Xueming Li has been researching electron microscopy for more than fifteen years, aiming to make the atomic resolution more obtainable. His previous efforts on an electron-counting camera significantly contributed to the resolution revolution of electron cryo-microscopy (CryoEM). His current research mostly focuses on the theoretical and methodological development of CryoEM and its application in structural biology to address important biological questions. He is particularly interested in developing and applying novel image processing and parallel computing technology to advance CryoEM for routine atomic-resolution structure analysis of biological macromolecules and future industrial application for drug discovery.
CryoEM is becoming a very powerful tool in determining structures of biological macromolecules and cells, which is essential to reveal the mechanism of life process and disease, as well as for drug discovery. Now, more CryoEM facilities are under construction or being planned. How to build a suitable computing cluster for CryoEM and how to make the algorithm better for high efficiency and high resolution are becoming key questions in this field. These are also issues that Tsinghua University aims to solve.
Our university has one of the largest CryoEM facilities in the world and hundreds of users. With this background, the Intel® Parallel Computing Center(s) (Intel® PCC) in Tsinghua aims to find a high-throughput solution for the CryoEM computing facility, better algorithms to achieve higher resolution and observe atomic details in life process.
Tsinghua University is planning to expand our computing power from the current approximately 600 Intel CPU nodes to more than a thousand in the next few years in order to fulfill the requirements of CryoEM computing for hundreds of users. Tsinghua University will not only optimize our current code but also develop new programs to make CryoEM computing work better on the newest Intel®-based CPU clusters with the latest generation of high-speed Intel® Omni-Path Architecture. We are also planning to develop our programs on Intel® Xeon Phi™ coprocessors to take advantage of newest generation of manycore parallel computing. The success of our computing facility and algorithms will set a benchmark for the future CryoEM computing.
Tsinghua University is now developing a new open-source CryoEM computing system to utilize the state-of-the-art machine learning combined with parallel computing techniques, and finally make CryoEM be more efficient and scalable. Tsinghua University has invested significant efforts in introducing new machine-learning techniques to the current CryoEM technology, such as statistical learning based on important sampling, deep learning, and compressed sensing. By better design and optimization, we expect our program will be able to take advantage of the newest generation of Intel processors.
The above efforts will definitely make our computing power more efficient and more productive, and accelerate scientific research and potential industrial application of CryoEM in drug discovery.
For life sciences, Tsinghua has hundreds of users and students working on CryoEM. Our facility will be opened to the entire country, including universities and companies, in the next few years by collaborations or public services. One of our targets is to train all users to understand and use the modern computing technology proposed in this project. We will also open some training classes and organize meetings for training and mind-storming purposes. This project is based on collaborations with labs in computing sciences and electronic engineering. Therefore, we will gather a wide range of new technologies, including parallel computing and signal processing, and help scientists in other fields understand how their techniques can be applied to life sciences.