Max H. Garzon

Genetic Programming 1998: Proceedings of the Third Annual Conference

Proceedings of the Annual Conferences on Genetic Programming. These proceedings present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, artificial life and evolution strategies, DNA computing, evolvable hardware, and genetic learning classifier systems.

Computability with low-dimensional dynamical systems

It has been known for a short time that a class of recurrent neural networks has universal computational abilities. These networks can be viewed as iterated piecewise-linear maps in a high-dimensional space. In this paper, we show that similar systems in dimension two are also capable of universal computations. On the contrary, it is necessary to resort to more complex systems (e.g., iterated piecewise-monotone maps) in order to retain this capability in dimension one.

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.

Biomolecular computing and programming

Molecular computing is a discipline that aims at harnessing individual molecules at nanoscales for computational purposes. The best-studied molecules for this purpose to date have been DNA and bacteriorhodopsin. Biomolecular computing allows one to realistically entertain, for the first time in history, the possibility of exploiting the massive parallelism at nanoscales inherent in natural phenomena to solve computational problems. The implementation of evolutionary algorithms in biomolecules would bring full circle the biological analogy and present an attractive alternative to meet large demands for computational power. The paper presents a review of the most important advances in biomolecular computing in the last few years. Major achievements to date are outlined, both experimental and theoretical, and major potential advances and challenges for practitioners in the foreseeable future are identified. A list of sources and major events in the field has been compiled in the Appendix, although no exhaustive survey of the expanding literature is intended.

A PCR-based Protocol for In Vitro Selection of Non-crosshybridizing Oligonucleotides

DNA computing often requires oligonucleotides that do not produce erroneous cross-hybridizations. By using in vitro evolution, huge libraries of non-crosshybridizing oligonucleotides might be evolved in the test tube. As a first step, a fitness function that corresponds to noncrosshybridization has to be implemented in an experimental protocol. Therefore, a modified version of PCR that selects non-crosshybridizing oligonucleotides was designed and tested. Experiments confirmed that the PCR-based protocol did amplify maximally mismatched oligonucleotides selectively over those that were more closely matched. In addition, a reaction temperature window was identified in which discrimination between matched and mismatched might be obtained. These results are a first step toward practical manufacture of very large libraries of non-crosshybridizing oligonucleotides in the test tube.

Codeword design and information encoding in DNA ensembles

Encoding of information in DNA-, RNA- and other biomolecules is animportant area of research in fields such as DNA computing,bioinformatics, and, conceivably, microbiology and genetics. This surveyfocuses on two fundamental problems, the codeword design problemand the representation problem of abiotic information, formassively parallel processing with DNA molecules. The first problemrequires libraries of DNA sequences to be designed so that specificduplexes are formed during annealing while simultaneously preventingother undesirable hybridizations from occurring in the course of acomputation in the tube. The second involves a search for efficient andcost-effective methods of representing non-biological information in DNAsequences for storage and retrieval of large amouns of data (tera- andpeta-byte scales). Two approaches are treated, namely thermodynamic andcombinatoric-computational. Both experimental and theoretical resultsare described. A reference list of major works in the area is given.Finally, some open problems deemed important for their possible impacton encoding of abiotic information representation and processing arediscussed.

The complexity of Grigorchuk groups with application to cryptography

Abstract The Turing complexity of the word problems of a class of groups introduced by Grigorchuk (1985) is examined. In particular, it is shown that such problems of permutation groups of the infinite complete binary tree yield natural complete sets that separate time and space complexity classes if they are distinct. A refinement of Savitchs translation theorem as well as a similar result restricted for time complexity follow. New families of nonfinitely presented groups are shown to have word problems uniformly solvable in simultaneous logspace and quadratic time. A new family of public- key cryptosystems based on these word problems is constructed.

Efficiency and Reliability of Semantic Retrieval in DNA-Based Memories

Associative memories based on DNA-affinity have been proposed. Here, the efficiency, reliability, and semantic capability for associative retrieval of three models of a DNA-based memory are quantified and compared to current conventional methods. In affinity-based memories[1], retrievals and deletions under stringent conditions occur reliably (98%) within very short times (100 milliseconds), regardless of the degree of stringency of the recall or the number of simultaneous queries in the input. In a more sophisticated type of DNA-based memory B proposed and experimentally verified by Chen et al. [2] with three genomes, the sensitivity of the discrimination ability remains unchanged when used on a library of 18 plasmids in the range of 1-4kbps and does appear to grow exponentially with the number of library strands used, even under simultaneous multiple queries in the same input. Finally, using a new type of memory compaction mechanism for data mining in vitro, DNA-based semantic retrieval compares favorably with statistically-based Latent Semantic Analysis (LSA), one of the best performers for semantic associative-based retrieval on text corpora.

Self-Assembly of DNA-like Structures In Silico

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 Ednas performance, reproduce and extend Adlemans 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.

In search of optimal codes for DNA computing

Encoding and processing information in DNA-, RNA- and other biomolecule-based devices is an important requirement for DNA-based computing with potentially important applications. Recent experimental and theoretical advances have produced and tested new methods to obtain large code sets of oligonucleotides to support virtually any kind of application. We report results of a tour de force to conduct an exhaustive search to produce code sets that are arguably of sizes comparable to that of maximal sets while guaranteeing high quality, as measured by the minimum Gibbs energy between any pair of code words and other criteria. The method is constructive and directly produces the actual composition of the sets, unlike their counterpart in vitro . The sequences allow a quantitative characterization of their composition. We also present a new technique to generate code sets with desirable more stringent constraints on their possible interaction under a variety of conditions, as measured by Gibbs energies of duplex formation. The results predict close agreement with known results in vitro for 20–mers. Consequences of these results are bounds on the capacity of DNA for information storage and processing in wet tubes for a given oligo length, as well as many other applications where specific and complex self-directed assembly of large number of components may be required.