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Sample records for aligned biological networks

  1. 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.

  2. 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.

  3. Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

    Energy Technology Data Exchange (ETDEWEB)

    Mohammadi, Shahin [Purdue Univ., West Lafayette, IN (United States); Gleich, David F. [Purdue Univ., West Lafayette, IN (United States); Kolda, Tamara G. [Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Grama, Ananth [Purdue Univ., West Lafayette, IN (United States)

    2015-11-01

    Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.

  4. AlignNemo: a local network alignment method to integrate homology and topology.

    Science.gov (United States)

    Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

    2012-01-01

    Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

  5. Metabolic network alignment in large scale by network compression

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    Ay Ferhat

    2012-03-01

    Full Text Available Abstract Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our

  6. Active network alignment: a matching-based approach

    CERN Document Server

    Malmi, Eric; Gionis, Aristides

    2016-01-01

    Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications - from biological networks to social networks to ontologies - where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting. The majority of the existing active methods focus on absolute queries ("are nodes $a$ and $b$ the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set $\\{b_1, \\ldots, b_n\\}$ is the most similar to node $a$?"). This paper introduces a nov...

  7. Functional alignment of regulatory networks: a study of temperate phages.

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    Ala Trusina

    2005-12-01

    Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.

  8. Lagrangian Relaxation Applied to Sparse Global Network Alignment

    CERN Document Server

    El-Kebir, Mohammed; Klau, Gunnar W

    2011-01-01

    Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. In this paper we present a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. It is based on an integer linear programming formulation, generalizing the well-studied quadratic assignment problem. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the software tool natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein inte...

  9. Opportunistic Interference Alignment for Random Access Networks

    OpenAIRE

    Jin, Hu; Jeon, Sang-Woon; Jung, Bang Chul

    2015-01-01

    An interference management problem among multiple overlapped random access networks (RANs) is investigated, each of which operates with slotted ALOHA protocol. Assuming that access points and users have multiple antennas, a novel opportunistic interference alignment~(OIA) is proposed to mitigate interference among overlapped RANs. The proposed technique intelligently combines the transmit beamforming technique at the physical layer and the opportunistic packet transmission at the medium acces...

  10. Interference Alignment in Dense Wireless Networks

    CERN Document Server

    Niesen, Urs

    2009-01-01

    We consider arbitrary dense wireless networks, in which $n$ nodes are placed in an arbitrary (deterministic) manner on a square region of unit area and communicate with each other over Gaussian fading channels. We provide inner and outer bounds for the $n\\times n$-dimensional unicast and the $n\\times 2^n$-dimensional multicast capacity regions of such a wireless network. These inner and outer bounds differ only by a factor $O(\\log(n))$, yielding a fairly tight scaling characterization of the entire regions. The communication schemes achieving the inner bounds use interference alignment as a central technique and are surprisingly simple.

  11. Networks in Cell Biology

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

    2010-05-01

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

  12. Dominating biological networks.

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    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.

  13. GraphCrunch 2: Software tool for network modeling, alignment and clustering

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    Hayes Wayne

    2011-01-01

    Full Text Available Abstract Background Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. Results We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL" for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other

  14. Finding optimal interaction interface alignments between biological complexes

    KAUST Repository

    Cui, Xuefeng

    2015-06-13

    Motivation: Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which are keys to understanding their evolutionary histories and functions. Although various structure alignment methods have been developed to successfully access the similarities of protein structures or certain types of interaction interfaces, existing alignment tools cannot directly align arbitrary types of interfaces formed by protein, DNA or RNA molecules. Specifically, they require a \\'blackbox preprocessing\\' to standardize interface types and chain identifiers. Yet their performance is limited and sometimes unsatisfactory. Results: Here we introduce a novel method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures. Our method uses sequentially remote fragments to search for the optimal superimposition. The optimal residue matching problem is then formulated as a maximum weighted bipartite matching problem to detect the optimal sequence order-independent alignment. Benchmark evaluation on all non-redundant protein-DNA complexes in PDB shows significant performance improvement of our method over TM-align and iAlign (with the \\'blackbox preprocessing\\'). Two case studies where our method discovers, for the first time, structural similarities between two pairs of functionally related protein-DNA complexes are presented. We further demonstrate the power of our method on detecting structural similarities between a protein-protein complex and a protein-RNA complex, which is biologically known as a protein-RNA mimicry case. © The Author 2015. Published by Oxford University Press.

  15. A Genetic Algorithm on Multiple Sequences Alignment Problems in Biology

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the "distance" between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now.

  16. Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment

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    Mohammed El-Kebir

    2015-11-01

    Full Text Available Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. We present a method for global network alignment that is fast and robust and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. We exploit that network alignment is a special case of the well-studied quadratic assignment problem (QAP. We focus on sparse network alignment, where each node can be mapped only to a typically small subset of nodes in the other network. This corresponds to a QAP instance with a symmetric and sparse weight matrix. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the open source software tool Natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative established and recent state-of-the-art methods.

  17. Logical impossibilities in biological networks

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    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.

  18. Waferscale assembly of Field-Aligned nanotube Networks (FANs)

    DEFF Research Database (Denmark)

    Dimaki, Maria; Bøggild, Peter

    2006-01-01

    We demonstrate the integration of nanotube networks on 512 individual devices on a full 4-inch wafer in less than 60 seconds with a roughly 80% yield using dielectrophoresis. We present here investigations of the morphology and electrical resistance of such field aligned networks for different...... frequencies of the electrical field used to attract the nanotubes to the electrodes. Preliminary data of response to visible light irradiation as well as changes in the humidity indicate that the field aligned networks could be used as sensor components that may well integrate with CMOS due to mild assembly...

  19. A Network Model of Interpersonal Alignment in Dialog

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    Alexander Mehler

    2010-06-01

    Full Text Available In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations.

  20. 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.…

  1. BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks

    DEFF Research Database (Denmark)

    Alkan, Ferhat; Erten, Cesim

    2014-01-01

    MOTIVATION: Global many-to-many alignment of biological networks has been a central problem in comparative biological network studies. Given a set of biological interaction networks, the informal goal is to group together related nodes. For the case of protein-protein interaction networks...... are conserved across the input networks. We provide a formal definition of the global many-to-many alignment of multiple protein-protein interaction networks that captures this informal objective. We show the computational intractability of the suggested definition. We provide a heuristic method based...... on backbone extraction and merge strategy (BEAMS) for the problem. We finally show, through experiments based on biological significance tests, that the proposed BEAMS algorithm performs better than the state-of-the-art approaches. Furthermore, the computational burden of the BEAMS algorithm in terms...

  2. 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.

  3. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

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

  4. Inferring Directed Road Networks from GPS Traces by Track Alignment

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    Xingzhe Xie

    2015-11-01

    Full Text Available This paper proposes a method to infer road networks from GPS traces. These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections. These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces. We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections. We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment. The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW algorithm. Our contribution is two-fold. First, we detect potential intersections by clustering the turning points on the GPS traces. Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a “stretch and then compress” strategy based on the DTW algorithm. This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track’s time alignment, for example the variance of speed along a road segment.

  5. A direct method for computing extreme value (Gumbel) parameters for gapped biological sequence alignments.

    Science.gov (United States)

    Quinn, Terrance; Sinkala, Zachariah

    2014-01-01

    We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.

  6. Variation Tendency of the Align Network for the 100MeV Proton Linac

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Daeil; Kwon, Hyeokjung; Kim, Hansung; Seo, Donghyuk; Cho, Yongsub [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-05-15

    In this paper, the variation tendency of the align networks is checked and the survey results are considered to compensate the position of the accelerator components in the tunnel. Two years ago, the align networks on the tunnel were installed and checked to align the accelerator components. In recent survey of align networks, the displacement by temperature was confirmed according to comparison of the position. When the accelerator components are realigned, the displacement should be used to compensate position of the accelerator components. 100MeV proton linac for KOMAC (Korea Multi-purpose Accelerator Complex) has been operated at the Gyeong-ju. Linac is composed of a 50keV proton injector, a 3MeV RFQ, DTL tanks and a beam dump. The align networks were built to align the accelerator components. The survey work of align networks should be accomplished whenever need the alignment of the accelerator by using the laser tracker.

  7. Multiple deep convolutional neural networks averaging for face alignment

    Science.gov (United States)

    Zhang, Shaohua; Yang, Hua; Yin, Zhouping

    2015-05-01

    Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

  8. 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

  9. 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

  10. Interference Alignment for Partially Connected MIMO Cellular Networks

    CERN Document Server

    Ruan, Liangzhong

    2012-01-01

    In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.

  11. Chimeric alignment by dynamic programming: Algorithm and biological uses

    Energy Technology Data Exchange (ETDEWEB)

    Komatsoulis, G.A.; Waterman, M.S. [Univ. of Southern California, Los Angeles, CA (United States)

    1997-12-01

    A new nearest-neighbor method for detecting chimeric 16S rRNA artifacts generated during PCR amplification from mixed populations has been developed. The method uses dynamic programming to generate an optimal chimeric alignment, defined as the highest scoring alignment between a query and a concatenation of a 5{prime} and a 3{prime} segment from two separate entries from a database of related sequences. Chimeras are detected by studying the scores and form of the chimeric and global sequence alignments. The chimeric alignment method was found to be marginally more effective than k-tuple based nearest-neighbor methods in simulation studies, but its most effective use is in concert with k-tuple methods. 15 refs., 3 figs., 1 tab.

  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. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...

  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. Attentional Networks and Biological Motion

    Directory of Open Access Journals (Sweden)

    Chandramouli Chandrasekaran

    2010-03-01

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

  19. Measuring the evolutionary rewiring of biological networks.

    Directory of Open Access Journals (Sweden)

    Chong Shou

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

  20. 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.

  1. Comparing biological networks via graph compression

    Directory of Open Access Journals (Sweden)

    Hayashida Morihiro

    2010-09-01

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

  2. Displacement Analysis of Building Movement by using the Survey of Align Network at KOMAC

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Dae-Il; Seo, Dong-Hyuk; Ahn, Tae-Sung; Kwon, Hyeok-Jung; Kim, Han-Sung; Cho, Yong-Sub [Korea Atomic Energy Research Institute, Gyeongju (Korea, Republic of)

    2014-10-15

    100MeV proton linac has been operated and provided to beam users in KOMAC (Korea Multi-purpose Accelerator Complex). Proton linac is composed of a 50keV proton injector, a 3MeV RFQ, 20MeV DTL tanks, 100MeV DTL tanks, beam dump and beam line for 20MeV and 100MeV. To align the accelerator components, the align networks based on reference point were installed on the wall inside tunnel. The survey works of align networks were accomplished by using the laser tracker. In this paper, the survey of align networks is performed and its results are presented. In recent survey of align networks, it was monitored to move the locations. The analysis of displacement was confirmed by compare the align networks. This analysis was used to re-align the accelerator components which can be compensated location of reference points. The displacement monitoring should be performed during the long-term period and need to find the other method for real-time, not the survey of align networks.

  3. High Throughput via Cross-Layer Interference Alignment for Mobile Ad Hoc Networks

    Science.gov (United States)

    2013-08-26

    hoc networks ( MANETS ) under practical assumptions. Several problems were posed and solved that provide insight into when and how interference alignment...REPORT High Throughput via Cross-Layer Interference Alignment for Mobile Ad Hoc Networks 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Recent...investigations into the fundamental limits of mobile ad hoc networks have produced a physical layer method for approaching their capacity. This strategy, known

  4. Alignment of the Fibrin Network Within an Autologous Plasma Clot.

    Science.gov (United States)

    Gessmann, Jan; Seybold, Dominik; Peter, Elvira; Schildhauer, Thomas Armin; Köller, Manfred

    2016-01-01

    Autologous plasma clots with longitudinally aligned fibrin fibers could serve as a scaffold for longitudinal axonal regrowth in cases of traumatic peripheral nerve injuries. Three different techniques for assembling longitudinally oriented fibrin fibers during the fibrin polymerization process were investigated as follows: fiber alignment was induced by the application of either a magnetic field or-as a novel approach-electric field or by the induction of orientated flow. Fiber alignment was characterized by scanning electron microscopy analysis followed by image processing using fast Fourier transformation (FFT). Besides FFT output images, area xmin to xmax, as well as full width at half maximum (FWHM) of the FFT graph plot peaks, was calculated to determine the relative degree of fiber alignment. In addition, fluorescently labeled human fibrinogen and mesenchymal stem cells (MSCs) were used to visualize fibrin and cell orientation in aligned and nonaligned plasma clots. Varying degrees of fiber alignment were achieved by the three different methods, with the electric field application producing the highest degree of fiber alignment. The embedded MSCs showed a longitudinal orientation in the electric field-aligned plasma clots. The key feature of this study is the ability to produce autologous plasma clots with aligned fibrin fibers using physical techniques. This orientated internal structure of an autologous biomaterial is promising for distinct therapeutic applications, such as a guiding structure for cell migration and growth dynamics.

  5. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting...... this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...... class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. CONCLUSION: The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http...

  6. 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.

  7. Identifying communities from multiplex biological networks

    Directory of Open Access Journals (Sweden)

    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.

  8. Biodiesel and Integrated STEM: Vertical Alignment of High School Biology/Biochemistry and Chemistry

    Science.gov (United States)

    Burrows, Andrea C.; Breiner, Jonathan M.; Keiner, Jennifer; Behm, Chris

    2014-01-01

    This article explores the vertical alignment of two high school classes, biology and chemistry, around the core concept of biodiesel fuel production. High school teachers and university faculty members investigated biodiesel as it relates to societal impact through a National Science Foundation Research Experience for Teachers. Using an action…

  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. Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach.

    Science.gov (United States)

    Paskiabi, Farnoush Asghari; Mirzaei, Esmaeil; Amani, Amir; Shokrgozar, Mohammad Ali; Saber, Reza; Faridi-Majidi, Reza

    2015-11-01

    This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers.

  11. 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.

  12. 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 pro...... to high-throughput experimental methods....... 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...

  13. Assessing business-IT alignment in networked organizations

    NARCIS (Netherlands)

    Santana Tapia, Roberto Guadalupe

    2009-01-01

    Concerns such as identifying ways to control costs, improve quality, increase effectiveness, and manage risk have become increasingly important for organizations as they face more and more pressure to gain and maintain their competitive edge. Business-IT alignment (B-ITa) is recognized as a solution

  14. 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.

  15. Business-IT alignment domains and principles for networked organizations: A qualitative multiple case study

    NARCIS (Netherlands)

    Santana Tapia, R.G.; Daneva, M.; Eck, van P.A.T.; Castro Cárdenas, N.; Oene, van L.

    2008-01-01

    Applying principles for business-IT alignment in networked organizations seems to be key for their survival in competitive environments. In this paper, we present a qualitative multiple case study conducted in three collaborative networked organizations: (i) an outsourcing relation between an intern

  16. 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.

  17. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  18. 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.

  19. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

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

  20. 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...

  1. 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.

  2. Assessing cognitive alignment in different types of dialog by means of a network model.

    Science.gov (United States)

    Mehler, Alexander; Lücking, Andy; Menke, Peter

    2012-08-01

    We present a network model of dialog lexica, called TiTAN (Two-layer Time-Aligned Network) series. TiTAN series capture the formation and structure of dialog lexica in terms of serialized graph representations. The dynamic update of TiTAN series is driven by the dialog-inherent timing of turn-taking. The model provides a link between neural, connectionist underpinnings of dialog lexica on the one hand and observable symbolic behavior on the other. On the neural side, priming and spreading activation are modeled in terms of TiTAN networking. On the symbolic side, TiTAN series account for cognitive alignment in terms of the structural coupling of the linguistic representations of dialog partners. This structural stance allows us to apply TiTAN in machine learning of data of dialogical alignment. In previous studies, it has been shown that aligned dialogs can be distinguished from non-aligned ones by means of TiTAN -based modeling. Now, we simultaneously apply this model to two types of dialog: task-oriented, experimentally controlled dialogs on the one hand and more spontaneous, direction giving dialogs on the other. We ask whether it is possible to separate aligned dialogs from non-aligned ones in a type-crossing way. Starting from a recent experiment (Mehler, Lücking, & Menke, 2011a), we show that such a type-crossing classification is indeed possible. This hints at a structural fingerprint left by alignment in networks of linguistic items that are routinely co-activated during conversation.

  3. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...... the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped...

  4. Impact of carbon nanotube length on electron transport in aligned carbon nanotube networks

    Science.gov (United States)

    Lee, Jeonyoon; Stein, Itai Y.; Devoe, Mackenzie E.; Lewis, Diana J.; Lachman, Noa; Kessler, Seth S.; Buschhorn, Samuel T.; Wardle, Brian L.

    2015-02-01

    Here, we quantify the electron transport properties of aligned carbon nanotube (CNT) networks as a function of the CNT length, where the electrical conductivities may be tuned by up to 10× with anisotropies exceeding 40%. Testing at elevated temperatures demonstrates that the aligned CNT networks have a negative temperature coefficient of resistance, and application of the fluctuation induced tunneling model leads to an activation energy of ≈14 meV for electron tunneling at the CNT-CNT junctions. Since the tunneling activation energy is shown to be independent of both CNT length and orientation, the variation in electron transport is attributed to the number of CNT-CNT junctions an electron must tunnel through during its percolated path, which is proportional to the morphology of the aligned CNT network.

  5. 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...

  6. 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...

  7. 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.

  8. MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks.

    Directory of Open Access Journals (Sweden)

    Konstantin Popov

    2016-04-01

    Full Text Available Active matter systems, and in particular the cell cytoskeleton, exhibit complex mechanochemical dynamics that are still not well understood. While prior computational models of cytoskeletal dynamics have lead to many conceptual insights, an important niche still needs to be filled with a high-resolution structural modeling framework, which includes a minimally-complete set of cytoskeletal chemistries, stochastically treats reaction and diffusion processes in three spatial dimensions, accurately and efficiently describes mechanical deformations of the filamentous network under stresses generated by molecular motors, and deeply couples mechanics and chemistry at high spatial resolution. To address this need, we propose a novel reactive coarse-grained force field, as well as a publicly available software package, named the Mechanochemical Dynamics of Active Networks (MEDYAN, for simulating active network evolution and dynamics (available at www.medyan.org. This model can be used to study the non-linear, far from equilibrium processes in active matter systems, in particular, comprised of interacting semi-flexible polymers embedded in a solution with complex reaction-diffusion processes. In this work, we applied MEDYAN to investigate a contractile actomyosin network consisting of actin filaments, alpha-actinin cross-linking proteins, and non-muscle myosin IIA mini-filaments. We found that these systems undergo a switch-like transition in simulations from a random network to ordered, bundled structures when cross-linker concentration is increased above a threshold value, inducing contraction driven by myosin II mini-filaments. Our simulations also show how myosin II mini-filaments, in tandem with cross-linkers, can produce a range of actin filament polarity distributions and alignment, which is crucially dependent on the rate of actin filament turnover and the actin filament's resulting super-diffusive behavior in the actomyosin-cross-linker system

  9. 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.

  10. 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.

  11. 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.

  12. Joint Optimization of Power Splitting and Allocation for SWIPT in Interference Alignment Networks

    OpenAIRE

    Zhao, Nan

    2017-01-01

    Interference alignment (IA) is a promising solution for interference management in wireless networks. On the other hand, simultaneous wireless information and power transfer (SWIPT) has become an emerging technique. Although some works have been done on IA and SWIPT, these two important areas have traditionally been addressed separately in the literature. In this paper, we propose to use a common framework to jointly study IA and SWIPT. We analyze the performance of SWIPT in IA networks. Spec...

  13. 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.

  14. 复杂网络结构比对算法研究进展%Advances in algorithms for construction alignment of complex networks research

    Institute of Scientific and Technical Information of China (English)

    刘富; 姜奕含; 邹青宇

    2015-01-01

    The construction alignment of complex networks problems in biological science、computer science、social science and other fields have practical signification.In recent years, different types of construction alignment of complex networks have been sprung up.In this paper, we mainly analysed the construction alignment algorithms based on graph and construction alignment algorithms and mathematical framework.Illustrating the key problem in the study of the networks alignment algorithm is analyzed and compared the algorithms of construction alignment. We explained their advantages and disadvantages,at last we forecast the future progress of algorithms for construc-tion alignment of complex networks.%复杂网络的结构比对问题在生物科学、计算机科学和社会科学等多个领域都具有很重要的现实意义。近年来涌现出了很多针对不同类型复杂网络的结构对比算法,对现有的网络结构比对算法进行梳理,重点分析了基于图的网络结构比对方法和基于数学框架网络结构比对方法。对这2种方法的特点进行了总结与比较,重点阐述了网络结构比对研究中的关键问题,分析和总结了现有的网络结构比对算法,阐述了网络结构比对中优势和不足。以此为基础提出了复杂网络结构比对问题未来的研究方向。

  15. Alignment and integration of complex networks by hypergraph-based spectral clustering

    CERN Document Server

    Michoel, Tom

    2012-01-01

    Complex networks possess a rich, multi-scale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address such problems. Here we introduce a framework for multi-network analysis based on hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for tripartite community detection in folksonomies, for local and global alignment of protein-protein interaction networks between multiple species and for detecting clusters of overlapping regulatory pathways in directed networks.

  16. Direct induction of molecular alignment in liquid crystal polymer network film by photopolymerization

    Science.gov (United States)

    Hisano, K.; Aizawa, M.; Ishizu, M.; Kurata, Y.; Shishido, A.

