Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917101988478976 |
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| author | Pawlak, Wiktoria Agata |
| author_facet | Pawlak, Wiktoria Agata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation Pawlak, Wiktoria Agata Biological Physics Neural and Evolutionary Computing Neurons and Cognition 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. |
| title | Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation |
| topic | Biological Physics Neural and Evolutionary Computing Neurons and Cognition |
| url | https://arxiv.org/abs/2511.19520 |