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Title: Bayesian inference of causal gene networks
Author: Morrissey, Edward R.
ISNI:       0000 0004 2725 6225
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2012
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Genes do not act alone, rather they form part of large interacting networks with certain genes regulating the activity of others. The structure of these networks is of great importance as it can produce emergent behaviour, for instance, oscillations in the expression of network genes or robustness to uctuations. While some networks have been studied in detail, most networks underpinning biological processes have not been fully characterised. Elucidating the structure of these networks is of paramount importance to understand these biological processes. With the advent of whole-genome gene expression measurement technology, a number of statistical methods have been put forward to predict the structure of gene networks from the individual gene measurements. This thesis focuses on the development of Bayesian statistical models for the inference of gene regulatory networks using time-series data. Most models used for network inference rely on the assumption that regulation is linear. This assumption is known to be incorrect and when the interactions are highly non-linear can affect the accuracy of the retrieved network. In order to address this problem we developed an inference model that allows for non-linear interactions and benchmarked the model against a linear interaction model. Next we addressed the problem of how to infer a network when replicate measurements are available. To analyse data with replicates we proposed two models that account for measurement error. The models were compared to the standard way of analysing replicate data, that is, calculating the mean/median of the data and treating it as a noise-free time-series. Following the development of the models we implemented GRENITS, an R/Bioconductor package that integrates the models into a single free package. The package is faster than the previous implementations and is also easier to use. Finally GRENITS was used to fit a network to a whole-genome time-series for the bacterium Streptomyces coelicolor. The accuracy of a sub-network of the inferred network was assessed by comparing gene expression dynamics across datasets collected under different experimental conditions.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council (EPSRC) ; Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA Mathematics ; QH426 Genetics