Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
SynthSys-Centre for Synthetic & Systems Biology, University of Edinburgh, Edinburgh, EH9 3BD, UK.
Nat Commun. 2018 Oct 30;9(1):4528. doi: 10.1038/s41467-018-06912-9.
Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.
生长会影响一系列表型反应。因此，确定生长变化的来源及其在细胞机制中的传播，可以揭示细胞决策的基础机制。我们提出了一个随机细胞模型，将基因表达、代谢和复制联系起来，以预测单个细菌细胞的生长动态。同时，我们提供了一种理论来分析与细胞分裂耦合的随机化学反应，从而能够有效地进行参数估计、敏感性分析和假设检验。该细胞模型恢复了关于细菌生理学生长依赖性的群体平均数据，以及单细胞生长变化如何随条件变化。我们确定了导致这种变化的过程，并重建了初始波动到生长和其他过程的传播。最后，我们研究了药物-营养相互作用，发现抗生素既可以增强也可以抑制生长异质性。我们的研究结果提供了一个预测框架，用于整合异质数据，并提出可测试的预测，对抗生素耐药性、进化和合成生物学具有重要意义。
