Plenary Speakers
Ryoichiro Kageyama
RIKEN Center for Brain Science, Japan
Website：
https://cbs.riken.jp/en/faculty/r.kageyama/index.html
Title of Presentation：The role of oscillatory gene expression in embryogenesis
Profile
His current research involves studies on the dynamics of gene expression during cellular proliferation and differentiation. His group has developed time-lapse imaging and light-controlled gene expression systems to analyze the functional significance of oscillatory gene expression with ultradian rhythms in many biological events including somitogenesis and neurogenesis. He has recently found that aged neural stem cells, which do not express the proneural gene Ascl1, can be rejuvenated by inducing oscillatory expression of Ascl1.
Abstract
The role of oscillatory gene expression in embryogenesis.
Many genes are expressed in an oscillatory manner in developmental processes, such as somitogenesis and neurogenesis. In mouse somitogenesis, Hes7 exhibits oscillatory expression with 2-hour periodicity and regulates periodic formation of somites. Hes7 oscillations are controlled by delayed negative feedback and delayed coupling; manipulation of these delays dampens Hes7 oscillations, leading to severe fusion of somites and somite-derived tissues such as the vertebrae and ribs. Similarly, Hes1 expression oscillates in embryonic neural stem cells (NSCs), which actively proliferate and generate neurons and glial cells. Hes1 oscillations periodically repress other cell fate determination factors such as the proneural gene Ascl1, thereby driving their oscillations. By using an optogenetic method, we found that oscillatory expression of these factors promotes proliferation of NSCs. Hes1 oscillations are also controlled by delayed negative feedback and delayed coupling, and manipulation of these delays dampens Hes1 oscillations, leading to microcephaly. Thus, oscillatory gene expression is required for proper developmental processes. I would also like to discuss the importance of mathematical modeling to deeper understanding of the mechanism of oscillatory gene expression.
Lei Zhang
Beijing International Center for Mathematical Research, Center for Quantitative Biology, Center for Machine Learning Research, Peking University, China
Website：http://bicmr.pku.edu.cn/~zhanglei
Title of Presentation：Prepareing for release
Profile
Lei Zhang is Boya Distinguished Professor at Beijing International Center for Mathematical Research, Peking University. He is also a Principle Investigator at Center for Quantitative Biology and Center for Machine Learning Research. He obtained his Ph.D in Mathematics at Penn State University in 2009. His research is in the area of computational and applied mathematics and interdisciplinary science in biology, materials, and machine learning. He has published the papers in Phys. Rev. Lett., PNAS, Acta Numerica, Science journals, Cell journals, SIAM journals. He was awarded/funded by NSFC Innovation Research Group, NSFC Outstanding Youth Award, National Key Research and Development Program of China, NSFC Excellent Youth Award, Royal Society Newton Advanced Fellowship, etc. He serves as an Associate Editor for SIAM J. Appl. Math, Science China Mathematics, CSIAM Trans. Appl. Math, DCDS-B, The Innovation, etc.
Abstract
Prepareing for release
Mukund Thattai
Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, India
Website：https://www.ncbs.res.in/faculty/thattai
Title of Presentation：Regulatory and Evolutionary Landscape of Eukaryotic Vesicle Traffic
Profile
Mukund Thattai is a physicist-turned computational biologist. He obtained a B.A. in physics from Cornell University in 1999, and a Ph.D. in physics from the Massachusetts Institute of Technology in 2004. Since 2004 he has been on the faculty at the National Centre for Biological Sciences in Bangalore, and is a member of the Simons Centre for the Study of Living Machines. Dr. Thattai's current research deals with the mechanistic and evolutionary basis of eukaryotic cell architecture. His work has contributed to our understanding of mitochondrial origins, organelle diversification, and the structure and function of the Golgi apparatus.
