Many biological phenomena are described within the framework of population dynamics, through a combination of elements such as proliferation, differentiation, infection, mutation, evolution, and adaptation, along with temporal evolution. Classical mathematical biology has long used mathematical models and computer simulations to explain and understand these dynamics. However, advancements in technology, such as next-generation flow cytometers and sequencers, now allow for comprehensive measurement of diverse cellular and molecular states. Furthermore, the integration of statistical science and machine learning methods with mathematical models, facilitated by improvements in computing power, has furthered quantitative understanding of population dynamics. The revival of "on-the-bench mathematical models," previously developed and debated, now as "practical mathematical models," alongside advances in measurement technologies and information statistics, marks the beginning of a new era in mathematical biology. In this symposium, we aim to share cutting-edge research results as concrete examples, with a focus on the integrated approach of mathematical models, computer simulations, bioinformatics, and artificial intelligence, to discuss the future of next-generation mathematical biology.

1. Carl de Boer (The University of British Columbia, Canada)
   Continual Improvement of Cis-Regulatory Models
2. Shingo Iwami (Nagoya University, Japan)
   Integrating Modeling and AI for Early Prediction of Disease Progression
3. Risa Karakida Kawaguchi (Kyoto University, Japan)
   Decoding the Role of Epigenetic Fluctuation in Stochastic Cell and Individual State Transitions
4. To Be Announced

Many biological phenomena are described within the framework of population dynamics, through a combination of elements such as proliferation, differentiation, infection, mutation, evolution, and adaptation, along with temporal evolution. Classical mathematical biology has long used mathematical models and computer simulations to explain and understand these dynamics. However, advancements in technology, such as next-generation flow cytometers and sequencers, now allow for comprehensive measurement of diverse cellular and molecular states. Furthermore, the integration of statistical science and machine learning methods with mathematical models, facilitated by improvements in computing power, has furthered quantitative understanding of population dynamics. The revival of "on-the-bench mathematical models," previously developed and debated, now as "practical mathematical models," alongside advances in measurement technologies and information statistics, marks the beginning of a new era in mathematical biology. In this symposium, we aim to share cutting-edge research results as concrete examples, with a focus on the integrated approach of mathematical models, computer simulations, bioinformatics, and artificial intelligence, to discuss the future of next-generation mathematical biology.

1. Nika Shakiba (The University of British Columbia, Canada)
   Simulating Embryonic Development: A Collective Cellular Society 
2. Yusuke Imoto (Kyoto University, Japan)
   Single-Cell Resolution Trajectory Inference via Multi-Layer Velocity Decomposition
3. Hikaru Sugimoto (The University of Tokyo, Japan)
   Inference of Diverse Biological Networks from Omics Data
4. Nozomu Yachie (The University of British Columbia, Canada)
   Deep Distributed Computing Toward Understanding the Molecular Cell Landscape of Mammalian Whole Bodies

 A cell is the smallest unit of life, and each component within a cell is a building block of a living system. Therefore, exploring how these intracellular components are organized and function is one of the best ways to approach the mystery of life. In these two sequential symposia, we invite six outstanding researchers studying intracellular structures. The first session will focus on chromosomes and biomolecular condensates, both of which are long polymers in highly compacted states. The physical and functional reasons behind their compaction are of our interest. The second session will cover the cytoskeleton and membrane organelles, which involve various molecules, including hundreds of types of proteins, lipids, and metal ions. Understanding the principles of self-organization in their structure and behavior is an exciting and current area of research. Throughout the two symposia, we aim to discuss how intracellular components are organized through their interactions with the intracellular environment, and how these interactions contribute to the overall organization of the cell.

