Reconstructing dynamic regulatory networks

描述(由申请人提供)：我们建议开发和实验验证在多种物种中重建动态调控网络的计算方法。转录基因调控是一个动态的过程，其正常的功能对所有活着的生物体都是必不可少的。一些疾病与部分或完全失去适当的转录调控有关。不同于以往侧重于这些网络的静态表示的方法，我们假设通过将丰富的静态调控数据与时间序列表达相结合，我们将能够重构这些网络的动态表示。这将导致确定这些过程的调节者、发生的一组相互作用以及它们的时间安排。这些模型产生可测试的时间假设，这些实验的结果将被用来进一步完善网络，从而为所研究的系统和响应建立准确的模型。这种监管网络的全球动态观点将是研究人员研究广泛的生物系统和疾病状态的非常有用的工具。拟议的项目将在由共生生物学家、分子生物学家和内科科学家组成的研究小组之间建立的、紧密整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，包括各种集群和建模计算方法(Z.Bar-Joseph)，细菌中调控网络的理论和实验研究(Z.N.Oltwai)，以及芽期酵母(I.Simon)和人类分裂中的调控网络的测量和绘制(N.Kaminski)。重构动态监管网络。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重建动态监管网络的新计算方法。这些方法将被用于研究几个物种的反应程序和疾病。到节目结束时，我们将有(a.)DREM是一种结合静态和时间序列数据以重建动态调控图的统一计算方法(B)支持多种物种和(C)允许用户使用新的时间序列数据对动态网络进行建模的软件包。该软件将对生物医学研究界具有实际应用价值。

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.