A Highly Accurate Pixel-Based FRAP Model Based on Spectral-Domain Numerical Methods
Artikel i vetenskaplig tidskrift, 2019

We introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluorescence recovery after photobleaching data. The model covers pure diffusion and diffusion and binding (reaction-diffusion) with immobile binding sites, as well as arbitrary bleach region shapes. Fitting of the model is supported using both conventional recovery-curve-based estimation and pixel-based estimation, in which all individual pixels in the data are utilized. The model explicitly accounts for multiple bleach frames, diffusion (and binding) during bleaching, and bleaching during imaging. To our knowledge, no other fluorescence recovery after photobleaching framework incorporates all these model features and estimation methods. We thoroughly validate the model by comparison to stochastic simulations of particle dynamics and find it to be highly accurate. We perform simulation studies to compare recovery-curve-based estimation and pixel-based estimation in realistic settings and show that pixel-based estimation is the better method for parameter estimation as well as for distinguishing pure diffusion from diffusion and binding. We show that accounting for multiple bleach frames is important and that the effect of neglecting this is qualitatively different for the two estimation methods. We perform a simple experimental validation showing that pixel-based estimation provides better agreement with literature values than recovery-curve-based estimation and that accounting for multiple bleach frames improves the result. Further, the software developed in this work is freely available online.


Magnus Roding

RISE Research Institutes of Sweden

Leander Lacroix

RISE Research Institutes of Sweden

Annika Krona

RISE Research Institutes of Sweden

Tobias Gebäck

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Niklas Lorén

Chalmers, Fysik, Eva Olsson Group

Biophysical Journal

0006-3495 (ISSN) 1542-0086 (eISSN)

Vol. 116 7 1348-1361


Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik






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