A genetic differential amplifier: design, simulation, construction, and testing
Bibliographic record
Abstract
Summary form only given. Differential amplifiers are devices that produce an output that is proportional to the difference between the two inputs. In electronics, they are useful in comparison/thresholding, feedback amplifiers and oscillators. A genetic analog to this circuit is equally useful in the construction of artificial gene networks. A genetic differential amplifier was developed using components from bacteriophage lambda. The activator protein CI was used as the non-inverting (positive) input. The repressor protein Cro was used as the inverting (negative) input. A mutated PRMpromoter formed the core of the amplifier. The gene for enhanced green fluorescent protein was placed after the PRMpromoter so that the output could be monitored. The mutations in PRMallow the representation of negative differences and correct an undesired feature of wild-type PRM. Negative differences are represented by a reduction in output from a baseline level. Wild-type PRMhas a low basal level of activity. The first mutation added a bias to correct this problem. Wild-type PRMis also repressed by high concentrations of CI. The second mutation removed the repression, making CI strictly an activator. The circuit was modeled using a statistical thermodynamic and stochastic approach. Different variants of the circuit were constructed. The mutated PRM was implemented on both low and high copy number plasmids. The inputs, CI and Cro, were placed on a separate plasmid under the control of Ptetand Plac, respectively, allowing control by aTc and IPTG. Both the low copy number and the high copy number variants of the circuit were tested over a range of aTc and IPTG concentrations. Output fluorescence levels were measured using a microplate fluorometer. Measurements of the low copy number variant showed that the output increased in response to CI and decreased in response to Cro, as expected. The circuit did not respond to very low input protein concentrations, and the output was clipped after a maximum concentration was reached. The high copy number variant showed similar results. This variant was also tested with the two inputs applied together. The output of the circuit reflected the difference between the two inputs, although the response was not linear. At very high levels of Cro, the circuit essentially shut down, regardless of CI concentration. The experimental results are consistent with the model predictions
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".