| 研究実績の概要 |
Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM) and X-ray free electron laser (XFEL) scattering require a large number of 2D images collected to resolve three-dimensional (3D) structures. In this project, we have been developing new efficient approaches to find 3D biological shapes from a few EM or XFEL images to serve as a starting point for further analyses.
We had first developed the protocol for using EM real space images as inputs and then have been developing algorithms to use XFEL diffraction patterns as inputs. This year, we are improving the algorithms so that it can deal with the diffraction patterns from actual experimental data. Diffraction patterns from XFEL experiments contain limited amount of signals due to the weak diffraction intensity. Thus only a certain region in the diffraction pattern (Region of Interest, ROI) can be used for the proposed match-finding algorithms, which poses a significant challenge in comparison to EM images. To automate the identification of ROI, we have developed a numerical algorithm, in which the approximate size of the sample in the input image is estimated via matching against a theoretical model and used to estimate the ROI. Using the estimated ROI, match-finding algorithms to identify plausible candidate 3D models from a few XFEL diffraction patterns has been improved.
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