2,394 research outputs found
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
Turing Automata and Graph Machines
Indexed monoidal algebras are introduced as an equivalent structure for
self-dual compact closed categories, and a coherence theorem is proved for the
category of such algebras. Turing automata and Turing graph machines are
defined by generalizing the classical Turing machine concept, so that the
collection of such machines becomes an indexed monoidal algebra. On the analogy
of the von Neumann data-flow computer architecture, Turing graph machines are
proposed as potentially reversible low-level universal computational devices,
and a truly reversible molecular size hardware model is presented as an
example
Complexity of Restricted and Unrestricted Models of Molecular Computation
In [9] and [2] a formal model for molecular computing was
proposed, which makes focused use of affinity purification.
The use of PCR was suggested to expand the range of
feasible computations, resulting in a second model. In this
note, we give a precise characterization of these two models
in terms of recognized computational complexity classes,
namely branching programs (BP) and nondeterministic
branching programs (NBP) respectively. This allows us to
give upper and lower bounds on the complexity of desired
computations. Examples are given of computable and
uncomputable problems, given limited time
One-Membrane P Systems with Activation and Blocking of Rules
We introduce new possibilities to control the application of rules based on
the preceding applications, which can be de ned in a general way for (hierarchical) P
systems and the main known derivation modes. Computational completeness can be
obtained even for one-membrane P systems with non-cooperative rules and using both
activation and blocking of rules, especially for the set modes of derivation.
When we allow the application of rules to in
uence the application of rules in previous
derivation steps, applying a non-conservative semantics for what we consider to be a
derivation step, we can even \go beyond Turing"
Massively parallel computing on an organic molecular layer
Current computers operate at enormous speeds of ~10^13 bits/s, but their
principle of sequential logic operation has remained unchanged since the 1950s.
Though our brain is much slower on a per-neuron base (~10^3 firings/s), it is
capable of remarkable decision-making based on the collective operations of
millions of neurons at a time in ever-evolving neural circuitry. Here we use
molecular switches to build an assembly where each molecule communicates-like
neurons-with many neighbors simultaneously. The assembly's ability to
reconfigure itself spontaneously for a new problem allows us to realize
conventional computing constructs like logic gates and Voronoi decompositions,
as well as to reproduce two natural phenomena: heat diffusion and the mutation
of normal cells to cancer cells. This is a shift from the current static
computing paradigm of serial bit-processing to a regime in which a large number
of bits are processed in parallel in dynamically changing hardware.Comment: 25 pages, 6 figure
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