Parameter estimation in fluorescence recovery after photobleaching: quantitative analysis of protein binding reactions and diffusion

Fluorescence recovery after photobleaching (FRAP) is a common experimental method for investigating rates of molecular redistribution in biological systems. Many mathematical models of FRAP have been developed, the purpose of which is usually the estimation of certain biological parameters such as t...

Full description

Saved in:
Bibliographic Details
Published in:Journal of mathematical biology 2021-07, Vol.83 (1), p.1-1, Article 1
Main Authors: Williamson, Daniel E., Sahai, Erik, Jenkins, Robert P., O’Dea, Reuben D., King, John R.
Format: Article
Language:English
Subjects:
Applications of Mathematics
Biological models (mathematics)
Chemical reactions
Computer applications
Experimental methods
Fisher information
Fluorescence
Fluorescence recovery after photobleaching
Mathematical and Computational Biology
Mathematical models
Mathematics
Mathematics and Statistics
Parameter estimation
Photobleaching
Proteins
Quantitative analysis
Recovery
Species diffusion
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fluorescence recovery after photobleaching (FRAP) is a common experimental method for investigating rates of molecular redistribution in biological systems. Many mathematical models of FRAP have been developed, the purpose of which is usually the estimation of certain biological parameters such as the diffusivity and chemical reaction rates of a protein, this being accomplished by fitting the model to experimental data. In this article, we consider a two species reaction–diffusion FRAP model. Using asymptotic analysis, we derive new FRAP recovery curve approximation formulae, and formally re-derive existing ones. On the basis of these formulae, invoking the concept of Fisher information, we predict, in terms of biological and experimental parameters, sufficient conditions to ensure that the values all model parameters can be estimated from data. We verify our predictions with extensive computational simulations. We also use computational methods to investigate cases in which some or all biological parameters are theoretically inestimable. In these cases, we propose methods which can be used to extract the maximum possible amount of information from the FRAP data.
ISSN:0303-6812
1432-1416
DOI:10.1007/s00285-021-01616-z