2. Aim of the project
The aim of our project is to develop methods and systems for analyzing the epigenetic information in cells. The project is based on the idea that, although genetic information makes a network of biochemical reactions, the history of the network as a parallel-processing recurrent network was ultimately determined by the environmental conditions of cells, which we call epigenetic information. As described above, if we are to understand the events in living systems at the cellular level, we need to keep in mind that epigenetic information is complementary to genetic information.
The advantage of this approach is that it bypasses the complexity of underlying physicochemical reactions which are not always completely understood and for which most of the necessary variables cannot be measured. Moreover, this approach shifts the view of cell regulatory processes from the basic chemical ground to the paradigm of a cell as an information-processing unit working as an intelligent machine capable of adaptation to changing environmental and internal conditions. It is an alternative representation of the cell and can bring new insight into cellular processes. Moreover, models derived from such a viewpoint can directly help in the more traditional biochemical and molecular biological analyses of cell control.
The basic part of the project is the development of on-chip single-cell-based cultivation and analysis systems for monitoring the dynamic processes in the cell. In addition we have employed these systems to examine a number of other processes eg; the variability of cells having the same genetic information, the inheritance of non-genetic information between adjacent generations of cells, the cellular adaptation processes caused by environmental change, the community effect of cells and network pattern formation in cell groups (Figs. 3 and 4). After making extensive experimental observations, we can understand the meaning of epigenetic information in the modeling of more complex signaling cascades. This field has been largely monopolized by physico-chemical models, which provide a good standard for the comparison, evaluation, and development of our approach. The ultimate aim of our project is to provide a comprehensive understanding of living systems as the products of both genetic information and epigenetic information.