Living systems exhibit remarkable diversity, dynamism, and interconnectivity. Mathematical biology has revealed the mechanisms behind biological complexity and the emergent phenomena it produces. This talk explores a diverse range of topics that reflect my research interests, spanning from microbes to human culture. I present a series of theoretical investigations, combined with some empirical data, that address the following key questions:
(i) How can cooperation evolve in microbial societies [1], [2]?
(ii) How do environmental factors influence community stability and biodiversity [3], [4], [5]?
(iii) How do environmental fluctuations shape eco-evolutionary dynamics [6]?
(iv) How does nature affect human culture and vice versa [7], [8]?
These studies demonstrate that mathematical biology is not merely a tool for analysis but a transformative lens—bridging disciplines, broadening perspectives, and shaping the future of integrative science.

References
[1] S. Shibasaki and M. Shimada, (2018) “Cyclic dominance emerges from the evolution of two inter-linked cooperative behaviours in the social amoeba,” Proc.Roy. Soc. B.
[2] S. Shibasaki and S. Mitri, (2020) “Controlling evolutionary dynamics to optimize microbial bioremediation,” Evol. Appl.
[3] S. Shibasaki, M. Mobilia, and S. Mitri, (2021) “Exclusion of the fittest predicts microbial community diversity in fluctuating environments,” J. R. Soc. Interface.
[4] S. Shibasaki and S. Mitri, (2023) “A spatially-structured mathematical model of the gut microbiome reveals factors that increase community stability,” iScience.
[5] S. Shibasaki and A. Terui, (2024) “Food web complexity modulates environmental impacts on food chain length,” Oikos.
[6] S. Shibasaki and M. Yamamichi, (under review) “The double-edged effect of environmental fluctuations on evolutionary rescue.”
[7] S. Shibasaki, Y. Nakawake, W. Tateishi, S. Fujii, and R. Nakadai, (under review) “Fear of supernatural punishment harmonises human societies with nature.”
[8] S. Shibasaki, R. Nakadai, and Y. Nakawake, (2024) “Biogeographical ‍distributions of trickster animals,” R. Soc. Open Sci.

　Recent advances in quantitative biology have highlighted the critical importance of heterogeneity among patients and cells in understanding infectious diseases and stem cell differentiation. Our research addresses these aspects through mathematical modeling that integrates experimental and clinical data to reveal underlying biological dynamics. In infectious disease modeling, we have focused on capturing patient-to-patient variability in viral infection dynamics, particularly for SARS-CoV-2. By developing models that incorporate heterogeneity in viral shedding, we quantitatively evaluated antiviral treatment efficacy and optimized clinical trial design. This approach demonstrated that early intervention and patient variability critically influence treatment outcomes and trial feasibility.
　In parallel, our work on hematopoietic stem cell differentiation explores cellular heterogeneity in self-renewal and lineage commitment. Using mathematical frameworks describing marker expression distributions and differentiation pathways, we elucidated age-related changes in stem cell division modes and quantitatively characterized single-clone proliferation dynamics across multiple blood lineages.
　Together, these studies contribute to advancing a quantitative and mechanistic understanding of biological heterogeneity, fostering integration of mathematical modeling with experimental and clinical research to deepen insights in biology and medicine.