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

Arizona State University, Scottsdale AZ

Investigators

Abstract

Intellectual Merit A 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.