ArticleACS synthetic biology2015
Predicting the Genetic Stability of Engineered DNA Sequences with the EFM Calculator.
Article in ACS synthetic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed, 85 citations in OpenAlex.
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- Long-duration environmental biosensing by recording analyte detection in DNA using recombinase memory.Applied and environmental microbiology · 2024Article
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- Toolbox of Characterized Genetic Parts forACS synthetic biology · 2024Article
- CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.Synthetic biology (Oxford, England) · 2024Article
- Mutation bias and adaptation in bacteria.Microbiology (Reading, England) · 2023Review
- Prolonging genetic circuit stability through adaptive evolution of overlapping genes.Nucleic acids research · 2023Article
- Modelling genetic stability in engineered cell populations.Nature communications · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Unwanted evolution can rapidly degrade the performance of genetically engineered circuits and metabolic pathways installed in living organisms. We created the Evolutionary Failure Mode (EFM) Calculator to computationally detect common sources of genetic instability in an input DNA sequence. It predicts two types of mutational hotspots: deletions mediated by homologous recombination and indels caused by replication slippage on simple sequence repeats. We tested the performance of our algorithm on genetic circuits that were previously redesigned for greater evolutionary reliability and analyzed the stability of sequences in the iGEM Registry of Standard Biological Parts. More than half of the parts in the Registry are predicted to experience >100-fold elevated mutation rates due to the inclusion of unstable sequence configurations. We anticipate that the EFM Calculator will be a useful negative design tool for avoiding volatile DNA encodings, thereby increasing the evolutionary lifetimes of synthetic biology devices.
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