WorldWideScience

Sample records for aligned biological networks

  1. Probabilistic biological network alignment.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

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

  2. GraphAlignment: Bayesian pairwise alignment of biological networks

    Czech Academy of Sciences Publication Activity Database

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

    2012-01-01

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

  3. 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.

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

    Science.gov (United States)

    Radu, Alex; Charleston, Michael

    2014-10-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  6. Use artificial neural network to align biological ontologies.

    Science.gov (United States)

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

    2008-09-16

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

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

    Science.gov (United States)

    Guzzi, Pietro Hiram; Milenković, Tijana

    2017-01-05

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

  8. Unified Alignment of Protein-Protein Interaction Networks.

    Science.gov (United States)

    Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

    2017-04-19

    Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

  9. Aligning Biomolecular Networks Using Modular Graph Kernels

    Science.gov (United States)

    Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant

    Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.

  10. 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.

  11. L-GRAAL: Lagrangian graphlet-based network aligner.

    Science.gov (United States)

    Malod-Dognin, Noël; Pržulj, Nataša

    2015-07-01

    Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at

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

    Directory of Open Access Journals (Sweden)

    Giovanni Ciriello

    Full Text Available 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.

  13. Multiple network alignment on quantum computers

    Science.gov (United States)

    Daskin, Anmer; Grama, Ananth; Kais, Sabre

    2014-12-01

    Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given graphs as input, alignment algorithms use topological information to assign a similarity score to each -tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.

  14. Synthetic biological networks

    International Nuclear Information System (INIS)

    Archer, Eric; Süel, Gürol M

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  16. Networks in Cell Biology

    Science.gov (United States)

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

    2010-05-01

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

  17. Dominating biological networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

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

  18. Finding optimal interaction interface alignments between biological complexes

    KAUST Repository

    Cui, Xuefeng; Naveed, Hammad; Gao, Xin

    2015-01-01

    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

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

    Science.gov (United States)

    Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

    2011-01-19

    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. 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 existing tool. Finally, GraphCruch 2 implements an

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

    Directory of Open Access Journals (Sweden)

    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

  1. 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.

  2. 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.…

  3. Characterizing the topology of probabilistic biological networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

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

  4. Modular analysis of biological networks.

    Science.gov (United States)

    Kaltenbach, Hans-Michael; Stelling, Jörg

    2012-01-01

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

  5. A Global Network Alignment Method Using Discrete Particle Swarm Optimization.

    Science.gov (United States)

    Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia

    2016-10-19

    Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.

  6. Value Systems Alignment Analysis in Collaborative Networked Organizations Management

    OpenAIRE

    Patricia Macedo; Luis Camarinha-Matos

    2017-01-01

    The assessment of value systems alignment can play an important role in the formation and evolution of collaborative networks, contributing to reduce potential risks of collaboration. For this purpose, an assessment tool is proposed as part of a collaborative networks information system, supporting both the formation and evolution of long-term strategic alliances and goal-oriented networks. An implementation approach for value system alignment analysis is described, which is intended to assis...

  7. [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.

  8. 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 fr...

  9. A Network Model of Interpersonal Alignment in Dialog

    Directory of Open Access Journals (Sweden)

    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.

  10. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

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

  11. An Alignment Model for Collaborative Value Networks

    Science.gov (United States)

    Bremer, Carlos; Azevedo, Rodrigo Cambiaghi; Klen, Alexandra Pereira

    This paper presents parts of the work carried out in several global organizations through the development of strategic projects with high tactical and operational complexity. By investing in long-term relationships, strongly operating in the transformation of the competitive model and focusing on the value chain management, the main aim of these projects was the alignment of multiple value chains. The projects were led by the Axia Transformation Methodology as well as by its Management Model and following the principles of Project Management. As a concrete result of the efforts made in the last years in the Brazilian market this work also introduces the Alignment Model which supports the transformation process that the companies undergo.

  12. Distributed interference alignment iterative algorithms in symmetric wireless network

    Directory of Open Access Journals (Sweden)

    YANG Jingwen

    2015-02-01

    Full Text Available Interference alignment is a novel interference alignment way,which is widely noted all of the world.Interference alignment overlaps interference in the same signal space at receiving terminal by precoding so as to thoroughly eliminate the influence of interference impacted on expected signals,thus making the desire user achieve the maximum degree of freedom.In this paper we research three typical algorithms for realizing interference alignment,including minimizing the leakage interference,maximizing Signal to Interference plus Noise Ratio (SINR and minimizing mean square error(MSE.All of these algorithms utilize the reciprocity of wireless network,and iterate the precoders between original network and the reverse network so as to achieve interference alignment.We use the uplink transmit rate to analyze the performance of these three algorithms.Numerical simulation results show the advantages of these algorithms.which is the foundation for the further study in the future.The feasibility and future of interference alignment are also discussed at last.

  13. Value Systems Alignment Analysis in Collaborative Networked Organizations Management

    Directory of Open Access Journals (Sweden)

    Patricia Macedo

    2017-11-01

    Full Text Available The assessment of value systems alignment can play an important role in the formation and evolution of collaborative networks, contributing to reduce potential risks of collaboration. For this purpose, an assessment tool is proposed as part of a collaborative networks information system, supporting both the formation and evolution of long-term strategic alliances and goal-oriented networks. An implementation approach for value system alignment analysis is described, which is intended to assist managers in virtual and networked organizations management. The implementation of the assessment and analysis methods is supported by a set of software services integrated in the information system that supports the management of the networked organizations. A case study in the solar energy sector was conducted, and the data collected through this study allow us to confirm the practical applicability of the proposed methods and the software services.

  14. Design principles in biological networks

    Science.gov (United States)

    Goyal, Sidhartha

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

  15. 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...... of execution speed and memory requirements is more reasonable than the competing algorithms. AVAILABILITY AND IMPLEMENTATION: Supplementary material including code implementations in LEDA C++, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/BEAMS.tar.gz....

  16. Biological transportation networks: Modeling and simulation

    KAUST Repository

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

    2015-01-01

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

  17. Biologically inspired EM image alignment and neural reconstruction.

    Science.gov (United States)

    Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

    2011-08-15

    Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

  18. 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.

  19. 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 ...

  20. 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

    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...

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. The Latin American Biological Dosimetry Network (LBDNet)

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-09-01

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

  7. Network Reconstruction of Dynamic Biological Systems

    OpenAIRE

    Asadi, Behrang

    2013-01-01

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

  8. Continuum Modeling of Biological Network Formation

    KAUST Repository

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

    2017-01-01

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

  9. Business-IT alignment in PSS value networks : a capability-based framework

    NARCIS (Netherlands)

    Bagheri, S.; Kusters, R.J.; Trienekens, J.J.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.

    2014-01-01

    Advanced information technology (IT) is regarded as a foundation for the operation of product-service system (PSS) value networks. This requires alignment between IT and PSS business strategy. Business‐IT alignment (BIA) in a value network can raise the ability of partners to collaborate effectively

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

    Science.gov (United States)

    Kojima, Kaname; Nagasaki, Masao; Miyano, Satoru

    2008-06-15

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

  11. SYNCHRONIZATION OF HETEROGENEOUS OSCILLATORS UNDER NETWORK MODIFICATIONS: PERTURBATION AND OPTIMIZATION OF THE SYNCHRONY ALIGNMENT FUNCTION

    Science.gov (United States)

    Taylor, Dane; Skardal, Per Sebastian; Sun, Jie

    2016-01-01

    Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications—for which proper functionality depends sensitively on the extent of synchronization—there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system’s ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments. PMID:27872501

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

    Science.gov (United States)

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

    2008-01-01

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

  13. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

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

  14. 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.

  15. 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.

  16. Proposal for an alignment method of the CLIC linear accelerator - From geodesic networks to the active pre-alignment

    International Nuclear Information System (INIS)

    Touze, T.

    2011-01-01

    The compact linear collider (CLIC) is the particle accelerator project proposed by the european organization for nuclear research (CERN) for high energy physics after the large hadron collider (LHC). Because of the nano-metric scale of the CLIC leptons beams, the emittance growth budget is very tight. It induces alignment tolerances on the positions of the CLIC components that have never been achieved before. The last step of the CLIC alignment will be done according to the beam itself. It falls within the competence of the physicists. However, in order to implement the beam-based feedback, a challenging pre-alignment is required: 10 μm at 3σ along a 200 m sliding window. For such a precision, the proposed solution must be compatible with a feedback between the measurement and repositioning systems. The CLIC pre-alignment will have to be active. This thesis does not demonstrate the feasibility of the CLIC active pre-alignment but shows the way to the last developments that have to be done for that purpose. A method is proposed. Based on the management of the Helmert transformations between Euclidean coordinate systems, from the geodetic networks to the metrological measurements, this method is likely to solve the CLIC pre-alignment problem. Large scale facilities have been built and Monte-Carlo simulations have been made in order to validate the mathematical modeling of the measurement systems and of the alignment references. When this is done, it will be possible to extrapolate the modeling to the entire CLIC length. It will be the last step towards the demonstration of the CLIC pre-alignment feasibility. (author)

  17. 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.

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

    Science.gov (United States)

    Jalili, Mahdi

    2018-03-01

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

  19. Dense module enumeration in biological networks

    Science.gov (United States)

    Tsuda, Koji; Georgii, Elisabeth

    2009-12-01

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

  20. Dense module enumeration in biological networks

    International Nuclear Information System (INIS)

    Tsuda, Koji; Georgii, Elisabeth

    2009-01-01

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

  1. Value-Based Business-IT Alignment in Networked Constellations of Enterprises

    NARCIS (Netherlands)

    Gordijn, Jaap; van Eck, Pascal; Cox, K.; Dubois, E.; Pigneur, Y.; Bleistein, S.J.; Verner, J.; Davis, A.M.; Wieringa, Roelf J.

    Business-ICT alignment is the problem of matching ICTservices with the requirements of the business. In businesses of any significant size, business-ICT alignment is a hard problem, which is currently not solved completely. With the advent of networked constellations of enterprises, the problem gets

  2. 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…

  3. Characterizing Topology of Probabilistic Biological Networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-09-06

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

  4. Alignment of global supply networks based on strategic groups of supply chains

    Directory of Open Access Journals (Sweden)

    Nikos G. Moraitakis

    2017-09-01

    Full Text Available Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic supply chain perspective in order to align regional competitive priorities and supply chain structures. The objective of this research is hence to develop an approach for the supply-chain-based alignment of complex global supply networks. Method: We review existing literature from the fields of supply chain and network management, strategic sourcing, and strategic management. Based on the literature review and theoretical and practical considerations we deduce a conceptual approach to consider upstream supply chain structures in supply network alignment initiatives. Results: On the basis of these considerations and current empirical literature we transfer strategic group theory to the supply network management context. The proposed approach introduces strategic groups of supply chains as a segmentation criterion for complex global supply networks which enables the network-wide alignment of competitive priorities. Conclusion: Supply-chain-based segmentation of global supply network structures can effectively reduce the complexity, firms face when aiming to strategically align their supply chains on a holistic level. The results of this research are applicable for certain types of global supply networks and can be used for network alignment and strategy development. The approach can furthermore generate insights useable for negotiation support with suppliers.

  5. Review of Biological Network Data and Its Applications

    Directory of Open Access Journals (Sweden)

    Donghyeon Yu

    2013-12-01

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

  6. 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.

