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

这项研究涉及创新的信号处理技术在与微生物、人类细胞及其代谢产物之间的复杂相互作用相关的动力系统中的开发和应用。该项目研究了评估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化途径发现和合理干预设计的计算工具，如治疗人类疾病的最优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观察到的布尔动力系统的系统识别和最优控制的计算方法，并应用于肠道微生物组代谢相互作用及其进化的建模。该项目促进了对多模式微生物组数据的综合分析，包括转录组、转录组、元基因组和代谢组数据，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。虽然以前关于布尔动态系统的工作是基于测量数据的特别二值化，并且忽略了不可观测变量的存在，但是这里所发展的方法允许隐藏状态过程，并且直接依赖于状态的间接或不完整的有噪声的时间序列测量。这项工作的一个新方面是，它是基于最小均方误差准则进行最优估计和控制的，而不是通常用于布尔和其他离散空间的最大后验概率方法。该项目包括构建各种形态的元基因组数据的似然函数，用于精确和近似的最优估计，以及使用有噪声的测量数据的系统识别方法。这项研究中开发的方法论使用由该项目的生命科学合作者提供的新的时间序列元基因组数据进行了验证。

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.