1,826 research outputs found
BOOM - A Heuristic Boolean Minimizer
This paper presents an algorithm for two-level Boolean minimization (BOOM) based on a new implicant generation paradigm. In contrast to all previous minimization methods, where the implicants are generated bottom-up, the proposed method uses a top-down approach. Thus, instead of increasing the dimensionality of implicants by omitting literals from their terms, the dimension of a term is gradually decreased by adding new literals. The method is advantageous especially for functions with many input variables (up to thousands) and with only few care terms defined, where other minimization tools are not applicable because of the long runtime. The method has been tested on several different kinds of problems and the results were compared with ESPRESSO
Phylogenetic surveillance of viral genetic diversity and the evolving molecular epidemiology of human immunodeficiency virus type 1
With ongoing generation of viral genetic diversity and increasing levels of migration, the global human immunodeficiency virus type 1 (HIV-1) epidemic is becoming increasingly heterogeneous. In this study, we investigate the epidemiological characteristics of 5,675 HIV-1 pol gene sequences sampled from distinct infections in the United Kingdom. These sequences were phylogenetically analyzed in conjunction with 976 complete-genome and 3,201 pol gene reference sequences sampled globally and representing the broad range of HIV-1 genetic diversity, allowing us to estimate the probable geographic origins of the various strains present in the United Kingdom. A statistical analysis of phylogenetic clustering in this data set identified several independent transmission chains within the United Kingdom involving recently introduced strains and indicated that strains more commonly associated with infections acquired heterosexually in East Africa are spreading among men who have sex with men. Coalescent approaches were also used and indicated that the transmission chains that we identify originated in the late 1980s to early 1990s. Similar changes in the epidemiological structuring of HIV epidemics are likely to be taking in place in other industrialized nations with large immigrant populations. The framework implemented here takes advantage of the vast amount of routinely generated HIV-1 sequence data and can provide epidemiological insights not readily obtainable through standard surveillance methods
Shapes in the Shadow: Evolutionary Dynamics of Morphogenesis
This article investigates the evolutionary dynamics
of morphogenesis. In this study, morphogenesis arises as a
side-effect of maximization of number of cell types. Thus, it
investigates the evolutionary dynamics of side-effects.
Morphogenesis is governed by the interplay between
differential cell adhesion, gene-regulation, and intercellular
signaling. Thus, it investigates the potential to generate
complex behavior by entanglement of relatively "boring"
processes, and the (automatic) coordination between these
processes.
The evolutionary dynamics shows all the hallmarks of
evolutionary dynamics governed by nonlinear genotype
phenotype mapping: for example, punctuated equilibria and
diffusion on neutral paths. More striking is the result that
interesting, complex morphogenesis occurs mainly in the
"shadow" of neutral paths which preserve cell differentiation,
that is, the interesting morphologies arise as mutants of the
fittest individuals.
Characteristics of the evolution of such side-effects in the
shadow appear to be the following: (1) The speci?c complex
morphologies are unique (or at least very rare) among the set
of de novo initiated evolutionary histories. (2) Similar
morphologies are reinvented at large temporal distances
during one evolutionary history and also when evolution is
restarted after the main cell differentiation pattern has been
established. (3) A mosaic-like evolution at the morphological
level, where different morphological features occur in many
combinations, while at the genotypic level recombination is
not implemented and genotypes diverge linearly and at a
constant rate
Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis
Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage
Compressed Genotyping
Significant volumes of knowledge have been accumulated in recent years
linking subtle genetic variations to a wide variety of medical disorders from
Cystic Fibrosis to mental retardation. Nevertheless, there are still great
challenges in applying this knowledge routinely in the clinic, largely due to
the relatively tedious and expensive process of DNA sequencing. Since the
genetic polymorphisms that underlie these disorders are relatively rare in the
human population, the presence or absence of a disease-linked polymorphism can
be thought of as a sparse signal. Using methods and ideas from compressed
sensing and group testing, we have developed a cost-effective genotyping
protocol. In particular, we have adapted our scheme to a recently developed
class of high throughput DNA sequencing technologies, and assembled a
mathematical framework that has some important distinctions from 'traditional'
compressed sensing ideas in order to address different biological and technical
constraints.Comment: Submitted to IEEE Transaction on Information Theory - Special Issue
on Molecular Biology and Neuroscienc
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