  7. Network biology concepts in complex disease comorbidities

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  8. Social traits, social networks and evolutionary biology.

    Science.gov (United States)

    Fisher, D N; McAdam, A G

    2017-12-01

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

  9. 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.

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

    Science.gov (United States)

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

    2013-11-01

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

  11. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

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

  12. Computing chemical organizations in biological networks.

    Science.gov (United States)

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

    2008-07-15

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

  13. Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.

    Science.gov (United States)

    Lan, Haidong; Chan, Yuandong; Xu, Kai; Schmidt, Bertil; Peng, Shaoliang; Liu, Weiguo

    2016-07-19

    Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators. This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency. Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .

  14. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jeonyoon; Stein, Itai Y. [Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Devoe, Mackenzie E. [Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Lewis, Diana J.; Lachman, Noa; Buschhorn, Samuel T.; Wardle, Brian L., E-mail: wardle@mit.edu [Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Kessler, Seth S. [Metis Design Corporation, 205 Portland St., Boston, Massachusetts 02114 (United States)

    2015-02-02

    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.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    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

  17. Learning and coding in biological neural networks

    Science.gov (United States)

    Fiete, Ila Rani

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

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

    Directory of Open Access Journals (Sweden)

    Jacques Demongeot

    2018-01-01

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

  19. An extensive assessment of network alignment algorithms for comparison of brain connectomes.

    Science.gov (United States)

    Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario

    2017-06-06

    Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

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

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

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

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

    Science.gov (United States)

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

    2010-10-27

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

  2. Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization.

    Science.gov (United States)

    Nenning, Karl-Heinz; Kollndorfer, Kathrin; Schöpf, Veronika; Prayer, Daniela; Langs, Georg

    2015-01-01

    Functional magnetic resonance imaging group studies rely on the ability to establish correspondence across individuals. This enables location specific comparison of functional brain characteristics. Registration is often based on morphology and does not take variability of functional localization into account. This can lead to a loss of specificity, or confounds when studying diseases. In this paper we propose multi-subject functional registration by manifold alignment via coupled joint diagonalization. The functional network structure of each subject is encoded in a diffusion map, where functional relationships are decoupled from spatial position. Two-step manifold alignment estimates initial correspondences between functionally equivalent regions. Then, coupled joint diagonalization establishes common eigenbases across all individuals, and refines the functional correspondences. We evaluate our approach on fMRI data acquired during a language paradigm. Experiments demonstrate the benefits in matching accuracy achieved by coupled joint diagonalization compared to previously proposed functional alignment approaches, or alignment based on structural correspondences.

  3. Organization of excitable dynamics in hierarchical biological networks.

    Directory of Open Access Journals (Sweden)

    Mark Müller-Linow

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

  4. 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

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

    Science.gov (United States)

    Bauer-Mehren, Anna

    2013-01-01

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

  6. Reachability Analysis in Probabilistic Biological Networks.

    Science.gov (United States)

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

    2015-01-01

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

  7. Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

    Science.gov (United States)

    Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

    1993-01-01

    This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

  8. Improved image alignment method in application to X-ray images and biological images.

    Science.gov (United States)

    Wang, Ching-Wei; Chen, Hsiang-Chou

    2013-08-01

    Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images. An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001). The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/). cweiwang@mail.ntust.edu.tw.

  9. 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.

  10. 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...... trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn...... 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...

  11. OWL Reasoning Framework over Big Biological Knowledge Network

    Science.gov (United States)

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

    2014-01-01

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

  12. Biology Question Generation from a Semantic Network

    Science.gov (United States)

    Zhang, Lishan

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

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

    DEFF Research Database (Denmark)

    Green, Sara; Serban, Maria; Scholl, Raphael

    2018-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

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

    DEFF Research Database (Denmark)

    Lynggaard, Per

    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...... is the interferences produced by WiFi access points in smart home networks which are expensive to overcome in terms of battery energy. Currently, different method exists to handle this. However, they use complex mechanisms such as sharing frequencies, sharing time slots, and spatial reuse of frequencies. This paper...... 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...

  16. A generic algorithm for layout of biological networks.

    Science.gov (United States)

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

    2009-11-12

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

  17. BioNSi: A Discrete Biological Network Simulator Tool.

    Science.gov (United States)

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

    2016-08-05

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

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

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

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

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Kato Mitsuru

    2007-03-01

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

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  2. Alignment of paired molecules of C60 within a hexagonal platform networked through hydrogen-bonds.

    Science.gov (United States)

    Hisaki, Ichiro; Nakagawa, Shoichi; Sato, Hiroyasu; Tohnai, Norimitsu

    2016-07-28

    We demonstrate, for the first time, that a hydrogen-bonded low-density organic framework can be applied as a platform to achieve periodic alignment of paired molecules of C60, which is the smallest example of a finite-numbered cluster of C60. The framework is a layered assembly of a hydrogen-bonded 2D hexagonal network (LA-H-HexNet) composed of dodecadehydrotribenzo[18]annulene derivatives.

  3. Mining biological networks from full-text articles.

    Science.gov (United States)

    Czarnecki, Jan; Shepherd, Adrian J

    2014-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2011-01-01

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

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

    Science.gov (United States)

    Michoel, Tom; Nachtergaele, Bruno

    2012-11-01

    Complex networks possess a rich, multiscale 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 these problems. Here we introduce a framework for clustering and community detection in such systems using 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 local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

  7. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

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

  8. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  9. Local and global control of ecological and biological networks

    OpenAIRE

    Alessandro Ferrarini

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Pei Wang

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

  11. Two-Stream Transformer Networks for Video-based Face Alignment.

    Science.gov (United States)

    Liu, Hao; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

    2017-08-01

    In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the fact that consistent movements of facial landmarks usually occur across consecutive frames, our TSTN aims to capture the complementary information of both the spatial appearance on still frames and the temporal consistency information across frames. To achieve this, we develop a two-stream architecture, which decomposes the video-based face alignment into spatial and temporal streams accordingly. Specifically, the spatial stream aims to transform the facial image to the landmark positions by preserving the holistic facial shape structure. Accordingly, the temporal stream encodes the video input as active appearance codes, where the temporal consistency information across frames is captured to help shape refinements. Experimental results on the benchmarking video-based face alignment datasets show very competitive performance of our method in comparisons to the state-of-the-arts.

  12. 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.

  13. 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.

  14. Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

    NARCIS (Netherlands)

    Broersen, A.; Liere, van R.; Altelaar, A.F.M.; Heeren, R.M.A.; McDonnell, L.A.

    2008-01-01

    High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment

  15. A three-dimensional microelectrode array composed of vertically aligned ultra-dense carbon nanotube networks

    Science.gov (United States)

    Nick, C.; Yadav, S.; Joshi, R.; Schneider, J. J.; Thielemann, C.

    2015-07-01

    Electrodes based on carbon nanotubes are a promising approach to manufacture highly sensitive sensors with a low limit of signal detection and a high signal-to-noise ratio. This is achieved by dramatically increasing the electrochemical active surface area without increasing the overall geometrical dimensions. Typically, carbon nanotube electrodes are nearly planar and composed of randomly distributed carbon nanotube networks having a limited surface gain for a specific geometrical surface area. To overcome this limitation, we have introduced vertically aligned carbon nanotube (VACNT) networks as electrodes, which are arranged in a microelectrode pattern of 60 single electrodes. Each microelectrode features a very high aspect ratio of more than 300 and thus a dramatically increased surface area. These microelectrodes composed of VACNT networks display dramatically decreased impedance over the entire frequency range compared to planar microelectrodes caused by the enormous capacity increase. This is experimentally verified by electrochemical impedance spectroscopy and cyclic voltammetry.

  16. Systems biology: properties of reconstructed networks

    National Research Council Canada - National Science Library

    Palsson, Bernhard

    2006-01-01

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

  17. 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…

  18. Improving accountability through alignment: the role of academic health science centres and networks in England.

    Science.gov (United States)

    Ovseiko, Pavel V; Heitmueller, Axel; Allen, Pauline; Davies, Stephen M; Wells, Glenn; Ford, Gary A; Darzi, Ara; Buchan, Alastair M

    2014-01-20

    As in many countries around the world, there are high expectations on academic health science centres and networks in England to provide high-quality care, innovative research, and world-class education, while also supporting wealth creation and economic growth. Meeting these expectations increasingly depends on partnership working between university medical schools and teaching hospitals, as well as other healthcare providers. However, academic-clinical relationships in England are still characterised by the "unlinked partners" model, whereby universities and their partner teaching hospitals are neither fiscally nor structurally linked, creating bifurcating accountabilities to various government and public agencies. This article focuses on accountability relationships in universities and teaching hospitals, as well as other healthcare providers that form core constituent parts of academic health science centres and networks. The authors analyse accountability for the tripartite mission of patient care, research, and education, using a four-fold typology of accountability relationships, which distinguishes between hierarchical (bureaucratic) accountability, legal accountability, professional accountability, and political accountability. Examples from North West London suggest that a number of mechanisms can be used to improve accountability for the tripartite mission through alignment, but that the simple creation of academic health science centres and networks is probably not sufficient. At the heart of the challenge for academic health science centres and networks is the separation of accountabilities for patient care, research, and education in different government departments. Given that a fundamental top-down system redesign is now extremely unlikely, local academic and clinical leaders face the challenge of aligning their institutions as a matter of priority in order to improve accountability for the tripartite mission from the bottom up. It remains to be

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

    Science.gov (United States)

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

    2004-12-01

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

  20. 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'…

  1. High-accuracy local positioning network for the alignment of the Mu2e experiment.

    Energy Technology Data Exchange (ETDEWEB)

    Hejdukova, Jana B. [Czech Technical Univ., Prague (Czech Republic)

    2017-06-01

    This Diploma thesis describes the establishment of a high-precision local positioning network and accelerator alignment for the Mu2e physics experiment. The process of establishing new network consists of few steps: design of the network, pre-analysis, installation works, measurements of the network and making adjustments. Adjustments were performed using two approaches. First is a geodetic approach of taking into account the Earth’s curvature and the metrological approach of a pure 3D Cartesian system on the other side. The comparison of those two approaches is performed and evaluated in the results and compared with expected differences. The effect of the Earth’s curvature was found to be significant for this kind of network and should not be neglected. The measurements were obtained with Absolute Tracker AT401, leveling instrument Leica DNA03 and gyrotheodolite DMT Gyromat 2000. The coordinates of the points of the reference network were determined by the Least Square Meth od and the overall view is attached as Annexes.

  2. 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.

  3. 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.

