General Applicability of Synthetic Gene-Overexpression for Cell-Type Ratio Control via Reprogramming

Ishimatsu, Kana; Hata, Takashi; Sekine, Ryoji; Yamamura, Masatoshi; Kiga, Daisuke

doi:10.1021/sb400102w

Cited by 8 publications

(12 citation statements)

References 21 publications

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“…A deep impact on biological systems was observed due to both classes of noises during replication, during variation observed at small reaction volumes within a cell and by bursts of translation caused by limited transcriptional activity [156][157][158]. Stochastic models have been applied to understand: sporulation dynamics of B subtilis, heftiness of a genetic circuit in response to divergent environmental conditions, exoprotease levels in bacterial populations and control of a bacterial population composition with a gene circuit [159][160][161][162]. Although In vitro systems are minimal, which seems that should simplify the development of computational models; due to this minimalistic, in vitro systems do not contain intrinsically the mechanisms of natural cells that could facilitate a strong behavior.…”

Section: -Stochastic Modelingmentioning

confidence: 99%

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Duschak¹

2015

Curr Synthetic Sys Biol

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The synthetic biology firstly refers to the design and fabrication of biological components and systems that do not already exist in the natural world and to the redesign and fabrication of existing biological systems. The link of computational tools to cell-free systems, converts to synthetic biology is an emerging field expert to build artificial biological systems through the combination of molecular biology and engineering approaches. Herein, most findings describing the differences between in vivo and in vitro reactions and systems have been extensively described. The specific applications of computational tools to the design of an in vitro gene expression platform known as the artificial cell, its components and the strategies developed to predict activities of processor modules and to control the expression of genes have been discussed in detail. Potential applications of artificial cells in drug delivery, in biosynthesis, among others, have been described. Two sources of models for the possible developing of the computational toolbox for cell-free synthetic biology include: i) Physical models of single cellular components able to be created from original principles, guiding to focus on tools to predict structure and dynamics of particular components;ii) A wide-range of mathematical models for predicting system dynamics of natural cells.Regarding modeling algorithms, there is a broad kind of models available for synthetic biologists and some areas of potential growth identified for researchers interested in developing tools for cell-free systems. Among them, deterministic, exploratory, molecular dynamic, stochastic, all atom models, among others, have been described and discussed. By using computational models to set up quantitative differences between in vitro reactions and in vivo systems, could identify specific mechanisms in living organisms to be further used in in vitro reactions in order to facilitate their processes. Thus, computational modeling would bridge the gap between in vitro and in vivo reactions. CSSB, an open access journalThe synthetic biology refers to • The design and fabrication of biological components and systems that do not already exist in the natural world and the redesign and fabrication of existing biological systems.

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Section: -Stochastic Modelingmentioning

confidence: 99%

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Duschak¹

2015

Curr Synthetic Sys Biol

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“…Both kinds of noise can have a profound impact on biological systems, including partitioning noise observed during replication (Huh and Paulsson, 2011a ), variance observed at small reaction volumes within a cell (Karig et al, 2013 ), and bursts of translation caused by limited transcriptional activity (Pedraza and Paulsson, 2008 ). Stochastic models have been applied to understand sporulation dynamics of Bacillus subtilis (Chastanet et al, 2010 ), robustness of a genetic circuit in response to varying environmental conditions (Toni and Tidor, 2013 ), exoprotease levels in bacterial populations (Davidson et al, 2012 ), and control of a bacterial population composition with a gene circuit (Sekine et al, 2011 ; Ishimatsu et al, 2013 ).…”

Section: Modeling Algorithmsmentioning

confidence: 99%

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

Lewis

Villarreal

et al. 2014

Front. Bioeng. Biotechnol.

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As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

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“…In synthetic biology, the dynamics of living cells with the mathematically-designed built-in circuits have been confirmed by testing the quantification of reporter proteins [6]. Based on the test results, synthetic biologists have redesigned synthetic genetic circuits to regulate the dynamics of living cells [7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Comparison between Effects of Retroactivity and Resource Competition upon Change in Downstream Reporter Genes of Synthetic Genetic Circuits

Moriya

Yamaoka

Wakayama

et al. 2019

Life

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Reporter genes have contributed to advancements in molecular biology. Binding of an upstream regulatory protein to a downstream reporter promoter allows quantification of the activity of the upstream protein produced from the corresponding gene. In studies of synthetic biology, analyses of reporter gene activities ensure control of the cell with synthetic genetic circuits, as achieved using a combination of in silico and in vivo experiments. However, unexpected effects of downstream reporter genes on upstream regulatory genes may interfere with in vivo observations. This phenomenon is termed as retroactivity. Using in silico and in vivo experiments, we found that a different copy number of regulatory protein-binding sites in a downstream gene altered the upstream dynamics, suggesting retroactivity of reporters in this synthetic genetic oscillator. Furthermore, by separating the two sources of retroactivity (titration of the component and competition for degradation), we showed that, in the dual-feedback oscillator, the level of the fluorescent protein reporter competing for degradation with the circuits’ components is important for the stability of the oscillations. Altogether, our results indicate that the selection of reporter promoters using a combination of in silico and in vivo experiments is essential for the advanced design of genetic circuits.

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General Applicability of Synthetic Gene-Overexpression for Cell-Type Ratio Control via Reprogramming

Cited by 8 publications

References 21 publications

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Synthetic Biology: Computational Modeling Bridging the Gap between In Vitro and In Vivo Reactions

Synthetic Biology Outside the Cell: Linking Computational Tools to Cell-Free Systems

Comparison between Effects of Retroactivity and Resource Competition upon Change in Downstream Reporter Genes of Synthetic Genetic Circuits

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