WorldWideScience

Sample records for physical biological networks

  1. Synthetic biological networks

    International Nuclear Information System (INIS)

    Archer, Eric; Süel, Gürol M

    2013-01-01

    Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics. (review article)

  2. RENEB - Running the European Network of biological dosimetry and physical retrospective dosimetry.

    Science.gov (United States)

    Kulka, Ulrike; Abend, Michael; Ainsbury, Elizabeth; Badie, Christophe; Barquinero, Joan Francesc; Barrios, Lleonard; Beinke, Christina; Bortolin, Emanuela; Cucu, Alexandra; De Amicis, Andrea; Domínguez, Inmaculada; Fattibene, Paola; Frøvig, Anne Marie; Gregoire, Eric; Guogyte, Kamile; Hadjidekova, Valeria; Jaworska, Alicja; Kriehuber, Ralf; Lindholm, Carita; Lloyd, David; Lumniczky, Katalin; Lyng, Fiona; Meschini, Roberta; Mörtl, Simone; Della Monaca, Sara; Monteiro Gil, Octávia; Montoro, Alegria; Moquet, Jayne; Moreno, Mercedes; Oestreicher, Ursula; Palitti, Fabrizio; Pantelias, Gabriel; Patrono, Clarice; Piqueret-Stephan, Laure; Port, Matthias; Prieto, María Jesus; Quintens, Roel; Ricoul, Michelle; Romm, Horst; Roy, Laurence; Sáfrány, Géza; Sabatier, Laure; Sebastià, Natividad; Sommer, Sylwester; Terzoudi, Georgia; Testa, Antonella; Thierens, Hubert; Turai, Istvan; Trompier, François; Valente, Marco; Vaz, Pedro; Voisin, Philippe; Vral, Anne; Woda, Clemens; Zafiropoulos, Demetre; Wojcik, Andrzej

    2017-01-01

    A European network was initiated in 2012 by 23 partners from 16 European countries with the aim to significantly increase individualized dose reconstruction in case of large-scale radiological emergency scenarios. The network was built on three complementary pillars: (1) an operational basis with seven biological and physical dosimetric assays in ready-to-use mode, (2) a basis for education, training and quality assurance, and (3) a basis for further network development regarding new techniques and members. Techniques for individual dose estimation based on biological samples and/or inert personalized devices as mobile phones or smart phones were optimized to support rapid categorization of many potential victims according to the received dose to the blood or personal devices. Communication and cross-border collaboration were also standardized. To assure long-term sustainability of the network, cooperation with national and international emergency preparedness organizations was initiated and links to radiation protection and research platforms have been developed. A legal framework, based on a Memorandum of Understanding, was established and signed by 27 organizations by the end of 2015. RENEB is a European Network of biological and physical-retrospective dosimetry, with the capacity and capability to perform large-scale rapid individualized dose estimation. Specialized to handle large numbers of samples, RENEB is able to contribute to radiological emergency preparedness and wider large-scale research projects.

  3. Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

    Science.gov (United States)

    Jalili, Mahdi

    2018-03-01

    I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

  4. Physical limits of feedback noise-suppression in biological networks

    International Nuclear Information System (INIS)

    Zhang, Jiajun; Yuan, Zhanjiang; Zhou, Tianshou

    2009-01-01

    Feedback is a ubiquitous control mechanism of biological networks, and has also been identified in a variety of regulatory systems and organisms. It has been shown that, for a given gain and with negligible intrinsic noise, negative feedback impairs noise buffering whereas positive feedback enhances noise buffering. We further investigate the influence of negative and positive feedback on noise in output signals by considering both intrinsic and extrinsic noise as well as operator noise. We find that, while maintaining the system sensitivity, either there exists a minimum of the output noise intensity corresponding to a biologically feasible feedback strength, or the output noise intensity is a monotonic function of feedback strength bounded by both biological and dynamical constraints. In both cases, feedback noise-suppression is physically limited. In other words, noise suppressed by negative or positive feedback cannot be reduced without limitation even in the case of slow transcription

  5. Quantum Processes and Dynamic Networks in Physical and Biological Systems.

    Science.gov (United States)

    Dudziak, Martin Joseph

    Quantum theory since its earliest formulations in the Copenhagen Interpretation has been difficult to integrate with general relativity and with classical Newtonian physics. There has been traditionally a regard for quantum phenomena as being a limiting case for a natural order that is fundamentally classical except for microscopic extrema where quantum mechanics must be applied, more as a mathematical reconciliation rather than as a description and explanation. Macroscopic sciences including the study of biological neural networks, cellular energy transports and the broad field of non-linear and chaotic systems point to a quantum dimension extending across all scales of measurement and encompassing all of Nature as a fundamentally quantum universe. Theory and observation lead to a number of hypotheses all of which point to dynamic, evolving networks of fundamental or elementary processes as the underlying logico-physical structure (manifestation) in Nature and a strongly quantized dimension to macroscalar processes such as are found in biological, ecological and social systems. The fundamental thesis advanced and presented herein is that quantum phenomena may be the direct consequence of a universe built not from objects and substance but from interacting, interdependent processes collectively operating as sets and networks, giving rise to systems that on microcosmic or macroscopic scales function wholistically and organically, exhibiting non-locality and other non -classical phenomena. The argument is made that such effects as non-locality are not aberrations or departures from the norm but ordinary consequences of the process-network dynamics of Nature. Quantum processes are taken to be the fundamental action-events within Nature; rather than being the exception quantum theory is the rule. The argument is also presented that the study of quantum physics could benefit from the study of selective higher-scale complex systems, such as neural processes in the brain

  6. Complex network problems in physics, computer science and biology

    Science.gov (United States)

    Cojocaru, Radu Ionut

    There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe

  7. Network science of biological systems at different scales: A review

    Science.gov (United States)

    Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

    2018-03-01

    Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

  8. Neural network models: from biology to many - body phenomenology

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

    Theoretical work in neural networks has a strange feel for most physicists. In some cases the aspect of design becomes paramount. More comfortable ground at least for many body theorists may be found in realistic biological simulation, although the complexity of most problems is so awesome that incisive results will be hard won. It has also shown the impressive capabilities of artificial networks in pattern recognition and classification may be exploited to solve management problems in experimental physics and for discovery of radically new theoretical description of physical systems. This advance represents an important step towards the ultimate goal of neuro biological paradigm. (A.B.)

  9. Design principles in biological networks

    Science.gov (United States)

    Goyal, Sidhartha

    Much of biology emerges from networks of interactions. Even in a single bacterium such as Escherichia coli, there are hundreds of coexisting gene and protein networks. Although biological networks are the outcome of evolution, various physical and biological constraints limit their functional capacity. The focus of this thesis is to understand how functional constraints such as optimal growth in mircoorganisms and information flow in signaling pathways shape the metabolic network of bacterium E. coli and the quorum sensing network of marine bacterium Vibrio harveyi, respectively. Metabolic networks convert basic elemental sources into complex building-blocks eventually leading to cell's growth. Therefore, typically, metabolic pathways are often coupled both by the use of a common substrate and by stoichiometric utilization of their products for cell growth. We showed that such a coupled network with product-feedback inhibition may exhibit limit-cycle oscillations which arise via a Hopf bifurcation. Furthermore, we analyzed several representative metabolic modules and find that, in all cases, simple product-feedback inhibition allows nearly optimal growth, in agreement with the predicted growth-rate by the flux-balance analysis (FBA). Bacteria have fascinating and diverse social lives. They display coordinated group behaviors regulated by quorum sensing (QS) systems. The QS circuit of V. harveyi integrates and funnels different ecological information through a common phosphorelay cascade to a set of small regulatory RNAs (sRNAs) that enables collective behavior. We analyzed the signaling properties and information flow in the QS circuit, which provides a model for information flow in signaling networks more generally. A comparative study of post-transcriptional and conventional transcriptional regulation suggest a niche for sRNAs in allowing cells to transition quickly yet reliably between distinct states. Furthermore, we develop a new framework for analyzing signal

  10. Dominating biological networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

    Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

  11. Physics and biology

    International Nuclear Information System (INIS)

    Frauenfelder, H.

    1988-01-01

    The author points out that the coupling between physics and biology is becoming closer as time goes on. He tries to show that physical studies on biological systems not only yield insight into biology but also provide results of interest to physics. Biological systems are extremly complex system. Ideally one would like to understand the behavior of such systems in terms of the behavior of its constituent atoms. Since in small organisms this may be 10 20 atoms, it is clear these are not simple many-body systems. He reviews the basic elements of cells and then considers the broader questions of structure, complexity, and function, which must be looked at on levels from the cell to the organism. Despite the vast amount of observational material already in existence, biophysics and biological physics are only at a beginning. We can expect that physics will continue to interact strongly with biology. Actually, the connection also includes chemistry and mathematics. New tools that become available in physics will continue to be applied to biological problems. We can expect that the flow of information will not be one way; biological systems will provide new information on many old and new parts of physics, from reaction theory and transport phenomena to complexity, cooperativity, and nonlinear processes

  12. Networks In Real Space: Characteristics and Analysis for Biology and Mechanics

    Science.gov (United States)

    Modes, Carl; Magnasco, Marcelo; Katifori, Eleni

    Functional networks embedded in physical space play a crucial role in countless biological and physical systems, from the efficient dissemination of oxygen, blood sugars, and hormonal signals in vascular systems to the complex relaying of informational signals in the brain to the distribution of stress and strain in architecture or static sand piles. Unlike their more-studied abstract cousins, such as the hyperlinked internet, social networks, or economic and financial connections, these networks are both constrained by and intimately connected to the physicality of their real, embedding space. We report on the results of new computational and analytic approaches tailored to these physical networks with particular implications and insights for mammalian organ vasculature.

  13. Biological and Environmental Research Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Balaji, V. [Princeton Univ., NJ (United States). Earth Science Grid Federation (ESGF); Boden, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Cowley, Dave [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dart, Eli [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Dattoria, Vince [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Desai, Narayan [Argonne National Lab. (ANL), Argonne, IL (United States); Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Foster, Ian [Argonne National Lab. (ANL), Argonne, IL (United States); Goldstone, Robin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gregurick, Susan [U.S. Dept. of Energy, Washington, DC (United States). Biological Systems Science Division; Houghton, John [U.S. Dept. of Energy, Washington, DC (United States). Biological and Environmental Research (BER) Program; Izaurralde, Cesar [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Johnston, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Joseph, Renu [U.S. Dept. of Energy, Washington, DC (United States). Climate and Environmental Sciences Division; Kleese-van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lipton, Mary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Monga, Inder [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Pritchard, Matt [British Atmospheric Data Centre (BADC), Oxon (United Kingdom); Rotman, Lauren [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Strand, Gary [National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Stuart, Cory [Argonne National Lab. (ANL), Argonne, IL (United States); Tatusova, Tatiana [National Inst. of Health (NIH), Bethesda, MD (United States); Tierney, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). ESNet; Thomas, Brian [Univ. of California, Berkeley, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Zurawski, Jason [Internet2, Washington, DC (United States)

    2013-09-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

  14. Interdisciplinary and physics challenges of network theory

    Science.gov (United States)

    Bianconi, Ginestra

    2015-09-01

    Network theory has unveiled the underlying structure of complex systems such as the Internet or the biological networks in the cell. It has identified universal properties of complex networks, and the interplay between their structure and dynamics. After almost twenty years of the field, new challenges lie ahead. These challenges concern the multilayer structure of most of the networks, the formulation of a network geometry and topology, and the development of a quantum theory of networks. Making progress on these aspects of network theory can open new venues to address interdisciplinary and physics challenges including progress on brain dynamics, new insights into quantum technologies, and quantum gravity.

  15. Mapping biological systems to network systems

    CERN Document Server

    Rathore, Heena

    2016-01-01

    The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully ...

  16. Comparing biological networks via graph compression

    Directory of Open Access Journals (Sweden)

    Hayashida Morihiro

    2010-09-01

    Full Text Available Abstract Background Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges. Results This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae, and B. subtilis, and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks. Conclusions Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.

  17. Modular analysis of biological networks.

    Science.gov (United States)

    Kaltenbach, Hans-Michael; Stelling, Jörg

    2012-01-01

    The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

  18. Using biological networks to improve our understanding of infectious diseases

    Directory of Open Access Journals (Sweden)

    Nicola J. Mulder

    2014-08-01

    Full Text Available Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.

  19. Uncovering the underlying physical mechanisms of biological systems via quantification of landscape and flux

    International Nuclear Information System (INIS)

    Xu Li; Chu Xiakun; Yan Zhiqiang; Zheng Xiliang; Zhang Kun; Zhang Feng; Yan Han; Wu Wei; Wang Jin

    2016-01-01

    In this review, we explore the physical mechanisms of biological processes such as protein folding and recognition, ligand binding, and systems biology, including cell cycle, stem cell, cancer, evolution, ecology, and neural networks. Our approach is based on the landscape and flux theory for nonequilibrium dynamical systems. This theory provides a unifying principle and foundation for investigating the underlying mechanisms and physical quantification of biological systems. (topical review)

  20. PREFACE: Nanobiology: from physics and engineering to biology

    Science.gov (United States)

    Nussinov, Ruth; Alemán, Carlos

    2006-03-01

    Biological systems are inherently nano in scale. Unlike nanotechnology, nanobiology is characterized by the interplay between physics, materials science, synthetic organic chemistry, engineering and biology. Nanobiology is a new discipline, with the potential of revolutionizing medicine: it combines the tools, ideas and materials of nanoscience and biology; it addresses biological problems that can be studied and solved by nanotechnology; it devises ways to construct molecular devices using biomacromolecules; and it attempts to build molecular machines utilizing concepts seen in nature. Its ultimate aim is to be able to predictably manipulate these, tailoring them to specified needs. Nanobiology targets biological systems and uses biomacromolecules. Hence, on the one hand, nanobiology is seemingly constrained in its scope as compared to general nanotechnology. Yet the amazing intricacy of biological systems, their complexity, and the richness of the shapes and properties provided by the biological polymers, enrich nanobiology. Targeting biological systems entails comprehension of how they work and the ability to use their components in design. From the physical standpoint, ultimately, if we are to understand biology we need to learn how to apply physical principles to figure out how these systems actually work. The goal of nanobiology is to assist in probing these systems at the appropriate length scale, heralding a new era in the biological, physical and chemical sciences. Biology is increasingly asking quantitative questions. Quantitation is essential if we are to understand how the cell works, and the details of its regulation. The physical sciences provide tools and strategies to obtain accurate measurements and simulate the information to allow comprehension of the processes. Nanobiology is at the interface of the physical and the biological sciences. Biology offers to the physical sciences fascinating problems, sophisticated systems and a rich repertoire of

  1. Identification of important nodes in directed biological networks: a network motif approach.

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  2. Review of Biological Network Data and Its Applications

    Directory of Open Access Journals (Sweden)

    Donghyeon Yu

    2013-12-01

    Full Text Available Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

  3. From biologically-inspired physics to physics-inspired biology From biologically-inspired physics to physics-inspired biology

    Science.gov (United States)

    Kornyshev, Alexei A.

    2010-10-01

    The conference 'From DNA-Inspired Physics to Physics-Inspired Biology' (1-5 June 2009, International Center for Theoretical Physics, Trieste, Italy) that myself and two former presidents of the American Biophysical Society—Wilma Olson (Rutgers University) and Adrian Parsegian (NIH), with the support of an ICTP team (Ralf Gebauer (Local Organizer) and Doreen Sauleek (Conference Secretary)), have organized was intended to establish stronger links between the biology and physics communities on the DNA front. The relationships between them were never easy. In 1997, Adrian published a paper in Physics Today ('Harness the Hubris') summarizing his thoughts about the main obstacles for a successful collaboration. The bottom line of that article was that physicists must seriously learn biology before exploring it and even having an interpreter, a friend or co-worker, who will be cooperating with you and translating the problems of biology into a physical language, may not be enough. He started his story with a joke about a physicist asking a biologist: 'I want to study the brain. Tell me something about it!' Biologist: 'First, the brain consists of two parts, and..' Physicist: 'Stop. You have told me too much.' Adrian listed a few direct avenues where physicists' contributions may be particularly welcome. This gentle and elegantly written paper caused, however, a stormy reaction from Bob Austin (Princeton), published together with Adrian's notes, accusing Adrian of forbidding physicists to attack big questions in biology straightaway. Twelve years have passed and many new developments have taken place in the biologist-physicist interaction. This was something I addressed in my opening conference speech, with my position lying somewhere inbetween Parsegian's and Austin's, which is briefly outlined here. I will first recall certain precepts or 'dogmas' that fly in the air like Valkyries, poisoning those relationships. Since the early seventies when I was a first year Ph

  4. Querying Large Biological Network Datasets

    Science.gov (United States)

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  5. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

    Science.gov (United States)

    Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

    2015-01-01

    With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

  6. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael

    2018-01-01

    of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological...... strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties...

  7. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  8. BiologicalNetworks 2.0 - an integrative view of genome biology data

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2010-12-01

    Full Text Available Abstract Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other and their relations (interactions, co-expression, co-citations, and other. The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org.

  9. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

    2015-01-01

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

  10. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Introduction. Over the past few years, network science has drawn attention from a large number of ... The qualitative properties of biological networks cannot ... Here, we study the underlying undirected structure of empirical biological networks.

  11. The Latin American Biological Dosimetry Network (LBDNet)

    International Nuclear Information System (INIS)

    Garcia, O.; Lamadrid, A.I.; Gonzalez, J.E.; Romero, I.; Mandina, T.; Di Giorgio, M.; Radl, A.; Taja, M.R.; Sapienza, C.E.; Deminge, M.M.; Fernandez Rearte, J.; Stuck Oliveira, M.; Valdivia, P.; Guerrero-Carbajal, C.; Arceo Maldonado, C.; Cortina Ramirez, G.E.; Espinoza, M.; Martinez-Lopez, W.; Di Tomasso, M.

    2016-01-01

    Biological Dosimetry is a necessary support for national radiation protection programmes and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 to provide early biological dosimetry assistance in case of radiation emergencies in the Latin American Region. Here are presented the main topics considered in the foundational document of the network, which comprise: mission, partners, concept of operation, including the mechanism to request support for biological dosimetry assistance in the region, and the network capabilities. The process for network activation and the role of the coordinating laboratory during biological dosimetry emergency response is also presented. This information is preceded by historical remarks on biological dosimetry cooperation in Latin America. A summary of the main experimental and practical results already obtained by the LBDNet is also included. (authors)

  12. Measuring the evolutionary rewiring of biological networks.

    Directory of Open Access Journals (Sweden)

    Chong Shou

    Full Text Available We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

  13. On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2011-01-01

    In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective. PMID:22084563

  14. Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

    Science.gov (United States)

    2018-01-01

    Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

  15. Mining biological networks from full-text articles.

    Science.gov (United States)

    Czarnecki, Jan; Shepherd, Adrian J

    2014-01-01

    The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network.

  16. OWL Reasoning Framework over Big Biological Knowledge Network

    Science.gov (United States)

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

  17. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  18. Nuclear Physics computer networking: Report of the Nuclear Physics Panel on Computer Networking

    International Nuclear Information System (INIS)

    Bemis, C.; Erskine, J.; Franey, M.; Greiner, D.; Hoehn, M.; Kaletka, M.; LeVine, M.; Roberson, R.; Welch, L.

    1990-05-01

    This paper discusses: the state of computer networking within nuclear physics program; network requirements for nuclear physics; management structure; and issues of special interest to the nuclear physics program office

  19. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

    Science.gov (United States)

    Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

    2004-12-01

    As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

  20. Network Reconstruction of Dynamic Biological Systems

    OpenAIRE

    Asadi, Behrang

    2013-01-01

    Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of differe...

  1. The common extremalities in biology and physics maximum energy dissipation principle in chemistry, biology, physics and evolution

    CERN Document Server

    Moroz, Adam

    2011-01-01

    This book is the first unified systemic description of dissipative phenomena, taking place in biology, and non-dissipative (conservative) phenomena, which is more relevant to physics. Fully updated and revised, this new edition extends our understanding of nonlinear phenomena in biology and physics from the extreme / optimal perspective. The first book to provide understanding of physical phenomena from a biological perspective and biological phenomena from a physical perspective Discusses emerging fields and analysis Provides examples.

  2. BioNSi: A Discrete Biological Network Simulator Tool.

    Science.gov (United States)

    Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

    2016-08-05

    Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

  3. The Latin American Biological Dosimetry Network (LBDNet).

    Science.gov (United States)

    García, O; Di Giorgio, M; Radl, A; Taja, M R; Sapienza, C E; Deminge, M M; Fernández Rearte, J; Stuck Oliveira, M; Valdivia, P; Lamadrid, A I; González, J E; Romero, I; Mandina, T; Guerrero-Carbajal, C; ArceoMaldonado, C; Cortina Ramírez, G E; Espinoza, M; Martínez-López, W; Di Tomasso, M

    2016-09-01

    Biological Dosimetry is a necessary support for national radiation protection programmes and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 to provide early biological dosimetry assistance in case of radiation emergencies in the Latin American Region. Here are presented the main topics considered in the foundational document of the network, which comprise: mission, partners, concept of operation, including the mechanism to request support for biological dosimetry assistance in the region, and the network capabilities. The process for network activation and the role of the coordinating laboratory during biological dosimetry emergency response is also presented. This information is preceded by historical remarks on biological dosimetry cooperation in Latin America. A summary of the main experimental and practical results already obtained by the LBDNet is also included. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  4. Towards physical principles of biological evolution

    Science.gov (United States)

    Katsnelson, Mikhail I.; Wolf, Yuri I.; Koonin, Eugene V.

    2018-03-01

    Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in ‘emergent phenomena’. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice

  5. A generic algorithm for layout of biological networks.

    Science.gov (United States)

    Schreiber, Falk; Dwyer, Tim; Marriott, Kim; Wybrow, Michael

    2009-11-12

    Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.

  6. Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks.

    Science.gov (United States)

    Tian, Ye; Zhang, Bai; Hoffman, Eric P; Clarke, Robert; Zhang, Zhen; Shih, Ie-Ming; Xuan, Jianhua; Herrington, David M; Wang, Yue

    2014-07-24

    Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowledge; missing significance assessment; and heuristic structural parameter learning. To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm. Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological

  7. The physics of communicability in complex networks

    International Nuclear Information System (INIS)

    Estrada, Ernesto; Hatano, Naomichi; Benzi, Michele

    2012-01-01

    A fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network. Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantum-mechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks.

  8. Reconstruction of biological networks based on life science data integration.

    Science.gov (United States)

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-10-27

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  9. Basic radiotherapy physics and biology

    CERN Document Server

    Chang, David S; Das, Indra J; Mendonca, Marc S; Dynlacht, Joseph R

    2014-01-01

    This book is a concise and well-illustrated review of the physics and biology of radiation therapy intended for radiation oncology residents, radiation therapists, dosimetrists, and physicists. It presents topics that are included on the Radiation Therapy Physics and Biology examinations and is designed with the intent of presenting information in an easily digestible format with maximum retention in mind. The inclusion of mnemonics, rules of thumb, and reader-friendly illustrations throughout the book help to make difficult concepts easier to grasp. Basic Radiotherapy Physics and Biology is a

  10. Controllability and observability of Boolean networks arising from biology

    Science.gov (United States)

    Li, Rui; Yang, Meng; Chu, Tianguang

    2015-02-01

    Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

  11. Networks in Cell Biology

    Science.gov (United States)

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  12. Reconstruction of biological networks based on life science data integration

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

    Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  13. Node fingerprinting: an efficient heuristic for aligning biological networks.

    Science.gov (United States)

    Radu, Alex; Charleston, Michael

    2014-10-01

    With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

  14. Inferring the physical connectivity of complex networks from their functional dynamics

    Directory of Open Access Journals (Sweden)

    Holm Liisa

    2010-05-01

    Full Text Available Abstract Background Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? Results We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees. Based on the observation of PPCs, we built a weighted network of synchronization (WNS, an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators. We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization. Finally, we correlated the topology of the

  15. Power Laws, Scale-Free Networks and Genome Biology

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

    Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

  16. The Physics of Proteins An Introduction to Biological Physics and Molecular Biophysics

    CERN Document Server

    Frauenfelder, Hans; Chan, Winnie S

    2010-01-01

    Physics and the life sciences have established new connections within the past few decades, resulting in biological physics as an established subfield with strong groups working in many physics departments. These interactions between physics and biology form a two-way street with physics providing new tools and concepts for understanding life, while biological systems can yield new insights into the physics of complex systems. To address the challenges of this interdisciplinary area, The Physics of Proteins: An Introduction to Biological Physics and Molecular Biophysics is divided into three interconnected sections. In Parts I and II, early chapters introduce the terminology and describe the main biological systems that physicists will encounter. Similarities between biomolecules, glasses, and solids are stressed with an emphasis on the fundamental concepts of living systems. The central section (Parts III and IV) delves into the dynamics of complex systems. A main theme is the realization that biological sys...

  17. Integration of genomic information with biological networks using Cytoscape.

    Science.gov (United States)

    Bauer-Mehren, Anna

    2013-01-01

    Cytoscape is an open-source software for visualizing, analyzing, and modeling biological networks. This chapter explains how to use Cytoscape to analyze the functional effect of sequence variations in the context of biological networks such as protein-protein interaction networks and signaling pathways. The chapter is divided into five parts: (1) obtaining information about the functional effect of sequence variation in a Cytoscape readable format, (2) loading and displaying different types of biological networks in Cytoscape, (3) integrating the genomic information (SNPs and mutations) with the biological networks, and (4) analyzing the effect of the genomic perturbation onto the network structure using Cytoscape built-in functions. Finally, we briefly outline how the integrated data can help in building mathematical network models for analyzing the effect of the sequence variation onto the dynamics of the biological system. Each part is illustrated by step-by-step instructions on an example use case and visualized by many screenshots and figures.

  18. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

    Gautier, Laurent; Taboureau, Olivier; Audouze, Karine Marie Laure

    2013-01-01

    Introduction: The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining...... it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity. Areas covered: This review describes the strengths and limitations...... of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity. Expert opinion: There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network...

  19. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  20. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  1. A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

    Science.gov (United States)

    Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

    2012-03-26

    Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

  2. From biological and social network metaphors to coupled bio-social wireless networks

    Science.gov (United States)

    Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

    2010-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  3. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  4. Physics of biological membranes

    Science.gov (United States)

    Mouritsen, Ole G.

    The biological membrane is a complex system consisting of an aqueous biomolecular planar aggregate of predominantly lipid and protein molecules. At physiological temperatures, the membrane may be considered a thin (˜50Å) slab of anisotropic fluid characterized by a high lateral mobility of the various molecular components. A substantial fraction of biological activity takes place in association with membranes. As a very lively piece of condensed matter, the biological membrane is a challenging research topic for both the experimental and theoretical physicists who are facing a number of fundamental physical problems including molecular self-organization, macromolecular structure and dynamics, inter-macromolecular interactions, structure-function relationships, transport of energy and matter, and interfacial forces. This paper will present a brief review of recent theoretical and experimental progress on such problems, with special emphasis on lipid bilayer structure and dynamics, lipid phase transitions, lipid-protein and lipid-cholesterol interactions, intermembrane forces, and the physical constraints imposed on biomembrane function and evolution. The paper advocates the dual point of view that there are a number of interesting physics problems in membranology and, at the same time, that the physical properties of biomembranes are important regulators of membrane function.

  5. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  6. A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

    Directory of Open Access Journals (Sweden)

    Tuikkala Johannes

    2012-03-01

    Full Text Available Abstract Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

  7. Robustness: confronting lessons from physics and biology.

    Science.gov (United States)

    Lesne, Annick

    2008-11-01

    The term robustness is encountered in very different scientific fields, from engineering and control theory to dynamical systems to biology. The main question addressed herein is whether the notion of robustness and its correlates (stability, resilience, self-organisation) developed in physics are relevant to biology, or whether specific extensions and novel frameworks are required to account for the robustness properties of living systems. To clarify this issue, the different meanings covered by this unique term are discussed; it is argued that they crucially depend on the kind of perturbations that a robust system should by definition withstand. Possible mechanisms underlying robust behaviours are examined, either encountered in all natural systems (symmetries, conservation laws, dynamic stability) or specific to biological systems (feedbacks and regulatory networks). Special attention is devoted to the (sometimes counterintuitive) interrelations between robustness and noise. A distinction between dynamic selection and natural selection in the establishment of a robust behaviour is underlined. It is finally argued that nested notions of robustness, relevant to different time scales and different levels of organisation, allow one to reconcile the seemingly contradictory requirements for robustness and adaptability in living systems.

  8. Applying differential dynamic logic to reconfigurable biological networks.

    Science.gov (United States)

    Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

    2017-09-01

    Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Organization of excitable dynamics in hierarchical biological networks.

    Directory of Open Access Journals (Sweden)

    Mark Müller-Linow

    Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

  10. Application of random matrix theory to biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov

    2006-09-25

    We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.

  11. Probabilistic biological network alignment.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

    Interactions between molecules are probabilistic events. An interaction may or may not happen with some probability, depending on a variety of factors such as the size, abundance, or proximity of the interacting molecules. In this paper, we consider the problem of aligning two biological networks. Unlike existing methods, we allow one of the two networks to contain probabilistic interactions. Allowing interaction probabilities makes the alignment more biologically relevant at the expense of explosive growth in the number of alternative topologies that may arise from different subsets of interactions that take place. We develop a novel method that efficiently and precisely characterizes this massive search space. We represent the topological similarity between pairs of aligned molecules (i.e., proteins) with the help of random variables and compute their expected values. We validate our method showing that, without sacrificing the running time performance, it can produce novel alignments. Our results also demonstrate that our method identifies biologically meaningful mappings under a comprehensive set of criteria used in the literature as well as the statistical coherence measure that we developed to analyze the statistical significance of the similarity of the functions of the aligned protein pairs.

  12. Bridging Physics and Biology Using Resistance and Axons

    Science.gov (United States)

    Dyer, Joshua M.

    2014-01-01

    When teaching physics, it is often difficult to get biology-oriented students to see the relevance of physics. A complaint often heard is that biology students are required to take physics for the Medical College Admission Test (MCAT) as part of a "weeding out" process, but that they don't feel like they need physics for biology. Despite…

  13. Characterizing the topology of probabilistic biological networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

    Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the joint-degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for node-degree distribution computation than the deterministic ones. all the data sets used, the software

  14. Characterizing Topology of Probabilistic Biological Networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-09-06

    Biological interactions are often uncertain events, that may or may not take place with some probability. Existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. Here, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. We develop a method that accurately describes the degree distribution of such networks. We also extend our method to accurately compute the joint degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. It also helps us find an adequate mathematical model using maximum likelihood estimation. Our results demonstrate that power law and log-normal models best describe degree distributions for probabilistic networks. The inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected.

  15. Multilayer network modeling of integrated biological systems. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    De Domenico, Manlio

    2018-03-01

    Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks - obtained by aggregating or neglecting temporal or categorical description of biological data - are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.

  16. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

    Directory of Open Access Journals (Sweden)

    Jacques Demongeot

    2018-01-01

    Full Text Available Networks used in biological applications at different scales (molecule, cell and population are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system as well as in their discrete Boolean versions (e.g., non-linear Hopfield system; in both cases, the notion of interaction graph G(J associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J, kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i attractor entropy, (ii isochronal entropy and (iii entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

  17. Bridging Physics and Biology Using Resistance and Axons

    Science.gov (United States)

    Dyer, Joshua M.

    2014-11-01

    When teaching physics, it is often difficult to get biology-oriented students to see the relevance of physics.1 A complaint often heard is that biology students are required to take physics for the Medical College Admission Test (MCAT) as part of a "weeding out" process, but that they don't feel like they need physics for biology. Despite this impression held by students, there have been calls for better physics education for future physicians and life scientists.2,3 Research is being performed to improve physics classes and labs by linking topics in biology and physics.4,5 Described here is a laboratory experiment covering the topics of resistance of materials and circuits/Kirchhoff's laws in a biology context with their direct application to neurons, axons, and electrical impulse transmission within animals. This experiment will also demonstrate the mechanism believed to cause multiple sclerosis. The apparatus was designed with low-cost and readily available materials in mind.

  18. Biology-inspired AMO physics

    Science.gov (United States)

    Mathur, Deepak

    2015-01-01

    This Topical Review presents an overview of increasingly robust interconnects that are being established between atomic, molecular and optical (AMO) physics and the life sciences. AMO physics, outgrowing its historical role as a facilitator—a provider of optical methodologies, for instance—now seeks to partner biology in its quest to link systems-level descriptions of biological entities to insights based on molecular processes. Of course, perspectives differ when AMO physicists and biologists consider various processes. For instance, while AMO physicists link molecular properties and dynamics to potential energy surfaces, these have to give way to energy landscapes in considerations of protein dynamics. But there are similarities also: tunnelling and non-adiabatic transitions occur both in protein dynamics and in molecular dynamics. We bring to the fore some such differences and similarities; we consider imaging techniques based on AMO concepts, like 4D fluorescence microscopy which allows access to the dynamics of cellular processes, multiphoton microscopy which offers a built-in confocality, and microscopy with femtosecond laser beams to saturate the suppression of fluorescence in spatially controlled fashion so as to circumvent the diffraction limit. Beyond imaging, AMO physics contributes with optical traps that probe the mechanical and dynamical properties of single ‘live’ cells, highlighting differences between healthy and diseased cells. Trap methodologies have also begun to probe the dynamics governing of neural stem cells adhering to each other to form neurospheres and, with squeezed light to probe sub-diffusive motion of yeast cells. Strong field science contributes not only by providing a source of energetic electrons and γ-rays via laser-plasma accelerations schemes, but also via filamentation and supercontinuum generation, enabling mainstream collision physics into play in diverse processes like DNA damage induced by low-energy collisions to

  19. Perspective: Reaches of chemical physics in biology

    Science.gov (United States)

    Gruebele, Martin; Thirumalai, D.

