Computational and Mathematical Bioinformatics and Biophysics

2019-12-09 ~ 2019-12-13 1654

Description of Activity

We propose a week-long annual workshop series: “Computational and Mathematical Bioinformatics” and Biophysics, to be held at Tsinghua Sanya International Mathematics Forum (TSIMF) in December every year, if possible. The exact timing of the workshop depends on the TSIMF availability. Biology became one of the most important forefronts in physical sciences since it transformed from macroscopic to microscopic (or molecular) in 1960s. Unfortunately, biological science and mathematics have been on divergent paths for about half century. Very few mathematicians pay any attention to important developments in biosciences. Due to rapid advances in biotechnology in the past few decades, the Protein Data Bank has accumulated more 130 thousand structures and the GenBank has collected near 200 million sequences. The exponential growth of biological data has set the stage for biological sciences to transform from qualitative, phenomenological and descriptive to quantitative, analytical and predictive in the 21st century. Mathematics, as it did to quantum physics, is becoming a driving force behind this historic transition.

The biological datasets are a key resource for genetics, structural biology, molecular biophysics and bioinformatics, and promises efficient drugs for curing various diseases. Utilizing these datasets, various biophysical models have been developed for the prediction and analysis of the hot spots of protein- protein interactions, protein functional domains, protein-DNA/RNA specificity, protein-ligand binding poses and binding affinity, protein folds, mutation induced protein stability changes, species evolution and the origin of life. These datasets play an indispensable role in protein homology analysis and protein design in general. However, data information extraction, interpretation and analysis as well as data-driven modeling of self-organizing biological systems become increasingly challenging due to the tremendous complexity, diversity and large quantity of the available data. A pressing challenge is how to develop mathematical models to solve important standing biological problems. Another challenge is how to absorb new technical advances in mathematics, statistics and data science for dealing with complex and diverse biological data and unveil the rule of life. The other challenge is how to create new mathematics from biology as did from quantum mechanics in the past century.

Mathematical approaches that are able to efficiently reduc the number of degrees of freedom, and model biomolecular structure, function, and dynamics have a potential to deal with the data complexity in biological data. Multiscale modeling, intrinsic manifold extraction, dimensionality reduction and machine learning techniques are introduced to reduce the complexity of macromolecular systems while maintaining an essential and adequate description of the molecule of interest. Morse theory, Conley index, Yau-Hausdorff distance, topological fingerprints and Floer homology o er unique descriptions of biological data evolution. Differential geometry and level set methods provide a natural description of the formation and evolution of biological systems. Lie algebra and Lie group are powerful tools for the description the symmetries, self-similarities and repeated patterns in complex biological data. Persistently invariant manifold and intrinsically low-dimensional manifold are utilized for the analysis of the structure-function relationships in biological datasets. Euler characteristic and persistent homology simplify the complexity of biological data. Algebraic geometry deciphers the intrinsic properties of the totality of solutions of signal transduction pathway models. Stochastic analysis and probability theory unveil the dynamical process of biological data. These ideas have been successfully paired with current progresses in biological science and technology. However, many mathematicians have lagged behind much recent exciting development in the field.

Impact

Currently, a major barrier for mathematicians, statisticians and data scientists to work in this field is the lack of knowledge in genetics, molecular biophysics and evolutionary biology, etc., while a major obstacle for biologists, biophysicists and biomolecular scientists is the lack of knowledge about mathematical apparatus, statistical algorithms and machine learning techniques that have been developed in the recent past. The proposed annual workshop series is designed to help bridge gaps between biologists and mathematicians and to facilitate their collaborations.

Sustainability plans

In a short term, this workshop series will bridge mathematics, computer science and biological science, and promote bio-inspired mathematics, such as Yau-Hausdorff , high dimensional persistence and deep learning, in the modeling and analysis of biological data. In a long term, it will integrate mathematical disciplines, such as differential geometry, partial differential equation, algebraic topology and algebraic geometry for understanding the emerging complexity and diversity of large biological datasets. It will educate young scientists and foster the collaboration at the interface of mathematics, statistics, computer science and biological sciences. There is enormous potential in this area for integrative interdisciplinary research in which mathematicians and biologists to develop solutions to data challenges in biological sciences. This workshop series will act as a catalyst to fully exploit these synergies, and create a network of collaborations that will sustain future activities in this area beyond these annual workshops.