    2016-09-01

    Liquid crystal (LC) is the promising material for the fabrication of high-performance soft, flexible devices. The fascinating and useful properties arise from their cooperative effect that inherently allows the macroscopic integration and control of molecular alignment through various external stimuli. To date, light-matter interaction is the most attractive stimuli and researchers developed photoalignment through photochemical or photophysical reactions triggered by linearly polarized light. Here we show the new choice based on molecular diffusion by photopolymerization. We found that photopolymerization of a LC monomer and a crosslinker through a photomask enables to direct molecular alignment in the resultant LC polymer network film. The key generating the molecular alignment is molecular diffusion due to the difference of chemical potentials between irradiated and unirradiated regions. This concept is applicable to various shapes of photomask and two-dimensional molecular alignments can be fabricated depending on the spatial design of photomask. By virtue of the inherent versatility of molecular diffusion in materials, the process would shed light on the fabrication of various high-performance flexible materials with molecular alignment having controlled patterns.

  17. An Active Pre-Alignment System and Metrology Network for CLIC

    CERN Document Server

    Becker, F; Pittin, R; Wilson, Ian H

    2003-01-01

    The pre-alignment tolerance on the transverse positions of the components of the CLIC linacs is typically ten microns over distances of 200 m. Such tight tolerances cannot be obtained by a static one-time alignment because normal seismic ground movement and cultural noise associated with human and industrial activity quickly creates significant errors. It is therefore foreseen to maintain the components in place using an active-alignment system which will be linked to a permanent metrology and geodetic network. This report describes the overall philosophy and implementation of such a system and proposes one possible solution for active-alignment which uses stepping-motors to move components and stretched-wires as reference lines. Special sensors have been developed to measure the position of the components with respect to the reference lines, and to measure local tilt and relative vertical position. An in-depth analysis has been made of the repercussions on the alignment system of perturbing effects due to th...

  18. 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.

  19. On the Feasibility of Precoding-Based Network Alignment for Three Unicast Sessions

    CERN Document Server

    Meng, Chun; Markopoulou, Athina; Jafar, Syed Ali

    2012-01-01

    We consider the problem of network coding across three unicast sessions over a directed acyclic graph, when each session has min-cut one. Previous work by Das et al. adapted a precoding-based interference alignment technique, originally developed for the wireless interference channel, specifically to this problem. We refer to this approach as precoding-based network alignment (PBNA). Similar to the wireless setting, PBNA asymptotically achieves half the minimum cut; different from the wireless setting, its feasibility depends on the graph structure. Das et al. provided a set of feasibility conditions for PBNA with respect to a particular precoding matrix. However, the set consisted of an infinite number of conditions, which is impossible to check in practice. Furthermore, the conditions were purely algebraic, without interpretation with regards to the graph structure. In this paper, we first prove that the set of conditions provided by Das. et al are also necessary for the feasibility of PBNA with respect to ...

  20. Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment

    NARCIS (Netherlands)

    El-Kebir, M.; Heringa, J.; Klau, G.W.

    2015-01-01

    Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological netw

  1. 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.

  2. A simulation model for aligning smart home networks and deploying smart objects

    DEFF Research Database (Denmark)

    Lynggaard, Per

    introduces a unique concept which saves battery energy and lowers the interference level by simulating the network alignment and assign the necessary amount of transmit power to each individual network node and finally, deploy the smart objects. The needed transmit powers are calculated by the presented...... and discussed simulation model which offers the packet error rate as a function of transmit power, wall losses, path losses, interference source power, and white Gaussian noise power. The deployed smart objects process the user events on location and thereby minimize the costly wireless handling of these......Smart homes use sensor based networks to capture activities and offer learned services to the user. These smart home networks are challenging because they mainly use wireless communication at frequencies that are shared with other services and equipments. One of the major challenges...

  3. Coverage probability of cellular networks using interference alignment under imperfect CSI

    Directory of Open Access Journals (Sweden)

    Raoul F. Guiazon

    2016-11-01

    Full Text Available Interference alignment (IA is well understood to approach the capacity of interference channels, and believed to be crucial in cellular networks in which the ability to control and exploit interference is key. However, the achievable performance of IA in cellular networks depends on the quality of channel state information (CSI and how effective IA is in practical settings is not known. This paper studies the use of IA to mitigate inter-cell interference of cellular networks under imperfect CSI conditions. Our analysis is based on stochastic geometry where the structure of the base station (BS locations is considered by a Poisson point process (PPP. Our main contribution is the coverage probability of the network and simulation results confirm the accuracy.

  4. Fast Switching of Vertical Alignment Liquid Crystal Cells with Liquid Crystalline Polymer Networks

    Science.gov (United States)

    Baek, Jong-In; Kim, Ki-Han; Kim, Jae Chang; Yoon, Tae-Hoon; Woo, Hwa Sung; Shin, Sung Tae; Souk, Jun Hyung

    2009-05-01

    This paper reports on the electro-optic characteristics of vertical alignment (VA) liquid crystal (LC) cells with liquid crystalline polymer networks. Optical bouncing, that occurs during the turn-on of VA cells, can be eliminated by introducing in-cell polymer networks. Furthermore, the turn-off also becomes much faster because of the anchoring effect caused by the anisotropy in the molecular shape of the liquid crystalline polymers. These response times have been found to vary for different LC/prepolymer mixtures. When the concentration of the liquid crystalline prepolymer in the initial LC/prepolymer mixture was 3, 5, or 10 wt %, the response times were measured to be 34, 56, and 87% faster than those of a VA cell with pure LC. These switching behaviors of VA cells with liquid crystalline polymer networks are demonstrated and compared with those using pure LC and with polymer networks made of isotropic prepolymers.

  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. 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...

  7. 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.

  8. 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.

  9. GeneWeaver: data driven alignment of cross-species genomics in biology and disease.

    Science.gov (United States)

    Baker, Erich; Bubier, Jason A; Reynolds, Timothy; Langston, Michael A; Chesler, Elissa J

    2016-01-04

    The GeneWeaver data and analytics website (www.geneweaver.org) is a publically available resource for storing, curating and analyzing sets of genes from heterogeneous data sources. The system enables discovery of relationships among genes, variants, traits, drugs, environments, anatomical structures and diseases implicitly found through gene set intersections. Since the previous review in the 2012 Nucleic Acids Research Database issue, GeneWeaver's underlying analytics platform has been enhanced, its number and variety of publically available gene set data sources has been increased, and its advanced search mechanisms have been expanded. In addition, its interface has been redesigned to take advantage of flexible web services, programmatic data access, and a refined data model for handling gene network data in addition to its original emphasis on gene set data. By enumerating the common and distinct biological molecules associated with all subsets of curated or user submitted groups of gene sets and gene networks, GeneWeaver empowers users with the ability to construct data driven descriptions of shared and unique biological processes, diseases and traits within and across species.

  10. Alternate MIMO AF relaying networks with interference alignment: Spectral efficient protocol and linear filter design

    KAUST Repository

    Park, Kihong

    2013-02-01

    In this paper, we study a two-hop relaying network consisting of one source, one destination, and three amplify-and-forward (AF) relays with multiple antennas. To compensate for the capacity prelog factor loss of 1/2$ due to the half-duplex relaying, alternate transmission is performed among three relays, and the inter-relay interference due to the alternate relaying is aligned to make additional degrees of freedom. In addition, suboptimal linear filter designs at the nodes are proposed to maximize the achievable sum rate for different fading scenarios when the destination utilizes a minimum mean-square error filter. © 1967-2012 IEEE.

  11. Registration algorithm for sensor alignment based on stochastic fuzzy neural network

    Institute of Scientific and Technical Information of China (English)

    Li Jiao; Jing Zhongliang; He Jiaona; Wang An

    2005-01-01

    Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.

  12. Evidence that prefibrotic myelofibrosis is aligned along a clinical and biological continuum featuring primary myelofibrosis.

    Directory of Open Access Journals (Sweden)

    Giovanni Barosi

    Full Text Available PURPOSE: In the WHO diagnostic classification, prefibrotic myelofibrosis (pre-MF is included in the category of primary myelofibrosis (PMF. However, strong evidence for this position is lacking. PATIENTS AND METHODS: We investigated whether pre-MF may be aligned along a clinical and biological continuum in 683 consecutive patients who received a WHO diagnosis of PMF. RESULTS: As compared with PMF-fibrotic type, pre-MF (132 cases showed female dominance, younger age, higher hemoglobin, higher platelet count, lower white blood cell count, smaller spleen index and higher incidence of splanchnic vein thrombosis. Female to male ratio and hemoglobin steadily decreased, while age increased from pre-MF to PMF- fibrotic type with early and to advanced bone marrow (BM fibrosis. Likely, circulating CD34+ cells, LDH levels, and frequency of chromosomal abnormalities increased, while CXCR4 expression on CD34+ cells and serum cholesterol decreased along the continuum of BM fibrosis. Median survival of the entire cohort of PMF cases was 21 years. Ninety-eight, eighty-one and fifty-six percent of patients with pre-MF, PMF-fibrotic type with early and with advanced BM fibrosis, respectively, were alive at 10 years from diagnosis. CONCLUSION: Pre-MF is a presentation mode of PMF with a very indolent phenotype. The major consequences of this contention is a new clinical vision of PMF, and the need to improve prognosis prediction of the disease.

  13. 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.

  14. 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.

  15. Exploring the Alignment of the Intended and Implemented Curriculum through Teachers' Interpretation: A Case Study of A-Level Biology Practical Work

    Science.gov (United States)

    Phaeton, Mukaro Joe; Stears, Michèle

    2017-01-01

    The research reported on here is part of a larger study exploring the alignment of the intended, implemented and attained curriculum with regard to practical work in the Zimbabwean A-level Biology curriculum. In this paper we focus on the alignment between the intended and implemented A-Level Biology curriculum through the lens of teachers'…

  16. Input/Output Scalability of Genomic Alignment: How to Configure a Computational Biology Cluster

    Energy Technology Data Exchange (ETDEWEB)

    Vaidyanathan, P; Madhyastha, T M; Jones, T

    2001-10-03

    Many scientific applications are I/O-intensive, which makes optimization and scaling difficult, especially on parallel architectures. The I/O requirements of computational biology applications are different from other scientific applications. The main difference is that many computational biology applications are embarrassingly parallel and require repeated read-only access to a large global database. In this paper we examine the scalability of an embarrassingly parallel computational biology application: psLayout, which played a crucial role in the mapping of the human genome. This study was carried out on three architecture: the native UCSC Linux cluster, a Linux cluster at Lawrence Livermore National Labs with a faster interconnect and NFS server, and the ASCI Blue-Pacific supercomputer. We show that a cluster equipped with a fast network and parallel file system or a scalable NFS server has reasonable I/O scalability. We believe that replication is an important issue when scaling to larger numbers of processors, and we introduce the design of a library for automatic data replication to address this issue.

  17. 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.

  18. 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.

  19. 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.

  20. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

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

  1. 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).

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

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

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

  3. 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

  4. 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.

  5. CTSS: a robust and efficient method for protein structure alignment based on local geometrical and biological features.

    Science.gov (United States)

    Can, Tolga; Wang, Yuan-Fang

    2003-01-01

    We present a new method for conducting protein structure similarity searches, which improves on the accuracy, robustness, and efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. To improve matching accuracy, we smooth the noisy raw atomic coordinate data with spline fitting. To improve matching efficiency, we adopt a hierarchical coarse-to-fine strategy. We use an efficient hashing-based technique to screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to discover new, meaningful motifs that were not reported by other structure alignment methods.

  6. 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.

  7. 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...

  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. USING ANALYTIC NETWORK FOR SELECTION OF ENTERPRISE RESOURCE PLANNING (ERP ALIGNED TO BUSINESS STRATEGY

    Directory of Open Access Journals (Sweden)

    Alberto de Medeiros Junior

    2014-10-01

    Full Text Available The choice of an Enterprise Resource Planning (ERP must be made judiciously by the high costs involved in the acquisition of such systems. Managers in areas such as accounting, financial and information technology need support and tools that help in selecting an appropriate ERP for their business. With this article, we present a study aimed at investigating the possibility of a Decision Support System (DSS to be used for this selection interrelating evaluation criteria, which could allow to contemplate the strategic alignment between Business and Information Technology. From the literature review 28 factors related to the selection of software packages, with special emphasis on ERP were identified. For the research procedures, the qualitative ones were adopted, in that the 18 factors considered relevant to a good selection of ERP were classified with the Delphi technique and used as input in a DSS: Analytical Network Process (ANP, applied as a Case Study in a small business that hired the ERP. The results showed that the ANP was efficient in interrelated criteria and evaluated the strategic alignment between Business and Information Technology.

  10. A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

    Science.gov (United States)

    Li, Shibao; He, Chang; Wang, Yixin; Zhang, Yang; Liu, Jianhang; Huang, Tingpei

    2017-01-01

    It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput. PMID:28287434

  11. 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.

  12. Automata networks model for alignment and least effort on vocabulary formation

    CERN Document Server

    Vera, Javier; Goles, Eric

    2015-01-01

    Can artificial communities of agents develop language with scaling relations close to the Zipf law? As a preliminary answer to this question, we propose an Automata Networks model of the formation of a vocabulary on a population of individuals, under two in principle opposite strategies: the alignment and the least effort principle. Within the previous account to the emergence of linguistic conventions (specially, the Naming Game), we focus on modeling speaker and hearer efforts as actions over their vocabularies and we study the impact of these actions on the formation of a shared language. The numerical simulations are essentially based on an energy function, that measures the amount of local agreement between the vocabularies. The results suggests that on one dimensional lattices the best strategy to the formation of shared languages is the one that minimizes the efforts of speakers on communicative tasks.

  13. 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

  14. 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.

  15. 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.

  16. Directional Freezing of Nanocellulose Dispersions Aligns the Rod-Like Particles and Produces Low-Density and Robust Particle Networks.

    Science.gov (United States)

    Munier, Pierre; Gordeyeva, Korneliya; Bergström, Lennart; Fall, Andreas B

    2016-05-01

    We show that unidirectional freezing of nanocellulose dispersions produces cellular foams with high alignment of the rod-like nanoparticles in the freezing direction. Quantification of the alignment in the long direction of the tubular pores with X-ray diffraction shows high orientation of cellulose nanofibrils (CNF) and cellulose nanocrystals (CNC) at particle concentrations above 0.2 wt % (CNC) and 0.08 wt % (CNF). Aggregation of CNF by pH decrease or addition of salt significantly reduces the particle orientation; in contrast, exceeding the concentration where particles gel by mobility constraints had a relatively small effect on the orientation. The dense nanocellulose network formed by directional freezing was sufficiently strong to resist melting. The formed hydrogels were birefringent and displayed anisotropic laser diffraction patterns, suggesting preserved nanocellulose alignment and cellular structure. Nondirectional freezing of the hydrogels followed by sublimation generates foams with a pore structure and nanocellulose alignment resembling the structure of the initial directional freezing.

  17. 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.

  18. The Structure and Function of Biological Networks

    Science.gov (United States)

    Wu, Daniel Duanqing

    2010-01-01

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

  19. Integrated crystal mounting and alignment system for high-throughput biological crystallography

    Energy Technology Data Exchange (ETDEWEB)

    Nordmeyer, Robert A.; Snell, Gyorgy P.; Cornell, Earl W.; Kolbe, William; Yegian, Derek; Earnest, Thomas N.; Jaklevic, Joseph M.; Cork, Carl W.; Santarsiero, Bernard D.; Stevens, Raymond C.

    2005-07-19

    A method and apparatus for the transportation, remote and unattended mounting, and visual alignment and monitoring of protein crystals for synchrotron generated x-ray diffraction analysis. The protein samples are maintained at liquid nitrogen temperatures at all times: during shipment, before mounting, mounting, alignment, data acquisition and following removal. The samples must additionally be stably aligned to within a few microns at a point in space. The ability to accurately perform these tasks remotely and automatically leads to a significant increase in sample throughput and reliability for high-volume protein characterization efforts. Since the protein samples are placed in a shipping-compatible layered stack of sample cassettes each holding many samples, a large number of samples can be shipped in a single cryogenic shipping container.

  20. Integrated crystal mounting and alignment system for high-throughput biological crystallography

    Energy Technology Data Exchange (ETDEWEB)

    Nordmeyer, Robert A. (San Leandro, CA); Snell, Gyorgy P. (Richmond, CA); Cornell, Earl W. (Antioch, CA); Kolbe, William F. (Moraga, CA); Yegian, Derek T. (Oakland, CA); Earnest, Thomas N. (Berkeley, CA); Jaklevich, Joseph M. (Lafayette, CA); Cork, Carl W. (Walnut Creek, CA); Santarsiero, Bernard D. (Chicago, IL); Stevens, Raymond C. (La Jolla, CA)

    2007-09-25

    A method and apparatus for the transportation, remote and unattended mounting, and visual alignment and monitoring of protein crystals for synchrotron generated x-ray diffraction analysis. The protein samples are maintained at liquid nitrogen temperatures at all times: during shipment, before mounting, mounting, alignment, data acquisition and following removal. The samples must additionally be stably aligned to within a few microns at a point in space. The ability to accurately perform these tasks remotely and automatically leads to a significant increase in sample throughput and reliability for high-volume protein characterization efforts. Since the protein samples are placed in a shipping-compatible layered stack of sample cassettes each holding many samples, a large number of samples can be shipped in a single cryogenic shipping container.

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

    Science.gov (United States)

    Motter, Adilson

    2013-03-01

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

  2. 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.

  3. Anisotropic quantum transport in a network of vertically aligned graphene sheets.

    Science.gov (United States)

    Huang, J; Guo, L-W; Li, Z-L; Chen, L-L; Lin, J-J; Jia, Y-P; Lu, W; Guo, Y; Chen, X-L

    2014-08-27

    Novel anisotropic quantum transport was observed in a network of vertically aligned graphene sheets (VAGSs), which can be regarded as composed of plenty of quasi-parallel, nearly intrinsic, freestanding monolayers of graphene. When a magnetic field was perpendicular to most graphene sheets, magnetoresistance (MR) curves showed a weak localization (WL) effect at low field and a maximum value at a critical field ascribed to diffusive boundary scattering. While the magnetic field was parallel to the graphene sheets, the MR maximum disappeared and exhibited a transition from WL to weak antilocalization (WAL) with increasing temperature and magnetic field. Edges as atomically sharp defects are the main elastic and inelastic intervalley scattering sources, and inelastic scattering is ascribed to electron-electron intervalley scattering in the ballistic regime. This is the first time simultaneously observing WL, WAL and diffusive boundary scattering in such a macroscopic three-dimensional graphene system. These indicate the VAGS network is a robust platform for the study of the intrinsic physical properties of graphene.

  4. On the Achievability of Interference Alignment for Three-Cell Multi-User MIMO Cellular Networks

    CERN Document Server

    Ma, Yanjun; Chen, Rui; Liu, Qin

    2011-01-01

    An interference alignment (IA) based scheme is proposed by joint design of transmit precoding matrices and receive beamforming matrices for the three-cell Multi-User MIMO (MU-MIMO) cellular network with constant channel between each node, where each base station (BS) is equipped with $M$ antennas, each mobile station (MS) is equipped with $N$ antennas, and $M \\geq N$. We assume $K$ cell-edge users are served per cell simultaneously where $K>1$, and BS sends $d$ data streams to each of its users. It is shown that $\\frac{M}{2}$ degrees of freedom (DoF) is achievable per cell when $M=N$, $K=\\frac{M}{2d}$, and $M$ is divisible by 2d, and $Kd$ DoF is achievable per cell if $M =KN$ and $d \\leq \\lfloor \\frac{M}{3K-1} \\rfloor$ or if $(3K -1)d \\leq M < KN $ and $d \\leq 3(KN-M)$. Furthermore, when there have extra dimensions at BSs, a space-division hybrid (SDH) scheme is proposed for the three-cell MU-MIMO cellular network, where cell-edge users and cell-center users are served simultaneously. Cell-edge users work ...

  5. 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.

  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 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.

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

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    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. MS4 - Multi-Scale Selector of Sequence Signatures: An alignment-free method for classification of biological sequences

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    Grasseau Gilles

    2010-07-01

    Full Text Available Abstract Background While multiple alignment is the first step of usual classification schemes for biological sequences, alignment-free methods are being increasingly used as alternatives when multiple alignments fail. Subword-based combinatorial methods are popular for their low algorithmic complexity (suffix trees ... or exhaustivity (motif search, in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the N-local decoding based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some "good" values of N, sufficiently relevant to form the basis of a reliable alignment-free classification. The aim of this paper is to develop a method that uses the similarities detected by N-local decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool. Results Among the equivalence classes produced by the N-local decodings for all N, we select a (relatively small number of "relevant" classes corresponding to variable length subwords that carry enough information to perform the classification. The parameter N, for which correct values are data-dependent and thus hard to guess, is here replaced by the average repetitivity κ of the sequences. We show that our approach yields classifications of several sets of HIV/SIV sequences that agree with the accepted taxonomy, even on usually discarded repetitive regions (like the non-coding part of LTR. Conclusions The method MS4 satisfactorily classifies a set of sequences that are notoriously hard to align. This suggests that our approach forms the basis of a reliable alignment-free classification tool. The only parameter κ of MS4 seems to give reasonable results even for its default value, which can be a great advantage for

  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. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

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    David Cárdenas-Peña

    2016-01-01

    Full Text Available Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD or mild cognitive impairment (MCI and healthy controls (NC. Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve at the time it reduces the class biasing.

  13. 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.

  14. 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.

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

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    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.

  16. 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.

  17. 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.

  18. 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.

