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This study introduces a new statistical framework to analyze biological data, accounting for inherent variability. The method enhances model identifiability and understanding of biological heterogeneity with comparable computational cost.

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Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Systems Biology

Background:

  • Biological processes exhibit significant heterogeneity, often overlooked in traditional analyses.
  • Existing methods for model identifiability struggle to incorporate parameter variability, limiting biological insights.
  • Underdeveloped approaches exist for analyzing models that explicitly account for biological heterogeneity.

Purpose of the Study:

  • To develop a novel likelihood-based framework for inference and identifiability analysis of differential equation models incorporating biological heterogeneity.
  • To provide a flexible method for analyzing models where parameters vary according to probability distributions.
  • To improve the understanding of biological heterogeneity by analyzing random parameter models.

Main Methods:

  • Developed a likelihood-based framework using moment matching for inference and identifiability analysis.
  • Applied both frequentist (profile likelihood) and Bayesian (Markov-chain Monte Carlo) approaches.
  • Utilized three case studies for a didactic guide on analyzing hyperparameters related to statistical moments.

Main Results:

  • The novel method offers a flexible framework for identifiability analysis of models with heterogeneous parameters.
  • Demonstrated effective inference and identifiability analysis using both frequentist and Bayesian techniques.
  • The computational cost is comparable to models that ignore heterogeneity, a significant advantage.

Conclusions:

  • The developed framework effectively addresses the challenge of biological heterogeneity in mathematical modeling.
  • This approach enhances the identifiability analysis of complex biological systems.
  • Analysis of random parameter models provides deeper insights into the sources of biological variability.