1. Tsuyoshi Terakawa (Kyoto University, Japan)
   Chromatin replication probed by molecular dynamics simulations and nanopore sequencing
2. Masaki Sasai (Kyoto University, Japan)
   Nonequilibrium structure formation and dynamics: from chromatin domains to genome
3. Yuji Sakai (Yokohama City University, Japan)
   Modeling structural changes and dynamics of mitotic chromosomes
4. Kyosuke Adachi (Riken, Japan)
   Decoding Rules for Condensation of Disordered Protein Sequences
5. Makito Miyazaki (Riken, Japan)
   Reconstitution and manipulation of actin cytoskeleton
6. Lu Lei (Nanyang Technological University, Singapore)
   Quantitative Insights into Intra-Golgi Secretory Transport and Cisternal Organization
7. Masashi Tachikawa (Yokohama City University, Japan)
   Physical mechanisms for shaping organelles
   
8. Hiroshi Noguchi (University of Tokyo, Japan)
   Spatiotemporal patterns in biomembranes

Qualitative theory for mathematical models plays an important role in obtaining insights into the mechanisms influencing complex biological systems including population dynamics and pattern formation in living forms. In this session, we study the recent development of mathematical analysis for dynamical behavior of those models. We invite researchers working in the qualitative analysis of ordinary differential equations, partial differential equations, and stochastic differential equations. Throughout the presentations, we discuss further applications and prospect of the theoretical studies. 

1. Yoshihisa Morita (Ryukoku University, Japan)
   Mass-conserved reaction-diffusion systems in biological pattern formations
2. Zhisheng Shuai (University of Central Florida, United States)
   Group inverses for understanding heterogeneity in metapopulation dynamics
3. Ta Viet Ton (Kyushu University, Japan)
   Modeling, analyzing, and simulating stochasticity in ecological systems

One of the main features that characterizes modern humans is their high prosociality. Humans are sometimes referred to as cooperative species, and it is no doubt that one of the reasons of current prosperity of human beings on the globe can be attributed to their ability to establish large scale cooperation. For these types of cooperation, we need cognitively demanding mechanisms such as reciprocity, coordination, and punishment. In this session, we would like to provide cutting-edge theories that explain how human cooperation has evolved and has been sustained stably in human populations. In those studies, evolutionary game theory models have been intensively used, and we would like to focus on mathematical aspects of those models, too. 

1. Seung Ki Baek (Pukyong National University)
   Cluster dynamics of indirect reciprocity in complete graphs
2. Hisashi Ohtsuki (SOKENDAI)
   Ancestral human cooperation is a key to explain modern human cooperation
3. Yohsuke Murase (RIKEN)
   Indirect Reciprocity under Opinion Synchronization

4. Genki Ichinose (Shizuoka University) (CONFIRMED) 
   On the existence of zero-determinant strategies in repeated asymmetric games 
   

5. To Be Announced

This symposium, divided into three sections, explores the control and dynamics of complex biological networks across scales, spanning gene regulation, metabolism, ecological interactions, and chemical reaction networks. By examining how network topology shapes stability, regulation, and evolutionary processes, the discussions will integrate graph-theoretical and analytical approaches to uncover shared principles across scales. Real-world applications will also be a key focus, ranging from controlling cell fates to managing ecosystems, highlighting the relevance of these approaches for advancing fundamental knowledge in biology.

1. Chen Jia (Beijing Computational Science Research Center, China)
   To Be Announced
2. Yuhei Yamauchi (Kyoto University, Japan)
   To Be Announced
3. Jose C Nacher (Toho University, Japan)
   To Be Announced
4. Atsushi Mochizuki (Kyoto University, Japan)
   To Be Announced

Another new wave is approaching mathematical biology: its full-scale entry into the field of medicine. The era has truly arrived where mathematical research, which was previously confined to theoretical studies, is now actively collaborating with medical doctors and researchers in the field. Medicine is broadly divided into basic medicine and clinical medicine. Basic medicine can be viewed as fundamental biology, aiming to unravel the core principles of life in order to save human lives. If basic medicine focuses on the future, striving to be useful ‘someday,’ clinical medicine focuses on the present, aiming to treat patients ‘now.’ To tackle these different but closely related fields of medicine, mathematical approaches must also adopt different perspectives. In this initiative, we share cutting-edge research at the forefront of mathematical medicine, bringing transformation through new approaches that integrate data and mathematical modeling.

