WorldWideScience

Sample records for biological network determination

  1. Efficient, sparse biological network determination

    Directory of Open Access Journals (Sweden)

    Papachristodoulou Antonis

    2009-02-01

    Full Text Available Abstract Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational and uncertainties in the data are taken into account. It also produces a network structure that can

  2. 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.

  3. 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.

  4. Dynamic properties of network motifs contribute to biological network organization.

    Directory of Open Access Journals (Sweden)

    Robert J Prill

    2005-11-01

    Full Text Available Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property--stability or robustness to small perturbations--is highly correlated with the relative abundance of small subnetworks (network motifs in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks.

  5. Logical impossibilities in biological networks

    Directory of Open Access Journals (Sweden)

    Monendra Grover

    2011-10-01

    Full Text Available Biological networks are complex and involve several kinds of molecules. For proper biological function it is important for these biomolecules to act at an individual level and act at the level of interaction of these molecules. In this paper some of the logical impossibilities that may arise in the biological networks and their possible solutions are discussed. It may be important to understand these paradoxes and their possible solutions in order to develop a holistic view of biological function.

  6. 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.…

  7. Correlation Effects in Biological Networks

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    A.A. Bagdasaryan

    2012-06-01

    Full Text Available Review of the complex network theory is presented and classification of such networks in accordance with the main statistical characteristics is considered. For the adjacency matrix of a real neural network the shortest distances for each pair of nodes as well as the node degree distribution and cluster coefficients are calculated. Comparison of the main statistical parameters with the random network is performed, and based on this, the conclusions about the correlation phenomena in biological system are made.

  8. Determinants of Network Outcomes

    DEFF Research Database (Denmark)

    Ysa, Tamyko; Sierra, Vicenta; Esteve, Marc

    2014-01-01

    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......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...... 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...

  9. Discovering large network motifs from a complex biological network

    Energy Technology Data Exchange (ETDEWEB)

    Terada, Aika; Sese, Jun, E-mail: terada@sel.is.ocha.ac.j, E-mail: sesejun@is.ocha.ac.j [Department of Computer Science, Ochanomizu University, 2-1-1 Ohtsuka, Bunkyo-ku, Tokyo 112-8610 (Japan)

    2009-12-01

    Graph structures representing relationships between entries have been studied in statistical analysis, and the results of these studies have been applied to biological networks, whose nodes and edges represent proteins and the relationships between them, respectively. Most of the studies have focused on only graph structures such as scale-free properties and cliques, but the relationships between nodes are also important features since most of the proteins perform their functions by connecting to other proteins. In order to determine such relationships, the problem of network motif discovery has been addressed; network motifs are frequently appearing graph structures in a given graph. However, the methods for network motif discovery are highly restrictive for the application to biological network because they can only be used to find small network motifs or they do not consider noise and uncertainty in observations. In this study, we introduce a new index to measure network motifs called AR index and develop a novel algorithm called ARIANA for finding large motifs even when the network has noise. Experiments using a synthetic network verify that our method can find better network motifs than an existing algorithm. By applying ARIANA to a real complex biological network, we find network motifs associated with regulations of start time of cell functions and generation of cell energies and discover that the cell cycle proteins can be categorized into two different groups.

  10. Attentional Networks and Biological Motion

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    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.

  11. Network dynamics and systems biology

    Science.gov (United States)

    Norrell, Johannes A.

    The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior. In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects---an asymmetry between on and off states, and a decaying memory of events in each element's inputs---that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors. Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the

  12. Programming and engineering biological networks.

    Science.gov (United States)

    Chin, Jason W

    2006-08-01

    Synthetic biology aims to build new functions in living organisms. Recent work has addressed the creation of synthetic epigenetic switches in mammalian cells and synthetic intracellular communication. Fundamentally new, and potentially scaleable, modes of gene regulation have been created that enable expansion of the scope of synthetic circuits. Increasingly sophisticated models of gene regulation that include stochastic effects are beginning to predict the behaviour of small synthetic networks. Overall, these advances suggest that a combination of molecular engineering and systems engineering should allow the creation of living matter capable of performing many useful and novel functions.

  13. 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 ...

  14. Some physics problems in biological networks

    Science.gov (United States)

    Bialek, William

    2007-03-01

    Most of the interesting things that happen in living organisms require interactions among many components, and it is convenient to think of these as a ``network'' of interactions. We use this language at the level of single molecules (the network of interactions among amino acids that determine protein structure), single cells (the network of protein-DNA interactions responsible for the regulation of gene expression) and complex multicellular organisms (the networks of neurons in our brain). In this talk I'll try to look at two very different kinds of theoretical physics problems that arise in thinking about such networks. The first problems are phenomenological: Given what our experimentalists friends can measure, can we generate a global view of network function and dynamics? I'll argue that maximum entropy methods can be useful here, and show how such methods have been used in very recent work on networks of neurons, enzymes, genes and (in disguise) amino acids. In this line of reasoning there are of course interesting connections to statistical mechanics, and we'll see that natural statistical mechanics questions about the underlying models actually teach us something about how the real biological system works, in ways that will be tested through new experiments. In the second half of the talk I'll ask if there are principles from which we might actually be able to predict the structure and dynamics of biological networks. I'll focus on optimization principles, in particular the optimization of information flow in transcriptional regulation. Even setting up these arguments forces us to think critically about our understanding of the signals, specificity and noise in these systems, all current topics of research. Although we don't know if we have the right principles, trying to work out the consequences of such optimization again suggests new experiments.

  15. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  16. 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.

  17. Network systems biology for targeted cancer therapies

    Institute of Scientific and Technical Information of China (English)

    Ting-Ting Zhou

    2012-01-01

    The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

  18. Network mapping and usage determination

    CSIR Research Space (South Africa)

    Senekal, FP

    2007-07-01

    Full Text Available A large computer network such as the Internet contains millions of computers, services and users, interconnected in a complicated and ever changing web. This article provides an introduction to network mapping and usage determination – the study...

  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.

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    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. Biologically inspired self-organizing networks

    Institute of Scientific and Technical Information of China (English)

    Naoki WAKAMIYA; Kenji LEIBNITZ; Masayuki MURATA

    2009-01-01

    Information networks are becoming more and more complex to accommodate a continuously increasing amount of traffic and networked devices, as well as having to cope with a growing diversity of operating environments and applications. Therefore, it is foreseeable that future information networks will frequently face unexpected problems, some of which could lead to the complete collapse of a network. To tackle this problem, recent attempts have been made to design novel network architectures which achieve a high level of scalability, adaptability, and robustness by taking inspiration from self-organizing biological systems. The objective of this paper is to discuss biologically inspired networking technologies.

  2. Activating and inhibiting connections in biological network dynamics

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    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.

  3. Comparing biological networks via graph compression

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    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.

  4. Understanding biological functions through molecular networks

    Institute of Scientific and Technical Information of China (English)

    Jing-Dong Jackie Han

    2008-01-01

    The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.

  5. Identifying communities from multiplex biological networks

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    Gilles Didier

    2015-12-01

    Full Text Available Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression. However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi.

  6. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Deyasi Krishanu; Upadhyay Shashankaditya; Banerjee Anirban

    2016-03-01

    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 information in a network. Here we investigate the underlying undirected topology of different biological networks which support faster spreading of information and are better in communication. We analyse the good expansion property by using the spectral gap and communicability between nodes. Different epidemic models are also used to study the transmission of information in terms of spreading of disease through individuals (nodes)in those networks. Moreover, we explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks, in a significant way, is qualitatively different from the same in other biologicalnetworks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication.

  7. Predicting biological networks from genomic data

    DEFF Research Database (Denmark)

    Harrington, Eoghan D; Jensen, Lars J; Bork, Peer

    2008-01-01

    Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks...... provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate...

  8. Quantifying evolvability in small biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Nemenman, Ilya [Los Alamos National Laboratory; Mugler, Andrew [COLUMBIA UNIV; Ziv, Etay [COLUMBIA UNIV; Wiggins, Chris H [COLUMBIA UNIV

    2008-01-01

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.

  9. 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.

  10. Reconstructing Causal Biological Networks through Active Learning.

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    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.

  11. Network biology methods integrating biological data for translational science.

    Science.gov (United States)

    Bebek, Gurkan; Koyutürk, Mehmet; Price, Nathan D; Chance, Mark R

    2012-07-01

    The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

  12. Application of Graph Coloring to Biological Networks

    CERN Document Server

    Khor, Susan

    2009-01-01

    We explore the application of graph coloring to biological networks, specifically protein-protein interaction (PPI) networks. First, we find that given similar conditions (i.e. number of nodes, number of links, degree distribution and clustering), fewer colors are needed to color disassortative (high degree nodes tend to connect to low degree nodes and vice versa) than assortative networks. Fewer colors create fewer independent sets which in turn imply higher concurrency potential for a network. Since PPI networks tend to be disassortative, we suggest that in addition to functional specificity and stability proposed previously by Maslov and Sneppen (Science 296, 2002), the disassortative nature of PPI networks may promote the ability of cells to perform multiple, crucial and functionally diverse tasks concurrently. Second, since graph coloring is closely related to the presence of cliques in a graph, the significance of node coloring information to the problem of identifying protein complexes, i.e. dense subg...

  13. Biological and Environmental Research Network Requirements

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-09-01

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

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

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael;

    2017-01-01

    The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research...

  15. Network biology concepts in complex disease comorbidities

    DEFF Research Database (Denmark)

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

    2016-01-01

    The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being...

  16. Discovery of Chemical Toxicity via Biological Networks and Systems Biology

    Energy Technology Data Exchange (ETDEWEB)

    Perkins, Edward; Habib, Tanwir; Guan, Xin; Escalon, Barbara; Falciani, Francesco; Chipman, J.K.; Antczak, Philipp; Edwards, Stephen; Taylor, Ronald C.; Vulpe, Chris; Loguinov, Alexandre; Van Aggelen, Graham; Villeneuve, Daniel L.; Garcia-Reyero, Natalia

    2010-09-30

    Both soldiers and animals are exposed to many chemicals as the result of military activities. Tools are needed to understand the hazards and risks that chemicals and new materials pose to soldiers and the environment. We have investigated the potential of global gene regulatory networks in understanding the impact of chemicals on reproduction. We characterized effects of chemicals on ovaries of the model animal system, the Fathead minnow (Pimopheles promelas) connecting chemical impacts on gene expression to circulating blood levels of the hormones testosterone and estradiol in addition to the egg yolk protein vitellogenin. We describe the application of reverse engineering complex interaction networks from high dimensional gene expression data to characterize chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis that governs reproduction in fathead minnows. The construction of global gene regulatory networks provides deep insights into how drugs and chemicals effect key organs and biological pathways.

  17. Review of biological network data and its applications.

    Science.gov (United States)

    Yu, Donghyeon; Kim, Minsoo; Xiao, Guanghua; Hwang, Tae Hyun

    2013-12-01

    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.

  18. 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.

  19. Algorithmic and analytical methods in network biology.

    Science.gov (United States)

    Koyutürk, Mehmet

    2010-01-01

    During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

  20. [Sodium determination in biological fluids].

    Science.gov (United States)

    Cristol, J-P; Balint, B; Canaud, B; Daurés, M-F

    2007-09-01

    Electrolyte disorders are frequently observed in nephrology and intensive care unit department and Na determination is routinely performed in biochemistry laboratories. Three methods are currently available. Flame photometry remains the reference method. With this method the serum sample is diluted before the actual measurement is obtained. Results are expressed as molarity (per Liter of plasma). Potentiometric methods have an increasing importance due to the advances in ion sensitive (selective) electrodes (ISE). Whereas the instruments for routine chemical analysis typically use indirect potentiometry (involving te dilution of samples) to measure sodium levels, the equipment for measuring arterial blood gases use direct potentiometry without any dilution. Thus, results obtained with indirect potentiometry are expressed in molarity (per liter of plasma) while results obtained with direct potentiometry are initially given in morality (per kg of plasma water) then converted in molarity. Analytical performances are in all cases satisfactory and therefore all the methods could be used in both normal and pathological ranges. Methods involving sample dilution such as flame photometry or indirect potentiometry, the serum sodium value would be expected to be low in case of decrease plasma water (pseudohyponatremia). By contrast, with direct potentiometry where no sample dilution takes place, no interference would be expected since the activity of sodium in the water phase only is being measured. Thus, the classical pseudohyponatremia observed with hyperlipemia or paraproteinemia are not further observed with direct potentiometry. These differences in methodology should be taken into account to explain discrepancies between results obtained with classical biochemistry analyser and with blood gas apparatus.

  1. 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.

  2. 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.

  3. 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.

  4. Exploration of biological network centralities with CentiBiN

    Directory of Open Access Journals (Sweden)

    Schreiber Falk

    2006-04-01

    Full Text Available Abstract Background The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability. Results CentiBiN (Centralities in Biological Networks is a tool for the computation and exploration of centralities in biological networks such as protein-protein interaction networks. It computes 17 different centralities for directed or undirected networks, ranging from local measures, that is, measures that only consider the direct neighbourhood of a network element, to global measures. CentiBiN supports the exploration of the centrality distribution by visualising central elements within the network and provides several layout mechanisms for the automatic generation of graphical representations of a network. It supports different input formats, especially for biological networks, and the export of the computed centralities to other tools. Conclusion CentiBiN helps systems biology researchers to identify crucial elements of biological networks. CentiBiN including a user guide and example data sets is available free of charge at http://centibin.ipk-gatersleben.de/. CentiBiN is available in two different versions: a Java Web Start application and an installable Windows application.

  5. Study of the structure and dynamics of complex biological networks

    Science.gov (United States)

    Samal, Areejit

    2008-12-01

    In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large scale metabolic networks inside three organisms, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the dynamics of the large scale genetic network controlling E. coli metabolism. We have tried to explain the observed system level dynamical properties of these networks in terms of their underlying structure. Our studies of the system level dynamics of these large scale biological networks provide a different perspective on their functioning compared to that obtained from purely structural studies. Our study also leads to some new insights on features such as robustness, fragility and modularity of these large scale biological networks. We also shed light on how different networks inside the cell such as metabolic networks and genetic networks are interrelated to each other.

  6. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

    Science.gov (United States)

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

    2016-06-02

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

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

    OpenAIRE

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

    2010-01-01

    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 V...

  8. Topological implications of negative curvature for biological and social networks

    Science.gov (United States)

    Albert, Réka; DasGupta, Bhaskar; Mobasheri, Nasim

    2014-03-01

    Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.

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

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael

    2017-01-01

    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...... complexity....

  10. Application of random matrix theory to biological networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-09-25

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

  11. 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.

  12. OWL Reasoning Framework over Big Biological Knowledge Network

    Directory of Open Access Journals (Sweden)

    Huajun Chen

    2014-01-01

    Full Text Available 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.

  13. A generic algorithm for layout of biological networks

    Directory of Open Access Journals (Sweden)

    Dwyer Tim

    2009-11-01

    Full Text Available Abstract Background 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. Results 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. Conclusion 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.

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

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    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...... cancer networks using Network Biology. Technologies key to this, such as Mass Spectrometry (MS), Next-Generation Sequencing (NGS) and High-Content Screening (HCS) are briefly described. In Chapter II, we cover how signaling networks and mutational data can be modeled in order to gain a better...

  15. Biological networks to the analysis of microarray data

    Institute of Scientific and Technical Information of China (English)

    FANG Zhuo; LUO Qingming; ZHANG Guoqing; LI Yixue

    2006-01-01

    Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations.Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods.

  16. Systems biology in the context of big data and networks.

    Science.gov (United States)

    Altaf-Ul-Amin, Md; Afendi, Farit Mochamad; Kiboi, Samuel Kuria; Kanaya, Shigehiko

    2014-01-01

    Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

  17. Bayesian networks: a powerful tool for systems biology study

    Institute of Scientific and Technical Information of China (English)

    Xiu-Jie WANG

    2010-01-01

    @@ Higher Education Press and Springer-Verlag Berlin Heidelberg 2010The wide application of omics research approaches caused a burst of biological data in the past decade, and also promoted the growth of systems biology, a research field that studies biological questions from a genome-wide point of view. One feature of systems biology study is to integrate and identify. Not only experiments are carried out at whole-genome scales, but also data from various resources, such as genomics, transcriptomics, proteomics,and metabolics data, need to be integrated to identify correlations among targeted entities. Therefore, plenty amounts of experimental data, robust statistical methods, and reliable network construction models are indispensable for systems biology study. Among the available network construction models, Bayesian network is considered as one of the most effective methods available so far for biological network predictions (Pe'er, 2005).

  18. 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...

  19. 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.

  20. 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

  1. BioFNet: biological functional network database for analysis and synthesis of biological systems.

    Science.gov (United States)

    Kurata, Hiroyuki; Maeda, Kazuhiro; Onaka, Toshikazu; Takata, Takenori

    2014-09-01

    In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.

  2. Duplication: a Mechanism Producing Disassortative Mixing Networks in Biology

    Institute of Scientific and Technical Information of China (English)

    ZHAO Dan; LIU Zeng-Rong; WANG Jia-Zeng

    2007-01-01

    Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks (PPI), gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks.Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.

  3. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

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

    2013-01-01

    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......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...... 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...

  4. Parameter identifiability-based optimal observation remedy for biological networks.

    Science.gov (United States)

    Wang, Yulin; Miao, Hongyu

    2017-05-04

    To systematically understand the interactions between numerous biological components, a variety of biological networks on different levels and scales have been constructed and made available in public databases or knowledge repositories. Graphical models such as structural equation models have long been used to describe biological networks for various quantitative analysis tasks, especially key biological parameter estimation. However, limited by resources or technical capacities, partial observation is a common problem in experimental observations of biological networks, and it thus becomes an important problem how to select unobserved nodes for additional measurements such that all unknown model parameters become identifiable. To the best knowledge of our authors, a solution to this problem does not exist until this study. The identifiability-based observation problem for biological networks is mathematically formulated for the first time based on linear recursive structural equation models, and then a dynamic programming strategy is developed to obtain the optimal observation strategies. The efficiency of the dynamic programming algorithm is achieved by avoiding both symbolic computation and matrix operations as used in other studies. We also provided necessary theoretical justifications to the proposed method. Finally, we verified the algorithm using synthetic network structures and illustrated the application of the proposed method in practice using a real biological network related to influenza A virus infection. The proposed approach is the first solution to the structural identifiability-based optimal observation remedy problem. It is applicable to an arbitrary directed acyclic biological network (recursive SEMs) without bidirectional edges, and it is a computerizable method. Observation remedy is an important issue in experiment design for biological networks, and we believe that this study provides a solid basis for dealing with more challenging design

  5. 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.

  6. 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

  7. On the Calculation of System Entropy in Nonlinear Stochastic Biological Networks

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2015-10-01

    Full Text Available Biological networks are open systems that can utilize nutrients and energy from their environment for use in their metabolic processes, and produce metabolic products. System entropy is defined as the difference between input and output signal entropy, i.e., the net signal entropy of the biological system. System entropy is an important indicator for living or non-living biological systems, as biological systems can maintain or decrease their system entropy. In this study, system entropy is determined for the first time for stochastic biological networks, and a computation method is proposed to measure the system entropy of nonlinear stochastic biological networks that are subject to intrinsic random fluctuations and environmental disturbances. We find that intrinsic random fluctuations could increase the system entropy, and that the system entropy is inversely proportional to the robustness and stability of the biological networks. It is also determined that adding feedback loops to shift all eigenvalues to the farther left-hand plane of the complex s-domain could decrease the system entropy of a biological network.

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

    Science.gov (United States)

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

    2013-01-01

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

  9. 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.

  10. Systems biology of plant molecular networks: from networks to models

    NARCIS (Netherlands)

    Valentim, F.L.

    2015-01-01

    Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs

  11. Rigidity and flexibility of biological networks

    CERN Document Server

    Gaspar, Merse E

    2012-01-01

    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.

  12. A method for developing regulatory gene set networks to characterize complex biological systems.

    Science.gov (United States)

    Suphavilai, Chayaporn; Zhu, Liugen; Chen, Jake Y

    2015-01-01

    Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

  13. Network benchmarking: a happy marriage between systems and synthetic biology.

    Science.gov (United States)

    Minty, Jeremy J; Varedi K, S Marjan; Nina Lin, Xiaoxia

    2009-03-27

    In their new Cell paper, Cantone et al. (2009) present exciting results on constructing and utilizing a small synthetic gene regulatory network in yeast that draws from two rapidly developing fields of systems and synthetic biology.

  14. 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

  15. Toward Network Biology in E. coli Cell.

    Science.gov (United States)

    Mori, Hirotada; Takeuchi, Rikiya; Otsuka, Yuta; Bowden, Steven; Yokoyama, Katsushi; Muto, Ai; Libourel, Igor; Wanner, Barry L

    2015-01-01

    E. coli has been a critically important model research organism for more than 50 years, particularly in molecular biology. In 1997, the E. coli draft genome sequence was published. Post-genomic techniques and resources were then developed that allowed E. coli to become a model organism for systems biology. Progress made since publication of the E. coli genome sequence will be summarized.

  16. 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…

  17. 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

  18. Mapping Transcriptional Networks in Plants: Data-Driven Discovery of Novel Biological Mechanisms.

    Science.gov (United States)

    Gaudinier, Allison; Brady, Siobhan M

    2016-04-29

    In plants, systems biology approaches have led to the generation of a variety of large data sets. Many of these data are created to elucidate gene expression profiles and their corresponding transcriptional regulatory mechanisms across a range of tissue types, organs, and environmental conditions. In an effort to map the complexity of this transcriptional regulatory control, several types of experimental assays have been used to map transcriptional regulatory networks. In this review, we discuss how these methods can be best used to identify novel biological mechanisms by focusing on the appropriate biological context. Translating network biology back to gene function in the plant, however, remains a challenge. We emphasize the need for validation and insight into the underlying biological processes to successfully exploit systems approaches in an effort to determine the emergent properties revealed by network analyses.

  19. 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.

  20. Stability of biological networks as represented in Random Boolean Nets.

    Energy Technology Data Exchange (ETDEWEB)

    Slepoy, Alexander; Thompson, Marshall

    2004-09-01

    We explore stability of Random Boolean Networks as a model of biological interaction networks. We introduce surface-to-volume ratio as a measure of stability of the network. Surface is defined as the set of states within a basin of attraction that maps outside the basin by a bit-flip operation. Volume is defined as the total number of states in the basin. We report development of an object-oriented Boolean network analysis code (Attract) to investigate the structure of stable vs. unstable networks. We find two distinct types of stable networks. The first type is the nearly trivial stable network with a few basins of attraction. The second type contains many basins. We conclude that second type stable networks are extremely rare.

  1. Composite nanowire networks for biological sensor platforms

    Science.gov (United States)

    Jabal, Jamie Marie Francisco

    The main goal of this research is to design, fabricate, and test a nanomaterial-based platform adequate for the measurement of physiological changes in living cells. The two primary objectives toward this end are (1) the synthesis and selection of a suitable nanomaterial and (2) the demonstration of cellular response to a direct stimulus. Determining a useful nanomaterial morphology and behavior within a sensor configuration presented challenges based on cellular integration and access to electrochemical characterization. The prospect for feasible optimization and eventual scale-up in technology were also significant. Constraining criteria are that the nanomaterial detector must (a) be cheap and relatively easy to fabricate controllably, (b) encourage cell attachment, (c) exhibit consistent wettability over time, and (d) facilitate electrochemical processes. The ultimate goal would be to transfer a proof-of-principle and proof-of-design for a whole-cell sensor technology that is cost effective and has a potential for hand-held packaging. Initial tasks were to determine an effective and highly-functional nanomaterial for biosensors by assessing wettability, morphology and conductivity behavior of several candidate materials: gallium nitride nanowires, silicon dioxide nanosprings and nanowires, and titania nanofibers. Electrospinning poly(vinyl pyrrolidone)-coated titania nano- and microfibers (O20 nm--2 microm) into a pseudo-random network is controllable to a uniformity of 1--2° in contact angle. The final electrode can be prepared with a precise wettability ranging from partial wetting to ultrahydrophobic (170°) on a variety of substrates: glass, indium tin oxide, silicon, and aluminum. Fiber mats exhibit excellent mechanical stability against rinsing, and support the incubation of epithelial (skin) and pancreatic cells. Impedance spectroscopy on the whole-cell sensor shows resistive changes attributed to cell growth as well as complex frequency

  2. Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.

    Science.gov (United States)

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.

  3. Polynomial-Time Algorithm for Controllability Test of a Class of Boolean Biological Networks

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2010-01-01

    Full Text Available In recent years, Boolean-network-model-based approaches to dynamical analysis of complex biological networks such as gene regulatory networks have been extensively studied. One of the fundamental problems in control theory of such networks is the problem of determining whether a given substance quantity can be arbitrarily controlled by operating the other substance quantities, which we call the controllability problem. This paper proposes a polynomial-time algorithm for solving this problem. Although the algorithm is based on a sufficient condition for controllability, it is easily computable for a wider class of large-scale biological networks compared with the existing approaches. A key to this success in our approach is to give up computing Boolean operations in a rigorous way and to exploit an adjacency matrix of a directed graph induced by a Boolean network. By applying the proposed approach to a neurotransmitter signaling pathway, it is shown that it is effective.

  4. Biologically inspired neural network controller for an infrared tracking system

    Science.gov (United States)

    Frigo, Janette R.; Tilden, Mark W.

    1999-01-01

    Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.

  5. Human diseases through the lens of network biology.

    Science.gov (United States)

    Furlong, Laura I

    2013-03-01

    One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models for genetic phenomena such as penetrance, epistasis, and modes of inheritance, all of which are integral aspects of Mendelian and complex diseases. Moreover, it can shed light on disease mechanisms via the identification of modules perturbed in those diseases. Current challenges include understanding disease as a result of the interplay between environmental and genetic perturbations and assessing the impact of personal sequence variations in the context of networks. Full realization of the potential of personal genomics will benefit from network biology approaches that aim to uncover the mechanisms underlying disease pathogenesis, identify new biomarkers, and guide personalized therapeutic interventions.

  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. Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

    Science.gov (United States)

    Podder, Avijit; Jatana, Nidhi; Latha, N

    2014-09-21

    Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network.

  8. 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.

