Source 1primary paper2023
Toolkit/Bayesian computational approach
Bayesian computational approach
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
This Bayesian computational approach is a data-analysis method developed to improve prediction of split protein behavior by contextualizing errors inherent to experimental procedures. In the cited study, it was applied to pooled, sequencing-based screening data from split Cre recombinase constructs generated with optogenetic dimers, enabling comprehensive analysis of split sites across the protein.
Usefulness & Problems
Why this is useful
The method is useful for interpreting high-throughput split-protein screening data in a way that accounts for experimental error, which improves predictive accuracy for construct behavior. In the reported application, it supported a streamlined workflow for identifying inducible post-translational control designs from pooled libraries with sequencing-based readout.
Source:
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Source:
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
Source:
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
Problem solved
It addresses the problem of inaccurate prediction of split protein behavior caused by errors inherent to experimental procedures in pooled screening datasets. The study specifically used it to analyze nearly all possible split Cre recombinase constructs screened in parallel.
Source:
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Problem links
Need tighter control over protein production
DerivedThis Bayesian computational approach is a data-analysis method developed to improve prediction of split protein behavior by contextualizing errors inherent to experimental procedures. In the cited study, it was applied within a pooled, sequencing-based screen of split Cre recombinase constructs generated with optogenetic dimers.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Target processes
translationImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builderswitch architecture: split
The method was implemented in the context of a pooled library approach with sequencing-based readout for split protein constructs. The reported application involved split Cre recombinase constructs generated with optogenetic dimers, but the evidence does not specify software, priors, input requirements, or computational infrastructure.
The available evidence describes development and application in a single 2023 study centered on split Cre recombinase, without broader validation across other proteins or assay formats. The source text does not provide implementation details for the statistical model, performance metrics, or independent replication.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
Applying the method to Cre recombinase with optogenetic dimers produced comprehensive data on split sites throughout the protein.
We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
A pooled library approach enables rapid generation and screening of nearly all possible split protein constructs in parallel, with sequencing-based readout.
we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing
Approval Evidence
1 source2 linked approval claimsfirst-pass slug bayesian-computational-approach
we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures
Source:
method advantagesupports
The overall method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.
Source:
method capabilitysupports
A Bayesian computational approach was developed to improve accuracy in predicting split protein behavior by contextualizing errors inherent to experimental procedures.
To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures.
Source:
Comparisons
Source-backed strengths
A key strength is explicit contextualization of experimental error through a Bayesian framework, which the source states improved prediction accuracy for split protein behavior. The method was demonstrated in a comprehensive pooled screen that generated data on split sites throughout Cre recombinase using optogenetic dimers and sequencing-based readout.
Compared with blue-light-activated DNA template ON switch
Bayesian computational approach and blue-light-activated DNA template ON switch address a similar problem space because they share translation.
Shared frame: shared target processes: translation; shared mechanisms: translation_control
Strengths here: looks easier to implement in practice.
Compared with intein
Bayesian computational approach and intein address a similar problem space because they share translation.
Shared frame: shared target processes: translation; shared mechanisms: translation_control
Strengths here: looks easier to implement in practice.
Compared with photo-crosslinking
Bayesian computational approach and photo-crosslinking address a similar problem space because they share translation.
Shared frame: shared target processes: translation; shared mechanisms: translation_control
Strengths here: looks easier to implement in practice.
Ranked Citations
- 1.