ArticleMolecular based mathematical biology2013
Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes.
Article in Molecular based mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 28 citations in OpenAlex.
- Near real-time adaptive isotropic and anisotropic image-to-mesh conversion for cerebral aneurysm simulations.Engineering with computers · 2026Article
- Image-to-mesh conversion method for multi-tissue medical image computing simulations.Engineering with computers · 2024Article
- A Vision for the Future of Multiscale Modeling.ACS physical chemistry Au · 2024Review
- Co-localization and confinement of ecto-nucleotidases modulate extracellular adenosine nucleotide distributions.PLoS computational biology · 2020Article
- Frontiers in biomolecular mesh generation and molecular visualization systems.Visual computing for industry, biomedicine, and art · 2018Review
- Chromatin Unfolding by Epigenetic Modifications Explained by Dramatic Impairment of Internucleosome Interactions: A Multiscale Computational Study.Journal of the American Chemical Society · 2015Article
- Parameterization for molecular Gaussian surface and a comparison study of surface mesh generation.Journal of molecular modeling · 2015Article
- Computational modeling of subcellular transport and signaling.Current opinion in structural biology · 2014Review
- Molecular and subcellular-scale modeling of nucleotide diffusion in the cardiac myofilament lattice.Biophysical journal · 2013Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaus-sian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.