Molecular Signal Reception in Complex Vessel Networks: The Role of the Network Topology

TL;DR

This paper develops an analytical model to understand how the topology of complex vessel networks affects molecular signal reception, with applications in medical diagnostics and targeted drug delivery within the human cardiovascular system.

Contribution

It introduces a generic end-to-end model for molecule propagation in branched vessel networks, specialized for SPION-based MC systems, and links network topology to signal quality metrics.

Findings

Validated the model with testbed data.

Proposed metrics for network-induced molecule dispersion.

Linked network topology to SNR at target locations.

Abstract

The notion of synthetic molecular communication (MC) refers to the transmission of information via molecules and is largely foreseen for use within the human body, where traditional electromagnetic wave (EM)-based communication is impractical. MC is anticipated to enable innovative medical applications, such as early-stage tumor detection, targeted drug delivery, and holistic approaches like the Internet of Bio-Nano Things (IoBNT). Many of these applications involve parts of the human cardiovascular system (CVS), here referred to as networks, posing challenges for MC due to their complex, highly branched vessel structures. To gain a better understanding of how the topology of such branched vessel networks affects the reception of a molecular signal at a target location, e.g., the network outlet, we present a generic analytical end-to-end model that characterizes molecule propagation and reception in linear branched vessel networks (LBVNs). We specialize this generic model to any MC system employing superparamagnetic iron-oxide nanoparticles (SPIONs) as signaling molecules and a planar coil as receiver (RX). By considering components that have been previously established in testbeds, we effectively isolate the impact of the network topology and validate our theoretical model with testbed data. Additionally, we propose two metrics, namely the molecule delay and the multi-path spread, that relate the LBVN topology to the molecule dispersion induced by the network, thereby linking the network structure to the signal-to-noise ratio (SNR) at the target location. This allows the characterization of the SNR at any point in the network solely based on the network topology. Consequently, our framework can, e.g., be exploited for optimal sensor placement in the CVS or identification of suitable testbed topologies for given SNR requirements.