Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1.
Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1.
复制标题
将通量平衡分析集成到动力学模型中，以破译 Shewanella oneidensis MR-1 的动态代谢。
DOI：
10.1371/journal.pcbi.1002376
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发表时间：
2012-02
影响因子：
4.3
通讯作者：
Tang YJ
中科院分区：
文献类型：
--
作者：
Feng X;Xu Y;Chen Y;Tang YJ
Shewanella oneidensis MR-1 在分批培养过程中依次利用乳酸及其废物（丙酮酸和乙酸）。为了破译 MR-1 代谢，我们将基因组规模的通量平衡分析 (FBA) 集成到多底物 Monod 模型中，以执行动态通量平衡分析 (dFBA)。 dFBA 采用静态优化方法 (SOA)，将批处理时间划分为小间隔（即~400 个迷你 FBA），然后 Monod 模型提供与时间相关的流入/流出通量来约束迷你 FBA 在每个时间间隔内描绘伪稳态通量。迷你 FBA 使用双目标函数（“最大化生长速率”和“最小化总通量”的加权组合）来捕获最佳生长和最小酶使用之间的权衡。通过拟合实验数据，dFBA的双层优化表明，双目标函数中的最佳权重是时间依赖性的：目标函数在生长早期保持恒定，而当乳酸稀缺时，最小酶使用的功能权重显着增加。 dFBA 描绘了具有生物学意义的动态 MR-1 代谢： 1. 氧化 TCA 循环通量在生长后期最初增加，然后减少； 2.在指数生长期，磷酸戊糖途径和糖异生的通量稳定； 3.当乙酸盐成为MR-1生长的主要碳源时，乙醛酸分流上调。这项研究整合了两种建模方法，即莫诺动力学模型和基因组规模通量平衡分析，来分析环境重要细菌（S. oneidensis MR-1）的动态代谢。建模结果表明，MR-1 代谢对于生物量生长而言不是最理想的，而 MR-1 不断重新编程细胞内通量分布以适应营养条件。这种创新的 dFBA 框架可广泛用于研究响应环境变化的瞬时细胞代谢。此外，dFBA 能够模拟 13C 示踪剂实验中的代谢物标记动力学，因此可以通过使用标记的蛋白氨基酸来改善通量结果，作为先进的 13C 辅助动态代谢通量分析的跳板。
Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth. This study integrates two modeling approaches, a Monod kinetic model and genome-scale flux balance analysis, to analyze the dynamic metabolism of an environmentally important bacterium (S. oneidensis MR-1). The modeling results reveal that MR-1 metabolism is suboptimal for biomass growth, while MR-1 continuously reprograms the intracellular flux distributions in adaption to nutrient conditions. This innovative dFBA framework can be widely used to investigate transient cell metabolisms in response to environmental variations. Furthermore, the dFBA is able to simulate metabolite-labeling dynamics in 13C-tracer experiments, and thus can serve as a springboard to advanced 13C-assisted dynamic metabolic flux analysis by using labeled proteinogenic amino acids to improve flux results.
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