Maximizing yield, purity and throughput of M13 bacteriophage bioprocessing
Bibliographic record
Abstract
The filamentous M13 bacteriophage has been used extensively as a building block for biomaterials fabrication due to its self-assembled structure and bulk assembling properties. With its genetic code encapsulated in its protein capsid, the M13 phage can be used in phage display or directed evolution mutagenesis for the creation of large libraries. Applying phage display to materials fabrication draws out bioprocessing challenges regarding high yields and high purity. Additionally, phage display introduces the need for high throughput production to parse mutant libraries. Here, we develop an optimized, high throughput process for upstream and downstream M13 phage production. We identify an optimal medium containing 17 g/L of both tryptone and yeast extract, maximizing phage production using standard polyethylene glycol/sodium chloride precipitation. Next, we add a centrifuge filtration step, which removes detectable traces of sodium ions and significantly lowers polyethylene glycol levels. The higher yields grown in the optimal medium remediate the loss of phages from added purification steps. We also applied this combined process to 96-well plates, recovering titers of purified phages proportional to those obtained with larger volumes. The method that we present here could allow for automatable, scalable M13 phage production for applications in genetically engineered biologically derived materials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".