Traveling chimeras and collective coordination in beta-cell networks

TL;DR

This study uses computational modeling to explore how different types of cell-to-cell interactions in pancreatic beta-cell networks lead to complex collective behaviors like synchronization and traveling chimera states, shedding light on insulin secretion regulation.

Contribution

It introduces a computational framework that models nonlocal electrical and metabolic coupling in beta-cell networks, revealing emergent dynamics such as traveling chimeras and waves.

Findings

Emergence of synchronization, traveling waves, and chimera states in beta-cell networks.

Insights into how coupling topology influences islet-wide cell dynamics.

Potential implications for understanding diabetes-related dysfunctions.

Abstract

Pancreatic $\beta$-cells play a central role in maintaining glucose homeostasis through the pulsatile secretion of insulin. This essential function relies not only on intracellular regulatory mechanisms but also on coordinated interactions among $\beta$-cells within the islets of Langerhans. Disruptions in this intercellular coordination are increasingly implicated in metabolic disorders such as type~I and type~II diabetes. In this work, we employ a computational framework to investigate the collective dynamics of a network of coupled $\beta$-cells interacting through a nonlocally coupled ring topology that incorporates both electrical and metabolic coupling pathways. This topology captures short- and long-range interactions known to shape islet communication. Numerical simulations reveal a variety of emergent behaviors, including synchronization, traveling waves, and traveling chimera states, in which coherent and incoherent domains coexist and propagate across the network. These findings provide new insight into the mechanisms governing coordinated $\beta$-cell activity and the regulation of pulsatile insulin secretion. By clarifying how coupling structure and intercellular communication shape islet-wide dynamics, this work contributes to a deeper understanding of the dysfunctions underlying diabetes.