Abstract
Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g. collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions–brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Molecular, Biological, and Multi-Scale Communications |
| DOIs | |
| Publication status | E-pub ahead of print - 2025 |
| Publication type | A1 Journal article-refereed |
Keywords
- bioengineered implants
- epilepsy biomarker
- heterogenous channel
- loss budget
- neuroengineering
- neuronal communications
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Biotechnology
- Bioengineering
- Modelling and Simulation
- Computer Networks and Communications
- Electrical and Electronic Engineering