940 research outputs found
Computational Biology and High Performance Computing 2000
Tutorial to be presented at Supercomputing 2000, Dallas TX, 6-10 November 2000.This work was supported by the Director, Office of Science, Office of Advanced Scientific computing Research, Mathematical, Information, and Computational Sciences Division of the U.S. Department of Energy under Contract No. DE-AC03-76SF0009
Computational Biology and High Performance Computing 2000
Tutorial to be presented at Supercomputing 2000, Dallas TX, 6-10 November 2000.This work was supported by the Director, Office of Science, Office of Advanced Scientific computing Research, Mathematical, Information, and Computational Sciences Division of the U.S. Department of Energy under Contract No. DE-AC03-76SF0009
On-premise containerized, light-weight software solutions for Biomedicine
Bioinformatics software systems are critical tools for analysing large-scale biological
data, but their design and implementation can be challenging due to the need for reliability, scalability, and performance. This thesis investigates the impact of several
software approaches on the design and implementation of bioinformatics software
systems. These approaches include software patterns, microservices, distributed
computing, containerisation and container orchestration. The research focuses on
understanding how these techniques affect bioinformatics software systems’ reliability, scalability, performance, and efficiency. Furthermore, this research highlights
the challenges and considerations involved in their implementation. This study also
examines potential solutions for implementing container orchestration in bioinformatics research teams with limited resources and the challenges of using container
orchestration. Additionally, the thesis considers microservices and distributed computing and how these can be optimised in the design and implementation process to
enhance the productivity and performance of bioinformatics software systems. The
research was conducted using a combination of software development, experimentation, and evaluation. The results show that implementing software patterns can
significantly improve the code accessibility and structure of bioinformatics software
systems. Specifically, microservices and containerisation also enhanced system reliability, scalability, and performance. Additionally, the study indicates that adopting
advanced software engineering practices, such as model-driven design and container
orchestration, can facilitate efficient and productive deployment and management of
bioinformatics software systems, even for researchers with limited resources. Overall, we develop a software system integrating all our findings. Our proposed system
demonstrated the ability to address challenges in bioinformatics. The thesis makes
several key contributions in addressing the research questions surrounding the design,
implementation, and optimisation of bioinformatics software systems using software
patterns, microservices, containerisation, and advanced software engineering principles and practices. Our findings suggest that incorporating these technologies can
significantly improve bioinformatics software systems’ reliability, scalability, performance, efficiency, and productivity.Bioinformatische Software-Systeme stellen bedeutende Werkzeuge für die Analyse
umfangreicher biologischer Daten dar. Ihre Entwicklung und Implementierung kann
jedoch aufgrund der erforderlichen Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit eine Herausforderung darstellen. Das Ziel dieser Arbeit ist es, die Auswirkungen von Software-Mustern, Microservices, verteilten Systemen, Containerisierung
und Container-Orchestrierung auf die Architektur und Implementierung von bioinformatischen Software-Systemen zu untersuchen. Die Forschung konzentriert sich
darauf, zu verstehen, wie sich diese Techniken auf die Zuverlässigkeit, Skalierbarkeit,
Leistungsfähigkeit und Effizienz von bioinformatischen Software-Systemen auswirken
und welche Herausforderungen mit ihrer Konzeptualisierungen und Implementierung
verbunden sind. Diese Arbeit untersucht auch potenzielle Lösungen zur Implementierung von Container-Orchestrierung in bioinformatischen Forschungsteams mit begrenzten Ressourcen und die Einschränkungen bei deren Verwendung in diesem Kontext. Des Weiteren werden die Schlüsselfaktoren, die den Erfolg von bioinformatischen Software-Systemen mit Containerisierung, Microservices und verteiltem Computing beeinflussen, untersucht und wie diese im Design- und Implementierungsprozess optimiert werden können, um die Produktivität und Leistung bioinformatischer
