Design, Engineering, and Discovery of Transcription Factor-Based Biosensors for Applications in Value-Added Chemical Production
Bibliographic record
Abstract
The rapid development of technology and techniques in synthetic biology and metabolic engineering has enabled the bio-based economy to develop microbial cell factories for the biological production of chemicals from renewable sources. As biological techniques and tools have developed and become cheaper, the ability to create strains and mutants has become essentially limitless. Traditional methods for screening and selecting viable mutants are low-throughput compared to the ability to build mutants, causing a bottleneck within the design-build-test-learn engineering cycle. To address the testing bottleneck, biosensors such as transcription factor-based biosensors have emerged as powerful biological tools that can be applied for the screening and control of microbial cell factories. Such biosensors can also be used in other applications for point-of-care diagnostics and environmental pollutant monitoring. While this technology has demonstrated its potential in the literature, the widespread development and deployment of biosensors is still limited by the availability of transcription factors for detecting target chemicals and the inconsistent design methods of biosensors. This project seeks to overcome current bottlenecks using multiple strategies to elucidate fundamental principles of transcription factor biosensor specificity and circuit engineering that can be harnessed to develop future biosensors. This was accomplished by establishing strategies for the design and construction of biosensors to guide biosensor development, developing structure-guided computational approaches for transcription factor specificity engineering to expand the breadth of potential biosensor applications, and creating chimeric transcription factors to expedite biosensor discovery and overcome the currently high levels of characterization typically required to develop biosensors. The accomplishment of these tasks improves the ability to design, engineer, and discover biosensors for molecules of interest, accelerating biosensor development and the implementation of biosensors towards harnessing biology for various applications.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".