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Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains

Zolaktaf, Sedigheh and Dannenberg, Frits and Winfree, Erik and Bouchard-Côté, Alexandre and Schmidt, Mark and Condon, Anne (2019) Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. In: DNA Computing and Molecular Programming. Lecture Notes in Computer Science. No.11648. Springer , Cham, pp. 80-99. ISBN 978-3-030-26806-0. https://resolver.caltech.edu/CaltechAUTHORS:20200811-134907797

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Abstract

Nucleic acid kinetic simulators aim to predict the kinetics of interacting nucleic acid strands. Many simulators model the kinetics of interacting nucleic acid strands as continuous-time Markov chains (CTMCs). States of the CTMCs represent a collection of secondary structures, and transitions between the states correspond to the forming or breaking of base pairs and are determined by a nucleic acid kinetic model. The number of states these CTMCs can form may be exponentially large in the length of the strands, making two important tasks challenging, namely, mean first passage time (MFPT) estimation and parameter estimation for kinetic models based on MFPTs. Gillespie’s stochastic simulation algorithm (SSA) is widely used to analyze nucleic acid folding kinetics, but could be computationally expensive for reactions whose CTMC has a large state space or for slow reactions. It could also be expensive for arbitrary parameter sets that occur in parameter estimation. Our work addresses these two challenging tasks, in the full state space of all non-pseudoknotted secondary structures of each reaction. In the first task, we show how to use a reduced variance stochastic simulation algorithm (RVSSA), which is adapted from SSA, to estimate the MFPT of a reaction’s CTMC. In the second task, we estimate model parameters based on MFPTs. To this end, first, we show how to use a generalized method of moments (GMM) approach, where we minimize a squared norm of moment functions that we formulate based on experimental and estimated MFPTs. Second, to speed up parameter estimation, we introduce a fixed path ensemble inference (FPEI) approach, that we adapt from RVSSA. We implement and evaluate RVSSA and FPEI using the Multistrand kinetic simulator. In our experiments on a dataset of DNA reactions, FPEI speeds up parameter estimation compared to inference using SSA, by more than a factor of three for slow reactions. Also, for reactions with large state spaces, it speeds up parameter estimation by more than a factor of two.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-030-26807-7_5DOIArticle
https://rdcu.be/b6cwwPublisherFree ReadCube access
ORCID:
AuthorORCID
Winfree, Erik0000-0002-5899-7523
Additional Information:© 2019 Springer Nature Switzerland AG. First Online: 24 July 2019.
Series Name:Lecture Notes in Computer Science
Issue or Number:11648
Record Number:CaltechAUTHORS:20200811-134907797
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200811-134907797
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104916
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Aug 2020 21:22
Last Modified:11 Aug 2020 21:22

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