  19. Prediction and testing of biological networks underlying intestinal cancer.

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    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.

  20. Uncovering Biological Network Function via Graphlet Degree Signatures

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    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.

  1. 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.

  2. Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST

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    Oliver Melvin J

    2005-04-01

    Full Text Available Abstract Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST, which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN. W.ND-BLAST provides intuitive Graphic User Interfaces (GUI for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is

  3. Biologically Inspired Optimization of Building District Heating Networks

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    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.

  4. 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

  5. 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

  6. Legal, medical and lay understanding of embryos in Portugal: alignment with biology?

    Directory of Open Access Journals (Sweden)

    Susana Silva

    Full Text Available This article provides a contribution to the debate about the processes of circulation of knowledge and meanings among experts and laymen regarding the status of human embryos in Portugal. It weighs up the expectations and concerns regarding the reliability, quality, safety and efficacy of medically assisted reproductive technologies. Individual interviews, aimed at exploring the complexities, similarities and differences among the views and values of jurists, doctors and couples involved in in vitro fertilization treatments, were the data source. This is a qualitative analysis on a case study. While jurists and doctors frame embryo status in biological, technical and/or legal terms, couples establish ontological relationships of moral, affective and social nature with embryos, which turns them into ethical beings, thus contrasting with the medical-legal biologization of the embryos.

  7. Curriculum alignment and higher order thinking in introductory biology in Arkansas public two-year colleges

    Science.gov (United States)

    Crandall, Elizabeth Diane

    This dissertation identified the cognitive levels of lecture objectives, lab objectives, and test questions in introductory majors' biology. The study group included courses offered by 27 faculty members at 18 of the 22 community colleges in Arkansas. Using Bloom's Taxonomy to identify cognitive levels, the median lecture learning outcomes were at level 2 (Comprehension) and test assessments at Level 1 (Knowledge). Lab learning outcomes were determined to have a median of level 3 (Analysis). A correlation analysis was performed using SPSS software to determine if there was an association between the Bloom's level of lecture objectives and test assessments. The only significant difference found was at the Analysis level, or Bloom's level 4 (p=.043). Correlation analyses were run for two other data sets. Years of college teaching experience and hours of training in writing objectives and assessments were compared to the Bloom's Taxonomy level of lecture objectives and test items. No significant difference was found for either of these independent variables. This dissertation provides Arkansas two-year college biology faculty with baseline information about the levels of cognitive skills that are required in freshman biology for majors courses. It can serve to initiate conversations about where we are compared to a national study, where we need to be, and how we get there.

  8. 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

  9. 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.

  10. 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.

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

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    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.

  12. 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.

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

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    Atsushi eFukushima

    2014-11-01

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

  14. 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...

  15. 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?

  16. 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.

  17. 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.

  18. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    2012-01-01

    With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological...... environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases...... and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical...

  19. 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.

  20. Balancing creativity and time efficiency in multi-team R&D projects: The alignment of formal and informal networks

    DEFF Research Database (Denmark)

    Kratzer, Jan; Gemuenden, Hans Georg; Lettl, Christopher

    2008-01-01

    and their effect on the challenge to balance project creativity and time efficiency. In order to analyse this issue data in two multi-team R&D projects in space industry are collected. There are two intriguing findings that are partly contradicting the state-of-the art knowledge. First, formally ascribed design......The business world is denoted by an increasing number of multi-team R&D projects, however, managerial knowledge about how to run them successfully is scarce. The present study attempts to shed light at this kind of projects by investigating the alignment of formal and informal network structures...

  1. 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

  2. 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.

  3. Eigen-Direction Alignment Based Physical-Layer Network Coding for MIMO Two-Way Relay Channels

    CERN Document Server

    Yang, Tao; Ping, Li; Collings, Iain B; Yuan, Jinhong

    2012-01-01

    In this paper, we propose a novel communication strategy which incorporates physical-layer network coding (PNC) into multiple-input multiple output (MIMO) two-way relay channels (TWRCs). At the heart of the proposed scheme lies a new key technique referred to as eigen-direction alignment (EDA) precoding. The EDA precoding efficiently aligns the two-user's eigen-modes into the same directions. Based on that, we carry out multi-stream PNC over the aligned eigen-modes. We derive an achievable rate of the proposed EDA-PNC scheme, based on nested lattice codes, over a MIMO TWRC. Asymptotic analysis shows that the proposed EDA-PNC scheme approaches the capacity upper bound as the number of user antennas increases towards infinity. For a finite number of user antennas, we formulate the design criterion of the optimal EDA precoder and present solutions. Numerical results show that there is only a marginal gap between the achievable rate of the proposed EDA-PNC scheme and the capacity upper bound of the MIMO TWRC, in ...

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

    Science.gov (United States)

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

    2014-01-01

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

  5. 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.

  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. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. From Spectrum Pooling to Space Pooling: Opportunistic Interference Alignment in MIMO Cognitive Networks

    CERN Document Server

    Perlaza, S M; Lasaulce, S; Debbah, M

    2009-01-01

    We describe a non-cooperative interference alignment (IA) technique which allows an opportunistic multiple input multiple output (MIMO) link (secondary) to harmlessly coexist with another MIMO link (primary) in the same frequency band. We assume perfect channel knowledge in the primary receiver and transmitter such that capacity is achieved by transmiting along the spatial directions (SD) associated with the singular values of its channel matrix using a water-filling power allocation (PA) scheme. Often, power limitations lead the primary transmitter to leave some of its SD unused. We show that the opportunistic link can transmit its own data if it is possible to align the interference produced on the primary link with such unused SDs. We provide both a processing scheme to perform IA and a PA scheme which maximizes the transmission rate of the opportunistic link. We determine the asymptotes of the achievable transmission rates of the opportunistic link in the regime of large numbers of antennas to show that d...

  17. 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.

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

    Science.gov (United States)

    Dudziak, Martin Joseph

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

  19. Notes on a PDE system for biological network formation

    KAUST Repository

    Haskovec, Jan

    2016-01-22

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

  20. 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

  1. Simultaneous Wireless Information and Power Transfer Mechanism in Interference Alignment Relay Networks

    Directory of Open Access Journals (Sweden)

    Fahui Wu

    2016-01-01

    Full Text Available This paper considers a simultaneous wireless information and power transfer (SWIPT mechanism in an interference alignment (IA relay system, in which source nodes send wireless information and energy simultaneously to relay nodes, and relay nodes forward the received signal to destination nodes powered by harvested energy. To manage interference and utilize interference as energy source, two-SWIPT receiver is designed, namely, power splitting (PS, and antennas switching (AS has been considered for relay system. The performance of AS- and PS-based IA relay systems is considered, as is a new energy cooperation (ECop scheme that is proposed to improve system performance. Numerical results are provided to evaluate the performance of all schemes and it is shown from the simulations that the performance of proposed ECop outperformed both AS and PS.

  2. 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...

  3. 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.

  4. 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/.

  5. 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.

  6. Title: Using Alignment and 2D Network Simulations to Study Charge Transport Through Doped ZnO Nanowire Thin Film Electrodes

    KAUST Repository

    Phadke, Sujay

    2011-09-30

    Factors affecting charge transport through ZnO nanowire mat films were studied by aligning ZnO nanowires on substrates and coupling experimental measurements with 2D nanowire network simulations. Gallium doped ZnO nanowires were aligned on thermally oxidized silicon wafer by shearing a nanowire dispersion in ethanol. Sheet resistances of nanowire thin films that had current flowing parallel to nanowire alignment direction were compared to thin films that had current flowing perpendicular to nanowire alignment direction. Perpendicular devices showed ∼5 fold greater sheet resistance than parallel devices supporting the hypothesis that aligning nanowires would increase conductivity of ZnO nanowire electrodes. 2-D nanowire network simulations of thin films showed that the device sheet resistance was dominated by inter-wire contact resistance. For a given resistivity of ZnO nanowires, the thin film electrodes would have the lowest possible sheet resistance if the inter-wire contact resistance was one order of magnitude lower than the single nanowire resistance. Simulations suggest that the conductivity of such thin film devices could be further enhanced by using longer nanowires. Solution processed Gallium doped ZnO nanowires are aligned on substrates using an innovative shear coating technique. Nanowire alignment has shown improvement in ZnO nanowire transparent electrode conductivity. 2D network simulations in conjunction with electrical measurements have revealed different regimes of operation of nanowire thin films and provided a guideline for improving electrical performance of nanowire electrodes. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Global alignment, coordination and collaboration in perinatal research: the Global Obstetrics Network (GONet) Initiative.

    Science.gov (United States)

    Mol, Ben Willem; Ruifrok, Anneloes Elisabeth

    2013-03-01

    Large clinical studies provide information and insight that are used to develop clinical guidelines. In view of the large sample sizes needed, many researchers have initiated multicenter studies. In some situations, the activities of these groups have led to networks, through which multiple trials have been executed over a longer period of time. The Global Obstetrics Network (GONet) was formed to link the different types of networks. The GONet mission is "to provide a forum for international interaction and collaboration among groups that perform clinical trials and observational studies in maternal fetal medicine and obstetrics." The purpose is to foster communication between groups to improve ongoing and future trials. This will open new avenues for cooperation in the design and conduct of large international trials, in seeking funding, and in highlighting evidence. The expectation is that this will lead to better studies and more efficient use of resources and minimize duplication. Furthermore, the group will provide insight and camaraderie, cooperate on data elements to allow future collaborations, and identify and highlight the pressing issues in maternal-fetal medicine. Here we describe the GONet mission, its objectives, structure and function, current collaborators, and plans for the future.

  8. 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

  9. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, R.C.

    1991-11-01

    This thesis involved the construction of (1) a grammar that incorporates knowledge on base invariancy and secondary structure in a molecule and (2) a parser engine that uses the grammar to position bases into the structural subunits of the molecule. These concepts were combined with a novel pinning technique to form a tool that semi-automates insertion of a new species into the alignment for the 16S rRNA molecule (a component of the ribosome) maintained by Dr. Carl Woese`s group at the University of Illinois at Urbana. The tool was tested on species extracted from the alignment and on a group of entirely new species. The results were very encouraging, and the tool should be substantial aid to the curators of the 16S alignment. The construction of the grammar was itself automated, allowing application of the tool to alignments for other molecules. The logic programming language Prolog was used to construct all programs involved. The computational linguistics approach used here was found to be a useful way to attach the problem of insertion into an alignment.

  10. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, R.C.

    1991-11-01

    This thesis involved the construction of (1) a grammar that incorporates knowledge on base invariancy and secondary structure in a molecule and (2) a parser engine that uses the grammar to position bases into the structural subunits of the molecule. These concepts were combined with a novel pinning technique to form a tool that semi-automates insertion of a new species into the alignment for the 16S rRNA molecule (a component of the ribosome) maintained by Dr. Carl Woese's group at the University of Illinois at Urbana. The tool was tested on species extracted from the alignment and on a group of entirely new species. The results were very encouraging, and the tool should be substantial aid to the curators of the 16S alignment. The construction of the grammar was itself automated, allowing application of the tool to alignments for other molecules. The logic programming language Prolog was used to construct all programs involved. The computational linguistics approach used here was found to be a useful way to attach the problem of insertion into an alignment.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. ABS: Sequence alignment by scanning

    KAUST Repository

    Bonny, Mohamed Talal

    2011-08-01

    Sequence alignment is an essential tool in almost any computational biology research. It processes large database sequences and considered to be high consumers of computation time. Heuristic algorithms are used to get approximate but fast results. We introduce fast alignment algorithm, called Alignment By Scanning (ABS), to provide an approximate alignment of two DNA sequences. We compare our algorithm with the well-known alignment algorithms, the FASTA (which is heuristic) and the \\'Needleman-Wunsch\\' (which is optimal). The proposed algorithm achieves up to 76% enhancement in alignment score when it is compared with the FASTA Algorithm. The evaluations are conducted using different lengths of DNA sequences. © 2011 IEEE.

  20. SPEAR3 Construction Alignment

    Energy Technology Data Exchange (ETDEWEB)

    LeCocq, Catherine; Banuelos, Cristobal; Fuss, Brian; Gaudreault, Francis; Gaydosh, Michael; Griffin, Levirt; Imfeld, Hans; McDougal, John; Perry, Michael; Rogers,; /SLAC

    2005-08-17

    An ambitious seven month shutdown of the existing SPEAR2 synchrotron radiation facility was successfully completed in March 2004 when the first synchrotron light was observed in the new SPEAR3 ring, SPEAR3 completely replaced SPEAR2 with new components aligned on a new highly-flat concrete floor. Devices such as magnets and vacuum chambers had to be fiducialized and later aligned on girder rafts that were then placed into the ring over pre-aligned support plates. Key to the success of aligning this new ring was to ensure that the new beam orbit matched the old SPEAR2 orbit so that existing experimental beamlines would not have to be reoriented. In this presentation a pictorial summary of the Alignment Engineering Group's surveying tasks for the construction of the SPEAR3 ring is provided. Details on the networking and analysis of various surveys throughout the project can be found in the accompanying paper.

  1. 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.

  2. 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.

  3. 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

  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. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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...

  12. 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.

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

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    , with genomic modifications giving rise to differential protein dynamics, ultimately resulting in disease. The exact molecular signaling networks underlying specific disease phenotypes remain elusive, as the definition thereof requires extensive analysis of not only the genomic and proteomic landscapes within...

  14. 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...... of advanced cell factories for production of fuels, chemicals, food ingredients and pharmaceuticals. The yeast Saccharomyces cerevisiae represents an excellent model system; the density of biological information available on this organism allows it to serve as a eukaryotic model for studying human diseases....... 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...

  15. 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.

  16. 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.

  17. 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.

  18. Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

    Directory of Open Access Journals (Sweden)

    Christoph Nick

    2014-09-01

    Full Text Available The growth of cortical neurons on three dimensional structures of spatially defined (structured randomly oriented, as well as on vertically aligned, carbon nanotubes (CNT is studied. Cortical neurons are attracted towards both types of CNT nano-architectures. For both, neurons form clusters in close vicinity to the CNT structures whereupon the randomly oriented CNTs are more closely colonised than the CNT pillars. Neurons develop communication paths via neurites on both nanoarchitectures. These neuron cells attach preferentially on the CNT sidewalls of the vertically aligned CNT architecture instead than onto the tips of the individual CNT pillars.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  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...

  4. 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...

  5. Imaging analysis of collagen fiber networks in cusps of porcine aortic valves: effect of their local distribution and alignment on valve functionality.

    Science.gov (United States)

    Mega, Mor; Marom, Gil; Halevi, Rotem; Hamdan, Ashraf; Bluestein, Danny; Haj-Ali, Rami

    2016-01-01

    The cusps of native aortic valve (AV) are composed of collagen bundles embedded in soft tissue, creating a heterogenic tissue with asymmetric alignment in each cusp. This study compares native collagen fiber networks (CFNs) with a goal to better understand their influence on stress distribution and valve kinematics. Images of CFNs from five porcine tricuspid AVs are analyzed and fluid-structure interaction models are generated based on them. Although the valves had similar overall kinematics, the CFNs had distinctive influence on local mechanics. The regions with dilute CFN are more prone to damage since they are subjected to higher stress magnitudes.

  6. 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.

  7. 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.

  8. Balancing creativity and time efficiency in multi-team R&D projects : the alignment of formal and informal networks

    NARCIS (Netherlands)

    Kratzer, Jan; Gemuenden, Hans Georg; Lettl, Christopher

    2008-01-01

    The business world is denoted by an increasing number of multi-team research and development (R&D) projects, however, managerial knowledge about how to run them successfully is scarce. The present study attempts to shed light at this kind of projects by investigating the alignment of formal and info

  9. 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/.

  10. 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.

  11. 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/

  12. 面向OpenCL架构的大规模生物序列比对%Mega-base Biological Sequence Alignment Targeting OpenCL Architecture

    Institute of Scientific and Technical Information of China (English)

    陈钢; 韦刚; 李国波; 裴颂文; 吴百锋

    2012-01-01

    In order to improve the performance and efficiency of biological sequence alignment algorithm, we propose a portable mega-base sequence alignment approach and its optimization method based on heterogeneous parallel processing platform. Our approach enhances the parallelism by altering the computation flows and data dependencies of the original Smith-Waterman algorithm. Meanwhile, we change memory layout to use vector data type for the sake of improving the global memory bandwidth. Finally, we employ offset inserting with the purpose of optimizing the local memory bank conflict, making best usage of the local memory. Experimental results show that our proposed optimization methods can achieve almost 100 times speedup.%为提高生物序列比对算法的性能和效率,提出一种异构处理平台下可移植的大规模生物序列比对算法及其优化方法.通过改变原有Smith-Waterman算法的计算流程和数据依赖关系,增加序列比对的并行性;通过改变存储器布局后使用向量数据类型,提高全局存储器的带宽利用率;通过增加偏移量改变存储器模块的映射方式,避免模块访问冲突,提高局部存储器的使用效率.实验结果表明,优化后的生物序列比对性能提升了近100倍.

  13. 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%.

  14. 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.

  15. 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.

  16. 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.

  17. 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...

  18. 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.

  19. An appraisal of biological responses and network of environmental interactions in non-mining and mining impacted coastal waters

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, C.E.G.; Malik, A; Jineesh, V.K.; Fernandes, S.O.; Das, A; Pandey, S.S.; Kanolkar, G.; Sujith, P.P.; Velip, D.; Shaikh, S.; Helekar, S.; Gonsalves, M.J.B.D.; Nair, S.; LokaBharathi, P.A

    iron brought from the hinterlands. We hypothesize that there could be a shift in biological response along with changes in network of interactions between environmental and biological variables in these mining and non-mining impacted regions, lying 160...

  20. 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.

  1. 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.

  2. 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

  3. Alignment validation

    Energy Technology Data Exchange (ETDEWEB)

    ALICE; ATLAS; CMS; LHCb; Golling, Tobias

    2008-09-06

    The four experiments, ALICE, ATLAS, CMS and LHCb are currently under constructionat CERN. They will study the products of proton-proton collisions at the Large Hadron Collider. All experiments are equipped with sophisticated tracking systems, unprecedented in size and complexity. Full exploitation of both the inner detector andthe muon system requires an accurate alignment of all detector elements. Alignmentinformation is deduced from dedicated hardware alignment systems and the reconstruction of charged particles. However, the system is degenerate which means the data is insufficient to constrain all alignment degrees of freedom, so the techniques are prone to converging on wrong geometries. This deficiency necessitates validation and monitoring of the alignment. An exhaustive discussion of means to validate is subject to this document, including examples and plans from all four LHC experiments, as well as other high energy experiments.

  4. 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...

  5. 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...

  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. 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

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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...

  17. Beyond Alignment

    DEFF Research Database (Denmark)

    Beyond Alignment: Applying Systems Thinking to Architecting Enterprises is a comprehensive reader about how enterprises can apply systems thinking in their enterprise architecture practice, for business transformation and for strategic execution. The book's contributors find that systems thinking...... is a valuable way of thinking about the viable enterprise and how to architect it....

  18. 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.

  19. 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/

  20. Hardware Acceleration of Bioinformatics Sequence Alignment Applications

    NARCIS (Netherlands)

    Hasan, L.

    2011-01-01

    Biological sequence alignment is an important and challenging task in bioinformatics. Alignment may be defined as an arrangement of two or more DNA or protein sequences to highlight the regions of their similarity. Sequence alignment is used to infer the evolutionary relationship between a set of pr

  1. 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.

  2. 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.

  3. 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...

  4. Visual analysis of transcriptome data in the context of anatomical structures and biological networks

    Directory of Open Access Journals (Sweden)

    Astrid eJunker

    2012-11-01

    Full Text Available The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Progressive multiple sequence alignments from triplets

    Directory of Open Access Journals (Sweden)

    Stadler Peter F

    2007-07-01

    Full Text Available Abstract Background The quality of progressive sequence alignments strongly depends on the accuracy of the individual pairwise alignment steps since gaps that are introduced at one step cannot be removed at later aggregation steps. Adjacent insertions and deletions necessarily appear in arbitrary order in pairwise alignments and hence form an unavoidable source of errors. Research Here we present a modified variant of progressive sequence alignments that addresses both issues. Instead of pairwise alignments we use exact dynamic programming to align sequence or profile triples. This avoids a large fractions of the ambiguities arising in pairwise alignments. In the subsequent aggregation steps we follow the logic of the Neighbor-Net algorithm, which constructs a phylogenetic network by step-wisely replacing triples by pairs instead of combining pairs to singletons. To this end the three-way alignments are subdivided into two partial alignments, at which stage all-gap columns are naturally removed. This alleviates the "once a gap, always a gap" problem of progressive alignment procedures. Conclusion The three-way Neighbor-Net based alignment program aln3nn is shown to compare favorably on both protein sequences and nucleic acids sequences to other progressive alignment tools. In the latter case one easily can include scoring terms that consider secondary structure features. Overall, the quality of resulting alignments in general exceeds that of clustalw or other multiple alignments tools even though our software does not included heuristics for context dependent (mismatch scores.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. Strategy-aligned fuzzy approach for market segment evaluation and selection: a modular decision support system by dynamic network process (DNP)

    Science.gov (United States)

    Mohammadi Nasrabadi, Ali; Hosseinpour, Mohammad Hossein; Ebrahimnejad, Sadoullah

    2013-05-01

    In competitive markets, market segmentation is a critical point of business, and it can be used as a generic strategy. In each segment, strategies lead companies to their targets; thus, segment selection and the application of the appropriate strategies over time are very important to achieve successful business. This paper aims to model a strategy-aligned fuzzy approach to market segment evaluation and selection. A modular decision support system (DSS) is developed to select an optimum segment with its appropriate strategies. The suggested DSS has two main modules. The first one is SPACE matrix which indicates the risk of each segment. Also, it determines the long-term strategies. The second module finds the most preferred segment-strategies over time. Dynamic network process is applied to prioritize segment-strategies according to five competitive force factors. There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts. To clarify, an example is illustrated by a case study in Iran's coffee market. The results show that success possibility of segments could be different, and choosing the best ones could help companies to be sure in developing their business. Moreover, changing the priority of strategies over time indicates the importance of long-term planning. This fact has been supported by a case study on strategic priority difference in short- and long-term consideration.