  9. Computation of the effective mechanical response of biological networks accounting for large configuration changes.

    Science.gov (United States)

    El Nady, K; Ganghoffer, J F

    2016-05-01

    The asymptotic homogenization technique is involved to derive the effective elastic response of biological membranes viewed as repetitive beam networks. Thereby, a systematic methodology is established, allowing the prediction of the overall mechanical properties of biological membranes in the nonlinear regime, reflecting the influence of the geometrical and mechanical micro-parameters of the network structure on the overall response of the equivalent continuum. Biomembranes networks are classified based on nodal connectivity, so that we analyze in this work 3, 4 and 6-connectivity networks, which are representative of most biological networks. The individual filaments of the network are described as undulated beams prone to entropic elasticity, with tensile moduli determined from their persistence length. The effective micropolar continuum evaluated as a continuum substitute of the biological network has a kinematics reflecting the discrete network deformation modes, involving a nodal displacement and a microrotation. The statics involves the classical Cauchy stress and internal moments encapsulated into couple stresses, which develop internal work in duality to microcurvatures reflecting local network undulations. The relative ratio of the characteristic bending length of the effective micropolar continuum to the unit cell size determines the relevant choice of the equivalent medium. In most cases, the Cauchy continuum is sufficient to model biomembranes. The peptidoglycan network may exhibit a re-entrant hexagonal configuration due to thermal or pressure fluctuations, for which micropolar effects become important. The homogenized responses are in good agreement with FE simulations performed over the whole network. The predictive nature of the employed homogenization technique allows the identification of a strain energy density of a hyperelastic model, for the purpose of performing structural calculations of the shape evolutions of biomembranes.

  10. 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.

  11. Relevance of Dynamic Clustering to Biological Networks

    CERN Document Server

    Kaneko, K

    1993-01-01

    Abstract Network of nonlinear dynamical elements often show clustering of synchronization by chaotic instability. Relevance of the clustering to ecological, immune, neural, and cellular networks is discussed, with the emphasis of partially ordered states with chaotic itinerancy. First, clustering with bit structures in a hypercubic lattice is studied. Spontaneous formation and destruction of relevant bits are found, which give self-organizing, and chaotic genetic algorithms. When spontaneous changes of effective couplings are introduced, chaotic itinerancy of clusterings is widely seen through a feedback mechanism, which supports dynamic stability allowing for complexity and diversity, known as homeochaos. Second, synaptic dynamics of couplings is studied in relation with neural dynamics. The clustering structure is formed with a balance between external inputs and internal dynamics. Last, an extension allowing for the growth of the number of elements is given, in connection with cell differentiation. Effecti...

  12. 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.

  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. Spectral algorithms for heterogeneous biological networks.

    Science.gov (United States)

    McDonald, Martin; Higham, Desmond J; Vass, J Keith

    2012-11-01

    Spectral methods, which use information relating to eigenvectors, singular vectors and generalized singular vectors, help us to visualize and summarize sets of pairwise interactions. In this work, we motivate and discuss the use of spectral methods by taking a matrix computation view and applying concepts from applied linear algebra. We show that this unified approach is sufficiently flexible to allow multiple sources of network information to be combined. We illustrate the methods on microarray data arising from a large population-based study in human adipose tissue, combined with related information concerning metabolic pathways.

  15. Oscillatory Activities in Regulatory Biological Networks and Hopf Bifurcation

    Institute of Scientific and Technical Information of China (English)

    YAN Shi-Wei; WANG Qi; XIE Bai-Song; ZHANG Feng-Shou

    2007-01-01

    Exploiting the nonlinear dynamics in the negative feedback loop, we propose a statistical signal-response model to describe the different oscillatory behaviour in a biological network motif. By choosing the delay as a bifurcation parameter, we discuss the existence of Hopf bifurcation and the stability of the periodic solutions of model equations with the centre manifold theorem and the normal form theory. It is shown that a periodic solution is born in a Hopf bifurcation beyond a critical time delay, and thus the bifurcation phenomenon may be important to elucidate the mechanism of oscillatory activities in regulatory biological networks.

  16. MAPPING OF NATURAL KAPOSI SARCOMA INHIBITOR USING NETWORK BIOLOGY APPROACH

    Directory of Open Access Journals (Sweden)

    Jayadeepa R. M.

    2012-03-01

    Full Text Available Identification of protein-ligand interaction networks on a proteome scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics. In this study we have developed a novel computational strategy to identify ligand binding profiles of proteins across gene families and applied it to predicting protein functions, elucidating molecular mechanisms of drug adverse effects, and repositioning safe pharmaceuticals to treat different diseases The resultant network is then extrapolated to proteomics level to sort out the genes only expressed in the specific cancer types. The network is statistically analyzed and represented by the graphical interpretation to encounter the hub nodes. The objective of developing a biological networking is for the evaluation and validation of cancer drugs and their targets. In the field of cancer biology, the drug and their targets holds a role of paramount importance. With the work conducted here it shows the study of relation between drug target networks. Kaposi’s sarcoma (KS is a systemic disease which can present with cutaneous lesions with or without internal involvement. Genes belonging to the group of proto-oncogenes and tumor suppressors are best targeted for cancer studies. Biological networks like gene regulatory networks, protein interaction network is usually created to simplify the studies. In the present study, 26 proteins as receptor were selected for the study; all the receptors were responsible for the cause of Kaposi’s sarcoma. Also, 121 natural anti-Kaposi Sarcoma compounds were selected from different sources the natural components were the best component for blocking of abnormal activity.

  17. Theory of interface: category theory, directed networks and evolution of biological networks.

    Science.gov (United States)

    Haruna, Taichi

    2013-11-01

    Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.

  18. 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.

  19. Decentralized control of ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-09-01

    Full Text Available Evolutionary Network Control (ENC has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data. In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets.

  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. Prediction and testing of biological networks underlying intestinal cancer.

    Directory of Open Access Journals (Sweden)

    Vishal N Patel

    Full Text Available Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called "driver" genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections--both precedented and novel--between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21, known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc(1638N+/- or Cdkn1a (Cdkn1a(-/-, followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional, then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.

  2. 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...

  3. 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 physiologic...

  4. Discovering networks of perturbed biological processes in hepatocyte cultures.

    Directory of Open Access Journals (Sweden)

    Christopher D Lasher

    Full Text Available The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs and collagen sandwiches (CS. Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs. CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior.

  5. Functional Genomics Assistant (FUGA: a toolbox for the analysis of complex biological networks

    Directory of Open Access Journals (Sweden)

    Ouzounis Christos A

    2011-10-01

    Full Text Available Abstract Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.

  6. Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions

    Science.gov (United States)

    Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.

    2016-09-01

    Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving

  7. Biologically Inspired Optimization of Building District Heating Networks

    Directory of Open Access Journals (Sweden)

    Leiming Shang

    2013-07-01

    Full Text Available In this paper we show that a biologically inspired model can be successfully applied to problems of building optimal district heating network. The model is based on physiological observations of the true slime mold Physarumpolycephalum, but can also be used for path-finding in the complicated networks of mazes and road maps. A strategy of optimally building heating distribution network was guided by the model and a well-tuned ant colony algorithm and genetic algorithm. The results indicate that although there are not large-scale efficiency savings to be made, the biologically inspired amoeboid movement model is capable of finding results of equal or better optimality than a comparable ant colony algorithm and genetic algorithm.

  8. 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.

  9. ALOUD biological: Adult Learning Open University Determinants study - Association of biological determinants with study success in formal lifelong learners

    NARCIS (Netherlands)

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

    2012-01-01

    Gijselaers, H. J. M., De Groot, R. H. M., & Kirschner, P. A. (2012, 15 March). ALOUD biological: Adult Learning Open University Determinants study - Association of biological determinants with study success in formal lifelong learners. Presentation given at the plenary meeting of Learning & Cognitio

  10. ALOUD biological: Adult Learning Open University Determinants study - Association of biological determinants with study success in formal lifelong learners

    NARCIS (Netherlands)

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

    2012-01-01

    Gijselaers, H. J. M., De Groot, R. H. M., & Kirschner, P. A. (2012, 15 March). ALOUD biological: Adult Learning Open University Determinants study - Association of biological determinants with study success in formal lifelong learners. Presentation given at the plenary meeting of Learning &

  11. Analysis of biological networks and biological pathways associated with residual feed intake in beef cattle.

    Science.gov (United States)

    Karisa, Brian; Moore, Stephen; Plastow, Graham

    2014-04-01

    In this study, biological networks were reconstructed from genes and metabolites significantly associated with residual feed intake (RFI) in beef cattle. The networks were then used to identify biological pathways associated with RFI. RFI is a measure of feed efficiency, which is independent of body size and growth; therefore selection for RFI is expected to result in cattle that consume less feed without adverse effects on growth rate and mature size. Although several studies have identified genes associated with RFI, the mechanisms of the biological processes are not well understood. In this study, we utilised the results obtained from two association studies, one using 24 genes and one using plasma metabolites to reconstruct biological networks associated with RFI using IPA software (Igenuity Systems). The results pointed to biological processes such as lipid and steroid biosynthesis, protein and carbohydrate metabolism and regulation of gene expression through DNA transcription, protein stability and degradation. The major canonical pathways included signaling of growth hormone, Oncostatin M, insulin-like growth factor and AMP activated protein kinase, and cholesterol biosynthesis. This study provides information on potential biological mechanisms, and genes and metabolites involved in feed efficiency in beef cattle. © 2013 Japanese Society of Animal Science.

  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. Human Metabolic Network: Reconstruction, Simulation, and Applications in Systems Biology

    Science.gov (United States)

    Wu, Ming; Chan, Christina

    2012-01-01

    Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. PMID:24957377

  14. Determining Application Runtimes Using Queueing Network Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Elliott, Michael L. [Univ. of San Francisco, CA (United States)

    2006-12-14

    Determination of application times-to-solution for large-scale clustered computers continues to be a difficult problem in high-end computing, which will only become more challenging as multi-core consumer machines become more prevalent in the market. Both researchers and consumers of these multi-core systems desire reasonable estimates of how long their programs will take to run (time-to-solution, or TTS), and how many resources will be consumed in the execution. Currently there are few methods of determining these values, and those that do exist are either overly simplistic in their assumptions or require great amounts of effort to parameterize and understand. One previously untried method is queuing network modeling (QNM), which is easy to parameterize and solve, and produces results that typically fall within 10 to 30% of the actual TTS for our test cases. Using characteristics of the computer network (bandwidth, latency) and communication patterns (number of messages, message length, time spent in communication), the QNM model of the NAS-PB CG application was applied to MCR and ALC, supercomputers at LLNL, and the Keck Cluster at USF, with average errors of 2.41%, 3.61%, and -10.73%, respectively, compared to the actual TTS observed. While additional work is necessary to improve the predictive capabilities of QNM, current results show that QNM has a great deal of promise for determining application TTS for multi-processor computer systems.

  15. Imposing early stability to ecological and biological networks through Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2015-03-01

    Full Text Available The stability analysis of the dynamical networks is a well-studied topic, both in ecology and in biology. In this work, I adopt a different perspective: instead of analysing the stability of an arbitrary ecological network, I seek here to impose such stability as soon as possible (or, contrariwise, as late as possible during network dynamics. Evolutionary Network Control (ENC is a theoretical and methodological framework aimed to the control of ecological and biological networks by coupling network dynamics and evolutionary modelling. ENC covers several topics of network control, for instance a the global control from inside and b from outside, c the local (step-by-step control, and the computation of: d control success, e feasibility, and f degree of uncertainty. In this work, I demonstrate that ENC can also be employed to impose early (but, also, late stability to arbitrary ecological and biological networks, and provide an applicative example based on the nonlinear, widely-used, Lotka-Volterra model.

  16. Passing messages between biological networks to refine predicted interactions.

    Directory of Open Access Journals (Sweden)

    Kimberly Glass

    Full Text Available Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation, a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  17. Passing messages between biological networks to refine predicted interactions.

    Science.gov (United States)

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  18. 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.

  19. 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...

  20. 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...

  1. Classification of biological and non-biological fluvial particles using image processing and artificial neural network

    Science.gov (United States)

    Shrestha, Bim Prasad; Shrestha, Nabin Kumar; Poudel, Laxman

    2009-04-01

    Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.

  2. Network news: innovations in 21st century systems biology.

    Science.gov (United States)

    Arkin, Adam P; Schaffer, David V

    2011-03-18

    A decade ago, seminal perspectives and papers set a strong vision for the field of systems biology, and a number of these themes have flourished. Here, we describe key technologies and insights that have elucidated the evolution, architecture, and function of cellular networks, ultimately leading to the first predictive genome-scale regulatory and metabolic models of organisms. Can systems approaches bridge the gap between correlative analysis and mechanistic insights?

  3. 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.

  4. 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.

  5. Importance of randomness in biological networks: A random matrix analysis

    Indian Academy of Sciences (India)

    Sarika Jalan

    2015-02-01

    Random matrix theory, initially proposed to understand the complex interactions in nuclear spectra, has demonstrated its success in diverse domains of science ranging from quantum chaos to galaxies. We demonstrate the applicability of random matrix theory for networks by providing a new dimension to complex systems research. We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate the importance of randomness in interactions for deducing crucial properties of the underlying system. This paper provides an overview of the importance of random matrix framework in complex systems research with biological systems as examples.

  6. Perturbation biology: inferring signaling networks in cellular systems.

    Science.gov (United States)

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; 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.

  7. Predicting genetic interactions with random walks on biological networks

    Directory of Open Access Journals (Sweden)

    Singh Ambuj K

    2009-01-01

    Full Text Available Abstract Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree

  8. Bit by bit control of nonlinear ecological and biological networks using Evolutionary Network Control

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-06-01

    Full Text Available Evolutionary Network Control (ENC has been first introduced in 2013 to effectively subdue network-like systems. ENC opposes the idea, very common in the scientific literature, that controllability of networks should be based on the identification of the set of driver nodes that can guide the system's dynamics, in other words on the choice of a subset of nodes that should be selected to be permanently controlled. ENC has proven to be effective in the global control (i.e. the focus is on mastery of the final state of network dynamics of linear and nonlinear networks, and in the local (i.e. the focus is on the step-by-step ascendancy of network dynamics control of linear networks. In this work, ENC is applied to the local control of nonlinear networks. Using the Lotka-Volterra model as a case study, I show here that ENC is capable of locally driving nonlinear networks as well, so that also intermediate steps (not only the final state are under our strict control. ENC can be readily applied to any kind of ecological, biological, economic and network-like system.

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Alina Nescerecka

    Full Text Available 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.

  11. 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

  12. [Concentration and determination of strychnine alkaloid in biological fluids].

    Science.gov (United States)

    Zhang, Jing; He, Lang-chong; Fu, Qiang

    2005-02-01

    To establish a new method for determination of strychnine alkaloid in biological fluids based on molecularly imprinted polymers. A strychnine molecularly imprinted monolithic column was prepared by in-situ molecularly imprinted technique. The polymer was filled to a 1cm column, and a method was developed to concentrate and determine strychnine alkaloids in biological fluids. the limit of detection of the method was 4.9 ng, and the recoveries were more than 92%. The relative standard deviations were smaller than 6.59%. The linear correlation coefficients of standard curves were 0.999 1 and 0.9966 respectively. This method was applied to concentrate and determine strychnine in plasma and urine of poisoned rabbit. The new method could concentrate and simultaneously determine strychnine alkaloids in biological fluids, and it was applied to forensic toxicological analysis.

  13. Factors determining nestedness in complex networks

    CERN Document Server

    Johnson, Samuel; Munoz, Miguel A

    2013-01-01

    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 and go on to 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 heterogeneity in the degree has a very strong influence on nestedness. Once such an influence has been discounted, we find that nestedness is strongly correlated with disassortativity and hence, as random (neutral) networks have been recently found to be naturally disassortative, they tend to be naturally nes...

  14. Motivation and requirements for determining a Network Warfare Capability

    CSIR Research Space (South Africa)

    Veerasamy, N

    2010-06-01

    Full Text Available to prevent misconceptions, as well as to ensure that the necessary input data is incorporated. They include the following: • The involved parties must grasp the difference between auditing and determining a Network Warfare Capability. It is encouraged... to incorporate the use of auditing in the technique set underlying Network Warfare. • To determine a Network Warfare Capability, assessments of the various techniques that contribute to Network Warfare are necessary. This will incorporate expert opinion...

  15. 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.

  16. Inference of asynchronous Boolean network from biological pathways.

    Science.gov (United States)

    Das, Haimabati; Layek, Ritwik Kumar

    2015-01-01

    Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.

  17. Similarities Between Biological and Social Networks in Their Structural Organization

    Science.gov (United States)

    Kahng, Byungnam; Lee, Deokjae; Kim, Pureun

    A branching tree is a tree that is generated through a multiplicative branching process starting from a root. A critical branching tree is a branching tree in which the mean branching number of each node is 1, so that the number of offspring neither decays to zero nor flourishes as the branching process goes on. Moreover, a scale-free branching tree is a branching tree in which the number of offspring is heterogeneous, and its distribution follows a power law. Here we examine three structures, two from biology (a phylogenetic tree and the skeletons of a yeast protein interaction network) and one from social science (a coauthorship network), and find that all these structures are scale-free critical branching trees. This suggests that evolutionary processes in such systems take place in bursts and in a self-organized manner.

  18. Molecular codes in biological and chemical reaction networks.

    Science.gov (United States)

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

    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.

  19. 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.

  20. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  1. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  2. Methods of information theory and algorithmic complexity for network biology.

    Science.gov (United States)

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Computational studies of gene regulatory networks: in numero molecular biology.

    Science.gov (United States)

    Hasty, J; McMillen, D; Isaacs, F; Collins, J J

    2001-04-01

    Remarkable progress in genomic research is leading to a complete map of the building blocks of biology. Knowledge of this map is, in turn, setting the stage for a fundamental description of cellular function at the DNA level. Such a description will entail an understanding of gene regulation, in which proteins often regulate their own production or that of other proteins in a complex web of interactions. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.

  4. A quantitative method for determining the robustness of complex networks

    Science.gov (United States)

    Qin, Jun; Wu, Hongrun; Tong, Xiaonian; Zheng, Bojin

    2013-06-01

    Most current studies estimate the invulnerability of complex networks using a qualitative method that analyzes the decay rate of network performance. This method results in confusion over the invulnerability of various types of complex networks. By normalizing network performance and defining a baseline, this paper defines the invulnerability index as the integral of the normalized network performance curve minus the baseline. This quantitative method seeks to measure network invulnerability under both edge and node attacks and provides a definition on the distinguishment of the robustness and fragility of networks. To demonstrate the proposed method, three small-world networks were selected as test beds. The simulation results indicate that the proposed invulnerability index can effectively and accurately quantify network resilience and can deal with both the node and edge attacks. The index can provide a valuable reference for determining network invulnerability in future research.

  5. Chapter 5: Network biology approach to complex diseases.

    Directory of Open Access Journals (Sweden)

    Dong-Yeon Cho

    Full Text Available Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.

  6. 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.

  7. Practical use of BiNoM: a biological network manager software.

    Science.gov (United States)

    Bonnet, Eric; Calzone, Laurence; Rovera, Daniel; Stoll, Gautier; Barillot, Emmanuel; Zinovyev, Andrei

    2013-01-01

    The Biological Network Manager (BiNoM) is a software tool for the manipulation and analysis of biological networks. It facilitates the import and conversion of a set of well-established systems biology file formats. It also provides a large set of graph-based algorithms that allow users to analyze and extract relevant subnetworks from large molecular maps. It has been successfully used in several projects related to the analysis of large and complex biological data, or networks from databases. In this tutorial, we present a detailed and practical case study of how to use BiNoM to analyze biological networks.

  8. Physics for Medicine and Biology: Determining Body Fat Content

    Science.gov (United States)

    Aaron, Ronald; Altman, Albert

    2011-04-01

    Hydrostatic weighing is a technique for determining body fat content that is based on Archimedes principle and varied applications of the ideal gas law. We use this procedure as an example of the types of physics material which should be presented in an introductory course for students that are interested in careers in biology and medicine.

  9. Integrative biology identifies shared transcriptional networks in CKD.

    Science.gov (United States)

    Martini, Sebastian; Nair, Viji; Keller, Benjamin J; Eichinger, Felix; Hawkins, Jennifer J; Randolph, Ann; Böger, Carsten A; Gadegbeku, Crystal A; Fox, Caroline S; Cohen, Clemens D; Kretzler, Matthias

    2014-11-01

    A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.

  10. 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

  11. Factors Determining Nestedness in Complex Networks

    Science.gov (United States)

    Jonhson, Samuel; Domínguez-García, Virginia; Muñoz, Miguel A.

    2013-01-01

    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. PMID:24069264

  12. A Network Biology Approach to Denitrification in Pseudomonas aeruginosa

    Science.gov (United States)

    Arat, Seda; Bullerjahn, George S.; Laubenbacher, Reinhard

    2015-01-01

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO2), nitric oxide (NO) and nitrous oxide (N2O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O2), nitrate (NO3), and phosphate (PO4) suggests that PO4 concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO4 on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N2O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide. PMID:25706405

  13. 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.

  14. Workshop to launch a network on biological opportunities for GHG management, Dec 15-16, 2010, Toronto: results and recommendations

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2010-12-15

    A workshop was conducted in Toronto, Ontario on December 15 and 16, 2010 in order to launch a network on biological opportunities for management of greenhouse gases (GHGs). The workshop was sponsored by the Climate Change and Emissions Management Corporation (CCEMC) and brought together 37 persons from the Canadian climate change community. The aim was to gather leading thinkers to evaluate potential bio GHG opportunities, to determine areas for bio GHG opportunity focus and to develop a knowledge network to support investment in bio GHG opportunities. During this workshop priorities for biological work were set through the selection of bio GHG sub-wedges which require further consideration. In addition 4 potential management models were studied for the creation of an effective biological knowledge network. This workshop permitted the organisation of a knowledge network on bio GHG opportunities and it was decided in conclusion that CCEMC would lead its development.

  15. Computationally efficient measure of topological redundancy of biological and social networks

    Science.gov (United States)

    Albert, Réka; Dasgupta, Bhaskar; Hegde, Rashmi; Sivanathan, Gowri Sangeetha; Gitter, Anthony; Gürsoy, Gamze; Paul, Pradyut; Sontag, Eduardo

    2011-09-01

    It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

  16. A New Computationally Efficient Measure of Topological Redundancy of Biological and Social Networks

    CERN Document Server

    Albert, Reka; Gitter, Anthony; Gursoy, Gamze; Hegde, Rashmi; Paul, Pradyut; Sivanathan, Gowri Sangeetha; Sontag, Eduardo

    2011-01-01

    It is well-known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient and applicable to a variety of directed networks such as cellular signaling, metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively...

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

    Science.gov (United States)

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

    2011-06-01

    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.

  18. 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/.

  19. MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology.

    Science.gov (United States)

    Vera, Julio; Lai, Xin; Schmitz, Ulf; Wolkenhauer, Olaf

    2013-01-01

    MicroRNAs (miRNAs) are involved in many regulatory pathways some of which are complex networks enriched in regulatory motifs like positive or negative feedback loops or coherent and incoherent feedforward loops. Their complexity makes the understanding of their regulation difficult and the interpretation of experimental data cumbersome. In this book chapter we claim that systems biology is the appropriate approach to investigate the regulation of these miRNA-regulated networks. Systems biology is an interdisciplinary approach by which biomedical questions on biochemical networks are addressed by integrating experiments with mathematical modelling and simulation. We here introduce the foundations of the systems biology approach, the basic theoretical and computational tools used to perform model-based analyses of miRNA-regulated networks and review the scientific literature in systems biology of miRNA regulation, with a focus on cancer.

  20. 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…

  1. 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.

  2. 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.

  3. Biologically relevant neural network architectures for support vector machines.

    Science.gov (United States)

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. 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

  5. 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.

  6. Managing biological networks by using text mining and computer-aided curation

    Science.gov (United States)

    Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

    2015-11-01

    In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

  7. Energy and time determine scaling in biological and computer designs.

    Science.gov (United States)

    Moses, Melanie; Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

    2016-08-19

    Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy-time minimization principle may govern the design of many complex systems that process energy, materials and information.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

  8. 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

  9. Harmonic analysis of Boolean networks: determinative power and perturbations

    Science.gov (United States)

    2013-01-01

    Consider a large Boolean network with a feed forward structure. Given a probability distribution on the inputs, can one find, possibly small, collections of input nodes that determine the states of most other nodes in the network? To answer this question, a notion that quantifies the determinative power of an input over the states of the nodes in the network is needed. We argue that the mutual information (MI) between a given subset of the inputs X={X1,...,Xn} of some node i and its associated function fi(X) quantifies the determinative power of this set of inputs over node i. We compare the determinative power of a set of inputs to the sensitivity to perturbations to these inputs, and find that, maybe surprisingly, an input that has large sensitivity to perturbations does not necessarily have large determinative power. However, for unate functions, which play an important role in genetic regulatory networks, we find a direct relation between MI and sensitivity to perturbations. As an application of our results, we analyze the large-scale regulatory network of Escherichia coli. We identify the most determinative nodes and show that a small subset of those reduces the overall uncertainty of the network state significantly. Furthermore, the network is found to be tolerant to perturbations of its inputs. PMID:23642003

  10. 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

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

    OpenAIRE

    Seebacher, Frank; Craig E. Franklin

    2012-01-01

    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 causa...

  12. 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.

  13. Design principles for the analysis and construction of robustly homeostatic biological networks.

    Science.gov (United States)

    Tang, Zhe F; McMillen, David R

    2016-11-07

    Homeostatic biological systems resist external disturbances, allowing cells and organisms to maintain a constant internal state despite perturbations from their surroundings. Many biological regulatory networks are known to act homeostatically, with examples including thermal adaptation, osmoregulation, and chemotaxis. Understanding the network topologies (sets of regulatory interactions) and biological parameter regimes that can yield homeostasis in a biological system is of interest both for the study of natural biological system, and in the context of designing new biological control schemes for use in synthetic biology. Here, we examine the mathematical properties of a function that maps a biological system's inputs to its outputs, we have formulated a novel criterion (the "cofactor condition") that compactly describes the conditions for homeostasis. We further analyze the problem of robust homeostasis, wherein the system is required to maintain homeostatic behavior when its parameter values are slightly altered. We use the cofactor condition to examine previously reported examples of robust homeostasis, showing that it is a useful way to unify a number of seemingly different analyses into a single framework. Based on the observation that all previous robustly homeostatic examples fall into one of three classes, we propose a "strong cofactor condition" and use it to provide an algorithm for designing new robustly homeostatic biological networks, giving both their topologies and constraints on their parameter values. Applying the design algorithm to a three-node biological network, we construct several robustly homeostatic genetic networks, uncovering network topologies not previously identified as candidates for exhibiting robust homeostasis.

