Modeling Core

建模核心项目摘要： 许多物理、生物和社会系统在性质不同的状态之间表现出突然的转变。 一个例子是营养物污染对水生生态系统的作用：当磷和氮水平 增加到阈值以上，溪流、河流或湖泊可以从低生物量/高多样性状态转变为 高生物量/低多样性状态。依赖于历史的系统表现出滞后性，并遵循以下原则- 称为S形分叉曲线。我们将探讨肺炎的发展及其 使用分叉理论的概念框架对治疗的响应的分辨率。在最简单的情况下， 只有两种状态时，该模型将区分具有低细菌负荷和高肺功能的状态 细菌含量高肺功能差这一框架的主要挑战是确定如何 用与肺炎发病机制相关的生物变量来表示控制参数。我们将 追求一种不可知的建模方法，以应对从这些高维数据中获得洞察力的挑战。 由于问题的复杂性，我们将迭代应用各种尖端方法， 系统生物学、数据科学、动力系统和生态学。通过克服这一挑战，我们将实现 两个目标。目标1。识别生物学变量（宿主和病原体），使我们能够预测临床 铜绿假单胞菌或鲍曼不动杆菌和其他菌种患者的结局肺炎 目标2.制定一套关于临床结局因果驱动因素的假设，并在 随后的人样品和使用人源化小鼠模型测试。我们将使用系统生物学方法 从收集的高通量、高维度数据中定义低维度变量。然后我们将 使用机器学习和动态系统方法-重点是已经证明的方法 它们在生态应用中的勇气-鉴定特定宿主/微生物组表型的生物标志物， 预测每种表型的不同临床结果的概率。我们将确定哪些生物 变量最有助于通过探测不同表型的敏感性来确定分类， 特定的生物变量，以产生机制假设，然后进行实验测试 人源化小鼠模型，并与人类样品验证。

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.