504 research outputs found
Two-Domain DNA Strand Displacement
We investigate the computing power of a restricted class of DNA strand
displacement structures: those that are made of double strands with nicks
(interruptions) in the top strand. To preserve this structural invariant, we
impose restrictions on the single strands they interact with: we consider only
two-domain single strands consisting of one toehold domain and one recognition
domain. We study fork and join signal-processing gates based on these
structures, and we show that these systems are amenable to formalization and to
mechanical verification
DNA as a universal substrate for chemical kinetics
Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior
A new model for classifying DNA code inspired by neural networks and FSA
This paper introduces a new model of classifiers CL(V,E,l,r)
designed for classifying DNA sequences and combining the flexibility of
neural networks and the generality of finite state automata. Our careful
and thorough verification demonstrates that the classifiers CL(V,E,l,r)
are general enough and will be capable of solving all classification tasks
for any given DNA dataset. We develop a minimisation algorithm for
these classifiers and include several open questions which could benefit
from contributions of various researchers throughout the world
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