6,311 research outputs found
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
JohnnyVon: Self-Replicating Automata in Continuous Two-Dimensional Space
JohnnyVon is an implementation of self-replicating automata 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
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
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
Programs as Polypeptides
We describe a visual programming language for defining behaviors manifested
by reified actors in a 2D virtual world that can be compiled into programs
comprised of sequences of combinators that are themselves reified as actors.
This makes it possible to build programs that build programs from components of
a few fixed types delivered by diffusion using processes that resemble
chemistry as much as computation.Comment: in European Conference on Artificial Life (ECAL '15), York, UK, 201
On the Growth Rate of Non-Enzymatic Molecular Replicators
It is well known that non-enzymatic template directed molecular replicators X
+ nO ---> 2X exhibit parabolic growth d[X]/dt = k [X]^{1/2}. Here, we analyze
the dependence of the effective replication rate constant k on hybridization
energies, temperature, strand length, and sequence composition. First we derive
analytical criteria for the replication rate k based on simple thermodynamic
arguments. Second we present a Brownian dynamics model for oligonucleotides
that allows us to simulate their diffusion and hybridization behavior. The
simulation is used to generate and analyze the effect of strand length,
temperature, and to some extent sequence composition, on the hybridization
rates and the resulting optimal overall rate constant k. Combining the two
approaches allows us to semi-analytically depict a fitness landscape for
template directed replicators. The results indicate a clear replication
advantage for longer strands at low temperatures.Comment: Submitted to: Entrop
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