  14. Characteristics of Molecular-biological Systems and Process-network Synthesis

    CERN Document Server

    Papp, L; Friedler, F; Fan, L T

    2002-01-01

    Graph Theoretic Process Network Synthesis is described as an introduction to biological networks. Genetic, protein and metabolic systems are considered. The theoretical work of Kauffman is discussed and amplified by critical property excursions. The scaling apparent in biological systems is shown. Applications to evolution and reverse engineering are construed. The use of several programs, such as the Synprops, Design of molecules, Therm and Knapsack are suggested as instruments to study biological process network synthesis. The properties of robust self-assembly and Self-Organizing synthesis are important contributors to the discussion. The bar code of life and intelligent design is reviewed. The need for better data in biological systems is emphasized.

  15. Identifying common components across biological network graphs using a bipartite data model.

    Science.gov (United States)

    Baker, Ej; Culpepper, C; Philips, C; Bubier, J; Langston, M; Chesler, Ej

    2014-01-01

    The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

  16. 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.

  17. 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

  18. Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

    Science.gov (United States)

    Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

    2008-10-01

    Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

  19. NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks

    Directory of Open Access Journals (Sweden)

    Yang Cao

    2017-01-01

    Full Text Available Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied “basic networks,” measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette.

  20. C-GRAAL: common-neighbors-based global GRAph ALignment of biological networks.

    Science.gov (United States)

    Memišević, Vesna; Pržulj, Nataša

    2012-07-01

    Networks are an invaluable framework for modeling biological systems. Analyzing protein-protein interaction (PPI) networks can provide insight into underlying cellular processes. It is expected that comparison and alignment of biological networks will have a similar impact on our understanding of evolution, biological function, and disease as did sequence comparison and alignment. Here, we introduce a novel pairwise global alignment algorithm called Common-neighbors based GRAph ALigner (C-GRAAL) that uses heuristics for maximizing the number of aligned edges between two networks and is based solely on network topology. As such, it can be applied to any type of network, such as social, transportation, or electrical networks. We apply C-GRAAL to align PPI networks of eukaryotic and prokaryotic species, as well as inter-species PPI networks, and we demonstrate that the resulting alignments expose large connected and functionally topologically aligned regions. We use the resulting alignments to transfer biological knowledge across species, successfully validating many of the predictions. Moreover, we show that C-GRAAL can be used to align human-pathogen inter-species PPI networks and that it can identify patterns of pathogen interactions with host proteins solely from network topology.

  1. 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

  2. Comparing the biological coherence of network clusters identified by different detection algorithms

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighborhoods based on network topology, many network cluster identification algorithms have been developed. However, each algorithm might dissect a network from a different aspect and may provide different insight on the network partition. In order to objectively evaluate the performance of four commonly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction networks with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    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 of an ...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....... 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...

  4. Some Limitations of BIOLOG System for Determining Soil Microbial Community

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A laboratory experiment was conducted to evaluate the effect of triphenyltetrazolium chloride (TTC)on soil microorganisms and the availability of pH characterization medium in BIOLOG plates. Applicationof TTC decreased the color development sharply and resulted in a great biocidal effect on the growth andreproduction of soil microorganisms, indicating that TTC can affect the discrimination on soil microbialcommunity. The microtitration plates with 21 carbon sources and two different pH levels (4.7 and 7.0) wereused to determine microbial community structure of eight red soils. The average utilization (average wellcolour development) of the carbon sources in the plates with different pH levels generally followed the samesigmoidal pattern as that in the traditional BIOLOG plates, but the pH 4.7 plates increased the discrimination of this technique, compared with the pH 7.0 plates. Since most tested soils are acid, it seemed that it's better to use a suitable pH characterization medium for a specific soil in the sole carbon source test.

  5. Putting the biological species concept to the test: using mating networks to delimit species.

    Directory of Open Access Journals (Sweden)

    Lélia Lagache

    Full Text Available Although interfertility is the key criterion upon which Mayr's biological species concept is based, it has never been applied directly to delimit species under natural conditions. Our study fills this gap. We used the interfertility criterion to delimit two closely related oak species in a forest stand by analyzing the network of natural mating events between individuals. The results reveal two groups of interfertile individuals connected by only few mating events. These two groups were largely congruent with those determined using other criteria (morphological similarity, genotypic similarity and individual relatedness. Our study, therefore, shows that the analysis of mating networks is an effective method to delimit species based on the interfertility criterion, provided that adequate network data can be assembled. Our study also shows that although species boundaries are highly congruent across methods of species delimitation, they are not exactly the same. Most of the differences stem from assignment of individuals to an intermediate category. The discrepancies between methods may reflect a biological reality. Indeed, the interfertility criterion is an environment-dependant criterion as species abundances typically affect rates of hybridization under natural conditions. Thus, the methods of species delimitation based on the interfertility criterion are expected to give results slightly different from those based on environment-independent criteria (such as the genotypic similarity criteria. However, whatever the criterion chosen, the challenge we face when delimiting species is to summarize continuous but non-uniform variations in biological diversity. The grade of membership model that we use in this study appears as an appropriate tool.

  6. Topological Small-World Organization of the Fibroblastic Reticular Cell Network Determines Lymph Node Functionality

    Science.gov (United States)

    Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V.; Turley, Shannon J.; Ludewig, Burkhard

    2016-01-01

    Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses. PMID:27415420

  7. Topological Small-World Organization of the Fibroblastic Reticular Cell Network Determines Lymph Node Functionality.

    Science.gov (United States)

    Novkovic, Mario; Onder, Lucas; Cupovic, Jovana; Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V; Bocharov, Gennady; Turley, Shannon J; Ludewig, Burkhard

    2016-07-01

    Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses.

  8. Topological Small-World Organization of the Fibroblastic Reticular Cell Network Determines Lymph Node Functionality.

    Directory of Open Access Journals (Sweden)

    Mario Novkovic

    2016-07-01

    Full Text Available Fibroblastic reticular cells (FRCs form the cellular scaffold of lymph nodes (LNs and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses.

  9. Genetic determinism in the Finnish upper secondary school biology textbooks

    Directory of Open Access Journals (Sweden)

    Tuomas Aivelo

    2015-05-01

    Full Text Available Genetics is a fast-developing field and it has been argued that genetics education is lagging behind. Genetics education has, for example, been suspected of indoctrinating strong genetic determinism. As the updating of the national upper secondary school curricula is about to start, we decided to study how the current curriculum manifests in Finnish biology textbooks. We studied the main four textbooks for historical gene models and definitions of genes using content analysis. Hybrid models were pervasive in textbooks. The textbooks expressed sometimes even strong genetic determinism, which might be linked to the dominance of older historical models in the textbooks. We also found instances of determinism which we call ‘weak determinism’: genes were depicted as more important factor than environment in relation to the expressed properties. Subsequently, there were no modern gene models found. We suggest gene models should be presented explicitly to reduce misconceptions about genes. We argue that genetics education needs to take more into account than environmental effects and there needs to be more emphasis on the temporal and developmental aspect of genotype-phenotype link. Specifically in Finland this could be done by a more explicit formulation of the national curriculum.

  10. Determination of Signaling Pathways in Proteins through Network Theory: Importance of the Topology.

    Science.gov (United States)

    Ribeiro, Andre A S T; Ortiz, Vanessa

    2014-04-08

    Network theory methods are being increasingly applied to proteins to investigate complex biological phenomena. Residues that are important for signaling processes can be identified by their condition as critical nodes in a protein structure network. This analysis involves modeling the protein as a graph in which each residue is represented as a node and edges are drawn between nodes that are deemed connected. In this paper, we show that the results obtained from this type of network analysis (i.e., signaling pathways, key residues for signal transmission, etc.) are profoundly affected by the topology of the network, with normally used determination of network edges by geometrical cutoff schemes giving rise to substantial statistical errors. We propose a method of determining protein structure networks by calculating inter-residue interaction energies and show that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme. We also show that including covalent interactions in the network topology is essential for accurate results to be obtained.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    Hao Dapeng

    2012-05-01

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

  13. 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.

  14. Simultaneous determination of salicylic acid and salicylamide in biological fluids

    Science.gov (United States)

    Murillo Pulgarín, J. A.; Alañón Molina, A.; Sánchez-Ferrer Robles, I.

    2011-09-01

    A new methodology for the simultaneous determination of salicylic acid and salicylamide in biological fluids is proposed. The strong overlapping of the fluorescence spectra of both analytes makes impossible the conventional fluorimetric determination. For that reason, the use of fluorescence decay curves to resolve mixtures of analytes is proposed; this is a novel technique that provides the benefits in selectivity and sensitivity of the fluorescence decay curves. In order to assess the goodness of the proposed method, a prediction set of synthetic samples were analyzed obtaining recuperation percentages between 98.2 and 104.6%. Finally, a study of the detection limits was done using a new criterion resulting in values for the detection limits of 8.2 and 11.6 μg L -1 for salicylic acid and salicylamide respectively. The validity of the method was tested in human serum and human urine spiked with aliquots of the analytes. Recoveries obtained were 96.2 and 94.5% for salicylic acid and salicylamide respectively.

  15. Application of source-receptor models to determine source areas of biological components (pollen and butterflies

    Directory of Open Access Journals (Sweden)

    M. Alarcón

    2010-01-01

    Full Text Available The source-receptor models allow the establishment of relationships between a receptor point (sampling point and the probable source areas (regions of emission through the association of concentration values at the receptor point with the corresponding atmospheric back-trajectories, and, together with other techniques, to interpret transport phenomena on a synoptic scale. These models are generally used in air pollution studies to determine the areas of origin of chemical compounds measured at a sampling point, and thus be able to target actions to reduce pollutants. However, until now, few studies have applied these types of models to describe the source areas of biological organisms. In Catalonia there are very complete records of pollen (data from the Xarxa Aerobiològica de Catalunya, Aerobiology Network of Catalonia and butterflies (data from the Catalan Butterfly Monitoring Scheme, a biological material that is also liable to be transported long distances and whose areas of origin could be interesting to know. This work presents the results of the use of the Seibert et al. model applied to the study of the source regions of: (1 certain pollen of an allergic nature, observed in Catalonia and the Canary Islands, and (2 the migratory butterfly Vanessa cardui, observed in Catalonia. Based on the results obtained we can corroborate the suitability of these models to determine the area of origin of several species, both chemical and biological, therefore expanding the possibilities of applying the original model to the wider field of Aerobiology.

  16. Systems Biology in the Context of Big Data and Networks

    OpenAIRE

    Md. Altaf-Ul-Amin; Farit Mochamad Afendi; Samuel Kuria Kiboi; Shigehiko Kanaya

    2014-01-01

    Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other...

  17. Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.

    Science.gov (United States)

    Pham, Lisa M; Carvalho, Luis; Schaus, Scott; Kolaczyk, Eric D

    Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

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

    Science.gov (United States)

    Cagua, E. F.; Berumen, M. L.; Tyler, E. H. M.

    2013-12-01

    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.

  19. 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.

  20. Optimization Techniques for Analysis of Biological and Social Networks

    Science.gov (United States)

    2012-03-28

    systematic fashion under a unifying theoretical and algorithmic framework . Optimization, Complex Networks, Social Network Analysis, Computational...analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms, test and fine...exact solutions are presented. In [3], we introduce the variable objective search framework for combinatorial optimization. The method utilizes

  1. 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.

  2. Biological Dosimetry by the Triage Dicentric Chromosome Assay - Further validation of International Networking.

    Science.gov (United States)

    Wilkins, Ruth C; Romm, Horst; Oestreicher, Ursula; Marro, Leonora; Yoshida, Mitsuaki A; Suto, Y; Prasanna, Pataje G S

    2011-09-01

    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 labour-, 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 labour-demanding and time-consuming metaphase-spread analysis to 20 to 50 metaphase spreads instead of routine 500 to 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.

  3. Determination of the scale of coarse graining in earthquake network

    CERN Document Server

    Abe, Sumiyoshi

    2009-01-01

    In a recent paper [S. Abe and N. Suzuki, Europhys. Lett., 65 (2004) 581], the concept of earthquake network has been introduced in order to describe complexity of seismicity. There, the cell size, which is the scale of coarse graining needed for constructing an earthquake network, has remained as a free parameter. Here, a method is presented for determining it based on the scaling behavior of the network. Quite remarkably, both the exponent of the power-law connectivity distribution and the clustering coefficient are found to approach the respective universal values and remain invariant as the cell size becomes larger than a certain value, $l_*$, which depends on the number of events contained in the analysis, in general. This $l_*$ fixes the scale of coarse graining. Universality of the result is demonstrated for all of the networks constructed from the data independently taken from California, Japan and Iran.

  4. 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…

  5. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

  6. Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data

    Institute of Scientific and Technical Information of China (English)

    Wei-Po Lee; Kung-Cheng Yang

    2008-01-01

    Constructing biological networks is one of the most important issues in system sbiology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.

  7. ModuLand plug-in for Cytoscape: extensively overlapping modules, community centrality and their use in biological networks

    CERN Document Server

    Szalay-Beko, Mate; Szappanos, Balazs; Kovacs, Istvan A; Papp, Balazs; Csermely, Peter

    2011-01-01

    Summary: The extensively overlapping structure of network modules is an increasingly recognized feature of biological networks. Here we introduce a user-friendly implementation of our previous network module determination method, ModuLand, as a plug-in of the widely used Cytoscape program. We show the utility of this approach a.) to identify an extensively overlapping modular structure; b.) to define a modular core and hierarchy allowing an easier functional annotation; c.) to identify key nodes of high community centrality, modular overlap or bridgeness in protein structure, protein-protein interaction and metabolic networks. Availability and implementation: The ModuLand Cytoscape plug-in was written in C++, has a JAVA-based graphical interface, can be installed as a single plug-in and can run on Windows, Linux, or Mac OS. The plug-in and its user guide can be downloaded from: http://www.linkgroup.hu/modules_bioinfo_download.php

  8. Fluctuation relations between hierarchical kinetically equivalent networks with Arrhenius-type transitions and their roles in systems and structural biology

    Science.gov (United States)

    Deng, De-Ming; Lu, Yi-Ta; Chang, Cheng-Hung

    2017-06-01

    The legality of using simple kinetic schemes to determine the stochastic properties of a complex system depends on whether the fluctuations generated from hierarchical equivalent schemes are consistent with one another. To analyze this consistency, we perform lumping processes on the stochastic differential equations and the generalized fluctuation-dissipation theorem and apply them to networks with the frequently encountered Arrhenius-type transition rates. The explicit Langevin force derived from those networks enables us to calculate the state fluctuations caused by the intrinsic and extrinsic noises on the free energy surface and deduce their relations between kinetically equivalent networks. In addition to its applicability to wide classes of network related systems, such as those in structural and systems biology, the result sheds light on the fluctuation relations for general physical variables in Keizer's canonical theory.

  9. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  10. Graphical methods for analysing feedback in biological networks - A survey

    Science.gov (United States)

    Radde, Nicole; Bar, Nadav S.; Banaji, Murad

    2010-01-01

    Observed phenotypes usually arise from complex networks of interacting cell components. Qualitative information about the structure of these networks is often available, while quantitative information may be partial or absent. It is natural then to ask what, if anything, we can learn about the behaviour of the system solely from its qualitative structure. In this article we review some techniques which can be applied to answer this question, focussing in particular on approaches involving graphical representations of model structure. By applying these techniques to various cellular network examples, we discuss their strengths and limitations, and point to future research directions.

  11. Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus.

    Science.gov (United States)

    Ferguson, K A; Njap, F; Nicola, W; Skinner, F K; Campbell, S A

    2015-12-01

    Determining the biological details and mechanisms that are essential for the generation of population rhythms in the mammalian brain is a challenging problem. This problem cannot be addressed either by experimental or computational studies in isolation. Here we show that computational models that are carefully linked with experiment provide insight into this problem. Using the experimental context of a whole hippocampus preparation in vitro that spontaneously expresses theta frequency (3-12 Hz) population bursts in the CA1 region, we create excitatory network models to examine whether cellular adaptation bursting mechanisms could critically contribute to the generation of this rhythm. We use biologically-based cellular models of CA1 pyramidal cells and network sizes and connectivities that correspond to the experimental context. By expanding our mean field analyses to networks with heterogeneity and non all-to-all coupling, we allow closer correspondence with experiment, and use these analyses to greatly extend the range of parameter values that are explored. We find that our model excitatory networks can produce theta frequency population bursts in a robust fashion.Thus, even though our networks are limited by not including inhibition at present, our results indicate that cellular adaptation in pyramidal cells could be an important aspect for the occurrence of theta frequency population bursting in the hippocampus. These models serve as a starting framework for the inclusion of inhibitory cells and for the consideration of additional experimental features not captured in our present network models.

  12. Determinants of Informal Coordination in Networked Supply Chains

    NARCIS (Netherlands)

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

    2010-01-01

    Purpose – Provide insight into the determinants or constructs that enable informally networked supply chains to operate in order to achieve improved operational performance. Design/methodology/approach – The research is based on a wide literature review, focused on the identification of dimensions o

  13. RENEB : running the European network of biological dosimetry and physical retrospective dosimetry

    OpenAIRE

    Kulka, Ulrike; Abend, Michael; Ainsbury, Elizabeth; Badie, Christophe; Francesc Barquinero, Joan; Barrios, Lleonard; Beinke, Christina; Bortolin, Emanuela; Cucu, Alexandra; De Amicis, Andrea; Domínguez, Inmaculada; Fattibene, Paola; Frøvig, Anne Marie; Gregoire, Eric; Guogyte, Kamile

    2017-01-01

    Purpose: 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. Results: 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 r...

  14. 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.

  15. 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.

  16. The interaction of intrinsic dynamics and network topology in determining network burst synchrony.

    Science.gov (United States)

    Gaiteri, Chris; Rubin, Jonathan E

    2011-01-01

    The pre-Bötzinger complex (pre-BötC), within the mammalian respiratory brainstem, represents an ideal system for investigating the synchronization properties of complex neuronal circuits via the interaction of cell-type heterogeneity and network connectivity. In isolation, individual respiratory neurons from the pre-BötC may be tonically active, rhythmically bursting, or quiescent. Despite this intrinsic heterogeneity, coupled networks of pre-BötC neurons en bloc engage in synchronized bursting that can drive inspiratory motor neuron activation. The region's connection topology has been recently characterized and features dense clusters of cells with occasional connections between clusters. We investigate how the dynamics of individual neurons (quiescent/bursting/tonic) and the betweenness centrality of neurons' positions within the network connectivity graph interact to govern network burst synchrony, by simulating heterogeneous networks of computational model pre-BötC neurons. Furthermore, we compare the prevalence and synchrony of bursting across networks constructed with a variety of connection topologies, analyzing the same collection of heterogeneous neurons in small-world, scale-free, random, and regularly structured networks. We find that several measures of network burst synchronization are determined by interactions of network topology with the intrinsic dynamics of neurons at central network positions and by the strengths of synaptic connections between neurons. Surprisingly, despite the functional role of synchronized bursting within the pre-BötC, we find that synchronized network bursting is generally weakest when we use its specific connection topology, which leads to synchrony within clusters but poor coordination across clusters. Overall, our results highlight the relevance of interactions between topology and intrinsic dynamics in shaping the activity of networks and the concerted effects of connectivity patterns and dynamic heterogeneities.

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

    OpenAIRE

    Jim Harkin; Fearghal Morgan; Liam McDaid; Steve Hall; Brian McGinley; Seamus Cawley

    2009-01-01

    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...

  18. Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations

    CERN Document Server

    Heckel, Reinhard; Bossert, Martin

    2011-01-01

    Consider a large Boolean network with a feed forward structure. Given a probability distribution for the inputs, can one find-possibly small-collections of input nodes that determine the states of most other nodes in the network? To identify these nodes, a notion that quantifies the determinative power of an input over states in the network is needed. We argue that the mutual information (MI) between a subset of the inputs X = {X_1, ..., X_n} of node i and the function f_i(X)$ associated with node i quantifies the determinative power of this subset of inputs over node i. To study the relation of determinative power to sensitivity to perturbations, we relate the MI to measures of perturbations, such as the influence of a variable, in terms of inequalities. The result shows that, maybe surprisingly, an input that has large influence does not necessarily have large determinative power. The main tool for the analysis is Fourier analysis of Boolean functions. Whether a function is sensitive to perturbations or not...

  19. Network-based drug discovery by integrating systems biology and computational technologies.

    Science.gov (United States)

    Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang

    2013-07-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

  20. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    Science.gov (United States)

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology.

    Science.gov (United States)

    Kirouac, D C; Onsum, M D

    2013-09-04

    If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e71; doi:10.1038/psp.2013.38; published online 4 September 2013.

  2. Clustering determines the dynamics of complex contagions in multiplex networks

    CERN Document Server

    Zhuang, Yong; Yağan, Osman

    2016-01-01

    We present the mathematical analysis of generalized complex contagions in clustered multiplex networks for susceptible-infected-recovered (SIR)-like dynamics. The model is intended to understand diffusion of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is specially useful in problems related to spreading and percolation. The results present non trivial dependencies between the clustering coefficient of the networks and its average degree. In particular, sev...

  3. Making the right connections: Network biology and plant immune system dynamics

    Directory of Open Access Journals (Sweden)

    Maggie E. McCormack

    2016-04-01

    Full Text Available Network analysis has been a recent focus in biological sciences due to its ability to synthesize global visualizations of cellular processes and predict functions based on inferences from network properties. A protein–protein interaction network, or interactome, captures the emergent cellular states from gene regulation and environmental conditions. Given that proteins are involved in extensive local and systemic molecular interactions such as signaling and metabolism, understanding protein functions and interactions are essential for a systems view of biology. However, in plant sciences these network-based approaches to data integration have been few and far between due to limited data, especially protein–protein interaction data. In this review, we cover network construction from experimental data, network analysis based on topological properties, and finally we discuss advances in networks in plants and other organisms in a comparative approach. We focus on applications of network biology to discover the dynamics of host–pathogen interactions as these have potential agricultural uses in improving disease resistance in commercial crops.

  4. On protocols and measures for the validation of supervised methods for the inference of biological networks

    Directory of Open Access Journals (Sweden)

    Marie eSchrynemackers

    2013-12-01

    Full Text Available Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature.In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.

  5. Depth Determination of an Abnormal Heat Source in Biological Tissues

    Institute of Scientific and Technical Information of China (English)

    WANG Qing-Hua; LI Zhen-Hua; LAI Jian-Cheng; HE An-Zhi

    2011-01-01

    We deduce the surface temperature distribution generated by the inner point heat source in biological tissues and propose a graphic method to retrieve the depth of the point heat source. The practical surface temperature distribution can be regarded as the convolution of the temperature distribution of the inner point heat source with the heat source shape function. The depth of an abnormal heat source in biological tissues can be retrieved by using the graphic method combined with the blind deconvolution scheme.%We deduce the surface temperature distribution generated by the inner point heat source in biological tissues and propose a graphic method to retrieve the depth of the point heat source.The practical surface temperature distribution can be regarded as the convolution of the temperature distribution of the inner point heat source with the heat source shape function.The depth of an abnormal heat source in biological tissues can be retrieved by using the graphic method combined with the blind deconvolution scheme.Surface temperature distribution of the biological tissues is closely related to the neighboring metabolic heat production,blood circulation in an organism and environmental temperature.[1] The abnormal metabolic performances of a local region in biological tissue imply malignant changes occurring,which can be distinguished from the variance of surface temperature.Modern development of thermal infrared (TIR) imaging has made the surface temperature measurement of biological tissue easier.Nowadays,several types of tumors,e.g.skin or breast can be recognized with TIR imaging.[2] The diagnostics with TIR imaging require more experienced operators and can not accurately ascertain the site of pathological changes,which limits the value of this technology.Therefore ascertaining the depth of inner heat source in biological body has the extremely important clinical value.

  6. Clustering determines the dynamics of complex contagions in multiplex networks

    Science.gov (United States)

    Zhuang, Yong; Arenas, Alex; Yaǧan, Osman

    2017-01-01

    We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

  7. Biologically Inspired Target Recognition in Radar Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qilian Liang

    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.

  8. 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...

  9. A network biology model of micronutrient related health

    NARCIS (Netherlands)

    Ommen, B. van; Fairweather-Tait, S.; Freidig, A.; Kardinaal, A.; Scalbert, A.; Wopereis, S.

    2008-01-01

    Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise healt

  10. A network biology model of micronutrient related health

    NARCIS (Netherlands)

    Ommen, B. van; Fairweather-Tait, S.; Freidig, A.; Kardinaal, A.; Scalbert, A.; Wopereis, S.

    2008-01-01

    Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise

  11. A network biology model of micronutrient related health

    NARCIS (Netherlands)

    Ommen, B. van; Fairweather-Tait, S.; Freidig, A.; Kardinaal, A.; Scalbert, A.; Wopereis, S.

    2008-01-01

    Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise healt

  12. 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.

  13. 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.

  14. BiNA: a visual analytics tool for biological network data.

    Directory of Open Access Journals (Sweden)

    Andreas Gerasch

    Full Text Available Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA--the Biological Network Analyzer--a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

  15. 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.

  16. Determination of Displacement Geodetic Network Points, Fredericton Approach

    OpenAIRE

    Vrečko, Anja

    2010-01-01

    This graduate thesis deals with the Fredericton approach for determining displacements in geodetic networks. In the introduction strain analysis is presented from a geodetic point of view. Special emphasis is placed on the problem of geodetic datum. It is followed by a theoretical explanation of the method in five steps: adjustment of observation for each epoch, preliminary identification of deformation models, estimation of deformation parameters, checking the deformation models and selectin...

  17. Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

    Science.gov (United States)

    Koehler, Jacob; Rawlings, Chris; Verrier, Paul; Mitchell, Rowan; Skusa, Andre; Ruegg, Alexander; Philippi, Stephan

    2005-01-01

    The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

  18. 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%.

