Improving Initial Pool Generation of Direct-Proportional Length-Based DNA Computing by Parallel Overlap Assembly

A direct-proportional length-based DNA computing approach for weighted graph problems has been proposed where the cost of each path is encoded by the length of oligonucleotides in a proportional way. During the initial pool generation, the hybridization/ligation phase is carried out where all the combinations are generated in the solution. However, this encoding suffers from biological behavior of hybridization since longer oligonucleotides are more likely to hybridize as oppose to the shorter ones. Recently, parallel overlap assembly (POA) has been recognized as an efficient initial pool generation of DNA computing for weighted graph problems. If POA is employed during the initial pool generation of direct-proportional length-based DNA computing, we expected that the biological influence contributed by various lengths of the oligonucleotides could be minimized as much as possible. Thus, in this paper we found that the hybridization/ligation method should be replaced with parallel overlap assembly, for a better and efficient initial pool generation of direct-proportional length-based DNA computing.

[1]  L F Landweber,et al.  Molecular computation: RNA solutions to chess problems , 2000, Proc. Natl. Acad. Sci. USA.

[2]  Marzuki Khalid,et al.  Direct-Proportional Length-Based DNA Computing for Shortest Path Problem , 2004, Int. J. Comput. Sci. Appl..

[3]  P D Kaplan,et al.  Parallel overlap assembly for the construction of computational DNA libraries. , 1997, Journal of theoretical biology.

[4]  Wolfgang Banzhaf,et al.  DNASequencesGenerator: A Program for the Construction of DNA Sequences , 2001, DNA.

[5]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[6]  K. Jayaraman,et al.  Polymerase chain reaction-mediated gene synthesis: synthesis of a gene coding for isozyme c of horseradish peroxidase. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Masahito Yamamoto,et al.  Local Search by Concentration-Controlled DNA Computing , 2001, Int. J. Comput. Intell. Appl..

[8]  Clifford R. Johnson,et al.  Solution of a 20-Variable 3-SAT Problem on a DNA Computer , 2002, Science.

[9]  Ajit Narayanan,et al.  DNA algorithms for computing shortest paths , 1998 .

[10]  W. Stemmer.  DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Byoung-Tak Zhang,et al.  Temperature Gradient-Based DNA Computing for Graph Problems with Weighted Edges , 2002, DNA.

[12]  W. Stemmer,et al.  Single-step assembly of a gene and entire plasmid from large numbers of oligodeoxyribonucleotides. , 1995, Gene.

[13]  S. Ho,et al.  Site-directed mutagenesis by overlap extension using the polymerase chain reaction. , 1989, Gene.

[14]  R. DeSalle,et al.  PCR jumping in clones of 30-million-year-old DNA fragments from amber preserved termites (Mastotermes electrodominicus) , 2005, Experientia.

[15]  Byoung-Tak Zhang,et al.  Efficient Initial Pool Generation for Weighted Graph Problems Using Parallel Overlap Assembly , 2004, DNA.