Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.

This dataset contains metadata after image segmentation and tracking. Segmentation was achieved by a watershed algorithm with a seed generated from the Hough circle detection. Tracking was performed by maximizing the segmentation feature correlation between two consecutive time frames. Average fluorescent intensity profiles of droplets were then extracted for further analysis. All analysis above is performed on MatLab 2019a.

This dataset contains 3 different levels of simplification, 1. raw data right after image processing. 2. cycle description after semi-automatic peak detection and oscillatory feature calculation. 3. droplet level statistics after peak detection. For each independent experiment, one or more levels of simplification may be provided. See 'readMe.doc' for detailed information about the simplification level of the dataset and data structure annotation. 

This dataset is mainly consisted of two types of files, MatLab metadata (*.mat ) after image processing and MatLab scripts (*.m) to generate figures. 

The dataset is categorized based on figures. Each script controls metadata import, data analysis, and figure generation. The scripts and corresponding figures are listed in readMe file.