Liebermeister Wolfram, Klipp Edda
Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Theor Biol Med Model. 2006 Dec 15;3:41. doi: 10.1186/1742-4682-3-41.
Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes.
We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme.
Convenience kinetics can be used to translate a biochemical network--manually or automatically--into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases.
将已知的代谢网络转化为动态模型需要所有化学反应的速率定律。数学表达式取决于潜在的酶促机制;它们可能会变得相当复杂,并且可能包含大量参数。大多数酶的速率定律和酶参数仍然未知。
我们引入了一种简单通用的速率定律,称为“便利动力学”。它可以从简单的随机顺序酶促机制推导得出。热力学定律可以对动力学参数施加依赖性。因此,为便于对大型网络进行模型拟合和参数优化,我们引入了热力学独立的系统参数:它们的值可以独立变化,而不会违反热力学约束。我们通过用生成吉布斯自由能或一组独立的平衡常数来表示平衡常数来实现这一点。其余的系统参数是平均周转速率、广义米氏常数以及抑制和激活常数。所有参数都对应于分子能量,例如反应物与酶之间的结合能。
便利动力学可用于将生化网络手动或自动转化为具有合理生物学特性的动态模型。它实现了酶的饱和以及激活剂和抑制剂的调节,涵盖了所有可能的反应化学计量关系,并且可以由少量参数指定。其数学形式使其特别适合参数估计和优化。可以通过对米氏值、周转速率、平衡常数以及在酶分析中常规测量并存储在动力学数据库中的其他量进行最小二乘拟合来轻松计算参数估计值。