iBLab (interdisciplinary Biology Laboratory) is Japan’s first interdisciplinary research group in biology, integrating mathematical modeling, computational simulation, and artificial intelligence (AI) to advance biological research across diverse domains. We operate entirely in a dry-lab setting, enabling data-driven and model-based approaches to be incorporated from the earliest stages of research design.

Biomedical science is entering a new era, characterized by approaches that extend beyond conventional big data analysis. There is growing interest in integrative frameworks that combine machine learning-based, data-driven methods with mathematical modeling, computational simulation, and bioinformatics. These developments reflect the evolution of data science into a more advanced and quantitatively rigorous stage.

Clinical research and drug development generate rich and diverse datasets, providing an ideal foundation for mathematical and computational approaches. Integrating data science enables quantitative risk assessment grounded in a holistic understanding of diseases through the incorporation of symptoms or underlying biological mechanisms, rather than relying solely on traditional organ-based diagnoses. Such perspectives support more equitable and evidence-based diagnosis and treatment in line with the evolution of modern medicine.

In the context of public health challenges, including emerging infectious diseases, data analysis and quantitative simulation can optimize disease containment strategies, accelerate drug development, and streamline clinical trial design. The integration of comprehensive simulation with data analysis also enables characterization and prediction of pathogen evolutionary dynamics.

Above all, we believe that truly mastering the data can unlock new possibilities and significantly accelerate research that has historically been difficult to advance. Mathematical modeling and computational simulation form the foundation of our work and serve as powerful interdisciplinary tools for biological research. By combining these approaches with AI and integrating model-driven and data-driven methodologies, we remain actively engaged at every stage of the research pipeline, from data acquisition through study design and analysis.

Through close partnership with collaborators and laboratory members, we aim to push the frontiers of life and medical sciences and contribute meaningfully to the emerging era of integrative research.