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Theoretical Computational and Biophysics Group - "Constructing diffusive modesl using Bayesian inference. Applications to conduction in synthetic membrance channels and permeability of lipid bilayers"

Event Type
Other
Topic
biophysics
Sponsor
Klaus Schulten - TCB Group
Date
Sep 30, 2013   3:00 - 4:00 pm  
Speaker
Dr. Jeff Comer of CNRS, Nancy Universite, Vandoeuvre-les-Nancy, France
Contact
Nancy Mallon
E-Mail
nmallon@illinois.edu
Phone
244-1586
Views
2023
Originating Calendar
Physics - Theoretical Biophysics Seminar

Constructing diffusive models using Bayesian inference. Applications to conduction in synthetic membrane channels and permeability of lipid bilayers

Methods for calculation of free energies have become essential tools to theoreticians in the field of molecular simulation; however, free energies alone are not sufficient to characterize many important features of biomolecular systems, including transition rates, conductances, permeabilities, and mean first-passage times. Complete diffusive models require knowledge of not only the free energy as a function the collective variable of interest, but also  the diffusivity, which normally exhibits significant dependence on this variable for heterogeneous systems. To complicate matters further, many approaches for determining diffusivities are  incompatible with importance-sampling methods for free-energy calculation, notably those that involve time-dependent biases (e.g. adaptive biasing force, metadynamics). This incompatibility explains why, up until now, free energies and diffusivities  often had to be computed independently. In this seminar, after reviewing  basic methods for computing diffusivity, I will present a Bayesian  inference scheme that consistently determines free-energy landscapes  and coordinate-dependent diffusivities. The advantages of this scheme  include compatibility with several importance-sampling algorithms used in free-energy calculation, as well as the ability to assume a variety diffusive models (e.g., Brownian dynamics, underdamped Langevin dynamics). As concrete examples, I will illustrate the use of the  Bayesian inference scheme in studying water transport through a peptide nanotube and the passive permeation of small molecules through lipid bilayers.

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