WorldWideScience

Sample records for aligned biological networks

  1. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

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

  2. An Improved Method for Completely Uncertain Biological Network Alignment

    OpenAIRE

    2015-01-01

    With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of curr...

  3. CytoGEDEVO - Global alignment of biological networks with Cytoscape

    DEFF Research Database (Denmark)

    Malek, Maximilian; Ibragimov, Rashid; Albrecht, Mario;

    2016-01-01

    MOTIVATION: In the systems biology era, high-throughput omics technologies have enabled the unraveling of the interplay of some biological entities on a large scale (e.g. genes, proteins, metabolites or RNAs). Huge biological networks have emerged, where nodes correspond to these entities and edges...... between them model their relations. Protein-protein-interaction (PPI) networks, for instance, show the physical interactions of proteins in an organism. The comparison of such networks promises additional insights into protein and cell function as well as knowledge-transfer across species. Several...... computational approaches have been developed previously to solve the network alignment problem, but only a few concentrate on the usability of the implemented tools for the evaluation of protein-protein interactions by the end-users (biologists and medical researchers). RESULTS: We have created CytoGEDEVO, a...

  4. Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks.

    Science.gov (United States)

    Radu, Alex; Charleston, Michael

    2015-07-01

    Due to recent advancements in high-throughput sequencing technologies, progressively more protein-protein interactions have been identified for a growing number of species. Subsequently, the protein-protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated, with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments. PMID:25695597

  5. FUSE: Multiple Network Alignment via Data Fusion

    OpenAIRE

    Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša

    2014-01-01

    Discovering 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. The objective of a multiple network alignment is to create clusters of nodes that are evolutionarily conserved and functionally consistent across all networks. Unfortunately, the alignment methods ...

  6. Algorithms for Large, Sparse Network Alignment Problems

    CERN Document Server

    Bayati, Mohsen; Gleich, David F; Saberi, Amin; Wang, Ying

    2009-01-01

    We propose a new distributed algorithm for sparse variants of the network alignment problem that occurs in a variety of data mining areas including systems biology, database matching, and computer vision. Our algorithm uses a belief propagation heuristic and provides near optimal solutions for an NP-hard combinatorial optimization problem. We show that our algorithm is faster and outperforms or nearly ties existing algorithms on synthetic problems, a problem in bioinformatics, and a problem in ontology matching. We also provide a unified framework for studying and comparing all network alignment solvers.

  7. 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.

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

    OpenAIRE

    Malod-Dognin, Noël; Pržulj, Nataša

    2015-01-01

    Motivation: 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...

  9. Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks

    OpenAIRE

    Farid Amir-Ghiasvand; Abbas Nowzari-Dalini; Vida Momenzadeh

    2014-01-01

    To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein...

  10. 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.

  11. GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

    OpenAIRE

    Giovanni Micale; Alfredo Pulvirenti; Rosalba Giugno; Alfredo Ferro

    2014-01-01

    The analysis of structure and dynamics of biological networks plays a central role in understanding the intrinsic complexity of biological systems. Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically ...

  12. Synthetic biological networks

    International Nuclear Information System (INIS)

    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)

  13. 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.

  14. Functional Aspects of Biological Networks

    Science.gov (United States)

    Sneppen, Kim

    2007-03-01

    We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042

  15. Networks in Cell Biology = Modelling cell biology with networks

    OpenAIRE

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

    2010-01-01

    The science of complex biological networks is transforming research in areas ranging from evolutionary biology to medicine. This is the first book on the subject, providing a comprehensive introduction to complex network science and its biological applications. With contributions from key leaders in both network theory and modern cell biology, this book discusses the network science that is increasingly foundational for systems biology and the quantitative understanding of living systems. It ...

  16. Mutual Community Detection across Multiple Partially Aligned Social Networks

    OpenAIRE

    Zhang, Jiawei; Yu, Philip S.

    2015-01-01

    Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formall...

  17. Services supporting collaborative alignment of engineering networks

    Science.gov (United States)

    Jansson, Kim; Uoti, Mikko; Karvonen, Iris

    2015-08-01

    Large-scale facilities such as power plants, process factories, ships and communication infrastructures are often engineered and delivered through geographically distributed operations. The competencies required are usually distributed across several contributing organisations. In these complicated projects, it is of key importance that all partners work coherently towards a common goal. VTT and a number of industrial organisations in the marine sector have participated in a national collaborative research programme addressing these needs. The main output of this programme was development of the Innovation and Engineering Maturity Model for Marine-Industry Networks. The recently completed European Union Framework Programme 7 project COIN developed innovative solutions and software services for enterprise collaboration and enterprise interoperability. One area of focus in that work was services for collaborative project management. This article first addresses a number of central underlying research themes and previous research results that have influenced the development work mentioned above. This article presents two approaches for the development of services that support distributed engineering work. Experience from use of the services is analysed, and potential for development is identified. This article concludes with a proposal for consolidation of the two above-mentioned methodologies. This article outlines the characteristics and requirements of future services supporting collaborative alignment of engineering networks.

  18. 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

  19. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?

    OpenAIRE

    Liao, Qianli; Leibo, Joel Z.; Mroueh, Youssef; Poggio, Tomaso

    2013-01-01

    The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be dissatisfied with it, among them is its lack of biological plausibility. A recent theory of invariant recognition by feedforward hierarchical networks, like HMAX, other convolutional networks, or possibly the ventral stream, implies an alternative approach to unconstrained face r...

  20. 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.

  1. Evolution, Interactions, and Biological Networks

    OpenAIRE

    Weitz, Joshua S.; Benfey, Philip N.; Wingreen, Ned S.

    2007-01-01

    Shifting the perspective of the questions we ask will ensure that network theory continues to excite the network theorists, but more importantly, that it remains vital to progress in biological research.

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

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

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

  3. Logical impossibilities in biological networks

    Directory of Open Access Journals (Sweden)

    Monendra Grover

    2011-10-01

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

  4. Querying Large Biological Network Datasets

    Science.gov (United States)

    Gulsoy, Gunhan

    2013-01-01

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

  5. Error estimation for accelerator alignment using surveying network

    International Nuclear Information System (INIS)

    Surveying networks are widely used for evaluating alignment of large particle accelerators. We analytically estimated error propagated to the results obtained by the surveying networks by using our stitching model. Here, we estimated effects of the measurement parameters, that is the total measurement length l, the partial measurement length lu, the measurement interval s, and overlapping ratio k for evaluating alignment of the components for approximately 100 m of circumference part of the cERL, which has been constructed in KEK. (author)

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

    DEFF Research Database (Denmark)

    Dimaki, Maria; Bøggild, Peter

    2006-01-01

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

  7. 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.

  8. Graphics processing unit-based alignment of protein interaction networks.

    Science.gov (United States)

    Xie, Jiang; Zhou, Zhonghua; Ma, Jin; Xiang, Chaojuan; Nie, Qing; Zhang, Wu

    2015-08-01

    Network alignment is an important bridge to understanding human protein-protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large-scale networks via sequential computing. In this study, the typical Hungarian-Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2-nearest neighbours (HGA-2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA-2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA-2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large-scale networks are considered. By using HGA-2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods. PMID:26243827

  9. Design principles in biological networks

    Science.gov (United States)

    Goyal, Sidhartha

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

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

    Directory of Open Access Journals (Sweden)

    Xingzhe Xie

    2015-11-01

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

  11. Alignment control network scheme design and measurement of HLS upgrade

    International Nuclear Information System (INIS)

    Background: In order to open up the application field and improve the source, the Hefei Light Source (HLS) is taken a major upgrade. Purpose: The paper mainly introduces the way of building and measuring of alignment control network during the HLS upgrade. Methods: The global horizontal control network points' data are measured by total station. All control network points' 3D-measurement data are obtained by laser tracker and elevation data are got by N3 Level. The function of Unified Spatial Metrology Network (USMN) of SA software is used to make a three dimensional adjustment combining data from different devices. The concept of measurement uncertainty is used to describe the measurement quality of network points. In order to check the correctness of the adjustment result, the software of MAA was employed to make three-dimensional adjustment as well as plane adjustment done by SURVEY adding elevation adjustment by NASEW2003. Results: Through the actual measurement and data processing, the accuracy of actual measurement is 0.09 mm, better than the designed accuracy of 0.15 mm. Compared with the results adjustment by different software, the result of SA is demonstrated to be reliable. Conclusion: The result demonstrates that the designed scheme of alignment control network of HLS upgrade is reasonable and feasible. (authors)

  12. Walking tree heuristics for biological string alignment, gene location, and phylogenies

    Science.gov (United States)

    Cull, P.; Holloway, J. L.; Cavener, J. D.

    1999-03-01

    Basic biological information is stored in strings of nucleic acids (DNA, RNA) or amino acids (proteins). Teasing out the meaning of these strings is a central problem of modern biology. Matching and aligning strings brings out their shared characteristics. Although string matching is well-understood in the edit-distance model, biological strings with transpositions and inversions violate this model's assumptions. We propose a family of heuristics called walking trees to align biologically reasonable strings. Both edit-distance and walking tree methods can locate specific genes within a large string when the genes' sequences are given. When we attempt to match whole strings, the walking tree matches most genes, while the edit-distance method fails. We also give examples in which the walking tree matches substrings even if they have been moved or inverted. The edit-distance method was not designed to handle these problems. We include an example in which the walking tree "discovered" a gene. Calculating scores for whole genome matches gives a method for approximating evolutionary distance. We show two evolutionary trees for the picornaviruses which were computed by the walking tree heuristic. Both of these trees show great similarity to previously constructed trees. The point of this demonstration is that WHOLE genomes can be matched and distances calculated. The first tree was created on a Sequent parallel computer and demonstrates that the walking tree heuristic can be efficiently parallelized. The second tree was created using a network of work stations and demonstrates that there is suffient parallelism in the phylogenetic tree calculation that the sequential walking tree can be used effectively on a network.

  13. Mapping biological systems to network systems

    CERN Document Server

    Rathore, Heena

    2016-01-01

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

  14. Network systems biology for targeted cancer therapies

    Institute of Scientific and Technical Information of China (English)

    Ting-Ting Zhou

    2012-01-01

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

  15. Measuring the evolutionary rewiring of biological networks.

    Science.gov (United States)

    Shou, Chong; Bhardwaj, Nitin; Lam, Hugo Y K; Yan, Koon-Kiu; Kim, Philip M; Snyder, Michael; Gerstein, Mark B

    2011-01-01

    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. PMID:21253555

  16. 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.

  17. Optimizing Nutrient Uptake in Biological Transport Networks

    Science.gov (United States)

    Ronellenfitsch, Henrik; Katifori, Eleni

    2013-03-01

    Many biological systems employ complex networks of vascular tubes to facilitate transport of solute nutrients, examples include the vascular system of plants (phloem), some fungi, and the slime-mold Physarum. It is believed that such networks are optimized through evolution for carrying out their designated task. We propose a set of hydrodynamic governing equations for solute transport in a complex network, and obtain the optimal network architecture for various classes of optimizing functionals. We finally discuss the topological properties and statistical mechanics of the resulting complex networks, and examine correspondence of the obtained networks to those found in actual biological systems.

  18. Biologically inspired self-organizing networks

    Institute of Scientific and Technical Information of China (English)

    Naoki WAKAMIYA; Kenji LEIBNITZ; Masayuki MURATA

    2009-01-01

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

  19. A Multithreaded Algorithm for Network Alignment Via Approximate Matching

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Arif; Gleich, David F.; Pothen, Alex; Halappanavar, Mahantesh

    2012-11-16

    Network alignment is an optimization problem to find the best one-to-one map between the vertices of a pair of graphs that overlaps in as many edges as possible. It is a relaxation of the graph isomorphism problem and is closely related to the subgraph isomorphism problem. The best current approaches are entirely heuristic, and are iterative in nature. They generate real-valued heuristic approximations that must be rounded to find integer solutions. This rounding requires solving a bipartite maximum weight matching problem at each step in order to avoid missing high quality solutions. We investigate substituting a parallel, half-approximation for maximum weight matching instead of an exact computation. Our experiments show that the resulting difference in solution quality is negligible. We demonstrate almost a 20-fold speedup using 40 threads on an 8 processor Intel Xeon E7-8870 system (from 10 minutes to 36 seconds).

  20. 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

    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...

  1. Reconstructing Causal Biological Networks through Active Learning

    OpenAIRE

    Cho, Hyunghoon; Berger, Bonnie; Peng, Jian

    2016-01-01

    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 ...

  2. On Crowd-verification of Biological Networks.

    Science.gov (United States)

    Ansari, Sam; Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Hayes, William; Hoeng, Julia; Iskandar, Anita; Kleiman, Robin; Norel, Raquel; O'Neel, Bruce; Peitsch, Manuel C; Poussin, Carine; Pratt, Dexter; Rhrissorrakrai, Kahn; Schlage, Walter K; Stolovitzky, Gustavo; Talikka, Marja

    2013-01-01

    Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. PMID:24151423

  3. Biological Networks for Cancer Candidate Biomarkers Discovery

    Science.gov (United States)

    Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

    2016-01-01

    Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

  4. Biological Networks for Cancer Candidate Biomarkers Discovery.

    Science.gov (United States)

    Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

    2016-01-01

    Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

  5. Discovering large network motifs from a complex biological network

    International Nuclear Information System (INIS)

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

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

    International Nuclear Information System (INIS)

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

  7. 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 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...

  8. Simplified models of biological networks.

    Science.gov (United States)

    Sneppen, Kim; Krishna, Sandeep; Semsey, Szabolcs

    2010-01-01

    The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations. PMID:20192769

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

    International Nuclear Information System (INIS)

    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)

  10. Network-Based Models in Molecular Biology

    Science.gov (United States)

    Beyer, Andreas

    Biological systems are characterized by a large number of diverse interactions. Interaction maps have been used to abstract those interactions at all biological scales ranging from food webs at the ecosystem level down to protein interaction networks at the molecular scale.

  11. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Deyasi Krishanu; Upadhyay Shashankaditya; Banerjee Anirban

    2016-03-01

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

  12. Predicting biological networks from genomic data

    DEFF Research Database (Denmark)

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

    2008-01-01

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

  13. BiologicalNetworks: visualization and analysis tool for systems biology

    OpenAIRE

    Baitaluk, Michael; Sedova, Mayya; Ray, Animesh; Gupta, Amarnath

    2006-01-01

    Systems level investigation of genomic scale information requires the development of truly integrated databases dealing with heterogeneous data, which can be queried for simple properties of genes or other database objects as well as for complex network level properties, for the analysis and modelling of complex biological processes. Towards that goal, we recently constructed PathSys, a data integration platform for systems biology, which provides dynamic integration over a diverse set of dat...

  14. Quantifying evolvability in small biological networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-01-01

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

  15. 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

  16. 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…

  17. 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.

  18. Reconstructing Causal Biological Networks through Active Learning.

    Science.gov (United States)

    Cho, Hyunghoon; Berger, Bonnie; Peng, Jian

    2016-01-01

    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. PMID:26930205

  19. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

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

  20. Attentional Networks and Biological Motion

    OpenAIRE

    Chandramouli Chandrasekaran; Lucy Turner; Heinrich H Bülthoff; Thornton, Ian M.

