Learning seeks to provide an understanding that aids in solving problems and finding solutions. Programming has been applied in different fields to find solutions including the application of dynamic programming in bioinformatics. The essay offers a review of two articles on the application of dynamic programming.
Articles reviewed
The first article reviewed is, "Comparing DNA Applying Dynamic Programming: A Historical Review" authored by Stojanov Done and published in the Tem Journal in 2017. The second article, "A systematic approach to dynamic programming in bioinformatics" was written by Giegerich Robert and published in the Bioinformatics
Journal in 2000.
Summary of the articles
The two articles focus on the same topic the application of dynamic programming in bioinformatics. The articles begin by underscoring the application of dynamic programming in applied sciences. The two authors concur that dynamic programming is the most popular paradigm in computational molecular biology (Giegerich 665 and Stojanov 888). The two articles provide a review of the application of dynamic programming including assembling of DNA sequence data DNA alignment determining structure of eukaryotic =genes predicting structure of functional RNA genes. The articles focus then differs with Stojanov providing a historical review of the application of dynamic programming in DNA alignment while Giegerich offers a systematic review.
Stojanov (889) reviews the application of dynamic programming algorithms in DNA alignment with the specification that the optimal solution has to achieve a maximal sum of all scores. The DNA alignment techniques reviewed in the article include Needleman-Wunsch algorithm which seeks to find end-to-end solutions through the maximization of total similarity of two sequences. The algorithm of sellers is then reviewed which seeks to provide a solution to DNA alignment by minimizing the total difference between two sequences. Therefore, the algorithm of sellers is the works in opposite to the Needleman-Wunsch algorithm. The third method Smith-Waterman algorithm seeks to create the best local solution while the Waterman-Eggert algorithm extends further to find the k-highest scoring local -solutions/alignments. The other solution reviewed in the article is diagonal alignments that aids in improving the computational complexity. The application diagonal alignment is, however, limited to the comparison of highly similar DNA samples (Stojanov 891). Lastly the article reviewed memory linear algorithms that seek to reduce memory complexity and aid in the detection of short sequence repeats (Stojanov 892). The article concludes by restating that the methods are a direct application of dynamic programming.
The second article by Giegerich provides a systematic review of algebraic dynamic programming (ADP). The high degree of abstractness in algebraic dynamic programming is considered the main virtue since it aids in supporting intuitive reasoning and formal validation resulting in reliable implementations and reusable algorithm components (Giegerich, 666). The approach aids in solving complex problems with better chances of success. The article provides an in-depth review of the algebraic dynamic programming and its application in aligning recombinant DNA sequences. The article concludes by offering the various aspects of algebraic dynamic programming in classical problems including global and local alignment and pattern alignment.
The two articles clearly depict the role of dynamic programming in bioinformatics and provide solutions to the problem. The articles show the role of dynamic programming in DNA alignment, which helps in bioinformatics.
Works Cited
Stojanov, Done. "Comparing DNA Applying Dynamic Programming: A Historical Review." Tem Journal 6.4 (2017): 888-893. Avaialble on http://www.temjournal.com/content/64/TemJournalNovember2017_888_893.pdf
Giegerich, Robert. "A systematic approach to dynamic programming in bioinformatics." Bioinformatics 16.8 (2000): 665-677. Available on https://pdfs.semanticscholar.org/5687/d7541c01e9e409e34eff644a640943413c57.pdf
