Guangyuan(Frank) Li

Biomedical Informatics first-year PhD student
University of Cincinnati
Cincinnati Children Hospital Medical Center(CCHMC)
Email: li2g2@mail.uc.edu

Education

09/2018 – 05/2019
Exchange student in Biological Design Institute, Arizona State University, U.S.A
Project: Analyzing GPCR structure by using lipidic phase technology (Supervisor: Dr. Wei Liu)
Click the link to know more about my GPCR project: GPCR project

09/2015 – 06/2019
Bachelor of biology in college of life sciences, Wuhan university, China
General GPA: 3.81/4.00
Courses: Biology category, Computer Science category, Mathematical category, etc.
Click each link will redirect to my course lists

Click the link to know more about my home institution: Wuhan University

Research experience

Shenzhen Huada Gene Research Institute, BGI-shenzhen, March 2018-July 2018
Intern, An novel cell therapy for melonoma and colon cancer

• Participated in drug pre-clinical testing and drug declaration process
• Used R programming language to analyze single cell RNA-seq data
• Took systematic bioinformatic training(Linux, Python, Perl, R programming language )
• Familiar with the cutting-edge progress of cancer immunotherapy
  Click on the link to know more: BGI internship

College of life science, Wuhan University, November 2016-November 2017
Core member, iGEM(international genetically engineered machine) competition

• Won silver medal in the final presentation(Boston,USA)
• Designed an engineered bacteria for disposing of sewage
• In charge of mathematical modelling-building and performance testing
• Took part in experimental part including molecular cloning, cell culture, etc
  Click on the link to know more：iGEM journal

Institute of hydrobiology, CAS, May 2017-May 2018
Research assistant, Early development of zebrafish Primodial Germ Cell(PGC)

• Studied the function of several critical genes involved in zebrafish PGC cell formation and sex determination
• Conducted zebrafish genetic experiment, including microinjection, in-situ hybridization, confocal microscopy, immunofluorescence
  Click on the link to know more: zebrafish research

Highlights

• Having systematic bioinformatic training
• Familiar in Perl, Python, R computer programming languages and MATLAB, can solve personalized programming demands
• Structural biology research experience
• Molecular cloning technology, cell culture operation, genetic operation in zebrafish
• Good at communicating with others
• Having high team spirits

Honors & Scholarships

• Excellent student in Wuhan University(Three consecutive years) 2016,2017,2018
• Excellent scholarship in Wuhan University(Two consecutive years) 2016,2017
• Best debater in Wuhan University debating tournament
• Vice director in department student union

Core Biology category

1. Molecular Biology(97/100)
2. Cell Biology(88/100)
3. Genetics(92/100)
4. Biochemistry(91/100)
5. Microbiology(92/100)
6. Virology(89/100)
7. Immunology(97/100)
8. Genomics(91/100)
9. Physiology(93/100)
10. Developmental Biology(96/100)

Computer Science category

1. Algoritm Coursera Online UCSD
2. Machine Learning Coursera Online Stanford
3. MATLAB Application(90/100)
4. C Programming Language(96/100)
5. Programming in HTML5, CSS3, JavaScript Coursera Online John Hopkins

Mathematical category

1. Advanced Mathematics(89/100)
2. Multivariate Calculus Coursera Online Imperial College London
3. Linear Algebra Coursera Online Imperial College London
4. Inferential Statistics Coursera Online Imperial College London
5. Bayesian Statistics Coursera Online UCSC

Solving crystal structure of GPCR-G protein complex

GPCR constitutes 30%-40% drug targets in current pharmaceutical industry, however, the lack of high-resolution structure largely hinder the further investigation to harness their desirable potential. As a membrane protein, GPCR is conformationally flexible and dynamic. A plethora of approaches has been proposed to resolve this issue, including protein engineering(site-directed mutation, fusion protein, protein truncation). Our lab employs the state-of-art lipidic cubic phase(LCP) technology to specifically assist in GPCR protein crystallization. Due to its amphiphilic property which perfectly mimicking natural phospholipid bilayer, LCP enables the hydrophobic and hydrophilic fragment of GPCR protein diffuse freely within the system and more amenable to form lattice. Followed by X-ray free-electron laser(XFEL) and Serial femtosecond crystallization(SFX) technology, which make it possible to generate high-quality data from microcrystals. We have elucidated dozens of GPCR proteins’ structure and their underlying regulatatory mechanisms.

