Genetic network modelling and inference

Modelling and reconstruction of genetic regulatory networks has developed in a wide field of study in the past few decades, with the application of ever sophisticated techniques. This thesis looks at how models for genetic networks have been developed from simple Boolean representations to more complicated models that take into account the inherent stochasticity of the biological system they are modelling. Statistical techniques are used to help predict the interaction between genes from microarray data in order to recover genetic regulatory networks and provide likely candidates for interactions that can be experimentally verified. The use of Granger causality is applied to statistically assess the effect of one gene upon another and modifications to this are presented, with bootstrapping used to understand the variability present within the parameters. Given the large amounts of data to be analysed from microarray experiments, clustering techniques are used to help reduce the computational burden and novel algorithms are developed to make use of such clustered data. Variability within clusters is also considered, by developing a novel approach with the use of principal component analysis. These algorithms that are developed are implemented with an observed dataset from Xenopus Laevis that has many genes but few timepoints in order to assess their effectiveness under such limited data. Predictions of likely interactions between genes are provided from the algorithms developed and their limitations discussed. Using extra information is considered, where a further dataset of gene knockout data is used to verify the predictions made for one particular gene.