    2013-01-01

    Chemical physics as a discipline contributes many experimental tools, algorithms, and fundamental theoretical models that can be applied to biological problems. This is especially true now as the molecular level and the systems level descriptions begin to connect, and multi-scale approaches are being developed to solve cutting edge problems in biology. In some cases, the concepts and tools got their start in non-biological fields, and migrated over, such as the idea of glassy landscapes, fluorescence spectroscopy, or master equation approaches. In other cases, the tools were specifically developed with biological physics applications in mind, such as modeling of single molecule trajectories or super-resolution laser techniques. In this introduction to the special topic section on chemical physics of biological systems, we consider a wide range of contributions, all the way from the molecular level, to molecular assemblies, chemical physics of the cell, and finally systems-level approaches, based on the contributions to this special issue. Chemical physicists can look forward to an exciting future where computational tools, analytical models, and new instrumentation will push the boundaries of biological inquiry. PMID:24089712

  20. Perspective: Reaches of chemical physics in biology.

    Science.gov (United States)

    Gruebele, Martin; Thirumalai, D

    2013-09-28

    Chemical physics as a discipline contributes many experimental tools, algorithms, and fundamental theoretical models that can be applied to biological problems. This is especially true now as the molecular level and the systems level descriptions begin to connect, and multi-scale approaches are being developed to solve cutting edge problems in biology. In some cases, the concepts and tools got their start in non-biological fields, and migrated over, such as the idea of glassy landscapes, fluorescence spectroscopy, or master equation approaches. In other cases, the tools were specifically developed with biological physics applications in mind, such as modeling of single molecule trajectories or super-resolution laser techniques. In this introduction to the special topic section on chemical physics of biological systems, we consider a wide range of contributions, all the way from the molecular level, to molecular assemblies, chemical physics of the cell, and finally systems-level approaches, based on the contributions to this special issue. Chemical physicists can look forward to an exciting future where computational tools, analytical models, and new instrumentation will push the boundaries of biological inquiry.

  1. Dynamics and thermodynamics in hierarchically organized systems applications in physics, biology and economics

    CERN Document Server

    Auger, P

    2013-01-01

    One of the most fundamental and efficient ways of conceptualizing complex systems is to organize them hierarchically. A hierarchically organized system is represented by a network of interconnected subsystems, each of which has its own network of subsystems, and so on, until some elementary subsystems are reached that are not further decomposed. This original and important book proposes a general mathematical theory of a hierarchical system and shows how it can be applied to very different topics such as physics (Hamiltonian systems), biology (coupling the molecular and the cellular levels), e

  2. Networks in biological systems: An investigation of the Gene Ontology as an evolving network

    International Nuclear Information System (INIS)

    Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

    2009-01-01

    Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

  3. European Marine Observation and Data Network EMODnet Physics

    Directory of Open Access Journals (Sweden)

    Antonio Novellino

    2014-10-01

    Full Text Available Recently the European Commission undertook steps towards a European Marine Observation and Data Network (EMODnet in order to standardize method for observing and assessing the grade of the Member States seas and improve access to high quality data. Since 2008-2009, European Commission, represented by the Directorate-General for Maritime Affairs and Fisheries (DG MARE, is running several service contracts for creating pilot thematic components of the ur-EMODNET: Biology, Bathymetry, Chemistry, Geology, Habitats, and Physics.The existing EMODnet-Physics portal (www.emodnet-physics.eu is based on a strong collaboration between EuroGOOS member institutes and its regional operational oceanographic systems (ROOSs, and the National Oceanographic Data Centres (NODCs, and it is a marine observation information system. It includes systems for physical data from the whole Europe (wave height andperiod, temperature of the water column, wind speed and direction, salinity of the water column, horizontal velocity of the water column, light attenuation, and sea level provided mainly by fixed stations and ferry-box platforms, discovering related data sets (both near real time and historical data sets, viewing and downloading of the data from about 2100 platforms (www.emodnetphysics.eu/map and thus contributing towards the definition of an operational European Marine Observation and Data Network (EMODnet.

  4. Applying Physical-Layer Network Coding in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Liew SoungChang

    2010-01-01

    Full Text Available A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11. This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.

  5. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

    Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.

  6. Quantum physics meets biology.

    Science.gov (United States)

    Arndt, Markus; Juffmann, Thomas; Vedral, Vlatko

    2009-12-01

    Quantum physics and biology have long been regarded as unrelated disciplines, describing nature at the inanimate microlevel on the one hand and living species on the other hand. Over the past decades the life sciences have succeeded in providing ever more and refined explanations of macroscopic phenomena that were based on an improved understanding of molecular structures and mechanisms. Simultaneously, quantum physics, originally rooted in a world-view of quantum coherences, entanglement, and other nonclassical effects, has been heading toward systems of increasing complexity. The present perspective article shall serve as a "pedestrian guide" to the growing interconnections between the two fields. We recapitulate the generic and sometimes unintuitive characteristics of quantum physics and point to a number of applications in the life sciences. We discuss our criteria for a future "quantum biology," its current status, recent experimental progress, and also the restrictions that nature imposes on bold extrapolations of quantum theory to macroscopic phenomena.

  7. Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

    Science.gov (United States)

    Rossin, Elizabeth J.; Lage, Kasper; Raychaudhuri, Soumya; Xavier, Ramnik J.; Tatar, Diana; Benita, Yair

    2011-01-01

    Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in

  8. SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

    Science.gov (United States)

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

  9. Physical properties of biological entities: an introduction to the ontology of physics for biology.

    Directory of Open Access Journals (Sweden)

    Daniel L Cook

    Full Text Available As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB, a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.

  10. Physical properties of biological entities: an introduction to the ontology of physics for biology.

    Science.gov (United States)

    Cook, Daniel L; Bookstein, Fred L; Gennari, John H

    2011-01-01

    As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration. © 2011 Cook et al.

  11. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    Science.gov (United States)

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

  12. Intermediate physics for medicine and biology

    CERN Document Server

    Hobbie, Russell K

    2015-01-01

    This classic text has been used in over 20 countries by advanced undergraduate and beginning graduate students in biophysics, physiology, medical physics, neuroscience, and biomedical engineering. It bridges the gap between an introductory physics course and the application of physics to the life and biomedical sciences. Extensively revised and updated, the fifth edition incorporates new developments at the interface between physics and biomedicine. New coverage includes cyclotrons, photodynamic therapy, color vision, x-ray crystallography, the electron microscope, cochlear implants, deep brain stimulation, nanomedicine, and other topics highlighted in the National Research Council report BIO2010. As with the previous edition, the first half of the text is primarily biological physics, emphasizing the use of ideas from physics to understand biology and physiology, and the second half is primarily medical physics, describing the use of physics in medicine for diagnosis (mainly imaging) and therapy. Among the m...

  13. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

    Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

  14. Emergence of communication in socio-biological networks

    CERN Document Server

    Berea, Anamaria

    2018-01-01

    This book integrates current advances in biology, economics of information and linguistics research through applications using agent-based modeling and social network analysis to develop scenarios of communication and language emergence in the social aspects of biological communications. The book presents a model of communication emergence that can be applied both to human and non-human living organism networks. The model is based on economic concepts and individual behavior fundamental for the study of trust and reputation networks in social science, particularly in economics; it is also based on the theory of the emergence of norms and historical path dependence that has been influential in institutional economics. Also included are mathematical models and code for agent-based models to explore various scenarios of language evolution, as well as a computer application that explores language and communication in biological versus social organisms, and the emergence of various meanings and grammars in human ...

  15. Link between physics and biology

    International Nuclear Information System (INIS)

    Zaider, M.; Brenner, D.J.; Hall, E.J.; Kliauga, P.

    1988-01-01

    In the general causative chain: radiation physics-radiation chemistry - radiobiology - cancer treatment, physics, the initiating and therefore cardinal agent, continues to play the role of the poor relation. The main reason for this state of affairs rests with the fact that most models of radiation action - the actual link between physics and biology - make very little or no use of information concerning the radiation field: they are simply convenient vehicles for describing a large body of radiobiological data with analytical expressions containing a minimum number of parameters. In spite of their practical usefulness such models will not be further considered here. It is a reasonable assertion that the main goal of radiation biophysics is to elucidate the mechanisms of radiation action on biological entities

  16. Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp

    Directory of Open Access Journals (Sweden)

    networkbiology@iaees.org

    Full Text Available Network Biology ISSN 2220-8879 URL: http://www.iaees.org/publications/journals/nb/online-version.asp RSS: http://www.iaees.org/publications/journals/nb/rss.xml E-mail: networkbiology@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS is an open access, peer-reviewed international journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology. It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: •Theories, algorithms and programs of network analysis •Innovations and applications of biological networks •Ecological networks, food webs and natural equilibrium •Co-evolution, co-extinction, biodiversity conservation •Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs •Physiological networksNetwork regulation of metabolic processes, human diseases and ecological systems •Social networks, epidemiological networks •System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal

  17. Biology-inspired AMO physics

    International Nuclear Information System (INIS)

    Mathur, Deepak

    2015-01-01

    This Topical Review presents an overview of increasingly robust interconnects that are being established between atomic, molecular and optical (AMO) physics and the life sciences. AMO physics, outgrowing its historical role as a facilitator—a provider of optical methodologies, for instance—now seeks to partner biology in its quest to link systems-level descriptions of biological entities to insights based on molecular processes. Of course, perspectives differ when AMO physicists and biologists consider various processes. For instance, while AMO physicists link molecular properties and dynamics to potential energy surfaces, these have to give way to energy landscapes in considerations of protein dynamics. But there are similarities also: tunnelling and non-adiabatic transitions occur both in protein dynamics and in molecular dynamics. We bring to the fore some such differences and similarities; we consider imaging techniques based on AMO concepts, like 4D fluorescence microscopy which allows access to the dynamics of cellular processes, multiphoton microscopy which offers a built-in confocality, and microscopy with femtosecond laser beams to saturate the suppression of fluorescence in spatially controlled fashion so as to circumvent the diffraction limit. Beyond imaging, AMO physics contributes with optical traps that probe the mechanical and dynamical properties of single ‘live’ cells, highlighting differences between healthy and diseased cells. Trap methodologies have also begun to probe the dynamics governing of neural stem cells adhering to each other to form neurospheres and, with squeezed light to probe sub-diffusive motion of yeast cells. Strong field science contributes not only by providing a source of energetic electrons and γ-rays via laser-plasma accelerations schemes, but also via filamentation and supercontinuum generation, enabling mainstream collision physics into play in diverse processes like DNA damage induced by low-energy collisions to

  18. Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

    Directory of Open Access Journals (Sweden)

    Martin Florian

    2012-05-01

    Full Text Available Abstract Background High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK

  19. Physics and Biology Collaborate to Color the World

    Science.gov (United States)

    Liu, Dennis W. C.

    2013-01-01

    To understand how life works, it is essential to understand physics and chemistry. Most biologists have a clear notion of where chemistry fits into their life sciences research and teaching. Although we are physical beings, physics does not always find a place in the biology curriculum. Physics informs and enlightens biology in myriad dimensions,…

  20. Toward University Modeling Instruction—Biology: Adapting Curricular Frameworks from Physics to Biology

    Science.gov (United States)

    Manthey, Seth; Brewe, Eric

    2013-01-01

    University Modeling Instruction (UMI) is an approach to curriculum and pedagogy that focuses instruction on engaging students in building, validating, and deploying scientific models. Modeling Instruction has been successfully implemented in both high school and university physics courses. Studies within the physics education research (PER) community have identified UMI's positive impacts on learning gains, equity, attitudinal shifts, and self-efficacy. While the success of this pedagogical approach has been recognized within the physics community, the use of models and modeling practices is still being developed for biology. Drawing from the existing research on UMI in physics, we describe the theoretical foundations of UMI and how UMI can be adapted to include an emphasis on models and modeling for undergraduate introductory biology courses. In particular, we discuss our ongoing work to develop a framework for the first semester of a two-semester introductory biology course sequence by identifying the essential basic models for an introductory biology course sequence. PMID:23737628

  1. Toward university modeling instruction--biology: adapting curricular frameworks from physics to biology.

    Science.gov (United States)

    Manthey, Seth; Brewe, Eric

    2013-06-01

    University Modeling Instruction (UMI) is an approach to curriculum and pedagogy that focuses instruction on engaging students in building, validating, and deploying scientific models. Modeling Instruction has been successfully implemented in both high school and university physics courses. Studies within the physics education research (PER) community have identified UMI's positive impacts on learning gains, equity, attitudinal shifts, and self-efficacy. While the success of this pedagogical approach has been recognized within the physics community, the use of models and modeling practices is still being developed for biology. Drawing from the existing research on UMI in physics, we describe the theoretical foundations of UMI and how UMI can be adapted to include an emphasis on models and modeling for undergraduate introductory biology courses. In particular, we discuss our ongoing work to develop a framework for the first semester of a two-semester introductory biology course sequence by identifying the essential basic models for an introductory biology course sequence.

  2. Network Analysis Tools: from biological networks to clusters and pathways.

    Science.gov (United States)

    Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

    2008-01-01

    Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

  3. Intermediate Physics for Medicine and Biology

    CERN Document Server

    Hobbie, Russell K

    2007-01-01

    Intended for advanced undergraduate and beginning graduate students in biophysics, physiology, medical physics, cell biology, and biomedical engineering, this wide-ranging text bridges the gap between introductory physics and its application to the life and biomedical sciences. This extensively revised and updated fourth edition reflects new developments at the burgeoning interface between physics and biomedicine. Among the many topics treated are: forces in the skeletal system; fluid flow, with examples from the circulatory system; the logistic equation; scaling; transport of neutral particles by diffusion and by solvent drag; membranes and osmosis; equipartition of energy in statistical mechanics; the chemical potential and free energy; biological magnetic fields; membranes and gated channels in membranes; linear and nonlinear feedback systems; nonlinear phenomena, including biological clocks and chaotic behavior; signal analysis, noise and stochastic resonance detection of weak signals; image formation and...

  4. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

    Science.gov (United States)

    2014-01-01

    Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism

  5. Novel topological descriptors for analyzing biological networks

    Directory of Open Access Journals (Sweden)

    Varmuza Kurt K

    2010-06-01

    Full Text Available Abstract Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.

  6. Physical models of biological information and adaptation.

    Science.gov (United States)

    Stuart, C I

    1985-04-07

    The bio-informational equivalence asserts that biological processes reduce to processes of information transfer. In this paper, that equivalence is treated as a metaphor with deeply anthropomorphic content of a sort that resists constitutive-analytical definition, including formulation within mathematical theories of information. It is argued that continuance of the metaphor, as a quasi-theoretical perspective in biology, must entail a methodological dislocation between biological and physical science. It is proposed that a general class of functions, drawn from classical physics, can serve to eliminate the anthropomorphism. Further considerations indicate that the concept of biological adaptation is central to the general applicability of the informational idea in biology; a non-anthropomorphic treatment of adaptive phenomena is suggested in terms of variational principles.

  7. Towards the understanding of network information processing in biology

    Science.gov (United States)

    Singh, Vijay

    Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

  8. Physics and biology of protein

    International Nuclear Information System (INIS)

    Go, Nobuhiro

    2008-01-01

    This is a record of my lecture given at the occasion of Yukawa-Tomonaga Centennial Symposium. At first I will mention very briefly how Yukawa contributed to the development of biophysics in Japan. Then I will be concerned with the relationship between physics and biology by discussing various aspects of protein. How far and in what sense can physics approach the essence of protein? In what aspects are something beyond physics important? (author)

  9. Social network size relates to developmental neural sensitivity to biological motion

    Directory of Open Access Journals (Sweden)

    L.A. Kirby

    2018-04-01

    Full Text Available The ability to perceive others’ actions and goals from human motion (i.e., biological motion perception is a critical component of social perception and may be linked to the development of real-world social relationships. Adult research demonstrates two key nodes of the brain’s biological motion perception system—amygdala and posterior superior temporal sulcus (pSTS—are linked to variability in social network properties. The relation between social perception and social network properties, however, has not yet been investigated in middle childhood—a time when individual differences in social experiences and social perception are growing. The aims of this study were to (1 replicate past work showing amygdala and pSTS sensitivity to biological motion in middle childhood; (2 examine age-related changes in the neural sensitivity for biological motion, and (3 determine whether neural sensitivity for biological motion relates to social network characteristics in children. Consistent with past work, we demonstrate a significant relation between social network size and neural sensitivity for biological motion in left pSTS, but do not find age-related change in biological motion perception. This finding offers evidence for the interplay between real-world social experiences and functional brain development and has important implications for understanding disorders of atypical social experience. Keywords: Biological motion, Social networks, Middle childhood, Neural specialization, Brain-behavior relations, pSTS

  10. Discriminative topological features reveal biological network mechanisms

    Directory of Open Access Journals (Sweden)

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  11. Network Physics - the only company to provide physics-based network management - secures additional funding and new executives

    CERN Multimedia

    2003-01-01

    "Network Physics, the only provider of physics-based network management products, today announced an additional venture round of $6 million in funding, as well as the addition of David Jones as president and CEO and Tom Dunn as vice president of sales and business development" (1 page).

  12. On the origin of distribution patterns of motifs in biological networks

    Directory of Open Access Journals (Sweden)

    Lesk Arthur M

    2008-08-01

    Full Text Available Abstract Background Inventories of small subgraphs in biological networks have identified commonly-recurring patterns, called motifs. The inference that these motifs have been selected for function rests on the idea that their occurrences are significantly more frequent than random. Results Our analysis of several large biological networks suggests, in contrast, that the frequencies of appearance of common subgraphs are similar in natural and corresponding random networks. Conclusion Indeed, certain topological features of biological networks give rise naturally to the common appearance of the motifs. We therefore question whether frequencies of occurrences are reasonable evidence that the structures of motifs have been selected for their functional contribution to the operation of networks.

  13. When physics is not "just physics": complexity science invites new measurement frames for exploring the physics of cognitive and biological development.

    Science.gov (United States)

    Kelty-Stephen, Damian; Dixon, James A

    2012-01-01

    The neurobiological sciences have struggled to resolve the physical foundations for biological and cognitive phenomena with a suspicion that biological and cognitive systems, capable of exhibiting and contributing to structure within themselves and through their contexts, are fundamentally distinct or autonomous from purely physical systems. Complexity science offers new physics-based approaches to explaining biological and cognitive phenomena. In response to controversy over whether complexity science might seek to "explain away" biology and cognition as "just physics," we propose that complexity science serves as an application of recent advances in physics to phenomena in biology and cognition without reducing or undermining the integrity of the phenomena to be explained. We highlight that physics is, like the neurobiological sciences, an evolving field and that the threat of reduction is overstated. We propose that distinctions between biological and cognitive systems from physical systems are pretheoretical and thus optional. We review our own work applying insights from post-classical physics regarding turbulence and fractal fluctuations to the problems of developing cognitive structure. Far from hoping to reduce biology and cognition to "nothing but" physics, we present our view that complexity science offers new explanatory frameworks for considering physical foundations of biological and cognitive phenomena.

  14. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias

    2017-01-01

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes

  15. Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

    DEFF Research Database (Denmark)

    Rossin, Elizabeth J.; Hansen, Kasper Lage; Raychaudhuri, Soumya

    2011-01-01

    Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these r......Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed...... in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more...... that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non...

  16. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  17. Physical and social activities mediate the associations between social network types and ventilatory function in Chinese older adults.

    Science.gov (United States)

    Cheng, Sheung-Tak; Leung, Edward M F; Chan, Trista Wai Sze

    2014-06-01

    This study examined the associations between social network types and peak expiratory flow (PEF), and whether these associations were mediated by social and physical activities and mood. Nine hundred twenty-four community-dwelling Chinese older adults, who were classified into five network types (diverse, friend-focused, family-focused, distant family, and restricted), provided data on demographics, social and physical activities, mood, smoking, chronic diseases, and instrumental activities of daily living. PEF and biological covariates, including blood lipids and glucose, blood pressure, and height and weight, were assessed. Two measures of PEF were analyzed: the raw reading in L/min and the reading expressed as percentage of predicted normal value on the basis of age, sex, and height. Diverse, friend-focused, and distant family networks were hypothesized to have better PEF values compared with restricted networks, through higher physical and/or social activities. No relative advantage was predicted for family-focused networks because such networks tend to be associated with lower physical activity. Older adults with diverse, friend-focused, and distant family networks had significantly better PEF measures than those with restricted networks. The associations between diverse network and PEF measures were partially mediated by physical exercise and socializing activity. The associations between friend-focused network and PEF measures were partially mediated by socializing activity. No significant PEF differences between family-focused and restricted networks were found. Findings suggest that social network types are associated with PEF in older adults, and that network-type differences in physical and socializing activity is partly responsible for this relationship. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  18. Local-area networks in nuclear physics (survey)

    International Nuclear Information System (INIS)

    Foteev, V.A.

    1987-01-01

    The design fundamentals, comparative characteristics, and possibilities of local-area networks as applied to physics experiments are examined. The example of Ethernet is used to explain the operation of local networks, and the results of a study of their functional characteristics are presented. Examples of operational local networks in nuclear physics research and atomic engineering are given: the Japan Research Institute of Atomic Energy, the University of California, and Los Alamos National Laboratory; atomic power plant control in Japan; DECnet and Fastbus; network developments at the Siberian Branch of the Academy of Sciences of the USSR and at the Laboratory of Neutron Physics of the Joint Institute for Nuclear Research; and others. It is shown that local networks are important means that considerably increase productivity in data processing

  19. Dense module enumeration in biological networks

    Science.gov (United States)

    Tsuda, Koji; Georgii, Elisabeth

    2009-12-01

    Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

  20. Dense module enumeration in biological networks

    International Nuclear Information System (INIS)

    Tsuda, Koji; Georgii, Elisabeth

    2009-01-01

    Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

  1. Biological physics in México: Review and new challenges.

    Science.gov (United States)

    Hernández-Lemus, Enrique

    2011-03-01

    Biological and physical sciences possess a long-standing tradition of cooperativity as separate but related subfields of science. For some time, this cooperativity has been limited by their obvious differences in methods and views. Biological physics has recently experienced a kind of revival (or better a rebirth) due to the growth of molecular research on animate matter. New avenues for research have been opened for both theoretical and experimental physicists. Nevertheless, in order to better travel for such paths, the contemporary biological physicist should be armed with a set of specialized tools and methods but also with a new attitude toward multidisciplinarity. In this review article, we intend to somehow summarize what has been done in the past (in particular, as an example we will take a closer look at the Mexican case), to show some examples of fruitful investigations in the biological physics area and also to set a proposal of new curricula for physics students and professionals interested in applying their science to get a better understanding of the physical basis of biological function.

  2. Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

    Directory of Open Access Journals (Sweden)

    Lemke Ney

    2009-09-01

    Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing

  3. The Caltech physics/engineering network

    International Nuclear Information System (INIS)

    Melvin, J.D.

    1985-01-01

    The California Institute of Technology Physics/Engineering network (referred to as the ''Caltech network'' in this paper) is a software system which has been developed over the last four years for high-speed data acquisition, graphics, and distributed computer resource communications. This paper presents: a brief history of past and current development of the network software; features currently implemented; current speed performance; and applications of the network to research and education at Caltech and at other institutions

  4. Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

    Science.gov (United States)

    Hao, Dapeng; Ren, Cong; Li, Chuanxing

    2012-05-01

    A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

  5. Optimizing Introductory Physics for the Life Sciences: Placing Physics in Biological Context

    Science.gov (United States)

    Crouch, Catherine

    2014-03-01

    Physics is a critical foundation for today's life sciences and medicine. However, the physics content and ways of thinking identified by life scientists as most important for their fields are often not taught, or underemphasized, in traditional introductory physics courses. Furthermore, such courses rarely give students practice using physics to understand living systems in a substantial way. Consequently, students are unlikely to recognize the value of physics to their chosen fields, or to develop facility in applying physics to biological systems. At Swarthmore, as at several other institutions engaged in reforming this course, we have reorganized the introductory course for life science students around touchstone biological examples, in which fundamental physics contributes significantly to understanding biological phenomena or research techniques, in order to make explicit the value of physics to the life sciences. We have also focused on the physics topics and approaches most relevant to biology while seeking to develop rigorous qualitative reasoning and quantitative problem solving skills, using established pedagogical best practices. Each unit is motivated by and culminates with students analyzing one or more touchstone examples. For example, in the second semester we emphasize electric potential and potential difference more than electric field, and start from students' typically superficial understanding of the cell membrane potential and of electrical interactions in biochemistry to help them develop a more sophisticated understanding of electric forces, field, and potential, including in the salt water environment of life. Other second semester touchstones include optics of vision and microscopes, circuit models for neural signaling, and magnetotactic bacteria. When possible, we have adapted existing research-based curricular materials to support these examples. This talk will describe the design and development process for this course, give examples of

  6. Biology meets Physics: Reductionism and Multi-scale Modeling of Morphogenesis

    DEFF Research Database (Denmark)

    Green, Sara; Batterman, Robert

    2017-01-01

    A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism ...... modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent....... from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom......-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem present a challenge to reductive explanations in both physics and biology. The problem refers to the scale...

  7. Exploitation of complex network topology for link prediction in biological interactomes

    KAUST Repository

    Alanis Lobato, Gregorio

    2014-06-01

    The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.

  8. Social traits, social networks and evolutionary biology.

    Science.gov (United States)

    Fisher, D N; McAdam, A G

    2017-12-01

    effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

  9. Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

    Directory of Open Access Journals (Sweden)

    Kim Man-Sun

    2012-05-01

    Full Text Available Abstract Background Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts. Results On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns. Conclusions Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

  10. PyPathway: Python Package for Biological Network Analysis and Visualization.

    Science.gov (United States)

    Xu, Yang; Luo, Xiao-Chun

    2018-05-01

    Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

  11. Pragmatic information in biology and physics.

    Science.gov (United States)

    Roederer, Juan G

    2016-03-13

    I will show how an objective definition of the concept of information and the consideration of recent results about information processing in the human brain help clarify some fundamental aspects of physics and biology. Rather than attempting to define information ab initio, I introduce the concept of interaction between material bodies as a primary concept. Two distinct categories can be identified: (i) interactions which can always be reduced to a superposition of physical interactions (forces) between elementary constituents; and (ii) interactions between complex bodies which cannot be expressed as a superposition of interactions between parts, and in which patterns and forms (in space and/or time) play the determining role. Pragmatic information is then defined as the link between a given pattern and the ensuing pattern-specific change. I will show that pragmatic information is a biological concept; it plays no active role in the purely physical domain-it only does so when a living organism intervenes. The consequences for physics (including foundations of quantum mechanics) and biology (including brain function) will be discussed. This will include speculations about three fundamental transitions, from the quantum to the classical domain, from natural inanimate to living systems, and from subhuman to human brain information-processing operations, introduced here in their direct connection with the concept of pragmatic information. © 2016 The Author(s).

  12. Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

    Directory of Open Access Journals (Sweden)

    Hao Dapeng

    2012-05-01

    Full Text Available Abstract Background A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling. Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. Results We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn’t show dependence of degree. Conclusions Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to “deterministic model” of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

  13. Neural network models: from biology to many - body phenomenology

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

    The current surge of research on practical side of neural networks and their utility in memory storage/recall, pattern recognition and classification is given in this article. The initial attraction of neural networks as dynamical and statistical system has been investigated. From the view of many-body theorist, the neurons may be thought of as particles, and the weighted connection between the units, as the interaction between these particles. Finally, the author has seen the impressive capabilities of artificial neural networks in pattern recognition and classification may be exploited to solve data management problems in experimental physics and the discovery of radically new theoretically description of physical problems and neural networks can be used in physics. (A.B.)

  14. Physical Biology : challenges for our second decade

    Science.gov (United States)

    Levine, Herbert

    2014-06-01

    It is quite an honor to be asked to become the third editor-in-chief of Physical Biology . I am following in the footsteps of Tim Newman, who served with energy and enthusiasm. Hopefully, the entire community fully appreciates his contributions to moving the field forward. Thank you, Tim! With the honor, however, goes a clear responsibility. Our journal has survived its birth pangs and emerged as a serious venue for publishing quality research papers using physical science to address the workings of living matter. With the support of scientists in this field and with the ongoing commitment of the IOP, we have successfully reached adolescence. Yet, there is clearly much room to grow and there are clear challenges in defining and maintaining our special niche in the publishing landscape. In this still-developing state, the journal very much mimics the state of the field of physical biology itself. Few scientists continue to question the relevance of physical science for the investigation of the living world. But, will our new perspective and the methods that come with it really lead to radically new principles of how life works? Or, will breakthroughs continue to come from experimental biology (perhaps aided by the traditional physicist-as-tool-builder paradigm), leaving us to put quantitative touches on established fundamentals? In thinking about these questions for the field and for the journal, I have tried to understand what is really unique about our joint endeavors. I have become convinced that living matter represents a new challenge to our physical-science based conceptual framework. Not only is it far from equilibrium, as has been generally recognized, but it violates our simple notions of the separability of constituents, their interactions and the resulting large-scale behavior. Unlike, say, atomic physicists who can do productive research while safely ignoring the latest developments in QCD (let alone particle physics at higher energies), we do not yet

  15. Epigenetics and Why Biological Networks are More Controllable than Expected

    Science.gov (United States)

    Motter, Adilson

    2013-03-01

    A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. In this talk, I will show that it is possible to exploit this same principle to control network behavior. This approach takes advantage of the nonlinear dynamics inherent to real networks, and allows bringing the system to a desired target state even when this state is not directly accessible or the linear counterpart is not controllable. Applications show that this framework permits both reprogramming a network to a desired task as well as rescuing networks from the brink of failure, which I will illustrate through various biological problems. I will also briefly review the progress our group has made over the past 5 years on related control of complex networks in non-biological domains.

  16. Analysis and logical modeling of biological signaling transduction networks

    Science.gov (United States)

    Sun, Zhongyao

    The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

  17. Two classes of bipartite networks: nested biological and social systems.

    Science.gov (United States)

    Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

    2008-10-01

    Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

  18. Biological dosimetry by the triage dicentric chromosome assay - Further validation of international networking

    Energy Technology Data Exchange (ETDEWEB)

    Wilkins, Ruth C., E-mail: Ruth.Wilkins@hc-sc.gc.ca [Health Canada, Ottawa, ON K1A 0K9 (Canada); Romm, Horst; Oestreicher, Ursula [Bundesamt fur Strahlenschutz, 38226 Salzgitter (Germany); Marro, Leonora [Health Canada, Ottawa, ON K1A 0K9 (Canada); Yoshida, Mitsuaki A. [Biological Dosimetry Section, Dept. of Dose Assessment, Research Center for Radiation Emergency Medicine, NIRS, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555 (Japan); Department Radiation Biology, Institute of Radiation Emergency Medicine, Hirosaki University Graduate School of Health Sciences, 66-1 Hon-cho, Hirosaki, Aomori 036-8564 (Japan); Suto, Y. [Biological Dosimetry Section, Dept. of Dose Assessment, Research Center for Radiation Emergency Medicine, NIRS, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555 (Japan); Prasanna, Pataje G.S. [National Cancer Institute, Division of Cancer Treatment and Diagnosis, Radiation Research Program, 6130 Executive Blvd., MSC 7440, Bethesda, MD 20892-7440 (United States)

    2011-09-15

    Biological dosimetry is an essential tool for estimating radiation doses received to personnel when physical dosimetry is not available or inadequate. The current preferred biodosimetry method is based on the measurement of radiation-specific dicentric chromosomes in exposed individuals' peripheral blood lymphocytes. However, this method is labor-, time- and expertise-demanding. Consequently, for mass casualty applications, strategies have been developed to increase its throughput. One such strategy is to develop validated cytogenetic biodosimetry laboratory networks, both national and international. In a previous study, the dicentric chromosome assay (DCA) was validated in our cytogenetic biodosimetry network involving five geographically dispersed laboratories. A complementary strategy to further enhance the throughput of the DCA among inter-laboratory networks is to use a triage DCA where dose assessments are made by truncating the labor-demanding and time-consuming metaphase spread analysis to 20 - 50 metaphase spreads instead of routine 500 - 1000 metaphase spread analysis. Our laboratory network also validated this triage DCA, however, these dose estimates were made using calibration curves generated in each laboratory from the blood samples irradiated in a single laboratory. In an emergency situation, dose estimates made using pre-existing calibration curves which may vary according to radiation type and dose rate and therefore influence the assessed dose. Here, we analyze the effect of using a pre-existing calibration curve on assessed dose among our network laboratories. The dose estimates were made by analyzing 1000 metaphase spreads as well as triage quality scoring and compared to actual physical doses applied to the samples for validation. The dose estimates in the laboratory partners were in good agreement with the applied physical doses and determined to be adequate for guidance in the treatment of acute radiation syndrome.

  19. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  20. Fusion of biological membranes

    Indian Academy of Sciences (India)

    Home; Journals; Pramana – Journal of Physics; Volume 64; Issue 6. Fusion of biological membranes. K Katsov M Müller M Schick. Invited Talks:- Topic 11. Biologically motivated problems (protein-folding models, dynamics at the scale of the cell; biological networks, evolution models, etc.) Volume 64 Issue 6 June 2005 pp ...

  1. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    Science.gov (United States)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

  2. The Physics of Marine Biology.

    Science.gov (United States)

    Conn, Kathleen

    1992-01-01

    Discusses ways in which marine biology can be integrated into the physics classroom. Topics suggested for incorporation include the harmonic motion of ocean waves, ocean currents, the interaction of visible light with ocean water, pressure, light absorption, and sound transfer in water. (MDH)

  3. Fast grid layout algorithm for biological networks with sweep calculation.

    Science.gov (United States)

    Kojima, Kaname; Nagasaki, Masao; Miyano, Satoru

    2008-06-15

    Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks. We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex. Java implementations of our layout algorithms are available in Cell Illustrator. masao@ims.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online.