  19. Self-Stabilizing Pulse Synchronization Inspired by Biological Pacemaker Networks

    CERN Document Server

    Daliot, Ariel; Parnas, Hanna

    2008-01-01

    We define the ``Pulse Synchronization'' problem that requires nodes to achieve tight synchronization of regular pulse events, in the settings of distributed computing systems. Pulse-coupled synchronization is a phenomenon displayed by a large variety of biological systems, typically overcoming a high level of noise. Inspired by such biological models, a robust and self-stabilizing Byzantine pulse synchronization algorithm for distributed computer systems is presented. The algorithm attains near optimal synchronization tightness while tolerating up to a third of the nodes exhibiting Byzantine behavior concurrently. Pulse synchronization has been previously shown to be a powerful building block for designing algorithms in this severe fault model. We have previously shown how to stabilize general Byzantine algorithms, using pulse synchronization. To the best of our knowledge there is no other scheme to do this without the use of synchronized pulses.

  20. 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.

  1. PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.

    Science.gov (United States)

    Trinh, Hung-Cuong; Le, Duc-Hau; Kwon, Yung-Keun

    2014-01-01

    It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.

  2. PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.

    Directory of Open Access Journals (Sweden)

    Hung-Cuong Trinh

    Full Text Available It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.

  3. Discriminating different classes of biological networks by analyzing the graphs spectra distribution

    CERN Document Server

    Takahashi, Daniel Yasumasa; Ferreira, Carlos Eduardo; Fujita, André

    2012-01-01

    The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibl...

  4. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    Science.gov (United States)

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Sex Roles: Their Relationship to Cultural and Biological Determinants. [Draft].

    Science.gov (United States)

    Sigmon, Scott B.

    This paper examines relevant research in comparative sociology, social anthropology with primitive societies, the behavior of primates, the hormonal control of social behavior, and contemporary social psychology. The reciprocal influence of social and biological factors on human societies is discussed. Moreover, the effect of attitudes on social…

  6. The BIOSCI electronic newsgroup network for the biological sciences. Final report, October 1, 1992--June 30, 1996

    Energy Technology Data Exchange (ETDEWEB)

    Kristofferson, D.; Mack, D.

    1996-10-01

    This is the final report for a DOE funded project on BIOSCI Electronic Newsgroup Network for the biological sciences. A usable network for scientific discussion, major announcements, problem solving, etc. has been created.

  7. Towards Systems Biology of Heterosis: A Hypothesis about Molecular Network Structure Applied for the Arabidopsis Metabolome

    Directory of Open Access Journals (Sweden)

    Gärtner Tanja

    2009-01-01

    Full Text Available We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity—a known phenomenon in the literature.

  8. Construction of biological networks from unstructured information based on a semi-automated curation workflow.

    Science.gov (United States)

    Szostak, Justyna; Ansari, Sam; Madan, Sumit; Fluck, Juliane; Talikka, Marja; Iskandar, Anita; De Leon, Hector; Hofmann-Apitius, Martin; Peitsch, Manuel C; Hoeng, Julia

    2015-06-17

    Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to capture and organize scientific knowledge and reduce the required curation skills and effort for this task. The workflow was used to build a network that represents the cellular and molecular mechanisms implicated in atherosclerotic plaque destabilization in an apolipoprotein-E-deficient (ApoE(-/-)) mouse model. The network was generated using knowledge extracted from the primary literature. The resultant atherosclerotic plaque destabilization network contains 304 nodes and 743 edges supported by 33 PubMed referenced articles. A comparison between the semi-automated and conventional curation processes showed similar results, but significantly reduced curation effort for the semi-automated process. Creating structured knowledge from unstructured text is an important step for the mechanistic interpretation and reusability of knowledge. Our new semi-automated knowledge extraction workflow reduced the curation skills and effort required to capture and organize scientific knowledge. The

  9. 9 CFR 113.29 - Determination of moisture content in desiccated biological products.

    Science.gov (United States)

    2010-01-01

    ... desiccated biological products. 113.29 Section 113.29 Animals and Animal Products ANIMAL AND PLANT HEALTH... biological products. Methods provided in this section must be used when a determination of moisture content in desiccated biological products is prescribed in an applicable Standard Requirement or in the...

  10. 21 CFR 601.26 - Reclassification procedures to determine that licensed biological products are safe, effective...

    Science.gov (United States)

    2010-04-01

    ... licensed biological products are safe, effective, and not misbranded under prescribed, recommended, or... Reclassification procedures to determine that licensed biological products are safe, effective, and not misbranded... for the reclassification of all biological products that have been classified into Category IIIA....

  11. Multichannel Convolutional Neural Network for Biological Relation Extraction

    Science.gov (United States)

    Quan, Chanqin; Sun, Xiao; Bai, Wenjun

    2016-01-01

    The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores. PMID:28053977

  12. 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.

  13. 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

    2017-09-05

    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.

  14. 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.

  15. 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...... studied. The exact quantities for ``mainly'' and ``most'' are modeled with two easy-to-interpret parameters that allow the user to control the number of outliers (not dysregulated genes/cases) in the solutions. We developed two slightly varying models that fall into the class of NP-Hard optimization...

  16. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

    Science.gov (United States)

    Wang, Ting; Ren, Zhao; Ding, Ying; Fang, Zhou; Sun, Zhe; MacDonald, Matthew L; Sweet, Robert A; Wang, Jieru; Chen, Wei

    2016-02-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM".

  17. Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes

    CERN Document Server

    Ouillon, Guy

    2010-01-01

    We present a new method of data clustering applied to earthquake catalogs, with the goal of reconstructing the seismically active part of fault networks. We first use an original method to separate clustered events from uncorrelated seismicity using the distribution of volumes of tetrahedra defined by closest neighbor events in the original and randomized seismic catalogs. The spatial disorder of the complex geometry of fault networks is then taken into account by defining faults as probabilistic anisotropic kernels, whose structures are motivated by properties of discontinuous tectonic deformation and previous empirical observations of the geometry of faults and of earthquake clusters at many spatial and temporal scales. Combining this a priori knowledge with information theoretical arguments, we propose the Gaussian mixture approach implemented in an Expectation-Maximization (EM) procedure. A cross-validation scheme is then used and allows the determination of the number of kernels that should be used to pr...

  18. Simultaneous Determination of Vitamin B Complex Using Wavelet Neural Network

    Institute of Scientific and Technical Information of China (English)

    YIN,Chun-Sheng(印春生); YIN,Chun-Sheng; GUO,Wei-Min(郭卫民); GUO,Wei-Min; LIU,Shu-Shen(刘树深); LIU,Shu-Shen; SHEN,Yang(沈阳); SHEN,Yang; Zhong-Xiao(潘忠孝); PAN,Zhong-Xiao; WANG,Lian-Sheng(王连生); WANG,Lian-Sheng

    2001-01-01

    A simultaneous determination of four conponents of B-groupvitamin, using a novel wavelet-based neural network (WNN), combined with correlation coefficient and standard deviation approach for wavelength selection, was reported in this work. Eleven representative wavelength points were selected from each o inginal UV spectrun, based on correlation coefficients and standard deviations of the observed data. A family of wavelet basic functions built from Morlet wavelet was adopted to improve the transfer quality of output data and solve the problems of training difficultly involved in neural networks, Tne predicted results, with fitting correlation coefficients (R = 0.9998-0.9999) and rooted mean squares errors (RMS =0.0578-0.1478), are satisfactory.

  19. Physical determinants of vascular network remodeling during tumor growth.

    Science.gov (United States)

    Welter, M; Rieger, H

    2010-10-01

    The process in which a growing tumor transforms a hierarchically organized arterio-venous blood vessel network into a tumor specific vasculature is analyzed with a theoretical model. The physical determinants of this remodeling involve the morphological and hydrodynamic properties of the initial network, generation of new vessels (sprouting angiogenesis), vessel dilation (circumferential growth), vessel regression, tumor cell proliferation and death, and the interdependence of these processes via spatio-temporal changes of blood flow parameters, oxygen/nutrient supply and growth factor concentration fields. The emerging tumor vasculature is non-hierarchical, compartmentalized into well-characterized zones, displays a complex geometry with necrotic zones and "hot spots" of increased vascular density and blood flow of varying size, and transports drug injections efficiently. Implications for current theoretical views on tumor-induced angiogenesis are discussed.

  20. Thermodynamic Rule Determining the Biological DNA Information Capacity

    CERN Document Server

    Widom, A; Srivastava, Y N; Sivasubramanian, S; Valenzi, V I

    2012-01-01

    A rigorous thermodynamic expression is derived for the total biological information capacity per unit length of a DNA molecule. The total information includes the usual four letter coding sequence information plus that excess information coding often erroneously referred to as "junk". We conclude that the currently understood human DNA code is about a hundred megabyte program written on a molecule with about a ten gigabyte memory. By far, most of the programing code is not presently understood.

  1. Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks

    CERN Document Server

    Banerjee, Soumya Jyoti; Roy, Soumen

    2014-01-01

    There have been many studies on malicious targeting of network nodes using degree, betweenness etc. We propose a new network metric, edge proximity, ${\\cal P}_e$, which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. Effects of removing edges with high ${\\cal P}_e$ might initially seem inconspicuous but is eventually shown to be very harmful for the network. When compared to existing strategies, removal of edges by ${\\cal P}_e$, leads to remarkable increase of diameter and average path length in real and random networks till the first disconnection and beyond. ${\\cal P}_e$ can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by ${\\cal P}_e$ causes notable efficiency loss in US and European power grid. ${\\cal P}_e$ identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections...

  2. Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

    Science.gov (United States)

    Christensen, Claire Petra

    Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author

  3. 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

    Background 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. Methodology/Principal Findings 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 () and latency of the optimized engineered gene networks. Conclusions/Significance 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. PMID:21850228

  4. Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways

    Science.gov (United States)

    Zhang, Kui; Busov, Victor; Wei, Hairong

    2017-01-01

    Background Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. Results A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. Conclusions BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental

  5. HPLC method for the determination of oxytocin in pharmaceutical dosage form and comparison with biological method.

    Science.gov (United States)

    Dudkiewicz-Wilczyńska, J; Snycerski, A; Tautt, J

    2000-01-01

    Conditions have been established for the determination of oxytocin by the HPLC method; the method has been validated. The results of HPLC determinations are compared with those obtained by the biological method.

  6. Discriminating different classes of biological networks by analyzing the graphs spectra distribution.

    Directory of Open Access Journals (Sweden)

    Daniel Yasumasa Takahashi

    Full Text Available The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1 protein-protein interaction networks of different species and (2 on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length failed.

  7. A simple method of determination of partition coefficient for biologically active molecules.

    Science.gov (United States)

    Sersen, F

    1995-02-01

    A simple method is presented for the determination of partition coefficient of an effector between water environment and biological material, based on concentration-dependent effects. The method allows the determination of partition coefficients for biological objects such as algae, bacteria and other microorganisms.

  8. [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.

  9. Intrinsic Disorder in Male Sex Determination: Disorderedness of Proteins from the Sry Transcriptional Network.

    Science.gov (United States)

    Merone, Jean; Nwogu, Onyekahi; Redington, Jennifer M; Uversky, Vladimir N

    2016-10-28

    Sex differentiation is a complex process where sexually indifferent embryo progressively acquires male or female characteristics via tightly controlled, perfectly timed, and sophisticatedly intertwined chain of events. This process is controlled and regulated by a set of specific proteins, with one of the first steps in sex differentiation being the activation of the Y-chromosomal Sry gene (sex-determining region Y) in males that acts as a switch from undifferentiated gonad somatic cells to testis development. There are several key players in this process, which constitute the Sry transcriptional network, and collective action of which governs testis determination. Although it is accepted now that many proteins engaged in signal transduction as well as regulation and control of various biological processes are intrinsically disordered (i.e., do not have unique structure and remain unstructured, or incompletely structured, under physiological conditions), the roles and profusion of intrinsic disorder in proteins involved in the male sex determination have not been accessed as of yet. The goal of this study is to cover this gap by analyzing some key players of the Sry transcriptional network. To this end, we employed a broad set of computational tools for intrinsic disorder analysis and conducted intensive literature search in order to gain information on the structural peculiarities of the Sry network-related proteins, their intrinsic disorder predispositions, and the roles of intrinsic disorder in their functions.

  10. Superficial topography of wound: a determinant of underlying biological events?

    Science.gov (United States)

    Farahani, Ramin Mostofi Zadeh; Aminabadi, Naser Asl; Kloth, Luther C

    2008-01-01

    Three-dimensional configuration of wounds varies considerably according to the etiology. Wounding of skin is proceeded by release of dermal pretension. Subsequent disruption of physical equilibrium with resulting development of force vectors alters the primary shape of wound to maintain a new dynamic physical equilibrium. This leads to the development of stress-relaxation and stress-concentration areas throughout the wound milieu. Mechanical strain produces piezoelectric current which is maximal in stress-relaxation regions due to lower tissue stiffness and higher mobility. Early surge in the tissue level of TGF-beta would be exaggerated through synergistic interaction with piezoelectric current in stress-relaxation areas. Subsequently, fibroblasts migrate to these areas due to galvanotaxis. The gradual dissipation of tissue tension, due to irreversible loss of viscous strain, reduces the synergistic action of TGF-beta and piezoelectricity. However, a similar pattern of activity of TGF-beta due to the polarized migration of fibroblasts, which are the main source of TGF-beta during secondary surge, may be continued. It seems that a biological-mechanical continuum exists for wounds so that even the superficial topography of wounds may affect the underlying biological activity and final healing outcome during healing of dermal wounds.

  11. Compressed Sensing Electron Tomography for Determining Biological Structure

    Science.gov (United States)

    Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.

    2016-06-01

    There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.

  12. 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...

  13. Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms

    Science.gov (United States)

    Li, Le; Yip, Kevin Y.

    2016-01-01

    Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature.Availability: Source code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/. PMID:27976738

  14. The Google matrix controls the stability of structured ecological and biological networks

    Science.gov (United States)

    Stone, Lewi

    2016-09-01

    May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.

  15. Constraints of Biological Neural Networks and Their Consideration in AI Applications

    Directory of Open Access Journals (Sweden)

    Richard Stafford

    2010-01-01

    Full Text Available Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances. Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

  16. Mass spectrometric determination of early and advanced glycation in biology.

    Science.gov (United States)

    Rabbani, Naila; Ashour, Amal; Thornalley, Paul J

    2016-08-01

    Protein glycation in biological systems occurs predominantly on lysine, arginine and N-terminal residues of proteins. Major quantitative glycation adducts are found at mean extents of modification of 1-5 mol percent of proteins. These are glucose-derived fructosamine on lysine and N-terminal residues of proteins, methylglyoxal-derived hydroimidazolone on arginine residues and N(ε)-carboxymethyl-lysine residues mainly formed by the oxidative degradation of fructosamine. Total glycation adducts of different types are quantified by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring mode. Metabolism of glycated proteins is followed by LC-MS/MS of glycation free adducts as minor components of the amino acid metabolome. Glycated proteins and sites of modification within them - amino acid residues modified by the glycating agent moiety - are identified and quantified by label-free and stable isotope labelling with amino acids in cell culture (SILAC) high resolution mass spectrometry. Sites of glycation by glucose and methylglyoxal in selected proteins are listed. Key issues in applying proteomics techniques to analysis of glycated proteins are: (i) avoiding compromise of analysis by formation, loss and relocation of glycation adducts in pre-analytic processing; (ii) specificity of immunoaffinity enrichment procedures, (iii) maximizing protein sequence coverage in mass spectrometric analysis for detection of glycation sites, and (iv) development of bioinformatics tools for prediction of protein glycation sites. Protein glycation studies have important applications in biology, ageing and translational medicine - particularly on studies of obesity, diabetes, cardiovascular disease, renal failure, neurological disorders and cancer. Mass spectrometric analysis of glycated proteins has yet to find widespread use clinically. Future use in health screening, disease diagnosis and therapeutic monitoring, and

  17. 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.

  18. VANESA - a software application for the visualization and analysis of networks in system biology applications.

    Science.gov (United States)

    Brinkrolf, Christoph; Janowski, Sebastian Jan; Kormeier, Benjamin; Lewinski, Martin; Hippe, Klaus; Borck, Daniela; Hofestädt, Ralf

    2014-06-23

    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.

  19. 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.

  20. Biological and behavioral determinants of fertility in Tierra del Fuego.

    Science.gov (United States)

    Pascual, J; García-Moro, C E; Hernández, M

    2005-05-01

    The reproductive history of 182 women in postreproductive life or near menopause from the Chilean part of Tierra del Fuego was traced back by means of familial interviews. These postmenopausal women represent the population since almost the beginning of the settlement, and their reproductive years were spent on the island. Path analysis was applied to analyze fertility determinants of these women and to propose a complex model of interconnections among factors. The reproductive history of these women is characterized by a long fertile span, a short childbearing period, and low fertility. Age at menarche is relatively late, and the age of the women at first birth is mainly determined by their late age at marriage. The use of contraception is related to both spacing and stopping behaviors. The late age of women at marriage, the rhythm of conception, and practices of contraception are proposed as the main determinants of fertility in Tierra del Fuego. Copyright 2004 Wiley-Liss, Inc.

  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.

  2. The structural molecular biology network of the State of São Paulo, Brazil

    Directory of Open Access Journals (Sweden)

    João A.R.G. Barbosa

    2006-06-01

    Full Text Available This article describes the achievements of the Structural Molecular Biology Network (SMolBNet, a collaborative program of structural molecular biology, centered in the State of São Paulo, Brazil, and supported by São Paulo State Funding Agency (FAPESP. It gathers twenty scientific groups and is coordinated by the scientific staff of the Center of Structural Molecular Biology, at the National Laboratory of Synchrotron Light (LNLS, in Campinas. The SMolBNet program has been aimed at 1 solving the structure of proteins of interest related to the research projects of the groups. In some cases, the choice has been to select proteins of unknown function or of possible novel structure obtained from the sequenced genomes of the FAPESP genomic program; 2 providing the groups with training in all the steps of the protein structure determination: gene cloning, protein expression, protein purification, protein crystallization and structure determination. Having begun in 2001, the program has been successful in both aims. Here, four groups reveal their participation in the program and describe the structural aspects of the proteins they have selected to study.Esse artigo descreve realizações do Programa SMolBNet (Rede de Biologia Molecular Estrutural do Estado de São Paulo, apoiado pela FAPESP (Fundação de Apoio à Pesquisa do Estado de São Paulo. Ele reúne vinte grupos de pesquisa e é coordenado pelos pesquisadores do Laboratório Nacional de Luz Síncrotron (LNLS, em Campinas. O Programa SMolBNet tem como metas: Elucidar a estrutura tridimensional de proteínas de interesse aos grupos de pesquisa componentes do Programa; Prover os grupos com treinamento em todas as etapas de determinação de estrutura: clonagem gênica, expressão de proteínas, purificação de proteínas, cristalização de proteínas e elucidação de suas estruturas. Tendo começado em 2001, o Programa alcançou sucesso em ambas as metas. Neste artigo, quatro dos grupos

  3. 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

  4. Time constant determination for electrical equivalent of biological cells

    Science.gov (United States)

    Dubey, Ashutosh Kumar; Dutta-Gupta, Shourya; Kumar, Ravi; Tewari, Abhishek; Basu, Bikramjit

    2009-04-01

    The electric field interactions with biological cells are of significant interest in various biophysical and biomedical applications. In order to study such important aspect, it is necessary to evaluate the time constant in order to estimate the response time of living cells in the electric field (E-field). In the present study, the time constant is evaluated by considering the hypothesis of electrical analog of spherical shaped cells and assuming realistic values for capacitance and resistivity properties of cell/nuclear membrane, cytoplasm, and nucleus. In addition, the resistance of cytoplasm and nucleoplasm was computed based on simple geometrical considerations. Importantly, the analysis on the basis of first principles shows that the average values of time constant would be around 2-3 μs, assuming the theoretical capacitance values and the analytically computed resistance values. The implication of our analytical solution has been discussed in reference to the cellular adaptation processes such as atrophy/hypertrophy as well as the variation in electrical transport properties of cellular membrane/cytoplasm/nuclear membrane/nucleoplasm.

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

    Directory of Open Access Journals (Sweden)

    Planas-Iglesias Joan

    2010-01-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  6. 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/

  7. Recovery Management in All Optical Networks Using Biologically-Inspired Complex Adaptive System

    Directory of Open Access Journals (Sweden)

    Inadyuti Dutt

    2013-01-01

    Full Text Available All-Optical Networks have the ability to display varied advantages like performance efficiency, throughput etc but their efficiency depends on their survivability as they are attack prone. These attacks can be categorised as active or passive because they try to access information within the network or alter the information in the network. The attack once detected has to be recovered by formulating back-up or alternative paths. The proposed heuristic uses biologically inspired Complex Adaptive System, inspired by Natural Immune System. The study shows that natural immune system exhibit unique behaviour of detecting foreign bodies in our body and removing them on their first occurrences. This phenomenon is being utilised in the proposed heuristic for recovery management in All-optical Network

  8. Determination of ferrous and ferric iron in aqueous biological solutions

    Energy Technology Data Exchange (ETDEWEB)

    Pepper, S.E. [Earth and Environmental Sciences Division, Los Alamos National Laboratory, 1400 University Drive, Carlsbad, NM 88220 (United States); Borkowski, M., E-mail: marian@lanl.gov [Earth and Environmental Sciences Division, Los Alamos National Laboratory, 1400 University Drive, Carlsbad, NM 88220 (United States); Richmann, M.K.; Reed, D.T. [Earth and Environmental Sciences Division, Los Alamos National Laboratory, 1400 University Drive, Carlsbad, NM 88220 (United States)

    2010-03-24

    A solvent extraction method was employed to determine ferrous and ferric iron in aqueous samples. Fe{sup 3+} is selectively extracted into the organic phase (n-heptane) using HDEHP (bis(2-ethylhexyl) hydrogen phosphate) and is then stripped using a strong acid. After separation, both oxidation states and the total iron content were determined directly by ICP-MS analysis. This extraction method was refined to allow determination of both iron oxidation states in the presence of strong complexing ligands, such as citrate, NTA and EDTA. The accuracy of the method was verified by crosschecking using a refinement of the ferrozine assay. Presented results demonstrate the ability of the extraction method to work in a microbiological system in the presence of strong chelating agents following the bioreduction of Fe{sup 3+} by the Shewanella alga BrY. Based on the results we report, a robust approach was defined to separately analyze Fe{sup 3+} and Fe{sup 2+} under a wide range of potential scenarios in subsurface environments where radionuclide/metal contamination may coexist with strongly complexing organic contaminants.

  9. Optoelectronic method for determining platinum in biological products

    Science.gov (United States)

    Radu, Simona; Ionicǎ, Mihai; Macovei, Radu Alexandru; Caragea, Genica; Forje, Mǎrgǎrita; Grecu, Iulia; Vlǎdescu, Marian; Viscol, Oana

    2016-12-01

    Of all platinum metals, platinum has the most uses and it's the most abundant and most easily to be processed. Its use in auto catalysts results in environmental contamination of crowded cities and high-traffic roads. In medicine, Pt is used as a cytostatic drug. In order to study the degree of contamination of the population with Pt or the correctness of treatment with Pt, it has been developed a method for its determination from urine or blood samples with a system Graphite Furnance - Atomic Absorption Spectrometer, (GF-AAS) Varian. There are presented the methods of sampling processing for blood or urine that followed the digest of the organic matrix. In the determination of the operating parameters for the system GF-AAS, was aimed the reducing of the nonanatomic absorbance by optimizing the drying temperatures, the calcination and atomization temperatures and the removal of the nonanatomic absorbance with D2 lamp. As a result of the use of the method are presented the concentrations of Pt in the blood or urine of a group of patients in Bucharest, a city with heavy traffic of vehicles. GF-AAS method presented is sensitive, reproducible, and relatively easy to apply with an acceptable cost. With this method, the concentration of Pt can be determined from blood and urine, both in order to establish the degree of contamination with Pt and for monitoring cancer therapy with platinum compounds.

  10. Research on Performance Evaluation of Biological Database based on Layered Queuing Network Model under the Cloud Computing Environment

    OpenAIRE

    Zhengbin Luo; Dongmei Sun

    2013-01-01

    To evaluate the performance of biological database based on layered queuing network model and under cloud computing environment is a premise, as well as an important step for biological database optimization. Based on predecessors’ researches concerning computer software and hardware performance evaluation under cloud environment, the study has further constructed a model system to evaluate the performance of biological database based on layered queuing network model and under cloud environme...

  11. Which properties of a spanning network of hydration water enable biological functions?

    Science.gov (United States)

    Brovchenko, Ivan; Oleinikova, Alla

    2008-12-22

    The central role of water in biological functions is well-recognized, but numerous questions concerning the physical mechanisms behind the importance of water for life remain unanswered. Water in biosystems exists mainly as hydration water. Analysis of the phase diagram of hydration water shows that biological functions are possible only when the surfaces of biomolecules are covered by spanning hydrogen-bonded networks of hydration water. The comparative studies of the various properties of hydrated biosystems in the presence and in the absence of a spanning water network should clarify its specific physical properties, which are crucial for biological functions. Herein, we summarize the recent progress in these studies. The biological activity of the living organisms is maximal in a narrow temperature interval, where the spanning network of hydration water breaks up with heating via a percolation transition. The entropy of the hydration water related to the diversity of cluster size diverges at this percolation threshold. The possible role of this phenomenon in life processes is discussed.

  12. A network-theoretic approach for decompositional translation across Open Biological Ontologies.

    Science.gov (United States)

    Patel, Chintan O; Cimino, James J

    2010-08-01

    Biological ontologies are now being widely used for annotation, sharing and retrieval of the biological data. Many of these ontologies are hosted under the umbrella of the Open Biological Ontologies Foundry. In order to support interterminology mapping, composite terms in these ontologies need to be translated into atomic or primitive terms in other, orthogonal ontologies, for example, gluconeogenesis (biological process term) to glucose (chemical ontology term). Identifying such decompositional ontology translations is a challenging problem. In this paper, we propose a network-theoretic approach based on the structure of the integrated OBO relationship graph. We use a network-theoretic measure, called the clustering coefficient, to find relevant atomic terms in the neighborhood of a composite term. By eliminating the existing GO to ChEBI Ontology mappings from OBO, we evaluate whether the proposed approach can re-identify the corresponding relationships. The results indicate that the network structure provides strong cues for decompositional ontology translation and the existing relationships can be used to identify new translations.

