The stochasticity of gene expression is manifested in the fluctuations of messenger ribonucleic acid and protein copy numbers within a cell lineage over time. While data of this type can be obtained for many generations, most mathematical models are unsuitable to interpret such data since they assume nongrowing cells. Here we develop a theoretical approach that quantitatively links the frequency content of lineage data to subcellular dynamics. We elucidate how the position, height, and width of the peaks in the power spectrum provide a distinctive fingerprint that encodes a wealth of information about mechanisms controlling transcription, translation, replication, degradation, bursting, promoter switching, cell cycle duration, cell division, gene dosage compensation, and cell-size homeostasis. Predictions are confirmed by analysis of single-cell Escherichia coli data obtained using fluorescence microscopy. Furthermore, by matching the experimental and theoretical power spectra, we infer the temperature-dependent gene expression parameters, without the need of measurements relating fluorescence intensities to molecule numbers.

中文翻译：

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细胞谱系中的mRNA和蛋白质拷贝数波动的频域分析：理论和实验验证

基因表达的随机性表现为信使核糖核酸和蛋白质谱系内蛋白质拷贝数随时间的波动。尽管这种类型的数据可以世代获得，但大多数数学模型都不适合解释此类数据，因为它们假定细胞不增长。在这里，我们开发了一种理论上的方法，该方法定量地将谱系数据的频率内容链接到亚细胞动力学。我们阐明了功率谱中峰的位置，高度和宽度如何提供独特的指纹，该指纹编码了有关控制转录，翻译，复制，降解，爆发，启动子切换，细胞周期持续时间，细胞分裂的机制的大量信息，基因剂量补偿和细胞大小的稳态。预测通过单细胞分析得到证实使用荧光显微镜获得的大肠杆菌数据。此外，通过匹配实验和理论功率谱，我们可以推断温度相关的基因表达参数，而无需进行将荧光强度与分子数量相关的测量。