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

Zolaktaf, Sedigheh and Dannenberg, Frits and Rudelis, Xander and Condon, Anne and Schaeffer, Joseph M. and Schmidt, Mark and Thachuk, Chris and Winfree, Erik (2017) Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates. In: DNA Computing and Molecular Programming. Lecture Notes in Computer Science. No.10467. Springer , Cham, Switzerland, pp. 172-187. ISBN 978-3-319-66798-0.

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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.

Item Type:Book Section
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Winfree, Erik0000-0002-5899-7523
Additional Information:© Springer International Publishing AG 2017. First Online: 24 August 2017. 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.
Funding AgencyGrant Number
Gordon and Betty Moore FoundationUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Series Name:Lecture Notes in Computer Science
Issue or Number:10467
Record Number:CaltechAUTHORS:20181126-091545698
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91155
Deposited By: Tony Diaz
Deposited On:26 Nov 2018 17:30
Last Modified:24 Feb 2020 21:34

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