Uncertainty Quantified Computational Analysis of the Energetics of Virus Capsid Assembly.

Most of the existing research in assembly pathway prediction/analysis of viral capsids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assumption does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continue throughout the assembly process until stabilization. This paper presents a statistical-ensemble based approach which samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Furthermore, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible subassemblies to bind in all possible combinations. We represent the resulting assembly graph in two different ways: First, we use the Wilcoxon signed rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. We applied these protocols on the feline panleukopenia virus and the Nudaurelia capensis virus. Results from these experiments showed significant departure from those one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and provide a statistical framework as an important first step towards assembly pathway prediction with high statistical confidence.

Dynamic Data-Driven Finite Element Models for Laser Treatment of Cancer.

Elevating the temperature of cancerous cells is known to increase their susceptibility to subsequent radiation or chemotherapy treatments, and in the case in which a tumor exists as a well-defined region, higher intensity heat sources may be used to ablate the tissue. These facts are the basis for hyperthermia based cancer treatments. Of the many available modalities for delivering the heat source, the application of a laser heat source under the guidance of real-time treatment data has the potential to provide unprecedented control over the outcome of the treatment process [7, 18]. The goals of this work are to provide a precise mathematical framework for the real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied to the equations of bioheat transfer and demonstrate that current finite element technology, parallel computer architecture, data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within the biological domain.

A comparative study of the quantitative accuracy of three-dimensional reconstructions of spinal cord from serial histological sections.

We evaluated the accuracy of estimating the volume of biological soft tissues from their three-dimensional (3D) computer wireframe models, reconstructed from histological data sets obtained from guinea-pig spinal cords. We compared quantification from two methods of three-dimensional surface reconstruction to standard quantitative techniques, Cavalieri method employing planimetry and point counting and Geometric Best-Fitting. This involved measuring a group of spinal cord segments and test objects to evaluate the accuracy of our novel quantification approaches. Once a quantitative methodology was standardized there was no statistical difference in volume measurement of spinal segments between quantification methods. We found that our 3D surface reconstructions' ability to model precisely actual soft tissues provided an accurate volume quantification of complex anatomical structures as standard approaches of Cavalieri estimation and Geometric Best-Fitting. Additionally, 3D reconstruction quantitatively interrogates and three-dimensionally images spinal cord segments and obscured internal pathological features with approximately the same effort required for standard quantification alone.

Advances in three-dimensional reconstruction of the experimental spinal cord injury.

Three-dimensional (3D) computer reconstruction is an ideal tool for evaluating the centralized pathology of mammalian spinal cord injury (SCI) where multiple anatomical features are embedded within each other. Here, we evaluate three different reconstruction algorithms to three-dimensionally visualize SCIs. We also show for the first time, that determination of the volume and surface area of pathological features is possible using the reconstructed 3D images themselves. We compare these measurements to those calculated by older morphometric approaches. Finally, we demonstrate dynamic navigation into a 3D spinal cord reconstruction.

Two- and three-dimensional computer graphic evaluation of the subacute spinal cord injury.

We have evaluated three-week-old compression lesions of the rat spinal cord using two-dimensional and three-dimensional morphometry, reconstruction, and visualization techniques. We offer a new computer assisted method to determine the number and density of macrophages within the spinal lesion using the macrophage specific monoclonal label ED1. We also provide quantitative information on pathological cyst formation and cavitation. This technique does not require: (1) subjective identification of the cell type, (2) human interaction with the data during the phase of quantification, and (3) can be applied to any sampling paradigm based on immunocytochemical labeling. Using novel algorithms based on solutions to 'correspondence' and 'branching' problems inherent in cross-sectional histological data, we provide three-dimensional reconstructions and visualizations of the macrophagic lesions and cysts imbedded within it. Our three-dimensional surface reconstructions can be interrogated to determine volumes and surface areas of structures within the data set. Using these methods we have learned that macrophage numbers approach the maximum density possible for such isodiametric cells (approximately 12 microm diameter) in the central lesion ranging from 4000-7000 cells per mm2 of lesion. At the time point studied, macrophage numbers would have peaked following the initial insult, and would not be expected to decline for several months. While the density of macrophages is highest in the region of most tissue damage, we show that the central regions of cavitated and cystic spinal parenchyma is not. We discuss how this density of cells may effect the secondary pathological responses of the spinal cord to injury.