Collaborative Research and RUI: Stochastic Dynamic Network Models of Gene Regulation under Environmental Stress

合作研究和 RUI：环境压力下基因调控的随机动态网络模型

• 批准号: 0921038
• 负责人: Kam Dahlquist
• 金额: $ 24.61万
• 依托单位: Loyola Marymount University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0921038&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Stochastic; Dynamic

This project is a collaboration between a biologist and a mathematician and their undergraduate students in stochastic dynamic modeling of gene regulatory networks under environmental stress. All organisms must respond to changes and stresses in their environment to survive and reproduce. Such environmental stresses include changes in nutrient or oxygen availability, changes in osmolarity or pH, the presence of reactive oxygen species or other damaging agents, and sudden or large changes in temperature. Organisms respond to environmental stresses through characteristic programs of gene expression. Among the most interesting and challenging problems in understanding this environmental stress response is the dynamic behavior of gene regulatory networks within the cell. The careful regulation of these networks is a fundamental activity of the organism. The objectives of this project are (1) to identify the network of transcription factors that regulate the response to cold shock in budding yeast, Saccharomyces cerevisiae, through a combination of mining of publicly available data, the genetic screening of systematic yeast deletion strains and the analysis of in-house microarray data, and a Bayesian approach to network reconstruction based on our model; (2) to analyze the model, comparing it to deterministic chemical kinetic and dynamic Bayesian network models in development and use in the research community; (3) to develop models of the additional exogenous perturbations of multiple temperature shifts and the resultant affect on growth rate for integration into the stochastic dynamic network model; (4) to test the model predictions experimentally using qRT-PCR and DNA microarrays on both total RNA and transcriptionally active mRNA, in both wild type and gene deletion strains, improving the model through successive rounds of simulation and experiment; and (5) to develop and analyze a general mathematical modeling framework suitable for studying a wide variety of gene regulatory networks. The invention of high-throughput genomics methods has transformed 21st century biology from a ?one gene at a time? approach to the analysis of whole systems. Baker?s yeast, Saccharomyces cerevisiae, is an ideal model organism to study because it grows quickly and the expression of all 6000 genes can be measured in a single experiment. While this ability to measure the expression of all the genes at once is a significant first step towards understanding fundamental cellular processes and the defects that lead to disease, it is still only a ?parts list?. Just as listing the numbers and kinds of boards, nails, bricks, and mortar it takes to build a house does not explain how the house is put together, simply measuring all of the genes does not explain how cells function and respond to environmental stresses. Instead, to understand cell function, we need to understand the rules that govern which genes are expressed under what circumstances and how the genes interact with each other. In short, we need to understand how this complex gene regulatory network changes over time. In this project, we will build a mathematical model that can be used to make testable predictions about cell function. Our approach offers the potential to synthesize a number of seemingly disparate techniques of gene regulatory analysis currently being used. The research program will also yield biological insight into the overall regulatory mechanism of the response to cold shock in yeast, which is poorly understood. Our work will determine the particular regulatory factors involved, the extent of environmental stress response pathway overlap, and will provide a measure of the direct and indirect effects of individual factors. The mathematical techniques we develop should provide a fruitful framework for the integration of dynamic modeling and statistical analysis of gene expression data and should be broadly applicable to the biology of complex organisms. In particular, a model that explicitly deals with the indirect effects of genes in a regulatory network should provide insight into the causes of complex diseases such as cancer where multiple genes and environmental effects are involved.

该项目是生物学家和数学家及其本科生之间的合作，对环境压力下的基因调控网络进行随机动态建模。 所有生物体都必须对其环境的变化和压力做出反应才能生存和繁殖。 此类环境应激包括营养物或氧气可用性的变化、渗透压或pH的变化、活性氧或其他破坏剂的存在以及温度的突然或大幅变化。 生物体通过基因表达的特征程序来应对环境压力。 在理解这种环境应激反应过程中，最有趣和最具挑战性的问题之一是细胞内基因调控网络的动态行为。 这些网络的仔细调节是生物体的一项基本活动。 该项目的目标是（1）通过公开数据的挖掘、系统性酵母缺失菌株的遗传筛选和内部微阵列数据分析以及基于我们的模型的贝叶斯网络重建方法相结合，确定调节芽殖酵母（酿酒酵母）冷休克反应的转录因子网络； (2) 分析该模型，将其与研究界开发和使用的确定性化学动力学和动态贝叶斯网络模型进行比较； (3) 开发多个温度变化的额外外源扰动及其对生长速率的影响的模型，以便集成到随机动态网络模型中； （4）使用qRT-PCR和DNA微阵列对野生型和基因缺失菌株的总RNA和转录活性mRNA进行实验测试模型预测，通过连续几轮的模拟和实验改进模型； (5)开发和分析适合研究各种基因调控网络的通用数学模型框架。高通量基因组学方法的发明使 21 世纪的生物学从“一次只有一个基因”发生了转变。整个系统的分析方法。 面包酵母（Saccharomyces cerevisiae）是一种理想的研究模式生物，因为它生长迅速，并且可以在一次实验中测量所有 6000 个基因的表达。 虽然这种同时测量所有基因表达的能力是了解基本细胞过程和导致疾病的缺陷的重要第一步，但它仍然只是一个“零件清单”。 正如列出建造房屋所需的木板、钉子、砖块和砂浆的数量和种类并不能解释房屋是如何建造的一样，简单地测量所有基因也不能解释细胞如何发挥作用以及对环境压力的反应。 相反，为了了解细胞功能，我们需要了解控制哪些基因在什么情况下表达以及基因如何相互作用的规则。 简而言之，我们需要了解这个复杂的基因调控网络如何随着时间的推移而变化。 在这个项目中，我们将建立一个数学模型，可用于对细胞功能进行可测试的预测。 我们的方法提供了综合目前正在使用的许多看似不同的基因调控分析技术的潜力。 该研究计划还将对酵母冷休克反应的整体调节机制产生生物学见解，而目前人们对此知之甚少。 我们的工作将确定所涉及的特定监管因素、环境应激反应途径重叠的程度，并将提供单个因素的直接和间接影响的衡量标准。 我们开发的数学技术应该为基因表达数据的动态建模和统计分析的整合提供一个富有成效的框架，并且应该广泛适用于复杂生物体的生物学。 特别是，一个明确处理调控网络中基因间接影响的模型应该能够深入了解复杂疾病的原因，例如涉及多个基因和环境影响的癌症。