Abstract
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.
Highlights
We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division
We first experimentally characterize the noisy dynamics of a synthetic gene oscillator at the single-cell level
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
Summary
Random fluctuations in gene networks have a variety of origins: e.g. small molecule numbers within cells [1,2,3,4,5,6], fluctuations in the environment [7, 8], spatial heterogeneity [9], or the cell cycle [10]. A number of experimental and theoretical studies have examined the impact of noise on gene networks in equilibrium. Such studies focus on decomposing fluctuations into an intrinsic component which affects individual genes independently, and an extrinsic component which impacts all reactions within the cell or population [3, 8, 11,12,13,14,15]. Different sources of extrinsic and intrinsic noise can have distinct impacts on network dynamics. An approach that identifies the effect and origin of different fluctuation sources would allow us to develop more accurate computational models of genetic circuits, and better understand the processes that affect their function
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