2020计算及数学生物信息学和生物物理学线上会议 (The Third Conference on Computational and Mathematical Bioinformatics and Biophysics)

2020-12-20 ~ 2020-12-24 1600

December 20 – 24, 2020, (Beijing Time)

December 19 – 23, 2020, (US Central Time)

 

Zoom Meeting ID: 981 2055 6545 Password: CMBB

Zoom Link:

https://uasystem.zoom.us/j/98120556545?pwd=OXJhZENlTDhpaUsrNDQ5a2NvQVNudz09


Synopsis and Organizers 

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 150 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 reduce 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, index theory, and Yau-Hausdorff distance provide unique descriptions of biological data evolution. Differential geometry and evolutionary de Rahm-Hodge theory offer 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, persistent homology and persistent spectral graph 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 distance, spectral 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.

Organizing Committee:

Stephen Shing-Toung Yau, Tsinghua University, China

Guowei Wei, Michigan State University, USA

Changchuan Yin, University of Illinois at Chicago, USA

Shan Zhao, University of Alabama, USA

 program

 

 

DAY 1

 

December 20, 2020, (Beijing Time)

December 19, 2020, (US Central Time)

 

 

 

Beijing Time

US Central Time

Session Chair:   Shi-Jie Chen, University of Missouri, USA

8:45 – 8:55

AM

6:45 – 6:55

PM

 

Zoom Registration

8:55 – 9:00

AM

6:55 – 7:00

PM

Welcoming Remark

Stephen Shing-Toung Yau, Tsinghua University,   China

 

9:00 – 9:25

AM

 

7:00 – 7:25

PM

Shi-Jie Chen, University of Missouri, USA

Energy-guided   iterative approach to computational prediction of ligand-RNA interaction

9:30 – 9:55

AM

7:30 – 7:55

PM

Jiali Gao, Shenzhen Bay Laboratory, China

Importance of quantum effects in biomolecular   simulations

10:00 – 10:25

AM

8:00 – 8:25

PM

Duc Nguyen, University of Kentucky, USA

Opportunities and challenges for AI and Math   in drug discovery

 

10:30 – 10:55

AM

 

8:30 – 8:55

PM

Xinqi Gong, Renmin University of China

Functional multi-body   protein interaction supercomplex structure prediction

 

11:00 – 11:25

AM

 

9:00 – 9:25

PM

Jinqiao Duan, Illinois Institute of Technology, USA

Xi Chen, Xi'an University of Finance and Economics, China Target search   of a protein on DNA in the presence of position- dependent bias


 

DAY 2

 

December 21, 2020, (Beijing Time)

December 20, 2020, (US Central Time)

  

Beijing Time

US Central Time

Session Chair:   Jinbo Xu, Toyota Tech Inst at Chicago, USA

8:50 – 9:00

AM

6:50 – 7:00

PM

 

Zoom Registration

9:00 – 9:25

AM

7:00 – 7:25

PM

Jinbo Xu, Toyota Tech Inst at Chicago, USA

Latest development of   protein structure prediction by deep learning

 

9:30 – 9:55

AM

 

7:30 – 7:55

PM

Qunfeng Dong, Loyola University Chicago, USA

A Bayesian Framework   for Estimating the Risk Ratio of Hospitalization for People with Comorbidity   Infected by the SARS-CoV-2 Virus

10:00 – 10:25

AM

8:00 – 8:25

PM

Yongshuai   Jiang, Harbin Medical University, China The   framework for population epigenetic study

 

10:30 – 10:55

AM

 

8:30 – 8:55

PM

Zhaoming   Wang, St. Jude Children's Research Hospital, USA   Genetic risk for subsequent breast cancer among female survivors of childhood   cancer

11:00 – 11:25

AM

9:00 – 9:25

PM

Lily He, Beijing University of Civil Engineering and Architecture, China

A novel alignment-free   method for HIV-1 subtype classification


 

DAY 3

 

December 22, 2020, (Beijing Time)

December 21, 2020, (US Central Time) 

 

