WorldWideScience

Sample records for biological network determination

  1. A neural network based approach for determination of optical scattering and absorption coefficients of biological tissue

    International Nuclear Information System (INIS)

    Warncke, D; Lewis, E; Leahy, M; Lochmann, S

    2009-01-01

    The propagation of light in biological tissue depends on the absorption and reduced scattering coefficient. The aim of this project is the determination of these two optical properties using spatially resolved reflectance measurements. The sensor system consists of five laser sources at different wavelengths, an optical fibre probe and five photodiodes. For these kinds of measurements it has been shown that an often used solution of the diffusion equation can not be applied. Therefore a neural network is being developed to extract the needed optical properties out of the reflectance data. Data sets for the training, validation and testing process are provided by Monte Carlo Simulations.

  2. 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)

  3. 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.

  4. 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.

  5. On the holistic approach in cellular and cancer biology: nonlinearity, complexity, and quasi-determinism of the dynamic cellular network.

    Science.gov (United States)

    Waliszewski, P; Molski, M; Konarski, J

    1998-06-01

    A keystone of the molecular reductionist approach to cellular biology is a specific deductive strategy relating genotype to phenotype-two distinct categories. This relationship is based on the assumption that the intermediary cellular network of actively transcribed genes and their regulatory elements is deterministic (i.e., a link between expression of a gene and a phenotypic trait can always be identified, and evolution of the network in time is predetermined). However, experimental data suggest that the relationship between genotype and phenotype is nonbijective (i.e., a gene can contribute to the emergence of more than just one phenotypic trait or a phenotypic trait can be determined by expression of several genes). This implies nonlinearity (i.e., lack of the proportional relationship between input and the outcome), complexity (i.e. emergence of the hierarchical network of multiple cross-interacting elements that is sensitive to initial conditions, possesses multiple equilibria, organizes spontaneously into different morphological patterns, and is controlled in dispersed rather than centralized manner), and quasi-determinism (i.e., coexistence of deterministic and nondeterministic events) of the network. Nonlinearity within the space of the cellular molecular events underlies the existence of a fractal structure within a number of metabolic processes, and patterns of tissue growth, which is measured experimentally as a fractal dimension. Because of its complexity, the same phenotype can be associated with a number of alternative sequences of cellular events. Moreover, the primary cause initiating phenotypic evolution of cells such as malignant transformation can be favored probabilistically, but not identified unequivocally. Thermodynamic fluctuations of energy rather than gene mutations, the material traits of the fluctuations alter both the molecular and informational structure of the network. Then, the interplay between deterministic chaos, complexity, self

  6. 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.

  7. Determination of complex reaction mechanisms: analysis of chemical, biological, and genetic networks

    National Research Council Canada - National Science Library

    Ross, John; Schreiber, Igor; Vlad, Marcel O

    2006-01-01

    ... Systems, 40 5 Response of Systems to Pulse Perturbations, 46 5.1 5.2 Theory, 46 An Example: The Glycolytic Pathway, 53 6 Experimental Test of the Pulse Perturbation Method for Determining Causa...

  8. 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.…

  9. 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.

  10. [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.

  11. 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.

  12. Determinants of Network Outcomes

    DEFF Research Database (Denmark)

    Ysa, Tamyko; Sierra, Vicenta; Esteve, Marc

    2014-01-01

    The literature on network management is extensive. However, it generally explores network structures, neglecting the impact of management strategies. In this article we assess the effect of management strategies on network outcomes, providing empirical evidence from 119 urban revitalization...... networks. We go beyond current work by testing a path model for the determinants of network outcomes and considering the interactions between the constructs: management strategies, trust, complexity, and facilitative leadership. Our results suggest that management strategies have a strong effect on network...... outcomes and that they enhance the level of trust. We also found that facilitative leadership has a positive impact on network management as well as on trust in the network. Our findings also show that complexity has a negative impact on trust. A key finding of our research is that managers may wield more...

  13. 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.

  14. 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

  15. Determination of the isotopic (C-13/C-12) discrimination by terrestrial biology from a global network of observations

    International Nuclear Information System (INIS)

    Bakwin, P.S.; Tans, P.P.; White, J.W.C.; Andres, R.J.

    1998-01-01

    Data from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory global air sampling network are analysed in order to extract the signatures of isotopic (C-13/C-12) discrimination by the terrestrial iota and of fossil fuel combustion for the regions surrounding the sampling sites. Measurements of carbon monoxide (CO) are used to give an estimate of the contribution of fossil fuel combustion to the short-term variability of carbon dioxide. In general, variations of CO 2 are more strongly dominated by biological exchange, so the isotopic signature of fossil fuel combustion, while consistent with inventory estimates, is not well constrained by the observations. Conversely, results for isotope discrimination by the terrestrial biosphere are not strongly dependent on assumptions about fossil fuel combustion. The analysis appears valid primarily for stations fairly near continental source/sink regions, particularly for midlatitude regions of the northern hemisphere. For these stations a mean discrimination of -16.8 per mil (%) is derived, with site-to-site variability of 0.8% and with little or no consistent latitudinal gradient

  16. 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

  17. 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 ...

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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)

  4. 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.

  5. 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.

  6. 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...

  7. 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

  8. Network mapping and usage determination

    CSIR Research Space (South Africa)

    Senekal, FP

    2007-07-01

    Full Text Available detection. Based on this information, topology determination techniques can be applied to infer network structure from the information. Techniques to visualise the information are discussed. IP geolocation (the ability to associate a geographical coordinate...

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Gallium determination in biological samples

    International Nuclear Information System (INIS)

    Stulzaft, O.; Maziere, B.; Ly, S.

    1980-01-01

    A sensitive, simple and time-saving method has been developed for the neutron activation analysis of gallium at concentrations around 10 -4 ppm in biological tissues. After a 24-hour irradiation in a thermal neutron flux of 2.8x10 13 nxcm -2 xs -1 and a purification by ion-exchange chromatography to eliminate troublesome elements such as sodium, iron and copper, the 72 Ga activity is measured with enough accuracy for the method to be applicable in animal physiology and clinical toxicology. (author)

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

    -inhibition affected cell cycle and inflammatory signaling were meaningfully determined. Conclusions The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

  14. 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.

  15. 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

  16. Biology Question Generation from a Semantic Network

    Science.gov (United States)

    Zhang, Lishan

    Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students' learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student's current competence so that a suitable question could be selected based on the student's previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from

  17. 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...

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. System for determining sizes of biological macromolecules

    International Nuclear Information System (INIS)

    Nelson, R.M.; Danby, P.C.

    1987-01-01

    An electrophoresis system for determining the sizes of radiolabelled biological macromolecules is described. It comprises a cell containing an electrophoresis gel and having at least one lane, a voltage source connected across the gel for effecting the movement of macromolecules in the lane, a detector fixed relative to the moving molecules for generating electrical pulses responsive to signals emitted by the radiolabelled molecules; a pulse processor for counting the pulse rate, and a computational device for comparing the pulse rate to a predetermined value. (author)

  4. 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.

  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. 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.

  7. 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

  8. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of ...

  9. 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...

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  17. Systems biology: properties of reconstructed networks

    National Research Council Canada - National Science Library

    Palsson, Bernhard

    2006-01-01

    ... between the mathematical ideas and biological processes are made clear, the book reflects the irreversible trend of increasing mathematical content in biology education. Therefore to assist both teacher and student, Palsson provides problem sets, projects, and PowerPoint slides in an associated web site and keeps the presentation in the book concrete with illustrat...

  18. The Structure and Function of Biological Networks

    Science.gov (United States)

    Wu, Daniel Duanqing

    2010-01-01

    Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding…

  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. 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

  1. 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.

  2. Growth and development and their environmental and biological determinants.

    Science.gov (United States)

    da Rocha Neves, Kelly; de Souza Morais, Rosane Luzia; Teixeira, Romero Alves; Pinto, Priscilla Avelino Ferreira

    2016-01-01

    To investigate child growth, cognitive/language development, and their environmental and biological determinants. This was a cross-sectional, predictive correlation study with all 92 children aged 24-36 months who attended the municipal early childhood education network in a town in the Vale do Jequitinhonha region, in 2011. The socioeconomic profile was determined using the questionnaire of the Associação Brasileira de Empresas de Pesquisa. The socio-demographicand maternal and child health profiles were created through a self-prepared questionnaire. The height-for-age indicator was selected to represent growth. Cognitive/language development was assessed through the Bayley Scale of Infant and Toddler Development. The quality of educational environments was assessed by Infant/Toddler Environment Scale; the home environment was assessed by the Home Observation for Measurement of the Environment. The neighborhood quality was determined by a self-prepared questionnaire. A multivariate linear regression analysis was performed. Families were predominantly from socioeconomic class D, with low parental education. The prevalence of stunted growth was 14.1%; cognitive and language development were below average at 28.6% and 28.3%, respectively. Educational institutions were classified as inadequate, and 69.6% of homes were classified as presenting a risk for development. Factors such as access to parks and pharmacies and perceived security received the worst score regarding neighborhood environment. Biological variables showed a greater association with growth and environmental variables with development. The results showed a high prevalence of stunting and below-average results for cognitive/language development among the participating children. Both environmental and biological factors were related to growth and development. However, biological variables showed a greater association with growth, whereas environmental variables were associated with development

  3. Growth and development and their environmental and biological determinants

    Directory of Open Access Journals (Sweden)

    Kelly da Rocha Neves

    2016-06-01

    Full Text Available Abstract Objective To investigate child growth, cognitive/language development, and their environmental and biological determinants. Methods This was a cross-sectional, predictive correlation study with all 92 children aged 24-36 months who attended the municipal early childhood education network in a town in the Vale do Jequitinhonha region, in 2011. The socioeconomic profile was determined using the questionnaire of the Associação Brasileira de Empresas de Pesquisa. The socio-demographicand maternal and child health profiles were created through a self-prepared questionnaire. The height-for-age indicator was selected to represent growth. Cognitive/language development was assessed through the Bayley Scale of Infant and Toddler Development. The quality of educational environments was assessed by Infant/Toddler Environment Scale; the home environment was assessed by the Home Observation for Measurement of the Environment. The neighborhood quality was determined by a self-prepared questionnaire. A multivariate linear regression analysis was performed. Results Families were predominantly from socioeconomic class D, with low parental education. The prevalence of stunted growth was 14.1%; cognitive and language development were below average at 28.6% and 28.3%, respectively. Educational institutions were classified as inadequate, and 69.6% of homes were classified as presenting a risk for development. Factors such as access to parks and pharmacies and perceived security received the worst score regarding neighborhood environment. Biological variables showed a greater association with growth and environmental variables with development. Conclusion The results showed a high prevalence of stunting and below-average results for cognitive/language development among the participating children. Both environmental and biological factors were related to growth and development. However, biological variables showed a greater association with growth, whereas

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Determination of Biological Oxygen Demand Rate Constant and ...

    African Journals Online (AJOL)

    Determination of Biological Oxygen Demand Rate Constant and Ultimate Biological Oxygen Demand for Liquid Waste Generated from Student Cafeteria at Jimma University: A Tool for Development of Scientific Criteria to Protect Aquatic Health in the Region.

  9. Biological impacts and context of network theory

    Energy Technology Data Exchange (ETDEWEB)

    Almaas, E

    2007-01-05

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  10. 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.

  11. 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.

  12. 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.

  13. Non-Hermitian localization in biological networks.

    Science.gov (United States)

    Amir, Ariel; Hatano, Naomichi; Nelson, David R

    2016-04-01

    We explore the spectra and localization properties of the N-site banded one-dimensional non-Hermitian random matrices that arise naturally in sparse neural networks. Approximately equal numbers of random excitatory and inhibitory connections lead to spatially localized eigenfunctions and an intricate eigenvalue spectrum in the complex plane that controls the spontaneous activity and induced response. A finite fraction of the eigenvalues condense onto the real or imaginary axes. For large N, the spectrum has remarkable symmetries not only with respect to reflections across the real and imaginary axes but also with respect to 90^{∘} rotations, with an unusual anisotropic divergence in the localization length near the origin. When chains with periodic boundary conditions become directed, with a systematic directional bias superimposed on the randomness, a hole centered on the origin opens up in the density-of-states in the complex plane. All states are extended on the rim of this hole, while the localized eigenvalues outside the hole are unchanged. The bias-dependent shape of this hole tracks the bias-independent contours of constant localization length. We treat the large-N limit by a combination of direct numerical diagonalization and using transfer matrices, an approach that allows us to exploit an electrostatic analogy connecting the "charges" embodied in the eigenvalue distribution with the contours of constant localization length. We show that similar results are obtained for more realistic neural networks that obey "Dale's law" (each site is purely excitatory or inhibitory) and conclude with perturbation theory results that describe the limit of large directional bias, when all states are extended. Related problems arise in random ecological networks and in chains of artificial cells with randomly coupled gene expression patterns.

  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. Toward Petascale Biologically Plausible Neural Networks

    Science.gov (United States)

    Long, Lyle

    This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

  16. 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.

  17. 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.)

  18. 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

  19. Determinants of public cooperation in multiplex networks

    Science.gov (United States)

    Battiston, Federico; Perc, Matjaž; Latora, Vito

    2017-07-01

    Synergies between evolutionary game theory and statistical physics have significantly improved our understanding of public cooperation in structured populations. Multiplex networks, in particular, provide the theoretical framework within network science that allows us to mathematically describe the rich structure of interactions characterizing human societies. While research has shown that multiplex networks may enhance the resilience of cooperation, the interplay between the overlap in the structure of the layers and the control parameters of the corresponding games has not yet been investigated. With this aim, we consider here the public goods game on a multiplex network, and we unveil the role of the number of layers and the overlap of links, as well as the impact of different synergy factors in different layers, on the onset of cooperation. We show that enhanced public cooperation emerges only when a significant edge overlap is combined with at least one layer being able to sustain some cooperation by means of a sufficiently high synergy factor. In the absence of either of these conditions, the evolution of cooperation in multiplex networks is determined by the bounds of traditional network reciprocity with no enhanced resilience. These results caution against overly optimistic predictions that the presence of multiple social domains may in itself promote cooperation, and they help us better understand the complexity behind prosocial behavior in layered social systems.

  20. 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.

  1. 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

  2. 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

  3. 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...

  4. Reachability Analysis in Probabilistic Biological Networks.

    Science.gov (United States)

    Gabr, Haitham; Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2015-01-01

    Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. Availability: All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.

  5. [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.

  6. 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.

  7. Creating the networking enterprises - logistics determinants

    Directory of Open Access Journals (Sweden)

    Ewa Kulińska

    2014-06-01

    Full Text Available Background: The article describes the determinants of creating network enterprises with peculiar consideration of logistic factors which are conditioning the organization of processes, exchange of resources and competences. On the basis of literature analysis, there is proposed a model of creating network enterprises. A model is verified in the application part of the thesis. Methods: Within the publication a literature review of submitted scope of the interest was presented, as well as the empirical research. A research substance attaches the enterprises created on the basis of the reactivation of organizations which has collapsed due to bankruptcy proceeding. The research was based upon direct interviews with employees of the net-forming entities. Results and conclusions: Results of the research shows that taking up the cooperation and net-cooperation was the only possibility for new entities to come into existence, that were  based upon old assets and human resources liquidated during bankruptcy proceeding. There was indentified many determinants of enterprises network cooperation, however due to the research a conclusion draws, that basic factors of creating network cooperation are those which are profit-achieving oriented.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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...

  16. Study of phosphors determination in biological samples

    International Nuclear Information System (INIS)

    Oliveira, Rosangela Magda de.

    1994-01-01

    In this paper, phosphors determination by neutron activation analysis in milk and bone samples was studied employing both instrumental and radiochemical separation methods. The analysis with radiochemistry separation consisted of the simultaneous irradiation of the samples and standards during 30 minutes, dissolution of the samples, hold back carrier, addition precipitation of phosphorus with ammonium phosphomolibdate (A.M.P.) and phosphorus-32 by counting by using Geiger-Mueller detector. The instrumental analysis consisted of the simultaneous irradiation of the samples and standards during 30 minutes, transfer of the samples into a counting planchet and measurement of the beta radiation emitted by phosphorus-32, after a suitable decay period. After the phosphorus analysis methods were established they were applied to both commercial milk and animal bone samples, and data obtained in the instrumental and radiochemical separation methods for each sample, were compared between themselves. In this work, it became possible to obtain analysis methods for phosphorus that can be applied independently of the sample quantity available, and the phosphorus content in the samples or interference that can be present in them. (author). 51 refs., 7 figs., 4 tabs

  17. 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...

  18. 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.

  19. INAA Application for Trace Element Determination in Biological Reference Material

    Science.gov (United States)

    Atmodjo, D. P. D.; Kurniawati, S.; Lestiani, D. D.; Adventini, N.

    2017-06-01

    Trace element determination in biological samples is often used in the study of health and toxicology. Determination change to its essentiality and toxicity of trace element require an accurate determination method, which implies that a good Quality Control (QC) procedure should be performed. In this study, QC for trace element determination in biological samples was applied by analyzing the Standard Reference Material (SRM) Bovine muscle 8414 NIST using Instrumental Neutron Activation Analysis (INAA). Three selected trace element such as Fe, Zn, and Se were determined. Accuracy of the elements showed as %recovery and precision as %coefficient of variance (%CV). The result showed that %recovery of Fe, Zn, and Se were in the range between 99.4-107%, 92.7-103%, and 91.9-112%, respectively, whereas %CV were 2.92, 3.70, and 5.37%, respectively. These results showed that INAA method is precise and accurate for trace element determination in biological matrices.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. Determination of chromium in biological matrices by neutron activation

    International Nuclear Information System (INIS)

    McClendon, L.T.

    1978-01-01

    Chromium is recognized to be an essential trace element in several biological systems. It exists in many biological materials in a variety of chemical forms and very low concentration levels which cause problems for many analytical techniques. Both instrumental and destructive neutron activation analysis were used to determine the chromium concentration in Orchard Leaves, SRM 1571, Brewers Yeast, SRM 1569, and Bovine Liver, SRM 1577. Some of the problems inherent with determining chromium in certain biological matrices and the data obtained here at the National Bureau of Standards using this technique are discussed. The results obtained from dissolution of brewers yeast in a closed system as described in the DNAA procedure are in good agreement with the INAA results. The same phenomenon existed in the determination of chromium in bovine liver. The radiochemical procedure described for chromium (DNAA) provides the analyst with a simple, rapid and selective technique for chromium determination in a variety of matrices. (T.G.)

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Determination of trace elements in KRISS biological CRMs by INAA

    International Nuclear Information System (INIS)

    Cho, Kyung Haeng; Park, Kwang Won; Zeisler, Rolf

    2005-01-01

    Two biological Certified Reference Materials (CRMs), KRISS 108-04-001 (oyster tissue) and 108-05-001 (water dropwort stem), were prepared by Korea Research Institute of Standards and Science (KRISS) during FY '01. The certified values of these materials had been determined by Isotope Dilution Mass Spectrometry (IDMS) for six elements (Cd, Cr, Cu, Fe, Pb and Zn). Additional analytical works are now progressing to certify the concentrations of a number of the environmental and nutrimental elements in these CRMs. The certified values in a CRM are usually determined by using a single primary method with confirmation by other method(s) or using two independent critically-evaluated methods. Instrumental Neutron Activation Analysis (INAA) plays an important role in determination of certified values. INAA procedure was used in determination of 20 elements in these two biological CRMs to acquire the concentration information and the results were compared with KRISS certified values

  14. 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.

  15. The determination of copper in biological materials by flame spectrophotometry

    Science.gov (United States)

    Newman, G. E.; Ryan, M.

    1962-01-01

    A method for the determination of the copper content of biological materials by flame spectrophotometry is described. The effects of interference by ions such as sodium and phosphate were eliminated by isolating copper as the dithizonate in CCl4. Results obtained for the urinary excretion of copper by a patient with Wilson's disease before and after treatment with penicillamine are reported. PMID:14479334

  16. Method of 10B determination in biological specimens

    International Nuclear Information System (INIS)

    Nikitina, R.G.; Frolova, E.I.

    1981-01-01

    The paper is concerned with the methods of 10 B determination in biological specimens (blood, skin and tissues of rats). On the basis of investigations conducted there have been proposed films based on cellulose triacetate with optimal characteristics in terms of their possible utilization as solid detectors to record α-particles and recoil nuclei. The conditions of film staining are also discussed

  17. [Progress on Determination and Analysis of Zopiclone in Biological Samples].

    Science.gov (United States)

    Shu, C X; Gong, D; Zhang, L P; Zhao, J X

    2017-12-01

    As a new hypnotic, zopiclone is widely used in clinical treatment. There are many methods for determination of zopiclone, including spectrophotometry, chromatography and chromatography mass spectrum, etc. Present paper reviews different kinds of biological samples associated with zopiclone, extraction and purification methods, and determination and analysis methods, which aims to provide references for the relevant research and practice. Copyright© by the Editorial Department of Journal of Forensic Medicine.

  18. 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

  19. 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.

  20. 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/.

  1. Factors determining nestedness in complex networks.

    Directory of Open Access Journals (Sweden)

    Samuel Jonhson

    Full Text Available Understanding the causes and effects of network structural features is a key task in deciphering complex systems. In this context, the property of network nestedness has aroused a fair amount of interest as regards ecological networks. Indeed, Bastolla et al. introduced a simple measure of network nestedness which opened the door to analytical understanding, allowing them to conclude that biodiversity is strongly enhanced in highly nested mutualistic networks. Here, we suggest a slightly refined version of such a measure of nestedness and study how it is influenced by the most basic structural properties of networks, such as degree distribution and degree-degree correlations (i.e. assortativity. We find that most of the empirically found nestedness stems from heterogeneity in the degree distribution. Once such an influence has been discounted - as a second factor - we find that nestedness is strongly correlated with disassortativity and hence - as random networks have been recently found to be naturally disassortative - they also tend to be naturally nested just as the result of chance.

  2. Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course

    Science.gov (United States)

    Ziegler, Brittany; Montplaisir, Lisa

    2014-01-01

    Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students' perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest…

  3. Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course

    OpenAIRE

    Ziegler, Brittany; Montplaisir, Lisa

    2014-01-01

    Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students’ perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest approach. Significant differences in students’ perception of their knowledge and their determined knowledge exist at the beginning (pretest) and end (postte...

  4. DETERMINANTS OF NETWORK OUTCOMES: THE IMPACT OF MANAGEMENT STRATEGIES.

    Science.gov (United States)

    Ysa, Tamyko; Sierra, Vicenta; Esteve, Marc

    2014-09-01

    The literature on network management is extensive. However, it generally explores network structures, neglecting the impact of management strategies. In this article we assess the effect of management strategies on network outcomes, providing empirical evidence from 119 urban revitalization networks. We go beyond current work by testing a path model for the determinants of network outcomes and considering the interactions between the constructs: management strategies, trust, complexity, and facilitative leadership. Our results suggest that management strategies have a strong effect on network outcomes and that they enhance the level of trust. We also found that facilitative leadership has a positive impact on network management as well as on trust in the network. Our findings also show that complexity has a negative impact on trust. A key finding of our research is that managers may wield more influence on network dynamics than previously theorized.

  5. 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.

  6. Biologically based neural network for mobile robot navigation

    Science.gov (United States)

    Torres Muniz, Raul E.

    1999-01-01

    The new tendency in mobile robots is to crete non-Cartesian system based on reactions to their environment. This emerging technology is known as Evolutionary Robotics, which is combined with the Biorobotic field. This new approach brings cost-effective solutions, flexibility, robustness, and dynamism into the design of mobile robots. It also provides fast reactions to the sensory inputs, and new interpretation of the environment or surroundings of the mobile robot. The Subsumption Architecture (SA) and the action selection dynamics developed by Brooks and Maes, respectively, have successfully obtained autonomous mobile robots initiating this new trend of the Evolutionary Robotics. Their design keeps the mobile robot control simple. This work present a biologically inspired modification of these schemes. The hippocampal-CA3-based neural network developed by Williams Levy is used to implement the SA, while the action selection dynamics emerge from iterations of the levels of competence implemented with the HCA3. This replacement by the HCA3 results in a closer biological model than the SA, combining the Behavior-based intelligence theory with neuroscience. The design is kept simple, and it is implemented in the Khepera Miniature Mobile Robot. The used control scheme obtains an autonomous mobile robot that can be used to execute a mail delivery system and surveillance task inside a building floor.

  7. The potential for biological structure determination with pulsed neutrons

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, C.C. [CLRC Rutherford Appleton Laboratory, Chilton Didcot Oxon (United Kingdom)

    1994-12-31

    The potential of pulsed neutron diffraction in structural determination of biological materials is discussed. The problems and potential solutions in this area are outlined, with reference to both current and future sources and instrumentation. The importance of developing instrumentation on pulsed sources in emphasized, with reference to the likelihood of future expansion in this area. The possibilities and limitations of single crystal, fiber and powder diffraction in this area are assessed.

  8. The potential for biological structure determination with pulsed neutrons

    International Nuclear Information System (INIS)

    Wilson, C.C.

    1994-01-01

    The potential of pulsed neutron diffraction in structural determination of biological materials is discussed. The problems and potential solutions in this area are outlined, with reference to both current and future sources and instrumentation. The importance of developing instrumentation on pulsed sources in emphasized, with reference to the likelihood of future expansion in this area. The possibilities and limitations of single crystal, fiber and powder diffraction in this area are assessed

  9. 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.

  10. 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

  11. Determining environmental causes of biological effects: the need for a mechanistic physiological dimension in conservation biology.

    Science.gov (United States)

    Seebacher, Frank; Franklin, Craig E

    2012-06-19

    The emerging field of Conservation Physiology links environmental change and ecological success by the application of physiological theory, approaches and tools to elucidate and address conservation problems. Human activity has changed the natural environment to a point where the viability of many ecosystems is now under threat. There are already many descriptions of how changes in biological patterns are correlated with environmental changes. The next important step is to determine the causative relationship between environmental variability and biological systems. Physiology provides the mechanistic link between environmental change and ecological patterns. Physiological research, therefore, should be integrated into conservation to predict the biological consequences of human activity, and to identify those species or populations that are most vulnerable.

  12. Determining Diagonal Branches in Mine Ventilation Networks

    Science.gov (United States)

    Krach, Andrzej

    2014-12-01

    The present paper discusses determining diagonal branches in a mine ventilation network by means of a method based on the relationship A⊗ PT(k, l) = M, which states that the nodal-branch incidence matrix A, modulo-2 multiplied by the transposed path matrix PT(k, l ) from node no. k to node no. l, yields the matrix M where all the elements in rows k and l - corresponding to the start and the end node - are 1, and where the elements in the remaining rows are 0, exclusively. If a row of the matrix M is to contain only "0" elements, the following condition has to be fulfilled: after multiplying the elements of a row of the matrix A by the elements of a column of the matrix PT(k, l), i.e. by the elements of a proper row of the matrix P(k, l ), the result row must display only "0" elements or an even number of "1" entries, as only such a number of "1" entries yields 0 when modulo-2 added - and since the rows of the matrix A correspond to the graph nodes, and the path nodes level is 2 (apart from the nodes k and l, whose level is 1), then the number of "1" elements in a row has to be 0 or 2. If, in turn, the rows k and l of the matrix M are to contain only "1" elements, the following condition has to be fulfilled: after multiplying the elements of the row k or l of the matrix A by the elements of a column of the matrix PT(k, l), the result row must display an uneven number of "1" entries, as only such a number of "1" entries yields 1 when modulo-2 added - and since the rows of the matrix A correspond to the graph nodes, and the level of the i and j path nodes is 1, then the number of "1" elements in a row has to be 1. The process of determining diagonal branches by means of this method was demonstrated using the example of a simple ventilation network with two upcast shafts and one downcast shaft. W artykule przedstawiono metodę wyznaczania bocznic przekątnych w sieci wentylacyjnej kopalni metodą bazującą na zależności A⊗PT(k, l) = M, która podaje, że macierz

  13. 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

  14. 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

  15. 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

  16. 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.