  4. Normal mode analysis and applications in biological physics.

    Science.gov (United States)

    Dykeman, Eric C; Sankey, Otto F

    2010-10-27

    Normal mode analysis has become a popular and often used theoretical tool in the study of functional motions in enzymes, viruses, and large protein assemblies. The use of normal modes in the study of these motions is often extremely fruitful since many of the functional motions of large proteins can be described using just a few normal modes which are intimately related to the overall structure of the protein. In this review, we present a broad overview of several popular methods used in the study of normal modes in biological physics including continuum elastic theory, the elastic network model, and a new all-atom method, recently developed, which is capable of computing a subset of the low frequency vibrational modes exactly. After a review of the various methods, we present several examples of applications of normal modes in the study of functional motions, with an emphasis on viral capsids.

  5. GraphAlignment: Bayesian pairwise alignment of biological networks

    Czech Academy of Sciences Publication Activity Database

    Kolář, Michal; Meier, J.; Mustonen, V.; Lässig, M.; Berg, J.

    2012-01-01

    Roč. 6, November 21 (2012) ISSN 1752-0509 Grant - others:Deutsche Forschungsgemeinschaft(DE) SFB 680; Deutsche Forschungsgemeinschaft(DE) SFB-TR12; Deutsche Forschungsgemeinschaft(DE) BE 2478/2-1 Institutional research plan: CEZ:AV0Z50520514 Keywords : Graph alignment * Biological networks * Parameter estimation * Bioconductor Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.982, year: 2012

  6. Introductory physics in biological context: An approach to improve introductory physics for life science students

    Science.gov (United States)

    Crouch, Catherine H.; Heller, Kenneth

    2014-05-01

    We describe restructuring the introductory physics for life science students (IPLS) course to better support these students in using physics to understand their chosen fields. Our courses teach physics using biologically rich contexts. Specifically, we use examples in which fundamental physics contributes significantly to understanding a biological system to make explicit the value of physics to the life sciences. This requires selecting the course content to reflect the topics most relevant to biology while maintaining the fundamental disciplinary structure of physics. In addition to stressing the importance of the fundamental principles of physics, an important goal is developing students' quantitative and problem solving skills. Our guiding pedagogical framework is the cognitive apprenticeship model, in which learning occurs most effectively when students can articulate why what they are learning matters to them. In this article, we describe our courses, summarize initial assessment data, and identify needs for future research.

  7. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  8. CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

    Science.gov (United States)

    Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

    2017-08-31

    Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

  9. Local and global control of ecological and biological networks

    OpenAIRE

    Alessandro Ferrarini

    2014-01-01

    Recently, I introduced a methodological framework so that ecological and biological networks can be controlled both from inside and outside by coupling network dynamics and evolutionary modelling. The endogenous control requires the network to be optimized at the beginning of its dynamics (by acting upon nodes, edges or both) so that it will then go inertially to the desired state. Instead, the exogenous control requires that exogenous controllers act upon the network at each time step. By th...

  10. High Energy Physics and Nuclear Physics Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Dart, Eli; Bauerdick, Lothar; Bell, Greg; Ciuffo, Leandro; Dasu, Sridhara; Dattoria, Vince; De, Kaushik; Ernst, Michael; Finkelson, Dale; Gottleib, Steven; Gutsche, Oliver; Habib, Salman; Hoeche, Stefan; Hughes-Jones, Richard; Ibarra, Julio; Johnston, William; Kisner, Theodore; Kowalski, Andy; Lauret, Jerome; Luitz, Steffen; Mackenzie, Paul; Maguire, Chales; Metzger, Joe; Monga, Inder; Ng, Cho-Kuen; Nielsen, Jason; Price, Larry; Porter, Jeff; Purschke, Martin; Rai, Gulshan; Roser, Rob; Schram, Malachi; Tull, Craig; Watson, Chip; Zurawski, Jason

    2014-03-02

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements needed by instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In August 2013, ESnet and the DOE SC Offices of High Energy Physics (HEP) and Nuclear Physics (NP) organized a review to characterize the networking requirements of the programs funded by the HEP and NP program offices. Several key findings resulted from the review. Among them: 1. The Large Hadron Collider?s ATLAS (A Toroidal LHC Apparatus) and CMS (Compact Muon Solenoid) experiments are adopting remote input/output (I/O) as a core component of their data analysis infrastructure. This will significantly increase their demands on the network from both a reliability perspective and a performance perspective. 2. The Large Hadron Collider (LHC) experiments (particularly ATLAS and CMS) are working to integrate network awareness into the workflow systems that manage the large number of daily analysis jobs (1 million analysis jobs per day for ATLAS), which are an integral part of the experiments. Collaboration with networking organizations such as ESnet, and the consumption of performance data (e.g., from perfSONAR [PERformance Service Oriented Network monitoring Architecture]) are critical to the success of these efforts. 3. The international aspects of HEP and NP collaborations continue to expand. This includes the LHC experiments, the Relativistic Heavy Ion Collider (RHIC) experiments, the Belle II Collaboration, the Large Synoptic Survey Telescope (LSST), and others. The international nature of these collaborations makes them heavily

  11. Quantifying the connectivity of scale-free and biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

    2004-07-01

    Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.

  12. Physical mechanisms of biological molecular motors

    International Nuclear Information System (INIS)

    Miller, John H. Jr.; Vajrala, Vijayanand; Infante, Hans L.; Claycomb, James R.; Palanisami, Akilan; Fang Jie; Mercier, George T.

    2009-01-01

    Biological motors generally fall into two categories: (1) those that convert chemical into mechanical energy via hydrolysis of a nucleoside triphosphate, usually adenosine triphosphate, regarded as life's chemical currency of energy and (2) membrane bound motors driven directly by an ion gradient and/or membrane potential. Here we argue that electrostatic interactions play a vital role for both types of motors and, therefore, the tools of physics can greatly contribute to understanding biological motors

  13. The degree distribution of fixed act-size collaboration networks

    Indian Academy of Sciences (India)

    ranging from the physical to biological, to even social sciences. Typical complex networks include the World Wide Web [1], biological interacting networks [2–4], ... and dynamical features of such complex networks has become overwhelming.

  14. Decoding network dynamics in cancer

    DEFF Research Database (Denmark)

    Linding, Rune

    2014-01-01

    Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...... and with an accuracy that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted molecular and biological studies have led to numerous pathway models of developmental and disease related processes. However, so far no global models have been derived from pathways, capable...

  15. Network biology concepts in complex disease comorbidities

    DEFF Research Database (Denmark)

    Hu, Jessica Xin; Thomas, Cecilia Engel; Brunak, Søren

    2016-01-01

    collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge......The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being...

  16. Physical Layer Network Coding

    DEFF Research Database (Denmark)

    Fukui, Hironori; Yomo, Hironori; Popovski, Petar

    2013-01-01

    of interfering nodes and usage of spatial reservation mechanisms. Specifically, we introduce a reserved area in order to protect the nodes involved in two-way relaying from the interference caused by neighboring nodes. We analytically derive the end-to-end rate achieved by PLNC considering the impact......Physical layer network coding (PLNC) has the potential to improve throughput of multi-hop networks. However, most of the works are focused on the simple, three-node model with two-way relaying, not taking into account the fact that there can be other neighboring nodes that can cause....../receive interference. The way to deal with this problem in distributed wireless networks is usage of MAC-layer mechanisms that make a spatial reservation of the shared wireless medium, similar to the well-known RTS/CTS in IEEE 802.11 wireless networks. In this paper, we investigate two-way relaying in presence...

  17. Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology

    Directory of Open Access Journals (Sweden)

    Jose A. Santiago

    2017-05-01

    Full Text Available Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD, Parkinson’s (PD and Huntington’s diseases (HD. We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.

  18. Physical mechanisms of biological molecular motors

    Energy Technology Data Exchange (ETDEWEB)

    Miller, John H. Jr. [Department of Physics and Texas Center for Superconductivity, University of Houston, 4800 Calhoun Road, Ste. 617 SR1 Houston, TX 77204-5005 (United States)], E-mail: jhmiller@uh.edu; Vajrala, Vijayanand; Infante, Hans L. [Department of Physics and Texas Center for Superconductivity, University of Houston, 4800 Calhoun Road, Ste. 617 SR1 Houston, TX 77204-5005 (United States); Claycomb, James R. [Department of Physics and Texas Center for Superconductivity, University of Houston, 4800 Calhoun Road, Ste. 617 SR1 Houston, TX 77204-5005 (United States); Department of Mathematics and Physics, Houston Baptist University, 7502 Fondren Road, Houston, TX 77074-3298 (United States); Palanisami, Akilan; Fang Jie; Mercier, George T. [Department of Physics and Texas Center for Superconductivity, University of Houston, 4800 Calhoun Road, Ste. 617 SR1 Houston, TX 77204-5005 (United States)

    2009-03-01

    Biological motors generally fall into two categories: (1) those that convert chemical into mechanical energy via hydrolysis of a nucleoside triphosphate, usually adenosine triphosphate, regarded as life's chemical currency of energy and (2) membrane bound motors driven directly by an ion gradient and/or membrane potential. Here we argue that electrostatic interactions play a vital role for both types of motors and, therefore, the tools of physics can greatly contribute to understanding biological motors.

  19. The Impact of the Physical Activity Policy Research Network.

    Science.gov (United States)

    Manteiga, Alicia M; Eyler, Amy A; Valko, Cheryl; Brownson, Ross C; Evenson, Kelly R; Schmid, Thomas

    2017-03-01

    Lack of physical activity is one of the greatest challenges of the 21st century. The Physical Activity Policy Research Network (PAPRN) is a thematic network established in 2004 to identify determinants, implementation, and outcomes of policies that are effective in increasing physical activity. The purpose of this study is to describe the products of PAPRN and make recommendations for future research and best practices. A mixed methods approach was used to obtain both quantitative and qualitative data on the network. First, in 2014, PAPRN's dissemination products from 2004 to 2014 were extracted and reviewed, including 57 publications and 56 presentations. Next, semi-structured qualitative interviews were conducted with 25 key network participants from 17 locations around the U.S. The transcripts were transcribed and coded. The results of the interviews indicated that the research network addressed several components of its mission, including the identification of physical activity policies, determinants of these policies, and the process of policy implementation. However, research focusing on physical activity policy outcomes was limited. Best practices included collaboration between researchers and practitioners and involvement of practitioners in research design, data collection, and dissemination of results. PAPRN is an example of a productive research network and has contributed to both the process and content of physical activity policy research over the past decade. Future research should emphasize physical activity policy outcomes. Additionally, increased partnerships with practitioners for collaborative, cross-sectoral physical activity policy research should be developed. Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.

  20. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits.

    Directory of Open Access Journals (Sweden)

    Danielle S Bassett

    2010-04-01

    Full Text Available Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.

  1. Physical activity and biological maturation: a systematic review

    Directory of Open Access Journals (Sweden)

    Eliane Denise Araújo Bacil

    2015-03-01

    Full Text Available OBJECTIVE: To analyze the association between physical activity (PA and biological maturation in children and adolescents. DATA SOURCE: We performed a systematic review in April 2013 in the electronic databases of PubMed/MEDLINE, SportDiscus, Web of Science and LILACS without time restrictions. A total of 628 potentially relevant articles were identified and 10 met the inclusion criteria for this review: cross-sectional or longitudinal studies, published in Portuguese, English or Spanish, with schoolchildren aged 9-15 years old of both genders. DATA SYNTHESIS: Despite the heterogeneity of the studies, there was an inverse association between PA and biological maturation. PA decreases with increased biological and chronological age in both genders. Boys tend to be more physically active than girls; however, when controlling for biological age, the gender differences disappear. The association between PA and timing of maturation varies between the genders. Variation in the timing of biological maturation affects the tracking of PA in early adolescent girls. This review suggests that mediators (BMI, depression, low self-esteem, and concerns about body weight can explain the association between PA and biological maturation. CONCLUSIONS: There is an association between PA and biological maturation. PA decreases with increasing biological age with no differences between genders. As for the timing of biological maturation, this association varies between genders.

  2. Physics with illustrative examples from medicine and biology

    CERN Document Server

    Benedek, George B

    Physics: with illustrative examples from medicine and biology is a three-volume set of textbooks in introductory physics written at the calculus level and designed primarily for students with career objectives in the life sciences.

  3. KeyPathwayMiner - De-novo network enrichment by combining multiple OMICS data and biological networks

    DEFF Research Database (Denmark)

    Baumbach, Jan; Alcaraz, Nicolas; Pauling, Josch K.

    We tackle the problem of de-novo pathway extraction. Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts and visualizes all maximal connected sub-networks that contain mainly genes that are dysregulated, e.g., differentially expressed, in most cases ...

  4. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

    Directory of Open Access Journals (Sweden)

    Shuo Gu

    2017-01-01

    Full Text Available With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

  5. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

    Science.gov (United States)

    Gu, Shuo; Pei, Jianfeng

    2017-01-01

    With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

  6. Physical biology of human brain development

    Directory of Open Access Journals (Sweden)

    Silvia eBudday

    2015-07-01

    Full Text Available Neurodevelopment is a complex, dynamic process that involves a precisely orchestrated sequence of genetic, environmental, biochemical, and physical events. Developmental biology and genetics have shaped our understanding of the molecular and cellular mechanisms during neurodevelopment. Recent studies suggest that physical forces play a central role in translating these cellular mechanisms into the complex surface morphology of the human brain. However, the precise impact of neuronal differentiation, migration, and connection on the physical forces during cortical folding remains unknown. Here we review the cellular mechanisms of neurodevelopment with a view towards surface morphogenesis, pattern selection, and evolution of shape. We revisit cortical folding as the instability problem of constrained differential growth in a multi-layered system. To identify the contributing factors of differential growth, we map out the timeline of neurodevelopment in humans and highlight the cellular events associated with extreme radial and tangential expansion. We demonstrate how computational modeling of differential growth can bridge the scales-from phenomena on the cellular level towards form and function on the organ level-to make quantitative, personalized predictions. Physics-based models can quantify cortical stresses, identify critical folding conditions, rationalize pattern selection, and predict gyral wavelengths and gyrification indices. We illustrate that physical forces can explain cortical malformations as emergent properties of developmental disorders. Combining biology and physics holds promise to advance our understanding of human brain development and enable early diagnostics of cortical malformations with the ultimate goal to improve treatment of neurodevelopmental disorders including epilepsy, autism spectrum disorders, and schizophrenia.

  7. Multilayer network modeling creates opportunities for novel network statistics. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Muldoon, Sarah Feldt

    2018-03-01

    As described in the review by Gosak et al., the field of network science has had enormous success in providing new insights into the structure and function of biological systems [1]. In the complex networks framework, system elements are network nodes, and connections between nodes represent some form of interaction between system elements [2]. The flexibility to define network nodes and edges to represent different aspects of biological systems has been employed to model numerous diverse systems at multiple scales.

  8. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  9. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Science.gov (United States)

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  10. Biological, chemical and medical physics

    International Nuclear Information System (INIS)

    1990-01-01

    This is an overview of the actual situation in Brazil, concerning three important areas of physics: biological, chemical and medical. It gives a brief historical of research in these areas. It talks as well, about perspectives and financing. It contains many tables with the main research groups in activity in Brazilian institutions. (A.C.A.S.)

  11. Life as physics and chemistry: A system view of biology.

    Science.gov (United States)

    Baverstock, Keith

    2013-04-01

    Cellular life can be viewed as one of many physical natural systems that extract free energy from their environments in the most efficient way, according to fundamental physical laws, and grow until limited by inherent physical constraints. Thus, it can be inferred that it is the efficiency of this process that natural selection acts upon. The consequent emphasis on metabolism, rather than replication, points to a metabolism-first origin of life with the adoption of DNA template replication as a second stage development. This order of events implies a cellular regulatory system that pre-dates the involvement of DNA and might, therefore, be based on the information acquired as peptides fold into proteins, rather than on genetic regulatory networks. Such an epigenetic cell regulatory model, the independent attractor model, has already been proposed to explain the phenomenon of radiation induced genomic instability. Here it is extended to provide an epigenetic basis for the morphological and functional diversity that evolution has yielded, based on natural selection of the most efficient free energy transduction. Empirical evidence which challenges the current genetic basis of cell and molecular biology and which supports the above proposal is discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  13. Physical Activity, Physical Performance, and Biological Markers of Health among Sedentary Older Latinos

    Directory of Open Access Journals (Sweden)

    Gerardo Moreno

    2014-01-01

    Full Text Available Background. Physical activity is associated with better physical health, possibly by changing biological markers of health such as waist circumference and inflammation, but these relationships are unclear and even less understood among older Latinos—a group with high rates of sedentary lifestyle. Methods. Participants were 120 sedentary older Latino adults from senior centers. Community-partnered research methods were used to recruit participants. Inflammatory (C-reactive protein and metabolic markers of health (waist circumference, HDL-cholesterol, triglycerides, insulin, and glucose, physical activity (Yale physical activity survey, and physical performance (short physical performance NIA battery were measured at baseline and 6-month followup. Results. Eighty percent of the sample was female. In final adjusted cross-sectional models, better physical activity indices were associated with faster gait speed (P<0.05. In adjusted longitudinal analyses, change in self-reported physical activity level correlated inversely with change in CRP (β=-0.05; P=0.03 and change in waist circumference (β=-0.16; P=0.02. Biological markers of health did not mediate the relationship between physical activity and physical performance. Conclusion. In this community-partnered study, higher physical activity was associated with better physical performance in cross-sectional analyses. In longitudinal analysis, increased physical activity was associated with improvements in some metabolic and inflammatory markers of health.

  14. Physics in Brazil in the next decade: atomic, molecular and optical physics, biological, chemical and medical physics, physics teaching and plasma physics

    International Nuclear Information System (INIS)

    1990-01-01

    This is an overview of physics in Brazil in the next decade. It is specially concerned with atomic, molecular and optical physics, biological chemical and medical physics, and also teaching of physics and plasma physics. It presents the main research groups in Brazil in the above mentioned areas. It talks as well, about financing new projects and the costs involved to improve these areas. (A.C.A.S.)

  15. Network Physics anounces first product to provide business-level management of the most complex and dynamic networks

    CERN Multimedia

    2003-01-01

    Network Physics, provider of business-level, traffic flow-based network management solutions, today announced the introduction of the Network Physics NP/BizFlow-1000. With the NP/BizFlow-1000, Fortune 1000 companies with complex and dynamic networks can analyze the flows that link business groups, critical applications, and network software and hardware (1 page).

  16. Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

    International Nuclear Information System (INIS)

    Thomson, Ty M.; Sewer, Alain; Martin, Florian; Belcastro, Vincenzo; Frushour, Brian P.; Gebel, Stephan; Park, Jennifer; Schlage, Walter K.; Talikka, Marja; Vasilyev, Dmitry M.; Westra, Jurjen W.; Hoeng, Julia; Peitsch, Manuel C.

    2013-01-01

    Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology. - Highlights: • The impact of biologically active substances is quantified at multiple levels. • The systems-level impact integrates the perturbations of individual networks. • The networks capture the relationships between

  17. BOOK REVIEW Handbook of Physics in Medicine and Biology Handbook of Physics in Medicine and Biology

    Science.gov (United States)

    Tabakov, Slavik

    2010-11-01

    This is a multi-author handbook (66 authors) aiming to describe various applications of physics to medicine and biology, from anatomy and physiology to medical equipment. This unusual reference book has 44 chapters organized in seven sections: 1. Anatomical physics; 2. Physics of perception; 3. Biomechanics; 4. Electrical physics; 5. Diagnostic physics; 6. Physics of accessory medicine; 7. Physics of bioengineering. Each chapter has separate page numbering, which is inconvenient but understandable with the number of authors. Similarly there is some variation in the emphasis of chapters: for some the emphasis is more technical and for others clinical. Each chapter has a separate list of references. The handbook includes hundreds of diagrams, images and tables, making it a useful tool for both medical physicists/engineers and other medical/biology specialists. The first section (about 40 pages) includes five chapters on physics of the cell membrane; protein signaling; cell biology and biophysics of the cell membrane; cellular thermodynamics; action potential transmission and volume conduction. The physics of these is well explained and illustrated with clear diagrams and formulae, so it could be a suitable reference for physicists/engineers. The chapters on cellular thermodynamics and action potential transmission have a very good balance of technical/clinical content. The second section (about 85 pages) includes six chapters on medical decision making; senses; somatic senses: touch and pain; hearing; vision; electroreception. Again these are well illustrated and a suitable reference for physicists/engineers. The chapter on hearing stands out with good balance and treatment of material, but some other chapters contain less physics and are close to typical physiological explanations. One could query the inclusion of the chapter on medical decision making, which also needs more detail. The third section (about 80 pages) includes eight chapters on biomechanics

  18. Physical layer network coding

    DEFF Research Database (Denmark)

    Fukui, Hironori; Popovski, Petar; Yomo, Hiroyuki

    2014-01-01

    Physical layer network coding (PLNC) has been proposed to improve throughput of the two-way relay channel, where two nodes communicate with each other, being assisted by a relay node. Most of the works related to PLNC are focused on a simple three-node model and they do not take into account...

  19. PREFACE: Complex Networks: from Biology to Information Technology

    Science.gov (United States)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  20. Statistical physics of interacting neural networks

    Science.gov (United States)

    Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

    2001-12-01

    Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

  1. Novel approaches to develop community-built biological network models for potential drug discovery.

    Science.gov (United States)

    Talikka, Marja; Bukharov, Natalia; Hayes, William S; Hofmann-Apitius, Martin; Alexopoulos, Leonidas; Peitsch, Manuel C; Hoeng, Julia

    2017-08-01

    Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

  2. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  3. Plasma Physics Network Newsletter, no. 5

    Science.gov (United States)

    1992-08-01

    The fifth Plasma Physics Network Newsletter (IAEA, Vienna, Aug. 1992) includes the following topics: (1) the availability of a list of the members of the Third World Plasma Research Network (TWPRN); (2) the announcement of the fourteenth IAEA International Conference on Plasma Physics and Controlled Nuclear Fusion Research to be held in Wuerzburg, Germany, from 30 Sep. to 7 Oct. 1992; (3) the announcement of a Technical Committee Meeting on research using small tokamaks, organized by the IAEA as a satellite meeting to the aforementioned fusion conference; (4) IAEA Fellowships and Scientific Visits for the use of workers in developing member states, and for which plasma researchers are encouraged to apply through Dr. D. Banner, Head, Physics Section, IAEA, P.O. Box 100, A-1400 Vienna, Austria; (5) the initiation in 1993 of a new Coordinated Research Programme (CRP) on 'Development of Software for Numerical Simulation and Data Processing in Fusion Energy Research', as well as a proposed CRP on 'Fusion Research in Developing Countries using Middle- and Small-Scale Plasma Devices'; (6) support from the International Centre for Theoretical Physics (ICTP) for meetings held in Third World countries; (7) a report by W. Usada on Fusion Research in Indonesia; (8) News on ITER; (9) the Technical Committee Meeting planned 8-12 Sep. 1992, Canada, on Tokamak Plasma Biasing; (10) software made available for the study of tokamak transport; (11) the electronic mail address of the TWPRN; (12) the FAX, e-mail, and postal address for contributions to this plasma physics network newsletter.

  4. Plasma physics network newsletter. No. 5

    International Nuclear Information System (INIS)

    1992-08-01

    The fifth Plasma Physics Network Newsletter (IAEA, Vienna, August 1992) includes the following topics: (i) the availability of a list of the members of the Third World Plasma Research Network (TWPRN); (ii) the announcement of the fourteenth IAEA International Conference on Plasma Physics and Controlled Nuclear Fusion Research to be held in Wuerzburg, Germany, from September 30 to October 7, 1992; (iii) the announcement of a Technical Committee Meeting on research using small tokamaks, organized by the IAEA as a satellite meeting to the aforementioned fusion conference; (iv) IAEA Fellowships and Scientific Visits for the use of workers in developing member states, and for which plasma researchers are encouraged to apply through Dr. D. Banner, Head, Physics Section, IAEA, P.O. Box 100, A-1400 Vienna, Austria; (v) the initiation in 1993 of a new Coordinated Research Programme (CRP) on ''Development of Software for Numerical Simulation and Data Processing in Fusion Energy Research'', as well as a proposed CRP on ''Fusion Research in Developing Countries using Middle- and Small-Scale Plasma Devices''; (vi) support from the International Centre for Theoretical Physics (ICTP) for meetings held in Third World countries; (vii) a report by W. Usada on Fusion Research in Indonesia; (viii) News on ITER; (ix) the Technical Committee Meeting planned September 8-12, 1992, Canada, on Tokamak Plasma Biasing; (x) software made available for the study of tokamak transport; (xi) the electronic mail address of the TWPRN; (xii) and the FAX, e-mail and postal address for contributions to this plasma physics network newsletter (FAX: (43-1)-234564)

  5. Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

    International Nuclear Information System (INIS)

    Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

    2009-01-01

    Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

  6. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

    OpenAIRE

    Shuo Gu; Jianfeng Pei

    2017-01-01

    With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regula...

  7. Physics of flow in weighted complex networks

    Science.gov (United States)

    Wu, Zhenhua

    This thesis uses concepts from statistical physics to understand the physics of flow in weighted complex networks. The traditional model for random networks is the Erdoḧs-Renyi (ER.) network, where a network of N nodes is created by connecting each of the N(N - 1)/2 pairs of nodes with a probability p. The degree distribution, which is the probability distribution of the number of links per node, is a Poisson distribution. Recent studies of the topology in many networks such as the Internet and the world-wide airport network (WAN) reveal a power law degree distribution, known as a scale-free (SF) distribution. To yield a better description of network dynamics, we study weighted networks, where each link or node is given a number. One asks how the weights affect the static and the dynamic properties of the network. In this thesis, two important dynamic problems are studied: the current flow problem, described by Kirchhoff's laws, and the maximum flow problem, which maximizes the flow between two nodes. Percolation theory is applied to these studies of the dynamics in complex networks. We find that the current flow in disordered media belongs to the same universality class as the optimal path. In a randomly weighted network, we identify the infinite incipient percolation cluster as the "superhighway", which contains most of the traffic in a network. We propose an efficient strategy to improve significantly the global transport by improving the superhighways, which comprise a small fraction of the network. We also propose a network model with correlated weights to describe weighted networks such as the WAN. Our model agrees with WAN data, and provides insight into the advantages of correlated weights in networks. Lastly, the upper critical dimension is evaluated using two different numerical methods, and the result is consistent with the theoretical prediction.

  8. Learning and coding in biological neural networks

    Science.gov (United States)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

  9. Biana: a software framework for compiling biological interactions and analyzing networks.

    Science.gov (United States)

    Garcia-Garcia, Javier; Guney, Emre; Aragues, Ramon; Planas-Iglesias, Joan; Oliva, Baldo

    2010-01-27

    The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

  10. Application of local computer networks in nuclear-physical experiments and technology

    International Nuclear Information System (INIS)

    Foteev, V.A.

    1986-01-01

    The bases of construction, comparative performance and potentialities of local computer networks with respect to their application in physical experiments are considered. The principle of operation of local networks is shown on the basis of the Ethernet network and the results of analysis of their operating performance are given. The examples of operating local networks in the area of nuclear-physics research and nuclear technology are presented as follows: networks of Japan Atomic Energy Research Institute, California University and Los Alamos National Laboratory, network realization according to the DECnet and Fast-bus programs, home network configurations of the USSR Academy of Sciences and JINR Neutron Physical Laboratory etc. It is shown that local networks allows significantly raise productivity in the sphere of data processing

  11. Cyber-Physical Architecture Assisted by Programmable Networking

    OpenAIRE

    Rubio-Hernan, Jose; Sahay, Rishikesh; De Cicco, Luca; Garcia-Alfaro, Joaquin

    2018-01-01

    Cyber-physical technologies are prone to attacks, in addition to faults and failures. The issue of protecting cyber-physical systems should be tackled by jointly addressing security at both cyber and physical domains, in order to promptly detect and mitigate cyber-physical threats. Towards this end, this letter proposes a new architecture combining control-theoretic solutions together with programmable networking techniques to jointly handle crucial threats to cyber-physical systems. The arch...

  12. A proposal to establish an international network in molecular microbiology and genetic engineering for scientific cooperation and prevention of misuse of biological sciences in the framework of science for peace

    International Nuclear Information System (INIS)

    Becker, Y.

    1998-01-01

    The conference on 'Science and Technology for Construction of Peace' which was organized by the Landau Network Coordination Center and A. Volta Center for Scientific Culture dealt with conversion of military and technological capacities into sustainable civilian application. The ideas regarding the conversion of nuclear warheads into nuclear energy for civilian-use led to the idea that the extension of this trend of thought to molecular biology and genetic engineering, will be a useful contribution to Science for Peace. This idea of developing a Cooperation Network in Molecular Biology and Genetic Engineering that will function parallel to and with the Landau Network Coordination in the 'A. Volta' Center was discussed in the Second International Symposium on Science for Peace, Jerusalem, January 1997. It is the reason for the inclusion of the biological aspects in the deliberations of our Forum. It is hoped that the establishment of an international network in molecular biology and genetic engineering, similar to the Landau Network in physics, will support and achieve the decommissioning of biological weapons. Such a network in microbiology and genetic engineering will contribute to the elimination of biological weapons and to contributions to Science for Peace and to Culture of Peace activities of UNESCO. (author)

  13. Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research.

    Science.gov (United States)

    Jinawath, Natini; Bunbanjerdsuk, Sacarin; Chayanupatkul, Maneerat; Ngamphaiboon, Nuttapong; Asavapanumas, Nithi; Svasti, Jisnuson; Charoensawan, Varodom

    2016-11-22

    With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians' point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world's major diseases.

  14. Physical integrity: the missing link in biological monitoring and TMDLs.

    Science.gov (United States)

    Asmus, Brenda; Magner, Joseph A; Vondracek, Bruce; Perry, Jim

    2009-12-01

    The Clean Water Act mandates that the chemical, physical, and biological integrity of our nation's waters be maintained and restored. Physical integrity has often been defined as physical habitat integrity, and as such, data collected during biological monitoring programs focus primarily on habitat quality. However, we argue that channel stability is a more appropriate measure of physical integrity and that channel stability is a foundational element of physical habitat integrity in low-gradient alluvial streams. We highlight assessment tools that could supplement stream assessments and the Total Maximum Daily Load stressor identification process: field surveys of bankfull cross-sections; longitudinal thalweg profiles; particle size distribution; and regionally calibrated, visual, stream stability assessments. Benefits of measuring channel stability include a more informed selection of reference or best attainable stream condition for an Index of Biotic Integrity, establishment of a baseline for monitoring changes in present and future condition, and indication of channel stability for investigations of chemical and biological impairments associated with sediment discontinuity and loss of habitat quality.

  15. Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

    Science.gov (United States)

    Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

    2016-10-01

    Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

  16. Parameterized neural networks for high-energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Baldi, Pierre; Sadowski, Peter [University of California, Department of Computer Science, Irvine, CA (United States); Cranmer, Kyle [NYU, Department of Physics, New York, NY (United States); Faucett, Taylor; Whiteson, Daniel [University of California, Department of Physics and Astronomy, Irvine, CA (United States)

    2016-05-15

    We investigate a new structure for machine learning classifiers built with neural networks and applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results. (orig.)

  17. Parameterized neural networks for high-energy physics

    International Nuclear Information System (INIS)

    Baldi, Pierre; Sadowski, Peter; Cranmer, Kyle; Faucett, Taylor; Whiteson, Daniel

    2016-01-01

    We investigate a new structure for machine learning classifiers built with neural networks and applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results. (orig.)

  18. Research at the interface of physics and biology: bridging the two fields

    Science.gov (United States)

    Shukla, Kamal

    2014-10-01

    I firmly believe that interaction between physics and biology is not only natural, but inevitable. Kamal Shukla provides a personal perspective on working at the interface between the physical and biological sciences.

  19. Getting the measure of things: the physical biology of stem cells.

    Science.gov (United States)

    Lowell, Sally

    2013-10-01

    In July 2013, the diverse fields of biology, physics and mathematics converged to discuss 'The Physical Biology of Stem Cells', the subject of the third annual symposium of the Cambridge Stem Cell Institute, UK. Two clear themes resonated throughout the meeting: the new insights gained from advances in the acquisition and interpretation of quantitative data; and the importance of 'thinking outside the nucleus' to consider physical influences on cell fate.

  20. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  1. A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Xin Lai

    2013-01-01

    Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

  2. Breaking Frontiers: Submicron Structures in Physics and Biology - 52 Zakopane School of Physics

    International Nuclear Information System (INIS)

    2008-01-01

    The 52 Zakopane School of Physics held in Zakopane from 19 to 24 May 2008. The main task of the symposium was to present the newest results of research in field of submicron structures in physics, biology and medicine. Some new technologies as well as their applications are also presented

  3. Breaking Frontiers: Submicron Structures in Physics and Biology - 52 Zakopane School of Physics

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-07-01

    The 52 Zakopane School of Physics held in Zakopane from 19 to 24 May 2008. The main task of the symposium was to present the newest results of research in field of submicron structures in physics, biology and medicine. Some new technologies as well as their applications are also presented.

  4. Biological instability in a chlorinated drinking water distribution network.

    Science.gov (United States)

    Nescerecka, Alina; Rubulis, Janis; Vital, Marius; Juhna, Talis; Hammes, Frederik

    2014-01-01

    The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia) was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM) intact cell concentrations, intracellular adenosine tri-phosphate (ATP), heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×10(3) cells mL(-1) to 4.66×10(5) cells mL(-1) in the network. While this parameter did not exceed 2.1×10(4) cells mL(-1) in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×10(5) cells mL(-1). This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability.

