Abstract
Understanding molecular regulation mechanisms of cells has been a central topic in biology. Recent advances in high-throughput technologies such as microarray, and Chip-Chip analysis have provided a large amount of data that reveal valuable information on regulatory mechanisms of cells. In systems biology, researchers have tried to build hypothetical regulation models based on those high-throughput data, and then predicted systems characteristic behavior of the models. One of the main obstacles of this research direction is to estimate a large number of parameters involved in a model of biological system. In this study, we address the problem of estimating parameters in systems biology model with measured data in a Bayesian framework.
