Toolkit/incoherent feed-forward loop
incoherent feed-forward loop
Also known as: iFFL
Taxonomy: Mechanism Branch / Architecture. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
We perform RNA-sequencing on cells expressing an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Published Workflows
Objective: Identify small molecules that increase productivity and gene expression in engineered mammalian cells without additional genetic modifications.
Why it works: The workflow uses the transcriptional state associated with an iFFL that enhances operational capacity as a reference, then searches for drugs whose induced profiles mimic that state, with candidate compounds subsequently tested for expression enhancement.
Stages
- 1.Transcriptomic profiling of iFFL-expressing cells(functional_characterization)
This stage creates the transcriptional reference used for downstream drug matching.
Selection: Generate a transcriptional signature from cells expressing the iFFL genetic circuit.
- 2.Computational drug-signature matching(in_silico_filter)
This stage narrows the search space from many possible compounds to candidates predicted to reproduce the beneficial iFFL-associated state.
Selection: Match the iFFL-derived transcriptional data with thousands of drug-induced profiles to find compounds that mimic the iFFL effect.
- 3.Candidate selection and expression testing(confirmatory_validation)
This stage confirms whether a computationally prioritized compound produces the desired productivity benefit in engineered-cell experiments.
Selection: Test a selected compound candidate for enhancement of payload expression across scenarios and cell lines.
Steps
- 1.Perform RNA-seq on iFFL-expressing cellsengineered reference circuit
Generate the transcriptional signature associated with the iFFL state.
The workflow first needs a reference transcriptional state before computational matching can be performed.
- 2.Match the iFFL transcriptional signature to drug-induced profiles using DECCODEcomputational prioritization method
Identify compounds predicted to mimic the iFFL effect.
This analysis uses the RNA-seq output from the previous step to prioritize candidate compounds before experimental testing.
- 3.Select Filgotinib from compound candidates and test expression enhancement across scenariosselected compound candidate
Confirm that a prioritized compound improves payload expression in practical engineered-cell settings.
Experimental testing follows computational prioritization to verify that a selected candidate produces the desired phenotype.
Taxonomy & Function
Primary hierarchy
Mechanism Branch
Architecture: A composed arrangement of multiple parts that instantiates one or more mechanisms.
Techniques
Sequence VerificationTarget processes
transcriptionInput: Chemical
Validation
Supporting Sources
Ranked Claims
The incoherent feed-forward loop is a genetic circuit that enhances operational capacity.
an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity
DECCODE was used to match the transcriptional signature from iFFL-expressing cells to thousands of drug-induced profiles to identify drugs that mimic the iFFL effect.
To find drugs that mimic this effect, we use DECCODE ... that matches our transcriptional data with thousands of drug-induced profiles.
Approval Evidence
We perform RNA-sequencing on cells expressing an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity.
Source:
The incoherent feed-forward loop is a genetic circuit that enhances operational capacity.
an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity
Source:
DECCODE was used to match the transcriptional signature from iFFL-expressing cells to thousands of drug-induced profiles to identify drugs that mimic the iFFL effect.
To find drugs that mimic this effect, we use DECCODE ... that matches our transcriptional data with thousands of drug-induced profiles.
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.