  5. NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

    Science.gov (United States)

    Theodosiou, Theodosios; Efstathiou, Georgios; Papanikolaou, Nikolas; Kyrpides, Nikos C; Bagos, Pantelis G; Iliopoulos, Ioannis; Pavlopoulos, Georgios A

    2017-07-14

    Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

  6. [Application of network biology on study of traditional Chinese medicine].

    Science.gov (United States)

    Tian, Sai-Sai; Yang, Jian; Zhao, Jing; Zhang, Wei-Dong

    2018-01-01

    With the completion of the human genome project, people have gradually recognized that the functions of the biological system are fulfilled through network-type interaction between genes, proteins and small molecules, while complex diseases are caused by the imbalance of biological processes due to a number of gene expression disorders. These have contributed to the rise of the concept of the "multi-target" drug discovery. Treatment and diagnosis of traditional Chinese medicine are based on holism and syndrome differentiation. At the molecular level, traditional Chinese medicine is characterized by multi-component and multi-target prescriptions, which is expected to provide a reference for the development of multi-target drugs. This paper reviews the application of network biology in traditional Chinese medicine in six aspects, in expectation to provide a reference to the modernized study of traditional Chinese medicine. Copyright© by the Chinese Pharmaceutical Association.

  7. Overview of physical dosimetry methods for triage application integrated in the new European network RENEB.

    Science.gov (United States)

    Trompier, François; Burbidge, Christopher; Bassinet, Céline; Baumann, Marion; Bortolin, Emanuela; De Angelis, Cinzia; Eakins, Jonathan; Della Monaca, Sara; Fattibene, Paola; Quattrini, Maria Cristina; Tanner, Rick; Wieser, Albrecht; Woda, Clemens

    2017-01-01

    In the EC-funded project RENEB (Realizing the European Network in Biodosimetry), physical methods applied to fortuitous dosimetric materials are used to complement biological dosimetry, to increase dose assessment capacity for large-scale radiation/nuclear accidents. This paper describes the work performed to implement Optically Stimulated Luminescence (OSL) and Electron Paramagnetic Resonance (EPR) dosimetry techniques. OSL is applied to electronic components and EPR to touch-screen glass from mobile phones. To implement these new approaches, several blind tests and inter-laboratory comparisons (ILC) were organized for each assay. OSL systems have shown good performances. EPR systems also show good performance in controlled conditions, but ILC have also demonstrated that post-irradiation exposure to sunlight increases the complexity of the EPR signal analysis. Physically-based dosimetry techniques present high capacity, new possibilities for accident dosimetry, especially in the case of large-scale events. Some of the techniques applied can be considered as operational (e.g. OSL on Surface Mounting Devices [SMD]) and provide a large increase of measurement capacity for existing networks. Other techniques and devices currently undergoing validation or development in Europe could lead to considerable increases in the capacity of the RENEB accident dosimetry network.

  8. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

    Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design....... Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single...... laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts, in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set...

  9. Computing chemical organizations in biological networks.

    Science.gov (United States)

    Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

    2008-07-15

    Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

  10. Physical approach to quantum networks with massive particles

    Science.gov (United States)

    Andersen, Molte Emil Strange; Zinner, Nikolaj Thomas

    2018-04-01

    Assembling large-scale quantum networks is a key goal of modern physics research with applications in quantum information and computation. Quantum wires and waveguides in which massive particles propagate in tailored confinement is one promising platform for realizing a quantum network. In the literature, such networks are often treated as quantum graphs, that is, the wave functions are taken to live on graphs of one-dimensional edges meeting in vertices. Hitherto, it has been unclear what boundary conditions on the vertices produce the physical states one finds in nature. This paper treats a quantum network from a physical approach, explicitly finds the physical eigenstates and compares them to the quantum-graph description. The basic building block of a quantum network is an X-shaped potential well made by crossing two quantum wires, and we consider a massive particle in such an X well. The system is analyzed using a variational method based on an expansion into modes with fast convergence and it provides a very clear intuition for the physics of the problem. The particle is found to have a ground state that is exponentially localized to the center of the X well, and the other symmetric solutions are formed so to be orthogonal to the ground state. This is in contrast to the predictions of the conventionally used so-called Kirchoff boundary conditions in quantum graph theory that predict a different sequence of symmetric solutions that cannot be physically realized. Numerical methods have previously been the only source of information on the ground-state wave function and our results provide a different perspective with strong analytical insights. The ground-state wave function has a spatial profile that looks very similar to the shape of a solitonic solution to a nonlinear Schrödinger equation, enabling an analytical prediction of the wave number. When combining multiple X wells into a network or grid, each site supports a solitonlike localized state. These

  11. Canadian Cytogenetic Emergency network (CEN) for biological dosimetry following radiological/nuclear accidents.

    Science.gov (United States)

    Miller, Susan M; Ferrarotto, Catherine L; Vlahovich, Slavica; Wilkins, Ruth C; Boreham, Douglas R; Dolling, Jo-Anna

    2007-07-01

    To test the ability of the cytogenetic emergency network (CEN) of laboratories, currently under development across Canada, to provide rapid biological dosimetry using the dicentric assay for triage assessment, that could be implemented in the event of a large-scale radiation/nuclear emergency. A workshop was held in May 2004 in Toronto, Canada, to introduce the concept of CEN and recruit clinical cytogenetic laboratories at hospitals across the country. Slides were prepared for dicentric assay analysis following in vitro irradiation of blood to a range of gamma-ray doses. A minimum of 50 metaphases per slide were analyzed by 41 people at 22 different laboratories to estimate the exposure level. Dose estimates were calculated based on a dose response curve generated at Health Canada. There were a total of 104 dose estimates and 96 (92.3%) of them fell within the expected range using triage scoring criteria. Half of the laboratories analyzed 50 metaphases in network were acceptable for emergency biological dosimetry. When this network is fully operational, it will be the first of its kind in Canada able to respond to radiological/nuclear emergencies by providing triage quality biological dosimetry for a large number of samples. This network represents an alternate expansion of existing international emergency biological dosimetry cytogenetic networks.

  12. A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

    Science.gov (United States)

    Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

    2013-01-01

    One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

  13. Physical-layer network coding for passive optical interconnect in datacenter networks.

    Science.gov (United States)

    Lin, Rui; Cheng, Yuxin; Guan, Xun; Tang, Ming; Liu, Deming; Chan, Chun-Kit; Chen, Jiajia

    2017-07-24

    We introduce physical-layer network coding (PLNC) technique in a passive optical interconnect (POI) architecture for datacenter networks. The implementation of the PLNC in the POI at 2.5 Gb/s and 10Gb/s have been experimentally validated while the gains in terms of network layer performances have been investigated by simulation. The results reveal that in order to realize negligible packet drop, the wavelengths usage can be reduced by half while a significant improvement in packet delay especially under high traffic load can be achieved by employing PLNC over POI.

  14. Biological mechanisms beyond network analysis via mathematical modeling. Comment on "Network science of biological systems at different scales: A review" by Marko Gosak et al.

    Science.gov (United States)

    Pedersen, Morten Gram

    2018-03-01

    Methods from network theory are increasingly used in research spanning from engineering and computer science to psychology and the social sciences. In this issue, Gosak et al. [1] provide a thorough review of network science applications to biological systems ranging from the subcellular world via neuroscience to ecosystems, with special attention to the insulin-secreting beta-cells in pancreatic islets.

  15. Biological/Genetic Regulation of Physical Activity Level: Consensus from GenBioPAC.

    Science.gov (United States)

    Lightfoot, J Timothy; DE Geus, Eco J C; Booth, Frank W; Bray, Molly S; DEN Hoed, Marcel; Kaprio, Jaakko; Kelly, Scott A; Pomp, Daniel; Saul, Michael C; Thomis, Martine A; Garland, Theodore; Bouchard, Claude

    2018-04-01

    Physical activity unquestionably maintains and improves health; however, physical activity levels globally are low and not rising despite all the resources devoted to this goal. Attention in both the research literature and the public policy domain has focused on social-behavioral factors; however, a growing body of literature suggests that biological determinants play a significant role in regulating physical activity levels. For instance, physical activity level, measured in various manners, has a genetic component in both humans and nonhuman animal models. This consensus article, developed as a result of an American College of Sports Medicine-sponsored round table, provides a brief review of the theoretical concepts and existing literature that supports a significant role of genetic and other biological factors in the regulation of physical activity. Future research on physical activity regulation should incorporate genetics and other biological determinants of physical activity instead of a sole reliance on social and other environmental determinants.

  16. WE-DE-202-00: Connecting Radiation Physics with Computational Biology

    International Nuclear Information System (INIS)

    2016-01-01

    Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological

  17. WE-DE-202-00: Connecting Radiation Physics with Computational Biology

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2016-06-15

    Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological

  18. Impact of heuristics in clustering large biological networks.

    Science.gov (United States)

    Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel

    2015-12-01

    Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Structural identifiability of cyclic graphical models of biological networks with latent variables.

    Science.gov (United States)

    Wang, Yulin; Lu, Na; Miao, Hongyu

    2016-06-13

    Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

  20. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

    Science.gov (United States)

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

  1. Applications of neural networks in high energy physics

    International Nuclear Information System (INIS)

    Cutts, D.; Hoftun, J.S.; Nesic, D.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.

    1990-01-01

    Neural network techniques provide promising solutions to pattern recognition problems in high energy physics. We discuss several applications of back propagation networks, and in particular describe the operation of an electron algorithm based on calorimeter energies. 5 refs., 5 figs., 1 tab

  2. Introduction to neural networks in high energy physics

    International Nuclear Information System (INIS)

    Therhaag, J.

    2013-01-01

    Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks. (authors)

  3. Conference: Statistical Physics and Biological Information

    International Nuclear Information System (INIS)

    Gross, David J.; Hwa, Terence

    2001-01-01

    In the spring of 2001, the Institute for Theoretical Physics ran a 6 month scientific program on Statistical Physics and Biological Information. This program was organized by Walter Fitch (UC Irvine), Terence Hwa (UC San Diego), Luca Peliti (University Federico II), Naples Gary Stormo (Washington University School of Medicine) and Chao Tang (NEC). Overall scientific supervision was provided by David Gross, Director, ITP. The ITP has an online conference/program proceeding which consists of audio and transparencies of almost all of the talks held during this program. Over 100 talks are available on the site at http://online.kitp.ucsb.edu/online/infobio01/

  4. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    Kolanoski, H.

    1995-04-01

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  5. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    Science.gov (United States)

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  6. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  7. A design of Biological Network in the Basin of Jucar; El diseno de la Red Biologica en la Cuenca del Jucar

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Mas, J F; Correcher Martinez, E; Pinon, A; Martinez Muro, M A; Pujante Mora, A M

    2002-07-01

    In this study results obtained in the design of the Biological Network carried out during the year 2000 in 221 sampling points in 106 rivers of the environment of the Confederation Hidrografica of the Jucar are presented. The Biological network is based in the analysis of hydro morphological physical, chemical and biological parameters (macro invertebrates, diatoms, macrofits and fishes) that allow to know the ecological status of the studied rivers. The quality of the rivers were stablished with BMWP index and completed with the calculation in some selected sampling sites of other indexes: 1CG, macrofits index (IM), diatoms index (ID), ecotrofic index (IE). The results indicate the division of the study area in three big groups: the sampling sites located in the headwaters and small streams, with an excellent conservation state and with the presence of indicative species, the medium upland river sites which are regulated by reservoirs and dams, and the lowland rivers with the biggest accumulation of contamination and the biggest population density. (Author) 13 refs.

  8. Content-rich biological network constructed by mining PubMed abstracts

    Directory of Open Access Journals (Sweden)

    Sharp Burt M

    2004-10-01

    Full Text Available Abstract Background The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality biological interactions. Programs with natural language processing (NLP capability have been created to address these limitations, however, they are in general not readily accessible to the public. Results We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. Conclusions Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

  9. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  10. MicroRNA functional network in pancreatic cancer: From biology to ...

    Indian Academy of Sciences (India)

    [Wang J and Sen S 2011 MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease. ... growth factor type I receptor; INSR, insulin receptor; IPA, Ingenuity Pathway Analysis; IPMN, ..... Prostate cancer signalling.

  11. Perspectives on theory at the interface of physics and biology

    Science.gov (United States)

    Bialek, William

    2018-01-01

    Theoretical physics is the search for simple and universal mathematical descriptions of the natural world. In contrast, much of modern biology is an exploration of the complexity and diversity of life. For many, this contrast is prima facie evidence that theory, in the sense that physicists use the word, is impossible in a biological context. For others, this contrast serves to highlight a grand challenge. I am an optimist, and believe (along with many colleagues) that the time is ripe for the emergence of a more unified theoretical physics of biological systems, building on successes in thinking about particular phenomena. In this essay I try to explain the reasons for my optimism, through a combination of historical and modern examples.

  12. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    2012-01-01

    With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological...... and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical...

  13. Biological network extraction from scientific literature: state of the art and challenges.

    Science.gov (United States)

    Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

    2014-09-01

    Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Integrated analysis of multiple data sources reveals modular structure of biological networks

    International Nuclear Information System (INIS)

    Lu Hongchao; Shi Baochen; Wu Gaowei; Zhang Yong; Zhu Xiaopeng; Zhang Zhihua; Liu Changning; Zhao, Yi; Wu Tao; Wang Jie; Chen Runsheng

    2006-01-01

    It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks

  15. Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin.

    Science.gov (United States)

    Guzzi, Pietro Hiram; Milenković, Tijana

    2017-01-05

    Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Ionising radiation - physical and biological effects

    International Nuclear Information System (INIS)

    Holter, Oe.; Ingebretsen, F.; Parr, H.

    1979-01-01

    The physics of ionising radiation is briefly presented. The effects of ionising radiation on biological cells, cell repair and radiosensitivity are briefly treated, where after the effects on man and mammals are discussed and related to radiation doses. Dose limits are briefly discussed. The genetic effects are discussed separately. Radioecology is also briefly treated and a table of radionuclides deriving from reactors, and their radiation is given. (JIW)

  17. Simbios: an NIH national center for physics-based simulation of biological structures.

    Science.gov (United States)

    Delp, Scott L; Ku, Joy P; Pande, Vijay S; Sherman, Michael A; Altman, Russ B

    2012-01-01

    Physics-based simulation provides a powerful framework for understanding biological form and function. Simulations can be used by biologists to study macromolecular assemblies and by clinicians to design treatments for diseases. Simulations help biomedical researchers understand the physical constraints on biological systems as they engineer novel drugs, synthetic tissues, medical devices, and surgical interventions. Although individual biomedical investigators make outstanding contributions to physics-based simulation, the field has been fragmented. Applications are typically limited to a single physical scale, and individual investigators usually must create their own software. These conditions created a major barrier to advancing simulation capabilities. In 2004, we established a National Center for Physics-Based Simulation of Biological Structures (Simbios) to help integrate the field and accelerate biomedical research. In 6 years, Simbios has become a vibrant national center, with collaborators in 16 states and eight countries. Simbios focuses on problems at both the molecular scale and the organismal level, with a long-term goal of uniting these in accurate multiscale simulations.

  18. The National Cancer Institute's Physical Sciences - Oncology Network

    Science.gov (United States)

    Espey, Michael Graham

    In 2009, the NCI launched the Physical Sciences - Oncology Centers (PS-OC) initiative with 12 Centers (U54) funded through 2014. The current phase of the Program includes U54 funded Centers with the added feature of soliciting new Physical Science - Oncology Projects (PS-OP) U01 grant applications through 2017; see NCI PAR-15-021. The PS-OPs, individually and along with other PS-OPs and the Physical Sciences-Oncology Centers (PS-OCs), comprise the Physical Sciences-Oncology Network (PS-ON). The foundation of the Physical Sciences-Oncology initiative is a high-risk, high-reward program that promotes a `physical sciences perspective' of cancer and fosters the convergence of physical science and cancer research by forming transdisciplinary teams of physical scientists (e.g., physicists, mathematicians, chemists, engineers, computer scientists) and cancer researchers (e.g., cancer biologists, oncologists, pathologists) who work closely together to advance our understanding of cancer. The collaborative PS-ON structure catalyzes transformative science through increased exchange of people, ideas, and approaches. PS-ON resources are leveraged to fund Trans-Network pilot projects to enable synergy and cross-testing of experimental and/or theoretical concepts. This session will include a brief PS-ON overview followed by a strategic discussion with the APS community to exchange perspectives on the progression of trans-disciplinary physical sciences in cancer research.

  19. Physical-layer Network Coding in Two-Way Heterogeneous Cellular Networks with Power Imbalance

    OpenAIRE

    Thampi, Ajay K; Liew, Soung Chang; Armour, Simon M D; Fan, Zhong; You, Lizhao; Kaleshi, Dritan

    2016-01-01

    The growing demand for high-speed data, quality of service (QoS) assurance and energy efficiency has triggered the evolution of 4G LTE-A networks to 5G and beyond. Interference is still a major performance bottleneck. This paper studies the application of physical-layer network coding (PNC), a technique that exploits interference, in heterogeneous cellular networks. In particular, we propose a rate-maximising relay selection algorithm for a single cell with multiple relays assuming the decode...

  20. ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

    Science.gov (United States)

    Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

    2008-08-12

    New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges

  1. Creating biological nanomaterials using synthetic biology

    International Nuclear Information System (INIS)

    Rice, MaryJoe K; Ruder, Warren C

    2014-01-01

    Synthetic biology is a new discipline that combines science and engineering approaches to precisely control biological networks. These signaling networks are especially important in fields such as biomedicine and biochemical engineering. Additionally, biological networks can also be critical to the production of naturally occurring biological nanomaterials, and as a result, synthetic biology holds tremendous potential in creating new materials. This review introduces the field of synthetic biology, discusses how biological systems naturally produce materials, and then presents examples and strategies for incorporating synthetic biology approaches in the development of new materials. In particular, strategies for using synthetic biology to produce both organic and inorganic nanomaterials are discussed. Ultimately, synthetic biology holds the potential to dramatically impact biological materials science with significant potential applications in medical systems. (review)

  2. Creating biological nanomaterials using synthetic biology.

    Science.gov (United States)

    Rice, MaryJoe K; Ruder, Warren C

    2014-02-01

    Synthetic biology is a new discipline that combines science and engineering approaches to precisely control biological networks. These signaling networks are especially important in fields such as biomedicine and biochemical engineering. Additionally, biological networks can also be critical to the production of naturally occurring biological nanomaterials, and as a result, synthetic biology holds tremendous potential in creating new materials. This review introduces the field of synthetic biology, discusses how biological systems naturally produce materials, and then presents examples and strategies for incorporating synthetic biology approaches in the development of new materials. In particular, strategies for using synthetic biology to produce both organic and inorganic nanomaterials are discussed. Ultimately, synthetic biology holds the potential to dramatically impact biological materials science with significant potential applications in medical systems.

  3. A primer on physical-layer network coding

    CERN Document Server

    Liew, Soung Chang; Zhang, Shengli

    2015-01-01

    The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of communications and networking with a wide following. This book is a primer on PNC. It is the outcome of a set of lecture notes for a course for beginning graduate students at The Chinese University of Hong Kong. The target audience is expected to have some prior background knowledge in communication theory and wireless communications, but not working knowledge at the research level. Indeed, a goal of this book/course is to allow the reader

  4. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    understanding of molecular processes which are fundamental to tumorigenesis. In Article 1, we propose a novel framework for how cancer mutations can be studied by taking into account their effect at the protein network level. In Article 2, we demonstrate how global, quantitative data on phosphorylation dynamics...... can be generated using MS, and how this can be modeled using a computational framework for deciphering kinase-substrate dynamics. This framework is described in depth in Article 3, and covers the design of KinomeXplorer, which allows the prediction of kinases responsible for modulating observed...... phosphorylation dynamics in a given biological sample. In Chapter III, we move into Integrative Network Biology, where, by combining two fundamental technologies (MS & NGS), we can obtain more in-depth insights into the links between cellular phenotype and genotype. Article 4 describes the proof...

  5. Coordinating an IPLS class with a biology curriculum: NEXUS/Physics

    Science.gov (United States)

    Redish, Edward

    2014-03-01

    A multi-disciplinary team of scientists has been reinventing the Introductory Physics for Life Scientists (IPLS) course at the University of Maryland. We focus on physics that connects elements common to the curriculum for all life scientists - molecular and cellular biology - with building general scientific competencies, such as mathematical modeling, reasoning from core principles, and multi-representation translation. The prerequisites for the class include calculus, chemistry, and biology. In addition to building the basic ideas of the Newtonian framework, electric currents, and optics, our prerequisites allow us to include topics such as atomic interactions and chemical bonding, random motion and diffusion, thermodynamics (including entropy and free energy), and spectroscopy. Our chemical bonding unit helps students link the view of energy developed in traditional macroscopic physics with the idea of chemical bonding as a source of energy presented in their chemistry and biology classes. Education research has played a central role in our design, as has a strong collaboration between our Discipline-Based Education and the Biophysics Research groups. These elements permit us to combine modern pedagogy with cutting-edge insights into the physics of living systems. Supported in part by a grant from HHMI and the US NSF grant #1122818/.

  6. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

    Science.gov (United States)

    Kriegeskorte, Nikolaus

    2015-11-24

    Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

  7. Main activities of the Latin American Network of Biological Dosimetry (LBDNet)

    International Nuclear Information System (INIS)

    Di Giorgio, M.; Vallerga, M.B.; Radl, A.; Taja, M.R.; Stuck Oliveira, M.; Valdivia, P.; Garcia Lima, O.; Lamadrid, A.; Gonzalez Mesa, J.E.; Romero Aguilera, I.; Mandina Cardoso, T.; Guerrero Carbajal, C.; Arceo Maldonado, C.; Espinoza, M.; Martinez Lopez, W.; Di Tomasso, M.; Barquinero, F.; Roy, L.

    2010-01-01

    The Latin American Biological Dosimetry Network (LBDNET) was constituted in 2007 for mutual assistance in case of a radiation emergency in the region supported by IAEA Technical Cooperation Projects RLA/9/054 and RLA/9/061. The main objectives are: a) to strengthen the technical capacities of Biological Dosimetry Services belonging to laboratories existing in the region (Argentine, Brazil, Chile, Cuba, Mexico, Peru and Uruguay) integrated in National Radiological Emergency Plans to provide a rapid biodosimetric response in a coordinated manner between countries and with RANET-IAEA/BioDoseNet-WHO, b) to provide support to other countries in the region lacking Biological Dosimetry laboratories, c) to consolidate the organization of the Latin American Biological Dosimetry Network for mutual assistance. The activities developed include technical meetings for protocols and chromosomal aberration scoring criteria unification, blood samples cultures exercises, chromosomal aberrations analysis at microscope, discussion of statistical methods and specialized software for dose calculation, the intercomparison between laboratory data after the analysis of slides with irradiated material and the intercomparison of the analysis of captured images distributed electronically in the WEB. The last exercise was the transportation of an irradiated human blood sample to countries inside and outside of the region. At the moment the exercises are concluded and they are pending to be published in reference journals. Results obtained show the capacity in the region for a biodosimetric response to a radiological accident. In the future the network will integrate techniques for high dose exposure evaluation and will enhance the interaction with other emergency systems in the region. (authors) [es

  8. MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

    Science.gov (United States)

    Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2017-01-01

    Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any

  9. Physical parameters and biological effects of the LVR-15 epithermal neutron beam

    International Nuclear Information System (INIS)

    Burian, J.; Marek, M.; Rejchrt, J.; Viererbl, L.; Gambarini, G.; Mares, V.; Vanossi, E.; Judas, L.

    2006-01-01

    Monitoring of the physical and biological properties of the epithermal neutron beam constructed at the multipurpose LVR-15 nuclear reactor for NCT therapy of brain tumors showed that its physical and biological properties are stable in time and independent on an ad hoc reconfiguration of the reactor core before its therapeutic use. Physical parameters were monitored by measurement of the neutron spectrum, neutron profile, fast neutron kerma rate in tissue and photon absorbed dose, the gel dosimetry was used with the group of standard measurement methods. The RBE of the beam, as evaluated by 3 different biological models, including mouse intestine crypt regeneration assay, germinative zones of the immature rat brain and C6 glioma cells in culture, ranged from 1.70 to 1.99. (author)

  10. A Methodology for Physical Interconnection Decisions of Next Generation Transport Networks

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Madsen, Ole Brun

    2011-01-01

    of possibilities when designing the physical network interconnection. This paper develops and presents a methodology in order to deal with aspects related to the interconnection problem of optical transport networks. This methodology is presented as independent puzzle pieces, covering diverse topics going from......The physical interconnection for optical transport networks has critical relevance in the overall network performance and deployment costs. As telecommunication services and technologies evolve, the provisioning of higher capacity and reliability levels is becoming essential for the proper...... development of Next Generation Networks. Currently, there is a lack of specific procedures that describe the basic guidelines to design such networks better than "best possible performance for the lowest investment". Therefore, the research from different points of view will allow a broader space...

  11. Biologically-inspired On-chip Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  12. Synchronization-optimized networks for coupled nearly identical ...

    Indian Academy of Sciences (India)

    From the stability criteria of the MSF, we construct optimal networks ... of intense research in physical, biological, chemical, technological and social sci- ..... In figure 3a, a sample of initial network of 32 coupled nearly identical Rössler oscilla-.

  13. Phylogenetically informed logic relationships improve detection of biological network organization

    Science.gov (United States)

    2011-01-01

    Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058

  14. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  15. Attentional Networks and Biological Motion

    Directory of Open Access Journals (Sweden)

    Chandramouli Chandrasekaran

    2010-03-01

    Full Text Available Our ability to see meaningful actions when presented with pointlight traces of human movement is commonly referred to as the perception of biological motion. While traditionalexplanations have emphasized the spontaneous and automatic nature of this ability, morerecent findings suggest that attention may play a larger role than is typically assumed. Intwo studies we show that the speed and accuracy of responding to point-light stimuli is highly correlated with the ability to control selective attention. In our first experiment we measured thresholds for determining the walking direction of a masked point-light figure, and performance on a range of attention-related tasks in the same set of observers. Mask-density thresholds for the direction discrimination task varied quite considerably from observer to observer and this variation was highly correlated with performance on both Stroop and flanker interference tasks. Other components of attention, such as orienting, alerting and visual search efficiency, showed no such relationship. In a second experiment, we examined the relationship between the ability to determine the orientation of unmasked point-light actions and Stroop interference, again finding a strong correlation. Our results are consistent with previous research suggesting that biological motion processing may requite attention, and specifically implicate networks of attention related to executive control and selection.

  16. Elucidation of time-dependent systems biology cell response patterns with time course network enrichment

    DEFF Research Database (Denmark)

    Wiwie, Christian; Rauch, Alexander; Haakonsson, Anders

    2018-01-01

    , no methods exist to integrate time series data with networks, thus preventing the identification of time-dependent systems biology responses. We close this gap with Time Course Network Enrichment (TiCoNE). It combines a new kind of human-augmented clustering with a novel approach to network enrichment...

  17. Advances in Physical and Biological Radiation Detectors. Proceedings of a Symposium on New Developments in Physical and Biological Radiation Detectors

    International Nuclear Information System (INIS)

    1971-01-01

    Radiation dosimetry is a fundamental part of all radiation protection work. The measurements are made with a variety of instruments, and health physicists, after professional interpretation of the data, can assess the levels of exposure which might be encountered in a given area or the individual doses received by workers, visitors and others at places where the possibility of radiation exposure exists. The types of radiation concerned here are photon radiations, ranging from soft X-rays to gamma rays, and particulate radiations such as β-rays, α-particles, protons, neutrons and fission fragments. The type of technique used depends not only on the type of radiation but also on such factors as whether the radiation is from a source internal or external to the body. Radiation dosimetry is not only used at nuclear facilities; it has diverse applications, for example in determining doses when radiation sources are employed for medical diagnostics and therapy, in safeguarding workers in any industry where isotopes are used, and in assessing the effect of both naturally occurring and man-made radiations on the general public and the environment. The advances of modern technology have increased the variety of sources; an example can be given from colour television, where the high potential necessary in certain colour cathode-ray tubes generates a non-negligible amount of X-rays. The Symposium on New Developments in Physical and Biological Radiation Detectors was one of a continuing series of meetings in which the International Atomic Energy Agency furthers the exchange of information on all aspects of personnel and area dosimetry. The Symposium was devoted in particular to a study of the dose meters themselves - their radiation-sensitive elements (both physical and biological),their instrumentation, and calibration and standardization. Several speakers suggested that the situation in the standardization and calibration of measuring equipment and sources was

  18. Microbiology and atmospheric processes: biological, physical and chemical characterization of aerosol particles

    Directory of Open Access Journals (Sweden)

    D. G. Georgakopoulos

    2009-04-01

    Full Text Available The interest in bioaerosols has traditionally been linked to health hazards for humans, animals and plants. However, several components of bioaerosols exhibit physical properties of great significance for cloud processes, such as ice nucleation and cloud condensation. To gain a better understanding of their influence on climate, it is therefore important to determine the composition, concentration, seasonal fluctuation, regional diversity and evolution of bioaerosols. In this paper, we will review briefly the existing techniques for detection, quantification, physical and chemical analysis of biological particles, attempting to bridge physical, chemical and biological methods for analysis of biological particles and integrate them with aerosol sampling techniques. We will also explore some emerging spectroscopy techniques for bulk and single-particle analysis that have potential for in-situ physical and chemical analysis. Lastly, we will outline open questions and further desired capabilities (e.g., in-situ, sensitive, both broad and selective, on-line, time-resolved, rapid, versatile, cost-effective techniques required prior to comprehensive understanding of chemical and physical characterization of bioaerosols.

  19. An efficient grid layout algorithm for biological networks utilizing various biological attributes

    Directory of Open Access Journals (Sweden)

    Kato Mitsuru

    2007-03-01

    Full Text Available Abstract Background Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. Results We propose a new grid layout algorithm. To address problem (i, we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii, we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii, we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates

  20. Bringing the physical sciences into your cell biology research.

    Science.gov (United States)

    Robinson, Douglas N; Iglesias, Pablo A

    2012-11-01

    Historically, much of biology was studied by physicists and mathematicians. With the advent of modern molecular biology, a wave of researchers became trained in a new scientific discipline filled with the language of genes, mutants, and the central dogma. These new molecular approaches have provided volumes of information on biomolecules and molecular pathways from the cellular to the organismal level. The challenge now is to determine how this seemingly endless list of components works together to promote the healthy function of complex living systems. This effort requires an interdisciplinary approach by investigators from both the biological and the physical sciences.

  1. Radiations at the physics-biology interface. Utilization of radiations for research

    International Nuclear Information System (INIS)

    Douzou, P.

    1997-01-01

    Structural biology, which study the relation between the structure of biomolecules and their function, is at the interface between physics and biology. With the help of large radiation instruments such as X ray diffraction and neutron scattering, important advancements have been accomplished in the understanding of specific biological functions and led to the development of protein engineering (such as directed mutagenesis)

  2. Robustness leads close to the edge of chaos in coupled map networks: toward the understanding of biological networks

    International Nuclear Information System (INIS)

    Saito, Nen; Kikuchi, Macoto

    2013-01-01

    Dynamics in biological networks are, in general, robust against several perturbations. We investigate a coupled map network as a model motivated by gene regulatory networks and design systems that are robust against phenotypic perturbations (perturbations in dynamics), as well as systems that are robust against mutation (perturbations in network structure). To achieve such a design, we apply a multicanonical Monte Carlo method. Analysis based on the maximum Lyapunov exponent and parameter sensitivity shows that systems with marginal stability, which are regarded as systems at the edge of chaos, emerge when robustness against network perturbations is required. This emergence of the edge of chaos is a self-organization phenomenon and does not need a fine tuning of parameters. (paper)

  3. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  4. Conference: Statistical Physics and Biological Information; F

    International Nuclear Information System (INIS)

    Gross, David J.; Hwa, Terence

    2001-01-01

    In the spring of 2001, the Institute for Theoretical Physics ran a 6 month scientific program on Statistical Physics and Biological Information. This program was organized by Walter Fitch (UC Irvine), Terence Hwa (UC San Diego), Luca Peliti (University Federico II), Naples Gary Stormo (Washington University School of Medicine) and Chao Tang (NEC). Overall scientific supervision was provided by David Gross, Director, ITP. The ITP has an online conference/program proceeding which consists of audio and transparencies of almost all of the talks held during this program. Over 100 talks are available on the site at http://online.kitp.ucsb.edu/online/infobio01/

  5. Interpreting physical flows in networks as a communication system

    Indian Academy of Sciences (India)

    However, the impact of information theory on networks (acting as the channel) is just starting. Here, we ... The physical universe is ruled by physical laws. These laws are ... their structure by setting electric currents that travel from sources to ...

  6. From systems biology to photosynthesis and whole-plant physiology: a conceptual model for integrating multi-scale networks.

    Science.gov (United States)

    Weston, David J; Hanson, Paul J; Norby, Richard J; Tuskan, Gerald A; Wullschleger, Stan D

    2012-02-01

    Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated with photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.

  7. Network motif frequency vectors reveal evolving metabolic network organisation.

    Science.gov (United States)

    Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

    2015-01-01

    At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

  8. The Cytoskeleton: Mechanical, Physical, and Biological Interactions

    Science.gov (United States)

    1996-01-01

    This workshop, entitled "The Cytoskeleton: Mechanical, Physical, and Biological Interactions," was sponsored by the Center for Advanced Studies in the Space Life Sciences at the Marine Biological Laboratory. This Center was established through a cooperative agreement between the MBL and the Life Sciences Division of the National Aeronautics and Space Administration. To achieve these goals, the Center sponsors a series of workshops on various topics in the life sciences. Elements of the cytoskeleton have been implicated in the effects of gravity on the growth of plants fungi. An intriguing finding in this regard is the report indicating that an integrin-like protein may be the gravireceptor in the internodal cells of Chara. Involvement of the cytoskeleton in cellular graviperception of the basidiomycete Flammulina velutipes has also been reported. Although the responses of mammalian cells to gravity are not well documented, it has been proposed that integrins can act as mechanochemical transducers in mammalian cells. Little is known about the integrated mechanical and physical properties of cytoplasm, this workshop would be the best place to begin developing interdisciplinary approaches to the effects of mechanical stresses on cells and their most likely responsive cytoplasmic elements- the fibrous proteins comprising the cytoskeleton.

