14,033 research outputs found
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
Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate
We have extended our previously developed 3D multi-scale agent-based brain
tumor model to simulate cancer heterogeneity and to analyze its impact across
the scales of interest. While our algorithm continues to employ an epidermal
growth factor receptor (EGFR) gene-protein interaction network to determine the
cells' phenotype, it now adds an explicit treatment of tumor cell adhesion
related to the model's biochemical microenvironment. We simulate a simplified
tumor progression pathway that leads to the emergence of five distinct glioma
cell clones with different EGFR density and cell 'search precisions'. The in
silico results show that microscopic tumor heterogeneity can impact the tumor
system's multicellular growth patterns. Our findings further confirm that EGFR
density results in the more aggressive clonal populations switching earlier
from proliferation-dominated to a more migratory phenotype. Moreover, analyzing
the dynamic molecular profile that triggers the phenotypic switch between
proliferation and migration, our in silico oncogenomics data display spatial
and temporal diversity in documenting the regional impact of tumorigenesis, and
thus support the added value of multi-site and repeated assessments in vitro
and in vivo. Potential implications from this in silico work for experimental
and computational studies are discussed.Comment: 37 pages, 10 figure
Efficient and realistic device modeling from atomic detail to the nanoscale
As semiconductor devices scale to new dimensions, the materials and designs
become more dependent on atomic details. NEMO5 is a nanoelectronics modeling
package designed for comprehending the critical multi-scale, multi-physics
phenomena through efficient computational approaches and quantitatively
modeling new generations of nanoelectronic devices as well as predicting novel
device architectures and phenomena. This article seeks to provide updates on
the current status of the tool and new functionality, including advances in
quantum transport simulations and with materials such as metals, topological
insulators, and piezoelectrics.Comment: 10 pages, 12 figure
The Effects of Task, Task Mapping, and Layout Space on User Performance in Information-Rich Virtual Environments
How should abstract information be displayed in Information-Rich Virtual Environments (IRVEs)? There are a variety of techniques available, and it is important to determine which techniques help foster a user’s understanding both within and between abstract and spatial information types. Our evaluation compared two such techniques: Object Space and Display Space. Users strongly prefer Display Space over Object Space, and those who use Display Space may perform better. Display Space was faster and more accurate than Object Space for tasks comparing abstract information. Object Space was more accurate for comparisons of spatial information. These results suggest that for abstract criteria, visibility is a more important requirement than perceptual coupling by depth and association cues. They also support the value of perceptual coupling for tasks with spatial criteria
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