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An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
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Graph topological transformations in space-filling cell aggregates.

Tanmoy Sarkar1, Matej Krajnc1

  • 1Department of Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia.

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|May 14, 2024
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Summary
This summary is machine-generated.

We introduce the Graph Vertex Model (GVM) to simulate cell rearrangements in tissues. This new method uses graph transformations for reproducible 3D cell simulations, generalizing 2D T1 transitions.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Tissue Engineering

Background:

  • Cell rearrangements drive tissue deformation but are hard to model in 3D.
  • Existing 3D vertex models are complex and lack reproducibility.

Purpose of the Study:

  • Reformulate the 3D vertex model for easier implementation and reproducibility.
  • Generalize cell rearrangement mechanisms across 2D and 3D tissue structures.

Main Methods:

  • Developed the Graph Vertex Model (GVM) using knowledge graphs and graph transformations.
  • Implemented GVM in a Python package utilizing the Neo4j graph database.
  • Applied GVM to study order-disorder transitions in 3D cell aggregates.

Main Results:

  • GVM simplifies cell rearrangement simulation via graph transformations.
  • GVM generalizes 2D T1 transitions to 3D cell packing.
  • Observed efficient ordering in 3D cell aggregates near an order-disorder transition point.

Conclusions:

  • Knowledge graphs provide a natural and suitable data model for tissue topology.
  • GVM enables reproducible and transparent analysis of cell rearrangements.
  • The GVM framework facilitates structured storage, analysis, and manipulation of tissue data.