When synthetic biology fails: a modular framework for modelling genetic stability in engineered cell populations

Abstract

AbstractPredicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology with the potential to transform our use of genetic devices. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce, where the vast combination of genetic parts and regulatory elements creates a unique modelling challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation variation to explore, after which our model automatically generates transition dynamics between different mutation phenotypes over time. Selection pressures are naturally accounted for using host-aware modelling, where the expression of synthetic genes is connected to the consumption of shared cellular resources and in turn cell growth rate. We show how our framework can provide new insights into industrial applications, such as how the design of synthetic constructs impacts long-term protein yield and genetic shelf-life. We also uncover new mutation-driven design paradigms for classic genetic network motifs: toggle switches regenerate their capacity for switching between stable states when subject to high degrees of mutational variation, while repressilators are resistant to single points of failure such that inactivating individual genes is selectively disadvantageous in a growing population.