CDI-Type I: Data-Based Prediction of Complex Networks and Applications

在科学、工程、工业和国防领域，有一种常见的情况是，从一个基本上联网的系统中可以获得大量数据，人们希望从可用数据中推断出未知网络的内在拓扑结构和交互模式。尽管在过去的十年中，网络科学和工程取得了巨大的进步，但预测复杂网络的逆问题却相对较少受到关注，主要是由于该问题的极端挑战性。该提案提出了一个全面的研究计划，基于广泛的初步研究，调查从数据中获取网络知识的问题，特别着眼于系统生物学中的应用。具体目标是设计一种通用的方法来预测枢纽节点和整个网络拓扑结构中存在的噪声，开发一种压缩感知方法来预测潜在的动态过程，并解决系统生物学中的一个重要应用：基于时空数据的基因调控网络的预测和表征。所提出的研究的预期成果是一个全面的理论范式预测复杂的动力系统和网络，和一些计算效率高的算法，可以直接在真实的时间在实际应用中实现。拟议的研究是跨学科的，因为它需要随机信号处理，非线性动力学，统计物理，应用数学，控制理论，系统生物学，计算机科学和工程的分析和计算工具。从拟议的研究产生的方法预计将直接转化到生物医学科学和工程，通信网络，国防和国土安全等领域。更广泛的影响拟议的活动不仅可以在物理和生物科学，工程，计算机和社会科学中找到应用，而且还可以在重大的社会关注的问题，特别是在国防和国土安全领域，由于复杂网络的普遍性和相关性。拟议的研究将有助于为研究生和本科生创造一个令人兴奋的跨学科意识和技能的环境。

Intellectual MeritA common situation in science, engineering, industry and defense is where a large amount of data is available from a system that is fundamentally networked, and one wishes to infer the intrinsic topology and interaction patterns of the unknown network from the available data. Despite tremendous progress in network science and engineering in the past decade, this inverse problem of predicting complex networks has received relatively little attention, mainly due to the extremely challenging nature of the problem. This proposal presents a comprehensive research plan, based on extensive preliminary studies, to investigate the problem of obtaining knowledge about network from data, with a particular eye toward applications in systems biology. The Specific Objectives are to devise a general method to predict hub nodes and the full network topology in the presence of noise, to develop a compressive-sensing approach to predicting the underlying dynamical processes, and to address a significant application in systems biology: prediction and characterization of gene regulatory networks based on spatiotemporal data. The anticipated outcome of the proposed research is a comprehensive theoretical paradigm for predicting complex dynamical systems and networks, and a number of computationally efficient algorithms that can be implemented directly in real time in practical applications. The proposed research is interdisciplinary as it requires analytic and computational tools from random signal processing, nonlinear dynamics, statistical physics, applied mathematics, control theory, systems biology, computer science and engineering. Methods resulting from the proposed research are expected to be directly transformable to areas such as biomedical science and engineering, communication networks, defense and homeland security.Broader Impact The proposed activity can find applications not only in physical and biological sciences, engineering, computer and social sciences, but also in problems of significant societal concern, especially those in defense and homeland security due to the ubiquity and relevance of complex networks. The proposed research will help create an exciting environment for graduate and undergraduate students in terms of interdisciplinary awareness and skills.