    2010-01-01

    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 ...

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

    OpenAIRE

    Wieringa, Roel; Gordijn, Jaap; Eck, van, C.F.; Cox, K.; Dubois, E.; Pigneur, Y.; Bleistein, S.J.; Verner, J; Davis, A.M.; Wieringa, R.J.

    2005-01-01

    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 a new dimension, because in such a network, there is not a single point of authority for making decisions about ICT support to solve conflicts in requirements these various enterprises may have. N...

  2. Application of Graph Coloring to Biological Networks

    CERN Document Server

    Khor, Susan

    2009-01-01

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

  3. 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.

  4. Dense module enumeration in biological networks

    International Nuclear Information System (INIS)

    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.

  5. Network biology methods integrating biological data for translational science

    OpenAIRE

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

    2012-01-01

    The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions betw...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-09-30

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

  7. Qualitative networks: a symbolic approach to analyze biological signaling networks

    Directory of Open Access Journals (Sweden)

    Henzinger Thomas A

    2007-01-01

    Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

  8. Application of graph colouring to biological networks.

    Science.gov (United States)

    Khor, S

    2010-05-01

    The author explores the application of graph colouring to biological networks, specifically protein-protein interaction (PPI) networks. First, the author finds that given similar conditions (i.e. graph size, degree distribution and clustering), fewer colours are needed to colour disassortative than assortative networks. Fewer colours create fewer independent sets which in turn imply higher concurrency potential for a network. Since PPI networks tend to be disassortative, the author suggests that in addition to functional specificity and stability proposed previously by Maslov and Sneppen (Science, 296, 2002), the disassortative nature of PPI networks may promote the ability of cells to perform multiple, crucial and functionally diverse tasks concurrently. Second, because graph colouring is closely related to the presence of cliques in a graph, the significance of node colouring information to the problem of identifying protein complexes (dense subgraphs in PPI networks), is investigated. The author finds that for PPI networks where 1-11% of nodes participate in at least one identified protein complex, such as H. sapien, DSATUR (a well-known complete graph colouring algorithm) node colouring information can improve the quality (homogeneity and separation) of initial candidate complexes. This finding may help improve existing protein complex detection methods, and/or suggest new methods. [Includes supplementary material]. PMID:20499999

  9. On Feasibility of Interference Alignment in MIMO Interference Networks

    CERN Document Server

    Yetis, Cenk M; Jafar, Syed A; Kayran, Ahmet H

    2009-01-01

    We explore the feasibility of interference alignment in signal vector space -- based only on beamforming -- for K-user MIMO interference channels. Our main contribution is to relate the feasibility issue to the problem of determining the solvability of a multivariate polynomial system, considered extensively in algebraic geometry. It is well known, e.g. from Bezout's theorem, that generic polynomial systems are solvable if and only if the number of equations does not exceed the number of variables. Following this intuition, we classify signal space interference alignment problems as either proper or improper based on the number of equations and variables. Rigorous connections between feasible and proper systems are made through Bernshtein's theorem for the case where each transmitter uses only one beamforming vector. The multi-beam case introduces dependencies among the coefficients of a polynomial system so that the system is no longer generic in the sense required by both theorems. In this case, we show tha...

  10. 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.

  11. Model-Driven Process Design : Aligning Value Networks, Enterprise Goals, Services and IT Systems

    OpenAIRE

    Perjons, Erik

    2011-01-01

    The purpose of business-IT alignment is to optimise the relation between business and IT in order to maximise the business value of IT. Successful business-IT alignment can be enabled by business processes and e-processes functioning as adaptive mediators between business and IT systems. Business processes are the ways actors work in enterprises and collaborate in value networks, while e-processes support a flexible flow of information between IT systems and business processes. The overall go...

  12. Aligning 3D nanofibrous networks from self-assembled phenylalanine nanofibers†

    OpenAIRE

    Wang, Xianfeng; CHEN, YI CHARLIE; Li, Bingyun

    2014-01-01

    Self-assembled synthetic materials are typically disordered, and controlling the alignment of such materials at the nanometer scale may be important for a variety of biological applications. In this study, we have applied directional freeze-drying, for the first time, to develop well aligned three dimensional (3D) nanofibrous materials using amino acid like L-phenylalanine (Phe). 3D free-standing Phe nanofibrous monoliths have been successfully prepared using directional freeze-drying, and ha...

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

    International Nuclear Information System (INIS)

    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

  14. 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.

  15. 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.

  16. 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

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

    Science.gov (United States)

    Papoian, Garegin A.

    2016-01-01

    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. We discuss the

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

    Science.gov (United States)

    Popov, Konstantin; Komianos, James; Papoian, Garegin A

    2016-04-01

    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. We discuss the

  19. Comparing artificial and biological dynamical neural networks

    Science.gov (United States)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  20. Survey and alignment report on the primary control network for the APS

    International Nuclear Information System (INIS)

    During November 1992 the survey and alignment team measured the entire primary control network for the APS. This task had to be finished before the enclosure of the EAA and the RF buildings were put in place, inhibiting several lines of sight necessary for the determination of the monument locations

  1. New scaling relation for information transfer in biological networks.

    Science.gov (United States)

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-12-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

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

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

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

  3. 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.

  4. Noncommutative Biology: Sequential Regulation of Complex Networks

    Science.gov (United States)

    Letsou, William; Cai, Long

    2016-01-01

    Single-cell variability in gene expression is important for generating distinct cell types, but it is unclear how cells use the same set of regulatory molecules to specifically control similarly regulated genes. While combinatorial binding of transcription factors at promoters has been proposed as a solution for cell-type specific gene expression, we found that such models resulted in substantial information bottlenecks. We sought to understand the consequences of adopting sequential logic wherein the time-ordering of factors informs the final outcome. We showed that with noncommutative control, it is possible to independently control targets that would otherwise be activated simultaneously using combinatorial logic. Consequently, sequential logic overcomes the information bottleneck inherent in complex networks. We derived scaling laws for two noncommutative models of regulation, motivated by phosphorylation/neural networks and chromosome folding, respectively, and showed that they scale super-exponentially in the number of regulators. We also showed that specificity in control is robust to the loss of a regulator. Lastly, we connected these theoretical results to real biological networks that demonstrate specificity in the context of promiscuity. These results show that achieving a desired outcome often necessitates roundabout steps. PMID:27560383

  5. Survey, data adjustment and accuracy computation of Indus-2 alignment control networks

    International Nuclear Information System (INIS)

    For accelerators like Indus-2, which is housed in a narrow circular tunnel, it is necessary to establish survey control networks, for controlling the overall shape and size of the machine and precise positioning of its various components in horizontal and azimuthal planes. These networks are normally in the form of fixed pillars (monuments) and wall brackets, with facilities for mounting survey instruments and optical targets etc. The coordinates of network points are determined by redundant distance, direction and elevation measurements and treating them by least square adjustment. This paper presents description of Indus-2 horizontal and elevation control networks, instruments used for their survey, data adjustment, accuracy and adequacy of networks in meeting the absolute and relative alignment tolerances. (author)

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

    CERN Document Server

    Michoel, Tom

    2012-01-01

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

  7. Aligning 3D nanofibrous networks from self-assembled phenylalanine nanofibers†

    Science.gov (United States)

    Wang, Xianfeng; Chen, Yi Charlie

    2015-01-01

    Self-assembled synthetic materials are typically disordered, and controlling the alignment of such materials at the nanometer scale may be important for a variety of biological applications. In this study, we have applied directional freeze-drying, for the first time, to develop well aligned three dimensional (3D) nanofibrous materials using amino acid like L-phenylalanine (Phe). 3D free-standing Phe nanofibrous monoliths have been successfully prepared using directional freeze-drying, and have presented a unique hierarchical structure with well-aligned nanofibers at the nanometer scale and an ordered compartmental architecture at the micrometer scale. We have found that the physical properties (e.g. nanofiber density and alignment) of the nanofibrous materials could be tuned by controlling the concentration and pH of the Phe solution and the freezing temperature. Moreover, the same strategy (i.e. directional freeze-drying) has been successfully applied to assemble peptide nanofibrous materials using a dipeptide (i.e. diphenylalanine), and to assemble Phe-based nanofibrous composites using polyethylenimine and poly(vinyl alcohol). The tunability of the nanofibrous structures together with the biocompatibility of Phe may make these 3D nanofibrous materials suitable for a variety of applications, including biosensor templates, tissue scaffolds, filtration membranes, and absorbents. The strategy reported here is likely applicable to create aligned nanofibrous structures using other amino acids, peptides, and polymers. PMID:25621167

  8. Modeling and measuring Business/IT Alignment by using a complex-network approach

    OpenAIRE

    Sousa, José Luís da Rocha

    2014-01-01

    Business/IT Alignment is an information systems research field with a long existence and a high number of researchers and represents a central thinking direction over the entanglement between business and information systems. lt aims to achieve a paradigm, on which there is a high degree of visibility and availability of information about the information systems sociomateriality. _ Complex-networks constitute an approach to the study of the emergent properties of complex-sys...

  9. 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

  10. Analyzing large biological datasets with association networks

    Energy Technology Data Exchange (ETDEWEB)

    Karpinets, Tatiana V [ORNL; Park, Byung H [ORNL; Uberbacher, Edward C [ORNL

    2012-01-01

    Due to advances in high throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches for timely processing of the collected data into new knowledge. In this study we address this problem by developing a new approach for discovering modular structure, relationships and regularities in complex data. These goals are achieved by converting records of biological annotations of an object, like organism, gene, chemical, sequence, into networks (Anets) and rules (Arules) of the associated annotations. Anets are based on similarity of annotation profiles of objects and can be further analyzed and visualized providing a compact birds-eye view of most significant relationships in the collected data and a way of their clustering and classification. Arules are generated by Apriori considering each record of annotations as a transaction and augmenting each annotation item by its type. Arules provide a way to validate relationships discovered by Anets producing comprehensive statistics on frequently associated annotations and specific confident relationships among them. A combination of Anets and Arules represents condensed information on associations among the collected data, helping to discover new knowledge and generate hypothesis. As an example we have applied the approach to analyze bacterial metadata from the Genomes OnLine Database. The analysis allowed us to produce a map of sequenced bacterial and archaeal organisms based on their genomic, metabolic and physiological characteristics with three major clusters of metadata representing bacterial pathogens, environmental isolates, and plant symbionts. A signature profile of clustered annotations of environmental bacteria if compared with pathogens linked the aerobic respiration, the high GC content and the large genome size to diversity of metabolic activities and physiological features of the organisms.

  11. BioNSi: A Discrete Biological Network Simulator Tool.

    Science.gov (United States)

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

    2016-08-01

    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. PMID:27354160

  12. 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...

  13. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

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

  14. Vertically aligned cobalt oxide nanowires on graphene networks for high-performance lithium storage

    International Nuclear Information System (INIS)

    Despite various electrochemically active materials, such as metals, metal oxides and sulfides, which have been widely utilized for lithium storage, these materials still encounter unsatisfied electrochemical performances including low reversible capacity, slow charge-discharge capability and poor cycle performance. Here, we demonstrate a simple approach to fabricate one-dimensional CoO nanowires vertically aligned on a 3D graphene network (denoted as a 3D CoO/graphene network) via a wet chemistry process. The resulting CoO/graphene network possesses an interconnected graphene network, hierarchical pores and a carpet-like structure. This unique network can (1) facilitate the easy access of the electrolyte, (2) prevent the aggregation of CoO nanowires, (3) accommodate the volume change of CoO during the cycle processes, (4) maintain a high electrical conductivity for the overall electrode and (5) give rise to a high content of CoO in the composite (∼92 wt%). As a result, the 3D CoO/graphene network can be directly used as an anode material without any binder or conductive additives for lithium storage, and it exhibits a high capacity of 857 mAh g−1, an excellent rate capability and good cycle performance. We believe that such a simple but efficient protocol will provide a new pathway for the fabrication of various 3D metal or metal oxide-graphene networks for wide applications in such fields as energy storage, sensors and catalysts. (paper)

  15. PicXAA-Web: a web-based platform for non-progressive maximum expected accuracy alignment of multiple biological sequences

    OpenAIRE

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2011-01-01

    In this article, we introduce PicXAA-Web, a web-based platform for accurate probabilistic alignment of multiple biological sequences. The core of PicXAA-Web consists of PicXAA, a multiple protein/DNA sequence alignment algorithm, and PicXAA-R, an extension of PicXAA for structural alignment of RNA sequences. Both PicXAA and PicXAA-R are probabilistic non-progressive alignment algorithms that aim to find the optimal alignment of multiple biological sequences by maximizing the expected accuracy...

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    The GeneWeaver data and analytics website (www.geneweaver.org) is a publically available resource for storing, curating and analyzing sets of genes from heterogeneous data sources. The system enables discovery of relationships among genes, variants, traits, drugs, environments, anatomical structures and diseases implicitly found through gene set intersections. Since the previous review in the 2012 Nucleic Acids Research Database issue, GeneWeaver's underlying analytics platform has been enhanced, its number and variety of publically available gene set data sources has been increased, and its advanced search mechanisms have been expanded. In addition, its interface has been redesigned to take advantage of flexible web services, programmatic data access, and a refined data model for handling gene network data in addition to its original emphasis on gene set data. By enumerating the common and distinct biological molecules associated with all subsets of curated or user submitted groups of gene sets and gene networks, GeneWeaver empowers users with the ability to construct data driven descriptions of shared and unique biological processes, diseases and traits within and across species. PMID:26656951

  18. A comparative analysis of thin-film transistors using aligned and random-network carbon nanotubes

    Energy Technology Data Exchange (ETDEWEB)

    Duan Yan [School of Engineering and Computer Science, Washington State University, Mechanical Engineering (United States); Juhala, Jason L.; Griffith, Benjamin W. [School of Engineering and Computer Science, Washington State University, Electrical Engineering (United States); Xue Wei, E-mail: wxue@wsu.edu [School of Engineering and Computer Science, Washington State University, Mechanical Engineering (United States)

    2013-03-15

    The purpose of this project is to investigate the characterization of carbon nanotube (CNT) thin-film transistors based on two solution-based fabrication methods: dielectrophoretic deposition of aligned CNTs and self-assembly of random-network CNTs. The electrical characteristics of aligned and random-network CNT transistors are studied comparatively. In particular, the selection effect of metallic and semiconducting CNTs in the dielectrophoresis process is evaluated experimentally by comparing the output characteristics of the two transistors. Our results demonstrate that the self-assembly method produces a stronger field effect with a much higher on/off ratio (I{sub on}/I{sub off}). This phenomenon provides evidence that the metallic CNTs are more responsive to dielectrophoretic forces than their semiconducting counterparts under common deposition conditions. In addition, the nanotube-nanotube cross-junctions in random-network CNT films create additional energy barriers and result in a reduced electric current. Thus, additional consideration must be applied when using different fabrication methods in building CNT-based electronic devices.