1. Naoki Honda (Nagoya University, Japan)
   To Be Announced
2. Yangin Kim (Kunkuk University, Korea)
   To Be Announced
3. Sungrim Seirin-Lee (Kyoto University, Japan)
   To Be Announced
4. Peijie Zhou (Peking University)To Be Announced
5. Hao Jiang (Renmin University)To Be Announced
6. Yeona Kang (Howard University)To Be Announced
7. Toshiaki Yachimura (Tohoku University)
   To Be Announced
8. Koichi Fujiwara (Nagoya University)
   To Be Announced
   

This mini-symposium aims to discuss recent advances in mathematical models of cancer to understand tumor progression and responses better. Featuring expert presentations, we will explore various mathematical and computational modeling approaches that simulate tumor growth, treatment effects, and the interplay between cancer cells and their microenvironment. Topics will include the development of multi-scale models that account for cellular heterogeneity, integrating clinical or biological data to refine predictive capabilities, and using agent-based models to simulate individual patient responses to therapy. By bringing together researchers from diverse fields, this symposium seeks to foster collaboration and inspire innovative strategies for personalizing cancer treatment.

1. Eunjung Kim (Korea Institute of Science and Technology (KIST), Korea)
   To Be Announced
2. Satbyul Seo (Kyongnam University, Korea) 
   To Be Announced
3. Wonmo Sung (Catholic University, Korea)
   To Be Announced
4. Heyrim Cho (Arizona State University, USA) 
   To Be Announced
5. Sungyoung Shin (University of Adelaide, Australia) 
   To Be Announced
6. Junho Lee (KIST, Korea)
   To Be Announced
7. Jibeom Choi (Kyong Hee University, Korea)
   To Be Announced
8. Joonhyun Song, (Stony Brook University, USA)
   To Be Announced

Microbial metabolism is a complex system composed of an extensive array of biochemical reactions. In ecosystems, microbial species with diverse metabolic pathways exchange metabolic products, expanding the metabolic network across entire communities into more complex forms. This community-level metabolic network forms the foundational framework that shapes biogeochemical cycles and energy flows within ecosystems. Understanding the detailed metabolic interactions of environmental microorganisms, along with the processes of their growth and environmental modification, is crucial for deciphering the co-development of organisms and their environment, and for predicting environmental changes. To achieve this, it is necessary to reconstruct traditional microbial modeling from the perspective of metabolic systems, enabling the advancement of mathematical models integrated with environmental omics data. This session brings together leading researchers at the forefront of cutting-edge efforts, fostering discussions aimed at advancing our understanding of microbial community interactions within metabolic contexts. 

1. Risa Sasaki (Nara Women's University, Japan)
   Functional reduction of microbial communities via metabolic trait modeling
2. Hugh C. McCullough (University of Nebraska-Lincoln, USA)
   Leveraging metabolic networks for multi-omics data interpretation
3. Hyun-Seob Song (University of Nebraska-Lincoln, USA)
   Integration of microbial metabolic networks with reactive-transport models

Recent trends in modeling and simulation of cancer have increasingly integrated Artificial Intelligence (AI) and Machine Learning (ML), Systems Biology, and Partial Differential Equation (PDE)-based models to better understand tumor dynamics and optimize treatment strategies. 

AI/ML methods are transforming cancer research by analyzing large-scale datasets, such as genomic data, medical imaging, and patient histories. These techniques can predict cancer progression, identify biomarkers, and recommend personalized treatments. Machine learning models, like neural networks and decision trees, can assist in early diagnosis by detecting patterns in medical images and genomic sequences that may be difficult for humans to discern. 

Systems biology approaches integrate molecular, cellular, and environmental data to simulate complex interactions within tumor ecosystems. This holistic perspective allows for modeling the multi-scale nature of cancer, including gene regulation, signaling pathways, and microenvironmental interactions. Systems biology helps uncover how tumors evolve, metastasize, and develop resistance to treatments, guiding the development of targeted therapies.

PDE-based models are crucial for simulating the spatiotemporal evolution of tumors. These models describe how factors like nutrient supply, drug diffusion, and cell proliferation spread through tissue. Coupled with computational methods, PDE models allow for the simulation of tumor growth, angiogenesis, and response to therapy under various conditions. By incorporating feedback loops and spatial heterogeneity, these models offer deeper insights into tumor behavior and help predict treatment outcomes.

