ArticleACS synthetic biology2022
Evolutionary Stability Optimizer (ESO): A Novel Approach to Identify and Avoid Mutational Hotspots in DNA Sequences While Maintaining High Expression Levels.
Article in ACS synthetic biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Design of bacterial DNT sensors based on computational models.Nucleic acids research · 2026Article
- Designing genetically stable multicopy gene constructs with the ChimeraUGEM web server.NAR genomics and bioinformatics · 2025Article
- AI-directed gene fusing prolongs the evolutionary half-life of synthetic gene circuits.Science advances · 2025Article
- Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.Journal of the Royal Society, Interface · 2025Article
- Design and regulation of engineered bacteria for environmental release.Nature microbiology · 2025Review
- Predicting gene sequences with AI to study codon usage patterns.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Yeast-derived low-purity FGF2 supports bovine ESC and MSC aggregates in suspension.Frontiers in nutrition · 2025Article
- CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.bioRxiv : the preprint server for biology · 2024Article
- Article
- CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria.Synthetic biology (Oxford, England) · 2024Article
- The Causes for Genomic Instability and How to Try and Reduce Them Through Rational Design of Synthetic DNA.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Israel and the global synthetic biology ecosystem.Engineering biology · 2023Review
- Modelling genetic stability in engineered cell populations.Nature communications · 2023Article
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9 authors.
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Abstract
Modern synthetic biology procedures rely on the ability to generate stable genetic constructs that keep their functionality over long periods of time. However, maintenance of these constructs requires energy from the cell and thus reduces the host's fitness. Natural selection results in loss-of-functionality mutations that negate the expression of the construct in the population. Current approaches for the prevention of this phenomenon focus on either small-scale, manual design of evolutionary stable constructs or the detection of mutational sites with unstable tendencies. We designed the Evolutionary Stability Optimizer (ESO), a software tool that enables the large-scale automatic design of evolutionarily stable constructs with respect to both mutational and epigenetic hotspots and allows users to define custom hotspots to avoid. Furthermore, our tool takes the expression of the input constructs into account by considering the guanine-cytosine (GC) content and codon usage of the host organism, balancing the trade-off between stability and gene expression, allowing to increase evolutionary stability while maintaining the high expression. In this study, we present the many features of the ESO and show that it accurately predicts the evolutionary stability of endogenous genes. The ESO was created as an easy-to-use, flexible platform based on the notion that directed genetic stability research will continue to evolve and revolutionize current applications of synthetic biology. The ESO is available at the following link: https://www.cs.tau.ac.il/~tamirtul/ESO/.
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