Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation

TL;DR

This paper presents a computational agent-based model inspired by ASAL to simulate bioelectric state transitions in glial cells, revealing how mitochondrial dysfunction can lead to glioblastoma-like states through bioelectrical and metabolic disruptions.

Contribution

It introduces a novel bioelectric agent-based framework that integrates mitochondrial efficiency, ion channels, and ROS dynamics to model glioblastoma initiation, employing evolutionary algorithms for analysis.

Findings

Reduced mitochondrial efficiency induces depolarization and ROS increase.

Evolutionary algorithms identify resilient tumor-like bioelectric states.

Disrupted bioelectric signaling can drive malignant transitions.

Abstract

Understanding how glioblastoma (GBM) emerges from initially healthy glial tissue requires models that integrate bioelectrical, metabolic, and multicellular dynamics. This work introduces an ASAL-inspired agent-based framework that simulates bioelectric state transitions in glial cells as a function of mitochondrial efficiency (Meff), ion-channel conductances, gap-junction coupling, and ROS dynamics. Using a 64x64 multicellular grid over 60,000 simulation steps, we show that reducing Meff below a critical threshold (~0.6) drives sustained depolarization, ATP collapse, and elevated ROS, reproducing key electrophysiological signatures associated with GBM. We further apply evolutionary optimization (genetic algorithms and MAP-Elites) to explore resilience, parameter sensitivity, and the emergence of tumor-like attractors. Early evolutionary runs converge toward depolarized, ROS-dominated regimes characterized by weakened electrical coupling and altered ionic transport. These results highlight mitochondrial dysfunction and disrupted bioelectric signaling as sufficient drivers of malignant-like transitions and provide a computational basis for probing the bioelectrical origins of oncogenesis.