Bioinspired decision architectures containing host and microbiome processing units

Heyde, Keith C.; Gallagher, P W; Ruder, Warren C.

doi:10.1088/1748-3190/11/5/056017

Cited by 2 publications

(4 citation statements)

References 36 publications

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“…In addition to a simplified membrane flux term, these mass balances use first‐order binding kinetics to relate the relative concentrations of bound and unbound internal inducer and repressor protein complexes. A full derivation may be found in previously published literature by the authors [57].

true \frac{normald false[{true normalI}_{normalint} false]}{normald normalt} = normalμ ([I_{ex}] - [I_{int}]) - {true normalK}_{normala} [I_{int}] \times [normalT normalP] + {true normalK}_{normald} [{T P : I}_{int}] - {true normalδ}_{normal1} \times [I_{int}] .

\frac{d false[T P false]}{d t} = - K_{a} false[{true normalI}_{normalint} false] \times false[T P false] + K_{d} false[{true normalT normalP : normalI}_{normalint} false] + g false[...

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Module one consists of a population of E. coli cells engineered to contain gene networks capable of synthesizing biotin synthase, which in turn enables the cells to produce biotin. In previous publications, we detail how a continuous model, depicting the system's dynamic behavior, may be developed for complex regulatory gene networks [45,57] as well as the biotin synthesizing processes [9]. This approach relies upon modeling four key subprocesses: i) Inducer mass balance and binding kinetics ii) Transcription of mRNA from regulatory gene components iii) Translation of proteins, including reporters, repressors, and biotin synthase iv) Biotin metabolism The first subprocess is modeled by establishing three mass balance equations for a given inducer.…”

Section: Module One: the Engineered Cellmentioning

confidence: 99%

“…In addition to a simplified membrane flux term, these mass balances use first-order binding kinetics to relate the relative concentrations of bound and unbound internal inducer and repressor protein complexes. A full derivation may be found in previously published literature by the authors [57].…”

Section: Module One: the Engineered Cellmentioning

confidence: 99%

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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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true \frac{normald false[{true normalI}_{normalint} false]}{normald normalt} = normalμ ([I_{ex}] - [I_{int}]) - {true normalK}_{normala} [I_{int}] \times [normalT normalP] + {true normalK}_{normald} [{T P : I}_{int}] - {true normalδ}_{normal1} \times [I_{int}] .

\frac{d false[T P false]}{d t} = - K_{a} false[{true normalI}_{normalint} false] \times false[T P false] + K_{d} false[{true normalT normalP : normalI}_{normalint} false] + g false[...

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Module One: the Engineered Cellmentioning

confidence: 99%

Section: Module One: the Engineered Cellmentioning

confidence: 99%

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Modeling information exchange between living and artificial cells

et al. 2017

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Fully 3D Printed Multi-Material Soft Bio-Inspired Whisker Sensor for Underwater-Induced Vortex Detection

Gul

Choi

2018

Soft Robotics

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Bio-mimicking the underwater sensors has tremendous potential in soft robotics, under water exploration and human interfaces. Pinniped are semiaquatic carnivores that use their whiskers to sense food by tracking the vortices left by potential prey. To detect and track the vortices inside the water, a fully 3D printed pinniped inspired multi-material whisker sensor is fabricated and characterized. The fabricated whisker is composed of a polyurethane rod with a length-to-diameter ratio (L/d) of 20:1 with four graphene patterns (length × diameter: 60 × 0.3 mm) perpendicular to each other. The graphene patterns are further connected with output signal wires via copper tape. The displacement (∼5 mm) of the whisker rod in any direction (0-360°) causes the change in resistance [Formula: see text] because of generated tensile. The analog signals (resistance change) are digitalized by using analog to digital modules and fed to a microcontroller to detect the vortex. A virtual environment is designed such that it consists of a 3D printed fish fin, a water tank, a camera, and data loggers to study the response of fabricated whisker. The underwater sensitivity of the whisker sensor in any direction is detectable and remarkably high ([Formula: see text]% ∼1180). The mechanical reliability of the whisker sensor is tested by bending it up to 2000 cycles. The fabricated whisker's structure and material are unique, and no one has fabricated them by using cost-effective 3D printing methods earlier. This fully 3D printable flexible whisker sensor should be applicable to a wide range of soft robotic applications.

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Bioinspired decision architectures containing host and microbiome processing units

Cited by 2 publications

References 36 publications

Modeling information exchange between living and artificial cells

Modeling information exchange between living and artificial cells

Fully 3D Printed Multi-Material Soft Bio-Inspired Whisker Sensor for Underwater-Induced Vortex Detection

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