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Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.

Ryan J Murphy1, Oliver J Maclaren2, Alivia R Calabrese3

  • 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.

Journal of the Royal Society, Interface
|December 7, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new computational framework to accurately predict biphasic population growth, improving biological population management. This method enhances understanding of growth dynamics across diverse life science applications.

Keywords:
identifiability analysispopulation dynamicsprofile likelihooduncertainty quantification

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Area of Science:

  • Life Sciences
  • Ecology
  • Mathematical Biology

Background:

  • Biological populations commonly exhibit biphasic growth, occurring across vast spatial and temporal scales.
  • Existing mathematical models and statistical methods for predicting biphasic growth can lead to inaccurate outcomes.
  • Inaccurate predictions can result in suboptimal management and intervention strategies.

Purpose of the Study:

  • To develop a general, computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth.
  • To improve the accuracy of predictions for biological population dynamics.
  • To provide a robust tool for applications across the life sciences.

Main Methods:

  • Developed a framework based on profile likelihood analysis.
  • Incorporated an efficient method for approximate confidence intervals for change points and model parameters.
  • Utilized parameter-wise profile predictions to assess individual parameter influence.

Main Results:

  • The framework provides a computationally efficient approach to biphasic growth analysis.
  • It enables accurate diagnosis, understanding, and prediction of population dynamics.
  • Case studies across life sciences demonstrate the framework's utility.

Conclusions:

  • The profile likelihood-based framework offers a significant advancement in analyzing biphasic population growth.
  • This approach enhances the reliability of predictions, supporting better management decisions.
  • The framework is broadly applicable to diverse biological systems exhibiting two-phase growth patterns.