applicability and efficiency. © 2017 Elsevier Inc.
| Original language | English |
|---|---|
| Pages (from-to) | 67-75 |
| Number of pages | 9 |
| Journal | MATHEMATICAL BIOSCIENCES |
| Volume | 292 |
| DOIs | |
| Publication status | Published - 2017 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Stochastic models
- Stochastic systems, Biochemical reaction network
- Biochemical reactions
- Biological models
- Computational advantages
- Computational biology
- Improving performance
- Stochastic simulation algorithms
- Stochastic simulations, Bioinformatics, Article
- benchmarking
- biochemistry
- biological model
- chemical reaction
- mathematical analysis
- mathematical computing
- stochastic model
- algorithm
- chemical model
- computer simulation
- Markov chain, Algorithms
- Computer Simulation
- Models, Biological
- Models, Chemical
- Stochastic Processes