Software System Design to Support Scale in Mammalian Cell Line Engineering
Bibliographic record
Abstract
Cell line engineering (CLE) is the process of gene editing cell lines for a variety of purposes including research and development or bioproduction processes. Traditionally, CLE workflows have been manual and low throughput. Here, we describe the development of several software-based processes, implemented alongside wet lab automation and robotics, built to improve the throughput of our CLE platform to three times its previous capacity. A markup language (GEML) was developed to enable e-commerce capabilities and connections to internal manufacturing systems. A laboratory information management system (LIMS), specifically designed to track CLE projects through all stages, was created to manufacture the cell line specified by the GEML. Cell line engineering required analysis of images in brightfield without fluorescent staining; therefore, a machine learning (ML)-based method for analysing engineered clones imaged captured on an automated imaging platform was created. Our work demonstrates that combining both wet lab automation and software approaches is essential to allow CLE workflows to reach their full potential, allowing the development of high-throughput robust platforms that meet the increasing demands of the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".