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Image analysis of fibroblast movement regulation and dynamic simulation

成纤维细胞运动调控的图像分析及动态模拟

基本信息

批准号：
15500198
负责人：
KURATA Hiroyuki
金额：
$ 2.3万
依托单位：
Kyushu Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

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.

通过将荧光标记到特定的蛋白质或细胞器上，已经产生了大量的图像数据来观察细胞内的各种现象。图像分析系统的发展为分子生物学提供了巨大的创新。利用这种先进的图像分析技术，我们成功地在成纤维细胞内观察到了WAVE与IRSP53的结合，并找到了与细胞运动有关的信号转导途径。另一方面，基于图像分析的结果，在时间和空间上模拟生化网络的动态行为是非常重要的。因此，我们开发了一种新的方法，通过积分扩散过程来数值模拟细胞内的时间和空间变化。除此之外，该模型通过使用Gillesbie算法来考虑随机过程。为了验证这种数值方法的可行性，我们模拟了果蝇胚胎发育过程中蛋白质，如双体和驼背的mRNAs的时空动态，并研究了随机波动对动态行为的影响。模拟再现了这些蛋白质的随机行为，这与实验观察相当一致。这种建模技术不仅导致了对细胞动力学的真实模拟，而且还阐明了细胞内噪声如何被抵消的机制，以及复杂的生化网络如何产生稳健性。由于计算的复杂性，模拟模型假定扩散发生在一个方向上。为了再现真实细胞的行为，需要进行高维建模。