Chemical communication between bacteria and cell-free gene expression systems within linear chains of emulsion droplets

Schwarz-Schilling, Matthaeus; Aufinger, Lukas; Mückl, Andrea; Simmel, Friedrich C.

doi:10.1039/c5ib00301f

Cited by 85 publications

(87 citation statements)

References 42 publications

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“…This module allows us to monitor the behavior of a cell-free system by examining an encapsulated droplet and analyzing the fluorescent response [51]. By modifying the concentration of DNA encoding for GFP within a cellfree system, previous studies have developed a reliable framework for modeling cell-free response as a function of DNA concentration [52].…”

Section: Resultsmentioning

confidence: 99%

Modeling information exchange between living and artificial cells

et al. 2017

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Background: The tools of synthetic biology have enabled researchers to explore multiple scientific phenomena by directly engineering signaling pathways within living cells and artificial protocells. Here, we explored the potential for engineered living cells themselves to assemble signaling pathways for non-living protocells. This analysis serves as a preliminary investigation into a potential origin of processes that may be utilized by complex living systems. Specifically, we suggest that if living cells can be engineered to direct the assembly of genetic signaling pathways from genetic biomaterials in their environment, then insight can be gained into how naturally occurring living systems might behave similarly.Methods: To this end, we have modeled and simulated a system consisting of engineered cells that control the assembly of DNA monomers on microparticle scaffolds. These DNA monomers encode genetic circuits, and therefore, these microparticles can then be encapsulated with minimal transcription and translation systems to direct protocell phenotype. The modeled system relies on multiple previously established synthetic systems and then links these together to demonstrate system feasibility.Results: In this specific model, engineered cells are induced to synthesize biotin, which competes with biotinylated, circuit-encoding DNA monomers for an avidinized-microparticle scaffold. We demonstrate that multiple synthetic motifs can be controlled in this way and can be tuned by manipulating parameters such as inducer and DNA concentrations. Conclusions:We expect that this system will provide insight into the origin of living systems as well as serve as a tool for engineering living cells that assemble complex biomaterials in their environment.

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Section: Resultsmentioning

confidence: 99%

Modeling information exchange between living and artificial cells

et al. 2017

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“…Finally, based on recent experimental studies (26)(27)(28)(29)(30), we propose potential routes for physically realizing our QS microcapsules. A key component for QS in our system is a regulated production of chemicals.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Encapsulation of cell-free biochemical networks has been achieved using phospholipid vesicles (30) and water-in-oil microemulsion droplets (27,29). Certain small molecules used in bacterial QS, such as N-(3-oxo-hexanoyl)-L-homoserine lactone, could pass through vesicle membranes (30) and the oil phase surrounding emulsion droplets (29), thus facilitating intercellular communication. Channel proteins could be used to allow transport of other signaling species across vesicle membranes (32), and stomatocytes with large openings would also freely exchange a wide range of materials with the external fluid (33).…”

Section: Discussionmentioning

confidence: 99%

“…The original realization of the repressilator was based on regulation of a synthetic gene network in living cells (12). The repressilator motif can, however, be implemented in cell-free alternatives, which use biochemical mixtures to execute the regulation scheme (26,29). In particular, in vitro transcriptional circuits can be systematically assembled to form arbitrary networks in an experimentally simple manner (31); oscillators, including the repressilator, have been successfully constructed using this technique (26,27).…”

Section: Discussionmentioning

confidence: 99%

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Synthetic quorum sensing in model microcapsule colonies

Shum

Balazs

2017

Proc. Natl. Acad. Sci. U.S.A.

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Biological quorum sensing refers to the ability of cells to gauge their population density and collectively initiate a new behavior once a critical density is reached. Designing synthetic materials systems that exhibit quorum sensing-like behavior could enable the fabrication of devices with both self-recognition and selfregulating functionality. Herein, we develop models for a colony of synthetic microcapsules that communicate by producing and releasing signaling molecules. Production of the chemicals is regulated by a biomimetic negative feedback loop, the "repressilator" network. Through theory and simulation, we show that the chemical behavior of such capsules is sensitive to both the density and number of capsules in the colony. For example, decreasing the spacing between a fixed number of capsules can trigger a transition in chemical activity from the steady, repressed state to largeamplitude oscillations in chemical production. Alternatively, for a fixed density, an increase in the number of capsules in the colony can also promote a transition into the oscillatory state. This configuration-dependent behavior of the capsule colony exemplifies quorum-sensing behavior. Using our theoretical model, we predict the transitions from the steady state to oscillatory behavior as a function of the colony size and capsule density.quorum sensing | repressilator | microcapsules Q uorum sensing (QS) refers to the ability of organisms in a population to assess the number and density of individuals present, allowing a specific behavior to be initiated only when a critical threshold in the population size and density is reached. QS plays a vital role in the life cycle of bacteria (1, 2), yeast (3, 4), and slime molds (5, 6), as well as social insects (7). In microorganisms, QS is based on chemical signaling among individuals in a colony. Bacteria (1, 8), for example, produce and secrete signaling molecules, which diffuse into the surrounding medium where they can be detected by other cells in the population. Through regulatory networks, the signaling molecule acts as an autoinducer; the rate of production of the autoinducer increases with its concentration. When the cell density is low, the concentration of the signaling molecule is low, and production is maintained at a low, basal rate. The cells are considered to be in the "off" QS state. When the population density is high, each cell detects a high concentration of the signaling molecule resulting from the collective production of many nearby neighbors. The cells are switched "on," increasing production of the signaling molecule and activating further metabolic pathways that are triggered by QS. Hence, spatially separated cells interact through regulatory networks, which coordinate the collective behavior of the colony.An intriguing challenge in both synthetic biology and materials science is to design man-made systems that exhibit behavior analogous to QS, i.e., sensing and responding to the system size and density. Such synthetic QS abilities could provide a route to ...

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Molecular Cybernetics: Challenges toward Cellular Chemical Artificial Intelligence

Murata

Toyota

Nomura

et al. 2022

Adv Funct Materials

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Research on so-called "chemical artificial intelligence" (CAI) is an emerging field with the aim of constructing information-processing systems with learning capabilities based on chemical methodologies. This can be regarded as an attempt to reconstruct Cybernetics using molecular based systems. Many chemical reaction systems with computational abilities are proposed, but most are fixed functions that deliver molecular output for a given molecular input. On the other hand, chemical AI is a system with learning capability; namely, the output should be variable and gradually change upon repeated molecular inputs. In this paper, a compartmentalization approach for implementing cellular chemical AI using liposomes is discussed. The existing studies in terms of the methods used for assembling systems consisting of many liposomes with different functions, methods for achieving recursiveness and plasticity in chemical reaction systems, and methods for reconfiguring the network topology by liposome deformation are reviewed. Issues that must be addressed in order to realize chemical AI are also identified.

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Chemical communication between bacteria and cell-free gene expression systems within linear chains of emulsion droplets

Cited by 85 publications

References 42 publications

Modeling information exchange between living and artificial cells

Modeling information exchange between living and artificial cells

Synthetic quorum sensing in model microcapsule colonies

Molecular Cybernetics: Challenges toward Cellular Chemical Artificial Intelligence

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