  19. A unified biological modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2013-12-01

    In order to understand the biological response in a cell, a researcher has to create a biological network and design an experiment to prove it. Although biological knowledge has been accumulated, we still don't have enough biological models to explain complex biological phenomena. If a new biological network is to be created, integrated modeling software supporting various biological models is required. In this research, we design and implement a unified biological modeling and simulation system, called ezBioNet, for analyzing biological reaction networks. ezBioNet designs kinetic and Boolean network models and simulates the biological networks using a server-side simulation system with Object Oriented Parallel Accelerator Library framework. The main advantage of ezBioNet is that a user can create a biological network by using unified modeling canvas of kinetic and Boolean models and perform massive simulations, including Ordinary Differential Equation analyses, sensitivity analyses, parameter estimates and Boolean network analysis. ezBioNet integrates useful biological databases, including the BioModels database, by connecting European Bioinformatics Institute servers through Web services Application Programming Interfaces. In addition, we employ Eclipse Rich Client Platform, which is a powerful modularity framework to allow various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool and a simulation system for understanding the control mechanism by monitoring the change of each component in a biological network. The simulation result can be managed and visualized on ezBioNet, which is available free of charge at http://ezbionet.sourceforge.net or http://ezbionet.cbnu.ac.kr.

  20. Statistical properties and robustness of biological controller-target networks.

    Directory of Open Access Journals (Sweden)

    Jacob D Feala

    Full Text Available Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a "many-to-many" control structure. Here we study several of these bipartite (two-layer networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates and a drug-target network composed of kinase inhibitors and of their kinase targets. Certain statistical properties of these biological bipartite structures seem universal across systems and species, suggesting the existence of common control strategies in biology. The number of controllers is ∼8% of targets and the density of links is 2.5%±1.2%. Links per node are predominantly exponentially distributed. We explain the conservation of the mean number of incoming links per target using a mathematical model of control networks, which also indicates that the "many-to-many" structure of biological control has properties of efficient robustness. The drug-target network has many statistical properties similar to the biological networks and we show that drug-target networks with biomimetic features can be obtained. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as "many-to-many" targeting, very wide coverage of the target set, and redundancy of incoming links per target.

  1. Organization principles of biological networks: An explorative study.

    Science.gov (United States)

    Kohestani, Havva; Giuliani, Alessandro

    2016-03-01

    The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure-function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models. A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p<0.0001) with a varying degree of 'peculiarity' across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid-amino acid interaction (AAI) networks. We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of 'not-physiological' (in this case cancer metabolic networks acting as test set) wiring patterns. We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations. PMID:26845173

  2. 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.

  3. Channels Reallocation In Cognitive Radio Networks Based On DNA Sequence Alignment

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Singh

    2010-06-01

    Full Text Available Nowadays, It has been shown that spectrum scarcity increased due to tremendous growth of new playersin wireless base system by the evolution of the radio communication. Resent survey found that there aremany areas of the radio spectrum that are occupied by authorized user/primary user (PU, which are notfully utilized. Cognitive radios (CR prove to next generation wireless communication system thatproposed as a way to reuse this under-utilised spectrum in an opportunistic and non-interfering basis. ACR is a self-directed entity in a wireless communications environment that senses its environment, trackschanges, and reacts upon its findings and frequently exchanges information with the networks forsecondary user (SU. However, CR facing collision problem with tracks changes i.e. reallocating of otherempty channels for SU while PU arrives. In this paper, channels reallocation technique based on DNAsequence alignment algorithm for CR networks has been proposed.

  4. Channels Reallocation In Cognitive Radio Networks Based On DNA Sequence Alignment

    CERN Document Server

    Singh, Santosh Kumar; Pathak, Vibhakar; 10.5121/ijngn.2010.2203

    2010-01-01

    Nowadays, It has been shown that spectrum scarcity increased due to tremendous growth of new players in wireless base system by the evolution of the radio communication. Resent survey found that there are many areas of the radio spectrum that are occupied by authorized user/primary user (PU), which are not fully utilized. Cognitive radios (CR) prove to next generation wireless communication system that proposed as a way to reuse this under-utilised spectrum in an opportunistic and non-interfering basis. A CR is a self-directed entity in a wireless communications environment that senses its environment, tracks changes, and reacts upon its findings and frequently exchanges information with the networks for secondary user (SU). However, CR facing collision problem with tracks changes i.e. reallocating of other empty channels for SU while PU arrives. In this paper, channels reallocation technique based on DNA sequence alignment algorithm for CR networks has been proposed.

  5. Modeling information flow in biological networks

    International Nuclear Information System (INIS)

    Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method

  6. Rigidity and flexibility of biological networks

    CERN Document Server

    Gaspar, Merse E

    2012-01-01

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

  7. Rigidity and flexibility of biological networks.

    Science.gov (United States)

    Gáspár, Merse E; Csermely, Peter

    2012-11-01

    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks. PMID:23165349

  8. Evolution of biological interaction networks: from models to real data

    OpenAIRE

    Sun, Mark GF; Kim, Philip M.

    2011-01-01

    We are beginning to uncover common mechanisms leading to the evolution of biological networks. The driving force behind these advances is the increasing availability of comparative data in several species.

  9. Bayesian variable selection and data integration for biological regulatory networks

    OpenAIRE

    Jensen, Shane T; Chen, Guang; Stoeckert, Jr, Christian J.

    2007-01-01

    A substantial focus of research in molecular biology are gene regulatory networks: the set of transcription factors and target genes which control the involvement of different biological processes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three dat...

  10. Summarizing cellular responses as biological process networks

    OpenAIRE

    Lasher, Christopher D; Rajagopalan, Padmavathy; Murali, T.M.

    2013-01-01

    Abstract Background Microarray experiments can simultaneously identify thousands of genes that show significant perturbation in expression between two experimental conditions. Response networks, computed through the integration of gene interaction networks with expression perturbation data, may themselves contain tens of thousands of interactions. Gene set enrichment has become standard for summarizing the results of these analyses in te...

  11. 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.

  12. Systematic Functional Annotation and Visualization of Biological Networks.

    Science.gov (United States)

    Baryshnikova, Anastasia

    2016-06-22

    Large-scale biological networks represent relationships between genes, but our understanding of how networks are functionally organized is limited. Here, I describe spatial analysis of functional enrichment (SAFE), a systematic method for annotating biological networks and examining their functional organization. SAFE visualizes the network in 2D space and measures the continuous distribution of functional enrichment across local neighborhoods, producing a list of the associated functions and a map of their relative positioning. I applied SAFE to annotate the Saccharomyces cerevisiae genetic interaction similarity network and protein-protein interaction network with gene ontology terms. SAFE annotations of the genetic network matched manually derived annotations, while taking less than 1% of the time, and proved robust to noise and sensitive to biological signal. Integration of genetic interaction and chemical genomics data using SAFE revealed a link between vesicle-mediate transport and resistance to the anti-cancer drug bortezomib. These results demonstrate the utility of SAFE for examining biological networks and understanding their functional organization. PMID:27237738

  13. Biologically Inspired Optimization of Building District Heating Networks

    OpenAIRE

    Leiming Shang; Xiaomin Zhao

    2013-01-01

    In this paper we show that a biologically inspired model can be successfully applied to problems of building optimal district heating network. The model is based on physiological observations of the true slime mold Physarumpolycephalum, but can also be used for path-finding in the complicated networks of mazes and road maps. A strategy of optimally building heating distribution network was guided by the model and a well-tuned ant colony algorithm and genetic algorithm. The results indicate th...

  14. Reduction of dynamical biochemical reactions networks in computational biology

    OpenAIRE

    Radulescu, O.; Gorban, A.N.; Zinovyev, A.; Noel, V.

    2012-01-01

    Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to...

  15. Inferring biological networks by sparse identification of nonlinear dynamics

    OpenAIRE

    Mangan, Niall M.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J Nathan

    2016-01-01

    Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological networks. The increasing quality and quantity of experimental data enable statistical approaches based on information theory for model selection and goodness-of-fit metrics. We propose an alternative method to infer networked nonlinear dynamical systems by using sparsity-promoting $\\ell_1$ optimization to select a sub...

  16. The effect of network biology on drug toxicology

    DEFF Research Database (Denmark)

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

    2013-01-01

    biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The...... 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...

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

    International Nuclear Information System (INIS)

    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.

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

    Directory of Open Access Journals (Sweden)

    Nicola J. Mulder

    2014-08-01

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

  19. Relevance of Dynamic Clustering to Biological Networks

    CERN Document Server

    Kaneko, K

    1993-01-01

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

  20. 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.

  1. Course 10: Three Lectures on Biological Networks

    Science.gov (United States)

    Magnasco, M. O.

    1 Enzymatic networks. Proofreading knots: How DNA topoisomerases disentangle DNA 1.1 Length scales and energy scales 1.2 DNA topology 1.3 Topoisomerases 1.4 Knots and supercoils 1.5 Topological equilibrium 1.6 Can topoisomerases recognize topology? 1.7 Proposal: Kinetic proofreading 1.8 How to do it twice 1.9 The care and proofreading of knots 1.10 Suppression of supercoils 1.11 Problems and outlook 1.12 Disquisition 2 Gene expression networks. Methods for analysis of DNA chip experiments 2.1 The regulation of gene expression 2.2 Gene expression arrays 2.3 Analysis of array data 2.4 Some simplifying assumptions 2.5 Probeset analysis 2.6 Discussion 3 Neural and gene expression networks: Song-induced gene expression in the canary brain 3.1 The study of songbirds 3.2 Canary song 3.3 ZENK 3.4 The blush 3.5 Histological analysis 3.6 Natural vs. artificial 3.7 The Blush II: gAP 3.8 Meditation

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

    Science.gov (United States)

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

    2016-05-01

    We show that unidirectional freezing of nanocellulose dispersions produces cellular foams with high alignment of the rod-like nanoparticles in the freezing direction. Quantification of the alignment in the long direction of the tubular pores with X-ray diffraction shows high orientation of cellulose nanofibrils (CNF) and cellulose nanocrystals (CNC) at particle concentrations above 0.2 wt % (CNC) and 0.08 wt % (CNF). Aggregation of CNF by pH decrease or addition of salt significantly reduces the particle orientation; in contrast, exceeding the concentration where particles gel by mobility constraints had a relatively small effect on the orientation. The dense nanocellulose network formed by directional freezing was sufficiently strong to resist melting. The formed hydrogels were birefringent and displayed anisotropic laser diffraction patterns, suggesting preserved nanocellulose alignment and cellular structure. Nondirectional freezing of the hydrogels followed by sublimation generates foams with a pore structure and nanocellulose alignment resembling the structure of the initial directional freezing. PMID:27071304

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

    Science.gov (United States)

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

    2007-09-25

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

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

    International Nuclear Information System (INIS)

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

  5. 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.

  6. Contribution made by biomarkers of status to an FP6 Network of Excellence, EURopean micronutrient RECommendations Aligned (EURRECA)12345

    OpenAIRE

    Fairweather-Tait, Susan J

    2011-01-01

    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 databas...

  7. Using biological networks to integrate, visualize and analyze genomics data.

    Science.gov (United States)

    Charitou, Theodosia; Bryan, Kenneth; Lynn, David J

    2016-01-01

    Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene's network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide 'omics' data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware. PMID:27036106

  8. An open system network for the biological sciences.

    OpenAIRE

    Springer, G K; Loch, J. L.; Patrick, T. B.

    1991-01-01

    A description of an open system, distributed computing environment for the Biological Sciences is presented. This system utilizes a transparent interface in a computer network using NCS to implement an application system for molecular biologists to perform various processing activities from their local workstation. This system accepts requests for the services of a remote database server, located across the network, to perform all of the database searches needed to support the activities of t...

  9. A Newtonian framework for community detection in undirected biological networks.

    Science.gov (United States)

    Narayanan, Tejaswini; Subramaniam, Shankar

    2014-02-01

    Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery. PMID:24681920

  10. Novel amphiphilic networks for biological use

    Czech Academy of Sciences Publication Activity Database

    Toman, Luděk; Janata, Miroslav; Spěváček, Jiří; Sikora, Antonín; Pleštil, Josef; Michálek, Jiří; Dvořánková, B.; Vlček, Petr; Látalová, Petra; Masař, Bohumil

    Prague: Czech Society for New Materials and Technologies, 2005. Poster Session II. [European Congress on Advanced Materials and Processes. 5.9.2005-8.9.2005, Prague] R&D Projects: GA ČR GA203/04/1050 Keywords : polyisobutylene * poly(2-hydroxyethyl methacrylate) * amphiphilic networks Subject RIV: CD - Macromolecular Chemistry http://webdb.dgm.de/dgm_lit/prg/FMPro?-db=w%5fprogram&- format =prog%5fpaper%5fresults.htm&-lay=standard&TB=%3d%3d688&tgb%5fsymposium%5fund%5fnr=B14%20Engineering%20and%20Design%20of%20Biomedical%20Materials&-max=20&-skip=20&-token.0=688&-token.1=B14%20Engineering%20and%20Design%20of%20Biomedical%20Materials&-find=

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

    Science.gov (United States)

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

    2011-01-01

    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

  12. 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.

  13. 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.

  14. Biologically plausible multi-dimensional reinforcement learning in neural networks

    NARCIS (Netherlands)

    Rombouts, J.O.; Ooyen, A. van; Roelfsema, P.R.; Bohte, S.M.

    2012-01-01

    How does the brain learn to map multi-dimensional sensory inputs to multi-dimensional motor outputs when it can only observe single rewards for the coordinated outputs of the whole network of neurons that make up the brain? We introduce Multi-AGREL, a novel, biologically plausible multi-layer neural

  15. Discovering Networks of Perturbed Biological Processes in Hepatocyte Cultures

    Science.gov (United States)

    Lasher, Christopher D.; Rajagopalan, Padmavathy; Murali, T. M.

    2011-01-01

    The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs) and collagen sandwiches (CS). Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs). CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior. PMID:21245926

  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...

  17. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    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...

  18. Biologically Inspired Optimization of Building District Heating Networks

    Directory of Open Access Journals (Sweden)

    Leiming Shang

    2013-07-01

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

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

    Science.gov (United States)

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2015-03-01

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

  2. The impact of network biology in pharmacology and toxicology

    DEFF Research Database (Denmark)

    Panagiotou, Gianni; Taboureau, Olivier

    2012-01-01

    With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological...... environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases...

  3. 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...

  4. 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.

  5. Facile: a command-line network compiler for systems biology

    OpenAIRE

    Ollivier Julien F; Siso-Nadal Fernando; Swain Peter S

    2007-01-01

    Abstract Background A goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values. Results We present Facile, a Perl...

