D Structural Investigation of Caveolae From Porcine Aorta Endothelial Cell by Electron Tomography*

Sun, Shuhong; 孙书峰,; Zhang, Kai; 张凯,; Xu, Wei; 徐伟,; Wang, Gang; Chen, Jianjun; Sun, Fenyong; 孙飞,

doi:10.3724/sp.j.1206.2008.00693

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…In order to evaluate the performance of the multilevel parallel strategy, the blob-based iterative reconstructions of the caveolae from the porcine aorta endothelial (PAE) cell have been performed [ 30 ]. Three different experimental datasets are used (denoted by small-sized, medium-sized, large-sized) with 56 images of 512 × 512 pixels, 112 images of 1024 × 1024 pixels, and 119 images of 2048 × 2048 pixels, to reconstruct tomograms of 512 × 512 × 190, 1024 × 1024 × 310 and 2048 × 2048 × 430 respectively.…”

Section: Resultsmentioning

confidence: 99%

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

et al. 2012

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BackgroundThree-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs) offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs.ResultsIn this paper we propose a multilevel parallel strategy combined with an asynchronous communication scheme and a blob-ELLR data structure to efficiently perform blob-based iterative reconstructions on multi-GPUs. The asynchronous communication scheme is used to minimize the idle GPU time so as to asynchronously overlap communications with computations. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR) data structure and yields significant acceleration.ConclusionsExperimental results indicate that the multilevel parallel scheme combined with the asynchronous communication scheme and the blob-ELLR data structure allows efficient implementations of 3D reconstruction in ET on multi-GPUs.

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Section: Resultsmentioning

confidence: 99%

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

et al. 2012

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Computational Methods in CryoElectron Microscopy 3D Structure Reconstruction

Pan

Wang

2013

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

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Towards dynamic structure of biological complexes at atomic resolution by cryo-EM

Zhang¹

2018

Chinese Phys. B

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Cryo-electron microscopy makes use of transmission electron microscopy to image vitrified biological samples and reconstruct their three-dimensional structures from two-dimensional projections via computational approaches. After over 40 years of development, this technique is now reaching its zenith and reforming the research paradigm of modern structural biology. It has been gradually taking over X-ray crystallography as the mainstream method. In this review, we briefly introduce the history of cryo-EM, recent technical development and its potential power to reveal dynamic structures. The technical barriers and possible approaches to tackle the upcoming challenges are discussed.

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D Structural Investigation of Caveolae From Porcine Aorta Endothelial Cell by Electron Tomography*

Cited by 3 publications

References 23 publications

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

Computational Methods in CryoElectron Microscopy 3D Structure Reconstruction

Towards dynamic structure of biological complexes at atomic resolution by cryo-EM

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