ArticleJournal of mathematical biology2021
Parameter estimation in fluorescence recovery after photobleaching: quantitative analysis of protein binding reactions and diffusion.
Article in Journal of mathematical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploring focal adhesion data: dynamic parameter extraction from FRAP and FLAP experiments using chemical master equation.Frontiers in molecular biosciences · 2025Article
- Target gene responses differ when transcription factor levels are acutely decreased by nuclear export versus degradation.Development (Cambridge, England) · 2024Article
- What's past is prologue: FRAP keeps delivering 50 years later.Biophysical journal · 2023Review
- Inferences from FRAP data are model dependent: A subdiffusive analysis.Biophysical journal · 2022Article
- Iron-Induced Oxidative Stress in Human Diseases.Cells · 2022Review
- Review
- An optogenetic method for interrogating YAP1 and TAZ nuclear-cytoplasmic shuttling.Journal of cell science · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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