Automated Cell-free Protein Synthesis for Distributed Biomanufacturing
Bibliographic record
Abstract
ABSTRACT Recombinant proteins are fundamental to modern medicine, enabling research, diagnostics, and drug discovery, yet their access is often restricted by centralized manufacturing and cold-chain distribution. Decentralized, on-site biomanufacturing can enable research, provide resilience, and advance personalized medicine. Here, we introduce MANGO (MANufacturing on the GO), a purpose-built, open-source device designed for automated, benchtop cell-free protein synthesis and purification. Computer-controlled synthesis and purification are mediated through biomedical-grade pumps, valves, and a microchannel manifold, enabling rapid protein production directly at the point-of-use. To demonstrate MANGO’s utility, we produced and validated a diverse range of proteins, including nanobodies against SARS-CoV-2 and TNF, cloning enzymes, and diagnostic enzymes, with performance equivalent to their commercial counterparts. We further deployed MANGO in Brazil and Colombia to support patient trials for SARS-CoV-2 and dengue virus diagnostics. These results establish MANGO as a low-cost, portable platform that can democratize access to bioreagents in both high- and low-resource settings and, ultimately, expand participation in the bioeconomy.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".