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

Abstract

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.

Resource-aware construct design in mammalian cells

“…This suggests that transcriptional resources might be more limiting than translational resources in the tested cell lines, and that the latter can accommodate a higher demand from synthetic constructs before being saturated. These results are in striking contrast with those previously reported in bacteria where 444 translational resources were shown to play the major role in gene expression burden 5,8,[29][30][31][32] .…”

Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware

“…The complexity of synthetic biology models dramatically increases in the recent years ,,,,,− in a rate higher than the increase in computation cost. In some cases, when the models and algorithms are too extensive, the simulations may become the bottleneck of the development process.…”

“…Biophysical models of intracellular processes such as gene expression have been used in recent years for studying numerous questions related to all biomedical disciplines. − The more advanced models in the field consider the “competition” of molecules in the cell (e.g., mRNAs) on resources (e.g., ribosomes). − In recent years, we understand that without considering this aspect, the models usually provide significantly biased prediction and miss important intracellular aspects. − ,− Thus, it is clear that in the near future, these models will be very frequently used for synthetic biology for designing cells and viruses; indeed, recent manuscripts emphasize the importance of such whole-cell simulations in synthetic biology. − However, when performing designs based on such models, the running time is orders of magnitude lower than just predicting a single intracellular stage. Thus, our approach is needed.…”

“…This is specifically challenging when various sets of parameters of the models are studied or optimized as in the case of synthetic biology, where the aim is to find a set of modifications in the cell that will optimize a certain objective that is affected by a large pool of factors in the cell. ,,,,,− In such cases, the optimizations can easily take many months or even years.…”

Experimental tools to reduce the burden of bacterial synthetic biology

“…Strategies to ease burden and its impact have focused on rewiring the native cellular machinery [10], the design of orthogonal systems for controlled allocation of cellular resources between endogenous and exogenous genes [11][12][13], identification of low-burden designs [14], adoption of biocontrollers to balance cellular fitness and exogenous expression [15][16][17] as well as less complex systems where gene expression resources are separated from the native cellular context [18][19][20]. While also a plethora of computational approaches is now available for host-aware bacterial engineering [21][22][23][24][25][26], in this review we aim at providing a schematic overview of some more recent experimental strategies adopted to reduce burden in bacteria, describing their advantages and limitations. From these examples it will become clear that we have improved in our ability to take burden into account at the design stage, even if we are still facing uncertainties in controlling the response of engineered cells.…”