在具有生长异质性的细菌群体中模拟遗传电路。
Simulating Genetic Circuits in Bacterial Populations with Growth Heterogeneity.
机构信息
Max Planck Institute of Colloids and Interfaces, Potsdam, Germany; Institute for Nonlinear Dynamics, University of Göttingen, Göttingen, Germany.
Max Planck Institute of Colloids and Interfaces, Potsdam, Germany; Institute for Nonlinear Dynamics, University of Göttingen, Göttingen, Germany.
出版信息
Biophys J. 2018 Jan 23;114(2):484-492. doi: 10.1016/j.bpj.2017.11.3745.
We computationally study genetic circuits in bacterial populations with heterogeneities in the growth rate. To that end, we present a stochastic simulation method for gene circuits in populations of cells and propose an efficient implementation that we call the "Next Family Method". Within this approach, we implement different population setups, specifically Chemostat-type growth and growth in an ideal Mother Machine and show that the population structure and its statistics are different for the different setups whenever there is growth heterogeneity. Such dependence on the population setup is demonstrated, in the case of bistable systems with different growth rates in the stable states, to have distinctive signatures on quantities including the distributions of protein concentration and growth rates, and hysteresis curves. Applying this method to a bistable antibiotic resistance circuit, we find that as a result of the different statistics in different population setups, the estimated minimal inhibitory concentration of the antibiotic becomes dependent on the population setup in which it is measured.
我们通过计算研究了具有不同生长率异质性的细菌群体中的遗传回路。为此，我们提出了一种用于细胞群体中基因回路的随机模拟方法，并提出了一种称为“Next Family Method”的有效实现方法。在这种方法中，我们实现了不同的群体设置，特别是恒化器类型的生长和在理想的母机中生长，并表明只要存在生长异质性，不同设置下的群体结构及其统计数据就会有所不同。在具有不同稳定状态下生长率的双稳态系统的情况下，这种对群体设置的依赖性在包括蛋白质浓度和生长速率分布以及滞后曲线在内的数量上具有独特的特征。将这种方法应用于双稳态抗生素耐药性电路，我们发现，由于不同群体设置下的统计数据不同，抗生素的最小抑菌浓度估计值变得取决于测量时所采用的群体设置。
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