Beijing Time

US Central Time

Session Chair:   Robert Krasny, University of Michigan, USA

8:50 – 9:00

AM

6:50 – 7:00

PM

 

Zoom Registration

 

9:00 – 9:25

AM

 

7:00 – 7:25

PM

Robert Krasny, University of Michigan, USA

A GPU-Accelerated Fast   Summation Method for Electrostatics of Biomolecules

 

9:30 – 9:55

AM

 

7:30 – 7:55

PM

Dexuan Xie, University of Wisconsin-Milwaukee, USA Advances in   Poisson–Nernst–Planck Ion Channel Models and Finite Element Solvers

 

10:00 – 10:25

AM

 

8:00 – 8:25

PM

Xiaoqin Zou, University of Missouri - Columbia, USA Dissimilar Ligands Bind   in a Similar Fashion: A Guide to Ligand Binding Mode Prediction

 

10:30 – 10:55

AM

 

8:30 – 8:55

PM

Zhan Chen, Georgia Southern University, USA Variational interface models   for implicit solvation of biomolecules

 

11:00 – 11:25

AM

 

9:00 – 9:25

PM

 

Shenggao Zhou,   Soochow University, China

Variational   implicit-solvent predictions of the dry–wet transition pathways for   ligand–receptor binding and unbinding kinetics


 

DAY 4

 

December 23, 2020, (Beijing Time)

December 22, 2020, (US Central Time)

  

Beijing Time

US Central   Time

Session   Chair: Huan-Xiang Zhou, University of Illinois at   Chicago, USA

8:50 – 9:00

AM

6:50 – 7:00

PM

 

Zoom Registration

 

9:00 – 9:25

AM

 

7:00 – 7:25

PM

Huan-Xiang   Zhou, University of Illinois at Chicago, USA   Correlated Segment and Fuzzy Membrane Association of Intrinsically Disordered   Proteins

9:30 – 9:55

AM

7:30 – 7:55

PM

Qiang Cui, Boston University, USA

Multi-scale models for ESCRT driven membrane   remodeling

 

10:00 – 10:25

AM

 

8:00 – 8:25

PM

Jing-an Cui, Beijing University of Civil Engineering and Architecture, China

Final size relation   and control for epidemic model with heterogeneous mixing

 

10:30 – 10:55

AM

 

8:30 – 8:55

PM

Shaojun Pei, Tsinghua University, China

A novel numerical   representation for proteins: Three- dimensional Chaos Game Representation and   its Extended Natural Vector

 

11:00 – 11:25

AM

 

9:00 – 9:25

PM

Duan Chen,   University of North Carolina at Charlotte, USA Fast stochastic compression   algorithms for biological data analysis


DAY 5

 

December 24, 2020, (Beijing Time)

December 23, 2020, (US Central Time)

  

Beijing Time

US Central   Time

Session   Chair: Ruhong Zhou, Zhejiang University, China   and Columbia University, USA

8:50 – 9:00

AM

6:50 – 7:00

PM

 

Zoom Registration

 

9:00 – 9:25

AM

 

7:00 – 7:25

PM

Ruhong Zhou, Zhejiang University, China and Columbia University, USA

Immunotherapy   Modeling: Molecular Interaction and Recognition of MHC/peptide/TCR Complexes

 

9:30 – 9:55

AM

 

7:30 – 7:55

PM

Yaoqi Zhou, Griffith University, Australia

RNA secondary   structure prediction by Evolutionary Profile and Mutational Coupling

10:00 – 10:25

AM

8:00 – 8:25

PM

Qi Wang, University of South Carolina, USA Collective dynamics of active   particles on surfaces

10:30 – 10:55

AM

8:30 – 8:55

PM

Zixuan Cang, University of California Irvine, USA

Spatial signaling in single-cell data via   optimal transport

 

11:00 – 11:25

AM

 

9:00 – 9:25

PM

Kelin Xia, Nanyang Technological University, Singapore Persistent   representation based machine learning models for drug design

Abstracts of Invited Talks

Abstracts of Invited Talks.pdf