Models of massive parallelism: analysis of cellular automata and neural networks
Models of massive parallelism: analysis of cellular automata and neural networks
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
DNA '00 Revised Papers from the 6th International Workshop on DNA-Based Computers: DNA Computing
Virtual Test Tubes: A New Methodology for Computing
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
Codes, Involutions, and DNA Encodings
Formal and Natural Computing - Essays Dedicated to Grzegorz Rozenberg [on occasion of his 60th birthday, March 14, 2002]
Codes, involutions, and DNA encodings
Formal and natural computing
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
Natural Computing: an international journal
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
Watson---Crick palindromes in DNA computing
Natural Computing: an international journal
DNA computing: a research snapshot
Algorithms and theory of computation handbook
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Biomolecular computing (BMC) aims to capture the innumerable advantages that biological molecules have gained in the course of millions of years for computational purposes. While biomolecules have resolved fundamental problems as a parallel computer system that we are just beginning to decipher, BMC still suffers from our inability to harness these properties to bring biomolecular computations to levels of reliability, efficiency and scalability that are now taken for granted with solidstate based computers. In the same way that evolutionary algorithms capture, in silico, the key properties of natural evolution, we explore an alternative approach to exploiting these properties by building virtual test tubes in electronics that would capture the best of both worlds. We describe a distributed implementation of a virtual tube, EdnaCo, on a cluster of PCs that aims to capture the massive asynchronous parallelism of BMC. We report several experimental results, such as solutions to the Hamiltonian Path problem (HPP) for large families of graphs than has been possible on a single processor or has been actually carried out in wet labs. The results show 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. Consequently, we pinpoint the appropriate range of problem sizes and properties where wet biomolecular solutions would offer superior solutions.