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Record W1995759967 · doi:10.1109/tnb.2003.820283

A genetic circuit amplifier: design and simulation

2003· article· en· W1995759967 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiscrete circuitLinear circuitAmplifierCircuit extractionComputer scienceEquivalent circuitElectronic circuit simulationRL circuitElectronic engineeringCircuit designCapacitorElectrical elementElectronic circuitRC circuitElectrical engineeringEngineeringVoltageCMOS

Abstract

fetched live from OpenAlex

A genetic circuit amplifier is designed using an electronic inverting amplifier as a starting point. Two simulation methods are used to analyze circuit performance in terms of the impulse and sinusoidal responses of electrical engineering. The first method is an exact stochastic simulation based on a kinetic model of the circuit. The second method incorporates statistical thermodynamic analysis. The simulations are used to analyze amplifier performance in response to classical systems analysis stimuli: impulses and sine waves. Degradation reactions, analogous to leakage off circuit capacitors, are found to have considerable impact on circuit response. For the nonlinear gain element used in our exemplary circuit, the selection of bias level based on controlling protein degradation rate plays an important role in determining circuit behavior. A parameter without electronic analog, the circuit plasmid copy number, is crucial to circuit operation. These simulations suggest that the copy number must be less than 50 for desired circuit operation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.240
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it