  13. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.

    Science.gov (United States)

    Yan, Jingwen; Risacher, Shannon L; Shen, Li; Saykin, Andrew J

    2017-06-30

    In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  14. Naziism, Biological Determinism, Sociobiology, and Evolutionary Theroy: Are They Necessairly Synonymous?

    OpenAIRE

    LAMB, Michael E.

    1993-01-01

    Richard Lerner's new book, Final Solutions: Biology, Prejudice, and Genocide, is a powerful and troubling treatise. It weaves together several topical strands into a direct, clear, and compelling argument. The chief strength of the book lies in its focus on a single aspect of Nazi ideology (biological determinism), the role played in the maintenance of that ideology by medical and biological scientists, and Lerner's warnings about those he views as the contemporary successors of these scienti...

  15. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2016-02-01

    Full Text Available Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM, a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM".

  16. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

    Science.gov (United States)

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  17. Determination of platinum, palladium, and lead in biological samples by atomic absorption spectrophotometry.

    Science.gov (United States)

    Tillery, J B; Johnson, D E

    1975-01-01

    A flameless atomic absorption method for the coextraction of platinum and palladium from biological and environmental samples by high molecular weight amine (HMWA) is given. Also, methods for lead determination in biological samples by use of extraction flameless analysis and direct aspiration-flame analysis are reported. A study of lead contamination of Vacutainer tubes is given. PMID:1227857

  18. 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...

  19. Communication issues in determining departmental local area networks requirements

    Science.gov (United States)

    Dabipi, I. K.; Donaldson, A.; Anderson, James A.

    1995-07-01

    One of the major issues when configuring networks is about the way in which the decision on what kind of a network to install is made. The Electrical Engineering Department at Southern University has recently gone through this process. This paper addresses the related network design issues as a class project simulation of the different network designs. The simulation details the network simulation and the network connectivity issues. A comparison of the various designs is presented and the reason for the choice of the final configuration is also given.

  20. Biological mechanisms determining the success of RNA interference in insects.

    Science.gov (United States)

    Wynant, Niels; Santos, Dulce; Vanden Broeck, Jozef

    2014-01-01

    Insects constitute the largest group of animals on this planet, having a huge impact on our environment, as well as on our quality of life. RNA interference (RNAi) is a posttranscriptional gene silencing mechanism triggered by double-stranded (ds)RNA fragments. This process not only forms the basis of a widely used reverse genetics research method in many different eukaryotes but also holds great promise to contribute to the species-specific control of agricultural pests and to combat viral infections in beneficial and disease vectoring insects. However, in many economically important insect species, such as flies, mosquitoes, and caterpillars, systemic delivery of naked dsRNA does not trigger effective gene silencing. Although many components of the RNAi pathway have initially been deciphered in the fruit fly, Drosophila melanogaster, it will be of major importance to investigate this process in a wider variety of species, including dsRNA-sensitive insects such as locusts and beetles, to elucidate the factors responsible for the remarkable variability in RNAi efficiency, as observed in different insects. In this chapter, we review the current knowledge on the RNAi pathway, as well as the most recent insights into the mechanisms that might determine successful RNAi in insects.

  1. Coenzyme Q10 analytical determination in biological matrices and pharmaceuticals.

    Science.gov (United States)

    Lucangioli, Silvia; Martinefski, Manuela; Tripodi, Valeria

    2016-06-01

    In recent years, the analytical determination of coenzyme Q10 (CoQ10) has gained importance in clinical diagnosis and in pharmaceutical quality control. CoQ10 is an important cofactor in the mitochondrial respiratory chain and a potent endogenous antioxidant. CoQ10 deficiency is often associated with numerous diseases and patients with these conditions may benefit from administration of supplements of CoQ10. In this regard, it has been observed that the best benefits are obtained when CoQ10 deficiency is diagnosed and treated early. Therefore, it is of great value to develop analytical methods for the detection and quantification of CoQ10 in this type of disease. The methods above mentioned should be simple enough to be used in routine clinical laboratories as well as in quality control of pharmaceutical formulations containing CoQ10. Here, we discuss the advantages and disadvantages of different methods of CoQ10 analysis.

  2. Adult Learning Open University Determinants study (ALOUD): Biological lifestyle factors associated with study success

    NARCIS (Netherlands)

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

    2012-01-01

    Gijselaers, H. J. M., De Groot, R. H. M., & Kirschner, P. A. (2012, 7 November). Adult Learning Open University Determinants study (ALOUD): Biological lifestyle factors associated with study success. Poster presentation at the International ICO Fall School, Girona, Spain.

  3. Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths

    Science.gov (United States)

    Li, Chunhe; Wang, Jin

    2013-01-01

    Cellular reprogramming has been recently intensively studied experimentally. We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes. We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states, quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process, connecting the stem cell state and differentiated cell state. Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly. Flux leads the kinetic paths to be deviated from the steepest descent gradient path, and the corresponding differentiation and reprogramming paths are irreversible. Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through. We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction. The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development. Through the global sensitivity analysis, we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process. Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics. PMID:23935477

  4. Cumulative Laws,Team Assembling Mechanisms Determining Network Structure

    Institute of Scientific and Technical Information of China (English)

    WU Bin; LIU Qi; YE Qi

    2008-01-01

    A number of researching works have shed light on the field of complex networks recently.We investigate a wide range of real-world networks and find several interesting phenomena.Firstly,almost all of these networks evolve by overlapping new small graphs on former networks.Secondly,not only the degree sequence of the mature network follows a power-law distribution,but also the distribution of the cumulative occurrence times during the growing process are revealed to have a heavy tail.Existing network evolving models do not provide interpretation to these phenomena.We suggest a model based on the team assembling mechanism,which is extracted from the growing processes of real-world networks and requires simple parameters,and produces networks exhibiting these properties observed in the present study and in previous works.

  5. 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

  6. 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.

  7. PiNGO: a Cytoscape plugin to find candidate genes in biological networks.

    Science.gov (United States)

    Smoot, Michael; Ono, Keiichiro; Ideker, Trey; Maere, Steven

    2011-04-01

    PiNGO is a tool to screen biological networks for candidate genes, i.e. genes predicted to be involved in a biological process of interest. The user can narrow the search to genes with particular known functions or exclude genes belonging to particular functional classes. PiNGO provides support for a wide range of organisms and Gene Ontology classification schemes, and it can easily be customized for other organisms and functional classifications. PiNGO is implemented as a plugin for Cytoscape, a popular network visualization platform. PiNGO is distributed as an open-source Java package under the GNU General Public License (http://www.gnu.org/), and can be downloaded via the Cytoscape plugin manager. A detailed user guide and tutorial are available on the PiNGO website (http://www.psb.ugent.be/esb/PiNGO.

  8. Determining biomass in biological processes. Methods for wastewater biological treatment; Determinacion de la biomasa en procesos biologicos

    Energy Technology Data Exchange (ETDEWEB)

    Arnaiz, C.; Isaac, L.; Lebrato, J. [Universidad Politecnica de Sevilla (Spain)

    2000-07-01

    Biomass concentration and activity are two important parameters for the successful design and control of biological processes in wastewater treatment. Widely used parameter for biomass characterization is dry weight concentration. This parameter is, however, not sufficient to describe biomass activity. Improved analytical methods are needed in order to understand the physiological behaviour of the biomass. In this work, conventional and advanced analytical methods for biomass determination in wastewater treatment are reviewed. (Author) 27 refs.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-01-01

    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 to 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.

  10. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks

    Science.gov (United States)

    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. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat). PMID:27853512

  11. 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.

  12. 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.

  13. EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks.

    Science.gov (United States)

    Sambaturu, Narmada; Mishra, Madhulika; Chandra, Nagasuma

    2016-08-18

    In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights. We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes. We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the

  14. Network-based analysis of affected biological processes in type 2 diabetes models.

    Directory of Open Access Journals (Sweden)

    Manway Liu

    2007-06-01

    Full Text Available Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

  15. 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.

  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. 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

    2014-01-21

    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.

  18. 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.

  19. Network news: prime time for systems biology of the plant circadian clock.

    Science.gov (United States)

    McClung, C Robertson; Gutiérrez, Rodrigo A

    2010-12-01

    Whole-transcriptome analyses have established that the plant circadian clock regulates virtually every plant biological process and most prominently hormonal and stress response pathways. Systems biology efforts have successfully modeled the plant central clock machinery and an iterative process of model refinement and experimental validation has contributed significantly to the current view of the central clock machinery. The challenge now is to connect this central clock to the output pathways for understanding how the plant circadian clock contributes to plant growth and fitness in a changing environment. Undoubtedly, systems approaches will be needed to integrate and model the vastly increased volume of experimental data in order to extract meaningful biological information. Thus, we have entered an era of systems modeling, experimental testing, and refinement. This approach, coupled with advances from the genetic and biochemical analyses of clock function, is accelerating our progress towards a comprehensive understanding of the plant circadian clock network. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. 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.

  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. Comparison between different earthquake magnitudes determined by China Seismograph Network

    Institute of Scientific and Technical Information of China (English)

    LIU Rui-feng; CHEN Yun-tai; REN Xiao; XU Zhi-guo; SUN Li; YANG Hui; LIANG Jian-hong; REN Ke-xin

    2007-01-01

    By linear regression and orthogonal regression methods, comparisons are made between different magnitudes (local magnitude ML, surface wave magnitudes MS and MS7, long-period body wave magnitude mB and short-period body wave magnitude mb) determined by Institute of Geophysics, China Earthquake Administration, on the basis of observation data collected by China Seismograph Network between 1983 and 2004. Empirical relations between different magnitudes have been obtained. The result shows that: ①As different magnitude scales reflect radiated energy by seismic waves within different periods, earthquake magnitudes can be described more objectively by using different scales for earthquakes of different magnitudes. When the epicentral distance is less than 1 000 km, local magnitude ML can be a preferable scale; In case MMS, i.e., MS underestimates magnitudes of such events, therefore, mB can be a better choice; In case M>6.0, MS>mB>mb, both mB and mb underestimate the magnitudes, so MS is a preferable scale for determining magnitudes of such events (6.08.5, a saturation phenomenon appears in MS, which cannot give an accurate reflection of the magnitudes of such large events; ②In China, when the epicentral distance is less than 1 000 km, there is almost no difference between ML and MS, and thus there is no need to convert between the two magnitudes in practice; ③Although MS and MS7 are both surface wave magnitudes, MS is in general greater than MS7 by 0.2~0.3 magnitude, because different instruments and calculation formulae are used; ④mB is almost equal to mb for earthquakes around mB4.0, but mB is larger than mb for those of mB(4.5, because the periods of seismic waves used for measuring mB and mb are different though the calculation formulae are the same.

  3. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  4. 78 FR 55326 - Determinations Regarding Use of Chemical Weapons in Syria Under the Chemical and Biological...

    Science.gov (United States)

    2013-09-10

    ... Determinations Regarding Use of Chemical Weapons in Syria Under the Chemical and Biological Weapons Control and..., 22 U.S.C. 5604(a), that the Government of Syria has used chemical weapons in violation of... Under Secretary of State for Political Affairs: (1) Determined that the Government of Syria has...

  5. 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.

  6. Workshop Report on the European Bone Sarcoma Networking Meeting: Integration of Clinical Trials with Tumor Biology.

    Science.gov (United States)

    Thomas, David M; Wilhelm, Miriam; Cleton-Jansen, Anne-Marie; Dirksen, Uta; Entz-Werlé, Natacha; Gelderblom, Hans; Hassan, Bass; Jürgens, Heribert; Koster, Jan; Kovar, Heinrich; Lankester, Arjan C; Lewis, Ian J; Myklebost, Ola; Nathrath, Michaela H M; Picci, Piero; Whelan, Jeremy S; Hogendoorn, Pancras C W; Bielack, Stefan S

    2011-09-01

    A key workshop was held in The Netherlands in June 2011, hosted by several European bone sarcoma networks and with a broad range of stakeholders from Europe and Australia. The purpose of the meeting was to identify the strengths and weaknesses in current clinical trials for bone sarcomas and to make recommendations as to how to accelerate progress in this field. Two areas of particular interest were discussed. First, all participants agreed upon the importance of tumor biology to understanding clinical responses for all types of bone sarcoma. Various barriers to biobanking tumor and germline specimens were canvassed and are outlined in this paper. Second, there was consideration of the particular challenges of dealing with adolescent and young adult cancers, exemplified by bone sarcomas. Participants recommended greater engagement of both pediatric and adult sarcoma trial organizations to address this issue. Specific opportunities were identified to develop biological sub-studies within osteosarcoma, focused on understanding germ line risk and pharmacogenomics defining toxicity and biological responses. In Ewing sarcoma, it was harder to define opportunities for biological insights. There was agreement that the results for insulin-like growth factor pathway inhibition in Ewing family tumors were disappointing, but represented a clear indication of the need for companion biologic studies to develop predictive biomarkers. The meeting ended with broad commitment to working together to make progress in this rare but important subgroup of cancers.

  7. 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

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

    Science.gov (United States)

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    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 such as BC.

  9. Some structural determinants of Pavlovian conditioning in artificial neural networks

    NARCIS (Netherlands)

    Sanchez, Jose M.; Galeazzi, Juan M.; Burgos, Jose E.

    2010-01-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hip

  10. Research on Performance Evaluation of Biological Database based on Layered Queuing Network Model under the Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Zhengbin Luo

    2013-06-01

    Full Text Available To evaluate the performance of biological database based on layered queuing network model and under cloud computing environment is a premise, as well as an important step for biological database optimization. Based on predecessors’ researches concerning computer software and hardware performance evaluation under cloud environment, the study has further constructed a model system to evaluate the performance of biological database based on layered queuing network model and under cloud environment. Moreover, traditional layered queuing network model is also optimized and upgraded in this process. After having constructed the performance evaluation system, the study applies laboratory experiment method to test the validity of the constructed performance model. Shown by the test result, this model is effective in evaluating the performance of biological system under cloud environment and the predicted result is quite close to the tested result. This has demonstrated the validity of the model in evaluating the performance of biological database.

  11. 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

  12. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  13. Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.; Baddeley, Robert L.; Riensche, Roderick M.; Jensen, Russell S.; Verhagen, Marc

    2010-08-02

    Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant links across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.

  14. 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.

  15. Dynamics and control at feedback vertex sets. II: a faithful monitor to determine the diversity of molecular activities in regulatory networks.

    Science.gov (United States)

    Mochizuki, Atsushi; Fiedler, Bernold; Kurosawa, Gen; Saito, Daisuke

    2013-10-21

    Modern biology provides many networks describing regulations between many species of molecules. It is widely believed that the dynamics of molecular activities based on such regulatory networks are the origin of biological functions. However, we currently have a limited understanding of the relationship between the structure of a regulatory network and its dynamics. In this study we develop a new theory to provide an important aspect of dynamics from information of regulatory linkages alone. We show that the "feedback vertex set" (FVS) of a regulatory network is a set of "determining nodes" of the dynamics. The theory is powerful to study real biological systems in practice. It assures that (i) any long-term dynamical behavior of the whole system, such as steady states, periodic oscillations or quasi-periodic oscillations, can be identified by measurements of a subset of molecules in the network, and that (ii) the subset is determined from the regulatory linkage alone. For example, dynamical attractors possibly generated by a signal transduction network with 113 molecules can be identified by measurement of the activity of only 5 molecules, if the information on the network structure is correct. Our theory therefore provides a rational criterion to select key molecules to control a system. We also demonstrate that controlling the dynamics of the FVS is sufficient to switch the dynamics of the whole system from one attractor to others, distinct from the original.

  16. A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homog

  17. 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

  18. The shortest path is not the one you know: application of biological network resources in precision oncology research.

    Science.gov (United States)

    Kuperstein, Inna; Grieco, Luca; Cohen, David P A; Thieffry, Denis; Zinovyev, Andrei; Barillot, Emmanuel

    2015-03-01

    Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision

  19. Networks and their applications to biological systems: From ecological dynamics to gene regulation

    Science.gov (United States)

    Sevim, Volkan

    In this dissertation, we study three biological applications of networks. The first one is a biological coevolution model, in which a species is defined by a genome in the form of a finite bitstring and the interactions between species are given by a fixed matrix with randomly distributed elements. Here we study a version of the model, in which the matrix elements are correlated to a controllable degree by means of an averaging scheme. This method allows creation of mutants resembling their ancestors (wildtype). We compare long kinetic Monte Carlo simulations of models with uncorrelated and correlated interactions. We find that while there are quantitative differences, most qualitative features, such as 1/f behavior in power spectral densities for the diversity indices and the power-law distribution of species lifetimes, are not significantly affected by the correlations in the interaction matrix. The second application is the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. This is a new preferential-attachment scheme, in which a new node attaches to an existing node i with probability pi(k i, k'i ) ∝ ( k'i /ki)gamma, where ki and k'i are the number of outgoing and incoming links at i, respectively, and gamma is a constant. First, we calculate the degree distribution for the outgoing links for a simplified form of this function, pi( ki) ∝ k-1i , both analytically and by Monte Carlo simulations. The distribution decays like kmuk/Gamma(k) for large k, where mu is a constant. We relate this mechanism to simple food-web models by implementing it in the cascade model. We also study the generalized case, pi(ki, k'i ) ∝ ( k'i /ki)gamma, by simulations. The third application is the evolution of robustness to mutations and noise in gene regulatory networks. It has been shown that robustness to mutations and noise can evolve through stabilizing selection for optimal phenotypes in model gene regulatory

  20. Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

    DEFF Research Database (Denmark)

    Audouze, Karine Marie Laure; Juncker, Agnieszka; Roque, Francisco José Sousa Simões Almeida

    2010-01-01

    Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemi...

  1. 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:…

  2. 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:…

  3. Determination of Optical Properties of Turbid Media from Spatially Resolved Diffuse Reflectance by Neural Network

    Institute of Scientific and Technical Information of China (English)

    REN Hongwu; FANG Zujie

    2000-01-01

    A backpropagation (BP) network is applied to the inversion of spatially resolved diffuse reflectance from turbid media and then to determine its optical properties. A standard BP network may be trapped to the local minimum. A BP network with variable momentum and variable leaning rate can reduce this effect. After being trained, this network will produce reduced scattering coefficients and absorption coefficients when the spatially resolved diffuse reflectance are fed to its input.

  4. 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.

  5. Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

    Directory of Open Access Journals (Sweden)

    Tina Toni

    Full Text Available 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

  6. Stochastic robustness and relative stability of multiple pathways in biological networks

    CERN Document Server

    Guo, Yongyi; Qian, Min; Ge, Hao

    2015-01-01

    Multiple dynamic pathways always exist in biological networks, but their robustness against internal fluctuations and relative stability have not been well recognized and carefully analyzed yet. Here we try to address these issues through an illustrative example, namely the Siah-1/beta-catenin/p14/19 ARF loop of protein p53 dynamics. Its deterministic Boolean network model predicts that two parallel pathways with comparable magnitudes of attractive basins should exist after the protein p53 is activated when a cell becomes harmfully disturbed. Once the low but non-neglectable intrinsic fluctuations are incorporated into the model, we show that a phase transition phenomenon is emerged: in one parameter region the probability weights of the normal pathway, reported in experimental literature, are comparable with the other pathway which is seemingly abnormal with the unknown functions, whereas, in some other parameter regions, the probability weight of the abnormal pathway can even dominate and become globally at...

  7. Collaboration Networks in the Brazilian Scientific Output in Evolutionary Biology: 2000-2012

    Directory of Open Access Journals (Sweden)

    Dirce M. Santin

    2016-03-01

    Full Text Available This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community.

  8. The MI bundle: enabling network and structural biology in genome visualization tools.

    Science.gov (United States)

    Céol, Arnaud; Müller, Heiko

    2015-11-15

    Prioritization of candidate genes emanating from large-scale screens requires integrated analyses at the genomics, molecular, network and structural biology levels. We have extended the Integrated Genome Browser (IGB) to facilitate these tasks. The graphical user interface greatly simplifies building disease networks and zooming in at atomic resolution to identify variations in molecular complexes that may affect molecular interactions in the context of genomic data. All results are summarized in genome tracks and can be visualized and analyzed at the transcript level. The MI Bundle is a plugin for the IGB. The plugin, help, video and tutorial are available at http://cru.genomics.iit.it/igbmibundle/ and https://github.com/CRUiit/igb-mi-bundle/wiki. The source code is released under the Apache License, Version 2. arnaud.ceol@iit.it Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  9. Algorithmic and complexity results for decompositions of biological networks into monotone subsystems.

    Science.gov (United States)

    DasGupta, Bhaskar; Enciso, German Andres; Sontag, Eduardo; Zhang, Yi

    2007-01-01

    A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [Goemans, M., Williamson, D., 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 (6), 1115-1145]. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally.

  10. Inference of biological networks using Bi-directional Random Forest Granger causality.

    Science.gov (United States)

    Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob

    2016-01-01

    The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer.

  11. Collaboration Networks in the Brazilian Scientific Output in Evolutionary Biology: 2000-2012.

    Science.gov (United States)

    Santin, Dirce M; Vanz, Samile A S; Stumpf, Ida R C

    2016-03-01

    This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community.

  12. 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.

  13. Determination of cadmium and lead in human biological samples by spectrometric techniques: a review.

    Science.gov (United States)

    Lemos, Valfredo Azevedo; de Carvalho, Anaildes Lago

    2010-12-01

    The analysis of human biological samples, such as blood, urine, nails, and hair, is generally used for the verification of human exposure to toxic metals. In this review, various spectrometric methods for the determination of cadmium and lead in biological samples are discussed and compared. Several spectrometric techniques are presented and discussed with respect to various characteristics such as sensitivity, selectivity, and cost. Special attention is drawn to the procedures for digestion prior to the determination of cadmium and lead in hair, nails, blood, and urine.

  14. Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE).

    Science.gov (United States)

    Le Novère, Nicolas; Hucka, Michael; Anwar, Nadia; Bader, Gary D; Demir, Emek; Moodie, Stuart; Sorokin, Anatoly

    2011-11-30

    The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.

  15. 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

  16. 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.

  17. 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)

  18. Determining the Permeable Efficiency of Elements in Transport Networks

    Directory of Open Access Journals (Sweden)

    V. Svoboda

    2001-01-01

    Full Text Available The transport network is simulated by a directed graph. Its edges are evaluated by length (in linear units or time units, by permeability and by the cost of driving through in a transport unit. Its peaks (nodes are evaluated in terms of permeability, the time of driving through the node in time units and the cost of driving a transport unit (set through this node.For such a conception of the transport network a role of optimisation and disintegration of transport flow was formulated, defined by a number of transport units (transport sets. These units enter the network at the initial node and exit the network (or vanish at the defined node. The aim of optimization was to disintegrate the transport flow so that the permeability was not exceeded in any element of the network (edge, nod, so that the relocation of the defined transport flow was completed in a prearranged time and so that the cost of driving through the transport net between the entry and exit knots was minimal.

  19. Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

    Directory of Open Access Journals (Sweden)

    Cheng Zhaolin

    2007-01-01

    Full Text Available We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length "feature digest" that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates ( can be achieved while maintaining low false alarm rates ( using a simulated 60-node outdoor camera network.

  20. PANADA: protein association network annotation, determination and analysis.

    Directory of Open Access Journals (Sweden)

    Alberto J M Martin

    Full Text Available Increasingly large numbers of proteins require methods for functional annotation. This is typically based on pairwise inference from the homology of either protein sequence or structure. Recently, similarity networks have been presented to leverage both the ability to visualize relationships between proteins and assess the transferability of functional inference. Here we present PANADA, a novel toolkit for the visualization and analysis of protein similarity networks in Cytoscape. Networks can be constructed based on pairwise sequence or structural alignments either on a set of proteins or, alternatively, by database search from a single sequence. The Panada web server, executable for download and examples and extensive help files are available at URL: http://protein.bio.unipd.it/panada/.

  1. Deciphering molecular determinants of chemotherapy in gastrointestinal malignancy using systems biology approaches.

    Science.gov (United States)

    Lin, Li-Ling; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

    2014-09-01

    Gastrointestinal cancers are asymptomatic in early tumor development, leading to high mortality rates. Peri- or postoperative chemotherapy is a common strategy used to prolong the life expectancy of patients with these diseases. Understanding the molecular mechanisms by which anticancer drugs exert their effect is crucial to the development of anticancer therapies, especially when drug resistance occurs and an alternative drug is needed. By integrating high-throughput techniques and computational modeling to explore biological systems at different levels, from gene expressions to networks, systems biology approaches have been successfully applied in various fields of cancer research. In this review, we highlight chemotherapy studies that reveal potential signatures using microarray analysis, next-generation sequencing (NGS), proteomic and metabolomic approaches for the treatment of gastrointestinal cancers.

  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. Attractive interactions among intermediate filaments determine network mechanics in vitro.

    Directory of Open Access Journals (Sweden)

    Paul Pawelzyk

    Full Text Available Mechanical and structural properties of K8/K18 and vimentin intermediate filament (IF networks have been investigated using bulk mechanical rheometry and optical microrheology including diffusing wave spectroscopy and multiple particle tracking. A high elastic modulus G0 at low protein concentration c, a weak concentration dependency of G0 (G0 ∼ c(0.5 ± 0.1 and pronounced strain stiffening are found for these systems even without external crossbridgers. Strong attractive interactions among filaments are required to maintain these characteristic mechanical features, which have also been reported for various other IF networks. Filament assembly, the persistence length of the filaments and the network mesh size remain essentially unaffected when a nonionic surfactant is added, but strain stiffening is completely suppressed, G0 drops by orders of magnitude and exhibits a scaling G0 ∼ c(1.9 ± 0.2 in agreement with microrheological measurements and as expected for entangled networks of semi-flexible polymers. Tailless K8Δ/K18ΔT and various other tailless filament networks do not exhibit strain stiffening, but still show high G0 values. Therefore, two binding sites are proposed to exist in IF networks. A weaker one mediated by hydrophobic amino acid clusters in the central rod prevents stretched filaments between adjacent cross-links from thermal equilibration and thus provides the high G0 values. Another strong one facilitating strain stiffening is located in the tail domain with its high fraction of hydrophobic amino acid sequences. Strain stiffening is less pronounced for vimentin than for K8/K18 due to electrostatic repulsion forces partly compensating the strong attraction at filament contact points.

