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SUMMARY:Bayesian Inference of Free-Energy Landscapes and Transition-Path T
 imes from Single-Molecule FRET Data Using the Langevin Equation  - Dr Tomo
 aki Yagi\, RIKEN
DTSTART:20251119T143000Z
DTEND:20251119T153000Z
UID:TALK232750@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Single-molecule Förster resonance energy transfer (smFRET) is
  a powerful technique for probing the structural dynamics of biomacromolec
 ules. However\, conventional analyses based on hidden Markov models (HMMs)
  impose a discrete-state framework that partitions conformational space in
 to a limited number of states and models dynamics as transitions between t
 hem. While effective for identifying stable states and their kinetics\, su
 ch approaches struggle to characterize transition states and continuous st
 ructural changes. Here\, we present a Bayesian statistical framework that 
 models smFRET time traces using the Langevin equation\, enabling the infer
 ence of continuous conformational dynamics without the need for artificial
  intermediate states. Parameter estimation is performed using the Expectat
 ion–Maximization (EM) algorithm. A central feature of this approach is i
 ts ability to estimate transition-path times (TPTs)\, which capture the du
 ration of barrier-crossing events and offer insight into transition proces
 ses that are otherwise inaccessible with discrete-state models. We validat
 e the method using synthetic smFRET data to assess its accuracy and tempor
 al resolution\, and we further demonstrate its applicability to experiment
 al datasets. This work provides a principled and flexible approach for rec
 onstructing free-energy landscapes and transition dynamics from smFRET dat
 a\, overcoming key limitations of traditional HMM-based analyses.\n
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
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