Source 1primary paper2023ACS Synthetic Biology
Toolkit/predictive tool for transcriptional program design
predictive tool for transcriptional program design
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
This computation method is a predictive design framework for transcriptional programs reported in Performance Prediction of Fundamental Transcriptional Programs. It uses experimentally characterized single-input logical operations and associated metrology to model and predict the performance of more complex compressed transcriptional logic programs, including two-input AND, NOR, and mixed-phenotype NIMPLY operations.
Usefulness & Problems
Why this is useful
The framework is useful because it guides and accelerates the design of transcriptional programs by predicting higher-order circuit behavior from foundational single-input data. The study specifically positions it as an enabling approach for predictive design of transcriptional programs of greater complexity.
Problem solved
It addresses the problem that predictive tools were needed to guide and accelerate transcriptional program design. In the reported work, this need was met by using single-input logic characterization and metrology to predict the performance of fundamental two-input compressed logic programs.
Problem links
Need tighter control over gene expression timing or amplitude
DerivedThis computation method is a predictive design framework for transcriptional programs described in Performance Prediction of Fundamental Transcriptional Programs. It uses single-input logical operation data and associated metrology to model and predict the performance of more complex compressed transcriptional logic programs, including two-input AND, NOR, and mixed-phenotype NIMPLY operations.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
performance predictionperformance predictiontranscriptional logic modelingtranscriptional logic modelingTarget processes
transcriptionImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
Implementation relies on experimentally characterized single-input logical operations and associated metrology, specifically including engineered BUFFER and engineered NOT elements. The available evidence does not specify software format, parameterization workflow, host organism, or data requirements beyond the use of single-input performance measurements.
The supplied evidence supports prediction for fundamental compressed two-input transcriptional logic operations, but it does not document validation for larger multi-layer programs or non-transcriptional systems. The evidence also does not provide quantitative accuracy metrics, implementation software details, or independent replication.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
The study developed and experimentally characterized a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations.
The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Approval Evidence
1 source4 linked approval claimsfirst-pass slug predictive-tool-for-transcriptional-program-design
Accordingly, we posited that a predictive tool is needed to guide and accelerate the design of transcriptional programs.
Source:
future design enablingsupports
This work set the stage for predictive design of transcriptional programs of greater complexity.
Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
Source:
performance predictionsupports
The authors were able to model and predict the performance of compressed mixed phenotype logical operations, including A NIMPLY B gates and complementary B NIMPLY A gates.
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
Source:
performance predictionsupports
Using single-input data and developed metrology, the authors were able to model and predict the performances of all fundamental two-input compressed logical operations, including compressed AND gates and compressed NOR gates.
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
Source:
predictive sufficiencysupports
Single-input data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
Source:
Comparisons
Source-backed strengths
The method was supported by experimental characterization of a large collection of network-capable single-input logical operations, including engineered BUFFER and engineered NOT operations. Using these data and the developed metrology, the authors were able to model and predict all fundamental two-input compressed logical operations tested, including compressed AND, compressed NOR, and mixed-phenotype A NIMPLY B and B NIMPLY A gates.
Source:
In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates).
Source:
Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates).
predictive tool for transcriptional program design and meta-analysis of transcriptomic datasets under varying light conditions address a similar problem space because they share transcription.
Shared frame: same top-level item type; shared target processes: transcription
Strengths here: looks easier to implement in practice.
Compared with opto-PKR
predictive tool for transcriptional program design and opto-PKR address a similar problem space because they share transcription.
Shared frame: shared target processes: transcription
Strengths here: looks easier to implement in practice.
Compared with Set2-LANS optogenetic switch
predictive tool for transcriptional program design and Set2-LANS optogenetic switch address a similar problem space because they share transcription.
Shared frame: shared target processes: transcription
Strengths here: looks easier to implement in practice.
Relative tradeoffs: appears more independently replicated.
Ranked Citations
- 1.