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Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics.
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern X-ray Free Electron Laser (XFEL) technology provides the possibility to acquire a large number of diffraction patterns from individual biological nano-particles, including proteins, viruses, and DNA. Ideally, the collected data frames are high-quality single-particle diffraction patterns. However, unfortunately, the raw dataset is noisy and also contains patterns with scatterings from multiple particles, contaminated particles, etc. The data complexity and the massive volumes of raw data make pattern selection a time-consuming and challenge task. Further, X-rays interact with particles at random and the captured patterns are the 2D intensities of the scattered waves, i.e. we cannot observe the particle orientations and the phase information from the 2D diffraction patterns. To reconstruct 2D diffraction patterns into 3D structures of the desired particle, we need a sufficiently large single-particle-pattern dataset. The computational methodology for this reconstruction task is still under development and in need of an improved understanding of the algorithmic uncertainties.

In this thesis, we tackle some of the challenges to obtain 3D structures of sample molecules from single-particle diffraction patterns. First, we have developed two classification methods to select single-particle diffraction patterns that are similar to provided templates. Second, we have accelerated the 3D reconstruction procedures by distributing the computations among Graphics Processing Units (GPUs) and by proposing an adaptive discretization of 3D space. Third, to better understand the uncertainties of the 3D reconstruction procedure, we have evaluated the impact of the different sources of resolution-limiting factors and introduced a practically applicable computational methodology in the form of bootstrap procedures for assessing the reconstruction uncertainty. These technologies form a data-analysis pipeline for recovering 3D structures from the raw X-ray single-particle data, which also analyzes the uncertainties. With the experimental developments of the X-ray single-particle technology, we expect that the data volumes will be increasing sharply, and hence, we believe such a computational pipeline will be critical to retrieve particle structures in the achievable resolution.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. , p. 79
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1905
Keywords [en]
X-ray Free Electron lasers (XFELs); 3D electron density determination; Machine learning; Image processing; High-performance computing; GPUs; Uncertainty quantification; X-ray single-particle Imaging; Flash X-ray single-particle diffraction Imaging (FXI);
National Category
Biophysics Computer Sciences Biological Sciences Other Computer and Information Science
Research subject
Physics with specialization in Biophysics
Identifiers
URN: urn:nbn:se:uu:diva-403878ISBN: 978-91-513-0877-7 (print)OAI: oai:DiVA.org:uu-403878DiVA, id: diva2:1393531
Public defence
2020-03-31, Room A1:107a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2020-03-06 Created: 2020-02-17 Last updated: 2020-03-24
List of papers
1. Supervised classification methods for flash X-ray single particle diffraction imaging
Open this publication in new window or tab >>Supervised classification methods for flash X-ray single particle diffraction imaging
2019 (English)In: Optics Express, ISSN 1094-4087, E-ISSN 1094-4087, Vol. 27, p. 3884-3899Article in journal (Refereed) Published
National Category
Biophysics Computer Sciences
Identifiers
urn:nbn:se:uu:diva-378990 (URN)10.1364/OE.27.003884 (DOI)000459152800024 ()
Projects
UPMARC
Available from: 2019-02-04 Created: 2019-03-19 Last updated: 2020-02-17Bibliographically approved
2. Assessing uncertainties in x-ray single-particle three-dimensional reconstruction
Open this publication in new window or tab >>Assessing uncertainties in x-ray single-particle three-dimensional reconstruction
2018 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 98, p. 013303:1-12, article id 013303Article in journal (Refereed) Published
National Category
Computer Sciences Biophysics
Identifiers
urn:nbn:se:uu:diva-355899 (URN)10.1103/PhysRevE.98.013303 (DOI)000437672700009 ()30110794 (PubMedID)
Projects
UPMARCeSSENCE
Available from: 2018-07-05 Created: 2018-07-07 Last updated: 2020-02-17Bibliographically approved
3. Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
Open this publication in new window or tab >>Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
2015 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 29, p. 233-243Article in journal (Refereed) Published
National Category
Computer Sciences Biophysics
Identifiers
urn:nbn:se:uu:diva-232813 (URN)10.1177/1094342015572030 (DOI)000353463200008 ()
Projects
UPMARCeSSENCE
Available from: 2015-02-23 Created: 2014-09-25 Last updated: 2020-02-17Bibliographically approved
4. Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline
Open this publication in new window or tab >>Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline
2020 (English)In: Computing Research Repository, no 1910.14029Article in journal (Other academic) Submitted
National Category
Biophysics Computer Sciences
Identifiers
urn:nbn:se:uu:diva-403874 (URN)
Projects
UPMARCeSSENCE
Available from: 2019-10-30 Created: 2020-02-17 Last updated: 2020-02-21Bibliographically approved

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12345671 of 16
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