Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Multiple-Input Multiple-Output Vascular Networks

Jakumeit T, Heinlein B, Tuccitto N, Schober R, Lotter S, Schäfer M (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Original Authors: Timo Jakumeit, Bastian Heinlein, Nunzio Tuccitto, Robert Schober, Sebastian Lotter, Maximilian Schafer

Book Volume: 12

Pages Range: 839 - 857

DOI: 10.1109/TMBMC.2026.3719307

Abstract

Synthetic molecular communication (MC) in the cardiovascular system (CVS) is a key enabler for many envisioned medical applications inside the human body, such as targeted drug delivery, early disease detection, and continuous health monitoring. The design of synthetic synthetic molecular communication (MC) systems for such applications requires suitable models for the signaling molecule propagation through complex vessel networks (VNs). Existing theoretical models offer limited analytical tractability and lack closed-form solutions, making the analysis of realistic large-scale VNs either infeasible or not insightful. To overcome these limitations, in this paper, we propose a novel closed-form physical model, termed mixture of inverse Gaussians for hemodynamic transport (MIGHT), for the advection-diffusion-driven transport of signaling molecules through complex VNs. The model represents the received molecule flux as a weighted sum of inverse Gaussian (IG) distributions, parameterized by the physical properties of the underlying VN. We show that MIGHT is capable of accurately representing the transport dynamics of signaling molecules in complex large-scale VNs ranging from simple single-input single-output (SISO) to complex multiple-input multiple-output (MIMO) network topologies. The accuracy of the proposed model is validated by comparison to the results from an existing convolution-based VN model and numerical finite-element simulations, with all finite-element simulation data openly available on Zenodo. Furthermore, we investigate three applications of the proposed model, namely (i) the reduction of large SISO-VNs to obtain simplified representations preserving the essential transport dynamics, (ii) the identification and analysis of network regions that are most important for molecule transport in MIMO-VNs comprising multiple transmitters (Txs) and multiple receivers (Rxs), and (iii) the estimation of representative SISO-VNs that can reproduce the received signal of an unknown SISO-VN.

Authors with CRIS profile

Involved external institutions

Università degli Studi di Catania Italy (IT)

How to cite

APA:

Jakumeit, T., Heinlein, B., Tuccitto, N., Schober, R., Lotter, S., & Schäfer, M. (2026). Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Multiple-Input Multiple-Output Vascular Networks. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 12, 839 - 857. https://doi.org/10.1109/TMBMC.2026.3719307

MLA:

Jakumeit, Timo, et al. "Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Multiple-Input Multiple-Output Vascular Networks." IEEE Transactions on Molecular, Biological and Multi-Scale Communications 12 (2026): 839 - 857.

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