Exploiting noise to engineer adaptability in synthetic multicellular systems

Aronson, Mark S.; Ricci-Tam, Chiara; Zhu, Xinwen; Sgro, Allyson E.

doi:10.1016/j.cobme.2020.100251

Cited by 8 publications

(3 citation statements)

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“…Gene expression convolution could also be used to engineer noisy phenotypes into populations of living cells. Example behaviors include genetically identical populations that automatically differentiate into specified ratios of cell sub-types 66 , form Turing-type patterns using activator and inhibitor morphogens with similar diffusion rates 67 , or stochastically lyse in order to release enzymes to enable the population to metabolize complex agricultural feedstocks 68 . Taken together, gene expression convolution is a simple strategy for studying and controlling gene expression noise in a wide range of organisms and biological pathways.…”

Section: Discussionmentioning

confidence: 99%

Independent control of mean and noise by convolution of gene expression distributions

et al. 2021

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Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

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Section: Discussionmentioning

confidence: 99%

Independent control of mean and noise by convolution of gene expression distributions

et al. 2021

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show abstract

Section: Discussionmentioning

confidence: 99%

Independent control of mean and noise by convolution of gene expression distributions

Gerhardt

Rao

Olson

et al. 2021

Preprint

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Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from a single promoter. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer high- and low-noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise, and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

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“…Regardless of the mechanism, a fundamental benefit of population heterogeneity is increased adaptability to fluctuating environmental conditions 6 . The widespread use of heterogeneity in nature suggests that synthetic genetic circuits could exploit gene expression noise to adapt to different environmental conditions and carry out complex functions 7, 8 .…”

Section: Figurementioning

confidence: 99%

Exploiting heterogeneity in coupled, two plasmid systems for dynamic population adaptation

Kumar

Lezia

Hasty

2023

Preprint

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In synthetic multi-plasmid systems, it is standard to use only plasmids with orthogonal replication mechanisms to avoid phenotypic heterogeneity and ensure plasmid stability. In nature, however, microbial populations actively exploit heterogeneity to survive in fluctuating environments. Here we show that the intentional use of distinct plasmids with identical origins of replications (oris) can help an engineered bacterial population adapt to its environment. We find that copy number coupling between distinct plasmids in such systems allows for copy number buffering of an essential, but high-burden construct through the action of a stably maintained, nonessential plasmid. Plasmid coupling also generates population state memory without additional layers of regulatory control. This work reimagines how we design synthetic populations to survive and adapt by strategically giving control back to the cells.

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Exploiting noise to engineer adaptability in synthetic multicellular systems

Cited by 8 publications

References 50 publications

Independent control of mean and noise by convolution of gene expression distributions

Independent control of mean and noise by convolution of gene expression distributions

Independent control of mean and noise by convolution of gene expression distributions

Exploiting heterogeneity in coupled, two plasmid systems for dynamic population adaptation

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