Image analysis of fibroblast movement regulation and dynamic simulation

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By labeling fluorescence to specific proteins or organelles, lots of image data have been produced to observe various phenomena within a cell. Development of the image analysis systems provides a great innovation to molecular biology. By using such advanced image analysis techniques, we successfully observed how WAVE binds to IRSp53 within fibroblast cells and found a signal transduction pathway regarding cell movement.On the other hand, it is very important to temporally and spatially simulate the dynamic behaviors of biochemical networks based on results of the image analysis. Thus, we have developed a novel method that numerically simulates the temporal and spatial changes within a cell by integrating diffusion processes. In addition to that, this modeling considers stochastic processes by employing the Gillespie algorithm. To demonstrate the feasibility of this numerical method, we simulated the spatial and temporal dynamics of mRNAs, proteins, such as bicoid and hunchback, in the embryogenesis of Drosophila, and investigated the effects of stochastic fluctuations to the dynamic behaviors. The simulation reproduced the stochastic behaviors of these proteins, which were fairly consistent with experimental observations. This modeling technology not only to leads to a real simulation of cell dynamics but also elucidates the mechanism of how noise is cancelled within a cell and of how robustness is generated by complicated biochemical networks. The simulation model assumes that diffusion occurs in one direction due to calculation complexity. It is required to approach to a high-dimensional modeling to reproduce the real cells behaviors.