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"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Computing vs. Genetics
This chapter first presents the interrelations between computing and genetics, which both are based on information and, particularly, self-reproducing artificial systems. It goes on to examine genetic code from a computational viewpoint. This raises a number of important questions about genetic code. These questions are stated in the form of an as yet unpublished working hypothesis. This hypothesis suggests that many genetic alterations are caused by the last base of certain codons. If this conclusive hypothesis were to be confirmed through experiementation if would be a significant advance for treating many genetic diseases
Sticker systems over monoids
Molecular computing has gained many interests among researchers since Head introduced the first theoretical model for DNA based computation using the splicing operation in 1987. Another model for DNA computing was proposed by using the sticker operation which Adlemanused in his successful experiment for the computation of Hamiltonian paths in a graph: a double stranded DNA sequence is composed by prolonging to the left and to the right a sequence of (single or double) symbols by using given single stranded strings or even more complex dominoes with sticky ends, gluing these ends together with the sticky ends of the current sequence according to a complementarity relation. According to this sticker operation, a language generative mechanism, called a sticker system, can be defined: a set of (incomplete) double-stranded sequences (axioms) and a set of pairs of single or double-stranded complementary sequences are given. The initial sequences are prolonged to the left and to the right by using sequences from the latter set, respectively. The iterations of these prolongations produce “computations” of possibly arbitrary length. These processes stop when a complete double stranded sequence is obtained. Sticker systems will generate only regular languages without restrictions. Additional restrictions can be imposed on the matching pairs of strands to obtain more powerful languages. Several types of sticker systems are shown to have the same power as regular grammars; one type is found to represent all linear languages whereas another one is proved to be able to represent any recursively enumerable language. The main aim of this research is to introduce and study sticker systems over monoids in which with each sticker operation, an element of a monoid is associated and a complete double stranded sequence is considered to be valid if the computation of the associated elements of the monoid produces the neutral element. Moreover, the sticker system over monoids is defined in this study
Mapping the Space of Genomic Signatures
We propose a computational method to measure and visualize interrelationships
among any number of DNA sequences allowing, for example, the examination of
hundreds or thousands of complete mitochondrial genomes. An "image distance" is
computed for each pair of graphical representations of DNA sequences, and the
distances are visualized as a Molecular Distance Map: Each point on the map
represents a DNA sequence, and the spatial proximity between any two points
reflects the degree of structural similarity between the corresponding
sequences. The graphical representation of DNA sequences utilized, Chaos Game
Representation (CGR), is genome- and species-specific and can thus act as a
genomic signature. Consequently, Molecular Distance Maps could inform species
identification, taxonomic classifications and, to a certain extent,
evolutionary history. The image distance employed, Structural Dissimilarity
Index (DSSIM), implicitly compares the occurrences of oligomers of length up to
(herein ) in DNA sequences. We computed DSSIM distances for more than
5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional
Scaling (MDS) to obtain Molecular Distance Maps that visually display the
sequence relatedness in various subsets, at different taxonomic levels. This
general-purpose method does not require DNA sequence homology and can thus be
used to compare similar or vastly different DNA sequences, genomic or
computer-generated, of the same or different lengths. We illustrate potential
uses of this approach by applying it to several taxonomic subsets: phylum
Vertebrata, (super)kingdom Protista, classes Amphibia-Insecta-Mammalia, class
Amphibia, and order Primates. This analysis of an extensive dataset confirms
that the oligomer composition of full mtDNA sequences can be a source of
taxonomic information.Comment: 14 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:1307.375
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