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Related Experiment Video

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability

Sabrina Hock, Jan Hasenauer, Fabian J Theis

BMC Bioinformatics

|November 26, 2013

Summary

This summary is machine-generated.

This study introduces a new method to analyze diffusion processes from microscopy images, improving parameter estimation and uncertainty quantification for biological models. The approach offers more rigorous bounds than traditional methods.

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Area of Science:

Quantitative Biology
Biophysics
Systems Biology

Background:

Diffusion is crucial for biological processes like chemotaxis and morphogenesis.
Microscopy imaging enables assessment of spatial diffusion gradients in vitro and in vivo.
Quantitative analysis of diffusion mechanisms is challenged by measurement noise and sparse data, leading to parameter uncertainties.

Purpose of the Study:

To develop a robust method for estimating parameters in diffusion models from image data.
To address challenges posed by noise and sparse observations in quantitative biological analysis.
To introduce a novel approach for assessing parameter uncertainty and identifiability in diffusion processes.

Main Methods:

Developed a likelihood function for image-based measurements with log-normal noise.
Formulated a maximum likelihood estimation problem solved via PDE-constrained optimization.
Introduced profile likelihoods for assessing parameter uncertainty and practical identifiability.

Main Results:

Successfully modeled dendritic cell guidance towards lymphatic vessels (haptotaxis) as a proof of concept.
Estimated five kinetic parameters of the haptotaxis model using artificial measurement data.
Demonstrated that profile likelihoods provide more rigorous uncertainty bounds compared to local approximation methods.

Conclusions:

The novel approach for parameter estimation and identifiability analysis is broadly applicable to diffusion processes.
The profile likelihood method enhances the rigor of uncertainty bounds in diffusion modeling.
This work advances quantitative analysis of biological mechanisms driven by diffusion.