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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A random effect multiplicative heteroscedastic model for bacterial growth.

Ricardo Cao1, Mario Francisco-Fernández, Emiliano J Quinto

  • 1Department of Mathematics, University of A Coruña, School of Computer Science, Campus de Elviña, s/n, 15071 A Coruña, Spain.

BMC Bioinformatics
|February 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using non-parametric models and bootstrap techniques to create prediction bands for microbial growth. This approach accurately models biological variability and environmental influences on bacterial growth dynamics.

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Area of Science:

  • Microbiology
  • Mathematical Modeling
  • Statistics

Background:

  • Predictive microbiology uses mathematical models to forecast microorganism growth rates under various environmental conditions.
  • Existing primary growth models often oversimplify biological variability, leading to inaccurate fits for bacterial growth curves.
  • Incorporating biological variability is crucial for robust microbial growth modeling.

Purpose of the Study:

  • To develop a robust method for modeling bacterial growth curves that accounts for biological variability.
  • To introduce prediction bands for microbial growth using non-parametric and bootstrap methods.
  • To improve the accuracy and interpretability of microbial growth predictions.

Main Methods:

  • Collected absorbance data for Listeria monocytogenes across various temperatures, pH levels, and NaCl concentrations.
  • Transformed absorbance data into viable count data for analysis.
  • Employed a random effect multiplicative heteroscedastic model and bootstrap resampling to generate prediction bands.
  • Developed an iterative algorithm for calculating simultaneous prediction intervals over time.

Main Results:

  • Proposed a novel concept of prediction bands for microbial growth.
  • Demonstrated that prediction bands are narrower before the inflection point and wider afterward.
  • Observed wider prediction bands at higher temperatures (42°C) compared to lower temperatures (22°C) as growth approached the asymptote.
  • Confirmed similar band patterns across tested temperatures (26°C and 38°C).

Conclusions:

  • The combination of non-parametric models and bootstrap techniques provides reliable prediction bands for microbial growth.
  • The proposed iterative algorithm ensures precise coverage probability for prediction bands.
  • Microbial growth bands effectively illustrate the impact of environmental factors on microorganism behavior, aiding in the interpretation of experimental growth curves.