Deep-learning-accelerated Bayesian inference for FRAP experiments
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Westling, Harald
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Fluorescence recovery after photobleaching (FRAP) is an experimental method for
determining properties such as the diffusion coefficient and binding rate of molecules
in solutions, and is used extensively in areas such as food science and biology. By
utilizing a high intensity laser the fluorescent molecules in a region of interest are
bleached. The mean fluorescence intensity in the region, and its development over
time, can be modeled as a function of the mobility parameters. Bayesian inference
with regards to the model parameters, using the likelihood function, can then
be performed. This likelihood function is very computationally heavy to evaluate.
In this work a neural network has been implemented to approximate the likelihood
function with regards to the four most central parameters in the model. The
method is promising since three of the four marginal posterior distributions of the
parameters were well approximated, with the result being comparable to traditional
methods for posterior sampling. We demonstrated that the method is much more
computationally efficient than traditional methods.
Beskrivning
Ämne/nyckelord
Deep-learning-accelerated Bayesian inference for FRAP experiments