  6. 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

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

    Science.gov (United States)

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

    2016-01-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

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

    Science.gov (United States)

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

    2016-06-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

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

    Indian Academy of Sciences (India)

    Sarika Jalan

    2015-02-01

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

  10. Systems analysis of biological networks in skeletal muscle function.

    Science.gov (United States)

    Smith, Lucas R; Meyer, Gretchen; Lieber, Richard L

    2013-01-01

    Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation-contraction coupling enabling Ca(2+) release. Ca(2+) then activates contractile proteins supporting actin and myosin cross-bridge cycling. Force generated by cross-bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high-throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems. PMID:23188744

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

    Science.gov (United States)

    Crandall, Elizabeth Diane

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

  12. 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.

  13. 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)

    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.

  14. 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.

  15. 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.

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

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2016-06-01

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

  17. 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

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

    Directory of Open Access Journals (Sweden)

    Martin Florian

    2012-05-01

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

  19. Classification of Approaches and Challenges of Frequent Subgraphs Mining in Biological Networks

    OpenAIRE

    Keyvanpour, Mohammadreza; Azizani, Fereshteh

    2012-01-01

    Understanding the structure and dynamics of biological networks is one of the important challenges in system biology. In addition, increasing amount of experimental data in biological networks necessitate the use of efficient methods to analyze these huge amounts of data. Such methods require to recognize common patterns to analyze data. As biological networks can be modeled by graphs, the problem of common patterns recognition is equivalent with frequent sub graph mining in a set of graphs. ...

  20. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2011-01-01

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

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

    DEFF Research Database (Denmark)

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

    2008-01-01

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

  3. Making the right connections: biological networks in the light of evolution

    OpenAIRE

    Knight, Christopher G.; Pinney, John W

    2009-01-01

    Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein–protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrat...

  4. Detecting modules in biological networks by edge weight clustering and entropy significance

    OpenAIRE

    Lecca, Paola; Re, Angela

    2015-01-01

    Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the informa...

  5. Multiple Sequence Alignment using GA and NN

    Directory of Open Access Journals (Sweden)

    Shuting Wu

    2008-12-01

    Full Text Available Multiple sequence alignment (MSA is an important tool in biological analysis. However, it is difficult to solve this class of problems, due to their exponential complexity. This paper presents an algorithm combining the genetic algorithm and a self-organizing neural network for solution to MSA. This approach demonstrates improved performance in long DNA and RNA data sets exhibiting small similarity.

  6. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

    Science.gov (United States)

    Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

    2016-01-01

    Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

  7. 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.

  8. Aligned, ultra-long graphene nanoribbon network fabrication by nanowire etch masks

    Science.gov (United States)

    Wood, Joshua; Sivapalan, Sean; Dorgan, Vincent; Murphy, Catherine; Pop, Eric; Lyding, Joseph

    2011-03-01

    Patterning semi-metallic graphene into quasi one-dimensional structures known as nanoribbons (GNRs) can open a ~ 0.5 eV bandgap by quantum confinement. To circumvent GNR lithographic difficulties, Si nanowires (NWs) were used previously as an etch mask for exfoliated graphene, but with no scalability or alignment control. Conversely, we transfer ~ 1 in 2 graphene sheets off copper to silicon dioxide, giving us a template for array fabrication. We meniscus align both Au NWs (w > = 20 nm , l = 400 nm) andAgNWs (w > = 200 nm , l > = 10 μ m) , respectively , onthegraphenesurface . Byreactiveionetch (RIE) , weremovetheunmaskedgraphene , andweetchtheNWs . BasedonthestartingNWs , theresultingGNRarrayshavelengthsrangingfrom 200 nmtotensofmicrons , andwidthsfrom 10 nmto 250 nm . WecreatesingleGNRsthatcanspanmicron - separatedcontactsandGNRnetworks , similartoagraphenenanomesh . UsingatomicforcemicroscopyandRamanspectroscopy , wedeterminethatwehavemonolayerGNRswithahighdisorderintensityI D / I G ~ 1 , indicating rough edges and graphene grain boundaries, which are deleterious to transport.

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

    CERN Document Server

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

    2011-01-01

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

  10. 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/. PMID:27446133

  11. 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 interf...

  12. What does biologically meaningful mean? A perspective on gene regulatory network validation

    OpenAIRE

    Walhout, Albertha JM

    2011-01-01

    Gene regulatory networks (GRNs) are rapidly being delineated, but their quality and biological meaning are often questioned. Here, I argue that biological meaning is challenging to define and discuss reasons why GRN validation should be interpreted cautiously.

  13. Alzheimer disease: modeling an Aβ-centered biological network.

    Science.gov (United States)

    Campion, D; Pottier, C; Nicolas, G; Le Guennec, K; Rovelet-Lecrux, A

    2016-07-01

    In genetically complex diseases, the search for missing heritability is focusing on rare variants with large effect. Thanks to next generation sequencing technologies, genome-wide characterization of these variants is now feasible in every individual. However, a lesson from current studies is that collapsing rare variants at the gene level is often insufficient to obtain a statistically significant signal in case-control studies, and that network-based analyses are an attractive complement to classical approaches. In Alzheimer disease (AD), according to the prevalent amyloid cascade hypothesis, the pathology is driven by the amyloid beta (Aβ) peptide. In past years, based on experimental studies, several hundreds of proteins have been shown to interfere with Aβ production, clearance, aggregation or toxicity. Thanks to a manual curation of the literature, we identified 335 genes/proteins involved in this biological network and classified them according to their cellular function. The complete list of genes, or its subcomponents, will be of interest in ongoing AD genetic studies. PMID:27021818

  14. 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

  15. Fabrication of Aligned Nanofiber Polymer Yarn Networks for Anisotropic Soft Tissue Scaffolds.

    Science.gov (United States)

    Wu, Shaohua; Duan, Bin; Liu, Penghong; Zhang, Caidan; Qin, Xiaohong; Butcher, Jonathan T

    2016-07-01

    Nanofibrous scaffolds with defined architectures and anisotropic mechanical properties are attractive for many tissue engineering and regenerative medicine applications. Here, a novel electrospinning system is developed and implemented to fabricate continuous processable uniaxially aligned nanofiber yarns (UANY). UANY were processed into fibrous tissue scaffolds with defined anisotropic material properties using various textile-forming technologies, i.e., braiding, weaving, and knitting techniques. UANY braiding dramatically increased overall stiffness and strength compared to the same number of UANY unbraided. Human adipose derived stem cells (HADSC) cultured on UANY or woven and knitted 3D scaffolds aligned along local fiber direction and were >90% viable throughout 21 days. Importantly, UANY supported biochemical induction of HADSC differentiation toward smooth muscle and osteogenic lineages. Moreover, we integrated an anisotropic woven fiber mesh within a bioactive hydrogel to mimic the complex microstructure and mechanical behavior of valve tissues. Human aortic valve interstitial cells (HAVIC) and human aortic root smooth muscle cells (HASMC) were separately encapsulated within hydrogel/woven fabric composite scaffolds for generating scaffolds with anisotropic biomechanics and valve ECM like microenvironment for heart valve tissue engineering. UANY have great potential as building blocks for generating fiber-shaped tissues or tissue microstructures with complex architectures. PMID:27304080

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

    CERN Document Server

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

    2009-01-01

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

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

    Science.gov (United States)

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

    2015-11-01

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

  18. A polarization-independent liquid crystal phase modulation using polymer-network liquid crystal with orthogonal alignment layers

    Science.gov (United States)

    Chen, Ming-Syuan; Lin, Wei-Chih; Tsou, Yu-Shih; Lin, Yi-Hsin

    2012-10-01

    A polarization-independent liquid crystal (LC) phase modulation using polymer-network liquid crystals with orthogonal alignments layers (T-PNLC) is demonstrated. T-PNLC consists of three layers. LC directors in the two layers near glass substrates are orthogonal to each other. In the middle layer, LC directors are perpendicular to the glass substrate. The advantages of such T-PNLC include polarizer-free, larger phase shift (~0.4π rad) than the residual phase type (<0.05π rad), and low operating voltage (< 30Vrms). It does not require bias voltage for avoiding scattering because the refractive index of liquid crystals matches that of polymers. The phase shift of T-PNLC is affected by the cell gap and the curing voltages. The potential applications are laser beam steering, spatial light modulators and electrically tunable micro-lens arrays.

  19. Fast statistical alignment.

    Directory of Open Access Journals (Sweden)

    Robert K Bradley

    2009-05-01

    Full Text Available We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment--previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches--yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

  20. Fast statistical alignment.

    Science.gov (United States)

    Bradley, Robert K; Roberts, Adam; Smoot, Michael; Juvekar, Sudeep; Do, Jaeyoung; Dewey, Colin; Holmes, Ian; Pachter, Lior

    2009-05-01

    We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment--previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches--yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/. PMID:19478997

  1. 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

  2. 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

  3. Aligned platinum nanowire networks from surface-oriented lipid cubic phase templates

    Science.gov (United States)

    Richardson, S. J.; Burton, M. R.; Staniec, P. A.; Nandhakumar, I. S.; Terrill, N. J.; Elliott, J. M.; Squires, A. M.

    2016-01-01

    Mesoporous metal structures featuring a bicontinuous cubic morphology have a wide range of potential applications and novel opto-electronic properties, often orientation-dependent. We describe the production of nanostructured metal films 1-2 microns thick featuring 3D-periodic `single diamond' morphology that show high out-of-plane alignment, with the (111) plane oriented parallel to the substrate. These are produced by electrodeposition of platinum through a lipid cubic phase (QII) template. Further investigation into the mechanism for the orientation revealed the surprising result that the QII template, which is tens of microns thick, is polydomain with no overall orientation. When thicker platinum films are grown, they also show increased orientational disorder. These results suggest that polydomain QII samples display a region of uniaxial orientation at the lipid/substrate interface up to approximately 2.8 +/- 0.3 μm away from the solid surface. Our approach gives previously unavailable information on the arrangement of cubic phases at solid interfaces, which is important for many applications of QII phases. Most significantly, we have produced a previously unreported class of oriented nanomaterial, with potential applications including metamaterials and lithographic masks.Mesoporous metal structures featuring a bicontinuous cubic morphology have a wide range of potential applications and novel opto-electronic properties, often orientation-dependent. We describe the production of nanostructured metal films 1-2 microns thick featuring 3D-periodic `single diamond' morphology that show high out-of-plane alignment, with the (111) plane oriented parallel to the substrate. These are produced by electrodeposition of platinum through a lipid cubic phase (QII) template. Further investigation into the mechanism for the orientation revealed the surprising result that the QII template, which is tens of microns thick, is polydomain with no overall orientation. When thicker

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

    OpenAIRE

    Ovseiko, PV; Heitmueller, A; Allen, P.; Davies, SM; Wells, G; Ford, GA; Darzi, A; Buchan, AM

    2014-01-01

    Background: 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 a...

  5. 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.

  6. 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.

  7. 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.

  8. Exploratory Analysis of Biological Networks through Visualization, Clustering, and Functional Annotation in Cytoscape.

    Science.gov (United States)

    Baryshnikova, Anastasia

    2016-01-01

    Biological networks define how genes, proteins, and other cellular components interact with one another to carry out specific functions, providing a scaffold for understanding cellular organization. Although in-depth network analysis requires advanced mathematical and computational knowledge, a preliminary visual exploration of biological networks is accessible to anyone with basic computer skills. Visualization of biological networks is used primarily to examine network topology, identify functional modules, and predict gene functions based on gene connectivity within the network. Networks are excellent at providing a bird's-eye view of data sets and have the power of illustrating complex ideas in simple and intuitive terms. In addition, they enable exploratory analysis and generation of new hypotheses, which can then be tested using rigorous statistical and experimental tools. This protocol describes a simple procedure for visualizing a biological network using the genetic interaction similarity network for Saccharomyces cerevisiae as an example. The visualization procedure described here relies on the open-source network visualization software Cytoscape and includes detailed instructions on formatting and loading the data, clustering networks, and overlaying functional annotations. PMID:26988373

  9. Fast Statistical Alignment

    OpenAIRE

    Bradley, Robert K.; Adam Roberts; Michael Smoot; Sudeep Juvekar; Jaeyoung Do; Colin Dewey; Ian Holmes; Lior Pachter

    2009-01-01

    We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multi...

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    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...

  11. Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction

    OpenAIRE

    Li, Kuochen; Chang, Dar-jen; Rouchka, Eric; Chen, Yuan Yan

    2007-01-01

    Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence ...

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2015-10-01

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

  15. Predicting metabolic pathways from metabolic networks with limited biological knowledge

    OpenAIRE

    Leung, HCM; Yiu, SM; Chin, FYL; Leung, SY; Xiang, CL

    2010-01-01

    Understanding the metabolism of new species (e.g. endophytic fungi that produce fuel) have tremendous impact on human lives. Based on predicted proteins and existing reaction databases, one can construct the metabolic network for the species. Next is to identify critical metabolic pathways from the network. Existing computational techniques identify conserved pathways based on multiple networks of related species, but have the following drawbacks. Some do not rely on additional information, s...

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

    OpenAIRE

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

    2014-01-01

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

  17. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

    Science.gov (United States)

    Hughes, Tyler B; Dang, Na Le; Miller, Grover P; Swamidass, S Joshua

    2016-08-24

    Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the

  18. Gene Network Biological Validity Based on Gene-Gene Interaction Relevance

    OpenAIRE

    Francisco Gómez-Vela; Norberto Díaz-Díaz

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in...

  19. 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…

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

    Institute of Scientific and Technical Information of China (English)

    Wei-Po Lee; Kung-Cheng Yang

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2010-01-01

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

  2. Uncovering disease mechanisms through network biology in the era of next generation sequencing.

    OpenAIRE

    Janet Piñero; Ariel Berenstein; Abel Gonzalez-Perez; Ariel Chernomoretz; Furlong, Laura I.

    2016-01-01

    Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants ...

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

    International Nuclear Information System (INIS)

    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.)

  4. 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.

  5. Yeast systems biology to unravel the network of life

    DEFF Research Database (Denmark)

    Mustacchi, Roberta; Hohmann, S; Nielsen, Jens

    2006-01-01

    Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design...... of advanced cell factories for production of fuels, chemicals, food ingredients and pharmaceuticals. The yeast Saccharomyces cerevisiae represents an excellent model system; the density of biological information available on this organism allows it to serve as a eukaryotic model for studying human...... diseases. Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and...

  6. Modeling Small Oscillating Biological Networks in Analog VLSI

    OpenAIRE

    Ryckebusch, Sylvie; Bower, James M.; Mead, Carver

    1989-01-01

    We have used analog VLSI technology to model a class of small oscillating biological neural circuits known as central pattern generators (CPG). These circuits generate rhythmic patterns of activity which drive locomotor behaviour in the animal. We have designed, fabricated, and tested a model neuron circuit which relies on many of the same mechanisms as a biological central pattern generator neuron, such as delays and internal feedback. We show that this neuron can be use...

