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.

Eccs-0802008Bamieh目标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。此类网络正受到越来越多的审查，其各个组成部分也得到了细致的细致研究。然而，仍然严重缺乏从详细的网络描述中推断动态和功能行为的通用方法。 We propose methodologies by which large components of such networks can be replaced by components of much smaller state dimension that have similar functionality.我们将这个问题称为“功能模型简化”，以强调与传统模型简化技术的区别。该方法背后的支持思想包括理解如何通过受限于特定类型构建块的动态网络来实现为规定功能（例如逻辑或混合操作）而设计的动态系统。我们在基本生化动力学中可用的构建模块的特定背景下对其进行研究。 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.我们的目标不是开发一种通用的非线性模型简化技术，而是一种专门针对生化动力学产生的微分方程的技术。系统理论的一些新颖方面需要发展，例如预先指定的网络组件的实现以及模型简化的功能目标。智力价值从生化反应网络的详细描述中发现和分类功能是系统生物学和动力系统理论的中心问题。拟议的工作将贡献专门针对生化动力学产生的动态网络的技术。将开发一种基于网络功能的模型缩减的新范式。 更广泛的影响 这项工作的更广泛影响包括将本项目中开发的模型缩减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​​人们对这种疾病的新认识和新的治疗方法成为可能。 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.