Reconstructing dynamic regulatory networks

描述（由申请人提供）：我们建议开发和实验验证用于重建多个物种中的动态调控网络的计算方法。转录基因调控是一个动态过程，其正常功能对所有生物体都至关重要。一些疾病与适当的转录调控的部分或完全丧失有关。与以前的方法，专注于这些网络的静态表示，我们假设，通过结合丰富的静态监管数据与时间序列表达，我们将能够重建这些网络的动态表示。这将导致识别这些过程的监管机构，发生的一系列相互作用，以及它们的时间。这些模型生成可测试的时间假设，这些实验的结果将用于进一步完善网络，从而为所研究的系统和响应提供准确的模型。这种调控网络的全局动态视图将是研究人员研究广泛的生物系统和疾病状态的一个非常有用的工具。拟议的项目将在计算生物学家，分子生物学家和医生科学家研究小组之间建立良好的，紧密结合的多学科努力的框架内进行。我们的团队在时间调控网络的理论和实验分析方面拥有丰富的经验，包括各种聚类和建模计算方法（Z。Bar-Joseph），细菌调控网络的理论和实验研究（Z. N. Oltvai），以及测量时间结合和绘制芽殖酵母中的调控网络（I. Simon）and in human diases（N. Kaminski）。重建动态监管网络。 公共卫生相关性：拟议的研究计划的目的是开发和实验测试新的计算方法重建动态调节网络。这些方法将用于研究几个物种的反应程序和疾病。在节目结束时，我们将有（a）。DREM，一种结合静态和时间序列数据用于重建动态调控图的统一计算方法（B）支持多个物种和（c.）一个软件包，允许用户使用新的时间序列数据来模拟动态网络。该软件将对生物医学研究界有实际用途。

DESCRIPTION (provided by applicant): We propose to develop and experimentally validate computational methods for reconstructing dynamic regulatory networks in multiple species. Transcriptional gene regulation is a dynamic process and its proper functioning is essential for all living organisms. Several diseases are associated with partial or complete loss of appropriate transcriptional regulation. Unlike prior methods that focused on static representation of these networks we hypothesize that by combining the abundant static regulatory data with time series expression we will be able to reconstruct dynamic representations for these networks. This will lead to identification of the regulators of these processes, the set of interactions taking place, and on their timing. The models generate testable temporal hypotheses and the results of these experiments will be used to further refine the network leading to accurate models for the systems and responses studied. This global dynamic view of regulatory networks will be a very useful tool for researchers studying a wide range of biological systems and disease states. The proposed project will be carried out in a framework of a well-established, tightly integrated multidisciplinary effort among research groups of comutational biologists, molecular biologists and physician-scientists. Our team has extensive experience in the theoretical and experimental analysis of temporal regulatory networks, including various clustering and modeling computational methods (Z. Bar-Joseph), theoretical and experimental studies of regulatory networks in bacteria (Z. N. Oltvai), and measurements of temporal binding and mapping out regulatory networks in budding yeast (I. Simon) and in human diaseses (N. Kaminski). Reconstructing dynamic regulatory networks. PUBLIC HEALTH RELEVANCE: The aim of the proposed research program is to develop and experimentally test new computational methods for reconstructing dynamic regulatory networks. The methods would be used to study response programs and diseases in several species. By the end of the program we will have (a.) DREM, a unified computational method that combines static and time series data for reconstructing dynamic regulatory maps (b) Support for multiple species and (c.) A software package allowing users to model dynamic networks using new time series data. This software will be of practical use to the biomedical research community.