Toward Bayesian inference of the spatial distribution of proteins from three-cube Förster resonance energy transfer data

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
173 Downloads (Pure)

Abstract

Főrster resonance energy transfer (FRET) is a quantum-physical phenomenon where energy may be transferred from one molecule to a neighbour molecule if the molecules are close enough. Using fluorophore molecule marking of proteins in a cell it is possible to measure in microscopic images to what extent FRET takes place between the fluorophores. This provides indirect information of the spatial distribution
of the proteins. Questions of particular interest are whether (and if so to which extent) proteins of possibly different types interact or whether they appear independently of each other.

In this paper we propose a new likelihood-based approach to statistical inference for FRET microscopic data. The likelihood function is obtained from a detailed modeling of the FRET data generating mechanism conditional on a protein configuration.
We next follow a Bayesian approach and introduce a spatial point process prior model for the protein configurations depending on hyper parameters quantifying the intensity of the point process. Posterior distributions are evaluated using Markov chain Monte Carlo. We propose to infer microscope related parameters in an initial step from reference data without interaction between the proteins. The new methodology is applied to simulated and real data sets.
Original languageEnglish
JournalThe Annals of Applied Statistics
Volume11
Issue number3
Pages (from-to)1711-1737
ISSN1932-6157
DOIs
Publication statusPublished - 5 Oct 2017

Keywords

  • Bayesian inference
  • Markov chain Monte Carlo
  • Forster resonance energy transfer
  • spatial point process
  • spatial distribution
  • proteins
  • fluorophores

Fingerprint

Dive into the research topics of 'Toward Bayesian inference of the spatial distribution of proteins from three-cube Förster resonance energy transfer data'. Together they form a unique fingerprint.

Cite this