Program
IMPORTANT DATES
  • Feb. 26-Mar. 31, 2024
    Minisymposia Abstract Submission
  • by Mar. 15, 2024
    Registration Open
  • by Apr. 30, 2024
    Speaker/Early Registration
  • by May. 31, 2024
    Registration Deadline

Plenary Speakers

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  • Plenary Speakers

July 1(Mon) 09:00-09:40

    • NameProfessor Alexander R.A. Anderson
    • AffiliationIntegrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, USA
    • Websitehttp://labpages.moffitt.org/andersona
    • Title of presentationEvolutionary Therapy
      • Education
        Dr. Anderson performed his doctoral work on hybrid mathematical models of nematode movement in heterogeneous environments at the Centre for Nonlinear Systems in Biology, Dundee University, UK. His postdoctoral work was on hybrid models of tumor-induced angiogenesis with Prof. Mark Chaplain at Bath University, UK. He moved back to Dundee in 1996 where he worked for the next 12 years on developing mathematical models of many different aspects of tumor progression and treatment, including anti-angiogenesis, radiotherapy, tumor invasion, intra-tumor heterogeneity, evolution of aggressive phenotypes and the role of the microenvironment. Due to his belief in the crucial role of mathematical models in cancer research he moved his group to the Moffitt Cancer Center in 2008 to establish the IMO department.

        Professional Experience
        Dr. Anderson is founding Richard O. Jacobson Chair of the Integrated Mathematical Oncology (IMO) Department and Director of the Center of Excellence for Evolutionary Therapy at Moffitt Cancer Center. For the last 20 years he has been developing mathematical models of many different aspects of tumor progression and treatment that require a tight dialogue between theory and experiment. Since his arrival at Moffitt, cancer treatment has become a significant driver of his research and using mathematical models that connect our basic science understanding of a given cancer with clinical translation. This has led to the development of evolutionary therapies that seek to control cancer rather than eradicate it. Through smart treatment scheduling and dosing, with combination therapies as well as microenvironment targeted treatments, he has developed novel treatments for prostate, breast, ovarian, lung and skin cancer. As director for the 1st center of Evolutionary Therapy he has helped facilitate 8 active evolutionary clinical trials at Moffitt that use mathematical models as part of their decision process. One of these trails is the Evolutionary Tumor Board (ETB), which consists of an integrated team of clinical physicians, evolutionary biologists, and mathematicians. The ETB provides guidance on optimal evolution based treatment strategies for individual patients by rigorously formulating and investigating underlying hypotheses for treatment failure and success using mathematical models, patient data and treatment efficacy.
        Evolutionary Therapy

        Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). Cancers are complex evolving systems that adapt to therapeutic intervention through a suite of resistance mechanisms, therefore whilst MTD therapies generally achieve impressive short-term responses, they unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression, metastasis and treatment response is becoming more widely accepted. However, MTD treatment strategies continue to dominate the precision oncology landscape. Here we discuss evolutionary therapy, a proactive therapeutic approach that changes and evolves with the tumor being treated. Due to the dynamic feedback between changing treatments and the evolving tumor, mathematical models are essential to drive treatment switch points and predict appropriate dosing and drug combinations. We will consider the importance of using treatment response as a critical driver of subsequent treatment decisions, rather than fixed MTD strategies that ignore it. We will also consider using mathematical models to drive treatment decisions based on limited clinical data. Through the integrated application of mathematical and experimental models as well as clinical data we will illustrate that, evolutionary therapy can drive either tumor control or extinction. Our results strongly indicate that the future of precision medicine shouldn’t only be in the development of new drugs but rather in the smarter evolutionary, and model informed, application of preexisting ones.

July 1(Mon) 13:00-13:40

    • NameProfessor Jae kyoung Kim
    • AffiliationDept of Mathematical Sciences, KAIST & Biomedical Mathematics Group, IBS, Korea
    • Websitehttps://www.ibs.re.kr/bimag/
    • Title of presentationInference of Dynamic Networks in Biological Systems
      • Jae Kyoung Kim is an associate professor in Dept. of Mathematical Sciences, KAIST and Chief Investigator of Biomedical Mathematics Group, IBS. He got his Ph. D. in Applied & Interdisciplinary Mathematics at University of Michigan and was a postdoctoral fellow in the Mathematical Biosciences Institute at the Ohio State University. He has combined nonlinear dynamics, the theory of stochastic processes, and scientific computing to solve critical biological and medical problems, including sleep disorders. In particular, his math models have been used for the development of a new drug and digital medicine for sleep disorders. He is a recipient of Human Frontier Science Program Young Investigator Award, Young Researcher Award from Korea SIAM, Sangsan Young Mathematician Award from Korea Math Society and 30 Young Scientists of Korea Award. He is the editor of J Theor Biol, Math Biosci, NPJ Syst Biol, NPJ Biol Time & Sleep, Current Opinions Syst Biol, J Biol Rhythms, and PLOS One.
        Inference of Dynamic Networks in Biological Systems

