Senger Ryan S, Papoutsakis Eleftherios T
Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Newark, Delaware 19711, USA.
Biotechnol Bioeng. 2008 Dec 1;101(5):1053-71. doi: 10.1002/bit.22009.
A regulated genome-scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux-vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo-exo-metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time-course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real-coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative-growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from approximately 3.5 mol of protons secreted per mole of weak acids at the start of the culture to approximately 0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub-systems of genome-scale metabolic networks here as a sub-network with a one-dimensional null space basis set. A numerically determined sub-system was constructed in the genome-scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D-alanine transaminase. These results were then used to establish additional constraints for the genome-scale model.
基于丙酮丁醇梭菌ATCC 824的代谢网络重建，开发了一种调控的基因组规模模型。为了帮助模型收敛并限制通量向量可能解的数量（表型解空间的大小），开发了建模策略，以在内-外代谢组界面施加一种新型约束。这种约束被称为特定质子通量状态，其使用能够准确预测分批培养营养生长期间细胞外培养基的pH值。特定质子通量是指游离质子（每单位生物量）跨细胞膜的流入或流出。特定质子通量状态包含特定质子通量的定义范围，并包括在此范围内导致特定质子通量的所有代谢通量分布。有效的时间进程分批发酵模拟需要使用来自一组最佳特定质子通量状态的独立通量平衡解。使用实数编码遗传算法优化特定质子通量状态的时间界限，我们表明需要六个单独的特定质子通量状态来模拟营养生长代谢并准确预测细胞外培养基的pH值。此外，我们定义了每单位弱酸流出的表观质子通量化学计量，并表明该值从培养开始时每摩尔弱酸分泌约3.5摩尔质子下降到营养生长结束时的约0。计算表明，当营养生长中特定弱酸产量最大化时，细胞与环境之间的净质子交换主要通过弱酸流出发生（表观质子通量化学计量为1）。然而，发现在产酸早期通过阳离子通道的质子流出是显著的。我们还在此处开发了基因组规模代谢网络的数值确定子系统的概念，作为具有一维零空间基集的子网络。在基因组规模代谢网络中构建了一个数值确定子系统，以研究乙酰鸟氨酸转氨酶、丙氨酸消旋酶和D-丙氨酸转氨酶的通量大小和方向。然后将这些结果用于为基因组规模模型建立额外的约束。
