TY - JOUR
T1 - A Bayesian method for inferring quantitative information from FRET data
AU - Lichten, Catherine A
AU - Swain, Peter S
PY - 2011
Y1 - 2011
N2 - Understanding biological networks requires identifying their elementary protein interactions and establishing the timing and strength of those interactions. Fluorescence microscopy and Förster resonance energy transfer (FRET) have the potential to reveal such information because they allow molecular interactions to be monitored in living cells, but it is unclear how best to analyze FRET data. Existing techniques differ in assumptions, manipulations of data and the quantities they derive. To address this variation, we have developed a versatile Bayesian analysis based on clear assumptions and systematic statistics.
AB - Understanding biological networks requires identifying their elementary protein interactions and establishing the timing and strength of those interactions. Fluorescence microscopy and Förster resonance energy transfer (FRET) have the potential to reveal such information because they allow molecular interactions to be monitored in living cells, but it is unclear how best to analyze FRET data. Existing techniques differ in assumptions, manipulations of data and the quantities they derive. To address this variation, we have developed a versatile Bayesian analysis based on clear assumptions and systematic statistics.
UR - http://www.scopus.com/inward/record.url?scp=80052972280&partnerID=8YFLogxK
U2 - 10.1186/2046-1682-4-10
DO - 10.1186/2046-1682-4-10
M3 - Article
C2 - 21595867
VL - 4
SP - 10
JO - BMC Biophysics
JF - BMC Biophysics
SN - 2046-1682
ER -