Novel algorithms for in vitro gene synthesis and gene optimization with applications to therapeutics and health research
In the development of a vaccine against four strains of the human papilloma virus (HPV), of particular note were studies involving innate immune response when genes of the virus were introduced into host cells. It was observed that increased levels of antibodies were produced by inoculation that used synthetic versions of two HPV genes. The application of gene vaccines, not only for cancer immunization, but also to aid the treatment of infectious diseases, is an ongoing and very active area of study. Developing an efficient and reliable method to produce synthetic DNA is a necessary tool for these studies to succeed. Due to the inherent difficulty of creating long strands of DNA, current technologies for gene synthesis use computational methods for design of shorter DNA fragments called oligos (oligonucleotides), which can be reliably synthesized and assembled. However, ensuring a set of oligos will self-assemble correctly into a longer DNA strand is difficult and complex, and previous software programs have failed to solve this issue. Chris Thachuk is a computer scientist who develops synthetic gene design algorithms. He and his colleagues are developing algorithms to successfully assemble long strands of DNA from oligos. These new algorithms outperform the current state-of-the-art and their effectiveness has been demonstrated through computational experiments on a large set of genes. Three average size genes have been produced with the aid of these algorithms. He now intends to build upon this success by extending the algorithms to produce reliable designs for synthesizing long genes and for synthesizing multiple genes in one step. By providing researchers with more advanced algorithms and accurate modeling software for gene synthesis, Thachuk hopes to contribute to new insights for treatment, detection and/or prevention of diseases.