12 research outputs found
A Random Force is a Force, of Course, of Coarse: Decomposing Complex Enzyme Kinetics with Surrogate Models
The temporal autocorrelation (AC) function associated with monitoring order
parameters characterizing conformational fluctuations of an enzyme is analyzed
using a collection of surrogate models. The surrogates considered are
phenomenological stochastic differential equation (SDE) models. It is
demonstrated how an ensemble of such surrogate models, each surrogate being
calibrated from a single trajectory, indirectly contains information about
unresolved conformational degrees of freedom. This ensemble can be used to
construct complex temporal ACs associated with a "non-Markovian" process. The
ensemble of surrogates approach allows researchers to consider models more
flexible than a mixture of exponentials to describe relaxation times and at the
same time gain physical information about the system. The relevance of this
type of analysis to matching single-molecule experiments to computer
simulations and how more complex stochastic processes can emerge from a mixture
of simpler processes is also discussed. The ideas are illustrated on a toy SDE
model and on molecular dynamics simulations of the enzyme dihydrofolate
reductase.Comment: 11 pages / 6 figure
Estimating eddy diffusivities from noisy Lagrangian observations
The problem of estimating the eddy diffusivity from Lagrangian observations
in the presence of measurement error is studied in this paper. We consider a
class of incompressible velocity fields for which is can be rigorously proved
that the small scale dynamics can be parameterised in terms of an eddy
diffusivity tensor. We show, by means of analysis and numerical experiments,
that subsampling of the data is necessary for the accurate estimation of the
eddy diffusivity. The optimal sampling rate depends on the detailed properties
of the velocity field. Furthermore, we show that averaging over the data only
marginally reduces the bias of the estimator due to the multiscale structure of
the problem, but that it does significantly reduce the effect of observation
error
Sparse Non Gaussian Component Analysis by Semidefinite Programming
Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of
extracting a linear structure from a high dimensional data based on estimating
a low-dimensional non-Gaussian data component. In this paper we discuss a new
approach to direct estimation of the projector on the target space based on
semidefinite programming which improves the method sensitivity to a broad
variety of deviations from normality. We also discuss the procedures which
allows to recover the structure when its effective dimension is unknown
Markov chain Monte Carlo for exact inference for diffusions
We develop exact Markov chain Monte Carlo methods for discretely-sampled,
directly and indirectly observed diffusions. The qualification "exact" refers
to the fact that the invariant and limiting distribution of the Markov chains
is the posterior distribution of the parameters free of any discretisation
error. The class of processes to which our methods directly apply are those
which can be simulated using the most general to date exact simulation
algorithm. The article introduces various methods to boost the performance of
the basic scheme, including reparametrisations and auxiliary Poisson sampling.
We contrast both theoretically and empirically how this new approach compares
to irreducible high frequency imputation, which is the state-of-the-art
alternative for the class of processes we consider, and we uncover intriguing
connections. All methods discussed in the article are tested on typical
examples.Comment: 23 pages, 6 Figures, 3 Table
Sequential Change Point Detection in Molecular Dynamics Trajectories
Motivated from a molecular dynamics context we propose a sequential change point detection algorithm for vector-valued autoregressive models based on Bayesian model selection. The algorithm does not rely on any sampling procedure or assumptions underlying the dynamics of the transitions and is designed to cope with high-dimensional data. We show the applicability of the algorithm on a time series obtained from numerical simulation of a penta-peptide molecule