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.
Journal of the Royal Society, Interface
|September 16, 2025
Summary
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.
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.
