At high concentrations, chemical reactions and related processes can be viewed as continuous and deterministic, and be well-described by ODEs and PDEs. However, down at the level of single cells, many biochemical processes take place at such low concentrations that the discreteness of the molecules involved cannot be ignored, and stochastic processes must be used to obtain satisfactory descriptions of the discrete random reaction dynamics. This project will be concerned with computational modelling and stochastic simulation of such continuous-time Markov processes, and the fitting of such models to time course experimental data.
Potential areas for more in-depth study include:
Fast exact and approximate simulation algorithms
Compositional modelling of large reaction networks
Bayesian inference for stochastic kinetic models
Simulation of stochastic reaction-diffusion processes
Detailed modelling and analysis for a real non-trivial genetic/biochemical network
Pre-requisites
You should have a strong background in probability and statistics, and must be comfortable with programming in R.