In this paper, a new approach to estimation problems of protein networks is proposed, based on an idea of systems biology. Generally, it is difficult to estimate complicated networks by molecular biology. However, it will be possible to solve the difficulty by using the proposed approach. This approach is based on system identification using least-squares method for state-space models. Moreover, the proposed approach is applied to an estimation problem of protein networks for cell cycle in yeast. Nine proteins are selected from 48 proteins concerned with cell cycle in yeast, then 9-dimensional protein networks are estimated.