  4. Computational modeling for prediction of the shear stress of three-dimensional isotropic and aligned fiber networks.

    Science.gov (United States)

    Park, Seungman

    2017-09-01

    Interstitial flow (IF) is a creeping flow through the interstitial space of the extracellular matrix (ECM). IF plays a key role in diverse biological functions, such as tissue homeostasis, cell function and behavior. Currently, most studies that have characterized IF have focused on the permeability of ECM or shear stress distribution on the cells, but less is known about the prediction of shear stress on the individual fibers or fiber networks despite its significance in the alignment of matrix fibers and cells observed in fibrotic or wound tissues. In this study, I developed a computational model to predict shear stress for different structured fibrous networks. To generate isotropic models, a random growth algorithm and a second-order orientation tensor were employed. Then, a three-dimensional (3D) solid model was created using computer-aided design (CAD) software for the aligned models (i.e., parallel, perpendicular and cubic models). Subsequently, a tetrahedral unstructured mesh was generated and flow solutions were calculated by solving equations for mass and momentum conservation for all models. Through the flow solutions, I estimated permeability using Darcy's law. Average shear stress (ASS) on the fibers was calculated by averaging the wall shear stress of the fibers. By using nonlinear surface fitting of permeability, viscosity, velocity, porosity and ASS, I devised new computational models. Overall, the developed models showed that higher porosity induced higher permeability, as previous empirical and theoretical models have shown. For comparison of the permeability, the present computational models were matched well with previous models, which justify our computational approach. ASS tended to increase linearly with respect to inlet velocity and dynamic viscosity, whereas permeability was almost the same. Finally, the developed model nicely predicted the ASS values that had been directly estimated from computational fluid dynamics (CFD). The present

  5. 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.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  7. High-speed all-optical DNA local sequence alignment based on a three-dimensional artificial neural network.

    Science.gov (United States)

    Maleki, Ehsan; Babashah, Hossein; Koohi, Somayyeh; Kavehvash, Zahra

    2017-07-01

    This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Nicola J. Mulder

    2014-08-01

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

  10. Applying differential dynamic logic to reconfigurable biological networks.

    Science.gov (United States)

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

    2017-09-01

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

  11. Analysis and logical modeling of biological signaling transduction networks

    Science.gov (United States)

    Sun, Zhongyao

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

  12. Non-Hermitian localization in biological networks.

    Science.gov (United States)

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

    2016-04-01

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

  13. Emergence of communication in socio-biological networks

    CERN Document Server

    Berea, Anamaria

    2018-01-01

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

  14. Toward Petascale Biologically Plausible Neural Networks

    Science.gov (United States)

    Long, Lyle

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

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

    Directory of Open Access Journals (Sweden)

    Ponomarenko Julia

    2010-12-01

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

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

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

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

  17. Alignment of the UMLS semantic network with BioTop: methodology and assessment.

    Science.gov (United States)

    Schulz, Stefan; Beisswanger, Elena; van den Hoek, László; Bodenreider, Olivier; van Mulligen, Erik M

    2009-06-15

    For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop. The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations. BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.

  18. 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.

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

    Science.gov (United States)

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

    2010-01-01

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

  20. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

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

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

    Science.gov (United States)

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

    2014-07-24

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

  2. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

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

  3. Engineering survey planning for the alignment of a particle accelerator: part II. Design of a reference network and measurement strategy

    Science.gov (United States)

    Junqueira Leão, Rodrigo; Raffaelo Baldo, Crhistian; Collucci da Costa Reis, Maria Luisa; Alves Trabanco, Jorge Luiz

    2018-03-01

    The building blocks of particle accelerators are magnets responsible for keeping beams of charged particles at a desired trajectory. Magnets are commonly grouped in support structures named girders, which are mounted on vertical and horizontal stages. The performance of this type of machine is highly dependent on the relative alignment between its main components. The length of particle accelerators ranges from small machines to large-scale national or international facilities, with typical lengths of hundreds of meters to a few kilometers. This relatively large volume together with micrometric positioning tolerances make the alignment activity a classical large-scale dimensional metrology problem. The alignment concept relies on networks of fixed monuments installed on the building structure to which all accelerator components are referred. In this work, the Sirius accelerator is taken as a case study, and an alignment network is optimized via computational methods in terms of geometry, densification, and surveying procedure. Laser trackers are employed to guide the installation and measure the girders’ positions, using the optimized network as a reference and applying the metric developed in part I of this paper. Simulations demonstrate the feasibility of aligning the 220 girders of the Sirius synchrotron to better than 0.080 mm, at a coverage probability of 95%.

  4. 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

    2016-05-09

    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.

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

    Science.gov (United States)

    Hart, Michael W; Sunday, Jennifer

    2007-10-22

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

  6. Mechanical property and biological performance of electrospun silk fibroin-polycaprolactone scaffolds with aligned fibers.

    Science.gov (United States)

    Yuan, Han; Shi, Hongfei; Qiu, Xushen; Chen, Yixin

    2016-01-01

    The mechanical strength, biocompatibility, and sterilizability of silk fibroin allow it to be a possible candidate as a natural bone regenerate material. To improve mechanical character and reinforce the cell movement induction, silk fibroin (SF)-polycaprolactone (PCL) alloy was fabricated by electrospinning techniques with a rotating collector to form aligned fibrous scaffolds and random-oriented scaffolds. The scanning electron microscope image of the scaffold and the mechanical properties of the scaffold were investigated by tensile mechanical tests, which were compared to random-oriented scaffolds. Furthermore, mesenchymal stem cells were planted on these scaffolds to investigate the biocompatibility, elongation, and cell movement in situ. Scanning electron microscopy shows that 91% fibers on the aligned fibroin scaffold were distributed between the dominant direction ±10°. With an ideal support for stem cell proliferation in vitro, the aligned fibrous scaffold induces cell elongation at a length of 236.46 ± 82 μm and distribution along the dominant fiber direction with a cell alignment angle at 6.57° ± 4.45°. Compared with random-oriented scaffolds made by artificial materials, aligned SF-PCL scaffolds could provide a moderate mesenchymal stem cell engraftment interface and speed up early stage cell movement toward the bone defect.

  7. Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores.

    Science.gov (United States)

    Bastien, Olivier; Maréchal, Eric

    2008-08-07

    Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2) following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory) is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure). Homologous sequences were considered as systems 1) having a high redundancy of information reflected by the magnitude of their alignment scores, 2) which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a constant rate, corresponding to the

  8. Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores

    Directory of Open Access Journals (Sweden)

    Maréchal Eric

    2008-08-01

    Full Text Available Abstract Background Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2 following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. Results We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure. Homologous sequences were considered as systems 1 having a high redundancy of information reflected by the magnitude of their alignment scores, 2 which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Jose A. Santiago

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Kim Man-Sun

    2012-05-01

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

  13. A value-based maturity model for IT alignment in networked businesses

    NARCIS (Netherlands)

    Santana Tapia, R.G.

    Business-IT alignment can be achieved at various levels of maturity. Supposing that an organization has tried to achieve business-IT alignment, a question to face is: how is that organization going to know the level of maturity of its alignment situation in order to plan future ways of action? That

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

    Directory of Open Access Journals (Sweden)

    Atsushi eFukushima

    2014-11-01

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

  15. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    2012-01-01

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

  16. 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...

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

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

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

  18. Analysis of complex networks from biology to linguistics

    CERN Document Server

    Dehmer, Matthias

    2009-01-01

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

  19. Impact of heuristics in clustering large biological networks.

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

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

  1. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Science.gov (United States)

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

    2013-01-01

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

  2. Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment

    Directory of Open Access Journals (Sweden)

    Anming Dong

    2017-09-01

    Full Text Available This paper considers power splitting (PS-based simultaneous wireless information and power transfer (SWIPT for multiple-input multiple-output (MIMO interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID and energy harvesting (EH, respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC networks.

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

    International Nuclear Information System (INIS)

    Zhang, Jiajun; Yuan, Zhanjiang; Zhou, Tianshou

    2009-01-01

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

  4. A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lihui Jiang

    2015-07-01

    Full Text Available Interference alignment (IA is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs when the signal-to-noise ratio (SNR is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.

  5. A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks.

    Science.gov (United States)

    Jiang, Lihui; Wu, Zhilu; Ren, Guanghui; Wang, Gangyi; Zhao, Nan

    2015-07-29

    Interference alignment (IA) is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs) when the signal-to-noise ratio (SNR) is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL) algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO) algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.

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

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

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

  7. Cyclic mechanical stretch contributes to network development of osteocyte-like cells with morphological change and autophagy promotion but without preferential cell alignment in rat.

    Science.gov (United States)

    Inaba, Nao; Kuroshima, Shinichiro; Uto, Yusuke; Sasaki, Muneteru; Sawase, Takashi

    2017-09-01

    Osteocytes play important roles in controlling bone quality as well as preferential alignment of biological apatite c -axis/collagen fibers. However, the relationship between osteocytes and mechanical stress remains unclear due to the difficulty of three-dimensional (3D) culture of osteocytes in vitro . The aim of this study was to investigate the effect of cyclic mechanical stretch on 3D-cultured osteocyte-like cells. Osteocyte-like cells were established using rat calvarial osteoblasts cultured in a 3D culture system. Cyclic mechanical stretch (8% amplitude at a rate of 2 cycles min -1 ) was applied for 24, 48 and 96 consecutive hours. Morphology, cell number and preferential cell alignment were evaluated. Apoptosis- and autophagy-related gene expression levels were measured using quantitative PCR. 3D-cultured osteoblasts became osteocyte-like cells that expressed osteocyte-specific genes such as Dmp1 , Cx43 , Sost , Fgf23 and RANKL , with morphological changes similar to osteocytes. Cell number was significantly decreased in a time-dependent manner under non-loaded conditions, whereas cyclic mechanical stretch significantly prevented decreased cell numbers with increased expression of anti-apoptosis-related genes. Moreover, cyclic mechanical stretch significantly decreased cell size and ellipticity with increased expression of autophagy-related genes, LC3b and atg7 . Interestingly, preferential cell alignment did not occur, irrespective of mechanical stretch. These findings suggest that an anti-apoptotic effect contributes to network development of osteocyte-like cells under loaded condition. Spherical change of osteocyte-like cells induced by mechanical stretch may be associated with autophagy upregulation. Preferential alignment of osteocytes induced by mechanical load in vivo may be partially predetermined before osteoblasts differentiate into osteocytes and embed into bone matrix.

  8. 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.

  9. 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

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

    Directory of Open Access Journals (Sweden)

    Martin Florian

    2012-05-01

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

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

    Science.gov (United States)

    2011-01-01

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

  12. Automated sample mounting and technical advance alignment system for biological crystallography at a synchrotron source

    International Nuclear Information System (INIS)

    Snell, Gyorgy; Cork, Carl; Nordmeyer, Robert; Cornell, Earl; Meigs, George; Yegian, Derek; Jaklevic, Joseph; Jin, Jian; Stevens, Raymond C.; Earnest, Thomas

    2004-01-01

    High-throughput data collection for macromolecular crystallography requires an automated sample mounting system for cryo-protected crystals that functions reliably when integrated into protein-crystallography beamlines at synchrotrons. Rapid mounting and dismounting of the samples increases the efficiency of the crystal screening and data collection processes, where many crystals can be tested for the quality of diffraction. The sample-mounting subsystem has random access to 112 samples, stored under liquid nitrogen. Results of extensive tests regarding the performance and reliability of the system are presented. To further increase throughput, we have also developed a sample transport/storage system based on 'puck-shaped' cassettes, which can hold sixteen samples each. Seven cassettes fit into a standard dry shipping Dewar. The capabilities of a robotic crystal mounting and alignment system with instrumentation control software and a relational database allows for automated screening and data collection to be developed

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

    Science.gov (United States)

    Rafegas, Ivet; Vanrell, Maria

    2018-05-11

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

  14. CellNet: Network Biology Applied to Stem Cell Engineering

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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

  16. Qualitative reasoning for biological network inference from systematic perturbation experiments.

    Science.gov (United States)

    Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco

    2012-01-01

    The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

  17. Molecular codes in biological and chemical reaction networks.

    Directory of Open Access Journals (Sweden)

    Dennis Görlich

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

  18. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)

    2010-02-05

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  19. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    International Nuclear Information System (INIS)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min

    2010-01-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  20. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Science.gov (United States)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun

    2010-02-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  1. High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP

    Directory of Open Access Journals (Sweden)

    Khaled Benkrid

    2012-01-01

    Full Text Available This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs, Graphics Processor Units (GPUs, and IBM’s Cell Broadband Engine (Cell BE, in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools, FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs.

