Liebermeister Wolfram
Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France.
Metabolites. 2022 May 11;12(5):434. doi: 10.3390/metabo12050434.
To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.
为了将代谢网络转化为动态模型，结构动力学建模框架（SKM）假定一个给定的参考状态，并用随机数替换该状态下的反应弹性。一种名为结构热动力学建模（STM）的新变体考虑了可逆反应和热力学。STM依赖于一种依赖模式，其中一些基本变量被采样、拟合数据或进行优化，而所有其他变量都可以轻松计算。相关弹性来自酶饱和度值和热力学力，它们在物理上是独立的。依赖模式中的概率分布定义了一个模型集合，即使数据稀缺也能进行概率预测。STM突出了生物变量的变异性、依赖性和协方差的重要性。通过改变网络结构、通量、热力学力、调节或速率定律的类型，可以评估这些模型特征的影响。通过选择基本变量，代谢网络可以转化为具有一致可逆速率定律的动力学模型。从这些模型中获得的代谢控制系数可以告诉我们代谢动力学的情况，包括对扰动的响应和最优适应、酶协同作用和代谢物相关性，以及由化学噪声引起的代谢波动。为了展示STM，我研究了代谢控制、代谢波动和酶协同作用，以及它们如何受到热力学力的影响。考虑热力学可以改进对通量控制、酶协同作用、相关通量和代谢物变化以及代谢噪声的出现和传播的预测。
