A genetic circuit to modulate noise and analyse its effects in synthetic devicesView all posters
University of Bologna, Italy
The stochastic nature of biochemical reactions together with their intrinsic nonlinearity complicates the analysis – and therefore the design – of synthetic biological circuitry. Thus, the transition from a conceptual deterministic analysis of gene expression to a stochastic approach is needed to overcome the present limitations in synthetic biology. As a minimal result this approach will limit the risks of unpredictability, but in a larger and most ambitious perspective it will offer instruments not limited to the control of noise, but aimed to the functional adjustment of the operational properties of synthetic biological systems. In this work the fluorescent output of a test circuit [Ceroni et al. (2012) ACS Synth. Biol. 1:163-171] is modeled using either ordinary differential equations or discrete reactions in the presence of an additional module, intended as a shuttle of intrinsic intracellular noise into the reporter molecular device. The test circuit combines a transcriptional (LacI-based) and a translational (through mRNA hybridization) control of gene expression, allowing to independently modulate and analyze the respective noise contributions to the output of the synthetic device. Noise modulation is achieved tuning the transcriptional rates of the mRNA molecules involved in the translational control. Numerical simulations, performed using the Gillespie’s algorithm, predict that increasing the noise at the translational level improves the sensitivity of the transcriptional control mechanism. These numerical results will be compared with experimental measurements performed in bacterial cells. As the control of intrinsic noise allows to exploit the unavoidable stochasticity of biological processes in the design of synthetic devices, the presented gene circuit is proposed as a tool for e.g. identifying the parameters that optimize the function performed by a synthetic circuit or analyzing the effects of noise on gene expression in nature.