Reconstructing Gene Regulatory Networks through Integration of Pertubation Screen

描述(由申请人提供)：该项目专注于通过整合来自扰动筛选和稳态和时间进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的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的内部工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。具体地说，从基因敲除/下调实验(干扰筛选)获得的数据通常样本大小有限，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。生物体的稳态或时程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 近在咫尺。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。具体地说，扰动数据被用来获得基因的因果排序；这种排序在很大程度上决定了哪些基因正在影响其他基因。由于调控网络的特征是反馈机制，并且由于扰动数据的潜在噪声性质，多个因果排序与扰动数据是一致的。引入了一种快速搜索算法来获得它们。随后，通过针对每个排序的计算高效的惩罚似然方法来估计网络链路，并且只有在重建中出现的具有非常高似然分数的那些被包括在共识图中。提出的方法在技术上是严格的，在计算上可扩展到大型网络，并且基于初步证据显示出优于现有方法的性能。此外，通过使用网络格兰杰因果关系框架来考虑集成时间进程表达式数据的扩展。将通过电子实验和从我们的合作者(见所附的支持信)和公开来源获得的真实数据来验证所提出的方法。请注意，真实数据涵盖不同的生物和不同的数据源。最后，计算方法将在一个开放源码软件工具中实施，该工具允许研究界添加增强网络重建的方法。该软件将用编程语言R开发，并且还将包含用于计算最密集的组件的可执行代码。它还将作为Taverna工作流程实施，以帮助向生物医学研究社区传播，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.