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

生物学中的网络物理系统（CPS）要求传感器输入尽可能接近细胞活动。许多工作花费在可穿戴传感器的开发上，这些传感器可以检测经过许多过程过滤的细胞活性的表达。最近的研究表明，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，24小时的光暗周期具有多种作用，然而，短周期和长周期不仅存在，而且在健康和疾病中发挥着重要作用。检测这些信号及其扰动可能在健壮的以健康为重点的CPS中有很大用处。这些信号的确切性质和它们背后的数学结构将构成这个提议的基础。社会影响超越了新的传感器，包括开源方法的发展，允许传播新的数学模型和见解。用于细胞过程的测量。该建议解决了生成细胞水平生理数据的关键问题，作为健康中有效CPS的基础。应用新的、无偏的信号处理技术，该团队最近发现了RNA随时间的新周期性。这个信号提供了对细胞功能及其变化的强有力的洞察。该小组将研究新技术在特定情况下的能力，以发现用作人类健康CPS传感器输入的信号。这种信号处理技术将用于识别与人类慢性代谢疾病（如糖尿病、炎症和癌症）相关的基因振荡。这些候选基因将用于构建输入到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确定的纵向DNA转录本的数学方程。综上所述，这两项活动将提供周期性基因转录的综合数学图像，然后为新型传感器设计奠定基础，从而在基于人类的CPS中提供预测和控制。该项目将开发一个新的理解单元的平台，该平台将通过基于web的开源平台广泛提供。

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.