Network-based Analysis of Kinetics and Regulation

描述(由申请人提供)：项目摘要近年来出现了越来越多的重建代谢反应网络。这样的重建可以被转换成数学格式，而该数学格式又可以用于分析网络的属性。对网络特性的基因组规模分析有助于了解它们的正常生理功能和导致病理生理状态的故障。该R01程序的第一部分集中于使用极端途径分析代谢网络可能的稳态通量分布。事实证明，这种分析在许多目的上都是有用的，但范围有限。幸运的是，我们发现解空间的随机抽样是极端路径的一种可行和有用的替代方案。在本程序的第二部分中，我们建议1)开发可行稳态解空间的均匀随机抽样，2)将这些算法应用于一系列生物和医学实例，以及3)开发大规模动力学模型的时间尺度分解。如果拟议的计划成功执行，它将推动建立基因组规模的模型来解释浓度并开发首个网络规模的动态模型的技术水平。这种基因组规模模型的出现将扩大它们的预测范围，从而扩大它们在各种生物和医疗保健问题上的应用。公共卫生相关性：项目叙述对DNA测序的公共投资已经导致对越来越多的生物的整个基因组进行测序。科学家们已经确定了如何将基因组序列中发现的信息以及科学文献中的数据整合到构成细胞功能的生化反应网络中。这一过程在新陈代谢方面尤其先进；这项建议侧重于对健康和疾病中代谢功能的数学描述和分析。

DESCRIPTION (provided by applicant): Project Summary A growing number of reconstructed metabolic reaction networks have appeared in recent years. Such reconstruction can be converted into a mathematical format that in turn can be used to analyze the properties of the networks. Genome-scale analysis of network properties leads to understanding of their normal physiological functions and malfunction leading to pathophysiological states. The first part of this R01 program was focused on the analysis of possible steady state flux distributions of metabolic networks using extreme pathways. This analysis has proven useful for a number of purposes but is limited in scope. Fortunately, we have found random sampling of the solution space to be a viable and useful alternative to extreme pathways. During the second part of this program we propose to 1) develop uniform randomized sampling of the feasible steady state solution spaces, 2) to apply these algorithms to a series of biological and medical examples, and 3) develop time-scale decomposition of large- scale kinetic models. If the proposed program is successfully executed it will advance the state of the art of building genome-scale models to account for concentrations and to develop first-pass network-scale dynamic models. The availability of such genome-scale models would expand their scope of predictions and thus their applications to various biological and health care issues. PUBLIC HEALTH RELEVANCE: Project Narrative Public investment in DNA sequencing has led to the sequencing of entire genomes of a growing number of organisms. Scientists have determined how to assemble the information found in genomic sequences, along with data from the scientific literature, into the biochemical reaction networks that underlie cellular functions. This process is particularly advanced for metabolism; this proposal is focused on the mathematical description and analysis of metabolic functions in health and disease.