CIF:Small:Minimum Mean Square Error Estimation and Control of Partially-Observed Boolean Dynamical Systems with Applications in Metagenomics

本研究涉及创新信号处理技术的开发和应用，以与微生物，人体细胞及其代谢产物之间的复杂相互作用相关的动力系统。该项目研究了用于估计和控制过程的创新方法，这些过程包括许多开关元件的复杂相互作用，例如人类肠道中特定微生物物种的“存在”和“不存在”，从而能够表征微生物群落及其与宿主细胞的相互作用。该项目为生命科学家提供了生物化学途径发现以及合理干预设计的计算工具，如治疗人类疾病的最佳药物调度和饮食调整。该项目开发了通过噪声时间序列数据部分可观察的布尔动力系统的系统识别和最佳控制的计算方法，应用于肠道微生物组中代谢相互作用及其演变的建模。该项目促进了多模式微生物群数据的综合分析，包括转录组学，元转录组学，宏基因组学和代谢组学数据，从而能够表征环境暴露，微生物群落及其与宿主肠道上皮细胞的相互作用。虽然以前的工作布尔动力系统的测量数据的ad-hoc二进制化的基础上，忽略了不可观测的变量的存在，这里开发的方法允许状态过程被隐藏，并直接依赖于间接或不完整的噪声时间序列测量的状态。这项工作的一个新的方面是，它是基于最小均方误差准则的最佳估计和控制，而不是最大后验方法通常用于布尔和其他离散空间。该项目包括构建各种形式的宏基因组数据的似然函数，用于精确和近似的最佳估计和使用噪声测量数据的系统识别方法。 本研究中开发的方法使用该项目的生命科学合作者提供的新的时间序列宏基因组数据进行验证。

This research concerns the development and application of innovative signal processing techniques to dynamical systems associated with the complex interactions among microbes, human cells, and their metabolic products. This project investigates innovative methods for estimation and control of processes that consist of the complex interactions of many switching elements, such as "presence" and "absence" of a particular microbial species in the human gut, enabling the characterization of microbial communities and their interactions with host cells. This project provides life scientists with computational tools for biochemical pathway discovery as well as rational intervention design, as in optimal drug scheduling and diet modifications to treat human disease.This project develops computational methods for systems identification and optimal control of Boolean dynamical systems partially observable through noisy time series data, with application in the modeling of metabolic interactions in the gut microbiome and their evolution. This project facilitates the integrative analysis of multimodal microbiota data, including transcriptomic, metatranscriptomic, metagenomic, and metabolomic data, enabling the characterization of environmental exposures, microbial communities, and their interactions with host gut epithelial cells. While previous work on Boolean dynamical systems has been based on ad-hoc binarization of measurement data and ignore the presence of unobservable variables, the methodology developed here allows the state process to be hidden and relies directly on indirect or incomplete noisy time series measurements of the states. A novel aspect of this work is that it is based on a minimum mean-square error criterion for optimal estimation and control, as opposed to maximum-a-posteriori methods typically used in Boolean and other discrete spaces. This project includes the construction of likelihood functions for various modalities of metagenomic data for use with exact and approximate optimal estimation and systems identification methods using the noisy measurement data. The methodology developed in this research is validated using novel time series metagenomic data provided by the project's life science collaborators.