Engineering of a genetic circuit with regulatable multistability

Li, Ting-Ting; Dong, Yun; Zhang, Xuanqi; Ji, Xiangyu; Luo, Chunxiong; Lou, Chunbo; Zhang, Haoqian; Ouyang, Qi

doi:10.1039/c8ib00030a

Cited by 18 publications

(11 citation statements)

References 47 publications

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“…Such multistable dynamics and consequent phenotypic changes has also been recently seen in disease progression [4,5] and in cellular reprogramming [6,7]. Thus, elucidating the dynamics of such multi-stable networks holds promise for advancing our understanding of embryonic development as well as the latest applications in synthetic biology [8,9] and regenerative therapies [10].…”

Section: Introductionmentioning

confidence: 94%

Operating principles of circular toggle polygons

Hati¹,

Duddu²,

Jolly³

2021

Phys. Biol.

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Decoding the dynamics of cellular decision-making and cell differentiation is a central question in cell and developmental biology. A common network motif involved in many cell-fate decisions is a mutually inhibitory feedback loop between two self-activating 'master regulators' A and B, also called as toggle switch. Typically, it can allow for three stable states -(high A, low B), (low A, high B) and (medium A, medium B). A toggle triad -three mutually repressing regulators A, B and C, i.e. three toggle switches arranged circularly (between A and B, between B and C, and between A and C) -can allow for six stable states: three 'single positive' and three 'double positive' ones. However, the operating principles of larger toggle polygons, i.e. toggle switches arranged circularly to form a polygon, remain unclear. Here, we simulate using both discrete and continuous methods the dynamics of different sized toggle polygons. We observed a pattern in their steady state frequency depending on whether the polygon was an even or odd numbered one. The even-numbered toggle polygons result in two dominant states with consecutive components of the network expressing alternating high and low levels. The oddnumbered toggle polygons, on the other hand, enable more number of states, usually twice the number of components with the states that follow 'circular permutation' patterns in their composition. Incorporating self-activations preserved these trends while increasing the frequency of multistability in the corresponding network. Our results offer insights into design principles of circular arrangement of regulatory units involved in cell-fate decision making, and can offer design strategies for synthesizing genetic circuits.

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Section: Introductionmentioning

confidence: 94%

Operating principles of circular toggle polygons

Hati¹,

Duddu²,

Jolly³

2021

Phys. Biol.

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“…Similar switching behavior can also be achieved using transcriptional regulation (Kim et al, 2006). Multi-stable switches have been theorized (Leon et al, 2016) and implemented (Li et al, 2018) which would allow for greater than two state memory. The information storage capability of DNA has also been exploited to create cellular memory devices (Siuti et al, 2013), lasting for over 100 generations (Bonnet et al, 2012).…”

Section: Engineering Bacteria To Computementioning

confidence: 99%

From Microbial Communities to Distributed Computing Systems

Karkaria¹,

Treloar²,

Barnes³

et al. 2020

Front. Bioeng. Biotechnol.

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A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

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“…2, pp. [25][26][27][28][29][30][31] This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) increase seems to be only weakly dependent on the inducer's concentration for the stronger induction conditions (green and blue lines in Fig. 1b).…”

Section: Challenges Of Model Fitting To Time-series In Vivo Datamentioning

confidence: 99%

“…The presence of positive feedback yield non-linear circuits with bistability [24][25][26]. Circuit bistability can lead to multimodal distributions of single-cell reporter expression levels.…”

Section: Single-cell Measurements and Stochastic Modelmentioning

confidence: 99%

Dynamical model fitting to a synthetic positive feedback circuit in E. coli

Tica

Zhu

Isalan

2020

Engineering Biology

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Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A singleequation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time-series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.

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Engineering of a genetic circuit with regulatable multistability

Cited by 18 publications

References 47 publications

Operating principles of circular toggle polygons

Operating principles of circular toggle polygons

From Microbial Communities to Distributed Computing Systems

Dynamical model fitting to a synthetic positive feedback circuit in E. coli

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