  17. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  18. The redox biology network in cancer pathophysiology and therapeutics

    Directory of Open Access Journals (Sweden)

    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  19. Approach of Complex Networks for the Determination of Brain Death

    Institute of Scientific and Technical Information of China (English)

    SUN Wei-Gang; CAO Jian-Ting; WANG Ru-Bin

    2011-01-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our Sndings might provide valuable insights on the determination of brain death.%@@ In clinical practice, brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis axe derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.

  20. Augmenting Plant Immune Responses and Biological Control by Microbial Determinants

    Directory of Open Access Journals (Sweden)

    Sang Moo Lee

    2015-09-01

    Full Text Available Plant have developed sophisticated defence mechanisms against microbial pathogens. The recent accumulated information allow us to understand the nature of plant immune responses followed by recognition of microbial factors/determinants through cutting-edge genomics and multi-omics techniques. However, the practical approaches to sustain plant health using enhancement of plant immunity is yet to be fully appreciated. Here, we overviewed the general concept and representative examples on the plant immunity. The fungal, bacterial, and viral determinants that was previously reported as the triggers of plant immune responses are introduced and described as the potential protocol of biological control. Specifically, the role of chitin, glucan, lipopolysaccharides/extracellular polysaccharides, microbe/pathogen-associated molecular pattern, antibiotics, mimic-phytohormones, N-acyl homoserine lactone, harpin, vitamins, and volatile organic compounds are considered. We hope that this review stimulates scientific community and farmers to broaden their knowledge on the microbial determinant-based biological control and to apply the technology on the integrated pest management program.

  1. Determination of platinum in biological samples by NAA

    International Nuclear Information System (INIS)

    Okada, Yukiko; Hirai, Shoji; Sakurai, Hiromu; Haraguchi, Hiroki.

    1990-01-01

    Recently, a Pt compound, Cisplatin (cis-dichlorodiamine platinum) has been used therapeutically as an effective anti-malignant-cancer drug. However, since this drug has a harmful aftereffect on kidney, an urgent study of how to reduce its toxicity without influencing the therapeutic effect is needed. We have to understand the behavior of Pt in biological organs in order to elucidate the mechanism of its toxicity reduction. In this study, the analytical conditions for the determination of Pt in biological samples by neutron activation analysis, such as cooling time, counting time and sample weight, are optimized. Freeze-dried samples of the liver, kidney and whole blood of a rat treated with Cisplatin were prepared to evaluate the precision of the analysis and the lower limit of determination. 199 Au (t 1/2 = 3.15 d) produced from 199 Pt (n, γ, β - ) was selected as the analytical radionuclide. A concentration of ca. 1 ppm Pt was determinable under the optimal conditions: a cooling time of 5 d and a counting time of 1 h. Pt in the respective organs of the control rat was not detected under the same analytical conditions. The concentrations of Pt in the liver, kidney, spleen, pancreas and lung of a rat treated with both Cisplatin and sodium selenite were higher than those of a rat treated only with Cisplatin. (author)

  2. Routine Determination of Arsenic in Biological Materials. RCN Report

    International Nuclear Information System (INIS)

    Kroon, J.J.; Das, H.A.

    1970-08-01

    This text describes a routine procedure for the determination of arsenic in biological materials by neutron activation analysis. Unlike most methods published in literature the present analysis is not based on chemical separation by destination. After a first purification by anion-exchange the 76 As-activity (T1/2 = 26,4 h) is isolated by precipitation as the metal. The method was tested by analysis of the standard kale powder. This material was prepared and issued by Bowen in 1966, to provide a reliable standard for the intercomparison of various methods. (author)

  3. Flame Spectrophotometric Determination of Strontium in Water and Biological Material

    Energy Technology Data Exchange (ETDEWEB)

    Joensson, G

    1964-10-15

    A flame spectrophotometric method has been developed for the determination of strontium in biological material and water samples. Strontium is determined in the presence of calcium at a wavelength of 4607 A. The intensity of the strontium emission from the sample is increased if n-butanol is added to a solution of the sample in water. With a 6 vol% solution of n-butanol in water, an optimum intensity of 3.5 times that obtained with pure water solution is obtained. Anions and alkali metals which might interfere with the flame spectrophotometric determination are separated from the sample by a simple ion exchange operation. The method allows determination of strontium in solutions down to 0.1{mu}g/ml. In this case the standard deviation is 3.1 % and with a strontium concentration of 1 {mu}g/ml the deviation is 0.9 %. This method has been used for the determination of strontium in samples of varying composition such as bone, meat and skin from fishes, samples of human bones, shell-fish, milk, and water, in which case Sr quantities of 5{mu}g were determined with an analytical error of less than 5 % and Sr{sub q}uantities greater than 10 {mu}g with an error of less than 3 %.

  4. Flame Spectrophotometric Determination of Strontium in Water and Biological Material

    International Nuclear Information System (INIS)

    Joensson, G.

    1964-10-01

    A flame spectrophotometric method has been developed for the determination of strontium in biological material and water samples. Strontium is determined in the presence of calcium at a wavelength of 4607 A. The intensity of the strontium emission from the sample is increased if n-butanol is added to a solution of the sample in water. With a 6 vol% solution of n-butanol in water, an optimum intensity of 3.5 times that obtained with pure water solution is obtained. Anions and alkali metals which might interfere with the flame spectrophotometric determination are separated from the sample by a simple ion exchange operation. The method allows determination of strontium in solutions down to 0.1μg/ml. In this case the standard deviation is 3.1 % and with a strontium concentration of 1 μg/ml the deviation is 0.9 %. This method has been used for the determination of strontium in samples of varying composition such as bone, meat and skin from fishes, samples of human bones, shell-fish, milk, and water, in which case Sr quantities of 5μg were determined with an analytical error of less than 5 % and Sr q uantities greater than 10 μg with an error of less than 3 %

  5. Analytical methodologies for the determination of benzodiazepines in biological samples.

    Science.gov (United States)

    Persona, Karolina; Madej, Katarzyna; Knihnicki, Paweł; Piekoszewski, Wojciech

    2015-09-10

    Benzodiazepine drugs belong to important and most widely used medicaments. They demonstrate such therapeutic properties as anxiolytic, sedative, somnifacient, anticonvulsant, diastolic and muscle relaxant effects. However, despite the fact that benzodiazepines possess high therapeutic index and are considered to be relatively safe, their use can be dangerous when: (1) co-administered with alcohol, (2) co-administered with other medicaments like sedatives, antidepressants, neuroleptics or morphine like substances, (3) driving under their influence, (4) using benzodiazepines non-therapeutically as drugs of abuse or in drug-facilitated crimes. For these reasons benzodiazepines are still studied and determined in a variety of biological materials. In this article, sample preparation techniques which have been applied in analysis of benzodiazepine drugs in biological samples have been reviewed and presented. The next part of the article is focused on a review of analytical methods which have been employed for pharmacological, toxicological or forensic study of this group of drugs in the biological matrices. The review was preceded by a description of the physicochemical properties of the selected benzodiazepines and two, very often coexisting in the same analyzed samples, sedative-hypnotic drugs. Copyright © 2015. Published by Elsevier B.V.

  6. 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...

  7. Neutron activation analysis on determination of arsenic in biological matrixes

    Energy Technology Data Exchange (ETDEWEB)

    Menezes, Maria Angela de B.C.; Silva, Maria Aparecida, E-mail: menezes@cdtn.br, E-mail: cida@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2013-07-01

    Aiming at giving support to the Worker's Health Awareness Program of the Municipal Department of Health of Belo Horizonte, an assessment related arsenic was carried out in two galvanising factories by means of hair and toenail samples analysis as biomonitors. The arsenic was determined in all matrixes from the factories where gold electrodeposition process was applied. This is because arsenic salts are usually added to gold bath to improve the metal covering. The high concentration results surprised the health surveillance professionals, and alerted for the need of assessing the influence of a long-term exposure. Studies concerning galvanising process have usually been developed broaching many aspects, but so far few works has pointed out the detection and measurement of other elements like arsenic. The k{sub 0}-Instrumental Neutron Activation method was applied confirming to be a suitable technique on determination of arsenic in biological matrixes. (author)

  8. Neutron activation analysis on determination of arsenic in biological matrixes

    International Nuclear Information System (INIS)

    Menezes, Maria Angela de B.C.; Silva, Maria Aparecida

    2013-01-01

    Aiming at giving support to the Worker's Health Awareness Program of the Municipal Department of Health of Belo Horizonte, an assessment related arsenic was carried out in two galvanising factories by means of hair and toenail samples analysis as biomonitors. The arsenic was determined in all matrixes from the factories where gold electrodeposition process was applied. This is because arsenic salts are usually added to gold bath to improve the metal covering. The high concentration results surprised the health surveillance professionals, and alerted for the need of assessing the influence of a long-term exposure. Studies concerning galvanising process have usually been developed broaching many aspects, but so far few works has pointed out the detection and measurement of other elements like arsenic. The k 0 -Instrumental Neutron Activation method was applied confirming to be a suitable technique on determination of arsenic in biological matrixes. (author)

  9. Wavelet analysis of polarization maps of polycrystalline biological fluids networks

    Science.gov (United States)

    Ushenko, Y. A.

    2011-12-01

    The optical model of human joints synovial fluid is proposed. The statistic (statistic moments), correlation (autocorrelation function) and self-similar (Log-Log dependencies of power spectrum) structure of polarization two-dimensional distributions (polarization maps) of synovial fluid has been analyzed. It has been shown that differentiation of polarization maps of joint synovial fluid with different physiological state samples is expected of scale-discriminative analysis. To mark out of small-scale domain structure of synovial fluid polarization maps, the wavelet analysis has been used. The set of parameters, which characterize statistic, correlation and self-similar structure of wavelet coefficients' distributions of different scales of polarization domains for diagnostics and differentiation of polycrystalline network transformation connected with the pathological processes, has been determined.

  10. 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

  11. 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 ...

  12. 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

  13. Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course

    Science.gov (United States)

    Montplaisir, Lisa

    2014-01-01

    Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students’ perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest approach. Significant differences in students’ perception of their knowledge and their determined knowledge exist at the beginning (pretest) and end (posttest) of the course. Alignment between student perception and determined knowledge was significantly more accurate on the posttest compared with the pretest. Students whose determined knowledge was in the upper quartile had significantly better alignment between their perception and determined knowledge on the pre- and posttest than students in the lower quartile. No difference exists between how students perceived their knowledge between upper- and lower-quartile students. There was a significant difference in alignment of perception and determined knowledge between males and females on the posttest, with females being more accurate in their perception of knowledge. This study provides evidence of discrepancies that exist between what students perceive they know and what they actually know. PMID:26086662

  14. Integration of biological networks and gene expression data using Cytoscape

    DEFF Research Database (Denmark)

    Cline, M.S.; Smoot, M.; Cerami, E.

    2007-01-01

    of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules......Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....

  15. Endogenous Molecular-Cellular Network Cancer Theory: A Systems Biology Approach.

    Science.gov (United States)

    Wang, Gaowei; Yuan, Ruoshi; Zhu, Xiaomei; Ao, Ping

    2018-01-01

    In light of ever apparent limitation of the current dominant cancer mutation theory, a quantitative hypothesis for cancer genesis and progression, endogenous molecular-cellular network hypothesis has been proposed from the systems biology perspective, now for more than 10 years. It was intended to include both the genetic and epigenetic causes to understand cancer. Its development enters the stage of meaningful interaction with experimental and clinical data and the limitation of the traditional cancer mutation theory becomes more evident. Under this endogenous network hypothesis, we established a core working network of hepatocellular carcinoma (HCC) according to the hypothesis and quantified the working network by a nonlinear dynamical system. We showed that the two stable states of the working network reproduce the main known features of normal liver and HCC at both the modular and molecular levels. Using endogenous network hypothesis and validated working network, we explored genetic mutation pattern in cancer and potential strategies to cure or relieve HCC from a totally new perspective. Patterns of genetic mutations have been traditionally analyzed by posteriori statistical association approaches in light of traditional cancer mutation theory. One may wonder the possibility of a priori determination of any mutation regularity. Here, we found that based on the endogenous network theory the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. Normal hepatocyte and cancerous hepatocyte stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable

  16. Biogastronomy: Factors that determine the biological response to meal ingestion.

    Science.gov (United States)

    Pribic, T; Azpiroz, F

    2018-02-02

    The biological response to a meal includes physiological changes, primarily related to the digestive process, and a sensory experience, involving sensations related to the homeostatic control of food consumption, eg, satiety and fullness, with a hedonic dimension, ie associated with changes in digestive well-being and mood. The responses to a meal include a series of events before, during and after ingestion. While much attention has been paid to the events before and during ingestion, relatively little is known about the postprandial sensations, which are key to the gastronomical experience. The aim of this narrative review is to provide a comprehensive overview and to define the framework to investigate the factors that determine the postprandial experience. Based on a series of proof-of-concept studies and related information, we propose that the biological responses to a meal depend on the characteristics of the meal, primarily its palatability and composition, and the responsiveness of the guest, which may be influenced by multiple previous and concurrent conditioning factors. This information provides the scientific backbone to the development of personalized gastronomy. © 2018 John Wiley & Sons Ltd.

  17. DETERMINATION OF POTENTIAL CRIMINALS IN SOCIAL NETWORK

    OpenAIRE

    Ceyhan, Eyüp Burak; Sağıroğlu, Şeref; Cesur, Ramazan; Kermen, Ayten

    2017-01-01

    This paper proposes a new approach to determine potential criminals by performing content analysis on Tweets. The analysis is done with the help of machine learning technologies and big data analysis. In this study, we have utilized from the MLP algorithm. A dataset consisting of 384 words are used to make the classification process. Dataset consist of two classes that are organized crime and cyber-crimes. In the analysis process, date, time, location values of sent twitter sharings are also ...

  18. Problems in the determination of chromium in biological materials

    International Nuclear Information System (INIS)

    Behne, D.; Braetter, P.; Gessner, H.; Hube, G.; Mertz, W.; Roesick, U.

    1976-01-01

    The effects of sample preparation on the analysis of chromium in biological matter have been investigated using brewer's yeast as a test material. The apparent chromium content of the yeast as determined by flameless atomic absorption spectrometry was significantly higher after destruction of the organic matter with HNO 3 in a closed pressure vessel than after wet-ashing in open vessels and after direct introduction of the sample into the graphite furnace. The results obtained by neutron activation analysis without any sample preparation, which corresponded to the atomic absorption values after digestion in the pressure vessel, showed that considerable errors arise in the other methods of sample treatment. Chromium analysis of dried and ashed yeast suggest that losses of volatile chromium compounds may occur during heating. (orig.) [de

  19. Approach of Complex Networks for the Determination of Brain Death

    International Nuclear Information System (INIS)

    Sun Wei-Gang; Cao Jian-Ting; Wang Ru-Bin

    2011-01-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death. (cross-disciplinary physics and related areas of science and technology)

  20. 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.

  1. 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.

  2. 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 .

  3. 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.

  4. Robustness of the p53 network and biological hackers.

    Science.gov (United States)

    Dartnell, Lewis; Simeonidis, Evangelos; Hubank, Michael; Tsoka, Sophia; Bogle, I David L; Papageorgiou, Lazaros G

    2005-06-06

    The p53 protein interaction network is crucial in regulating the metazoan cell cycle and apoptosis. Here, the robustness of the p53 network is studied by analyzing its degeneration under two modes of attack. Linear Programming is used to calculate average path lengths among proteins and the network diameter as measures of functionality. The p53 network is found to be robust to random loss of nodes, but vulnerable to a targeted attack against its hubs, as a result of its architecture. The significance of the results is considered with respect to mutational knockouts of proteins and the directed attacks mounted by tumour inducing viruses.

  5. 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.

  6. 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.

  7. A biological radioimmunological microassay to determine hypophysiotropic factors

    International Nuclear Information System (INIS)

    Sand, T.

    1980-01-01

    The thesis presented there describes a combined biological-radioimmunological assay for hypophysiotropic substances. The secretion reaction of adenohypophysial rat cells cultured by a long-term monolayer technique is used as a measure of hypophysiotropic activity. For hypophysial hormones released into the culture medium are then determined directly with the aid of specific radio immunoassay. This method can also be used for substances not yet characterized chemically or for tissue extracts, as shown here using hypothalamus stalk median eminance extract as example. The method is technically quite simple and economical. At the same time the technique is exact and reliable and offers, for TRH and LHRH determination, a degree of sensitivity in the pg range similar to that obtained by radioimmunological methods. The sensitivity towards CRH activity exceeds that obtained by other methods to date. From a morphological viewpoint and from comparisons of spontaneous secretion behaviour (or stimulation reactions following TRH, LHRH, dopamine and vasopressin application) with in-vivo findings it was shown that the long-term cell cultures were intact and that, overall, the culture model used closely approximates in its functional behaviour the physiological situation. (orig./MG) [de

  8. Topography and biological noise determine acoustic detectability on coral reefs

    KAUST Repository

    Cagua, Edgar F.

    2013-08-19

    Acoustic telemetry is an increasingly common tool for studying the movement patterns, behavior and site fidelity of marine organisms, but to accurately interpret acoustic data, the variability, periodicity and range of detectability between acoustic tags and receivers must be understood. The relative and interactive effects of topography with biological and environmental noise have not been quantified on coral reefs. We conduct two long-term range tests (1- and 4-month duration) on two different reef types in the central Red Sea to determine the relative effect of distance, depth, topography, time of day, wind, lunar phase, sea surface temperature and thermocline on detection probability. Detectability, as expected, declines with increasing distance between tags and receivers, and we find average detection ranges of 530 and 120 m, using V16 and V13 tags, respectively, but the topography of the reef can significantly modify this relationship, reducing the range by ~70 %, even when tags and receivers are in line-of-sight. Analyses that assume a relationship between distance and detections must therefore be used with care. Nighttime detection range was consistently reduced in both locations, and detections varied by lunar phase in the 4-month test, suggesting a strong influence of biological noise (reducing detection probability up to 30 %), notably more influential than other environmental noises, including wind-driven noise, which is normally considered important in open-water environments. Analysis of detections should be corrected in consideration of the diel patterns we find, and range tests or sentinel tags should be used for more than 1 month to quantify potential changes due to lunar phase. Some studies assume that the most usual factor limiting detection range is weather-related noise; this cannot be extrapolated to coral reefs. © 2013 Springer-Verlag Berlin Heidelberg.

  9. Network 'small-world-ness': a quantitative method for determining canonical network equivalence.

    Directory of Open Access Journals (Sweden)

    Mark D Humphries

    Full Text Available BACKGROUND: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges. This semi-quantitative definition leads to a categorical distinction ('small/not-small' rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model--the Watts-Strogatz (WS model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. METHODOLOGY/PRINCIPAL FINDINGS: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S>1--an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. CONCLUSIONS/SIGNIFICANCE: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.

  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. 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

  12. Determining the confidence levels of sensor outputs using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

    1996-12-31

    This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

  13. Commentary: Biochemistry and Molecular Biology Educators Launch National Network

    Science.gov (United States)

    Bailey, Cheryl; Bell, Ellis; Johnson, Margaret; Mattos, Carla; Sears, Duane; White, Harold B.

    2010-01-01

    The American Society of Biochemistry and Molecular Biology (ASBMB) has launched an National Science Foundation (NSF)-funded 5 year project to support biochemistry and molecular biology educators learning what and how students learn. As a part of this initiative, hundreds of life scientists will plan and develop a rich central resource for…

  14. 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.

  15. 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.

  16. 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...

  17. 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.

  18. 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.

  19. 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.)

  20. 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.

  1. Notes on a PDE system for biological network formation

    KAUST Repository

    Haskovec, Jan; Markowich, Peter A.; Perthame, Benoî t; Schlottbom, Matthias

    2016-01-01

    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

  2. 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

  3. Fluorine determinations in biological materials by instrumental neutron activation analysis

    International Nuclear Information System (INIS)

    Demiralp, R.; Guinn, V.P.; Becker, D.A.

    1992-01-01

    Exploratory studies were carried out at the University of California, Irvine on several freeze-dried human diet materials and on two freeze-dried vegetation materials - all prospective reference materials. The University of California, Irvine equipment includes a 250-kW TRIGA Mark 1 reactor, 2.5 x 10 12 n/cm 2 ·s thermal flux, 3-s sample transfer time, and a typical 18% Ge(Li)/4,096-channel gamma-ray spectrometer with a detector resolution of 3.3 keV at 1,332 keV. In these exploratory studies, it was found that it was not feasible to measure fluorine with adequate precision or accuracy at fluorine concentrations much less than ∼100 ppm. These initial studies, however, defined the magnitudes of the various difficulties. One good outcome of these studies was the demonstration that the otherwise excellent Teflon-mill brittle-fracture method for homogenizing freeze-dried biological samples was not suitable if fluorine was to be determined. Abrasion of the Teflon increased the fluorine content of a human diet sample about sevenfold (compared with similar treatment of the same material in an all-titanium mill)

  4. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

    Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

  5. Impact parameter determination in experimental analysis using neural network

    International Nuclear Information System (INIS)

    Haddad, F.; David, C.; Freslier, M.; Aichelin, J.; Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.

    1997-01-01

    A neural network is used to determine the impact parameter in 40 Ca + 40 Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure have been studied carefully. An overall improvement of the impact parameter determination of 25 % is obtained using this technique. The analysis of Amphora 40 Ca+ 40 Ca data at 35 MeV per nucleon using a neural network shows two well separated classes of events among the selected 'complete' events. (authors)

  6. Biological condition gradient: Applying a framework for determining the biological integrity of coral reefs

    Science.gov (United States)

    The goals of the U.S. Clean Water Act (CWA) are to restore and maintain the chemical, physical and biological integrity of water resources. Although clean water is a goal, another is to safeguard biological communities by defining levels of biological integrity to protect aquatic...

  7. Using function approximation to determine neural network accuracy

    International Nuclear Information System (INIS)

    Wichman, R.F.; Alexander, J.

    2013-01-01

    Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

  8. Evolutionary biology and the determinants of morality 2 | Odozor ...

    African Journals Online (AJOL)

    This second essay continues the reflection on this problem through an exploration of the extra-biological factors and how they effect human moral development—a facet which is only too obvious, but which the evolutionary approach sidelines in its desperate effort to put up purely biological accounts of morality and ethics.

  9. Growth and development and their environmental and biological determinants

    Directory of Open Access Journals (Sweden)

    Kelly da Rocha Neves

    2016-05-01

    Conclusion: The results showed a high prevalence of stunting and below‐average results for cognitive/language development among the participating children. Both environmental and biological factors were related to growth and development. However, biological variables showed a greater association with growth, whereas environmental variables were associated with development.

  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. 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

  12. 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.

  13. BinAligner: a heuristic method to align biological networks.

    Science.gov (United States)

    Yang, Jialiang; Li, Jun; Grünewald, Stefan; Wan, Xiu-Feng

    2013-01-01

    The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and

  14. Modeling Wireless Sensor Networks for Monitoring in Biological Processes

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil

    parameters, as the use of wired sensors is impractical. In this thesis, a ZigBee based wireless sensor network was employed and only a part of the herd was monitored, as monitoring each individual animal in a large herd under practical conditions is inefficient. Investigations to show that the monitored...... (MMAE) approach to the data resulted in the highest classification success rate, due to the use of precise forth-order mathematical models which relate the feed offer to the pitch angle of the neck. This thesis shows that wireless sensor networks can be successfully employed to monitor the behavior...

  15. Determinants of informal coordination in networked supply chains

    NARCIS (Netherlands)

    Ashayeri, J.; Ogulin, R.; Selen, W.

    2012-01-01

    Purpose – The purpose of this paper is to empirically examine capability connectivity, relationship alignment and the ability to informally network in the supply chain as determinants for better utilizing capabilities amongst supply chain partners. In particular, the paper focuses on how the above

  16. The Determinants of International News Flow: A Network Analysis.

    Science.gov (United States)

    Kim, Kyungmo; Barnett, George A.

    1996-01-01

    Examines the structure of international news flow and its determinants. Reveals inequality of international news flow between core and periphery, with Western industrialized countries at the center. Finds that the news flow network is structured into eight geographic-linguistic groups. Indicates flow is influenced by a country's economic…

  17. 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)

  18. Biologically Inspired Target Recognition in Radar Sensor Networks

    Directory of Open Access Journals (Sweden)

    Liang Qilian

    2010-01-01

    Full Text Available One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC. Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.

  19. Bernstein approximations in glasso-based estimation of biological networks

    NARCIS (Netherlands)

    Purutcuoglu, Vilda; Agraz, Melih; Wit, Ernst

    The Gaussian graphical model (GGM) is one of the common dynamic modelling approaches in the construction of gene networks. In inference of this modelling the interaction between genes can be detected mainly via graphical lasso (glasso) or coordinate descent-based approaches. Although these methods

  20. Network formation determined by the diffusion process of random walkers

    International Nuclear Information System (INIS)

    Ikeda, Nobutoshi

    2008-01-01

    We studied the diffusion process of random walkers in networks formed by their traces. This model considers the rise and fall of links determined by the frequency of transports of random walkers. In order to examine the relation between the formed network and the diffusion process, a situation in which multiple random walkers start from the same vertex is investigated. The difference in diffusion rate of random walkers according to the difference in dimension of the initial lattice is very important for determining the time evolution of the networks. For example, complete subgraphs can be formed on a one-dimensional lattice while a graph with a power-law vertex degree distribution is formed on a two-dimensional lattice. We derived some formulae for predicting network changes for the 1D case, such as the time evolution of the size of nearly complete subgraphs and conditions for their collapse. The networks formed on the 2D lattice are characterized by the existence of clusters of highly connected vertices and their life time. As the life time of such clusters tends to be small, the exponent of the power-law distribution changes from γ ≅ 1-2 to γ ≅ 3

  1. 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....

  2. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

    NARCIS (Netherlands)

    He, F.; Murabito, E.; Westerhoff, H.V.

    2016-01-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental

  3. Resilience of caregivers of people with dementia: a systematic review of biological and psychosocial determinants

    Directory of Open Access Journals (Sweden)

    Rachel Dias

    2015-03-01

    Full Text Available Introduction: Although caregivers of people with dementia may face difficulties, some positive feelings of caregiving may be associated with resilience.Objective: This study systematically reviewed the definitions, methodological approaches and determinant models associated with resilience among caregivers of people with dementia.Methods: Search for articles published between 2003 and 2014 in ISI, PubMed/MEDLINE, SciELO and Lilacs using the search terms resilience, caregivers and dementia.Results and conclusions: Resilience has been defined as positive adaptation to face adversity, flexibility, psychological well-being, strength, healthy life, burden, social network and satisfaction with social support. No consensus was found about the definition of resilience associated with dementia. We classified the determinant variables into biological, psychological and social models. Higher levels of resilience were associated with lower depression rates and greater physical health. Other biological factors associated with higher levels of resilience were older age, African-American ethnicity and female sex. Lower burden, stress, neuroticism and perceived control were the main psychological factors associated with resilience. Social support was a moderating factor of resilience, and different types of support seemed to relieve the physical and mental overload caused by stress.