        Biological systems are complex dynamic networks. In this talk, I will introduce GOBI (General Model-based Inference), a simple and scalable method for inferring regulatory networks from time-series data. GOBI can infer gene regulatory networks and ecological networks that cannot be obtained with previous causation detection methods(e.g., Granger, CCM, PCM). I will then introduce Density-PINN (Physics-Informed Neural Network), a method for inferring the shape of the time-delay distribution of interactions in a network. The inferred shape of time-delay distribution can be used to identify the number of pathways that induce a signaling response against antibiotics, which solves the long-standing mystery, the major source of cell-to-cell heterogeneity in response to stress. Finally, I will talk how to infer the dynamic information from just network structure information, which can be used to identify the targets (nodes) perturbing the homeostasis of the systems.

July 2(Tue) 09:00-09:40

    • NameProfessor Lisa J. Fauci
    • AffiliationSchool of Science & Engineering, Tulane University, New Orleans, USA
    • Websitehttps://sse.tulane.edu/lisa-j-fauci
    • Title of presentationLong, flexible, and actuated: Flagella Moving Through Heterogeneous Fluid Environments
      • Lisa Fauci received her PhD from the Courant Institute of Mathematical Sciences at New York University, and directly after that joined the Department of Mathematics at Tulane University in New Orleans. Her research focuses on biological fluid dynamics, with an emphasis on using modeling and simulation to study the basic biophysics of organismal locomotion and reproductive mechanics. Lisa served as president of the Society for Industrial and Applied Mathematics (SIAM) in 2019-2020. She is a fellow of the American Physical Society, the American Mathematical Society, the Society for Industrial and Applied Mathematics, the Association for Women in Mathematics, and the American Association for the Advancement of Science. In 2023, she was elected to the US National Academy of Sciences.
        Long, flexible, and actuated: Flagella Moving Through Heterogeneous Fluid Environments

        In microscopic biological systems, rarely does an undulating flagellum beat freely, but may move through passive elastic structures such as mucosal strands or narrow ducts in the female reproductive tract. In this talk, we will focus on two intriguing systems: (a) flagellar motility through a viscous fluid with suspended polymeric networks, and (b) the journey of (incredibly long) insect sperm through (incredibly narrow) sperm storage organs in the female reproductive tract. We will discuss mathematical models and computational methods that capture these coupled fluid-elastic systems, what we have learned, and what challenges lie ahead.

July 2(Tue) 13:00-13:40

    • NameProfessor Eunok Jung
    • AffiliationDepartment of Mathematics, Konkuk University, Korea
    • Website
    • Title of presentationContributions to public health policies through data-driven mathematical modeling during the COVID-19 pandemic and future perspectives in this field
      • Eunok Jung has been a professor in Department of Mathematics at Konkuk University in Korea since 2002. She earned her Ph.D. in Applied Mathematics from the Courant Institute, New York University in the USA. After completing her Ph.D., she joined the Oak Ridge National Laboratory as a postdoctoral researcher and later a senior researcher for three years.
        Dr. Jung is an expert in the field of Mathematical Biology, especially in Mathematical Modeling and Simulations of Transmission Dynamics of Infectious Diseases, Multi-dimensional Modeling of Cardiovascular Diseases, Mathematical Modeling in Biomedical and Bio-industrial Applications, Numerical Methods in Computational Biofluid Dynamics, Optimal Control Theory: Biomedical Applications and Epidemic Diseases. During the COVID-19 pandemic, Dr. Jung has played an important role in collaborating with government agencies such as the Korea Disease Control and Prevention Agency (KDCA) to provide scientific evidence for COVID-19 policies based on mathematical modeling. In addition, based on the recommendation of the Korean Mathematical Society, she received the Presidential Commendation in the field of science and technology in 2020. Furthermore, in 2022, she was awarded the Minister's Commendation from the Ministry of Health and Welfare based on the recommendation of the Korea Disease Control and Prevention Agency.
        Dr. Jung served as the 7th President of the Korean Society for Industrial and Applied Mathematics. Currently, she is the Chairperson of the International Cooperation Committee at the Korean Foundation of Women's Science & Technology Associations, and also serves as the Chairperson of the COVID-19 Mathematical Modeling Task Force (TF) team in Korea.
        Contributions to public health policies through data-driven mathematical modeling during the COVID-19 pandemic and future perspectives in this field

