A new design framework for engineering evolutionarily robust gene circuits

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Synthetic Biology, EngineeringEngineering artificial gene networks in microorganisms has a range of applications in medicine, industrial bioprocessing and environmental science. However, such engineered strains often show poor growth. During population growth, non-functional strains emerge due to the natural process of DNA mutation. These new mutant strains have higher growth rate and so dominate the population resulting in a breakdown of engineered function over time. This creates a fundamental roadblock to the implementation of engineered biological solutions. The ability to construct synthetic gene circuits reliably and efficiently with evolutionary robustness would represent a milestone in our efforts to scale-up the engineering of biology. However, evolution remains the 'elephant in the room' for the effective application of these engineered biological systems with evolution often neglected during the system design process. We aim to develop a design framework which enable gene circuits to be engineered with long term function. We will do this by: (1) using state of the art mathematical modelling to obtain detailed quantitative understanding of how evolutionary pressures affect the performance and functionality of engineered gene circuits over time (2) leveraging this understanding to generate design principles that will allow the efficient and reliable design of engineered circuits with increased evolutionary robustness Methodology: In this project, we will develop multiscale models of microbial gene expression, growth and population competition capable of predicting the population-wide performance of a gene circuit. We will develop mutation algorithms which realistically capture the impact DNAmutations have on key circuit parameters, such as transcription and translation rates. We will couple our mutation scheme with our multiscale models to enable the simulation of gene circuits over the course of volutionary time. We will use tools to identify the modes of failure in gene circuit topologies over the course of evolutionary time and develop control strategies to increase long term performance.