When synthetic biology fails: a modular framework for modelling genetic stability in engineered cell populations
Predicting the evolution of engineered cell populations is a highly soughtafter goal in biotechnology. 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 challenge. To address this gap, we herein 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 heterogeneity to explore, after which our model generates hostaware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a devices components can be tweaked to optimise longterm protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.