Stanley Edward Stevens

A DNA based implementation of an evolutionary search for good encodings for DNA computation

Computation based on manipulation of DNA molecules has the potential to solve problems with massive parallelism. DNA computation, however, is implemented with chemical reactions between the nucleotide bases, and therefore, the results can be error-prone. Application of DNA based computation to traditional computing paradigms requires error-free computation, which the DNA chemistry is unable to support. Careful encoding of the nucleotide sequences can alleviate the production of errors, but these good encodings are difficult to find. In this paper, an algorithm for evolutionary computation with DNA is sketched. Evolutionary computation does not require error-free DNA chemistry, and in fact, takes advantage of errors to produce change and variation in the population. An application of the DNA based evolution program to a search for good DNA encodings is sketched.

DNA computing: a review

DNA computing holds out the promise of important and significant connections between computers and living systems, as well as promising massively parallel computations. Before these promises are fulfilled, however, important challenges related to errors and practicality have to be addressed. On the other hand, new directions toward a synthesis of molecular evolution and DNA computing might circumvent the problems that have hindered development, so far.

A DNA based artificial immune system for self-nonself discrimination

Artificial immune systems attempt to distinguish self from nonself through string matching operations. A detector set of strings is selected by eliminating random strings that match the self strings. DNA based computers have been proposed to solve complex problems that defy solution on conventional computers. They are based on (hydrogen bonding based) matchings (called hybridizations) between Watson-Crick complementary pairs, Adenine-Thymine or Cytosine-Guanine. Therefore, a single strand (an oligonucleotide) will bind with other oligonucleotides that match most closely its sequence under the operation of Watson-Crick complementation. In this paper, an algorithm for implementing an artificial immune system for self-nonself discrimination based on DNA is described. This procedure takes advantage of the inherent pattern matching capability of DNA hybridization reactions and the notion of similarity naturally found in DNA hybridization.

A new algorithm for DNA based computation

A common feature of DNA computing involves the use of molecular biology techniques to extract molecules representing the solution to a computation from a reaction mixture. Current applied extraction methods often employ PCR (polymerase chain reactions) and/or gel electrophoresis, both of which we believe are too time-consuming and error-prone to yield a practical DNA-based molecular computing capability. This paper suggests a new approach to solving the Hamiltonian graph and similar combinatorial problems that avoids these traditional techniques in favor of a purely enzymatic methodology.

In Vitro Implementation of Finite-State Machines

We explore the information processing capabilities and efficiency of DNA computations by giving two different types of implementations of finite-state machines. A ligation-based approach allows input of arbitrary length and can be readily implemented with current biotechnology, but requires sequential input feed and different molecules for different machines. In a second implementation not based on ligation, transitions are represented by reusable molecules, and the input, coded as a molecule, can be introduced at once. We extend the technique for programmable fault-tolerant implementation of nondeterministic finite-state machines by enforcings the basic conditions in the subset constructions that permit efficient computation. All implementations allow optical extraction of the status of the machine.