An In Silico Model of Vascular Organoid Growth and Fusion Dynamics for Early Detection of Neurodegenerative Diseases

Abstract

Neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are characterized by progressive neuron death. Early diagnosis remains challenging due to the lack of specific biomarkers at the preclinical stage. Recently, in silico models have gained attention as a complement to in vitro experiments. They allow researchers to vary parameters and predict disease progression without additional biological testing.

Vascular organoids are three-dimensional cell structures that mimic vascular tissue. Their fusion reflects collective cell behavior and is sensitive to changes in cell properties that occur in disease, making it useful for quantitative analysis.

This work presents an agent-based model of vascular spheroid fusion implemented in PhysiCell. The model accounts for cell mechanics, migration, proliferation, and adhesion. For comparison with experiments, we developed a contour comparison method using polar coordinates and an integral discrepancy metric. We calibrated the model against experimental data by varying four parameters: growth rate, motility, adhesion strength, and adhesion distance. Based on 61 simulations, we identified an optimal parameter set that minimizes the discrepancy between model and experiment. Visual comparison of contours and radial profiles confirmed good agreement throughout the fusion process.

The results show that our calibration approach can reproduce experimental data quantitatively. In the future, this model could be useful for studying cell abnormalities in neurodegenerative diseases, but it requires further validation.

Speaker

Krasina Allina
Bauman Moscow State Technical University
Russia

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