ArticleEngineering biology
Software System Design to Support Scale in Mammalian Cell Line Engineering.
Article in Engineering biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Software System Design to Support Scale in Mammalian Cell Line Engineering.Engineering biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.