  4. Implementation of Biological Routing Protocol in Tunnel Wireless Sensor Network (TWSN

    Directory of Open Access Journals (Sweden)

    M.Muzaffar Zahar

    2013-08-01

    Full Text Available A routing protocol is a core issue in Wireless Sensor Network (WSN especially on undetermined situationand crucial condition to guarantee the transmission of data. Therefore, any implementation of routingprotocol in a tunnel environment will suit with their application to minimize dropped data in itscommunication. This paper presents a biological routing protocol named as Biological Tunnel RoutingProtocol (BioTROP in Tunnel Wireless Sensor Network (TWSN. BioTROP has been tested with fourchallenging situations for marking its standard soon. By setting it in low power transmission, all nodesappear as source node and intermediate nodes concurrently, faster transmission rate and free-locationsetup for each node; these conditions make BioTROP as an ad-hoc protocol with lightweight coding size intunnel environment. This protocol is tested only in real test bed experiment using 7 TelosB nodes at apredetermined distance. The results have shown more than 70 percent of the transmitted data packets weresuccessfully delivered at the base station.

  5. 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

  6. Incorporation and characterization of biological molecules in droplet-interface bilayer networks for novel active systems

    Science.gov (United States)

    Sarles, Stephen A.; Ghanbari Bavarsad, Pegah; Leo, Donald J.

    2009-03-01

    Biological molecules including phospholipids and proteins offer scientists and engineers a diverse selection of materials to develop new types of active materials and smart systems based on ion conduction. The inherent energy-coupling abilities of these components create novel kinds of transduction elements. Networks formed from droplet-interface bilayers (DIB) are a promising construct for creating cell mimics that allow for the assembly and study of these active biological molecules. The current-voltage relationship of symmetric, "lipid-in" dropletinterface bilayers are characterized using electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV). "Lipid-in" diphytanoyl phosphatidylcholine (DPhPC) droplet-interface bilayers have specific resistances of nearly 10MΩ•cm2 and rupture at applied potentials greater than 300mV, indicating the "lipid-in" approach produces higher quality interfacial membranes than created using the original "lipid-out" method. The incorporation of phospholipids into the droplet interior allows for faster monolayer formation but does not inhibit the selfinsertion of transmembrane proteins into bilayer interfaces that separate adjacent droplets. Alamethicin proteins inserted into single and multi-DIB networks produce a voltage-dependent membrane conductance and current measurements on bilayers containing this type of protein exhibit a reversible, 3-4 order-of-magnitude conductance increase upon application of voltage.

  7. 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.

  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. 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…

  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 ichthiomycin concentration by biological test with Cyprinus carpio L.

    Science.gov (United States)

    Gameyska, Y; Popov, K; Ognianov, I; Tishinova, R; Rachev, R

    1993-01-01

    We have found out that one-year-old carps are extremely sensitive to ichthiomycin in concentrations between 25-125 micrograms/dm3. This fact permitted the use of biological methods for determination of ichthiomycin concentrations in cultural medium of Streptomyces levoris 1107 or crude preparations of the antibiotic.

  12. Do public networks really work?: An essay on public network performance and its determinants

    OpenAIRE

    Macciò, Laura; Cristofoli, Daniela

    2014-01-01

    Since the early Nineties, public networks have been placed centre stage to solve “wicked” problems and are considered the multi-organizational arrangement ‘par excellence’ to achieve solutions that are difficult to obtain by individual organizations. Despite this euphoria on networks as the best solution, in 1997 O’Toole’s call to “treat networks seriously” implied understanding how they perform, how to measure their performance and what affects their results. Firstly, this dissertation...

  13. 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...

  14. dNSP: a biologically inspired dynamic Neural network approach to Signal Processing.

    Science.gov (United States)

    Cano-Izquierdo, José Manuel; Ibarrola, Julio; Pinzolas, Miguel; Almonacid, Miguel

    2008-09-01

    The arriving order of data is one of the intrinsic properties of a signal. Therefore, techniques dealing with this temporal relation are required for identification and signal processing tasks. To perform a classification of the signal according with its temporal characteristics, it would be useful to find a feature vector in which the temporal attributes were embedded. The correlation and power density spectrum functions are suitable tools to manage this issue. These functions are usually defined with statistical formulation. On the other hand, in biology there can be found numerous processes in which signals are processed to give a feature vector; for example, the processing of sound by the auditory system. In this work, the dNSP (dynamic Neural Signal Processing) architecture is proposed. This architecture allows representing a time-varying signal by a spatial (thus statical) vector. Inspired by the aforementioned biological processes, the dNSP performs frequency decomposition using an analogical parallel algorithm carried out by simple processing units. The architecture has been developed under the paradigm of a multilayer neural network, where the different layers are composed by units whose activation functions have been extracted from the theory of Neural Dynamic [Grossberg, S. (1988). Nonlinear neural networks principles, mechanisms and architectures. Neural Networks, 1, 17-61]. A theoretical study of the behavior of the dynamic equations of the units and their relationship with some statistical functions allows establishing a parallelism between the unit activations and correlation and power density spectrum functions. To test the capabilities of the proposed approach, several testbeds have been employed, i.e. the frequencial study of mathematical functions. As a possible application of the architecture, a highly interesting problem in the field of automatic control is addressed: the recognition of a controlled DC motor operating state.

  15. Measuring information flow in cellular networks by the systems biology method through microarray data.

    Science.gov (United States)

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

  16. 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.

  17. 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.

  18. Hydrologic and biologic influences on stream network nutrient concentrations: Interactions of hydrologic turnover and concentration-dependent nutrient uptake

    Science.gov (United States)

    Mallard, John; McGlynn, Brian; Covino, Tim

    2016-04-01

    Stream networks lie in a crucial landscape position between terrestrial ecosystems and downstream water bodies. As such, whether inferring terrestrial watershed processes from watershed outlet nutrient signals or predicting the effect of observed terrestrial processes on stream nutrient signals, it is requisite to understand how stream networks can modulate terrestrial nutrient inputs. To date integrated understanding and modeling of physical and biological influences on nutrient concentrations at the stream network scale have been limited. However, watershed scale groundwater - surface water exchange (hydrologic turnover), concentration-variable biological uptake, and the interaction between the two can strongly modify stream water nutrient concentrations. Stream water and associated nutrients are lost to and replaced from groundwater with distinct nutrient concentrations while in-stream nutrients can also be retained by biological processes at rates that vary with concentration. We developed an empirically based network scale model to simulate the interaction between hydrologic turnover and concentration-dependent nutrient uptake across stream networks. Exchange and uptake parameters were measured using conservative and nutrient tracer addition experiments in the Bull Trout Watershed, central Idaho. We found that the interaction of hydrologic turnover and concentration-dependent uptake combined to modify and subsequently stabilize in-stream concentrations, with specific concentrations dependent on the magnitude of hydrologic turnover, groundwater concentrations, and the shape of nutrient uptake kinetic curves. We additionally found that by varying these physical and biological parameters within measured ranges we were able to generate a spectrum of stream network concentration distributions representing a continuum of shifting magnitudes of physical and biological influences on in-stream concentrations. These findings elucidate the important and variable role of

  19. Analytical methods for vancomycin determination in biological fluids and in pharmaceuticals

    Directory of Open Access Journals (Sweden)

    Marta Maria Duarte Carvalho Vila

    2007-04-01

    Full Text Available Vancomycin is a glycopeptide antibiotic employed in the treatment of infections caused by certain methicillin-resistant staphylococci. It is indicated also for patients allergic to penicillin or when there is no response to penicillins or cephalosporins. The adequate vancomycin concentration levels in blood serum lies between 5 and 10 mg/L. Higher values are toxic, causing mainly nephrotoxicity and ototoxicity. Various analytical methods are described in the literature: spectrophotometric, immunologic, biologic and chromatographic methods. This paper reviews the main analytical methods for vancomycin determination in biological fluids and in pharmaceutical preparations.

  20. 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.

  1. A review on determination of steroids in biological samples exploiting nanobio-electroanalytical methods.

    Science.gov (United States)

    Yadav, Saurabh K; Chandra, Pranjal; Goyal, Rajendra N; Shim, Yoon-Bo

    2013-01-31

    The applications of nanomaterial modified sensors, molecularly imprinting polymer based, aptamer based, and immunosensors have been described in the determination of steroids using electroanalytical techniques. After a brief description of the steroids and assays in biological fluids, the principles of electrochemical detection with the advantages and the limitations of the various sensors are presented. The nanomaterial modified sensors catalyze the oxidation/reduction of steroids and are suitable for sensing them in environmental samples and biological fluids. The determination of steroids based on their reduction has been found more useful in comparison to oxidation as the common metabolites present in the biological fluids do not undergo reduction in the usual potential window and hence, do not interfere in the determination. The sensors based on immunosensors and aptamers were found more sensitive and selective for steroid determination. Conducting polymer modified bio-sensors and microchip devices are suggested as possible future prospects for the ultra sensitive and simultaneous determination of steroids and their metabolites in various samples.

  2. Vulnerability and Cosusceptibility Determine the Size of Network Cascades

    Science.gov (United States)

    Yang, Yang; Nishikawa, Takashi; Motter, Adilson E.

    2017-01-01

    In a network, a local disturbance can propagate and eventually cause a substantial part of the system to fail in cascade events that are easy to conceptualize but extraordinarily difficult to predict. Here, we develop a statistical framework that can predict cascade size distributions by incorporating two ingredients only: the vulnerability of individual components and the cosusceptibility of groups of components (i.e., their tendency to fail together). Using cascades in power grids as a representative example, we show that correlations between component failures define structured and often surprisingly large groups of cosusceptible components. Aside from their implications for blackout studies, these results provide insights and a new modeling framework for understanding cascades in financial systems, food webs, and complex networks in general.

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    Thomas eHoellinger

    2013-05-01

    Full Text Available 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 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.

  5. Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

    Directory of Open Access Journals (Sweden)

    Richard J. Radke

    2007-01-01

    Full Text Available We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length “feature digest” that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates (>0.8 can be achieved while maintaining low false alarm rates (<0.05 using a simulated 60-node outdoor camera network.

  6. Determining a bisection bandwidth for a multi-node data communications network

    Science.gov (United States)

    Faraj, Ahmad A.

    2010-01-26

    Methods, systems, and products are disclosed for determining a bisection bandwidth for a multi-node data communications network that include: partitioning nodes in the network into a first sub-network and a second sub-network in dependence upon a topology of the network; sending, by each node in the first sub-network to a destination node in the second sub-network, a first message having a predetermined message size; receiving, by each node in the first sub-network from a source node in the second sub-network, a second message; measuring, by each node in the first sub-network, the elapsed communications time between the sending of the first message and the receiving of the second message; selecting the longest elapsed communications time; and calculating the bisection bandwidth for the network in dependence upon the number of the nodes in the first sub-network, the predetermined message size of the first test message, and the longest elapsed communications time.

  7. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  8. A New Approach to Determine the Critical Path in Stochastic Activity Network

    Directory of Open Access Journals (Sweden)

    Weng-Ming Chu

    2014-01-01

    Full Text Available The determination of the critical path (CP in stochastic networks is difficult. It is partly due to the randomness of path durations and partly due to the probability issue of the selection of the critical path in the network. What we are confronted with is not only the complexity among random variables but also the problem of path dependence of the network. Besides, we found that CP is not necessarily the longest (or shortest path in the network, which was a conventional assumption in use. The Program Evaluation and Review Technique (PERT and Critical Path Index (CPI approaches are not able to deal with this problem efficiently. In this study, we give a new definition on the CP in stochastic network and propose a modified label-correcting tracing algorithm (M-LCTA to solve it. Based on the numerical results, compared with Monte Carlo simulation (MCS, the proposed approach can accurately determine the CP in stochastic networks.

  9. A systems biology approach identifies a regulatory network in parotid acinar cell terminal differentiation.

    Directory of Open Access Journals (Sweden)

    Melissa A Metzler

    Full Text Available The transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.A systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.Clustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1 activates the Mist1 promoter [corrected]. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.This study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a

  10. Recent developments in fatty acids profile determination in biological samples - a review

    Directory of Open Access Journals (Sweden)

    Tiuca Ioana

    2015-12-01

    Full Text Available The present paper is a literature review of the recent years dealing with the most important separation techniques of fatty acids in biological samples. Our aim was to make a synthesis of the analytical methods used, to note the most used ones, but also to mention other methods that are less utilized, which can have important advantages (such as less time consuming, greener reagents, etc.. Gas-chromatographic separation methods were described and compared to liquid chromatographic separations of fatty acids in different types of biological samples. In the same time, the importance of determining fatty acids profiles in biological samples was revealed, pointing out the possible implications in diagnostics of different types of disorders or remarking different profiles compared to healthy states.

  11. 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.

  12. Determination of Cu, Zn, and Se in microvolumes of liquid biological samples

    Science.gov (United States)

    Shaban, H. A.; Shaltout, A. A.; Abdou, M.; Al Ashker, E. A.; Elgohary, M.

    2011-01-01

    Cu, Zn, and Se were successfully determined in a few microliters (<100 μl) of biological samples using discrete injection atomic absorption spectrometry. Different factors were investigated in order to obtain a biological sample volume which is valid for analysis. Among them are the effect of microsampling volume variations (starting from 40 to 200 μl), nebulization efficiency, detection limits, precision, and finally the calibration and sensitivity of the proposed method. It was found that 60 μl of the biological sample was adequate for the quantitative analysis with reasonable precision. The advantages of the proposed method are not only rapidity, simplicity, sensitivity, and good precision, but also, contrary to conventional flame atomic absorption spectrometry, the capability of analyzing microvolumes of samples.

  13. The determination of retainable phosphorus, relative biological availability, and relative biological value of phosphorus sources for broilers.

    Science.gov (United States)

    Coon, C N; Seo, S; Manangi, M K

    2007-05-01

    A 10- to 21-d chick bioassay was conducted to determine the absolute retention value (ARV) for 2 different defluorinated phosphates (DF-1 and DF-2) and a reagent grade dicalcium phosphate (DCP). The total and test P in excreta regressed on feed P levels were subjected to general straight-line (linear), 1-slope broken-line, 2-slope broken-line, and polynomial regression methods to find the best analysis model. The relative biological availability (RBA) and relative biological value (RBV) for P from the 2 different defluorinated phosphates (DF-1 and DF-2) were obtained by the slope ratio method using 3 different bone measurements (% tibia ash, tibia breaking force, tibia weight) and RBV calculated using percentage tibia ash, weight gain, and feed/gain. The DCP was used as reference standard for RBA and RBV. The ARV measured at the breakpoints for test P by 2-slope analysis were determined to be 82.99% for DCP, 76.34% for DF-1, and 70.30% for DF-2. The ARV of test P determined at 0.45% NPP was 62.41% for DCP, 63.58% for DF-1, and 59.25% for DF-2. The relationship of ARV and RBA were similar in that DCP was 6% higher in ARV at the breakpoint compared with DF-1 and the RBA of DF-1 was 71 and 91% from tibia weight and tibia breaking force, respectively, compared with the bone parameters from chicks fed DCP. The DF-1 phosphate had 3 and 7% higher ARV at the breakpoint and 0.45% NPP, respectively, compared with DF-2. The RBA of DF-2 was 59 and 80% from tibia weight and bone-breaking force. The ARV of phosphate sources were independent of an arbitrary reference. The ARV for P sources provide retainable P information for industry-based feed formulation that can reduce excess P in poultry waste. The excreta P data from broilers fed increasing levels of DCP indicates that the data are best described statistically with a 1-slope broken-line regression, 2-slope broken-line regression, or polynomial regression.

  14. A round-robin determination of boron in botanical and biological samples.

    Science.gov (United States)

    Downing, R G; Strong, P L

    1998-01-01

    The accurate determination of boron (B) at trace and ultratrace concentrations is an important step toward establishing the role of B in biological functions. However, low-level B concentrations are difficult to determine accurately, especially for many botanical and biological matrices. A round-robin study was conducted to assess analytical agreement for low-level B determinations. Ten experienced research groups from analytical laboratories extending across Europe, Asia, and the US participated in this study. These groups represent a cross-section of academic, commercial, and government facilities. The researchers employed both ion-coupled plasma and neutron techniques in the study. Results from this round-robin study indicate good agreement between participating laboratories at the mg/kg level, but at the lowest levels, microg/kg, only three laboratories participated, and agreement was poor. By encouraging discussion among scientists over these data, the secondary goal of this round-robin study is to stimulate continued improvement in analytical procedures and techniques for accurate low-level B determinations. Furthermore, it is intended to encourage the development of a variety of low-level (low mg/kg and microg/kg) B certified reference samples in biological and botanical matrices. The results from the round-robin analyses were compiled and are summarized in this article.

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

    DEFF Research Database (Denmark)

    Jensen, Lars Wittrup

    2015-01-01

    Within the railway industry, high quality estimates on railway capacity is crucial information, that helps railway companies to utilise the expensive (infrastructure) resources as efficiently as possible. This paper therefore proposes an optimisation framework to estimate the capacity of a railway...... to the train types. This is done using a mathematical model which is solved with a heuristic. The developed approach is used on a case network to obtain the capacity of the given railway system. Furthermore, we test different parameters to explore computation time, precision and sensitivity to input...

  16. A systems biology approach identifies molecular networks defining skeletal muscle abnormalities in chronic obstructive pulmonary disease.

    Directory of Open Access Journals (Sweden)

    Nil Turan

    2011-09-01

    Full Text Available Chronic Obstructive Pulmonary Disease (COPD is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. Our model shows that failure to co-ordinately activate expression of several tissue remodelling and bioenergetics pathways is a specific landmark of COPD diseased muscles. Our findings also suggest that this phenomenon may be linked to an abnormal expression of a number of histone modifiers, which we discovered correlate with oxygen utilization. These observations raised the interesting possibility that cell hypoxia may be a key factor driving skeletal muscle degeneration in COPD patients.

  17. Orientational tomography of optical axes directions distributions of multilayer biological tissues birefringent polycrystalline networks

    Science.gov (United States)

    Zabolotna, Natalia I.; Dovhaliuk, Rostyslav Y.

    2013-09-01

    We present a novel measurement method of optic axes orientation distribution which uses a relatively simple measurement setup. The principal difference of our method from other well-known methods lies in direct approach for measuring the orientation of optical axis of polycrystalline networks biological crystals. Our test polarimetry setup consists of HeNe laser, quarter wave plate, two linear polarizers and a CCD camera. We also propose a methodology for processing of measured optic axes orientation distribution which consists of evaluation of statistical, correlational and spectral moments. Such processing of obtained data can be used to classify particular tissue sample as "healthy" or "pathological". For our experiment we use thin layers of histological section of normal and muscular dystrophy tissue sections. It is shown that the difference between mentioned moments` values of normal and pathological samples can be quite noticeable with relative difference up to 6.26.

  18. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference.

    Science.gov (United States)

    Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence

    2007-12-01

    Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.

  19. Host centrality in food web networks determines parasite diversity.

    Directory of Open Access Journals (Sweden)

    Tavis K Anderson

    Full Text Available BACKGROUND: Parasites significantly alter topological metrics describing food web structure, yet few studies have explored the relationship between food web topology and parasite diversity. METHODS/PRINCIPAL FINDINGS: This study uses quantitative metrics describing network structure to investigate the relationship between the topology of the host food web and parasite diversity. Food webs were constructed for four restored brackish marshes that vary in species diversity, time post restoration and levels of parasitism. Our results show that the topology of the food web in each brackish marsh is highly nested, with clusters of generalists forming a distinct modular structure. The most consistent predictors of parasite diversity within a host were: trophic generality, and eigenvector centrality. These metrics indicate that parasites preferentially colonise host species that are highly connected, and within modules of tightly interacting species in the food web network. CONCLUSIONS/SIGNIFICANCE: These results suggest that highly connected free-living species within the food web may represent stable trophic relationships that allow for the persistence of complex parasite life cycles. Our data demonstrate that the structure of host food webs can have a significant effect on the establishment of parasites, and on the potential for evolution of complex parasite life cycles.

  20. Determinants of Research and Development Intensity from a Network Perspective

    Directory of Open Access Journals (Sweden)

    Teng Joe K.L.

    2016-12-01

    Full Text Available We model and examine the research and development (R&D intensity of a focal industry from an inter-industry network perspective. More specifically, we estimate how a focal industry’s R&D investment is affected by partner (suppliers and customers industries’ R&D expenditure. We also investigate the impact of the overall economy on the focal industry’s R&D expenditure and finally how a focal industry’s position in the supply chain network moderates the overall economy’s impact on the focal industry’s R&D expenditure. We found that, in general, a focal industry’s R&D intensity is positively associated with its partner industries’ R&D intensity. In addition, an industry’s R&D intensity is positively associated with the growth rate of the overall economy. Finally, we found that a more central industry is subjected to a stronger impact of macroeconomic shocks on its R&D intensity though there is no significant association between an industry’s centrality and its R&D intensity.

  1. Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

    Science.gov (United States)

    de Franciscis, Sebastiano; Caravagna, Giulio; Mauri, Giancarlo; D’Onofrio, Alberto

    2016-06-01

    Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting.

  2. Determination of cobalt and nickel in biological materials using catalytic adsorptive stripping voltammetry

    OpenAIRE

    Bobrowski, Andrzej; Zarębski, Jerzy; Królicka, Agnieszka

    2012-01-01

    The paper discusses the utilisation of the catalytic adsorptive stripping voltammetric method for the simultaneous determination of Co and Ni in biological materials such as hair, oyster tissue, bovine liver and oriental tobacco leaves. For this purpose the most sensitive and selective catalytic-adsorptive system with nioxime and nitrite has been selected. The optimal parameters, including concentration of the supporting electrolyte, pH and accumulation time and potential, have be...

  3. Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.

    Science.gov (United States)

    Chatterjee, Abhijit; Mayawala, Kapil; Edwards, Jeremy S; Vlachos, Dionisios G

    2005-05-01

    Developing a quantitative understanding of intracellular networks requires simulations and computational analyses. However, traditional differential equation modeling tools are often inadequate due to the stochasticity of intracellular reaction networks that can potentially influence the phenotypic characteristics. Unfortunately, stochastic simulations are computationally too intense for most biological systems. Herein, we have utilized the recently developed binomial tau-leap method to carry out stochastic simulations of the epidermal growth factor receptor induced mitogen activated protein kinase cascade. Results indicate that the binomial tau-leap method is computationally 100-1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial tau-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size. http://www.dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email. Details about the binomial tau-leap algorithm, software and a manual are available at the above website.

  4. Disulfide bond formation network in the three biological kingdoms, bacteria, fungi and mammals.

    Science.gov (United States)

    Sato, Yoshimi; Inaba, Kenji

    2012-07-01

    Almost all organisms, from bacteria to humans, possess catalytic systems that promote disulfide bond formation-coupled protein folding, i.e. oxidative protein folding. These systems are necessary for the biosynthesis of many secretory and membrane proteins, such as antibodies, major histocompatibility complex molecules, growth factors, and insulin. Over the last decade, structural studies have made striking progress in this field of research, identifying how oxidative systems operate in a specific and regulated manner to maintain redox and protein homeostasis within cells. Interestingly, more and more novel catalysts that promote disulfide bond formation have been discovered in mammals, suggesting that the oxidative protein folding network is even more complicated in higher eukaryotes than previously thought. This review highlights the physiological roles and molecular bases of the disulfide bond formation pathways that have evolved in the bacterial periplasm and the endoplasmic reticulum of fungi and mammals. Accumulating knowledge about disulfide bond formation networks widely distributed throughout the biological kingdom has significantly advanced our understanding of the cellular mechanisms dedicated to protein quality control.

  5. A biologically plausible learning rule for the Infomax on recurrent neural networks.

    Science.gov (United States)

    Hayakawa, Takashi; Kaneko, Takeshi; Aoyagi, Toshio

    2014-01-01

    A fundamental issue in neuroscience is to understand how neuronal circuits in the cerebral cortex play their functional roles through their characteristic firing activity. Several characteristics of spontaneous and sensory-evoked cortical activity have been reproduced by Infomax learning of neural networks in computational studies. There are, however, still few models of the underlying learning mechanisms that allow cortical circuits to maximize information and produce the characteristics of spontaneous and sensory-evoked cortical activity. In the present article, we derive a biologically plausible learning rule for the maximization of information retained through time in dynamics of simple recurrent neural networks. Applying the derived learning rule in a numerical simulation, we reproduce the characteristics of spontaneous and sensory-evoked cortical activity: cell-assembly-like repeats of precise firing sequences, neuronal avalanches, spontaneous replays of learned firing sequences and orientation selectivity observed in the primary visual cortex. We further discuss the similarity between the derived learning rule and the spike timing-dependent plasticity of cortical neurons.

  6. Semantic data integration and knowledge management to represent biological network associations.

    Science.gov (United States)

    Losko, Sascha; Heumann, Klaus

    2009-01-01

    The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data including experimental data from "-omics" platforms, phenotype information, and clinical data. For bioinformatics, several challenges remain: to structure this information as biological networks enabling scientists to identify relevant information; to integrate this information as specific "knowledge bases"; and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation and, thus, the generation of new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we will introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.

  7. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    Science.gov (United States)

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    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 that 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.

  8. Network Motif: The Smallest Unit of a Biological Network%网络基序:生物网络的最小研究单位

    Institute of Scientific and Technical Information of China (English)

    陈长水; 刘少飞

    2011-01-01

    The network motif (motif for short) is the smallest decomposable unit of a biological network or its smallest building block. It is an important research issue in systems biology. Motifs exist in various types of biological networks with information-processed functions. Simulations and experiments were carried out to study their dynamical properties. This paper reviews the styles and functions of motifs and the databases and tools to find motifs. The motifs we discuss in the paper include Negative Auto-Regulation(NAR), Positive Auto-Regulation (PAR), Forward Feedback Loop(FFL), Single Input Module(SIM), Multiple Input Modules (MIMs), regulator chain motifs, multi-component loop, bridge and brick motif in transcriptional networks, and motifs in signal transduction networks, as well as those in neural networks. The motifs might be viewed as the electric devices. This review also discusses the properties and the evolutions of network motifs, and related applications to synthetic biology. Finally, it is pointed out that the network motif study might be the first step in the studies of biological networks in systems biology to provide a good research method to study the module and synthetic biology. More types of motifs in various networks should be found out and more in-vivo experiments should be carried out. The further study might produce some general principles in biological networks.%网络基序( network motif),是从生物网络中分解得到的最小研究单位,是构成生物网络的“砖块”,是系统生物学中最简单的研究对象.网络基序存在于各种生物网络中,具有信息处理的功能,通过理论和实验分析发现网络基序有重要的动力学功能.本文总结了网络基序的类型和功能研究方面的工作,包括转录网络中的基序(自我调节或者是反馈(负反馈(NAR)和正反馈(PAR)),正反馈环(FFL),单输入基序(SIM),多级输入基序(MIMs),链式调节子基序(Regulator Chain Motifs

  9. 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.

