Simulation of Virus Detection and Immune Cell Behavior Control

Abstract

research into new approaches and their simulation for identification and search mechanisms at the cellular level is of great practical importance for bioinformatics. We developed simple, efficient algorithms to simulate interactions among cells. We simulated viruses as targets (reflectors) and immune effector cells (searching cells) as emitters. We tested the hypothesis that body cells can interact via electromagnetic radiation. Our methods demonstrated high efficiency in simulating the searching (hunting) process. The neural network classifier achieved 98% accuracy. The mean angular error for predicted direction vectors was 8^∘, and the error in estimated distance to the virus was 16%. These results suggest that the proposed simulation approach is effective for modeling search-and-detect processes at the cellular level and support further investigation of non-chemical signaling hypotheses.