A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs
Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2BU, UK
Department of Bioengineering, Imperial College London, London SW7 2BU, UK
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 14 November 2020 / Revised: 26 December 2020 / Accepted: 31 December 2020 / Published: 7 January 2021
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.