  9. Assembling the puzzle for promoting physical activity in Brazil: a social network analysis.

    Science.gov (United States)

    Brownson, Ross C; Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Hallal, Pedro C; Hoehner, Christine; Malta, Deborah Carvalho; Reis, Rodrigo S; Ramos, Luiz Roberto; Ribeiro, Isabela C; Soares, Jesus; Pratt, Michael

    2010-07-01

    Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil. An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization. Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy. Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

  10. Analysis of complex networks from biology to linguistics

    CERN Document Server

    Dehmer, Matthias

    2009-01-01

    Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.

  11. An introduction to nuclear physics, with applications in medicine and biology

    International Nuclear Information System (INIS)

    Dyson, N.A.

    1981-01-01

    A concise account of the applications of nuclear physics to medical and biological science is given. Half the book is devoted to the basic aspects of nuclear and radiation physics such as interactions between radiation and matter, nuclear reactions and the production of isotopes, an introduction to α, β and γ-radiation detectors and finally the radiation from nuclear decay. Information is then given on the applications of radioisotopes and neutrons and other accelerator-based applications in medicine and biology. The book is aimed at not only those undergraduates and postgraduates who are devoting their main effort to medical physics, but also to those students who are looking primarily for an introduction to nuclear physics together with an account of some of the ways in which it impinges on the work of other scientists. (U.K.)

  12. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    Science.gov (United States)

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

  13. Exploitation of complex network topology for link prediction in biological interactomes

    KAUST Repository

    Alanis Lobato, Gregorio

    2014-01-01

    In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable

  14. Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model.

    Science.gov (United States)

    Jaikuna, Tanwiwat; Khadsiri, Phatchareewan; Chawapun, Nisa; Saekho, Suwit; Tharavichitkul, Ekkasit

    2017-02-01

    To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL) model. The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR), and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD 2 ) was calculated using biological effective dose (BED) based on the LQL model. The software calculation and the manual calculation were compared for EQD 2 verification with pair t -test statistical analysis using IBM SPSS Statistics version 22 (64-bit). Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS) in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV) determined by D 90% , 0.56% in the bladder, 1.74% in the rectum when determined by D 2cc , and less than 1% in Pinnacle. The difference in the EQD 2 between the software calculation and the manual calculation was not significantly different with 0.00% at p -values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT) and 0.240, 0.320, and 0.849 for brachytherapy (BT) in HR-CTV, bladder, and rectum, respectively. The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

  15. Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection.

    Science.gov (United States)

    Chen, Bor-Sen; Yeh, Chin-Hsun

    2017-12-01

    We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

    OpenAIRE

    Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

    2012-01-01

    Abstract Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, suc...

  17. Physics and the molecular revolution in plant biology: union needed for managing the future

    Directory of Open Access Journals (Sweden)

    Ulrich Lüttge

    2016-10-01

    Full Text Available The question was asked if there is still a prominent role of biophysics in plant biology in an age when molecular biology appears to be dominating. Mathematical formation of theory is essential in systems biology, and mathematics is more inherent in biophysics than in molecular biology. A survey is made identifying and briefly characterizing fields of plant biology where approaches of biophysics remain essential. In transport at membranes electrophysiology and thermodynamics are biophysical topics. Water is a special molecule. Its transport follows the physical laws of osmosis and gradients of water potential on the background of physics of hydraulic architecture. Photobiology needs understanding of the physics of electro-magnetic radiation of quantitative nature in photosynthesis and of qualitative nature in perception by the photo-sensors cryptochromes, phototropins and phytochrome in environmental responses and development. Biophysical oscillators can play a role in biological timing by the circadian clock. Integration in the self-organization of modules, such as roots, stems and leaves, for the emergence of whole plants as unitary organisms needs storage and transport of information where physical modes of signaling are essential with cross talks between electrical and hydraulic signals and with chemical signals. Examples are gravitropism and root-shoot interactions in water relations. All of these facets of plant biophysics overlie plant molecular biology and exchange with it. It is advocated that a union of approaches of plant molecular biology and biophysics needs to be cultivated. In many cases it is already operative. In bionics biophysics is producing output for practical applications linking biology with technology. Biomimetic engineering intrinsically uses physical approaches. An extreme biophysical perspective is looking out for life in space. Sustained and increased practice of biophysics with teaching and research deserves strong

  18. Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

    Directory of Open Access Journals (Sweden)

    Wu Xiaogang

    2012-06-01

    Full Text Available In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease, and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI database, and pathway enrichment from the human pathway database (HPD. We use a recently-published microarray dataset (GSE24215 related to insulin resistance and type 2 diabetes (T2D as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

  19. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  20. Interface between Physics and Biology: Training a New Generation of Creative Bilingual Scientists.

    Science.gov (United States)

    Riveline, Daniel; Kruse, Karsten

    2017-08-01

    Whereas physics seeks for universal laws underlying natural phenomena, biology accounts for complexity and specificity of molecular details. Contemporary biological physics requires people capable of working at this interface. New programs prepare scientists who transform respective disciplinary views into innovative approaches for solving outstanding problems in the life sciences. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Future directions for radiological physics: An interface with molecular biology

    International Nuclear Information System (INIS)

    Braby, L.A.

    1987-01-01

    Recent experiments with low energy x-rays and fast molecular ions have shown that the products of the interaction of several ionizations within a few nanometers dominate radiation effects. However, the authors still can only make assumptions about the physical and chemical nature of this initial damage. Enzymatic repair of DNA damage is another key factor, but they have little idea of what governs the success or failure (misrepair) of these processes. Unresolved problems like these dictate the future direction of radiological physics. Molecular biology techniques are being applied to determine molecular alterations which result in observed damage. Interpretation of these experiments will require new data on the physics of energy transfer to macromolecules and the stochastics of energy deposition in time. Future studies will attempt to identify the initial damage, before biological processes have amplified it. This will require a detailed understanding of the role of chromatin structure in governing gene expression, the transport of energy within macromolecules, the transport of ions and radicals in the semiordered environment near DNA strands, and many other physical characteristics within the living cell

  2. International network connectivity and performance -- The challenge from high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Matthews, W.

    2000-03-20

    The requirements of the new generation of High Energy and Nuclear Physics (HENP) experiments such as the BaBar detector at the Stanford Linear Accelerator Center (SLAC), the Relativistic Heavy Ion Collider (RHIC) groups at the Brookhaven National Laboratory (BNL) and the LHC projects currently under development at the European Center for Particle Physics (CERN) are a huge challenge to networking. In order to increase understanding and to improve performance and connectivity by identifying bottlenecks and allocating resources, the HENP networking community has been actively monitoring the network for over five years.

  3. Physical heterogeneity and aquatic community function in river networks: A case study from the Kanawha River Basin, USA

    Science.gov (United States)

    Thoms, M. C.; Delong, M. D.; Flotemersch, J. E.; Collins, S. E.

    2017-08-01

    The geomorphological character of a river network provides the template upon which evolution acts to create unique biological communities. Deciphering commonly observed patterns and processes within riverine landscapes resulting from the interplay between physical and biological components is a central tenet for the interdisciplinary field of river science. Relationships between the physical heterogeneity and food web character of functional process zones (FPZs) - large tracts of river with a similar geomorphic character -in the Kanawha River (West Virginia, USA) are examined in this study. Food web character was measured as food chain length (FCL), which reflects ecological community structure and ecosystem function. Our results show that the same basal resources were present throughout the Kanawha River but that their assimilation into the aquatic food web by primary consumers differed between FPZs. Differences in the trophic position of higher consumers (fish) were also recorded between FPZs. Overall, the morphological heterogeneity and heterogeneity of the river bed sediment of FPZs were significantly correlated with FCL. Specifically, FCL increases with greater FPZ physical heterogeneity. The result of this study does not support the current paradigm that ecosystem size is the primary determinant of food web character in river ecosystems.

  4. Mapping university students’ epistemic framing of computational physics using network analysis

    Directory of Open Access Journals (Sweden)

    Madelen Bodin

    2012-04-01

    Full Text Available Solving physics problem in university physics education using a computational approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modeling. These competences are in turn related to students’ beliefs about the domains as well as about learning. These knowledge and beliefs components are referred to here as epistemic elements, which together represent the students’ epistemic framing of the situation. The purpose of this study was to investigate university physics students’ epistemic framing when solving and visualizing a physics problem using a particle-spring model system. Students’ epistemic framings are analyzed before and after the task using a network analysis approach on interview transcripts, producing visual representations as epistemic networks. The results show that students change their epistemic framing from a modeling task, with expectancies about learning programming, to a physics task, in which they are challenged to use physics principles and conservation laws in order to troubleshoot and understand their simulations. This implies that the task, even though it is not introducing any new physics, helps the students to develop a more coherent view of the importance of using physics principles in problem solving. The network analysis method used in this study is shown to give intelligible representations of the students’ epistemic framing and is proposed as a useful method of analysis of textual data.

  5. Optimising Transport in a Homogeneous Network

    OpenAIRE

    WEAIRE, DENIS LAWRENCE

    2004-01-01

    PUBLISHED Many situations in physics, biology, and engineering consist of the transport of some physical quantity through a network of narrow channels. The ability of a network to transport such a quantity in every direction can be described by the average conductivity associated with it. When the flow through each channel is conserved and derives from a potential function, we show that there exists an upper bound of the average conductivity and explicitly give the expression f...

  6. Optimizing transport in a homogeneous network

    OpenAIRE

    WEAIRE, DENIS LAWRENCE

    2004-01-01

    PUBLISHED Many situations in physics, biology, and engineering consist of the transport of some physical quantity through a network of narrow channels. The ability of a network to transport such a quantity in every direction can be described by the average conductivity associated with it. When the flow through each channel is conserved and derives from a potential function, we show that there exists an upper bound of the average conductivity and explicitly give the expression f...

  7. Associations within school-based same-sex friendship networks of children's physical activity and sedentary behaviours: a cross-sectional social network analysis.

    Science.gov (United States)

    Salway, Ruth E; Sebire, Simon J; Solomon-Moore, Emma; Thompson, Janice L; Jago, Russell

    2018-02-21

    Physical activity in children is associated with better physical and mental health but many children do not meet physical activity guidelines. Friendship groups are potentially an important influence on children's physical activity and sedentary time. This paper examines the association between children of physical activity and sedentary time in school-based same-sex friendship networks, for both moderate-to-vigorous intensity physical activity (MVPA) and sedentary time. Moreover, considering the methodological challenges of conducting and interpreting these analyses, we provide examples of how to analyse these data and interpret results to encourage further work in the area. Accelerometer data for 1223 children, aged 8-9 years, were collected in 2015-2016 and analysed in 2017. Mean accelerometer minutes of MVPA and sedentary time were calculated. Children named up to four school friends and same-sex school-based friendship networks were constructed. Network models, which include correlation between friends, were fitted by sex. Both MVPA and sedentary time were found to be associated via the friendship networks, for both boys and girls. The network autocorrelation was 0.21 (95% CI: 0.15 to 0.26) for boys' MVPA, and 0.14 (95% CI: 0.07 to 0.21) for sedentary time. Network autocorrelation between girls was weaker, with 0.13 (95% CI: 0.06 to 0.19) for MVPA and 0.11 (95% CI: 0.05 to 0.17) for sedentary time. Physical activity and sedentary time of boys and girls are associated with the physical activity and sedentary time respectively of others within same-sex friendship networks, and these associations are comparable to other known factors. In this study, the correlation between friends was stronger for boys than girls, and stronger for MVPA than for sedentary time. These findings suggest that friendship networks play a part in understanding children's physical activity and sedentary time and could play a valuable role in developing effective interventions.

  8. Biological mechanisms underlying the role of physical fitness in health and resilience

    OpenAIRE

    Silverman, Marni N.; Deuster, Patricia A.

    2014-01-01

    Physical fitness, achieved through regular exercise and/or spontaneous physical activity, confers resilience by inducing positive psychological and physiological benefits, blunting stress reactivity, protecting against potentially adverse behavioural and metabolic consequences of stressful events and preventing many chronic diseases. In this review, we discuss the biological mechanisms underlying the beneficial effects of physical fitness on mental and physical health. Physical fitness appear...

  9. Molecular codes in biological and chemical reaction networks.

    Directory of Open Access Journals (Sweden)

    Dennis Görlich

    Full Text Available Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio- chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries, biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades, an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

  10. The Effects Of Physical And Biological Cohesion On Bedforms

    Science.gov (United States)

    Parsons, D. R.; Schindler, R.; Baas, J.; Hope, J. A.; Malarkey, J.; Paterson, D. M.; Peakall, J.; Manning, A. J.; Ye, L.; Aspden, R.; Alan, D.; Bass, S. J.

    2014-12-01

    Most coastal sediments consist of complex mixtures of cohesionless sands, physically-cohesive clays and extra cellular polymeric substances (EPS) that impart biological cohesion. Yet, our ability to predict bedform dimensions in these substrates is reliant on predictions based exclusively on cohesionless sand. We present findings from the COHBED project - which explicitly examines how bedform dynamics are modified by natural cohesion. Our experimental results show that for ripples, height and length are inversely proportional to initial clay content and bedforms take longer to appear, with no ripples when clay content exceeds 18%. When clay is replaced by EPS the development time and time of first appearance of ripples both increase by two orders of magnitude, with no bedforms above 0.125% EPS. For dunes, height and length are also inversely proportional to initial substrate clay content, resulting in a transition from dunes to ripples normally associated with velocity decreases. Addition of low EPS concentrations into the substrate results in yet smaller bedforms at the same clay contents and at high EPS concentrations, biological cohesion supersedes all electrostatic bonding, and bedform size is no longer related to mud content. The contrast in physical and biological cohesion effects on bedform development result from the disparity between inter-particle electrostatic bonding of clay particles and EPS grain coating and strands that physically link sediments together, which effects winnowing rates as bedforms evolve. These findings have wide ranging implications for bedform predictions in both modern and ancient environments. Coupling of biological and morphological processes not only requires an understanding of how bedform dimensions influence biota and habitat, but also how benthic species can modify bedform dimensions. Consideration of both aspects provides a means in which fluid dynamics, sediment transport and ecosystem energetics can be linked to yield

  11. Scandium: its occurrence, chemistry, physics, metallurgy, biology, and technology

    International Nuclear Information System (INIS)

    Horovitz, C.T.

    1975-01-01

    This book describes the following aspects of scandium: discovery and history, occurrence in nature, geochemistry and mineralogy, chemical, physical and technological properties, fabrication and metallurgy, its biological significance and toxicology, and its uses. (Extensive references for each chapter)

  12. DNA confinement in nanochannels: physics and biological applications

    DEFF Research Database (Denmark)

    Reisner, Walter; Pedersen, Jonas Nyvold; Austin, Robert H

    2012-01-01

    in nanochannels, creating a linear unscrolling of the genome along the channel for analysis. We will first review the fundamental physics of DNA nanochannel confinement—including the effect of varying ionic strength—and then discuss recent applications of these systems to genomic mapping. Apart from the intense...... direct assessment of the genome in its native state). In this review, we will discuss how the information contained in genomic-length single DNA molecules can be accessed via physical confinement in nanochannels. Due to self-avoidance interactions, DNA molecules will stretch out when confined...... biological interest in extracting linear sequence information from elongated DNA molecules, from a physics view these systems are fascinating as they enable probing of single-molecule conformation in environments with dimensions that intersect key physical length-scales in the 1 nm to 100μm range. (Some...

  13. Building gene co-expression networks using transcriptomics data for systems biology investigations

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

    Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four......) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT...... (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended....

  14. Biology and physics competencies for pre-health and other life sciences students.

    Science.gov (United States)

    Hilborn, Robert C; Friedlander, Michael J

    2013-06-01

    The recent report on the Scientific Foundations for Future Physicians (SFFP) and the revised Medical College Admissions Test (MCAT) reframe the preparation for medical school (and other health professional schools) in terms of competencies: what students should know and be able to do with that knowledge, with a strong emphasis on scientific inquiry and research skills. In this article, we will describe the thinking that went into the SFFP report and what it says about scientific and quantitative reasoning, focusing on biology and physics and the overlap between those fields. We then discuss how the SFFP report set the stage for the discussion of the recommendations for the revised MCAT, which will be implemented in 2015, again focusing the discussion on biology and physics. Based on that framework, we discuss the implications for undergraduate biology and physics education if students are to be prepared to demonstrate these competencies.

  15. Networking for High Energy and Nuclear Physics

    Science.gov (United States)

    Newman, Harvey B.

    2007-07-01

    This report gives an overview of the status and outlook for the world's research networks and major international links used by the high energy physics and other scientific communities, network technology advances on which our community depends and in which we have an increasingly important role, and the problem of the Digital Divide, which is a primary focus of ICFA's Standing Committee on Inter-regional Connectivity (SCIC). Wide area networks of sufficient, and rapidly increasing end-to-end capability are vital for every phase of high energy physicists' work. Our bandwidth usage, and the typical capacity of the major national backbones and intercontinental links used by our field have progressed by a factor of more than 1000 over the past decade, and the outlook is for a similar increase over the next decade. This striking exponential growth trend, outstripping the growth rates in other areas of information technology, has continued in the past year, with many of the major national, continental and transoceanic networks supporting research and education progressing from a 10 Gigabits/sec (Gbps) backbone to multiple 10 Gbps links in their core. This is complemented by the use of point-to-point "light paths" to support the most demanding applications, including high energy physics, in a growing list of cases. As we approach the era of LHC physics, the growing need to access and transport Terabyte-scale and later 10 to 100 Terabyte datasets among more than 100 "Tier1" and "Tier2" centers at universities and laboratories spread throughout the world has brought the key role of networks, and the ongoing need for their development, sharply into focus. Bandwidth itself on an increasing scale is not enough. Realizing the scientific wealth of the LHC and our other major scientific programs depends crucially on our ability to use the bandwidth efficiently and reliably, with reliable high rates of data throughput, and effectively, where many parallel large-scale data

  16. Double network bacterial cellulose hydrogel to build a biology-device interface

    Science.gov (United States)

    Shi, Zhijun; Li, Ying; Chen, Xiuli; Han, Hongwei; Yang, Guang

    2013-12-01

    Establishing a biology-device interface might enable the interaction between microelectronics and biotechnology. In this study, electroactive hydrogels have been produced using bacterial cellulose (BC) and conducting polymer (CP) deposited on the BC hydrogel surface to cover the BC fibers. The structures of these composites thus have double networks, one of which is a layer of electroactive hydrogels combined with BC and CP. The electroconductivity provides the composites with capabilities for voltage and current response, and the BC hydrogel layer provides good biocompatibility, biodegradability, bioadhesion and mass transport properties. Such a system might allow selective biological functions such as molecular recognition and specific catalysis and also for probing the detailed genetic and molecular mechanisms of life. A BC-CP composite hydrogel could then lead to a biology-device interface. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) are used here to study the composite hydrogels' electroactive property. BC-PAni and BC-PPy respond to voltage changes. This provides a mechanism to amplify electrochemical signals for analysis or detection. BC hydrogels were found to be able to support the growth, spreading and migration of human normal skin fibroblasts without causing any cytotoxic effect on the cells in the cell culture. These double network BC-CP hydrogels are biphasic Janus hydrogels which integrate electroactivity with biocompatibility, and might provide a biology-device interface to produce implantable devices for personalized and regenerative medicine.

  17. Resume: networking in high energy physics

    International Nuclear Information System (INIS)

    Hutton, J.S.

    1985-11-01

    Networking in High Energy Physics covers communications inside the experiment and internationally. Inside the experiment the need for agreed 'codes of practice' is now accepted. Within Europe it is accepted that a common infrastructure based on the use of the ISO OSI protocols should be used. In the USA a community initiative has been proposed. The background to these approaches is discussed. (author)

  18. Time in physics and biology

    Directory of Open Access Journals (Sweden)

    BRUNO GÜNTHER

    2004-01-01

    Full Text Available In contrast with classical physics, particularly with Sir Isaac Newton, where time is a continuous function, generally valid, eternally and evenly flowing as an absolute time dimension, in the biological sciences, time is in essence of cyclical nature (physiological periodicities, where future passes to past through an infinitely thin boundary, the present. In addition, the duration of the present (DP leads to the so-called 'granulation of time' in living beings, so that by the fusion of two successive pictures of the world, which are not entirely similar, they attain the perception of 'movement,' both in the real world as well as in the sham-movement in the mass media (TV.

  19. Plasma Physics Network Newsletter. No. 1

    International Nuclear Information System (INIS)

    1989-08-01

    This is the first issue of a quarterly newsletter published by the International Atomic Energy Agency in order to provide news of potential interest of fusion scientists in developing countries. According to the foreword to this first issue, the purpose of the newsletter, as well as the organization called ''Third World Network'', is to ''start the process of unifying the developing country fusion community into some type of cohesive entity and to bring the efforts of the developing countries in the plasma physics research area to the attention of the world fusion community at large''. Furthermore, this first issue contains information about (i) Nuclear Fusion Research in Argentina, (ii) Chinese Fusion Efforts, (iii) Plasma and Fusion Physics in Egypt, (iv) Fusion Research in India, (v) Fusion Research in the Republic of Korea, (vi) Fusion Programmes in Malaysia, (vi) the Agency's Fusion Programme, (vii) a proposal for a workshop on computational plasma physics, sponsored by the Third World Plasma Research Network, (viii) the announcement of the formation of the ''Asian African Association for Plasma Training'', - for the promotion of the initiation/strengthening of plasma research, especially experimental, in developing countries in Asia and Africa, as well as the cooperation and sharing of technology among plasma physicists in the developing countries in the region; (ix) a communication entitled ''Fusion Research in ''Small'' Countries'', I.R. Jones, School of Physical Sciences, The Flinders University of South Australia, Bedford Park, Australia, on the desirability of the pursuit of fusion research in ''small'' countries, i.e., those countries that do not have a national fusion research programme; (x) and, finally, a newsletter on the ITER project

  20. An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

    Science.gov (United States)

    Mallik, Mrinmay Kumar

    2018-02-07

    Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

  1. High Energy Physics Computer Networking: Report of the HEPNET Review Committee

    International Nuclear Information System (INIS)

    1988-06-01

    This paper discusses the computer networks available to high energy physics facilities for transmission of data. Topics covered in this paper are: Existing and planned networks and HEPNET requirements

  2. Use artificial neural network to align biological ontologies.

    Science.gov (United States)

    Huang, Jingshan; Dang, Jiangbo; Huhns, Michael N; Zheng, W Jim

    2008-09-16

    Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

  3. Integrated Network Analysis and Effective Tools in Plant Systems Biology

    Directory of Open Access Journals (Sweden)

    Atsushi eFukushima

    2014-11-01

    Full Text Available One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1 network visualization tools, (2 pathway analyses, (3 genome-scale metabolic reconstruction, and (4 the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

  4. Cyber-physical system design with sensor networking technologies

    CERN Document Server

    Zeadally, Sherali

    2016-01-01

    This book describes how wireless sensor networking technologies can help in establishing and maintaining seamless communications between the physical and cyber systems to enable efficient, secure, reliable acquisition, management, and routing of data.

  5. Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study.

    Science.gov (United States)

    Mones, Enys; Stopczynski, Arkadiusz; Pentland, Alex 'Sandy'; Hupert, Nathaniel; Lehmann, Sune

    2018-01-01

    Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the 'holy grails' of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call 'cyber-directed vaccination') can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission. © 2018 The Author(s).

  6. OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space.

    Science.gov (United States)

    Zhou, Guangyan; Xia, Jianguo

    2018-06-07

    Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.

  7. Friendship networks and physical activity and sedentary behavior among youth: a systematized review.

    Science.gov (United States)

    Sawka, Keri Jo; McCormack, Gavin R; Nettel-Aguirre, Alberto; Hawe, Penelope; Doyle-Baker, Patricia K

    2013-12-01

    Low levels of physical activity and increased participation in sedentary leisure-time activities are two important obesity-risk behaviors that impact the health of today's youth. Friend's health behaviors have been shown to influence individual health behaviors; however, current evidence on the specific role of friendship networks in relation to levels of physical activity and sedentary behavior is limited. The purpose of this review was to summarize evidence on friendship networks and both physical activity and sedentary behavior among children and adolescents. After a search of seven scientific databases and reference scans, a total of thirteen articles were eligible for inclusion. All assessed the association between friendship networks and physical activity, while three also assessed sedentary behavior. Overall, higher levels of physical activity among friends are associated with higher levels of physical activity of the individual. Longitudinal studies reveal that an individual's level of physical activity changes to reflect his/her friends' higher level of physical activity. Boys tend to be influenced by their friendship network to a greater extent than girls. There is mixed evidence surrounding a friend's sedentary behavior and individual sedentary behavior. Friends' physical activity level appears to have a significant influence on individual's physical activity level. Evidence surrounding sedentary behavior is limited and mixed. Results from this review could inform effective public health interventions that harness the influence of friends to increase physical activity levels among children and adolescents.

  8. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

    Science.gov (United States)

    Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

    2012-01-01

    Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

  9. Biological-based and physical-based optimization for biological evaluation of prostate patient's plans

    Science.gov (United States)

    Sukhikh, E.; Sheino, I.; Vertinsky, A.

    2017-09-01

    Modern modalities of radiation treatment therapy allow irradiation of the tumor to high dose values and irradiation of organs at risk (OARs) to low dose values at the same time. In this paper we study optimal radiation treatment plans made in Monaco system. The first aim of this study was to evaluate dosimetric features of Monaco treatment planning system using biological versus dose-based cost functions for the OARs and irradiation targets (namely tumors) when the full potential of built-in biological cost functions is utilized. The second aim was to develop criteria for the evaluation of radiation dosimetry plans for patients based on the macroscopic radiobiological criteria - TCP/NTCP. In the framework of the study four dosimetric plans were created utilizing the full extent of biological and physical cost functions using dose calculation-based treatment planning for IMRT Step-and-Shoot delivery of stereotactic body radiation therapy (SBRT) in prostate case (5 fractions per 7 Gy).

  10. 8. International Conference on Pulse Investigations in Chemistry, Biology and Physics - PULS'2008. Abstracts

    International Nuclear Information System (INIS)

    2008-01-01

    The Report comprises abstracts of 68 communications (oral and posters) presented during the 8. International Conference on Pulse Investigations in Chemistry, Biology and Physics - PULS'2008, held on September 6 - 12, 2008 in Cracow. Presentations cover a variety of research fields representing different fields of pulse radiolysis in chemistry, biology and physics

  11. 8. International Conference on Pulse Investigations in Chemistry, Biology and Physics - PULS'2008. Abstracts

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-07-01

    The Report comprises abstracts of 68 communications (oral and posters) presented during the 8. International Conference on Pulse Investigations in Chemistry, Biology and Physics - PULS'2008, held on September 6 - 12, 2008 in Cracow. Presentations cover a variety of research fields representing different fields of pulse radiolysis in chemistry, biology and physics.

  12. From the Physical World to the Biological Universe: Historical Developments Underlying SETI

    Science.gov (United States)

    Dick, Steven J.

    More than thirty years ago the French historian of science Alexandre Koyré (1957) wrote his classic volume, From the Closed World to the Infinite Universe, in which he argued that a fundamental shift in world view had taken place in 17th century cosmology. Between Nicholas of Cusa in the fifteenth century and Newton and Leibniz in the seventeenth, he found that the very terms in which humans thought about their universe had changed. These changes he characterized broadly as the destruction of the closed finite cosmos and the geometrization of space. The occasion of the Third International Bioastronomy Symposium in France is an especially appropriate time to argue that the SETI endeavor represents a test for a similar fundamental shift in cosmological world view, from the physical world to the biological universe. I define the biological universe, equivalent to what I have called before the biophysical cosmology (Dick, 1989), as the scientific world view which holds that life is widespread throughout the universe. In this case the biological universe does not necessarily supersede the physical universe, but a universe filled with life would certainly fundamentally alter our attitude toward the universe, and our place in it. Although Koyré mentioned life beyond the Earth as an adjunct to the revolution from the closed world to the infinite universe, only in the 1980s has the history of science begun to give full treatment to the subject. What follows is meant to be a contribution to that ongoing endeavor to understand where the extraterrestrial life debate fits in the history of science. The modern era in the extraterrestrial life debate is normally dated from Cocconi and Morrison's paper in 1959, and though one can always find precursors, this in my view is a valid perception. Cocconi and Morrison gave definite form to SETI, Frank Drake independently first carried out the experiment, a network of interested scientists began to form and met in Green Bank in

  13. Effects of social networks on physical health among people with serious mental illness.

    Science.gov (United States)

    Lee, Sungkyu; Wong, Yin-Ling Irene; Rothbard, Aileen

    2014-12-01

    This study examined the effects of social network characteristics on physical health among people with serious mental illness using social transactions that are reciprocal, and the combination of objective and subjective health measures. The sample consisted of a probability sample of 231 adults with serious mental illness who resided in permanent supportive housing in Philadelphia, Pennsylvania. Path analyses were conducted to examine the relationships between social network characteristics and two aspects of medical comorbidity, objective health and subjective health. Bivariate statistics showed that individuals with medical comorbidity were more likely to have contact with their network members and had a higher level of reciprocal positive tangible support when compared to those who did not have medical comorbidity. The results of the path analyses revealed that none of the social network characteristics were associated with better physical health. The lack of a significant relationship between social networks and better physical health is contrary to prior research findings. However, this is the first study to include both types of social transactions simultaneously as predictors of better physical health for individuals with serious mental illness. A longitudinal study would provide more insight into the temporal relationship of social networks and physical health conditions of people with serious mental illness. Furthermore, the transactional nature of social relationships, particularly for those with mental health issues, requires greater exploration.

  14. Color encoding in biologically-inspired convolutional neural networks.

    Science.gov (United States)

    Rafegas, Ivet; Vanrell, Maria

    2018-05-11

    Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

    Directory of Open Access Journals (Sweden)

    Lo Kenneth

    2012-08-01

    Full Text Available Abstract Background Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. Results We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. Conclusions We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

  16. Summer School Mathematical Foundations of Complex Networked Information Systems

    CERN Document Server

    Fosson, Sophie; Ravazzi, Chiara

    2015-01-01

    Introducing the reader to the mathematics beyond complex networked systems, these lecture notes investigate graph theory, graphical models, and methods from statistical physics. Complex networked systems play a fundamental role in our society, both in everyday life and in scientific research, with applications ranging from physics and biology to economics and finance. The book is self-contained, and requires only an undergraduate mathematical background.

  17. How Can We Improve Problem Solving in Undergraduate Biology? Applying Lessons from 30 Years of Physics Education Research

    Science.gov (United States)

    Hoskinson, A.-M.; Caballero, M. D.; Knight, J. K.

    2013-01-01

    If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research. PMID:23737623

  18. How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research.

    Science.gov (United States)

    Hoskinson, A-M; Caballero, M D; Knight, J K

    2013-06-01

    If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research.

  19. S100A4 and its role in metastasis – computational integration of data on biological networks.

    Science.gov (United States)

    Buetti-Dinh, Antoine; Pivkin, Igor V; Friedman, Ran

    2015-08-01

    Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.

  20. The relative importance of physical and biological energy in landscape evolution

    Science.gov (United States)

    Turowski, J. M.; Schwanghart, W.

    2017-12-01

    Landscapes are formed by the interplay of uplift and geomorphic processes, including interacting and competing physical and biological processes. For example, roots re-inforce soil and thereby stabilize hillslopes and the canopy cover of the forest may mediate the impact of precipitation. Furthermore, plants and animals act as geomorphic agents, directly altering landscape response and dynamics by their actions: tree roots may crack rocks, thus changing subsurface water flows and exposing fresh material for denudation; fungi excrete acids that accelerate rates of chemical weathering, and burrowing animals displace soil and rocks while digging holes for shelter or in search of food. Energetically, landscapes can be viewed as open systems in which topography stores potential energy above a base level. Tectonic processes add energy to the system by uplift and mechanically altering rock properties. Especially in unvegetated regions, erosion and transport by wind can be an important geomorphic process. Advection of atmospheric moisture in high altitudes provides potential energy that is converted by water fluxes through catchments. At the same time, the conversion of solar energy through atmospheric and biological processes drives primary production of living organisms. If we accept that biota influence geomorphic processes, then what is their energetic contribution to landscape evolution relative to physical processes? Using two case studies, we demonstrate that all components of energy input are negligible apart from biological production, quantified by net primary productivity (NPP) and potential energy conversion by water that is placed high up in the landscape as rainfall and leaves it as runoff. Assuming that the former is representative for biological energy and the latter for physical energy, we propose that the ratio of these two values can be used as a proxy for the relative importance of biological and physical processes in landscape evolution. All necessary

  1. Synchronization in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Arenas, A.; Diaz-Guilera, A.; Moreno, Y.; Zhou, C.; Kurths, J.

    2007-12-12

    Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.

  2. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    Energy Technology Data Exchange (ETDEWEB)

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  3. Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

    Science.gov (United States)

    Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

    2016-01-01

    Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

  4. Statistical physics, neural networks, brain studies

    International Nuclear Information System (INIS)

    Toulouse, G.

