Comprehensive Parameter Space Mapping of Cell Cycle Dynamics under Network Perturbations

Comprehensive Parameter Space Mapping of Cell Cycle Dynamics under Network Perturbations
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DOI:
10.1021/acssynbio.3c00631
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发表时间:
2024-02-29
影响因子:
4.7
通讯作者:
Yang,Qiong
Yang,Qiong
中科院分区:
生物学2区
文献类型:
--
作者:
Li,Zhengda;Wang,Shiyuan;Yang,Qiong

文献摘要

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定量系统和合成生物学的研究已经广泛地利用模型来解释数据,进行预测和指导实验设计。然而,模型往往简化复杂的生物系统,缺乏实验验证的参数,使其在扰动系统的可靠性不清楚。在这里,我们开发了一种基于液滴的合成细胞系统,以在多个维度上连续调整单细胞水平的参数,并具有完整的动态范围,为全局参数空间扫描提供了一个实验框架。我们系统地扰动了以细胞周期蛋白依赖性激酶(Cdk 1)为中心的细胞周期振荡器,从而能够全面绘制响应网络扰动的周期景观。这些数据使我们能够挑战现有的模型,并改进一个与观察到的反应相匹配的新模型。我们的分析表明,Cdk 1正反馈抑制限制了细胞周期的频率范围,证实了模型的预测,此外,它揭示了新的细胞反应的Cdk 1-抵消磷酸酶PP 2A的抑制:单峰或双峰分布在不同的抑制水平,强调细胞周期调控的复杂性,可以解释我们的模型。该平台可以推广到其它复杂动力学系统的研究。
Studies of quantitative systems and synthetic biology have extensively utilized models to interpret data, make predictions, and guide experimental designs. However, models often simplify complex biological systems and lack experimentally validated parameters, making their reliability in perturbed systems unclear. Here, we developed a droplet-based synthetic cell system to continuously tune parameters at the single-cell level in multiple dimensions with full dynamic ranges, providing an experimental framework for global parameter space scans. We systematically perturbed a cell-cycle oscillator centered on cyclin-dependent kinase (Cdk1), enabling comprehensive mapping of period landscapes in response to network perturbations. The data allowed us to challenge existing models and refine a new model that matches the observed response. Our analysis demonstrated that Cdk1 positive feedback inhibition restricts the cell cycle frequency range, confirming model predictions; furthermore, it revealed new cellular responses to the inhibition of the Cdk1-counteracting phosphatase PP2A: monomodal or bimodal distributions across varying inhibition levels, underscoring the complex nature of cell cycle regulation that can be explained by our model. This comprehensive perturbation platform may be generalizable to exploring other complex dynamic systems.