The Feynman Path Integral and Machine Learning Algorithms to Characterize and Anticipate Bacteria Chemotaxis in a Host Healthy Body

摘要

In this paper, the idea of Feynman's path integral is introduced inside the framework of nano biological systems such as bacteria population where due to their property of chemotaxis, a stochastic modeling might be drawn to describe their mobility due essentially to electrical interactions among them as a recurrent resource to protect themselves against antibacterial agents. Due to composition of ions exists there a net charge along the internal and external phospholipid membrane of bacteria. The idea of the path's integral assumes a spacetime pathway where the space-time bacteria displacements are governed by physics interactions that gives rise to changes of position in the space- time plane in a fully accordance to biological and physics laws.