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

    Science.gov (United States)

    De Domenico, Manlio

    2018-03-01

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

  3. Application of the Strategic Alignment Model and Information Technology Governance Concepts to Support Network Centric Warfare

    National Research Council Canada - National Science Library

    Valentine, Jennifer R

    2006-01-01

    .... This thesis proposes that while the two areas? success fundamentally resides in the implementation and exploitation of technology, it is only through sound IT Governance policies and strategic alignment practices that success can be measured...

  4. 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

  5. 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.

  6. Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

    Directory of Open Access Journals (Sweden)

    Lijun Song

    2018-01-01

    Full Text Available The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

  7. Accelerated reliability testing of highly aligned single-walled carbon nanotube networks subjected to DC electrical stressing.

    Science.gov (United States)

    Strus, Mark C; Chiaramonti, Ann N; Kim, Young Lae; Jung, Yung Joon; Keller, Robert R

    2011-07-01

    We investigate the electrical reliability of nanoscale lines of highly aligned, networked, metallic/semiconducting single-walled carbon nanotubes (SWCNTs) fabricated through a template-based fluidic assembly process. We find that these SWCNT networks can withstand DC current densities larger than 10 MA cm(-2) for several hours and, in some cases, several days. We develop test methods that show that the degradation rate, failure predictability and total device lifetime can be linked to the initial resistance. Scanning electron and transmission electron microscopy suggest that fabrication variability plays a critical role in the rate of degradation, and we offer an empirical method of quickly determining the long-term performance of a network. We find that well-fabricated lines subject to constant electrical stress show a linear accumulation of damage reminiscent of electromigration in metallic interconnects, and we explore the underlying physical mechanisms that could cause such behavior.

  8. 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/.

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

    Science.gov (United States)

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

    2014-08-14

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

  10. Biologically based neural network for mobile robot navigation

    Science.gov (United States)

    Torres Muniz, Raul E.

    1999-01-01

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

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

    Science.gov (United States)

    Muldoon, Sarah Feldt

    2018-03-01

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

  12. 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

  13. 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

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

    Science.gov (United States)

    Cojocaru, Radu Ionut

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

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

    Science.gov (United States)

    Pedersen, Morten Gram

    2018-03-01

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

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

    Science.gov (United States)

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

    2011-12-15

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

  17. 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

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

    DEFF Research Database (Denmark)

    Wiwie, Christian; Rauch, Alexander; Haakonsson, Anders

    2018-01-01

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

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

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

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

  20. Alignment of global supply networks based on strategic groups of supply chains

    OpenAIRE

    Nikos G. Moraitakis; Jiazhen Huo; Hans-Christian Pfohl

    2017-01-01

    Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic su...

  1. 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 ...

  2. Contribution made by biomarkers of status to an FP6 Network of Excellence, EURopean micronutrient RECommendations Aligned (EURRECA).

    Science.gov (United States)

    Fairweather-Tait, Susan J

    2011-08-01

    Dietary reference values for micronutrients vary considerably among countries, and harmonization is needed to facilitate nutrition policy and public health strategies at the European and global levels. The EURopean micronutrient RECommendations Aligned (EURRECA) Network of Excellence is developing generic instruments for systematically deriving and updating micronutrient reference values and dietary recommendations. These include best practice guidelines, interlinked web pages, online databases, and decision trees. Journal supplements have been published on micronutrient intakes and status, and an ongoing activity of EURRECA is the completion of systematic reviews on associations between intakes, status, and various health outcomes for priority micronutrients (ie, iron, zinc, folate, vitamin B-12, and iodine), which were selected by using a triage technique. Future activities include meta-analyses to identify dose-response relations and the variability, factorial estimates of requirements, bioavailability from whole diets, effects of genotype, and modeling techniques for addressing dietary recommendations for combinations of nutrients with common health endpoints.

  3. Aligning the unalignable: bacteriophage whole genome alignments.

    Science.gov (United States)

    Bérard, Sèverine; Chateau, Annie; Pompidor, Nicolas; Guertin, Paul; Bergeron, Anne; Swenson, Krister M

    2016-01-13

    In recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences. In this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressive Mauve aligner - which implements a partial order strategy, but whose alignments are linearized - shows a greatly improved interactive graphic display, while avoiding misalignments. Multiple alignments of whole bacteriophage genomes work, and will become an important conceptual and visual tool in comparative genomics of sets of related strains. A python implementation of Alpha, along with installation instructions for Ubuntu and OSX, is available on bitbucket (https://bitbucket.org/thekswenson/alpha).

  4. 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.

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

    Science.gov (United States)

    2018-01-01

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

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

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  7. 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.

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

    KAUST Repository

    Park, Kihong; Alouini, Mohamed-Slim

    2013-01-01

    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

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

    NARCIS (Netherlands)

    Mol, Ben Willem; Ruifrok, Anneloes Elisabeth

    2013-01-01

    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

  10. Alignment of the UMLS semantic network with BioTop: Methodology and assessment

    NARCIS (Netherlands)

    S. Schulz; E. Beisswanger (Elena); L. van den Hoek (László); O. Bodenreider (Olivier); E.M. van Mulligen (Erik)

    2009-01-01

    textabstractMotivation: For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for

  11. Nano-soldering of magnetically aligned three-dimensional nanowire networks

    International Nuclear Information System (INIS)

    Gao Fan; Gu Zhiyong

    2010-01-01

    It is extremely challenging to fabricate 3D integrated nanostructures and hybrid nanoelectronic devices. In this paper, we report a simple and efficient method to simultaneously assemble and solder nanowires into ordered 3D and electrically conductive nanowire networks. Nano-solders such as tin were fabricated onto both ends of multi-segmented nanowires by a template-assisted electrodeposition method. These nanowires were then self-assembled and soldered into large-scale 3D network structures by magnetic field assisted assembly in a liquid medium with a high boiling point. The formation of junctions/interconnects between the nanowires and the scale of the assembly were dependent on the solder reflow temperature and the strength of the magnetic field. The size of the assembled nanowire networks ranged from tens of microns to millimeters. The electrical characteristics of the 3D nanowire networks were measured by regular current-voltage (I-V) measurements using a probe station with micropositioners. Nano-solders, when combined with assembling techniques, can be used to efficiently connect and join nanowires with low contact resistance, which are very well suited for sensor integration as well as nanoelectronic device fabrication.

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

    Directory of Open Access Journals (Sweden)

    Shuo Gu

    2017-01-01

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

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

    Science.gov (United States)

    Gu, Shuo; Pei, Jianfeng

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-07-14

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C. [Case Western Reserve Univ., Cleveland, OH (United States)

    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.

  16. 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.

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

    Science.gov (United States)

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

    2016-11-22

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

  18. Robustness of the p53 network and biological hackers.

    Science.gov (United States)

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

    2005-06-06

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

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

    Science.gov (United States)

    Hao, Dapeng; Ren, Cong; Li, Chuanxing

    2012-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Hao Dapeng

    2012-05-01

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

  1. A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhilu Wu

    2015-01-01

    Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

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

    Science.gov (United States)

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

    2017-08-31

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

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

    Directory of Open Access Journals (Sweden)

    L.A. Kirby

    2018-04-01

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

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

    Indian Academy of Sciences (India)

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

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

    KAUST Repository

    Alanis Lobato, Gregorio

    2014-01-01

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

  6. 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…

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lesk Arthur M

    2008-08-01

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

  9. Using Pathfinder networks to discover alignment between expert and consumer conceptual knowledge from online vaccine content.

    Science.gov (United States)

    Amith, Muhammad; Cunningham, Rachel; Savas, Lara S; Boom, Julie; Schvaneveldt, Roger; Tao, Cui; Cohen, Trevor

    2017-10-01

    This study demonstrates the use of distributed vector representations and Pathfinder Network Scaling (PFNETS) to represent online vaccine content created by health experts and by laypeople. By analyzing a target audience's conceptualization of a topic, domain experts can develop targeted interventions to improve the basic health knowledge of consumers. The underlying assumption is that the content created by different groups reflects the mental organization of their knowledge. Applying automated text analysis to this content may elucidate differences between the knowledge structures of laypeople (heath consumers) and professionals (health experts). This paper utilizes vaccine information generated by laypeople and health experts to investigate the utility of this approach. We used an established technique from cognitive psychology, Pathfinder Network Scaling to infer the structure of the associational networks between concepts learned from online content using methods of distributional semantics. In doing so, we extend the original application of PFNETS to infer knowledge structures from individual participants, to infer the prevailing knowledge structures within communities of content authors. The resulting graphs reveal opportunities for public health and vaccination education experts to improve communication and intervention efforts directed towards health consumers. Our efforts demonstrate the feasibility of using an automated procedure to examine the manifestation of conceptual models within large bodies of free text, revealing evidence of conflicting understanding of vaccine concepts among health consumers as compared with health experts. Additionally, this study provides insight into the differences between consumer and expert abstraction of domain knowledge, revealing vaccine-related knowledge gaps that suggest opportunities to improve provider-patient communication. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    OpenAIRE

    Shuo Gu; Jianfeng Pei

    2017-01-01

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

  11. 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

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

    International Nuclear Information System (INIS)

    Clark, J.W.

    1993-01-01

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

  13. 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.

  14. Notes on a PDE system for biological network formation

    KAUST Repository

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

    2016-01-01

    Darcy’s type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. The analytical part

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

    KAUST Repository

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

    2015-01-01

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

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

    Science.gov (United States)

    Xu, Yang; Luo, Xiao-Chun

    2018-05-01

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

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

    Science.gov (United States)

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

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

  18. Modeling Wireless Sensor Networks for Monitoring in Biological Processes

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil

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

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

    International Nuclear Information System (INIS)

    Saito, Nen; Kikuchi, Macoto

    2013-01-01

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

  20. 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.

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

    NARCIS (Netherlands)

    Purutcuoglu, Vilda; Agraz, Melih; Wit, Ernst

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

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

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  4. 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.

  5. 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%.

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  8. 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.

  9. 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.

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

    Science.gov (United States)

    Mallik, Mrinmay Kumar

    2018-02-07

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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

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

    Science.gov (United States)

    Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

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

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Lo Kenneth

    2012-08-01

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

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

    Science.gov (United States)

    Kriegeskorte, Nikolaus

    2015-11-24

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  18. 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.

  19. ABS: Sequence alignment by scanning

    KAUST Repository

    Bonny, Mohamed Talal; Salama, Khaled N.

    2011-01-01

    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. 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...

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

    Science.gov (United States)

    Chen, Bor-Sen; Yeh, Chin-Hsun

    2017-12-01

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

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

    Science.gov (United States)

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

    2007-07-01

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

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

    Science.gov (United States)

    Ushenko, Y. A.