    1999-01-01

    An overview of some aspects of a vast domain, located at the crossroads of physics, biology and computer science is presented: (1) During the last fifteen years, physicists advancing along various pathways have come into contact with biology (computational neurosciences) and engineering (formal neural nets). (2) This move may actually be viewed as one component in a larger picture. A prominent trend of recent years, observable over many countries, has been the establishment of interdisciplinary centers devoted to the study of: cognitive sciences; natural and artificial intelligence; brain, mind and behaviour; perception and action; learning and memory; robotics; man-machine communication, etc. What are the promising lines of development? What opportunities for physicists? An attempt will be made to address such questions and related issues

  5. A system of networks and continuing education for physical therapists in rheumatology: a feasibility study

    Directory of Open Access Journals (Sweden)

    J. Verhoef

    2004-07-01

    Full Text Available Purpose: To evaluate the feasibility of regional physical therapy networks including continuing education in rheumatology. The aim of these networks was to improve care provided by primary care physical therapists by improving specific knowledge, technical and communicative skills and the collaboration with rheumatologists. Methods: In two regions in The Netherlands continuing education (CE programmes, consisting of a 5-day postgraduate training course followed by bimonthly workshops and teaching practices, were organised simultaneously. Network activities included consultations, newsletters and the development of a communication guideline. Endpoint measures included the participation rate, compliance, quality of the CE programme, teaching practices, knowledge, network activities, communication, number of patients treated and patient satisfaction. Results: Sixty-three physical therapists out of 193 practices (33% participated in the project. They all completed the education programmes and were formally registered. All evaluations of the education programmes showed positive scores. Knowledge scores increased significantly directly after the training course and at 18 months. A draft guideline on communication between physical therapists and rheumatologists was developed, and 4 newsletters were distributed. A substantial proportion of physical therapists and rheumatologists reported improved communication at 18 months. The mean number of patients treated by physical therapists participating in the networks increased significantly. Patients' satisfaction scores within the networks were significantly higher than those from outside the networks at 18 months. Conclusions: Setting up a system of networks for continuing education for physical therapists regarding the treatment of patients with rheumatic diseases is feasible. Further research will focus on the effectiveness of the system and its implementation on a larger scale.

  6. What Motivates Young Adults to Talk About Physical Activity on Social Network Sites?

    Science.gov (United States)

    Zhang, Ni; Campo, Shelly; Yang, Jingzhen; Eckler, Petya; Snetselaar, Linda; Janz, Kathleen; Leary, Emily

    2017-06-22

    Electronic word-of-mouth on social network sites has been used successfully in marketing. In social marketing, electronic word-of-mouth about products as health behaviors has the potential to be more effective and reach more young adults than health education through traditional mass media. However, little is known about what motivates people to actively initiate electronic word-of-mouth about health behaviors on their personal pages or profiles on social network sites, thus potentially reaching all their contacts on those sites. This study filled the gap by applying a marketing theoretical model to explore the factors associated with electronic word-of-mouth on social network sites about leisure-time physical activity. A Web survey link was sent to undergraduate students at one of the Midwestern universities and 439 of them completed the survey. The average age of the 439 participants was 19 years (SD=1 year, range: 18-24). Results suggested that emotional engagement with leisure-time physical activity (ie, affective involvement in leisure-time physical activity) predicted providing relevant opinions or information on social network sites. Social network site users who perceived stronger ties with all their contacts were more likely to provide and seek leisure-time physical activity opinions and information. People who provided leisure-time physical activity opinions and information were more likely to seek opinions and information, and people who forwarded information about leisure-time physical activity were more likely to chat about it. This study shed light on the application of the electronic word-of-mouth theoretical framework in promoting health behaviors. The findings can also guide the development of future social marketing interventions using social network sites to promote leisure-time physical activity. ©Ni Zhang, Shelly Campo, Jingzhen Yang, Petya Eckler, Linda Snetselaar, Kathleen Janz, Emily Leary. Originally published in the Journal of Medical

  7. Enhancement of COPD biological networks using a web-based collaboration interface [v2; ref status: indexed, http://f1000r.es/5ew

    Directory of Open Access Journals (Sweden)

    The sbv IMPROVER project team (in alphabetical order

    2015-05-01

    Full Text Available The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/ and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD

  8. Statistical assessment of crosstalk enrichment between gene groups in biological networks.

    Science.gov (United States)

    McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

    2013-01-01

    Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

  9. Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge.

    Science.gov (United States)

    Zhang, Yuji

    2015-01-01

    Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly "watch" the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems. In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among

  10. Influence of different natural physical fields on biological processes

    Science.gov (United States)

    Mashinsky, A. L.

    2001-01-01

    In space flight conditions gravity, magnetic, and electrical fields as well as ionizing radiation change both in size, and in direction. This causes disruptions in the conduct of some physical processes, chemical reactions, and metabolism in living organisms. In these conditions organisms of different phylogenetic level change their metabolic reactions undergo changes such as disturbances in ionic exchange both in lower and in higher plants, changes in cell morphology for example, gyrosity in Proteus ( Proteus vulgaris), spatial disorientation in coleoptiles of Wheat ( Triticum aestivum) and Pea ( Pisum sativum) seedlings, mutational changes in Crepis ( Crepis capillaris) and Arabidopsis ( Arabidopsis thaliana) seedling. It has been found that even in the absence of gravity, gravireceptors determining spatial orientation in higher plants under terrestrial conditions are formed in the course of ontogenesis. Under weightlessness this system does not function and spatial orientation is determined by the light flux gradient or by the action of some other factors. Peculiarities of the formation of the gravireceptor apparatus in higher plants, amphibians, fish, and birds under space flight conditions have been observed. It has been found that the system in which responses were accompanied by phase transition have proven to be gravity-sensitive under microgravity conditions. Such reactions include also the process of photosynthesis which is the main energy production process in plants. In view of the established effects of microgravity and different natural physical fields on biological processes, it has been shown that these processes change due to the absence of initially rigid determination. The established biological effect of physical fields influence on biological processes in organisms is the starting point for elucidating the role of gravity and evolutionary development of various organisms on Earth.

  11. Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.

    Science.gov (United States)

    Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N

    2016-12-01

    Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).

  12. Mathematical Analysis of a PDE System for Biological Network Formation

    KAUST Repository

    Haskovec, Jan; Markowich, Peter A.; Perthame, Benoit

    2015-01-01

    Motivated by recent physics papers describing rules for natural network formation, we study an elliptic-parabolic system of partial differential equations proposed by Hu and Cai [13, 15]. The model describes the pressure field thanks to Darcy's type

  13. Research on condensed matter and atomic physics using major experimental facilities and devices: Physics, chemistry, biology. Reports on results. Vol. 3. 4. Chemistry. 5. Biology. 6. Development of methods and instruments

    International Nuclear Information System (INIS)

    1993-01-01

    This report in three volumes substantiates the contents of the programme survey published in September 1989. The progress reports cover the following research areas: Vol. I, (1). Atomic and molecular physics - free atoms, molecules, macromolecules, clusters, matrix-isolated atoms and molecules. (2) Physics and chemistry of surfaces and interfaces - epitaxy, surface structure, adsorption, electrical, magnetic, and optical properties, thin films, synthetic layer structure. Vol. II, (3). Solid-state physics, and materials science -structural research, lattice dynamics, magnetic structure and dynamics, electronic states; load; spin and pulse density fluctuations; diffusion and internal motion, defects, unordered systems and liquids. Vol. III, (4). Chemistry - bonding and structure, kinetics and reaction mechanisms, polymer research, analysis and synthesis. (5). Biology, - structure and dynamics of biological macromolecules, membrane and cell biology. (6) Development of methods and instruments - neutron sources, synchrotron sources, special accelerators, research with interlinked systems and devices. (orig.) [de

  14. Non-dimensional physics of pulsatile cardiovascular networks and energy efficiency.

    Science.gov (United States)

    Yigit, Berk; Pekkan, Kerem

    2016-01-01

    In Nature, there exist a variety of cardiovascular circulation networks in which the energetic ventricular load has both steady and pulsatile components. Steady load is related to the mean cardiac output (CO) and the haemodynamic resistance of the peripheral vascular system. On the other hand, the pulsatile load is determined by the simultaneous pressure and flow waveforms at the ventricular outlet, which in turn are governed through arterial wave dynamics (transmission) and pulse decay characteristics (windkessel effect). Both the steady and pulsatile contributions of the haemodynamic power load are critical for characterizing/comparing disease states and for predicting the performance of cardiovascular devices. However, haemodynamic performance parameters vary significantly from subject to subject because of body size, heart rate and subject-specific CO. Therefore, a 'normalized' energy dissipation index, as a function of the 'non-dimensional' physical parameters that govern the circulation networks, is needed for comparative/integrative biological studies and clinical decision-making. In this paper, a complete network-independent non-dimensional formulation that incorporates pulsatile flow regimes is developed. Mechanical design variables of cardiovascular flow systems are identified and the Buckingham Pi theorem is formally applied to obtain the corresponding non-dimensional scaling parameter sets. Two scaling approaches are considered to address both the lumped parameter networks and the distributed circulation components. The validity of these non-dimensional number sets is tested extensively through the existing empirical allometric scaling laws of circulation systems. Additional validation studies are performed using a parametric numerical arterial model that represents the transmission and windkessel characteristics, which are adjusted to represent different body sizes and non-dimensional haemodynamic states. Simulations demonstrate that the proposed non

  15. Status of networking for high energy physics in the United States

    International Nuclear Information System (INIS)

    Kunz, P.F.

    1985-06-01

    Networks are used extensively for High Energy Physics in the United States. Although the networks have grown in an ad hoc manner with connections typically being made to satisfy the needs of one detector group, they now encompass to large fraction of the US HEP community in one form or another. This paper summarizes the current status and experience with networks

  16. Russian science readings (chemistry, physics, biology)

    CERN Document Server

    Light, L

    1949-01-01

    Some years' experience in teaching Russian to working scientists who had already acquired the rudiments of the grammar convinced me of the need for a reader of the present type that would smooth the path of those wishing to study Russian scientific literature in the original. Although the subject matter comprises what I have described for convenience as chemistry, physics and biology, it could be read with equal profit by those engaged in any branch of pure or applied science. All the passages are taken from school textbooks, and acknowledgements are due to the authors of the works listed at the foot of the contents page.

  17. PersonA: Persuasive social network for physical Activity.

    Science.gov (United States)

    Ayubi, Soleh U; Parmanto, Bambang

    2012-01-01

    Advances in physical activity (PA) monitoring devices provide ample opportunities for innovations in the way the information produced by these devices is used to encourage people to have more active lifestyles. One such innovation is expanding the current use of the information from self-management to social support. We developed a Persuasive social network for physical Activity (PersonA) that combines automatic input of physical activity data, a smartphone, and a social networking system (SNS). This paper describes the motivation for and overarching design of the PersonA and its functional and non-functional features. PersonA is designed to intelligently and automatically receive raw PA data from the sensors in the smartphone, calculate the data into meaningful PA information, store the information on a secure server, and show the information to the users as persuasive and real-time feedbacks or publish the information to the SNS to generate social support. The implementation of self-monitoring, social support, and persuasive concepts using currently available technologies has the potential for promoting healthy lifestyle, greater community participation, and higher quality of life. We also expect that PersonA will enable health professionals to collect in situ data related to physical activity. The platform is currently being used and tested to improve PA level of three groups of users in Pittsburgh, PA, USA.

  18. Designing and testing a classroom curriculum to teach preschoolers about the biology of physical activity: The respiration system as an underlying biological causal mechanism

    Science.gov (United States)

    Ewing, Tracy S.

    The present study examined young children's understanding of respiration and oxygen as a source of vital energy underlying physical activity. Specifically, the purpose of the study was to explore whether a coherent biological theory, characterized by an understanding that bodily parts (heart and lungs) and processes (oxygen in respiration) as part of a biological system, can be taught as a foundational concept to reason about physical activity. The effects of a biology-based intervention curriculum designed to teach preschool children about bodily functions as a part of the respiratory system, the role of oxygen as a vital substance and how physical activity acts an energy source were examined. Participants were recruited from three private preschool classrooms (two treatment; 1 control) in Southern California and included a total of 48 four-year-old children (30 treatment; 18 control). Findings from this study suggested that young children could be taught relevant biological concepts about the role of oxygen in respiratory processes. Children who received biology-based intervention curriculum made significant gains in their understanding of the biology of respiration, identification of physical and sedentary activities. In addition these children demonstrated that coherence of conceptual knowledge was correlated with improved accuracy at activity identification and reasoning about the inner workings of the body contributing to endurance. Findings from this study provided evidence to support the benefits of providing age appropriate but complex coherent biological instruction to children in early childhood settings.

  19. Radiation physics, biophysics, and radiation biology

    International Nuclear Information System (INIS)

    Hall, E.J.; Zaider, M.

    1993-05-01

    Research at the Center for Radiological Research is a multidisciplenary blend of physics, chemistry and biology aimed at understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. The focus is increased on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights of the program from the past year are described. A mathematical model describing the production of single-strand and double-strand breaks in DNA as a function radiation quality has been completed. For the first time Monte Carlo techniques have been used to obtain directly the spatial distribution of DNA moieties altered by radiation. This information was obtained by including the transport codes a realistic description of the electronic structure of DNA. We have investigated structure activity relationships for the potential oncogenicity of a new generation of bioreductive drugs that function as hypoxic cytotoxins. Experimental and theoretical investigation of the inverse dose rate effect, whereby medium LET radiations actually produce an c effect when the dose is protracted, is now at a point where the basic mechanisms are reasonably understood and the complex interplay between dose, dose rate and radiation quality which is necessary for the effect to be present can now be predicted at least in vitro. In terms of early radiobiological damage, a quantitative link has been established between basic energy deposition and locally multiply damaged sites, the radiochemical precursor of DNA double strand breaks; specifically, the spatial and energy deposition requirements necessary to form LMDs have been evaluated. For the first time, a mechanically understood ''biological fingerprint'' of high-LET radiation has been established. Specifically measurement of the ratio of inter-to intra-chromosomal aberrations produces a unique signature from alpha-particles or neutrons

  20. Radiation physics, biophysics, and radiation biology

    Energy Technology Data Exchange (ETDEWEB)

    Hall, E.J.; Zaider, M.

    1993-05-01

    Research at the Center for Radiological Research is a multidisciplenary blend of physics, chemistry and biology aimed at understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. The focus is increased on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights of the program from the past year are described. A mathematical model describing the production of single-strand and double-strand breaks in DNA as a function radiation quality has been completed. For the first time Monte Carlo techniques have been used to obtain directly the spatial distribution of DNA moieties altered by radiation. This information was obtained by including the transport codes a realistic description of the electronic structure of DNA. We have investigated structure activity relationships for the potential oncogenicity of a new generation of bioreductive drugs that function as hypoxic cytotoxins. Experimental and theoretical investigation of the inverse dose rate effect, whereby medium LET radiations actually produce an c effect when the dose is protracted, is now at a point where the basic mechanisms are reasonably understood and the complex interplay between dose, dose rate and radiation quality which is necessary for the effect to be present can now be predicted at least in vitro. In terms of early radiobiological damage, a quantitative link has been established between basic energy deposition and locally multiply damaged sites, the radiochemical precursor of DNA double strand breaks; specifically, the spatial and energy deposition requirements necessary to form LMDs have been evaluated. For the first time, a mechanically understood biological fingerprint'' of high-LET radiation has been established. Specifically measurement of the ratio of inter-to intra-chromosomal aberrations produces a unique signature from alpha-particles or neutrons.

  1. Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis.

    Science.gov (United States)

    Shimansky, Yury P

    2009-12-01

    Learning processes in the brain are usually associated with plastic changes made to optimize the strength of connections between neurons. Although many details related to biophysical mechanisms of synaptic plasticity have been discovered, it is unclear how the concurrent performance of adaptive modifications in a huge number of spatial locations is organized to minimize a given objective function. Since direct experimental observation of even a relatively small subset of such changes is not feasible, computational modeling is an indispensable investigation tool for solving this problem. However, the conventional method of error back-propagation (EBP) employed for optimizing synaptic weights in artificial neural networks is not biologically plausible. This study based on computational experiments demonstrated that such optimization can be performed rather efficiently using the same general method that bacteria employ for moving closer to an attractant or away from a repellent. With regard to neural network optimization, this method consists of regulating the probability of an abrupt change in the direction of synaptic weight modification according to the temporal gradient of the objective function. Neural networks utilizing this method (regulation of modification probability, RMP) can be viewed as analogous to swimming in the multidimensional space of their parameters in the flow of biochemical agents carrying information about the optimality criterion. The efficiency of RMP is comparable to that of EBP, while RMP has several important advantages. Since the biological plausibility of RMP is beyond a reasonable doubt, the RMP concept provides a constructive framework for the experimental analysis of learning in natural neural networks.

  2. Starnet, a high-speed fiber optical network for particle physics application

    International Nuclear Information System (INIS)

    Bacilieri, P.; Ghiselli, A.; Caccia, B.; Valentini, S.; Ciaffoni, O.; Di Pirro, G.; Ferrer, M.L.; Martini, A.; Pace, E.; Trasatti, L.

    1990-01-01

    An asynchronous data transmission optical network using single-mode fibers and capable of transmitting frequencies of a few Gbit/s at distances of tens of kilometers is presented. This network (or part of it) is of interest for application in particle physics. (orig.)

  3. A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

    Science.gov (United States)

    Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip

    2013-01-01

    Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855

  4. A framework to find the logic backbone of a biological network.

    Science.gov (United States)

    Maheshwari, Parul; Albert, Réka

    2017-12-06

    Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network. In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures. The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

  5. Complex networks principles, methods and applications

    CERN Document Server

    Latora, Vito; Russo, Giovanni

    2017-01-01

    Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, ...

  6. Students' network integration vs. persistence in introductory physics courses

    Science.gov (United States)

    Zwolak, Justyna; Brewe, Eric

    2017-01-01

    Society is constantly in flux, which demands the continuous development of our educational system to meet new challenges and impart the appropriate knowledge/skills to students. In order to improve student learning, among other things, the way we are teaching has significantly changed over the past few decades. We are moving away from traditional, lecture-based teaching towards more interactive, engagement-based strategies. A current, major challenge for universities is to increase student retention. While students' academic and social integration into an institution seems to be vital for student retention, research on the effect of interpersonal interactions is rare. I use of network analysis to investigate academic and social experiences of students in and beyond the classroom. In particular, there is a compelling case that transformed physics classes, such as Modeling Instruction (MI), promote persistence by the creation of learning communities that support the integration of students into the university. I will discuss recent results on pattern development in networks of MI students' interactions throughout the semester, as well as the effect of students' position within the network on their persistence in physics.

  7. Fast Inference of Deep Neural Networks in FPGAs for Particle Physics

    Energy Technology Data Exchange (ETDEWEB)

    Duarte, Javier [Fermilab; Han, Song [MIT; Harris, Philip [MIT; Jindariani, Sergo [Fermilab; Kreinar, Edward [EIS Intl., Herndon; Kreis, Benjamin [Fermilab; Ngadiuba, Jennifer [CERN; Pierini, Maurizio [CERN; Rivera, Ryan [Fermilab; Tran, Nhan [Fermilab; Wu, Zhenbin [Illinois U., Chicago

    2018-04-16

    Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA hardware has only just begun. FPGA-based trigger and data acquisition (DAQ) systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. We develop a package based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

  8. Physical Activity: A Tool for Improving Health (Part 1--Biological Health Benefits)

    Science.gov (United States)

    Gallaway, Patrick J.; Hongu, Nobuko

    2015-01-01

    Extension educators have been promoting and incorporating physical activities into their community-based programs and improving the health of individuals, particularly those with limited resources. This article is the first of a three-part series describing the benefits of physical activity for human health: 1) biological health benefits of…

  9. Physical explosion analysis in heat exchanger network design

    Science.gov (United States)

    Pasha, M.; Zaini, D.; Shariff, A. M.

    2016-06-01

    The failure of shell and tube heat exchangers is being extensively experienced by the chemical process industries. This failure can create a loss of production for long time duration. Moreover, loss of containment through heat exchanger could potentially lead to a credible event such as fire, explosion and toxic release. There is a need to analyse the possible worst case effect originated from the loss of containment of the heat exchanger at the early design stage. Physical explosion analysis during the heat exchanger network design is presented in this work. Baker and Prugh explosion models are deployed for assessing the explosion effect. Microsoft Excel integrated with process design simulator through object linking and embedded (OLE) automation for this analysis. Aspen HYSYS V (8.0) used as a simulation platform in this work. A typical heat exchanger network of steam reforming and shift conversion process was presented as a case study. It is investigated from this analysis that overpressure generated from the physical explosion of each heat exchanger can be estimated in a more precise manner by using Prugh model. The present work could potentially assist the design engineer to identify the critical heat exchanger in the network at the preliminary design stage.

  10. Network science

    CERN Document Server

    Barabasi, Albert-Laszlo

    2016-01-01

    Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network sci...

  11. A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma

    Directory of Open Access Journals (Sweden)

    Liwei Zhuang

    2014-01-01

    Full Text Available In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.

  12. Neural networks and cellular automata in experimental high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B

    1988-06-01

    Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high energy physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hard-wired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions.

  13. Neural networks and cellular automata in experimental high energy physics

    International Nuclear Information System (INIS)

    Denby, B.

    1987-11-01

    Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hardwired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions

  14. Neural networks and cellular automata in experimental high energy physics

    International Nuclear Information System (INIS)

    Denby, B.

    1988-01-01

    Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high energy physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hard-wired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions. (orig.)

  15. Application of neural networks in experimental physics

    International Nuclear Information System (INIS)

    Kisel', I.V.; Neskromnyj, V.N.; Ososkov, G.A.

    1993-01-01

    The theoretical foundations of numerous models of artificial neural networks (ANN) and their applications to the actual problems of associative memory, optimization and pattern recognition are given. This review contains also numerous using of ANN in the experimental physics both as the hardware realization of fast triggering systems for even selection and for the following software implementation of the trajectory data recognition

  16. Inferring network structure from cascades

    Science.gov (United States)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  17. Physical impairment aware transparent optical networks

    Science.gov (United States)

    Antona, Jean-Christophe; Morea, Annalisa; Zami, Thierry; Leplingard, Florence

    2009-11-01

    As illustrated by optical fiber and optical amplification, optical telecommunications have appeared for the last ten years as one of the most promising candidates to increase the transmission capacities. More recently, the concept of optical transparency has been investigated and introduced: it consists of the optical routing of Wavelength Division Multiplexed (WDM) channels without systematic optoelectronic processing at nodes, as long as propagation impairments remain acceptable [1]. This allows achieving less power-consuming, more scalable and flexible networks, and today partial optical transparency has become a reality in deployed systems. However, because of the evolution of traffic features, optical networks are facing new challenges such as demand for higher transmitted capacity, further upgradeability, and more automation. Making all these evolutions compliant on the same current network infrastructure with a minimum of upgrades is one of the main issues for equipment vendors and operators. Hence, an automatic and efficient management of the network needs a control plan aware of the expected Quality of Transmission (QoT) of the connections to set-up with respect to numerous parameters such as: the services demanded by the customers in terms of protection/restoration; the modulation rate and format of the connection under test and also of its adjacent WDM channels; the engineering rules of the network elements traversed with an accurate knowledge of the associated physical impairments. Whatever the method and/or the technology used to collect this information, the issue about its accuracy is one of the main concerns of the network system vendors, because an inaccurate knowledge could yield a sub-optimal dimensioning and so additional costs when installing the network in the field. Previous studies [1], [2] illustrated the impact of this knowledge accuracy on the ability to predict the connection feasibility. After describing usual methods to build

  18. Revealing networks from dynamics: an introduction

    International Nuclear Information System (INIS)

    Timme, Marc; Casadiego, Jose

    2014-01-01

    What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity. (topical review)

  19. The Gravity of Regenerative Medicine; Physics, Chemistry & Biology behind it

    Directory of Open Access Journals (Sweden)

    Dedeepiya V

    2008-01-01

    Full Text Available The in-vitro expansion of cells of the organs/tissues and their re-implantation into the affected region/ tissue for treating cell/organ failure have been in practice for long, but in limited specialties. The in-vitro cell culture protocols use variety of biological reagents derived from animal sources and recombinant technologies. However, the optimal quantity of such biological components such as growth factors, cytokines etc.,needed for such cells to be grown in a non-physiological environment is still unknown. The use of such biological components have started to stir a controversy of late, due to the recognition of its potential hazards such as spread of prion diseases and contamination with non-human sialic acid proteins. Therefore synthetic reproducible biomaterials are gaining popularity in cell culture and tissue engineering. The biomaterials made of several chemical components based on physical parameters are starting to change certain concepts about the niche of cell culture and that of stem cell expansion and differentiation to specific lineages. Engler et al have already proven that a simple change in the matrix elasticity alone could change the lineage of the cells. Spencer et al have reported that a change in bioelectricity could change the morphogenesis during development. NCRM has been involved in cell culture and tissue engineering using approximately 240 different materials ranging from polymer hydrogel, gel with adherent inserts, nano composite materials, nano-coating technologies, nano-sheets and nano-films. These materials are used in cell culture in different hybrid combinations such as Floating 3D cell culture without adherent components in a homogenous hydrogel. Floating 3D cell culture with anchorage inserts. Flat surface- 2D adherent cell culture. Combined flat surface 2D cell culture (for differentiating cells and floating 3D culture (for undifferentiated cells. These combinations have started yielding several

  20. Loosening the shackles of scientific disciplines with network science. Reply to comments on "Network science of biological systems at different scales: A review"

    Science.gov (United States)

    Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Rupnik, Marjan Slak; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

    2018-03-01

    We would like to thank all the experts for their insightful and very interesting comments that have been submitted in response to our review "Network science of biological systems at different scales" [1]. We are delighted with the number of comments that have been written, and even more so with the positive opinions that these comments communicate to the wider audience [2-9]. Although methods of network science have long proven their value in relevantly addressing various challenges in the biological sciences, such interdisciplinary research often still struggles for funding and recognition at many academic levels.

  1. Sensitivity of the Action Observation Network to Physical and Observational Learning

    NARCIS (Netherlands)

    Cross, E.S.; Kraemer, D.J.M.; Hamilton, A.F.D.C.; Kelley, W.M.; Grafton, S.T.

    2009-01-01

    Human motor skills can be acquired by observation without the benefit of immediate physical practice. The current study tested if physical rehearsal and observational learning share common neural substrates within an action observation network (AON) including premotor and inferior parietal regions,

  2. QCD-Aware Neural Networks for Jet Physics

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based neural network topology varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating...

  3. Charge Migration in DNA Perspectives from Physics, Chemistry, and Biology

    CERN Document Server

    Chakraborty, Tapash

    2007-01-01

    Charge migration through DNA has been the focus of considerable interest in recent years. A deeper understanding of the nature of charge transfer and transport along the double helix is important in fields as diverse as physics, chemistry and nanotechnology. It has also important implications in biology, in particular in DNA damage and repair. This book presents contributions from an international team of researchers active in this field. It contains a wide range of topics that includes the mathematical background of the quantum processes involved, the role of charge transfer in DNA radiation damage, a new approach to DNA sequencing, DNA photonics, and many others. This book should be of value to researchers in condensed matter physics, chemical physics, physical chemistry, and nanoscale sciences.

  4. Logic-based models in systems biology: a predictive and parameter-free network analysis method.

    Science.gov (United States)

    Wynn, Michelle L; Consul, Nikita; Merajver, Sofia D; Schnell, Santiago

    2012-11-01

    Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

  5. Elasticity in Physically Cross-Linked Amyloid Fibril Networks

    Science.gov (United States)

    Cao, Yiping; Bolisetty, Sreenath; Adamcik, Jozef; Mezzenga, Raffaele

    2018-04-01

    We provide a constitutive model of semiflexible and rigid amyloid fibril networks by combining the affine thermal model of network elasticity with the Derjaguin-Landau-Vervey-Overbeek (DLVO) theory of electrostatically charged colloids. When compared to rheological experiments on β -lactoglobulin and lysozyme amyloid networks, this approach provides the correct scaling of elasticity versus both concentration (G ˜c2.2 and G ˜c2.5 for semiflexible and rigid fibrils, respectively) and ionic strength (G ˜I4.4 and G ˜I3.8 for β -lactoglobulin and lysozyme, independent from fibril flexibility). The pivotal role played by the screening salt is to reduce the electrostatic barrier among amyloid fibrils, converting labile physical entanglements into long-lived cross-links. This gives a power-law behavior of G with I having exponents significantly larger than in other semiflexible polymer networks (e.g., actin) and carrying DLVO traits specific to the individual amyloid fibrils.

  6. Local computer network of the JINR Neutron Physics Laboratory

    International Nuclear Information System (INIS)

    Alfimenkov, A.V.; Vagov, V.A.; Vajdkhadze, F.

    1988-01-01

    New high-speed local computer network, where intelligent network adapter (NA) is used as hardware base, is developed in the JINR Neutron Physics Laboratory to increase operation efficiency and data transfer rate. NA consists of computer bus interface, cable former, microcomputer segment designed for both program realization of channel-level protocol and organization of bidirectional transfer of information through direct access channel between monochannel and computer memory with or witout buffering in NA operation memory device

  7. Organization of physical interactomes as uncovered by network schemas.

    Science.gov (United States)

    Banks, Eric; Nabieva, Elena; Chazelle, Bernard; Singh, Mona

    2008-10-01

    Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks.

  8. Surface treatments for biological, chemical and physical applications

    CERN Document Server

    Karaman, Mustafa

    2017-01-01

    A step-by-step guide to the topic with a mix of theory and practice in the fields of biology, chemistry and physics. Straightforward and well-structured, the first chapter introduces fundamental aspects of surface treatments, after which examples from nature are given. Subsequent chapters discuss various methods to surface modification, including chemical and physical approaches, followed by the characterization of the functionalized surfaces. Applications discussed include the lotus effect, diffusion barriers, enzyme immobilization and catalysis. Finally, the book concludes with a look at future technology advances. Throughout the text, tutorials and case studies are used for training purposes to grant a deeper understanding of the topic, resulting in an essential reference for students as well as for experienced engineers in R&D.

  9. Dynamic modeling of physical phenomena for PRAs using neural networks

    International Nuclear Information System (INIS)

    Benjamin, A.S.; Brown, N.N.; Paez, T.L.

    1998-04-01

    In most probabilistic risk assessments, there is a set of accident scenarios that involves the physical responses of a system to environmental challenges. Examples include the effects of earthquakes and fires on the operability of a nuclear reactor safety system, the effects of fires and impacts on the safety integrity of a nuclear weapon, and the effects of human intrusions on the transport of radionuclides from an underground waste facility. The physical responses of the system to these challenges can be quite complex, and their evaluation may require the use of detailed computer codes that are very time consuming to execute. Yet, to perform meaningful probabilistic analyses, it is necessary to evaluate the responses for a large number of variations in the input parameters that describe the initial state of the system, the environments to which it is exposed, and the effects of human interaction. Because the uncertainties of the system response may be very large, it may also be necessary to perform these evaluations for various values of modeling parameters that have high uncertainties, such as material stiffnesses, surface emissivities, and ground permeabilities. The authors have been exploring the use of artificial neural networks (ANNs) as a means for estimating the physical responses of complex systems to phenomenological events such as those cited above. These networks are designed as mathematical constructs with adjustable parameters that can be trained so that the results obtained from the networks will simulate the results obtained from the detailed computer codes. The intent is for the networks to provide an adequate simulation of the detailed codes over a significant range of variables while requiring only a small fraction of the computer processing time required by the detailed codes. This enables the authors to integrate the physical response analyses into the probabilistic models in order to estimate the probabilities of various responses

  10. IMOS National Reference Stations: A Continental-Wide Physical, Chemical and Biological Coastal Observing System

    Science.gov (United States)

    Lynch, Tim P.; Morello, Elisabetta B.; Evans, Karen; Richardson, Anthony J.; Rochester, Wayne; Steinberg, Craig R.; Roughan, Moninya; Thompson, Peter; Middleton, John F.; Feng, Ming; Sherrington, Robert; Brando, Vittorio; Tilbrook, Bronte; Ridgway, Ken; Allen, Simon; Doherty, Peter; Hill, Katherine; Moltmann, Tim C.

    2014-01-01

    Sustained observations allow for the tracking of change in oceanography and ecosystems, however, these are rare, particularly for the Southern Hemisphere. To address this in part, the Australian Integrated Marine Observing System (IMOS) implemented a network of nine National Reference Stations (NRS). The network builds on one long-term location, where monthly water sampling has been sustained since the 1940s and two others that commenced in the 1950s. In-situ continuously moored sensors and an enhanced monthly water sampling regime now collect more than 50 data streams. Building on sampling for temperature, salinity and nutrients, the network now observes dissolved oxygen, carbon, turbidity, currents, chlorophyll a and both phytoplankton and zooplankton. Additional parameters for studies of ocean acidification and bio-optics are collected at a sub-set of sites and all data is made freely and publically available. Our preliminary results demonstrate increased utility to observe extreme events, such as marine heat waves and coastal flooding; rare events, such as plankton blooms; and have, for the first time, allowed for consistent continental scale sampling and analysis of coastal zooplankton and phytoplankton communities. Independent water sampling allows for cross validation of the deployed sensors for quality control of data that now continuously tracks daily, seasonal and annual variation. The NRS will provide multi-decadal time series, against which more spatially replicated short-term studies can be referenced, models and remote sensing products validated, and improvements made to our understanding of how large-scale, long-term change and variability in the global ocean are affecting Australia's coastal seas and ecosystems. The NRS network provides an example of how a continental scaled observing systems can be developed to collect observations that integrate across physics, chemistry and biology. PMID:25517905

  11. IMOS National Reference Stations: a continental-wide physical, chemical and biological coastal observing system.

    Directory of Open Access Journals (Sweden)

    Tim P Lynch

    Full Text Available Sustained observations allow for the tracking of change in oceanography and ecosystems, however, these are rare, particularly for the Southern Hemisphere. To address this in part, the Australian Integrated Marine Observing System (IMOS implemented a network of nine National Reference Stations (NRS. The network builds on one long-term location, where monthly water sampling has been sustained since the 1940s and two others that commenced in the 1950s. In-situ continuously moored sensors and an enhanced monthly water sampling regime now collect more than 50 data streams. Building on sampling for temperature, salinity and nutrients, the network now observes dissolved oxygen, carbon, turbidity, currents, chlorophyll a and both phytoplankton and zooplankton. Additional parameters for studies of ocean acidification and bio-optics are collected at a sub-set of sites and all data is made freely and publically available. Our preliminary results demonstrate increased utility to observe extreme events, such as marine heat waves and coastal flooding; rare events, such as plankton blooms; and have, for the first time, allowed for consistent continental scale sampling and analysis of coastal zooplankton and phytoplankton communities. Independent water sampling allows for cross validation of the deployed sensors for quality control of data that now continuously tracks daily, seasonal and annual variation. The NRS will provide multi-decadal time series, against which more spatially replicated short-term studies can be referenced, models and remote sensing products validated, and improvements made to our understanding of how large-scale, long-term change and variability in the global ocean are affecting Australia's coastal seas and ecosystems. The NRS network provides an example of how a continental scaled observing systems can be developed to collect observations that integrate across physics, chemistry and biology.

