ITR: Statistical Mechanics of Sloppy Models: From Signal Transduction in the Cell Cycle to Forest Modeling and the Nitrogen Cycle

ITR：草率模型的统计力学：从细胞周期中的信号转导到森林模型和氮循环

• 批准号: 0218475
• 负责人: James Sethna
• 金额: $ 45万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218475&HistoricalAwards=false
• 关键词: ITR; Statistical; Mechanics; Sloppy; Models

This grant is made in response to a small proposal submitted to the Information Technology Research (ITR) Initiative. The PI will leverage information technology and sophisticated methods from the statistical mechanics of disordered systems to systematically explore large, complex biological models. These models are sloppy, and the key challenge is to extract reliable and falsifiable predictions from them. The PI will apply statistical mechanics not to the physical system, but to the parameters in the dynamical models of the system: this meta-modeling technique thus draws predictions from the entire ensemble of models that are consistent with the currently available data. Meta-modeling will be applied to four problems. Three of these are systems studied in the field of cellular signal transduction: the Erk system in PC12 cells; growth factor receptor trafficking and Cdc42; and the cell cycle in the colon cell line Caco-2. The final application is in ecology: the nutrient cycles in forested ecosystems. The PI bypasses traditional analytical methods in mathematical statistics, by leveraging the computational power made possible by the information technology revolution. The sampling methods are direct, flexible, and easily implemented; the methods properly average over multiple locally optimal states and properly cope with nonlinearity in the modeling process. The PI will use many technical ideas drawn from materials simulations. Numerical efficiency is enhanced through the use of a soft-mode symmetry-breaking field to penalize computer time, and through the use of algorithms developed to accelerate simulations of glassy materials. Weakly-coupled replicas of the model will be used to avoid problems due to soft modes when comparing different cell types: renormalization of parameters will be studied when comparing different theories of the same system. These methods are efficiently run in parallel on current large-scale computers; ensembel genration could be Grid enabled, delocalized over the Web.The meta-modeling concept of abstracting the modeling process into a statistical mechanics problem transforms a tedious, ill-posed search into an exciting new problem in complex, disordered systems. From an applications view, insights drawn from the materials physics community form powerful tools, allowing real predictive power where only qualitative exploration was previously possible. From a physics view, the prevalence of soft modes in parameter space provides new insights and provokes new methods. From an information technology view, intensive computational analysis can now be brought to bear on a large new class of problems of importance to science and society.The three signal transduction networks being studied are of particular interest to the development of drug therapies in cancer, and are a well-studied preview of the kinds of challenges the bioinformatics and proteomics revolution will present in the coming decade. The extension to ecological meta-modeling will lead to applications in many other complex systems where incomplete or preliminary data nonetheless need rigorous analysis.%%% This grant is made in response to a small proposal submitted to the Information Technology Research (ITR) Initiative. The PI will leverage information technology and sophisticated methods from the statistical mechanics of disordered systems to systematically explore large, complex biological models. These models are sloppy, and the key challenge is to extract reliable and falsifiable predictions from them. The PI will apply statistical mechanics not to the physical system, but to the parameters in the dynamical models of the system: this meta-modeling technique thus draws predictions from the entire ensemble of models that are consistent with the currently available data. Meta-modeling will be applied to four problems. Three of these are systems studied in the field of cellular signal transduction: the Erk system in PC12 cells; growth factor receptor trafficking and Cdc42; and the cell cycle in the colon cell line Caco-2. The final application is in ecology: the nutrient cycles in forested ecosystems.***

这项赠款是为了响应提交给信息技术研究（ITR）倡议的一项小型提案。 PI将利用信息技术和无序系统统计力学的复杂方法，系统地探索大型复杂的生物模型。 这些模型是草率的，关键的挑战是从中提取可靠和可证伪的预测。 PI将不将统计力学应用于物理系统，而是应用于系统的动力学模型中的参数：这种元建模技术因此从与当前可用数据一致的整个模型集合中得出预测。 元建模将应用于四个问题。 其中三个是在细胞信号转导领域研究的系统：PC 12细胞中的Erk系统;生长因子受体运输和Cdc 42;以及结肠细胞系Caco-2中的细胞周期。 最后一个应用是生态学：森林生态系统的养分循环。PI通过利用信息技术革命所带来的计算能力，绕过了数理统计中的传统分析方法。 采样方法是直接的，灵活的，易于实现的方法适当平均多个局部最优状态，并妥善科普建模过程中的非线性。 PI将使用许多来自材料模拟的技术思想。 通过使用软模式的破环场来惩罚计算机时间，并通过使用开发的算法来加速玻璃材料的模拟，提高了数值效率。 将使用模型的弱耦合副本来避免在比较不同细胞类型时由于软模式引起的问题：在比较同一系统的不同理论时将研究参数的重正化。 这些方法在当前的大规模计算机上并行高效地运行; ensembel genration可以是网格使能的，在Web.The抽象的建模过程到一个统计力学问题的元建模概念转换成一个令人兴奋的新问题，在复杂的，无序的系统中的一个乏味的，不适定的搜索。 从应用的角度来看，从材料物理学社区得出的见解形成了强大的工具，允许真实的预测能力，而以前只能定性探索。 从物理学的角度来看，参数空间中软模的普遍存在提供了新的见解，并引发了新的方法。 从信息技术的角度来看，密集的计算分析现在可以承担一个大的新的一类问题的重要性，科学和society.The三个信号转导网络正在研究的是特别感兴趣的癌症药物治疗的发展，是一个良好的研究预览的各种挑战的生物信息学和蛋白质组学革命将在未来十年。 生态元建模的扩展将导致在许多其他复杂系统中的应用，其中不完整或初步的数据仍然需要严格的分析。这项赠款是为了响应提交给信息技术研究（ITR）倡议的一项小型提案。 PI将利用信息技术和无序系统统计力学的复杂方法，系统地探索大型复杂的生物模型。 这些模型是草率的，关键的挑战是从中提取可靠和可证伪的预测。 PI将不将统计力学应用于物理系统，而是应用于系统的动力学模型中的参数：这种元建模技术因此从与当前可用数据一致的整个模型集合中得出预测。 元建模将应用于四个问题。 其中三个是在细胞信号转导领域研究的系统：PC 12细胞中的Erk系统;生长因子受体运输和Cdc 42;以及结肠细胞系Caco-2中的细胞周期。 最后一个应用是生态学：森林生态系统中的养分循环。