EAGER: Collaborative Research: Data Science Applications In Cyberphysical Systems for Health

生物学中的计算机物理系统(CPS)要求传感器输入尽可能接近地代表细胞活动。许多工作都花在开发可穿戴传感器上，这种传感器可以检测通过许多过程过滤的细胞活动的表达。最近的工作揭示，基因转录可以被认为是一个信号，随着时间的推移有周期性的振荡。众所周知的24小时明暗周期具有千变万化的影响，然而，更短更长的周期不仅存在，而且在健康和疾病中扮演着重要的角色。对这些信号及其扰动的检测很可能在关注健康的健壮CPS中大有用处。这些信号的确切性质和它们背后的数学结构将构成这一提议的基础。其社会影响不仅限于新的传感器，还包括开放源码方法的开发，允许传播新的数学模型和见解。用来测量细胞过程。这项建议解决了产生细胞水平的生理数据作为健康中有效的CPS的底物的关键问题。应用新的、无偏见的信号处理技术，该团队最近发现了RNA随时间变化的新周期。这一信号提供了对细胞功能及其变化的强大洞察力。该团队将研究新技术在特定情况下发现用作人类健康CPS传感器输入的信号的能力。这项信号处理技术将用于识别与定义的人类慢性代谢性疾病(如糖尿病、炎症和癌症)相关的基因振荡。这些候选基因将被用来构建输入到CPS传感器的精确签名。这些概念和数据将用于构建描述先前识别的纵向DNA转录本的数学方程。综上所述，这两项活动将为周期性基因转录提供一幅完整的数学图景，然后为新的传感器设计奠定基础，该设计将在以人为基础的CPS中提供预测和控制。该项目将开发一个了解细胞的新平台，并将通过基于网络的开放源码平台广泛提供。

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.