  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. Comparison between earthquake magnitudes determined by China seismograph network and US seismograph network (Ⅱ):Surface wave magnitude

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    By using orthogonal regression method, a systematic comparison is made between surface wave magnitudes determined by Institute of Geophysics of China Earthquake Administration (IGCEA) and National Earthquake Information Center of US Geological Survey (USGS/NEIC) on the basis of observation data collected by the two institutions between 1983 and 2004. A formula is obtained which reveals the relationship between surface wave magnitudes determined by China seismograph network and US seismograph network. The result shows that, as different calculation formulae and observational instruments are used, surface wave magnitude determined by IGCEA is generally greater by 0.2 than that determined by NEIC: for M=3.5~4.5 earthquakes, it is greater by 0.3;for M=5.0~6.5 earthquakes, it is greater by 0.2;and for M≥7.0 earthquakes, it is greater by no more than 0.1.

  12. 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.

  13. 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.

  14. In vivo and in vitro techniques to determine the biological activity of food allergens

    DEFF Research Database (Denmark)

    Poulsen, Lars K.

    2001-01-01

    Methods for determination of the biological activity of food allergens comprise both determination of the allergenic potency, i.e. the capability to elicit an allergic reaction in an already sensitized individual, and the allergenic potential, i.e. the risk for sensitizing a hitherto non-allergic......Methods for determination of the biological activity of food allergens comprise both determination of the allergenic potency, i.e. the capability to elicit an allergic reaction in an already sensitized individual, and the allergenic potential, i.e. the risk for sensitizing a hitherto non......-allergic individual. Several methods are discussed for determination of potency including the double-blinded placebo-controlled food challenge, skin testing, in vitro effector cell assays such as basophil histamine release, and IgE-based techniques such as RAST and RAST inhibition. No reliable methods have yet been...... developed which can predict the allergenic potential of a food or a food allergen. The progress in the areas of stability studies and animal models for food allergy are discussed....

  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. The conquest of vitalism or the eclipse of organicism? The 1930s Cambridge organizer project and the social network of mid-twentieth-century biology.

    Science.gov (United States)

    Peterson, Erik

    2014-06-01

    In the 1930s, two concepts excited the European biological community: the organizer phenomenon and organicism. This essay examines the history of and connection between these two phenomena in order to address the conventional 'rise-and-fall' narrative that historians have assigned to each. Scholars promoted the 'rise-and-fall' narrative in connection with a broader account of the devitalizing of biology through the twentieth century. I argue that while limited evidence exists for the 'fall of the organizer concept' by the 1950s, the organicism that often motivated the organizer work had no concomitant fall--even during the mid-century heyday of molecular biology. My argument is based on an examination of shifting social networks of life scientists from the 1920s to the 1970s, many of whom attended or corresponded with members of the Cambridge Theoretical Biology Club (1932-1938). I conclude that the status and cohesion of these social networks at the micro scale was at least as important as macro-scale conceptual factors in determining the relative persuasiveness of organicist philosophy.

  17. Analytical approaches to determination of carnitine in biological materials, foods and dietary supplements.

    Science.gov (United States)

    Dąbrowska, Monika; Starek, Małgorzata

    2014-01-01

    l-Carnitine is a vitamin-like amino acid derivative, which is an essential factor in fatty acid metabolism as acyltransferase cofactor and in energy production processes, such as interconversion in the mechanisms of regulation of cetogenesis and termogenesis, and it is also used in the therapy of primary and secondary deficiency, and in other diseases. The determination of carnitine and acyl-carnitines can provide important information about inherited or acquired metabolic disorders, and for monitoring the biochemical effect of carnitine therapy. The endogenous carnitine pool in humans is maintained by biosynthesis and absorption of carnitine from the diet. Carnitine has one asymmetric carbon giving two stereoisomers d and l, but only the l form has a biological positive effect, thus chiral recognition of l-carnitine enantiomers is extremely important in biological, chemical and pharmaceutical sciences. In order to get more insight into carnitine metabolism and synthesis, a sensitive analysis for the determination of the concentration of free carnitine, carnitine esters and the carnitine precursors is required. Carnitine has been investigated in many biochemical, pharmacokinetic, metabolic and toxicokinetic studies and thus many analytical methods have been developed and published for the determination of carnitine in foods, dietary supplements, pharmaceutical formulations, biological tissues and body fluid. The analytical procedures presented in this review have been validated in terms of basic parameters (linearity, limit of detection, limit of quantitation, sensitivity, accuracy, and precision). This article presented the impact of different analytical techniques, and provides an overview of applications that address a diverse array of pharmaceutical and biological questions and samples. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. 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.

  19. Determining the input dimension of a neural network for nonlinear time series prediction

    Institute of Scientific and Technical Information of China (English)

    张胜; 刘红星; 高敦堂; 都思丹

    2003-01-01

    Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the mostimportant feature of it,the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Finally,some wlidation examples and results are given.

  20. Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

    Science.gov (United States)

    Yang, Gang; Campbell, Colin; Albert, Réka

    2016-12-01

    Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

  1. Biologic

    CERN Document Server

    Kauffman, L H

    2002-01-01

    In this paper we explore the boundary between biology and the study of formal systems (logic). In the end, we arrive at a summary formalism, a chapter in "boundary mathematics" where there are not only containers but also extainers ><, entities open to interaction and distinguishing the space that they are not. The boundary algebra of containers and extainers is to biologic what boolean algebra is to classical logic. We show how this formalism encompasses significant parts of the logic of DNA replication, the Dirac formalism for quantum mechanics, formalisms for protein folding and the basic structure of the Temperley Lieb algebra at the foundations of topological invariants of knots and links.

  2. MORO: a Cytoscape app for relationship analysis between modularity and robustness in large-scale biological networks.

    Science.gov (United States)

    Truong, Cong-Doan; Tran, Tien-Dzung; Kwon, Yung-Keun

    2016-12-23

    Although there have been many studies revealing that dynamic robustness of a biological network is related to its modularity characteristics, no proper tool exists to investigate the relation between network dynamics and modularity. Accordingly, we developed a novel Cytoscape app called MORO, which can conveniently analyze the relationship between network modularity and robustness. We employed an existing algorithm to analyze the modularity of directed graphs and a Boolean network model for robustness calculation. In particular, to ensure the robustness algorithm's applicability to large-scale networks, we implemented it as a parallel algorithm by using the OpenCL library. A batch-mode simulation function was also developed to verify whether an observed relationship between modularity and robustness is conserved in a large set of randomly structured networks. The app provides various visualization modes to better elucidate topological relations between modules, and tabular results of centrality and gene ontology enrichment analyses of modules. We tested the proposed app to analyze large signaling networks and showed an interesting relationship between network modularity and robustness. Our app can be a promising tool which efficiently analyzes the relationship between modularity and robustness in large signaling networks.

  3. 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.

  4. Determining important regulatory relations of amino acids from dynamic network analysis of plasma amino acids.

    Science.gov (United States)

    Shikata, Nahoko; Maki, Yukihiro; Nakatsui, Masahiko; Mori, Masato; Noguchi, Yasushi; Yoshida, Shintaro; Takahashi, Michio; Kondo, Nobuo; Okamoto, Masahiro

    2010-01-01

    The changes in the concentrations of plasma amino acids do not always follow the flow-based metabolic pathway network. We have previously shown that there is a control-based network structure among plasma amino acids besides the metabolic pathway map. Based on this network structure, in this study, we performed dynamic analysis using time-course data of the plasma samples of rats fed single essential amino acid deficient diet. Using S-system model (conceptual mathematical model represented by power-law formalism), we inferred the dynamic network structure which reproduces the actual time-courses within the error allowance of 13.17%. By performing sensitivity analysis, three of the most dominant relations in this network were selected; the control paths from leucine to valine, from methionine to threonine, and from leucine to isoleucine. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from methionine to threonine.

  5. Assessing Vermont's stream health and biological integrity using artificial neural networks and Bayesian methods

    Science.gov (United States)

    Rizzo, D. M.; Fytilis, N.; Stevens, L.

    2012-12-01

    Environmental managers are increasingly required to monitor and forecast long-term effects and vulnerability of biophysical systems to human-generated stresses. Ideally, a study involving both physical and biological assessments conducted concurrently (in space and time) could provide a better understanding of the mechanisms and complex relationships. However, costs and resources associated with monitoring the complex linkages between the physical, geomorphic and habitat conditions and the biological integrity of stream reaches are prohibitive. Researchers have used classification techniques to place individual streams and rivers into a broader spatial context (hydrologic or health condition). Such efforts require environmental managers to gather multiple forms of information - quantitative, qualitative and subjective. We research and develop a novel classification tool that combines self-organizing maps with a Naïve Bayesian classifier to direct resources to stream reaches most in need. The Vermont Agency of Natural Resources has developed and adopted protocols for physical stream geomorphic and habitat assessments throughout the state of Vermont. Separate from these assessments, the Vermont Department of Environmental Conservation monitors the biological communities and the water quality in streams. Our initial hypothesis is that the geomorphic reach assessments and water quality data may be leveraged to reduce error and uncertainty associated with predictions of biological integrity and stream health. We test our hypothesis using over 2500 Vermont stream reaches (~1371 stream miles) assessed by the two agencies. In the development of this work, we combine a Naïve Bayesian classifier with a modified Kohonen Self-Organizing Map (SOM). The SOM is an unsupervised artificial neural network that autonomously analyzes inherent dataset properties using input data only. It is typically used to cluster data into similar categories when a priori classes do not exist. The

  6. 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.

  7. Determination of acrylamide in local and commercial cultivar of potatoes from biological farm.

    Science.gov (United States)

    Marchettini, Nadia; Focardi, Silvia; Guarnieri, Massimo; Guerranti, Cristiana; Perra, Guido

    2013-02-15

    This paper reports the results of a preliminary study on the characterization of parameters influencing formation of acrylamide in fried potatoes, from biological cultivation. The formation of acrylamide was investigated in relation to frying in biological extra virgin olive oil and commercial seed oil. Three different cultivars (Rossa di Colfiorito, Quarantina bianca genovese and Kennebec) were chosen. Asparagine, glucose, fructose and sucrose concentrations were determined in potato slice before frying, while acrylamide content was analysed by LC-ESI-MS/MS in the slices fried in seed and extra virgin olive oil. The Kennebec cultivar showed differences in its potential for acrylamide formation, which was primarily related to its relatively high asparagine and reducing sugars contents, respect the other local cultivars (particulary Quarantina). Values of acrylamide below detection limit (LOD) were found in Quarantina bianca genovese cultivar samples fried in extra virgin olive oil and peanuts seed oil and higher in peanuts seed oil fried potatoes of Kennebec cultivar.

  8. 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.

  9. 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.

  10. 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

  11. Determination of steroid hormones in biological and environmental samples using green microextraction techniques: an overview.

    Science.gov (United States)

    Aufartová, Jana; Mahugo-Santana, Cristina; Sosa-Ferrera, Zoraida; Santana-Rodríguez, José Juan; Nováková, Lucie; Solich, Petr

    2011-10-17

    Residues of steroid hormones have become a cause for concern because they can affect the biological activity of non-target organisms. Steroid hormones are a potential risk for wildlife and humans through the consumption of contaminated food or water. Their determination requires extraction and clean-up steps, prior to detection, to reach low concentration levels. In recent years, a great effort has been made to develop new analytical methodologies, such as microextraction techniques, that reduce environmental pollution. Researchers have modified old methods to incorporate procedures that use less-hazardous chemicals or that use smaller amounts of them. They are able to do direct analysis using miniaturised equipment and reduced amounts of solvents and wastes. These accomplishments are the main objectives of green analytical chemistry. In this overview, we focus on microextraction techniques for the determination of steroid hormones in biological (e.g., human urine, human serum, fish, shrimp and prawn tissue and milk) and environmental (e.g., wastewaters, surface waters, tap waters, river waters, sewage sludges, marine sediments and river sediments) samples. We comment on the most recent applications in sorptive-microextraction modes, such as solid phase microextraction (SPME) with molecularly imprinted polymers (MIPs), in-tube solid-phase microextraction (IT-SPME), stir-bar sorptive extraction (SBSE) and microextraction in packed sorbent (MEPS). We also describe liquid-phase microextraction (LPME) approaches reported in the literature that are applied to the determination of steroid hormones.

  12. 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....

  13. Biological age as a basis for determining prenozological states in elementary school-age children.

    Directory of Open Access Journals (Sweden)

    Omelchenko T.G.

    2011-08-01

    Full Text Available The necessity of determining the biological age (BA in elementary school-age children as an important criterion for prenozological diagnostics is justified from the theoretical as well as practical prospective. The classification of prenozological states based on the BA is presented. The experiment features data of 159 children aged 7-10 years. Analyses of the obtained mean functional age (FA data shows deviation from the calendar age (CA in all age and gender groups which enables to diagnose prenozological state of elementary school-age children.

  14. 77 FR 7229 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-02-10

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF STATE Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art... April 15, 2003), I hereby determine that the objects to be included in the exhibition ``Nomads...

  15. 77 FR 37730 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-06-22

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF STATE Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art... April 15, 2003), I hereby determine that the objects to be included in the exhibition ``Nomads...

  16. DETERMINATION OF COCAINE AND BENZOYLECGONINE IN BIOLOGICAL MATRICES BY HPLC AND LC-MS/MS.

    Directory of Open Access Journals (Sweden)

    Zinar Pinar GUMUS

    2016-10-01

    Full Text Available Cocaine is a powerfully addictive illicit drug. Cocaine abuse and addiction continue to increase in the world. Most analytical tests for the detection of cocaine use include the analysis of the metabolite, benzoylecgonine, in the urine. Benzoylecgonine is a major urinary metabolite of cocaine. Generally, the most common biologic matrices used for the analysis of cocaine contain the urine and blood however saliva was also added as a matrix in this study. Practical, quick, reliable, precise and accurate, reproducible analytical methods have been developed and validated for cocaine and benzoylecgonine. In addition, this chromatographic techniques were used as both initial test and confirmatory. The validated chromatographic method was successfully applied to the analysis of cocaine and benzoylecgonine compounds in synthetic biological matrix. Confirmation analyses were made by LC-MS/MS to support reliability of HPLC results. As a result of this study, it can be claimed that HPLC could be a good alternative for the analyses of various biological matrices in forensic studies. Also the matrices and concentrations were determined by HPLC instrument could be used where necessary.

  17. Improved FIA-ABTS method for antioxidant capacity determination in different biological samples.

    Science.gov (United States)

    Bompadre, Stefano; Leone, Luciana; Politi, Alessia; Battino, Maurizio

    2004-08-01

    In order to evaluate the actual antioxidant features of foods, beverages and also plasma from patients, a number of assays have been developed in the last few years to determine the so called total antioxidant activity (TAA), intended as the cumulative capacity of a biological sample to scavenge free radicals. Most of the assays partially failed in obtaining a good reproducibility when using plasma because it is composed of a large number of substances, some of which are present at very high concentrations and possess masking features. For these reasons we have improved the widely known ABTS method by means of a FIA system where both temperature and dispersion of sample and reagent were strictly controlled. We found that temperature may be a critical aspect in the measurement of plasma TAA whilst its influence may be less important in the assay of non-complex biological samples. We demonstrated that also the reaction time may be critical, depending on the nature of the substance employed. Data confirmed the high TAA of a methylsalicylate-containing mouthrinse as well as the negligible TAA offered by the chlorhexidine containing one. White wines (Verdicchio) also displayed interesting TAA values. The improved method was useful to screen rapidly, without dilution, with very limited handling of the sample and with high repeatability the TAA of plasma in addition to chemical products, beverages and non-complex biological mixtures.

  18. The Biological Safety of Condom Material Can Be Determined Using an In Vitro Cell Culture System

    Directory of Open Access Journals (Sweden)

    N. A. Motsoane

    2001-01-01

    Full Text Available Latex products have long been recognized as a cause of latex protein allergy. The increased usage of latex gloves, with the consequent increased occurrence of latex allergies appears to have escalated with increasing awareness of the transmission of HIV–AIDS and other infections. The use of condoms as a means to prevent the transmission of STD's (sexually transmitted diseases and HIV–AIDS has been widely promoted. Although extensive testing is done to evaluate the physical quality of condoms, no information is available regarding the biological safety of condoms. This study was undertaken to determine the effects of short‐term exposure to physiological levels of condom surface material on cell viability (MTT assay and cell growth (crystal violet assay. A direct contact cell culture testing method (FDA test method F813‐83 used to evaluate the cytotoxic potential of medical materials and devices was used. The modified test method was found to be a sensitive test system for the evaluation of the biological safety of condoms. This study reveals the importance of evaluating the biological safety of all condoms that are commercially available, because of the potential health risk that may be associated with prolonged use of certain types of condoms.

  19. 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.

  20. Determination of the edge of criticality in echo state networks through Fisher information maximization

    CERN Document Server

    Livi, Lorenzo; Alippi, Cesare

    2016-01-01

    It is a widely accepted fact that the computational capability of recurrent neural networks is maximized on the so-called "edge of criticality". Once in this configuration, the network performs efficiently on a specific application both in terms of (i) low prediction error and (ii) high short-term memory capacity. Since the behavior of recurrent networks is strongly influenced by the particular input signal driving the dynamics, a universal, application-independent method for determining the edge of criticality is still missing. In this paper, we propose a theoretically motivated method based on Fisher information for determining the edge of criticality in recurrent neural networks. It is proven that Fisher information is maximized for (finite-size) systems operating in such critical regions. However, Fisher information is notoriously difficult to compute and either requires the probability density function or the conditional dependence of the system states with respect to the model parameters. The paper expl...

  1. Does the Compelled Cooperation Determine the Structure of a Complex Network?

    Institute of Scientific and Technical Information of China (English)

    XUAN Qi; LI Yan-Jun; WU Tie-Jun

    2008-01-01

    Cooperation among individuals is considered to play an important role in the evolution of complex networked systems in physical,biological,economical and even epidemiological worlds,but its effects on the development of the systems is not so clear.We consider a specific kind of primal cooperation in a group of individuals,i.e.,an individual never cooperates with others except when compelled to do so.The lowest level of compelled cooperation,in which cooperators share no message or resources,is investigated in the background of complex networks driven by the simple game rock-paper-scissors.Simulation results show that with the evolution of the systems,the cooperation will spread all over the networks,and finally results in systems with modular structures and a scale-free property.

  2. Physical-chemical determinant properties of biological communities in continental semi-arid waters.

    Science.gov (United States)

    da Rocha, Francisco Cleiton; de Andrade, Eunice Maia; Lopes, Fernando Bezerra; de Paula Filho, Francisco José; Filho, José Hamilton Costa; da Silva, Merivalda Doroteu

    2016-08-01

    Throughout human history, water has undergone changes in quality. This problem is more serious in dry areas, where there is a natural water deficit due to climatic factors. The aims of this study, therefore, were (i) to verify correlations between physical attributes, chemical attributes and biological metrics and (ii) from the biological attributes, to verify the similarity between different points of a body of water in a tropical semi-arid region. Samples were collected every 2 months, from July 2009 to July 2011, at seven points. Four physical attributes, five chemical attributes and four biological metrics were investigated. To identify the correlations between the physicochemical properties and the biological metrics, hierarchical cluster analysis (HCA) and canonical correlation analysis (CCA) were applied. Nine classes of phytoplankton were identified, with the predominance of species of cyanobacteria, and ten families of macroinvertebrates. The use of HCA resulted in the formation of three similar groups, showing that it was possible to reduce the number of sampling points when monitoring water quality with a consequent reduction in cost. Group I was formed from the waters at the high end of the reservoir (points P1, P2 and P3), group II by the waters from the middle third (points P4 and P5), and group III by the waters from the lower part of the reservoir (points P6 and P7). Richness of the phytoplanktons Cyanophyceae, Chorophyceae and Bacillariophyceae was the attribute which determined dissimilarity in water quality. Using CCA, it was possible to identify the spatial variability of the physicochemical attributes (TSS, TKN, nitrate and total phosphorus) that most influence the metrics of the macroinvertebrates and phytoplankton present in the water. Low macroinvertebrate diversity, with a predominance of indicator families for deterioration in water quality, and the composition of phytoplankton showing a predominance of cyanobacteria, suggests greater

  3. Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach.

    Science.gov (United States)

    Lee, Hyun-Jeong; Park, Hye-Sun; Kim, Woonsu; Yoon, Duhak; Seo, Seongwon

    2014-01-01

    The interrelationship between muscle and adipose tissues plays a major role in determining the quality of carcass traits. The objective of this study was to compare metabolic differences between muscle and intramuscular adipose (IMA) tissues in the longissimus dorsi (LD) of Hanwoo (Bos taurus coreanae) using the RNA-seq technology and a systems biology approach. The LD sections between the 6th and 7th ribs were removed from nine (each of three cows, steers, and bulls) Hanwoo beef cattle (carcass weight of 430.2 ± 40.66 kg) immediately after slaughter. The total mRNA from muscle, IMA, and subcutaneous adipose and omental adipose tissues were isolated and sequenced. The reads that passed quality control were mapped onto the bovine reference genome (build bosTau6), and differentially expressed genes across tissues were identified. The KEGG pathway enrichment tests revealed the opposite direction of metabolic regulation between muscle and IMA. Metabolic gene network analysis clearly indicated that oxidative metabolism was upregulated in muscle and downregulated in IMA. Interestingly, pathways for regulating cell adhesion, structure, and integrity and chemokine signaling pathway were upregulated in IMA and downregulated in muscle. It is thus inferred that IMA may play an important role in the regulation of development and structure of the LD tissues and muscle/adipose communication.

  4. The SOL Genomics Network. A Comparative Resource for Solanaceae Biology and Beyond1

    Science.gov (United States)

    Mueller, Lukas A.; Solow, Teri H.; Taylor, Nicolas; Skwarecki, Beth; Buels, Robert; Binns, John; Lin, Chenwei; Wright, Mark H.; Ahrens, Robert; Wang, Ying; Herbst, Evan V.; Keyder, Emil R.; Menda, Naama; Zamir, Dani; Tanksley, Steven D.

    2005-01-01

    The SOL Genomics Network (SGN; http://sgn.cornell.edu) is a rapidly evolving comparative resource for the plants of the Solanaceae family, which includes important crop and model plants such as potato (Solanum tuberosum), eggplant (Solanum melongena), pepper (Capsicum annuum), and tomato (Solanum lycopersicum). The aim of SGN is to relate these species to one another using a comparative genomics approach and to tie them to the other dicots through the fully sequenced genome of Arabidopsis (Arabidopsis thaliana). SGN currently houses map and marker data for Solanaceae species, a large expressed sequence tag collection with computationally derived unigene sets, an extensive database of phenotypic information for a mutagenized tomato population, and associated tools such as real-time quantitative trait loci. Recently, the International Solanaceae Project (SOL) was formed as an umbrella organization for Solanaceae research in over 30 countries to address important questions in plant biology. The first cornerstone of the SOL project is the sequencing of the entire euchromatic portion of the tomato genome. SGN is collaborating with other bioinformatics centers in building the bioinformatics infrastructure for the tomato sequencing project and implementing the bioinformatics strategy of the larger SOL project. The overarching goal of SGN is to make information available in an intuitive comparative format, thereby facilitating a systems approach to investigations into the basis of adaptation and phenotypic diversity in the Solanaceae family, other species in the Asterid clade such as coffee (Coffea arabica), Rubiaciae, and beyond. PMID:16010005

  5. Formal modeling and analysis of the MAL-associated biological regulatory network: insight into cerebral malaria.

    Directory of Open Access Journals (Sweden)

    Jamil Ahmad

    Full Text Available The discrete modeling formalism of René Thomas is a well known approach for the modeling and analysis of Biological Regulatory Networks (BRNs. This formalism uses a set of parameters which reflect the dynamics of the BRN under study. These parameters are initially unknown but may be deduced from the appropriately chosen observed dynamics of a BRN. The discrete model can be further enriched by using the model checking tool HyTech along with delay parameters. This paves the way to accurately analyse a BRN and to make predictions about critical trajectories which lead to a normal or diseased response. In this paper, we apply the formal discrete and hybrid (discrete and continuous modeling approaches to characterize behavior of the BRN associated with MyD88-adapter-like (MAL--a key protein involved with innate immune response to infections. In order to demonstrate the practical effectiveness of our current work, different trajectories and corresponding conditions that may lead to the development of cerebral malaria (CM are identified. Our results suggest that the system converges towards hyperinflammation if Bruton's tyrosine kinase (BTK remains constitutively active along with pre-existing high cytokine levels which may play an important role in CM pathogenesis.

  6. Determination of Sodium Cromoglycate by a New Kinetic Spectrophotometric Method in Biological Samples

    Directory of Open Access Journals (Sweden)

    Mohsen Keyvanfard

    2013-01-01

    Full Text Available A new kinetic spectrophotometric method is described for the determination of ultratrace amounts of sodium cromoglycate (SCG. The method based on catalytic action of SCG on the oxidation of amaranth with periodate in acidic and micellar medium. The reaction was monitored spectrophotometrically by measuring the decrease in absorbance of the amaranth at 518 nm, for the first 4 min from initiation of the reaction. Calibration curve was linear in the range of 4.0−36.0 ng mL−1 SCG. The limit of detection is 2.7 ng mL−1 SCG. The relative standard deviation (RSD for ten replicate analyses of 12, 20, and 28 ng mL−1 SCG was 0.40%, 0.32%, and 0.53%, respectively. The proposed method was used for the determination of SCG in biological samples.

