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Article

A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs

by 1,†, 2,3,† and 2,3,*
1
Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
2
Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2BU, UK
3
Department of Bioengineering, Imperial College London, London SW7 2BU, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biology 2021, 10(1), 37; https://doi.org/10.3390/biology10010037
Received: 14 November 2020 / Revised: 26 December 2020 / Accepted: 31 December 2020 / Published: 7 January 2021
(This article belongs to the Special Issue Computational Methods in Synthetic Biology)
In 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. View Full-Text
Keywords: synthetic biology; whole-cell model; translation; stochastic simulation; TASEP; construct design; burden; ribosomal queues; slow codon synthetic biology; whole-cell model; translation; stochastic simulation; TASEP; construct design; burden; ribosomal queues; slow codon
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MDPI and ACS Style

Sarvari, P.; Ingram, D.; Stan, G.-B. A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs. Biology 2021, 10, 37. https://doi.org/10.3390/biology10010037

AMA Style

Sarvari P, Ingram D, Stan G-B. A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs. Biology. 2021; 10(1):37. https://doi.org/10.3390/biology10010037

Chicago/Turabian Style

Sarvari, Peter, Duncan Ingram, and Guy-Bart Stan. 2021. "A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs" Biology 10, no. 1: 37. https://doi.org/10.3390/biology10010037

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