Reconstructing Gene Regulatory Networks through Integration of Pertubation Screen

描述（由申请人提供）：该项目的重点是通过整合来自扰动筛选和稳态及时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又具有挑战性的问题。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的内部运作及其对外部刺激和环境变化的反应中起着关键作用。这些挑战主要是由于现有数据的局限性。具体而言，从敲除/下调实验（扰动筛选）获得的数据通常在样本量方面受到限制，因此可能存在噪声，此外还提供了关于基因相互作用的间接证据。生物体在稳态或时间过程中的观测数据更容易获得，但它们的信息内容通常不足以完成任务 身边.所提出的方法代表了一种新的计算方法来整合这两个数据源来解决重建问题。具体来说，扰动数据用于获得基因的因果排序;这种排序在很大程度上决定哪些基因影响其他基因。由于调节网络的特点是反馈机制，由于扰动数据的潜在噪声性质，多个因果排序是一致的扰动数据。一个快速搜索算法被引入到获得它们。随后，通过计算高效的惩罚似然方法估计每个排序的网络链接，只有那些出现在重建中具有非常高的似然得分被包括在共识图中。所提出的方法在技术上是严格的，计算上可扩展到大型网络，并根据初步的证据表现出上级性能，现有的方法。此外，扩展到集成时程表达数据被认为是通过采用网络格兰杰因果关系的框架。将通过计算机模拟实验和从我们的合作者（见随附的支持信）和公开来源获得的真实的数据对所提出的方法进行验证。请注意，真实的数据涵盖不同的生物体和不同的数据源。最后，计算方法将在一个开源软件工具中实现，该工具允许研究界添加增强网络重建的方法。该软件将用R编程语言开发，还将包含计算量最大的组成部分的可执行代码。它还将作为Taverna工作流程实施，以帮助向生物医学研究界传播，并允许科学家共享输入数据，工作流程结果以及比较网络重建。

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.