The Ascona B-DNA Consortium (or ABC) has generated very large data sets of molecular dynamics simulations of double stranded nucleic acids (or dsNA). Predictive Gaussian coarse-grain sequence-dependent models of dsNA can then be parametrised by fitting to statistics drawn from this training set data using Kullback-Leibler divergence (or relative entropy) as objective function. However the precision, or stiffness, matrices in these Gaussian models have very particular structures arising from the specifics of the application, e.g. block bandedness. I will focus on describing special features of the parameter fitting process for such structured Gaussians, which seem to be of interest beyond the specific application.
This video was produced by the Isaac Newton Institute, as part of the workshop Mathematical mechanical biology: old school and new school, methods and applications, forming part of the programme Uncertainty quantification and stochastic modelling of materials.
