Automated design of assemblable, modular, synthetic chromosomes

  • Authors:

  • Sarah M. Richardson;Brian S. Olson;Jessica S. Dymond;Randal Burns;Srinivasan Chandrasegaran;Jef D. Boeke;Amarda Shehu;Joel S. Bader

  • Affiliations:

  • High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimor ...;Department of Computer Science, George Mason University, Fairfax, VA;High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD and Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, ...;Department of Computer Science, Johns Hopkins University, Baltimore, MD;Department of Environmental Health Sciences, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD;High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD and Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, ...;Department of Computer Science, George Mason University, Fairfax, VA;High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD

  • Venue:
  • PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part II
  • Year:
  • 2009

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Abstract

The goal of the Saccharomyces cerevisiae v2.0 project is the complete synthesis of a re-designed genome for baker's yeast. The resulting organism will permit systematic studies of eukaryotic chromosome structure that have been impossible to explore with traditional gene-ata-time experiments. The efficiency of chemical synthesis of DNA does not yet permit direct synthesis of an entire chromosome, although it is now feasible to synthesize multi-kilobase pieces of DNA that can be combined into larger molecules. Designing a chromosome-sized sequence that can be assembled from smaller pieces has to date been accomplished by biological experts in a laborious and error-prone fashion. Here we pose DNA design as an optimization problem and obtain optimal solutions with a parallelizable dynamic programming algorithm.