  7. Selective spectrofluorimetric determination of zinc in biological samples by Flow Injection Analysis (FIA)

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez, P.; Perez Conde, C.; Gutierrez, A.; Camara, C. (Universidad Complutense, Madrid (Spain). Dept. de Quimica Analitica)

    1992-03-01

    The automatization of a spectrofluorimetric method for the determination of zinc at trace level is described. It is based on the formation of the fluorescent complex Zn(II)-5,7-dibromo-8-quinolinol (Zn(II)-DBQ) followed by extraction into diethylether using flow injection analysis. The optimum fluorescent emission is reached in hexamethylenetetramine (H{sub 2}MTA{sup +}/HMTA) buffer pH 6.0. A membrane phase separator was used. The calibration graph is linear up to 1.5 {mu}g/ml of Zn(II). The proposed method (detection limit 3 ng/ml) is very selective and has been successfully applied to determine Zn(II) in biological samples, tap waters and various food items. (orig.).

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

    Science.gov (United States)

    Sosa-Ferrera, Zoraida; Mahugo-Santana, Cristina; Santana-Rodríguez, José Juan

    2013-01-01

    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. PMID:23738329

  9. Analytical methodologies for the determination of endocrine disrupting compounds in biological and environmental samples.

    Science.gov (United States)

    Sosa-Ferrera, Zoraida; Mahugo-Santana, Cristina; Santana-Rodríguez, José Juan

    2013-01-01

    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.

  10. 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.

  11. Spectrofluorimetric determination of certain biologically active phenothiazines in commercial dosage forms and human plasma.

    Science.gov (United States)

    Mohamed, Abdel-Maaboud I; Abdelmageed, Osama H; Salem, Hesham; Nagy, Dalia M; Omar, Mahmoud A

    2013-01-01

    A validated simple and sensitive spectrofluorimetric method was developed for the determination of chlorpromazine hydrochloride, promethazine hydrochloride, trifluperazine hydrochloride, thioridazine hydrochloride, perazine maleate and oxomemazine. The method was based on condensation of malonic acid/acetic anhydride (MAA) under the catalytic effect of the tertiary amine moiety of the studied phenothiazines to provide a deep yellow to brown colour with green fluorescence. Relative fluorescence intensity of the products was measured at λ exc 398 nm and λ em 432 nm. Different variables affecting the reaction were studied and optimized. The method was successfully applied for the determination of the studied drugs in commercial dosage forms. The lower detection limits allowed the application of this method for the determination of the compounds in plasma as an example of a biological fluid. In addition, the method was considered specific for the determination of tertiary amines in the presence of primary and secondary amines; as a result, it was deemed suitable for the determination of the cited drugs in the presence of their degradation products resulting from N-dealkylation or oxidation of the corresponding sulphoxides or sulphones.

  12. 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; Phani Shilpa, P.; 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.

  13. Potentiometric detection in UPLC as an easy alternative to determine cocaine in biological samples.

    Science.gov (United States)

    Daems, Devin; van Nuijs, Alexander L N; Covaci, Adrian; Hamidi-Asl, Ezat; Van Camp, Guy; Nagels, Luc J

    2015-07-01

    The analytical methods which are often used for the determination of cocaine in complex biological matrices are a prescreening immunoassay and confirmation by chromatography combined with mass spectrometry. We suggest an ultra-high-pressure liquid chromatography combined with a potentiometric detector, as a fast and practical method to detect and quantify cocaine in biological samples. An adsorption/desorption model was used to investigate the usefulness of the potentiometric detector to determine cocaine in complex matrices. Detection limits of 6.3 ng mL(-1) were obtained in plasma and urine, which is below the maximum residue limit (MRL) of 25 ng mL(-1). A set of seven plasma samples and 10 urine samples were classified identically by both methods as exceeding the MRL or being inferior to it. The results obtained with the UPLC/potentiometric detection method were compared with the results obtained with the UPLC/MS method for samples spiked with varying cocaine concentrations. The intraclass correlation coefficient was 0.997 for serum (n =7) and 0.977 for urine (n =8). As liquid chromatography is an established technique, and as potentiometry is very simple and cost-effective in terms of equipment, we believe that this method is potentially easy, inexpensive, fast and reliable.

  14. Ambient and biological monitoring of cokeoven workers: determinants of the internal dose of polycyclic aromatic hydrocarbons.

    Science.gov (United States)

    Jongeneelen, F J; van Leeuwen, F E; Oosterink, S; Anzion, R B; van der Loop, F; Bos, R P; van Veen, H G

    1990-07-01

    Polycyclic aromatic hydrocarbons (PAH) were measured in the breathing zone air of 56 battery workers at two cokeovens during three consecutive days. The concentration of total PAH ranged up to 186 micrograms/m3. Preshift and end of shift urine samples were collected to determine 1-hydroxypyrene, a metabolite of pyrene. Control urine samples were available from 44 workers in the shipping yard of a hot rolling mill. The median values of 1-hydroxypyrene in urine of smoking and non-smoking controls were 0.51 and 0.17 mumol/mol creatinine, respectively. Concentrations of 1-hydroxypyrene up to 11.2 mumol/mol were found in the urine of the cokeoven workers. At the start of the three day working period after 32 hours off work, the 1-hydroxypyrene concentrations were four times higher and at the end of the working period 10 times higher compared with control concentrations. Excretion of 1-hydroxypyrene occurred with a half life of 6-35 hours. Both the ambient air monitoring data and the biological monitoring data showed that the topside workers were the heaviest exposed workers. The relation between air monitoring data and biological monitoring data was not strong. Multiple regression analysis was performed to identify determinants of the internal dose. The combination of exposure and smoking amplify each other and the use of a protective airstream helmet decreases the internal dose. An effect of alcohol consumption and the use of medication on the toxicokinetics of pyrene was not found.

  15. Applications of a DAD-HPLC method for determination of loratadine on biological samples

    Directory of Open Access Journals (Sweden)

    Pavalache Georgeta

    2015-06-01

    Full Text Available The aim of research is to assess the active substance by a HPLC method for the separation and quantitative determination of loratadine. The method has been developed and validated on the standard solutions, in previous research. The current study was undertaken to present the results obtained from loratadine determination in biological samples (human serum, urine and breast milk. These results may be applicable on patients with different physiological conditions (aging, pregnancy or recently giving birth, etc. and pathological conditions which may interfere with the metabolism of loratadine. The used HPLC method detected loratadine concentrations in human serum samples, respectively urine samples, at 2 hours after drug administration. The method detected traces of loratadine which passed into breast milk, as well. Data were statistically interpreted using MED CALC 10.2 software. These results show that the applied method can be used for quantitative analysis of loratadine in biological fluids (all permissible limits of quality specifications being in the range 95- 105%.

  16. Determination of gadolinium-based MRI contrast agents in biological and environmental samples: A review

    Energy Technology Data Exchange (ETDEWEB)

    Telgmann, Lena [University of Münster, Institute of Inorganic and Analytical Chemistry, Münster (Germany); Sperling, Michael [University of Münster, Institute of Inorganic and Analytical Chemistry, Münster (Germany); European Virtual Institute for Speciation Analysis (EVISA), Münster (Germany); Karst, Uwe, E-mail: uk@uni-muenster.de [University of Münster, Institute of Inorganic and Analytical Chemistry, Münster (Germany)

    2013-02-18

    Highlights: ► All major methods for the analysis of Gd-based MRI contrast agents are discussed. ► Biological and environmental samples are covered. ► Pharmacokinetics and species transformation can be investigated. ► The figures of merit as limit of detection and analysis time are described. -- Abstract: The development of analytical methods and strategies to determine gadolinium and its complexes in biological and environmental matrices is evaluated in this review. Gadolinium (Gd) chelates are employed as contrast agents for magnetic resonance imaging (MRI) since the 1980s. In general they were considered as safe and well-tolerated, when in 2006, the disease nephrogenic systemic fibrosis (NSF) was connected to the administration of MRI contrast agents based on Gd. Pathogenesis and etiology of NSF are yet unclear and called for the development of several analytical methods to obtain elucidation in this field. Determination of Gd complex stability in vitro and in vivo, as well as the quantification of Gd in body fluids like blood and urine was carried out. Separation of the Gd chelates was achieved with high performance liquid chromatography (HPLC) and capillary electrophoresis (CE). For detection, various methods were employed, including UV–vis absorbance and fluorescence spectroscopy, electrospray ionization mass spectrometry (ESI-MS) and inductively coupled plasma mass spectrometry (ICP-MS). A second challenge for analysts was the discovery of high concentrations of anthropogenic Gd in surface waters draining populated areas. The source could soon be determined to be the increasing administration of Gd complexes during MRI examinations. Identification and quantification of the contrast agents was carried out in various surface and groundwater samples to determine the behavior and fate of the Gd chelates in the environment. The improvement of limits of detection (LOD) and limits of quantification (LOQ) was and still is the goal of past and ongoing

  17. Evaluation of flow injection analysis for determination of cholinesterase activities in biological material.

    Science.gov (United States)

    Cabal, Jiri; Bajgar, Jiri; Kassa, Jiri

    2010-09-06

    The method for automatic continual monitoring of acetylcholinesterase (AChE) activity in biological material is described. It is based on flexible system of plastic pipes mixing samples of biological material with reagents for enzyme determination; reaction product penetrates through the semipermeable membrane and it is spectrophotometrically determined (Ellman's method). It consists of sampling (either in vitro or in vivo), adding the substrate and flowing to dialyzer; reaction product (thiocholine) is dialyzed and mixed with 5,5'-dithio-bis-2-nitrobenzoic acid (DTNB) transported to flow spectrophotometer. Flowing of all materials is realised using peristaltic pump. The method was validated: time for optimal hydratation of the cellophane membrane; type of the membrane; type of dialyzer; conditions for optimal permeation of reaction components; optimization of substrate and DTNB concentrations (linear dependence); efficacy of peristaltic pump; calibration of analytes after permeation through the membrane; excluding of the blood permeation through the membrane. Some examples of the evaluation of the effects of AChE inhibitors are described. It was demonstrated very good uniformity of peaks representing the enzyme activity (good reproducibility); time dependence of AChE inhibition caused by VX in vitro in the rat blood allowing to determine the half life of inhibition and thus, bimolecular rate constants of inhibition; reactivation of inhibited AChE by some reactivators, and continual monitoring of the activity in the whole blood in vivo in intact and VX-intoxicated rats. The method is simple and not expensive, allowing automatic determination of AChE activity in discrete or continual samples in vitro or in vivo. It will be evaluated for further research of cholinesterase inhibitors.

  18. Mechanisms of stochastic focusing and defocusing in biological reaction networks: insight from accurate chemical master equation (ACME) solutions.

    Science.gov (United States)

    Giirsoy, Gamze; Terebus, Anna; Cao, Youfang; Liang, Jie; Gursoy, Gamze; Terebus, Anna; Youfang Cao; Jie Liang; Gursoy, Gamze; Cao, Youfang; Terebus, Anna; Liang, Jie

    2016-08-01

    Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small. Studies based on Stochastic Simulation Algorithm (SSA) has shown that a basic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the signal noise. Although SSA has been widely used to study stochastic networks, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method to solve the exact solution of the discrete Chemical Master Equations and to study a network where SF was reported. We showed that the level of SF depends on the degree of the fluctuations of signal molecule. We discovered that signaling noise under certain conditions in the same reaction network can lead to a decrease in the system sensitivities, thus the network can experience stochastic defocusing. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system.

  19. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    Science.gov (United States)

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

  20. GeneNet Toolbox for MATLAB: a flexible platform for the analysis of gene connectivity in biological networks.

    Science.gov (United States)

    Taylor, Avigail; Steinberg, Julia; Andrews, Tallulah S; Webber, Caleb

    2015-02-01

    We present GeneNet Toolbox for MATLAB (also available as a set of standalone applications for Linux). The toolbox, available as command-line or with a graphical user interface, enables biologists to assess connectivity among a set of genes of interest ('seed-genes') within a biological network of their choosing. Two methods are implemented for calculating the significance of connectivity among seed-genes: 'seed randomization' and 'network permutation'. Options include restricting analyses to a specified subnetwork of the primary biological network, and calculating connectivity from the seed-genes to a second set of interesting genes. Pre-analysis tools help the user choose the best connectivity-analysis algorithm for their network. The toolbox also enables visualization of the connections among seed-genes. GeneNet Toolbox functions execute in reasonable time for very large networks (∼10 million edges) on a desktop computer. GeneNet Toolbox is open source and freely available from http://avigailtaylor.github.io/gntat14. Supplementary data are available at Bioinformatics online. avigail.taylor@dpag.ox.ac.uk. © The Author 2014. Published by Oxford University Press.

  1. Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines.

    Science.gov (United States)

    Crowgey, Erin L; Matlock, Andrea; Venkatraman, Vidya; Fert-Bober, Justyna; Van Eyk, Jennifer E

    2017-01-01

    Data-independent acquisition mass spectrometry (DIA-MS) strategies and applications provide unique advantages for qualitative and quantitative proteome probing of a biological sample allowing constant sensitivity and reproducibility across large sample sets. These advantages in LC-MS/MS are being realized in fundamental research laboratories and for clinical research applications. However, the ability to translate high-throughput raw LC-MS/MS proteomic data into biological knowledge is a complex and difficult task requiring the use of many algorithms and tools for which there is no widely accepted standard and best practices are slowly being implemented. Today a single tool or approach inherently fails to capture the full interpretation that proteomics uniquely supplies, including the dynamics of quickly reversible chemically modified states of proteins, irreversible amino acid modifications, signaling truncation events, and, finally, determining the presence of protein from allele-specific transcripts. This chapter highlights key steps and publicly available algorithms required to translate DIA-MS data into knowledge.

  2. Comparison between earthquake magnitudes determined by China seismograph network and US seismograph networks (Ⅰ): Body wave magnitude

    Institute of Scientific and Technical Information of China (English)

    LIU Rui-feng; CHEN Yun-tai; Peter Bormann; REN Xiao; HOU Jian-min; ZOU Li-ye; YANG Hui

    2005-01-01

    By using orthogonal regression method, a systematic comparison is made between body wave magnitudes determined by Institute of Geophysics of China Earthquake Administration (IGCEA) and National Earthquake Information Center of US Geological Survey (USGS/NEIC) on the basis of observation data from China and US seismograph networks between 1983 and 2004. The result of orthogonal regression shows no systematic error between body wave magnitude mb determined by IGCEA and mb (NEIC). Provided that mb (NEIC) is taken as the benchmark, body wave magnitude determined by IGCEA is greater by 0.2~0.1 than the magnitude determined by NEIC for M=3.5~4.5 earthquakes; for M=5.0~5.5 earthquakes, there is no difference; and for M≥6.0 earthquakes, it is smaller by no more than 0.2. This is consistent with the result of comparison by IDC (International Data Center).

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

    Institute of Scientific and Technical Information of China (English)

    YUAN Wu-Jie; LUO Xiao-Shu; JIANG Pin-Qun

    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 nolse and coupling.The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

  4. Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

  5. The use of self-determination theory to foster environmental motivation in an environmental biology course

    Science.gov (United States)

    Darner, Rebekka

    A scientifically literate person is one who understands the nature of science, its processes, products, and their appropriate application to decision-making contexts. The impetus to make informed decisions about environmental issues is environmental motivation. I examined students' environmental motivation, its relationship to scientific knowledge, and how environmental motivation can be fostered in a science classroom. This study took place in a college-level environmental biology course in which the instructor attempted to support students' basic psychological needs, as defined by self-determination theory (SDT). The first question was to what extent does an SDT-guided environmental biology course differ from a non-SDT-guided course in the degree to which it fostered self-determined motivation toward the environment. The administration of a well-validated scale to two sections before, after, and six months following the end of the course indicated that SDT-guided instruction is a plausible way to foster environmental motivation in the classroom. The second question was what are the multiple influences on fostering self-determined motivation toward the environment in an SDT-guided course. Path analysis indicated that environmental motivation can be partially accomplished in an environmental biology course by conveying to students that they are cared for, are connected to others, and can trust others while solving environmental problems. The third question sought to characterize students' scientific conceptualizations as they solve environmental problems and the extent to which their conceptualizations relate to the satisfaction of their need for competence. Students were videotaped during in-class problem-solving, after which stimulated-recall interviews were conducted. Grounded theory and an established coding scheme were combined to analyze these data, which resulted in three grounded hypotheses about what characterizes students' scientific knowledge when they

  6. 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...

  7. Common biological networks underlie genetic risk for alcoholism in African- and European-American populations.

    Science.gov (United States)

    Kos, M Z; Yan, J; Dick, D M; Agrawal, A; Bucholz, K K; Rice, J P; Johnson, E O; Schuckit, M; Kuperman, S; Kramer, J; Goate, A M; Tischfield, J A; Foroud, T; Nurnberger, J; Hesselbrock, V; Porjesz, B; Bierut, L J; Edenberg, H J; Almasy, L

    2013-07-01

    Alcohol dependence (AD) is a heritable substance addiction with adverse physical and psychological consequences, representing a major health and economic burden on societies worldwide. Genes thus far implicated via linkage, candidate gene and genome-wide association studies (GWAS) account for only a small fraction of its overall risk, with effects varying across ethnic groups. Here we investigate the genetic architecture of alcoholism and report on the extent to which common, genome-wide SNPs collectively account for risk of AD in two US populations, African-Americans (AAs) and European-Americans (EAs). Analyzing GWAS data for two independent case-control sample sets, we compute polymarker scores that are significantly associated with alcoholism (P = 1.64 × 10(-3) and 2.08 × 10(-4) for EAs and AAs, respectively), reflecting the small individual effects of thousands of variants derived from patterns of allelic architecture that are population specific. Simulations show that disease models based on rare and uncommon causal variants (MAF gene location and examined for constituent biological networks, gene enrichment is observed for several cellular processes and functions in both EA and AA populations, transcending their underlying allelic differences. Our results reveal key insights into the complex etiology of AD, raising the possibility of an important role for rare and uncommon variants, and identify polygenic mechanisms that encompass a spectrum of disease liability, with some, such as chloride transporters and glycine metabolism genes, displaying subtle, modifying effects that are likely to escape detection in most GWAS designs.

  8. 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

  9. 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; Antzack, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J.; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

    2016-01-01

    Abstract 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

  10. 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

  11. 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.

  12. [Determination of ethylene glycol in biological fluids--propylene glycol interferences].

    Science.gov (United States)

    Gomółka, Ewa; Cudzich-Czop, Sylwia; Sulka, Adrianna

    2013-01-01

    Many laboratories in Poland do not use gas chromatography (GC) method for determination of ethylene glycol (EG) and methanol in blood of poisoned patients, they use non specific spectrophotometry methods. One of the interfering substances is propylene glycol (PG)--compound present in many medical and cosmetic products: drops, air freshens, disinfectants, electronic cigarettes and others. In Laboratory of Analytical Toxicology and Drug Monitoring in Krakow determination of EG is made by GC method. The method enables to distinguish and make resolution of (EG) and (PG) in biological samples. In the years 2011-2012 in several serum samples from diagnosed patients PG was present in concentration from several to higher than 100 mg/dL. The aim of the study was to estimate PG interferences of serum EG determination by spectrophotometry method. Serum samples containing PG and EG were used in the study. The samples were analyzed by two methods: GC and spectrophotometry. Results of serum samples spiked with PG with no EG analysed by spectrophotometry method were improper ("false positive"). The results were correlated to PG concentration in samples. Calculated cross-reactivity of PG in the method was 42%. Positive results of EG measured by spectrophotometry method must be confirmed by reference GC method. Spectrophotometry method shouldn't be used for diagnostics and monitoring of patients poisoned by EG.

  13. Determination of phenolic compounds derived from hydrolysable tannins in biological matrices by RP-HPLC.

    Science.gov (United States)

    Díez, María Teresa; García del Moral, Pilar; Resines, José Antonio; Arín, María Jesús

    2008-08-01

    An RP-HPLC method for the determination of four phenolic compounds: gallic acid (GA), pyrogallol (PY), resorcinol (RE) and ellagic acid (EA), derived from hydrolysable tannins is reported. Separation was achieved on a SunFire C18 (250 x 4.6 mm id, 5 microm) column at 40 degrees C with gradient elution. UV detection at 280 nm was applied. The developed method was validated in terms of linearity, accuracy and precision. Satisfactory repeatability and between day precision were noticed with RSD values lower than 3%. Recoveries from different biological samples ranged from 91.50 to 105.25%. The LODs were estimated as 1.70 mg/L for PY, 1.68 mg/L for GA, 1.52 mg/L for RE and 0.98 mg/L for EA with a 20 microL injection volume. The method was applied for the determination of these compounds in oak leaves and in ruminal fluid and urine samples taken from beef cattle fed with oak leaves. The proposed method could be used in ruminant nutrition studies to verify the effect that a diet rich in tannins have on ruminal fermentation and to determine the toxicity of these compounds.

  14. 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.

  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 t

  16. The ALOUD Study: Adult Learning Open University Determinants Study - Influence of biological and psychological determinants on study success in formal lifelong learning in adults

    NARCIS (Netherlands)

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

    2011-01-01

    Gijselaers, H. J. M., Neroni, J., De Groot, R. H. M., & Kirschner, P. A. (2011, September). The ALOUD Study: Adult Learning Open University Determinants Study - Influence of biological and psychological determinants on study success in formal lifelong learning in adults. Presentation given for visit

  17. RNA-Sequencing Reveals Biological Networks during Table Grapevine (‘Fujiminori’) Fruit Development

    Science.gov (United States)

    Shangguan, Lingfei; Mu, Qian; Fang, Xiang; Zhang, Kekun; Jia, Haifeng; Li, Xiaoying; Bao, Yiqun; Fang, Jinggui

    2017-01-01

    Grapevine berry development is a complex and genetically controlled process, with many morphological, biochemical and physiological changes occurring during the maturation process. Research carried out on grapevine berry development has been mainly concerned with wine grape, while barely focusing on table grape. ‘Fujiminori’ is an important table grapevine cultivar, which is cultivated in most provinces of China. In order to uncover the dynamic networks involved in anthocyanin biosynthesis, cell wall development, lipid metabolism and starch-sugar metabolism in ‘Fujiminori’ fruit, we employed RNA-sequencing (RNA-seq) and analyzed the whole transcriptome of grape berry during development at the expanding period (40 days after full bloom, 40DAF), véraison period (65DAF), and mature period (90DAF). The sequencing depth in each sample was greater than 12×, and the expression level of nearly half of the expressed genes were greater than 1. Moreover, greater than 64% of the clean reads were aligned to the Vitis vinifera reference genome, and 5,620, 3,381, and 5,196 differentially expressed genes (DEGs) were identified between different fruit stages, respectively. Results of the analysis of DEGs showed that the most significant changes in various processes occurred from the expanding stage to the véraison stage. The expression patterns of F3’H and F3’5’H were crucial in determining red or blue color of the fruit skin. The dynamic networks of cell wall development, lipid metabolism and starch-sugar metabolism were also constructed. A total of 4,934 SSR loci were also identified from 4,337 grapevine genes, which may be helpful for the development of phylogenetic analysis in grapevine and other fruit trees. Our work provides the foundation for developmental research of grapevine fruit as well as other non-climacteric fruits. PMID:28118385

  18. 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.

  19. Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties.

    Science.gov (United States)

    Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Sun, Dianjun; Lv, Yingli

    2016-06-01

    Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms.

  20. 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.

  1. Determination of kinetics and stoichiometry of chemical sulfide oxidation in wastewater of sewer networks

    DEFF Research Database (Denmark)

    Nielsen, A.H.; Vollertsen, Jes; Hvitved-jacobsen, Thorkild

    2003-01-01

    A method for determination of kinetics and stoichiometry of chemical sulfide oxidation by dissolved oxygen (DO) in wastewater is presented. The method was particularly developed to investigate chemical sulfide oxidation in wastewater of sewer networks at low DO concentrations. The method is based...

  2. BiologicalNetworks - tools enabling the integration of multi-scale data for the host-pathogen studies

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2011-01-01

    Full Text Available Abstract Background Understanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage. Results To study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use. Conclusions The developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org and web interface (at http://flu.sdsc.edu.

  3. Identifying influential nodes in a wound healing-related network of biological processes using mean first-passage time

    Science.gov (United States)

    Arodz, Tomasz; Bonchev, Danail

    2015-02-01

    In this study we offer an approach to network physiology, which proceeds from transcriptomic data and uses gene ontology analysis to identify the biological processes most enriched in several critical time points of wound healing process (days 0, 3 and 7). The top-ranking differentially expressed genes for each process were used to build two networks: one with all proteins regulating the transcription of selected genes, and a second one involving the proteins from the signaling pathways that activate the transcription factors. The information from these networks is used to build a network of the most enriched processes with undirected links weighted proportionally to the count of shared genes between the pair of processes, and directed links weighted by the count of relationships connecting genes from one process to genes from the other. In analyzing the network thus built we used an approach based on random walks and accounting for the temporal aspects of the spread of a signal in the network (mean-first passage time, MFPT). The MFPT scores allowed identifying the top influential, as well as the top essential biological processes, which vary with the progress in the healing process. Thus, the most essential for day 0 was found to be the Wnt-receptor signaling pathway, well known for its crucial role in wound healing, while in day 3 this was the regulation of NF-kB cascade, essential for matrix remodeling in the wound healing process. The MFPT-based scores correctly reflected the pattern of the healing process dynamics to be highly concentrated around several processes between day 0 and day 3, and becoming more diffuse at day 7.

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

    Science.gov (United States)

    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. PMID:27806074

  5. Network news: prime time for systems biology of the plant circadian clock truncated form of the title: Plant circadian clocks

    Science.gov (United States)

    McClung, C. Robertson; Gutiérrez, Rodrigo A.

    2011-01-01

    Summary Whole-transcriptome analyses have established that the plant circadian clock regulates virtually every plant biological process and most prominently hormonal and stress response pathways. Systems biology efforts have successfully modeled the plant central clock machinery and an iterative process of model refinement and experimental validation has contributed significantly to the current view of the central clock machinery. The challenge now is to connect this central clock to the output pathways for understanding how the plant circadian clock contributes to plant growth and fitness in a changing environment. Undoubtedly, systems approaches will be needed to integrate and model the vastly increased volume of experimental data in order to extract meaningful biological information. Thus, we have entered an era of systems modeling, experimental testing, and refinement. This approach, coupled with advances from the genetic and biochemical analyses of clock function, is accelerating our progress towards a comprehensive understanding of the plant circadian clock network. PMID:20889330

  6. 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

  7. 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.