k-cone analysis:: Determining all candidate values for kinetic parameters on a network scale
k-cone analysis:: Determining all candidate values for kinetic parameters on a network scale
复制标题
DOI：
10.1529/biophysj.104.050385
复制
发表时间：
2005-03-01
影响因子：
3.4
通讯作者：
Palsson, BO
中科院分区：
文献类型：
--
作者：
Famili, I;Mahadevan, R;Palsson, BO
由于缺乏全面的测量动力学数值和观察到的体外动力学数据的不一致，阻碍了生化反应网络的网络尺度动力学模型的建立。为了应对这一挑战，我们提出了一种构造一个凸空间的方法，称为k-锥，它包含了大规模生化网络中所有允许的动力学常数的数值。K-锥体的定义依赖于体内浓度数据的结合，以及在已建立的基于约束的建模方法中表示酶动力学的简化方法。采用k-锥体法进行求解。一个完整的人体红细胞代谢动力学模型的数值允许组合，并研究其相关的动力学参数。当用于网络尺度的动力学模型时，k-锥体法可以用来确定体外测量的动力学数值与体内浓度和通量测量之间的一致性。K-Cone分析成功地确定了用于重建基于动力学的酿酒酵母中心代谢模型的体外测量的动力学值是否可以重现体内的测量结果。此外，如果不再现体内测量值，则可以使用k-锥体来确定在动力学模型中需要改变体外测量参数的哪些数值。K-Cone分析可以确定在酿酒酵母模型中需要调整的体外确定的动力学参数的最小数目，以与体内数据一致。将先验的k-锥分析应用于动力学模型的开发，可以减少建模和参数调整的时间和工作量。随着高通量PRO的最新发展。随着代谢物浓度在全细胞水平的变化和代谢组学技术的进步，本文提出的k-锥体方法有望在全细胞水平上对代谢网络和其他生物功能进行动力学表征。
The absence of comprehensive measured kinetic values and the observed inconsistency in the available in vitro kinetic data has hindered the formulation of network-scale kinetic models of biochemical reaction networks. To meet this challenge we present an approach to construct a convex space, termed the k-cone, which contains all the allowable numerical values of the kinetic constants in large-scale biochemical networks. The definition of the k-cone relies on the incorporation of in vivo concentration data and a simplified approach to represent enzyme kinetics within an established constraint-based modeling approach. The k-cone approach was implemented to de. ne the allowable combination of numerical values for a full kinetic model of human red blood cell metabolism and to study its correlated kinetic parameters. The k-cone approach can be used to determine consistency between in vitro measured kinetic values and in vivo concentration and flux measurements when used in a network- scale kinetic model. k-Cone analysis was successful in determining whether in vitro measured kinetic values used in the reconstruction of a kinetic-based model of Saccharomyces cerevisiae central metabolism could reproduce in vivo measurements. Further, the k-cone can be used to determine which numerical values of in vitro measured parameters are required to be changed in a kinetic model if in vivo measured values are not reproduced. k-Cone analysis could identify what minimum number of in vitro determined kinetic parameters needed to be adjusted in the S. cerevisiae model to be consistent with the in vivo data. Applying the k-cone analysis a priori to kinetic model development may reduce the time and effort involved in model building and parameter adjustment. With the recent developments in high-throughput pro. ling of metabolite concentrations at a whole-cell scale and advances in metabolomics technologies, the k-cone approach presented here may hold the promise for kinetic characterization of metabolic networks as well as other biological functions at a whole-cell level.