Alfonso Sepúlveda-Gálvez, Jesús Agustín Badillo-Corona, Isaac Chairez
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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.
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