Abstract
Analyzing the expression of single genes in single cells appears minimalistic in comparison to gene expression studies based on more global approaches. However, stimulated by advances in imaging technologies, single-cell studies have become an essential tool in understanding the rules that govern gene expression. This quantitative view of single-cell gene expression is based on counting mRNAs in single cells, monitoring transcription in real time, and visualizing single proteins. Parallel advances in mathematical models based on stochastic, discrete descriptions of biochemical processes have provided crucial insights into the underlying cellular mechanisms that control expression. The view that has emerged is rooted in a probabilistic understanding of cellular processes that quantitatively explains both the mean and the variation observed in gene-expression patterns among single cells. Thus, the close coupling between imaging and mathematical theory has established single-cell analysis as an essential branch of systems biology.