In my project(also my undergraduate thesis), I try to optimize current pipelines for construct designing, GPCR protein extracting and to explore the novel approaches to stablize GPCR protein. To be specific, we adopt the concept of solving GPCR-G protein complex instead of merely solving GPCR structure. There are there reasons for that:1) GPCR protein is highly dynamic and flexible, G protein could serve as a stablizer to immobilize GPCR protein and force them retaining natural and functional state. 2) By using G protein to form the complex, we are able to increase the overall molecular weight of GPCR protein, which may offer us a new method to solve its protein through Cryo-EM. 3) We hope to illustrate how GPCR interact with downstream signal partner and further extend our understanding.

Using Machine-learning approach to streamline trial-and-error process during sample preparation

In order to simplify and streamline the time and labor consuming process during sample preparation, I endeavored to modify existing machine learning algorithm and apply them into predicting the effect of particular protein engineering approaches, including fusion protein, site-directed mutaton and protein truncation. The idea here is to adopt multi-classfication instead of current binary classifier. It is exceedingly benefit, when we hope to carry on one kind of engineeing approaches(Say mutate site K6.43), binary classfier could only tell you if it could work, if not, the only thing left we can do is to arbitrarily find another site. In contrast, a multi-classification could let us know which step could be exactly most likely getting stuck, then we are able to apply our biochemistry knowledge to do subsequent analysis and derive another possible site with higher confidence.

By doing that, firstly we need to collect training data set as mush as possible. Fortunately, there are tons of negative data generated from our lab(Which is imaginable, optimizing protocol is a high-failure rate process). We assign them a series of score from 0 to n corresponding to the certain stip upon which they would get stuck. Next we try to find feature vectors as following:

Sequence-based features (Hydrophobicity, Hydrophilicity, Amino acid percentiles .. )

Structure-based features (Van del Waals Force, Salt bridge, Hydrogen bond ..)

Energy-based features (Gibbs free energy ..)

Then we fit those parameter depending on which machine learning algorithm we chose and finally it may work!

Reference

1. Vilardaga, Jean-Pierre, et al. “G-protein-coupled receptor heteromer dynamics.” J Cell Sci 123.24 (2010): 4215-4220.
2. Li, J. et al. The Molecule Pages database. Nature 420, 716-717 (2002).
3. Popov, Petr, et al. “Computational design of thermostabilizing point mutations for G protein-coupled receptors.” eLife 7 (2018): e34729.

A novel set of cancer therapy

I am now working as an intern in BGI-Shenzhen. BGI is one of the world’s genome sequencing center, my job is to participate in developing a novel anti-tumor method called TSA-CTL. Based upon the powerful and accurate sequencing platform, we can firstly identify tumor specific antigen(TSA) by using whole exome sequencing(WES) and RNA-seq, and use these antigens to activate patients’ self-derived cytotoxic lymphocyte(CTL) in vitro, finally reinfuse the activated T cell into patients’ body. 2 out of 5 patients of malignant melanoma have appeared partial tumor regression during our clinical trial. More importantly, different from CAR-T therapy, our method is free from off-target effect or cytokine syndrome, all of our clinical trials are reported to be safe.

The biggest harvest for me is the systematic bioinformatic training, ranging from sequencing technology, processing single-cell RNA data, to learning python, R，and perl to solve series of programming probelm. And I am currently learning how to make good use of the TCGA(The Cancer Genome Atlas) database to do data mining and bioinformatic analysis like survival analysis and gene expression profile.