    2011-12-01

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

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

    Science.gov (United States)

    Maheshwari, Parul; Albert, Réka

    2017-12-06

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

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

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

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

  7. RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS1

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

    Wang, Yulin; Lu, Na; Miao, Hongyu

    2016-06-13

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

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

    Science.gov (United States)

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

    2017-12-19

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

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

    Science.gov (United States)

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

    2014-09-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  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. © 2016 The Author(s).

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Brinkrolf Christoph

    2014-06-01

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

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  16. Simulation of beamline alignment operations

    International Nuclear Information System (INIS)

    Annese, C; Miller, M G.

    1999-01-01

    The CORBA-based Simulator was a Laboratory Directed Research and Development (LDRD) project that applied simulation techniques to explore critical questions about distributed control systems. The simulator project used a three-prong approach that studied object-oriented distribution tools, computer network modeling, and simulation of key control system scenarios. The National Ignition Facility's (NIF) optical alignment system was modeled to study control system operations. The alignment of NIF's 192 beamlines is a large complex operation involving more than 100 computer systems and 8000 mechanized devices. The alignment process is defined by a detailed set of procedures; however, many of the steps are deterministic. The alignment steps for a poorly aligned component are similar to that of a nearly aligned component; however, additional operations/iterations are required to complete the process. Thus, the same alignment operations will require variable amounts of time to perform depending on the current alignment condition as well as other factors. Simulation of the alignment process is necessary to understand beamline alignment time requirements and how shared resources such as the Output Sensor and Target Alignment Sensor effect alignment efficiency. The simulation has provided alignment time estimates and other results based on documented alignment procedures and alignment experience gained in the laboratory. Computer communication time, mechanical hardware actuation times, image processing algorithm execution times, etc. have been experimentally determined and incorporated into the model. Previous analysis of alignment operations utilized average implementation times for all alignment operations. Resource sharing becomes rather simple to model when only average values are used. The time required to actually implement the many individual alignment operations will be quite dynamic. The simulation model estimates the time to complete an operation using

  17. 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.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  19. Alignment for CSR

    International Nuclear Information System (INIS)

    Wang Shoujin; Man Kaidi; Guo Yizhen; Cai Guozhu; Guo Yuhui

    2002-01-01

    Cooled Storage Ring of Heavy Ion Research Facility in Lanzhou (HIRFL-CSR) belongs to China great scientific project in China. The alignment for it is very difficult because of very large area and very high accuracy. For the special case in HIRFL-CSR, some new methods and new instruments are used, including the construction of survey control network, the usage of laser tracker, and CSR alignment database system with applications developed to store and analyze data. The author describes the whole procedure of CSR alignment

  20. 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.

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

    Science.gov (United States)

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

    2010-01-27

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

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

    Science.gov (United States)

    2014-01-01

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

  3. 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/

  4. Differential pulse adsorptive stripping voltammetric determination of nanomolar levels of atorvastatin calcium in pharmaceutical and biological samples using a vertically aligned carbon nanotube/graphene oxide electrode.

    Science.gov (United States)

    Silva, Tiago Almeida; Zanin, Hudson; Vicentini, Fernando Campanhã; Corat, Evaldo José; Fatibello-Filho, Orlando

    2014-06-07

    A novel vertically aligned carbon nanotube/graphene oxide (VACNT-GO) electrode is proposed, and its ability to determine atorvastatin calcium (ATOR) in pharmaceutical and biological samples by differential pulse adsorptive stripping voltammetry (DPAdSV) is evaluated. VACNT films were prepared on a Ti substrate by a microwave plasma chemical vapour deposition method and then treated with oxygen plasma to produce the VACNT-GO electrode. The oxygen plasma treatment exfoliates the carbon nanotube tips exposing graphene foils and inserting oxygen functional groups, these effects improved the VACNT wettability (super-hydrophobic) which is crucial for its electrochemical application. The electrochemical behaviour of ATOR on the VACNT-GO electrode was studied by cyclic voltammetry, which showed that it underwent an irreversible oxidation process at a potential of +1.08 V in pHcond 2.0 (0.2 mol L(-1) buffer phosphate solution). By applying DPAdSV under optimized experimental conditions the analytical curve was found to be linear in the ATOR concentration range of 90 to 3.81 × 10(3) nmol L(-1) with a limit of detection of 9.4 nmol L(-1). The proposed DPAdSV method was successfully applied in the determination of ATOR in pharmaceutical and biological samples, and the results were in close agreement with those obtained by a comparative spectrophotometric method at a confidence level of 95%.

  5. 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.

  6. A review of active learning approaches to experimental design for uncovering biological networks

    Science.gov (United States)

    2017-01-01

    Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

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

    Science.gov (United States)

    Modes, Carl; Magnasco, Marcelo; Katifori, Eleni

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

  8. 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.

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

    Science.gov (United States)

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

    2017-01-01

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

  10. 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

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

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

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

  12. 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.

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

    OpenAIRE

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

    2016-01-01

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

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

    Science.gov (United States)

    Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

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

    Directory of Open Access Journals (Sweden)

    Wu Xiaogang

    2012-06-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  18. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    Science.gov (United States)

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

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

    Science.gov (United States)

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

    2012-02-01

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

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

    KAUST Repository

    Gomez-Cabrero, David

    2017-05-09

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

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

    Science.gov (United States)

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

    2017-03-10

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

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

    Science.gov (United States)

    Shimansky, Yury P

    2009-12-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

  4. CUFID-query: accurate network querying through random walk based network flow estimation.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2017-12-28

    Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

  5. 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.

  6. 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.

  7. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  8. Community Alignment ANADP

    OpenAIRE

    Halbert, Martin; Bicarregui, Juan; Anglada, Lluis; Duranti, Luciana

    2014-01-01

    Aligning National Approaches to Digital Preservation: An Action Assembly Biblioteca de Catalunya (National Library of Catalonia) November 18-20, 2013, Barcelona, Spain Auburn University Council on Library and Information Resources (CLIR) Digital Curation Centre (DCC) Digital Preservation Network (DPN) Joint Information Systems Committee (JISC) University of North Texas Virginia Tech Interuniversity Consortium for Political and Social Research (ICPSR) Innovative Inte...

  9. Resource Alignment ANADP

    OpenAIRE

    Grindley, Neil; Cramer, Tom; Schrimpf, Sabine; Wilson, Tom

    2014-01-01

    Aligning National Approaches to Digital Preservation: An Action Assembly Biblioteca de Catalunya (National Library of Catalonia) November 18-20, 2013, Barcelona, Spain Auburn University Council on Library and Information Resources (CLIR) Digital Curation Centre (DCC) Digital Preservation Network (DPN) Joint Information Systems Committee (JISC) University of North Texas Virginia Tech Interuniversity Consortium for Political and Social Research (ICPSR) Innovative Inte...

  10. Capacity Alignment ANADP

    OpenAIRE

    Davidson, Joy; Whitehead, Martha; Molloy, Laura; Molinaro, Mary

    2014-01-01

    Aligning National Approaches to Digital Preservation: An Action Assembly Biblioteca de Catalunya (National Library of Catalonia) November 18-20, 2013, Barcelona, Spain Auburn University Council on Library and Information Resources (CLIR) Digital Curation Centre (DCC) Digital Preservation Network (DPN) Joint Information Systems Committee (JISC) University of North Texas Virginia Tech Interuniversity Consortium for Political and Social Research (ICPSR) Innovative Inte...

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

    Science.gov (United States)

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

    2008-04-01

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

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Samra Khalid

    2016-10-01

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

  16. 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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2012-11-01

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

  19. Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.

    Science.gov (United States)

    Di Silvestre, Dario; Bergamaschi, Andrea; Bellini, Edoardo; Mauri, PierLuigi

    2018-06-03

    The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

  20. 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.

  1. Students Mental Representation of Biology Diagrams/Pictures Conventions Based on Formation of Causal Network

    Science.gov (United States)

    Sampurno, A. W.; Rahmat, A.; Diana, S.

    2017-09-01

    Diagrams/pictures conventions is one form of visual media that often used to assist students in understanding the biological concepts. The effectiveness of use diagrams/pictures in biology learning at school level has also been mostly reported. This study examines the ability of high school students in reading diagrams/pictures biological convention which is described by Mental Representation based on formation of causal networks. The study involved 30 students 11th grade MIA senior high school Banten Indonesia who are studying the excretory system. MR data obtained by Instrument worksheet, developed based on CNET-protocol, in which there are diagrams/drawings of nephron structure and urinary mechanism. Three patterns formed MR, namely Markov chain, feedback control with a single measurement, and repeated feedback control with multiple measurement. The third pattern is the most dominating pattern, differences in the pattern of MR reveal the difference in how and from which point the students begin to uncover important information contained in the diagram to establish a causal networks. Further analysis shows that a difference in the pattern of MR relate to how complex the students process the information contained in the diagrams/pictures.

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

    Cameron, L C

    2013-01-01

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

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

    Science.gov (United States)

    Cameron, L. C.

    2013-01-01

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

  5. 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

  6. 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:…

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

    Science.gov (United States)

    Toni, Tina; Tidor, Bruce

    2013-01-01

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

  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. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  9. Visual data mining of biological networks: one size does not fit all.

    Directory of Open Access Journals (Sweden)

    Chiara Pastrello

    Full Text Available High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

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

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

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

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

    Science.gov (United States)

    Ferentinos, Konstantinos P

    2005-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Maria Simak

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  15. Field-Effect Transistors Based on Networks of Highly Aligned, Chemically Synthesized N = 7 Armchair Graphene Nanoribbons.

    Science.gov (United States)

    Passi, Vikram; Gahoi, Amit; Senkovskiy, Boris V; Haberer, Danny; Fischer, Felix R; Grüneis, Alexander; Lemme, Max C

    2018-03-28

    We report on the experimental demonstration and electrical characterization of N = 7 armchair graphene nanoribbon (7-AGNR) field effect transistors. The back-gated transistors are fabricated from atomically precise and highly aligned 7-AGNRs, synthesized with a bottom-up approach. The large area transfer process holds the promise of scalable device fabrication with atomically precise nanoribbons. The channels of the FETs are approximately 30 times longer than the average nanoribbon length of 30 nm to 40 nm. The density of the GNRs is high, so that transport can be assumed well-above the percolation threshold. The long channel transistors exhibit a maximum I ON / I OFF current ratio of 87.5.

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

    Directory of Open Access Journals (Sweden)

    Primig Michael

    2008-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-10-15

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

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

    International Nuclear Information System (INIS)

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

    2010-10-01

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

  19. 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...

  20. A shear-induced network of aligned wormlike micelles in a sugar-based molecular gel. From gelation to biocompatibility assays.

