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

EAGER：协作研究：数据科学在健康网络物理系统中的应用

基本信息

批准号：
1703170
负责人：
Clifford Dacso
金额：
$ 14.61万
依托单位：
Baylor College of Medicine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1703170&HistoricalAwards=false
关键词：
EAGER Collaborative Research Data Science

项目摘要

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.

生物学中的信息物理系统（CPS）需要尽可能接近地代表细胞活动的传感器输入。许多工作都花费在可穿戴传感器的开发上，这些传感器检测通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种信号，随着时间的推移而周期性振荡。众所周知的24小时光暗周期具有多变的效果，然而更短和更长的周期不仅存在，而且在健康和疾病中发挥重要作用。这些信号的检测和它们的扰动可能在稳健的健康集中CPS中有很大的用途。这些信号的确切性质及其背后的数学结构将构成本提案的基础。社会影响超越了新的传感器，包括开源方法的开发，允许传播新的数学模型和见解。细胞过程的测量。该建议解决了产生细胞水平的生理数据作为有效的CPS在健康的基板的关键问题。应用新的无偏信号处理技术，该团队最近发现了RNA随时间推移的新周期性。该信号提供了对细胞功能及其变化的强大洞察力。该团队将解决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）相关的基因中的振荡。这些候选基因将用于构建输入CPS传感器的精确标记。这些概念和数据将用于构建描述先前鉴定的纵向DNA转录本的数学方程。总之，这两个活动将提供一个完整的数学图像的周期性基因转录，然后为新的传感器设计，将提供预测和控制在一个基于人类的CPS的阶段。该项目将开发一个新的平台，用于了解细胞，并将通过基于Web的开源平台广泛使用。