Controlling uncertainty in aptamer selection

Research output: Contribution to journalArticle

Authors

  • Zohar B Weinstein
  • Atena Irani Shemirani
  • Nga Ho
  • Darash Desai
  • Muhammad H Zaman

Colleges, School and Institutes

External organisations

  • Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA 02118.
  • Department of Biomedical Engineering, Boston University, Boston MA 02215.
  • Department of Biomedical Engineering, Boston University, Boston MA 02215; ddesai@bu.edu zaman@bu.edu.
  • Department of Biomedical Engineering, Boston University, Boston MA 02215; Howard Hughes Medical Institute, Boston University, Boston, MA 02215 ddesai@bu.edu zaman@bu.edu.

Abstract

The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a stochastic hybrid model that describes the evolutionary selection of aptamers to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.

Details

Original languageEnglish
Pages (from-to)12076-12081
Number of pages6
JournalNational Academy of Sciences. Proceedings
Volume113
Issue number43
Early online date25 Oct 2016
Publication statusE-pub ahead of print - 25 Oct 2016

Keywords

  • Oligonucleotides, Stochastic model, Hybrid model, Evolution, in vitro selection