Work

Self-organizing network representation of human heart

Year: 2024

Type: article

Abstract: Network represents adjacent relationships, connections, and interactions among constituent elements in complex systems but often loses critical information about spatial configurations. However, structure-function relationships in biological systems, e.g., the human heart, are highly dependent on both connectivity relationships and geometric details. Therefore, this paper presents a new self-organizing approach to derive the geometric structure from a network representation of the heart. We propose to simulate the network as a physical system, where nodes are treated as charged particles and edges as springs and then let these nodes self-organize to reconstruct geometric details. Despite random initiations, this network evolves into a steady topology when its energy is minimized. This study addresses the open question, i.e., "whether a network representation can effectively resemble spatial geometry of a biological system," thereby paving a stepstone to leverage network theory to investigate disease-altered biological functions.

Source: Chaos An Interdisciplinary Journal of Nonlinear Science

DOI: 10.1063/5.0243391

Authors: Runsang Liu, Hui Yang

Institution: Pennsylvania State University

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: G9000155902, G937646386