Paper List
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
The 30-Second View
IN SHORT: This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is a prerequisite for designing reliable biomedical applications.
Innovation (TL;DR)
- Methodology Proposes a novel, comprehensive channel model for molecular communication in vessel networks, incorporating advection, molecular/turbulent diffusion, and adsorption/desorption at vessel walls.
- Methodology Introduces two novel metrics—molecule delay and multi-path spread—to quantify the impact of vessel network topology on signal dispersion and the resulting signal-to-noise ratio (SNR).
- Methodology Provides the first end-to-end experimental validation of a molecular communication model in branched vessel network topologies using a dedicated SPION (superparamagnetic iron-oxide nanoparticle) testbed.
Key conclusions
- The proposed channel model, validated against experimental data from branched topologies, accurately captures key transport dynamics (advection, diffusion, sorption) in vessel networks.
- The introduced metrics (molecule delay, multi-path spread) successfully establish a quantifiable link between vessel network structure and the resulting signal-to-noise ratio (SNR) at the receiver.
- The framework enables practical applications such as optimizing sensor placement in the cardiovascular system under specific SNR constraints and guiding the design of experimental testbeds.
Abstract: The notion of synthetic molecular communication (MC) refers to the transmission of information via signaling molecules and is foreseen to enable innovative medical applications in the human cardiovascular system (CVS). Crucially, the design of such applications requires accurate and experimentally validated channel models that characterize the propagation of signaling molecules, not just in individual blood vessels, but in complex vessel networks, as prevalent in the CVS. However, experimentally validated models for MC in VNs remain scarce. To address this gap, we propose a novel channel model for MC in complex VN topologies, which captures molecular transport via advection, molecular and turbulent diffusion, as well as adsorption and desorption at the vessel walls. We specialize this model for superparamagnetic iron-oxide nanoparticles as signaling molecules by introducing a new receiver (RX) model for planar coil inductive sensors, enabling end-to-end experimental validation with a dedicated SPION testbed. Validation covers a range of channel topologies, from single-vessel topologies to branched VNs with multiple paths between transmitter (TX) and RX. Additionally, to quantify how the VN topology impacts signal quality, and inspired by multi-path propagation models in conventional wireless communications, we introduce two metrics, namely molecule delay and multi-path spread. We show that these metrics link the VN structure to molecule dispersion induced by the VN and mediately to the resulting signal-to-noise ratio (SNR) at the RX. The proposed VN structure-SNR link is validated experimentally, demonstrating that the proposed framework can support tasks such as optimal sensor placement in the CVS or the identification of suitable testbed topologies for specific SNR requirements. All experimental data are openly available on Zenodo.