Mathematical modelling applied to biological systems allows for the inferring of changes in the dynamic behaviour of organisms associated with variations in the environment. Models based on ordinary differential equations are most commonly used because of their ability to describe the mechanisms of biological systems such as transcription. The disadvantage of using this approach is that there is a large number of parameters involved and that it is difficult to obtain them experimentally. This study presents an algorithm to obtain a finite-time parameter characterization of the model used to describe changes in the metabolic behaviour of Escherichia coli associated with environmental changes. In this scheme, super-twisting algorithm was proposed to recover the derivative of all the proteins and mRNA of E. coli associated to changes in the concentration of oxygen available in the growth media. The 75 identified parameters in this study maintain the biological coherence of the system and they were estimated with no more than 20% error with respect to the real ones included in the proposed model.

中文翻译：

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该模型的有限时间参数识别代表了大肠杆菌中顺序有氧呼吸和厌氧发酵过程的代谢和遗传调控作用。

应用于生物系统的数学模型可以推断与环境变化相关的生物体动态行为的变化。基于普通微分方程的模型具有描述生物系统（例如转录）机制的能力，因此最为常用。使用这种方法的缺点是涉及大量参数，并且很难通过实验获得它们。这项研究提出了一种算法，用于获得模型的有限时间参数表征，该模型用于描述与环境变化相关的大肠杆菌代谢行为的变化。在该方案中，提出了超扭曲算法以回收E的所有蛋白质和mRNA的衍生物。大肠杆菌与生长培养基中可用氧气浓度的变化有关。在这项研究中，确定的75个参数保持了系统的生物一致性，并且相对于所建议模型中包含的真实参数，估计这些参数的误差不超过20％。