  4. Resilience of caregivers of people with dementia: a systematic review of biological and psychosocial determinants.

    Science.gov (United States)

    Dias, Rachel; Santos, Raquel Luiza; Sousa, Maria Fernanda Barroso de; Nogueira, Marcela Moreira Lima; Torres, Bianca; Belfort, Tatiana; Dourado, Marcia Cristina Nascimento

    2015-01-01

    Although caregivers of people with dementia may face difficulties, some positive feelings of caregiving may be associated with resilience. This study systematically reviewed the definitions, methodological approaches and determinant models associated with resilience among caregivers of people with dementia. Search for articles published between 2003 and 2014 in ISI, PubMed/MEDLINE, SciELO and Lilacs using the search terms resilience, caregivers and dementia. Resilience has been defined as positive adaptation to face adversity, flexibility, psychological well-being, strength, healthy life, burden, social network and satisfaction with social support. No consensus was found about the definition of resilience associated with dementia. We classified the determinant variables into biological, psychological and social models. Higher levels of resilience were associated with lower depression rates and greater physical health. Other biological factors associated with higher levels of resilience were older age, African-American ethnicity and female sex. Lower burden, stress, neuroticism and perceived control were the main psychological factors associated with resilience. Social support was a moderating factor of resilience, and different types of support seemed to relieve the physical and mental overload caused by stress.

  5. 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.

  6. Things fall apart: biological species form unconnected parsimony networks.

    Science.gov (United States)

    Hart, Michael W; Sunday, Jennifer

    2007-10-22

    The generality of operational species definitions is limited by problematic definitions of between-species divergence. A recent phylogenetic species concept based on a simple objective measure of statistically significant genetic differentiation uses between-species application of statistical parsimony networks that are typically used for population genetic analysis within species. Here we review recent phylogeographic studies and reanalyse several mtDNA barcoding studies using this method. We found that (i) alignments of DNA sequences typically fall apart into a separate subnetwork for each Linnean species (but with a higher rate of true positives for mtDNA data) and (ii) DNA sequences from single species typically stick together in a single haplotype network. Departures from these patterns are usually consistent with hybridization or cryptic species diversity.

  7. Laser tracker error determination using a network measurement

    International Nuclear Information System (INIS)

    Hughes, Ben; Forbes, Alistair; Lewis, Andrew; Sun, Wenjuan; Veal, Dan; Nasr, Karim

    2011-01-01

    We report on a fast, easily implemented method to determine all the geometrical alignment errors of a laser tracker, to high precision. The technique requires no specialist equipment and can be performed in less than an hour. The technique is based on the determination of parameters of a geometric model of the laser tracker, using measurements of a set of fixed target locations, from multiple locations of the tracker. After fitting of the model parameters to the observed data, the model can be used to perform error correction of the raw laser tracker data or to derive correction parameters in the format of the tracker manufacturer's internal error map. In addition to determination of the model parameters, the method also determines the uncertainties and correlations associated with the parameters. We have tested the technique on a commercial laser tracker in the following way. We disabled the tracker's internal error compensation, and used a five-position, fifteen-target network to estimate all the geometric errors of the instrument. Using the error map generated from this network test, the tracker was able to pass a full performance validation test, conducted according to a recognized specification standard (ASME B89.4.19-2006). We conclude that the error correction determined from the network test is as effective as the manufacturer's own error correction methodologies

  8. Probabilistic Inference of Biological Networks via Data Integration

    Directory of Open Access Journals (Sweden)

    Mark F. Rogers

    2015-01-01

    Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

  9. System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

    Science.gov (United States)

    Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

    2018-01-01

    Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

  10. 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.

  11. Spatial-Frequency Azimuthally Stable Cartography of Biological Polycrystalline Networks

    Directory of Open Access Journals (Sweden)

    V. A. Ushenko

    2013-01-01

    Full Text Available A new azimuthally stable polarimetric technique processing microscopic images of optically anisotropic structures of biological tissues histological sections is proposed. It has been used as a generalized model of phase anisotropy definition of biological tissues by using superposition of Mueller matrices of linear birefringence and optical activity. The matrix element M44 has been chosen as the main information parameter, whose value is independent of the rotation angle of both sample and probing beam polarization plane. For the first time, the technique of concerted spatial-frequency filtration has been used in order to separate the manifestation of linear birefringence and optical activity. Thereupon, the method of azimuthally stable spatial-frequency cartography of biological tissues histological sections has been elaborated. As the analyzing tool, complex statistic, correlation, and fractal analysis of coordinate distributions of M44 element has been performed. The possibility of using the biopsy of the uterine wall tissue in order to differentiate benign (fibromyoma and malignant (adenocarcinoma conditions has been estimated.

  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. 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

  14. Determining the confidence levels of sensor outputs using neural networks

    International Nuclear Information System (INIS)

    Broten, G.S.; Wood, H.C.

    1995-01-01

    This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

  15. 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

  16. Determination of biologically active phenols and polyphenols in various objects by chromatographic techniques

    International Nuclear Information System (INIS)

    Kochetova, M V; Semenistaya, E N; Larionov, Oleg G; Revina, A A

    2007-01-01

    Chromatographic techniques for determination of biologically active phenols and polyphenols are considered. Various methods for sample preparation and detection are compared. The advantages of high performance liquid chromatography with spectrophotometric detection for determination of antioxidants are demonstrated. Data on determination of biologically active phenols and polyphenols published in the period from 1995 to 2005 are analysed.

  17. A routine chromium determination in biological materials; application to various reference materials and standard reference materials

    International Nuclear Information System (INIS)

    Tjioe, P.S.; Goeij, J.J.M. de; Volkers, K.J.

    1979-01-01

    The determination limit under standard working conditions of chromium in biological materials is discussed. Neutron activation analysis and atomic spectrometry have been described for some analytical experiences with NBS SRM 1577 reference material. The chromium determination is a part of a larger multi-element scheme for the determination of 12 elements in biological materials

  18. Topology determines force distributions in one-dimensional random spring networks

    Science.gov (United States)

    Heidemann, Knut M.; Sageman-Furnas, Andrew O.; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F.; Wardetzky, Max

    2018-02-01

    Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N ,z ) . Despite the universal properties of such (N ,z ) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.

  19. Robust Learning of High-dimensional Biological Networks with Bayesian Networks

    Science.gov (United States)

    Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

    Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

  20. 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.

  1. 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.

  2. Physical and biological factors determining the effective proton range

    International Nuclear Information System (INIS)

    Grün, Rebecca; Friedrich, Thomas; Krämer, Michael; Scholz, Michael; Zink, Klemens; Durante, Marco; Engenhart-Cabillic, Rita

    2013-01-01

    Purpose: Proton radiotherapy is rapidly becoming a standard treatment option for cancer. However, even though experimental data show an increase of the relative biological effectiveness (RBE) with depth, particularly at the distal end of the treatment field, a generic RBE of 1.1 is currently used in proton radiotherapy. This discrepancy might affect the effective penetration depth of the proton beam and thus the dose to the surrounding tissue and organs at risk. The purpose of this study was thus to analyze the impact of a tissue and dose dependent RBE of protons on the effective range of the proton beam in comparison to the range based on a generic RBE of 1.1.Methods: Factors influencing the biologically effective proton range were systematically analyzed by means of treatment planning studies using the Local Effect Model (LEM IV) and the treatment planning software TRiP98. Special emphasis was put on the comparison of passive and active range modulation techniques.Results: Beam energy, tissue type, and dose level significantly affected the biological extension of the treatment field at the distal edge. Up to 4 mm increased penetration depth as compared to the depth based on a constant RBE of 1.1. The extension of the biologically effective range strongly depends on the initial proton energy used for the most distal layer of the field and correlates with the width of the distal penumbra. Thus, the range extension, in general, was more pronounced for passive as compared to active range modulation systems, whereas the maximum RBE was higher for active systems.Conclusions: The analysis showed that the physical characteristics of the proton beam in terms of the width of the distal penumbra have a great impact on the RBE gradient and thus also the biologically effective penetration depth of the beam

  3. Causal inference in biology networks with integrated belief propagation.

    Science.gov (United States)

    Chang, Rui; Karr, Jonathan R; Schadt, Eric E

    2015-01-01

    Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

  4. 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

  5. Neural network determination of parton distributions: the nonsinglet case

    International Nuclear Information System (INIS)

    Del Debbio, Luigi; Forte, Stefano; Latorre, Jose I.; Piccione, Andrea; Rojo, Joan

    2007-01-01

    We provide a determination of the isotriplet quark distribution from available deep-inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next-to-next-to-leading order, and compare them with those obtained using other approaches

  6. 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...

  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. 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.

  9. 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.

  10. Identification of Bacteria and Determination of Biological Indicators

    Science.gov (United States)

    Venkateswaran, Kasthuri; La Duc, Myron T.; Vaishampayan, Parag A.

    2009-01-01

    The ultimate goal of planetary protection research is to develop superior strategies for inactivating resistance bearing micro-organisms like Rummeli - bacillus stabekisii. By first identifying the particular physiologic pathway and/or structural component of the cell/spore that affords it such elevated tolerance, eradication regimes can then be designed to target these resistance-conferring moieties without jeopardizing the structural integrity of spacecraft hardware. Furthermore, hospitals and government agencies frequently use biological indicators to ensure the efficacy of a wide range of sterilization processes. The spores of Rummelibacillus stabekisii, which are far more resistant to many of such perturbations, could likely serve as a more significant biological indicator for potential survival than those being used currently.

  11. 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.

  12. A biologically inspired neural network controller for ballistic arm movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

    Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

  13. Spectrophotometric determination of vanadium in environmental and biological samples

    International Nuclear Information System (INIS)

    Rekha, D.; Krishnapriya, B.; Subrahmanyam, P.; Reddyprasad, P.; Dilip Kumar, J.; Chiranjeevi, P.

    2007-01-01

    The method is based on oxidation of p-nitro aniline by vanadium (V) followed by coupling reaction with N-(1-naphthalene-1-y1)ethane-1, 2-diaminedihydrochloride (NEDA) in basic medium of pH 8 to give purple colored derivative. The derivative having an λ max 525nm is stable for 10 days. Beer's law is obeyed for vanadium (V) in the concentration range of 0.03-4.5 μg ml -1 . The proposed method was successfully applied to the analysis of vanadium in environmental and biological samples. (author)

  14. RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS1

    Science.gov (United States)

    Silva, Ricardo; Heller, Katherine; Ghahramani, Zoubin; Airoldi, Edoardo M.

    2013-01-01

    Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided. PMID:24587838

  15. [Determination of glutamic acid in biological material by capillary electrophoresis].

    Science.gov (United States)

    Narezhnaya, E; Krukier, I; Avrutskaya, V; Degtyareva, A; Igumnova, E A

    2015-01-01

    The conditions for the identification and determination of Glutamic acid by capillary zone electrophoresis without their preliminary derivatization have been optimized. The effect of concentration of buffer electrolyte and pH on determination of Glutamic acid has been investigated. It is shown that the 5 Mm borate buffer concentration and a pH 9.15 are optimal. Quantitative determination of glutamic acid has been carried out using a linear dependence between the concentration of the analyte and the area of the peak. The accuracy and reproducibility of the determination are confirmed by the method "introduced - found". Glutamic acid has been determined in the placenta homogenate. The duration of analysis doesn't exceed 30 minutes. The results showed a decrease in the level of glutamic acid in cases of pregnancy complicated by placental insufficiency compared with the physiological, and this fact allows to consider the level of glutamic acid as a possible marker of complicated pregnancy.

  16. 78 FR 55326 - Determinations Regarding Use of Chemical Weapons in Syria Under the Chemical and Biological...

    Science.gov (United States)

    2013-09-10

    ... DEPARTMENT OF STATE [Public Notice 8460] Determinations Regarding Use of Chemical Weapons in Syria Under the Chemical and Biological Weapons Control and Warfare Elimination Act of 1991 AGENCY: Bureau of... Government has determined on August 2, pursuant to Section 306(a) of the Chemical and Biological Weapons...

  17. 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.

  18. 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.

  19. 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.

  20. Determination of 18O concentrations in microsamples of biological fluids

    International Nuclear Information System (INIS)

    Cohen, D.D.; Bradshaw, S.D.; Bradshaw, F.J.

    1990-01-01

    The 18 O(p, α) 15 N reaction has been developed for the analysis of microsamples of biological fluids containing 18 O-enriched water. Samples as small as 50 μl have been used. Well characterized Ta 2 O 5 targets were prepared from these fluids and irradiated with several hundred nA of protons from the 3 MV Van de Graaff at Lucas Heights. The broad (47 keV) 846 kV resonance in this reaction was used to measure 18 O concentrations down to natural backgrounds (0.2 at.%) in a few minutes of accelerator running time. Concentrations of 18 O from 0 to 5 at.% were measured in body fluids in small Australian lizards directly after doping with 18 O-enriched water and then again after several days of roaming around in their natural desert environments. The changes in these levels were related to the metabolic rates of these animals. (orig.)

  1. Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

    Science.gov (United States)

    He, Fei; Murabito, Ettore; Westerhoff, Hans V

    2016-04-01

    Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  7. Thorium determination in water and biological materials by fission track

    International Nuclear Information System (INIS)

    Melo Ferreira, A.C. de.

    1989-01-01

    As a segment of a research programme on the study of bioaccumulation of radionuclides, in animals and vegetables from Morro do Ferro, Pocos de Caldas, MG, a fission track method for the determination of low levels of thorium in environmental samples was developed as an alternative for alpha spectroscopy. The study was carried out in early alpha spectroscopy samples, containing high levels of 228 Th activity, which makes difficult the 232 Th determination. A dry way method for thorium evaluation was developed. Pieces of membrane filters, containing La F 3 (Th), coupled to Makrofol detectors, were irradiated in the core of a research reactor, IEA-R1 (IPEN). (author)

  8. Latinamerican Biological Dosimetry Network (LBDNET). International Biological Dosimetry intercomparison Program (exercise 2007-2008)

    International Nuclear Information System (INIS)

    Di Giorgio, Marina; Vallerga, Maria B.; Radl, A.; Taja, Maria R.

    2009-01-01

    This paper describes the International Biological Dosimetry Intercomparison Program (exercise 2007-2008) - developed within the framework of the IAEA regional project - RLA/9/054 (Establishment of national capabilities for response to radiological and nuclear emergency) whose general objectives are: assess reproducibility inter-laboratory; identify problems and provide the necessary modifications for collaborative work in accidental situations requiring activation of mutual assistance mechanisms which will form the basis of the Organization of LBDNET. This exercise involves the laboratories of the region: Argentina (laboratory support), Brazil, Chile, Cuba, Mexico, Peru and Uruguay and the laboratory of the Autonomous University of Barcelona-Espana (Joan Francesc Barquinero and staff). Finally, these countries will meet the next time for the drafting of a final report and later publication. (author)

  9. Determination of the Corona model parameters with artificial neural networks

    International Nuclear Information System (INIS)

    Ahmet, Nayir; Bekir, Karlik; Arif, Hashimov

    2005-01-01

    Full text : The aim of this study is to calculate new model parameters taking into account the corona of electrical transmission line wires. For this purpose, a neural network modeling proposed for the corona frequent characteristics modeling. Then this model was compared with the other model developed at the Polytechnic Institute of Saint Petersburg. The results of development of the specified corona model for calculation of its influence on the wave processes in multi-wires line and determination of its parameters are submitted. Results of obtained calculation equations are brought for electrical transmission line with allowance for superficial effect in the ground and wires with reference to developed corona model

  10. Amines as extracting agents for the quantitative determinations of actinides in biological samples

    International Nuclear Information System (INIS)

    Singh, N.P.

    1987-01-01

    The use of amines (primary, secondary and tertiary chains and quaternary ammonium salts) as extracting agents for the quantitative determination of actinides in biological samples is reviewed. Among the primary amines, only Primene JM-T is used to determine Pu in urine and bone. No one has investigated the possibility of using secondary amines to quantitatively extract actinides from biological samples. Among the tertiary amines, tri-n-octylamine, tri-iso-octylamine, tyricaprylamine (Alamine) and trilaurylamine (tridodecylamine) are used extensively to extract and separate the actinides from biological samples. Only one quaternary ammonium salt, methyltricapryl ammonium chloride (Aliquat-336), is used to extract Pu from biological samples. (author) 28 refs

  11. 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.

  12. Nitrate biosensors and biological methods for nitrate determination.

    Science.gov (United States)

    Sohail, Manzar; Adeloju, Samuel B

    2016-06-01

    The inorganic nitrate (NO3‾) anion is present under a variety of both natural and artificial environmental conditions. Nitrate is ubiquitous within the environment, food, industrial and physiological systems and is mostly present as hydrated anion of a corresponding dissolved salt. Due to the significant environmental and toxicological effects of nitrate, its determination and monitoring in environmental and industrial waters are often necessary. A wide range of analytical techniques are available for nitrate determination in various sample matrices. This review discusses biosensors available for nitrate determination using the enzyme nitrate reductase (NaR). We conclude that nitrate determination using biosensors is an excellent non-toxic alternative to all other available analytical methods. Over the last fifteen years biosensing technology for nitrate analysis has progressed very well, however, there is a need to expedite the development of nitrate biosensors as a suitable alternative to non-enzymatic techniques through the use of different polymers, nanostructures, mediators and strategies to overcome oxygen interference. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Nonoxidized, biologically active parathyroid hormone determines mortality in hemodialysis patients

    DEFF Research Database (Denmark)

    Tepel, Martin; Armbruster, Franz Paul; Grön, Hans Jürgen

    2013-01-01

    Background: It was shown that nonoxidized PTH (n-oxPTH) is bioactive, whereas the oxidation of PTH results in a loss of biological activity. Methods: In this study we analyzed the association of n-oxPTH on mortality in hemodialysis patients using a recently developed assay system. Results......: Hemodialysis patients (224 men, 116 women) had a median age of 66 years. One hundred seventy patients (50%) died during the follow-up period of 5 years. Median n-oxPTH levels were higher in survivors (7.2 ng/L) compared with deceased patients (5.0 ng/L; P = .002). Survival analysis showed an increased survival...... in the highest n-oxPTH tertile compared with the lowest n-oxPTH tertile (χ(2), 14.3; P = .0008). Median survival was 1702 days in the highest n-oxPTH tertile, whereas it was only 453 days in the lowest n-oxPTH tertile. Multivariable-adjusted Cox regression showed that higher age increased odds for death, whereas...

  14. 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.

  15. 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

  16. MODA: an efficient algorithm for network motif discovery in biological networks.

    Science.gov (United States)

    Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali

    2009-10-01

    In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/

  17. RNAA determination of As, Cd and Zn in biological materials

    International Nuclear Information System (INIS)

    Taskaev, E.; Penev, I.; Kinova, L.

    1985-01-01

    In connection with the IAEA project 3739/RB the elements Hg, As, Cd and Zn in human spleen, kidney, heart, liver, SRM Bovine Liver 1577a and Bowen's Kale. The consecutive extraction have been chosen as the most rational approach determining these elements in a single specimen. Aiming at getting reliable results well established systems were used (ditizone to separate Hg, Cu and DEDTC to extract As, Cd and Zn). Special attention was paid to the accuracy of the determination. Monitoring, optimization and cooling time and control of chemical yield were carried out in each case. The samples were irradiated in the vertical channel of the IRT-2000 reactor in Sofia, in thermal neutron flux of about 5.10 12 n.cm -2 .s -1 for 24 h. Iron monitor was used and cooling time varied from 20 h to 30 h

  18. Quantification of the impact of PSI:Biology according to the annotations of the determined structures.

    Science.gov (United States)

    DePietro, Paul J; Julfayev, Elchin S; McLaughlin, William A

    2013-10-21

    Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure. One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure. We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i

  19. 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

  20. 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.

  1. Determination of palladium in biological samples applying nuclear analytical techniques

    International Nuclear Information System (INIS)

    Cavalcante, Cassio Q.; Sato, Ivone M.; Salvador, Vera L. R.; Saiki, Mitiko

    2008-01-01

    This study presents Pd determinations in bovine tissue samples containing palladium prepared in the laboratory, and CCQM-P63 automotive catalyst materials of the Proficiency Test, using instrumental thermal and epithermal neutron activation analysis and energy dispersive X-ray fluorescence techniques. Solvent extraction and solid phase extraction procedures were also applied to separate Pd from interfering elements before the irradiation in the nuclear reactor. The results obtained by different techniques were compared against each other to examine sensitivity, precision and accuracy. (author)

  2. 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

  3. Determination of methylmercury salts in various kinds of biological material

    Energy Technology Data Exchange (ETDEWEB)

    Westoeoe, G

    1968-01-01

    The cysteine acetate modification of the method for determining methylmercury salts in foods, which was useful for analysis of fish, egg white, and meat, was not efficient when applied to egg yolk with low methylmercury content, liver, sediments in aquaria, or sludge. Therefore some modifications of the procedure have been investigated. A combination of the mercuric chloride and cysteine acetate procedures gave good results for sediments in aquaria and sludge and could also be used for, e.g., fish, egg white, bile, kidney, blood, meat, and moss. Precipitation of the proteins with molybdic acid at the first extraction improved the results for liver but not for egg yolk. For egg yolk an increase of the concentration of the cysteine acetate solution from 1 to 10% gave 90% recovery of added methylmercury, repeated extractions 100% recovery. 5 references, 2 tables.

  4. Determination of uranium in biological objects using adsorption

    International Nuclear Information System (INIS)

    Zakharova, Eh.A.; Filichkina, O.G.; Pikula, N.P.; Slepchenko, G.B.

    2002-01-01

    A method of U 6+ (0.0001-0.005 mg/liter) determination in urine using adsorption cathode voltammetry is presented. The signal was measured using supporting 0.01 M KCl + 5 x 10 -5 M oxyquinoline + 1x10 -4 M trilon B at pH 6.8 - 7.0. A stationary mercury film electrode on a silver substrate was restituted mechanically and electrochemically. The interfering matrix effect was eliminated using wet mineralization and calcination of the samples (up to 100 ml) and subsequent isolation of oxyquinolinate complex of uranyl with chloroform. The total error, within which the error falls with the probability p=0.95, is ±33%. A systematic error attributed to partial isolation is eliminated via a correction coefficient equal to 1.3 [ru

  5. 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...

  6. Determination of fluorine in biological materials: reaction paper.

    Science.gov (United States)

    Ophaug, R

    1994-06-01

    Although the fluorine in human tissues may exist in both inorganic and organic (covalently bound) forms, the inorganic fraction is clearly the most relevant for assessing human exposure to, and utilization of, environmental fluoride. There is now general agreement that the inorganic fraction of total tissue fluorine can be accurately determined by a variety of analytical techniques. One of the basic questions considered at this workshop is whether the analysis of a specific tissue or body fluid can provide an estimate of how much of the fluoride to which an individual is exposed actually enters and accumulates in the body. The analysis of hair and nails has been used as an indicator of exposure and utilization for several trace elements, including fluoride. Due to methodological uncertainties regarding sampling and pre-analysis treatment, however, it is presently not possible clearly to distinguish fluoride which is incorporated into hair and nails during formation (endogenous) from that which becomes associated with the tissues following exposure to the environment (exogenous). Consequently, although the fluoride content of hair and nails is clearly increased by environmental exposure to fluoride, the conclusion that these tissues are suitable indicators of fluoride utilization and accumulation in the body is premature.

  7. 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.

  8. Specific determination of clinical and toxicological important substances in biological samples by LC-MS

    International Nuclear Information System (INIS)

    Mitulovic, G.

    2001-02-01

    This thesis of this dissertation is the specific determination of clinical and toxicological important substances in biological samples by LC-MS. Nicotine was determined in serum after application of nicotine plaster and nicotine nasal spray with HPLC-ESI-MS. Cotinine was determined direct in urine with HPLC-ESI-MS. Short time anesthetics were determined in blood and cytostatics were determined in liquor with HPLC-ESI-MS. (botek)

  9. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

    OpenAIRE

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of tra...

  10. 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).

  11. 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.

  12. 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

  13. Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

    Science.gov (United States)

    Scott, RE; Ghule, PN; Stein, JL; Stein, GS

    2015-01-01

    The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with p = e−13 to e−36. Cell cycle expression networks show species, sex and tissue variability and they are enriched in mRNA transcripts associated with mitosis many of which are associated with chromosomal instability. PMID:25808367

  14. 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.

  15. 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.

  16. Iterative Systems Biology for Medicine – time for advancing from network signature to mechanistic equations

    KAUST Repository

    Gomez-Cabrero, David

    2017-05-09

    The rise and growth of Systems Biology following the sequencing of the human genome has been astounding. Early on, an iterative wet-dry methodology was formulated which turned out as a successful approach in deciphering biological complexity. Such type of analysis effectively identified and associated molecular network signatures operative in biological processes across different systems. Yet, it has proven difficult to distinguish between causes and consequences, thus making it challenging to attack medical questions where we require precise causative drug targets and disease mechanisms beyond a web of associated markers. Here we review principal advances with regard to identification of structure, dynamics, control, and design of biological systems, following the structure in the visionary review from 2002 by Dr. Kitano. Yet, here we find that the underlying challenge of finding the governing mechanistic system equations enabling precision medicine remains open thus rendering clinical translation of systems biology arduous. However, stunning advances in raw computational power, generation of high-precision multi-faceted biological data, combined with powerful algorithms hold promise to set the stage for data-driven identification of equations implicating a fundamental understanding of living systems during health and disease.

  17. Biologics or tofacitinib for people with rheumatoid arthritis unsuccessfully treated with biologics: a systematic review and network meta-analysis.

    Science.gov (United States)

    Singh, Jasvinder A; Hossain, Alomgir; Tanjong Ghogomu, Elizabeth; Mudano, Amy S; Maxwell, Lara J; Buchbinder, Rachelle; Lopez-Olivo, Maria Angeles; Suarez-Almazor, Maria E; Tugwell, Peter; Wells, George A

    2017-03-10

    Biologic disease-modifying anti-rheumatic drugs (DMARDs: referred to as biologics) are effective in treating rheumatoid arthritis (RA), however there are few head-to-head comparison studies. Our systematic review, standard meta-analysis and network meta-analysis (NMA) updates the 2009 Cochrane overview, 'Biologics for rheumatoid arthritis (RA)' and adds new data. This review is focused on biologic or tofacitinib therapy in people with RA who had previously been treated unsuccessfully with biologics. To compare the benefits and harms of biologics (abatacept, adalimumab, anakinra, certolizumab pegol, etanercept, golimumab, infliximab, rituximab, tocilizumab) and small molecule tofacitinib versus comparator (placebo or methotrexate (MTX)/other DMARDs) in people with RA, previously unsuccessfully treated with biologics. On 22 June 2015 we searched for randomized controlled trials (RCTs) in CENTRAL, MEDLINE, and Embase; and trials registries (WHO trials register, Clinicaltrials.gov). We carried out article selection, data extraction, and risk of bias and GRADE assessments in duplicate. We calculated direct estimates with 95% confidence intervals (CI) using standard meta-analysis. We used a Bayesian mixed treatment comparison (MTC) approach for NMA estimates with 95% credible intervals (CrI). We converted odds ratios (OR) to risk ratios (RR) for ease of understanding. We have also presented results in absolute measures as risk difference (RD) and number needed to treat for an additional beneficial outcome (NNTB). Outcomes measured included four benefits (ACR50, function measured by Health Assessment Questionnaire (HAQ) score, remission defined as DAS tofacitinib (399 participants). The majority of the trials (10/12) lasted less than 12 months.We judged 33% of the studies at low risk of bias for allocation sequence generation, allocation concealment and blinding, 25% had low risk of bias for attrition, 92% were at unclear risk for selective reporting; and 92% had low risk

  18. 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.

