Understanding and harnessing the mechanics in multi-cellular systems.

An embryo produces cells with specific fates, forms, and functions during development. These cells are self-organized into an ordered pattern through collective interactions of biomolecules and mechanical forces at various spatio-temporal scales. Through holistic understanding of how mechanical forces elicit self-organizing feedback leading to progressive self-tuning transformation of multicellular systems, we aim at developing new paradigms of the fundamental design principles of biological systems.
With cutting-edge technologies needed to interrogate the mechanical processes, we will establish a unique model for multi-disciplinary research that harnesses expertise from biomedical sciences, engineering, mathematics, physics, and chemistry. Our research projects will advance innovation at the interface of biology and physics, which will create seeds and contribute to social transformation.

The blue parts show how the group of cells interacts with each other to create an organized pattern, such as embryos and lumens. The orange and blue arrows indicate direction and strength of mechanical forces acting on the cells and the extracellular space, respectively. The gear surrounding the group of cells describes how the balance of complex forces orchestrates the emergent properties of self-organizing multi-cellular system.







Self-organization of multi-cellular systems by mechanical forces within cells

Elucidate how “forces generated by cells” orchestrate cell-to-cell communication in the self-organization process of living organisms.


Self-organization of multi-cellular systems by mechanical forces from extracellular spaces

Elucidate how “forces derived from the extracellular environment” control the emergence of tissue function.


Development of techniques for measurement, manipulation, and analysis of mechanical self-organization

Measure and manipulate forces applied to cell populations and the extracellular environment in living organisms, and quantitatively analyze their spatiotemporal patterns.