  12. VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications

    Directory of Open Access Journals (Sweden)

    Brinkrolf Christoph

    2014-06-01

    Full Text Available VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.

  13. Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

    Science.gov (United States)

    Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

    2013-01-01

    Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

  14. Physical basis for biological effect

    International Nuclear Information System (INIS)

    Goodhead, D.T.

    1987-01-01

    Absorbed dose, or particle fluence, alone, are poor predictors of the biological effectiveness of ionizing radiations. Various radiation 'quality' parameters have been proposed to account quantitatively for the differences due to type of radiation. These include LET, quality factor (Q), lineal energy, specific energy and Z 2 /β 2 . However, all of these have major shortcomings, largely because they fail to describe adequately the microscopic stochastic properties of radiation which are primarily responsible for their relative effectiveness. Most biophysical models of radiation action now agree that the biological effectiveness of radiations are to a large extent determined by their very localized spatial properties of energy deposition (perhaps DNA and associated structures) and that the probability of residual permanent cellular damage (after cellular repair) depends on the nature of this initial macromolecular damage. Common features of these models make it clear that major future advances in identifying critical physical parameters of radiations for general practical application, or to describe their fundamental mechanisms of action, require accurate knowledge of the spatial patterns of energy deposition down to distances of the order of nanometres. Therefore, adequate descriptions are required of the nature and spatial distribution of the initial charged particles and of the interaction-by-interaction structure of the ensuing charged particle tracks. Recent development and application of Monte Carlo track structure simulations have already made it possible to commence such analyses of radiobiological data. (author). 56 refs, 7 figs

  15. Status of (US) High Energy Physics Networking

    International Nuclear Information System (INIS)

    Montgomery, H.E.

    1987-02-01

    The current status of Networking to and between computers used by the High Energy Physics community is discussed. Particular attention is given to developments over the last year and to future prospects. Comparison between the current status and that of two years ago indicates that considerable strides have been made but that much remains to be done to achieve an acceptable level of functionality

  16. openBEB: open biological experiment browser for correlative measurements

    OpenAIRE

    Ramakrishnan, Chandrasekhar; Bieri, Andrej; Sauter, Nora; Roizard, Sophie; Ringler, Philippe; Müller, Shirley A; Goldie, Kenneth N; Enimanev, Kaloyan; Stahlberg, Henning; Rinn, Bernd; Braun, Thomas

    2014-01-01

    Background: New experimental methods must be developed to study interaction networks in systems biology. To reduce biological noise, individual subjects, such as single cells, should be analyzed using high throughput approaches. The measurement of several correlative physical properties would further improve data consistency. Accordingly, a considerable quantity of data must be acquired, correlated, catalogued and stored in a database for subsequent analysis. Results: We have developed openBE...

  17. From Molecules to Living Organisms : an Interplay between Biology and Physics : Lecture Notes of the Les Houches School of Physics

    CERN Document Server

    Nury, Hughes; Parcy, François; Ruigrok, Rob W H; Ziegler, Christine; Cugliandolo, Leticia F; Session CII

    2016-01-01

    The aim of this book is to provide new ideas for studying living matter by a simultaneous understanding of behavior from molecules to the cell, to the whole organism in the light of physical concepts. Indeed, forces guide most biological phenomena. In some cases these forces can be well-described and thus used to model a particular biological phenomenon. This is exemplified here by the study of membranes, where their shapes and curvatures can be modeled using a limited number of parameters that are measured experimentally. The growth of plants is another example where the combination of physics, biology and mathematics leads to a predictive model. The laws of thermodynamics are essential, as they dictate the behavior of proteins, or more generally biological molecules, in an aqueous environment. Integrated studies from the molecule to a larger scale need a combination of cutting-edge approaches, such as the use of new X-ray sources, in-cell NMR, cryo-electron microscopy or single-molecule microscopy. Some are...

  18. Milkweed Seed Dispersal: A Means for Integrating Biology and Physics.

    Science.gov (United States)

    Bisbee, Gregory D.; Kaiser, Cheryl A.

    1997-01-01

    Describes an activity that integrates biology and physics concepts by experimenting with the seed dispersal of common milkweed or similar wind-dispersed seeds. Student teams collect seeds and measure several parameters, review principles of trajectory motion, perform experiments, and graph data. Students examine the ideas of…

  19. Essential concepts and underlying theories from physics, chemistry, and mathematics for "biochemistry and molecular biology" majors.

    Science.gov (United States)

    Wright, Ann; Provost, Joseph; Roecklein-Canfield, Jennifer A; Bell, Ellis

    2013-01-01

    Over the past two years, through an NSF RCN UBE grant, the ASBMB has held regional workshops for faculty members from around the country. The workshops have focused on developing lists of Core Principles or Foundational Concepts in Biochemistry and Molecular Biology, a list of foundational skills, and foundational concepts from Physics, Chemistry, and Mathematics that all Biochemistry or Molecular Biology majors must understand to complete their major coursework. The allied fields working group created a survey to validate foundational concepts from Physics, Chemistry, and Mathematics identified from participant feedback at various workshops. One-hundred twenty participants responded to the survey and 68% of the respondents answered yes to the question: "We have identified the following as the core concepts and underlying theories from Physics, Chemistry, and Mathematics that Biochemistry majors or Molecular Biology majors need to understand after they complete their major courses: 1) mechanical concepts from Physics, 2) energy and thermodynamic concepts from Physics, 3) critical concepts of structure from chemistry, 4) critical concepts of reactions from Chemistry, and 5) essential Mathematics. In your opinion, is the above list complete?" Respondents also delineated subcategories they felt should be included in these broad categories. From the results of the survey and this analysis the allied fields working group constructed a consensus list of allied fields concepts, which will help inform Biochemistry and Molecular Biology educators when considering the ASBMB recommended curriculum for Biochemistry or Molecular Biology majors and in the development of appropriate assessment tools to gauge student understanding of how these concepts relate to biochemistry and molecular biology. © 2013 by The International Union of Biochemistry and Molecular Biology.

  20. [Chemical, physical and biological risks in law enforcement].

    Science.gov (United States)

    Magrini, Andrea; Grana, Mario; Vicentini, Laura

    2014-01-01

    Chemical, physical and biological risks among public safety and security forces. Law enforcement personnel, involved in routine tasks and in emergency situations, are exposed to numerous and several occupational hazards (chemical, physical and biological) whith likely health and security consequences. These risks are particularly high when the organization and preparation are inadequate, there is a lacking or insufficient coordination, information, education and communication and safety and personal protective equipment are inadequate or insufficient. Despite the objective difficulties, caused by the actual special needs related to the service performed or the organizational peculiarities, the risk identification and assessment is essential for worker health and safety of personnel, as provided for by Legislative Decree no. 81/2008. Chemical risks include airborne pollutants due to vehicular traffic (carbon monoxide, ultrafine particles, benzene, polycyclic aromatic hydrocarbons, aldehydes, nitrogen and sulfur oxides, lead), toxic gases generated by combustion process following fires (aromatic hydrocarbons, PAHs, dioxins and furans, biphenyls, formaldehyde, metals and cyanides), substances emitted in case of chemical accidents (solvents, pesticides, toxic gases, caustics), drugs (methylamphetamine), riot control agents and self-defence spray, lead at firing ranges, and several materials and reagents used in forensic laboratory. The physical hazards are often caused by activities that induce biomechanical overload aid the onset of musculoskeletal disorders, the use of visual display terminals and work environments that may expose to heat stress and discomfort, high and low pressure, noise, vibrations, ionizing and non-ionizing radiation. The main biological risks are blood-borne diseases (viral hepatitis, AIDS), airborne diseases (eg, tuberculosis, meningitis, SARS, anthrax), MRSA, and vector-borne diseases. Many of these risk factors are unavoidable or are not

  1. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  2. Integration of physics and biology: synergistic undergraduate education for the 21st century.

    Science.gov (United States)

    Woodin, Terry; Vasaly, Helen; McBride, Duncan; White, Gary

    2013-06-01

    This is an exciting time to be a biologist. The advances in our field and the many opportunities to expand our horizons through interaction with other disciplines are intellectually stimulating. This is as true for people tasked with helping the field move forward through support of research and education projects that serve the nation's needs as for those carrying out that research and educating the next generation of biologists. So, it is a pleasure to contribute to this edition of CBE-Life Sciences Education. This column will cover three aspects of the interactions of physics and biology as seen from the viewpoint of four members of the Division of Undergraduate Education of the National Science Foundation. The first section places the material to follow in context. The second reviews some of the many interdisciplinary physics-biology projects we support. The third highlights mechanisms available for supporting new physics-biology undergraduate education projects based on ideas that arise, focusing on those needing and warranting outside support to come to fruition.

  3. Mathematical Analysis of a PDE System for Biological Network Formation

    KAUST Repository

    Haskovec, Jan

    2015-02-04

    Motivated by recent physics papers describing rules for natural network formation, we study an elliptic-parabolic system of partial differential equations proposed by Hu and Cai [13, 15]. The model describes the pressure field thanks to Darcy\\'s type equation and the dynamics of the conductance network under pressure force effects with a diffusion rate D >= 0 representing randomness in the material structure. We prove the existence of global weak solutions and of local mild solutions and study their long term behavior. It turns out that, by energy dissipation, steady states play a central role to understand the network formation capacity of the system. We show that for a large diffusion coefficient D, the zero steady state is stable, while network formation occurs for small values of D due to the instability of the zero steady state, and the borderline case D = 0 exhibits a large class of dynamically stable (in the linearized sense) steady states.

  4. Biological Physics : Poincaré seminar

    CERN Document Server

    Bio-physique : séminaire Poincaré

    2011-01-01

    This new volume in the Poincaré Seminar Series, describing recent developments at the interface between physics and biology, is directed towards a broad audience of physicists, biologists, and mathematicians. Both the theoretical and experimental aspects are covered, and particular care is devoted to the pedagogical nature of the presentations. The first survey article, by Jean-Francois Joanny and Jacques Prost, describes the theoretical advances made in the study of "active gels", with applications to liquid crystals and cell motility. Jasper van der Gucht and Cécile Sykes then report on recent advances made with biomimetic model systems in the understanding of cytokinesis. The next article, by Jonathon Howard, presents several molecular models for motor proteins, which are compared with experimental results for kinesin. David Lacoste and Kirone Mallick then show theoretically that similar ratchet models of motor proteins naturally satisfy a fundamental time-reversal symmetry, the Gallavotti-Cohen fluctuat...

  5. A generic framework for individual-based modelling and physical-biological interaction

    DEFF Research Database (Denmark)

    Christensen, Asbjørn; Mariani, Patrizio; Payne, Mark R.

    2018-01-01

    The increased availability of high-resolution ocean data globally has enabled more detailed analyses of physical-biological interactions and their consequences to the ecosystem. We present IBMlib, which is a versatile, portable and computationally effective framework for conducting Lagrangian...... scales. The open-source framework features a minimal robust interface to facilitate the coupling between individual-level biological models and oceanographic models, and we provide application examples including forward/backward simulations, habitat connectivity calculations, assessing ocean conditions...

  6. Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems

    Directory of Open Access Journals (Sweden)

    Abdul-Wahid Mohammed

    2017-01-01

    Full Text Available The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home.

  7. Introduction to solitons and their applications in physics and biology

    International Nuclear Information System (INIS)

    Peyrard, M.

    1995-01-01

    The response of most of the physical systems to combined excitations is not a simple superposition of their response to individual stimuli. This is particularly true for biological systems in which the nonlinear effects are often the dominant ones. The intrinsic treatment of nonlinearities in mathematical models and physical systems has led to the emergence of the chaos and solitons concepts. The concept of soliton, relevant for systems with many degrees of freedom, provides a new tool in the studies of biomolecules because it has no equivalent in the world of linear excitations. The aim of this lecture is to present the main ideas that underline the soliton concept and to discuss some applications. Solitons are solitary waves, that propagate at constant speed without changing their shape. They are extremely stable to perturbations, in particular to collisions with small amplitude linear waves and with other solitons. Conditions to have solitons and equations of solitons propagation are analysed. Solitons can be divided into two main classes: topological and non-topological solitons which can be found at all scales and in various domains of physics and chemistry. Using simple examples, this paper shows how linear expansions can miss completely essential physical properties of a system. This is particularly characteristic for the pendulum chain example. Soliton theory offers alternative methods. Multiple scale approximations, or expansion on a soliton basis, can be very useful to provide a description of some physical phenomena. Nonlinear energy localization is also a very important concept valid for a large variety of systems. These concepts are probably even more relevant for biological molecules than for solid state physics, because these molecules are very deformable objects where large amplitude nonlinear motions or conformational changes are crucial for function. (J.S.). 14 refs., 9 figs

  8. Exploring biological and social networks to better understand and treat diabetes mellitus. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Belgardt, Bengt-Frederik; Jarasch, Alexander; Lammert, Eckhard

    2018-03-01

    Improvements and breakthroughs in computational sciences in the last 20 years have paralleled the rapid gain of influence of social networks on our daily life. As timely reviewed by Perc and colleagues [1], understanding and treating complex human diseases, such as type 2 diabetes (T2D), from which already more than 5% of the global population suffer, will necessitate analyzing and understanding the multi-layered and interconnected networks that usually keep physiological functions intact, but are disturbed in disease states. These networks range from intra- and intercellular networks influencing cell behavior (e.g., secretion of insulin in response to food intake and anabolic response to insulin) to social networks influencing human behavior (e.g., food intake and physical activity). This commentary first expands on the background of pancreatic beta cell networks in human health and T2D, briefly introduces exosomes as novel signals exchanged between distant cellular networks, and finally discusses potential pitfalls and chances in network analyses with regards to experimental data acquisition and processing.

  9. Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

    Science.gov (United States)

    Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

    2016-05-01

    Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

    Science.gov (United States)

    Voss, Michelle W.; Weng, Timothy B.; Burzynska, Agnieszka Z.; Wong, Chelsea N.; Cooke, Gillian E.; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P.; Olson, Erin A.; McAuley, Edward; Kramer, Arthur F.

    2015-01-01

    Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the Default Mode Network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. PMID:26493108

  11. CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

    Science.gov (United States)

    Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

    2017-09-13

    The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

  12. After the Greeting: Realizing the Potential of Physical Models in Cell Biology.

    Science.gov (United States)

    Paluch, Ewa K

    2015-12-01

    Biophysics is increasingly taking center stage in cell biology as the tools for precise quantifications of cellular behaviors expand. Interdisciplinary approaches, combining quantitative physical modeling with cell biology, are of growing interest to journal editors, funding agencies, and hiring committees. However, despite an ever-increasing emphasis on the importance of interdisciplinary research, the student trained in biology may still be at a loss as to what it actually means. I discuss here some considerations on how to achieve meaningful and high-quality interdisciplinary work. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Neuromorphic atomic switch networks.

    Directory of Open Access Journals (Sweden)

    Audrius V Avizienis

    Full Text Available Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.

  14. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

  15. A neighbourhood evolving network model

    International Nuclear Information System (INIS)

    Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

    2006-01-01

    Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

  16. Mechanobiology by the numbers: a close relationship between biology and physics.

    Science.gov (United States)

    Schwarz, Ulrich S

    2017-12-01

    Studies of mechanobiology lie at the interface of various scientific disciplines from biology to physics. Accordingly, quantification and mathematical modelling have been instrumental in fuelling the progress in this rapidly developing research field, assisting experimental work on many levels.

  17. Research on condensed matter and atomic physics using major experimental facilities and devices: Physics, chemistry, biology. Reports on results. Vol. 2. 3. Solid state physics and materials science

    International Nuclear Information System (INIS)

    1993-01-01

    This report in three volumes substantiates the contents of the programme survey published in September 1989. The progress reports cover the following research areas: Vol. I, (1). Atomic and molecular physics - free atoms, molecules, macromolecules, clusters, matrix-isolated atoms and molecules. (2) Physics and chemistry of surfaces and interfaces - epitaxy, surface structure, adsorption, electrical, magnetic, and optical properties, thin films, synthetic layer structure. Vol. II, (3). Solid-state physics, and materials science -structural research, lattice dynamics, magnetic structure and dynamics, electronic states; load; spin and pulse density fluctuations; diffusion and internal motion, defects, unordered systems and liquids. Vol. III, (4). Chemistry - bonding and structure, kinetics and reaction mechanisms, polymer research, analysis and synthesis. (5). Biology, - structure and dynamics of biological macromolecules, membrane and cell biology. (6) Development of methods and instruments - neutron sources, synchrotron sources, special accelerators, research with interlinked systems and devices. (orig.) [de

  18. CellNet: Network Biology Applied to Stem Cell Engineering

    Science.gov (United States)

    Cahan, Patrick; Li, Hu; Morris, Samantha A.; da Rocha, Edroaldo Lummertz; Daley, George Q.; Collins, James J.

    2014-01-01

    SUMMARY Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population, and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering. PMID:25126793

  19. CellNet: network biology applied to stem cell engineering.

    Science.gov (United States)

    Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J

    2014-08-14

    Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. A first course in network theory

    CERN Document Server

    Estrada, Ernesto

    2015-01-01

    The study of network theory is a highly interdisciplinary field, which has emerged as a major topic of interest in various disciplines ranging from physics and mathematics, to biology and sociology. This book promotes the diverse nature of the study of complex networks by balancing the needs of students from very different backgrounds. It references the most commonly used concepts in network theory, provides examples of their applications in solving practicalproblems, and clear indications on how to analyse their results.In the first part of the book, students and researchers will discover the

  1. Health: The No-Man's-Land Between Physics and Biology.

    Science.gov (United States)

    Mansfield, Peter J

    2015-10-01

    Health as a positive attribute is poorly understood because understanding requires concepts from physics, of which physicians and other life scientists have a very poor grasp. This paper reviews the physics that bears on biology, in particular complex quaternions and scalar fields, relates these to the morphogenetic fields proposed by biologists, and defines health as an attribute of living action within these fields. The distinction of quality, as juxtaposed with quantity, proves essential. Its basic properties are set out, but a science and mathematics of quality are awaited. The implications of this model are discussed, particularly as proper health enhancement could set a natural limit to demand for, and therefore the cost of, medical services.

  2. Notes on a PDE system for biological network formation

    KAUST Repository

    Haskovec, Jan

    2016-01-22

    We present new analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transport networks. The model describes the pressure field using a Darcy’s type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. The analytical part extends the results of Haskovec et al. (2015) regarding the existence of weak and mild solutions to the whole range of meaningful relaxation exponents. Moreover, we prove finite time extinction or break-down of solutions in the spatially one-dimensional setting for certain ranges of the relaxation exponent. We also construct stationary solutions for the case of vanishing diffusion and critical value of the relaxation exponent, using a variational formulation and a penalty method. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on mixed finite elements and study the qualitative properties of network structures for various parameter values. Furthermore, we indicate numerically that some analytical results proved for the spatially one-dimensional setting are likely to be valid also in several space dimensions.

  3. On the Interplay between Entropy and Robustness of Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2010-05-01

    Full Text Available The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H∞ stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology.

  4. Simulated, Emulated, and Physical Investigative Analysis (SEPIA) of networked systems.

    Energy Technology Data Exchange (ETDEWEB)

    Burton, David P.; Van Leeuwen, Brian P.; McDonald, Michael James; Onunkwo, Uzoma A.; Tarman, Thomas David; Urias, Vincent E.

    2009-09-01

    This report describes recent progress made in developing and utilizing hybrid Simulated, Emulated, and Physical Investigative Analysis (SEPIA) environments. Many organizations require advanced tools to analyze their information system's security, reliability, and resilience against cyber attack. Today's security analysis utilize real systems such as computers, network routers and other network equipment, computer emulations (e.g., virtual machines) and simulation models separately to analyze interplay between threats and safeguards. In contrast, this work developed new methods to combine these three approaches to provide integrated hybrid SEPIA environments. Our SEPIA environments enable an analyst to rapidly configure hybrid environments to pass network traffic and perform, from the outside, like real networks. This provides higher fidelity representations of key network nodes while still leveraging the scalability and cost advantages of simulation tools. The result is to rapidly produce large yet relatively low-cost multi-fidelity SEPIA networks of computers and routers that let analysts quickly investigate threats and test protection approaches.

  5. Cellular response to ionizing radiations: a study of the roles of physics and biology

    International Nuclear Information System (INIS)

    DeWyngaert, J.K.

    1982-01-01

    A study of the complementary roles of physics and biology in determining the response of cellular systems to ionizing radiations has been conducted. Upon exposure to radiation, a cell responds in a binary (yes/no) manner in terms of its proliferative ability (survival). The relationship between the survival probability and absorbed dose may then be examined in terms of relevant physical and biological parameters. The approach to these studies was to vary the physics and biology independently and observe separately their influences upon the measured effect. Unique to these studies was the use of heterogeneous tumor systems. These are solid tumors found to consist of genetically related but identifiably distinct populations of cells. The two heterogeneous systems studied, a murine system consisting of four subpopulations and a human tumor system with two subpopulations, were exposed to graded doses of 14 MeV neutrons or x-rays and their effectiveness in inducing cell lethality compared. A further examination of the radiation effect involved a study at the chemical level, measuring the ability of oxygen to potentiate the damage produced by photon irradiation. To summarize, the physics, biology and the environment have all been varied, and the systematics of the responses studied. The data were analyzed within the formalisms of the dual theory of radiation action, the repair-misrepair model, and the repair saturation model of cell killing. The change in survival curve shape and the increased effectiveness in cell killing for higher Linear Energy Transfer (LET) radiations (neutrons vs. x-rays) are discussed in relation to explanations in terms of either physical or biochemical processes

  6. Effects of the soil pore network architecture on the soil's physical functionalities

    Science.gov (United States)

    Smet, Sarah; Beckers, Eléonore; Léonard, Angélique; Degré, Aurore

    2017-04-01

    The soil fluid movement's prediction is of major interest within an agricultural or environmental scope because many processes depend ultimately on the soil fluids dynamic. It is common knowledge that the soil microscopic pore network structure governs the inner-soil convective fluids flow. There isn't, however, a general methodthat consider the pore network structure as a variable in the prediction of thecore scale soil's physical functionalities. There are various possible representations of the microscopic pore network: sample scale averaged structural parameters, extrapolation of theoretic pore network, or use of all the information available by modeling within the observed pore network. Different representations implydifferent analyzing methodologies. To our knowledge, few studies have compared the micro-and macroscopic soil's characteristics for the same soil core sample. The objective of our study is to explore the relationship between macroscopic physical properties and microscopic pore network structure. The saturated hydraulic conductivity, the air permeability, the retention curve, and others classical physical parameters were measured for ten soil samples from an agricultural field. The pore network characteristics were quantified through the analyses of X-ray micro-computed tomographic images(micro-CT system Skyscan-1172) with a voxel size of 22 µm3. Some of the first results confirmed what others studies had reported. Then, the comparison between macroscopic properties and microscopic parameters suggested that the air movements depended mostly on the pore connectivity and tortuosity than on the total porosity volume. We have also found that the fractal dimension calculated from the X-ray images and the fractal dimension calculated from the retention curve were significantly different. Our communication will detailthose results and discuss the methodology: would the results be similar with a different voxel size? What are the calculated and measured

  7. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An

  8. Visiting Power Laws in Cyber-Physical Networking Systems

    Directory of Open Access Journals (Sweden)

    Ming Li

    2012-01-01

    Full Text Available Cyber-physical networking systems (CPNSs are made up of various physical systems that are heterogeneous in nature. Therefore, exploring universalities in CPNSs for either data or systems is desired in its fundamental theory. This paper is in the aspect of data, aiming at addressing that power laws may yet be a universality of data in CPNSs. The contributions of this paper are in triple folds. First, we provide a short tutorial about power laws. Then, we address the power laws related to some physical systems. Finally, we discuss that power-law-type data may be governed by stochastically differential equations of fractional order. As a side product, we present the point of view that the upper bound of data flow at large-time scaling and the small one also follows power laws.

  9. UNCERTAINTY ON RADIATION DOSES ESTIMATED BY BIOLOGICAL AND RETROSPECTIVE PHYSICAL METHODS.

    Science.gov (United States)

    Ainsbury, Elizabeth A; Samaga, Daniel; Della Monaca, Sara; Marrale, Maurizio; Bassinet, Celine; Burbidge, Christopher I; Correcher, Virgilio; Discher, Michael; Eakins, Jon; Fattibene, Paola; Güçlü, Inci; Higueras, Manuel; Lund, Eva; Maltar-Strmecki, Nadica; McKeever, Stephen; Rääf, Christopher L; Sholom, Sergey; Veronese, Ivan; Wieser, Albrecht; Woda, Clemens; Trompier, Francois

    2018-03-01

    Biological and physical retrospective dosimetry are recognised as key techniques to provide individual estimates of dose following unplanned exposures to ionising radiation. Whilst there has been a relatively large amount of recent development in the biological and physical procedures, development of statistical analysis techniques has failed to keep pace. The aim of this paper is to review the current state of the art in uncertainty analysis techniques across the 'EURADOS Working Group 10-Retrospective dosimetry' members, to give concrete examples of implementation of the techniques recommended in the international standards, and to further promote the use of Monte Carlo techniques to support characterisation of uncertainties. It is concluded that sufficient techniques are available and in use by most laboratories for acute, whole body exposures to highly penetrating radiation, but further work will be required to ensure that statistical analysis is always wholly sufficient for the more complex exposure scenarios.

  10. Radiation physics, biophysics, and radiation biology

    International Nuclear Information System (INIS)

    Hall, E.J.; Zaider, M.

    1991-05-01

    Research at the Radiological Research Laboratory is a blend of physics, chemistry, and biology, involving research at the basic level with the admixture of a small proportion of pragmatic or applied research in support of radiation protection and/or radiotherapy. Current research topics include: oncogenic transformation assays, mutation studies involving interactions between radiation and environmental contaminants, isolation, characterization and sequencing of a human repair gene, characterization of a dominant transforming gene found in C3H 10T1/2 cells, characterize ab initio the interaction of DNA and radiation, refine estimates of the radiation quality factor Q, a new mechanistic model of oncogenesis showing the role of long-term low dose medium LET radiation, and time dependent modeling of radiation induced chromosome damage and subsequent repair or misrepair

  11. Biological Movement and Laws of Physics.

    Science.gov (United States)

    Latash, Mark L

    2017-07-01

    Living systems may be defined as systems able to organize new, biology-specific, laws of physics and modify their parameters for specific tasks. Examples include the force-length muscle dependence mediated by the stretch reflex, and the control of movements with modification of the spatial referent coordinates for salient performance variables. Low-dimensional sets of referent coordinates at a task level are transformed to higher-dimensional sets at lower hierarchical levels in a way that ensures stability of performance. Stability of actions can be controlled independently of the actions (e.g., anticipatory synergy adjustments). Unintentional actions reflect relaxation processes leading to drifts of corresponding referent coordinates in the absence of changes in external load. Implications of this general framework for movement disorders, motor development, motor skill acquisition, and even philosophy are discussed.

  12. Ontology-supported research on vaccine efficacy, safety and integrative biological networks.

    Science.gov (United States)

    He, Yongqun

    2014-07-01

    While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

  13. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  14. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  15. Research on condensed matter and atomic physics, using major experimental facilities and devices: Physics, chemistry, biology. Reports on results. Vol. 1. 1. Atomic and molecular physics. 2. Physics and chemistry of surfaces and interfaces

    International Nuclear Information System (INIS)

    1993-01-01

    This report in three volumes substantiates the contents of the programme survey published in September 1989. The progress reports cover the following research areas: Vol. I, (1). Atomic and molecular physics - free atoms, molecules, macromolecules, clusters, matrix-isolated atoms and molecules. (2) Physics and chemistry of surfaces and interfaces - epitaxy, surface structure, adsorption, electrical, magnetic, and optical properties, thin films, synthetic layer structure. Vol. II, (3). Solid-state physics, and materials science -structural research, lattice dynamics, magnetic structure and dynamics, electronic states; load; spin and pulse density fluctuations; diffusion and internal motion, defects, unordered systems and liquids. Vol. III, (4). Chemistry - bonding and structure, kinetics and reaction mechanisms, polymer research, analysis and synthesis. (5). Biology, - structure and dynamics of biological macromolecules, membrane and cell biology. (6) Development of methods and instruments - neutron sources, synchrotron sources, special accelerators, research with interlinked systems and devices. (orig.) [de

  16. Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

    Science.gov (United States)

    Pusuluri, Sai Teja

    Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features

  17. 4D-Var data assimilation system for a coupled physical-biological ...

    Indian Academy of Sciences (India)

    A 3-compartment model of phytoplankton growth dynamics has been coupled with a primitive-equation circulation model to better understand and quantify physical and biological processes in the Adriatic Sea. This paper presents the development and application of a data assimilation procedure based on optimal.

  18. S.E.A. Lab. Science Experiments and Activities. Marine Science for High School Students in Chemistry, Biology and Physics.

    Science.gov (United States)

    Hart, Kathy, Ed.

    A series of science experiments and activities designed for secondary school students taking biology, chemistry, physics, physical science or marine science courses are outlined. Each of the three major sections--chemistry, biology, and physics--addresses concepts that are generally covered in those courses but incorporates aspects of marine…

  19. Networking for high energy physics in Japan

    International Nuclear Information System (INIS)

    Karita, Yukio; Abe, Fumio; Hirose, Hitoshi; Goto, Hiroyuki; Ogasawara, Ryusuke; Yuasa, Fukuko; Banno, Yoshiaki; Yasu, Yoshiji

    1989-01-01

    The computer network for high energy physics in Japan has grown over the last five or six years and is still expanding. Its original purpose was to provide the collaborators in universities access to the computing resources in KEK. Adding to the remote login from terminals, VAXs or Fujitsu computers located in universities have been connected to KEK's computers by DECnet or FNA (Fujitsu's SNA) and have formed the ''Japanese HEPnet''. Since the link between LBL and KEK was established in June 1987, the Japanese HEPnet is combined with the American HEPnet and is an indispensable tool for international collaboration. The current communication media for Japanese HEPnet, leased lines and public X.25, are being replaced by Gakujo-net (Monbusho's inter-university private X.25 network). DECnet, FNA, IP and Ethernet-bridge will run on Gakujo-net for the Japanese HEPnet. (orig.)

  20. Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

    Directory of Open Access Journals (Sweden)

    Luan Yihui

    2009-09-01

    Full Text Available Abstract Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Results Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Conclusion Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

  1. Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.

    Science.gov (United States)

    Wang, Wenhui; Nunez-Iglesias, Juan; Luan, Yihui; Sun, Fengzhu

    2009-09-03

    Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

  2. Radiation physics, biophysics, and radiation biology. Progress report, December 1, 1993--November 30, 1994

    Energy Technology Data Exchange (ETDEWEB)

    Hall, E.J.; Zaider, M.

    1994-05-01

    Research at the Center for Radiological Research is a blend of physics, chemistry and biology and epitomizes the multidisciplinary approach towards understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. To an increasing extent, the focus of attention is on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights from the past year are briefly described.

  3. Radiation physics, biophysics, and radiation biology. Progress report, December 1, 1993--November 30, 1994

    International Nuclear Information System (INIS)

    Hall, E.J.; Zaider, M.

    1994-05-01

    Research at the Center for Radiological Research is a blend of physics, chemistry and biology and epitomizes the multidisciplinary approach towards understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. To an increasing extent, the focus of attention is on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights from the past year are briefly described

  4. Visual analysis of transcriptome data in the context of anatomical structures and biological networks

    Directory of Open Access Journals (Sweden)

    Astrid eJunker

    2012-11-01

    Full Text Available The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

  5. Biological and Physical Space Research Laboratory 2002 Science Review

    Science.gov (United States)

    Curreri, P. A. (Editor); Robinson, M. B. (Editor); Murphy, K. L. (Editor)

    2003-01-01

    With the International Space Station Program approaching core complete, our NASA Headquarters sponsor, the new Code U Enterprise, Biological and Physical Research, is shifting its research emphasis from purely fundamental microgravity and biological sciences to strategic research aimed at enabling human missions beyond Earth orbit. Although we anticipate supporting microgravity research on the ISS for some time to come, our laboratory has been vigorously engaged in developing these new strategic research areas.This Technical Memorandum documents the internal science research at our laboratory as presented in a review to Dr. Ann Whitaker, MSFC Science Director, in July 2002. These presentations have been revised and updated as appropriate for this report. It provides a snapshot of the internal science capability of our laboratory as an aid to other NASA organizations and the external scientific community.

  6. An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships.

    Directory of Open Access Journals (Sweden)

    Daniel Vaiman

    Full Text Available Preeclampsia (PE is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs. However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

  7. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  8. An appraisal of biological responses and network of environmental interactions in non-mining and mining impacted coastal waters

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, C.E.G.; Malik, A; Jineesh, V.K.; Fernandes, S.O.; Das, A; Pandey, S.S.; Kanolkar, G.; Sujith, P.P.; Velip, D.; Shaikh, S.; Helekar, S.; Gonsalves, M.J.B.D.; Nair, S.; LokaBharathi, P.A

    iron brought from the hinterlands. We hypothesize that there could be a shift in biological response along with changes in network of interactions between environmental and biological variables in these mining and non-mining impacted regions, lying 160...