  19. 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.

  20. Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

    Directory of Open Access Journals (Sweden)

    Gao Shouguo

    2011-08-01

    Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

  1. 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.

  2. 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.

  3. Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity.

    Directory of Open Access Journals (Sweden)

    J R Managbanag

    Full Text Available BACKGROUND: Identification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking. METHODOLOGY/PRINCIPAL FINDINGS: We have utilized a shortest-path network analysis to identify novel genes that modulate longevity in Saccharomyces cerevisiae. Based on a set of previously reported genes associated with increased life span, we applied a shortest-path network algorithm to a pre-existing protein-protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single-gene deletion strains corresponding to predicted components of the shortest-path longevity network was determined. Here we report that the single-gene deletion strains identified by our shortest-path longevity analysis are significantly enriched for mutations conferring either increased or decreased replicative life span, relative to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. Further, we report the identification of previously unknown longevity genes, several of which function in a conserved longevity pathway believed to mediate life span extension in response to dietary restriction. CONCLUSIONS/SIGNIFICANCE: This work demonstrates that shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity and represents the first application of

  4. Using chemistry and microfluidics to understand the spatial dynamics of complex biological networks.

    Science.gov (United States)

    Kastrup, Christian J; Runyon, Matthew K; Lucchetta, Elena M; Price, Jessica M; Ismagilov, Rustem F

    2008-04-01

    Understanding the spatial dynamics of biochemical networks is both fundamentally important for understanding life at the systems level and also has practical implications for medicine, engineering, biology, and chemistry. Studies at the level of individual reactions provide essential information about the function, interactions, and localization of individual molecular species and reactions in a network. However, analyzing the spatial dynamics of complex biochemical networks at this level is difficult. Biochemical networks are nonequilibrium systems containing dozens to hundreds of reactions with nonlinear and time-dependent interactions, and these interactions are influenced by diffusion, flow, and the relative values of state-dependent kinetic parameters. To achieve an overall understanding of the spatial dynamics of a network and the global mechanisms that drive its function, networks must be analyzed as a whole, where all of the components and influential parameters of a network are simultaneously considered. Here, we describe chemical concepts and microfluidic tools developed for network-level investigations of the spatial dynamics of these networks. Modular approaches can be used to simplify these networks by separating them into modules, and simple experimental or computational models can be created by replacing each module with a single reaction. Microfluidics can be used to implement these models as well as to analyze and perturb the complex network itself with spatial control on the micrometer scale. We also describe the application of these network-level approaches to elucidate the mechanisms governing the spatial dynamics of two networkshemostasis (blood clotting) and early patterning of the Drosophila embryo. To investigate the dynamics of the complex network of hemostasis, we simplified the network by using a modular mechanism and created a chemical model based on this mechanism by using microfluidics. Then, we used the mechanism and the model to

  5. 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.

  6. 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

  7. Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

    Science.gov (United States)

    Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

    2016-02-01

    The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

  8. Quantitative Evaluation of Biologic Therapy Options for Psoriasis: A Systematic Review and Network Meta-Analysis.

    Science.gov (United States)

    Jabbar-Lopez, Zarif K; Yiu, Zenas Z N; Ward, Victoria; Exton, Lesley S; Mohd Mustapa, M Firouz; Samarasekera, Eleanor; Burden, A David; Murphy, Ruth; Owen, Caroline M; Parslew, Richard; Venning, Vanessa; Warren, Richard B; Smith, Catherine H

    2017-08-01

    Multiple biologic treatments are licensed for psoriasis. The lack of head-to-head randomized controlled trials makes choosing between them difficult for patients, clinicians, and guideline developers. To establish their relative efficacy and tolerability, we searched MEDLINE, PubMed, Embase, and Cochrane for randomized controlled trials of licensed biologic treatments for skin psoriasis. We performed a network meta-analysis to identify direct and indirect evidence comparing biologics with one another, methotrexate, or placebo. We combined this with hierarchical cluster analysis to consider multiple outcomes related to efficacy and tolerability in combination for each treatment. Study quality, heterogeneity, and inconsistency were evaluated. Direct comparisons from 41 randomized controlled trials (20,561 participants) were included. All included biologics were efficacious compared with placebo or methotrexate at 3-4 months. Overall, cluster analysis showed adalimumab, secukinumab, and ustekinumab were comparable in terms of high efficacy and tolerability. Ixekizumab and infliximab were differentiated by very high efficacy but poorer tolerability. The lack of longer term controlled data limited our analysis to short-term outcomes. Trial performance may not equate to real-world performance, and so results need to be considered alongside real-world, long-term safety and effectiveness data. These data suggest that it is possible to discriminate between biologics to inform clinical practice and decision making (PROSPERO 2015:CRD42015017538). Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Plasticity of gene-regulatory networks controlling sex determination: of masters, slaves, usual suspects, newcomers, and usurpators.

    Science.gov (United States)

    Herpin, Amaury; Schartl, Manfred

    2015-10-01

    Sexual dimorphism is one of the most pervasive and diverse features of animal morphology, physiology, and behavior. Despite the generality of the phenomenon itself, the mechanisms controlling how sex is determined differ considerably among various organismic groups, have evolved repeatedly and independently, and the underlying molecular pathways can change quickly during evolution. Even within closely related groups of organisms for which the development of gonads on the morphological, histological, and cell biological level is undistinguishable, the molecular control and the regulation of the factors involved in sex determination and gonad differentiation can be substantially different. The biological meaning of the high molecular plasticity of an otherwise common developmental program is unknown. While comparative studies suggest that the downstream effectors of sex-determining pathways tend to be more stable than the triggering mechanisms at the top, it is still unclear how conserved the downstream networks are and how all components work together. After many years of stasis, when the molecular basis of sex determination was amenable only in the few classical model organisms (fly, worm, mouse), recently, sex-determining genes from several animal species have been identified and new studies have elucidated some novel regulatory interactions and biological functions of the downstream network, particularly in vertebrates. These data have considerably changed our classical perception of a simple linear developmental cascade that makes the decision for the embryo to develop as male or female, and how it evolves. © 2015 The Authors.

  10. An independent accurate reference method for the determination of chromium in biological materials

    NARCIS (Netherlands)

    Lagerwaard, A.; Woittiez, J.R.W.; de Goeij, J.J.M.

    1994-01-01

    A method for the determination of Cr in biological materials with high accuracy is reported for use as an independent reference method. It is based on radiochemical neutron activation analysis (RNAA) in combination with an individual yield determination based on the online yield principle. A

  11. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

    Directory of Open Access Journals (Sweden)

    István A Kovács

    Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

  12. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Directory of Open Access Journals (Sweden)

    Samra Khalid

    2016-10-01

    Full Text Available Background Breast cancer (BC is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s. It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α associated Biological Regulatory Network (BRN for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR for wet lab experiments as well as provided valuable insights in the treatment of cancers

  13. Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

    Science.gov (United States)

    Riccardo, Flavia; Shigematsu, Mika; Chow, Catherine; McKnight, C Jason; Linge, Jens; Doherty, Brian; Dente, Maria Grazia; Declich, Silvia; Barker, Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Blench, Michael; Arthur, Ray

    2014-01-01

    The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. On Determining if Tree-based Networks Contain Fixed Trees.

    Science.gov (United States)

    Anaya, Maria; Anipchenko-Ulaj, Olga; Ashfaq, Aisha; Chiu, Joyce; Kaiser, Mahedi; Ohsawa, Max Shoji; Owen, Megan; Pavlechko, Ella; St John, Katherine; Suleria, Shivam; Thompson, Keith; Yap, Corrine

    2016-05-01

    We address an open question of Francis and Steel about phylogenetic networks and trees. They give a polynomial time algorithm to decide if a phylogenetic network, N, is tree-based and pose the problem: given a fixed tree T and network N, is N based on T? We show that it is [Formula: see text]-hard to decide, by reduction from 3-Dimensional Matching (3DM) and further that the problem is fixed-parameter tractable.

  20. Topologically determined optimal stochastic resonance responses of spatially embedded networks

    International Nuclear Information System (INIS)

    Gosak, Marko; Marhl, Marko; Korosak, Dean

    2011-01-01

    We have analyzed the stochastic resonance phenomenon on spatial networks of bistable and excitable oscillators, which are connected according to their location and the amplitude of external forcing. By smoothly altering the network topology from a scale-free (SF) network with dominating long-range connections to a network where principally only adjacent oscillators are connected, we reveal that besides an optimal noise intensity, there is also a most favorable interaction topology at which the best correlation between the response of the network and the imposed weak external forcing is achieved. For various distributions of the amplitudes of external forcing, the optimal topology is always found in the intermediate regime between the highly heterogeneous SF network and the strong geometric regime. Our findings thus indicate that a suitable number of hubs and with that an optimal ratio between short- and long-range connections is necessary in order to obtain the best global response of a spatial network. Furthermore, we link the existence of the optimal interaction topology to a critical point indicating the transition from a long-range interactions-dominated network to a more lattice-like network structure.

  1. Determining Cloud Thermodynamic Phase from Micropulse Lidar Network Data

    Science.gov (United States)

    Lewis, Jasper R.; Campbell, James; Lolli, Simone; Tan, Ivy; Welton, Ellsworth J.

    2017-01-01

    Determining cloud thermodynamic phase is a critical factor in studies of Earth's radiation budget. Here we use observations from the NASA Micro Pulse Lidar Network (MPLNET) and thermodynamic profiles from the Goddard Earth Observing System, version 5 (GEOS-5) to distinguish liquid water, mixed-phase, and ice water clouds. The MPLNET provides sparse global, autonomous, and continuous measurements of clouds and aerosols which have been used in a number of scientific investigations to date. The use of a standardized instrument and a common suite of data processing algorithms with thorough uncertainty characterization allows for straightforward comparisons between sites. Lidars with polarization capabilities have recently been incorporated into the MPLNET project which allows, for the first time, the ability to infer a cloud thermodynamic phase. This presentation will look specifically at the occurrence of ice and mixed phase clouds in the temperature region of -10 C to -40 C for different climatological regions and seasons. We compare MPLNET occurrences of mixed-phase clouds to an historical climatology based on observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft.

  2. International institute for collaborative cell biology and biochemistry--history and memoirs from an international network for biological sciences.

    Science.gov (United States)

    Cameron, L C

    2013-01-01

    I was invited to write this essay on the occasion of my selection as the recipient of the 2012 Bruce Alberts Award for Excellence in Science Education from the American Society for Cell Biology (ASCB). Receiving this award is an enormous honor. When I read the email announcement for the first time, it was more than a surprise to me, it was unbelievable. I joined ASCB in 1996, when I presented a poster and received a travel award. Since then, I have attended almost every ASCB meeting. I will try to use this essay to share with readers one of the best experiences in my life. Because this is an essay, I take the liberty of mixing some of my thoughts with data in a way that it not usual in scientific writing. I hope that this sacrifice of the format will achieve the goal of conveying what I have learned over the past 20 yr, during which time a group of colleagues and friends created a nexus of knowledge and wisdom. We have worked together to build a network capable of sharing and inspiring science all over the world.

  3. International Institute for Collaborative Cell Biology and Biochemistry—History and Memoirs from an International Network for Biological Sciences

    Science.gov (United States)

    Cameron, L. C.

    2013-01-01

    I was invited to write this essay on the occasion of my selection as the recipient of the 2012 Bruce Alberts Award for Excellence in Science Education from the American Society for Cell Biology (ASCB). Receiving this award is an enormous honor. When I read the email announcement for the first time, it was more than a surprise to me, it was unbelievable. I joined ASCB in 1996, when I presented a poster and received a travel award. Since then, I have attended almost every ASCB meeting. I will try to use this essay to share with readers one of the best experiences in my life. Because this is an essay, I take the liberty of mixing some of my thoughts with data in a way that it not usual in scientific writing. I hope that this sacrifice of the format will achieve the goal of conveying what I have learned over the past 20 yr, during which time a group of colleagues and friends created a nexus of knowledge and wisdom. We have worked together to build a network capable of sharing and inspiring science all over the world. PMID:24006381

  4. 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

  5. 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:…

  6. Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

    Science.gov (United States)

    Toni, Tina; Tidor, Bruce

    2013-01-01

    Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady

  7. Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

    Science.gov (United States)

    Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

    2014-12-06

    Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  8. 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.

  9. A survey on social networks to determine requirements for Learning Networks for professional development of university staff

    NARCIS (Netherlands)

    Brouns, Francis; Berlanga, Adriana; Fetter, Sibren; Bitter-Rijpkema, Marlies; Van Bruggen, Jan; Sloep, Peter

    2009-01-01

    Brouns, F., Berlanga, A. J., Fetter, S., Bitter-Rijpkema, M. E., Van Bruggen, J. M., & Sloep, P. B. (2011). A survey on social networks to determine requirements for Learning Networks for professional development of university staff. International Journal of Web Based Communities, 7(3), 298-311.

  10. 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.

  11. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

    Science.gov (United States)

    Ferentinos, Konstantinos P

    2005-09-01

    Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

  12. Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Maria Simak

    Full Text Available The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN. For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN model based on techniques of hidden Markov model (HMM, likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.

  13. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

    Science.gov (United States)

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

  14. The Annotation, Mapping, Expression and Network (AMEN suite of tools for molecular systems biology

    Directory of Open Access Journals (Sweden)

    Primig Michael

    2008-02-01

    Full Text Available Abstract Background High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently. Results We developed the Annotation, Mapping, Expression and Network (AMEN software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i uploading and pre-processing data from microarray expression profiling experiments, (ii detecting groups of significantly co-expressed genes, and (iii searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human. Conclusion AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.

  15. The Latin American Biological Dosimetry Network (LBDNet): Argentina, Brazil, Chile, Cuba, Mexico, Peru, Uruguay

    Energy Technology Data Exchange (ETDEWEB)

    Guerrero C, C.; Arceo M, C. [ININ, Carretera Mexico-Toluca s/n, Ocoyoacac 52750, Estado de Mexico (Mexico); Di Giorgio, M.; Vallerga, M.; Radl, A. [Autoridad Regulatoria Nuclear, Av. del Libertador 8250, C1429 BNP CABA (Argentina); Taja, M.; Seoane, A.; De Luca, J. [Universidad Nacionald de La Plata, Av. 7 No. 1776, La Plata 1900, Buenos Aires (Argentina); Stuck O, M. [Instituto de Radioproteccion y Dosimetria, Av. Salvador Allende s/n, Recreio dos Bandeirantes, Rio de Janeiro (Brazil); Valdivia, P., E-mail: lbdnet@googlegroups.co [Comision Chilena de Energia, Amutanegui 95, Santiago Centro, Santiago (Chile)

    2010-10-15

    Biological dosimetry is a necessary support for national radiation protection programs and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 for mutual assistance in case of radiation emergencies and for providing support to other Latin American countries that do not have bio dosimetry laboratories. In the frame of the IAEA Technical Cooperation Projects RLA/9/54 and RLA/9/61 the following activities have been performed: a) An international intercomparison exercise organized during 2007-2008 included six European countries and LBDNet laboratories. Relevant parameters related with dose assessment were evaluated through triage and conventional scoring criteria. A new approach for statistical data analysis was developed including assessment of inter-laboratory reproducibility and intra-laboratory repeatability. Overall, the laboratory performance was satisfactory for mutual cooperation purposes. b) In 2009, LBDNet and two European countries carried out a digital image intercomparison exercise involving dose assessment from metaphase images distributed electronically through internet. The main objectives were to evaluate scoring feasibility on metaphase images and time response. In addition a re-examination phase was considered in which the most controversial images were discussed jointly, this allowed for the development of a homogeneous scoring criteria within the network. c) A further exercise was performed during 2009 involving the shipment of biological samples for biological dosimetry assessment. The aim of this exercise was to test the timely and properly sending and receiving blood samples under national and international regulations. A total of 14 laboratories participated in this joint IAEA, PAHO and WHO. (Author)

  16. The Latin American Biological Dosimetry Network (LBDNet): Argentina, Brazil, Chile, Cuba, Mexico, Peru, Uruguay

    International Nuclear Information System (INIS)

    Guerrero C, C.; Arceo M, C.; Di Giorgio, M.; Vallerga, M.; Radl, A.; Taja, M.; Seoane, A.; De Luca, J.; Stuck O, M.; Valdivia, P.

    2010-10-01

    Biological dosimetry is a necessary support for national radiation protection programs and emergency response schemes. The Latin American Biological Dosimetry Network (LBDNet) was formally founded in 2007 for mutual assistance in case of radiation emergencies and for providing support to other Latin American countries that do not have bio dosimetry laboratories. In the frame of the IAEA Technical Cooperation Projects RLA/9/54 and RLA/9/61 the following activities have been performed: a) An international intercomparison exercise organized during 2007-2008 included six European countries and LBDNet laboratories. Relevant parameters related with dose assessment were evaluated through triage and conventional scoring criteria. A new approach for statistical data analysis was developed including assessment of inter-laboratory reproducibility and intra-laboratory repeatability. Overall, the laboratory performance was satisfactory for mutual cooperation purposes. b) In 2009, LBDNet and two European countries carried out a digital image intercomparison exercise involving dose assessment from metaphase images distributed electronically through internet. The main objectives were to evaluate scoring feasibility on metaphase images and time response. In addition a re-examination phase was considered in which the most controversial images were discussed jointly, this allowed for the development of a homogeneous scoring criteria within the network. c) A further exercise was performed during 2009 involving the shipment of biological samples for biological dosimetry assessment. The aim of this exercise was to test the timely and properly sending and receiving blood samples under national and international regulations. A total of 14 laboratories participated in this joint IAEA, PAHO and WHO. (Author)

  17. Quantitative HPLC determination of [99mTc]-pertechnetate in radiopharmaceuticals and biological samples: Pt. 1

    International Nuclear Information System (INIS)

    Tianze Zhou; Hirth, W.W.; Heineman, W.R.; Deutsch, Edward

    1988-01-01

    Techniques have been developed which allow HPLC (high performance liquid chromatography) to be used for the quantitative determination of [ 99m Tc]pertechnetate in radiopharmaceuticals and biological samples. An instrumental technique accounts for 99m Tc species which do not elute from the HPLC column, while a chemical technique obviates interferences caused by Sn(II). These two techniques are incorporated into an anion exchange HPLC procedure which is applied to the determination of [ 99m Tc]pertechnetate in 99m Tc-diphosphonate radiopharmaceuticals and biological samples. (author)

  18. Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

    Directory of Open Access Journals (Sweden)

    de los Reyes Benildo G

    2008-04-01

    Full Text Available Abstract Background Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM, facilitates the inference. Identifying NMs defined by specific transcription factors (TF establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO information. Results The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1 Gene set enrichment

  19. Mueller-matrix mapping of biological tissues in differential diagnosis of optical anisotropy mechanisms of protein networks

    Energy Technology Data Exchange (ETDEWEB)

    Ushenko, V A; Sidor, M I [Yuriy Fedkovych Chernivtsi National University, Chernivtsi (Ukraine); Marchuk, Yu F; Pashkovskaya, N V; Andreichuk, D R [Bukovinian State Medical University, Chernivtsi (Ukraine)

    2015-03-31

    We report a model of Mueller-matrix description of optical anisotropy of protein networks in biological tissues with allowance for the linear birefringence and dichroism. The model is used to construct the reconstruction algorithms of coordinate distributions of phase shifts and the linear dichroism coefficient. In the statistical analysis of such distributions, we have found the objective criteria of differentiation between benign and malignant tissues of the female reproductive system. From the standpoint of evidence-based medicine, we have determined the operating characteristics (sensitivity, specificity and accuracy) of the Mueller-matrix reconstruction method of optical anisotropy parameters and demonstrated its effectiveness in the differentiation of benign and malignant tumours. (laser applications and other topics in quantum electronics)

  20. 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.

  1. The use of skewness, kurtosis and neural networks for determining corrosion mechanism from electrochemical noise data

    International Nuclear Information System (INIS)

    Reid, S.; Bell, G.E.C.; Edgemon, G.L.

    1998-01-01

    This paper describes the work undertaken to de-skill the complex procedure of determining corrosion mechanisms derived from electrochemical noise data. The use of neural networks is discussed and applied to the real time generated electrochemical noise data files with the purpose of determining characteristics particular to individual types of corrosion mechanisms. The electrochemical noise signals can have a wide dynamic range and various methods of raw data pre-processing prior to neural network analysis were investigated. Normalized data were ultimately used as input to the final network analysis. Various network schemes were designed, trained and tested. Factors such as the network learning schedule and network design were considered before a final network was implemented to achieve a solution. Neural networks trained using general and localized corrosion data from various material environment systems were used to analyze data from simulated nuclear waste tank environments with favorable results

  2. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

    Science.gov (United States)

    Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

    2017-02-01

    Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

  3. Integration of gene expression and methylation to unravel biological networks in glioblastoma patients.

    Science.gov (United States)

    Gadaleta, Francesco; Bessonov, Kyrylo; Van Steen, Kristel

    2017-02-01

    The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors. © 2016 WILEY PERIODICALS, INC.

  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.

    2012-01-01

    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. PMID:23072820

  5. Interconnection between biological abnormalities in borderline personality disorder: use of the Bayesian networks model.

    Science.gov (United States)

    De la Fuente, José Manuel; Bengoetxea, Endika; Navarro, Felipe; Bobes, Julio; Alarcón, Renato Daniel

    2011-04-30

    There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  6. Landslide Susceptibility Index Determination Using Aritificial Neural Network

    Science.gov (United States)

    Kawabata, D.; Bandibas, J.; Urai, M.

    2004-12-01

    The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic features, rock types and geologic structure are especially important base factors of the landslide occurrence. Generating landslide susceptibility index by defining the relationship between landslide occurrence and that base factors using conventional mathematical and statistical methods is very difficult and inaccurate. This study focuses on generating landslide susceptibility index using artificial neural networks in Southern Japanese Alps. The training data are geomorphic (e.g. altitude, slope and aspect) and geologic parameters (e.g. rock type, distance from geologic boundary and geologic dip-strike angle) and landslides. Artificial neural network structure and training scheme are formulated to generate the index. Data from areas with and without landslide occurrences are used to train the network. The network is trained to output 1 when the input data are from areas with landslides and 0 when no landslide occurred. The trained network generates an output ranging from 0 to 1 reflecting the possibility of landslide occurrence based on the inputted data. Output values nearer to 1 means higher possibility of landslide occurrence. The artificial neural network model is incorporated into the GIS software to generate a landslide susceptibility map.

  7. [Financing Regional Dementia Networks in Germany: Determinants of Sustainable Healthcare Networks].

    Science.gov (United States)

    Michalowsky, B; Wübbeler, M; Thyrian, J R; Holle, B; Gräske, J; Schäfer-Walkmann, S; Fleßa, S; Hoffmann, W

    2017-12-01

    Analysis of practice-based financing concepts in German dementia networks (DN); Provision of sustainable financing structures and their determinants in DN. Qualitative expert interviews with leaders of 13 DN were conducted. A semi-structured interview guide was used to analyse four main topics: Finance-related organization, cost, sources of funding and financial sustainability. DN were primarily financed by membership fees, earnings of services provided, public funds and payments by municipalities or health care providers. 63% of the DN reported a financial sustainability. Funds to support the interpersonal expanding, a mix of internal and external financing sources and investments of the municipality were determinants of a sustainable financing. Overall, DN in rural areas seemed to be disadvantaged due to a lack of potential linkable service providers. DN in urban regions are more likely able to gather sustainable funding resources. A minimum funding of 50.000 €/year for human resources coordinating the DN, seems to be a threshold for a sustainable DN. © Georg Thieme Verlag KG Stuttgart · New York.

  8. Simultaneous Determination of Arsenic, Manganese, and Selenium in Biological Materials by Neutron-Activation Analysis

    DEFF Research Database (Denmark)

    Heydorn, Kaj; Damsgaard, Else

    1973-01-01

    A new method was developed for the simultaneous determination of arsenic, manganese, and selenium in biological material by thermal-neutron activation analysis. The use of 81 mSe as indicator for selenium permitted a reduction of activation time to 1 hr for a 1 g sample, and the possibility of loss...

  9. Selenium determination in biological material by atomic absorption spectrophotometry in graphite furnace and using vapor generation

    International Nuclear Information System (INIS)

    Carvalho Vidal, M. de F. de.

    1984-01-01

    The applicability of the atomic absorption spectrophotometry to the determination of selenium in biological material using vapor generation and electrothermal atomization in the graphite furnace was investigated. Instrumental parameters and the analytical conditions of the methods were studied. Decomposition methods for the samples were tested, and the combustion in the Wickbold apparatus was chosen. (author) [pt

  10. Genetic Diseases and Genetic Determinism Models in French Secondary School Biology Textbooks

    Science.gov (United States)

    Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

    2008-01-01

    The presentation of genetic diseases in French secondary school biology textbooks is analysed to determine the major conceptions taught in the field of human genetics. References to genetic diseases, and the processes by which they are explained (monogeny, polygeny, chromosomal anomaly and environmental influence) are studied in recent French…

  11. Determination of carbon-14 content in biological materials and its application to vinegars

    International Nuclear Information System (INIS)

    Grau Malonda, A.; Martin-Casallo, M.T.; Chereguini, S.

    1976-01-01

    A radiometric method for the determination of synthetic acetic acid in the presence of biological vinegar has been developed. The activity of 1 4C is measured by liquid scintillation counting and the sensitivity is optimized by taking into account the composition of several liquid scintillation solutions and the concentration of their components. (author) [es

  12. Determination of trace elements in biological material by neutron activation analysis

    International Nuclear Information System (INIS)

    Tran Van, L.; Teherani, D.K.

    1989-01-01

    Eighteen trace elements in biological materials [grass (Imperata cylindrica), mimosa plant (Mimosa pudica), rice] by neutron activation method were determined. In the comparative analysis the content of the same element was different in each material, although they were collected at the same place and the same sampling method was applied. (author) 4 refs.; 1 fig.; 1 tab

  13. Determination of arsenic in biological materials using ammonium molybdate labelled with 99Mo

    International Nuclear Information System (INIS)

    Maruyama, Y.; Nagaoka, Y.

