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J Biol Eng. 2012 Mar 30;6(1):2. doi: 10.1186/1754-1611-6-2.

A synthetic biology approach to self-regulatory recombinant protein production in Escherichia coli.

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1
UC Davis Genome Center, University of California, Davis, USA. iliast@ucdavis.edu.

Abstract

BACKGROUND:

Recombinant protein production is a process of great industrial interest, with products that range from pharmaceuticals to biofuels. Since high level production of recombinant protein imposes significant stress in the host organism, several methods have been developed over the years to optimize protein production. So far, these trial-and-error techniques have proved laborious and sensitive to process parameters, while there has been no attempt to address the problem by applying Synthetic Biology principles and methods, such as integration of standardized parts in novel synthetic circuits.

RESULTS:

We present a novel self-regulatory protein production system that couples the control of recombinant protein production with a stress-induced, negative feedback mechanism. The synthetic circuit allows the down-regulation of recombinant protein expression through a stress-induced promoter. We used E. coli as the host organism, since it is widely used in recombinant processes. Our results show that the introduction of the self-regulatory circuit increases the soluble/insoluble ratio of recombinant protein at the expense of total protein yield. To further elucidate the dynamics of the system, we developed a computational model that is in agreement with the observed experimental data, and provides insight on the interplay between protein solubility and yield.

CONCLUSION:

Our work introduces the idea of a self-regulatory circuit for recombinant protein products, and paves the way for processes with reduced external control or monitoring needs. It demonstrates that the library of standard biological parts serves as a valuable resource for initial synthetic blocks that needs to be further refined to be successfully applied in practical problems of biotechnological significance. Finally, the development of a predictive model in conjunction with experimental validation facilitates a better understanding of the underlying dynamics and can be used as a guide to experimental design.

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