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Molecular biology has changed dramatically over the past two decades. Until the early 1990s genes were studied one at a time by small teams of researchers; today entire genomes are sequenced by internationally collaborating laboratories. In the bygone gene-centered era the accumulation of data was the rate-limiting step in research. Now that step is often data interpretation. This is increasingly dependent on computational methods and as a consequence, computational biology has emerged in the past decade as a new subdiscipline of biology. This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors. Introduction to Computational Biology is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists. Bernhard Haubold is associate professor at the University of Applied Sciences, Weihenstephan, Germany. Thomas Wiehe is associate professor at the University of Cologne, Germany.

Mathematical statistics --- Biomathematics. Biometry. Biostatistics --- Computational biology.

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The biological sciences are now in the midst of a true life sciences revolution akin to what physics experienced just after the turn of the last century. We are now in a phase of unparalleled growth that is reflected by the amount of data generated from each experiment. At the time of this writing, the rate of data acquisition was approaching 2 terabytes over the course of 5 days with first pass analysis proceeding over the following 2-3 week period. This fundamental shift has provided unprecedented opportunities that for the first time afford us the ability, i.e., means, breadth, and depth of data, to truly address human biology at the systems level. This wealth of information from seemingly disparate datasets and its integration is being realized through bioinformatics. It is with this philosophy that the text Bioinformatics for Systems Biology was born. This revolution has spawned true personalized medicine that encompasses diagnostics and treatment through to cure. For the physical and computer scientist, this text provides an introduction to the basic biological principles governing a cell. This quickly moves from the fundamentals to exploring the underlying genetic processes. While providing a rudimentary and necessary overview for the life scientist, the physical and computer scientist will be apprised of various nuances within the field reflecting the reality of "wet-bench" science. For those in the life sciences, it I rapidly becoming appreciated that we now progressing from examining our favorite "pet" gene to the system. Statistics is now an essential component to understand the vast datasets and this is emphasized throughout the text. The majority of the text is devoted to the common ground that these groups share. It provides rich examples of tools, databases, and strategies to mine the databases to reveal novel insights. A host of examples of parsing the data into a series of overlays that use various presentation systems are reviewed. The goal is to provide a representation most comfortable to the user to enable the user to thoroughly explore the data. The text concludes with examples of how the systems information is used to inform personalized medicine in a true "bench to bedside" manner. Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward in the mostly uncharted and emerging field of Systems Biology.

Bioinformatics --- Systems biology --- Computational biology --- Cytology --- Data processing. --- Bioinformatique --- Cytologie --- Traitement des données

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Robert Rosen was not only a biologist, he was also a brilliant mathematician whose extraordinary contributions to theoretical biology were tremendous. Founding, with this book, the area of Anticipatory Systems Theory is a remarkable outcome of his work in theoretical biology. This second edition of his book Anticipatory Systems, has been carefully revised and edited, and includes an Introduction by Judith Rosen. It has also been expanded with a set of Prolegomena by Dr. Mihai Nadin, who offers an historical survey of this fast growing field since the original work was published. There is also some exciting new work, in the form of an additional chapter on the Ontology of Anticipation, by Dr. John Kineman.  An addendum-- with autobiographical reminiscences by Robert Rosen, himself, and a short story by Judith Rosen about her father-- adds a personal touch.  This work, now available again, serves as the guiding foundations for the growing field of Anticipatory Systems and, indeed, any area of science that deals with living organisms in some way, including the study of Life and Mind. It will also be of interest to graduate students and researchers in the field of Systems Science.

systeemtheorie --- biotechnologie --- Biology --- Biotechnology --- Engineering sciences. Technology --- systeembeheer --- biologie --- Biological models. --- Systems theory. --- Models, Biological --- Matemáticas I (49151101) --- Bibliografía recomendada --- Biomathematics. --- Systems biology. --- System theory. --- Mathematical and Computational Biology. --- Systems Biology. --- Systems Theory, Control. --- Systems, Theory of --- Systems science --- Science --- Computational biology --- Bioinformatics --- Biological systems --- Molecular biology --- Mathematics --- Philosophy --- Biology. --- Philosophy and the life sciences.

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Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include * import and preprocessing of data from various sources * statistical modeling of differential gene expression * biological metadata * application of graphs and graph rendering * machine learning for clustering and classification problems * gene set enrichment analysis Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table. The authors of this book have longtime experience in teaching introductory and advanced courses to the application of Bioconductor software. Florian Hahne is a Postdoc at the Fred Hutchinson Cancer Research Center in Seattle, developing novel methodologies for the analysis of high-throughput cell-biological data. Wolfgang Huber is a research group leader in the European Molecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has wide-ranging experience in the development of methods for the analysis of functional genomics experiments. Robert Gentleman is Head of the Program in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle, and he is one of the two authors of the original R system. Seth Falcon is a member of the R core team and former project manager and developer for the Bioconductor project.

Statistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Computational Biology/Bioinformatics. --- Bioinformatics. --- Mathematical Biology in General. --- Biology --- Statistique --- Bio-informatique --- Mathematics. --- Bioconductor (Computer file). --- R (Computer program language). --- Bioinformatics --- R (Computer program language) --- Mathematics --- Biology - General --- Mathematical Statistics --- Health & Biological Sciences --- Physical Sciences & Mathematics --- Bioconductor (Computer file) --- GNU-S (Computer program language) --- Bio-informatics --- Biological informatics --- Life sciences. --- Life Sciences, general. --- Mathematical and Computational Biology. --- Information science --- Computational biology --- Systems biology --- Data processing --- Domain-specific programming languages --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Biosciences --- Sciences, Life --- Science --- Statistics . --- Biomathematics.

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This textbook provides an introduction to the field of mathematical biology through the integration of classical applications in ecology with more recent applications to epidemiology, particularly in the context of spread of infectious diseases. It integrates modeling, mathematics, and applications in a semi-rigorous way, stating theoretical results and giving references but not necessarily giving detailed proofs, providing a solid introduction to the field to undergraduates (junior and senior level), graduate students in applied mathematics, ecology, epidemiology or evolutionary biology, sustainability scientists, and to researchers who must routinely read the practical and theoretical results that come from modeling in ecology and epidemiology. This new edition has been updated throughout. In particular the chapters on epidemiology have been updated and extended considerably, and there is a new chapter on spatially structured populations that incorporates dispersal. The number of problems has been increased and the number of projects has more than doubled, in particular those stressing connections to data. In addition some examples, exercises, and projects include use of Maple and Matlab. Review of first edition: "A strength of the book is the large number of biologically-motivated problem sets. These and the references to the original biological papers would be valuable resources for an instructor." (UK Nonlinear News, 2001)

Statistical science --- Biomathematics. Biometry. Biostatistics --- General ecology and biosociology --- Human ecology. Social biology --- Epidemiology --- sociale ecologie --- ecologie --- biometrie --- Mathematics --- Ecology --- Mathematics. --- Mathematical and Computational Biology. --- Community & Population Ecology. --- Ecology. --- Mathématiques --- Ecologie --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B