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Author: Metin Balaban
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Thanks to the advances in sequencing technologies in the last two decades, the set of available whole-genome sequences has been expanding rapidly. One of the challenges in phylogenetics is accurate large-scale phylogenetic inference based on whole-genome sequences. A related challenge is using incomplete genome-wide data in an assembly-free manner for accurate sample identification with reference to phylogeny. This dissertation proposes new scalable and accurate algorithms to address these two challenges. First, I present a family of scalable methods called TreeCluster for breaking a large set of sequences into evolutionary homogeneous clusters. Second, I present two algorithms for accurate phylogenetic placement of genomic sequences on ultra-large single-gene and whole-genome based trees. The first version, APPLES, scales linearly with the reference size while APPLES-2 scales sub-linearly thanks to a divide-and-conquer strategy based on the TreeCluster method. Third, I develop a solution for assembly-free sample phylogenetic placement for a particularly challenging case when the specimen is a mixture of two cohabiting species or a hybrid of two species. Fourth, I address one limitation of assembly-free methods--their reliance on simple models of sequence evolution--by developing a technique to compute evolutionary distances under a complex 4-parameter model called TK4. Finally, I introduce a divide-and-conquer workflow for incrementally growing and updating ultra-large phylogenies using many of the ingredients developed in other chapters. This workflow (uDance) is accurate in simulations and can build a 200,000-genome microbial tree-of-life based on 388 marker genes.
Author: Metin Balaban
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Thanks to the advances in sequencing technologies in the last two decades, the set of available whole-genome sequences has been expanding rapidly. One of the challenges in phylogenetics is accurate large-scale phylogenetic inference based on whole-genome sequences. A related challenge is using incomplete genome-wide data in an assembly-free manner for accurate sample identification with reference to phylogeny. This dissertation proposes new scalable and accurate algorithms to address these two challenges. First, I present a family of scalable methods called TreeCluster for breaking a large set of sequences into evolutionary homogeneous clusters. Second, I present two algorithms for accurate phylogenetic placement of genomic sequences on ultra-large single-gene and whole-genome based trees. The first version, APPLES, scales linearly with the reference size while APPLES-2 scales sub-linearly thanks to a divide-and-conquer strategy based on the TreeCluster method. Third, I develop a solution for assembly-free sample phylogenetic placement for a particularly challenging case when the specimen is a mixture of two cohabiting species or a hybrid of two species. Fourth, I address one limitation of assembly-free methods--their reliance on simple models of sequence evolution--by developing a technique to compute evolutionary distances under a complex 4-parameter model called TK4. Finally, I introduce a divide-and-conquer workflow for incrementally growing and updating ultra-large phylogenies using many of the ingredients developed in other chapters. This workflow (uDance) is accurate in simulations and can build a 200,000-genome microbial tree-of-life based on 388 marker genes.
Author: Thomas Jansen
Publisher: Springer Science & Business Media
ISBN: 364217339X
Category : Computers
Languages : en
Pages : 264
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Book Description
Evolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years. In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods. The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms.
Author: Matthew Andres Moreno
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 0
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Book Description
Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such major transitions in individuality have profoundly shaped complexity, novelty, and adaptation over the course of natural history. Regard for their causes and consequences drives many fundamental questions in biology. Likewise, evolutionary transitions have been highlighted as a hallmark of true open-ended evolution in artificial life. As such, experiments with digital multicellularity promise to help realize computational systems with properties that more closely resemble those of biological systems, ultimately providing insights about the origins of complex life in the natural world and contributing to bio-inspired distributed algorithm design.Major challenges exist, however, in applying high-performance computing hardware to realize the dynamic, large-scale digital artificial life simulations required for such work. This dissertation presents two new tools designed to facilitate digital multicellularity experiments at scale: the Conduit library for best-effort communication and the hstrat ("hereditary stratigraphy") library, which debuts novel decentralized algorithms to estimate phylogenetic distance between evolving agents.Most current parallel and distributed high-performance computing work emphasizes logical determinism: extra effort is expended to guarantee reliable communication and, when necessary, computation halts in order to await expected messages. Determinism does enable hardware-independent algorithmic results and perfect reproducibility, however adopting a best-effort communication model can substantially reduce synchronization overhead and allow dynamic (albeit, potentially lossy) scaling of communication load to fully utilize available resources. We present a set of experiments to empirically characterize the best-effort communication model implemented by the Conduit library on commercially available high-performance computing hardware. We find that best-effort communication through Conduit enables significantly better computational performance under high thread and process counts and can help achieve significantly better solution quality within a fixed time constraint.In a similar vein, existing digital evolution work that incorporates phylogenetic analysis