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Characterization of nitrogen substrate limitation on Escherichia coli's growth by parameter identification tools

机译:用参数识别工具表征氮底物对大肠杆菌生长的限制

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Carbon-to-nitrogen ratio (CNR) has shown to be a relevant factor in microorganisms growth and metabolites production. It is usual that this factor compromises the productivity yield of different microorganisms. However, CNR has been rarely modeled and therefore the nature of its specific influence on metabolites production has not been understood clearly. This paper describes a parametric characterization of the CNR effect on the Escherichia coli metabolism. A set of parameters was proposed to introduce a mathematical model that considers the biomass, substrate and several byproducts dynamical behavior under batch regimen and CNR influence. Identification algorithm used to calculate the parameters considers a novel least mean square strategy that formalizes the CNR influence in E. coli metabolism. This scheme produced a step-by-step method that was suitable for obtaining the set of parameters that describes the model. This method was evaluated under two scenarios: (a) using the data from a set of numerical simulations where the model was tested under the presence of artificial noises and (b) the information obtained from a set of experiments under different CNR. In both cases, a leaveone- experiment-out cross-validation study was considered to evaluate the model prediction capabilities. Feasibility of the parametric identification method was proven in both considered scenarios.
机译:碳氮比(CNR)已显示是微生物生长和代谢产物产生的相关因素。通常,该因素损害了不同微生物的生产率。但是,很少对CNR进行建模,因此尚未清楚了解其对代谢产物产生的特定影响的性质。本文描述了CNR对大肠杆菌代谢的影响的参数表征。提出了一组参数以引入一个数学模型,该模型考虑了分批方案和CNR影响下的生物量,底物和几种副产物的动力学行为。用于计算参数的识别算法考虑了一种新颖的最小均方策略,该策略将CNR对大肠杆菌代谢的影响形式化。该方案产生了一种逐步方法,适用于获得描述模型的参数集。在两种情况下对该方法进行了评估:(a)使用来自一组数值模拟的数据,其中在存在人工噪声的情况下对模型进行了测试;(b)从一组在不同CNR下的实验获得的信息。在这两种情况下,均考虑采用无酵解试验交叉验证研究来评估模型的预测能力。在两种考虑的情况下都证明了参数识别方法的可行性。

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