Modelling genetic stability in engineered cell populations

Abstract

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.

Genomic barcoding for clonal diversity monitoring and control in cell-based complex antibody production

“…2 e and 4 g), result in clones which do express reduced or misfolded protein. We speculate that protein expression utilizes a finite supply of cellular energy, and cells with lower expression burden can divert more resources to growth-supporting processes 35 . This is supported by our previous work where we showed that high producer cell clones devote a substantial proportion (up to 29%) of their global transcriptome towards antibody transgenes 36 .…”

Engineering a novel probiotic toolkit inEscherichia coli Nissle1917for sensing and mitigating gut inflammatory diseases

“…The stable colonization of engineered probiotics in the gut can be negatively impacted by metabolic burden—the allocation of resources towards the engineered system— which can hinder bacterial growth in the complex microbiome environment 79 . Moreover, evolutionary changes might disrupt circuit functionality over short time periods 94 . The heterogeneity in bacterial expression due to background genetic mutations or expression noise might also lead to variability in treatment efficacy 94 .…”

“…Moreover, evolutionary changes might disrupt circuit functionality over short time periods 94 . The heterogeneity in bacterial expression due to background genetic mutations or expression noise might also lead to variability in treatment efficacy 94 . Additionally, the interactions between the host immune system and engineered probiotics require thorough investigation to ensure long-term efficacy and safety 95,96 .…”

“…Additionally, the interactions between the host immune system and engineered probiotics require thorough investigation to ensure longterm efficacy and safety 95,96 . To address some of these challenges, strategies such as integrating the genetic circuit into the genome can enhance the stability and robustness of the device's functionality 78,94 . Combining whole-cell and host-microbiome metabolic models with in vivo assays of viability and prevalence of engineered probiotics is also important for predicting the long-term maintenance of such systems 93,[97][98][99][100][101] .…”

Competition and evolutionary selection among core regulatory motifs in gene expression control

“…The results presented in this paper focus on the impact of autoregulation by putting the spotlight on fitness advantage, as this factor plays a pivotal role in establishing the dominant strategies that emerge as a result of competition and selection. In synthetic biology applications, however, while the fitness cost can be crucial in certain scenarios, e.g., when studying the cost of plasmid acquisition and maintenance 76 , often the rate at which non-binders emerge is instead of primary concern to avoid compromising the genetic stability of engineered synthetic circuits 77 . Importantly, our approach can be applied to characterize how autoregulation impacts the proliferation of deleterious mutations considering timescales and population sizes typical in synthetic biology applications (Supplementary Section 8 ) ranging from microfluidics experiments to bioreactor-based contexts 78 – 82 .…”