Codeword design and information encoding in DNA ensembles
Natural Computing: an international journal
Development, evaluation and benchmarking of simulation software for biomolecule-based computing
Natural Computing: an international journal
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Emergence of genomic self-similarity in location independent representations
Genetic Programming and Evolvable Machines
On codeword design in metric DNA spaces
Natural Computing: an international journal
Efficiency and reliability of DNA-based memories
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
DNA-like genomes for evolution in silico
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
In search of optimal codes for DNA computing
DNA'06 Proceedings of the 12th international conference on DNA Computing
The capacity of DNA for information encoding
DNA'04 Proceedings of the 10th international conference on DNA computing
Bacterially inspired evolving system with an application to time series prediction
Applied Soft Computing
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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.