Baroukh Caroline, Muñoz-Tamayo Rafael, Steyer Jean-Philippe, Bernard Olivier
INRA UR050, Laboratoire des Biotechnologies de l'Environnement, Narbonne, France; INRIA-BIOCORE, Sophia-Antipolis, France.
INRIA-BIOCORE, Sophia-Antipolis, France.
PLoS One. 2014 Aug 8;9(8):e104499. doi: 10.1371/journal.pone.0104499. eCollection 2014.
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.
代谢建模是理解、预测和优化生物过程的有力工具,特别是当这些过程涉及感兴趣的细胞内分子时。不幸的是,由于缺乏定义和校准代谢途径动力学反应速率所需的实验数据,代谢模型在时变代谢通量方面的应用受到了阻碍。因此,代谢模型通常在平衡生长假设下使用。然而,对于一些过程,如微藻的光合自养代谢,由于其昼夜节律与日常光照同步,平衡生长假设似乎并不合理。然而,了解微藻代谢对于优化基于这种微生物的生物过程的产量是必要的,例如第三代生物燃料的生产。在本文中,我们提出了DRUM,这是一种新的动态代谢建模框架,它能够处理非平衡生长条件以及细胞内代谢物的积累。该方法的第一阶段包括将代谢网络划分为子网络,这些子网络描述空间上接近且假定满足平衡生长条件的反应。连接子网络的剩余代谢物表现出动态行为。然后,借助基本通量模式分析,每个子网络被简化为宏观反应,并假定其具有简单的动力学。最后,得到一个常微分方程系统来描述底物消耗、生物量生产、产物排泄以及一些内部代谢物的积累。DRUM被应用于在昼夜循环下微藻金藻的脂质和碳水化合物积累。所得模型准确地描述了在昼夜条件下获得的实验数据。它有效地预测了脂质和碳水化合物的积累和消耗。