Genetic Algorithms

Preface 1. Background in linear algebra 2. Discretization of partial differential equations 3. Sparse matrices 4. Basic iterative methods 5. Projection methods 6. Krylov subspace methods Part I 7. Krylov subspace methods Part II 8. Methods related to the normal equations 9. Preconditioned iterations 10. Preconditioning techniques 11. Parallel implementations 12. Parallel preconditioners 13. Multigrid methods 14. Domain decomposition methods Bibliography Index.

/pdf/iterative-methods-for-sparse-linear-systems-34ozhpwq89.pdf

Iterative Methods for Sparse Linear Systems

The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated. >

Principal warps: thin-plate splines and the decomposition of deformations

On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms

Carbohydrate-active enzymes (CAZymes) are very important to the biotech industry, particularly the emerging biofuel industry because CAZymes are responsible for the synthesis, degradation and modification of all the carbohydrates on Earth. We have developed a web resource, dbCAN (http://csbl.bmb .uga.edu/dbCAN/annotate.php), to provide a capability for automated CAZyme signature domainbased annotation for any given protein data set (e.g. proteins from a newly sequenced genome) submitted to our server. To accomplish this, we have explicitly defined a signature domain for every CAZyme family, derived based on the CDD (conserved domain database) search and literature curation. We have also constructed a hidden Markov model to represent the signature domain of each CAZyme family. These CAZyme family-specific HMMs are our key contribution and the foundation for the automated CAZyme annotation.

dbCAN: a web resource for automated carbohydrate-active enzyme annotation.

Since the first two complete bacterial genome sequences were published in 1995, the science of bacteria has dramatically changed. Using third-generation DNA sequencing, it is possible to completely sequence a bacterial genome in a few hours and identify some types of methylation sites along the genome as well. Sequencing of bacterial genome sequences is now a standard procedure, and the information from tens of thousands of bacterial genomes has had a major impact on our views of the bacterial world. In this review, we explore a series of questions to highlight some insights that comparative genomics has produced. To date, there are genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. However, the distribution is quite skewed towards a few phyla that contain model organisms. But the breadth is continuing to improve, with projects dedicated to filling in less characterized taxonomic groups. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system provides bacteria with immunity against viruses, which outnumber bacteria by tenfold. How fast can we go? Second-generation sequencing has produced a large number of draft genomes (close to 90 % of bacterial genomes in GenBank are currently not complete); third-generation sequencing can potentially produce a finished genome in a few hours, and at the same time provide methlylation sites along the entire chromosome. The diversity of bacterial communities is extensive as is evident from the genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. Genome sequencing can help in classifying an organism, and in the case where multiple genomes of the same species are available, it is possible to calculate the pan- and core genomes; comparison of more than 2000 Escherichia coli genomes finds an E. coli core genome of about 3100 gene families and a total of about 89,000 different gene families. Why do we care about bacterial genome sequencing? There are many practical applications, such as genome-scale metabolic modeling, biosurveillance, bioforensics, and infectious disease epidemiology. In the near future, high-throughput sequencing of patient metagenomic samples could revolutionize medicine in terms of speed and accuracy of finding pathogens and knowing how to treat them.

/pdf/insights-from-20-years-of-bacterial-genome-sequencing-34ipqtujgf.pdf

Insights from 20 years of bacterial genome sequencing

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

CAZymes Analysis Toolkit (CAT): Web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database

parallel genetic algorithm

A parallel genetic algorithm

Despite its potential importance in obesity and related disorders, little is known about regulation of lipogenesis in human adipose tissue. To investigate this area at the molecular and mechanistic levels, we studied lipogenesis and the regulation of 1 of its core enzymes, fatty acid synthase (FAS), in human adipose tissue in response to hormonal and nutritional manipulation. As a paradigm for lipogenic genes, we cloned the upstream region of the human FAS gene, compared its sequence to that of FAS orthologs from other species, and identified important regulatory elements that lie upstream of the FAS coding region. Lipogenesis, as assessed by glucose incorporation into lipids, was increased by insulin and more so by the combination of insulin and dexamethasone (Dex, a potent glucocorticoid analogue). In parallel, FAS expression, activity, and gene transcription rate were also significantly increased by these treatments. We also showed that linoleic acid, a representative PUFA, attenuated the actions of insulin and Dex on fatty acid and lipid synthesis as well as FAS activity and expression. Using reporter assays, we determined that the regions responsible for hormonal regulation of the FAS gene lie in the proximal portion of the gene's 5'-flanking region, within which we identified an insulin response element similar to the E-box sequence we identified previously in the rat FAS gene. In summary, we demonstrated that lipogenesis occurs in human adipose tissue and can be induced by insulin, further enhanced by glucocorticoids, and suppressed by PUFA in a hormone-dependent manner.

/pdf/the-human-fatty-acid-synthase-gene-and-de-novo-lipogenesis-28yoshzb3r.pdf

Michael R. Leuze

Papers

Insights from 20 years of bacterial genome sequencing

CAZymes Analysis Toolkit (CAT): Web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database

parallel genetic algorithm

A parallel genetic algorithm

The human fatty acid synthase gene and de novo lipogenesis are coordinately regulated in human adipose tissue.