Single cell RNA-seq analysis

By using single cell RNA-seq, we aim to detect those unknown critical genes in cancer development. Plus, we can compare the gene expression profile in different area and different time, we can track the effects of a certain kind of drug and the whole developmental timeline. Combined with enrichment analysis, we can determine which set of gene would be most correlated with our observed phenotype.

Training program ( Bulk RNA-seq analysis)

Analyzing bulk RNA-seq data is the most basic and easy-to-learn pipeline in bioinformatics analysis. We chose Bowtie to map and align our RNA reads, then TopHat came up to fix the “junction site” problem, which is caused by alternative splicing. Cufflink is a basic package that can help us calculate gene expression, along with analyzing significant diffrences between two or more samples. Finally，we deployed R package to visulize the results.

November 8th, Boston

This is my first time to be in America, I feel so excited to get this opportunity to present our project on behalf our school. We’ve been working on this project for the last 6 months. I dare not to say how great our project is, we are just a bunch of junior student. We confronted a variety of obstacles, failing to get decent eletrophoresis bands, running into PCR issues, etc. Maybe it sounds easy for a well-trained graduate students, but it baffles us a lot at the first several months. The most fascinating part of iGEM competition is that we did everything by ourselves, ranging from being a lab manager to procure all the instruments and materials, to perform every experiments, analyzing data and construct mathematic modeling. Plus, I have learned how to design an nice-looking webpage, since we have to present all of our result on our webpage, which will be a necessary part for judges’ appraisal.

iGEM opening ceremony

Wow, fastanstic opening events, people from all over the world. There are 301 teams this year and we are iGEMers! iGEM competition is a prestigious interdisciplinary contest for synthetic biology, held by MIT annually. Every team is aimed to construct one kind of biological designs using the philosophy of synthetic biology – our body is like a car which constitutes lots of parts, all these parts assemble together comes the car. With the advanced development of science, we know that life is also built upon each “part”, the most basic part – the permutation of four base part ATCG. If appropriately controlled and designed, we can make these parts assemble and act in a certain way that might be different from its natural counterpart, but may benefit our life and society.

If you would like to know more about iGEM, click the link below.

iGEM homepage

Our team and project

Here are our team members. In general, we aim to apply a novel dehalogenase, RdhANP, to dispose of industrial wastewaters by degrading the persistent and toxic pollutant, halogenated organics. To optimize its application, on the one hand, we used conventional synthetic biological methods for reference to increase the production of functional dehalogenase. On the other hand, we planned to introduce our engineered bacterium, Bacillus megaterium into hybrid membrane bioreactor(HMBR) to make our project more practical. Engineering and modification were carried out upon our B. megaterium.

If you are interested in our project, click the link below!

WHU-China 2017 project website

Visiting Harvard University

Visiting Harvard University, so much squirrels!

Abstract for my work

Primordial germ cell (PGC) is a group of embryonic cells that will develop into mature gametes in the early stages of development. Buckyball is a factor found in fish that plays an important role in germ plasma assembly. However, the effects of its tissue-specific overexpression in primordial germ cells (PGC) remains unclear. This project used the unique Gal4-UAS expression system to ensure PGC-specific overexpression of Buckyball, combined with the strong advantages of the China zebrafish resource center(CZRC), to study the effect of buckyball overexpression on the number of PGCs and gonadal development at DNA level. Unlike temporary gene knockouts at RNA level, changes in DNA sequence allows us to observe the sustained effects of buckyball genes on following PGC differentiation. We have found some intriguing facts and the work is still going on.

Reference

Asakawa K, Abe G, Kawakami K. Cellular dissection of the spinal cord motor column by BAC transgenesis and gene trapping in zebrafish[J]. Frontiers in neural circuits, 2013, 7: 100.