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

Gerhardt, Karl; Rao, Satyajit D; Olson, Evan J.; Igoshin, Oleg A.; Tabor, Jeffrey J.

doi:10.1038/s41467-021-27070-5

Cited by 5 publications

(6 citation statements)

References 72 publications

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“…Reference [119] first showed that the stationary mean and variance of the controlled species could be independently assigned within their admissibility region by considering two independent control inputs. Experiments reported by by Gerhardt et al [122] also led to the same conclusions, albeit using a different approach. This result needs to be contrasted with in-vivo control where it is possible to act on the mean and the variance of the controlled species independently but using one single control input [113,105].…”

Section: Centralized Population and Single-cell Controlsupporting

confidence: 63%

Noise in Biomolecular Systems: Modeling, Analysis, and Control Implications

Briat¹,

Khammash²

2022

Preprint

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While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement certain functions that would have been difficult or even impossible otherwise. In this article, we review the role and impact of noise in systems and synthetic biology, with a particular emphasis on its role in the genetic control of biological systems, an area we refer to as cybergenetics. The main modeling paradigm is that of stochastic reaction networks, whose applicability goes beyond biology, as these networks can represent any population dynamics system, including ecological, epidemiological, and opinion dynamics networks. We review different ways to mathematically represent these systems, and we notably argue that the concept of ergodicity presents a particularly suitable way to characterize their stability. We then discuss noise-induced properties and show that noise can be both an asset and a nuisance in this setting. Finally, we discuss recent results on (stochastic) cybergenetics and explore their relationships to noise. Along the way, we detail the different technical and biological constraints that need to be respected when designing synthetic biological circuits. Finally, we discuss the concepts, problems, and solutions exposed in the article; raise criticisms and concerns about current ideas and approaches; suggest current (open) problems with potential solutions; and provide some ideas for future research directions.

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Section: Centralized Population and Single-cell Controlsupporting

confidence: 63%

Noise in Biomolecular Systems: Modeling, Analysis, and Control Implications

Briat¹,

Khammash²

2022

Preprint

View full text Add to dashboard Cite

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“…Therefore, it is necessary to understand the origin of the noise, as well as control noise rationally for various applications. Regarding noise regulation, various strategies have been proposed to control gene expression noise independently, including engineering transcription and translation in synthetic gene circuits ( 61 , 62 ), introducing pulsatile input to control the promoter activation frequency and transcription rate independently ( 63 ) and expressing two copies of the target gene from separate circuits with different characteristics ( 64 ), among others. Through dSort-Seq profiling of different combinations of promoters and RBSs in E. coli , the transcriptional effect on gene expression noise was revealed.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning–assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Feng,

Li,

Wang

et al. 2023

Sci. Adv.

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Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning–assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli , from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli .

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“…Therefore, it is necessary to understand the origin of the noise, as well as control noise rationally for various applications. Regarding noise regulation, various strategies have been proposed to control gene expression noise independently, including engineering transcription and translation in synthetic gene circuits 58,59 , introducing pulsatile input to control the promoter activation frequency and transcription rate independently 60 and expressing two copies of the target gene from separate circuits with different characteristics 61 , among others. Through dSort-Seq profiling of different combinations of promoters and RBSs in E. coli , the transcriptional effect on gene expression noise was revealed.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Feng

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

As an essential physiological process, gene expression determines the function of each cell. However, owing to the complex nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. Therefore, quantitative description of these characteristics is an important goal in biology. This task, however, has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose‒response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in E. coli, from which we discovered that the expression noise is strongly coupled with the mean expression level instead of translation strength, even in the case of weak transcription. We also discovered that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli. Overall, dSort-Seq is able to efficiently determine the strength-noise landscape, which has promising applications in studies related to gene expression.

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

Cited by 5 publications

References 72 publications

Noise in Biomolecular Systems: Modeling, Analysis, and Control Implications

Noise in Biomolecular Systems: Modeling, Analysis, and Control Implications

Deep-learning–assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

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