Description

Systems Biology is a rapidly expanding area which involves the investigation of chemical reaction networks found (usually) within cells. The vast majority of these networks have been studied using standard coupled differential equations. While these differential equation models are suitable if the numbers of molecules of all chemicals are large, such deterministic models break down for low numbers of molecules, a case which often occurs for processes that involve gene translation.

In these situations, the stochastic or random nature of the system becomes evident. Fortunately, stochastic versions of the chemical reaction network models can be successfully used to represent and understand the system. These models contain several rate parameters, one for each proposed reaction in the network. If observed data is available, the main problem of interest is how to learn about the rate parameters using both the model and the observations.

In this project the student will learn about a class of stochastic systems biology models and how to simulate from them using R. The student will then examine one of the models in more detail using a variety of statistical techniques, with the goal of performing Bayesian inference to learn about the model's rate parameters.

This project has both theoretical and computational aspects, the later occurring through use of the statistical package R. While the balance between theory and computation can be weighted toward the student's preferred direction, some familiarity with R and with the methods described in 2H Statistical Concepts are essential.

Prerequisites

Resources

The systems biology networks that feature in this project, along with many relevant statistical techniques, are clearly described in Prof Darren Wilkinson's excellent introductory text:

Stochastic Modelling for Systems Biology, Darren Wilkinson, published by Chapman & Hall/CRC, 2006.

For more details see the book's website , which gives a flavour of the subject. Also to be found on this site are several examples of R functions that can be used to simulate such stochastic models (along with a lot of useful R code).