Conveners
Reaction networks & stochasticity
- Robyn Araujo ()
A common modelling technique to include stochastic effects in biochemical reaction networks is the use of discrete-state continuous time Markov chains. Analytic results for the resulting systems are rare and one often has to rely on stochastic simulation approaches to quantitatively probe their dynamics. Many conventional simulation methods, however, become prohibitively slow if the system is...
The complexity of biochemical reaction networks means that we often rely on stochastic simulation to investigate their potential behaviours, generating multiple sample paths from the model and using them to estimate summary statistics of interest. However, for realistic models, existing Monte Carlo methods are often prohibitive when it comes to exploring the range of possible model behaviours,...
Continuous-time Markov chain models are often used to describe the stochastic dynamics of networks of reacting chemical species, especially in the growing field of systems biology. Discrete-event stochastic simulation of these models rapidly becomes computationally intensive. Consequently, more tractable diffusion approximations are commonly used in numerical computation, even for modest-sized...
