Micro-quantitative and macro-qualitative gene network models from ChIP-sequencing and microarray data.

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It is a key aim in biology to discover the major causes of a disease at the genetic level, as this can lead to the development of a cure for that disease. Particular emphasis in research is given to the study of proteins and, amongst these, to transcription factors. These are key to the regulation of a cell, by activating or repressing other proteins in the organism and in so doing regulating their transcription rate. This is the rate by which genes are turned into proteins which then perform the various distinct functions in a cell. In this proposal, we look closely at two specific proteins, p300 and CBP, whose incorrect functioning has been linked to a number of major diseases, such as different types of cancer. In particular, it is of interest to discover differences in the roles played by p300 and CBP in the regulatory process. These two proteins are known to be transcription coactivators, thus activating a number of other proteins. It is the first aim of this proposal to detect exactly which proteins in the biological system are activated by p300 and CBP, either jointly or separately. To achieve this aim, we will use ChIP-sequencing data, produced by the recent deep-sequencing technology. This has shown greater advances over the traditional approaches, such as microarray experiments, in terms of accuracy, depth and speed, so we expect to obtain more accurate results using these data. As the first deep-sequencing datasets are becoming available, it is now timely to develop appropriate statistical models to analyse these data. In particular, it is an objective of this proposal to develop a statistical model to analyse ChIP-sequencing data, when more than one transcription factor is available. The challenge is to include in the model the effect of the two different antibodies used in the experimental set-up for the two different transcription factors. As a result of this analysis, a number of targets will be detected as regulated by both or by just one of the two transcription factors. As a second step of the proposal, we aim to integrate the ChIP-sequencing analysis with time-course microarray data on the same system. A dedicated statistical model on gene expression data will give further insight into the regulatory mechanisms of p300 and CBP, their activity and the kinetics of regulation of their target genes. Finally, we aim to extend the small refined regulatory network consisting of p300/CBP and their target genes into a larger scale network, by exploring possible interactions between any component in the network and other transcription factors in the biological system. So as a final result, the proposal will produce a small refined statistical analysis of the set of genes regulated by the two transcription activators as well as a global view of the network of regulation of the whole biological system. This will shed light, both at the local and global level, onto the regulatory mechanism of these two proteins and potentially lead to advances in the cure of the diseases underlying their malfunctioning. The work in this proposal will be important to biologists, who will be able to gain insight into the specific biological problem under study, to mathematicians and statisticians interested in the advances made on the statistical methodology, and to the wider community, that would benefit from any knowledge gained on extremely important health-related regulatory mechanisms.