Development of anti-cancer drug medication scheduling method based on the systematic understanding of biochemical networks

通过使用昼夜节律-细胞周期模型，我们确定了正常细胞和癌细胞之间细胞周期的差异，并提出了有效的药物给药策略。为了建立仿真模型，需要对动力学参数值进行估计，使模型与实验数据拟合。通常，体外测量的参数值与体内测量的参数值不同。然而，体外测量可以作为体内值的线索。在这项研究中，我们开发了一种新的参数估计方法，使用体外测量。我们揭示并数学证明了长负反馈回路使振荡对动力学参数的波动具有鲁棒性。

By using a circadian rhythm-cell cycle model, we identified differences in cell cycles between normal and cancer cells, and proposed an efficient drug administration strategy. In order to construct simulation models, the values of kinetic parameters need to be estimated so that the models fit to experimental data. Generally, the parameter values measured in vitro are different from those in vivo. However, the in vitro measurements can be clues to in vivo values. In this study, we developed a novel parameter estimation method that uses in vitro measurements. We revealed and mathematically proved that the long negative feedback loops make oscillations robust to fluctuations in kinetic parameters.