2,081 research outputs found
Self-Replication and Self-Assembly for Manufacturing
It has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach
to very low-cost manufacturing. The research presented here is intended to
be a step towards this vision. We describe a computational simulation of
nanoscale machines floating in a virtual liquid. The machines can bond
together to form strands (chains) that self-replicate and self-assemble
into user-specified meshes. There are four types of machines and the
sequence of machine types in a strand determines the shape of the mesh
they will build. A strand may be in an unfolded state, in which the bonds
are straight, or in a folded state, in which the bond angles depend on the
types of machines. By choosing the sequence of machine types in a strand,
the user can specify a variety of polygonal shapes. A simulation typically
begins with an initial unfolded seed strand in a soup of unbonded machines.
The seed strand replicates by bonding with free machines in the soup. The
child strands fold into the encoded polygonal shape, and then the polygons
drift together and bond to form a mesh. We demonstrate that a variety of
polygonal meshes can be manufactured in the simulation, by simply changing
the sequence of machine types in the seed
Self-Replicating Strands that Self-Assemble into User-Specified Meshes
It has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach to
very low-cost manufacturing. The research presented here is intended to be a
step towards this vision. In previous work (JohnnyVon 1.0), we simulated
machines that bonded together to form self-replicating strands. There were two
types of machines (called types 0 and 1), which enabled strands to encode
arbitrary bit strings. However, the information encoded in the strands had no
functional role in the simulation. The information was replicated without being
interpreted, which was a significant limitation for potential manufacturing
applications. In the current work (JohnnyVon 2.0), the information in a strand
is interpreted as instructions for assembling a polygonal mesh. There are now
four types of machines and the information encoded in a strand determines how
it folds. A strand may be in an unfolded state, in which the bonds are straight
(although they flex slightly due to virtual forces acting on the machines), or
in a folded state, in which the bond angles depend on the types of machines. By
choosing the sequence of machine types in a strand, the user can specify a
variety of polygonal shapes. A simulation typically begins with an initial
unfolded seed strand in a soup of unbonded machines. The seed strand replicates
by bonding with free machines in the soup. The child strands fold into the
encoded polygonal shape, and then the polygons drift together and bond to form
a mesh. We demonstrate that a variety of polygonal meshes can be manufactured
in the simulation, by simply changing the sequence of machine types in the
seed
Network Automata: Coupling structure and function in real-world networks
We introduce Network Automata, a framework which couples the topological
evolution of a network to its structure. It is useful for dealing with networks
in which the topology evolves according to some specified microscopic rules
and, simultaneously, there is a dynamic process taking place on the network
that both depends on its structure but is also capable of modifying it. It is a
generic framework for modeling systems in which network structure, dynamics,
and function are interrelated. At the practical level, this framework allows
for easy implementation of the microscopic rules involved in such systems. To
demonstrate the approach, we develop a class of simple biologically inspired
models of fungal growth.Comment: 7 pages, 5 figures, 1 tables. Revised content - surplus text and
figures remove
Primordial Evolution in the Finitary Process Soup
A general and basic model of primordial evolution--a soup of reacting
finitary and discrete processes--is employed to identify and analyze
fundamental mechanisms that generate and maintain complex structures in
prebiotic systems. The processes---machines as defined in
computational mechanics--and their interaction networks both provide well
defined notions of structure. This enables us to quantitatively demonstrate
hierarchical self-organization in the soup in terms of complexity. We found
that replicating processes evolve the strategy of successively building higher
levels of organization by autocatalysis. Moreover, this is facilitated by local
components that have low structural complexity, but high generality. In effect,
the finitary process soup spontaneously evolves a selection pressure that
favors such components. In light of the finitary process soup's generality,
these results suggest a fundamental law of hierarchical systems: global
complexity requires local simplicity.Comment: 7 pages, 10 figures;
http://cse.ucdavis.edu/~cmg/compmech/pubs/pefps.ht
Computing vs. Genetics
This chapter first presents the interrelations between computing and genetics, which both are based on information and, particularly, self-reproducing artificial systems. It goes on to examine genetic code from a computational viewpoint. This raises a number of important questions about genetic code. These questions are stated in the form of an as yet unpublished working hypothesis. This hypothesis suggests that many genetic alterations are caused by the last base of certain codons. If this conclusive hypothesis were to be confirmed through experiementation if would be a significant advance for treating many genetic diseases
COMPUTER SIMULATION AND COMPUTABILITY OF BIOLOGICAL SYSTEMS
The ability to simulate a biological organism by employing a computer is related to the
ability of the computer to calculate the behavior of such a dynamical system, or the "computability" of the system.* However, the two questions of computability and simulation are not equivalent. Since the question of computability can be given a precise answer in terms of recursive functions, automata theory and dynamical systems, it will be appropriate to consider it first. The more elusive question of adequate simulation of biological systems by a computer will be then addressed and a possible connection between the two answers given will be considered. A conjecture is formulated that suggests the possibility of employing an algebraic-topological, "quantum" computer (Baianu, 1971b)
for analogous and symbolic simulations of biological systems that may include chaotic processes that are not, in genral, either recursively or digitally computable. Depending on the biological network being modelled, such as the Human Genome/Cell Interactome or a trillion-cell Cognitive Neural Network system, the appropriate logical structure for such simulations might be either the Quantum MV-Logic (QMV) discussed in recent publications (Chiara, 2004, and references cited therein)or Lukasiewicz Logic Algebras that were shown to be isomorphic to MV-logic algebras (Georgescu et al, 2001)
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