  7. Pattern Learning, Damage and Repair within Biological Neural Networks

    Science.gov (United States)

    Siu, Theodore; Fitzgerald O'Neill, Kate; Shinbrot, Troy

    2015-03-01

    Traumatic brain injury (TBI) causes damage to neural networks, potentially leading to disability or even death. Nearly one in ten of these patients die, and most of the remainder suffer from symptoms ranging from headaches and nausea to convulsions and paralysis. In vitro studies to develop treatments for TBI have limited in vivo applicability, and in vitro therapies have even proven to worsen the outcome of TBI patients. We propose that this disconnect between in vitro and in vivo outcomes may be associated with the fact that in vitro tests assess indirect measures of neuronal health, but do not investigate the actual function of neuronal networks. Therefore in this talk, we examine both in vitro and in silico neuronal networks that actually perform a function: pattern identification. We allow the networks to execute genetic, Hebbian, learning, and additionally, we examine the effects of damage and subsequent repair within our networks. We show that the length of repaired connections affects the overall pattern learning performance of the network and we propose therapies that may improve function following TBI in clinical settings.

  8. Characterization of Adaptation by Morphology in a Planar Biological Network of Plasmodial Slime Mold

    Science.gov (United States)

    Ito, Masateru; Okamoto, Riki; Takamatsu, Atsuko

    2011-07-01

    Growth processes of a planar biological network of plasmodium of a true slime mold, Physarum polycephalum, were analyzed quantitatively. The plasmodium forms a transportation network through which protoplasm conveys nutrients, oxygen, and cellular organelles similarly to blood in a mammalian vascular network. To analyze the network structure, vertices were defined at tube bifurcation points. Then edges were defined for the tubes connecting both end vertices. Morphological analysis was attempted along with conventional topological analysis, revealing that the growth process of the plasmodial network structure depends on environmental conditions. In an attractive condition, the network is a polygonal lattice with more than six edges per vertex at the early stage and the hexagonal lattice at a later stage. Through all growing stages, the tube structure was not highly developed but an unstructured protoplasmic thin sheet was dominantly formed. The network size is small. In contrast, in the repulsive condition, the network is a mixture of polygonal lattice and tree-graph. More specifically, the polygonal lattice has more than six edges per vertex in the early stage, then a tree-graph structure is added to the lattice network at a later stage. The thick tube structure was highly developed. The network size, in the meaning of Euclidean distance but not topological one, grows considerably. Finally, the biological meaning of the environment-dependent network structure in the plasmodium is discussed.

  9. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    Science.gov (United States)

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene. PMID:27294188

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

    Science.gov (United States)

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

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. PMID:26701126

  11. Logical Reduction of Biological Networks to Their Most Determinative Components.

    Science.gov (United States)

    Matache, Mihaela T; Matache, Valentin

    2016-07-01

    Boolean networks have been widely used as models for gene regulatory networks, signal transduction networks, or neural networks, among many others. One of the main difficulties in analyzing the dynamics of a Boolean network and its sensitivity to perturbations or mutations is the fact that it grows exponentially with the number of nodes. Therefore, various approaches for simplifying the computations and reducing the network to a subset of relevant nodes have been proposed in the past few years. We consider a recently introduced method for reducing a Boolean network to its most determinative nodes that yield the highest information gain. The determinative power of a node is obtained by a summation of all mutual information quantities over all nodes having the chosen node as a common input, thus representing a measure of information gain obtained by the knowledge of the node under consideration. The determinative power of nodes has been considered in the literature under the assumption that the inputs are independent in which case one can use the Bahadur orthonormal basis. In this article, we relax that assumption and use a standard orthonormal basis instead. We use techniques of Hilbert space operators and harmonic analysis to generate formulas for the sensitivity to perturbations of nodes, quantified by the notions of influence, average sensitivity, and strength. Since we work on finite-dimensional spaces, our formulas and estimates can be and are formulated in plain matrix algebra terminology. We analyze the determinative power of nodes for a Boolean model of a signal transduction network of a generic fibroblast cell. We also show the similarities and differences induced by the alternative complete orthonormal basis used. Among the similarities, we mention the fact that the knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. In a special case, we obtain a stronger result than in previous

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

    OpenAIRE

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

    2009-01-01

    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 ...

  13. 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.

  14. 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

    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......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...... and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to...

  15. 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.

  16. Revealing gene regulation and association through biological networks

    Science.gov (United States)

    This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...

  17. 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.

  18. Hardware Accelerated Sequence Alignment with Traceback

    OpenAIRE

    Scott Lloyd; Snell, Quinn O

    2009-01-01

    Biological sequence alignment is an essential tool used in molecular biology and biomedical applications. The growing volume of genetic data and the complexity of sequence alignment present a challenge in obtaining alignment results in a timely manner. Known methods to accelerate alignment on reconfigurable hardware only address sequence comparison, limit the sequence length, or exhibit memory and I/O bottlenecks. A space-efficient, global sequence alignment algorithm and architecture is pres...

  19. 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. PMID:21576756

  20. Discovering Networks of Perturbed Biological Processes in Hepatocyte Cultures

    OpenAIRE

    Lasher, Christopher D; Rajagopalan, Padmavathy; Murali, T.M.

    2011-01-01

    The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs) and collagen sandwiches (CS). Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We inte...

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

    OpenAIRE

    Schmid Maurizio; Accornero Neri; Capozza Marco; Conforto Silvia; Bernabucci Ivan; D'Alessio Tommaso

    2007-01-01

    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 ...

  2. Perturbation Biology: inferring signaling networks in cellular systems

    OpenAIRE

    Molinelli, Evan J.; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo

    2013-01-01

    Author Summary Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of...

  3. Detecting modules in biological networks by edge weight clustering and entropy significance.

    Science.gov (United States)

    Lecca, Paola; Re, Angela

    2015-01-01

    Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be

  4. 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.

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

    International Nuclear Information System (INIS)

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Thomson, Ty M. [Selventa, One Alewife Center, Cambridge, MA 02140 (United States); Sewer, Alain, E-mail: Alain.Sewer@pmi.com [Philip Morris International R and D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel (Switzerland); Martin, Florian; Belcastro, Vincenzo [Philip Morris International R and D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel (Switzerland); Frushour, Brian P. [Selventa, One Alewife Center, Cambridge, MA 02140 (United States); Gebel, Stephan [Philip Morris International R and D, Philip Morris Research Laboratories GmbH, Edmund-Rumpler-Strasse 5, 51149 Koeln (Germany); Park, Jennifer [Selventa, One Alewife Center, Cambridge, MA 02140 (United States); Schlage, Walter K. [Philip Morris International R and D, Philip Morris Research Laboratories GmbH, Edmund-Rumpler-Strasse 5, 51149 Koeln (Germany); Talikka, Marja [Philip Morris International R and D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel (Switzerland); Vasilyev, Dmitry M.; Westra, Jurjen W. [Selventa, One Alewife Center, Cambridge, MA 02140 (United States); Hoeng, Julia; Peitsch, Manuel C. [Philip Morris International R and D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel (Switzerland)

    2013-11-01

    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

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

    Science.gov (United States)

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

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. PMID:26691180

  8. 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.

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

    CERN Document Server

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

    2012-01-01

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

  10. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    Science.gov (United States)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  11. Alignment for CSR

    International Nuclear Information System (INIS)

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Kristofferson, D.; Mack, D.

    1996-10-01

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

  13. Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

    Science.gov (United States)

    Stevens, Hallam

    2015-10-01

    The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource. PMID:26593711

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

    International Nuclear Information System (INIS)

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

  15. Modeling Wireless Sensor Networks for Monitoring in Biological Processes

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil

    signal strength). Fusing the two measured behavioral data resulted in an improvement of the classification results regarding the animal behavior mode (activity/inactivity), compared to the results achieved by only monitoring one of the behavioral parameters. Applying a multiple-model adaptive estimation...... (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...

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of...... chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential...

  17. 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.

  18. Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

    OpenAIRE

    Bošnački Dragan; Odenbrett Maximilian R; Wijs Anton; Ligtenberg Willem; Hilbers Peter

    2012-01-01

    Abstract Background Techniques for reconstruction of biological networks which are based on perturbation experiments often predict direct interactions between nodes that do not exist. Transitive reduction removes such relations if they can be explained by an indirect path of influences. The existing algorithms for transitive reduction are sequential and might suffer from too long run times for large networks. They also exhibit the anomaly that some existing direct interactions are also remove...

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

    Science.gov (United States)

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

    2016-02-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM". PMID:26872036

  20. ezBioNet: A modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2012-10-01

    To achieve robustness against living environments, a living organism is composed of complicated regulatory mechanisms ranging from gene regulations to signal transduction. If such life phenomena are to be understand, an integrated analysis tool that should have modeling and simulation functions for biological reactions, as well as new experimental methods for measuring biological phenomena, is fundamentally required. We have designed and implemented modeling and simulation software (ezBioNet) for analyzing biological reaction networks. The software can simultaneously perform an integrated modeling of various responses occurring in cells, ranging from gene expressions to signaling processes. To support massive analysis of biological networks, we have constructed a server-side simulation system (VCellSim) that can perform ordinary differential equations (ODE) analysis, sensitivity analysis, and parameter estimates. ezBioNet integrates the BioModel database by connecting the european bioinformatics institute (EBI) servers through Web services APIs and supports the handling of systems biology markup language (SBML) files. In addition, we employed eclipse RCP (rich client platform) which is a powerful modularity framework allowing various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool, as well as a simulation system, to understand the control mechanism by monitoring the change of each component in a biological network. A researcher may perform the kinetic modeling and execute the simulation. The simulation result can be managed and visualized on ezBioNet, which is freely available at http://ezbionet.cbnu.ac.kr.

  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...... problems and designed a set of algorithms to tackle the combinatorial explosion of the search space. During the presentation we will demonstrate how to: Import and process the data, set the parameters for the two models, compute and visualize the key pathways, judge and statistically evaluate the results...

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

    CERN Document Server

    Banerjee, Soumya Jyoti; Roy, Soumen

    2014-01-01

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

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

    Science.gov (United States)

    Christensen, Claire Petra

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

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

    Science.gov (United States)

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

    2016-07-01

    The cusps of native aortic valve (AV) are composed of collagen bundles embedded in soft tissue, creating a heterogenic tissue with asymmetric alignment in each cusp. This study compares native collagen fiber networks (CFNs) with a goal to better understand their influence on stress distribution and valve kinematics. Images of CFNs from five porcine tricuspid AVs are analyzed and fluid-structure interaction models are generated based on them. Although the valves had similar overall kinematics, the CFNs had distinctive influence on local mechanics. The regions with dilute CFN are more prone to damage since they are subjected to higher stress magnitudes. PMID:26406926

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

    OpenAIRE

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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

  8. The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

    Science.gov (United States)

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology. PMID:23577081

  9. 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.

  10. 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 throughin silicotheoretical studies with the aim to guide and complement furtherin vitroandin vivoexperimental efforts. Clearly, what counts is the resultin 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. PMID:27075000

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

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

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

  12. GPCODON ALIGNMENT: A GLOBAL PAIRWISE CODON BASED SEQUENCE ALIGNMENT APPROACH

    Directory of Open Access Journals (Sweden)

    Zeinab A. Fareed

    2016-02-01

    Full Text Available The alignment of two DNA sequences is a basic step in the analysis of biological data. Sequencing a long DNA sequence is one of the most interesting problems in bioinformatics. Several techniques have been developed to solve this sequence alignment problem like dynamic programming and heuristic algorithms. In this paper, we introduce (GPCodon alignment a pairwise DNA-DNA method for global sequence alignment that improves the accuracy of pairwise sequence alignment. We use a new scoring matrix to produce the final alignment called the empirical codon substitution matrix. Using this matrix in our technique enabled the discovery of new relationships between sequences that could not be discovered using traditional matrices. In addition, we present experimental results that show the performance of the proposed technique over eleven datasets of average length of 2967 bps. We compared the efficiency and accuracy of our techniques against a comparable tool called “Pairwise Align Codons” [1].

  13. Alignment validation

    CERN Document Server

    Golling, T

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Inadyuti Dutt

    2013-01-01

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

  15. Insights into biological information processing: structural and dynamical analysis of a human protein signalling network

    International Nuclear Information System (INIS)

    We present an investigation on the structural and dynamical properties of a 'human protein signalling network' (HPSN). This biological network is composed of nodes that correspond to proteins and directed edges that represent signal flows. In order to gain insight into the organization of cell information processing this network is analysed taking into account explicitly the edge directions. We explore the topological properties of the HPSN at the global and the local scale, further applying the generating function formalism to provide a suitable comparative model. The relationship between the node degrees and the distribution of signals through the network is characterized using degree correlation profiles. Finally, we analyse the dynamical properties of small sub-graphs showing high correlation between their occurrence and dynamic stability

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

    Science.gov (United States)

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

    2009-10-01

    In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/ PMID:20154426

  17. Application of Kohonen Neural Networks in classification of biologically active compounds.

    Science.gov (United States)

    Kirew, D B; Chretien, J R; Bernard, P; Ros, F

    1998-01-01

    Automated data classification is an indispensable tool in Drug Design. It allows to select homogeneous training sets or to distinguish compounds with required biological properties. The Kohonen Neural Networks (KNN) suggest new means for classification of biologically interesting compounds. In this paper, first, capabilities of KNN in data dimensionality reduction are presented as compared with the capabilities of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The advantages of KNN become evident with increasing data dimensionality and size of the training set. Then, new methods are suggested to evaluate the quality of KNN models. Finally, a case study on chemical and biological data is presented. The database studied includes more than 2000 organophosphorous potent pesticides. The Kohonen maps were obtained which allow to distinguish compounds with different biological behavior. PMID:9517011

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

    Directory of Open Access Journals (Sweden)

    Xin Lai

    2013-01-01

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

  19. Maria Goeppert-Mayer Award Talk: Probing the structure and dynamics of biological networks

    Science.gov (United States)

    Albert, Reka

    2011-03-01

    The relationship between the structure and dynamics of networks is one of the central topics in network science. In the context of biological regulatory networks at the molecular to cellular level, the dynamics in question is often thought of as information propagation through the network. Quantitative dynamic models help to achieve an understanding of this process, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting biological signal transduction, and at the same time probing the structure-function relation of these networks. This analysis, however, necessitates the extension of current graph theoretical frameworks to incorporate features such as the positive or negative nature of interactions and synergistic behaviors among multiple components. This talk will present a method for structural analysis in an augmented graph framework that can probe the dynamics of information transfer. The first step is to expand the network to a richer representation that incorporates negative and synergistic regulation by the addition of pseudo-nodes and new edges. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. As a first application of this method we ranked the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on various regulatory networks shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and agrees with dynamic simulation results and experimental observations. Future applications include determining the ESMs that (do

  20. 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 that...

  1. Secure Degrees of Freedom of Multi-user Networks: One-Time-Pads in the Air via Alignment

    OpenAIRE

    Xie, Jianwei; Ulukus, Sennur

    2015-01-01

    We revisit the recent secure degrees of freedom (s.d.o.f.) results for one-hop multi-user wireless networks by considering three fundamental wireless network structures: Gaussian wiretap channel with helpers, Gaussian multiple access wiretap channel, and Gaussian interference channel with secrecy constraints. We present main enabling tools and resulting communication schemes in an expository manner, along with key insights and design principles emerging from them. The main achievable schemes ...

  2. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy.