        This presentation aims to explore the strategic implementation and significance of data-driven mathematical modeling within epidemic control policies during the COVID-19 pandemic. Infectious diseases pose a continuous and significant threat to humanity, making mathematical modeling essential for an effective response. We will discuss the importance of mathematical modeling from various perspectives, including the spread mechanism of infectious diseases, predictive modeling, and effective pharmaceutical/non-pharmaceutical strategies.
        In this presentation, we will introduce current research findings and methodological approaches, showcasing practical examples of how mathematical modeling of infectious diseases contributes to prevention and response strategies in Korea. Additionally, we will engage in discussions regarding the potential advancements and challenges in infectious disease modeling that may arise in the future.

July 3(Wed) 09:00-09:40

    • NameProfessor Qing Nie
    • AffiliationDept. of Mathematics, Dept. of Developmental and Cell Biology, University of California, Irvine, USA
    • Websitehttps://faculty.sites.uci.edu/qnie/
    • Title of presentationMulticellular organization: from biophysical models to single-cell genomics
      • Dr. Qing Nie is a Distinguished Professor of Mathematics and Developmental and Cell Biology with an affiliated appointment in Department of Biomedical Engineering at University of California, Irvine. In research, Dr. Nie uses multiscale modeling and data-driven methods to study complex biological systems with focuses on single-cell analysis, cellular plasticity, stem cells, embryonic development, and their applications to regeneration, aging, and diseases. Dr. Nie has published more than 220 research articles. In training, Dr. Nie has supervised nearly 60 postdoctoral fellows and PhD students, with many of them working in academic institutions. Dr. Nie is a fellow of the American Association for the Advancement of Science, a fellow of American Physical Society, and a fellow of Society for Industrial and Applied Mathematics.
        Multicellular organization: from biophysical models to single-cell genomics

        Cells make fate decisions in response to their dynamic environments, and multicellular structures emerge from close interplays among cells and genes in space and time. Mechanistic models, based on a small number of selected biochemical and physical regulators, have provided many insights into how cells organize their spatiotemporal patterns for relatively simple biological systems. The recent single-cell genomics technologies provide an unprecedented opportunity to explore complex spatial tissues systematically and comprehensively from in vivo animal models and human diseases. However, since its start emergence five years ago, spatiotemporal analysis of multi-modal single-cell genomics datasets is still at its early stage and many major challenges remain. In this talk, by motivating via mechanistic models of “small” data of genes and cells for multicellular systems, I will present new methods to reconstruct spatiotemporal tissue properties from large single-cell genomics datasets. Specifically, we derive dynamic transitions of cell fate from static measurements, infer cell-cell communication from nonspatial data, and uncover spatiotemporal cellular interaction and organization from spatial data. Through several applications to development, regeneration, and disease, we show the discovery power of these methods as well the need of new sophisticated mechanistic models and inference tools that enable better understanding of principles governing the complex multicellular organization as new data grow exponentially in many forms.

July 4(Thu) 09:00-09:40

    • NameProfessor Ulf Dieckmann
    • AffiliationOIST in Japan and at IIASA in Austria
    • Website
    • Title of presentation

July 4(Thu) 13:00-13:40

    • NameProfessor Atsushi Mochizuki, Ph.D.
    • AffiliationInstitute for Life and Medical Sciences, Kyoto University, Japan
    • Websitehttps://mathbio.infront.Kyoto-u.ac.jp/
    • Title of presentationBiological functions and functional modules originated in the structure of networks
      • Atsushi Mochizuki is a Professor at Institute for Life and Medical Sciences, Kyoto University. He graduated from the Faculty of Sciences, Kyoto University, in 1994, and obtained his PhD in 1999 from Kyushu University. He was promoted to assistant professor in 1998 at Kyushu University, to associate professor in 2002 at National Institute for Basic Biology. He has been a full-PI, Chief Scientist at RIKEN since 2008, and a full professor at Kyoto University since 2018. His researches focus on dynamical properties of network systems in biology using mathematical approaches. One of his largest achievements is to establish "Structural Theories" for network systems, by which important aspects of dynamical properties of complex systems are determined from topologies of networks alone. He was awarded 11th JSPS PRIZE (2015) from Japan Society for the Promotion of Science, and 1st MIMS Mimura Award (2017).
        Biological functions and functional modules originated in structure of chemical reaction networks.