    Science.gov (United States)

    Fitremann, Juliette; Lonetti, Barbara; Fratini, Emiliano; Fabing, Isabelle; Payré, Bruno; Boulé, Christelle; Loubinoux, Isabelle; Vaysse, Laurence; Oriol, Luis

    2017-10-15

    A new low molecular weight hydrogelator with a saccharide (lactobionic) polar head linked by azide-alkyne click chemistry was prepared in three steps. It was obtained in high purity without chromatography, by phase separation and ultrafiltration of the aqueous gel. Gelation was not obtained reproducibly by conventional heating-cooling cycles and instead was obtained by shearing the aqueous solutions, from 2 wt% to 0.25 wt%. This method of preparation favored the formation of a quite unusual network of interconnected large but thin 2D-sheets (7nm-thick) formed by the association side-by-side of long and aligned 7nm diameter wormlike micelles. It was responsible for the reproducible gelation at the macroscopic scale. A second network made of helical fibres with a 10-13nm diameter, more or less intertwined was also formed but was scarcely able to sustain a macroscopic gel on its own. The gels were analysed by TEM (Transmission Electronic Microscopy), cryo-TEM and SAXS (Small Angle X-ray Scattering). Molecular modelling was also used to highlight the possible conformations the hydrogelator can take. The gels displayed a weak and reversible transition near 20°C, close to room temperature, ascribed to the wormlike micelles 2D-sheets network. Heating over 30°C led to the loss of the gel macroscopic integrity, but gel fragments were still observed in suspension. A second transition near 50°C, ascribed to the network of helical fibres, finally dissolved completely these fragments. The gels showed thixotropic behaviour, recovering slowly their initial elastic modulus, in few hours, after injection through a needle. Stable gels were tested as scaffold for neural cell line culture, showing a reduced biocompatibility. This new gelator is a clear illustration of how controlling the pathway was critical for gel formation and how a new kind of self-assembly was obtained by shearing. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Business-IT alignment in PSS value networks linking customer knowledge management to social customer relationship management

    NARCIS (Netherlands)

    Bagheri, S.; Kusters, R.J.; Trienekens, J.J.M.; Hammoudi, S.; Cordeiro, J.; Maciaszek, L.

    2015-01-01

    Offering a PSS that is based on co-creating value with customer, starts with understanding customer needs. Customer understanding is realized through the process of managing customer knowledge across a PSS value network. In this respect, customer knowledge management (CKM) is seen as a core business

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

    Science.gov (United States)

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

    2015-08-01

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

  3. 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.

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

    Science.gov (United States)

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

    2017-02-01

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

  5. 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.

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

    Science.gov (United States)

    Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

    2012-01-01

    Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

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

    Science.gov (United States)

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

    2011-04-30

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

  8. Aligning everyday life priorities with people's self-management support networks: an exploration of the work and implementation of a needs-led telephone support system.

    Science.gov (United States)

    Blickem, Christian; Kennedy, Anne; Jariwala, Praksha; Morris, Rebecca; Bowen, Robert; Vassilev, Ivaylo; Brooks, Helen; Blakeman, Tom; Rogers, Anne

    2014-06-17

    Recent initiatives to target the personal, social and clinical needs of people with long-term health conditions have had limited impact within primary care. Evidence of the importance of social networks to support people with long-term conditions points to the need for self-management approaches which align personal circumstances with valued activities. The Patient-Led Assessment for Network Support (PLANS) intervention is a needs-led assessment for patients to prioritise their health and social needs and provide access to local community services and activities. Exploring the work and practices of patients and telephone workers are important for understanding and evaluating the workability and implementation of new interventions. Qualitative methods (interviews, focus group, observations) were used to explore the experience of PLANS from the perspectives of participants and the telephone support workers who delivered it (as part of an RCT) and the reasons why the intervention worked or not. Normalisation Process Theory (NPT) was used as a sensitising tool to evaluate: the relevance of PLANS to patients (coherence); the processes of engagement (cognitive participation); the work done for PLANS to happen (collective action); the perceived benefits and costs of PLANS (reflexive monitoring). 20 patients in the intervention arm of a clinical trial were interviewed and their telephone support calls were recorded and a focus group with 3 telephone support workers was conducted. Analysis of the interviews, support calls and focus group identified three themes in relation to the delivery and experience of PLANS. These are: formulation of 'health' in the context of everyday life; trajectories and tipping points: disrupting everyday routines; precarious trust in networks. The relevance of these themes are considered using NPT constructs in terms of the work that is entailed in engaging with PLANS, taking action, and who is implicated this process. PLANS gives scope to align

  9. Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

    Science.gov (United States)

    Pusuluri, Sai Teja

    Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features

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

    Science.gov (United States)

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

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

  11. 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.

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

    Science.gov (United States)

    Tsai, Kuan-Yao; Wang, Feng-Sheng

    2005-04-01

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

  13. Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.

    Science.gov (United States)

    Saithong, Treenut; Painter, Kevin J; Millar, Andrew J

    2010-12-16

    A number of studies have previously demonstrated that "goodness of fit" is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain. Here, we propose a novel robustness analysis that aims to determine the "common robustness" of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network. Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

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

    Science.gov (United States)

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

    2008-08-12

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

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

    Science.gov (United States)

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

    2018-03-01

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

  16. Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

    Science.gov (United States)

    Li, Man; Ling, Cheng; Xu, Qi; Gao, Jingyang

    2018-02-01

    Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    Valborg Gudmundsdottir

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

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

    Science.gov (United States)

    Chang, K. C.; Kotari, Vikas

    2009-05-01

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

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

    Science.gov (United States)

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

    2015-02-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Mehmet eKocaturk

    2015-08-01

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

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

    Science.gov (United States)

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

    2011-07-01

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

  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. © 2014 American Institute of Chemical Engineers.

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

    Science.gov (United States)

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

    2017-03-20

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

  6. 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.

  7. 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.

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

    Science.gov (United States)

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

    2012-03-21

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

  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. 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.

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

    Science.gov (United States)

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

    2018-03-01

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

  12. 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

  13. Ancestral sequence alignment under optimal conditions

    Directory of Open Access Journals (Sweden)

    Brown Daniel G

    2005-11-01

    Full Text Available Abstract Background Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences. Results We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang. Conclusion We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Sommer Björn

    2010-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Lemke Ney

    2009-09-01

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

  18. 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

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

    Science.gov (United States)

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

    2006-12-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  1. Injectable Anisotropic Nanocomposite Hydrogels Direct in Situ Growth and Alignment of Myotubes

    International Nuclear Information System (INIS)

    De France, Kevin J.; Yager, Kevin G.; Chan, Katelyn J. W.; Corbett, Brandon; Cranston, Emily D.; Hoare, Todd

    2017-01-01

    Here, while injectable in situ cross-linking hydrogels have attracted increasing attention as minimally invasive tissue scaffolds and controlled delivery systems, their inherently disorganized and isotropic network structure limits their utility in engineering oriented biological tissues. Traditional methods to prepare anisotropic hydrogels are not easily translatable to injectable systems given the need for external equipment to direct anisotropic gel fabrication and/or the required use of temperatures or solvents incompatible with biological systems. Herein, we report a new class of injectable nanocomposite hydrogels based on hydrazone cross-linked poly(oligoethylene glycol methacrylate) and magnetically aligned cellulose nanocrystals (CNCs) capable of encapsulating skeletal muscle myoblasts and promoting their differentiation into highly oriented myotubes in situ. CNC alignment occurs on the same time scale as network gelation and remains fixed after the removal of the magnetic field, enabling concurrent CNC orientation and hydrogel injection. The aligned hydrogels show mechanical and swelling profiles that can be rationally modulated by the degree of CNC alignment and can direct myotube alignment both in two- and three-dimensions following coinjection of the myoblasts with the gel precursor components. As such, these hydrogels represent a critical advancement in anisotropic biomimetic scaffolds that can be generated noninvasively in vivo following simple injection.

  2. A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

    Science.gov (United States)

    Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

    2016-09-01

    Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

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

    Science.gov (United States)

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

    2017-04-12

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

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

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

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

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

    KAUST Repository

    Phadke, Sujay; Lee, Jung-Yong; West, Jack; Peumans, Peter; Salleo, Alberto

    2011-01-01

    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

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

    KAUST Repository

    Gomez-Cabrero, David; Tegner, Jesper

    2017-01-01

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

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

    Science.gov (United States)

    Whoriskey, F. G.

    2016-02-01

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

  8. Propagating semantic information in biochemical network models

    Directory of Open Access Journals (Sweden)

    Schulz Marvin

    2012-01-01

    Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.

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

    OpenAIRE

    Alessandro Giuliani

    2014-01-01

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

  10. Enhanced Dynamic Algorithm of Genome Sequence Alignments

    OpenAIRE

    Arabi E. keshk

    2014-01-01

    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 se...

  11. 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

  12. 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

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lan Chung-Yu

    2008-09-01

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

  17. 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.

  18. Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.

    Science.gov (United States)

    Wang, Wenhui; Nunez-Iglesias, Juan; Luan, Yihui; Sun, Fengzhu

    2009-09-03

    Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.

  19. 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

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

    International Nuclear Information System (INIS)

    Gaillard-Lecanu, E.; Coffigny, H.; Poncy, J.L.; Authier, N.; Verrey, B.; Bailly, I.; Baldacchino, G.; Bordy, J.M.; Carriere, M.; Leplat, J.J.; Pin, S.; Pommeret, S.; Thuret, J.Y.; Renault, J.P.; Cortella, I.; Duval, D.; Khodja, H.; Testard, I.

    2006-01-01

    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)

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

    Science.gov (United States)

    Waliszewski, P; Molski, M; Konarski, J

    1998-06-01

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

  2. 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...

  3. 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...

  4. 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.

  5. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

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

    International Nuclear Information System (INIS)

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

    2012-01-01

    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).

  7. A Biologically Inspired Energy-Efficient Duty Cycle Design Method for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jie Zhou

    2017-01-01

    Full Text Available The recent success of emerging wireless sensor networks technology has encouraged researchers to develop new energy-efficient duty cycle design algorithm in this field. The energy-efficient duty cycle design problem is a typical NP-hard combinatorial optimization problem. In this paper, we investigate an improved elite immune evolutionary algorithm (IEIEA strategy to optimize energy-efficient duty cycle design scheme and monitored area jointly to enhance the network lifetimes. Simulation results show that the network lifetime of the proposed IEIEA method increased compared to the other two methods, which means that the proposed method improves the full coverage constraints.

  8. 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

  9. Latin-American Biological Dosimetry Network (LBDNET) Intercomparison Exercise. Evaluation through triage and conventional scoring criteria. Development of a new approach for statistical data analysis

    International Nuclear Information System (INIS)

    Di Giorgio, M.; Vallerga, M.B.; Radl, A.

    2011-01-01

    Biological Dosimetry is a necessary support for National Radiation Protection Programs and Emergency Response Schemes. A Latin-American Biological Dosimetry Network (LBDNET) has been constituted by the biological dosimetry laboratories from: Argentina, Brazil, Chile, Cuba, Mexico, Peru, and Uruguay (IAEA Regional Project RLA9/054, 2007). The biological dosimetry laboratory of Argentina organized an international biological dosimetry intercomparison for the analysis of some relevant parameters involved in dose assessment, to reinforce the response capability in accidental situations requiring the activation of mutual assistance mechanisms and thus, constituting the bases of the LBDNET organization. (authors)

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

    Science.gov (United States)

    Zhou, Guangyan; Xia, Jianguo

    2018-06-07

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

  11. 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.

