Pedraza Juan M, Paulsson Johan
Department of Systems Biology, Harvard University, Boston, MA 02115, USA.
Science. 2008 Jan 18;319(5861):339-43. doi: 10.1126/science.1144331.
Many cellular components are present in such low numbers per cell that random births and deaths of individual molecules can cause substantial "noise" in concentrations. But biochemical events do not necessarily occur in single steps of individual molecules. Some processes are greatly randomized when synthesis or degradation occurs in large bursts of many molecules during a short time interval. Conversely, each birth or death of a macromolecule could involve several small steps, creating a memory between individual events. We present a generalized theory for stochastic gene expression, formulating the variance in protein abundance in terms of the randomness of the individual gene expression events. We show that common types of molecular mechanisms can produce gestation and senescence periods that reduce noise without requiring higher abundances, shorter lifetimes, or any concentration-dependent control loops. We also show that most single-cell experimental methods cannot distinguish between qualitatively different stochastic principles, although this in turn makes such methods better suited for identifying which components introduce fluctuations. Characterizing the random events that give rise to noise in concentrations instead requires dynamic measurements with single-molecule resolution.
许多细胞成分在每个细胞中的数量非常少，以至于单个分子的随机产生和死亡会在浓度上造成显著的“噪声”。但是生化事件不一定以单个分子的单步形式发生。当合成或降解在短时间间隔内以许多分子的大量爆发形式发生时，一些过程会变得高度随机。相反，大分子的每次产生或死亡可能涉及几个小步骤，从而在各个事件之间产生记忆。我们提出了一种随机基因表达的广义理论，根据单个基因表达事件的随机性来阐述蛋白质丰度的方差。我们表明，常见类型的分子机制可以产生孕育期和衰老期，从而降低噪声，而无需更高的丰度、更短的寿命或任何浓度依赖性控制回路。我们还表明，大多数单细胞实验方法无法区分定性不同的随机原理，尽管这反过来使这些方法更适合识别哪些成分会引入波动。相反，要表征导致浓度噪声的随机事件需要具有单分子分辨率的动态测量。
