The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues.

Predicting the dynamics of mutation spread in engineered cell populations is a sought-after goal in synthetic biology. Until now, models that capture these processes have been lacking, either by failing to account for the diversity of mutation types, or by failing to link the growth rate of a cell to the consumption of shared cellular resources by synthetic constructs. In this study we address these shortcomings by building a novel mutation-aware modelling framework of cell growth in a turbidostat. Our framework allows users to input essential design elements of their synthetic constructs so as to predict the time evolution of different mutation phenotypes and protein production dynamics. Its structure allows quick mutation-based analysis of any construct design, from single-gene constructs to multi-gene devices with regulatory elements. We show how our framework can generate new insights into industrial applications, such as how the design of synthetic constructs impacts long-term protein yield and genetic shelf-life. Our framework also uncovers new mutation-driven design paradigms for synthetic gene regulatory networks, such as how mutations can temporarily increase the bistability of toggle switches, or how repressilators can be resistant to single points of failure.

Predicting the evolution of engineered cell populations is a highly sought-after 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 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 heterogeneity to explore, after which our model generates host-aware 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 device’s components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.

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Predicting the dynamics of mutation spread in engineered cell populations is a sought- after goal in synthetic biology. Until now, models that capture these processes have been lacking, either by failing to account for the diversity of mutation types, or by failing to link the growth rate of a cell to the consumption of shared cellular resources by synthetic constructs. In this study we address these shortcomings by building a novel mutation-aware modelling framework of cell growth in a turbidostat. Our framework allows users to input essential design elements of their synthetic constructs so as to predict the time evolution of different mutation phenotypes and protein production dynamics. Its structure allows quick mutation-based analysis of any construct design, from single-gene constructs to multi-gene devices with regulatory elements. We show how our framework can generate new insights into industrial applications, such as how the design of synthetic constructs impacts long-term protein yield and genetic shelf-life. Our framework also uncovers new mutation-driven design paradigms for synthetic gene regulatory networks, such as how mutations can temporarily increase the bistability of toggle switches, or how repressilators can be resistant to single points of failure.