Department of Chemical Engineering, University of Michigan, Ann Arbor, MI48109, USA.
J Theor Biol. 1993 Dec 21;165(4):477-502. doi: 10.1006/jtbi.1993.1202.
Metabolism of living cells converts substrates into metabolic energy, redox potential and metabolic end products that are essential to maintain cellular function. The flux distribution among the various biochemical pathways is determined by the kinetic properties of enzymes which are subject to strict regulatory control. Simulation of metabolic behavior therefore requires the complete knowledge of biochemical pathways, enzyme kinetics as well as their regulation. Unfortunately, complete kinetic and regulatory information is not available for microbial cells, thus preventing accurate dynamic simulation of their metabolic behavior. However, it is possible to define wider limits on metabolic behavior based solely on flux balances of biochemical pathways. We present here comprehensive information about the catabolic pathways of the bacterium Escherichia coli. Using this biochemical database, we formulate a stoichiometric model of the bacterial network of fueling reactions. After logical structural reduction, the network consists of 53 metabolic fluxes and 30 metabolites. The solution space of this under-determined system of equations presents the bounds of metabolic flux distribution that the bacterial cell can achieve. We use specific objective functions and linear optimization to investigate the capability of E. coli catabolism to maximally produce the 12 biosynthetic precursors and three key cofactors within this solution space. For the three cofactors, the maximum yields are calculated to be 18.67 ATP, 11.6 NADH and 11 NADPH per glucose molecule, respectively. The yields of NADH and NADPH are less than 12 owing to the energy costs of importing glucose. These constraints are made explicit by the interpretation of shadow prices. The optimal yields of the 12 biosynthetic precursors are computed. Four of the 12 precursors (3-phosphoglycerate, phosphoenolpyruvate, pyruvate and oxaloacetate) can be made by E. coli with complete carbon conversion. Conversely, none of the sugar monophosphates can be made with 100% carbon conversion and analysis of the shadow prices reveals that this conversion is constrained by the energy cost of importing glucose. Three of the 12 precursors (acetyl-coA, α-ketoglutarate, and succinyl-coA) cannot be made with full carbon conversion owing to stoichiometric constraints; there is no route to these compounds without carrying out a decarboxylation reaction. Metabolite flux balances and linear optimization have thus been used to determine the catabolic capabilities of E. coli .
活细胞的代谢将底物转化为代谢能、氧化还原电势和代谢终产物,这些对于维持细胞功能至关重要。各种生化途径之间的通量分布由酶的动力学特性决定,而酶又受到严格的调控。因此,代谢行为的模拟需要完全了解生化途径、酶动力学及其调控。不幸的是,微生物细胞的完整动力学和调控信息并不可用,从而阻止了对其代谢行为的准确动态模拟。然而,仅基于生化途径的通量平衡,可以定义代谢行为的更广泛限制。我们在这里提供了关于细菌大肠杆菌分解代谢途径的综合信息。使用这个生化数据库,我们制定了细菌供能反应网络的计量模型。经过逻辑结构简化,网络由 53 个代谢通量和 30 种代谢物组成。这个欠定方程组的解空间提供了细菌细胞可以实现的代谢通量分布的边界。我们使用特定的目标函数和线性优化来研究大肠杆菌分解代谢最大限度地产生 12 种生物合成前体和三种关键辅酶的能力。对于三种辅酶,葡萄糖分子的最大产率分别计算为 18.67ATP、11.6NADH 和 11NADPH。由于葡萄糖的输入能量成本,NADH 和 NADPH 的产率低于 12。这些约束通过影子价格的解释来明确表示。计算了 12 种生物合成前体的最佳产率。12 种前体中的 4 种(3-磷酸甘油酸、磷酸烯醇丙酮酸、丙酮酸和草酰乙酸)可以被大肠杆菌完全转化为碳进行合成。相反,没有一种单磷酸糖可以 100%完全转化为碳,并且影子价格的分析表明,这种转化受到葡萄糖输入能量成本的限制。12 种前体中的 3 种(乙酰辅酶 A、α-酮戊二酸和琥珀酰辅酶 A)由于化学计量限制而无法完全转化为碳;如果不进行脱羧反应,就无法合成这些化合物。因此,代谢物通量平衡和线性优化已被用于确定大肠杆菌的分解代谢能力。