  1. Micro/nanoscale self-aligned optical couplings of the self-organized lightwave network (SOLNET) formed by excitation lights from outside

    Science.gov (United States)

    Yoshimura, Tetsuzo; Nawata, Hideyuki

    2017-01-01

    The self-organized lightwave network (SOLNET) provides "optical solder," which enables self-aligned optical couplings between misaligned optical devices with different core sizes. We propose a low-cost SOLNET formation method, in which write beams are generated within optical devices by excitation lights from outside. Simulations based on the finite-difference time-domain method reveal that the two-photon processes enhance optical-solder capabilities. In couplings between 600-nm-wide waveguides opposed with 32-μm distance a wide lateral misalignment tolerance of 2 μm to maintain <1 dB loss at 650 nm in wavelength is obtained. The coupling loss at 1-μm lateral misalignment is 0.4 dB. In couplings between 3-μm-wide and 600-nm-wide waveguides, losses at 650 nm are 0.1 dB for no misalignments and 0.9 dB for 1-μm misalignment. These results suggest that SOLNETs provide optical solder with mode size converting functions.

  2. 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

  3. 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:…

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  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. 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.

  16. 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)

  17. 基于联邦网络的传递对准滤波补偿算法%Transfer Alignment Filtering Compensating Algorithm Based on Federal Neural Network

    Institute of Scientific and Technical Information of China (English)

    赵剡; 王纪南; 解春明

    2012-01-01

    针对空中环境各种干扰因素对空空导弹传递对准(TA)滤波的影响,提出一种基于联邦网络的补偿算法.将干扰误差考虑为量测输入纳入滤波系统,改进标准Kalman滤波结构.将补偿神经网络设计成联邦结构,两个子系统分别用于训练量测输入估计误差、输出层权值误差和隐层权值误差.推导了联邦网络的训练算法并对算法进行了稳定性证明,保证了网络在结构上计算量小,系统反馈能力强,能够对干扰误差进行有效在线预测,辅助改进Kalman滤波器对失准角进行精确估计.仿真比较实验验证了该算法能够在不需任何先验信息的条件下,及时适应对准环境,预测校正干扰误差,滤波收敛快、精度高,适合空空导弹在具体设备和环境条件下的快速精确传递对准.%Focusing on the influence of various disturbing sources under air environment on transfer alignment (TA), a compensating algorithm based on federal neural network was put forward. Firstly, standard Kalman filtering structure was improved by regarding disturbing errors as measurement input. Then, the neural network was designed to form federal structure with two subsystems, which were used to train measurement input estimating error, output layer weight error and hidden layer weight error. Further, the training algorithm of the federal neural network was deduced and its stability was proved, which ensured low computing load and strong feedback capability. The online disturbing errors were efficiently predicted and aided to modified Kalman filter for accurate estimation of misalignment. Results of simulation experiments validate that, without knowing any priori information, the proposed algorithm could timely adapt to TA environment, predict and rectify disturbing errors, achieve fast convergence and high accuracy. It is feasible for air-to-air missile to implement rapid and high accuracy TA under hard air environment with different apparatus.

  18. Modulating the forces between self-assembling molecules to control the shape of vesicles and the mechanics and alignment of nanofiber networks

    Science.gov (United States)

    Greenfield, Megan Ann

    One of the great challenges in supramolecular chemistry is the design of molecules that can self-assemble into functional aggregates with well-defined three-dimensional structures and bulk material properties. Since the self-assembly of nanostructures is greatly influenced by both the nature of the self-assembling components and the environmental conditions in which the components assemble, this work explores how changes in the molecular design and the environment affect the properties of self-assembled structures. We first explore how to control the mechanical properties of self-assembled fibrillar networks by changing environmental conditions. We report here on how changing pH, screening ions, and solution temperature affect the gelation, stiffness, and response to deformation of peptide amphiphile gels. Although the morphology of PA gels formed by charge neutralization and salt-mediated charge screening are similar by electron microscopy, rheological measurements indicate that the calcium-mediated ionic bridges in CaCl2-PA gels form stronger intra- and inter-fiber crosslinks than the hydrogen bonds formed by the protonated carboxylic acid residues in HCl-PA gels. In contrast, the structure of PA gels changes drastically when the PA solution is annealed prior to gel formation. Annealed PA solutions are birefringent and can form viscoelastic strings of aligned nanofibers when manually dragged across a thin film of CaCl2. These aligned arrays of PA nanofibers hold great promise in controlling the orientation of cells in three-dimensions. Separately, we applied the principles of molecular design to create buckled membrane nanostructures that mimic the shape of viruses. When oppositely charged amphiphilic molecules are mixed they can form vesicles with a periodic two-dimensional ionic lattice that opposes the membrane's natural curvature and can result in vesicle buckling. Our results demonstrate that a large +3 to -1 charge imbalance between the cationic and anionic

  19. 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.

  20. 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.

  1. 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.

  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. 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.

  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. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. A scheme for multiple sequence alignment optimization--an improvement based on family representative mechanics features.

    Science.gov (United States)

    Liu, Xin; Zhao, Ya-Pu

    2009-12-21

    As a basic tool of modern biology, sequence alignment can provide us useful information in fold, function, and active site of protein. For many cases, the increased quality of sequence alignment means a better performance. The motivation of present work is to increase ability of the existing scoring scheme/algorithm by considering residue-residue correlations better. Based on a coarse-grained approach, the hydrophobic force between each pair of residues is written out from protein sequence. It results in the construction of an intramolecular hydrophobic force network that describes the whole residue-residue interactions of each protein molecule, and characterizes protein's biological properties in the hydrophobic aspect. A former work has suggested that such network can characterize the top weighted feature regarding hydrophobicity. Moreover, for each homologous protein of a family, the corresponding network shares some common and representative family characters that eventually govern the conservation of biological properties during protein evolution. In present work, we score such family representative characters of a protein by the deviation of its intramolecular hydrophobic force network from that of background. Such score can assist the existing scoring schemes/algorithms, and boost up the ability of multiple sequences alignment, e.g. achieving a prominent increase (approximately 50%) in searching the structurally alike residue segments at a low identity level. As the theoretical basis is different, the present scheme can assist most existing algorithms, and improve their efficiency remarkably.

  14. Protein sequence alignment analysis by local covariation: coevolution statistics detect benchmark alignment errors.

    Directory of Open Access Journals (Sweden)

    Russell J Dickson

    Full Text Available The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files/.

  15. 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

  16. 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.

  17. 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.

  18. The effects of alignment error and alignment filtering on the sitewise detection of positive selection.

    Science.gov (United States)

    Jordan, Gregory; Goldman, Nick

    2012-04-01

    When detecting positive selection in proteins, the prevalence of errors resulting from misalignment and the ability of alignment filters to mitigate such errors are not well understood, but filters are commonly applied to try to avoid false positive results. Focusing on the sitewise detection of positive selection across a wide range of divergence levels and indel rates, we performed simulation experiments to quantify the false positives and false negatives introduced by alignment error and the ability of alignment filters to improve performance. We found that some aligners led to many false positives, whereas others resulted in very few. False negatives were a problem for all aligners, increasing with sequence divergence. Of the aligners tested, PRANK's codon-based alignments consistently performed the best and ClustalW performed the worst. Of the filters tested, GUIDANCE performed the best and Gblocks performed the worst. Although some filters showed good ability to reduce the error rates from ClustalW and MAFFT alignments, none were found to substantially improve the performance of PRANK alignments under most conditions. Our results revealed distinct trends in error rates and power levels for aligners and filters within a biologically plausible parameter space. With the best aligner, a low false positive rate was maintained even with extremely divergent indel-prone sequences. Controls using the true alignment and an optimal filtering method suggested that performance improvements could be gained by improving aligners or filters to reduce the prevalence of false negatives, especially at higher divergence levels and indel rates.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. Hayes; Pelletier, Dale A.; Schmoyer, Denise D.; Cannon, William R.

    2007-12-01

    One of the core tasks of the emerging discipline of systems biology is the reconstruction of the various biological networks in an organism. The importance of understanding such regulatory, interaction, and signaling networks has fueled the development by bioinformatics researchers of many inference algorithms for determining their structure. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, testing, and improvement of algorithms used to reconstruct the structures of regulatory and interaction networks from high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, a software package for exploratory data analysis that allows basic integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. Thus, the combined SEBINI–CABIN platform aids in the more accurate determination of biological networks, in less time, with less effort. In this paper, we present a case study demonstrating the use of the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein-protein interaction networks from sets of mass spectrometry bait-prey experiment data. To the best of our knowledge the pipeline so designed is the first to be publicly available for such use. A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/NIT/NIT.html. Source code and PostgreSQL database schema are available under open source license. Contact: ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. The

  8. A Report on the Implementation of the Blooming Biology Tool: Aligning Course Learning Outcomes with Assessments and Promoting Consistency in a Large Multi-Section First-Year Biology Course

    Directory of Open Access Journals (Sweden)

    Carol Pollock

    2010-06-01

    Full Text Available The objectives of this study were to investigate the alignment of exam questions with course learning outcomes in a first year biology majors course, to examine gaps and overlaps in assessment of content amongst the sections of the course, and to use this information to provide feedback to the teaching team to further improve the course. Our ultimate goal was to provide students with learning outcomes that would clearly indicate the content and the level at which they would be expected to learn the content for this course, regardless of the section in which they were registered. We took an evidence-based approach to course evaluation and employed the Blooming Biology Tool to compare the learning outcomes and the exam questions of the course, investigating whether the cognitive skill level of each learning outcome as written matched the level at which it was assessed. We identified misalignments and recommended revising the learning outcomes to better reflect the intended level of learning for the course. We also investigated student performance on exam questions of different cognitive levels and found that students scored statistically significantly higher (p < .05 on questions in which learning outcomes were tested at the stated cognitive skill level compared to at a higher level.Les objectifs de cette étude étaient (1 d’examiner la correspondance entre les questions d’examen et les résultats en matière d’apprentissage pour un cours de première année d’une majeure en biologie, (2 d’étudier les écarts et les chevauchements en matière d’évaluation du contenu des sections du cours et (3 d’utiliser ces informations pour fournir de la rétroaction à l’équipe des enseignants afin d’améliorer le cours. Notre but ultime était de faire en sorte que les résultats de l’apprentissage des étudiants indiquent clairement le contenu à apprendre et le niveau cognitif qu’ils devraient avoir atteint, peu

  9. 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

  10. 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.

  11. Dose-response aligned circuits in signaling systems.

    Science.gov (United States)

    Yan, Long; Ouyang, Qi; Wang, Hongli

    2012-01-01

    Cells use biological signal transduction pathways to respond to environmental stimuli and the behavior of many cell types depends on precise sensing and transmission of external information. A notable property of signal transduction that was characterized in the Saccharomyces cerevisiae yeast cell and many mammalian cells is the alignment of dose-response curves. It was found that the dose response of the receptor matches closely the dose responses of the downstream. This dose-response alignment (DoRA) renders equal sensitivities and concordant responses in different parts of signaling system and guarantees a faithful information transmission. The experimental observations raise interesting questions about the nature of the information transmission through DoRA signaling networks and design principles of signaling systems with this function. Here, we performed an exhaustive computational analysis on network architectures that underlie the DoRA function in simple regulatory networks composed of two and three enzymes. The minimal circuits capable of DoRA were examined with Michaelis-Menten kinetics. Several motifs that are essential for the dynamical function of DoRA were identified. Systematic analysis of the topology space of robust DoRA circuits revealed that, rather than fine-tuning the network's parameters, the function is primarily realized by enzymatic regulations on the controlled node that are constrained in limiting regions of saturation or linearity.

  12. 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.

  13. Bayesian coestimation of phylogeny and sequence alignment

    Directory of Open Access Journals (Sweden)

    Jensen Jens

    2005-04-01

    Full Text Available Abstract Background Two central problems in computational biology are the determination of the alignment and phylogeny of a set of biological sequences. The traditional approach to this problem is to first build a multiple alignment of these sequences, followed by a phylogenetic reconstruction step based on this multiple alignment. However, alignment and phylogenetic inference are fundamentally interdependent, and ignoring this fact leads to biased and overconfident estimations. Whether the main interest be in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both. Results We developed a fully Bayesian Markov chain Monte Carlo method for coestimating phylogeny and sequence alignment, under the Thorne-Kishino-Felsenstein model of substitution and single nucleotide insertion-deletion (indel events. In our earlier work, we introduced a novel and efficient algorithm, termed the "indel peeling algorithm", which includes indels as phylogenetically informative evolutionary events, and resembles Felsenstein's peeling algorithm for substitutions on a phylogenetic tree. For a fixed alignment, our extension analytically integrates out both substitution and indel events within a proper statistical model, without the need for data augmentation at internal tree nodes, allowing for efficient sampling of tree topologies and edge lengths. To additionally sample multiple alignments, we here introduce an efficient partial Metropolized independence sampler for alignments, and combine these two algorithms into a fully Bayesian co-estimation procedure for the alignment and phylogeny problem. Our approach results in estimates for the posterior distribution of evolutionary rate parameters, for the maximum a-posteriori (MAP phylogenetic tree, and for the posterior decoding alignment. Estimates for the evolutionary tree and multiple alignment are augmented with confidence estimates for each node height and alignment column

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. Conformational fluctuations affect protein alignment in dilute liquid crystal media

    DEFF Research Database (Denmark)

    Louhivuori, M.; Otten, R.; Lindorff-Larsen, Kresten

    2006-01-01

    The discovery of dilute liquid crystalline media to align biological macromolecules has opened many new possibilities to study protein and nucleic acid structures by NMR spectroscopy. We inspect the basic alignment phenomenon for an ensemble of protein conformations to deduce relative contributions...... molecular surfaces. Furthermore, we consider the implications of a dynamic bias to structure determination using data from the weak alignment method....

  19. Algorithm Engineering for Optimal Alignment of Protein Structure Distance Matrices

    NARCIS (Netherlands)

    Wohlers, I.; Andonov, R.; Klau, G.W.

    2011-01-01

    Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular DALI scoring function. We introduce the structural alignment problem formally,

  20. 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.

  1. Vibrating wire alignment technique

    CERN Document Server

    Xiao-Long, Wang; lei, Wu; Chun-Hua, Li

    2013-01-01

    Vibrating wire alignment technique is a kind of method which through measuring the spatial distribution of magnetic field to do the alignment and it can achieve very high alignment accuracy. Vibrating wire alignment technique can be applied for magnet fiducialization and accelerator straight section components alignment, it is a necessary supplement for conventional alignment method. This article will systematically expound the international research achievements of vibrating wire alignment technique, including vibrating wire model analysis, system frequency calculation, wire sag calculation and the relation between wire amplitude and magnetic induction intensity. On the basis of model analysis this article will introduce the alignment method which based on magnetic field measurement and the alignment method which based on amplitude and phase measurement. Finally, some basic questions will be discussed and the solutions will be given.

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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.

  7. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez.

    Since June of 2009, the muon alignment group has focused on providing new alignment constants and on finalizing the hardware alignment reconstruction. Alignment constants for DTs and CSCs were provided for CRAFT09 data reprocessing. For DT chambers, the track-based alignment was repeated using CRAFT09 cosmic ray muons and validated using segment extrapolation and split cosmic tools. One difference with respect to the previous alignment is that only five degrees of freedom were aligned, leaving the rotation around the local x-axis to be better determined by the hardware system. Similarly, DT chambers poorly aligned by tracks (due to limited statistics) were aligned by a combination of photogrammetry and hardware-based alignment. For the CSC chambers, the hardware system provided alignment in global z and rotations about local x. Entire muon endcap rings were further corrected in the transverse plane (global x and y) by the track-based alignment. Single chamber track-based alignment suffers from poor statistic...

  8. A grading matrix assessment approach to align student performance to Threshold Learning Outcomes (TLOs in a large first year biology class

    Directory of Open Access Journals (Sweden)

    Lesley Lluka

    2015-03-01

    Full Text Available In our large first year biology course, we aim to provide students with clear links between the course delivery framework and assessment. In the first semester of offering in 2008, we determined grades using the traditional weighted average of marks for the assessment tasks. However, of the 99% of students who passed with an aggregate of at least 50%, the lowest performing student obtained only 21% for the examination. Since Semester 2 2008, we have graded the course using a grading matrix approach with specified standards for all the individual assessment components. Analysis of results from this approach showed that 85-89% of students obtained a passing grade, for which a minimum score of 45% for the examination was required. The grading matrix approach provides a measure of each student’s higher order learning of concepts and skills that can be mapped to threshold learning outcomes for the students’ programs of study.

  9. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez and J. Pivarski

    2011-01-01

    Alignment efforts in the first few months of 2011 have shifted away from providing alignment constants (now a well established procedure) and focussed on some critical remaining issues. The single most important task left was to understand the systematic differences observed between the track-based (TB) and hardware-based (HW) barrel alignments: a systematic difference in r-φ and in z, which grew as a function of z, and which amounted to ~4-5 mm differences going from one end of the barrel to the other. This difference is now understood to be caused by the tracker alignment. The systematic differences disappear when the track-based barrel alignment is performed using the new “twist-free” tracker alignment. This removes the largest remaining source of systematic uncertainty. Since the barrel alignment is based on hardware, it does not suffer from the tracker twist. However, untwisting the tracker causes endcap disks (which are aligned ...

  10. 基于社会网络的IT与业务匹配多主体仿真%Multi-agent Simulation Approach to Business-IT Alignment in Social Network

    Institute of Scientific and Technical Information of China (English)

    张延林; 肖静华; 李礼; 谢康

    2011-01-01

    Business-IT alignment(BIA) has been one of the top concerns of practitioners and scholars for decades because it enables organizations to improve their business performance.Although numerous studies concerned on how to accomplish BIA look alignment as an end state or as an ongoing process,multiagent simulation based on complex adaptive system is still rarely discussed in the existing literatures.This paper attempts to fulfill this gap by designing and simulating a comprehensive BIA game model to discuss how the different network structures influence the whole alignment.Drawing on prior literature,the coordination and communication between IT and business managers are identified as critical success factors for BIA.Regarding the IT and business units or individuals as rational agents in view of game theory,a BIA game model is established.Using multiagent simulation methodology based on the social network,the results show: the threshold of alignment cost for the whole alignment increases from regular lattice to small world and then to random network.Moreover,strategic evolution process is one of key reasons for the difference between whole alignment and whole misalignment.Overall,the main contribution of this study is a dynamic analysis framework of BIA key factors proposed from the perspective of multi-agent simulation in social network,which provides a more ample prescriptive insight to understand complex dynamic and emergent nature of alignment for practitioners and scholars in IS fields.%IT与业务匹配(BIA)能创造更高的组织绩效,如何取得BIA成为信息系统理论与实践的热点研究问题之一。本文将BIA的静态研究和动态研究相结合,构建多主体交互的BIA博弈模型,并基于社会网络进行多主体仿真,探讨不同的社会网络结构对总体匹配的影响。试验结果显示:由规则格子到小世界以及随机网络,提升了匹配成本对总体匹配的阈值,策略演化过程是造成总

  11. 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.

  12. 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.

  13. Alignment-free phylogenetics and population genetics.

    Science.gov (United States)

    Haubold, Bernhard

    2014-05-01

    Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on comparative data, today usually DNA sequences. These have become so plentiful that alignment-free sequence comparison is of growing importance in the race between scientists and sequencing machines. In phylogenetics, efficient distance computation is the major contribution of alignment-free methods. A distance measure should reflect the number of substitutions per site, which underlies classical alignment-based phylogeny reconstruction. Alignment-free distance measures are either based on word counts or on match lengths, and I apply examples of both approaches to simulated and real data to assess their accuracy and efficiency. While phylogeny reconstruction is based on the number of substitutions, in population genetics, the distribution of mutations along a sequence is also considered. This distribution can be explored by match lengths, thus opening the prospect of alignment-free population genomics.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

    2010-01-01

    The main developments in muon alignment since March 2010 have been the production, approval and deployment of alignment constants for the ICHEP data reprocessing. In the barrel, a new geometry, combining information from both hardware and track-based alignment systems, has been developed for the first time. The hardware alignment provides an initial DT geometry, which is then anchored as a rigid solid, using the link alignment system, to a reference frame common to the tracker. The “GlobalPositionRecords” for both the Tracker and Muon systems are being used for the first time, and the initial tracker-muon relative positioning, based on the link alignment, yields good results within the photogrammetry uncertainties of the Tracker and alignment ring positions. For the first time, the optical and track-based alignments show good agreement between them; the optical alignment being refined by the track-based alignment. The resulting geometry is the most complete to date, aligning all 250 DTs, ...

  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. 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.

  1. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    Z. Szillasi and G. Gomez.

