ArticleProceedings of the National Academy of Sciences of the United States of America2003
Computational and experimental analysis of DNA shuffling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Strategies to improve photosynthesis by modifying the RuBisCO system and its limitations.Molecular biology reports · 2025Review
- Generation of Cry11 Variants ofEvolutionary bioinformatics online · 2020Article
- Codon-Optimization of Wild-Type Adeno-Associated Virus Capsid Sequences Enhances DNA Family Shuffling while Conserving Functionality.Molecular therapy. Methods & clinical development · 2019Article
- DNA secondary structure formation by DNA shuffling of the conserved domains of the Cry protein ofBMC biophysics · 2017Article
- Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.Methods (San Diego, Calif.) · 2013Review
- Algorithms for optimizing cross-overs in DNA shuffling.BMC bioinformatics · 2012Article
- Revealing biases inherent in recombination protocols.BMC biotechnology · 2007Article
- IPRO: an iterative computational protein library redesign and optimization procedure.Biophysical journal · 2006Article
- AAV hybrid serotypes: improved vectors for gene delivery.Current gene therapy · 2005Review
- Computationally mapping sequence space to understand evolutionary protein engineering.Biotechnology progressArticle
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We describe a computational model of DNA shuffling based on the thermodynamics and kinetics of this process. The model independently tracks a representative ensemble of DNA molecules and records their states at every stage of a shuffling reaction. These data can subsequently be analyzed to yield information on any relevant metric, including reassembly efficiency, crossover number, type and distribution, and DNA sequence length distributions. The predictive ability of the model was validated by comparison to three independent sets of experimental data, and analysis of the simulation results led to several unique insights into the DNA shuffling process. We examine a tradeoff between crossover frequency and reassembly efficiency and illustrate the effects of experimental parameters on this relationship. Furthermore, we discuss conditions that promote the formation of useless "junk" DNA sequences or multimeric sequences containing multiple copies of the reassembled product. This model will therefore aid in the design of optimal shuffling reaction conditions.
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