Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.
Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.
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DOI：
10.1371/journal.pcbi.1002575
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发表时间：
2012
影响因子：
4.3
通讯作者：
Shlomi T
中科院分区：
文献类型：
--
作者：
Adadi R;Volkmer B;Milo R;Heinemann M;Shlomi T
确定在各种环境和遗传条件下决定微生物生长速率的因素是系统生物学的主要挑战。虽然目前的基因组尺度代谢建模方法使我们能够成功地预测各种代谢表型，包括最大生物量产量，但预测实际生长速率是一个长期的目标。这一差距源于严格依赖于关于反应化学计量和方向性的数据，而没有考虑酶动力学因素。在这里，我们提出了一种新的代谢网络为基础的方法，代谢建模与酶动力学（MOMENT），它预测代谢通量率和增长率，通过利用酶周转率和酶分子量的先验数据，而不需要测量的营养吸收率。该方法基于代谢的确定的设计原理，其中催化跨不同介质的高通量反应的酶在具有更高的周转数方面往往更有效。在以前的尝试，利用动力学数据在基因组规模的代谢建模扩展，我们的方法考虑到催化预测的代谢通量率，考虑同工酶，蛋白质复合物和多功能酶的特定酶浓度的要求。结果表明，MOMENT能显著提高大肠杆菌各种代谢表型的预测精度。包括细胞内通量速率和不同生长速率下基因表达水平的变化。最重要的是，MOMENT被证明可以预测E.大肠杆菌在与实验测量相关的多种培养基下进行，显着改进了现有的最先进的化学计量建模方法。这些结果支持这样的观点，即细胞酶浓度的生理结合是决定微生物生长速率的关键因素。虽然目前的基因组规模的代谢建模方法使我们能够成功地预测各种代谢表型，但确定决定微生物生长速率的因素以及预测各种条件下的生长速率仍然是一个开放的挑战。在这里，我们提出了一种基于代谢网络的方法，代谢建模与酶动力学（MOMENT），它预测的增长率整合标准的化学计量模型与酶周转率和酶分子量的先验数据，考虑总酶的浓度的生理约束。该方法是基于这样的发现，即催化高通量反应的酶在具有更高的周转数方面往往更有效。MOMENT预测了E.大肠杆菌在一组24个不同的媒体，显着相关的实验测量，而现有的国家的最先进的化学计量建模方法不能做到这一点。这些结果表明，细胞酶浓度的结合是决定微生物生长速率的关键因素。
Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, identifying the factors that determine microbial growth rate and the prediction of growth rates under various conditions is still an open challenge. Here we present a metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts growth rates by integrating standard stoichiometric modeling with prior data on enzyme turnover rates and enzyme molecular weights, considering a physiological bound on total enzymes' concentration. The method is based on a finding that enzymes catalyzing high flux reactions tend to be more efficient in terms of having higher turnover numbers. MOMENT predicts growth rates of E. coli across a set of 24 different media that are significantly correlated with experimental measurements, while existing state-of-the art stoichiometric modeling approaches fail to do so. These results suggest that a bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.
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影响因子：
56.9
作者：
Ishii, Nobuyoshi;Nakahigashi, Kenji;Tomita, Masaru
通讯作者：
Tomita, Masaru
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
--
作者：
Kim J;Reed JL
通讯作者：
Reed JL
影响因子：
9.9
作者：
Shlomi, Tomer;Eisenberg, Yariv;Sharan, Roded;Ruppin, Eytan
通讯作者：
Ruppin, Eytan
影响因子：
4.8
作者：
Daran-Lapujade, P;Jansen, MLA;Pronk, JT
通讯作者：
Pronk, JT