ArticleEngineering biology2024
Supercharging engineering biology with automation.
Article in Engineering biology, 2024. 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.
- Supercharging engineering biology with automation.Engineering biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breakthroughs in engineering biology will solve the challenges facing humanity, by harnessing life itself. Standing in the way of these breakthroughs are the technical challenges of collecting the requisite data. Data variability and reproducibility problems, mean the odds are stacked against emerging biotechs. Automation has long been known to solve both problems; Let a robot do the pipetting and get reproducible data with less hands-on time. Although transitioning to automation has clear benefits, it can introduce additional complexity if done incorrectly. Analytik Jena UK has focused on supporting this transition to automation. We have found the combining of industry expertise with the biology know-how at the bench is paramount. Great automation should empower the scientist. Scientists should be trained on how to harness their automation. Through industry-customer relationships, we have successfully automated platforms for building DNA to antibody development. Through this partnership, we can enable a smooth translation of engineering biology to scalable industrial solutions. In this communication we have highlighted some successful examples where translating engineering biology workflows onto automation has proven beneficial, paving the way to industry ready solutions.
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What OpenQuestion holds
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.