Source 1primary paper2021
Toolkit/dynamic multiplexing
dynamic multiplexing
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Dynamic multiplexing is a computational design principle for synthetic gene networks that encodes and decodes time-varying inputs into distinct gene expression states. In the cited 2021 study, it increased information transmission from signal to gene expression and enabled dynamic signal decoding using engineered regulators with different response kinetics.
Usefulness & Problems
Why this is useful
This approach is useful for extracting more regulatory information from a single dynamic input by mapping temporal features onto different transcriptional outputs. The cited work also applied it 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 that static or single-channel regulation limits how much information can be transmitted from an input signal to gene expression state. The reported design principle uses temporal structure and kinetic differences among regulators to decode complex signals into differential expression programs.
Source:
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Problem links
Need conditional recombination or state switching
DerivedDynamic multiplexing is a computational design principle for encoding and decoding time-varying signals to control gene expression states. In the cited 2021 synthetic gene network study, it was used to increase information transmission from signal to gene expression and to enable dynamic signal decoding in engineered circuits.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
dynamic signal decodingdynamic signal decodingkinetic discrimination based on differing regulator response timeskinetic discrimination based on differing regulator response timestemporal multiplexingtemporal multiplexingTechniques
Computational DesignTarget processes
recombinationImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The reported implementation used synthetic gene networks and light-responsive transcriptional regulators with differing response kinetics to construct dynamic decoders such as a falling-edge pulse detector. The supplied evidence also mentions domain-level circuit integration with dCas9-based regulators, but practical construct details, host system, and delivery conditions are not described in the provided text.
The provided evidence is limited to a single 2021 study and does not report independent replication in the supplied material. Quantitative performance details, implementation generality across organisms, and direct evidence for recombination-related applications are not provided here.
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.
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.
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.
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.
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.
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
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
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.
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.
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 source3 linked approval claimsfirst-pass slug dynamic-multiplexing
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state
Source:
application demosupports
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.
Source:
design principlesupports
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.
Source:
performance improvementsupports
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.
Source:
Comparisons
Source-backed strengths
The source states that dynamic multiplexing significantly increases information transmission capacity from signal to gene expression state, based on an information-theoretic analysis. It was also demonstrated in synthetic gene networks that decode complex signals, including a falling-edge pulse detector built from light-responsive transcriptional regulators with differing response kinetics.
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 computational design strategy
dynamic multiplexing and computational design strategy address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Compared with FRASE
dynamic multiplexing and FRASE address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Compared with NCBI sequence screening for 2A/2A-like occurrence
dynamic multiplexing and NCBI sequence screening for 2A/2A-like occurrence address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: looks easier to implement in practice.
Ranked Citations
- 1.