Cells, the smallest unit of living organisms, have a mechanism for fate determination. This mechanism can be imagined as a network resembling an electric circuit connecting gene regulation and protein expression. However, it is still difficult to elucidate all the connections of the networks containing more than 20,000 genes even today when the entire human genome has been decoded. Therefore, this circuit is obliged to switch on and off according to the external environment where the cells are present, internal environment, and also time.
We focus on some of the major molecules that play crucial roles in regulating the environment and follow the kinetics of their activity over time to clarify the relationship between the network centered on that molecule and cell regulation. The number of circuits based on kinetics is somewhat limited, and the fate of a cell can be thought of as determined by the combination of multiple types of circuits.
Our Laboratory has been carrying out researches to identify biological networks and regulatory principles using computational, theoretical and experimental approaches. Our research interest is to identify and reconstruct biological regulatory circuits from experimentally observable data using computational and theoretical approaches to reveal the mechanism of signal transduction networks, which determine cell fate in mammalian cells. For this purpose, we take both bottom-up kinetic analysis and top-down omics approaches. These approaches complement each other in order to understand objects at a system level.
The intracellular signal transduction system is composed of a combination of linear and nonlinear responses. Non-linear responses are often caused by circuit structures such as feedback control, and it is difficult to clarify the complete regulatory mechanism using only experimental methods. This study combines mathematical modeling of reactions with differential equations, live cell imaging and biochemical experimental measurements to try and elucidate the complete regulatory mechanism underlying these responses. Currently, we are working on all models of signal-dependent cell growth control (cell cycle control) and aiming to find patterns among seemingly complex regulatory mechanisms. For this reason, we have also built a mathematical model analysis platform (see BioMASS: mathematical model page) designed to be friendly even for experimental researchers.
In addition to biochemical substance recognition, the signal transduction system is also involved in mechanical recognition such as cell density and adhesion. In this study, we are trying to clarify the relationship between cell shape and metabolism. Metabolism changes as the shape of the cell changes. The reason will be clarified using omics analysis and inhibitor screening, incorporating Artificial intelligence for classification.
Transcription factors receive signal information, bind to DNA in the nucleus, and play a role in regulating gene expression. Transcription factors are activated only when needed. In this study, we focus on changes in transcription factor activity and combine omics, live cell imaging, and mathematical models to clarify the relationship between transcription factor kinetics and gene expression in a data-driven and quantitative manner. Currently, we are focusing on NF-κB transcription factors that are closely related to immune responses and cancer development.