Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates

  • Sedigheh Zolaktaf
  • Frits Dannenberg
  • Xander Rudelis
  • Anne Condon
  • Joseph M. Schaeffer
  • Mark Schmidt
  • Chris Thachuk
  • Erik WinfreeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10467)


Models of nucleic acid thermal stability are calibrated to a wide range of experimental observations, and typically predict equilibrium probabilities of nucleic acid secondary structures with reasonable accuracy. By comparison, a similar calibration and evaluation of nucleic acid kinetic models to a broad range of measurements has not been attempted so far. We introduce an Arrhenius model of interacting nucleic acid kinetics that relates the activation energy of a state transition with the immediate local environment of the affected base pair. Our model can be used in stochastic simulations to estimate kinetic properties and is consistent with existing thermodynamic models. We infer parameters for our model using an ensemble Markov chain Monte Carlo (MCMC) approach on a training dataset with 320 kinetic measurements of hairpin closing and opening, helix association and dissociation, bubble closing and toehold-mediated strand exchange. Our new model surpasses the performance of the previously established Metropolis model both on the training set and on a testing set of size 56 composed of toehold-mediated 3-way strand displacement with mismatches and hairpin opening and closing rates: reaction rates are predicted to within a factor of three for \(93.4\%\) and \(78.5\%\) of reactions for the training and testing sets, respectively.



We thank the U.S. National Science Foundation (awards 0832824, 1213127, 1317694, 1643606), the Gordon and Betty Moore Foundation’s Programmable Molecular Technology Initiative, and the Natural Sciences and Engineering Research Council of Canada for support. We also thank the anonymous reviewers for their helpful comments and suggestions. XR’s current address is Descartes Labs, Los Alamos, NM, USA.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sedigheh Zolaktaf
    • 1
  • Frits Dannenberg
    • 2
  • Xander Rudelis
    • 2
  • Anne Condon
    • 1
  • Joseph M. Schaeffer
    • 3
  • Mark Schmidt
    • 1
  • Chris Thachuk
    • 2
  • Erik Winfree
    • 2
    Email author
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.California Institute of TechnologyPasadenaUSA
  3. 3.Autodesk ResearchSan FranciscoUSA

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