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A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential

Nikita Sivakumar1, Helen V Warner1, Shayn M Peirce1

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.

Plos Computational Biology
|November 28, 2022
PubMed
Summary

This study developed computational models to understand how cell signaling and adhesion influence multicellular pattern formation. The models predict how to design synthetic cell systems for specific tissue patterns.

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Area of Science:

  • * Developmental Biology
  • * Systems Biology
  • * Computational Biology

Background:

  • * Multicellular pattern formation is crucial for embryogenesis and tumorigenesis.
  • * Cell-cell signaling and differential adhesion drive tissue patterning in heterogeneous systems.
  • * Understanding parameter sensitivity in pattern formation is key but not well-elucidated.

Purpose of the Study:

  • * To develop and validate agent-based models (ABMs) for spheroid patterning.
  • * To systematically explore how signaling and adhesion parameters affect pattern emergence.
  • * To enable the design of synthetic cell signaling circuits for desired patterns.

Main Methods:

  • * Developed 2D and 3D agent-based models (ABMs) of spheroid patterning.
  • * Utilized previously engineered cells with bidirectional signaling regulating N- and P-cadherin expression.
  • * Employed unsupervised clustering to map parameter combinations to spheroid patterns.

Main Results:

  • * Revealed how cell seeding, signaling order, cadherin expression probabilities, and adhesion strengths impact pattern formation.
  • * Identified specific parameter combinations leading to unique spheroid patterns.
  • * Experimentally validated some model predictions.

Conclusions:

  • * Computational models offer a systematic approach to study multicellular pattern formation.
  • * The developed ABM can predict and guide the design of synthetic cell signaling circuits.
  • * This work advances the understanding of how cellular interactions create complex biological structures.