講座內容：The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behavior. In recent years, computational models of these networks based on differential equations have become useful tools for quantifying the complex regulatory relationship sunderlying the clocks oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of this approach. In this talk, a complementary approach based on Boolean logic will be introduced that dramatically reduces the parametrization, making the state and parameter spaces more computationally tractable. Through the construction of Boolean models fitted to both synthetic and experimental time courses, it will be shown that logic models can reproduce the complex responses to environmental inputs generated by more detailed differential equation formulations. In particular, it will be demonstrated that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. This suggests that the capacity of logic models to provide a computationally efficient representation of system behaviour could facilitate the reverse-engineering of large-scale biochemical networks.