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Published June 1, 2020 | Published + Submitted
Journal Article Open

A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures

Abstract

Information technologies enable programmers and engineers to design and synthesize systems of startling complexity that nonetheless behave as intended. This mastery of complexity is made possible by a hierarchy of formal abstractions that span from high-level programming languages down to low-level implementation specifications, with rigorous connections between the levels. DNA nanotechnology presents us with a new molecular information technology whose potential has not yet been fully unlocked in this way. Developing an effective hierarchy of abstractions may be critical for increasing the complexity of programmable DNA systems. Here, we build on prior practice to provide a new formalization of 'domain-level' representations of DNA strand displacement systems that has a natural connection to nucleic acid biophysics while still being suitable for formal analysis. Enumeration of unimolecular and bimolecular reactions provides a semantics for programmable molecular interactions, with kinetics given by an approximate biophysical model. Reaction condensation provides a tractable simplification of the detailed reactions that respects overall kinetic properties. The applicability and accuracy of the model is evaluated across a wide range of engineered DNA strand displacement systems. Thus, our work can serve as an interface between lower-level DNA models that operate at the nucleotide sequence level, and high-level chemical reaction network models that operate at the level of interactions between abstract species.

Additional Information

© 2020 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 17/12/2019; Manuscript accepted 21/04/2020; Published online 03/06/2020; Published in print 24/06/2020. The authors thank Chris Thachuk, Niles Pierce, Andrew Phillips, Peng Yin, Dave Zhang and Justin Werfel for discussion and support. We declare we have no competing interests. This work was supported by the National Science Foundation grant nos. CCF-0832824 (The Molecular Programming Project), 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 S.B. was, in part, provided by a postdoctoral fellowship from the Caltech Biology and Biological Engineering Division. C.G. received support from the NIH/NIGMS Medical Scientist Training Program training grant, T32GM007205. K.V.S. acknowledges support from NIH NCI F30CA210329 and the UCLA-Caltech Medical Scientist Training Program.

Attached Files

Published - rsif.2019.0866.pdf

Submitted - 1505.03738.pdf

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Additional details

Created:
August 22, 2023
Modified:
December 22, 2023