Natural fluctuations in gene expression (“noise”) impact many cell processes such as differentiation, proliferation, and apoptosis. Negative feedback gene circuits, a gene self-repressing its own transcription e.g. through protein binding, are often used to reduce the single-gene expression noise and control its impact on investigated phenomena. However, the detailed, quantitative understanding of how temporal and population-level noise is affected by natural mammalian gene expression processes and how this dependence is modified by transcriptional negative feedback is lacking. Here, we present a comprehensive framework combining Live-cell Imaging of Single-Cell Arrays (LISCA) with likelihood-free inference methods to characterize stochastic gene expression in the time domain and at single-cell resolution. We investigate the temporal and population-level variability in protein levels driven by a negative feedback circuit and compare the results with constitutive expression where the feedback mechanism is broken. We use inferred kinetic rate parameters to validate several hybrid stochastic/deterministic models at their predictive capacity regarding Coefficient of Variation (CV) versus mean protein level relation. Our analysis shows significant differences between temporal and population-level noise profiles. We show how feedback strength and cooperativity quantitatively control not only mean protein levels per cell but also their variation range within cell population (CV p ) and across time in individual cells (CV t ).
Azukas et al. (Sun,) studied this question.