Modeling Core

建模核心

• 批准号: 10326813
• 负责人: LUIS A. Nunes AMARAL
• 金额: $ 34.7万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-17 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10326813
• 关键词: Acinetobacter baumannii; Behavior; Biological; Biological Markers; Biomass; Cells; Characteristics; Classification; Clinical; Data; Data Science; Dimensions; Ecology; Ecosystem; Environment; Evaluation; Genetic; Growth; Human; Immune; Immune response; Inflammatory Response; Learning; Location; Machine Learning; Measures; Medical History; Metabolism; Metagenomics; Methods; Modeling; Nitrogen; Noise; Nutrient; Outcome; Pathogenesis; Pathway interactions; Patients; Phenotype; Phosphorus; Pneumonia; Pollution; Probability; Pseudomonas aeruginosa; Recording of previous events; Resolution; Rivers; Role; Sampling; Stream; System; Systems Biology; Testing; Virulence; clinical phenotype; dynamic system; genetic element; host microbiome; humanized mouse; in silico; in vivo; insight; machine learning method; microbial; microbial community; microbiome; model development; mouse model; multidimensional data; neural network; outcome prediction; pathogen; pneumonia treatment; predict clinical outcome; pulmonary function; random forest; response; social; specific biomarkers; theories; transcriptome; transcriptome sequencing; treatment response; trigger point

Modeling Core Project Summary: Many physical, biological, and social systems display sudden transitions between qualitatively different states. An example is the role of nutrient pollution on aquatic ecosystems: when phosphorus and nitrogen levels increase above a threshold value, a stream, river, or lake can transition from a low biomass/high diversity state to a high biomass/low diversity state. Systems that are history-dependent demonstrate hysteresis and follow so- called S-shaped bifurcation curves. We will approach the modeling of the development of pneumonia and its resolution in response to treatment using the conceptual framework of bifurcation theory. In the simplest case, with only two states, the model would distinguish a state with low bacterial load and high lung function from one with a high bacterial load and low lung function. The major challenge in this framework is to determine how to express the control parameter in terms of biological variables pertinent to pneumonia pathogenesis. We will pursue an agnostic modeling approach to the challenge of obtaining insight from these high-dimensional data. Because of the complexity of the problem, we will iteratively apply a variety of cutting-edge methods from systems biology, data science, dynamical systems, and ecology. By overcoming this challenge, we will achieve two aims. Aim 1. To identify biological variables (both host and pathogen) that will enable us to predict clinical outcomes in patients with Pseudomonas aeruginosa or Acinetobacter baumannii and other spp. pneumonia. Aim 2. To develop a set of hypotheses on the causal drivers of clinical outcomes that will be validated in subsequent human samples and tested using humanized mouse models. We will use systems biology methods to define low-dimensionality variables from the high-throughput, high-dimensionality data collected. We will then use machine learning, and dynamical systems methods — with a focus on methods that have demonstrated their mettle in ecological applications — to identify biomarkers for specific host/microbiome phenotypes and to predict the probability of different clinical outcomes for each phenotype. We will determine which biological variables most contribute to determining the classification by probing the sensitivities of different phenotypes to specific biological variables in order to generate mechanistic hypotheses that will then be tested experimentally with humanized mouse models and validated with human samples.

建模核心项目摘要： 许多物理，生物学和社会系统在定性不同的状态之间突然表现出过渡。 一个例子是养分污染对水生生态系统的作用：磷和氮水平 增加高于阈值，溪流，河流或湖泊可以从低生物质/高多样性状态过渡 具有高生物量/低多样性状态。依赖历史的系统表明滞后并遵循 称为S形分叉曲线。我们将处理肺炎及其开发的建模 使用分叉理论的概念框架响应治疗的解决方案。在最简单的情况下， 只有两个状态，该模型将区分低细菌负荷和高肺功能的状态与一个状态 具有高细菌负荷和低肺功能。该框架中的主要挑战是确定如何 用与肺炎发病机理有关的生物变量来表达控制参数。我们将 追求一种不可知论的建模方法，以从这些高维数据中获得见解的挑战。 由于问题的复杂性，我们将迭代地采用各种尖端方法 系统生物学，数据科学，动态系统和生态学。通过克服这一挑战，我们将实现 两个目标。目的1。确定生物学变量（宿主和病原体），这将使我们能够预测临床 铜绿假单胞菌或鲍曼尼杆菌和其他SPP患者的结果。肺炎。 目标2。建立一组关于临床结果因果驱动因素的假设 随后的人类样品并使用人源化小鼠模型进行了测试。我们将使用系统生物学方法 从收集的高通量，高维数据中定义低维变量。然后我们会 使用机器学习和动态系统方法 - 专注于已演示的方法 它们在生态应用中的勇气 - 识别特定宿主/微生物组表型的生物标志物，并 预测每种表型的不同临床结果的概率。我们将确定哪种生物学 变量最有助于通过探测不同表型的敏感性来确定分类 特定的生物学变量以生成机械假设，然后将在实验中测试 使用人源化的小鼠模型，并用人类样品验证。