Information Rates of Controlled Protein Interactions Using Terahertz Communication
September 02, 2020 Β· Declared Dead Β· π IEEE Transactions on Nanobioscience
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hadeel Elayan, Andrew W. Eckford, Raviraj Adve
arXiv ID
2009.01060
Category
q-bio.MN
Cross-listed
cs.IT
Citations
17
Venue
IEEE Transactions on Nanobioscience
Last Checked
3 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.MN
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Large-scale analysis of disease pathways in the human interactome
R.I.P.
π»
Ghosted
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
R.I.P.
π»
Ghosted
AptRank: An Adaptive PageRank Model for Protein Function Prediction on Bi-relational Graphs
R.I.P.
π»
Ghosted
Learning of signaling networks: molecular mechanisms
R.I.P.
π»
Ghosted
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted