Improving Biosensor-based Circuits for Dynamic Control of Metabolic Pathways
Bibliographic record
Abstract
Bioproduction of chemicals in microorganisms using renewable feedstocks is a sustainable and greener alternative to traditional processes. Dynamic control of metabolism using biosensors has evolved as a common approach to partition cellular resources efficiently However, biosensor development requires several iterations of DBTL cycles to optimize for any application. In this work, we modeled and studied the effect of various parameters on the switching characteristics of an autonomous biosensor-regulator system, qCRIPSRi by simulating the dynamics of the circuit and performing sensitivity analysis. In addition, the effect of stringency of transcriptional activator LuxR on the switching characteristics and dynamics was investigated and validated through in vivo experiments. We also designed and modeled a novel biosensor architecture that enables tuning dose-response curve using small molecule inducers. Furthermore, we demonstrated the ability of the system to tune dynamic range, threshold, and sensitivity of dose response curves of adipic acid and resorcinol biosensors in silico.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".