15,016 research outputs found
A DNA Codification for Genetic Algorithms Simulation
In this paper we propose a model of encoding data into DNA strands so that this data can be used in
the simulation of a genetic algorithm based on molecular operations. DNA computing is an impressive
computational model that needs algorithms to work properly and efficiently. The first problem when trying to apply
an algorithm in DNA computing must be how to codify the data that the algorithm will use. In a genetic algorithm
the first objective must be to codify the genes, which are the main data. A concrete encoding of the genes in a
single DNA strand is presented and we discuss what this codification is suitable for. Previous work on DNA
coding defined bond-free languages which several properties assuring the stability of any DNA word of such a
language. We prove that a bond-free language is necessary but not sufficient to codify a gene giving the correct
codification
Towards Understanding the Origin of Genetic Languages
Molecular biology is a nanotechnology that works--it has worked for billions
of years and in an amazing variety of circumstances. At its core is a system
for acquiring, processing and communicating information that is universal, from
viruses and bacteria to human beings. Advances in genetics and experience in
designing computers have taken us to a stage where we can understand the
optimisation principles at the root of this system, from the availability of
basic building blocks to the execution of tasks. The languages of DNA and
proteins are argued to be the optimal solutions to the information processing
tasks they carry out. The analysis also suggests simpler predecessors to these
languages, and provides fascinating clues about their origin. Obviously, a
comprehensive unraveling of the puzzle of life would have a lot to say about
what we may design or convert ourselves into.Comment: (v1) 33 pages, contributed chapter to "Quantum Aspects of Life",
edited by D. Abbott, P. Davies and A. Pati, (v2) published version with some
editin
A Process Calculus for Molecular Interaction Maps
We present the MIM calculus, a modeling formalism with a strong biological
basis, which provides biologically-meaningful operators for representing the
interaction capabilities of molecular species. The operators of the calculus
are inspired by the reaction symbols used in Molecular Interaction Maps (MIMs),
a diagrammatic notation used by biologists. Models of the calculus can be
easily derived from MIM diagrams, for which an unambiguous and executable
interpretation is thus obtained. We give a formal definition of the syntax and
semantics of the MIM calculus, and we study properties of the formalism. A case
study is also presented to show the use of the calculus for modeling
biomolecular networks.Comment: 15 pages; 8 figures; To be published on EPTCS, proceedings of MeCBIC
200
Territorial Developments Based on Graffiti: a Statistical Mechanics Approach
We study the well-known sociological phenomenon of gang aggregation and
territory formation through an interacting agent system defined on a lattice.
We introduce a two-gang Hamiltonian model where agents have red or blue
affiliation but are otherwise indistinguishable. In this model, all
interactions are indirect and occur only via graffiti markings, on-site as well
as on nearest neighbor locations. We also allow for gang proliferation and
graffiti suppression. Within the context of this model, we show that gang
clustering and territory formation may arise under specific parameter choices
and that a phase transition may occur between well-mixed, possibly dilute
configurations and well separated, clustered ones. Using methods from
statistical mechanics, we study the phase transition between these two
qualitatively different scenarios. In the mean-field rendition of this model,
we identify parameter regimes where the transition is first or second order. In
all cases, we have found that the transitions are a consequence solely of the
gang to graffiti couplings, implying that direct gang to gang interactions are
not strictly necessary for gang territory formation; in particular, graffiti
may be the sole driving force behind gang clustering. We further discuss
possible sociological -- as well as ecological -- ramifications of our results
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
The Triplet Genetic Code had a Doublet Predecessor
Information theoretic analysis of genetic languages indicates that the
naturally occurring 20 amino acids and the triplet genetic code arose by
duplication of 10 amino acids of class-II and a doublet genetic code having
codons NNY and anticodons . Evidence for this scenario
is presented based on the properties of aminoacyl-tRNA synthetases, amino acids
and nucleotide bases.Comment: 10 pages (v2) Expanded to include additional features, including
likely relation to the operational code of the tRNA-acceptor stem. Version to
be published in Journal of Theoretical Biolog
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