Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates. In: Brijder R, Qian L, editors. DNA Computing and Molecular Programming. Cham: Springer International Publishing; 2017. pp. 172-87. [DOI: 10.1007/978-3-319-66799-7_12]

Controlling Uncertainty of Empirical First-Passage Times in the Small-Sample Regime

We derive general bounds on the probability that the empirical first-passage time τ[over ¯]_{n}≡∑_{i=1}^{n}τ_{i}/n of a reversible ergodic Markov process inferred from a sample of n independent realizations deviates from the true mean first-passage time by more than any given amount in either direction. We construct nonasymptotic confidence intervals that hold in the elusive small-sample regime and thus fill the gap between asymptotic methods and the Bayesian approach that is known to be sensitive to prior belief and tends to underestimate uncertainty in the small-sample setting. We prove sharp bounds on extreme first-passage times that control uncertainty even in cases where the mean alone does not sufficiently characterize the statistics. Our concentration-of-measure-based results allow for model-free error control and reliable error estimation in kinetic inference, and are thus important for the analysis of experimental and simulation data in the presence of limited sampling.

Hybridization kinetics of out-of-equilibrium mixtures of short RNA oligonucleotides

Hybridization and strand displacement kinetics determine the evolution of the base paired configurations of mixtures of oligonucleotides over time. Although much attention has been focused on the thermodynamics of DNA and RNA base pairing in the scientific literature, much less work has been done on the time dependence of interactions involving multiple strands, especially in RNA. Here we provide a study of oligoribonucleotide interaction kinetics and show that it is possible to calculate the association, dissociation and strand displacement rates displayed by short oligonucleotides (5nt–12nt) that exhibit no expected secondary structure as simple functions of oligonucleotide length, CG content, ΔG of hybridization and ΔG of toehold binding. We then show that the resultant calculated kinetic parameters are consistent with the experimentally observed time dependent changes in concentrations of the different species present in mixtures of multiple competing RNA strands. We show that by changing the mixture composition, it is possible to create and tune kinetic traps that extend by orders of magnitude the typical sub-second hybridization timescale of two complementary oligonucleotides. We suggest that the slow equilibration of complex oligonucleotide mixtures may have facilitated the nonenzymatic replication of RNA during the origin of life.

The stability and number of nucleating interactions determine DNA hybridization rates in the absence of secondary structure

The kinetics of DNA hybridization are fundamental to biological processes and DNA-based technologies. However, the precise physical mechanisms that determine why different DNA sequences hybridize at different rates are not well understood. Secondary structure is one predictable factor that influences hybridization rates but is not sufficient on its own to fully explain the observed sequence-dependent variance. In this context, we measured hybridization rates of 43 different DNA sequences that are not predicted to form secondary structure and present a parsimonious physically justified model to quantify our observations. Accounting only for the combinatorics of complementary nucleating interactions and their sequence-dependent stability, the model achieves good correlation with experiment with only two free parameters. Our results indicate that greater repetition of Watson-Crick pairs increases the number of initial states able to proceed to full hybridization, with the stability of those pairings dictating the likelihood of such progression, thus providing new insight into the physical factors underpinning DNA hybridization rates.

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

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.