2016 Synthetic Biology: Engineering, Evolution & Design (SEED)
Protease-Based Feedback for Overcoming Growth Rate and Enzymatic Queueing Limitations
An increasing number of synthetic genetic circuits are relying on degradation tags for achieving fast dynamics that are not rate-limited by dilution due to cell division. In bacteria, the SsrA tags are commonly used to target proteins to the native-to-E. coli ClpXP protease. However, the over-expression of tagged proteins interferes with the physiological activity of ClpXP by overloading it and consequently reducing its degradation activity. This ‘enzymatic queuing’ effect has an impact on the anticipated dynamics of the expressed synthetic genetic circuits as tagged transcription factors become post-translational coupled. More importantly, this can lead to pathological host phenotypes, where cells grow slower or even fail to divide. In this work we are employing biomolecular feedback control in order to a) sense the load imposed on proteases, such as the native ClpXP or mf-Lon from mycoplasma, by increasing amounts of tagged proteins and b) automatically adjust the total level of proteases to compensate and alleviate the pathological effects caused by the post-translational coupling between the tagged species. The basic architecture of the network comprises a self-regulated protease. This is realized by either introducing a self-targeting tag on the protease, or through a protease-degradable transcriptional activator circuit. From an engineering design viewpoint, our in silico analysis has highlighted unique and interesting properties of this feedback architecture. First, the varying levels of tagged proteins, in the closed loop network, exhibit negligible sensitivities with respect to each other. In addition, the system is significantly more robust and shows faster recovery from transient perturbations, but more importantly it has reduced sensitivity to dilution rate changes. This suggests that our protease feedback mechanisms can be used to maintain protein levels at a stable, almost constant level during and between cell growth phases. Undergoing experimental work aims to implement, demonstrate, and inform further the modelling aspects of this work in order to optimize it’s ‘buffering’ capacity. Potential applications include delivering a biomolecular controller that can significantly increase robustness in synthetic circuits in terms of perturbations and changes in cell growth rates and allow for the predictable fine-tuning of the dynamics of synthetic biology systems.