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Related Experiment Video

Updated: Mar 23, 2026

Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
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Bayesian model selection framework for identifying growth patterns in filamentous fungi.

Xiao Lin1, Gabriel Terejanu1, Sajan Shrestha2

  • 1Department of Computer Science and Engineering, University of South Carolina, 315 Main St, Swearingen Bldg. 3A01L, Columbia, SC 29208, USA.

Journal of Theoretical Biology
|March 23, 2016
PubMed
Summary

This study introduces a Bayesian framework to quantify errors in fungal growth models, enabling better model selection and refinement for improved predictions in fungal biology research.

Keywords:
Fungal growthModel calibrationModel discrepancyStatistical modeling

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Area of Science:

  • Fungal Biology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Mathematical modeling of filamentous fungi is crucial for understanding hyphal and colony behaviors.
  • Fungal colony architecture is complex due to diverse environmental and intracellular signals.
  • A gap exists in connecting fungal growth models with empirical measurement data.

Purpose of the Study:

  • To introduce a unified computational framework for quantifying model errors in fungal growth models.
  • To provide a method for ranking statistical models based on their descriptive power.
  • To bridge the gap between fungal growth models and measurement data.

Main Methods:

  • Development of a computational framework utilizing Bayesian inference.
  • Quantification of individual model errors.
  • Ranking of statistical models using Bayesian model comparison.

Main Results:

  • The proposed Bayesian framework effectively quantifies model errors.
  • Model comparison balances data fitness with model complexity (Occam's razor).
  • The framework aids in calibrating, comparing, and refining fungal growth models.

Conclusions:

  • The Bayesian framework offers a rigorous approach to model selection in fungal growth studies.
  • Quantified model errors facilitate improved predictions and guide future modeling efforts.
  • This methodology enhances the reliability and applicability of fungal growth models.