CIF: Small: Optimal Estimation and Network Inference for Boolean Dynamical Systems

CIF：小：布尔动力系统的最优估计和网络推理

• 批准号: 1320884
• 负责人: Ulisses Braga Neto
• 金额: $ 40.09万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320884&HistoricalAwards=false
• 关键词: CIF; Small; Optimal; Estimation; Network

This research concerns complex dynamical systems based on networks of logical gates updated and observed at discrete time intervals. Examples of such systems in contemporary technology abound, including digital logic systems, digital communication systems, and more. This is also the case, notably, of many biochemical systems, such as DNA regulatory circuits. In fact, Norbert Wiener, one of the pioneers of the modern information age, famously wrote about ?the essential unity of the set of problems centering about communication, control, and statistical mechanics, whether in the machine or in living tissue.?This research studies a novel signal model, and corresponding optimal estimation methods, for Boolean dynamical system models under noisy observational conditions. The optimal recursive MMSE estimator for the proposed model is called the Boolean Kalman Filter (BKF). The terminology comes from the fact that this estimator is reminiscent of the (extended, unscented) Kalman filter for continuous state-spaces. The BKF also has similarities with the forward (and backward) algorithm used for state estimation in Hidden Markov Models (HMM). The methodology is applied to the inference of biochemical regulatory networks, in collaboration with the Translational Genomics Research Institute (TGen), the University of Pittsburgh, and the Oswaldo Cruz Foundation (FIOCRUZ), in Brazil.

本研究涉及基于逻辑门网络的复杂动态系统，该系统以离散时间间隔更新和观察。这种系统在当代技术中的例子比比皆是，包括数字逻辑系统、数字通信系统等等。许多生物化学系统，例如DNA调控回路，也是如此。事实上，现代信息时代的先驱之一诺伯特·维纳（Norbert Wiener）曾写过一篇关于？无论是在机器中还是在活体组织中，以通信、控制和统计力学为中心的一系列问题的本质统一。研究了噪声观测条件下布尔动力系统模型的一种新的信号模型及其最优估计方法。该模型的最优递归MMSE估计被称为布尔卡尔曼滤波器（BKF）。这个术语来自于这样一个事实，即这个估计器让人想起连续状态空间的（扩展的，无味的）卡尔曼滤波器。BKF与隐马尔可夫模型（HMM）中用于状态估计的前向（和后向）算法也有相似之处。与转化基因组学研究所（TGen）、匹兹堡大学和巴西奥斯瓦尔多·克鲁兹基金会（FIOCRUZ）合作，将该方法应用于生化调节网络的推断。