803 research outputs found
DNA Computing by Self-Assembly
Information and algorithms appear to be central to biological organization
and processes, from the storage and reproduction of genetic information to
the control of developmental processes to the sophisticated computations
performed by the nervous system. Much as human technology uses electronic
microprocessors to control electromechanical devices, biological
organisms use biochemical circuits to control molecular and chemical events.
The engineering and programming of biochemical circuits, in vivo and in
vitro, would transform industries that use chemical and nanostructured
materials. Although the construction of biochemical circuits has been
explored theoretically since the birth of molecular biology, our practical
experience with the capabilities and possible programming of biochemical
algorithms is still very young
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Background: Many biological systems are modeled qualitatively with discrete
models, such as probabilistic Boolean networks, logical models, Petri nets, and
agent-based models, with the goal to gain a better understanding of the system.
The computational complexity to analyze the complete dynamics of these models
grows exponentially in the number of variables, which impedes working with
complex models. Although there exist sophisticated algorithms to determine the
dynamics of discrete models, their implementations usually require
labor-intensive formatting of the model formulation, and they are oftentimes
not accessible to users without programming skills. Efficient analysis methods
are needed that are accessible to modelers and easy to use. Method: By
converting discrete models into algebraic models, tools from computational
algebra can be used to analyze their dynamics. Specifically, we propose a
method to identify attractors of a discrete model that is equivalent to solving
a system of polynomial equations, a long-studied problem in computer algebra.
Results: A method for efficiently identifying attractors, and the web-based
tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other
analysis methods for discrete models. ADAM converts several discrete model
types automatically into polynomial dynamical systems and analyzes their
dynamics using tools from computer algebra. Based on extensive experimentation
with both discrete models arising in systems biology and randomly generated
networks, we found that the algebraic algorithms presented in this manuscript
are fast for systems with the structure maintained by most biological systems,
namely sparseness, i.e., while the number of nodes in a biological network may
be quite large, each node is affected only by a small number of other nodes,
and robustness, i.e., small number of attractors
Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype
<p>Abstract</p> <p>Background</p> <p>A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.</p> <p>Results</p> <p>We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in <it>in vitro </it>translation system that a viable protein can be autonomously assembled.</p> <p>Conclusion</p> <p>Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.</p
Self-Replicating Machines in Continuous Space with Virtual Physics
JohnnyVon is an implementation of self-replicating machines in
continuous two-dimensional space. Two types of particles drift
about in a virtual liquid. The particles are automata with
discrete internal states but continuous external relationships.
Their internal states are governed by finite state machines but
their external relationships are governed by a simulated physics
that includes Brownian motion, viscosity, and spring-like attractive
and repulsive forces. The particles can be assembled into patterns
that can encode arbitrary strings of bits. We demonstrate that, if
an arbitrary "seed" pattern is put in a "soup" of separate individual
particles, the pattern will replicate by assembling the individual
particles into copies of itself. We also show that, given sufficient
time, a soup of separate individual particles will eventually
spontaneously form self-replicating patterns. We discuss the implications
of JohnnyVon for research in nanotechnology, theoretical biology, and
artificial life
Experimental Progress in Computation by Self-Assembly of DNA Tilings
Approaches to DNA-based computing by self-assembly require the
use of D. T A nanostructures, called tiles, that have efficient chemistries, expressive
computational power: and convenient input and output (I/O) mechanisms.
We have designed two new classes of DNA tiles: TAO and TAE, both
of which contain three double-helices linked by strand exchange. Structural
analysis of a TAO molecule has shown that the molecule assembles efficiently
from its four component strands. Here we demonstrate a novel method for
I/O whereby multiple tiles assemble around a single-stranded (input) scaffold
strand. Computation by tiling theoretically results in the formation of structures
that contain single-stranded (output) reported strands, which can then
be isolated for subsequent steps of computation if necessary. We illustrate the
advantages of TAO and TAE designs by detailing two examples of massively
parallel arithmetic: construction of complete XOR and addition tables by linear
assemblies of DNA tiles. The three helix structures provide flexibility for
topological routing of strands in the computation: allowing the implementation
of string tile models
Proofreading tile sets: Error correction for algorithmic self-assembly
For robust molecular implementation of tile-based algorithmic
self-assembly, methods for reducing errors must be developed. Previous
studies suggested that by control of physical conditions, such as
temperature and the concentration of tiles, errors (ε) can be reduced
to an arbitrarily low rate - but at the cost of reduced speed (r) for
the self-assembly process. For tile sets directly implementing blocked
cellular automata, it was shown that r ≈ βε^2 was optimal. Here, we
show that an improved construction, which we refer to as proofreading
tile sets, can in principle exploit the cooperativity of tile assembly reactions
to dramatically improve the scaling behavior to r ≈ βε and better.
This suggests that existing DNA-based molecular tile approaches may be
improved to produce macroscopic algorithmic crystals with few errors.
Generalizations and limitations of the proofreading tile set construction
are discussed
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