    Science.gov (United States)

    Tyler, Anna L; Asselbergs, Folkert W; Williams, Scott M; Moore, Jason H

    2009-02-01

    Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene-gene interaction, has also been treated as an exception to the Mendelian one gene-one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena. PMID:19204994

  3. A Structure of Biological System and Functionality using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Khaled Mohammed Beer Gamal

    2014-07-01

    Full Text Available Artificial neural networks have, as initial motivation, the structure of biological systems, and constitute an alternative computability paradigm. For that reason we will review some aspects of the way in which biological systems perform information processing. The fascination which still pervades this research field has much to do with the points of contact with the surprisingly elegant methods used by neurons in order to process information at the cellular level. Several million years of evolution have led to very sophisticated solutions to the problem of dealing with an uncertain environment. In this chapter we will discuss some elements of these strategies in order to determine what features we want to adopt in our abstract models of neural networks.

  4. ATLAS Muon Endcap Alignment

    CERN Document Server

    Bensinger, J R

    2005-01-01

    To align the endcap muon chambers of the ATLAS experiment, an optical grid is set up between aluminum “alignment bars” nested in each layer of chambers. Optical lines are made of laser diodes and CCD cameras that form an alignment grid. The alignment bars are self-aligning. They are then carefully measured using a large coordinate measuring machine (CMM). The subsequent shape changes of the bar are determined by calculations that are corrected by the readings of the internal monitors. The relationship between the bars is then established by a network of sensors that measure the bearing angle of light sources on the other parts of the system. The system is over-determined and the location and orientation of each bar is determined using a fitting program. Chambers are then referenced to the alignment grid using proximity sensors. This information is used to provide corrections to the nominal chamber positions before calculating track momentum. The performance of the system has been validated in a test beam ...

  5. Pattern recognition and classification of images of biological macromolecules using artificial neural networks

    OpenAIRE

    Marabini, Roberto; Carazo García, José María

    1994-01-01

    The goal of this work was to analyze an image data set and to detect the structural variability within this set. Two algorithms for pattern recognition based on neural networks are presented, one that performs an unsupervised classification (the self-organizing map) and the other a supervised classification (the learning vector quantization). The approach has a direct impact in current strategies for structural determination from electron microscopic images of biological macromolecules. In th...

  6. Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

    OpenAIRE

    Liwei Qian; Haoran Zheng; Hong Zhou; Ruibin Qin; Jinlong Li

    2013-01-01

    The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture mode...

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

    OpenAIRE

    Nil Turan; Susana Kalko; Anna Stincone; Kim Clarke; Ayesha Sabah; Katherine Howlett; S John Curnow; Rodriguez, Diego A.; Marta Cascante; Laura O'Neill; Stuart Egginton; Josep Roca; Francesco Falciani

    2011-01-01

    Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular an...

  8. Multi-level dynamic modeling in biological systems : application of hybrid Petri nets to network simulation

    OpenAIRE

    Costa, Rafael S.; Machado, C. D.; Neves, Ana Rute; Vinga, Susana

    2012-01-01

    The recent progress in the high-throughput experimental technologies allows the reconstruction of many biological networks and to evaluate changes in proteins, genes and metabolites levels in different conditions. On the other hand, computational models, when complemented with regulatory information, can be used to predict the phenotype of an organism under different genetic and environmental conditions. These computational methods can be used for example to identify molecular targets capable...

  9. ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

    Science.gov (United States)

    Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

    2015-01-01

    Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks. PMID:26357321

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

    Directory of Open Access Journals (Sweden)

    Manway Liu

    2007-06-01

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

  11. 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.

  12. Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

    Science.gov (United States)

    Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

    2016-03-01

    A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

  13. 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.

  14. 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.

  15. 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. PMID:25502388

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

    International Nuclear Information System (INIS)

    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)

  17. Beyond Alignment

    DEFF Research Database (Denmark)

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

  18. 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:…

  19. 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

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

    Science.gov (United States)

    Furqan, Mohammad Shaheryar; Siyal, Mohammad Yakoob

    2016-01-01

    The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality. This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information. We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets, i.e., fMRI and HeLa cell. fMRI data was used to map brain network involved in deductive reasoning while HeLa cell dataset was used to map gene network involved in cancer. PMID:27186478

  1. Partitioning Biological Networks into Highly Connected Clusters with Maximum Edge Coverage.

    Science.gov (United States)

    Hüffner, Falk; Komusiewicz, Christian; Liebtrau, Adrian; Niedermeier, Rolf

    2014-01-01

    A popular clustering algorithm for biological networks which was proposed by Hartuv and Shamir identifies nonoverlapping highly connected components. We extend the approach taken by this algorithm by introducing the combinatorial optimization problem Highly Connected Deletion, which asks for removing as few edges as possible from a graph such that the resulting graph consists of highly connected components. We show that Highly Connected Deletion is NP-hard and provide a fixed-parameter algorithm and a kernelization. We propose exact and heuristic solution strategies, based on polynomial-time data reduction rules and integer linear programming with column generation. The data reduction typically identifies 75 percent of the edges that are deleted for an optimal solution; the column generation method can then optimally solve protein interaction networks with up to 6,000 vertices and 13,500 edges within five hours. Additionally, we present a new heuristic that finds more clusters than the method by Hartuv and Shamir. PMID:26356014

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

    CERN Document Server

    Guo, Yongyi; Qian, Min; Ge, Hao

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-03-01

    This article analyzes the existing collaboration networks in the Brazilian scientific output in Evolutionary Biology, considering articles published during the period from 2000 to 2012 in journals indexed by Web of Science. The methodology integrates bibliometric techniques and Social Network Analysis resources to describe the growth of Brazilian scientific output and understand the levels, dynamics and structure of collaboration between authors, institutions and countries. The results unveil an enhancement and consolidation of collaborative relationships over time and suggest the existence of key institutions and authors, whose influence on research is expressed by the variety and intensity of the relationships established in the co-authorship of articles. International collaboration, present in more than half of the publications, is highly significant and unusual in Brazilian science. The situation indicates the internationalization of scientific output and the ability of the field to take part in the science produced by the international scientific community. PMID:26871500

  4. 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

    Library, MEDLINE, and EMBASE (to January 2010). Identifying search results and data extraction were performed independently and in duplicate. For the network meta-analysis, we performed mixed-effects logistic regression using an arm-based, random-effects model within an empirical Bayes framework. Main...... results We included 163 RCTs with 50,010 participants and 46 extension studies with 11,954 participants. The median duration of RCTs was six months and 13 months for OLEs. Data were limited for tuberculosis (TB) reactivation, lymphoma, and congestive heart failure. Adjusted for dose, biologics as a group...... increased risk of TB reactivation (OR 4.68, 95% CI 1.18 to 18.60; NNTH = 681, 95% CI 143 to 14706) compared to control. The rate of serious adverse events, serious infections, lymphoma, and congestive heart failure were not statistically significantly different between biologics and control treatment...

  5. 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.

  6. Hardware Acceleration of Bioinformatics Sequence Alignment Applications

    OpenAIRE

    Hasan, L.

    2011-01-01

    Biological sequence alignment is an important and challenging task in bioinformatics. Alignment may be defined as an arrangement of two or more DNA or protein sequences to highlight the regions of their similarity. Sequence alignment is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment can provide valuable information for experimentation on the newly found sequences. It is indispensable in basic research as well as in practical applic...

  7. 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.

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

    International Nuclear Information System (INIS)

    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)

  9. 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)

  10. Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score

    Directory of Open Access Journals (Sweden)

    Skolnick Jeffrey

    2008-12-01

    Full Text Available Abstract Background Protein tertiary structure comparisons are employed in various fields of contemporary structural biology. Most structure comparison methods involve generation of an initial seed alignment, which is extended and/or refined to provide the best structural superposition between a pair of protein structures as assessed by a structure comparison metric. One such metric, the TM-score, was recently introduced to provide a combined structure quality measure of the coordinate root mean square deviation between a pair of structures and coverage. Using the TM-score, the TM-align structure alignment algorithm was developed that was often found to have better accuracy and coverage than the most commonly used structural alignment programs; however, there were a number of situations when this was not true. Results To further improve structure alignment quality, the Fr-TM-align algorithm has been developed where aligned fragment pairs are used to generate the initial seed alignments that are then refined using dynamic programming to maximize the TM-score. For the assessment of the structural alignment quality from Fr-TM-align in comparison to other programs such as CE and TM-align, we examined various alignment quality assessment scores such as PSI and TM-score. The assessment showed that the structural alignment quality from Fr-TM-align is better in comparison to both CE and TM-align. On average, the structural alignments generated using Fr-TM-align have a higher TM-score (~9% and coverage (~7% in comparison to those generated by TM-align. Fr-TM-align uses an exhaustive procedure to generate initial seed alignments. Hence, the algorithm is computationally more expensive than TM-align. Conclusion Fr-TM-align, a new algorithm that employs fragment alignment and assembly provides better structural alignments in comparison to TM-align. The source code and executables of Fr-TM-align are freely downloadable at: http://cssb.biology.gatech.edu/skolnick/files/FrTMalign/.

  11. Image alignment

    Science.gov (United States)

    Dowell, Larry Jonathan

    2014-04-22

    Disclosed is a method and device for aligning at least two digital images. An embodiment may use frequency-domain transforms of small tiles created from each image to identify substantially similar, "distinguishing" features within each of the images, and then align the images together based on the location of the distinguishing features. To accomplish this, an embodiment may create equal sized tile sub-images for each image. A "key" for each tile may be created by performing a frequency-domain transform calculation on each tile. A information-distance difference between each possible pair of tiles on each image may be calculated to identify distinguishing features. From analysis of the information-distance differences of the pairs of tiles, a subset of tiles with high discrimination metrics in relation to other tiles may be located for each image. The subset of distinguishing tiles for each image may then be compared to locate tiles with substantially similar keys and/or information-distance metrics to other tiles of other images. Once similar tiles are located for each image, the images may be aligned in relation to the identified similar tiles.

  12. 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.

  13. Classification of time series gene expression in clinical studies via integration of biological network.

    Directory of Open Access Journals (Sweden)

    Liwei Qian

    Full Text Available The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.

  14. Classification of time series gene expression in clinical studies via integration of biological network.

    Science.gov (United States)

    Qian, Liwei; Zheng, Haoran; Zhou, Hong; Qin, Ruibin; Li, Jinlong

    2013-01-01

    The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction. PMID:23516469

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

    Science.gov (United States)

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

    2008-09-01

    The arriving order of data is one of the intrinsic properties of a signal. Therefore, techniques dealing with this temporal relation are required for identification and signal processing tasks. To perform a classification of the signal according with its temporal characteristics, it would be useful to find a feature vector in which the temporal attributes were embedded. The correlation and power density spectrum functions are suitable tools to manage this issue. These functions are usually defined with statistical formulation. On the other hand, in biology there can be found numerous processes in which signals are processed to give a feature vector; for example, the processing of sound by the auditory system. In this work, the dNSP (dynamic Neural Signal Processing) architecture is proposed. This architecture allows representing a time-varying signal by a spatial (thus statical) vector. Inspired by the aforementioned biological processes, the dNSP performs frequency decomposition using an analogical parallel algorithm carried out by simple processing units. The architecture has been developed under the paradigm of a multilayer neural network, where the different layers are composed by units whose activation functions have been extracted from the theory of Neural Dynamic [Grossberg, S. (1988). Nonlinear neural networks principles, mechanisms and architectures. Neural Networks, 1, 17-61]. A theoretical study of the behavior of the dynamic equations of the units and their relationship with some statistical functions allows establishing a parallelism between the unit activations and correlation and power density spectrum functions. To test the capabilities of the proposed approach, several testbeds have been employed, i.e. the frequencial study of mathematical functions. As a possible application of the architecture, a highly interesting problem in the field of automatic control is addressed: the recognition of a controlled DC motor operating state. PMID:18579344

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

    Science.gov (United States)

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

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

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

    Science.gov (United States)

    Mallard, John; McGlynn, Brian; Covino, Tim

    2016-04-01

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

  18. Ni foam supported three-dimensional vertically aligned and networked layered CoO nanosheet/graphene hybrid array as a high-performance oxygen evolution electrode

    Science.gov (United States)

    Yuan, Weiyong; Zhao, Ming; Yuan, Jia; Li, Chang Ming

    2016-07-01

    The sluggish oxygen evolution reaction (OER) represents a major kinetic bottleneck in water splitting. Herein we report the synthesis of a novel Ni foam (NF) supported 3-D vertically aligned and interconnected layered CoO nanosheet array with controlled density, layer thickness, and interlayer spacing, and the conformal self-assembly of graphene on this nanosheet array. The obtained CoO layered nanosheet/graphene hybrid nanoarray was directly used as an OER electrode, showing a current density of 10 mA cm-2 at an overpotential of 330 mV and a Tafel slope of 79 mV dec-1, both of which are much lower than pristine NF and the nanosheet array without graphene, and are among the lowest reported for Co-based OER catalysts and transition metal oxide-based ones measured under the same conditions. In addition, it can retain 92.4% of the current density after 66 h of chronoamperometry testing at a potential of 1.0 V vs. SCE, and 94.3% of the current density at 1.0 V vs. SCE after 200 cyclic voltammetry cycles (0-1.0 V vs. SCE). The excellent catalytic activity and stability toward OER are ascribed to the 3-D NF supported robustly grown networked layered nanosheet array structure and the synergistic effects between CoO layered nanosheets and graphene.

  19. 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.

  20. Alignments of RNA structures.

    Science.gov (United States)

    Blin, Guillaume; Denise, Alain; Dulucq, Serge; Herrbach, Claire; Touzet, Hélène

    2010-01-01

    We describe a theoretical unifying framework to express the comparison of RNA structures, which we call alignment hierarchy. This framework relies on the definition of common supersequences for arc-annotated sequences and encompasses the main existing models for RNA structure comparison based on trees and arc-annotated sequences with a variety of edit operations. It also gives rise to edit models that have not been studied yet. We provide a thorough analysis of the alignment hierarchy, including a new polynomial-time algorithm and an NP-completeness proof. The polynomial-time algorithm involves biologically relevant edit operations such as pairing or unpairing nucleotides. It has been implemented in a software, called gardenia, which is available at the Web server http://bioinfo.lifl.fr/RNA/gardenia. PMID:20431150

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

    Directory of Open Access Journals (Sweden)

    Melissa A Metzler

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

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

    Directory of Open Access Journals (Sweden)

    Nil Turan

    2011-09-01

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

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

    OpenAIRE

    Grzegorczyk, M.; Husmeier, D.

    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 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 pr...

