A new design framework for engineering evolutionarily robust gene circuits

合成生物学，工程学微生物中的人工基因网络在医学、工业生物加工和环境科学中有广泛的应用。然而，这类基因工程菌株的生长往往很差。在种群增长过程中，由于DNA突变的自然过程，出现了非功能菌株。这些新的突变菌株具有更高的生长速度，因此在种群中占据主导地位，导致工程功能随着时间的推移而崩溃。这对工程生物解决方案的实施造成了根本障碍。能够可靠、高效地构建具有进化稳健性的合成基因电路，将是我们努力扩大生物学工程的一个里程碑。然而，进化仍然是有效应用这些工程生物系统的“房间里的大象”，而进化在系统设计过程中往往被忽视。我们的目标是开发一种设计框架，使基因电路能够被设计成具有长期功能。我们将通过以下方式做到这一点：(1)使用最先进的数学模型来获得详细的定量了解进化压力如何随时间影响工程基因电路的性能和功能(2)利用这些了解来生成设计原则，以便能够高效和可靠地设计具有更强进化健壮性的工程电路：在这个项目中，我们将开发能够预测基因电路种群范围性能的微生物基因表达、生长和种群竞争的多尺度模型。我们将开发突变算法，真实地捕获DNA突变对关键电路参数的影响，如转录和翻译速率。我们将把我们的突变方案与我们的多尺度模型结合起来，以便能够在进化过程中模拟基因电路。我们将使用工具来识别基因电路拓扑在进化过程中的故障模式，并开发控制策略来提高长期性能。

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.