    2013-01-01

    When CMS is opened up, major components of the Link and Barrel Alignment systems will be removed. This operation, besides allowing for maintenance of the detector underneath, is needed for making interventions that will reinforce the alignment measurements and make the operation of the alignment system more reliable. For that purpose and also for their general maintenance and recalibration, the alignment components will be transferred to the Alignment Lab situated in the ISR area. For the track-based alignment, attention is focused on the determination of systematic uncertainties, which have become dominant, since now there is a large statistics of muon tracks. This will allow for an improved Monte Carlo misalignment scenario and updated alignment position errors, crucial for high-momentum muon analysis such as Z′ searches.

  2. Elements of Cellular Blind Interference Alignment --- Aligned Frequency Reuse, Wireless Index Coding and Interference Diversity

    CERN Document Server

    Jafar, Syed A

    2012-01-01

    We explore degrees of freedom (DoF) characterizations of partially connected wireless networks, especially cellular networks, with no channel state information at the transmitters. Specifically, we introduce three fundamental elements --- aligned frequency reuse, wireless index coding and interference diversity --- through a series of examples, focusing first on infinite regular arrays, then on finite clusters with arbitrary connectivity and message sets, and finally on heterogeneous settings with asymmetric multiple antenna configurations. Aligned frequency reuse refers to the optimality of orthogonal resource allocations in many cases, but according to unconventional reuse patterns that are guided by interference alignment principles. Wireless index coding highlights both the intimate connection between the index coding problem and cellular blind interference alignment, as well as the added complexity inherent to wireless settings. Interference diversity refers to the observation that in a wireless network ...

  3. 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.

  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. 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.

  6. Interference Alignment for Secrecy

    CERN Document Server

    Koyluoglu, Onur Ozan; Lai, Lifeng; Poor, H Vincent

    2008-01-01

    This paper studies the frequency/time selective $K$-user Gaussian interference channel with secrecy constraints. Two distinct models, namely the interference channel with confidential messages and the one with an external eavesdropper, are analyzed. The key difference between the two models is the lack of channel state information (CSI) about the external eavesdropper. Using interference alignment along with secrecy pre-coding, it is shown that each user can achieve non-zero secure Degrees of Freedom (DoF) for both cases. More precisely, the proposed coding scheme achieves $\\frac{K-2}{2K-2}$ secure DoF {\\em with probability one} per user in the confidential messages model. For the external eavesdropper scenario, on the other hand, it is shown that each user can achieve $\\frac{K-2}{2K}$ secure DoF {\\em in the ergodic setting}. Remarkably, these results establish the {\\em positive impact} of interference on the secrecy capacity region of wireless networks.

  7. Deciphering the Molecular Basis of Human Cardiovascular Disease through Network Biology

    Science.gov (United States)

    Chan, Stephen Y.; White, Kevin; Loscalzo, Joseph

    2012-01-01

    Purpose of review This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods on predicting and ameliorating individual manifestations of human cardiovascular disease. Recent findings Complex cardiovascular diseases rarely result from an abnormality in a single molecular effector, but, rather, nearly always are the net result of multiple pathobiological pathways that interact through an interconnected network. In the post-genomic era, a framework has emerged of the potential complexity of the interacting pathways that govern molecular actions in the human cell. As a result, network approaches have been developed to understand more comprehensively those interconnections that influence human disease. “Network medicine” has already led to tangible discoveries of novel disease genes and pathways as well as improved mechanisms for rational drug development. Summary As methodologies evolve, network medicine may better capture the complexity of human pathogenesis and, thus, re-define personalized disease classification and therapies. PMID:22382498

  8. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

    Science.gov (United States)

    Kuperstein, I; Bonnet, E; Nguyen, H-A; Cohen, D; Viara, E; Grieco, L; Fourquet, S; Calzone, L; Russo, C; Kondratova, M; Dutreix, M; Barillot, E; Zinovyev, A

    2015-01-01

    Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses

  9. Pattern-recognition by an artificial network derived from biologic neuronal systems.

    Science.gov (United States)

    Alkon, D L; Blackwell, K T; Barbour, G S; Rigler, A K; Vogl, T P

    1990-01-01

    A novel artificial neural network, derived from neurobiological observations, is described and examples of its performance are presented. This DYnamically STable Associative Learning (DYSTAL) network associatively learns both correlations and anticorrelations, and can be configured to classify or restore patterns with only a change in the number of output units. DYSTAL exhibits some particularly desirable properties: computational effort scales linearly with the number of connections, i.e., it is O(N) in complexity; performance of the network is stable with respect to network parameters over wide ranges of their values and over the size of the input field; storage of a very large number of patterns is possible; patterns need not be orthogonal; network connections are not restricted to multi-layer feed-forward or any other specific structure; and, for a known set of deterministic input patterns, the network weights can be computed, a priori, in closed form. The network has been associatively trained to perform the XOR function as well as other classification tasks. The network has also been trained to restore patterns obscured by binary or analog noise. Neither global nor local feedback connections are required during learning; hence the network is particularly suitable for hardware (VLSI) implementation.

  10. Ontology alignment with OLA

    OpenAIRE

    Euzenat, Jérôme; Loup, David; Touzani, Mohamed; Valtchev, Petko

    2004-01-01

    euzenat2004d; International audience; Using ontologies is the standard way to achieve interoperability of heterogeneous systems within the Semantic web. However, as the ontologies underlying two systems are not necessarily compatible, they may in turn need to be aligned. Similarity-based approaches to alignment seems to be both powerful and flexible enough to match the expressive power of languages like OWL. We present an alignment tool that follows the similarity-based paradigm, called OLA. ...

  11. Supramolecular chirality in organo-, hydro-, and metallogels derived from bis-amides of L-(+)-tartaric acid: formation of highly aligned 1D silica fibers and evidence of 5-c net SnS topology in a metallogel network.

    Science.gov (United States)

    Das, Uttam Kumar; Dastidar, Parthasarathi

    2012-10-01

    A series of bis-amides derived from L-(+)-tartaric acid was synthesized as potential low-molecular-weight gelators. Out of 14 bis-amides synthesized, 13 displayed organo-, hydro-, and ambidextrous gelation behavior. The gels were characterized by methods including circular dichroism, differential scanning calorimetry, optical and electron microscopy, and rheology. One of the gels derived from di-3-pyridyltartaramide (D-3-PyTA) displayed intriguing nanotubular morphology of the gel network, which was exploited as a template to generate highly aligned 1D silica fibers. The gelator D-3-PyTA was also exploited to generate metallogels by treatment with various Cu(II) /Zn(II) salts under suitable conditions. A structure-property correlation on the basis of single-crystal and powder X-ray diffraction data was attempted to gain insight into the structures of the gel networks in both organo- and metallogels. Such study led to the determination of the gel-network structure of the Cu(II) coordination-polymer-based metallogel, which displayed a 2D sheet architecture made of a chloride-bridged double helix that resembled a 5-c net SnS topology.

  12. FiBi - A French network of facilities for irradiation in biology: The organisation of the network and the research opportunities associated

    Energy Technology Data Exchange (ETDEWEB)

    Gaillard-Lecanu, E.; Coffigny, H.; Poncy, J.L. [CEA Fontenay aux Roses, 92 (France); Authier, N.; Verrey, B. [CEA Valduc, 21 - Is sur Tille (France); Bailly, I. [CEA Bruyeres le Chatel, 91 (France); Baldacchino, G.; Bordy, J.M.; Carriere, M.; Leplat, J.J.; Pin, S.; Pommeret, S.; Thuret, J.Y.; Renault, J.P. [CEA Saclay, 91 - Gif sur Yvette (France); Cortella, I. [CEA Grenoble, Dept. Etudes des Reacteurs (DER), 38 (France); Duval, D. [Schering-CIS bio International, 91 - Saclay (France); Khodja, H.; Testard, I. [Atomic Energy Commission, 14 - Caen (France)

    2006-07-01

    The Life Science Division of the Atomic Energy Commission has developed a national network of available irradiation facilities for biological studies. One aim is to optimise the irradiation of biological samples, through a compendium of existing facilities allowing for the preserving and the irradiation of these samples in good conditions, and for providing an appropriate and reliable dosimetry. Given the high cost of the facilities and their specialization (nature and precision of irradiation on a cell scale, dose and dose rate), closeness is no longer the only criteria of choice for biologists. Development is leaning towards the implementation of irradiation platforms gathering irradiation tools associated with specific methods belonging to biology: cell culture, molecular biology and even animal care houses. The aim is to be able to offer biologists the most appropriate experimental tools, and to modify them according to the changing needs of radiobiology. This work is currently in progress and the database is still not exhaustive and shall be implemented as and when new documents are drawn up and new facilities are opened. (author)

  13. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

    2010-01-01

    Most of the work in muon alignment since December 2009 has focused on the geometry reconstruction from the optical systems and improvements in the internal alignment of the DT chambers. The barrel optical alignment system has progressively evolved from reconstruction of single active planes to super-planes (December 09) to a new, full barrel reconstruction. Initial validation studies comparing this full barrel alignment at 0T with photogrammetry provide promising results. In addition, the method has been applied to CRAFT09 data, and the resulting alignment at 3.8T yields residuals from tracks (extrapolated from the tracker) which look smooth, suggesting a good internal barrel alignment with a small overall offset with respect to the tracker. This is a significant improvement, which should allow the optical system to provide a start-up alignment for 2010. The end-cap optical alignment has made considerable progress in the analysis of transfer line data. The next set of alignment constants for CSCs will there...

  14. Computer-assisted curation of a human regulatory core network from the biological literature

    NARCIS (Netherlands)

    Thomas, P.; Durek, P.; Solt, I.; Klinger, B.; Witzel, F.; Schulthess, P.; Mayer, Y.; Tikk, D.; Blüthgen, N.; Leser, U.

    2015-01-01

    Motivation: A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plag

  15. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    NARCIS (Netherlands)

    Herrgård, Markus J.; Swainston, Neil; Dobson, Paul; Dunn, Warwick B.; Arga, K. Yalçin; Arvas, Mikko; Blüthgen, Nils; Borger, Simon; Costenoble, Roeland; Heinemann, Matthias; Hucka, Michael; Novère, Nicolas Le; Li, Peter; Liebermeister, Wolfram; Mo, Monica L.; Oliveira, Ana Paula; Petranovic, Dina; Pettifer, Stephen; Simeonidis, Evangelos; Smallbone, Kieran; Spasić, Irena; Weichart, Dieter; Brent, Roger; Broomhead, David S.; Westerhoff, Hans V.; Kırdar, Betül; Penttilä, Merja; Klipp, Edda; Palsson, Bernhard Ø.; Sauer, Uwe; Oliver, Stephen G.; Mendes, Pedro; Nielsen, Jens; Kell, Douglas B.

    2008-01-01

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and cont

  16. Development in a biologically inspired spinal neural network for movement control

    NARCIS (Netherlands)

    van Heijst, JJ; Vos, JE; Bullock, D

    1998-01-01

    In two phases, we develop increasingly complex neural network models of spinal circuitry that self-organizes into networks with opponent channels for the control of an antagonistic muscle pair. The self-organization is enabled by a Hebbian learning rule operating during spontaneous activity present

  17. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

    Science.gov (United States)

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

  18. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

    Science.gov (United States)

    Martin, O. C.; Krzywicki, A.; Zagorski, M.

    2016-07-01

    Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent "motifs", that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.

  19. Systems biology of the cell cycle of Saccharomyces cerevisiae: From network mining to system-level properties.

    Science.gov (United States)

    Alberghina, Lilia; Coccetti, Paola; Orlandi, Ivan

    2009-01-01

    Following a brief description of the operational procedures of systems biology (SB), the cell cycle of budding yeast is discussed as a successful example of a top-down SB analysis. After the reconstruction of the steps that have led to the identification of a sizer plus timer network in the G1 to S transition, it is shown that basic functions of the cell cycle (the setting of the critical cell size and the accuracy of DNA replication) are system-level properties, detected only by integrating molecular analysis with modelling and simulation of their underlying networks. A detailed network structure of a second relevant regulatory step of the cell cycle, the exit from mitosis, derived from extensive data mining, is constructed and discussed. To reach a quantitative understanding of how nutrients control, through signalling, metabolism and transcription, cell growth and cycle is a very relevant aim of SB. Since we know that about 900 gene products are required for cell cycle execution and control in budding yeast, it is quite clear that a purely systematic approach would require too much time. Therefore lines for a modular SB approach, which prioritises molecular and computational investigations for faster cell cycle understanding, are proposed. The relevance of the insight coming from the cell cycle SB studies in developing a new framework for tackling very complex biological processes, such as cancer and aging, is discussed.

  20. Adverse effects of biologics: a network meta-analysis and Cochrane overview

    DEFF Research Database (Denmark)

    Singh, J. A.; Wells, G. A.; Christensen, Robin Daniel Kjersgaard

    2011-01-01

    Background Biologics are used for the treatment of rheumatoid arthritis and many other conditions. While the efficacy of biologics has been established, there is uncertainty regarding the adverse effects of this treatment. Since serious risks such as tuberculosis (TB) reactivation, serious...

  1. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

    DEFF Research Database (Denmark)

    Herrgard, Markus; Swainston, Neil; Dobson, Paul

    2008-01-01

    and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology...

  2. Algorithms for Automatic Alignment of Arrays

    Science.gov (United States)

    Chatterjee, Siddhartha; Gilbert, John R.; Oliker, Leonid; Schreiber, Robert; Sheffler, Thomas J.

    1996-01-01

    Aggregate data objects (such as arrays) are distributed across the processor memories when compiling a data-parallel language for a distributed-memory machine. The mapping determines the amount of communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: an alignment that maps all the objects to an abstract template, followed by a distribution that maps the template to the processors. This paper describes algorithms for solving the various facets of the alignment problem: axis and stride alignment, static and mobile offset alignment, and replication labeling. We show that optimal axis and stride alignment is NP-complete for general program graphs, and give a heuristic method that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. We also show how local graph contractions can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. We show how to model the static offset alignment problem using linear programming, and we show that loop-dependent mobile offset alignment is sometimes necessary for optimum performance. We describe an algorithm with for determining mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself or can be used to improve performance. We describe an algorithm based on network flow that replicates objects so as to minimize the total amount of broadcast communication in replication.

  3. Synthetic biology approaches in cancer immunotherapy, genetic network engineering, and genome editing.

    Science.gov (United States)

    Chakravarti, Deboki; Cho, Jang Hwan; Weinberg, Benjamin H; Wong, Nicole M; Wong, Wilson W

    2016-04-18

    Investigations into cells and their contents have provided evolving insight into the emergence of complex biological behaviors. Capitalizing on this knowledge, synthetic biology seeks to manipulate the cellular machinery towards novel purposes, extending discoveries from basic science to new applications. While these developments have demonstrated the potential of building with biological parts, the complexity of cells can pose numerous challenges. In this review, we will highlight the broad and vital role that the synthetic biology approach has played in applying fundamental biological discoveries in receptors, genetic circuits, and genome-editing systems towards translation in the fields of immunotherapy, biosensors, disease models and gene therapy. These examples are evidence of the strength of synthetic approaches, while also illustrating considerations that must be addressed when developing systems around living cells.

  4. A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

    Science.gov (United States)

    de Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu, Huizhong; Pichevar, Ramin; Rouat, Jean

    2012-02-01

    A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

  5. Dynamics on and of complex networks applications to biology, computer science, and the social sciences

    CERN Document Server

    Ganguly, Niloy; Mukherjee, Animesh

    2009-01-01

    This self-contained book systematically explores the statistical dynamics on and of complex networks having relevance across a large number of scientific disciplines. The theories related to complex networks are increasingly being used by researchers for their usefulness in harnessing the most difficult problems of a particular discipline. The book is a collection of surveys and cutting-edge research contributions exploring the interdisciplinary relationship of dynamics on and of complex networks. Towards this goal, the work is thematically organized into three main sections: Part I studies th

  6. 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/.

  7. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

    2011-01-01

    The Muon Alignment work now focuses on producing a new track-based alignment with higher track statistics, making systematic studies between the results of the hardware and track-based alignment methods and aligning the barrel using standalone muon tracks. Currently, the muon track reconstruction software uses a hardware-based alignment in the barrel (DT) and a track-based alignment in the endcaps (CSC). An important task is to assess the muon momentum resolution that can be achieved using the current muon alignment, especially for highly energetic muons. For this purpose, cosmic ray muons are used, since the rate of high-energy muons from collisions is very low and the event statistics are still limited. Cosmics have the advantage of higher statistics in the pT region above 100 GeV/c, but they have the disadvantage of having a mostly vertical topology, resulting in a very few global endcap muons. Only the barrel alignment has therefore been tested so far. Cosmic muons traversing CMS from top to bottom are s...

  8. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    Gervasio Gomez

    The main progress of the muon alignment group since March has been in the refinement of both the track-based alignment for the DTs and the hardware-based alignment for the CSCs. For DT track-based alignment, there has been significant improvement in the internal alignment of the superlayers inside the DTs. In particular, the distance between superlayers is now corrected, eliminating the residual dependence on track impact angles, and good agreement is found between survey and track-based corrections. The new internal geometry has been approved to be included in the forthcoming reprocessing of CRAFT samples. The alignment of DTs with respect to the tracker using global tracks has also improved significantly, since the algorithms use the latest B-field mapping, better run selection criteria, optimized momentum cuts, and an alignment is now obtained for all six degrees of freedom (three spatial coordinates and three rotations) of the aligned DTs. This work is ongoing and at a stage where we are trying to unders...

  9. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    Since December, the muon alignment community has focused on analyzing the data recorded so far in order to produce new DT and CSC Alignment Records for the second reprocessing of CRAFT data. Two independent algorithms were developed which align the DT chambers using global tracks, thus providing, for the first time, a relative alignment of the barrel with respect to the tracker. These results are an important ingredient for the second CRAFT reprocessing and allow, for example, a more detailed study of any possible mis-modelling of the magnetic field in the muon spectrometer. Both algorithms are constructed in such a way that the resulting alignment constants are not affected, to first order, by any such mis-modelling. The CSC chambers have not yet been included in this global track-based alignment due to a lack of statistics, since only a few cosmics go through the tracker and the CSCs. A strategy exists to align the CSCs using the barrel as a reference until collision tracks become available. Aligning the ...

  10. Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.

    Science.gov (United States)

    Linel, Patrice; Wu, Shuang; Deng, Nan; Wu, Hulin

    2014-10-01

    Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

  11. Faster exon assembly by sparse spliced alignment

    CERN Document Server

    Tiskin, Alexander

    2007-01-01

    Assembling a gene from candidate exons is an important problem in computational biology. Among the most successful approaches to this problem is \\emph{spliced alignment}, proposed by Gelfand et al., which scores different candidate exon chains within a DNA sequence of length $m$ by comparing them to a known related gene sequence of length n, $m = \\Theta(n)$. Gelfand et al.\\ gave an algorithm for spliced alignment running in time O(n^3). Kent et al.\\ considered sparse spliced alignment, where the number of candidate exons is O(n), and proposed an algorithm for this problem running in time O(n^{2.5}). We improve on this result, by proposing an algorithm for sparse spliced alignment running in time O(n^{2.25}). Our approach is based on a new framework of \\emph{quasi-local string comparison}.

  12. Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction.

    Science.gov (United States)

    Street, Maria E; Buscema, Massimo; Smerieri, Arianna; Montanini, Luisa; Grossi, Enzo

    2013-12-01

    One of the specific aims of systems biology is to model and discover properties of cells, tissues and organisms functioning. A systems biology approach was undertaken to investigate possibly the entire system of intra-uterine growth we had available, to assess the variables of interest, discriminate those which were effectively related with appropriate or restricted intrauterine growth, and achieve an understanding of the systems in these two conditions. The Artificial Adaptive Systems, which include Artificial Neural Networks and Evolutionary Algorithms lead us to the first analyses. These analyses identified the importance of the biochemical variables IL-6, IGF-II and IGFBP-2 protein concentrations in placental lysates, and offered a new insight into placental markers of fetal growth within the IGF and cytokine systems, confirmed they had relationships and offered a critical assessment of studies previously performed.

  13. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2011-01-01

    A new set of muon alignment constants was approved in August. The relative position between muon chambers is essentially unchanged, indicating good detector stability. The main changes concern the global positioning of the barrel and of the endcap rings to match the new Tracker geometry. Detailed studies of the differences between track-based and optical alignment of DTs have proven to be a valuable tool for constraining Tracker alignment weak modes, and this information is now being used as part of the alignment procedure. In addition to the “split-cosmic” analysis used to investigate the muon momentum resolution at high momentum, a new procedure based on reconstructing the invariant mass of di-muons from boosted Zs is under development. Both procedures show an improvement in the momentum precision of Global Muons with respect to Tracker-only Muons. Recent developments in track-based alignment include a better treatment of the tails of residual distributions and accounting for correla...

  14. Physics of Grain Alignment

    CERN Document Server

    Lazarian, A

    2000-01-01

    Aligned grains provide one of the easiest ways to study magnetic fields in diffuse gas and molecular clouds. How reliable our conclusions about the inferred magnetic field depends critically on our understanding of the physics of grain alignment. Although grain alignment is a problem of half a century standing recent progress achieved in the field makes us believe that we are approaching the solution of this mystery. I review basic physical processes involved in grain alignment and show why mechanisms that were favored for decades do not look so promising right now. I also discuss why the radiative torque mechanism ignored for more than 20 years looks right now the most powerful means of grain alignment.

  15. MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

    Directory of Open Access Journals (Sweden)

    Sucaet Yves

    2010-11-01

    Full Text Available Abstract Background Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results MetNetAPI is a versatile Application Programming Interface (API to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle, interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates. Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup. The API is available for Java, Microsoft.NET and R programming environments and offers

  16. Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

  17. Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

    Science.gov (United States)

    Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

    2014-05-01

    Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation.