  4. Monotonicity, frustration, and ordered response: an analysis of the energy landscape of perturbed large-scale biological networks

    Directory of Open Access Journals (Sweden)

    Altafini Claudio

    2010-06-01

    Full Text Available Abstract Background For large-scale biological networks represented as signed graphs, the index of frustration measures how far a network is from a monotone system, i.e., how incoherently the system responds to perturbations. Results In this paper we find that the frustration is systematically lower in transcriptional networks (modeled at functional level than in signaling and metabolic networks (modeled at stoichiometric level. A possible interpretation of this result is in terms of energetic cost of an interaction: an erroneous or contradictory transcriptional action costs much more than a signaling/metabolic error, and therefore must be avoided as much as possible. Averaging over all possible perturbations, however, we also find that unlike for transcriptional networks, in the signaling/metabolic networks the probability of finding the system in its least frustrated configuration tends to be high also in correspondence of a moderate energetic regime, meaning that, in spite of the higher frustration, these networks can achieve a globally ordered response to perturbations even for moderate values of the strength of the interactions. Furthermore, an analysis of the energy landscape shows that signaling and metabolic networks lack energetic barriers around their global optima, a property also favouring global order. Conclusion In conclusion, transcriptional and signaling/metabolic networks appear to have systematic differences in both the index of frustration and the transition to global order. These differences are interpretable in terms of the different functions of the various classes of networks.

  5. Biological network module-based model for the analysis of differential expression in shotgun proteomics.

    Science.gov (United States)

    Xu, Jia; Wang, Lily; Li, Jing

    2014-12-01

    Protein differential expression analysis plays an important role in the understanding of molecular mechanisms as well as the pathogenesis of complex diseases. With the rapid development of mass spectrometry, shotgun proteomics using spectral counts has become a prevailing method for the quantitative analysis of complex protein mixtures. Existing methods in differential proteomics expression typically carry out analysis at the single-protein level. However, it is well-known that proteins interact with each other when they function in biological processes. In this study, focusing on biological network modules, we proposed a negative binomial generalized linear model for differential expression analysis of spectral count data in shotgun proteomics. In order to show the efficacy of the model in protein expression analysis at the level of protein modules, we conducted two simulation studies using synthetic data sets generated from theoretical distribution of count data and a real data set with shuffled counts. Then, we applied our method to a colorectal cancer data set and a nonsmall cell lung cancer data set. When compared with single-protein analysis methods, the results showed that module-based statistical model which takes account of the interactions among proteins led to more effective identification of subtle but coordinated changes at the systems level. PMID:25327611

  6. Biologic

    CERN Document Server

    Kauffman, L H

    2002-01-01

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

  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. 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

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

    Science.gov (United States)

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

    2012-12-01

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

  10. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

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

  11. 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

  12. 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...

  13. DNA Align Editor: DNA Alignment Editor Tool

    Science.gov (United States)

    The SNPAlignEditor is a DNA sequence alignment editor that runs on Windows platforms. The purpose of the program is to provide an intuitive, user-friendly tool for manual editing of multiple sequence alignments by providing functions for input, editing, and output of nucleotide sequence alignments....

  14. 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.

  15. A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network

    Science.gov (United States)

    Lachkar, Z.; Gruber, N.

    2012-01-01

    Eastern Boundary Upwelling Systems (EBUS) are highly productive ocean regions. Yet, substantial differences in net primary production (NPP) exist within and between these systems for reasons that are still not fully understood. Here, we explore the leading physical processes and environmental factors controlling NPP in EBUS through a comparative study of the California, Canary, Benguela, and Humboldt Current systems. The NPP drivers are identified with the aid of an artificial neural network analysis based on self-organizing-maps (SOM). Our results suggest that in addition to the expected NPP enhancing effect of stronger equatorward alongshore wind, three factors have an inhibiting effect: (1) strong eddy activity, (2) narrow continental shelf, and (3) deep mixed layer. The co-variability of these 4 drivers defines in the context of the SOM a continuum of 100 patterns of NPP regimes in EBUS. These are grouped into 4 distinct classes using a Hierarchical Agglomerative Clustering (HAC) method. Our objective classification of EBUS reveals important variations of NPP regimes within each of the four EBUS, particularly in the Canary and Benguela Current systems. Our results show that the Atlantic EBUS are generally more productive and more sensitive to upwelling favorable winds because of weaker factors inhibiting NPP. Perturbations of alongshore winds associated with climate change may therefore lead to contrasting biological responses in the Atlantic and the Pacific EBUS.

  16. A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network

    Directory of Open Access Journals (Sweden)

    Z. Lachkar

    2011-10-01

    Full Text Available Eastern Boundary Upwelling Systems (EBUS are highly productive ocean regions. Yet, substantial differences in net primary production (NPP exist within and between these systems for reasons that are still not fully understood. Here, we explore the leading physical processes and environmental factors controlling NPP in EBUS through a comparative study of the California, Canary, Benguela, and Humboldt Current systems. The identification of NPP drivers is done with the aid of an artificial neural network analysis based on self-organizing-maps (SOMs. We show that in addition to the expected NPP enhancing effect of stronger alongshore wind, three factors have an inhibiting effect: (1 strong eddy activity, (2 narrow continental shelf, and (3 deep mixed layer. The co-variability of these 4 drivers defines in the context of the SOM a continuum of 100 patterns of NPP regimes in EBUS. These are grouped into 4 distinct classes using a Hierarchical Agglomerative Clustering (HAC method. Our objective classification of EBUS reveals important variations of NPP regimes within each of the four EBUS, particularly in the Canary and Benguela Current systems. Our results show that the Atlantic EBUS are generally more productive and more sensitive to upwelling favorable winds because of a weaker factors inhibiting NPP. Perturbations of alongshore winds associated with climate change may therefore lead to contrasting biological responses in the Atlantic and the Pacific EBUS.

  17. The biological networks in studying cell signal transduction complexity: The examples of sperm capacitation and of endocannabinoid system

    Science.gov (United States)

    Bernabò, Nicola; Barboni, Barbara; Maccarrone, Mauro

    2014-01-01

    Cellular signal transduction is a complex phenomenon, which plays a central role in cell surviving and adaptation. The great amount of molecular data to date present in literature, together with the adoption of high throughput technologies, on the one hand, made available to scientists an enormous quantity of information, on the other hand, failed to provide a parallel increase in the understanding of biological events. In this context, a new discipline arose, the systems biology, aimed to manage the information with a computational modeling-based approach. In particular, the use of biological networks has allowed the making of huge progress in this field. Here we discuss two possible application of the use of biological networks to explore cell signaling: the study of the architecture of signaling systems that cooperate in determining the acquisition of a complex cellular function (as it is the case of the process of activation of spermatozoa) and the organization of a single specific signaling systems expressed by different cells in different tissues (i.e. the endocannabinoid system). In both the cases we have found that the networks follow a scale free and small world topology, likely due to the evolutionary advantage of robustness against random damages, fastness and specific of information processing, and easy navigability. PMID:25379139

  18. A distributed system for fast alignment of next-generation sequencing data

    Science.gov (United States)

    Srimani, Jaydeep K.; Wu, Po-Yen; Phan, John H.; Wang, May D.

    2016-01-01

    We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

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

    International Nuclear Information System (INIS)

    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.

  1. Tissue culture on a chip: Developmental biology applications of self-organized capillary networks in microfluidic devices.

    Science.gov (United States)

    Miura, Takashi; Yokokawa, Ryuji

    2016-08-01

    Organ culture systems are used to elucidate the mechanisms of pattern formation in developmental biology. Various organ culture techniques have been used, but the lack of microcirculation in such cultures impedes the long-term maintenance of larger tissues. Recent advances in microfluidic devices now enable us to utilize self-organized perfusable capillary networks in organ cultures. In this review, we will overview past approaches to organ culture and current technical advances in microfluidic devices, and discuss possible applications of microfluidics towards the study of developmental biology. PMID:27272910

  2. Dose-response aligned circuits in signaling systems.

    Directory of Open Access Journals (Sweden)

    Long Yan

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

  3. Data-driven, data-intensive computing for modelling and analysis of biological networks: application to bioethanol production

    Science.gov (United States)

    Park, Byung-Hoon; Samatova, Nagiza F.; Karpinets, Tatiana; Jallouk, Andrew; Molony, Scott; Horton, Scott; Arcangeli, Steven

    2007-07-01

    Modelling biological networks is inherently data-driven and data-intensive. The combinatorial nature of this type of modelling, however, requires new methods capable of dealing with the enormous size and irregularity of the search. Searching via 'backtracking' is one possible solution that avoids exhaustive searches by constraining the search space to the subspace of feasible solutions. Despite its wide use in many combinatorial optimization problems, there are currently few parallel implementations of backtracking capable of effectively dealing with the memory-intensive nature of the process and the extremely unbalanced loads present. In this paper, a parallel, scalable, and memory-efficient backtracking algorithm within the context of maximal clique enumeration is presented, and its applicability to large-scale biological networks aimed at studying the mechanisms for efficient bioethanol production is discussed.

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

    OpenAIRE

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

    2008-01-01

    Abstract Background 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 c...

  5. 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

  6. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

    Science.gov (United States)

    Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antzack, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; 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

  7. In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

    OpenAIRE

    Yin, Lianhong; Zheng, Lingli; Xu, Lina; Dong, Deshi; Han, Xu; Qi, Yan; Zhao, Yanyan; Xu, Youwei; Peng, Jinyong

    2015-01-01

    Background Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. Methods In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targe...

  8. A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

    OpenAIRE

    Hvidsten Torgeir R; Jansson Stefan; Street Nathaniel

    2011-01-01

    Abstract Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods ...

  9. Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data

    OpenAIRE

    Kadarmideen, Haja N; 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 different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared ...

  10. 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...

  11. The virome: a missing component of biological interaction networks in health and disease.

    Science.gov (United States)

    Handley, Scott A

    2016-01-01

    Host-associated viral populations, viromes, have been understudied relative to their contribution to human physiology. Viruses interact with host gene networks, influencing both health and disease. Analysis of host gene networks in the absence of virome analysis risks missing important network information. PMID:27037032

  12. Computing alignment plots efficiently

    CERN Document Server

    Krusche, Peter

    2009-01-01

    Dot plots are a standard method for local comparison of biological sequences. In a dot plot, a substring to substring distance is computed for all pairs of fixed-size windows in the input strings. Commonly, the Hamming distance is used since it can be computed in linear time. However, the Hamming distance is a rather crude measure of string similarity, and using an alignment-based edit distance can greatly improve the sensitivity of the dot plot method. In this paper, we show how to compute alignment plots of the latter type efficiently. Given two strings of length m and n and a window size w, this problem consists in computing the edit distance between all pairs of substrings of length w, one from each input string. The problem can be solved by repeated application of the standard dynamic programming algorithm in time O(mnw^2). This paper gives an improved data-parallel algorithm, running in time $O(mnw/\\gamma/p)$ using vector operations that work on $\\gamma$ values in parallel and $p$ processors. We show ex...

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

    Science.gov (United States)

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

    2016-06-01

    Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms. PMID:26897376

  14. 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.

  15. Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

    Directory of Open Access Journals (Sweden)

    Urmi Sengupta

    Full Text Available Type 2 diabetes mellitus (T2D is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d Lastly, from our fourth network we have inferred that the interaction of beta-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better

  16. Properties of Random Complex Chemical Reaction Networks and Their Relevance to Biological Toy Models

    OpenAIRE

    Bigan, Erwan; Steyaert, Jean-Marc; Douady, Stéphane

    2013-01-01

    We investigate the properties of large random conservative chemical reaction networks composed of elementary reactions endowed with either mass-action or saturating kinetics, assigning kinetic parameters in a thermodynamically-consistent manner. We find that such complex networks exhibit qualitatively similar behavior when fed with external nutrient flux. The nutrient is preferentially transformed into one specific chemical that is an intrinsic property of the network. We propose a self-consi...

  17. Large-scale photonic neural networks with biology-like processing elements: the role of electron-trapping materials

    Science.gov (United States)

    Farhat, Nabil H.; Wen, Zhimin

    1995-08-01

    Neural networks employing pulsating biology-oriented integrate-and-fire (IF) model neurons, that can exhibit synchronicity (phase-locking), bifurcation, and chaos, have features that make them potentially useful for learning and recognition of spatio-temporal patterns, generation of complex motor control, emulating higher-level cortical functions like feature binding, separation of object from background, cognition and other higher-level functions; all of which are beyond the ready reach of nonpulsating sigmoidal neuron networks. The spiking nature of biology-oriented neural networks makes their study in digital hardware impractical. Prange and Klar convincingly argued that the best way of realizing such networks is through analog CMOS technology rather than digital hardware. They showed, however, that the number of neurons one can accommodate on a VLSI chip limited to a hundred or so, even when submicron CMOS technology is used, because of the relatively large size of the neuron/dendrite cell. One way of reducing the size of neuron/dendrite cell is to reduce the structural complexity of the cell by realizing some of the processes needed in the cell's operation externally to the chip and by coupling these processes to the cell optically. Two such processes are the relaxation mechanism of the IF neuron and dendritic-tree processing. We have shown, by examining the blue light impulse response of electron trapping materials (ETMs) used under simultaneous infrared and blue light bias, that these materials offer features that can be used in realizing both the optical relaxation and synapto-dendritic response mechanisms. Experimental results demonstrating the potential of this approach in realizing dense arrays of biology-oriented neuron/dendrite cells will be presented, focusing on the concept and design of ETM-based image intensifier as new enabling technology.

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

    OpenAIRE

    Shuqiang Wang; Yanyan Shen; Changhong Shi; Tao Wang; Zhiming Wei; Hanxiong Li

    2014-01-01

    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 ...

  19. Pathosystems Biology: Computational Prediction and Analysis of Host-Pathogen Protein Interaction Networks

    OpenAIRE

    Dyer, Matthew David

    2008-01-01

    An important aspect of systems biology is the elucidation of the protein-protein interactions (PPIs) that control important biological processes within a cell and between organisms. In particular, at the cellular and molecular level, interactions between a pathogen and its host play a vital role in initiating infection and a successful pathogenesis. Despite recent successes in the advancement of the systems biology of model organisms to understand complex diseases, the analysis of infectious ...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-07-01

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

  1. 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)

    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)

  2. A comparison of four pair-wise sequence alignment methods

    OpenAIRE

    Essoussi, Nadia; Fayech, Sondes

    2007-01-01

    Protein sequence alignment has become an essential task in modern molecular biology research. A number of alignment techniques have been documented in literature and their corresponding tools are made available as freeware and commercial software. The choice and use of these tools for sequence alignment through the complete interpretation of alignment results is often considered non-trivial by end-users with limited skill in Bioinformatics algorithm development. Here, we discuss the compariso...

  3. 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...

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    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 of...

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

    Science.gov (United States)

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

    2016-07-01

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

  6. 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 ...

  7. An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm

    OpenAIRE

    Kumar, Manish

    2015-01-01

    One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the pop...

  8. 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)

    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)

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

    International Nuclear Information System (INIS)

    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).

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

    Science.gov (United States)

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

    2012-02-01

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

  11. Exploring the topological sources of robustness against invasion in biological and technological networks

    Science.gov (United States)

    Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

    2016-02-01

    For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity.

