Source 1primary paper2021
Toolkit/demultiplexer for dynamically encoded signals
demultiplexer for dynamically encoded signals
Also known as: demultiplexer
Taxonomy: Mechanism Branch / Architecture. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
The demultiplexer for dynamically encoded signals is a synthetic gene circuit architecture that decodes temporally encoded inputs into distinct gene-expression outputs. In a 2021 study, this circuit was shown to demultiplex dynamic signals and was further combined with dCas9-based gene networks to build pulsatile-signal filters and decoders.
Usefulness & Problems
Why this is useful
This tool is useful for converting complex temporal input patterns into separable transcriptional responses, enabling dynamic information processing in engineered cells. The cited work also applied dynamic multiplexing to precise multidimensional regulation of a heterologous metabolic pathway, indicating utility for biotechnological control tasks.
Source:
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Source:
show that this circuit can be employed to demultiplex dynamically encoded signals
Problem solved
It addresses the problem of how to decode dynamically encoded signals and route them into different gene-expression programs within a synthetic network. The reported systems were presented as synthetic platforms for decoding complex signals and elucidating design principles of dynamic information processing.
Source:
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Taxonomy & Function
Primary hierarchy
Mechanism Branch
Architecture: A reusable architecture pattern for arranging parts into an engineered system.
Mechanisms
crispri/dcas9-based transcriptional regulationdynamic signal decodingfalling-edge pulse detectionkinetic filteringlight-responsive transcriptional regulationtemporal demultiplexingTechniques
No technique tags yet.
Target processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
The reported implementations used light-responsive transcriptional regulators with differing response kinetics to construct a falling-edge pulse detector. The demultiplexer was also combined with dCas9-based gene networks, indicating that practical deployment involves synthetic circuit composition and kinetic tuning of regulatory components.
The supplied evidence comes from a single 2021 source and does not provide detailed quantitative performance metrics, host range, or comparative benchmarking. Independent replication, long-term stability, and implementation constraints beyond the reported circuit combinations are not established by the provided evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Approval Evidence
1 source2 linked approval claimsfirst-pass slug demultiplexer-for-dynamically-encoded-signals
show that this circuit can be employed to demultiplex dynamically encoded signals
Source:
engineering resultsupports
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Source:
functional capabilitysupports
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
Source:
Comparisons
Source-backed strengths
The architecture was experimentally reported to demultiplex dynamically encoded signals, and its integration with dCas9-based gene networks enabled pulsatile-signal filters and decoders. The study also demonstrated a falling-edge pulse detector built from light-responsive transcriptional regulators with differing response kinetics, supporting kinetic selectivity as a functional strength.
Source:
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Source:
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Source:
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Compared with dCas9-based gene networks
demultiplexer for dynamically encoded signals and dCas9-based gene networks address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding
Compared with falling-edge pulse-detector
demultiplexer for dynamically encoded signals and falling-edge pulse-detector address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding, light-responsive transcriptional regulation
Strengths here: looks easier to implement in practice.
Compared with pulsatile-signal filters and decoders
demultiplexer for dynamically encoded signals and pulsatile-signal filters and decoders address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding
Ranked Citations
- 1.