Selective Phenome Growth Adapted NK Model: A Novel Landscape to Represent Aptamer Ligand Binding. (24th July 2017)

• Record Type: Journal Article
• Authors: Kinghorn, Andrew Brian; Tanner, Julian Alexander
• Other Names: De Lellis Pietro Academic Editor.
• Is Part Of: Complexity. Volume 2017(2017)
• Publication Date: 2017-07-24
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2017/6760852
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.