Collaborative Research: FRG: Stochastic models for intracellular reaction networks

合作研究：FRG：细胞内反应网络的随机模型

• 批准号: 0553687
• 负责人: Thomas Kurtz
• 金额: $ 65万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0553687&HistoricalAwards=false
• 关键词: Collaborative; Research; FRG; Stochastic; models

With the completion of numerous genome projects for bacteria, yeast, and humans, there is an increasing interest in understanding how molecules encoded within the genomes interact to define various functional networks of the cell. Network of integrated molecular reactions tend to involve many different molecular species, thus posing complex analytical problems. For prediction and simulation purposes it is essential to reduce both the model and computational complexity of the problem, while still capturing all the essential characteristics and potential behavior of the network. This project will systematically develop stochastic models for chemical reaction networks, beginning with classical Markov chain models and developing new models that take into account the stepwise development of reactions involving RNA and DNA molecules. Specific issues to be addressed include scaling limits based on the wide range of time and other quantitative scales in the system, model reduction through scaling limit approximations and other approaches, the implications of the combinatorial restrictions the reaction structure places on the system, sensitivity analysis for the parameters of the stochastic models, and statistical methods for model validation based on data that is frequently obtained through indirect and/or aggregated measurements.At the level of the cell, the chemical dynamics may well be dominated by the action of regulatory molecules that are present at levels of only a few copies per cell. Therefore, the molecular fluctuations of these components may determine the dynamics of the reaction network. These molecular fluctuations appear to have significant consequences; the observed large variation in rates of development, morphology and concentration of molecular species in a cell often lead to a randomization of phenotypic outcomes and non-genetic population heterogeneity. Since these fluctuations may have profound effects on the physiology of the cell, stochastic models for the intra-cellular reaction networks and careful statistical analysis appear to be essential if the system is to be well understood. The project will also provide a fertile training ground for graduate students and postdoctoral researchers. There is a high demand for well-trained mathematical scientists with the interest and expertise necessary to contribute to the solution of problems arising in cell and molecular biology.

随着大量细菌、酵母和人类基因组计划的完成，人们越来越有兴趣了解基因组中编码的分子如何相互作用以定义细胞的各种功能网络。 集成的分子反应网络往往涉及许多不同的分子物种，从而构成复杂的分析问题。 为了预测和仿真的目的，必须降低问题的模型和计算复杂性，同时仍然捕获网络的所有基本特征和潜在行为。该项目将系统地开发化学反应网络的随机模型，从经典的马尔可夫链模型开始，并开发考虑到涉及RNA和DNA分子的反应逐步发展的新模型。 要解决的具体问题包括基于系统中广泛的时间和其他定量尺度的尺度限制，通过尺度限制近似和其他方法的模型简化，反应结构对系统的组合限制的影响，随机模型参数的敏感性分析，以及用于基于通过间接和/或聚合测量经常获得的数据的模型验证的统计方法。在单元级别，化学动力学很可能受调节分子的作用支配，所述调节分子以每个细胞仅几个拷贝的水平存在。 因此，这些组分的分子波动可以决定反应网络的动力学。 这些分子波动似乎会产生重大后果;观察到的细胞中分子种类的发育速率、形态和浓度的巨大变化通常会导致表型结果的随机化和非遗传群体异质性。 由于这些波动可能对细胞的生理产生深远的影响，细胞内反应网络的随机模型和仔细的统计分析似乎是必不可少的，如果要很好地理解系统。 该项目还将为研究生和博士后研究人员提供肥沃的培训基地。 有一个训练有素的数学科学家的兴趣和必要的专业知识，以促进细胞和分子生物学中出现的问题的解决方案的高需求。