Collaborative Research: A Constrained Optimal Control Approach to Nonparametric Estimation with Applications to Biological, Biomedical and Engineering Systems

协作研究：非参数估计的约束最优控制方法及其在生物、生物医学和工程系统中的应用

• 批准号: 1030246
• 负责人: Xiao Wang
• 金额: $ 14.2万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1030246&HistoricalAwards=false
• 关键词: Collaborative; Research; Constrained; Optimal; Control

The research objective of this award is to develop a control theoretic framework and efficient numerical schemes for nonparametric estimation of shape and dynamics constrained functions, with applications to emerging fields such as systems biology. The project will focus on three important and interrelated components: (i) smoothing spline estimation of functions subject to constraints; (ii) computation and analysis of penalized polynomial spline estimators for constrained functions; and (iii) applications to genetic regulatory networks, degradation analysis in reliability engineering, and joint-drug treatment in biomedical research. The underlying theoretical foundation is based on constrained optimal control, complementarity theory, and asymptotic statistics. The obtained estimation algorithms will be implemented on various biological, biomedical and engineering systems subject to constraints.Constraints are pervasive in engineering and science. Efficient estimation methods to be developed in this research will yield better understanding of complex biological systems, and provide accurate predication of products' lifetime and joint-drug effects on patients. The proposed research goes beyond traditional areas of control technology with many novel applications in statistics, systems biology, reliability engineering, and biomedical practice. The research findings of this project will be disseminated through PIs' close collaboration with engineers and an epidemiologist. This project also intends to integrate research with education and outreach activities at University of Maryland Baltimore County and Purdue University. Examples include developing new curricula and recruiting students to gain hands-on research experience. Undergraduate summer research and K-12 training will be supported with particular focus on minority and women participants.

该奖项的研究目标是为形状和动力学约束函数的非参数估计开发一个控制理论框架和有效的数值方案，并将其应用于系统生物学等新兴领域。该项目将集中于三个重要和相互关联的组成部分：(i)受约束函数的平滑样条估计；（ii）约束函数的惩罚多项式样条估计的计算和分析；（三）基因调控网络、可靠性工程中的退化分析和生物医学研究中的联合药物治疗的应用。潜在的理论基础是基于约束最优控制，互补理论和渐近统计。所获得的估计算法将在各种生物、生物医学和工程系统中实施。约束在工程和科学中是普遍存在的。本研究中开发的高效评估方法将有助于更好地理解复杂的生物系统，并准确预测产品的使用寿命和对患者的联合药物效应。拟议的研究超越了传统的控制技术领域，在统计学、系统生物学、可靠性工程和生物医学实践中有许多新的应用。该项目的研究成果将通过pi与工程师和流行病学家的密切合作进行传播。该项目还打算将研究与马里兰大学巴尔的摩县分校和普渡大学的教育和外展活动结合起来。例子包括开发新课程和招收学生获得实践研究经验。本科暑期研究和K-12培训将特别关注少数民族和妇女参与者。