Modeling Core

建模核心

基本信息

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
项目类别：
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

项目摘要

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/高多样性状态转变依赖于历史的系统表现出滞后性并遵循这样的状态。我们将探讨肺炎及其发展的模型。在最简单的情况下，使用分叉理论的概念框架来响应治疗。在只有两种状态的情况下，该模型可以将细菌负荷低且肺功能高的状态与一种状态区分开来。具有高细菌负荷和低肺功能的情况下，该框架的主要挑战是确定如何进行。我们将用与肺炎发病机制相关的生物学变量来表达控制参数。追求一种不可知的建模方法来应对从这些高维数据中获取洞察力的挑战。由于问题的复杂性，我们会迭代应用多种前沿方法通过克服这一挑战，我们将实现系统生物学、数据科学、动力系统和生态学。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肺炎患者的结果。目标 2. 提出一套关于临床结果因果驱动因素的假设，这些假设将在随后的人体样本并使用人源化小鼠模型进行测试，我们将使用系统生物学方法。然后，我们将从收集的高通量、高维数据中定义低维变量。使用机器学习和动力系统方法——重点关注已经证明的方法他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不同临床结果的概率，我们将确定哪种生物学结果。通过探索不同表型的敏感性，变量对确定分类最有贡献特定的生物变量，以产生机械假设，然后进行实验测试使用人源化小鼠模型并用人类样本进行验证。