In combination, AI/ML, systems biology, and PDE-based models are revolutionizing cancer research, enabling more precise, data-driven approaches to diagnosis, treatment planning, and understanding cancer progression.

1. B.V.Rathish Kumar（IIT Kanpur）
   To Be Announced
2. Bishal Chethri（IIT Kanpur）
   To Be Announced
3. Mubashir Raschid（IIT Kanpur）
   To Be Announced
4. Rinki（IIT Kanpur）
   To Be Announced

Mathematical models are increasingly applied to address complex challenges in both ecology and neurology. This symposium explores the intersection of mathematical modeling and biological systems, focusing on key topics: calcium dysregulation in Alzheimer's disease (AD), population dynamics, and the impacts of deoxygenation on coral reefs.

In ecology, researchers simulate ecosystems, helping to understand biodiversity, species interactions, and environmental changes while aiding in predicting the impacts of climate change and human activities for conservation efforts. In neurology, models are used to study brain function, neural networks, and neurological disorders like Alzheimer's and Epilepsy offering insights into disease mechanisms and guiding treatment developments. This interdisciplinary approach helps address global challenges in health and sustainability. 

This symposium will provide a platform for exploring the power of mathematical models in understanding complex neurological and ecological systems. Bringing together experts from both fields will foster interdisciplinary discussions on how modeling can enhance our understanding of brain function, neurological disorders, and ecosystem dynamics. Participants will gain insights into innovative modeling techniques and their applications in addressing global health, sustainability, and conservation challenges. The exchange of ideas will encourage collaborative approaches to tackling pressing issues in ecology and neurology.

1. Ranjit Kumar Upadhyay（IIT(ISM) Dhanbad）
   To Be Announced
2. Vikas Pandey（Fujita Health University, Japan, and RiKEN Centre for Brain Sciences, Japan）
   To Be Announced
3. Brajesh Kumar Jha（School of Technology Pandit Deendayal Energy University Gandhinagar, Gujarat, India）
   To Be Announced

4. Rana D. Parshad（Iowa State University, USA）
   To Be Announced
   

5. Parimita Roy（Thapar Institute of Engineering & Technology, Patiala, India）
   To Be Announced
   

6. Jyotiska Datta（Central University of Odhisa, India）
   To Be Announced
   

The symposium will focus on the dynamical modelling of the spread of infectious diseases, exploring how mathematical modelling can utilize empirical data to enrich our understanding of infectious diseases. Topics of interest include model development, data-driven approaches, statistical analysis of population-level contagious disease data, predictive analytics, control and eradication strategies for endemic infections, and guiding policymakers in resource allocation, public health measures, and allied areas. Additionally, the symposium aims to promote interdisciplinary collaboration, targeting researchers, data scientists, and public health officials to leverage modelling techniques and data for control and eradication strategies for endemic infections.

1. Sandip Banerjee (IIT Roorkee)
   To Be Announced
2. Parthasarathi Dutta (IIT Ropar)
   To Be Announced
3. Bapan Ghosh (IIT Indore)
   To Be Announced
4. Anuraj Singh (IIITM Gwalior)
   To Be Announced
5. Samit Bhattacharyya (Shiv Nadar University）
   To Be Announced

This symposium, divided into three sections, explores the control and dynamics of complex biological networks across scales, spanning gene regulation, metabolism, ecological interactions, and chemical reaction networks. By examining how network topology shapes stability, regulation, and evolutionary processes, the discussions will integrate graph-theoretical and analytical approaches to uncover shared principles across scales. Real-world applications will also be a key focus, ranging from controlling cell fates to managing ecosystems, highlighting the relevance of these approaches for advancing fundamental knowledge in biology.

1. Mukund Thattai (National Centre for Biological Sciences, India)
   To Be Announced
2. Keiichiro Tokita (Nagoya University, Japan)
   To Be Announced
3. Hirokazu Toju (Kyoto University, Japan)
   To Be Announced
4. To Be Announced
   To Be Announced