Title
The feynman path integral and machine learning algorithms to characterize and anticipate bacteria chemotaxis in a host healthy body
Date Issued
12 March 2019
Access level
metadata only access
Resource Type
conference paper
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
Start page
969
End page
973
Language
English
OCDE Knowledge area
Biología celular, Microbiología
Métodos de investigación bioquímica
Subjects
Scopus EID
2-s2.0-85063913398
ISBN of the container
9781728105543
Conference
2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
Sources of information:
Directorio de Producción Científica
Scopus