  18. A systems biology approach to identify the signalling network regulated by Rho-GDI-γ during neural stem cell differentiation.

    Science.gov (United States)

    Wang, Jiao; Hu, Fuyan; Cheng, Hua; Zhao, Xing-Ming; Wen, Tieqiao

    2012-11-01

    Understanding the molecular mechanism that underlies the differentiation of neural stem cells (NSCs) is vital to develop regenerative medicines for neurological disorders. In our previous work, Rho-GDI-γ was found to be able to prompt neuronal differentiation when it was down regulated. However, it is unclear how Rho-GDI-γ regulates this differentiation process. Therefore, a novel systems biology approach is presented here to identify putative signalling pathways regulated by Rho-GDI-γ during NSC differentiation, and these pathways can provide insights into the NSC differentiation mechanisms. In particular, our proposed approach combines the predictive power of computational biology and molecular experiments. With different biological experiments, the genes in the computationally identified signalling network were validated to be indeed regulated by Rho-GDI-γ during the differentiation of NSCs. In particular, one randomly selected pathway involving Vcp, Mapk8, Ywhae and Ywhah was experimentally verified to be regulated by Rho-GDI-γ. These promising results demonstrate the effectiveness of our proposed systems biology approach, indicating the potential predictive power of integrating computational and experimental approaches.

  19. DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks.

    Science.gov (United States)

    Nguyen, Lan K; Degasperi, Andrea; Cotter, Philip; Kholodenko, Boris N

    2015-07-29

    Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Diseases such as cancer commonly arise due to changes in the dynamics of signalling and gene regulatory networks caused by genetic alternations. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. More efficient and reliable methods for multi-dimensional systems analysis and visualisation are thus required. To address this issue, we here present an integrated analysis and visualisation framework for high-dimensional network behaviour which exploits the advantages provided by parallel coordinates graphs. We demonstrate the applicability of the framework, named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC), to a variety of signalling networks ranging in topological wirings and dynamic properties. The framework was proved useful in acquiring an integrated understanding of systems behaviour.

  20. Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions

    Science.gov (United States)

    2014-01-01

    Background An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network. Results We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes. Conclusions The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure. PMID:25189194

  1. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

    Directory of Open Access Journals (Sweden)

    Shuqiang Wang

    2014-01-01

    Full Text Available Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC method based on sequential Monte Carlo (SMC to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

  2. Mulan: Multiple-Sequence Local Alignment and Visualization for Studying Function and Evolution

    Energy Technology Data Exchange (ETDEWEB)

    Ovcharenko, I; Loots, G; Giardine, B; Hou, M; Ma, J; Hardison, R; Stubbs, L; Miller, W

    2004-07-14

    Multiple sequence alignment analysis is a powerful approach for understanding phylogenetic relationships, annotating genes and detecting functional regulatory elements. With a growing number of partly or fully sequenced vertebrate genomes, effective tools for performing multiple comparisons are required to accurately and efficiently assist biological discoveries. Here we introduce Mulan (http://mulan.dcode.org/), a novel method and a network server for comparing multiple draft and finished-quality sequences to identify functional elements conserved over evolutionary time. Mulan brings together several novel algorithms: the tba multi-aligner program for rapid identification of local sequence conservation and the multiTF program for detecting evolutionarily conserved transcription factor binding sites in multiple alignments. In addition, Mulan supports two-way communication with the GALA database; alignments of multiple species dynamically generated in GALA can be viewed in Mulan, and conserved transcription factor binding sites identified with Mulan/multiTF can be integrated and overlaid with extensive genome annotation data using GALA. Local multiple alignments computed by Mulan ensure reliable representation of short-and large-scale genomic rearrangements in distant organisms. Mulan allows for interactive modification of critical conservation parameters to differentially predict conserved regions in comparisons of both closely and distantly related species. We illustrate the uses and applications of the Mulan tool through multi-species comparisons of the GATA3 gene locus and the identification of elements that are conserved differently in avians than in other genomes allowing speculation on the evolution of birds. Source code for the aligners and the aligner-evaluation software can be freely downloaded from http://bio.cse.psu.edu/.

  3. Vapor-Phase Epitaxial Growth of Aligned Nanowire Networks of Cesium Lead Halide Perovskites (CsPbX3, X = Cl, Br, I).

    Science.gov (United States)

    Chen, Jie; Fu, Yongping; Samad, Leith; Dang, Lianna; Zhao, Yuzhou; Shen, Shaohua; Guo, Liejin; Jin, Song

    2017-01-11

    With the intense interest in inorganic cesium lead halide perovskites and their nanostructures for optoelectronic applications, high-quality crystalline nanomaterials with controllable morphologies and growth directions are desirable. Here, we report a vapor-phase epitaxial growth of horizontal single-crystal CsPbX3 (X = Cl, Br, I) nanowires (NWs) and microwires (MWs) with controlled crystallographic orientations on the (001) plane of phlogopite and muscovite mica. Moreover, single NWs, Y-shaped branches, interconnected NW or MW networks with 6-fold symmetry, and, eventually, highly dense epitaxial network of CsPbBr3 with nearly continuous coverage were controllably obtained by varying the growth time. Detailed structural study revealed that the CsPbBr3 wires grow along the [001] directions and have the (100) facets exposed. The incommensurate heteroepitaxial lattice match between the CsPbBr3 and mica crystal structures and the growth mechanism of these horizontal wires due to asymmetric lattice mismatch were proposed. Furthermore, the photoluminescence waveguiding and good performance from the photodetector device fabricated with these CsPbBr3 networks demonstrated that these well-connected CsPbBr3 NWs could serve as straightforward platforms for fundamental studies and optoelectronic applications.

  4. Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

  5. Galaxy alignments: An overview

    CERN Document Server

    Joachimi, Benjamin; Kitching, Thomas D; Leonard, Adrienne; Mandelbaum, Rachel; Schäfer, Björn Malte; Sifón, Cristóbal; Hoekstra, Henk; Kiessling, Alina; Kirk, Donnacha; Rassat, Anais

    2015-01-01

    The alignments between galaxies, their underlying matter structures, and the cosmic web constitute vital ingredients for a comprehensive understanding of gravity, the nature of matter, and structure formation in the Universe. We provide an overview on the state of the art in the study of these alignment processes and their observational signatures, aimed at a non-specialist audience. The development of the field over the past one hundred years is briefly reviewed. We also discuss the impact of galaxy alignments on measurements of weak gravitational lensing, and discuss avenues for making theoretical and observational progress over the coming decade.

  6. Discriminative Shape Alignment

    DEFF Research Database (Denmark)

    Loog, M.; de Bruijne, M.

    2009-01-01

    The alignment of shape data to a common mean before its subsequent processing is an ubiquitous step within the area shape analysis. Current approaches to shape analysis or, as more specifically considered in this work, shape classification perform the alignment in a fully unsupervised way......, not taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two......-dimensional shapes from a two-class recognition problem....

  7. 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

  8. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

    Directory of Open Access Journals (Sweden)

    Alex Greenfield

    Full Text Available BACKGROUND: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. METHODOLOGY: We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. CONCLUSION/SIGNIFICANCE: Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations. Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

  9. Artificial neural networks in biology and chemistry: the evolution of a new analytical tool.

    Science.gov (United States)

    Cartwright, Hugh M

    2008-01-01

    Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the neural network has been developed in the last decade into a powerful computational tool. Its use now spans all areas of science, from the physical sciences and engineering to the life sciences and allied subjects. Applications range from the assessment of epidemiological data or the deconvolution of spectra to highly practical applications, such as the electronic nose. This introductory chapter considers briefly the growth in the use of neural networks and provides some general background in preparation for the more detailed chapters that follow.

  10. 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

  11. [Tabular excel editor for analysis of aligned nucleotide sequences].

    Science.gov (United States)

    Demkin, V V

    2010-01-01

    Excel platform was used for transition of results of multiple aligned nucleotide sequences obtained using the BLAST network service to the form appropriate for visual analysis and editing. Two macros operators for MS Excel 2007 were constructed. The array of aligned sequences transformed into Excel table and processed using macros operators is more appropriate for analysis than initial html data.

  12. Multiple sequence alignment with user-defined anchor points

    Directory of Open Access Journals (Sweden)

    Pöhler Dirk

    2006-04-01

    Full Text Available Abstract Background Automated software tools for multiple alignment often fail to produce biologically meaningful results. In such situations, expert knowledge can help to improve the quality of alignments. Results Herein, we describe a semi-automatic version of the alignment program DIALIGN that can take pre-defined constraints into account. It is possible for the user to specify parts of the sequences that are assumed to be homologous and should therefore be aligned to each other. Our software program can use these sites as anchor points by creating a multiple alignment respecting these constraints. This way, our alignment method can produce alignments that are biologically more meaningful than alignments produced by fully automated procedures. As a demonstration of how our method works, we apply our approach to genomic sequences around the Hox gene cluster and to a set of DNA-binding proteins. As a by-product, we obtain insights about the performance of the greedy algorithm that our program uses for multiple alignment and about the underlying objective function. This information will be useful for the further development of DIALIGN. The described alignment approach has been integrated into the TRACKER software system.

  13. 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...

  14. Models of neural networks temporal aspects of coding and information processing in biological systems

    CERN Document Server

    Hemmen, J; Schulten, Klaus

    1994-01-01

    Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...

  15. A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks

    KAUST Repository

    Lautenschläger, Karin

    2013-06-01

    Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (±0.6)×104cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, sofar for unknown reasons, recorded a slight but significantly higher TCC (1.3(±0.1)×105cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used provides a powerful

  16. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

    Since September, the muon alignment system shifted from a mode of hardware installation and commissioning to operation and data taking. All three optical subsystems (Barrel, Endcap and Link alignment) have recorded data before, during and after CRAFT, at different magnetic fields and during ramps of the magnet. This first data taking experience has several interesting goals: •    study detector deformations and movements under the influence of the huge magnetic forces; •    study the stability of detector structures and of the alignment system over long periods, •    study geometry reproducibility at equal fields (specially at 0T and 3.8T); •    reconstruct B=0T geometry and compare to nominal/survey geometries; •    reconstruct B=3.8T geometry and provide DT and CSC alignment records for CMSSW. However, the main goal is to recons...

  17. MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network.

    Science.gov (United States)

    Hwang, Sohyun; Kim, Eiru; Yang, Sunmo; Marcotte, Edward M; Lee, Insuk

    2014-07-01

    Despite recent advances in human genetics, model organisms are indispensable for human disease research. Most human disease pathways are evolutionally conserved among other species, where they may phenocopy the human condition or be associated with seemingly unrelated phenotypes. Much of the known gene-to-phenotype association information is distributed across diverse databases, growing rapidly due to new experimental techniques. Accessible bioinformatics tools will therefore facilitate translation of discoveries from model organisms into human disease biology. Here, we present a web-based discovery tool for human disease studies, MORPHIN (model organisms projected on a human integrated gene network), which prioritizes the most relevant human diseases for a given set of model organism genes, potentially highlighting new model systems for human diseases and providing context to model organism studies. Conceptually, MORPHIN investigates human diseases by an orthology-based projection of a set of model organism genes onto a genome-scale human gene network. MORPHIN then prioritizes human diseases by relevance to the projected model organism genes using two distinct methods: a conventional overlap-based gene set enrichment analysis and a network-based measure of closeness between the query and disease gene sets capable of detecting associations undetectable by the conventional overlap-based methods. MORPHIN is freely accessible at http://www.inetbio.org/morphin.

  18. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

    Science.gov (United States)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-07-12

    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 homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

  19. Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

    Science.gov (United States)

    Williams, Tim D; Turan, Nil; Diab, Amer M; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L; Hrydziuszko, Olga; Lyons, Brett P; Stentiford, Grant D; Herbert, John M; Abraham, Joseph K; Katsiadaki, Ioanna; Leaver, Michael J; Taggart, John B; George, Stephen G; Viant, Mark R; Chipman, Kevin J; Falciani, Francesco

    2011-08-01

    The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

  20. Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

    Directory of Open Access Journals (Sweden)

    Tim D Williams

    2011-08-01

    Full Text Available The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

  1. Epigenetics and its Implications for Plant Biology. 1. The Epigenetic Network in Plants

    OpenAIRE

    GRANT-DOWNTON, R. T.; Dickinson, H. G.

    2005-01-01

    • Background Epigenetics has rapidly evolved in the past decade to form an exciting new branch of biology. In modern terms, ‘epigenetics’ studies molecular pathways regulating how the genes are packaged in the chromosome and expressed, with effects that are heritable between cell divisions and even across generations.

  2. WormBase: network access to the genome and biology of Caenorhabditis elegans.

    Science.gov (United States)

    Stein, L; Sternberg, P; Durbin, R; Thierry-Mieg, J; Spieth, J

    2001-01-01

    WormBase (http://www.wormbase.org) is a web-based resource for the Caenorhabditis elegans genome and its biology. It builds upon the existing ACeDB database of the C.elegans genome by providing data curation services, a significantly expanded range of subject areas and a user-friendly front end.

  3. Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions.

    Directory of Open Access Journals (Sweden)

    Paul De Barro

    Full Text Available BACKGROUND: A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally. METHODOLOGY/PRINCIPAL FINDINGS: Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010. Only two species proposed in Dinsdale et al. (2010 departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED and Middle East - Asia Minor 1 (MEAM1, showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region. CONCLUSION/SIGNIFICANCE: The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of

  4. 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.

  5. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2012-01-01

      A new muon alignment has been produced for 2012 A+B data reconstruction. It uses the latest Tracker alignment and single-muon data samples to align both DTs and CSCs. Physics validation has been performed and shows a modest improvement in stand-alone muon momentum resolution in the barrel, where the alignment is essentially unchanged from the previous version. The reference-target track-based algorithm using only collision muons is employed for the first time to align the CSCs, and a substantial improvement in resolution is observed in the endcap and overlap regions for stand-alone muons. This new alignment is undergoing the approval process and is expected to be deployed as part of a new global tag in the beginning of December. The pT dependence of the φ-bias in curvature observed in Monte Carlo was traced to a relative vertical misalignment between the Tracker and barrel muon systems. Moving the barrel as a whole to match the Tracker cures this pT dependence, leaving only the &phi...

  6. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    S. Szillasi

    2013-01-01

    The CMS detector has been gradually opened and whenever a wheel became exposed the first operation was the removal of the MABs, the sensor structures of the Hardware Barrel Alignment System. By the last days of June all 36 MABs have arrived at the Alignment Lab at the ISR where, as part of the Alignment Upgrade Project, they are refurbished with new Survey target holders. Their electronic checkout is on the way and finally they will be recalibrated. During LS1 the alignment system will be upgraded in order to allow more precise reconstruction of the MB4 chambers in Sector 10 and Sector 4. This requires new sensor components, so called MiniMABs (pictured below), that have already been assembled and calibrated. Image 6: Calibrated MiniMABs are ready for installation For the track-based alignment, the systematic uncertainties of the algorithm are under scrutiny: this study will enable the production of an improved Monte Carlo misalignment scenario and to update alignment position errors eventually, crucial...

  7. Incremental Alignment Manifold Learning

    Institute of Scientific and Technical Information of China (English)

    Zhi Han; De-Yu Meng; Zong-Sen Xu; Nan-Nan Gu

    2011-01-01

    A new manifold learning method, called incremental alignment method (IAM), is proposed for nonlinear dimensionality reduction of high dimensional data with intrinsic low dimensionality. The main idea is to incrementally align low-dimensional coordinates of input data patch-by-patch to iteratively generate the representation of the entire dataset. The method consists of two major steps, the incremental step and the alignment step. The incremental step incrementally searches neighborhood patch to be aligned in the next step, and the alignment step iteratively aligns the low-dimensional coordinates of the neighborhood patch searched to generate the embeddings of the entire dataset. Compared with the existing manifold learning methods, the proposed method dominates in several aspects: high efficiency, easy out-of-sample extension, well metric-preserving, and averting of the local minima issue. All these properties are supported by a series of experiments performed on the synthetic and real-life datasets. In addition, the computational complexity of the proposed method is analyzed, and its efficiency is theoretically argued and experimentally demonstrated.

  8. Erasing errors due to alignment ambiguity when estimating positive selection.

    Science.gov (United States)

    Redelings, Benjamin

    2014-08-01

    Current estimates of diversifying positive selection rely on first having an accurate multiple sequence alignment. Simulation studies have shown that under biologically plausible conditions, relying on a single estimate of the alignment from commonly used alignment software can lead to unacceptably high false-positive rates in detecting diversifying positive selection. We present a novel statistical method that eliminates excess false positives resulting from alignment error by jointly estimating the degree of positive selection and the alignment under an evolutionary model. Our model treats both substitutions and insertions/deletions as sequence changes on a tree and allows site heterogeneity in the substitution process. We conduct inference starting from unaligned sequence data by integrating over all alignments. This approach naturally accounts for ambiguous alignments without requiring ambiguously aligned sites to be identified and removed prior to analysis. We take a Bayesian approach and conduct inference using Markov chain Monte Carlo to integrate over all alignments on a fixed evolutionary tree topology. We introduce a Bayesian version of the branch-site test and assess the evidence for positive selection using Bayes factors. We compare two models of differing dimensionality using a simple alternative to reversible-jump methods. We also describe a more accurate method of estimating the Bayes factor using Rao-Blackwellization. We then show using simulated data that jointly estimating the alignment and the presence of positive selection solves the problem with excessive false positives from erroneous alignments and has nearly the same power to detect positive selection as when the true alignment is known. We also show that samples taken from the posterior alignment distribution using the software BAli-Phy have substantially lower alignment error compared with MUSCLE, MAFFT, PRANK, and FSA alignments.

  9. Enhanced Dynamic Algorithm of Genome Sequence Alignments

    Directory of Open Access Journals (Sweden)

    Arabi E. keshk

    2014-05-01

    Full Text Available The merging of biology and computer science has created a new field called computational biology that explore the capacities of computers to gain knowledge from biological data, bioinformatics. Computational biology is rooted in life sciences as well as computers, information sciences, and technologies. The main problem in computational biology is sequence alignment that is a way of arranging the sequences of DNA, RNA or protein to identify the region of similarity and relationship between sequences. This paper introduces an enhancement of dynamic algorithm of genome sequence alignment, which called EDAGSA. It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution with decreasing the execution time and therefore the performance is increased. To illustrate the effectiveness of optimizing the performance of the proposed algorithm, it is compared with the traditional methods such as Needleman-Wunsch, Smith-Waterman and longest common subsequence algorithms. Also, database is implemented for using the algorithm in multi-sequence alignments for searching the optimal sequence that matches the given sequence.

  10. 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.

  11. Theoretical approaches to holistic biological features: Pattern formation, neural networks and the brain-mind relation

    Indian Academy of Sciences (India)

    Alfred Gierer

    2002-06-01

    The topic of this article is the relation between bottom-up and top-down, reductionist and ``holistic” approaches to the solution of basic biological problems. While there is no doubt that the laws of physics apply to all events in space and time, including the domains of life, understanding biology depends not only on elucidating the role of the molecules involved, but, to an increasing extent, on systems theoretical approaches in diverse fields of the life sciences. Examples discussed in this article are the generation of spatial patterns in development by the interplay of autocatalysis and lateral inhibition; the evolution of integrating capabilities of the human brain, such as cognition-based empathy; and both neurobiological and epistemological aspects of scientific theories of consciousness and the mind.

  12. MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease

    Indian Academy of Sciences (India)

    Jin Wang; Subrata Sen

    2011-08-01

    MicroRNAs (miRs), the 17- to 25-nucleotide-long non-coding RNAs, regulate expression of approximately 30% of the protein-coding genes at the post-transcriptional level and have emerged as critical components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis. Abnormal expression levels of miRs, regulating critical cancerassociated pathways, have been implicated to play important roles in the oncogenic processes, functioning both as oncogenes and as tumour suppressor genes. Elucidation of the genetic networks regulated by the abnormally expressing miRs in cancer cells is proving to be extremely significant in understanding the role of these miRs in the induction of malignant-transformation-associated phenotypic changes. As a result, the miRs involved in the oncogenic transformation process are being investigated as novel biomarkers of disease detection and prognosis as well as potential therapeutic targets for human cancers. In this \\article, we review the existing literature in the field documenting the significance of aberrantly expressed miRs in human pancreatic cancer and discuss how the oncogenic miRs may be involved in the genetic networks regulating functional pathways deregulated in this malignancy.

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

    OpenAIRE

    Riccardo, Flavia; Shigematsu, Mika; Chow, Catherine; Linge, Jens; Doherty, Brian; DENTE Maria Grazia; Declich, Silvia; BARKER Mike; Barboza, Philippe; Vaillant, Laetitia; Donachie, Alastair; Mawudeku, Abla; Arthur, Ray; MCKNIGHT Jason

    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, radio-nuclear (CBRN) and pandemic influenza threats. At a time when no international collaborations existed in the field of event based surveillance, EAR’s innovative approach consisted in the involvement of both epidemic intelligence experts and internet-based biosur...