        In living cells, chemical reactions are connected by sharing their products and substrates, and construct complex network systems. Biological functions are emerging from dynamics of chemical reaction networks, and regulated by changes in amount/activities of enzymes mediating reactions in the system. In this talk, I present our recent theoretical approaches to determine behaviors of chemical reaction systems induced by changes in enzyme amount/activities from network topology alone. We found that (1) qualitative responses of chemical concentrations (and reaction fluxes) by enzymatic changes are determined from a network structure alone. (2) The nonzero responses are localized in finite extents in a network, and each of the extent is determined by a subnetwork called a "buffering structure". A buffering structure is defined from local topology of the network by an equation -(#" of chemicals" )+(#" of reactions" )-(#" of cycles" )=0, where the index is an analogous to the Euler characteristic. We proved that any perturbation on a reaction parameter in a buffering structure does not influence concentrations and fluxes outside the buffering structure. Finally, (3) buffering structures govern the bifurcation of steady stats of reaction networks. The bifurcation behaviors are localized in finite extents in a network, and the extents are determined by buffering structures, again. These results suggest that the buffering structures may be the origin of the modularity in biological functions originated in reaction networks. We apply our method to some real networks, including cell-cycle or carbon metabolism systems, and demonstrate how we can understand behaviors of biological systems from network structures alone.

July 5(Fri) 09:00-09:40

    • NameProfessor Xianning Liu
    • AffiliationSchool of Mathematics and Statistics, Southwest University, China
    • Websitehttp://math.swu.edu.cn/info/1014/2359.htm
    • Title of presentation Modelling the prudent predation in predator-prey interactions
      • Dr. Xianning Liu received his Ph. D. degree from Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2003. He is currently the dean and a professor at School of Mathematics and Statistics, Southwest University, Chongqing China. His current research interests include Mathematical Biology, Differential Equations and Dynamical Systems. Dr. Liu has published more than 100 research articles. He was selected as the “Academic and Technical Leaders in Chongqing” and serves as the vice president of Chinese Society of Mathematical Biology and the president of the Chongqing Mathematical Society, etc.
        Modelling the prudent predation in predator-prey interactions

        In this talk, we introduce prudent predation into a two prey and one predator model through the way of formulating a function of predation rate, which depends upon the abundance of each prey species. Based on the Holling functional responses, which divide an individual predator's time into two periods: searching for and handling of prey, we take the process of perception and testing into account. A prudent predator will adjust the predation rate, even cannibalize, and switch to the enfeebled and dead (acting as scavenger) preys, to avoid over-exploitation of resources. We find that the prudent predation can stabilize the system from chaos and suitable level of prudence will benefit the predator while sustaining the prey. These results may reveal the important role of predator initiative and provide us further understanding the dynamics of predator-prey interactions in the real environment.

July 5(Fri) 13:00-13:40

    • NameProfessor Dagmar Iber, PhD
    • AffiliationDepartment of Biosystems Science and Engineering, ETH Zurich, Switzerland
    • Websitehttps://bsse.ethz.ch/cobi
    • Title of presentation(tentative) Computational Modelling of Morphogenesis
      • Dagmar Iber studied mathematics and biochemistry in Regensburg, Cambridge, and Oxford. She holds Master degrees and PhDs in both disciplines. After three years as a Junior Research Fellow in St John’s College, Oxford, Dagmar became a lecturer in Applied Mathematics at Imperial College London. Dagmar has joined ETH Zurich in 2008 after returning from an investment bank where she worked as an oil option trader for one year. She leads the Computational Biology Group (CoBi), which focuses on the delineation of fundamental developmental mechanisms and which develops data-driven mechanistic 4D in silico models of a wide range of developmental processes, covering gamete formation, early embryogenesis, and organogenesis, including kidney development. The group runs a wet lab to obtain imaging data, but also teams up with biologists, and collaborates with clinicians on problems in personalised medicine.