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

生物学的一个关键目标是在遗传水平上发现疾病的主要原因，因为这可以导致该疾病的治疗方法的发展。研究中特别强调的是蛋白质的研究，其中包括转录因子。这些是调节细胞的关键，通过激活或抑制生物体中的其他蛋白质，从而调节它们的转录速率。这是基因转化为蛋白质的速率，然后蛋白质在细胞中执行各种不同的功能。在这项提案中，我们仔细研究了两种特定的蛋白质，p300和CBP，它们的不正确功能与许多重大疾病有关，例如不同类型的癌症。特别是，它是感兴趣的，以发现在监管过程中所发挥的作用的p300和CBP的差异。已知这两种蛋白质是转录共激活因子，从而激活许多其他蛋白质。该提案的第一个目的是准确检测生物系统中哪些蛋白质被p300和CBP联合或单独激活。为了实现这一目标，我们将使用最近的深度测序技术产生的ChIP测序数据。这表明在准确性，深度和速度方面比传统方法（如微阵列实验）有更大的进步，因此我们期望使用这些数据获得更准确的结果。随着第一批深度测序数据集的出现，现在是时候开发适当的统计模型来分析这些数据了。特别是，本提案的一个目标是开发一种统计模型，以分析ChIP测序数据，当有一种以上的转录因子可用时。挑战是在模型中包括在实验设置中使用的两种不同抗体对两种不同转录因子的影响。作为该分析的结果，许多靶将被检测为由两种转录因子中的两种或仅一种调节。作为该提案的第二步，我们的目标是将ChIP测序分析与同一系统上的时程微阵列数据相结合。一个专门的基因表达数据的统计模型将进一步深入了解p300和CBP的调控机制，它们的活性和它们靶基因的调控动力学。最后，我们的目标是通过探索网络中任何组分与生物系统中其他转录因子之间可能的相互作用，将由p300/CBP及其靶基因组成的小型精细调控网络扩展为更大规模的网络。因此，作为最终结果，该提案将产生对两种转录激活因子调控的基因集的小型精细统计分析，以及整个生物系统调控网络的全局视图。这将在地方和全球层面上揭示这两种蛋白质的调控机制，并可能导致其故障背后的疾病的治疗进展。本提案中的工作对生物学家、对统计方法学进展感兴趣的数学家和统计学家以及对更广泛的社区都很重要，生物学家将能够深入了解正在研究的具体生物学问题，更广泛的社区将受益于在极其重要的健康相关监管机制方面获得的任何知识。

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.