Multi-Resolution Partitioning and Kinetic Function Estimation for Dynamic Biochemical Network Model Development

动态生化网络模型开发的多分辨率分区和动力学函数估计

• 批准号: 0829899
• 负责人: Soha Hassoun
• 金额: $ 85万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0829899&HistoricalAwards=false
• 关键词: Multi; Resolution; Partitioning; Kinetic; Function

This interdisciplinary research investigates creating dynamic models of biological systems with predictive power that is beyond the capabilities of current generation of descriptive, static models. A biological system such as a cell can be conceptualized as a complex integrated network of biochemical reactions. Metabolic reactions are an important class of reactions performing essential cellular functions such as energy generation, biosynthesis, and harmful waste and byproduct elimination. Predictive models of cellular metabolism offer broad benefits as tools for both basic and applied research. In the context of public health, metabolic models can be used to integrate new laboratory and clinical data on drug efficacy, to compare healthy and diseased tissues, and to predict potentially harmful side effects of new drugs under development. Metabolic models also play an essential role in biotechnology as they enable the design and optimization of genetically engineered microbial cells that produce industrially useful bulk and value-added chemicals (e.g. biofuels).This research focuses on two innovative modeling techniques for metabolic networks. The driving principle for both techniques is integration of structural and functional analyses. The first technique is multi-resolution structural modeling, which combines top-down modularization and bottom-up functional abstraction of individual reactions. The second technique compensates for incomplete information during mathematical modeling. This work investigates a co-estimation of reaction rate law functions and relevant parameters for each dominant reaction set while using noisy data for model calibration. The two modeling techniques and their associated algorithms are tested on experimental data collected from cultures of liver cells (hepatocytes), a representative and well-studied model system with biochemical complexity and relevance to public health. The modeling techniques obtained through this study feature generic aspects, e.g. mathematical abstraction of directed and time-varying interactions, which apply not only to metabolic networks but also other types of important biochemical (e.g. signaling, gene regulatory, etc.) networks.

这一跨学科的研究探讨了如何创建具有预测能力的生物系统动态模型，这超出了当前一代描述性静态模型的能力。一个生物系统，如细胞，可以被概念化为一个复杂的生化反应的综合网络。代谢反应是一类重要的反应，具有基本的细胞功能，如能量产生、生物合成、有害废物和副产品的消除。细胞代谢的预测模型为基础研究和应用研究提供了广泛的好处。在公共卫生方面，代谢模型可用于整合关于药物疗效的新的实验室和临床数据，比较健康和患病组织，并预测正在开发的新药的潜在有害副作用。代谢模型在生物技术中也起着至关重要的作用，因为它们能够设计和优化基因工程微生物细胞，从而生产工业上有用的散装和增值化学品（例如生物燃料）。本研究的重点是代谢网络的两种创新建模技术。这两种技术的驱动原则是结构和功能分析的集成。第一种技术是多分辨率结构建模，它结合了自上而下的模块化和自下而上的单个反应的功能抽象。第二种技术是对数学建模过程中不完全信息的补偿。这项工作研究了在使用噪声数据进行模型校准时，对每个主要反应集的反应速率定律函数和相关参数的共同估计。这两种建模技术及其相关算法在从肝细胞（肝细胞）培养物收集的实验数据上进行了测试，肝细胞是具有生化复杂性和与公共卫生相关的代表性和研究充分的模型系统。通过本研究获得的建模技术具有通用方面的特点，例如定向和时变相互作用的数学抽象，不仅适用于代谢网络，也适用于其他类型的重要生化网络（如信号传导、基因调控等）。