  12. From cell biology to immunology: Controlling metastatic progression of cancer via microRNA regulatory networks.

    Science.gov (United States)

    Park, Jae Hyon; Theodoratou, Evropi; Calin, George A; Shin, Jae Il

    2016-01-01

    Recently, the study of microRNAs has expanded our knowledge of the fundamental processes of cancer biology and the underlying mechanisms behind tumor metastasis. Extensive research in the fields of microRNA and its novel mechanisms of actions against various cancers has more recently led to the trial of a first cancer-targeted microRNA drug, MRX34. Yet, these microRNAs are mostly being studied and clinically trialed solely based on the understanding of their cell biologic effects, thus, neglecting the important immunologic effects that are sometimes opposite of the cell biologic effects. Here, we summarize both the cell biologic and immunologic effects of various microRNAs and discuss the importance of considering both effects before using them in clinical settings. We stress the importance of understanding the miRNA's effect on cancer metastasis from a "systems" perspective before developing a miRNA-targeted therapeutic in treating cancer metastasis.

  13. 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.

  14. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate

    International Nuclear Information System (INIS)

    Gulliford, Sarah L.; Webb, Steve; Rowbottom, Carl G.; Corne, David W.; Dearnaley, David P.

    2004-01-01

    Background and purpose: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. Patients and methods: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. Results: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. Conclusion: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes

  15. 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

  16. New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.

    Science.gov (United States)

    Gogoshin, Grigoriy; Boerwinkle, Eric; Rodin, Andrei S

    2017-04-01

    Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology-type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types-single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite

  17. Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Loppini, Alessandro

    2018-03-01

    Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

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

    Science.gov (United States)

    Zhang, Yuji

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-09-13

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

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

    DEFF Research Database (Denmark)

    Gudmundsdottir, Valborg; Pedersen, Helle Krogh; Allebrandt, Karla Viviani

    2018-01-01

    Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin...... secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS...... P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P

  1. Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

    Science.gov (United States)

    Cabana, Alvaro; Mizraji, Eduardo; Pomi, Andrés; Valle-Lisboa, Juan Carlos

    2008-04-01

    Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language). From our social net, we constructed two different graph representations based on the relationships among researchers revealed by their co-participation in Master's thesis committees. We studied these networks at different times and found that they achieve connectedness early in their evolution and exhibit the small-world property (i.e. high clustering with short path lengths). The data seem compatible with power law distributions of connectivity, clustering coefficients and betweenness centrality. Evidence of preferential attachment of new nodes and of new links between old nodes was also found in both representations. These results suggest that there are topological properties observed throughout the growth of the network that do not depend on the representations we have chosen but reflect intrinsic properties of the academic collective under study. Researchers in PEDECIBA are classified according to their specialties. We analysed the community structure detected by a standard algorithm in both representations. We found that much of the pre-specified structure is recovered and part of the mismatches can be attributed to convergent interests between scientists from different sub-disciplines. This result shows the potentiality of some clustering methods for the analysis of partially known natural systems.

  2. 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

  3. Biological and theoretical relevance of some connectionist assumptions. The development of conceptual networks

    NARCIS (Netherlands)

    Delgado, JFR; Dalenoort, GJ; Gracia, AP

    2000-01-01

    For the study of psychological processes in cognitive science modelling two general approaches rule nowadays research: Artificial Intelligence (top-down) functional symbolic models, and Connectionist (bottom-up) neural networks modelling. Our goal in this paper is to show that analyzing the

  4. Biological and theoretical relevance of some connectionist assumptions : The development of conceptual networks

    NARCIS (Netherlands)

    Delgado, JFR; Dalenoort, GJ; Pitarque Gracia, A

    2000-01-01

    For the study of psychological processes in cognitive science modelling two general approaches rule nowadays research: Artificial Intelligence (top-down) functional symbolic models, and Connectionist (bottom-up) neural networks modelling. Our goal in this paper is to show that analyzing the

  5. 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...

  6. A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks

    Directory of Open Access Journals (Sweden)

    Hyun-Ho Choi

    2016-03-01

    Full Text Available Most of the energy used to operate a cellular network is consumed by a base station (BS, and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network.

  7. 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

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

    KAUST Repository

    Lautenschlä ger, Karin; Hwang, Chiachi; Liu, Wentso; Boon, Nico; Kö ster, Oliver; Vrouwenvelder, Johannes S.; Egli, Thomas; Hammes, Frederik A.

    2013-01-01

    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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. Functional Module Analysis for Gene Coexpression Networks with Network Integration.

    Science.gov (United States)

    Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

  15. [Bone Cell Biology Assessed by Microscopic Approach. Response to mechanical stress by osteocyte network].

    Science.gov (United States)

    Komori, Toshihisa

    2015-10-01

    Osteocytes were considered to be involved in the response to mechanical stress from their network structure. However, it was difficult to prove the function because of the lack of animal models for a long time. Recently, the function of osteocytes was clarified using various knockout and transgenic mice. Osteocyte death causes bone remodeling, which is a repair process induced by osteocyte necrosis but not by the loss of the function of live osteocytes. The osteocyte network mildly inhibits bone formation and mildly stimulates bone resorption in physiological condition. In unloaded condition, it strongly inhibits bone formation and strongly stimulates bone resorption, at least in part, through the induction of Sost in osteocytes and Rankl in osteoblasts.

  16. 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 ...

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

    Science.gov (United States)

    Stokes, M.; Perron, J. T.

    2017-12-01

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

  18. 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.

  19. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Recent advances in modeling languages for pathway maps and computable biological networks.

    Science.gov (United States)

    Slater, Ted

    2014-02-01

    As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. 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.

  2. BioMart Central Portal: an open database network for the biological community

    OpenAIRE

    Guberman, Jonathan M.; Ai, J.; Arnaiz, O.; Baran, Joachim; Blake, Andrew; Baldock, Richard; Chelala, Claude; Croft, David; Cros, Anthony; Cutts, Rosalind J.; Di Genova, A.; Forbes, Simon; Fujisawa, T.; Gadaleta, E.; Goodstein, D. M.

    2011-01-01

    International audience; BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common inte...

  3. A Problem-Solving Environment for Biological Network Informatics: Bio-Spice

    Science.gov (United States)

    2007-06-01

    user an environment to access software tools. The Dashboard is built upon the NetBeans Integrated Development Environment (IDE), an open source Java...based integration platform was demonstrated. During the subsequent six month development cycle, the first version of the NetBeans based Bio-SPICE...frameworks (OAA, NetBeans , and Systems Biology Workbench (SBW)[15]), it becomes possible for Bio-SPICE tools to truly interoperate. This interoperation

  4. A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

    Science.gov (United States)

    Baur, Brittany; Bozdag, Serdar

    2015-04-01

    One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-03-31

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

  6. BioMart Central Portal: an open database network for the biological community

    Science.gov (United States)

    Guberman, Jonathan M.; Ai, J.; Arnaiz, O.; Baran, Joachim; Blake, Andrew; Baldock, Richard; Chelala, Claude; Croft, David; Cros, Anthony; Cutts, Rosalind J.; Di Génova, A.; Forbes, Simon; Fujisawa, T.; Gadaleta, E.; Goodstein, D. M.; Gundem, Gunes; Haggarty, Bernard; Haider, Syed; Hall, Matthew; Harris, Todd; Haw, Robin; Hu, S.; Hubbard, Simon; Hsu, Jack; Iyer, Vivek; Jones, Philip; Katayama, Toshiaki; Kinsella, R.; Kong, Lei; Lawson, Daniel; Liang, Yong; Lopez-Bigas, Nuria; Luo, J.; Lush, Michael; Mason, Jeremy; Moreews, Francois; Ndegwa, Nelson; Oakley, Darren; Perez-Llamas, Christian; Primig, Michael; Rivkin, Elena; Rosanoff, S.; Shepherd, Rebecca; Simon, Reinhard; Skarnes, B.; Smedley, Damian; Sperling, Linda; Spooner, William; Stevenson, Peter; Stone, Kevin; Teague, J.; Wang, Jun; Wang, Jianxin; Whitty, Brett; Wong, D. T.; Wong-Erasmus, Marie; Yao, L.; Youens-Clark, Ken; Yung, Christina; Zhang, Junjun; Kasprzyk, Arek

    2011-01-01

    BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities. Database URL: http://central.biomart.org. PMID:21930507

  7. 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, ...

  8. Fast global sequence alignment technique

    KAUST Repository

    Bonny, Mohamed Talal; Salama, Khaled N.

    2011-01-01

    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

  9. 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.

  10. Biomine: predicting links between biological entities using network models of heterogeneous databases

    Directory of Open Access Journals (Sweden)

    Eronen Lauri

    2012-06-01

    Full Text Available Abstract Background Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Results Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. Conclusions The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable

  11. Chemokines and Heart Disease: A Network Connecting Cardiovascular Biology to Immune and Autonomic Nervous Systems

    Science.gov (United States)

    Dusi, Veronica; Ghidoni, Alice; Ravera, Alice; De Ferrari, Gaetano M.; Calvillo, Laura

    2016-01-01

    Among the chemokines discovered to date, nineteen are presently considered to be relevant in heart disease and are involved in all stages of cardiovascular response to injury. Chemokines are interesting as biomarkers to predict risk of cardiovascular events in apparently healthy people and as possible therapeutic targets. Moreover, they could have a role as mediators of crosstalk between immune and cardiovascular system, since they seem to act as a “working-network” in deep linkage with the autonomic nervous system. In this paper we will describe the single chemokines more involved in heart diseases; then we will present a comprehensive perspective of them as a complex network connecting the cardiovascular system to both the immune and the autonomic nervous systems. Finally, some recent evidences indicating chemokines as a possible new tool to predict cardiovascular risk will be described. PMID:27242392

  12. 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

  13. 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

    2013-09-10

    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.

  14. Supramolecular assembly of biological molecules purified from bovine nerve cells: from microtubule bundles and necklaces to neurofilament networks

    International Nuclear Information System (INIS)

    Needleman, Daniel J; Jones, Jayna B; Raviv, Uri; Ojeda-Lopez, Miguel A; Miller, H P; Li, Y; Wilson, L; Safinya, C R

    2005-01-01

    With the completion of the human genome project, the biosciences community is beginning the daunting task of understanding the structures and functions of a large number of interacting biological macromolecules. Examples include the interacting molecules involved in the process of DNA condensation during the cell cycle, and in the formation of bundles and networks of filamentous actin proteins in cell attachment, motility and cytokinesis. In this proceedings paper we present examples of supramolecular assembly based on proteins derived from the vertebrate nerve cell cytoskeleton. The axonal cytoskeleton in vertebrate neurons provides a rich example of bundles and networks of neurofilaments, microtubules (MTs) and filamentous actin, where the nature of the interactions, structures, and structure-function correlations remains poorly understood. We describe synchrotron x-ray diffraction, electron microscopy, and optical imaging data, in reconstituted protein systems purified from bovine central nervous system, which reveal unexpected structures not predicted by current electrostatic theories of polyelectrolyte bundling, including three-dimensional MT bundles and two-dimensional MT necklaces

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

    Science.gov (United States)

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

    2017-09-01

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

  16. MP-GeneticSynth: inferring biological network regulations from time series.

    Science.gov (United States)

    Castellini, Alberto; Paltrinieri, Daniele; Manca, Vincenzo

    2015-03-01

    MP-GeneticSynth is a Java tool for discovering the logic and regulation mechanisms responsible for observed biological dynamics in terms of finite difference recurrent equations. The software makes use of: (i) metabolic P systems as a modeling framework, (ii) an evolutionary approach to discover flux regulation functions as linear combinations of given primitive functions, (iii) a suitable reformulation of the least squares method to estimate function parameters considering simultaneously all the reactions involved in complex dynamics. The tool is available as a plugin for the virtual laboratory MetaPlab. It has graphical and interactive interfaces for data preparation, a priori knowledge integration, and flux regulator analysis. Availability and implementation: Source code, binaries, documentation (including quick start guide and videos) and case studies are freely available at http://mplab.sci.univr.it/plugins/mpgs/index.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. High performance computing on biological sequence alignment

    OpenAIRE

    Orobitg Cortada, Miquel

    2013-01-01

    L'Alineament Múltiple de Seqüències (MSA) és una eina molt potent per a aplicacions biològiques importants. Els MSA són computacionalment complexos de calcular, i la majoria de les formulacions porten a problemes d'optimització NP-Hard. Per a dur a terme alineaments de milers de seqüències, nous desafiaments necessiten ser resolts per adaptar els algoritmes a l'era de la computació d'altes prestacions. En aquesta tesi es proposen tres aportacions diferents per resoldre algunes limitacion...

