A dynamic data structure for flexible molecular maintenance and informatics

Bajaj, Chandrajit; Chowdhury, Rezaul; Rasheed, Muhibur

doi:10.1145/1629255.1629287

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…All experiments were conducted on a PC with an Intel Core i7 3.20GHz, 6GB RAM and an NVIDIA GeForce GTX 480 GPU. We use the Dynamic Packing Grid (DPG) data structure [BCR09] for a memory-efficient grid structure in our simulation. Our final results are rendered using an off-line rendering software, POVRay 3.7.…”

Section: Resultsmentioning

confidence: 99%

Wetting Effects in Hair Simulation

Rungjiratananon

Kanamori

Nishita

2012

Computer Graphics Forum

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There is considerable recent progress in hair simulations, driven by the high demands in computer animated movies. However, capturing the complex interactions between hair and water is still relatively in its infancy. Such interactions are best modeled as those between water and an anisotropic permeable medium as water can flow into and out of the hair volume biased in hair fiber direction. Modeling the interaction is further challenged when the hair is allowed to move. In this paper, we introduce a simulation model that reproduces interactions between water and hair as a dynamic anisotropic permeable material. We utilize an Eulerian approach for capturing the microscopic porosity of hair and handle the wetting effects using a Cartesian bounding grid. A Lagrangian approach is used to simulate every single hair strand including interactions with each other, yielding fine-detailed dynamic hair simulation. Our model and simulation generate many interesting effects of interactions between fine-detailed dynamic hair and water, i.e., water absorption and diffusion, cohesion of wet hair strands, water flow within the hair volume, water dripping from the wet hair strands and morphological shape transformations of wet hair.

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Section: Resultsmentioning

confidence: 99%

Wetting Effects in Hair Simulation

Rungjiratananon

Kanamori

Nishita

2012

Computer Graphics Forum

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“…We decimate this mesh using the cost function fqe and a modified version of f gb which uses fqe instead of |v1 − v2|. We use a Dynamic Packing Grid data structure [5] to efficiently compute the set {xj} of nearby centers for each mesh edge. We set ρ = 5 and λ = 10 −8 and compute the polarized and non-polarized energy for each mesh using the nFFGB code described in [7].…”

Section: Experimental Results and Con-clusionsmentioning

confidence: 99%

Stable mesh decimation

Bajaj

Gillette

Qin

2009

2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling

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Current mesh reduction techniques, while numerous, all primarily reduce mesh size by successive element deletion (e.g. edge collapses) with the goal of geometric and topological feature preservation. The choice of geometric error used to guide the reduction process is chosen independent of the function the end user aims to calculate, analyze, or adaptively refine. In this paper, we argue that such a decoupling of structure from function modeling is often unwise as small changes in geometry may cause large changes in the associated function. A stable approach to mesh decimation, therefore, ought to be guided primarily by an analysis of functional sensitivity, a property dependent on both the particular application and the equations used for computation (e.g. integrals, derivatives, or integral/partial differential equations). We present a methodology to elucidate the geometric sensitivity of functionals via two major functional discretization techniques: Galerkin finite element and discrete exterior calculus. A number of examples are given to illustrate the methodology and provide numerical examples to further substantiate our choices.

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“…We used MolEnergy (Bajaj and Zhao, 2010;Bajaj et al, 2011) to compute the surface area and volume, and a graphical processing unit (GPU)-accelerated algorithm, PMEOPA (Cha et al, 2015), for computing the van der Waals, Coulombic, and polarization energies. The accuracy of these algorithms was established in Cha et al (2015) by comparison with AMBER (Case et al, 2005).…”

Section: Distribution Of E and Dementioning

confidence: 99%

Viral Capsid Assembly: A Quantified Uncertainty Approach

Clement

Rasheed

Bajaj

2018

Journal of Computational Biology

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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 article presents a statistical-ensemble-based approach that samples the configurational space for each monomer with the relative local orientation between monomers, to capture the uncertainties in binding and conformations. Further, 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 a significant departure from those that 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 we provide a statistical framework as an important first step toward assembly pathway prediction with high statistical confidence.

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A dynamic data structure for flexible molecular maintenance and informatics

Cited by 6 publications

References 51 publications

Wetting Effects in Hair Simulation

Wetting Effects in Hair Simulation

Stable mesh decimation

Viral Capsid Assembly: A Quantified Uncertainty Approach

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