Fig. 1
From: Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it
Different approaches to single-cell analysis based on FRAP data. a The basic principle behind FRAP experiments: a part of a cell is bleached, and the recovery is followed. b The most common analysis of FRAP data: to fit an exponential function to the data. c PDE simulations, where the gradients are continuous in the cytosol. d The reversed-engineering approach to FRAP data, to draw conclusions in terms of model rejections and estimation of parameters and predictions. e The STS approach: fit a model to each data separately, and then combine the estimations to get the distributions. f The NONMEM approach: to add a postulated distribution for the parameter distributions among the population, and then fit to all the data at the same time using a joint likelihood function