Systems Bioinformatics, IBIVU, VU University, Amsterdam, The Netherlands; Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands; Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University, The Netherlands.
FEBS J. 2014 Mar;281(6):1547-55. doi: 10.1111/febs.12722. Epub 2014 Feb 12.
Specific product formation rates and cellular growth rates are important maximization targets in biotechnology and microbial evolution. Maximization of a specific rate (i.e. a rate expressed per unit biomass amount) requires the expression of particular metabolic pathways at optimal enzyme concentrations. In contrast to the prediction of maximal product yields, any prediction of optimal specific rates at the genome scale is currently computationally intractable, even if the kinetic properties of all enzymes are available. In the present study, we characterize maximal-specific-rate states of metabolic networks of arbitrary size and complexity, including genome-scale kinetic models. We report that optimal states are elementary flux modes, which are minimal metabolic networks operating at a thermodynamically-feasible steady state with one independent flux. Remarkably, elementary flux modes rely only on reaction stoichiometry, yet they function as the optimal states of mathematical models incorporating enzyme kinetics. Our results pave the way for the optimization of genome-scale kinetic models because they offer huge simplifications to overcome the concomitant computational problems.
特定产物形成速率和细胞生长速率是生物技术和微生物进化中的重要优化目标。特定速率（即单位生物量表示的速率）的最大化需要在最佳酶浓度下表达特定的代谢途径。与最大产物产率的预测相比，即使所有酶的动力学特性都可用，目前在基因组尺度上对最佳特定速率的任何预测在计算上都是难以处理的。在本研究中，我们描述了任意大小和复杂程度的代谢网络（包括基因组尺度的动力学模型）的最大特定速率状态。我们报告说，最优状态是基本通量模式，这是在热力学可行的稳态下运行的最小代谢网络，具有一个独立的通量。值得注意的是，基本通量模式仅依赖于反应计量，然而它们作为包含酶动力学的数学模型的最优状态发挥作用。我们的研究结果为基因组尺度的动力学模型的优化铺平了道路，因为它们提供了巨大的简化，以克服随之而来的计算问题。
