42,023 research outputs found
Systems biology in animal sciences
Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits
Synthetic biology—putting engineering into biology
Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.
A Systematic Identification and Analysis of Scientists on Twitter
Metrics derived from Twitter and other social media---often referred to as
altmetrics---are increasingly used to estimate the broader social impacts of
scholarship. Such efforts, however, may produce highly misleading results, as
the entities that participate in conversations about science on these platforms
are largely unknown. For instance, if altmetric activities are generated mainly
by scientists, does it really capture broader social impacts of science? Here
we present a systematic approach to identifying and analyzing scientists on
Twitter. Our method can identify scientists across many disciplines, without
relying on external bibliographic data, and be easily adapted to identify other
stakeholder groups in science. We investigate the demographics, sharing
behaviors, and interconnectivity of the identified scientists. We find that
Twitter has been employed by scholars across the disciplinary spectrum, with an
over-representation of social and computer and information scientists;
under-representation of mathematical, physical, and life scientists; and a
better representation of women compared to scholarly publishing. Analysis of
the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a
small fraction of shared URLs are science-related. We find an assortative
mixing with respect to disciplines in the networks between scientists,
suggesting the maintenance of disciplinary walls in social media. Our work
contributes to the literature both methodologically and conceptually---we
provide new methods for disambiguating and identifying particular actors on
social media and describing the behaviors of scientists, thus providing
foundational information for the construction and use of indicators on the
basis of social media metrics
Implementation of QoS onto virtual bus network
Quality of Service (QoS) is a key issue in a multimedia environment because multimedia applications are sensitive to delay. The virtual bus architecture is a hierarchical access network structure that has been proposed to simplify network signaling. The network employs an interconnection of hierarchical database to support advanced routing of the signaling and traffic load. Therefore, the requirements and management of quality of service is important in the virtual bus network particularly to support multimedia applications. QoS and traffic parameters are specified for each class type and the OMNeT model has been described
Schooling effects and earnings of French University graduates: school quality matters, but choice of discipline matters more
Our aim in this article is to study the relation between earnings of French universities graduates and some characteristics of their universities. We exploit data from the Céreq's "Génération 98" survey, enriched with information on university characteristics primarily from the ANETES (yearbook of French institutions of higher education). We employ multilevel modeling, enabling us to take advantage of the natural hierarchy in our separate datasets, and thus to identify, and even to measure potential effects of institutional quality. Since we take into account many individual students characteristics, we are able to obtain an income hierarchy among the different disciplines : students who graduated in science, economics or management obtain the highest earnings. Below them, we and students who graduated in law, political science, communication or language and literature, while the ones who graduated in social studies earn the lowest incomes. On the institutional level, we need two significant quality effects : the rest is from the socioeconomic composition of the university's student population, and the second effect is from the university's network in the job market. These last two results remain stable when we examine subsamples of universities according to their dominant teaching fields, except for universities that are particularly concentrated in science.Demand for schooling, educational economics, human capital, salaries wage differentials, school choice
A machine learning pipeline for discriminant pathways identification
Motivation: Identifying the molecular pathways more prone to disruption
during a pathological process is a key task in network medicine and, more in
general, in systems biology.
Results: In this work we propose a pipeline that couples a machine learning
solution for molecular profiling with a recent network comparison method. The
pipeline can identify changes occurring between specific sub-modules of
networks built in a case-control biomarker study, discriminating key groups of
genes whose interactions are modified by an underlying condition. The proposal
is independent from the classification algorithm used. Three applications on
genomewide data are presented regarding children susceptibility to air
pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's.
Availability: Details about the software used for the experiments discussed
in this paper are provided in the Appendix
The hidden impact of inter-individual genomic variations on cellular function
An analysis of the degree of genomic variation between two individual genomes suggests that there may be considerable biochemical differences among individuals. Examination of DNA sequence variations in 14 canonical signaling pathways and Monte-Carlo simulation modeling suggest that the kinetic and quantitative behavior of signaling pathways in many individuals may be significantly perturbed from the 'healthy' norm. Signal transduction pathways in some individuals may suffer context-specific failures, or they may function normally but fail easily in the face of additional environmental perturbations or somatic mutations. These findings argue for new systems biology approaches that can predict pathway status in individuals using personal genome sequences and biomarker data
Howard Hughes Medical Institute 2013 Year in Review
The Howard Hughes Medical Institute is the nation's largest private supporter of academic biomedical research. As a medical research organization, the Institute spends at least 3.5 percent of its endowment each year on research activities and related overhead, excluding grants and investment management expenses. At the close of fiscal year 2013, the Institute had 1.1 billion from the previous fiscal year's end. Since 2004, the Institute has provided over $7 billion in direct support for research and science education.This annual report includes financial information as well as an overview of the work done by the center in 2013
Meeting report: a hard look at the state of enamel research.
The Encouraging Novel Amelogenesis Models and Ex vivo cell Lines (ENAMEL) Development workshop was held on 23 June 2017 at the Bethesda headquarters of the National Institute of Dental and Craniofacial Research (NIDCR). Discussion topics included model organisms, stem cells/cell lines, and tissues/3D cell culture/organoids. Scientists from a number of disciplines, representing institutions from across the United States, gathered to discuss advances in our understanding of enamel, as well as future directions for the field
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