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

In this paper the idea of Feynman's path integral is introduced inside a nano biological system 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 such as macrophages. Due to composition of K+, Cl− and Na+ exists there a net charge along the internal and external phospholipid membrane of bacteria.

The model of path's integration invented by Richard Feynman has been extensively used to tackle crucial problems in quantum mechanics for various decades essentially in atomic physics and nano physics. The idea of the path's integral assumes a space-time pathway where the space­time 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. We worked out the idea of the Feynmans path integral to describe space-time dynamics of aggregations of bacteria trying to host a healthy body. We assumed that the bacteria interactions is governed by electric fields and potentials.

While the net charge is predominantly positive due to the high concentrations of Cl+, there are clearly external electric fields and potentials that might seriously affect the behavior of bacteria space-time dynamics. In this manner this phenomenology would fit the path integral theory. Therefore the change of the net charge in bacteria due to the presence of others charged nano organisms would affect their translational dynamics by being vulnerable to macrophages. Thus the knowledge of the pathway of these bacteria populations is seen as an advantage to tackle the beginning of diseases inside the framework of Internet of Bio-nano Things that targets to anticipate infections using electromagnetic pulses through advanced software-hardware interfaces. In order to assess possible advantages and disadvantages of this theory we use the Machine Learning algorithm.