Disentangling Intrinsic and Extrinsic Gene Expression Noise in Growing Cells
Disentangling Intrinsic and Extrinsic Gene Expression Noise in Growing Cells
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DOI：
10.1103/physrevlett.126.078101
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发表时间：
2021-02-16
影响因子：
8.6
通讯作者：
Amir, Ariel
中科院分区：
文献类型：
--
作者：
Lin, Jie;Amir, Ariel
基因表达是一个随机过程。尽管生长细胞中蛋白质数量增加，但在整个细胞周期中蛋白质浓度通常被限制在小范围内。通常，蛋白质浓度中的噪声可以分解为内在和外在分量，其中前者在高表达水平下消失。将蛋白质浓度的时间轨迹视为浓度空间中的随机步行者，必须存在有效的恢复力（具有相应的“弹簧常数”）以防止由于随机波动而导致的浓度发散。在这项工作中，我们证明了有效弹簧常数的大小直接关系到总蛋白质浓度噪声中的固有噪声的分数。我们发现，人们可以推断的幅度的内在，外在的，和测量噪声的基因表达的蛋白质浓度的时间分辨数据的基础上，没有任何先验知识的基础基因表达动态。我们将此方法应用于单细胞细菌基因表达的实验数据。结果使我们能够估计所研究的蛋白质的平均拷贝数和翻译爆发参数。
Gene expression is a stochastic process. Despite the increase of protein numbers in growing cells, the protein concentrations are often found to be confined within small ranges throughout the cell cycle. Generally, the noise in protein concentration can be decomposed into an intrinsic and an extrinsic component, where the former vanishes for high expression levels. Considering the time trajectory of protein concentration as a random walker in the concentration space, an effective restoring force (with a corresponding "spring constant") must exist to prevent the divergence of concentration due to random fluctuations. In this work, we prove that the magnitude of the effective spring constant is directly related to the fraction of intrinsic noise in the total protein concentration noise. We show that one can infer the magnitude of intrinsic, extrinsic, and measurement noises of gene expression solely based on time-resolved data of protein concentration, without any a priori knowledge of the underlying gene expression dynamics. We apply this method to experimental data of single-cell bacterial gene expression. The results allow us to estimate the average copy numbers and the translation burst parameters of the studied proteins.