Cells, the smallest unit of living organisms, have a sophisticated mechanism for determining their fate. This mechanism can be imagined as a network resembles an electric circuit that connects genes and proteins. However, even after the entire human genome has been decoded, it is still difficult to identify all connections in networks containing more than 20,000 genes. As one of the showcases of these networks, we study signal transduction networks and clarify the relationship between the network structure, that is regulatory rules, the dynamics of molecules in the network and the cell fate.
Our Laboratory has been carrying out researches to identify the regulatory principles of cell fate decisions by computational, theoretical and experimental approaches. For this purpose, we employ both bottom-up mechanistic mathematical modeling and top-down omics methods. These methods complement each other to understand the cells at the system level.
Signaling network consists of a combination of linear and nonlinear responses. Nonlinear responses often arise from network structures such as feedback loops, and it is difficult to elucidate the mechanism using only experimental methods. The study combines ODE modeling, live cell imaging and biochemical experiments to identify the regulatory mechanism associated with irreversible cell transition. Currently, we are working on signal-dependent cell growth control (cell cycle control) and aiming to find the rules in seemingly complex cellular dynamics. We have also developed a mathematical modeling platform (see https://github.com/okadalabipr/biomass) designed for experimental researchers. Using this platform, analysis of clinical cancer data (development of patient-specific models) is ongoing.
The signaling networks are involved in mechanical recognition such as cell density and adhesion, in addition to the recognition of biochemical substances. In this study, we try to clarify the relationship between cell shape and metabolism. Metabolism shift occurs as the shape of the cell changes (see Imamoto et al. link [PubMed]). The mechanism is clarified using cell shape analysis combined with omics analysis and drug screening. Modeling of integrin signaling network is also in progress.
Transcription factors receive information from signaling networks, bind to nuclear DNA, and regulate gene expression. We use omics, live cell imaging, and mathematical models to reveal the relationship between signal dynamics, transcription factor activity and gene expression in a data-driven and quantitative manner. Currently, we are focusing on NF-κB transcription factor that is closely linked to immune responses and cancer development (see Michida et al. https://pubmed.ncbi.nlm.nih.gov/32492432/).
Our laboratory has been conducting research by combining mathematical models and omics analysis focusing on cancer and immune signaling network. These studies have found that some regulatory principles are universally preserved, even in different cells and tissues. We also found that epigenetic data often includes a "footprint" of signaling activity. Given this background, we have begun to analyze the epigenetics of neuronal development at the bulk and single-cell level to reveal cell development as a result of signaling networks.