2,122 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
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
Fitting Effective Diffusion Models to Data Associated with a "Glassy Potential": Estimation, Classical Inference Procedures and Some Heuristics
A variety of researchers have successfully obtained the parameters of low
dimensional diffusion models using the data that comes out of atomistic
simulations. This naturally raises a variety of questions about efficient
estimation, goodness-of-fit tests, and confidence interval estimation. The
first part of this article uses maximum likelihood estimation to obtain the
parameters of a diffusion model from a scalar time series. I address numerical
issues associated with attempting to realize asymptotic statistics results with
moderate sample sizes in the presence of exact and approximated transition
densities. Approximate transition densities are used because the analytic
solution of a transition density associated with a parametric diffusion model
is often unknown.I am primarily interested in how well the deterministic
transition density expansions of Ait-Sahalia capture the curvature of the
transition density in (idealized) situations that occur when one carries out
simulations in the presence of a "glassy" interaction potential. Accurate
approximation of the curvature of the transition density is desirable because
it can be used to quantify the goodness-of-fit of the model and to calculate
asymptotic confidence intervals of the estimated parameters. The second part of
this paper contributes a heuristic estimation technique for approximating a
nonlinear diffusion model. A "global" nonlinear model is obtained by taking a
batch of time series and applying simple local models to portions of the data.
I demonstrate the technique on a diffusion model with a known transition
density and on data generated by the Stochastic Simulation Algorithm.Comment: 30 pages 10 figures Submitted to SIAM MMS (typos removed and slightly
shortened
Robust Hypothesis Tests for Detecting Statistical Evidence of 2D and 3D Interactions in Single-Molecule Measurements
A variety of experimental techniques have improved the 2D and 3D spatial
resolution that can be extracted from \emph{in vivo} single-molecule
measurements. This enables researchers to quantitatively infer the magnitude
and directionality of forces experienced by biomolecules in their native
cellular environments. Situations where such forces are biologically relevant
range from mitosis to directed transport of protein cargo along cytoskeletal
structures. Models commonly applied to quantify single-molecule dynamics assume
that effective forces and velocity in the (or ) directions are
statistically independent, but this assumption is physically unrealistic in
many situations. We present a hypothesis testing approach capable of
determining if there is evidence of statistical dependence between positional
coordinates in experimentally measured trajectories; if the hypothesis of
independence between spatial coordinates is rejected, then a new model
accounting for 2D (3D) interactions should be considered to more faithfully
represent the underlying experimental kinetics. The technique is robust in the
sense that 2D (3D) interactions can be detected via statistical hypothesis
testing even if there is substantial inconsistency between the physical
particle's actual noise sources and the simplified model's assumed noise
structure. For example, 2D (3D) interactions can be reliably detected even if
the researcher assumes normal diffusion, but the experimental data experiences
"anomalous diffusion" and/or is subjected to a measurement noise characterized
by a distribution differing from that assumed by the fitted model. The approach
is demonstrated on control simulations and on experimental data (IFT88 directed
transport in the primary cilium).Comment: 7 pages, 6 figure
Using stochastic models calibrated from nanosecond nonequilibrium simulations to approximate mesoscale information
doi:10.1063/1.3106225We demonstrate how the surrogate process approximation (SPA) method can be used to compute both the potential of mean force along a reaction coordinate and the associated diffusion coefficient using a relatively small number (10-20) of bidirectional nonequilibrium trajectories coming from a complex system. Our method provides confidence bands which take the variability of the initial configuration of the high-dimensional system, continuous nature of the work paths, and thermal fluctuations into account. Maximum-likelihood-type methods are used to estimate a stochastic differential equation (SDE) approximating the dynamics. For each observed time series, we estimate a new SDE resulting in a collection of SPA models. The physical significance of the collection of SPA models is discussed and methods for exploiting information in the population of estimated SPA models are demonstrated and suggested. Molecular dynamics simulations of potassium ion dynamics inside a gramicidin A channel are used to demonstrate the methodology, although SPA-type modeling has also proven useful in analyzing single-molecule experimental time series.C.P.C. thanks Benoît Roux for providing comments on this paper, Riccardo Chelli for helpful discussions related to PMF computations, a referee for helpful comments on the first version, and NIH Grant No. T90 DK070121-04. L.J. and I.K. gratefully acknowledge the computer time provided by
the University of Missouri Bioinformatics Consortium. Partial computational support was obtained from the Rice Computational Research Cluster funded by NSF under Grant
No. CNS-0421109 and a partnership between Rice University, AMD, and Cray
Approximating nonequilibrium processes using a collection of surrogate diffusion models
The surrogate process approximation (SPA) is applied to model the
nonequilibrium dynamics of a reaction coordinate (RC) associated with the
unfolding and refolding processes of a deca-alanine peptide at 300 K. The RC
dynamics, which correspond to the evolution of the end-to-end distance of the
polypeptide, are produced by steered molecular dynamics (SMD) simulations and
approximated using overdamped diffusion models. We show that the collection of
(estimated) SPA models contain structural information "orthogonal" to the RC
monitored in this study. Functional data analysis ideas are used to correlate
functions associated with the fitted SPA models with the work done on the
system in SMD simulations. It is demonstrated that the shape of the
nonequilibrium work distributions for the unfolding and refolding processes of
deca-alanine can be predicted with functional data analysis ideas using a
relatively small number of simulated SMD paths for calibrating the SPA
diffusion models.Comment: 13 pages, 7 figure
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