  14. Curriculum Alignment Research Suggests that Alignment Can Improve Student Achievement

    Science.gov (United States)

    Squires, David

    2012-01-01

    Curriculum alignment research has developed showing the relationship among three alignment categories: the taught curriculum, the tested curriculum and the written curriculum. Each pair (for example, the taught and the written curriculum) shows a positive impact for aligning those results. Following this, alignment results from the Third…

  15. An Ant-Based Model for Multiple Sequence Alignment

    CERN Document Server

    Guinand, Frédéric

    2008-01-01

    Multiple sequence alignment is a key process in today's biology, and finding a relevant alignment of several sequences is much more challenging than just optimizing some improbable evaluation functions. Our approach for addressing multiple sequence alignment focuses on the building of structures in a new graph model: the factor graph model. This model relies on block-based formulation of the original problem, formulation that seems to be one of the most suitable ways for capturing evolutionary aspects of alignment. The structures are implicitly built by a colony of ants laying down pheromones in the factor graphs, according to relations between blocks belonging to the different sequences.

  16. MaxAlign: maximizing usable data in an alignment

    DEFF Research Database (Denmark)

    Oliveira, Rodrigo Gouveia; Sackett, Peter Wad; Pedersen, Anders Gorm

    2007-01-01

    BACKGROUND: The presence of gaps in an alignment of nucleotide or protein sequences is often an inconvenience for bioinformatical studies. In phylogenetic and other analyses, for instance, gapped columns are often discarded entirely from the alignment. RESULTS: MaxAlign is a program that optimizes...... the alignment prior to such analyses. Specifically, it maximizes the number of nucleotide (or amino acid) symbols that are present in gap-free columns - the alignment area - by selecting the optimal subset of sequences to exclude from the alignment. MaxAlign can be used prior to phylogenetic and bioinformatical...... analyses as well as in other situations where this form of alignment improvement is useful. In this work we test MaxAlign's performance in these tasks and compare the accuracy of phylogenetic estimates including and excluding gapped columns from the analysis, with and without processing with MaxAlign...

  17. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    M. Dallavalle

    2013-01-01

    A new Muon misalignment scenario for 2011 (7 TeV) Monte Carlo re-processing was re-leased. The scenario is based on running of standard track-based reference-target algorithm (exactly as in data) using single-muon simulated sample (with the transverse-momentum spectrum matching data). It used statistics similar to what was used for alignment with 2011 data, starting from an initially misaligned Muon geometry from uncertainties of hardware measurements and using the latest Tracker misalignment geometry. Validation of the scenario (with muons from Z decay and high-pT simulated muons) shows that it describes data well. The study of systematic uncertainties (dominant by now due to huge amount of data collected by CMS and used for muon alignment) is finalised. Realistic alignment position errors are being obtained from the estimated uncertainties and are expected to improve the muon reconstruction performance. Concerning the Hardware Alignment System, the upgrade of the Barrel Alignment is in progress. By now, d...

  18. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2010-01-01

    For the last three months, the Muon Alignment group has focussed on providing a new, improved set of alignment constants for the end-of-year data reprocessing. These constants were delivered on time and approved by the CMS physics validation team on November 17. The new alignment incorporates several improvements over the previous one from March for nearly all sub-systems. Motivated by the loss of information from a hardware failure in May (an entire MAB was lost), the optical barrel alignment has moved from a modular, super-plane reconstruction, to a full, single loop calculation of the entire geometry for all DTs in stations 1, 2 and 3. This makes better use of the system redundancy, mitigating the effect of the information loss. Station 4 is factorised and added afterwards to make the system smaller (and therefore faster to run), and also because the MAB calibration at the MB4 zone is less precise. This new alignment procedure was tested at 0 T against photogrammetry resulting in precisions of the order...

  19. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    Gervasio Gomez

    2012-01-01

      The new alignment for the DT chambers has been successfully used in physics analysis starting with the 52X Global Tag. The remaining main areas of development over the next few months will be preparing a new track-based CSC alignment and producing realistic APEs (alignment position errors) and MC misalignment scenarios to match the latest muon alignment constants. Work on these items has been delayed from the intended timeline, mostly due to a large involvement of the muon alignment man-power in physics analyses over the first half of this year. As CMS keeps probing higher and higher energies, special attention must be paid to the reconstruction of very-high-energy muons. Recent muon POG reports from mid-June show a φ-dependence in curvature bias in Monte Carlo samples. This bias is observed already at the tracker level, where it is constant with muon pT, while it grows with pT as muon chamber information is added to the tracks. Similar studies show a much smaller effect in data, at le...

  20. Ergodic Secret Alignment

    CERN Document Server

    Bassily, Raef

    2010-01-01

    In this paper, we introduce two new achievable schemes for the fading multiple access wiretap channel (MAC-WT). In the model that we consider, we assume that perfect knowledge of the state of all channels is available at all the nodes in a causal fashion. Our schemes use this knowledge together with the time varying nature of the channel model to align the interference from different users at the eavesdropper perfectly in a one-dimensional space while creating a higher dimensionality space for the interfering signals at the legitimate receiver hence allowing for better chance of recovery. While we achieve this alignment through signal scaling at the transmitters in our first scheme (scaling based alignment (SBA)), we let nature provide this alignment through the ergodicity of the channel coefficients in the second scheme (ergodic secret alignment (ESA)). For each scheme, we obtain the resulting achievable secrecy rate region. We show that the secrecy rates achieved by both schemes scale with SNR as 1/2log(SNR...

  1. Partial Alignment for Improvement of Beam Transmission at KOMAC

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Dong-Hyuk; Kim, Dae-Il; Ahn, Tae-Sung; Kim, Han-Sung; Kwon, Hyeok-Jung; Cho, Yong-Sub [KOMAC, Gyeongju (Korea, Republic of)

    2015-05-15

    The ion source and steering magnets were aligned for improving the beam transmission. It is expected that the re-alignment of accelerator components can reduce the beam loss which can occur for the dislocation among them. The center displacements of RFQ and 20MeV DTL are different to 100MeV DTL, so it is necessary to re-align in next maintenance period. 100MeV proton linac placed in KOMAC (Korea Multi-purpose Accelerator Complex) has been operated and provided to beam users. There are two maintenance periods every year, winter (Jan-Feb) and summer (Jul-Aug). In maintenance period, proton linac is re-aligned for the improvement of beam transmission. 4 newly steering magnet are installed in beam line. To align the steering magnet, network align in tunnel is measured using by laser tracker. In addition, the position of ion source is away from the position of RFQ in the result of the survey of network align. The alignment of steering magnet after installation is performed. At the same time, the position of accelerator component is checked and aligned partially.

  2. Yeast Biological Networks Unfold the Interplay of Antioxidants, Genome and Phenotype, and Reveal a Novel Regulator of the Oxidative Stress Response

    DEFF Research Database (Denmark)

    Otero, José Manuel; Papadakis, M.A.; Udatha, D.B.R.K.G.

    2010-01-01

    Background: Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional...... network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae. Methodology/Principal Findings: By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions...

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

    OpenAIRE

    SOMMER, BJÖRN; Tiys, Evgeny S; Kormeier, Benjamin; Hippe, Klaus; Janowski, Sebastian J.; Ivanisenko, Timofey V; Bragin, Anatoly O.; Arrigo, Patrizio; Demenkov, Pavel S; Kochetov, Alexey V.; Ivanisenko, Vladimir A; Kolchanov, Nikolay A.; Hofestädt, Ralf

    2010-01-01

    Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to creat...

  4. A Coauthorship Network as an Indicator for Scientifi c Collaboration: A Case Study for the School of Biology and Biotechnology, National University of Mongolia

    OpenAIRE

    Bazartseren Boldgiv

    2012-01-01

    This case study analyzes coauthorship collaboration, or lack thereof, among individual faculty members and departments in the School of Biology and Biotechnology of the National University of Mongolia. I found that publication rates and coauthorship networks in impact-factor journals between 2008 and 2012 (as of October 31, 2012) are highly variable among the eight biology departments we studied, both within and among departments. Even in the best ...

  5. Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology.

    Directory of Open Access Journals (Sweden)

    David A Ball

    Full Text Available The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.

  6. Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology.

    Science.gov (United States)

    Ball, David A; Lux, Matthew W; Adames, Neil R; Peccoud, Jean

    2014-01-01

    The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.

  7. Real-Time Illumination Invariant Face Detection Using Biologically Inspired Feature Set and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2014-06-01

    Full Text Available In recent years, face detection has been thoroughly studied due to its wide potential applications, including face recognition, human-computer interaction, video surveillance, etc.In this paper, a new and illumination invariant face detection method, based on features inspired by the human's visual cortexand applying BP neural network on the extracted featureset is proposed.A feature set is extracted from face and non-face images, by means of a feed-forward model, which contains a view and illumination invariant C2 features from all images in the dataset. Then, these C2 feature vector which derived from a cortex-like mechanism passed to a BP neural network. In the result part, the proposed approach is applied on FEI and Wild face detection databases and high accuracy rate is achieved. In addition, experimental results have demonstrated our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the first best result on all tested challenging face detection databases.

  8. Five Years of Designing Wireless Sensor Networks in the Doñana Biological Reserve (Spain): An Applications Approach

    Science.gov (United States)

    Larios, Diego F.; Barbancho, Julio; Sevillano, José L.; Rodríguez, Gustavo; Molina, Francisco J.; Gasull, Virginia G.; Mora-Merchan, Javier M.; León, Carlos

    2013-01-01

    Wireless Sensor Networks (WSNs) are a technology that is becoming very popular for many applications, and environmental monitoring is one of its most important application areas. This technology solves the lack of flexibility of wired sensor installations and, at the same time, reduces the deployment costs. To demonstrate the advantages of WSN technology, for the last five years we have been deploying some prototypes in the Doñana Biological Reserve, which is an important protected area in Southern Spain. These prototypes not only evaluate the technology, but also solve some of the monitoring problems that have been raised by biologists working in Doñana. This paper presents a review of the work that has been developed during these five years. Here, we demonstrate the enormous potential of using machine learning in wireless sensor networks for environmental and animal monitoring because this approach increases the amount of useful information and reduces the effort that is required by biologists in an environmental monitoring task. PMID:24025554

  9. Five Years of Designing Wireless Sensor Networks in the Doñana Biological Reserve (Spain: An Applications Approach

    Directory of Open Access Journals (Sweden)

    Javier M. Mora-Merchan

    2013-09-01

    Full Text Available Wireless Sensor Networks (WSNs are a technology that is becoming very popular for many applications, and environmental monitoring is one of its most important application areas. This technology solves the lack of flexibility of wired sensor installations and, at the same time, reduces the deployment costs. To demonstrate the advantages of WSN technology, for the last five years we have been deploying some prototypes in the Doñana Biological Reserve, which is an important protected area in Southern Spain. These prototypes not only evaluate the technology, but also solve some of the monitoring problems that have been raised by biologists working in Doñana. This paper presents a review of the work that has been developed during these five years. Here, we demonstrate the enormous potential of using machine learning in wireless sensor networks for environmental and animal monitoring because this approach increases the amount of useful information and reduces the effort that is required by biologists in an environmental monitoring task.

  10. TOLKIN--Tree of Life Knowledge and Information Network: filling a gap for collaborative research in biological systematics.

    Directory of Open Access Journals (Sweden)

    Reed S Beaman

    Full Text Available The development of biological informatics infrastructure capable of supporting growing data management and analysis environments is an increasing need within the systematics biology community. Although significant progress has been made in recent years on developing new algorithms and tools for analyzing and visualizing large phylogenetic data and trees, implementation of these resources is often carried out by bioinformatics experts, using one-off scripts. Therefore, a gap exists in providing data management support for a large set of non-technical users. The TOLKIN project (Tree of Life Knowledge and Information Network addresses this need by supporting capabilities to manage, integrate, and provide public access to molecular, morphological, and biocollections data and research outcomes through a collaborative, web application. This data management framework allows aggregation and import of sequences, underlying documentation about their source, including vouchers, tissues, and DNA extraction. It combines features of LIMS and workflow environments by supporting management at the level of individual observations, sequences, and specimens, as well as assembly and versioning of data sets used in phylogenetic inference. As a web application, the system provides multi-user support that obviates current practices of sharing data sets as files or spreadsheets via email.

  11. Strategic Alignment of Business Intelligence

    OpenAIRE

    Cederberg, Niclas

    2010-01-01

    This thesis is about the concept of strategic alignment of business intelligence. It is based on a theoretical foundation that is used to define and explain business intelligence, data warehousing and strategic alignment. By combining a number of different methods for strategic alignment a framework for alignment of business intelligence is suggested. This framework addresses all different aspects of business intelligence identified as relevant for strategic alignment of business intelligence...

  12. Orientation and Alignment Echoes

    CERN Document Server

    Karras, G; Billard, F; Lavorel, B; Hartmann, J -M; Faucher, O; Gershnabel, E; Prior, Y; Averbukh, I Sh

    2015-01-01

    We present what is probably the simplest classical system featuring the echo phenomenon - a collection of randomly oriented free rotors with dispersed rotational velocities. Following excitation by a pair of time-delayed impulsive kicks, the mean orientation/alignment of the ensemble exhibits multiple echoes and fractional echoes. We elucidate the mechanism of the echo formation by kick-induced filamentation of phase space, and provide the first experimental demonstration of classical alignment echoes in a thermal gas of CO_2 molecules excited by a pair of femtosecond laser pulses.

  13. Group Based Interference Alignment

    CERN Document Server

    Ma, Yanjun; Chen, Rui; Yao, Junliang

    2010-01-01

    in $K$-user single-input single-output (SISO) frequency selective fading interference channels, it is shown that the achievable multiplexing gain is almost surely $K/2$ by using interference alignment (IA). However when the signaling dimensions is limited, allocating all the resource to all the users simultaneously is not optimal. According to this problem, a group based interference alignment (GIA) scheme is proposed and a search algorithm is designed to get the group patterns and the resource allocation among them. Analysis results show that our proposed scheme achieves a higher multiplexing gain when the resource is limited.

  14. PILOT optical alignment

    Science.gov (United States)

    Longval, Y.; Mot, B.; Ade, P.; André, Y.; Aumont, J.; Baustista, L.; Bernard, J.-Ph.; Bray, N.; de Bernardis, P.; Boulade, O.; Bousquet, F.; Bouzit, M.; Buttice, V.; Caillat, A.; Charra, M.; Chaigneau, M.; Crane, B.; Crussaire, J.-P.; Douchin, F.; Doumayrou, E.; Dubois, J.-P.; Engel, C.; Etcheto, P.; Gélot, P.; Griffin, M.; Foenard, G.; Grabarnik, S.; Hargrave, P..; Hughes, A.; Laureijs, R.; Lepennec, Y.; Leriche, B.; Maestre, S.; Maffei, B.; Martignac, J.; Marty, C.; Marty, W.; Masi, S.; Mirc, F.; Misawa, R.; Montel, J.; Montier, L.; Narbonne, J.; Nicot, J.-M.; Pajot, F.; Parot, G.; Pérot, E.; Pimentao, J.; Pisano, G.; Ponthieu, N.; Ristorcelli, I.; Rodriguez, L.; Roudil, G.; Salatino, M.; Savini, G.; Simonella, O.; Saccoccio, M.; Tapie, P.; Tauber, J.; Torre, J.-P.; Tucker, C.

    2016-07-01

    PILOT is a balloon-borne astronomy experiment designed to study the polarization of dust emission in the diffuse interstellar medium in our Galaxy at wavelengths 240 μm with an angular resolution about two arcminutes. Pilot optics is composed an off-axis Gregorian type telescope and a refractive re-imager system. All optical elements, except the primary mirror, are in a cryostat cooled to 3K. We combined the optical, 3D dimensional measurement methods and thermo-elastic modeling to perform the optical alignment. The talk describes the system analysis, the alignment procedure, and finally the performances obtained during the first flight in September 2015.

  15. 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.

  16. 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.

  17. Sample-Align-D: A High Performance Multiple Sequence Alignment System using Phylogenetic Sampling and Domain Decomposition

    CERN Document Server

    Saeed, Fahad

    2009-01-01

    Multiple Sequence Alignment (MSA) is one of the most computationally intensive tasks in Computational Biology. Existing best known solutions for multiple sequence alignment take several hours (in some cases days) of computation time to align, for example, 2000 homologous sequences of average length 300. Inspired by the Sample Sort approach in parallel processing, in this paper we propose a highly scalable multiprocessor solution for the MSA problem in phylogenetically diverse sequences. Our method employs an intelligent scheme to partition the set of sequences into smaller subsets using kmer count based similarity index, referred to as k-mer rank. Each subset is then independently aligned in parallel using any sequential approach. Further fine tuning of the local alignments is achieved using constraints derived from a global ancestor of the entire set. The proposed Sample-Align-D Algorithm has been implemented on a cluster of workstations using MPI message passing library. The accuracy of the proposed solutio...

  18. Aligned Nanofibers for Regenerating Arteries, Nerves, and Muscles

    Science.gov (United States)

    McClendon, Mark Trosper

    Cells are the fundamental unit of the human body, and therefore the ability to control cell behavior is the most important challenge in regenerative medicine. Peptides are the language of biology which is why synthetic peptide amphiphile (PA) molecules hold great potential as a biomaterial. The work presented in this dissertation explores a variety of liquid crystalline PA nanofibers as a means for directing cell growth. Shaping the alignment of these nanofiber networks requires a deep understanding of their rheological properties which presents a difficult challenge as they exist in complex solid and liquid environments. Using PA molecules that self-assemble into high aspect ratio nanofibers and liquid crystalline solutions, this work investigates the influence of shear flow on macroscopic and microscopic nanofiber alignment. To this end, a shear force applied to PA solutions was systematically varied while the alignment was probed using small angle x-ray scattering. Nanofibers were found to respond to shear flow by aligning parallel to the flow direction. By changing pH and PA chemical sequence it was observed that increasing the interfiber electrostatic repulsive interactions resulted in a greater dependence on shear rate. Nanofiber solutions having greater repulsion did not drastically increase in alignment when the applied strain was increased by two orders of magnitude (1 s -1 to 100 s-1), while solutions with nanofibers having less repulsion increased there alignment four fold with the same strain increase. say exactly what you mean by resulted in greater dependence: did it result in fibers aligning under lower shear rates or higher rates--give the results Anionic PA solutions typically used to encapsulate living cells at neutral pH were found to require minimal shear rates, Bioluminescence and histology showed that stem cell engraftment into myofibers was greatly enhanced when delivered by PA gels compared to saline solution. The final section of this

  19. Aligning Theory with Practice

    Science.gov (United States)

    Kurz, Terri L.; Batarelo, Ivana

    2009-01-01

    This article describes a structure to help preservice teachers get invaluable field experience by aligning theory with practice supported by the integration of elementary school children into their university mathematics methodology course. This course structure allowed preservice teachers to learn about teaching mathematics in a nonthreatening…

  20. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez and Y. Pakhotin

    2012-01-01

      A new track-based alignment for the DT chambers is ready for deployment: an offline tag has already been produced which will become part of the 52X Global Tag. This alignment was validated within the muon alignment group both at low and high momentum using a W/Z skim sample. It shows an improved mass resolution for pairs of stand-alone muons, improved curvature resolution at high momentum, and improved DT segment extrapolation residuals. The validation workflow for high-momentum muons used to depend solely on the “split cosmics” method, looking at the curvature difference between muon tracks reconstructed in the upper or lower half of CMS. The validation has now been extended to include energetic muons decaying from heavily boosted Zs: the di-muon invariant mass for global and stand-alone muons is reconstructed, and the invariant mass resolution is compared for different alignments. The main areas of development over the next few months will be preparing a new track-based C...

  1. Alignment of concerns

    DEFF Research Database (Denmark)

    Andersen, Tariq Osman; Bansler, Jørgen P.; Kensing, Finn;

    2014-01-01

    The emergence of patient-centered eHealth systems introduces new challenges, where patients come to play an increasingly important role. Realizing the promises requires an in-depth understanding of not only the technology, but also the needs of both clinicians and patients. However, insights from...... as a design rationale for successful eHealth, termed 'alignment of concerns'....

  2. Aligning Mental Representations

    DEFF Research Database (Denmark)

    Kano Glückstad, Fumiko

    2013-01-01

    This work introduces a framework that implements asymmetric communication theory proposed by Sperber and Wilson [1]. The framework applies a generalization model known as the Bayesian model of generalization (BMG) [2] for aligning knowledge possessed by two communicating parties. The work focuses...

  3. New Markov-autocorrelation indices for re-evaluation of links in chemical and biological complex networks used in metabolomics, parasitology, neurosciences, and epidemiology.

    Science.gov (United States)

    González-Díaz, Humberto; Riera-Fernández, Pablo

    2012-12-21

    The development of new methods for the computational re-evaluation of links in chemical and biological complex networks is very important to save time and resources. The Moreau-Broto autocorrelation indices (MBis) are well-known topological indices (TIs) used in QSAR/QSPR studies to encode the structural information contained in molecular graphs. In addition, MBis and similar autocorrelation measures have been used to study other systems like, for example, proteins. In the present work, MBis are combined with Markov chains to develop a general class of stochastic MBis of order k (MB(k)) that is used to encode the structural information contained in different types of large complex networks. The MB(k) values obtained for the nodes (centralities) of these networks are used as input variables to seek QSPR-like equations (by means of linear discriminant analysis) in which the outputs are numerical scores S(L(ij)) that allow us to discriminate between connected and nonconnected nodes and therefore re-evaluate the connectivity of the whole network. The models developed in this work produced the following results in terms of overall accuracy for network reconstruction: metabolic networks (72.10%), parasite-host networks (88.70%), CoCoMac brain cortex coactivation network (81.89%), and fasciolosis spreading network (86.39%).

