Research of Professor Fujisawa¶
Extracting the maximum information from experimental data through analysis¶
From one- and two-dimensional data to a three-dimensional world of shapes¶
Many experimental data, such as spectra (one-dimensional) and projections (two-dimensional), are used to obtain information about shapes (three-dimensional). While the fundamental principles for obtaining this information have been well established, real-world experimental data often present various challenges.
In this research, we analyze experimental data such as negative stain electron micrographs and small-angle X-ray scattering curves to determine the structures of proteins.
What you can learn through this research¶
You can learn the following:
Applied mathematics and machine learning: The foundation of all technologies.
Programming: Writing numerical analysis codes in Python.
Experimental techniques: Acquiring various experimental techniques such as sample preparation and electron microscopy.
Data analysis: Learning how to analyze experimental data using custom software and extract meaning from it.
For those who are not good at mathematics and computers:¶
For those who are not good at mathematics and computers, we are conducting experiments on the expression, purification, and characterization of pathogenic proteins in collaboration with Dr. Yuji Kamatari of the Gifu University Center for Integrated Research in Life Sciences ( https://researchmap.jp/kamatari?lang=en ).
Regarding student guidance:¶
We focus on:
Developing fundamental engineering skills
Avoiding simple imitation
Cultivating logical thinking