Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

Creators: Berleant, Joseph; Berlind, Christopher; Badelt, Stefan; Dannenberg, Frits; Schaeffer, Joseph; Winfree, Erik

Abstract

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson–Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

Additional Information

© 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Manuscript received 11/02/2018; Manuscript accepted 05/11/2018; Published online 19/12/2018; Published in print 12/2018. Data accessibility: This article has no additional data. Authors' contributions: J.B. helped design the analysis framework, helped create the Python implementation, performed data collection and data analysis, and wrote the manuscript; C.B. helped design the analysis framework and helped create the Python implementation; S.B. helped perform data collection and data analysis; F.D. assisted with the Multistrand back-end and rate formula derivations; J.S. helped design the analysis framework and gave guidance during its implementation; E.W. conceived of and designed the framework, helped with its implementation, and helped perform data collection and data analysis. All authors helped draft the manuscript and gave final approval for publication. We declare that we have no competing interests. This research was funded by NSF grant nos. CCF/HCC-1213127, CHE/CCF-1643606 and CCF-1317694 (the Expedition in Computing on 'Molecular Programming Architectures, Abstractions, Algorithms, and Applications') and by the Gordon and Betty Moore Foundation through grant no. GBMF2809 to the Caltech Programmable Molecular Technology Initiative. Funding for J.B. was in part provided by the NSF grant no. CCF-1564025 to Mark Bathe and by an NSF Graduate Research Fellowship. Funding for S.B. was in part provided by a postdoctoral fellowship from the Caltech Biology and Biological Engineering Division. The authors appreciate discussion and guidance from Niranjan Srinivas and Chris Thachuk.

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Supplemental Material - rsif20180107supp1.pdf

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