Realization Theory and Functional Model Reduction in Biochemical Networks

生化网络的实现理论与功能模型还原

• 批准号: 0802008
• 负责人: Bassam Bamieh
• 金额: $ 33万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0802008&HistoricalAwards=false
• 关键词: Realization; Theory; Functional; Model; Reduction

Eccs-0802008BamiehObjectiveOne of the central problems in the emerging field of Systems Biology is the analysis and functional classification of large complex biochemical reaction networks. Such networks are increasingly being scrutinized and their individual components painstakingly investigated in detail. However, a general methodology for inferring dynamical and functional behavior from the detailed network description is still sorely lacking. We propose methodologies by which large components of such networks can be replaced by components of much smaller state dimension that have similar functionality. We term this problem Functional Model Reduction to emphasize distinctions with traditional model reduction techniques. The enabling ideas behind this methodology consist of understanding how dynamical systems that are designed for prescribed functions (such as logical or hybrid operations) can be implemented with dynamical networks constrained to have specific types of building blocks. We investigate it in the specific context of building blocks that are available from basic biochemical kinetics. Enabled with this analysis, we pose the problem of carrying out this analysis in reverse, that is, given networks with specific types of building blocks, we ask what type of functional behavior they represent, and whether it is possible to mirror that behavior with dynamical system of much lower dimension. Our goal is not to develop a general nonlinear model reduction technique, but rather one that is particularly tailored to the differential equations that result from biochemical kinetics. Some novel aspects of systems theory will need to be developed such as realizations with prespecified network components as well as functional objectives for model reduction.Intellectual MeritUncovering and classification of function from the detailed description of biochemical reaction networks is a central problem in systems biology and dynamical systems theory. The proposed work will contribute techniques that are particularly tailored to the dynamical network that arise from biochemical kinetics. A new paradigm for model reduction based on network function will be developed.Broader ImpactThe broader impacts of this work include the application of the model reduction techniques developed in this project to a high order complex model of ischemic stroke that is being developed, which will make possible new understanding of this disease and new treatments for it. The multi-disciplinary nature of this work will ensure that graduate students from dynamical systems and control and those from the life sciences will develop new skill sets from the other disciplines and will help create graduates who are comfortable working at the boundary of their disciplines.

系统生物学新兴领域的核心问题之一是大型复杂生化反应网络的分析和功能分类。这些网络正受到越来越多的审查，其各个组成部分也受到细致的调查。然而，从详细的网络描述中推断动力学和功能行为的一般方法仍然非常缺乏。我们提出的方法，这种网络的大组件可以被替换为具有类似功能的小得多的状态维度的组件。我们把这个问题的功能模型约简强调与传统的模型约简技术的区别。这种方法背后的使能思想包括理解为指定功能（如逻辑或混合操作）设计的动态系统如何可以用受限于特定类型构建块的动态网络来实现。我们调查它的特定背景下，可从基本的生化动力学的积木。有了这个分析，我们提出了反向执行这个分析的问题，也就是说，给定具有特定类型构建块的网络，我们问它们代表什么类型的功能行为，以及是否有可能用低得多的维度的动力系统来反映这种行为。我们的目标是不开发一个通用的非线性模型简化技术，而是一个特别适合的微分方程，结果从生化动力学。一些新的方面的系统理论将需要开发，如实现与预先指定的网络组件以及功能的modelreduction.Intellectual MeritUncovering和分类的功能从详细描述的生化反应网络是一个中心问题，在系统生物学和动力系统理论。拟议的工作将有助于技术，特别是针对动态网络，从生化动力学。将开发基于网络功能的模型简化的新范例。更广泛的影响这项工作的更广泛的影响包括将本项目中开发的模型简化技术应用于正在开发的缺血性中风的高阶复杂模型，这将使人们有可能对这种疾病有新的认识和新的治疗方法。这项工作的学科性质将确保来自动力系统和控制的研究生以及来自生命科学的研究生将从其他学科发展新的技能，并将有助于培养在其学科边界工作的毕业生。