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Discrete stochastic models of SELEX: Aptamer capture probabilities and protocol optimization.

Yue Wang1, Bhaven A Mistry2, Tom Chou1

  • 1Department of Computational Medicine, University of California, Los Angeles, California 90095-1766, USA.

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A new stochastic model for aptamer selection (SELEX) reveals that optimal protocols differ from deterministic predictions, especially in small systems. This model better captures the probability of losing high-affinity aptamers during selection.

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Area of Science:

  • Biotechnology
  • Molecular Biology
  • Computational Biology

Background:

  • Antibodies are effective but costly and large biomolecules for target recognition.
  • Aptamers, short nucleic acid sequences, offer an alternative with high specificity and affinity.
  • Systematic Evolution of Ligands by EXponential enrichment (SELEX) is a common method for aptamer generation.

Purpose of the Study:

  • To develop a fully discrete stochastic model for the SELEX process.
  • To analyze SELEX in small system sizes where deterministic models are insufficient.
  • To identify optimal SELEX protocols under stochastic conditions.

Main Methods:

  • Development of a fully discrete stochastic model for SELEX.
  • Mathematical analysis of the stochastic model, comparing it to mass-action (deterministic) models.
  • Investigation of statistical quantities, including loss probability of aptamers in small systems.

Main Results:

  • The stochastic model converges to the mass-action model in the large system-size limit.
  • The model allows for the study of statistical effects in small SELEX systems.
  • Optimal SELEX protocols derived from the stochastic model differ from those predicted by deterministic models.

Conclusions:

  • A discrete stochastic model provides a more accurate representation of SELEX, particularly for small system sizes.
  • Stochastic effects can significantly influence aptamer selection outcomes, leading to different optimal strategies.
  • This model enhances understanding of aptamer development and optimization.