Software-Systeme zu steigern. Die vorliegende Arbeit wurde mittels einer Kombination aus Software-Entwicklung, Experimenten und Evaluation durchgefĂĽhrt. Die
erzielten Ergebnisse zeigen, dass die Implementierung von Software-Mustern, die Zuverlässigkeit und Skalierbarkeit von bioinformatischen Software-Systemen erheblich
verbessern kann. Der Einsatz von Microservices und Containerisierung trug ebenfalls zur Steigerung der Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit des
Systems bei. DarĂĽber hinaus legt die Arbeit dar, dass die Anwendung von SoftwareEngineering-Praktiken, wie modellgesteuertem Design und Container-Orchestrierung,
die effiziente und produktive Bereitstellung und Verwaltung von bioinformatischen
Software-Systemen erleichtern kann. Zudem löst die Implementierung dieses SoftwareSystems, Herausforderungen für Forschungsgruppen mit begrenzten Ressourcen. Insgesamt hat das System gezeigt, dass es in der Lage ist, Herausforderungen im Bereich
der Bioinformatik zu bewältigen und stellt somit ein wertvolles Werkzeug für Forscher in diesem Bereich dar. Die vorliegende Arbeit leistet mehrere wichtige Beiträge
zur Beantwortung von Forschungsfragen im Zusammenhang mit dem Entwurf, der
Implementierung und der Optimierung von Software-Systemen fĂĽr die Bioinformatik unter Verwendung von Prinzipien und Praktiken der Softwaretechnik. Unsere
Ergebnisse deuten darauf hin, dass die Einbindung dieser Technologien die Zuverlässigkeit, Skalierbarkeit, Leistungsfähigkeit, Effizienz und Produktivität bioinformatischer Software-Systeme erheblich verbessern kann
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Evolution from the ground up with Amee – From basic concepts to explorative modeling
Evolutionary theory has been the foundation of biological research for about a century
now, yet over the past few decades, new discoveries and theoretical advances have rapidly
transformed our understanding of the evolutionary process. Foremost among them are
evolutionary developmental biology, epigenetic inheritance, and various forms of evolu-
tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led
to the conceptualization of an extended evolutionary synthesis. Starting from abstract
principles rooted in complexity theory, this thesis aims to provide a unified conceptual
understanding of any kind of evolution, biological or otherwise. This is used in the second
part to develop Amee, an agent-based model that unifies development, niche construction,
and phenotypic plasticity with natural selection based on a simulated ecology. Amee
is implemented in Utopia, which allows performant, integrated implementation and
simulation of arbitrary agent-based models. A phenomenological overview over Amee’s
capabilities is provided, ranging from the evolution of ecospecies down to the evolution
of metabolic networks and up to beyond-species-level biological organization, all of
which emerges autonomously from the basic dynamics. The interaction of development,
plasticity, and niche construction has been investigated, and it has been shown that while
expected natural phenomena can, in principle, arise, the accessible simulation time and
system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes
From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics
Understanding how genotypes map onto phenotypes, fitness, and eventually
organisms is arguably the next major missing piece in a fully predictive theory
of evolution. We refer to this generally as the problem of the
genotype-phenotype map. Though we are still far from achieving a complete
picture of these relationships, our current understanding of simpler questions,
such as the structure induced in the space of genotypes by sequences mapped to
molecular structures, has revealed important facts that deeply affect the
dynamical description of evolutionary processes. Empirical evidence supporting
the fundamental relevance of features such as phenotypic bias is mounting as
well, while the synthesis of conceptual and experimental progress leads to
questioning current assumptions on the nature of evolutionary dynamics-cancer
progression models or synthetic biology approaches being notable examples. This
work delves into a critical and constructive attitude in our current knowledge
of how genotypes map onto molecular phenotypes and organismal functions, and
discusses theoretical and empirical avenues to broaden and improve this
comprehension. As a final goal, this community should aim at deriving an
updated picture of evolutionary processes soundly relying on the structural
properties of genotype spaces, as revealed by modern techniques of molecular
and functional analysis.Comment: 111 pages, 11 figures uses elsarticle latex clas
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