Kleessen Sabrina, Nikoloski Zoran
Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
BMC Syst Biol. 2012 Mar 12;6:16. doi: 10.1186/1752-0509-6-16.
Flux balance analysis (FBA) together with its extension, dynamic FBA, have proven instrumental for analyzing the robustness and dynamics of metabolic networks by employing only the stoichiometry of the included reactions coupled with adequately chosen objective function. In addition, under the assumption of minimization of metabolic adjustment, dynamic FBA has recently been employed to analyze the transition between metabolic states.
Here, we propose a suite of novel methods for analyzing the dynamics of (internally perturbed) metabolic networks and for quantifying their robustness with limited knowledge of kinetic parameters. Following the biochemically meaningful premise that metabolite concentrations exhibit smooth temporal changes, the proposed methods rely on minimizing the significant fluctuations of metabolic profiles to predict the time-resolved metabolic state, characterized by both fluxes and concentrations. By conducting a comparative analysis with a kinetic model of the Calvin-Benson cycle and a model of plant carbohydrate metabolism, we demonstrate that the principle of regulatory on/off minimization coupled with dynamic FBA can accurately predict the changes in metabolic states.
Our methods outperform the existing dynamic FBA-based modeling alternatives, and could help in revealing the mechanisms for maintaining robustness of dynamic processes in metabolic networks over time.
通量平衡分析（FBA）及其扩展动态FBA已被证明有助于通过仅利用所包含反应的化学计量学以及适当选择的目标函数来分析代谢网络的稳健性和动态性。此外，在代谢调节最小化的假设下，动态FBA最近已被用于分析代谢状态之间的转变。
在此，我们提出了一套新颖的方法，用于分析（内部扰动的）代谢网络的动态性，并在动力学参数知识有限的情况下量化其稳健性。遵循代谢物浓度呈现平滑时间变化这一具有生物化学意义的前提，所提出的方法依赖于最小化代谢谱的显著波动来预测以通量和浓度为特征的时间分辨代谢状态。通过与卡尔文 - 本森循环的动力学模型和植物碳水化合物代谢模型进行比较分析，我们证明了调节开/关最小化原理与动态FBA相结合能够准确预测代谢状态的变化。
我们的方法优于现有的基于动态FBA的建模方法，并且有助于揭示随着时间推移代谢网络中维持动态过程稳健性的机制。
