Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression ‘noise’, has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and mRNA levels, and have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive ‘fingerprint’ with which to explore fundamental questions on gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.

Although much has been done to elucidate the biochemistry of signal transduction and gene regulatory pathways, it remains difficult to understand or predict quantitative responses. We integrate single-cell experiments with stochastic analyses, to identify predictive models of transcriptional dynamics for the osmotic stress response pathway in Saccharomyces cerevisiae. We generate models with varying complexity and use parameter estimation and cross-validation analyses to select the most predictive model. This model yields insight into several dynamical features, including multi-step regulation and switch-like activation for several osmosensitive genes. Furthermore, the model correctly predicts the transcriptional dynamics of cells in response to different environmental and genetic perturbations. Since our approach is general, it should facilitate a predictive understanding for signal-activated transcription of other genes in other pathways or organisms.

Summary The cell fate decision leading to gametogenesis is essential for sexual reproduction. In S. cerevisiae, only diploid MATa/α but not haploid MATa or MATα cells undergo gametogenesis, known as sporulation. We find that transcription of two long non-coding RNAs (lncRNAs) mediates mating type control of sporulation. In MATa or MATα haploids expression of IME1, the central inducer of gametogenesis, is inhibited in cis by transcription of the lncRNA IRT1, located in the IME1 promoter. IRT1 transcription recruits the Set2 histone methyltransferase and the Set3 histone deacetylase complex to establish repressive chromatin at the IME1 promoter. Inhibiting expression of IRT1 and an antisense transcript that antagonizes the expression of the meiotic regulator IME4, allows cells expressing the haploid mating-type to sporulate with kinetics that are indistinguishable from that of MATa/α diploids. Conversely, expression of the two lncRNAs abolishes sporulation in MATa/α diploids. Thus, transcription of two lncRNAs governs mating type control of gametogenesis in yeast.

SignificanceSystems biology seeks to combine experiments with computation to predict biological behaviors. However, despite tremendous data and knowledge, biological models make less-accurate predictions compared with other fields. By analyzing single-cell, single-molecule measurements of mRNA during yeast stress response, we explore why and how the shapes of experimental distributions control prediction accuracy. We show how asymmetric data distributions with long tails cause standard modeling approaches to yield excellent fits but make meaningless predictions. We show how these biases arise from the violation of fundamental assumptions in standard modeling approaches. We demonstrate how advanced computational tools solve this dilemma and achieve predictive understanding of spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport, and decay.