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Mon. Jul 7, 2025

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Decoding the Role of Epigenetic Fluctuation in Stochastic Cell and Individual State Transitions

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.

Next-Generation Mathematical Biology: Where It Meets AI

Joint Meeting of Asian Conference for Mathematical Biology and Annual Meeting of Japanese Society for Mathematical Biology (ACMB-JSMB2025)

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[SS15-03]Decoding the Role of Epigenetic Fluctuation in Stochastic Cell and Individual State Transitions

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