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Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
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Machine Learning Optimization of Non-Kasha Behavior and of Transient Dynamics in Model Retinal Isomerization.

Davinder Singh1, Chern Chuang2, Paul Brumer1

  • 1Center for Quantum Information and Quantum Control and Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.

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Summary

This study optimized a minimal two-state-two-mode model for retinal photoisomerization in rhodopsin using Bayesian optimization. The refined model accurately predicts experimental fluorescence spectra and dynamics, advancing our understanding of vision initiation.

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Area of Science:

  • Photochemistry
  • Molecular Biophysics
  • Vision Science

Background:

  • Understanding the initial steps of vision, specifically retinal isomerization in rhodopsin, is crucial.
  • Existing models face challenges in accurately representing both transient and stationary experimental data.

Purpose of the Study:

  • To refine a minimal two-state-two-mode (TM) model for retinal photoisomerization in rhodopsin.
  • To achieve excellent agreement with experimental observables across different timescales and states.

Main Methods:

  • Employed multiobjective Bayesian optimization to refine TM model parameters.
  • Adjusted the potential energy surface within the TM model.

Main Results:

  • The optimized TM model accurately predicts excitation wavelength-dependent fluorescence spectra.
  • The model successfully captures experimentally observed non-Kasha behavior in the nonequilibrium steady state.
  • Discrepancies in the time domain were significantly reduced.

Conclusions:

  • The refined TM model provides an excellent description of retinal photoisomerization in rhodopsin.
  • This work advances computational modeling of photochemical processes relevant to vision.