MATHEMATICAL MODELING OF CHEMICAL PROCESSES IN ANIMAL PHYSIOLOGY: A COMPUTATIONAL APPROACH

Abstract

  This study aims at investigating the computational methods and mathematical modeling in the chemical ecology and animal physiology. Thus, the analysis methodology of the serious physiological processes built by the study incorporates the principles of the biological physics, neurobiological models, and machine learning algorithmics. In particular, it involves such research topics as mechanisms of neuronal excitability, cell-cell interactions, and disease progression. We used sophisticated computing simulations and estimated action potentials of neurons with an error of 1.8% and important treatment points in retinitis pigmentosa with a success rate of 92%. Also, it was possible to perfectly model microfluidic systems under the research to enhance an efficiency gain of 15% in diagnostics. These results further express the possibility of computational techniques accompanied by biomedical research and respective practices. It thus shows that the use of a combinatorial strategy is robust in addressing multilayered biological issues hence the identification of new treatments and enhancing the other treatments in existence. In conclusion, this work benefits the theory and practice of animal physiology knowledge of future studies and applications in medical sciences.