    1983-01-01

    A new radiometric method for the determination of arsenic in biological materials has been developed. An excess of ammonium molybdate labelled with 99 Mo was added to the sample solution and the arsenomolybdic acid formed was extracted into n-butyl alcohol and ethyl acetate mixture. The activity of the organic phase was directly proportional to the amount of arsenic. The method was applied for the determination of arsenic in Orchard Leaves obtained from the National Bureau of Standards. (author)

  14. 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

  15. Determination of drugs in biological fluids by direct injection of samples for liquid-chromatographic analysis.

    Science.gov (United States)

    Mullett, Wayne M

    2007-03-10

    The analysis of drugs in various biological fluids is an important criterion for the determination of the physiological performance of a drug. After sampling of the biological fluid, the next step in the analytical process is sample preparation. The complexity of biological fluids adds to the challenge of direct determination of the drug by chromatographic analysis, therefore demanding a sample preparation step that is often time-consuming, tedious, and frequently overlooked. However, direct on-line injection methods offer the advantage of reducing sample preparation steps and enabling effective pre-concentration and clean-up of biological fluids. These procedures can be automated and therefore reduce the requirements for handling potentially infectious biomaterial, improve reproducibility, and minimize sample manipulations and potential contamination. The objective of this review is to present an overview of the existing literature with emphasis on advances in automated sample preparation methods for liquid-chromatographic methods. More specifically, this review concentrates on the use of direct injection techniques, such as restricted-access materials, turbulent-flow chromatography and other automated on-line solid-phase extraction (SPE) procedures. It also includes short overviews of emerging automated extraction-phase technologies, such as molecularly imprinted polymers, in-tube solid-phase micro-extraction, and micro-extraction in a packed syringe for a more selective extraction of analytes from complex samples, providing further improvements in the analysis of biological materials. Lastly, the outlook for these methods and potential new applications for these technologies are briefly discussed.

  16. Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology

    Science.gov (United States)

    Ukil, Sanchaita; Sinha, Meenakshee; Varshney, Lavneesh; Agrawal, Shipra

    Type 2 Diabetes is a complex multifactorial disease, which alters several signaling cascades giving rise to serious complications. It is one of the major risk factors for cardiovascular diseases. The present research work describes an integrated functional network biology approach to identify pathways that get transcriptionally altered and lead to complex complications thereby amplifying the phenotypic effect of the impaired disease state. We have identified two sub-network modules, which could be activated under abnormal circumstances in diabetes. Present work describes key proteins such as P85A and SRC serving as important nodes to mediate alternate signaling routes during diseased condition. P85A has been shown to be an important link between stress responsive MAPK and CVD markers involved in fibrosis. MAPK8 has been shown to interact with P85A and further activate CTGF through VEGF signaling. We have traced a novel and unique route correlating inflammation and fibrosis by considering P85A as a key mediator of signals. The next sub-network module shows SRC as a junction for various signaling processes, which results in interaction between NF-kB and beta catenin to cause cell death. The powerful interaction between these important genes in response to transcriptionally altered lipid metabolism and impaired inflammatory response via SRC causes apoptosis of cells. The crosstalk between inflammation, lipid homeostasis and stress, and their serious effects downstream have been explained in the present analyses.

  17. 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.

  18. Determining Regulatory Networks Governing the Differentiation of Embryonic Stem Cells to Pancreatic Lineage

    Science.gov (United States)

    Banerjee, Ipsita

    2009-03-01

    Knowledge of pathways governing cellular differentiation to specific phenotype will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of the cellular environment. With this aim, we have developed a novel method to reconstruct the underlying regulatory architecture of a differentiating cell population from discrete temporal gene expression data. We utilize an inherent feature of biological networks, that of sparsity, in formulating the network reconstruction problem as a bi-level mixed-integer programming problem. The formulation optimizes the network topology at the upper level and the network connectivity strength at the lower level. The method is first validated by in-silico data, before applying it to the complex system of embryonic stem (ES) cell differentiation. This formulation enables efficient identification of the underlying network topology which could accurately predict steps necessary for directing differentiation to subsequent stages. Concurrent experimental verification demonstrated excellent agreement with model prediction.

  19. Evolutionary optimization with data collocation for reverse engineering of biological networks.

    Science.gov (United States)

    Tsai, Kuan-Yao; Wang, Feng-Sheng

    2005-04-01

    Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

  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. Of arrows and flows. Causality, determination, and specificity in the Central Dogma of molecular biology.

    Science.gov (United States)

    Fantini, Bernardino

    2006-01-01

    From its first proposal, the Central Dogma had a graphical form, complete with arrows of different types, and this form quickly became its standard presentation. In different scientific contexts, arrows have different meanings and in this particular case the arrows indicated the flow of information among different macromolecules. A deeper analysis illustrates that the arrows also imply a causal statement, directly connected to the causal role of genetic information. The author suggests a distinction between two different kinds of causal links, defined as 'physical causality' and 'biological determination', both implied in the production of biological specificity.

  2. Application of genetic algorithms for determination biological half-life of 137 Cs in milk

    International Nuclear Information System (INIS)

    Pantelic, G.

    1998-01-01

    Genetic algorithm an optimization method involving natural selection mechanisms, was used to determine biological half-life of sup 1 sup 3 sup 7 Cs in the milk, after the Chernobyl accident, based on a two compartment linear system model. Genetic algorithms operate on populations of strings. Reproduction, crossover and mutation are applied to successive string population to create new string population. A model parameter estimation is performed by minimizing square differences between fitting function and experimental data. The calculated biological half-life of sup 1 sup 3 sup 7 Cs in milk is (32(+(-) days (author)

  3. Determination of silicon in biological and botanical reference materials by epithermal INAA and Compton suppression

    International Nuclear Information System (INIS)

    Landsberger, S.; Peshev, S.; Becker, D.A.

    1994-01-01

    Silicon determination in sixteen botanical and biological standard reference materials is described using the 29 Si(n, p) 29 Al reaction through instrumental epithermal neutron activation analysis and Compton suppression gamma-ray spectroscopy. By simultaneous utilization of both cadmium and boron epithermal filters along with anticoincidence gamma-counting, detection limits as low as 12 ppm were obtained for certain matrices, much lower than previously reported values for this type of analysis. The method is applicable to many botanical and biological matrices and is attractive with its interference free, purely instrumental nature, compared with methods using the 28 Si(n, p) 28 Al reaction or chemical separation techniques. ((orig.))

  4. 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.

  5. Possibilities of nondestructive determination of fluorine in coal and biological materials by IPAA

    International Nuclear Information System (INIS)

    Randa, Zdenek; Mizera, Jiri; Chvatil, David

    2009-01-01

    The possibilities of nondestructive determination of fluorine in coal and biological materials by instrumental photon activation analysis (IPAA) were studied. The determination was based on counting the non-specific 511 keV annihilation gamma rays of 18 F, a pure positron emitter which is the product of the photonuclear reaction 19 F(γ, n) 18 F. The simultaneous formation of some additional positron emitters, particularly 45 Ti and 34m Cl, is an interfering factor. When using correction standards for Ti and Cl and optimization of the beam energy and irradiation-decay-counting times, fluorine could be determined by IPAA in selected coal and biological samples at the ten ppm level. The feasibility of additional optimization for further improvements of the proposed IPAA procedure are discussed

  6. Determination of chorionic gonadotrophin. Comparison of biological, immunological and radioimmunological methods

    International Nuclear Information System (INIS)

    Munier, M.-P.

    1978-07-01

    Three types of analysis were used to quantify chorionic gonadotrophic hormone: biological determination (rana-reaction); immunological determination (simplified pregnosticon test of the Organon Teknika laboratories); radioimmunological determination (Commissariat a l'Energie Atomique - CEA kits). While the immunochemical technique is specially suited to analysis of the urine, the radioimmunological measurement is carried out on the plasma. This method is extremely sensitive; when traditional biological and immunological methods are used the quantity of CGH detectable is of the order of some hundreds or at best a few tens of international units. The radioimmunological method is a thousand times more sensitive and can therefore measure CGH in amounts of the milli-unit order. Until recently it was not specific enough to differentiate between CGH and LH, but not long ago a β CGH-specific antibody was discovered and it is now possible to detect small amounts of CGH in the presence of LH [fr

  7. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  8. Determination of Liquefaction Potential using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Farrokhzad, F; Choobbasti, A.J; Barari, Amin

    2011-01-01

    The authors propose an alternative general regression model based on neural networks, which enables analysis of summary data obtained by liquefaction analysis according to usual methods. For that purpose, the data from some thirty boreholes made during field investigations in Babol, in the Iranian...

  9. Determinant factors in dynamics of global manufacturing virtual networks

    Directory of Open Access Journals (Sweden)

    José Ramón Vilana

    2011-04-01

    Full Text Available Purpose: The purpose of this work is to demonstrate the existence of a number of structural mechanisms that networks actors use to exert tacit power in the GMVNs. Design/methodology/approach: The analysis of the engine manufacturing aeronautical industry based on a quantitative approach with a binomial logistic model with a logit function. Findings and Originality/value: Original Equipment Manufacturers (OEMs exert a tacit power by occupying central positions in the network and having many structural holes. Suppliers, nevertheless, establish many strong and direct ties to increase trust with other network actors; while new actors establish, primarily, indirect ties to gain explicit knowledge. Research limitations/implications: Another similar works focused in other industrial segments like electronics, chemical o automotive would strengthen the findings of this work.Practical implications: The results of this work can facilitate, ex ante, the design of new GMVNs and increase drastically their efficiency.Originality/value: It classifies the internal operations of these organizations that, so far, were not analysed under this approach. Its conclusions can be used as a prescriptive model for other manufacturing networks.

  10. An optimisation framework for determination of capacity in railway networks

    DEFF Research Database (Denmark)

    Jensen, Lars Wittrup

    2015-01-01

    network based on a mix of train types, the infrastructure and rolling stock used. The framework consist of two steps. In the first step the maximum number of trains is found according to the predefined mix of train types. In the second step additional trains are added based on weights assigned...

  11. Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion: A DIRECT study.

    Directory of Open Access Journals (Sweden)

    Valborg Gudmundsdottir

    Full Text Available Glucagon-like peptide 1 (GLP-1 stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126. This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100. Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P < 0.05 with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated

  12. Determination of the dynamical behaviour of biological materials during impact using a pendulum device

    Science.gov (United States)

    Van Zeebroeck, M.; Tijskens, E.; Van Liedekerke, P.; Deli, V.; De Baerdemaeker, J.; Ramon, H.

    2003-09-01

    A pendulum device has been developed to measure contact force, displacement and displacement rate of an impactor during its impact on the sample. Displacement, classically measured by double integration of an accelerometer, was determined in an alternative way using a more accurate incremental optical encoder. The parameters of the Kuwabara-Kono contact force model for impact of spheres have been estimated using an optimization method, taking the experimentally measured displacement, displacement rate and contact force into account. The accuracy of the method was verified using a rubber ball. Contact force parameters for the Kuwabara-Kono model have been estimated with success for three biological materials, i.e., apples, tomatoes and potatoes. The variability in the parameter estimations for the biological materials was quite high and can be explained by geometric differences (radius of curvature) and by biological variation of mechanical tissue properties.

  13. Biophysical and biological factors determining the ability to achieve long-term cryobiological preservation

    Energy Technology Data Exchange (ETDEWEB)

    Mazur, P. [Oak Ridge National Lab., TN (United States). Life Sciences Div.

    1997-12-01

    The BESTCapsule will maintain appropriate biological specimens for decades or centuries at cryogenic temperatures in the living state. Maintenance at temperatures below {approximately} {minus}140 C is not a problem. No ordinary chemical reactions in aqueous solutions can occur. The only source of damage will be the slow accumulation of physical damage to DNA from background ionizing radiation. But this source of damage should not become serious in less than a millennium. Rather, the main problem in cryopreservation is to devise procedures for cooling the biological specimens to {minus}196 C and returning them to normal temperatures without inflicting lethal injury. Regardless of the cell type, there are certain encompassing biophysical factors and constraints that determine whether they will survive or die during freezing and thawing. Superimposed on these may be special biological factors that apply to specific cell types. This paper will emphasize the former and give illustrative examples of the latter.

  14. Determination of azide in biological fluids by use of electron paramagnetic resonance

    International Nuclear Information System (INIS)

    Minakata, Kayoko; Suzuki, Osamu

    2005-01-01

    A simple and sensitive method has been developed for the determination of azide ion (N 3 - ) in biological fluids and beverages. The procedure was based on the formation of a ternary complex Cu(N 3 ) 2 (4-methylpyridine) x in benzene, followed by its detection by electron paramagnetic resonance. The complex in benzene showed a characteristic four-peak hyperfine structure with a g-value of 2.115 at room temperature. Cu 2+ reacted with N 3 - most strongly among common metals found in biological fluids. Several anions and metal ions in biological fluids did not interfere with the determination of N 3 - in the presence of large amounts of Cu 2+ and oxidants. In the present method, N 3 - at the concentration from 5 μM to 2 mM in 100 μl solution could be determined with the detection limit of 20 ng. The recoveries were more than 95% for N 3 - added to 100 μl of blood, urine, milk and beverages at 200 μM. Our method is recommendable because it takes less than 10 min to determine N 3 - and the produced complex is quite stable

  15. Chemical preparation of biological materials for accurate chromium determination by isotope dilution mass spectrometry

    International Nuclear Information System (INIS)

    Dunstan, L.P.; Garner, E.L.

    1977-01-01

    The current interest in trace elements in biological materials has created a need for accurate methods of analysis. The source of discrepancies and variations in chromium concentration determinations is often traceable to inadequate methods of sample preparation. Any method of Cr analysis that requires acid digestion of a biological matrix must take into consideration the existence or formation of a volatile Cr component. In addition, because Cr is often present at concentrations less than 1 μg/g, the analytical blank becomes a potential source of error. Chemical procedures have been developed for the digestion of the biological matrix and the separation of Cr without either large analytical blanks or significant losses by volatilization. These procedures have been used for the analysis of NBS Standard Reference Material (SRM) 1569 Brewers Yeast; SRM 1577 Bovine Liver; SRM 1570 Spinach and other biological materials including human hair and nails. At this time, samples containing 1 μg of Cr can be determined with an estimated accuracy of 2 percent

  16. An epidemic model for biological data fusion in ad hoc sensor networks

    Science.gov (United States)

    Chang, K. C.; Kotari, Vikas

    2009-05-01

    Bio terrorism can be a very refined and a catastrophic approach of attacking a nation. This requires the development of a complete architecture dedicatedly designed for this purpose which includes but is not limited to Sensing/Detection, Tracking and Fusion, Communication, and others. In this paper we focus on one such architecture and evaluate its performance. Various sensors for this specific purpose have been studied. The accent has been on use of Distributed systems such as ad-hoc networks and on application of epidemic data fusion algorithms to better manage the bio threat data. The emphasis has been on understanding the performance characteristics of these algorithms under diversified real time scenarios which are implemented through extensive JAVA based simulations. Through comparative studies on communication and fusion the performance of channel filter algorithm for the purpose of biological sensor data fusion are validated.

  17. [RESAOLAB: West African network of laboratories to enhance the quality of clinical biology].

    Science.gov (United States)

    Delorme, L; Machuron, J L; Sow, I; Diagne, R; Sakandé, J; Nikiéma, A; Bougoudogo, F; Keita, A; Longuet, C

    2015-02-01

    The Fondation Mérieux, in partnership with the Ministries of Health of Burkina Faso, Mali and Senegal, implemented for four years a project to reinforce the laboratory sector in the three participating countries: the RESAOLAB project (West African Network of Biomedical Analysis Laboratories).The objective of RESAOLAB project, in partnership with the WHO Office for West Africa and the West African Health Organization, was to strengthen the systems of biomedical laboratories to improve diagnostic services, access, monitoring and management of infectious diseases. Following the successful results achieved under the RESAOLAB project and due to the demand of the neighbour countries ministries, the RESAOLAB project is now extended to four other countries of the West African region: Benin, Guinea-Conakry, Niger and Togo. The RESAOLAB project has become the RESAOLAB programme, its purpose is to strengthen the quality of the medical biology services thanks to a regional and transversal approach.

  18. Structural equation modelling of determinants of customer satisfaction of mobile network providers: Case of Kolkata, India

    Directory of Open Access Journals (Sweden)

    Shibashish Chakraborty

    2014-12-01

    Full Text Available The Indian market of mobile network providers is growing rapidly. India is the second largest market of mobile network providers in the world and there is intense competition among existing players. In such a competitive market, customer satisfaction becomes a key issue. The objective of this paper is to develop a customer satisfaction model of mobile network providers in Kolkata. The results indicate that generic requirements (an aggregation of output quality and perceived value, flexibility, and price are the determinants of customer satisfaction. This study offers insights for mobile network providers to understand the determinants of customer satisfaction.

  19. A Comprehensive Experiment for Molecular Biology: Determination of Single Nucleotide Polymorphism in Human REV3 Gene Using PCR-RFLP

    Science.gov (United States)

    Zhang, Xu; Shao, Meng; Gao, Lu; Zhao, Yuanyuan; Sun, Zixuan; Zhou, Liping; Yan, Yongmin; Shao, Qixiang; Xu, Wenrong; Qian, Hui

    2017-01-01

    Laboratory exercise is helpful for medical students to understand the basic principles of molecular biology and to learn about the practical applications of molecular biology. We have designed a lab course on molecular biology about the determination of single nucleotide polymorphism (SNP) in human REV3 gene, the product of which is a subunit of…

  20. Security management based on trust determination in cognitive radio networks

    Science.gov (United States)

    Li, Jianwu; Feng, Zebing; Wei, Zhiqing; Feng, Zhiyong; Zhang, Ping

    2014-12-01

    Security has played a major role in cognitive radio networks. Numerous researches have mainly focused on attacking detection based on source localization and detection probability. However, few of them took the penalty of attackers into consideration and neglected how to implement effective punitive measures against attackers. To address this issue, this article proposes a novel penalty mechanism based on cognitive trust value. The main feature of this mechanism has been realized by six functions: authentication, interactive, configuration, trust value collection, storage and update, and punishment. Data fusion center (FC) and cluster heads (CHs) have been put forward as a hierarchical architecture to manage trust value of cognitive users. Misbehaving users would be punished by FC by declining their trust value; thus, guaranteeing network security via distinguishing attack users is of great necessity. Simulation results verify the rationality and effectiveness of our proposed mechanism.

  1. On the accuracy of protein determination in large biological samples by prompt gamma neutron activation analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kasviki, K. [Institute of Nuclear Technology and Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Attikis 15310 (Greece); Medical Physics Laboratory, Medical School, University of Ioannina, Ioannina 45110 (Greece); Stamatelatos, I.E. [Institute of Nuclear Technology and Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Attikis 15310 (Greece)], E-mail: ion@ipta.demokritos.gr; Yannakopoulou, E. [Institute of Physical Chemistry, NCSR ' Demokritos' , Aghia Paraskevi, Attikis 15310 (Greece); Papadopoulou, P. [Institute of Technology of Agricultural Products, NAGREF, Lycovrissi, Attikis 14123 (Greece); Kalef-Ezra, J. [Medical Physics Laboratory, Medical School, University of Ioannina, Ioannina 45110 (Greece)

    2007-10-15

    A prompt gamma neutron activation analysis (PGNAA) facility has been developed for the determination of nitrogen and thus total protein in large volume biological samples or the whole body of small animals. In the present work, the accuracy of nitrogen determination by PGNAA in phantoms of known composition as well as in four raw ground meat samples of about 1 kg mass was examined. Dumas combustion and Kjeldahl techniques were also used for the assessment of nitrogen concentration in the meat samples. No statistically significant differences were found between the concentrations assessed by the three techniques. The results of this work demonstrate the applicability of PGNAA for the assessment of total protein in biological samples of 0.25-1.5 kg mass, such as a meat sample or the body of small animal even in vivo with an equivalent radiation dose of about 40 mSv.

  2. On the accuracy of protein determination in large biological samples by prompt gamma neutron activation analysis

    International Nuclear Information System (INIS)

    Kasviki, K.; Stamatelatos, I.E.; Yannakopoulou, E.; Papadopoulou, P.; Kalef-Ezra, J.

    2007-01-01

    A prompt gamma neutron activation analysis (PGNAA) facility has been developed for the determination of nitrogen and thus total protein in large volume biological samples or the whole body of small animals. In the present work, the accuracy of nitrogen determination by PGNAA in phantoms of known composition as well as in four raw ground meat samples of about 1 kg mass was examined. Dumas combustion and Kjeldahl techniques were also used for the assessment of nitrogen concentration in the meat samples. No statistically significant differences were found between the concentrations assessed by the three techniques. The results of this work demonstrate the applicability of PGNAA for the assessment of total protein in biological samples of 0.25-1.5 kg mass, such as a meat sample or the body of small animal even in vivo with an equivalent radiation dose of about 40 mSv

  3. Sieve-based relation extraction of gene regulatory networks from biological literature.

    Science.gov (United States)

    Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

    2015-01-01

    Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming

  4. Determination of biological transport of oxygen-15 and carbon-11 generated in rats

    International Nuclear Information System (INIS)

    Archambeau, B.; Bennett, G.W.; Archambeau, J.O.

    1976-02-01

    The distribution of induced 15 O and 11 C activity in live and dead rats was determined following local irradiation with a 32 MeV proton beam. Results indicate that rapid biological redistribution of some of the induced activity occurs within a minute following irradiation. Sufficient activity remains, bound in the intracellular water, to define the proton beam in tissue. Thus, mapping of the induced 15 O activity proves to be a valid means of beam localization

  5. Determination of trace metals in Cladophora glomerata: C. glomerata as a potential biological monitor

    Energy Technology Data Exchange (ETDEWEB)

    Keeny, W.L.; Breck, W.G.; Vanloon, G.W.; Page, J.A.

    1976-01-01

    A differential pulse anodic stripping voltammetry method has been developed for the determination of Zn, Cd, Pb and Cu in Cladophora glomerata. The method has been applied to samples taken in August from a remote island in Lake Ontario (Main Duck) and a shore site near Kingston, Ontario (Deadman Bay). It is postulated that C. glomerata can act as a biological monitor, concentrating the trace metals present in the aqueous environment with a reasonably constant CF for each element.

  6. Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control

    Directory of Open Access Journals (Sweden)

    Mehmet eKocaturk

    2015-08-01

    Full Text Available In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE as a practical platform for the development of novel brain machine interface (BMI controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

  7. A mathematical framework for agent based models of complex biological networks.

    Science.gov (United States)

    Hinkelmann, Franziska; Murrugarra, David; Jarrah, Abdul Salam; Laubenbacher, Reinhard

    2011-07-01

    Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agreed-upon standard way to specify such models, it is not always easy to use published models. Also, since model descriptions are not usually given in mathematical terms, it is difficult to bring mathematical analysis tools to bear, so that models are typically studied through simulation. In order to address this issue, Grimm et al. proposed a protocol for model specification, the so-called ODD protocol, which provides a standard way to describe models. This paper proposes an addition to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis. The mathematical framework is that of algebraic models, that is, time-discrete dynamical systems with algebraic structure. It is shown by way of several examples how this mathematical specification can help with model analysis. This mathematical framework can also accommodate other model types such as Boolean networks and the more general logical models, as well as Petri nets.

  8. In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

    Science.gov (United States)

    Klinke, David J

    2014-01-01

    Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers.

  9. SWIM: a computational tool to unveiling crucial nodes in complex biological networks.

    Science.gov (United States)

    Paci, Paola; Colombo, Teresa; Fiscon, Giulia; Gurtner, Aymone; Pavesi, Giulio; Farina, Lorenzo

    2017-03-20

    SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.

  10. FUSE: a profit maximization approach for functional summarization of biological networks

    Directory of Open Access Journals (Sweden)

    Seah Boon-Siew

    2012-03-01

    Full Text Available Abstract Background The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

  11. FUSE: a profit maximization approach for functional summarization of biological networks.

    Science.gov (United States)

    Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes; Yu, Hanry

    2012-03-21

    The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

  12. Analytic determination of the activation of essential and toxic trace elements in biological material

    International Nuclear Information System (INIS)

    Schelenz, R.

    1980-01-01

    A neutron activation-analysis technique for the multielement determination in biological material was developed. The individual steps of this procedure comprise radiochemical and also instrumental analytic techniques. After radiochemical separation 34 elements can be determined, after only instrumental procedures 26 elements can be detected in biological material. The radiochemical analysis of 34 elements lasts 4 days. Tracer investigations on the radionuclide retention of the anorganic separators HAP, TiP and ZP in 9N aqueous HNO 3 solution indicated that apart from Na-24, K-42 and P-32 the radionuclides Cs-134, Rb-86 and Se-75 are almost quantitatively adsorbed at the separators. For the remaining investigated radionuclides different but well-reproducible retention values resulted. The pH-value only slightly influences the extent of the radionuclide retention. Kinetic investigations on the radiochemical precipitation of some radionuclides on Cu and Cu(Hg)sub(x) were carried out. The depositing of the radionuclides Ag-110m, Hg-203 and Se-75 at 0 0 C and room temperature on Cu(Hg)sub(x) and Cu foil is a first order reaction. The half-life periods and the velocity constants of the depositing on Cu and Cu(Hg)sub(x) were determined for the investigated radionuclides in dependency of the temperature. The technique was examined by means of international biological multielement standards of known element combinations. The realisation of ring tests for the multielement determination in potatoe and milk powder showed that this method provides precise results. The applicability of the radiochemical method was confirmed by the simultaneous determination of 25 elements in overall nutrition samples. The instrumental technique was applied for the multielement determination in human hair (of the head) and in river water. (orig./MG) [de

  13. Molecular mechanisms of temperature-dependent sex determination in the context of ecological developmental biology.

    Science.gov (United States)

    Matsumoto, Yuiko; Crews, David

    2012-05-06

    Temperature-dependent sex determination (TSD) is a prime example of phenotypic plasticity in that gonadal sex is determined by the temperature of the incubating egg. In the red-eared slider turtle (Trachemys scripta), the effect of temperature can be overridden by exogenous ligands, i.e., sex steroid hormones and steroid metabolism enzyme inhibitors, during the temperature-sensitive period (TSP) of development. Precisely how the physical signal of temperature is transduced into a biological signal that ultimately results in sex determination remains unknown. In this review, we discuss the sex determining pathway underlying TSD by focusing on two candidate sex determining genes, Forkhead box protein L2 (FoxL2) and Doublesex mab3- related transcription factor 1 (Dmrt1). They appear to be involved in transducing the environmental temperature signal into a biological signal that subsequently determines gonadal sex. FoxL2 and Dmrt1 exhibit gonad-typical patterns of expression in response to temperature during the TSP in the red-eared slider turtle. Further, the biologically active ligands regulate the expression of FoxL2 and Dmrt1 during development to modify gonad trajectory. The precise regulatory mechanisms of expression of these genes by temperature or exogenous ligands are not clear. However, the environment often influences developmental gene expression by altering the epigenetic status in regulatory regions. Here, we will discuss if the regulation of FoxL2 and Dmrt1 expression by environment is mediated through epigenetic mechanisms during development in species with TSD. Published by Elsevier Ireland Ltd.

  14. The use of a single technique for the separation and determination of actinides in biological materials

    International Nuclear Information System (INIS)

    Camera, V.; Giubileo.