  4. Fast global sequence alignment technique

    KAUST Repository

    Bonny, Mohamed Talal

    2011-11-01

    Bioinformatics database is growing exponentially in size. Processing these large amount of data may take hours of time even if super computers are used. One of the most important processing tool in Bioinformatics is sequence alignment. We introduce fast alignment algorithm, called \\'Alignment By Scanning\\' (ABS), to provide an approximate alignment of two DNA sequences. We compare our algorithm with the wellknown sequence alignment algorithms, the \\'GAP\\' (which is heuristic) and the \\'Needleman-Wunsch\\' (which is optimal). The proposed algorithm achieves up to 51% enhancement in alignment score when it is compared with the GAP Algorithm. The evaluations are conducted using different lengths of DNA sequences. © 2011 IEEE.

  5. Visualization and analysis of a cardio vascular disease- and MUPP1-related biological network combining text mining and data warehouse approaches.

    Science.gov (United States)

    Sommer, Björn; Tiys, Evgeny S; Kormeier, Benjamin; Hippe, Klaus; Janowski, Sebastian J; Ivanisenko, Timofey V; Bragin, Anatoly O; Arrigo, Patrizio; Demenkov, Pavel S; Kochetov, Alexey V; Ivanisenko, Vladimir A; Kolchanov, Nikolay A; Hofestädt, Ralf

    2010-11-11

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

  6. Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.

    Science.gov (United States)

    Salazar, Diego A; Rodríguez-López, Alexander; Herreño, Angélica; Barbosa, Hector; Herrera, Juliana; Ardila, Andrea; Barreto, George E; González, Janneth; Alméciga-Díaz, Carlos J

    2016-02-01

    Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.

  7. MUSE alignment onto VLT

    Science.gov (United States)

    Laurent, Florence; Renault, Edgard; Boudon, Didier; Caillier, Patrick; Daguisé, Eric; Dupuy, Christophe; Jarno, Aurélien; Lizon, Jean-Louis; Migniau, Jean-Emmanuel; Nicklas, Harald; Piqueras, Laure

    2014-07-01

    MUSE (Multi Unit Spectroscopic Explorer) is a second generation Very Large Telescope (VLT) integral field spectrograph developed for the European Southern Observatory (ESO). It combines a 1' x 1' field of view sampled at 0.2 arcsec for its Wide Field Mode (WFM) and a 7.5"x7.5" field of view for its Narrow Field Mode (NFM). Both modes will operate with the improved spatial resolution provided by GALACSI (Ground Atmospheric Layer Adaptive Optics for Spectroscopic Imaging), that will use the VLT deformable secondary mirror and 4 Laser Guide Stars (LGS) foreseen in 2015. MUSE operates in the visible wavelength range (0.465-0.93 μm). A consortium of seven institutes is currently commissioning MUSE in the Very Large Telescope for the Preliminary Acceptance in Chile, scheduled for September, 2014. MUSE is composed of several subsystems which are under the responsibility of each institute. The Fore Optics derotates and anamorphoses the image at the focal plane. A Splitting and Relay Optics feed the 24 identical Integral Field Units (IFU), that are mounted within a large monolithic structure. Each IFU incorporates an image slicer, a fully refractive spectrograph with VPH-grating and a detector system connected to a global vacuum and cryogenic system. During 2012 and 2013, all MUSE subsystems were integrated, aligned and tested to the P.I. institute at Lyon. After successful PAE in September 2013, MUSE instrument was shipped to the Very Large Telescope in Chile where that was aligned and tested in ESO integration hall at Paranal. After, MUSE was directly transported, fully aligned and without any optomechanical dismounting, onto VLT telescope where the first light was overcame the 7th of February, 2014. This paper describes the alignment procedure of the whole MUSE instrument with respect to the Very Large Telescope (VLT). It describes how 6 tons could be move with accuracy better than 0.025mm and less than 0.25 arcmin in order to reach alignment requirements. The success

  8. Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment

    Directory of Open Access Journals (Sweden)

    Daniels Noah M

    2012-10-01

    Full Text Available Abstract Background The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult. Results We present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD. Conclusions Considering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.

  9. Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network

    Science.gov (United States)

    Hall, Molly A; Verma, Shefali S; Wallace, John; Lucas, Anastasia; Berg, Richard L; Connolly, John; Crawford, Dana C; Crosslin, David R; de Andrade, Mariza; Doheny, Kimberly F; Haines, Jonathan L; Harley, John B; Jarvik, Gail P; Kitchner, Terrie; Kuivaniemi, Helena; Larson, Eric B; Carrell, David S; Tromp, Gerard; Vrabec, Tamara R; Pendergrass, Sarah A; McCarty, Catherine A; Ritchie, Marylyn D

    2015-01-01

    Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset. PMID:25982363

  10. Aligning component upgrades

    Directory of Open Access Journals (Sweden)

    Roberto Di Cosmo

    2011-08-01

    Full Text Available Modern software systems, like GNU/Linux distributions or Eclipse-based development environment, are often deployed by selecting components out of large component repositories. Maintaining such software systems by performing component upgrades is a complex task, and the users need to have an expressive preferences language at their disposal to specify the kind of upgrades they are interested in. Recent research has shown that it is possible to develop solvers that handle preferences expressed as a combination of a few basic criteria used in the MISC competition, ranging from the number of new components to the freshness of the final configuration. In this work we introduce a set of new criteria that allow the users to specify their preferences for solutions with components aligned to the same upstream sources, provide an efficient encoding and report on the experimental results that prove that optimising these alignment criteria is a tractable problem in practice.

  11. Inflation by alignment

    Energy Technology Data Exchange (ETDEWEB)

    Burgess, C.P. [PH -TH Division, CERN,CH-1211, Genève 23 (Switzerland); Department of Physics & Astronomy, McMaster University,1280 Main Street West, Hamilton ON (Canada); Perimeter Institute for Theoretical Physics,31 Caroline Street North, Waterloo ON (Canada); Roest, Diederik [Van Swinderen Institute for Particle Physics and Gravity, University of Groningen,Nijenborgh 4, 9747 AG Groningen (Netherlands)

    2015-06-08

    Pseudo-Goldstone bosons (pGBs) can provide technically natural inflatons, as has been comparatively well-explored in the simplest axion examples. Although inflationary success requires trans-Planckian decay constants, f≳M{sub p}, several mechanisms have been proposed to obtain this, relying on (mis-)alignments between potential and kinetic energies in multiple-field models. We extend these mechanisms to a broader class of inflationary models, including in particular the exponential potentials that arise for pGB potentials based on noncompact groups (and so which might apply to moduli in an extra-dimensional setting). The resulting potentials provide natural large-field inflationary models and can predict a larger primordial tensor signal than is true for simpler single-field versions of these models. In so doing we provide a unified treatment of several alignment mechanisms, showing how each emerges as a limit of the more general setup.

  12. Alignment of concerns

    DEFF Research Database (Denmark)

    Andersen, Tariq Osman; Bansler, Jørgen P.; Kensing, Finn;

    E-health promises to enable and support active patient participation in chronic care. However, these fairly recent innovations are complicated matters and emphasize significant challenges, such as patients’ and clinicians’ different ways of conceptualizing disease and illness. Informed by insight...... from medical phenomenology and our own empirical work in telemonitoring and medical care of heart patients, we propose a design rationale for e-health systems conceptualized as the ‘alignment of concerns’....

  13. Nuclear reactor alignment plate configuration

    Energy Technology Data Exchange (ETDEWEB)

    Altman, David A; Forsyth, David R; Smith, Richard E; Singleton, Norman R

    2014-01-28

    An alignment plate that is attached to a core barrel of a pressurized water reactor and fits within slots within a top plate of a lower core shroud and upper core plate to maintain lateral alignment of the reactor internals. The alignment plate is connected to the core barrel through two vertically-spaced dowel pins that extend from the outside surface of the core barrel through a reinforcement pad and into corresponding holes in the alignment plate. Additionally, threaded fasteners are inserted around the perimeter of the reinforcement pad and into the alignment plate to further secure the alignment plate to the core barrel. A fillet weld also is deposited around the perimeter of the reinforcement pad. To accomodate thermal growth between the alignment plate and the core barrel, a gap is left above, below and at both sides of one of the dowel pins in the alignment plate holes through with the dowel pins pass.

  14. Orbit IMU alignment: Error analysis

    Science.gov (United States)

    Corson, R. W.

    1980-01-01

    A comprehensive accuracy analysis of orbit inertial measurement unit (IMU) alignments using the shuttle star trackers was completed and the results are presented. Monte Carlo techniques were used in a computer simulation of the IMU alignment hardware and software systems to: (1) determine the expected Space Transportation System 1 Flight (STS-1) manual mode IMU alignment accuracy; (2) investigate the accuracy of alignments in later shuttle flights when the automatic mode of star acquisition may be used; and (3) verify that an analytical model previously used for estimating the alignment error is a valid model. The analysis results do not differ significantly from expectations. The standard deviation in the IMU alignment error for STS-1 alignments was determined to the 68 arc seconds per axis. This corresponds to a 99.7% probability that the magnitude of the total alignment error is less than 258 arc seconds.

  15. Seeking the perfect alignment

    CERN Multimedia

    2002-01-01

    The first full-scale tests of the ATLAS Muon Spectrometer are about to begin in Prévessin. The set-up includes several layers of Monitored Drift Tubes Chambers (MDTs) and will allow tests of the performance of the detectors and of their highly accurate alignment system.   Monitored Drift Chambers in Building 887 in Prévessin, where they are just about to be tested. Muon chambers are keeping the ATLAS Muon Spectrometer team quite busy this summer. Now that most people go on holiday, the beam and alignment tests for these chambers are just starting. These chambers will measure with high accuracy the momentum of high-energy muons, and this implies very demanding requirements for their alignment. The MDT chambers consist of drift tubes, which are gas-filled metal tubes, 3 cm in diameter, with wires running down their axes. With high voltage between the wire and the tube wall, the ionisation due to traversing muons is detected as electrical pulses. With careful timing of the pulses, the position of the muon t...

  16. RECAT - Redundant Channel Alignment Technique

    Science.gov (United States)

    2016-06-07

    distribution unlimited 13. SUPPLEMENTARY NOTES NUWC2015 14. ABSTRACT A problem in the analog-to- digital , (A/D), conversion of broadband tape recorded...Alignment Technique, is used to align data taken on one pass with data from any other pass. The accuracy of this alignment is a function of the digital ...Redundant Channel Alignment Technique; analog-to- digital ; A/D; Broadband Bearing Time Processing 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

  17. Method for alignment of microwires

    Energy Technology Data Exchange (ETDEWEB)

    Beardslee, Joseph A.; Lewis, Nathan S.; Sadtler, Bryce

    2017-01-24

    A method of aligning microwires includes modifying the microwires so they are more responsive to a magnetic field. The method also includes using a magnetic field so as to magnetically align the microwires. The method can further include capturing the microwires in a solid support structure that retains the longitudinal alignment of the microwires when the magnetic field is not applied to the microwires.

  18. 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.

  19. Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

    Science.gov (United States)

    Carrieri, Arthur H; Copper, Jack; Owens, David J; Roese, Erik S; Bottiger, Jerold R; Everly, Robert D; Hung, Kevin C

    2010-01-20

    An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance.

  20. A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure

    Science.gov (United States)

    Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram

    2016-05-01

    Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.

  1. A paradigm for viewing biologic systems as scale-free networks based on energy efficiency: implications for present therapies and the future of evolution.

    Science.gov (United States)

    Yun, Anthony J; Lee, Patrick Y; Doux, John D

    2006-01-01

    A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death.

  2. General space-efficient sampling algorithm for suboptimal alignment

    Institute of Scientific and Technical Information of China (English)

    CHEN; Yi; BAI; Yan-qin

    2009-01-01

    Suboptimal alignments always reveal additional interesting biological features and have been successfully used to informally estimate the significance of an optimal alignment. Besides, traditional dynamic programming algorithms for sequence comparison require quadratic space, and hence are infeasible for long protein or DNA sequences. In this paper, a space-efficient sampling algorithm for computing suboptimal alignments is described. The algorithm uses a general gap model, where the cost associated with gaps is given by an affine score, and randomly selects an alignment according to the distribution of weights of all potential alignments. If x and y are two sequences with lengths n and m, respectively, then the space requirement of this algorithm is linear to the sum of n and m. Finally, an example illustrates the utility of the algorithm.

  3. Improving your target-template alignment with MODalign.

    KAUST Repository

    Barbato, Alessandro

    2012-02-04

    SUMMARY: MODalign is an interactive web-based tool aimed at helping protein structure modelers to inspect and manually modify the alignment between the sequences of a target protein and of its template(s). It interactively computes, displays and, upon modification of the target-template alignment, updates the multiple sequence alignments of the two protein families, their conservation score, secondary structure and solvent accessibility values, and local quality scores of the implied three-dimensional model(s). Although it has been designed to simplify the target-template alignment step in modeling, it is suitable for all cases where a sequence alignment needs to be inspected in the context of other biological information. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://modorama.biocomputing.it/modalign. Website implemented in HTML and JavaScript with all major browsers supported. CONTACT: jan.kosinski@uniroma1.it.

  4. Epitaxial growth of aligned GaN nanowires and nanobridges

    OpenAIRE

    2007-01-01

    Homo-epitaxialy grown aligned GaN nanowires were prepared on crystalline GaN mesas. The GaN nanowires showed preferential growth along the 〈100〉 direction (m-axis direction). By using selectively positioned and crystallographically well defined GaN epitaxial lateral overgrowth (ELO) mesas as substrate, we obtained horizontally aligned GaN nanowires, in comb-like arrays and hexagonal network interconnecting the ELO mesas. Preliminary testing of the nanomechanical behavior of horizontal nanowir...

  5. A Brief Introduction to Chinese Biological Biological

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Chinese Biological Abstracts sponsored by the Library, the Shanghai Institutes for Biological Sciences, the Biological Documentation and Information Network, all of the Chinese Academy of Sciences, commenced publication in 1987 and was initiated to provide access to the Chinese information in the field of biology.

  6. RNA-RNA interaction prediction based on multiple sequence alignments

    CERN Document Server

    Li, Andrew X; Qin, Jing; Reidys, Christian M

    2010-01-01

    Recently, $O(N^6)$ time and $O(N^4)$ space dynamic programming algorithms have become available that compute the partition function of RNA-RNA interaction complexes for pairs of RNA sequences. These algorithms and the biological requirement of more reliable interactions motivate to utilize the additional information contained in multiple sequence alignments and to generalize the above framework to the partition function and base pairing probabilities for multiple sequence alignments.

  7. Polymer Alignment Behavior with Molecular Switching of Ferroelectric Liquid Crystal

    Science.gov (United States)

    Murashige, Takeshi; Fujikake, Hideo; Sato, Hiroto; Kikuchi, Hiroshi; Kurita, Taiichiro; Sato, Fumio

    2007-01-01

    This paper describes the molecular alignment behavior of polymer networks with switching of a ferroelectric liquid crystal (FLC) in a molecularly aligned FLC/polymer composite film. The polymer alignment in the composite film, which was slowly formed by photopolymerization-induced phase separation of a heated nematic-phase solution of FLC and monomers, was observed by polarization Raman spectral microscopy. Raman peak intensities originating from the polymers were changed with those from the FLC, when the applied voltage polarity was changed. The trace patterns of the Raman peak intensity with in-plane rotation of the composite film indicated that the formed flexible polymers can follow FLC molecular switching.

  8. The known unknowns of the human dendritic cell network

    Directory of Open Access Journals (Sweden)

    Mélanie eDurand

    2015-03-01

    Full Text Available Dendritic cells (DC initiate and orient immune responses and comprise several subsets that display distinct phenotypes and properties. Most of our knowledge of DC subsets biology is based on mouse studies. In the past few years, the alignment of the human DC network with the mouse DC network has been the focus of much attention. Although comparative phenotypic and transcriptomic analysis have shown a high level of homology between mouse and human DC subsets, significant differences in phenotype and function have also been evidenced. Here we review recent advances in our understanding of the human DC network and discuss some remaining gaps and future challenges of the human DC field.

  9. Semiautomated improvement of RNA alignments

    DEFF Research Database (Denmark)

    Andersen, Ebbe Sloth; Lind-Thomsen, Allan; Knudsen, Bjarne

    2007-01-01

    We have developed a semiautomated RNA sequence editor (SARSE) that integrates tools for analyzing RNA alignments. The editor highlights different properties of the alignment by color, and its integrated analysis tools prevent the introduction of errors when doing alignment editing. SARSE readily...... connects to external tools to provide a flexible semiautomatic editing environment. A new method, Pcluster, is introduced for dividing the sequences of an RNA alignment into subgroups with secondary structure differences. Pcluster was used to evaluate 574 seed alignments obtained from the Rfam database...... and we identified 71 alignments with significant prediction of inconsistent base pairs and 102 alignments with significant prediction of novel base pairs. Four RNA families were used to illustrate how SARSE can be used to manually or automatically correct the inconsistent base pairs detected by Pcluster...

  10. ATLAS Inner Detector Alignment

    CERN Document Server

    Bocci, A

    2008-01-01

    The ATLAS experiment is a multi-purpose particle detector that will study high-energy particle collisions produced by the Large Hadron Collider at CERN. In order to achieve its physics goals, the ATLAS tracking requires that the positions of the silicon detector elements have to be known to a precision better than 10 μm. Several track-based alignment algorithms have been developed for the Inner Detector. An extensive validation has been performed with simulated events and real data coming from the ATLAS. Results from such validation are reported in this paper.

  11. CELT optics Alignment Procedure

    Science.gov (United States)

    Mast, Terry S.; Nelson, Jerry E.; Chanan, Gary A.; Noethe, Lothar

    2003-01-01

    The California Extremely Large Telescope (CELT) is a project to build a 30-meter diameter telescope for research in astronomy at visible and infrared wavelengths. The current optical design calls for a primary, secondary, and tertiary mirror with Ritchey-Chretién foci at two Nasmyth platforms. The primary mirror is a mosaic of 1080 actively-stabilized hexagonal segments. This paper summarizes a CELT report that describes a step-by-step procedure for aligning the many degrees of freedom of the CELT optics.

  12. TSGC and JSC Alignment

    Science.gov (United States)

    Sanchez, Humberto

    2013-01-01

    NASA and the SGCs are, by design, intended to work closely together and have synergistic Vision, Mission, and Goals. The TSGC affiliates and JSC have been working together, but not always in a concise, coordinated, nor strategic manner. Today we have a couple of simple ideas to present about how TSGC and JSC have started to work together in a more concise, coordinated, and strategic manner, and how JSC and non-TSG Jurisdiction members have started to collaborate: Idea I: TSGC and JSC Technical Alignment Idea II: Concept of Clusters.

  13. Physicochemical characterisation and biological evaluation of hydrogel-poly(epsilon-caprolactone) interpenetrating polymer networks as novel urinary biomaterials.

    Science.gov (United States)

    Jones, David S; McLaughlin, David W J; McCoy, Colin P; Gorman, Sean P

    2005-05-01

    Hydrogels are frequently employed as medical device biomaterials due to their advantageous biological properties, e.g. resistance to infection and encrustation, biocompatibility; however, their poor mechanical properties generally limit the scope of application to coatings of medical devices. To address this limitation, this study described the formulation of sequential interpenetrating polymer networks (IPN) of poly(-caprolactone) (PCL) and poly(hydroxyethylmethacrylate) (p(HEMA)). IPN containing 20% w/w PCL, p(HEMA), both in the presence or absence of ethyleneglycol dimethacrylate (EGDMA 1% w/w), were prepared by free radical polymerisation. Following preparation the degradation and the mechanical and surface properties of the biomaterials and, in addition, the resistances to microbial adherence and encrustation in vitro were examined. In comparison to p(HEMA) the various IPN exhibited substantially greater tensile properties (ultimate tensile strength, % elongation, Young's modulus) that were accredited to the discrete distribution of PCL within the hydrogel network. The IPN exhibited two glass transition temperatures that were statistically similar to those of the individual components, thereby providing evidence of the immiscible nature of the two polymers. The IPN possessed higher receding contact angles and lower equilibrium water contents in comparison to p(HEMA), whereas the limited degradation of the IPN at both pH 7 and 9 was deemed suitable for clinical usage for periods of at least 4 weeks. The resistances of the various IPN to bacterial adherence and urinary encrustation were examined using in vitro models. Importantly the resistance of the IPN to encrustation was, in general, similar to that of p(HEMA) but greater than that of PCL whereas, the resistance of the IPN to bacterial adherence was frequently greater than that of p(HEMA) and PCL. Therefore, this study has shown that the mechanical properties of p(HEMA) may be substantially increased by the

  14. All about alignment

    CERN Multimedia

    2006-01-01

    The ALICE absorbers, iron wall and superstructure have been installed with great precision. The ALICE front absorber, positioned in the centre of the detector, has been installed and aligned. Weighing more than 400 tonnes, the ALICE absorbers and the surrounding support structures have been installed and aligned with a precision of 1-2 mm, hardly an easy task but a very important one. The ALICE absorbers are made of three parts: the front absorber, a 35-tonne cone-shaped structure, and two small-angle absorbers, long straight cylinder sections weighing 18 and 40 tonnes. The three pieces lined up have a total length of about 17 m. In addition to these, ALICE technicians have installed a 300-tonne iron filter wall made of blocks that fit together like large Lego pieces and a surrounding metal support structure to hold the tracking and trigger chambers. The absorbers house the vacuum chamber and are also the reference surface for the positioning of the tracking and trigger chambers. For this reason, the ab...

  15. Testing the tidal alignment model of galaxy intrinsic alignment