  9. Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

    Directory of Open Access Journals (Sweden)

    Jesper Bruun

    2013-07-01

    Full Text Available The role of student interactions in learning situations is a foundation of sociocultural learning theory, and social network analysis can be used to quantify student relations. We discuss how self-reported student interactions can be viewed as processes of meaning making and use this to understand how quantitative measures that describe the position in a network, called centrality measures, can be understood in terms of interactions that happen in the context of a university physics course. We apply this discussion to an empirical data set of self-reported student interactions. In a weekly administered survey, first year university students enrolled in an introductory physics course at a Danish university indicated with whom they remembered having communicated within different interaction categories. For three categories pertaining to (1 communication about how to solve physics problems in the course (called the PS category, (2 communications about the nature of physics concepts (called the CD category, and (3 social interactions that are not strictly related to the content of the physics classes (called the ICS category in the introductory mechanics course, we use the survey data to create networks of student interaction. For each of these networks, we calculate centrality measures for each student and correlate these measures with grades from the introductory course, grades from two subsequent courses, and the pretest Force Concept Inventory (FCI scores. We find highly significant correlations (p<0.001 between network centrality measures and grades in all networks. We find the highest correlations between network centrality measures and future grades. In the network composed of interactions regarding problem solving (the PS network, the centrality measures hide and PageRank show the highest correlations (r=-0.32 and r=0.33, respectively with future grades. In the CD network, the network measure target entropy shows the highest correlation

  10. A Brief Introduction to Chinese Biological Biological

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Chinese Biological Abstracts sponsored by the Library, the Shanghai Institutes for Biological Sciences, the Biological Documentation and Information Network, all of the Chinese Academy of Sciences, commenced publication in 1987 and was initiated to provide access to the Chinese information in the field of biology.

  11. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    Science.gov (United States)

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

  12. Investigating student communities with network analysis of interactions in a physics learning center

    Directory of Open Access Journals (Sweden)

    Eric Brewe

    2012-01-01

    Full Text Available Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  13. Investigating student communities with network analysis of interactions in a physics learning center

    Science.gov (United States)

    Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

    2012-06-01

    Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  14. Collective Dynamics in Physical and Social Networks

    Science.gov (United States)

    Isakov, Alexander

    We study four systems where individual units come together to display a range of collective behavior. First, we consider a physical system of phase oscillators on a network that expands the Kuramoto model to include oscillator-network interactions and the presence of noise: using a Hebbian-like learning rule, oscillators that synchronize in turn strengthen their connections to each other. We find that the average degree of connectivity strongly affects rates of flipping between aligned and anti-aligned states, and that this result persists to the case of complex networks. Turning to a fully multi-player, multi-strategy evolutionary dynamics model of cooperating bacteria that change who they give resources to and take resources from, we find several regimes that give rise to high levels of collective structure in the resulting networks. In this setting, we also explore the conditions in which an intervention that affects cooperation itself (e.g. "seeding the network with defectors") can lead to wiping out an infection. We find a non-monotonic connection between the percent of disabled cooperation and cure rate, suggesting that in some regimes a limited perturbation can lead to total population collapse. At a larger scale, we study how the locomotor system recovers after amputation in fruit flies. Through experiment and a theoretical model of multi-legged motion controlled by neural oscillators, we find that proprioception plays a role in the ability of flies to control leg forces appropriately to recover from a large initial turning bias induced by the injury. Finally, at the human scale, we consider a social network in a traditional society in Africa to understand how social ties lead to group formation for collective action (stealth raids). We identify critical and distinct roles for both leadership (important for catalyzing a group) and friendship (important for final composition). We conclude with prospects for future work.

  15. 4D-Var data assimilation system for a coupled physical biological ...

    Indian Academy of Sciences (India)

    A 3-compartment model of phytoplankton growth dynamics has been coupled with a primitive-equation circulation model to better understand and quantify physical and biological processes in the Adriatic Sea. This paper presents the development and application of a data assimilation procedure based on optimal control ...

  16. Physics of non-Newtonian fluids and interdisciplinary relations (biology and criminology)

    Science.gov (United States)

    Holubova, R.

    2018-03-01

    The aim of the paper is the presentation of an interdisciplinary topic that allows applying content knowledge in physics, mathematics and biology in real life environment. Students use to play games and view crime scenes but in common they have little knowledge about the science used during crime scene investigation. In this paper the science background of blood spatter analysis is presented—the physics of non-Newtonian fluids, the biology of blood and mathematics—the measurement and calculation of the angle of inpact, the relationship between height and spatter diameter. This topic was choosen according to the analysis of interviews with secondary and high school learners realized at four schools in Moravia, Czech Republic. The topic can be taught at secondary schools so as at a higher level at high schools. Hands-on activities are included. The teaching strategy supports group work. The appropriateness and reasonableness of the topic was checked in the real teaching process and the activities have had a positive feedback.

  17. Modelling the Influence of Shielding on Physical and Biological Organ Doses

    CERN Document Server

    Ballarini, Francesca; Ferrari, Alfredo; Ottolenghi, Andrea; Pelliccioni, Maurizio; Scannicchio, Domenico

    2002-01-01

    Distributions of "physical" and "biological" dose in different organs were calculated by coupling the FLUKA MC transport code with a geometrical human phantom inserted into a shielding box of variable shape, thickness and material. While the expression "physical dose" refers to the amount of deposited energy per unit mass (in Gy), "biological dose" was modelled with "Complex Lesions" (CL), clustered DNA strand breaks calculated in a previous work based on "event-by-event" track-structure simulations. The yields of complex lesions per cell and per unit dose were calculated for different radiation types and energies, and integrated into a version of FLUKA modified for this purpose, allowing us to estimate the effects of mixed fields. As an initial test simulation, the phantom was inserted into an aluminium parallelepiped and was isotropically irradiated with 500 MeV protons. Dose distributions were calculated for different values of the shielding thickness. The results were found to be organ-dependent. In most ...

  18. Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics.

    Directory of Open Access Journals (Sweden)

    Ryan P Womack

    Full Text Available This study takes a stratified random sample of articles published in 2014 from the top 10 journals in the disciplines of biology, chemistry, mathematics, and physics, as ranked by impact factor. Sampled articles were examined for their reporting of original data or reuse of prior data, and were coded for whether the data was publicly shared or otherwise made available to readers. Other characteristics such as the sharing of software code used for analysis and use of data citation and DOIs for data were examined. The study finds that data sharing practices are still relatively rare in these disciplines' top journals, but that the disciplines have markedly different practices. Biology top journals share original data at the highest rate, and physics top journals share at the lowest rate. Overall, the study finds that within the top journals, only 13% of articles with original data published in 2014 make the data available to others.

  19. Handbook of Graphs and Networks From the Genome to the Internet

    CERN Document Server

    Bornholdt, Stefan

    2002-01-01

    Complex interacting networks are observed in systems from such diverse areas as physics, biology, economics, ecology, and computer science. For example, economic or social interactions often organize themselves in complex network structures. Similar phenomena are observed in traffic flow and in communication networks as the internet. In current problems of the Biosciences, prominent examples are protein networks in the living cell, as well as molecular networks in the genome. On larger scales one finds networks of cells as in neural networks, up to the scale of organisms in ecological food web

  20. Students Mental Representation of Biology Diagrams/Pictures Conventions Based on Formation of Causal Network

    Science.gov (United States)

    Sampurno, A. W.; Rahmat, A.; Diana, S.

    2017-09-01

    Diagrams/pictures conventions is one form of visual media that often used to assist students in understanding the biological concepts. The effectiveness of use diagrams/pictures in biology learning at school level has also been mostly reported. This study examines the ability of high school students in reading diagrams/pictures biological convention which is described by Mental Representation based on formation of causal networks. The study involved 30 students 11th grade MIA senior high school Banten Indonesia who are studying the excretory system. MR data obtained by Instrument worksheet, developed based on CNET-protocol, in which there are diagrams/drawings of nephron structure and urinary mechanism. Three patterns formed MR, namely Markov chain, feedback control with a single measurement, and repeated feedback control with multiple measurement. The third pattern is the most dominating pattern, differences in the pattern of MR reveal the difference in how and from which point the students begin to uncover important information contained in the diagram to establish a causal networks. Further analysis shows that a difference in the pattern of MR relate to how complex the students process the information contained in the diagrams/pictures.

  1. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Science.gov (United States)

    Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antczak, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; Guindani, Michele; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

    2016-04-01

    The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks

  2. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Directory of Open Access Journals (Sweden)

    Victor Trevino

    2016-04-01

    Full Text Available The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell

  3. Supporting students in building interdisciplinary connections across physics and biology

    Science.gov (United States)

    Turpen, Chandra

    2014-03-01

    Our research team has been engaged in the iterative redesign of an Introductory Physics course for Life Science (IPLS) majors to explicitly bridge biology and physics in ways that are authentic to the disciplines. Our interdisciplinary course provides students opportunities to examine how modeling decisions (e.g. knowing when and how to use different concepts, identifying implicit assumptions, making and justifying assumptions) may differ depending on canonical disciplinary aims and interests. Our focus on developing students' interdisciplinary reasoning skills requires 1) shifting course topics to focus on core ideas that span the disciplines, 2) shifting epistemological expectations, and 3) foregrounding typically tacit disciplinary assumptions. In working to build an authentic interdisciplinary course that bridges physics and biology, we pay careful attention to supporting students in constructing these bridges. This course has been shown to have important impacts: a) students seek meaningful connections between the disciplines, b) students perceive relevance and utility of ideas from different disciplines, and c) students reconcile challenging disciplinary ideas. Although our focus has been on building interdisciplinary coherence, we have succeeded in maintaining strong student learning gains on fundamental physics concepts and allowed students to deepen their understanding of challenging concepts in thermodynamics. This presentation will describe the shifts in course content and the modern pedagogical approaches that have been integrated into the course, and provide an overview of key research results from this project. These results may aid physicists in reconsidering how they can meaningfully reach life-science students. This work is supported by NSF-TUES DUE 11-22818, the HHMI NEXUS grant, and a NSF Graduate Research Fellowship (DGE 0750616).

  4. BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

    Science.gov (United States)

    Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

    2009-02-15

    BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

  5. Radiation-induced physical ageing in network arsenic-sulfide/selenide glasses

    International Nuclear Information System (INIS)

    Shpotyuk, M; Golovchak, R; Kozdras, A; Shpotyuk, O

    2010-01-01

    Effect of radiation-induced physical ageing is investigated by differential scanning calorimetry method in As x Se 100-x (10 ≤ x ≤ 42) and As x S 100-x (30 ≤ x ≤ 42) glasses. Obtained results are compared with conventional physical ageing at normal conditions. Significant radiation-induced physical ageing is recorded for glassy As x S 100-x within 30 ≤ x x Se 100-x glasses from the same compositional interval do not show any measurable changes in DSC curves after γ-irradiation. Observed difference in radiation-induced physical ageing in arsenic-sulfide/selenide glasses is explained by a greater lifetime of γ-induced excitations within sulfur-based network in comparison with selenium-based one.

  6. Managing health physics departmental data via a local area network

    International Nuclear Information System (INIS)

    Weber, P.J.; Castronovo, F.P. Jr.

    1993-01-01

    The authors describe the multiuser data management system that can be accessed simultaneously by all department members, in use at the Dept of Health Physics, Brigham and Women's Hospital, U.S.A., which makes use of the Local Area Network. (UK)

  7. Tutorial on neural network applications in high energy physics: A 1992 perspective

    International Nuclear Information System (INIS)

    Denby, B.

    1992-04-01

    Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given

  8. Physical and Cross-Layer Security Enhancement and Resource Allocation for Wireless Networks

    Science.gov (United States)

    Bashar, Muhammad Shafi Al

    2011-01-01

    In this dissertation, we present novel physical (PHY) and cross-layer design guidelines and resource adaptation algorithms to improve the security and user experience in the future wireless networks. Physical and cross-layer wireless security measures can provide stronger overall security with high efficiency and can also provide better…

  9. Plasma Physics Network Newsletter. No. 2

    International Nuclear Information System (INIS)

    1990-06-01

    The IAEA Fellowship Programme providing for in general up to two years of training at a host laboratory or university is accessible for Member State scientists (contact the editor); so are IAEA research contracts (up to $ 5000 per year for up to 3 years). An overview of meetings on fusion or fusion-related topics is given for June-October 1990. It is announced that the full IFCR status report on fusion is due to be published in the September issues of Nuclear Fusion, and that the ''Third World Plasma Research Network'' (TWPRN) has been set up to ''provide an international forum for plasma research centres of the Third World countries'' to promote ''closer interactions among them'' and to strengthen their scientific programmes. The network also ''envisages active participation of small scale research programmes from developed countries that pursue basic plasma studies and development objectives''. Furthermore, this newsletter contains (1) the minutes of the steering committee meeting of the TWPRN, New Delhi, November 1989; (2) a contribution from A. Rodrigo, Argentina, entitled ''Collaboration and Scientific Exchange in Latin American Plasma Physics Laboratories'', listing for each country (Argentina, Brazil, Chile, Colombia, Mexico, and Venezuela) (i) key contact persons, (ii) main areas for collaboration/scientific exchange, and (iii) list of foreign laboratories having close contacts; (3) ''Plasma Research at the Institute of Nuclear Science and Technology of Bangladesh'', by U.A. Mofiz, giving an overview of plasma research activities there; (4) A summary by P.K. Kaw and A. Sen of the 1989 International Conference on Plasma Physics held in New Delhi; (5) the announcement of the first South-North International Workshop on Fusion Theory, Tipaza, Algeria, September 16-23, 1990

  10. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  11. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  12. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  13. A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

    Science.gov (United States)

    Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

    2016-09-01

    Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

  14. Leveraging a Relationship with Biology to Expand a Relationship with Physics

    Science.gov (United States)

    Sawtelle, Vashti; Turpen, Chandra

    2016-01-01

    This work examines how experiences in one disciplinary domain (biology) can impact the relationship a student builds with another domain (physics). We present a model for disciplinary relationships using the constructs of identity, affect, and epistemology. With these constructs we examine an ethnographic case study of a student who experienced a…

  15. Radiation physics, biophysics, and radiation biology: Progress report, December 1, 1987-November 30, 1988

    International Nuclear Information System (INIS)

    Hall, E.J.; Zaider, M.; Delegianis, M.J.

    1988-07-01

    Research at the Radiological Research Laboratory is a blend of physics, chemistry, and biology, involving research at the basic level with the admixture of a small proportion of pragmatic or applied research in support of radiation protection and/or radiation therapy. At the current level of funding, approximately one quarter of the research of the Laboratory could be regarded as in support of radiotherapy, with the remainder addressing more basic issues. The new initiatives have been in two directions. First, there has been an increased emphasis on research in radiation chemistry, inasmuch as this subject which involves the study of free radicals and fast radiation chemistry processes starts to bridge the gap between physics and biology, between the initial deposition of radiant energy and its final expression in terms of biological consequences. Second, the emphasis in the biological research has moved towards studies at the molecular level, with the appointment of new members of staff with expertise in this area. Individual chapters were processed separately for the data base

  16. A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional

  17. Physical-layer network coding in coherent optical OFDM systems.

    Science.gov (United States)

    Guan, Xun; Chan, Chun-Kit

    2015-04-20

    We present the first experimental demonstration and characterization of the application of optical physical-layer network coding in coherent optical OFDM systems. It combines two optical OFDM frames to share the same link so as to enhance system throughput, while individual OFDM frames can be recovered with digital signal processing at the destined node.

  18. A review of active learning approaches to experimental design for uncovering biological networks

    Science.gov (United States)

    2017-01-01

    Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

  19. Statistical physics of networks, information and complex systems

    Energy Technology Data Exchange (ETDEWEB)

    Ecke, Robert E [Los Alamos National Laboratory

    2009-01-01

    In this project we explore the mathematical methods and concepts of statistical physics that are fmding abundant applications across the scientific and technological spectrum from soft condensed matter systems and bio-infonnatics to economic and social systems. Our approach exploits the considerable similarity of concepts between statistical physics and computer science, allowing for a powerful multi-disciplinary approach that draws its strength from cross-fertilization and mUltiple interactions of researchers with different backgrounds. The work on this project takes advantage of the newly appreciated connection between computer science and statistics and addresses important problems in data storage, decoding, optimization, the infonnation processing properties of the brain, the interface between quantum and classical infonnation science, the verification of large software programs, modeling of complex systems including disease epidemiology, resource distribution issues, and the nature of highly fluctuating complex systems. Common themes that the project has been emphasizing are (i) neural computation, (ii) network theory and its applications, and (iii) a statistical physics approach to infonnation theory. The project's efforts focus on the general problem of optimization and variational techniques, algorithm development and infonnation theoretic approaches to quantum systems. These efforts are responsible for fruitful collaborations and the nucleation of science efforts that span multiple divisions such as EES, CCS, 0 , T, ISR and P. This project supports the DOE mission in Energy Security and Nuclear Non-Proliferation by developing novel infonnation science tools for communication, sensing, and interacting complex networks such as the internet or energy distribution system. The work also supports programs in Threat Reduction and Homeland Security.

  20. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

    Science.gov (United States)

    Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

  1. The definitions of information and meaning two possible boundaries between physics and biology.

    Science.gov (United States)

    Barbieri, Marcello

    2004-01-01

    The standard approach to the definition of the physical quantities has not produced satisfactory results with the concepts of information and meaning. In the case of information we have at least two unrelated definitions, while in the case of meaning we have no definition at all. Here it is shown that both information and meaning can be defined by operative procedures, but it is also pointed out that we need to recognize them as a new type of natural entities. They are not quantities (neither fundamental nor derived) because they cannot be measured, and they are not qualities because are not subjective features. Here it is proposed to call them nominable entities, i.e., entities which can be specified only by naming their components in their natural order. If the genetic code is not a linguistic metaphor but a reality, we must conclude that information and meaning are real natural entities, and now we must also conclude that they are not equivalent to the quantities and qualities of our present theoretical framework. This gives us two options. One is to extend the definition of physics and say that the list of its fundamental entities must include information and meaning. The other is to say that physics is the science of quantities only, and in this case information and meaning become the exclusive province of biology. The boundary between physics and biology, in short, is a matter of convention, but the existence of information and meaning is not. We can decide to study them in the framework of an extended physics or in a purely biological framework, but we cannot avoid studying them for what they are, i.e., as fundamental components of the fabric of Nature.

  2. Design in nature how the constructal law governs evolution in biology, physics, technology, and social organization

    CERN Document Server

    Bejan, Adrian

    2013-01-01

    In this groundbreaking book, Adrian Bejan takes the recurring patterns in nature—trees, tributaries, air passages, neural networks, and lightning bolts—and reveals how a single principle of physics, the constructal law, accounts for the evolution of these and many other designs in our world. Everything—from biological life to inanimate systems—generates shape and structure and evolves in a sequence of ever-improving designs in order to facilitate flow. River basins, cardiovascular systems, and bolts of lightning are very efficient flow systems to move a current—of water, blood, or electricity. Likewise, the more complex architecture of animals evolve to cover greater distance per unit of useful energy, or increase their flow across the land. Such designs also appear in human organizations, like the hierarchical “flowcharts” or reporting structures in corporations and political bodies. All are governed by the same principle, known as the constructal law, and configure and reconfigure themselves...

  3. Radiation-induced physical ageing in network arsenic-sulfide/selenide glasses

    Energy Technology Data Exchange (ETDEWEB)

    Shpotyuk, M; Golovchak, R; Kozdras, A; Shpotyuk, O, E-mail: shpotyuk@novas.lviv.ua

    2010-11-15

    Effect of radiation-induced physical ageing is investigated by differential scanning calorimetry method in As{sub x}Se{sub 100-x} (10 {<=} x {<=} 42) and As{sub x}S{sub 100-x} (30 {<=} x {<=} 42) glasses. Obtained results are compared with conventional physical ageing at normal conditions. Significant radiation-induced physical ageing is recorded for glassy As{sub x}S{sub 100-x} within 30 {<=} x < 40 range, while As{sub x}Se{sub 100-x} glasses from the same compositional interval do not show any measurable changes in DSC curves after {gamma}-irradiation. Observed difference in radiation-induced physical ageing in arsenic-sulfide/selenide glasses is explained by a greater lifetime of {gamma}-induced excitations within sulfur-based network in comparison with selenium-based one.

  4. The Use of Textbooks for Advanced-Level GCE Courses in Physics, Chemistry and Biology by Sixth-Form Students.

    Science.gov (United States)

    Newton, D. P.

    1984-01-01

    A survey of sixth-form students to determine the level of A-level textbook use in physics, chemistry, and biology in English schools found that texts are used primarily after the lesson, at the student's discretion, and with great variations between students. Biology texts were used most, and physics texts used least. (MBR)

  5. Romanian knowledge transfer network in nuclear physics and engineering - REFIN

    International Nuclear Information System (INIS)

    Ghitescu, Petre; Prisecaru, Ilie

    2007-01-01

    According to the requirements of the Romanian Nuclear Programme regarding the education and training of the skilled personnel for the nuclear facilities, a knowledge transfer network named REFIN (in Romanian: Retea Educationala in Fizica si Ingineria Nucleara) was developed since 2005. The knowledge target field is nuclear physics and engineering. The main objective of this network is to develop an effective, flexible and modern educational system in the nuclear physics and engineering area which could meet the requirements of all known types of nuclear facilities and therewith be redundant with the perspectives of the European Research Area (FP7, EURATOM). A global strategy was proposed in order to harmonize the curricula between the network facilities to implement pilot modern teaching programs (courses/modules), to introduce advanced learning methods (as Systematic Approach to Training, e-learning and distance-learning), to strengthen and better use the existing research infrastructures of the research institutes in network. The education and training strategy is divided into several topics: university engineering , master, post-graduate, Ph.D. degree, post-doctoral activity, training for industry, improvement. For the first time in our country, a modular scheme is used allowing staff with different technical background to participate at different levels. In this respect, the European system with transferable credits (ECTS) is used. Based on this strategy, courses in 'Radioactive Waste Management' and 'Numerical and Experimental Methods in Reactor Physics' for both MS students and for industry. This way the training activity which a student attends will allow him or her to be involved, depending on specific professional needs, into a flexible educational scheme. This scheme will ensure competence and enhancement and also the possibility of qualification development and a better mobility on labour market. This kind of activity is already in progress in the

  6. Qualitative reasoning for biological network inference from systematic perturbation experiments.

    Science.gov (United States)

    Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco

    2012-01-01

    The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

  7. Restorability on 3-connected WDM Networks Under Single and Dual Physical Link Failures

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Jensen, Michael; Riaz, Tahir

    2013-01-01

    This work studies the influence the network interconnection has over restoration techniques. The way physical links are distributed to interconnect network nodes has a great impact on parameters such as path distances when failures occur and restoration is applied. The work focuses on single and ...... to network planning, the trade-off network length vs. performance of the different topological options is studied. The results show how 3-connected graphs could provide a reasonable trade-off between costs, link failure rates, and restored path parameters....

  8. Preservice Teachers' Epistemological Beliefs in Physics, Chemistry, and Biology: A Mixed Study

    Science.gov (United States)

    Topcu, Mustafa Sami

    2013-01-01

    The purposes of the study were to assess preservice teachers' domain-specific epistemological beliefs and to investigate whether preservice teachers distinguish disciplinary differences (physics, chemistry, and biology) in domain-specific epistemological beliefs. Mixed-method research design guided the present research. The researcher explored…

  9. Cost and Availability Analysis of 2- and 3-Connected WDM Networks Physical Interconnection

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Pedersen, Jens Myrup

    2012-01-01

    for the best trade-off among the relevant parameters for the network. In this paper we analyze this trade-off by studying 2-and 3-connected graphs to be used as WDM (Wavelength Division Multiplexing) networks physical infrastructure. The experiments show how the way links are distributed to interconnect...

  10. Physical Chemistry for the Chemical and Biological Sciences (by Raymond Chang)

    Science.gov (United States)

    Pounds, Andrew

    2001-05-01

    This book does offer an alternative approach to physical chemistry that is particularly well suited for those who want to pursue a course of study more focused on the biological sciences. It could also be an excellent choice for schools that mainly serve preprofessional programs or for schools that have split physical chemistry tracks to independently serve the B.S. and B.A. degrees. Since the book focuses on single-variable mathematics, schools that require only one year of calculus for their chemistry degree could also think about adopting it. However, in general, the use of the text as a drop-in replacement for physical chemistry for the B.S. degree is questionable owing to its lack of focus on quantum mechanics and its implications for spectroscopy.

  11. Associations within school-based same-sex friendship networks of children’s physical activity and sedentary behaviours: a cross-sectional social network analysis

    OpenAIRE

    Salway, Ruth E.; Sebire, Simon J.; Solomon-Moore, Emma; Thompson, Janice L.; Jago, Russell

    2018-01-01

    Background Physical activity in children is associated with better physical and mental health but many children do not meet physical activity guidelines. Friendship groups are potentially an important influence on children’s physical activity and sedentary time. This paper examines the association between children of physical activity and sedentary time in school-based same-sex friendship networks, for both moderate-to-vigorous intensity physical activity (MVPA) and sedentary time. Moreover, ...

  12. Integrating soil physical and biological properties in contrasting tillage systems in organic and conventional farming

    NARCIS (Netherlands)

    Crittenden, S.J.; Goede, de R.G.M.

    2016-01-01

    Though soil physical and soil biological properties are intrinsically linked in the soil environment they are often studied separately. This work adds value to analyses of soil biophysical quality of tillage systems under organic and conventional farming systems by correlating physical and

  13. Physics of Non-Newtonian Fluids and Interdisciplinary Relations (Biology and Criminology)

    Science.gov (United States)

    Holubova, R.

    2018-01-01

    The aim of the paper is the presentation of an interdisciplinary topic that allows applying content knowledge in physics, mathematics and biology in real life environment. Students use to play games and view crime scenes but in common they have little knowledge about the science used during crime scene investigation. In this paper the science…

  14. Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

    Science.gov (United States)

    Chen, Bor-Sen; Wu, Chia-Chou

    2013-01-01

    Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

  15. Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2013-10-01

    Full Text Available Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

  16. Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.

    Science.gov (United States)

    Saithong, Treenut; Painter, Kevin J; Millar, Andrew J

    2010-12-16

    A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain. Here, we propose a novel robustness analysis that aims to determine the "common robustness" of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network. Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

  17. Helium ions for radiotherapy? Physical and biological verifications of a novel treatment modality

    Energy Technology Data Exchange (ETDEWEB)

    Krämer, Michael, E-mail: m.kraemer@gsi.de; Scifoni, Emanuele; Schuy, Christoph; Rovituso, Marta; Maier, Andreas; Kaderka, Robert; Kraft-Weyrather, Wilma [Biophysics, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Planckstr. 1, 64291 Darmstadt (Germany); Tinganelli, Walter; Durante, Marco [Biophysics, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Planckstr. 1, 64291 Darmstadt, Germany and Trento Institute for Fundamental Physics and Application (TIFPA-INFN), 38123, via Sommarive 14, Trento (Italy); Brons, Stephan; Tessonnier, Thomas [Heidelberger Ionenstrahl-Therapiezentrum (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg, Germany and Radioonkologie und Strahlentherapie, Universitätsklinikums Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg (Germany); Parodi, Katia [Heidelberger Ionenstrahl-Therapiezentrum (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg (Germany); Radioonkologie und Strahlentherapie, Universitätsklinikums Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg (Germany); Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Department of Medical Physics, Am Coulombwall 1, 85748 Munich (Germany)

    2016-04-15

    Purpose: Modern facilities for actively scanned ion beam radiotherapy allow in principle the use of helium beams, which could present specific advantages, especially for pediatric tumors. In order to assess the potential use of these beams for radiotherapy, i.e., to create realistic treatment plans, the authors set up a dedicated {sup 4}He beam model, providing base data for their treatment planning system TRiP98, and they have reported that in this work together with its physical and biological validations. Methods: A semiempirical beam model for the physical depth dose deposition and the production of nuclear fragments was developed and introduced in TRiP98. For the biological effect calculations the last version of the local effect model was used. The model predictions were experimentally verified at the HIT facility. The primary beam attenuation and the characteristics of secondary charged particles at various depth in water were investigated using {sup 4}He ion beams of 200 MeV/u. The nuclear charge of secondary fragments was identified using a ΔE/E telescope. 3D absorbed dose distributions were measured with pin point ionization chambers and the biological dosimetry experiments were realized irradiating a Chinese hamster ovary cells stack arranged in an extended target. Results: The few experimental data available on basic physical processes are reproduced by their beam model. The experimental verification of absorbed dose distributions in extended target volumes yields an overall agreement, with a slight underestimation of the lateral spread. Cell survival along a 4 cm extended target is reproduced with remarkable accuracy. Conclusions: The authors presented a simple simulation model for therapeutical {sup 4}He beams which they introduced in TRiP98, and which is validated experimentally by means of physical and biological dosimetries. Thus, it is now possible to perform detailed treatment planning studies with {sup 4}He beams, either exclusively or in

  18. Physical layer impairments tolerance based lightpath provision in software defined optical network

    Institute of Scientific and Technical Information of China (English)

    Zhao Xianlong; Xu Xianze; Bai Huifeng

    2017-01-01

    As all-optical networks grow with ever increasing ultra-high speed, the communication quality suffers seriously from physical layer impairments ( PLIs) .The same problem still exists in software defined optical network ( SDON) controlled by OpenFlow.Aimed to solve this problem, a PLIs tol-erance based lightpath provision scheme is proposed for OpenFlow controlled optical networks.This proposed approach not only takes the OSNR model to represent those linear PLIs factors, but also in-troduces those nonlinear factors into the OSNR model.Thus, the proposed scheme is able to cover most PLIs factors of each optical link and conduct optical lightpath provison with better communica-tion quality.Moreover, PLIs tolerance model is also set up and considered in this work with some necessary extension to OpenFlow protocols to achieve better compatibility between physical layer im-pairments factors and various services connections.Simulation results show that the proposed scheme is able to get better performance in terms of packet loss rate and connection setup time.

  19. Visual data mining of biological networks: one size does not fit all.

    Directory of Open Access Journals (Sweden)

    Chiara Pastrello

    Full Text Available High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

  20. BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

    Science.gov (United States)

    Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

    2016-01-01

    Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

  1. Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.

    Science.gov (United States)

    Di Silvestre, Dario; Bergamaschi, Andrea; Bellini, Edoardo; Mauri, PierLuigi

    2018-06-03

    The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

  2. Integrative Analysis of the Physical Transport Network into Australia.

    Directory of Open Access Journals (Sweden)

    Robert C Cope

    Full Text Available Effective biosecurity is necessary to protect nations and their citizens from a variety of threats, including emerging infectious diseases, agricultural or environmental pests and pathogens, and illegal wildlife trade. The physical pathways by which these threats are transported internationally, predominantly shipping and air traffic, have undergone significant growth and changes in spatial distributions in recent decades. An understanding of the specific pathways and donor-traffic hotspots created by this integrated physical transport network is vital for the development of effective biosecurity strategies into the future. In this study, we analysed the physical transport network into Australia over the period 1999-2012. Seaborne and air traffic were weighted to calculate a "weighted cumulative impact" score for each source region worldwide, each year. High risk source regions, and those source regions that underwent substantial changes in risk over the study period, were determined. An overall risk ranking was calculated by integrating across all possible weighting combinations. The source regions having greatest overall physical connectedness with Australia were Singapore, which is a global transport hub, and the North Island of New Zealand, a close regional trading partner with Australia. Both those regions with large amounts of traffic across multiple vectors (e.g., Hong Kong, and those with high levels of traffic of only one type (e.g., Bali, Indonesia with respect to passenger flights, were represented among high risk source regions. These data provide a baseline model for the transport of individuals and commodities against which the effectiveness of biosecurity controls may be assessed, and are a valuable tool in the development of future biosecurity policy.

  3. Networks in social policy problems

    CERN Document Server

    Scotti, marco

    2012-01-01

    Network science is the key to managing social communities, designing the structure of efficient organizations and planning for sustainable development. This book applies network science to contemporary social policy problems. In the first part, tools of diffusion and team design are deployed to challenges in adoption of ideas and the management of creativity. Ideas, unlike information, are generated and adopted in networks of personal ties. Chapters in the second part tackle problems of power and malfeasance in political and business organizations, where mechanisms in accessing and controlling informal networks often outweigh formal processes. The third part uses ideas from biology and physics to understand global economic and financial crises, ecological depletion and challenges to energy security. Ideal for researchers and policy makers involved in social network analysis, business strategy and economic policy, it deals with issues ranging from what makes public advisories effective to how networks influenc...

  4. Unraveling spurious properties of interaction networks with tailored random networks.

    Directory of Open Access Journals (Sweden)

    Stephan Bialonski

    Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

  5. Foundations of anticipatory logic in biology and physics.

    Science.gov (United States)

    Bettinger, Jesse S; Eastman, Timothy E

    2017-12-01

    Recent advances in modern physics and biology reveal several scenarios in which top-down effects (Ellis, 2016) and anticipatory systems (Rosen, 1980) indicate processes at work enabling active modeling and inference such that anticipated effects project onto potential causes. We extrapolate a broad landscape of anticipatory systems in the natural sciences extending to computational neuroscience of perception in the capacity of Bayesian inferential models of predictive processing. This line of reasoning also comes with philosophical foundations, which we develop in terms of counterfactual reasoning and possibility space, Whitehead's process thought, and correlations with Eastern wisdom traditions. Copyright © 2017 Elsevier Ltd. All rights reserved.