Tools for engineering coordinated system behaviour in synthetic microbial consortia

Kylilis, Nicolas; Tuza, Zoltán A.; Stan, Guy-Bart; Polizzi, Karen M.

doi:10.1038/s41467-018-05046-2

Cited by 168 publications

(120 citation statements)

References 48 publications

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“…x 1 , α 0 the three populations are available and have been tested in [20]. A crucial open problem when constructing synthetic cell consortia is to guarantee and maintain a desired ratio between the cell populations to avoid exiting the regions shown in Figure 5 where the required functions are guaranteed.…”

Section: Discussionmentioning

confidence: 99%

“…so that the control input is strong enough to trigger the transition from one state to the other and render the toggleswitch monostable. d) Parameters' constraints: Substituting equations (17)- (19) in conditions (20), (21) and (23), we obtain that, at steady state, the Controllers can activate or deactivate the Targets in response to the presence or absence of the external reference signal r in (t) if the system parameters satisfy (22) and the following conditions are satisfied:…”

Section: Consortium Engineeringmentioning

confidence: 99%

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Multicellular feedback control of a genetic toggle-switch in microbial consortia

Fiore

Salzano

Cristòbal-Cóppulo

et al. 2020

Preprint

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We describe a multicellular approach to control a target cell population endowed with a bistable toggle-switch. The idea is to engineer a synthetic microbial consortium consisting of three different cell populations. In such a consortium, two populations, the Controllers, responding to some reference input, can induce the switch of a bistable memory mechanism in a third population, the Targets, so as to activate or deactivate some additional functionalities in the cells. Communication among the three populations is established by orthogonal quorum sensing molecules that are used to close a feedback control loop across the populations. The control design is validated via in-silico experiments in BSim, a realistic agentbased simulator of bacterial populations.

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Section: Discussionmentioning

confidence: 99%

Section: Consortium Engineeringmentioning

confidence: 99%

Multicellular feedback control of a genetic toggle-switch in microbial consortia

Fiore

Salzano

Cristòbal-Cóppulo

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…We think that the number of solutions that is evaluated in parallel within this context is not something that can be achieved by a traditional computer. However, it is also true that even though the proposed model is extensible, a lack of well-characterized synthetic parts may pose a problem in terms of orthogonal intercell communication [42][43][44][45] and variety of elements to 24 construct large synthetic circuits. Future in-vivo implementations can be assisted by software to help select the proper parts for the design 46,47 .…”

Section: Discussionmentioning

confidence: 99%

A framework for implementing metaheuristic algorithms using intercellular communication

Gutiérrez

Ortiz

Carrión

2020

Preprint

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Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH are used to approximate the solution to difficult problems but require a large amount of computational resources. Cell colonies harboring synthetic distributed circuits using intercell communication offer a direction for tackling this problem, as they process information in a massively parallel fashion. In this work, we propose a framework that maps MH elements to synthetic circuits in growing cell colonies. The framework relies on cell-cell communication mechanisms such as quorum sensing (QS) and bacterial conjugation. As a proofof-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic 2 circuits on the gro simulator. Furthermore, we show an example of how our framework can be extended by implementing another kind of computational model: The Cellular Automaton. This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies. MAIN TEXTEvolution is a key element involved in all microbiology processes. It is the process that drives organism adaptation to better survive and thrive in their surrounding environment. This process occurs at a genetic level, involving mainly genetic recombination and mutation. The genetic diversity produced by evolution is studied and used as inspiration in computational methods such as Evolutionary Algorithms (EAs) 1,2 . These algorithms are generally used for approximating solutions to optimization problems. Since evolution is a standard occurring process, it is natural to relate EAs to microbiology experiments, and more specifically, to Synthetic Biology constructs. This relationship has already been addressed by Directed Evolution 3,4 . However, the control level of Directed Evolution is not as specific as the one reached in EAs. Furthermore, several other computational methods can be translated to Synthetic Biology constructs that emulate their operation. Metaheuristic procedures (MH) 5-7 are a larger class of procedures that contain EAs.Inspiration upon which these techniques are designed range from metallurgy processes 8-10 through bird flock movement patterns [11][12][13] , and ant colony food foraging 14,15 . A general mapping, relating Synthetic Biology constructs to MH elements can be proposed such that any procedure of that class can be modeled as a synthetic circuit. This is due to MH sharing common elements and similarities that can be generalized.

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“…For example, cell communities have been programmed to undergo synchronized gene expression dynamics ( 1 – 5 ), act as distributed computers ( 6 – 8 ), and differentiate into simple patterns ( 9 – 13 ). However, due to challenges such as molecular cross-talk, no more than two communication systems have been combined in a single design ( 14 – 16 ). In stark contrast, evolution utilizes dozens of communication signals to orchestrate complex behaviors such as embryo patterning ( 17 ), the precise wiring of the nervous ( 18 ), circulatory and lymphatic systems ( 19 ), and innate and adaptive immunity.…”

Section: Main Textmentioning

confidence: 99%

Multiplexing cell-cell communication

Sexton

Tabor

2019

Preprint

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The engineering of advanced multicellular behaviors, such as the programmed growth 6 of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. 7Unfortunately, few cell-cell communication systems have been developed for synthetic biology. 8Here, we engineer a genetically-encoded channel selector device that enables a single 9communication system to transmit two separate intercellular conversations. Our design comprises 10 multiplexer and demultiplexer sub-circuits constructed from a total of 12 CRISPRi-based 11 transcriptional logic gates, an acyl homoserine lactone-based communication module, and three 12 inducible promoters that enable small molecule control over the conversations. Experimentally-13parameterized mathematical models of the sub-components predict the steady state and dynamical 14 performance of the full system. Multiplexed cell-cell communication has applications in synthetic 15 development, metabolic engineering, and other areas requiring the coordination of multiple 16pathways amongst a community of cells. 17 One Sentence Summary: We have engineered a synthetic genetic system that enables bacteria to 18 have two separate conversations over a single chemical "wire" by separating the conversations in 19 time. 20

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Tools for engineering coordinated system behaviour in synthetic microbial consortia

Cited by 168 publications

References 48 publications

Multicellular feedback control of a genetic toggle-switch in microbial consortia

Multicellular feedback control of a genetic toggle-switch in microbial consortia

A framework for implementing metaheuristic algorithms using intercellular communication

Multiplexing cell-cell communication

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