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Volume 2017 (2017), Article ID 6760852, 12 pages
Research Article

Selective Phenome Growth Adapted Model: A Novel Landscape to Represent Aptamer Ligand Binding

School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong

Correspondence should be addressed to Andrew Brian Kinghorn

Received 16 January 2017; Revised 10 May 2017; Accepted 23 May 2017; Published 24 July 2017

Academic Editor: Pietro De Lellis

Copyright © 2017 Andrew Brian Kinghorn and Julian Alexander Tanner. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.