热力学约束下代谢网络流量的蒙特卡洛采样方法

Monte Carlo sampling of metabolic fluxes under thermodynamic constraints

发展了一种新的基于约束的代谢网络分析方法:将代谢网络中可能的稳态类比成一个热力学系综进行蒙特卡洛采样,定义以代谢流量为变量的势能函数,既包括能使网络的生物量产出达到最优的目标量,也包含网络需要满足的一些约束条件.该方法可以对大肠杆菌稳态代谢流量空间在自动满足热力学约束和稳态约束的条件下进行采样,比已有的方法更方便有效,样本比完全随机采样具有更好的分布.优化生物量产出得到的模拟结果与真实实验结果一致.此外,使用不同势能函数,例如使乙醇产量最大化,可获得不同目标下的流量分布,对于代谢工程的遗传操作有指导作用.

A new constraint-based analysis method for metabolic networks has been developed. Thepossible steady-states in metabolic network were treated as a thermodynamic ensemble and a potential energy function enforcing additional constraints and virtual biomass was defined. The sampling in the stead-state flux space of the central metabolic network of Escherichia coli can avoid irrational fluxes violating thermodynamic constraints and mass balance and the results were consistent with the experimental data. The proposed method is more efficient than those reported, and the flux samples have better distribution than the random sampling method. Besides, other samples can be obtained, such as ethanol optimizing,via modifying the network and the potential function, which can be helpful to metabolic engineering.