does so through a perfect tracking model where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where agents may migrate between a dynamic set of disjoint processing elements. Additionally, this perfect tracking approach is not robust to data loss or corruption. To provide for phylogenetic analyses in these environments, we propose an approach to infer phylogenies via heritable genetic annotations. We introduce hereditary stratigraphy, an algorithm that enables efficient, fault-tolerant phylogenetic reconstruction with tunable trade-offs between annotation memory footprint and reconstruction accuracy. For example, this approach can estimate the most recent common ancestor (MRCA) generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We simulate inference over known lineages, recovering up to 85% of the information contained in the original tree using only a 64-bit annotation.We harness these tools in DISHTINY, a distributed digital evolution system designed to study digital organisms as they undergo major evolutionary transitions in individuality. This system allows digital cells to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enables preferential communication and cooperation between cells. We report group-level traits characteristic of fraternal transitions in the natural world. These include reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. In one detailed case study, we track the co-evolution of novelty, complexity, and adaptation over the evolutionary history of an experiment. We characterize ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. Our case study suggests a loose, sometimes divergent, relationship can exist among novelty, complexity, and adaptation.The constructive potential inherent in major evolutionary transitions holds great promise for progress toward replicating the capability and robustness of natural organisms. Coupled with shrewd software engineering and innovative model design informed by evolutionary theory, contemporary hardware systems could plausibly already suffice to realize paradigm-shifting advances in open-ended evolution and, ultimately, scientific understanding of major transitions themselves. This work establishes important new tools and methodologies to support continuing progress in this direction.
Author: Shang-Hua Teng
Publisher:
ISBN: 9781680831306
Category : Computers
Languages : en
Pages : 292
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Book Description
In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.
Author: Emmanuel Paradis
Publisher: Springer Science & Business Media
ISBN: 0387351000
Category : Science
Languages : en
Pages : 221
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Book Description
This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.
Author: Kalyanmoy Deb
Publisher: John Wiley & Sons
ISBN: 9780471873396
Category : Mathematics
Languages : en
Pages : 540
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Book Description
Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.
Author: Edson Borin
Publisher: Springer Nature
ISBN: 3031297695
Category : Computers
Languages : en
Pages : 337
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Book Description
This book brings a thorough explanation on the path needed to use cloud computing technologies to run High-Performance Computing (HPC) applications. Besides presenting the motivation behind moving HPC applications to the cloud, it covers both essential and advanced issues on this topic such as deploying HPC applications and infrastructures, designing cloud-friendly HPC applications, and optimizing a provisioned cloud infrastructure to run this family of applications. Additionally, this book also describes the best practices to maintain and keep running HPC applications in the cloud by employing fault tolerance techniques and avoiding resource wastage. To give practical meaning to topics covered in this book, it brings some case studies where HPC applications, used in relevant scientific areas like Bioinformatics and Oil and Gas industry were moved to the cloud. Moreover, it also discusses how to train deep learning models in the cloud elucidating the key components and aspects necessary to train these models via different types of services offered by cloud providers. Despite the vast bibliography about cloud computing and HPC, to the best of our knowledge, no existing manuscript has comprehensively covered these topics and discussed the steps, methods and strategies to execute HPC applications in clouds. Therefore, we believe this title is useful for IT professionals and students and researchers interested in cutting-edge technologies, concepts, and insights focusing on the use of cloud technologies to run HPC applications.
Author: National Academy of Engineering
Publisher: National Academies Press
ISBN: 0309466040
Category : Technology & Engineering
Languages : en
Pages : 141
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Book Description
This volume presents papers on the topics covered at the National Academy of Engineering's 2017 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2017 symposium was held September 25-27 at the United Technologies Research Center in East Hartford, Connecticut. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work.
Author: Setubal
Publisher: Frontiers Media SA
ISBN: 2832506402
Category : Science
Languages : en
Pages : 223
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Book Description
Author: Khosrow-Pour, Mehdi
Publisher: IGI Global
ISBN: 1466660996
Category : Computers
Languages : en
Pages : 379
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Book Description
In the digital age, technological solutions are being developed and integrated into every aspect of our everyday lives. The ever-changing scope of research in systems and software advancements allows for further improvements and applications. Systems and Software Development, Modeling, and Analysis: New Perspectives and Methodologies presents diverse, interdisciplinary research on topics pertaining to the management, integration, evaluation, and architecture of modern computational systems and software. Presenting the most up-to-date research in this rapidly evolving field, this title is ideally designed for use by computer engineers, academicians, graduate and post-graduate students, and computer science researchers.
