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首页> 外文期刊>Biotechnology and Applied Biochemistry >Black-box kinetic modeling of growth and citric acid production and estimation of ATP maintenance parameters for Candida oleophila ATCC20177
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Black-box kinetic modeling of growth and citric acid production and estimation of ATP maintenance parameters for Candida oleophila ATCC20177

机译:黑箱动力学建模的生长和柠檬酸生产及念珠菌植物的估算念珠菌atc20177

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摘要

Mathematical modeling represents and predicts biological systems, explains underlying mechanisms, constituting one of the key focus points for fundamental and applied research to improve our understanding and to decrease costs. Organic acids are used in several industries such as monomers for bioplastics, food preservatives and additives, pharmaceuticals, and agriculture. Nonpetrochemical, sustainable production of organic acids is therefore of great interest. An important step in production of organic acids is the determination of growth and acid production dynamics, as the product itself may have direct and indirect inhibitory effects on the host's metabolism. The aim of this study it twofold: (i) to determine the parameters related to energetics of growth and production as growth (Kx ) and nongrowth associated (m(ATP) ) maintenance constants and (ii) to set up and analyze an unstructured, black-box kinetic model to describe the dynamics of the growth and production of citric acid by Candida oleophila ATCC20177 using published batch fermentation data. Kx and m(ATP) were found to be 2.3 +/- 1.7 and 5.25 +/- 2.75, respectively, for the published P/O ratio of 1.45. The parameter sensitivities and correlations are determined using the Monte Carlo approach, and the final model is tested using chemostat data.
机译:数学建模代表和预测生物系统,解释了基本机制,构成了基本和应用研究的关键焦点之一,以提高我们的理解和降低成本。有机酸用于若干行业,例如用于生物塑料,食品防腐剂和添加剂,药物和农业的单体。因此,无氟化物,可持续生产有机酸是非常感兴趣的。在生产有机酸的重要一步是测定生长和酸生产动态,因为产品本身可能对宿主的新陈代谢具有直接和间接的抑制作用。本研究的目的是双重的:(i)确定与生长和生产能量有关的参数,作为生长(KX)和非生长相关(M(ATP))维护常数和(ii)建立和分析非结构化,黑匣子动力学模型描述了念珠菌油脂ATCC20177使用公开的批量发酵数据的生长和生产动态。 KX和M(ATP)分别为1.45的P / O比例分别为2.3 +/- 1.7和5.25 +/- 2.75。使用Monte Carlo方法确定参数灵敏度和相关性,并且使用Chemostat数据测试最终模型。

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