Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Vascular Networks

2025-10-11

AI Summary

Modeling molecular communication in cardiovascular systems faces challenges due to the analytical intractability of convection–diffusion processes within complex, heterogeneous vascular networks and the computational infeasibility of large-scale analysis. To address this, we propose MIGHT—a novel closed-form physical model based on a mixture of inverse Gaussian distributions—that explicitly characterizes molecular flux propagation dynamics across anatomically realistic, heterogeneous vascular networks. By integrating physical constraints with statistical properties, MIGHT enables both network-order reduction and signal-driven topology-agnostic inversion. Validated via finite-element simulations and convolutional basis analysis, MIGHT achieves <5% error across diverse real vascular topologies while accelerating computation by two to three orders of magnitude. This work establishes the first analytically tractable, invertible, and scalable modeling framework for molecular communication in complex physiological networks, providing foundational theory and design tools for synthetic biology and targeted theranostics.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

Synthetic molecular communication (MC) in the cardiovascular system (CVS) is a key enabler for many envisioned medical applications in the human body, such as targeted drug delivery, early cancer detection, and continuous health monitoring. The design of 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 large-scale VNs either infeasible or not insightful. To overcome these limitations, in this paper, we propose a novel closed-form physical model, termed MIGHT, for 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 physical properties of the network. The proposed model is validated by comparison with an existing convolution-based model and finite-element simulations. Further, we show that the model can be applied for the reduction of large VNs to simplified representations preserving the essential transport dynamics and for estimating representative VN based on received signals from unknown VNs.

Problem

• Modeling molecular signal propagation in complex vascular networks
• Overcoming limited analytical tractability in existing transport models
• Providing closed-form solutions for advection-diffusion transport analysis

Innovation

• MIGHT models molecule flux with inverse Gaussian distributions
• Parameterizes distributions using physical network properties
• Enables network reduction and estimation from signals