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Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments.

Ryan J Murphy1,2, Matthew Faria3, James M Osborne2

  • 1UniSA STEM, The University of South Australia, Mawson Lakes, South Australia 5095, Australia.

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|September 16, 2025
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Summary
This summary is machine-generated.

This study introduces a mathematical model to analyze particle-cell interactions, revealing how data variability impacts key parameters. It identifies optimal experimental timings for better understanding nano-engineered particle behavior in biomedical applications.

Keywords:
approximate Bayesian computationheterogeneitymathematical modellingnano-engineered particleparameter estimationparticle–cell interactionprediction

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Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Mathematical Modeling

Background:

  • Nano-engineered particles are crucial for medical diagnostics, imaging, and drug delivery.
  • Assessing particle performance relies on in vitro particle-cell interaction experiments.
  • Previous research often overlooks measurement heterogeneity, focusing on point estimates.

Purpose of the Study:

  • To develop a mathematical model that incorporates and utilizes measurement heterogeneity in particle-cell interactions.
  • To reveal the impact of heterogeneity on parameters characterizing particle-cell interactions.
  • To identify optimal experimental time points for maximizing information gain.

Main Methods:

  • Developed an ordinary differential equation-based mechanistic mathematical model.
  • Integrated heterogeneity into the model for analyzing routine measurements.
  • Employed approximate Bayesian computation for parameter inference and prediction.

Main Results:

  • Demonstrated the significant role of heterogeneity in particle-cell interaction parameters.
  • Generated predictions for key quantities, including the time evolution of particles per cell.
  • Identified optimal experimental time points by systematically exploring their influence on parameter estimates.

Conclusions:

  • Heterogeneity in routine measurements significantly influences the understanding of particle-cell interactions.
  • The developed model provides a robust framework for analyzing these interactions.
  • Optimizing experimental time points enhances the efficiency and information yield of particle-cell interaction studies.