Using information metrics and molecular communication to detect cellular tissue deformation

Area of Science:

• Biophysics
• Nanotechnology
• Biomedical Engineering

Background:

• Cellular tissues utilize calcium (Ca2+) signaling for short-range molecular communication.
• Tissue flexibility and dynamic shape changes under strain can disrupt nanomachine communication channels.
• Existing communication methods lack robustness against environmental deformations.

Purpose of the Study:

• To propose a novel process for nanomachines to infer and detect tissue state during Ca2+-based molecular communication.
• To enable nanomachines to identify tissue deformation type, magnitude, and communication parameters.
• To enhance the reliability of nanomachine communication in dynamic biological environments.

Main Methods:

• Development of the Molecular Nanonetwork Inference Process utilizing a threshold-based classifier.
• Training the classifier to identify threshold boundaries for accurate inference.
• Evaluation of information metrics including mutual information and generalized entropy across two network topologies.

Main Results:

• The proposed process successfully infers tissue deformation type and amount.
• Nanomachines can determine Ca2+ concentration and distance from other nanomachines.
• Mutual information with generalized entropy achieved an average accuracy of 80% in inferencing and detection.

Conclusions:

• The Molecular Nanonetwork Inference Process significantly improves nanomachine communication reliability in deformable tissues.
• Mutual information with generalized entropy is the most effective metric for this application.
• This approach offers a robust solution for inter-nanomachine communication in biological settings.