Topologic properties of metabolic networks

代谢网络的拓扑特性

• 批准号: 6703064
• 负责人: ALBERT-LASZLO BARABASI
• 金额: $ 46.6万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2006-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6703064
• 关键词: Escherichia coli; bacterial genetics; cell biology; computer simulation; gene environment interaction; gene expression; gene mutation; gene targeting; intermolecular interaction; mathematical model; microorganism metabolism; model design /development; molecular biology information system; nucleic acid hybridization; protein structure function; regulatory gene; Escherichia coli; bacterial genetics; cell biology; computer simulation; gene environment interaction; gene expression; gene mutation; gene targeting; intermolecular interaction; mathematical model; microorganism metabolism; model design /development; molecular biology information system; nucleic acid hybridization; protein structure function; regulatory gene

Over the past several decades, the biomedical research community has acquired an enormous amount of valuable data across a wide spectrum of the biological world. Nearly all these efforts reflect reductive, analytical approaches to investigating important biological questions, in which biologists typically have deliberately restricted their analyses to well-defined systems with relatively few components, implicitly attempting to reduce biological phenomena to the behavior of individual molecules. Yet, despite the value of these approaches, a discrete biological function cannot be attributed only to individual molecules. Instead, the robust behavior of biological systems arises to a large extent from complex interactions among its various building constituents (i.e., cellular networks). In this application, we propose to conduct a highly integrated program in which we will examine in quantitative terms the structure of complex metabolic networks that are required to maintain the proper function of a cell. This goal will be aided by two recent scientific developments: the emergence of integrated pathway-genome databases providing detailed connectivity maps of metabolic networks, and by theoretical advances in comprehending and quantifying the topology of complex (non-biological) networks. This research represents a unique collaboration between a theoretical physicist (A. -L. Barabasi), a physician-molecular biologist (Z. N. Oltvai), and a bacterial molecular geneticist (B. L. Wanner). Our aims are threefold: (1) We will analyze in quantitative terms the structure and functional activity of complex metabolic and genetic networks of model organisms, such as Escherichia coli; (2) We will examine the effects of perturbing the levels of proteins in central metabolic networks to aid in model building and to test rules that evolve from computer simulations of these models, including examining their tolerance to targeted mutations; and (3) We will attempt to develop an understanding of the dynamic changes that take place in metabolic networks in response to a changing environment, in studies of the Escherichia coli physiome. Understanding the principles of interactions among various metabolic network components of a living cell and the generic large-scale feature of metabolic networks will not only provide an important contribution to basic biology, but will also have applicability to translational research, such as pharmaceutical target identification.

在过去的几十年中，生物医学研究界获得了广泛的生物世界中的大量有价值的数据。 几乎所有这些努力反映了研究重要生物学问题的还原性，分析方法，其中生物学家通常故意将其分析限制在相对较少的成分的定义明确的系统中，隐含地试图将生物学现象减少到单个分子的行为。 然而，尽管这些方法具有价值，但离散的生物学功能不能仅归因于单个分子。 取而代之的是，生物系统的稳健行为在很大程度上源于其各种建筑成分（即蜂窝网络）之间的复杂相互作用。在此应用程序中，我们建议进行一个高度集成的程序，在该程序中，我们将以定量的术语检查复杂的代谢网络的结构，以维持细胞的正常功能。 该目标将得到两个最近的科学发展的帮助：综合途径基因组数据库的出现提供了代谢网络的详细连接图，以及理解和量化复杂（非生物学）网络拓扑的理论进步。 这项研究代表了理论物理学家（A. -L。Barabasi），医师 - 分子生物学家（Z. N. Oltvai）与细菌分子遗传学家（B. L. Wanner）之间的独特合作。我们的目标是三个方面：（1）我们将以定量的术语分析模型生物的复杂代谢和遗传网络的结构和功能活性，例如大肠杆菌； （2）我们将研究中央代谢网络中蛋白质水平的影响，以帮助建立模型并测试从这些模型的计算机模拟演变的规则，包括检查其对靶向突变的耐受性； （3）我们将试图对代谢网络中的动态变化进行理解，以响应不断变化的环境，研究大肠杆菌的研究。 了解活细胞的各种代谢网络组成部分之间的相互作用原理以及代谢网络的通用大规模特征不仅会为基本生物学提供重要的贡献，而且还将适用于转化研究，例如药物目标识别。