Reconstructing Gene Regulatory Networks through Integration of Pertubation Screen

描述（申请人提供）：本项目专注于通过整合来自摄动筛选和稳态和时间过程基因表达谱的数据来构建转录调控网络。这是功能基因组学中一个重要而具有挑战性的问题。它的重要性源于这样一个事实，即调控网络在我们理解细胞内部运作及其对外部刺激和环境变化的反应方面发挥着关键作用。这些挑战主要是由于现有数据的限制。具体来说，从敲除/下调实验（扰动筛选）中获得的数据通常样本量有限，因此可能存在噪声，此外还提供了有关基因相互作用的间接证据。生物在稳定状态或时间过程中的观测数据更容易获得，但其信息内容通常不足以完成任务

DESCRIPTION (provided by applicant): This project focuses on econstructing transcriptional regulatory networks by integrating data from perturbation screens and steady state and time course gene expression profiles. This is an important and challenging problem in functional genomics. Its importance stems from the fact that regulatory networks play a key role in our understanding of the inner workings of the cell and their response to external stimuli and environmental changes. The challenges are mainly due to limitations in the available data. Specifically, data obtained from knock-out/down experiments (perturbation screens) are usually limited in sample size and thus potentially noisy and in addition provide indirect evidence regarding gene interactions. Observational data of the organism in steady state or time course ones are more readily available, but their informational content is usually inadequate for the task at hand. The proposed methodology represents a novel computational approach to integrate these two data sources for solving the reconstruction problem. Specifically, the perturbation data are used to obtain causal orderings of the genes; such orderings determine to a large extent which genes are affecting other genes. Since regulatory networks are characterized by feedback mechanisms and due to the potential noisy nature of the perturbation data, multiple causal orderings are consistent with the perturbation data. A fast search algorithm is introduced to obtain them. Subsequently, the network links are estimated through a computationally efficient penalized likelihood method for each ordering and only those appearing in the reconstructions with very high likelihood scores are included in a consensus graph. The proposed approach is technically rigorous, computationally scalable to large networks and based on preliminary evidence exhibits superior performance to existing methods. Further, extensions to integrate time course expression data are considered by employing the framework of network Granger causality. Validation of the proposed methodology will be pursued both with in silico experiments and with real data obtained both from our collaborators (see attached letters of support) and publicly available sources. Note that the real data cover different organisms and different data sources. Finally, the computationally methodology will be implemented in an open source software tool that allows the research community to add methods that enhance network reconstructions. The software will be developed in the programming language R and would also contain executable code for the most computationally intensive components. It would also be implemented as a Taverna workflow, to aid dissemination to the biomedical research community and allow scientists to share input data, workflow results, as well as compare network reconstructions.