Through evolution, biomolecules have resolved
fundamental problems as a highly interactive parallel
and distributed system that we are just beginning to
decipher. Biomolecular Computing (BMC) protocols,
however, are unreliable, inefficient and unscalable
when compared to computational algorithms run in
silico. An alternative approach is explored to
exploiting these properties by building biomolecular
analogs (eDNA) and virtual test tubes in electronics
that would capture the best of both worlds. A
distributed implementation is described of a virtual
tube, Edna, on a cluster of PCs that does capture the
massive asynchronous parallel interactions typical of
BMC. Results are reported from over 1000 experiments
that calibrate and benchmark Edna's performance,
reproduce and extend Adleman's solution to the
Hamiltonian Path problem for larger families of graphs
than has been possible on a single processor or has
been actually carried out in wet labs, and benchmark
the feasibility and performance of DNA-based
associative memories. The results required a
million-fold less molecules and are at least as
reliable as in vitro experiments, and so provide strong
evidence that the paradigm of molecular computing can
be implemented much more efficiently (in terms of time,
cost, and probability of success) in silico than the
corresponding wet experiments, at least in the range
where Edna can be practically run. This approach also
demonstrates intrinsic advantages in using electronic
analogs of DNA as genomes for genetic algorithms and
evolutionary computation.
%0 Journal Article
%1 garzon:2003:GPEM
%A Garzon, Max
%A Blain, Derrel
%A Bobba, Kiran
%A Neel, Andrew
%A West, Michael
%D 2003
%J Genetic Programming and Evolvable Machines
%K DNA DNA-based Hamiltonian algorithms, associative computational computing, efficiency genetic in kinetics memories, of online path problem, protocols reaction
%N 2
%P 185--200
%R doi:10.1023/A:1023989130306
%T Self-Assembly of DNA-like Structures In Silico
%V 4
%X Through evolution, biomolecules have resolved
fundamental problems as a highly interactive parallel
and distributed system that we are just beginning to
decipher. Biomolecular Computing (BMC) protocols,
however, are unreliable, inefficient and unscalable
when compared to computational algorithms run in
silico. An alternative approach is explored to
exploiting these properties by building biomolecular
analogs (eDNA) and virtual test tubes in electronics
that would capture the best of both worlds. A
distributed implementation is described of a virtual
tube, Edna, on a cluster of PCs that does capture the
massive asynchronous parallel interactions typical of
BMC. Results are reported from over 1000 experiments
that calibrate and benchmark Edna's performance,
reproduce and extend Adleman's solution to the
Hamiltonian Path problem for larger families of graphs
than has been possible on a single processor or has
been actually carried out in wet labs, and benchmark
the feasibility and performance of DNA-based
associative memories. The results required a
million-fold less molecules and are at least as
reliable as in vitro experiments, and so provide strong
evidence that the paradigm of molecular computing can
be implemented much more efficiently (in terms of time,
cost, and probability of success) in silico than the
corresponding wet experiments, at least in the range
where Edna can be practically run. This approach also
demonstrates intrinsic advantages in using electronic
analogs of DNA as genomes for genetic algorithms and
evolutionary computation.
@article{garzon:2003:GPEM,
abstract = {Through evolution, biomolecules have resolved
fundamental problems as a highly interactive parallel
and distributed system that we are just beginning to
decipher. Biomolecular Computing (BMC) protocols,
however, are unreliable, inefficient and unscalable
when compared to computational algorithms run in
silico. An alternative approach is explored to
exploiting these properties by building biomolecular
analogs (eDNA) and virtual test tubes in electronics
that would capture the best of both worlds. A
distributed implementation is described of a virtual
tube, Edna, on a cluster of PCs that does capture the
massive asynchronous parallel interactions typical of
BMC. Results are reported from over 1000 experiments
that calibrate and benchmark Edna's performance,
reproduce and extend Adleman's solution to the
Hamiltonian Path problem for larger families of graphs
than has been possible on a single processor or has
been actually carried out in wet labs, and benchmark
the feasibility and performance of DNA-based
associative memories. The results required a
million-fold less molecules and are at least as
reliable as in vitro experiments, and so provide strong
evidence that the paradigm of molecular computing can
be implemented much more efficiently (in terms of time,
cost, and probability of success) in silico than the
corresponding wet experiments, at least in the range
where Edna can be practically run. This approach also
demonstrates intrinsic advantages in using electronic
analogs of DNA as genomes for genetic algorithms and
evolutionary computation.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Garzon, Max and Blain, Derrel and Bobba, Kiran and Neel, Andrew and West, Michael},
biburl = {https://www.bibsonomy.org/bibtex/2fea4ac78a4531d4d5f89c805a9599e9b/brazovayeye},
doi = {doi:10.1023/A:1023989130306},
interhash = {bb7861558f6d9770898af93bdf44212e},
intrahash = {fea4ac78a4531d4d5f89c805a9599e9b},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {DNA DNA-based Hamiltonian algorithms, associative computational computing, efficiency genetic in kinetics memories, of online path problem, protocols reaction},
month = {June},
notes = {Special Issue on Biomolecular Machines and Artificial
Evolution Article ID: 5122745},
number = 2,
pages = {185--200},
timestamp = {2008-06-19T17:40:07.000+0200},
title = {Self-Assembly of {DNA}-like Structures In Silico},
volume = 4,
year = 2003
}