Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding
Aptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the...
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| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/6760852 |
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| author | Andrew Brian Kinghorn Julian Alexander Tanner |
| author_facet | Andrew Brian Kinghorn Julian Alexander Tanner |
| author_sort | Andrew Brian Kinghorn |
| collection | DOAJ |
| description | Aptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the vast complexity of aptamer selection. Simulation of aptamer selection has been used to characterise and optimise the selection process; however, the absence of a good model for aptamer-target binding limits this field of study. Here, we generate theoretical fitness landscapes which appear to more accurately represent aptamer-target binding. The method used to generate these landscapes, selective phenome growth, is a new approach in which phenotypic contributors are added to a genotype/phenotype interaction map sequentially in such a way so as to increase the fitness of a selected fit sequence. In this way, a landscape is built around the selected fittest sequences. Comparison to empirical aptamer microarray data shows that our theoretical fitness landscapes more accurately represent aptamer ligand binding than other theoretical models. These improved fitness landscapes have potential for the computational analysis and optimisation of other complex systems. |
| format | Article |
| id | doaj-art-cc7c22d560ab45e293e79a21614835ff |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-cc7c22d560ab45e293e79a21614835ff2025-02-03T01:21:14ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/67608526760852Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand BindingAndrew Brian Kinghorn0Julian Alexander Tanner1School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong KongSchool of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong KongAptamers are single-stranded oligonucleotides selected by evolutionary approaches from massive libraries with significant potential for specific molecular recognition in diagnostics and therapeutics. A complete empirical characterisation of an aptamer selection experiment is not feasible due to the vast complexity of aptamer selection. Simulation of aptamer selection has been used to characterise and optimise the selection process; however, the absence of a good model for aptamer-target binding limits this field of study. Here, we generate theoretical fitness landscapes which appear to more accurately represent aptamer-target binding. The method used to generate these landscapes, selective phenome growth, is a new approach in which phenotypic contributors are added to a genotype/phenotype interaction map sequentially in such a way so as to increase the fitness of a selected fit sequence. In this way, a landscape is built around the selected fittest sequences. Comparison to empirical aptamer microarray data shows that our theoretical fitness landscapes more accurately represent aptamer ligand binding than other theoretical models. These improved fitness landscapes have potential for the computational analysis and optimisation of other complex systems.http://dx.doi.org/10.1155/2017/6760852 |
| spellingShingle | Andrew Brian Kinghorn Julian Alexander Tanner Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding Complexity |
| title | Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding |
| title_full | Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding |
| title_fullStr | Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding |
| title_full_unstemmed | Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding |
| title_short | Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding |
| title_sort | selective phenome growth adapted nk model a novel landscape to represent aptamer ligand binding |
| url | http://dx.doi.org/10.1155/2017/6760852 |
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