Abstract
Eukaryotic cells are defined by their membrane traffic systems: transport vesicles move cargo between membrane-bounded compartments such as the ER, Golgi, and plasma membrane. This system allows eukaryotes to sample their environment, change shape, and communicate by contact, traits that are essential for organised sexual reproduction and multicellularity. Understanding the origins of compartmentalised membrane traffic is therefore key to understanding eukaryote evolution. The loading of cargo into budding vesicles, and the fusion of these vesicles to target compartments, is regulated by modules of interacting proteins such as coats, cargo adaptors, and fusogenic SNARE proteins. The system is self-organised, because the resulting flux of cargo determines the composition of each compartment. We explore a rule-based multilevel model of vesicle traffic involving three layers: evolutionary (genes), regulatory (protein interactions), and phenotypic (cargo transport graph). We use this framework to ask the following questions: How is the membrane traffic system assembled through dynamic protein interactions and information flow? How did it get this way over billions of years of evolution? How does it benefit the cell to have such a system? By bringing together threads from biology, physics and computer science, we can weave the story of the past, present and future of cellular life.
Il hyo Jung
Department of Mathematics & Institute of Mathematical Sciences & Graduate School of Date Science Pusan National University, Korea
Website：https://math.pusan.ac.kr/matheng/index..do
Title of Presentation：Mathematical Modeling for Anticancer Drug Therapy
Profile
Il Hyo Jung received his B. S. degree in Mathematics from the Pusan National University, South Korea in 1991. He then received his M.S, and Ph.D. degrees in Mathematics from KAIST, South Korea in 1993 and 1997, respectively. Dr. Jung is currently working a Professor at the Department of Mathematics and the Graduate School of Data Science, the Division director of Institute of Mathematical Sciences, Pusan Nation University, South Korea. He is also serving as the President of the Korean Society for Mathematical Biology. Dr. Jung's research interests include Biomathematics, Mathematical Modeling Framework for Personalized Anticancer Drug Therapy, Infectious Disease Mathematical Modeling, Qualitative Analysis of Differential Equations (ODE/PDE), and Control and Optimal Control Problem. In November 2022, he was awarded the Korea Disease Control and Prevention Agency (KDCA) Commissioner's Award for contributing to the protection of public life through his work in COVID-19 pandemic forecasting by mathematical modeling.
Abstract
Mathematical modeling of anticancer drug therapy has a history of more than 50 years. This research field has contributed to the development of ideas about drug therapy scheduling, multi-drug protocols, the recruitment and synchronization of normal cells and cancer cells etc. The phenomena and relationships that relate a drug dose to its concentration in plasma or its effect on cancer or normal cells are defined by the so-called pharmacokinetics (PK) and pharmacodynamics (PD). PK describes the variation between dose and concentration in plasma, while PD describes the effect of a given concentration on cell viability. In this talk, I will introduce some biological background and mathematical modeling techniques in this field that have the potential to contribute to anticancer drug therapy and consider several topics, which may help improve the situation in the mathematical approach.
Chih-hao Hsieh
Institute of Oceanography, National Taiwan University, Taiwan
Website：https://ecoinformaticsc.webnode.tw/professor/
Title of Presentation：Empirical Dynamic Modeling toward mechanistic understanding and forecasting complex systems
Profile
Chih-hao (Zac) Hsieh is a Professor of Oceanography at National Taiwan University. Chih-hao Hsieh’s academic journey began with an MS/BA in Zoology from National Taiwan University, followed by a PhD in Oceanography from the University of California-San Diego. Since joining the faculty of National Taiwan University in 2007, he has played a major role in research and teaching. He served as Director of the Institute of Oceanography from August 2020 to July 2023. His notable achievements include receiving the Biwako Prize for Ecology, the Ministry of Science and Technology Outstanding Research Award of Taiwan, recognition as one of the Ten Outstanding Young Persons of Taiwan, and the Young Scientist Research Innovation Award. Chih-hao Hsieh’s research prowess is evidenced by his authorship of over 130 research articles, many of which have been published in prestigious journals such as Nature and Science. His research focuses on developing methods for time series analysis in dynamical systems, with applications in environmental management, economics, epidemiology, ecology, medical sciences, and neural sciences.
