Borger Simon, Uhlendorf Jannis, Helbig Anselm, Liebermeister Wolfram
Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
In Silico Biol. 2007;7(2 Suppl):S73-9.
We describe a workflow to translate a given metabolic network into a kinetic model; the model summarises kinetic information collected from different data sources. All reactions are modelled by convenience kinetics; where detailed kinetic laws are known, they can also be incorporated. Confidence intervals and correlations of the resulting model parameters are obtained from Bayesian parameter estimation; they can be used to sample parameter sets for Monte-Carlo simulations. The integration method ensures that the resulting parameter distributions are thermodynamically feasible. Here we summarise different previous works on this topic: we give an overview over the convenience kinetics, thermodynamic criteria for parameter sets, Bayesian parameter estimation, the collection of kinetic data, and different machine learning techniques that can be used to obtain prior distributions for kinetic parameters. All methods have been assembled into a workflow that facilitates the integration of biochemical data and the modelling of metabolic networks from scratch.
我们描述了一种将给定代谢网络转化为动力学模型的工作流程；该模型总结了从不同数据源收集的动力学信息。所有反应均采用便捷动力学进行建模；在已知详细动力学定律的情况下，也可将其纳入。通过贝叶斯参数估计获得所得模型参数的置信区间和相关性；它们可用于为蒙特卡罗模拟采样参数集。积分方法确保所得参数分布在热力学上是可行的。在此，我们总结了此前关于该主题的不同研究工作：我们概述了便捷动力学、参数集的热力学标准、贝叶斯参数估计、动力学数据的收集以及可用于获取动力学参数先验分布的不同机器学习技术。所有方法都已整合到一个工作流程中，该流程便于整合生化数据并从头开始对代谢网络进行建模。
