Discrete-time stochastic modeling and simulation of biochemical networks
1University of Bamberg, Feldkirchenstr. 21, D-96045, Bamberg, Germany. werner.sandmann@uni-bamberg.de
Computational Biology and Chemistry
|May 24, 2008
Summary
This study introduces a discrete-time Markov chain model for chemical kinetics, offering a more efficient simulation method. It preserves the exactness of the Gillespie algorithm while reducing computational effort in stochastic modeling.
Area of Science:
- Computational Biology
- Chemical Kinetics
- Systems Biology
Background:
- Stochastic modeling is crucial for understanding inherent randomness in chemical reacting systems.
- Continuous-time Markov chains governed by the chemical master equation are commonly used for biological network simulation.
Purpose of the Study:
- To develop a more efficient stochastic simulation method for chemical kinetics.
- To convert continuous-time Markov chains to stochastically identical discrete-time Markov chains.
Main Methods:
- Conversion of continuous-time Markov chains to discrete-time Markov chains.
- Derivation of a discrete-time chemical master equation.
- Simulation of the discrete-time Markov chain.
Main Results:
- A discrete-time version of the chemical master equation was obtained.
- Simulation of the discrete-time Markov chain is equivalent to the Gillespie algorithm.
- The new method eliminates the need for generating exponential random variables, reducing simulation effort.
Conclusions:
- The discrete-time approach preserves the exactness of the Gillespie algorithm.
- This method allows for more efficient stochastic simulation of complex biological networks.
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