A DNA Sequence Design for Direct-Proportional Length-Based DNA Computing using DNASequenceGenerator

2007 ◽  
Author(s):  
Zuwairie Ibrahim ◽  
Tri Basuki Kurniawan ◽  
Marzuki Khalid ◽  
Osamu Ono
Keyword(s):  
Dna Sequence ◽  
Dna Computing ◽  
Sequence Design ◽  
Dna Sequence Design

Hierarchy Evolutionary Searching Algorithm for DNA Encoding Sequence

2010 ◽  
Vol 20-23 ◽  
pp. 94-98
Author(s):  
Lian Ye ◽  
Jing Chen ◽  
Yong Kang Xing
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Dna Computing ◽  
Reaction Process ◽  
Dna Encoding ◽  
Molecular Computation ◽  
Sequence Design ◽  
Dna Sequence Design ◽  
Molecular Reaction

DNA sequence design is the basic and important step for DNA computing. Good codeword could avoid some error may possibly occurred in practical computing. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. In this paper, a new hierarchy evolutionary searching algorithm is proposed to obtain good DNA encoding sequences, this approach is based on combinatorial constraints which affect the molecular reaction process, and can provide some reliable and effective encoding sequences for controllable DNA computing.

Evolutionary Multi-Objective Optimization for DNA Sequence Design

2011 ◽  
pp. 239-264
Author(s):  
Soo-Yong Shin ◽  
In-Hee Lee ◽  
Byoung-Tak Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Dna Microarrays ◽  
Superior Performance ◽  
Final Decision ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design ◽  
Np Problems

Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

scholarly journals EGNAS: an exhaustive DNA sequence design algorithm

2012 ◽  
Vol 13 (1) ◽  
pp. 138 ◽  
Author(s):  
Alfred Kick ◽  
Martin Bönsch ◽  
Michael Mertig
Keyword(s):  
Dna Sequence ◽  
Design Algorithm ◽  
Sequence Design ◽  
Dna Sequence Design

scholarly journals DNA Chisel, a versatile sequence optimizer

2020 ◽  
Vol 36 (16) ◽  
pp. 4508-4509 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Optimization Problems ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequence Optimization ◽  
Sequence Design ◽  
Optimization Framework ◽  
Dna Sequence Design

Abstract Motivation Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. Results DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Availability and implementation The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). Supplementary information Supplementary data are available at Bioinformatics online.

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