25,213 research outputs found
In silico estimation of annealing specificity of query searches in DNA databases
We consider DNA implementations of databases for digital signals with retrieval and mining capabilities. Digital signals are encoded in DNA sequences and retrieved through annealing between query DNA primers and data carrying DNA target sequences. The hybridization between query and target can be non-specific containing multiple mismatches thus implementing similarity-based searches. In this paper we examine theoretically and by simulation the efficiency of such a system by estimating the concentrations of query-target duplex formations at equilibrium. A coupled kinetic model is used to estimate the concentrations. We offer a derivation that results in an equation that is guaranteed to have a solution and can be easily and accurately solved computationally with bi-section root-finding methods. Finally, we also provide an approximate solution at dilute query concentrations that results in a closed form expression. This expression is used to improve the speed of the bi-section algorithm and also to find a closed form expression for the specificity ratios
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Investigating the cyclization of enediyne analogs using density functional theory
Enediynes are organic molecules that readily undergo a thermal rearrangement, now commonly known as the Bergman cyclization, to a cyclic para diradical form. Interest in this rearrangement was renewed when it was found to be crucial to the mechanism of cytotoxicity in a variety of natural products containing the enediyne structural moiety. Cyclization of these molecules leads to DNA strand scission and ultimately cell death. Recent efforts by medicinal chemists to discover therapeutically relevant enediyne derivatives have been complemented by computational approaches, which seek to compute energies and energetic barriers to cyclization that can accurately predict the behavior of these molecules in vivo. Here we demonstrate this approach for cis-hex-3-ene-1,5-diyne and two of its analogs using density functional theory, discuss the validity of its predictions, and investigate the effect of basis set on the description of these moleculesâ reactivity.Pharmac
Programmable Control of Nucleation for Algorithmic Self-Assembly
Algorithmic self-assembly, a generalization of crystal growth processes, has
been proposed as a mechanism for autonomous DNA computation and for bottom-up
fabrication of complex nanostructures. A `program' for growing a desired
structure consists of a set of molecular `tiles' designed to have specific
binding interactions. A key challenge to making algorithmic self-assembly
practical is designing tile set programs that make assembly robust to errors
that occur during initiation and growth. One method for the controlled
initiation of assembly, often seen in biology, is the use of a seed or catalyst
molecule that reduces an otherwise large kinetic barrier to nucleation. Here we
show how to program algorithmic self-assembly similarly, such that seeded
assembly proceeds quickly but there is an arbitrarily large kinetic barrier to
unseeded growth. We demonstrate this technique by introducing a family of tile
sets for which we rigorously prove that, under the right physical conditions,
linearly increasing the size of the tile set exponentially reduces the rate of
spurious nucleation. Simulations of these `zig-zag' tile sets suggest that
under plausible experimental conditions, it is possible to grow large seeded
crystals in just a few hours such that less than 1 percent of crystals are
spuriously nucleated. Simulation results also suggest that zig-zag tile sets
could be used for detection of single DNA strands. Together with prior work
showing that tile sets can be made robust to errors during properly initiated
growth, this work demonstrates that growth of objects via algorithmic
self-assembly can proceed both efficiently and with an arbitrarily low error
rate, even in a model where local growth rules are probabilistic.Comment: 37 pages, 14 figure
DNA as a medium for storing digital signals
Motivated by the storage capacity and efficiency of the DNA molecule in this paper we propose to utilize DNA molecules to store digital signals. We show that hybridization of DNA molecules can be used as a similarity criterion for retrieving digital signals encoded and stored in a DNA database. Since retrieval is achieved through hybridization of query and data carrying DNA molecules, we present a mathematical model to estimate hybridization efficiency (also known as selectivity annealing). We show that selectivity annealing is inversely proportional to the mean squared error (MSE) of the encoded signal values. In addition, we show that the concentration of the molecules plays the same role as the decision threshold employed in digital signal matching algorithms. Finally, similarly to the digital domain, we define a DNA signal-to-noise ratio (SNR) measure to assess the performance of the DNA-based retrieval scheme. Simulations are presented to validate our arguments
DNA extraction from formalin-fixed tissue: new light from the Deep-Sea
DNA samples were extracted from ethanol and formalin-fixed decapod crustacean tissue using a new method based on Tetramethylsilane (TMS)-Chelex. It is shown that neither an indigestible matrix of cross-linked protein nor soluble PCR inhibitors impede PCR success when dealing with formalin-fixed material. Instead, amplification success from formalin-fixed tissue appears to depend on the presence of unmodified DNA in the extracted sample. A staining method that facilitates the targeting of samples with a high content of unmodified DNA is provided
Active Self-Assembly of Algorithmic Shapes and Patterns in Polylogarithmic Time
We describe a computational model for studying the complexity of
self-assembled structures with active molecular components. Our model captures
notions of growth and movement ubiquitous in biological systems. The model is
inspired by biology's fantastic ability to assemble biomolecules that form
systems with complicated structure and dynamics, from molecular motors that
walk on rigid tracks and proteins that dynamically alter the structure of the
cell during mitosis, to embryonic development where large-scale complicated
organisms efficiently grow from a single cell. Using this active self-assembly
model, we show how to efficiently self-assemble shapes and patterns from simple
monomers. For example, we show how to grow a line of monomers in time and
number of monomer states that is merely logarithmic in the length of the line.
Our main results show how to grow arbitrary connected two-dimensional
geometric shapes and patterns in expected time that is polylogarithmic in the
size of the shape, plus roughly the time required to run a Turing machine
deciding whether or not a given pixel is in the shape. We do this while keeping
the number of monomer types logarithmic in shape size, plus those monomers
required by the Kolmogorov complexity of the shape or pattern. This work thus
highlights the efficiency advantages of active self-assembly over passive
self-assembly and motivates experimental effort to construct general-purpose
active molecular self-assembly systems
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