The power of surface-based DNA computation (extended abstract)
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Simulating Boolean circuits on a DNA computer
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
DNA models and algorithms for NP-complete problems
CCC '96 Proceedings of the 11th Annual IEEE Conference on Computational Complexity
Parallel Function Application on a DNA Substrate
Parallel Function Application on a DNA Substrate
General Purpose Parallel Computation on a DNA Substrate
General Purpose Parallel Computation on a DNA Substrate
Solving Constraint Satisfaction Problems with DNA Computing
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Fastest parallel molecular algorithms for the elliptic curve discrete logarithm problem over GF(2n)
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
System identification using DNA computing approach
CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
An Ant Colony System for DNA sequence design based on thermodynamics
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
DNA-based evolutionary algorithm for cable trench problem
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
| Hi-index | 0.00 |
L. M. Adleman launched the field of DNA computing with a demonstration in 1994 that strands of DNA could be used to solve the Hamiltonian path problem for a simple graph. He also identified three broad categories of open questions for the field. First, is DNA capable of universal computation? Second, what kinds of algorithms can DNA implement? Third, can the error rates in the manipulations of the DNA be controlled enough to allow for useful computation? In the two years that have followed, theoretical work has shown that DNA is in fact capable of universal computation. Furthermore, algorithms for solving interesting questions, like breaking the Data Encryption Standard, have been described using currently available technology and methods. Finally, a few algorithms have been proposed to handle some of the apparently crippling error rates in a few of the common processes used to manipulate DNA. It is thus unlikely that DNA computation is doomed to be only a passing curiosity. However, much work remains to be done on the containment and correction of errors. It is far from clear if the problems in the error rates can be solved sufficiently to ever allow for general-purpose computation that will challenge the more popular substrates for computation. Unfortunately, biological demonstrations of the theoretical results have been sadly lacking. To date, only the simplest of computations have been carried out in DNA. To make significant progress, the field will require both the assessment of the practicality of the different manipulations of DNA and the implementation of algorithms for realistic problems. Theoreticians, in collaboration with experimentalists, can contribute to this research program by settling on a small set of practical and efficient models for DNA computation.