Organizers

Professor Guowei Wei, Department of Mathematics Michigan State University

Professor Stephen S.-T. Yau, Department of Mathematical Sciences,Tsinghua University

Dr. Changchuan Yin, Department of Mathematics, University of Illinois at Chicago

Professor Shan Zhao, Department of Mathematics, University of Alabama

Participants

English Name

Chinese Name

Employer's Name in English

 Employer's Name in Chinese

Yin, Changchuan

殷长传

University of Illinois at   Chicago


Wei, Guowei


Michigan State University


Yau, Stephen Shing-Toung


University of Illinois at   Chicago


Chen, Minxin


Soochow University

苏州大学

Dong, Rui

董睿

Tsinghua University


Duan, Haibao

段海豹

Chinese Academicy of Science

中科院

Gao, Yiqin

高毅勤

Peking University

北京大学

Gong, Xinqi

龚新奇

Renmin University


Gong, Haipeng

龚海鹏

Tsinghua University


Han, Fei


National University of   Singapore

新加坡国立大学

Hao, Wenrui


Penn State University


Duan, Jinqiao


Illinois Institute of   Technology


Huang, Shi


Xiangya Medical School,   Central South University


Lei, Fengchun


Dalian University of   Technology


Lei, Jinzhi

雷锦志

Tsinghua University

清华大学

Lin, En-Bing


Central Michigan University,

中央密歇根大学

Liu, Chun


Penn State University


Liu, Haiyan


Univ. Science and Technology   of China


Pei, Shaojun


Tsinghua University


Sun, Fengzhu


University of Southern   California

南加州大学

Wang, Chunmei


Texas State University


Wang, Yanying

王彦英

Hebei Normal University

河北师范大学

Waterman, S Michael


University of Southern   California


Wen, Jia


The Chinese University of   Hong Kong


Wu, Jie


National University of   Singapore

新加坡国立大学

Xia, Kelin


Nanyang Technological   University


Xiao, Yi

肖奕

Huazhong University Science   and Technology


Yang, Minghui

杨明晖

WIPM, China

中国科学院武汉物理与数学研究所

Alexov, Emil


Clemson University

克莱姆森大学

Zhang, John

张增辉

East China Normal University

华东师范大学

Zhou, Huan-Xiang


Beijing Normal   University-HongKong Baptist University United International College

北京师范大学-浸会大学联合学院

Yang, Xiwu

杨希武

Liaoning Normal University

辽宁师范大学

Zhang, Kam Y.


University of Tokyo, Japan


Jianshu Cao

曹建树

CSRC

北京计算科学研究中心

Dmytro Kozakov


Stony Brook University


Chen Song


Peking University

北京大学

Jinbo Xu


Toyota Technological   Institute at Chicago

芝加哥丰田科技学院

Limei Cheng


BMS

百时美施贵宝

Xiaoqin Zou


University of  Missouri

密苏里大学

Shi-Jie Chen


University of  Missouri

密苏里大学

zhengqu


Peking University

北京大学

Kun Tian

田堃

Renmin University of China

中国人民大学

Jie Yang


University of Illinois at   Chicago


Lei Wang


Renmin University of China

中国人民大学

Xin Zhao

赵鑫

Tsinghua University

清华大学

 Schedule

Time \ Date

Monday (Dec. 9)  

Tuesday (Dec.   10)

Wednesday (Dec.   11)

Thursday (Dec.   12)

Friday (Dec. 13)  

7:30-8:30

Breakfast

Breakfast

Breakfast

Breakfast

Breakfast

Chair

Stephen Yau

(5 minute open remarks)

 Jianshu   Cao

 

Haiyan Liu

 

John Zhang


8:50-9:30

Shi-Jie Chen

Emil Alexov

Shi Huang

Minghui Yang

Discussion

9:30-9:55

Jianshu Cao

Fengzhu Sun

John Zhang

 

Chen Song

Discussion

9:55-10:25

Tea break (5 min Photo)

Tea break

Tea break

Tea break

  Tea break

10:25-11:05

Yi Xiao

Limei Cheng

Guowei Wei

Rui Dong

 Discussion

11:05-11:30

Jinqiao Duan

Dmytro Kozakov

Jinbo Xu

Shaojun Pei

 Discussion

11:30-11:55

Haipeng Gong 

 Jie Wu

 

Yiqing Gao

Jinzhi Lei

 Discussion

11:55-13:25

Lunch

Lunch

Lunch

Lunch

Lunch

Chair

Guowei Wei   

Kam Zhang   


Jie Wu

Departure

13:30-14:10

Huan-Xiang Zhou

Xinqi Gong

Free   discussion

Jia Wen 

Departure

14:10-14:35

Kam Zhang

Haibao Duan

Free   discussion

Fei Han

Departure

14:35-15:00

Wenrui Hao

Yanying Wang

Free   discussion

Fengchun Lei

Departure

15:00-15:30

Tea break

Tea break

Free discussion

Tea break

Departure

15:30-16:10

Changchuan Yin

 En-Bing Lin

Free   discussion

Lei Wang

Departure

16:10-16:35

Kelin Xia

Minxin Chen

Free   discussion

Xiwu Yang

Departure

16:35-17:00

Haiyan Liu


Free   discussion

Discussion

Departure

17:00-17:25

Discussion

 Discussion

Free   discussion

Discussion

Departure

17:30-19:30

Banquet 18:00-20:00

Dinner

Dinner

Dinner


Titles and Abstracts

 

Sanya_Bioinformatics_Final_12042019.pdf

 

 Group Photo

HD Image

2019计算数学、生物信息学和生物物理学会议.jpg

2019计算数学、生物信息学和生物物理学会议 A.jpg