<i>In vitro</i> prototyping and rapid optimization of biosynthetic enzymes for cellular design
Bibliographic record
Abstract
Abstract Microbial cell factories offer an attractive approach for production of biobased products. Unfortunately, designing, building, and optimizing biosynthetic pathways remains a complex challenge, especially for industrially-relevant, non-model organisms. To address this challenge, we describe a platform for in vitro P rototyping and R apid O ptimization of B iosynthetic E nzymes (iPROBE). In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE with two examples. First, we tested and ranked 54 different pathways for 3-hydroxybutyrate production, improving in vivo production in Clostridium by 20-fold to 14.63 ± 0.48 g/L and identifying a new biosynthetic route to (S) -(+)-1,3-butanediol. Second, we used iPROBE and data-driven design to optimize a 6-step n -butanol pathway, increasing titers 4-fold across 205 pathways, and showed strong correlation between cell-free and cellular performance. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".