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Related Experiment Video

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The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

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Published on: July 9, 2017

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate

Brenda N Vo¹, Christopher C Drovandi¹, Anthony N Pettitt¹

¹Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Queensland 4001, Australia.

Mathematical Biosciences

|March 10, 2015

Summary

This study introduces approximate Bayesian computation (ABC) to precisely estimate cell diffusivity (D) and proliferation rate (λ) in collective cell spreading models. The method offers accurate parameter estimation with quantified uncertainty, improving biological insights.

Keywords:

Approximate Bayesian computation Cell diffusivity Cell proliferation Collective cell spreading Random walk model

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Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

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Published on: May 22, 2021

Area of Science:

Biophysics
Mathematical Biology
Cell Biology

Background:

Collective cell spreading is crucial for wound healing and tumor growth, driven by cell motility and proliferation.
Mathematical models are essential for analyzing cell spreading data and estimating key parameters.
Current parameter estimation methods often assume constant parameters and neglect uncertainty.

Purpose of the Study:

To apply approximate Bayesian computation (ABC) for estimating cell diffusivity (D) and proliferation rate (λ) in collective cell spreading.
To quantify the uncertainty in these parameter estimates using Bayesian inference.
To analyze collective cell spreading data from 3T3 fibroblast cells under different experimental conditions.

Main Methods:

Utilized approximate Bayesian computation (ABC) coupled with Bayesian inference for parameter estimation.
Employed a discrete model of collective cell spreading.
Analyzed experimental data based on the position of the leading edge of cell populations.

Main Results:

Achieved precise estimation of cell diffusivity (D) with a coefficient of variation (CV) of 2-6%.
Observed that cell diffusivity (D) appears to be dependent on experimental time, a previously overlooked factor.
Obtained precise estimates for cell proliferation rate (λ) (CV 4-12%) by leveraging diffusivity estimates.

Conclusions:

The ABC method provides a robust and accurate approach for estimating parameters in collective cell spreading models.
The findings highlight the time-dependent nature of cell diffusivity and offer precise estimates for both diffusivity and proliferation.
This Bayesian approach offers a cost-effective alternative to cell counting techniques for parameter estimation.