Abstract
Simple SummaryIn synthetic biology, it is commonplace to design and insert gene expression constructs into cells for the production of useful proteins. In order to maximise production yield, it is useful to predict the performance of these “engineered cells” in advance of conducting experiments. This is typically a complex task, which in recent years has motivated the use of “whole-cell models” (WCMs) that act as computational tools for predicting different aspects of cell growth. Many useful WCMs exist, however a common problem is their over-simplification of ribosome movement on mRNA transcripts during translation. WCMs typically don’t consider that, for constructs with inefficient (“slow”) codons, ribosomes can stall and form “traffic jams”, thereby becoming unavailable for translation of other proteins. To more accurately address these scenarios, we have built a computational framework that combines whole-cell modelling with a detailed account of ribosome movement on mRNA. We show how our framework can be used to link the modular design of a gene expression construct (via its promoter, ribosome binding site and codon composition) to protein yield during continuous cell culture, with a particular focus on how the optimal design can change over time in the presence or absence of “slow” codons.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.
Highlights
How cell production is affected, is difficult to predict, and so, whole-cell models” (WCMs) are often used to explore how various genetic construct designs affect the performance of engineered cells, e.g., in terms of the burden their expression imposes on their host cell and/or the dynamics of the protein production yields achievable by different designs
We increased the efficiency of an existing totally asymmetric simple exclusion process (TASEP) framework by removing the possibility of selecting a queuing ribosome [56] and merged this modified TASEP with a stochastic implementation of the whole-cell model introduced in [4]
We primarily explored how the sustained expression of a synthetic gene construct is coupled to cell growth through the re-distribution of finite cellular resources
Summary
An effective method that allows researchers to explore how burden affects cellular growth is via the construction of resource-limited “whole-cell models” (WCMs), which typically aim to describe key cellular processes (e.g., transcription, translation, metabolism, etc.) given finite cellular resources, such as energy, amino acids, polymerases and ribosomes [3,4,5,6,7,8]. Within such frameworks, the expression of synthetic gene constructs drains resources that are required for normal cell growth, effectively coupling heterologous gene expression to the growth rate. How cell production is affected, is difficult to predict, and so, WCMs are often used to explore how various genetic construct designs affect the performance of engineered cells, e.g., in terms of the burden their expression imposes on their host cell and/or the dynamics of the protein production yields achievable by different designs
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