Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|State||Published - Jul 2009|
Bibliographical noteFunding Information:
This work was supported by a grant from the US National Science Foundation (CBET-0425882 and CAREER award CBET-0644792 to Y.N.K.). Computational support from the Minnesota Supercomputing Institute (MSI) is gratefully acknowledged. This work was also supported by the National Computational Science Alliance under TG-MCA04N033.
- Chemical langevin equations (CLEs)
- Model reduction
- Multiscale models
- Stiff biomolecular systems
- Stochastic chemical kinetics