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

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

doi:10.1098/rsif.2018.0107

Joseph Berleant, Erik Winfree + Show 4 more

Open Access

https://doi.org/10.1098/rsif.2018.0107

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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.

Highlights

DNA is a widely used engineering substrate for biochemical circuits and systems
Experimental demonstrations have shown that DNA-based circuits can carry out a diverse range of information-processing tasks, including amplification and analogue computation [4,5,6,7,8,9,10,11,12], digital logic gates and circuits [13,14,15,16,17,18], neural network pattern recognition [19 –21], probabilistic circuits [22] and the implementation of chemical reaction network (CRN) dynamics [23,24]
Theoretical studies have established that DNAbased circuits are capable of arbitrarily complex digital and analogue circuits [25 –27], efficient neural network computation and autonomous learning [28,29], the full range of dynamical behaviours supported by mass-action kinetics of abstract CRNs [30 –32], and even the full range of algorithmic behaviours supported by Turing machines [33,34]

Summary

Introduction

DNA is a widely used engineering substrate for biochemical circuits and systems. Using simple Watson –Crick base-pairing rules, molecules can be designed to fold into stable conformations and large assemblies [1], but they can be programmed to implement dynamic systems using toeholdmediated DNA strand displacement [2] for triggered rearrangement of molecular components [3]. Experimental demonstrations have shown that DNA-based circuits can carry out a diverse range of information-processing tasks, including amplification and analogue computation [4,5,6,7,8,9,10,11,12], digital logic gates and circuits [13,14,15,16,17,18], neural network pattern recognition [19 –21], probabilistic circuits [22] and the implementation of chemical reaction network (CRN) dynamics [23,24]. A valid secondary structure must have each domain unbound, or completely bound to a single complementary domain. We further dictate that valid secondary structures be non-pseudoknotted (i.e. have a well-defined dot-parens-plus representation [49]). Examples of valid structures are shown in figure 1a, with accompanying dot-parens-plus structure representations [45]

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