    1975-01-01

    For the radiotoxicological survey of workers exposed to different types of alpha-emitting contaminants, a procedure was developed which permits the estimate of Th, Pa, U, Np, Pu, Am and Cm in biological samples with a single technique. The radionuclides are extracted on a column by tri-n-octylphosphine oxide and separated by elution at different pH values. Afterwards, the quantitative determinations are done by physical methods (alpha counting or spectrometry). In the case of an accident it is possible to use a simplification of the procedure (extraction in a beaker) for checks. A procedure for the rapid determination of actinides in faeces and in nasal secretions is described

  15. A biologically inspired neural network model to transformation invariant object recognition

    Science.gov (United States)

    Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

    2007-09-01

    Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

  16. Mass determination based on electron scattering in electron probe X-ray microanalysis of thin biological specimens

    International Nuclear Information System (INIS)

    Linders, P.W.J.

    1984-01-01

    This thesis describes the development of a method for mass determination of thin biological objects by quantitative electron microscopy. The practical realization of the mass determination consists of photographical recording with subsequent densitometry. (Auth.)

  17. Biological psychological and social determinants of old age: Bio-psycho-social aspects of human aging

    Directory of Open Access Journals (Sweden)

    Małgorzata Dziechciaż

    2014-11-01

    Full Text Available Biological psychological and social determinants of old age: Bio-psycho-social aspects of human aging. The aging of humans is a physiological and dynamic process ongoing with time. In accordance with most gerontologists’ assertions it starts in the fourth decade of life and leads to death. The process of human aging is complex and individualized, occurs in the biological, psychological and social sphere. Biological aging is characterized by progressive age-changes in metabolism and physicochemical properties of cells, leading to impaired self-regulation, regeneration, and to structural changes and functional tissues and organs. It is a natural and irreversible process which can run as successful aging, typical or pathological. Biological changes that occur with age in the human body affect mood, attitude to the environment, physical condition and social activity, and designate the place of seniors in the family and society. Psychical ageing refers to human awareness and his adaptability to the ageing process. Among adaptation attitudes we can differentiate: constructive, dependence, hostile towards others and towards self attitudes. With progressed age, difficulties with adjustment to the new situation are increasing, adverse changes in the cognitive and intellectual sphere take place, perception process involutes, perceived sensations and information received is lowered, and thinking processes change. Social ageing is limited to the role of an old person is culturally conditioned and may change as customs change. Social ageing refers to how a human being perceives the ageing process and how society sees it.

  18. 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.

  19. 77 FR 37730 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-06-22

    ... DEPARTMENT OF STATE [Public Notice 7928] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  20. 77 FR 7229 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-02-10

    ... DEPARTMENT OF STATE [Public Notice 7794] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  1. Competitive network determines the direction of the diversity–function relationship

    NARCIS (Netherlands)

    Maynard, Daniel S.; Crowther, Thomas W.; Bradford, Mark A.

    2017-01-01

    The structure of the competitive network is an important driver of biodiversity and coexistence in natural communities. In addition to determining which species survive, the nature and intensity of competitive interactions within the network also affect the growth, productivity, and abundances of

  2. Magnesium degradation as determined by artificial neural networks.

    Science.gov (United States)

    Willumeit, Regine; Feyerabend, Frank; Huber, Norbert

    2013-11-01

    Magnesium degradation under physiological conditions is a highly complex process in which temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial neural networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role. Copyright © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  3. Determine point-to-point networking interactions using regular expressions

    Directory of Open Access Journals (Sweden)

    Konstantin S. Deev

    2015-06-01

    Full Text Available As Internet growth and becoming more popular, the number of concurrent data flows start to increasing, which makes sense in bandwidth requested. Providers and corporate customers need ability to identify point-to-point interactions. The best is to use special software and hardware implementations that distribute the load in the internals of the complex, using the principles and approaches, in particular, described in this paper. This paper represent the principles of building system, which searches for a regular expression match using computing on graphics adapter in server station. A significant computing power and capability to parallel execution on modern graphic processor allows inspection of large amounts of data through sets of rules. Using the specified characteristics can lead to increased computing power in 30…40 times compared to the same setups on the central processing unit. The potential increase in bandwidth capacity could be used in systems that provide packet analysis, firewalls and network anomaly detectors.

  4. COMPLEMENTARY SEX DETERMINATION IN HYMENOPTERAN PARASITOIDS AND ITS IMPLICATIONS FOR BIOLOGICAL CONTROL

    Institute of Scientific and Technical Information of China (English)

    WUZhishan; KeithR.Hopper; PaulJ.Ode; RogerW.Fuester; CHENJia-hua; GeorgeE.Heimpel

    2003-01-01

    In haplodiploid Hymenoptera, unfertilized eggs produce haploid males while fertilized eggs lead to diploid females under most circumstances. Diploid males can also be produced from fertilization under a system of sex determination known as complementary sex determination (CSD). Under single-locus CSD, sex is determined by multiple alleles at a single sex locus. Individuals heterozygous at the sex locus are female while hemizygous and homozygous individuals develop as haploid and diploid males, respectively. In multiple-locus CSD, two or more loci, each with two or more alleles, determine sex. Diploid individuals are female if one or more sex loci are het-erozygous, while a diploid is male only if homozygous at all sex loci. Diploid males are known to occur in 43 hym-enopteran species and single-locus CSD has been demonstrated in 22 of these species. Diploid males are either developmentally inviable or sterile, so their production constitutes a genetic load. Because diploid male production is more likely under inbreeding, CSD is a form of inbreeding depression. It is crucial to preserve the diversity of sex alleles and reduce the loss of genetic variation in biological control. In the parasitoid species with single-locus CSD, certain precautionary procedures can prevent negative effects of single-locus CSD on biological control.

  5. Sensitive kinetic spectrophotometric determination of captopril and ethamsylate in pharmaceutical preparations and biological fluids.

    Science.gov (United States)

    El-Shabrawy, Y; El-Enany, N; Salem, K

    2004-10-01

    A highly sensitive kinetic spectrophotometric method was developed for the determination of captopril (CPL) and ethamsylate (ESL) in pharmaceutical preparations and biological fluids. The method is based on a catalytic acceleration of the reaction between sodium azide and iodine in an aqueous solution. Concentration range of 0.1-1.5 microg ml(-1) for CPL and 0.3-3 microg ml(-1) for ESL was determined by measuring the decrease in the absorbance of iodine at 348 nm by a fixed time method. The decrease in absorbance after 5 min was markedly correlated to the concentration. The relative standard deviations obtained were 1.30 and 1.87 for CPL and ESL, respectively, in pure forms. Correlation coefficients were 0.9997 and 0.9999 for CPL and ESL, respectively. The detection limits were determined as (S/N = 3) were 20 ng ml(-1) for CPL and 50 ng ml(-1) for ESL. The proposed procedure was successively applied for the determination of both drugs in pharmaceutical preparations and in biological fluids.

  6. The determination of iodine in biological media using radioactivation analysis (1962)

    International Nuclear Information System (INIS)

    Comar, D.

    1962-06-01

    The object of this study is to show that the application of radioactivation analysis to the determination of iodine in biological media makes it possible to measure iodine concentrations of the order of 0.0001 μg. After a review of the chemical methods with a mention of the difficulties they present, the optimum conditions for the determination of iodine in biological liquids are given. Three methods are described: - the first consists of a chemical treatment which liberates the protein bound iodine in an inorganic form. After distillation this iodine is irradiated in a flux of thermal neutrons. The induced radioactivity is compared to that of a standard sample irradiated in the same conditions by γ spectrometry. - the second method which is of more general application consists in irradiating the sample and then extracting the iodine; its induced radio-activity is then measured by β-counting. - the third method measures the iodine directly in the thyroid tissue by anti-compton spectrometry. The sensitivity, the reproducibility and the accuracy are discussed. Some applications are described: determination of iodine in its various organic forms in serum, determination of iodine in urines, in food-stuffs, etc., in the thyroid tissue, etc. (author) [fr

  7. Biological signatures of dynamic river networks from a coupled landscape evolution and neutral community model

    Science.gov (United States)

    Stokes, M.; Perron, J. T.

    2017-12-01

    Freshwater systems host exceptionally species-rich communities whose spatial structure is dictated by the topology of the river networks they inhabit. Over geologic time, river networks are dynamic; drainage basins shrink and grow, and river capture establishes new connections between previously separated regions. It has been hypothesized that these changes in river network structure influence the evolution of life by exchanging and isolating species, perhaps boosting biodiversity in the process. However, no general model exists to predict the evolutionary consequences of landscape change. We couple a neutral community model of freshwater organisms to a landscape evolution model in which the river network undergoes drainage divide migration and repeated river capture. Neutral community models are macro-ecological models that include stochastic speciation and dispersal to produce realistic patterns of biodiversity. We explore the consequences of three modes of speciation - point mutation, time-protracted, and vicariant (geographic) speciation - by tracking patterns of diversity in time and comparing the final result to an equilibrium solution of the neutral model on the final landscape. Under point mutation, a simple model of stochastic and instantaneous speciation, the results are identical to the equilibrium solution and indicate the dominance of the species-area relationship in forming patterns of diversity. The number of species in a basin is proportional to its area, and regional species richness reaches its maximum when drainage area is evenly distributed among sub-basins. Time-protracted speciation is also modeled as a stochastic process, but in order to produce more realistic rates of diversification, speciation is not assumed to be instantaneous. Rather, each new species must persist for a certain amount of time before it is considered to be established. When vicariance (geographic speciation) is included, there is a transient signature of increased

  8. Parametric motion control of robotic arms: A biologically based approach using neural networks

    Science.gov (United States)

    Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

    1993-01-01

    A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

  9. Differential determination of 203Hg and 14C or 35S in double labelled biological samples

    International Nuclear Information System (INIS)

    Bem, E.M.; Bem, H.; Reimschussel, W.

    1979-01-01

    The differential determination of 203 Hg and 14 C or 35 S in double labelled biological samples is presented. The biological samples were mineralized with 70% HClO 4 and 30% H 2 O 2 in glass vials, MILLI-6. The γ-activity of 203 Hg was measured on a well scintillation counter. The total activity, due to 203 Hg and 14 C or 35 S, was measured by the liquid scintillation technique after addition of Aquasol into the same vials. The method of external standard channel ratio was used for standardization. Very good recoveries were obtained: 100+-0.7% for 203 Hg and 94.6-101.0% for 14 C and 35 S. This method could be used for other β, γ and β-active nuclides with similar β-spectra. (author)

  10. Development of capillary electrophoresis methods for quantitative determination of taurine in vehicle system and biological media.

    Science.gov (United States)

    da Silva, Dayse L P; Rüttinger, Hans H; Mrestani, Yahia; Baum, Walter F; Neubert, Reinhard H H

    2006-06-01

    CE methods have been developed for the determination of taurine in pharmaceutical formulation (microemulsion) and in biological media such as sweat. The CE system with end-column pulsed amperometric detection has been found to be an interesting method in comparison with UV and fluorescence detection for its simplicity and rapidity. A gold-disk electrode of 100 mm diameter was used as the working electrode. The effects of a field decoupler at the end of the capillary, separation voltage, injection and pressure times were investigated. A detection limit of 4 x 10(-5) mol/L was reached using integrated pulsed amperometric detection, a method successfully applied to taurine analysis of the biological samples such as sweat. For taurine analysis of oil-in-water microemulsion, fluorescence detector was the favored method, the detection limit of which was 4 x 10(-11) mol/L.

  11. 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.

  12. Visualization and Analysis of a Cardio Vascular Diseaseand MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches

    Directory of Open Access Journals (Sweden)

    Sommer Björn

    2010-03-01

    Full Text Available Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein.

  13. 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.

  14. Determination of rhenium in biological and environmental samples by radiochemical neutron activation analysis

    International Nuclear Information System (INIS)

    Kucera, J.; Mizera, J.; Randa, Z.; Byrne, A.R.; Lucanikova, M.

    2006-01-01

    Radiochemical neutron activation procedures using liquid-liquid extraction with tetraphenylarsonium chloride in chloroform from 1 M HCl and solid extraction with ALIQUAT 336 incorporated in a polyacrylonitrile binding matrix from 0.1 M HCl were developed for accurate determination of rhenium in biological and environmental samples at the sub-ng.g -1 level. Concentrations of Re in the range of 0.1 to 2.4 ng.g -1 were determined in several botanical reference materials (RM), while in a RM of road dust a value of approx. 10 ng.g -1 was found. Significantly elevated values of Re, up to 90 ng.g -1 , were found in seaweed (brown algae). Results for Re in the brown algae Fucus vesiculosus in which elevated 99 Tc values had previously been determined suggest possible competition between Re and Tc in the accumulation process. (author)

  15. Impact parameter determination for 40Ca + 40Ca reactions using a neural network

    International Nuclear Information System (INIS)

    Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

    1995-01-01

    A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs

  16. 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

  17. Track Detection Technique Using CR-39 for Determining Depleted Uranium in Biological Specimens

    International Nuclear Information System (INIS)

    Murbat, S.M.

    2013-01-01

    Track detecting technique using CR-39 track detector has been implemented for determining depleted uranium concentration in biological specimens (tissues, bones, and blood) of patients infected with cancer diseases. Results were compared with specimens of patients infected with conventional diseases (noncancerous). Specimens were collected from middle and south of Iraq have been contaminated with depleted uranium in the Gulf war in 1991. Results show that this technique is efficient for determining depleted uranium concentration in biological specimens. It was found that all studies samples determine for patients infected with cancer diseases contain a high concentration of depleted uranium (more than the international standard) comparing with noncancerous diseases. Moreover, it was found that persons infected with Leukemia show more sensitive to uranium concentrations to induce the diseases (66-202 ppb), while (116- 1910 ppb) concentrations were needed for inducing cancer diseases in organs and tissues. Result confirmed the correlation between cancerous diseases and the munitions made of depleted uranium used in the Gulf war in 1991 leads to contaminate the Iraqi environment and causes a high risk against people in Iraq.

  18. Liquid chromatographic determination of CPZEN-45, a novel anti-tubercular drug, in biological samples.

    Science.gov (United States)

    Hanif, S N M; Hickey, A J; Garcia-Contreras, L

    2014-01-01

    CPZEN-45 is a new drug candidate being considered for the treatment of tuberculosis (TB). The aim of this study was to develop and validate a reverse-phase high-performance liquid chromatographic (HPLC) method suitable to determine CPZEN-45 concentrations in biological samples. CPZEN-45 was extracted from biological fluids and tissues (plasma, lung and spleen from guinea pig) by sequential extraction with acetonitrile and quantified by a Waters HPLC Alliance System coupled with a ZORBAX Bonus-RP column, guard column and UV detection at 263nm. The mobile phase was 20:80 acetonitrile:ultrapure-water with 0.05% TFA. The CPZEN-45 peak was eluted at 5.1min with no interference from the inherent peaks of plasma, bronchoalveolar lavage (BAL), lung or spleen tissues. Recovery of CPZEN-45 from biological samples was >96% of the spiked amount. The limit of detection was 0.05μg/ml and the limit of quantitation was 0.29μg/ml which was more than 5 and 21 times lower than the reported minimal inhibitory concentration of CPZEN-45 (MIC=1.56μg/ml for Mycobacterium tuberculosis and 6.25μg/ml for MDR-TB, respectively). Thus, HPLC method was deemed reliable, sensitive, reproducible and accurate for the determination of CPZEN-45 concentrations in plasma, BAL, lung and spleen tissues. Therefore, this method was used in subsequent studies in the guinea pig model to determine the disposition of CPZEN-45 after administration of solutions by the IV and SC routes. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. Determination of phosphorus in biological samples by thermal neutron activation followed by β--counting

    International Nuclear Information System (INIS)

    Weginwar, R.G.; Samudralwar, D.L.; Garg, A.N.

    1989-01-01

    Phosphorus was determined using the β - emitter 32 P by instrumental neutron activation analysis (INAA) in several NBS and IAEA standards, and in samples of biological origin such as human and animal blood, cancerous tissue, edible plant leaves, diets, milk samples, etc. The method involves thermal neutron irradiation (for 2-10 h in a reactor) followed by β - counting on an end-window gas flow proportional counter using aluminium filter. The results are within ±10% of the certified values in most cases. (author) 29 refs.; 3 tabs

  20. Method for determining the biological effects of air pollution by transplanted lichens

    Energy Technology Data Exchange (ETDEWEB)

    Schoenbeck, H

    1969-01-01

    The natural sensitivity of the leaf Parmelia physodes to air pollutants can be used for their detection. For this purpose the lichens are transplanted in many parallel lines onto boards made of wood or of any other material (lichen exposure boards) and are exposed in the area to be investigated at a height of 1.50 m. Their reactions are recorded photographically at definite intervals. The biological test values determined permit evidence of the existing emission load to be obtained, which can then be used in addition to the chemical air analysis.

  1. A method to determine site-specific, anisotropic fracture toughness in biological materials

    International Nuclear Information System (INIS)

    Bechtle, Sabine; Özcoban, Hüseyin; Yilmaz, Ezgi D.; Fett, Theo; Rizzi, Gabriele; Lilleodden, Erica T.; Huber, Norbert; Schreyer, Andreas; Swain, Michael V.; Schneider, Gerold A.

    2012-01-01

    Many biological materials are hierarchically structured, with highly anisotropic structures and properties on several length scales. To characterize the mechanical properties of such materials, detailed testing methods are required that allow precise and site-specific measurements on several length scales. We propose a fracture toughness measurement technique based on notched focused ion beam prepared cantilevers of lower and medium micron size scales. Using this approach, site-specific fracture toughness values in dental enamel were determined. The usefulness and challenges of the method are discussed.

  2. A Fast Radiochemical Method for the Determination of Some Essential Trace Elements in Biology and Medicine

    International Nuclear Information System (INIS)

    Samsahl, K.

    1964-12-01

    A method has been developed for the determination with neutron-activation analysis of the following trace elements in soft biological tissues: Br, Ca, Cl, Cu, K, Mg, Mn, Mo, Na, P, Sr and Zn. The method consists in performing a short-term irradiation of the samples with a high thermal neutron flux, followed by fast chemical separations and gamma-spectrometric measurements. The chemical separations and the measurements of short-lived nuclides from a run are finished within 70 min, after the end of irradiation

  3. Ionometric determination of boron in natural, waste waters and biological materials

    International Nuclear Information System (INIS)

    Yakimov, V.P.; Markova, O.L.

    1992-01-01

    Method have been developed for the determination of boron in natural, waste waters and biological materials using direct potentiometry with a BF 4 - selective electrode. In order to estimate the matrix effects in plotting the calibration graphs, it is recommended to and the test water or solution of biomaterial mineralizates, containing boron in electrode-inactive form, to the calibration solutions before e.m.f. measurements version of boron into tetrafluoroborate in solid phase on heating the mineralized samples with ammonium bifluoride at 150-180 deg C

  4. A Fast Radiochemical Method for the Determination of Some Essential Trace Elements in Biology and Medicine

    Energy Technology Data Exchange (ETDEWEB)

    Samsahl, K

    1964-12-15

    A method has been developed for the determination with neutron-activation analysis of the following trace elements in soft biological tissues: Br, Ca, Cl, Cu, K, Mg, Mn, Mo, Na, P, Sr and Zn. The method consists in performing a short-term irradiation of the samples with a high thermal neutron flux, followed by fast chemical separations and gamma-spectrometric measurements. The chemical separations and the measurements of short-lived nuclides from a run are finished within 70 min, after the end of irradiation.

  5. Evaluation of Botanical Reference Materials for the Determination of Vanadium in Biological Samples

    DEFF Research Database (Denmark)

    Heydorn, Kaj; Damsgaard, Else

    1982-01-01

    Three botanical reference materials prepared by the National Bureau of Standards have been studied by neutron activation analysis to evaluate their suitability with respect to the determination of vanadium in biological samples. Various decomposition methods were applied in connection with chemic....... A reference value of 1.15 mg/kg of this material is recommended, based on results from 3 different methods. All three materials are preferable to SRM 1571 Orchard Leaves, while Bowen's Kale remains the material of choice because of its lower concentration....

  6. NBS SRM 1569 Brewer's Yeast: Is it an adequate standard reference material for testing a chromium determination in biological materials tion in biological materials

    International Nuclear Information System (INIS)

    Goeij, J.J.M. de; Volkers, K.J.; Tjioe, P.S.; Kroon, J.J.

    1978-01-01

    Some analytical experiences with NBS SRM 1569 Brewer's Yeast are presented. Against this background the adequacy of this standard reference material for the determination of chromium in biological materials is discussed. Authors have three main objections. Due to its high content of insoluble chromium-containing particles, SRM 1569 is not typical for biological materials, possibly not even for Brewer's Yeast. The chromium level of SRM 1569 is not typical for the chromium levels normally encountered in pure biological materials. The major fraction (69 +- 3 percent) of the chromium is present in a form which is insoluble under the conditions used in Author's analysis. (T.I.)

  7. Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

    Directory of Open Access Journals (Sweden)

    Harun Akif Kabuk

    2015-01-01

    Full Text Available This study investigated the biological treatability of textile wastewater. For this purpose, a membrane bioreactor (MBR was utilized for biological treatment after the ozonation process. Due to the refractory organic contents of textile wastewater that has a low biodegradability capacity, ozonation was implemented as an advanced oxidation process prior to the MBR system to increase the biodegradability of the wastewater. Textile wastewater, oxidized by ozonation, was fed to the MBR at different hydraulic retention times (HRT. During the process, color, chemical oxygen demand (COD, and biochemical oxygen demand (BOD removal efficiencies were monitored for 24-hour, 12-hour, 6-hour, and 3-hour retention times. Under these conditions, 94% color, 65% COD, and 55% BOD removal efficiencies were obtained in the MBR system. The experimental outputs were modeled with multiple linear regressions (MLR and fuzzy logic. MLR results suggested that color removal is more related to COD removal relative to BOD removal. A surface map of this issue was prepared with a fuzzy logic model. Furthermore, fuzzy logic was employed to the whole modeling of the biological system treatment. Determination coefficients for COD, BOD, and color removal efficiencies were 0.96, 0.97, and 0.92, respectively.

  8. Disclosure Level of CPC 29 Biological Assets: Analysis of Determining Factors in Brazilian companies

    Directory of Open Access Journals (Sweden)

    Daniel Ramos Nogueira

    2017-04-01

    Full Text Available The research question guiding this research is "What are the Determining Factors of CPC 29 Disclosure in Brazilian Companies?". In this aspect, the research objective was to evaluate the main factors that affect the disclosure of information related to biological assets. For this, 5 variables highlighted in the literature were selected as evidence influencers. The sample was composed of Brazilian companies with biological assets in the Balance Sheet. From this list, financial statements, explanatory notes, corporate management level and independent auditing company for the 6 years (2010 to 2015 were collected. With the collected information, the dependent variable (Disclosure level of CPC 29 and the independent variables of each year were verified. At the end (after exclusions, 100 observations were analyzed. The results indicated that the variables Size, Representativeness of Biological Assets and Effectiveness of OCPC 07 positively impacted the level of Disclosure. The first two confirmed the predicted hypothesis and OCPC 07 presented a relation that was different from what was expected, showing an increase and not a reduction in the number of disclosures in the years 2014 and 2015.

  9. Quantitative determination of nitrogen biological fixation by the N-15 isotopic method

    International Nuclear Information System (INIS)

    Basantes, Emilio; Trivelin, Paulo; Mui Tsai, Siu

    1993-01-01

    In order to quantify the biological nitrogen fixation (BNF) and to evaluate the mycorrhiza effect in the BNF, an experiment was carried on by applying 1 5 N -ammonium sulphate and mycorrhiza fungi to the soil. The treatments included legumes: mucuna negra(Stizolobium atterrinum Piper et Tracv) and caupi (Vigna unguiculoata L. Walp). Two control plants: non nodulating soybean (Glycine max L.Merril) and rice (Oryza sativa), were used for measuring the fixed N in the legumes by isotope dilution method. Both legumes and control plants assimmilated the same ammounts of nitrogen from the soil and fertilizer. The greater N content in the legumnes was determined as coming from the fixed nitrogen. Rice and non nodulating soybean showed to be good controls for measuring biological nitrogen fixation using isotopic dilution method. The values of fixed nitrogen for legumes calculated using rice as control plant were slightly greater than those with non nodulating soybean, nevertheless there were no significant statistical differences between the values. The mucuna fixed more N than caupi in both mycorrhiza treatments (76.7, 66.6 and 56. 7 per cent of N fixed, respectively). The mycorrhiza increased dry matter yield (13.84 per cent), accumulation of N in the plant(14.85 per cent N) and the biological N fixation (16.06 per cent N-fixed) in caupi

  10. A Simulated Annealing Algorithm for Maximum Common Edge Subgraph Detection in Biological Networks

    DEFF Research Database (Denmark)

    Larsen, Simon; Alkærsig, Frederik G.; Ditzel, Henrik

    2016-01-01

    Network alignment is a challenging computational problem that identifies node or edge mappings between two or more networks, with the aim to unravel common patterns among them. Pairwise network alignment is already intractable, making multiple network comparison even more difficult. Here, we intr...

  11. Research Coordination Network: Geothermal Biology and Geochemistry in Yellowstone National Park

    Science.gov (United States)

    Inskeep, W. P.; Young, M. J.; Jay, Z.

    2006-12-01

    The number and diversity of geothermal features in Yellowstone National Park (YNP) represent a fascinating array of high temperature geochemical environments that host a corresponding number of unique and potentially novel organisms in all of the three recognized domains of life: Bacteria, Archaea and Eukarya. The geothermal features of YNP have long been the subject of scientific inquiry, especially in the fields of microbiology, geochemistry, geothermal hydrology, microbial ecology, and population biology. However, there are no organized forums for scientists working in YNP geothermal areas to present research results, exchange ideas, discuss research priorities, and enhance synergism among research groups. The primary goal of the YNP Research Coordination Network (GEOTHERM) is to develop a more unified effort among scientists and resource agencies to characterize, describe, understand and inventory the diverse biota associated with geothermal habitats in YNP. The YNP RCN commenced in January 2005 as a collaborative effort among numerous university scientists, governmental agencies and private industry. The YNP RCN hosted a workshop in February 2006 to discuss research results and to form three working groups focused on (i) web-site and digital library content, (ii) metagenomics of thermophilic microbial communities and (iii) development of geochemical methods appropriate for geomicrobiological studies. The working groups represent one strategy for enhancing communication, collaboration and most importantly, productivity among the RCN participants. If you have an interest in the geomicrobiology of geothermal systems, please feel welcome to join and or participate in the YNP RCN.

  12. Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks.

    Science.gov (United States)

    D'Souza, Mark; Sulakhe, Dinanath; Wang, Sheng; Xie, Bing; Hashemifar, Somaye; Taylor, Andrew; Dubchak, Inna; Conrad Gilliam, T; Maltsev, Natalia

    2017-01-01

    Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

  13. Analytical Methodologies for the Determination of Endocrine Disrupting Compounds in Biological and Environmental Samples

    Directory of Open Access Journals (Sweden)

    Zoraida Sosa-Ferrera

    2013-01-01

    Full Text Available Endocrine-disruptor compounds (EDCs can mimic natural hormones and produce adverse effects in the endocrine functions by interacting with estrogen receptors. EDCs include both natural and synthetic chemicals, such as hormones, personal care products, surfactants, and flame retardants, among others. EDCs are characterised by their ubiquitous presence at trace-level concentrations and their wide diversity. Since the discovery of the adverse effects of these pollutants on wildlife and human health, analytical methods have been developed for their qualitative and quantitative determination. In particular, mass-based analytical methods show excellent sensitivity and precision for their quantification. This paper reviews recently published analytical methodologies for the sample preparation and for the determination of these compounds in different environmental and biological matrices by liquid chromatography coupled with mass spectrometry. The various sample preparation techniques are compared and discussed. In addition, recent developments and advances in this field are presented.

