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

• URL: http://arxiv.org/abs/2511.19520v1
• Date: Mon, 24 Nov 2025 04:59:51 GMT
• Title: Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation
• Authors: Wiktoria Agata Pawlak,
• Abstract summary: This work introduces an ASAL-inspired agent-based framework that simulates bioelectric state transitions in glial cells.<n>Using a 64x64 multicellular grid over 60,000 simulation steps, we show that reducing Meff below a critical threshold drives sustained depolarization, ATP collapse, and elevated ROS.<n>We further apply evolutionary optimization (genetic algorithms and MAP-Elites) to explore resilience, parameter sensitivity, and the emergence of tumor-like attractors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Latent Causal Diffusions for Single-Cell Perturbation Modeling [83.47931153555321]
  We present a generative model that frames single-cell gene expression as a stationary diffusion process observed under measurement noise.<n> LCD outperforms established approaches in predicting the distributional shifts of unseen perturbation combinations in single-cell RNA-sequencing screens.<n>We develop an approach we call causal linearization via perturbation responses (CLIPR), which yields an approximation of the direct causal effects between all genes.
  arXiv  Detail & Related papers  (2026-01-20T16:15:38Z)
• Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling [74.25438319700929]
  We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that models local-global dependencies between molecules and cellular responses.<n> evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines.<n>Results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations.
  arXiv  Detail & Related papers  (2025-11-26T07:15:00Z)
• Digitizing Spermatogenesis Lineage at Nanoscale Resolution In Tissue-Level Electron Microscopy [12.470262476858053]
  DeepOrganelle is capable of segmenting and extracting organelles within different cell types, performing statistical quantitative analysis, as well as visualizing and quantifying the spatial distribution of organelle morphologies and interactions.<n>It uncovers a waved pattern of mitochondria(Mito)-endoplasmic reticulum(ER) contact with a significant increase specifically at Stage X pachytene.
  arXiv  Detail & Related papers  (2025-11-02T05:19:59Z)
• CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types [7.197454335008732]
  We present CFM-GP, a method for cell type-agnostic gene perturbation prediction.<n>CFM-GP learns a continuous, time-dependent transformation between unperturbed and perturbed gene expression distributions.<n>We evaluate on five datasets: SARS-CoV-2 infection, IFN-beta stimulated PBMCs, treated with glioblastoma Panobinostat, lupus under IFN-beta stimulation, and Statefate progenitor fate mapping.
  arXiv  Detail & Related papers  (2025-08-09T00:00:17Z)
• A scalable gene network model of regulatory dynamics in single cells [88.48246132084441]
  We introduce a Functional Learnable model of Cell dynamicS, FLeCS, that incorporates gene network structure into coupled differential equations to model gene regulatory functions.<n>Given (pseudo)time-series single-cell data, FLeCS accurately infers cell dynamics at scale.
  arXiv  Detail & Related papers  (2025-03-25T19:19:21Z)
• CellFlux: Simulating Cellular Morphology Changes via Flow Matching [44.8979602893102]
  We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations.<n>CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes.<n>These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research.
  arXiv  Detail & Related papers  (2025-02-13T21:10:00Z)
• Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen [76.02070962797794]
  This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data.<n>CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.