Visible to the public EAGER: Collaborative Research: Data Science Applications In Cyberphysical Systems for HealthConflict Detection Enabled

Project Details
Lead PI:Clifford Dacso
Co-PI(s):Bokai Zhu
Performance Period:09/15/17 - 08/31/19
Institution(s):Baylor College of Medicine
Sponsor(s):National Science Foundation
Award Number:1703170
88 Reads. Placed 501 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: A cyberphysical system (CPS) in biology requires sensor input that represents, as closely as possible, cell activity. Much work is expended on the development of wearable sensors that detect the expression of cell activity filtered through many processes. Recent work discloses that gene transcription can be thought of as a signal, with periodic oscillations over time. The well-known 24 hour light-dark cycle has protean effects however shorter and longer cycles not only exist but have important roles to play in health and disease. Detection of these signals and their perturbation is likely to be of great use in a robust health focused CPS. The exact nature of these signals and the mathematical structure underlying them will form the basis of this proposal. The societal impacts go beyond the new sensors to include the development of open source methods allowing the dissemination of new mathematical models and insights. into measurement of cellular processes. This proposal addresses the critical problem of generating cell-level physiologic data as a substrate for an effective CPS in health. Applying new, unbiased signal processing techniques, the team has recently identified new periodicity in RNA over time. This signal provides a robust insight into cell function and its changes. The team will address the ability of the new techniques in specific situations to uncover signals to be used as inputs for a human health CPS sensor. This signal processing technique will be used to identify oscillations in genes associated with defined chronic metabolic diseases of humans such as diabetes, inflammation, and cancer). These candidate genes will be used to construct a precision signature for input into a CPS sensor. The concepts and data will be used to construct mathematical equations describing the longitudinal DNA transcripts previously identified. Taken together, these two activities will provide an integrated mathematical picture of periodic gene transcription that then sets the stage for novel sensor design that will provide prediction and control in a human-based CPS. The project will develop a new platform for understanding the cell that will be made widely available via a Web-based open source platform.