Abstract
Mechanistic understanding and forecasting are important for effective system policy and management recommendations. However, these tasks are challenging because the real world is complex, where correlation does not necessarily imply causation. Here, I present a time-series analytical framework, known as Empirical Dynamic Modeling (EDM). EDM enables detection of causality among interacting components in nonlinear dynamical systems, construction of time-varying interaction networks, forecasting of effects of external forcing, and provision of early warning signals for critical transition. I will demonstrate the efficacy of EDM in various systems, including environmental change effects on aquatic ecosystems, fisheries, climate, and bioreactor for clean energy. The information can shed light on identifying drivers of system stability and translating this science into policy-relevant information.
Yan Jie
Department of Physics and Mechanobiology Institute, National University of Singapore, SingaporeWebsite：https://phyweb.physics.nus.edu.sg/~biosmm/
Title of Presentation：Force-dependent interactions of biomolecules
Profile
Dr. Yan Jie is a single-molecule biophysicist who earned his PhD from the University of Illinois at Chicago in 2005. He is currently a Full Professor in the Department of Physics and a Principal Investigator at the Mechanobiology Institute, National University of Singapore. His research focuses on the micromechanics of DNA and proteins, with the goal of uncovering how force-bearing proteins within cells detect and respond to mechanical forces and how these proteins can be targeted pharmaceutically. His contributions to the field have been widely recognized; he has published over 150 papers and has been elected a Fellow of the American Physical Society and a Singapore NRF Investigator.
Abstract
Cells sense (mechanosensing) and respond (mechanotransduction) to mechanical signals in their environment, which is crucial for many fundamental cellular functions. This mechanosensing process relies on the formation of tension-transmitting linkages composed of non-covalently connected proteins. Cells detect dynamic tensile forces through these linkages, and protein domains and their interfaces undergo changes in response to intracellular tensile forces. These changes lead to complex connectivity within the linkages, domain alterations, and interactions among proteins in the linkage and signaling proteins in the cytosol. In my talk, I will outline the biophysical principles of force-dependent biomolecular interactions and explore their roles in cell-matrix adhesions, adhesion GPCR activation, and bacterial adhesions. Specifically, I will discuss in detail the critical role of conformational entropy in protein domains and complexes as a key factor in their equilibrium and dynamic mechanical responses. Finally, I will address how this knowledge enhances our understanding of cellular mechanosensing and mechanotransduction and may help the development of mechanopharmacology.
Yang-Yu Liu
Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
Website：https://yangyuliu.bwh.harvard.edu/
Title of Presentation：Controlling the Human Microbiome
Profile
Dr. Yang-Yu Liu is currently an Associate Professor of Medicine at Harvard Medical School and an Associate Scientist at Brigham and Women's Hospital. Dr. Liu earned his Ph.D. in Physics from the University of Illinois in 2009, with his research on phase transitions in disordered systems being recognized as the Best of 2009 Collection in Europhysics Letters. From 2009 to 2012, he was a Postdoctoral Research Fellow and Research Assistant Professor in the Department of Physics and the Center for Complex Networks at Northeastern University. During this time, his groundbreaking work on the controllability and observability of complex networks was featured as a cover story in Nature and PNAS, and was widely reported by media outlets including Nature, Science, Science News, Science Daily, and WIRED. In 2013, Dr. Liu joined Harvard Medical School and Brigham and Women's Hospital, where his laboratory (https://yangyuliu.bwh.harvard.edu) explores complex microbial communities through the lenses of community ecology, network science, control theory, and machine learning. His research focuses on fundamental questions related to the human microbiome and its applications in disease treatment and precision nutrition. To date, Dr. Liu has published over 140 academic papers with more than 12,000 citations. He also serves as an associate editor for several academic journals, has reviewed for nearly 70 journals, and has participated in the organization or program committees of over 10 international conferences.
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays crucial roles in human physiology and diseases. While we have gained significant insights into the composition and metabolic functions of the microbiome, the true measure of our understanding lies in our ability to manipulate it for health benefits. To enable the rational design of microbiome-based therapies, several fundamental systems-level questions still need to be addressed. In this talk, I will present recent progress achieved through tools from diverse fields—such as community ecology, network science, control theory, and machine learning—that are advancing us toward the ultimate goal of controlling the human microbiome for health benefits.