Sources of Variability in a Synthetic Gene Oscillator

Synthetic gene oscillators are small, engineered genetic circuits that produce periodic variations in target protein expression. Like other gene circuits, synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period. Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits. Here, we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells. We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations. We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division. To understand how such variability arises, we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell. When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations, and accurately predicted outcomes under novel experimental conditions. Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits.Author Summary: A goal of synthetic biology is to design genetic circuits using mathematical models that predict circuit function. However, various sources of noise impact gene regulation in different ways. This hinders the development of accurate mathematical models, especially when single-cell accuracy is required. Here, we first experimentally characterize the noisy dynamics of a synthetic gene oscillator at the single-cell level. Then, using measurements obtained from the experiments, we develop a minimal computational model that correctly predicts the statistical behavior of single cells within a growing colony. Our method can be used to construct simple computational models that not only capture the average dynamics of gene circuits, but also the statistical properties of single cells.