Modelling and visualizing morphology in the fungus Alternaria

Area of Science:

• Mycology and Computational Biology
• Fungal morphology and taxonomy

Background:

• Alternaria fungi are ubiquitous, causing significant impacts in agriculture, health, and material science.
• Accurate identification of Alternaria species is crucial but challenging due to complex sporulation morphology.
• Current identification methods are often labor-intensive, time-consuming, and require specialized expertise.

Purpose of the Study:

• To develop a generalized statistical model for characterizing the 3D geometric structure of Alternaria sporulation apparatus.
• To create a more accessible and automated tool for Alternaria morphology analysis and species identification.
• To explore the relationship between local growth rules and overall fungal morphology.

Main Methods:

• Developed a generalized statistical model inspired by Lindenmayer-systems (L-systems) for plant modeling.
• Utilized probability distributions to generate variations in fungal morphology by adjusting model parameters.
• Employed statistical inference for fitting models to image data and enabling automated analysis.

Main Results:

• The model successfully generates diverse Alternaria morphologies by varying parameters, allowing exploration of growth patterns.
• Parameters can be validated against published data and microscopy images for species-specific model development.
• The approach enables automated quantification of phenotypic structure and supports automated species identification from images.

Conclusions:

• The developed statistical modeling approach offers a powerful tool for visualizing and quantifying fungal morphology.
• This method significantly enhances the accessibility and efficiency of Alternaria identification.
• The study paves the way for automated, form-based fungal species identification using computational methods.