Network-based Analysis of Kinetics and Regulation

描述（由申请人提供）：极端途径是代谢途径的一种独特的、基于网络的数学定义，因此可用于严格定义和研究生物系统的涌现特性。它们是直接从化学计量矩阵，代表了一个生化网络和完全表征所有可能的稳态通量分布通过网络。极端路径已经给出了许多关于重构反应网络的拓扑性质及其与生物功能的关系的有见地的结论。然而，极端途径的效用目前受到以下事实的限制：基因组规模的极端途径的完全枚举在计算上具有挑战性。此外，需要开发新的分析工具，以加强极端途径特性与实验数据之间的联系。因此，我们的具体目标是：（1）从基因组规模模型中有效计算极端途径;（2）应用在具体目标#1中开发的技术来计算极端途径：（a）细胞器（来自酿酒酵母的线粒体和来自拟南芥的叶绿体），（B）生长条件依赖性人类病原体（幽门螺杆菌和流感嗜血杆菌以及本提案期间可能出现的其他细菌），和（c）完全自主的生物体大肠杆菌）;（3）通过分析极端途径矩阵的奇异值分解（SVD）和将通量锥转换为动力学常数锥，开发分析工具，以产生更多的极端途径的生物学意义和相关性（K-锥）使用测量的浓度（蛋白质组学和代谢组学数据）。迄今为止开发和验证的概念和分析方法必须向前推进，将概念直接与生物数据和应用联系起来。如果实施，该计划将导致我们研究和表征重建网络的能力以及将计算机结果与实际细胞功能联系起来的能力的重大进步。

DESCRIPTION (provided by applicant): Extreme pathways are a unique, network-based, mathematical definition of metabolic pathways and thus can be used to rigorously define and study the emergent properties of biological systems. They are derived directly from the stoichiometric matrix that represents a biochemical network and completely characterize all possible steady-state flux distributions through the network. Extreme pathways have already given many insightful conclusions about the topological properties of reconstructed reaction networks and their relationships to biological functionalities. However, the utility of extreme pathways is currently limited by the fact that the complete enumeration of genome-scale extreme pathways is computationally challenging. Additionally, the development of new analysis tools is needed to strengthen the link between extreme pathway properties and experimental data. Accordingly, our specific aims are to: (1) enable the efficient calculation of extreme pathways from genome-scale models; (2) apply the techniques developed in Specific Aim #1 to compute the extreme pathways for: (a) organelles (the mitochondria from Saccharomyces cerevisiae and chloroplasts from Arabidopsis thaliana), (b) growth condition dependent human pathogens (Helicobacter pylori and Haemophilus influenzae and others that may become available during the period of this proposal), and (c) a fully autonomous organism Escherichia coil); and (3) develop analysis tools to yield more biological meaning and relevance of extreme pathways by analyzing singular value decomposition (SVD) of extreme pathway matrices and converting the flux cone into a cone of kinetic constants (the K-cone) using measured concentrations (proteomics and metabolomic data). The concepts and analysis methods developed and verified to date must be moved forward to tie concepts directly to biological data and applications. If implemented, this proposed program will result in a significant advancement in our ability to study and characterize the capabilities of reconstructed networks and to relate in silico results to actual cellular functions.