Biosystem Dynamics

Associate Professor: MATSUI Takaaki
Labs HP: https://sites.google.com/view/matsui-labnaist/

Outline of Research and Education

Living organisms are composed of many different types of cells that are precisely organized. By sharing roles among these cells, tissues, organs, and ultimately the entire organism can function properly. To truly understand how life works, it is essential to comprehensively capture how the shapes and behaviors of cells and tissues, the dynamics of molecules, and physiological functions change over time. In our laboratory, we focus on such temporally and spatially changing biological phenomena and refer to them collectively as “biosystem dynamics” (Fig. 1). Our goal is to elucidate the mechanisms that govern these dynamic processes. By combining experiments using cultured cells and animal models with live imaging technologies, we observe and analyze biological phenomena across multiple scales, from the cellular level to the whole organism level. Furthermore, we apply mathematical modeling and machine learning approaches to large datasets obtained from experiments to identify common patterns, predictive rules, and causal relationships underlying biological systems. Through the complementary use of experiments and data analysis, we aim to understand how living systems are dynamically regulated and to translate this knowledge into future applications.

On the educational side, we provide an environment in which students and early-career researchers can acquire a balanced skill set in both experimental techniques and data analysis. While fostering the ability to “observe, measure, and analyze” biological phenomena, we place great importance on nurturing individuals who can formulate their own scientific questions, think independently, and actively drive their research forward.

Major Research Topics

Understanding the Mechanisms of Aging and Challenges toward Anti-aging

In today’s super-aging society, understanding the mechanisms of aging is a critically important research topic. Although molecular mechanisms underlying cellular senescence and aging have gradually been elucidated in recent years, how senescent cells accumulate in the body during aging remains poorly understood. Our recent studies have suggested the existence of a previously unrecognized mechanism involved in the accumulation of senescent cells. Based on this discovery, our laboratory aims to clarify how aging progresses and to explore strategies for suppressing or controlling aging, with the ultimate goal of contributing to anti-aging research (Fig. 2).

Elucidation of Organ Regeneration Mechanisms

With growing expectations for regenerative medicine, understanding how damaged organs regenerate is of great importance. While most human organs have limited regenerative capacity, zebrafish are a powerful model organism capable of regenerating various tissues, including nerves, muscles, fins, and neuromasts (sensory organs corresponding to the inner ear in humans). Using zebrafish, our laboratory investigates how cells behave and which signaling pathways are activated during organ regeneration. Based on these findings, we aim to identify fundamental principles that can be applied to organ regeneration and regenerative medicine using iPS and ES cells. Currently, we are particularly focusing on the regeneration of neuromasts (Fig. 3).

Utilization and Development of Cell Dynamics Measurement Systems

At the Bioscience & Materials Science Research Foundry, research is conducted to uncover the physiological functions of polymers and novel materials whose biological activities are not yet fully understood. Our laboratory plays a central role in this effort by quantitatively analyzing how such materials influence cellular dynamics, including cell survival, aging, and differentiation. By leveraging live imaging and automated measurement systems, we efficiently evaluate a large number of conditions and identify factors that affect cellular functions. In addition, by promoting automation in measurement and analysis, we aim to achieve a more large-scale and high-precision understanding of dynamic biological phenomena (Fig. 4).