Controlling uncertainty in aptamer selection
Research output: Contribution to journal › Article
Colleges, School and Institutes
- 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; email@example.com firstname.lastname@example.org.
- Department of Biomedical Engineering, Boston University, Boston MA 02215; Howard Hughes Medical Institute, Boston University, Boston, MA 02215 email@example.com firstname.lastname@example.org.
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.
|Number of pages||6|
|Journal||National Academy of Sciences. Proceedings|
|Early online date||25 Oct 2016|
|Publication status||E-pub ahead of print - 25 Oct 2016|
- Oligonucleotides, Stochastic model, Hybrid model, Evolution, in vitro selection