Source 1primary paper2023
Toolkit/Lustro
Lustro
Also known as: high-throughput optogenetics platform
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Lustro is a high-throughput optogenetics platform for studying and controlling blue light-sensitive optogenetic systems. In the cited 2023 work, it was combined with machine learning to achieve multiplexed control of split transcription factor responses in Saccharomyces cerevisiae.
Usefulness & Problems
Why this is useful
Lustro is useful for systematically probing blue light induction conditions in optogenetic systems and for enabling multiplexed regulation of transcriptional outputs. The cited study shows that the platform can support identification of illumination regimes for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors.
Source:
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
Problem solved
It addresses the problem of how to dynamically control multiple blue light-responsive optogenetic transcription systems within the same experimental framework. The reported application specifically tackles multiplexed control over pairs of split transcription factors in Saccharomyces cerevisiae.
Problem links
Need precise spatiotemporal control with light input
DerivedLustro is a high-throughput optogenetics platform used to study and control blue light-sensitive optogenetic systems. In the cited work, it was integrated with machine learning to enable multiplexed control of transcriptional optogenetic responses in Saccharomyces cerevisiae.
Need tighter control over gene expression timing or amplitude
DerivedLustro is a high-throughput optogenetics platform used to study and control blue light-sensitive optogenetic systems. In the cited work, it was integrated with machine learning to enable multiplexed control of transcriptional optogenetic responses in Saccharomyces cerevisiae.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Mechanisms
multiplexed control of split transcription factor activitymultiplexed control of split transcription factor activityoptical control by blue light illuminationoptical control by blue light illuminationTarget processes
transcriptionInput: Light
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: spectral hardware requirementoperating role: sensorswitch architecture: split
The documented use case involves blue light-sensitive optogenetic systems and split transcription factors in Saccharomyces cerevisiae. The platform was used in conjunction with machine learning, but the provided evidence does not specify illumination hardware, software architecture, construct design, or culture format.
The supplied evidence is limited to a single cited study and provides little detail on hardware configuration, throughput metrics, quantitative performance, or generalizability beyond Saccharomyces cerevisiae transcriptional systems. Independent replication and validation in other organisms, modalities, or non-transcriptional outputs are not documented in the provided material.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
Approval Evidence
1 source4 linked approval claimsfirst-pass slug lustro
Lustro, a powerful high-throughput optogenetics platform
Source:
capabilitysupports
An integrated framework combining Lustro and machine learning enables multiplexed control over blue light-sensitive optogenetic systems.
we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems
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experimental findingsupports
The study identified light induction conditions for sequential activation, preferential activation, and switching between pairs of light-sensitive split transcription factors in Saccharomyces cerevisiae.
Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae
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foundational implicationsupports
This work lays the foundation for designing more advanced synthetic biological circuits in which multiple circuit components can be controlled using designer light induction programs.
This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs
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modeling capabilitysupports
High-throughput data generated from Lustro were used to build a Bayesian optimization framework for predicting system behavior and identifying optimal conditions for multiplexed control.
We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control
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Comparisons
Source-backed strengths
The available evidence describes Lustro as a high-throughput optogenetics platform and reports successful integration with machine learning for multiplexed control. In the cited yeast study, it enabled identification of light induction conditions that produced sequential activation, preferential activation, and switching behaviors between paired blue light-sensitive split transcription factors.
Lustro and automated 96-well microplate illumination and measurement address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription; same primary input modality: light
Compared with Iris
Lustro and Iris address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription; same primary input modality: light
Relative tradeoffs: appears more independently replicated; looks easier to implement in practice.
Compared with open-source microplate reader
Lustro and open-source microplate reader address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription; same primary input modality: light
Ranked Citations
- 1.