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

    Science.gov (United States)

    Lautenschlager, Karin; Hwang, Chiachi; Liu, Wen-Tso; Boon, Nico; Köster, Oliver; Vrouwenvelder, Hans; Egli, Thomas; Hammes, Frederik

    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 (6-52 h) 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) × 10(4) cells/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, so far for unknown reasons, recorded a slight but significantly higher TCC (1.3 (± 0.1) × 10(5) cells/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

  19. Control rod housing alignment

    International Nuclear Information System (INIS)

    Dixon, R.C.; Deaver, G.A.; Punches, J.R.; Singleton, G.E.; Erbes, J.G.; Offer, H.P.

    1990-01-01

    This patent describes a process for measuring the vertical alignment between a hole in a core plate and the top of a corresponding control rod drive housing within a boiling water reactor. It comprises: providing an alignment apparatus. The alignment apparatus including a lower end for fitting to the top of the control rod drive housing; an upper end for fitting to the aperture in the core plate, and a leveling means attached to the alignment apparatus to read out the difference in angularity with respect to gravity, and alignment pin registering means for registering to the alignment pin on the core plate; lowering the alignment device on a depending support through a lattice position in the top guide through the hole in the core plate down into registered contact with the top of the control rod drive housing; registering the upper end to the sides of the hole in the core plate; registering the alignment pin registering means to an alignment pin on the core plate to impart to the alignment device the required angularity; and reading out the angle of the control rod drive housing with respect to the hole in the core plate through the leveling devices whereby the angularity of the top of the control rod drive housing with respect to the hole in the core plate can be determined

  20. Semantic Data Integration and Knowledge Management to Represent Biological Network Associations.

    Science.gov (United States)

    Losko, Sascha; Heumann, Klaus

    2017-01-01

    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, originating from a multitude of "-omics" platforms, phenotype information, and clinical data. For bioinformatics, the challenge remains to structure this information so that scientists can 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. Here we report on progress made in building a generic knowledge management environment capable of representing and mining both explicit and implicit knowledge and, thus, generating new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we 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.

  1. A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions.

    Science.gov (United States)

    Taylor, Duncan; Biedermann, Alex; Hicks, Tacha; Champod, Christophe

    2018-03-01

    The hierarchy of propositions has been accepted amongst the forensic science community for some time. It is also accepted that the higher up the hierarchy the propositions are, against which the scientist are competent to evaluate their results, the more directly useful the testimony will be to the court. Because each case represents a unique set of circumstances and findings, it is difficult to come up with a standard structure for evaluation. One common tool that assists in this task is Bayesian networks (BNs). There is much diversity in the way that BN can be constructed. In this work, we develop a template for BN construction that allows sufficient flexibility to address most cases, but enough commonality and structure that the flow of information in the BN is readily recognised at a glance. We provide seven steps that can be used to construct BNs within this structure and demonstrate how they can be applied, using a case example. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  2. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere.

    Science.gov (United States)

    Delgado-Baquerizo, Manuel; Reith, Frank; Dennis, Paul G; Hamonts, Kelly; Powell, Jeff R; Young, Andrew; Singh, Brajesh K; Bissett, Andrew

    2018-03-01

    The ecological drivers of soil biodiversity in the Southern Hemisphere remain underexplored. Here, in a continental survey comprising 647 sites, across 58 degrees of latitude between tropical Australia and Antarctica, we evaluated the major ecological patterns in soil biodiversity and relative abundance of ecological clusters within a co-occurrence network of soil bacteria, archaea and eukaryotes. Six major ecological clusters (modules) of co-occurring soil taxa were identified. These clusters exhibited strong shifts in their relative abundances with increasing distance from the equator. Temperature was the major environmental driver of the relative abundance of ecological clusters when Australia and Antarctica are analyzed together. Temperature, aridity, soil properties and vegetation types were the major drivers of the relative abundance of different ecological clusters within Australia. Our data supports significant reductions in the diversity of bacteria, archaea and eukaryotes in Antarctica vs. Australia linked to strong reductions in temperature. However, we only detected small latitudinal variations in soil biodiversity within Australia. Different environmental drivers regulate the diversity of soil archaea (temperature and soil carbon), bacteria (aridity, vegetation attributes and pH) and eukaryotes (vegetation type and soil carbon) across Australia. Together, our findings provide new insights into the mechanisms driving soil biodiversity in the Southern Hemisphere. © 2018 by the Ecological Society of America.

  3. Quantitative modeling of gene networks of biological systems using fuzzy Petri nets and fuzzy sets

    Directory of Open Access Journals (Sweden)

    Raed I. Hamed

    2018-01-01

    Full Text Available Quantitative demonstrating of organic frameworks has turned into an essential computational methodology in the configuration of novel and investigation of existing natural frameworks. Be that as it may, active information that portrays the framework's elements should be known keeping in mind the end goal to get pertinent results with the routine displaying strategies. This information is frequently robust or even difficult to get. Here, we exhibit a model of quantitative fuzzy rational demonstrating approach that can adapt to obscure motor information and hence deliver applicable results despite the fact that dynamic information is fragmented or just dubiously characterized. Besides, the methodology can be utilized as a part of the blend with the current cutting edge quantitative demonstrating strategies just in specific parts of the framework, i.e., where the data are absent. The contextual analysis of the methodology suggested in this paper is performed on the model of nine-quality genes. We propose a kind of FPN model in light of fuzzy sets to manage the quantitative modeling of biological systems. The tests of our model appear that the model is practical and entirely powerful for information impersonation and thinking of fuzzy expert frameworks.

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

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

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

  5. Toward Synthetic Biology Strategies for Adipic Acid Production: An in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks

    DEFF Research Database (Denmark)

    Averesch, Nils J. H.; Martínez, Verónica S.; Nielsen, Lars K.

    2018-01-01

    Adipic acid, a nylon-6,6 precursor, has recently gained popularity in synthetic biology. Here, 16 different production routes to adipic acid were evaluated using a novel tool for network-embedded thermodynamic analysis of elementary flux modes. The tool distinguishes between thermodynamically...

  6. 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...

  7. Tidal alignment of galaxies

    Energy Technology Data Exchange (ETDEWEB)

    Blazek, Jonathan; Vlah, Zvonimir; Seljak, Uroš

    2015-08-01

    We develop an analytic model for galaxy intrinsic alignments (IA) based on the theory of tidal alignment. We calculate all relevant nonlinear corrections at one-loop order, including effects from nonlinear density evolution, galaxy biasing, and source density weighting. Contributions from density weighting are found to be particularly important and lead to bias dependence of the IA amplitude, even on large scales. This effect may be responsible for much of the luminosity dependence in IA observations. The increase in IA amplitude for more highly biased galaxies reflects their locations in regions with large tidal fields. We also consider the impact of smoothing the tidal field on halo scales. We compare the performance of this consistent nonlinear model in describing the observed alignment of luminous red galaxies with the linear model as well as the frequently used "nonlinear alignment model," finding a significant improvement on small and intermediate scales. We also show that the cross-correlation between density and IA (the "GI" term) can be effectively separated into source alignment and source clustering, and we accurately model the observed alignment down to the one-halo regime using the tidal field from the fully nonlinear halo-matter cross correlation. Inside the one-halo regime, the average alignment of galaxies with density tracers no longer follows the tidal alignment prediction, likely reflecting nonlinear processes that must be considered when modeling IA on these scales. Finally, we discuss tidal alignment in the context of cosmic shear measurements.

  8. Tevatron alignment issues 2003-2004

    International Nuclear Information System (INIS)

    Volk, J.T.; Annala, J.; Elementi, L.; Gelfand, N.; Gollwitzer, K.E.; Greenwood, J.; Martens, M.; Moore, C.; Nobrega, A.; Russell, A.D.; Shiltsev, V.; Stefanski, R.; Sager, T.; Syphers, M.J.; Wojcik, G.

    2005-01-01

    It was observed during the early part of Run II that dipole corrector currents in the Tevatron were changing over time. Measurement of the roll for dipoles and quadrupoles confirmed that there was a slow and systematic movement of the magnets from their ideal position. A simple system using a digital protractor and laptop computer was developed to allow roll measurements of all dipoles and quadrupoles. These measurements showed that many magnets in the Tevatron had rolled more than 1 milliradian. To aid in magnet alignment a new survey network was built in the Tevatron tunnel. This network is based on the use of free centering laser tracker. During the measurement of the network coordinates for all dipole, quadrupole and corrector magnets were obtained. This paper discusses roll measurement techniques and data, the old and new Tevatron alignment network

  9. BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

    Science.gov (United States)

    Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

    2016-01-01

    Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

  10. Alignment for new Subaru ring

    International Nuclear Information System (INIS)

    Zhang, Ch.; Matsui, S.; Hashimoto, S.

    1999-01-01

    The New SUBARU is a synchrotron light source being constructed at the SPring-8 site. The main facility is a 1.5 GeV electron storage ring that provides light beam in the region from VUV to soft X-ray using SPring-8's 1 GeV linac as an injector. The ring, with a circumference of about 119 meters, is composed of six bending cells. Each bending cell has two normal dipoles of 34 degree and one inverse dipole of -8 degree. The ring has six straight sections: two very long straight sections for a 11-m long undulator and an optical klystron, four short straight sections for a 2.3-m undulator, a super-conducting wiggler, rf cavity and injection, etc. The magnets of the storage ring are composed of 12 dipoles (BMs), 6 invert dipoles (BIs), 56 quadrupoles and 44 sextupoles, etc. For the magnet alignment, positions of the dipoles (the BMs and BIs) are determined by network survey method. The multipoles, which are mounted on girders between the dipoles, are aligned with a laser-CCD camera system. This article presents the methodology used to position the different components and particularly to assure the precise alignment of the multipoles. (authors)

  11. Creating biological nanomaterials using synthetic biology

    International Nuclear Information System (INIS)

    Rice, MaryJoe K; Ruder, Warren C

    2014-01-01

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

  12. Creating biological nanomaterials using synthetic biology.

    Science.gov (United States)

    Rice, MaryJoe K; Ruder, Warren C

    2014-02-01

    Synthetic biology is a new discipline that combines science and engineering approaches to precisely control biological networks. These signaling networks are especially important in fields such as biomedicine and biochemical engineering. Additionally, biological networks can also be critical to the production of naturally occurring biological nanomaterials, and as a result, synthetic biology holds tremendous potential in creating