  14. Development of radiochemical separation method for determination of toxic elements in biological samples

    International Nuclear Information System (INIS)

    Maihara, V.A.; Vasconcellos, M.B.A.; Favaro, D.I.T.; Armelin, M.J.A.

    1990-01-01

    In order to determine Hg, Sb, As and Se in biological materials by neutron activation analysis, a radiochemical separation was developed. The chemical separation procedure used was based on the digestion of the irradiated sample in a mixture of H NO 3 and H 2 SO 4 in a teflon bomb, at 130 0 C for 1 to 4 hours. After the dissolution of organic matter, Hg and Sb were retained by a Dowex 2-X8 resin column in 6 M HCl. The effluent was passed through a TDO, tin dioxide column which retains As and Se in 3 M HCl medium. Radioactive tracers of these elements were used to determine the yields of the separation process. Certified reference materials were analyzed to check the precision and accuracy of the method. (author)

  15. Development of a radiochemical separation method for toxic elements determination from biologic samples

    International Nuclear Information System (INIS)

    Malhara, V.A.; Vasconcellos, M.B.A.; Favaro, D.I.T.; Armelin, M.J.A.

    1990-01-01

    In order to determine Hg, Sb, As and Se in biological materials by neutron activation analysis, a radiochemical separation was developed. The chemical separation procedure used was based on the digestion of the irradiated sample in a mixture of HNO 3 and H 2 SO 4 in a teflon bomb, at 130 0 C for 1 to 4 hours. After the dissolution of organic matter, Hg and Sb were retained by a Dowex 2-XB resin column in 6M HCI. The efluent was passed through a TDO, tin dioxide column which retains As and Se in 3M HCI medium. Radioactive tracers of these elements were used to determine the yields of the separation process. Certified reference materials were analyzed to check the precision and accuracy of the method. (author)

  16. Ultratrace determination of platinum in biological materials via neutron activation and radiochemical separation

    International Nuclear Information System (INIS)

    Zeisler, R.; Greenberg, R.R.

    1982-01-01

    A neutron activation analysis scheme based upon a radiochemical separation of the activation products has been developed. The method utilizes the inherent sensitivity of the activation reaction 198 Pt(n,ν) 199 Pt and counting of the daughter nuclide 199 Au. This nuclide is radiochemically separated from interfering activities by homogeneous precipitation as elemental gold. The remaining interference of the secondary reaction 197 Au(n,ν) 198 Au(n,ν) 199 Au from gold in the samples is quantitatively assessed and corrected. During this process accurate gold concentrations in the samples are obtained at ultratrace levels. The analysis scheme is applied to gold and platinum determinations in biological Standard Reference Materials and human liver specimens. Gold and platinum are determined at concentrations of 5x10 - 11 g/g, and at higher levels. (author)

  17. Activation analytical determination of essential and toxic trace elements in biological material

    International Nuclear Information System (INIS)

    Schelenz, R.

    1980-01-01

    In order to determine the essential trace elements Hg, Ag, Cu and Se in food (potatoes, milk powder) and biological standard materials (fruit tree leaves), simple, fast radiochemical separation methods are worked out. Following oxidative decomposition and destillation of Hg, the elements silver, copper and selenium are found in the destillation residue and can be electrochemically enriched on an amalgamated Cu foil (determination of Ag and Se in the concentration range of 10 -9 to 10 -8 g, of Cu in the range of 10 -12 to 10 -10 g), whilst the matrix elements Na, K, P are adsorbed on a column with 3 different inorganic ion exchangers. The eluate of the ion exchanger can be added directly to the multielement gamma spectroscopy. The possiblity of working purely instrumentally is demonstrated by 2 examples: multielement analysis of human hair and river water. (RB) [de

  18. The application of extraction chromatography to the determination of radionuclides in biological and environmental samples

    International Nuclear Information System (INIS)

    Testa, C.; Delle Site, A.

    1976-01-01

    The paper describe the application of extraction chromatography to the determination of several alpha and beta emitters in biological and environmental samples. Both column extraction chromatography and batch extraction process have been used to isolate the radionuclides from the samples. The effect of several parameters (extractant concentration, support granulometry, stirring time, temperature, presence of a complexing agent) on the extraction and elution has been examined. The application of redox extraction chromatography is also described. A very simple and rapid determination of the activity retained on the column can be obtained by transferring the slurry to a counting vial and by adding the scintillation liquid for a direct detection of the α or β emission. The counting efficiencies obtained with this technique are compared with those obtained with ion exchange resins. The organic polymers used for the extraction chromatography give about 100% counting efficiency. The conventional ion exchange resin cannot be used to this purpose because of their strong light absorption. (T.G.)

  19. ANALYTICAL TECHNIQUES FOR THE DETERMINATION OF MELOXICAM IN PHARMACEUTICAL FORMULATIONS AND BIOLOGICAL SAMPLES

    Directory of Open Access Journals (Sweden)

    Aisha Noreen

    2016-06-01

    Full Text Available Meloxicam (MX belongs to the family of oxicams which is the most important group of non steroidal anti-inflammatory drugs (NSAIDs and is widely used for their analgesics and antipyretic activities. It inhibits both COX-I and COX-II enzymes with less gastric and local tissues irritation. A number of analytical techniques have been used for the determination of MX in pharmaceutical as well as in biological fluids. These techniques include titrimetry, spectrometry, chromatography, flow injection spectrometry, fluorescence spectrometry, capillary zone electrophoresis and electrochemical techniques. Many of these techniques have also been used for the simultaneous determination of MX with other compounds. A comprehensive review of these analytical techniques has been done which could be useful for the analytical chemists and quality control pharmacists.

  20. Analytical Strategies for the Determination of Norepinephrine Reuptake Inhibitors in Pharmaceutical Formulations and Biological Fluids.

    Science.gov (United States)

    Saka, Cafer

    2016-01-01

    Norepinephrine reuptake inhibitors (NRIs) are a class of antidepressant drugs that act as reuptake inhibitors for the neurotransmitters norepinephrine and epinephrine. The present review provides an account of analytical methods published in recent years for the determination of NRI drugs. NRIs are atomoxetine, reboxetine, viloxazine and maprotiline. NRIs with less activity at other sites are mazindol, bupropion, tapentadol, and teniloxazine. This review focuses on the analytical methods including chromatographic, spectrophotometric, electroanalytical, and electrophoresis techniques for NRI analysis from pharmaceutical formulations and biological samples. Among all of the published methods, liquid chromatography with UV-vis or MS-MS detection is the most popular technique. The most the common sample preparation techniques in the analytical methods for NRIs include liquid-liquid extraction and solid-phase extraction. Besides the analytical methods for single components, some of the simultaneous determinations are also included in this review.

  1. 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.

  2. Azimuthally invariant Mueller-matrix mapping of biological optically anisotropic network

    Science.gov (United States)

    Ushenko, Yu. O.; Vanchuliak, O.; Bodnar, G. B.; Ushenko, V. O.; Grytsyuk, M.; Pavlyukovich, N.; Pavlyukovich, O. V.; Antonyuk, O.

    2017-09-01

    A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular dichroism of histological sections liver tissue of mice with different degrees of severity of diabetes are found. The interconnections between such distributions and parameters of linear and circular dichroism of liver of mice tissue histological sections are defined. The comparative investigations of coordinate distributions of parameters of amplitude anisotropy formed by Liver tissue with varying severity of diabetes (10 days and 24 days) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular dichroism are defined. The objective criteria of cause of the degree of severity of the diabetes differentiation are determined.

  3. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

    Science.gov (United States)

    Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

    2017-04-12

    Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

  4. Relating Topological Determinants of Complex Networks to Their Spectral Properties: Structural and Dynamical Effects

    Science.gov (United States)

    Castellano, Claudio; Pastor-Satorras, Romualdo

    2017-10-01

    The largest eigenvalue of a network's adjacency matrix and its associated principal eigenvector are key elements for determining the topological structure and the properties of dynamical processes mediated by it. We present a physically grounded expression relating the value of the largest eigenvalue of a given network to the largest eigenvalue of two network subgraphs, considered as isolated: the hub with its immediate neighbors and the densely connected set of nodes with maximum K -core index. We validate this formula by showing that it predicts, with good accuracy, the largest eigenvalue of a large set of synthetic and real-world topologies. We also present evidence of the consequences of these findings for broad classes of dynamics taking place on the networks. As a by-product, we reveal that the spectral properties of heterogeneous networks built according to the linear preferential attachment model are qualitatively different from those of their static counterparts.

  5. Methylphenidate administration determines enduring changes in neuroglial network in rats.

    Science.gov (United States)

    Cavaliere, Carlo; Cirillo, Giovanni; Bianco, Maria Rosaria; Adriani, Walter; De Simone, Antonietta; Leo, Damiana; Perrone-Capano, Carla; Papa, Michele

    2012-01-01

    Repeated exposure to psychostimulant drugs induces complex molecular and structural modifications in discrete brain regions of the meso-cortico-limbic system. This structural remodeling is thought to underlie neurobehavioral adaptive responses. Administration to adolescent rats of methylphenidate (MPH), commonly used in attention deficit and hyperactivity disorder (ADHD), triggers alterations of reward-based behavior paralleled by persistent and plastic synaptic changes of neuronal and glial markers within key areas of the reward circuits. By immunohistochemistry, we observe a marked increase of glial fibrillary acidic protein (GFAP) and neuronal nitric oxide synthase (nNOS) expression and a down-regulation of glial glutamate transporter GLAST in dorso-lateral and ventro-medial striatum. Using electron microscopy, we find in the prefrontal cortex a significant reduction of the synaptic active zone length, paralleled by an increase of dendritic spines. We demonstrate that in limbic areas the MPH-induced reactive astrocytosis affects the glial glutamatergic uptake system that in turn could determine glutamate receptor sensitization. These processes could be sustained by NO production and synaptic rearrangement and contribute to MPH neuroglial induced rewiring. Copyright © 2011. Published by Elsevier B.V.

  6. Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

    In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  7. A novel visible spectrophotometric method for the determination of ethamsylate in pharmaceutical preparations and biological samples

    Science.gov (United States)

    Zhang, Meiyun; Zhang, Yan; Li, Quanmin

    2010-03-01

    A highly sensitive visible spectrophotometric method has been developed to determine ethamsylate in this paper, which is based on using Cu(II) as spectroscopic probe reagent. The study indicates that in the presence of SCN - and KNO 3, Cu(II) is reduced to Cu(I) by ethamsylate at pH 5.0, and the in situ formed Cu(I) reacts with SCN - to form into the white emulsion CuSCN that could be stayed upon the surface of water. According to the amount of residual Cu(II), the amount of ethamsylate can be indirectly determined. Under the optimal conditions, Beer's law is applicable in the range of 0.2-9.0 μg/mL (7.60 × 10 -7-3.42 × 10 -5 mol/L) for aqueous standard solution of ethamsylate with linear correlation coefficient of 0.9998. The detection limit and relative standard deviation are 0.12 μg/mL and 1.5%, respectively. And the molar absorption coefficient of the indirect determination of ethamsylate is 1.0 × 10 5 L/mol cm. The method is successfully applied to determine ethamsylate in pharmaceutical preparations and biological samples.

  8. Optimization and validation of spectrophotometric methods for determination of finasteride in dosage and biological forms

    Science.gov (United States)

    Amin, Alaa S.; Kassem, Mohammed A.

    2012-01-01

    Aim and Background: Three simple, accurate and sensitive spectrophotometric methods for the determination of finasteride in pure, dosage and biological forms, and in the presence of its oxidative degradates were developed. Materials and Methods: These methods are indirect, involve the addition of excess oxidant potassium permanganate for method A; cerric sulfate [Ce(SO4)2] for methods B; and N-bromosuccinimide (NBS) for method C of known concentration in acid medium to finasteride, and the determination of the unreacted oxidant by measurement of the decrease in absorbance of methylene blue for method A, chromotrope 2R for method B, and amaranth for method C at a suitable maximum wavelength, λmax: 663, 528, and 520 nm, for the three methods, respectively. The reaction conditions for each method were optimized. Results: Regression analysis of the Beer plots showed good correlation in the concentration ranges of 0.12–3.84 μg mL–1 for method A, and 0.12–3.28 μg mL–1 for method B and 0.14 – 3.56 μg mL–1 for method C. The apparent molar absorptivity, Sandell sensitivity, detection and quantification limits were evaluated. The stoichiometric ratio between the finasteride and the oxidant was estimated. The validity of the proposed methods was tested by analyzing dosage forms and biological samples containing finasteride with relative standard deviation ≤ 0.95. Conclusion: The proposed methods could successfully determine the studied drug with varying excess of its oxidative degradation products, with recovery between 99.0 and 101.4, 99.2 and 101.6, and 99.6 and 101.0% for methods A, B, and C, respectively. PMID:23781478

  9. Conditions for addressing environmental determinants of health behavior in intersectoral policy networks: A fuzzy set Qualitative Comparative Analysis.

    Science.gov (United States)

    Peters, D T J M; Verweij, S; Grêaux, K; Stronks, K; Harting, J

    2017-12-01

    Improving health requires changes in the social, physical, economic and political determinants of health behavior. For the realization of policies that address these environmental determinants, intersectoral policy networks are considered necessary for the pooling of resources to implement different policy instruments. However, such network diversity may increase network complexity and therefore hamper network performance. Network complexity may be reduced by network management and the provision of financial resources. This study examined whether network diversity - amidst the other conditions - is indeed needed to address environmental determinants of health behavior. We included 25 intersectoral policy networks in Dutch municipalities aimed at reducing overweight, smoking, and alcohol/drugs abuse. For our fuzzy set Qualitative Comparative Analysis we used data from three web-based surveys among (a) project leaders regarding network diversity and size (n = 38); (b) project leaders and project partners regarding management (n = 278); and (c) implementation professionals regarding types of environmental determinants addressed (n = 137). Data on budgets were retrieved from project application forms. Contrary to their intentions, most policy networks typically addressed personal determinants. If the environment was addressed too, it was mostly the social environment. To address environmental determinants of health behavior, network diversity (>50% of the actors are non-public health) was necessary in networks that were either small (policy networks in improving health behaviors by addressing a variety of environmental determinants. Copyright © 2017. Published by Elsevier Ltd.

  10. Iterative Systems Biology for Medicine – time for advancing from network signature to mechanistic equations

    KAUST Repository

    Gomez-Cabrero, David; Tegner, Jesper

    2017-01-01

    The rise and growth of Systems Biology following the sequencing of the human genome has been astounding. Early on, an iterative wet-dry methodology was formulated which turned out as a successful approach in deciphering biological complexity

  11. The Ocean Tracking Network and its contribution to ocean biological observation

    Science.gov (United States)

    Whoriskey, F. G.

    2016-02-01

    Animals move to meet their needs for food, shelter, reproduction and to avoid unfavorable environments. In aquatic systems, it is essential that we understand these movements if we are to sustainably manage populations and maintain healthy ecosystems. Thus the ability to document and monitor changes in aquatic animal movements is a biological observing system need. The Ocean Tracking Network (OTN) is a global research, technology development, and data management platform headquartered at Dalhousie University, in Halifax, Nova Scotia working to fill this need. OTN uses electronic telemetry to document the local-to-global movements and survival of aquatic animals, and to correlate them to oceanographic or limnological variables that are influencing movements. Such knowledge can assist with planning for and managing of anthropogenic impacts on present and future animal distributions, including those due to climate change. OTN works with various tracking methods including satellite and data storage tag systems, but its dominant focus is acoustic telemetry. OTN is built on global partnerships for the sharing of equipment and data, and has stimulated technological development in telemetry by bringing researchers with needs for new capabilities together with manufacturers to generate, test, and operationalize new technologies. This has included pioneering work into the use of marine autonomous vehicles (Slocum electric gliders; Liquid Robotics Wave Glider) in animal telemetry research. Similarly, OTN scientists worked with the Sea Mammal Research Unit to develop mobile acoustic receiver that have been placed on grey seals and linked via Bluetooth to a satellite transmitter/receiver. This provided receiver coverage in areas occupied by the seals during their typically extensive migrations and allowed for the examination of ecosystem linkages by documenting behavioral interactions the seals had with the physical environment, conspecifics, and other tagged species.

  12. Networks as a Privileged Way to Develop Mesoscopic Level Approaches in Systems Biology

    OpenAIRE

    Alessandro Giuliani

    2014-01-01

    The methodologies advocated in computational biology are in many cases proper system-level approaches. These methodologies are variously connected to the notion of “mesosystem” and thus on the focus on relational structures that are at the basis of biological regulation. Here, I describe how the formalization of biological systems by means of graph theory constitutes an extremely fruitful approach to biology. I suggest the epistemological relevance of the notion of graph resides in its multil...

  13. Biological and chemical tests of contaminated soils to determine bioavailability and environmentally acceptable endpoints (EAE)

    International Nuclear Information System (INIS)

    Montgomery, C.R.; Menzie, C.A.; Pauwells, S.J.

    1995-01-01

    The understanding of the concept of bioavailability of soil contaminants to receptors and its use in supporting the development of EAE is growing but still incomplete. Nonetheless, there is increased awareness of the importance of such data to determine acceptable cleanup levels and achieve timely site closures. This presentation discusses a framework for biological and chemical testing of contaminated soils developed as part of a Gas Research Institute (GRI) project entitled ''Environmentally Acceptable Endpoints in Soil Using a Risk Based Approach to Contaminated Site Management Based on Bioavailability of Chemicals in Soil.'' The presentation reviews the GRI program, and summarizes the findings of the biological and chemical testing section published in the GRI report. The three primary components of the presentation are: (1) defining the concept of bioavailability within the existing risk assessment paradigm, (2) assessing the usefulness of the existing tests to measure bioavailability and test frameworks used to interpret these measurements, and (3) suggesting how a small selection of relevant tests could be incorporated into a flexible testing scheme for soils to address this issue

  14. Bioanalytical methods for the determination of cocaine and metabolites in human biological samples.

    Science.gov (United States)

    Barroso, M; Gallardo, E; Queiroz, J A

    2009-08-01

    Determination of cocaine and its metabolites in biological specimens is of great importance, not only in clinical and forensic toxicology, but also in workplace drug testing. These compounds are normally screened for using sensitive immunological methods. However, screening methods are unspecific and, therefore, the posterior confirmation of presumably positive samples by a specific technique is mandatory. Although GC-MS-based techniques are still the most commonly used for confirmation purposes of cocaine and its metabolites in biological specimens, the advent of LC-MS and LC-MS/MS has enabled the detection of even lower amounts of these drugs, which assumes particular importance when sample volume available is small, as frequently occurs with oral fluid. This paper will review recently-published papers that describe procedures for detection of cocaine and metabolites, not only in the most commonly used specimens, such as blood and urine, but also in other 'alternative' matrices (e.g., oral fluid and hair) with a special focus on sample preparation and chromatographic analysis.

  15. DETERMINATION OF EDUCATIONAL EFFICIENCY AND STUDENTS’ INVOLVEMENT IN THE FLIPPED BIOLOGY CLASSROOM IN PRIMARY SCHOOL

    Directory of Open Access Journals (Sweden)

    Vera S. Županec

    2018-02-01

    Full Text Available The Flipped Classroom (FC is a teaching approach in which students gain the first-exposure learning with online materials outside the classroom, and then, in the classroom, they focus on interactive or engaging exercises. Despite its considerable publicity, the studies focused on the FC in primary education are deficient. The aim of this research is to determine efficiency and students’ involvement in the flipped Biology classroom in primary school, compared to the conventional classroom (CC approach. Educational efficiency and students’ involvement are measured by combining the values of the students’ performance and mental effort on the test. Each task in the test was followed by the 5-point Likert scale for evaluation of invested mental effort. The total sample of this research included 112 students, aged from 12 to 13. The results show that the FC approach contributes to the reduction of the students’ mental effort and an increase in the students’ performance. On the basis of calculated efficiency and students’ involvement of applied teaching approaches, it was concluded that the FC represents a feasible and efficient approach to Biology primary education.

  16. Experience of an inter-laboratory exercise for the determination of Carbon-14 in biological samples

    International Nuclear Information System (INIS)

    Baburajan, A.; Rajaram, S.; D'Souza, Renita Shiny; Nayak, Rasmi; Karunakara, N.; Ravi, P.M.; Tripathi, R.M.

    2018-01-01

    Carbon-14 is one of the naturally occurring cosmogenic nuclide with long half life of 5730 y and beta energy, E max : 156 keV produced continuously in the outer atmosphere. It is also produced by the anthropogenic activities like nuclear weapon test, nuclear power plant etc. contributing to the atmospheric inventory. The 14 CO 2 gets incorporated with the plant species during photosynthesis and ultimately reaches to man through food chain. It is important to accurately quantify the level of 14 C in different biological matrices for the computation of radiation dose due to ingestion. There are different methods available for the determination of 14 C in biological samples. The oxidation of the dried sample is one of the methods used for liberating the 14 CO 2 and which in turn re-absorbed using Carbo Sorb and subjected to Liquid scintillation analyses with Permaflour scintillator solution. The paper deals with the quality assurance programme initiated by ESL, Tarapur along with ESL, Kalpakkam and CARER, Mangalore University and share the experience of the inter-laboratory comparison exercise

  17. Determination of zinc stable isotopes in biological materials using isotope dilution inductively coupled plasma mass spectrometry

    International Nuclear Information System (INIS)

    Patterson, K.Y.; Veillon, Claude

    1992-01-01

    A method is described for using isotope dilution to determine both the amount of natural zinc and enriched isotopes of zinc in biological samples. Isotope dilution inductively coupled plasma mass spectrometry offers a way to quantify not only the natural zinc found in a sample but also the enriched isotope tracers of zinc. Accurate values for the enriched isotopes and natural zinc are obtained by adjusting the mass count rate data for measurable instrumental biases. Analytical interferences from the matrix are avoided by extracting the zinc from the sample matrix using diethylammonium diethyldithiocarbamate. The extraction technique separates the zinc from elements which form interfering molecular ions at the same nominal masses as the zinc isotopes. Accuracy of the method is verified using standard reference materials. The detection limit is 0.06 μg Zn per sample. Precision of the abundance ratios range from 0.3-0.8%. R.S.D. for natural zinc concentrations is about 200-600 μg g -1 . The accuracy and precision of the measurements make it possible to follow enriched isotopic tracers of zinc in biological samples in metabolic tracer studies. (author). 19 refs.; 1 fig., 4 tabs

  18. 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

  19. 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

  20. Cancer-related marketing centrality motifs acting as pivot units in the human signaling network and mediating cross-talk between biological pathways.

    Science.gov (United States)

    Li, Wan; Chen, Lina; Li, Xia; Jia, Xu; Feng, Chenchen; Zhang, Liangcai; He, Weiming; Lv, Junjie; He, Yuehan; Li, Weiguo; Qu, Xiaoli; Zhou, Yanyan; Shi, Yuchen

    2013-12-01

    Network motifs in central positions are considered to not only have more in-coming and out-going connections but are also localized in an area where more paths reach the networks. These central motifs have been extensively investigated to determine their consistent functions or associations with specific function categories. However, their functional potentials in the maintenance of cross-talk between different functional communities are unclear. In this paper, we constructed an integrated human signaling network from the Pathway Interaction Database. We identified 39 essential cancer-related motifs in central roles, which we called cancer-related marketing centrality motifs, using combined centrality indices on the system level. Our results demonstrated that these cancer-related marketing centrality motifs were pivotal units in the signaling network, and could mediate cross-talk between 61 biological pathways (25 could be mediated by one motif on average), most of which were cancer-related pathways. Further analysis showed that molecules of most marketing centrality motifs were in the same or adjacent subcellular localizations, such as the motif containing PI3K, PDK1 and AKT1 in the plasma membrane, to mediate signal transduction between 32 cancer-related pathways. Finally, we analyzed the pivotal roles of cancer genes in these marketing centrality motifs in the pathogenesis of cancers, and found that non-cancer genes were potential cancer-related genes.

  1. The determination of plutonium alpha activity in urine, faeces and biological materials

    International Nuclear Information System (INIS)

    Bains, M.E.D.

    1963-07-01

    Methods have been developed for the determination of plutonium alpha activity in urine, faeces and biological materials. The chemical stages involved give practically complete separation of all extraneous material from the plutonium, which is electrodeposited on to a 0.5 inch stainless steel disc to produce a thin high resolution source. The limit of detection is 0.025 μμc/sample (sixteen-hour count) when the sources are counted in a small scintillator counter, but is lowest when counted in a counter which counts particles of energy 5.05-5.25 MeV only, and which therefore discriminates against small quantities of α-active materials introduced with the reagents in the final electrodeposition stage of the process. (Any such alpha activity may readily be identified by alpha pulse height analysis). (author)

  2. Isolation of thymus gland fractions and the determination of their biological activity

    Directory of Open Access Journals (Sweden)

    MILENA RADETA

    2007-03-01

    Full Text Available A calf thymus extract was prepared and fractionated into lipid and non-lipid fractions. The non-lipid fraction was isolated from the calf thymus extract using the Folch method. The components isolated from the non-lipid fraction were characterized by IR, NMR, biuret and HPLC method. The results of the analyses indicated the presence of peptides. The lipid fraction contained phospholipids, glycolipids and neutral lipids. The biological activity of both the isolated lipid and peptide fractions was determined by the in vivo hemolytic plaques method in Wistar rats with an involuted thymus. The peptide and phospholipid fractions of the thymus extract showed a significant increase of hemolytic plaques. The glycolipid and neutral lipid fraction failed to express a significant immunological response.

  3. Determination of selenium in biological material by instrumental neutron activation analysis using 77m Se radioisotope

    International Nuclear Information System (INIS)

    Vasconcellos, Marina B.A.; Moreira, Edson G.; Catharino, Marilia G.M.; Tokura, Alexandra M.; Saiki, Mitiko

    1999-01-01

    Selenium is an essential element in human diet due to its relation to the protection against carcinogenic substances, heart disease, hypertension, sexual performance enhancement, and others. In this work Se concentration in samples of the biological certificate reference materials Human Hair BCR-CRM 397, Spiked Human Hair IAEA-085, Unspiked Human Hair IAEA-086; Dogfish Liver DOLT-1 and Dogfish Muscle DORM-1 were determined in order to improve the instrumental neutron activation analysis, INAA, method using 77m Se radioisotope. The application of this method allows the analysis of a large number of samples of samples with reduced time of experimental and cost. the best results were obtained with the reactor operating at 5 MW and time of irradiation between 10 and 20 s. In these experimental conditions the relative standard deviation and error were generally lower than 10%. (author)

  4. Method for determination of radioactive iodine isotopes in environmental objects and biologic materials

    International Nuclear Information System (INIS)

    Dubynin, O.D.; Pogodin, R.I.

