Intrinsic and extrinsic contributions to stochasticity in gene expression
Intrinsic and extrinsic contributions to stochasticity in gene expression
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DOI：
10.1073/pnas.162041399
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发表时间：
2002-10-01
影响因子：
11.1
通讯作者：
Siggia, ED
中科院分区：
文献类型：
--
作者：
Swain, PS;Elowitz, MB;Siggia, ED
基因表达是一个随机的，或“噪声”的过程。这种噪音来自两种方式。生物化学过程如转录和翻译的固有随机性产生“内在”噪声。此外，其他细胞成分的量或状态的波动间接导致特定基因表达的变化，因此代表“外在”噪声。在这里，我们展示了如何在一个给定的基因的表达水平的总变化可以分解成其内在和外在的组件。我们从理论上证明，同时测量每个细胞的两个相同的基因，使这两种类型的噪音的歧视。给出了一个涉及转录和翻译中所有主要步骤的模型的固有噪声的解析表达式。这些表达式给出了对各种参数的敏感性，量化了与泊松统计量的偏差，并提供了拟合实验的方法。当每个mRNA转录物产生的蛋白质平均数量大于或等于2时，转录占主导地位。低于这个数字，翻译效果也变得重要。模型中包含的基因复制和细胞分裂导致蛋白质数量趋于极限循环。我们计算的外在噪音的一般形式，并说明它与一个单一的波动外在变量的特定情况下，阻遏蛋白，它作用于感兴趣的基因。所有结果都证实了随机模拟大肠杆菌使用合理的参数。
Gene expression is a stochastic, or "noisy," process. This noise comes about in two ways. The inherent stochasticity of biochemical processes such as transcription and translation generates "intrinsic" noise. In addition, fluctuations in the amounts or states of other cellular components lead indirectly to variation in the expression of a particular gene and thus represent "extrinsic" noise. Here, we show how the total variation in the level of expression of a given gene can be decomposed into its intrinsic and extrinsic components. We demonstrate theoretically that simultaneous measurement of two identical genes per cell enables discrimination of these two types of noise. Analytic expressions for intrinsic noise are given for a model that involves all the major steps in transcription and translation. These expressions give the sensitivity to various parameters, quantify the deviation from Poisson statistics, and provide a way of fitting experiment. Transcription dominates the intrinsic noise when the average number of proteins made per mRNA transcript is greater than similar or equal to2. Below this number, translational effects also become important. Gene replication and cell division, included in the model, cause protein numbers to tend to a limit cycle. We calculate a general form for the extrinsic noise and illustrate it with the particular case of a single fluctuating extrinsic variable-a repressor protein, which acts on the gene of interest. All results are confirmed by stochastic simulation using plausible parameters for Escherichia coli.