Srienc Friedrich, Barrett John
Department of Chemical Engineering and Materials Science and BioTechnology Institute, University of Minnesota, Minneapolis/St. Paul, MN 55455/55108, USA.
Metabolites. 2025 Mar 13;15(3):200. doi: 10.3390/metabo15030200.
: When glucose molecules are metabolized by a biological cell, the molecules are constrained to flow along distinct reaction trajectories, which are defined by the cell's underlying metabolic network. : Using the computational technique of Elementary Mode Analysis, the entire set of all possible trajectories can be enumerated, effectively allowing metabolism to be viewed in a discretized space. : With the resulting set of Elementary Flux Modes (EMs), macroscopic fluxes, (of both mass and energy) that cross the cell envelope can be computed by a simple, linear combination of the individual EM trajectories. The challenge in this approach is that the usage probability of each EM is unknown. But, because the analytical framework we have adopted allows metabolism to be viewed in a discrete space, we can use the mathematics of statistical thermodynamics to derive the usage probabilities when the system entropy is maximized. The resulting probabilities, which obey a Boltzmann-type distribution, predict a rate structure for the metabolic network that is in remarkable agreement with experimentally measured rates of adaptively evolved strains. : Thus, in principle, the intracellular dynamic properties of such bacteria can be predicted, using only the knowledge of the DNA sequence, to reconstruct the metabolic reaction network, and the measurement of the specific glucose uptake rate.
当生物细胞代谢葡萄糖分子时，这些分子被限制沿着由细胞潜在代谢网络定义的不同反应轨迹流动。使用基本模式分析的计算技术，可以枚举所有可能轨迹的完整集合，从而有效地在离散空间中观察代谢过程。利用由此得到的基本通量模式（EMs）集合，可以通过各个EM轨迹的简单线性组合来计算穿过细胞膜的宏观通量（质量和能量）。这种方法的挑战在于每个EM的使用概率是未知的。但是，由于我们采用的分析框架允许在离散空间中观察代谢过程，当系统熵最大化时，我们可以使用统计热力学的数学方法来推导使用概率。由此得到的概率服从玻尔兹曼型分布，预测了代谢网络的速率结构，这与适应性进化菌株的实验测量速率显著一致。因此，原则上，仅利用DNA序列知识来重建代谢反应网络以及特定葡萄糖摄取速率的测量，就可以预测此类细菌的细胞内动态特性。
