Biochemical networks with uncertain parameters
Biochemical networks with uncertain parameters
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DOI：
10.1049/ip-syb:20045033
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发表时间：
2005-09-01
期刊：
影响因子：
--
通讯作者：
Klipp, E
中科院分区：
文献类型：
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作者：
Liebermeister, W;Klipp, E
如果动力学参数是变化的、不确定的或未知的，则生化网络的建模变得微妙。面对这种情况，我们通过概率分布来量化有关参数的不确定知识或信念。我们展示了如何使用参数分布来推断动态网络特性的概率陈述，如稳态通量和浓度，信号特性或控制系数。参数分布也可以作为贝叶斯统计分析中的先验。我们提出了一个图形化的方案，“依赖图”，带出已知的参数之间的依赖关系，例如，由于平衡常数。如果参数分布很窄，则可以通过围绕一组平均参数值展开它们来计算变量的结果分布。我们计算的浓度分布，通量和概率的定性变量，如通量方向。概率框架允许代谢相关性的研究，它提供了变异性和随机敏感性的简单措施。它还清楚地表明了生物系统的变异性是如何与代谢反应系数相关的。
The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients.