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We developed a novel visibility graph method to quantify and classify complex cell shapes. This network-driven approach accurately analyzes cell features and aids in plant classification.

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Area of Science:

  • Morphology and developmental biology
  • Network science and computational biology

Background:

  • Cell shape is fundamental to organismal function and development.
  • Existing frameworks for cell shape quantification, comparison, and classification are limited.

Purpose of the Study:

  • To introduce a versatile framework for cell shape characterization using visibility graphs.
  • To demonstrate the framework's efficacy in quantifying complex cell shapes and aiding classification.

Main Methods:

  • Representing shapes as visibility graphs to enable network-driven analysis.
  • Applying the framework to quantify protrusions and invaginations in leaf pavement cells.
  • Analyzing structural properties of visibility graphs for shape complexity quantification.

Main Results:

  • The visibility graph framework accurately quantifies cell shape features like protrusions and invaginations.
  • It offers enhanced functionality compared to existing methods for shape analysis.
  • Structural properties of visibility graphs correlate with pavement cell shape complexity and plant phylogeny.

Conclusions:

  • Visibility graphs provide a robust and unique framework for accurate cell shape quantification and classification.
  • This network-based approach has broad applicability across different biological domains.
  • The method facilitates a deeper understanding of shape-function relationships in biological systems.