Development of MANufacturing on the GO (MANGO): A Cell-Free Synthetic Biology Approach to Portable On-Demand Biomanufacturing
Bibliographic record
Abstract
Proteins are a diverse group of macromolecules that are in all living organisms and, accordingly, are integral to a variety of biological applications such as medicine, research, and diagnostics. Currently, recombinant proteins are primarily synthesized in well-resourced commercial laboratories using cell-based production. The global distribution of commercial protein products from centralized biomanufacturing hubs poses logistical challenges due to storage conditions dependent on cold chains, which can impede access in remote or resource-limited regions, and during public health crises. To address this, this study introduces a pioneering approach that automates cell-free protein synthesis systems to provide a compact, portable solution for distributed biomanufacturing. Cell-free protein expression systems are compatible with freeze-drying for stable room-temperature distribution and storage, eliminating storage condition and cold chain limitations. To de-skill cell-free protein expression, we have developed MANGO (MANufacturing on the GO), a purpose-built device for small-scale protein production and purification and demonstrated this approach in a series of biomanufacturing vignettes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".