代表大肠杆菌顺序有氧呼吸和厌氧发酵过程代谢和遗传调控效应的模型的有限时间参数识别。

IF 0.8 4区 数学 Q4 BIOLOGY Mathematical Medicine and Biology-A Journal of the Ima Pub Date : 2018-09-11 DOI:10.1093/imammb/dqx004
Alfonso Sepúlveda-Gálvez, Jesús Agustín Badillo-Corona, Isaac Chairez
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引用次数: 0

摘要

应用于生物系统的数学模型可以推断与环境变化有关的生物体动态行为的变化。基于常微分方程的模型是最常用的,因为它们能够描述生物系统的机制,如转录。使用这种方法的缺点是涉及大量的参数,并且很难通过实验获得它们。本研究提出了一种算法,以获得用于描述与环境变化相关的大肠杆菌代谢行为变化的模型的有限时间参数表征。在该方案中,提出了超扭转算法来恢复与生长介质中可用氧浓度变化相关的大肠杆菌所有蛋白质和mRNA的衍生物。本研究中确定的75个参数保持了系统的生物一致性,并且相对于所提出模型中包含的真实参数,它们的估计误差不超过20%。
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Finite-time parametric identification for the model representing the metabolic and genetic regulatory effects of sequential aerobic respiration and anaerobic fermentation processes in Escherichia coli.

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.

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来源期刊
CiteScore
2.20
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Formerly the IMA Journal of Mathematics Applied in Medicine and Biology. Mathematical Medicine and Biology publishes original articles with a significant mathematical content addressing topics in medicine and biology. Papers exploiting modern developments in applied mathematics are particularly welcome. The biomedical relevance of mathematical models should be demonstrated clearly and validation by comparison against experiment is strongly encouraged. The journal welcomes contributions relevant to any area of the life sciences including: -biomechanics- biophysics- cell biology- developmental biology- ecology and the environment- epidemiology- immunology- infectious diseases- neuroscience- pharmacology- physiology- population biology
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