Information Rates of Controlled Protein Interactions Using Terahertz Communication

TL;DR

This paper explores a novel communication paradigm that uses Terahertz waves to control protein interactions within the body, modeling the system with Markov chains to analyze information capacity and signaling potential.

Contribution

It introduces a combined electromagnetic and molecular communication model using THz stimulation of proteins and derives capacity expressions for this bio-communication system.

Findings

Derived closed-form mutual information expressions for protein communication

Maximized capacity between nanoantenna force and protein states

Demonstrated physical significance of controlled protein signaling

Abstract

In this work, we present a paradigm bridging electromagnetic (EM) and molecular communication through a stimuli-responsive intra-body model. It has been established that protein molecules, which play a key role in governing cell behavior, can be selectively stimulated using Terahertz (THz) band frequencies. By triggering protein vibrational modes using THz waves, we induce changes in protein conformation, resulting in the activation of a controlled cascade of biochemical and biomechanical events. To analyze such an interaction, we formulate a communication system composed of a nanoantenna transmitter and a protein receiver. We adopt a Markov chain model to account for protein stochasticity with transition rates governed by the nanoantenna force. Both two-state and multi-state protein models are presented to depict different biological configurations. Closed form expressions for the mutual information of each scenario is derived and maximized to find the capacity between the input nanoantenna force and the protein state. The results we obtain indicate that controlled protein signaling provides a communication platform for information transmission between the nanoantenna and the protein with a clear physical significance. The analysis reported in this work should further research into the EM-based control of protein networks.