    1981-01-01

    The method proposed for determination of radioactive iodine isotopes content in environmental objects and biologic materials is based on the extraction of iodine with carbon tetrachloride and subsequent precipitation of bismuthyl iodine (BiOI) in perchloric medium. Sample preparation for analysis is carried out using conventional alkaline ashing methods. Quantitative iodine separation is hampered if macroquantities of Cl - , Br - , SO 4 2 - , SO 8 2 - , Cr 2 O 7 2 - and other ions are present in the solution. Iodine extraction is carried out before its precipitation. Separated iodine preparation activity is measured using scintillation (NaI) Tl gamma spectrometer. The method's sensitivity when measuring iodine-131 preparations makes up 0.07 Bq per 1 sample with the error +-25 %

  5. Evaluation of botanical reference materials for the determination of vanadium in biological samples

    International Nuclear Information System (INIS)

    Heydorn, K.; Damsgaard, E.

    1982-01-01

    Three botanical reference materials prepared by the National Bureau of Standards have been studied by neutron activation analysis to evaluate their suitability with respect to the determination of vanadium in biological samples. Various decomposition methods were applied in connection with chemical or radiochemical separations, and results for vanadium were compared with those found by purely instrumental neutron activation analysis. Significantly lower results indicate losses or incomplete dissolution, which makes SRM 1575 Pine Needles and SRM 1573 Tomato Leaves less satisfactory than SRM 1570 Spinach. A reference value of 1.15 mg/kg of this material is recommended, based on results from 3 different methods. All three materials are preferable to SRM 1571 Orchard Leaves, while Bowen's Kale remains the material of choice because of its lower concentration. (author)

  6. Applicability of discovery science approach to determine biological effects of mobile phone radiation.

    Science.gov (United States)

    Leszczynski, Dariusz; Nylund, Reetta; Joenväärä, Sakari; Reivinen, Jukka

    2004-02-01

    We argue that the use of high-throughput screening techniques, although expensive and laborious, is justified and necessary in studies that examine biological effects of mobile phone radiation. The "case of hsp27 protein" presented here suggests that even proteins with only modestly altered (by exposure to mobile phone radiation) expression and activity might have an impact on cell physiology. However, this short communication does not attempt to present the full scientific evidence that is far too large to be presented in a single article and that is being prepared for publication in three separate research articles. Examples of the experimental evidence presented here were designed to show the flow of experimental process demonstrating that the use of high-throughput screening techniques might help in rapid identification of the responding proteins. This, in turn, can help in speeding up of the process of determining whether these changes might affect human health.*

  7. The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

    Science.gov (United States)

    Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J; Inzé, Dirk; Van de Peer, Yves

    2013-03-01

    Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

  8. Determination of 25 elements in biological standard reference materials by neutron activation analysis

    International Nuclear Information System (INIS)

    Guzzi, G.; Pietra, R.; Sabbioni, E.

    1974-12-01

    Standard and Certified Reference Materials programme of the JRC includes the determination of trace elements in complex biological samples delivered by the U.S. National Bureau of Standards: Bovine liver (NBS SRM 1577), Orchard Leaves (NBS SRM 1571) and Tomato Leaves. The study has been performed by the use of neutron activation analysis. Due to the very low concentration of some elements, radiochemical groups or elemental separation procedures were necessary. The paper describes the techniques used to analyse 25 elements. Computer assisted instrumental neutron activation analysis with high resolution Ge(Li) spectrometry was considerably advantageous in the determination of Na, K, Cl, Mn, Fe, Rb and Co and in some cases of Ca, Zn, Cs, Sc, and Cr. For low contents of Ca, Mg, Ni and Si special chemical separation schemes, followed by Cerenkov counting have been developped. Two other separation procedures allowing the determination of As, Cd, Ga, Hg, Mo, Cu, Sr Se, Ba and P have been set up. The first, the simplified one involves the use of high resolution Ge(Li) detectors, the second, the more complete one involves a larger number of shorter measurements performed by simpler and more sensitive techniques, such as NaI(Tl) scintillation spectrometry and Cerenkov counting. The results obtained are presented and discussed

  9. Analytical applications of oscillatory chemical reactions: determination of some pharmaceuticaly and biologically important compounds

    Directory of Open Access Journals (Sweden)

    Pejić Nataša D.

    2012-01-01

    Full Text Available Novel analytical methods for quantitive determination of analytes based on perturbations of oscillatory chemical reactions realized under open reactor conditions (continuosly fed well stirred tank reactor, CSTR, have been developed in the past twenty years. The proposed kinetic methods are generally based on the ability of the analyzed substances to change the kinetics of the chemical reactions matrix. The unambiguous correlation of quantitative characteristics of perturbations, and the amount (concentration of analyte expressed as a regression equation, or its graphics (calibration curve, enable the determination of the unknown analyte concentration. Attention is given to the development of these methods because of their simple experimental procedures, broad range of linear regression ( 10-7 10-4 mol L-1 and low limits of detection of analytes ( 10-6 10-8 mol L1, in some cases even lower than 10-12 mol L-1. Therefore, their application is very convenient for routine analysis of various inorganic and organic compounds as well as gases. This review summarizes progress made in the past 5 years on quantitative determination of pharmaceutically and biologically important compounds.

  10. Preconcentration and determination of heavy metals in water, sediment and biological samples

    Directory of Open Access Journals (Sweden)

    Shirkhanloo Hamid

    2011-01-01

    Full Text Available In this study, a simple, sensitive and accurate column preconcentration method was developed for the determination of Cd, Cu and Pb ions in river water, urine and sediment samples by flame atomic absorption spectrometry. The procedure is based on the retention of the analytes on a mixed cellulose ester membrane (MCEM column from buffered sample solutions and then their elution from the column with nitric acid. Several parameters, such as pH of the sample solution, volume of the sample and eluent and flow rates of the sample were evaluated. The effects of diverse ions on the preconcentration were also investigated. The recoveries were >95 %. The developed method was applied to the determination of trace metal ions in river water, urine and sediment samples, with satisfactory results. The 3δ detection limits for Cu, Pb and Cd were found to be 2, 3 and 0.2 μg dm−3, respectively. The presented procedure was successfully applied for determination of the copper, lead and cadmium contents in real samples, i.e., river water and biological samples.

  11. Determination of total mercury and methylmercury in biological samples by photochemical vapor generation

    Energy Technology Data Exchange (ETDEWEB)

    Vieira, Mariana A.; Ribeiro, Anderson S.; Curtius, Adilson J. [Universidade Federal de Santa Catarina, Departamento de Quimica, Florianopolis, SC (Brazil); Sturgeon, Ralph E. [National Research Council Canada, Institute for National Measurement Standards, Ottawa, ON (Canada)

    2007-06-15

    Cold vapor atomic absorption spectrometry (CV-AAS) based on photochemical reduction by exposure to UV radiation is described for the determination of methylmercury and total mercury in biological samples. Two approaches were investigated: (a) tissues were digested in either formic acid or tetramethylammonium hydroxide (TMAH), and total mercury was determined following reduction of both species by exposure of the solution to UV irradiation; (b) tissues were solubilized in TMAH, diluted to a final concentration of 0.125% m/v TMAH by addition of 10% v/v acetic acid and CH{sub 3}Hg{sup +} was selectively quantitated, or the initial digests were diluted to 0.125% m/v TMAH by addition of deionized water, adjusted to pH 0.3 by addition of HCl and CH{sub 3}Hg{sup +} was selectively quantitated. For each case, the optimum conditions for photochemical vapor generation (photo-CVG) were investigated. The photochemical reduction efficiency was estimated to be {proportional_to}95% by comparing the response with traditional SnCl{sub 2} chemical reduction. The method was validated by analysis of several biological Certified Reference Materials, DORM-1, DORM-2, DOLT-2 and DOLT-3, using calibration against aqueous solutions of Hg{sup 2+}; results showed good agreement with the certified values for total and methylmercury in all cases. Limits of detection of 6 ng/g for total mercury using formic acid, 8 ng/g for total mercury and 10 ng/g for methylmercury using TMAH were obtained. The proposed methodology is sensitive, simple and inexpensive, and promotes ''green'' chemistry. The potential for application to other sample types and analytes is evident. (orig.)

  12. Determination of tin in biological reference materials by atomic absorption spectrophotometry and neutron activation analysis

    International Nuclear Information System (INIS)

    Chiba, M.; Iyengar, V.; Gills, T.

    1991-01-01

    Because of a lack of reliable analytical techniques for the determination of tin in biological materials, there have been no reference materials certified for this element. However, the authors' experience has shown that it is feasible to use both atomic absorption and nuclear activation techniques at least for selected matrices. Therefore, an investigation was undertaken to determine tin in several biological materials such as non-fat milk powder (NBS-SRM-1549), citrus leaves (NBS-SRM-1572), total diet (NIST-SRM-1548), mixed diet (NBS-RM-8431), and USDIET-I by atomic absorption spectrophotometry (AAS) and neutron activation analysis (NAA). AAS-ashed samples were extracted with MIBK and assayed using a Perkin Elmer model 5000 apparatus. NAA was carried out by irradiating the samples at the NIST reactor in the RT-4 facility and counting with the help of a Ge(Li) detector connected to a multichannel analyzer. The concentration of tin measured by both AAS and NAA agree well for USDIET-I, total diet, citrus leaves and non-fat milk powder (the concentration ranges for tin in these matrices were from 0.0025 to 3.8 micro g/g). However, in the case of mixed diet (RM-8431), the mean values found were 47 ± 5.6 (n = 19) by AAS and 55.5 ± 2.5 (n = 6) by INAA. Since RM-8431 is not certified it is difficult to draw conclusions. For apple and peach leaves, a distillation step was required. The results were apple leaves 0.085 ± 0.015 (n = 10) by AAS and < 0.2 (n = 3) by RNAA; for peach leaves 0.077 ± 0.02 (n = 9) by AAS and < 0.1 (n = 3) by RNAA. All concentrations are expressed in micro g/g dry weight

  13. Mixing regime as a key factor to determine DON formation in drinking water biological treatment.

    Science.gov (United States)

    Lu, Changqing; Li, Shuai; Gong, Song; Yuan, Shoujun; Yu, Xin

    2015-11-01

    Dissolved organic nitrogen (DON) can act as precursor of nitrogenous disinfection by-products formed during chlorination disinfection. The performances of biological fluidized bed (continuous stirred tank reactor, CSTR) and bio-ceramic filters (plug flow reactor, PFR) were compared in this study to investigate the influence of mixing regime on DON formation in drinking water treatment. In the shared influent, DON ranged from 0.71mgL(-1) to 1.20mgL(-1). The two biological fluidized bed reactors, named BFB1 (mechanical stirring) and BFB2 (air agitation), contained 0.12 and 0.19mgL(-1) DON in their effluents, respectively. Meanwhile, the bio-ceramic reactors, labeled as BCF1 (no aeration) and BCF2 (with aeration), had 1.02 and 0.81mgL(-1) DON in their effluents, respectively. Comparative results showed that the CSTR mixing regime significantly reduced DON formation. This particular reduction was further investigated in this study. The viable/total microbial biomass was determined with propidium monoazide quantitative polymerase chain reaction (PMA-qPCR) and qPCR, respectively. The results of the investigation demonstrated that the microbes in BFB2 had higher viability than those in BCF2. The viable bacteria decreased more sharply than the total bacteria along the media depth in BCF2, and DON in BCF2 accumulated in the deeper media. These phenomena suggested that mixing regime determined DON formation by influencing the distribution of viable, total biomass, and ratio of viable biomass to total biomass. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. TLC-spectrophometric separation and trace determination of monocrotophos and dichlorvos in enviromental and biological samples.

    Science.gov (United States)

    Janghel, Etesh K; Rai, J K; Khan, S; Rai, M K; Gupta, V K

    2007-04-01

    Organophosphorus insecticides, monocrotophos and dichlrovos are increasingly being used in agriculture to control insects on a wide range of crops. Their ready access has resulted in misuse in many instances of homicidal and suicidal poisoning cases. This paper describes about a chromogenic spray reagent for the detection/determination of monocrophos and dichlrovos in environmental and biological samples by TLC and spectrophotometric method. Monocrotophos and dichlorvos on alkaline hydrolysis yield N-methyl acetoacetamide and dichlroacetaldehyde respectively, which in turn react with diazotized p-amino acetophenone to give red-violet and red coloured compounds. Other organophosphorus insecticides do not give this reaction. Moreover, organochlorine and synthetic pyrethroid insecticides and constituents of viscera (amino acids, peptides, proteins etc), which are generally coextracted with the insecticides, do not interfere. However, phenolic compounds and hydrolysed product of carbamate insecticides may interfere and differentiate from monocrotophos and dichlrovos by Rf values. The lower limit of detection is 0.2 mg for monocrotophos and 0.1 mg for dichlorovos. The absorption maxima of the reddish-violet and red colour formed by monocrotophos and dichlrovos, are measured at 560 nm and 540 nm respectively. Beer's Law is obeyed over the concentration range of 1.2 to 6.8 mg and 6.2 to 35 mg in the final solution volume of 25 mL. The molar absorptivity and Sandell's sensitivity of monocrotophos and dichlrovos were found to be 7.1 x 10(5) (+100) 1 mole(-1) cm(-1) and 0.008 mg cm(-2), 1.2 x 10(5) 1 mole(-1) cm(-1) and 0.003 mg cm(-2) respectively. The standard deviation and relative standard deviation were found be +/- 0.005 and 2.05% +/- 0.007 and 2.02% respectively. The developed method has been successfully applied to the detection and determination of monocrotophos and dichlrovos in environmental and biological samples.

  15. ALOUD: Adult Learning Open University Determinants Study: Association between biological and psychological determinants and study success in adult formal distance education

    NARCIS (Netherlands)

    De Groot, Renate; Neroni, Joyce; Gijselaers, Jérôme; Kirschner, Paul A.

    2012-01-01

    De Groot, R. H. M., Neroni, J., Gijselaers, J., & Kirschner, P. A. (2012, 6 December). ALOUD: Adult Learning Open University Determinants Study: Associations between biological and psychological determinants and study success in adult formal distance education. Presented at the Open University for

  16. Nodes and biological processes identified on the basis of network analysis in the brain of the senescence accelerated mice as an Alzheimer’s disease animal model

    Directory of Open Access Journals (Sweden)

    Xiao-Rui eCheng

    2013-10-01

    Full Text Available Harboring the behavioral and histopathological signatures of Alzheimer’s disease (AD, senescence accelerated mouse-prone 8 (SAMP8 mice are currently considered a robust model for studying AD. However, the underlying mechanisms, prioritized pathways and genes in SAMP8 mice linked to AD remain unclear. In this study, we provide a biological interpretation of the molecular underpinnings of SAMP8 mice. Our results were derived from differentially expressed genes in the hippocampus and cerebral cortex of SAMP8 mice compared to age-matched SAMR1 mice at 2, 6, and 12 months of age using cDNA microarray analysis. On the basis of PPI, MetaCore and the co-expression network, we constructed a distinct genetic sub-network in the brains of SAMP8 mice. Next, we determined that the regulation of synaptic transmission and apoptosis were disrupted in the brains of SAMP8 mice. We found abnormal gene expression of RAF1, MAPT, PTGS2, CDKN2A, CAMK2A, NTRK2, AGER, ADRBK1, MCM3AP and STUB1, which may have initiated the dysfunction of biological processes in the brains of SAMP8 mice. Specifically, we found microRNAs, including miR-20a, miR-17, miR-34a, miR-155, miR-18a, miR-22, miR-26a, miR-101, miR-106b and miR-125b, that might regulate the expression of nodes in the sub-network. Taken together, these results provide new insights into the biological and genetic mechanisms of SAMP8 mice and add an important dimension to our understanding of the neuro-pathogenesis in SAMP8 mice from a systems perspective.

  17. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  18. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  19. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

    Directory of Open Access Journals (Sweden)

    Lan Chung-Yu

    2008-09-01

    Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

  20. Determination of parathion in biological fluids by means of direct solid-phase microextraction.

    Science.gov (United States)

    Gallardo, E; Barroso, M; Margalho, C; Cruz, A; Vieira, D N; López-Rivadulla, M

    2006-11-01

    A new and simple procedure for the determination of parathion in human whole blood and urine using direct immersion (DI) solid-phase microextraction (SPME) and gas chromatography/mass spectrometry (GC/MS) is presented. This technique was developed using only 100 microL of sample, and ethion was used as internal standard (IS). A 65-microm Carbowax/divinylbenzene (CW/DVB) SPME fibre was selected for sampling, and the main parameters affecting the SPME process such as extraction temperature, adsorption and desorption time, salt addition, agitation and pH effect were optimized to enhance the sensitivity of the method. This optimization was also performed to allow the qualitative determination of parathion's main metabolite, paraoxon, in blood. The limits of detection and quantitation for parathion were 3 and 10 ng/mL for urine and 25 and 50 ng/mL for blood, respectively. For paraoxon, the limit of detection was 50 ng/mL in blood. The method showed linearity between the LOQ and 50 microg/mL for both matrices, with correlation coefficients ranging from 0.9954 to 0.9999. Precision and accuracy were in conformity with the criteria normally accepted in bioanalytical method validation. The mean absolute recoveries were 35.1% for urine and 6.7% for blood. Other parameters such as dilution of sample and stability were also validated. Its simplicity and the fact that only 100 microL of sample is required to accomplish the analysis make this method useful in forensic toxicology laboratories to determine this compound in intoxications, and it can be considered an alternative to other methods normally used for the determination of this compound in biological media.

  1. 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...

  2. 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.

  3. 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.

  4. Application of ion mobility spectrometry for the determination of tramadol in biological samples

    Directory of Open Access Journals (Sweden)

    Ali Sheibani

    2014-12-01

    Full Text Available In this study, a simple and rapid ion mobility spectrometry (IMS method has been described for the determination of tramadol. The operating instrumental parameters that could influence IMS were investigated and optimized (temperature; injection: 220 and IMS cell: 190°C, flow rate; carrier: 300 and drift: 600 mL/minute, voltage; corona: 2300 and drift: 7000 V, pulse width: 100 μs. Under optimum conditions, the calibration curves were linear within two orders of magnitude with R2 ≥ 0.998 for the determination of tramadol in human plasma, saliva, serum, and urine samples. The limits of detection and the limits of quantitation were between 0.1 and 0.3 and 0.3 and 1 ng/mL, respectively. The relative standard deviations were between 7.5 and 8.8%. The recovery results (90–103.9% indicate that the proposed method can be applied for tramadol analysis in different biological samples.

  5. Determination of traces of lithium in biological, environmental and metal samples by neutron activation analysis

    International Nuclear Information System (INIS)

    Yang, J.Y.; Tseng, C.L.; Lo, J.M.; Yang, M.H.

    1985-01-01

    Lithium in environmental, biological and metal samples was determined by neutron activation analysis via the 6 Li(n,α)T and 16 O(T,n) 18 F reactions. The samples were converted to aqueous solutions either by dissolution or by digestion and their aliquots were irradiated in a nuclear reactor for 2 h. The irradiated sample solution, was placed in a ZrO 2 column on which the 18 F nuclide was adsorbed. Most of the coexisting nuclides 24 Na, 82 Br, 38 Cl, 64 Cu, etc. were separated by elution with pH 1proportional3 solution. The column was subjected to a Ge(Li) detector for γ-ray spectrometry. The lithium content in the sample was estimated from the 18 F activity obtained. The matrix effect can be eliminated by either strong dilution of the samples in aqueous medium or by the method of standard addition. Lithium can be determined with high precision and accuracy in sub-ppm samples. (orig.) [de

  6. Simultaneous determination of arsenic, copper, manganese, selenium, and zinc in biological materials by neutron activation analysis

    International Nuclear Information System (INIS)

    Damsgaard, E.; Heydorn, K.

    1976-08-01

    A method for the simultaneous determination of arsenic, copper, manganese, selenium, and zinc in biological material was developed by the incorporation of separation procedures for copper and zinc into an existing procedure. Investigation of the performance characteristics of the method was carried out with reference to copper and zinc. For certain materials characterized by a high Cu/Zn ratio, or a high zinc content, or both, such as liver, copper ihterferes in the determination of zinc thus requiring a small correction by an iterative procedure. Blank values for copper depend on the rinsing of the irradiation container, and a single rinsing with redistilled water was found superior to other rinsing procedures. Nuclear interference was negligible. The accuracy of the method was checked by analysis of Standard Reference Materials and the precision verified by analysis of Intercomparison Samples. Results are presented for 5 male foetuses of 3-5 months' gestational age. The distribution of arsenic, manganese and selenium is similar to that previously reported for adults. With the exception of liver, concentrations of copper in foetal organs were lower than values in the literature indicate. (author)

  7. Study on Solid Phase Extraction and Spectrophotometric Determination of Nickel in Waters and Biological Samples

    International Nuclear Information System (INIS)

    Hu, Qiufen; Yang, Guangyu; Huang, Zhangjie; Yin, Jiayuan

    2004-01-01

    A sensitive, selective and rapid method for the determination of nickel based on the rapid reaction of nickel(II) with QADMAA and the solid phase extraction of the Ni(II)-QADMAA chelate with C 18 membrane disks has been developed. In the presence of pH 6.0 buffer solution and sodium dodecyl sulfonate (SDS) medium, QADMAA reacts with nickel to form a violet complex of a molar ratio of 1 : 2 (nickel to QADMAA). This chelate was enriched by solid phase extraction with C 18 membrane disks. An enrichment factor of 50 was obtained by elution of the chelates form the disks with the minimal amount of isopentyl alcohol. The molar absorptivity of the chelate was 1.32 x 10 5 L mol -1 cm -1 at 590 nm in the measured solution. Beer's law was obeyed in the range of 0.01-0.6 μg/mL. This method was applied to the determination of nickel in water and biological samples with good results

  8. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  9. Simple Sensitive Spectrophotometric Determination of Vanadium in Biological and Environmental Samples

    Directory of Open Access Journals (Sweden)

    B. Krishna Priya

    2006-01-01

    Full Text Available Novel, rapid, highly sensitive and selective spectrophotometric method for the determination of traces of vanadium (V in environmental and biological samples, pharmaceutical and steel samples was studied. The method is based on oxidation of 2,4- dinitro phenyl hydrazine(2,4-DNPH by vanadium (V followed by coupling reaction with N-(1-naphthalene-1-ylethane-1,2-diamine-dihydrochloride (NEDA in acidic medium to give red colored derivative or on oxidation of 4-Amino Pyridine by vanadium (V followed by coupling reaction with NEDA in basic medium to give pink colored derivative. The red colored derivative having an λmax 495 nm which is stable for 8 days and the pink colored derivative with 525 nm is stable for more than 7 days at 350C. Beer's law is obeyed for vanadium (V in the concentration range of 0.02 - 3.5 μg mL–1 (red derivative and 0.03 – 4.5 μg mL–1 (pink derivative at the wave length of maximum absorption. The optimum reaction conditions and other analytical parameters were investigated to enhance the sensitivity of the present method. The detailed study of various interferences made the method more selective. The proposed method was successfully applied to the analysis of vanadium in natural water samples, plant material, soil samples, synthetic mixtures, pharmaceutical samples and biological samples. The results obtained were agreed with the reported methods at the 95 % confidence level. The performance of proposed method was evaluated in terms of Student's t-test and Variance ratio f-test which indicates the significance of proposed method over reported method.

  10. Study to Determine the Biological Feasibility of a New Fish Tagging System : Annual Report 1983.

    Energy Technology Data Exchange (ETDEWEB)

    Prentice, Earl F.; Park, D.L.

    1984-05-01

    Pacific salmon are tagged or marked as a critical part of numerous research and management studies. A new tag called the PIT (passive integrated transponder) tag measuring 7.5 mm long by 1.5 mm in diameter has a great potential for marking fish if it proves to be biologically compatible. A study was conducted to evaluate the potential of the PIT tag for marking salmonids. The objectives of the first year's research were to determine: (1) the anatomical areas in which the tag could be placed; (2) tissue response to the tag; and (3) tag retention. Juvenile coho, Oncorhynchus kisutch, and chinook O. tshawytscha, salmon and adult chinook salmon held at Manchester or Big Beef Creek, Washington, were used as test animals. Juvenile salmon were injected with sham PIT tags in the body cavity and opercular, dorsal, and caudal masculature. The fish ranged in length from 126 to 212 mm. Observations based on three tests, from 44 to 102 days long, indicated that the dorsal musculature and body cavity were the best locations to inject the tag from biological and social standpoints. Sham PIT tags were injected into the nose; body cavity; and opercular, dorsal, and caudal musculature of jack chinook salmon. The test was conducted for 23 days. Although all five anatomical areas were acceptable from a technical standpoint, the body cavity appeared to be the best area for tag placement. Initial test results with the Sham PIT tag were very encouraging. Apparently the PIT tag can be successfully injected into and carried by salmon, making it a potentially useful tool for fisheries biologists. 5 refs., 8 figs., 6 tabs.

  11. Adaptive online state-of-charge determination based on neuro-controller and neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

    2010-05-15

    This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

  12. The feasibility of using neural networks for determination of control rod elevation in a PWR

    International Nuclear Information System (INIS)

    Garis, N.S.; Temesvari, E.; Pazsit, I.

    1996-08-01

    This paper presents the results of a preliminary study on using neural networks for determination of the axial position of control rods in PWRs. The method is based on the dependence of the axial flux profile on control rod elevation in a reactor. This flux profile can be measured by e.g. a moveable detector in an operating plant. However, in this preliminary study the flux profile is only calculated using an advanced core code for several axial positions of a partially inserted control rod. The calculated fluxes with corresponding positions of the control rod are used for training a neural network. Using the trained network it is then possible to determine the unknown axial position of a control rod elevation from the corresponding axial flux profile. 10 refs

  13. Evaluation rate of aging person based on determination of biological age

    Directory of Open Access Journals (Sweden)

    V. Fil

    2015-01-01

    2Kazimierz Wielki University, Bydgoszcz, Poland   Abstract The article considers the value of biological and passport age of student youth. In experiment participated 140 students aged 17-19 years. Calculated their biological age. Reviewed pace of aging of the body of students. Detected indexes that have the strongest relationship with indicators of biological age. The results compared with researchers of other regions of Ukraine.   Keywords: chronological (passport age, biological age, appropriate biological age, pulse blood pressure, rate of aging, static balancing, self-reported level of health.

  14. European network on the determination of site end points for radiologically contaminated land

    International Nuclear Information System (INIS)

    Booth, Peter; Lennon, Chris

    2007-01-01

    Nexia Solutions are currently running a small European network entitled 'European Network on the Determination of Site End Points for Radiologically Contaminated Land (ENDSEP)'. Other network members include NRG (Netherlands), UKAEA (UK), CEA (France), SOGIN (Italy), Wismut (Germany), Saxon State Agency of Environment and Geology (Germany). The network is focused on the technical and socio-economical issues associated with the determination of end points for sites potentially, or actually, impacted by radiological contamination. Such issues will cover: - Those associated with the run up to establishing a site end point; - Those associated with verifying that the end points have been met; and Those associated with post closure. The network's current high level objectives can be summarized as follows: Share experience and best practice in the key issues running up to determining site end points; Gain a better understanding of the potential effects of recent and forthcoming EU legislation; Assess consistency between approaches; Highlight potential gaps within the remit of site end point determination and management; and - Consider the formulation o