  12. 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

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

    Directory of Open Access Journals (Sweden)

    Hung-Cuong Trinh

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

  14. The effect of nano scale inhomogeneity and silicate network connectivity on the activity of glasses with biological applications

    International Nuclear Information System (INIS)

    Classical molecular dynamics simulations of six soda-lime phospho silicate glasses are analyzed to identify correlations between specific structural features of the glasses and their biological activity, namely the dissolution in physiological fluids and the rate of deposition of a bone-like apatite film on their surface. Structural markers of the bioactivity can be identified in the silicate connectivity and the formation of nanometer-size calcium clusters separated from the silicate network, with the latter associated with less bioactive or bio-inactive compositions. The simulations show how these key structural features are affected by the glass composition, and suggest that different strategies (i.e. compositional ranges)can be employed to modify them in a rational and systematic way, in order to steer the biological activity of the glass towards the level required by different biomedical applications.

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

    Directory of Open Access Journals (Sweden)

    Sucaet Yves

    2010-11-01

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

  16. Social insect colony as a biological regulatory system: Information flow in dominance networks

    OpenAIRE

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

    2014-01-01

    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...

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

    OpenAIRE

    Poole, Matthew; Kentzoglanakis, Kyriakos

    2011-01-01

    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 modelling the dynamical behaviour of gene regulatory systems. More specifically, ACO is used for searching the discre...

  18. 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.

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

    Science.gov (United States)

    Fernandes, Christabelle E G; Malik, Ashish; Jineesh, V K; Fernandes, Sheryl O; Das, Anindita; Pandey, Sunita S; Kanolkar, Geeta; Sujith, P P; Velip, Dhillan M; Shaikh, Shagufta; Helekar, Samita; Gonsalves, Maria Judith; Nair, Shanta; LokaBharathi, P A

    2015-08-01

    The coastal waters of Goa and Ratnagiri lying on the West coast of India are influenced by terrestrial influx. However, Goa is influenced anthropogenically by iron-ore mining, while Ratnagiri is influenced by deposition of heavy minerals containing 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 nmi apart. Biological and environmental parameters were analyzed during pre-monsoon season. Except silicates, the measured parameters were higher at Goa and related significantly, suggesting bacteria centric, detritus-driven region. At Ratnagiri, phytoplankton biomass related positively with silicate suggesting a region dominated by primary producers. This dominance perhaps got reflected as a higher tertiary yield. Thus, even though the regions are geographically proximate, the different biological response could be attributed to the differences in the web of interactions between the measured variables. PMID:25907627

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

    International Nuclear Information System (INIS)

    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

  1. Underlying Principles of Natural Selection in Network Evolution: Systems Biology Approach

    OpenAIRE

    Bor-Sen Chen; Wei-Sheng Wu

    2007-01-01

    Systems biology is a rapidly expanding field that integrates diverse areas of science such as physics, engineering, computer science, mathematics, and biology toward the goal of elucidating the underlying principles of hierarchical metabolic and regulatory systems in the cell, and ultimately leading to predictive understanding of cellular response to perturbations. Because post-genomics research is taking place throughout the tree of life, comparative approaches offer a way for combining data...

  2. Visual Analysis of Transcriptome Data in the Context of Anatomical Structures and Biological Networks

    OpenAIRE

    Junker, Astrid; Rohn, Hendrik; Schreiber, Falk

    2012-01-01

    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 n...

  3. The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

    OpenAIRE

    Moulos, Panagiotis; Klein, Julie; Jupp, Simon; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost

    2013-01-01

    BACKGROUND: Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics ...

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

    OpenAIRE

    Eronen Lauri; Toivonen Hannu

    2012-01-01

    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, ge...

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

    OpenAIRE

    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...

  6. Dynamic Sensor Scheduling for Thermal Management in Biological Wireless Sensor Networks

    OpenAIRE

    Yahya Osais; F. Richard Yu; Marc St-Hilaire

    2013-01-01

    Biological sensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated...

  7. 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.

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

    CERN Document Server

    Jafar, Syed A

    2012-01-01

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

  9. 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, ...

  10. 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.

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

    Science.gov (United States)

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

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

  12. Creating biological nanomaterials using synthetic biology

    OpenAIRE

    MaryJoe K Rice; 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 bi...

  13. 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

  14. Identification and classification of galaxies using a biologically-inspired neutral network

    Science.gov (United States)

    Somanah, Radhakhrishna; Rughooputh, Soonil D. D. V.; Rughooputh, Harry C. S.

    2002-10-01

    Recognition/Classification of galaxies is an important issue in the large-scale study of the Universe; it is not a simple task. According to estimates computed from the Hubble Deep Field (HDF), astronomers predict that the universe may potentially contain over 100 billion galaxies. Several techniques have been reported for the classification of galaxies. Parallel developments in the field of neural networks have come to a stage that they can participate well in the recognition of objects. Recently, the Pulse-Coupled Neural Network (PCNN) has been shown to be useful for image pre-processing. In this paper, we present a novel way to identify optical galaxies by presenting the images of the galaxies to a hierarchical neural network involving two PCNNs. The image is presented to the network to generate binary barcodes (one per iteration) of the galaxies; the barcodes are unique to the input galactic image. In the current study, we exploit this property to identify optical galaxies by comparing the signatures (binary barcode) from a corresponding database.

  15. 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...

  16. 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

  17. 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

    2012-01-01

    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 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. PMID:22850067

  18. Horizontal carbon nanotube alignment.

    Science.gov (United States)

    Cole, Matthew T; Cientanni, Vito; Milne, William I

    2016-09-21

    The production of horizontally aligned carbon nanotubes offers a rapid means of realizing a myriad of self-assembled near-atom-scale technologies - from novel photonic crystals to nanoscale transistors. The ability to reproducibly align anisotropic nanostructures has huge technological value. Here we review the present state-of-the-art in horizontal carbon nanotube alignment. For both in and ex situ approaches, we quantitatively assess the reported linear packing densities alongside the degree of alignment possible for each of these core methodologies. PMID:27546174

  19. 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...

  20. Orthodontics and Aligners

    Science.gov (United States)

    ... Repairing Chipped Teeth Teeth Whitening Tooth-Colored Fillings Orthodontics and Aligners Straighten teeth for a healthier smile. Orthodontics When consumers think about orthodontics, braces are the ...

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

    Indian Academy of Sciences (India)

    Alfred Gierer

    2002-06-01

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

  2. The dynamics of living and thinking systems, biological networks, and the laws of physics

    OpenAIRE

    2004-01-01

    Discrete chaotic dynamics (DCD) of living and thinking systems are presented in a form of networks of interacting agents with the abilities of energy and information exchange. Special dynamical principles followed by the systems of basic discrete time and space difference equations are introduced. Emergent, self-organized behavior of complex living and thinking systems is presented by the different patterns generated by the DCD algorithms. Artificial life and brain systems based on DCD p...

  3. Autworks: a cross-disease network biology application for Autism and related disorders

    OpenAIRE

    Nelson Tristan H; Jung Jae-Yoon; DeLuca Todd F; Hinebaugh Byron K; St Gabriel Kristian; Wall Dennis P

    2012-01-01

    Abstract Background The genetic etiology of autism is heterogeneous. Multiple disorders share genotypic and phenotypic traits with autism. Network based cross-disorder analysis can aid in the understanding and characterization of the molecular pathology of autism, but there are few tools that enable us to conduct cross-disorder analysis and to visualize the results. Description We have designed Autworks as a web portal to bring together gene interaction and gene-disease association data on au...

  4. Species interactions–area relationships: biological invasions and network structure in relation to island area

    OpenAIRE

    Sugiura, Shinji

    2010-01-01

    The relationship between species number and island area is a fundamental rule in ecology. However, the extent to which interactions with exotic species and how the structure of species interactions is related to island area remain unexplored. Here, I document the relationship between island area and (i) interactions with exotic species and (ii) network structure of species interactions in the context of mutualistic interactions between ants and extrafloral nectary-bearing plants on the oceani...

  5. The dynamics of living and thinking systems, biological networks, and the laws of physics

    OpenAIRE

    V. Gontar

    2004-01-01

    Discrete chaotic dynamics (DCD) of living and thinking systems are presented in a form of networks of interacting agents with the abilities of energy and information exchange. Special dynamical principles followed by the systems of basic discrete time and space difference equations are introduced. Emergent, self-organized behavior of complex living and thinking systems is presented by the different patterns generated by the DCD algorithms. Artificial life and brai...

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

    Science.gov (United States)

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

    1993-01-01

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

  7. De novo deleterious genetic variations target a biological network centered on Aβ peptide in early-onset Alzheimer disease.

    Science.gov (United States)

    Rovelet-Lecrux, A; Charbonnier, C; Wallon, D; Nicolas, G; Seaman, M N J; Pottier, C; Breusegem, S Y; Mathur, P P; Jenardhanan, P; Le Guennec, K; Mukadam, A S; Quenez, O; Coutant, S; Rousseau, S; Richard, A-C; Boland, A; Deleuze, J-F; Frebourg, T; Hannequin, D; Campion, D

    2015-09-01

    We hypothesized that de novo variants (DNV) might participate in the genetic determinism of sporadic early-onset Alzheimer disease (EOAD, onset before 65 years). We investigated 14 sporadic EOAD trios first by array-comparative genomic hybridization. Two patients carried a de novo copy number variation (CNV). We then performed whole-exome sequencing in the 12 remaining trios and identified 12 non-synonymous DNVs in six patients. The two de novo CNVs (an amyloid precursor protein (APP) duplication and a BACE2 intronic deletion) and 3/12 non-synonymous DNVs (in PSEN1, VPS35 and MARK4) targeted genes from a biological network centered on the Amyloid beta (Aβ) peptide. We showed that this a priori-defined genetic network was significantly enriched in amino acid-altering DNV, compared with the rest of the exome. The causality of the APP de novo duplication (which is the first reported one) was obvious. In addition, we provided evidence of the functional impact of the following three non-synonymous DNVs targeting this network: the novel PSEN1 variant resulted in exon 9 skipping in patient's RNA, leading to a pathogenic missense at exons 8-10 junction; the VPS35 missense variant led to partial loss of retromer function, which may impact neuronal APP trafficking and Aβ secretion; and the MARK4 multiple nucleotide variant resulted into increased Tau phosphorylation, which may trigger enhanced Aβ-induced toxicity. Despite the difficulty to recruit Alzheimer disease (AD) trios owing to age structures of the pedigrees and the genetic heterogeneity of the disease, this strategy allowed us to highlight the role of de novo pathogenic events, the putative involvement of new genes in AD genetics and the key role of Aβ network alteration in AD. PMID:26194182

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

    OpenAIRE

    Guberman, Jonathan M.; Gundem, Gunes; L??pez Bigas, N??ria; P??rez Llamas, Christian, 1976-; 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...

  9. Systems biology beyond networks: generating order from disorder through self-organization

    OpenAIRE

    Saetzler, K.; Sonnenschein, C; Soto, A. M.

    2011-01-01

    Erwin Schrödinger pointed out in his 1944 book “What is Life” that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known a...

  10. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle

    OpenAIRE

    Alexandre, Pamela A.; Kogelman, Lisette; Santana, Miguel H. A.; Passarelli, Danielle; Pulz, Lidia H.; Fantinato-Neto, Paulo; Silva, Paulo L.; Leme, Paulo R; Strefezzi, Ricardo F.; Coutinho, Luiz L.; Ferraz, José B. S.; Eler, Joanie P.; Kadarmideen, Haja; Fukumasu, Heidge

    2015-01-01

    Background The selection of beef cattle for feed efficiency (FE) traits is very important not only for productive and economic efficiency but also for reduced environmental impact of livestock. Considering that FE is multifactorial and expensive to measure, the aim of this study was to identify biological functions and regulatory genes associated with this phenotype. Results Eight genes were differentially expressed between high and low feed efficient animals (HFE and LFE, respectively). Co-e...

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

    Indian Academy of Sciences (India)

    Jin Wang; Subrata Sen

    2011-08-01

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

  12. On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience.

    Science.gov (United States)

    Bowers, Jeffrey S

    2009-01-01

    A fundamental claim associated with parallel distributed processing (PDP) theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts (e.g. "dog"), that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned in connectionist models and neural coding in brain and often dismiss localist (grandmother cell) theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes (sparse and coarse coding) and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models. PMID:19159155

  13. Meta-Alignment with Crumble and Prune: Partitioning very large alignment problems for performance and parallelization

    Directory of Open Access Journals (Sweden)

    Paten Benedict

    2011-05-01

    Full Text Available Abstract Background Continuing research into the global multiple sequence alignment problem has resulted in more sophisticated and principled alignment methods. Unfortunately these new algorithms often require large amounts of time and memory to run, making it nearly impossible to run these algorithms on large datasets. As a solution, we present two general methods, Crumble and Prune, for breaking a phylogenetic alignment problem into smaller, more tractable sub-problems. We call Crumble and Prune meta-alignment methods because they use existing alignment algorithms and can be used with many current alignment programs. Crumble breaks long alignment problems into shorter sub-problems. Prune divides the phylogenetic tree into a collection of smaller trees to reduce the number of sequences in each alignment problem. These methods are orthogonal: they can be applied together to provide better scaling in terms of sequence length and in sequence depth. Both methods partition the problem such that many of the sub-problems can be solved independently. The results are then combined to form a solution to the full alignment problem. Results Crumble and Prune each provide a significant performance improvement with little loss of accuracy. In some cases, a gain in accuracy was observed. Crumble and Prune were tested on real and simulated data. Furthermore, we have implemented a system called Job-tree that allows hierarchical sub-problems to be solved in parallel on a compute cluster, significantly shortening the run-time. Conclusions These methods enabled us to solve gigabase alignment problems. These methods could enable a new generation of biologically realistic alignment algorithms to be applied to real world, large scale alignment problems.

  14. Tevatron Alignment Issues 2003-2004

    CERN Document Server

    Volk, James T; Elementi, Luciano; Gelfand, Norman M; Gollwitzer, Keith; Greenwood, John A; Martens, Michael A; Moore, Craig D; Nobrega, Alfred; Russell, Allison D; Sager, Terry; Shiltsev, Vladimir; Stefanski, Raymond; Syphers, Michael J; Wojcik, George

    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 milli-radian. 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.

  15. Faster exon assembly by sparse spliced alignment

    CERN Document Server

    Tiskin, Alexander

    2007-01-01

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

  16. Regulatory networks, genes and glycerophospholipid biosynthesis pathway in schistosomiasis: a systems biology view for pharmacological intervention.

    Science.gov (United States)

    Shinde, Sonali; Mol, Milsee; Singh, Shailza

    2014-10-25

    Understanding network topology through embracing the global dynamical regulation of genes in an active state space rather than traditional one-gene-one trait approach facilitates the rational drug development process. Schistosomiasis, a neglected tropical disease, has glycerophospholipids as abundant molecules present on its surface. Lack of effective clinical solutions to treat pathogens encourages us to carry out systems-level studies that could contribute to the development of an effective therapy. Development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses through in silico approaches provides tantalizing opportunity to investigate the role of protein/substrate metabolism. A genome-scale metabolic network model reconstruction represents choline-phosphate cytidyltransferase as the rate limiting enzyme and regulates the rate of phosphatidylcholine (PC) biosynthesis. The uptake of choline was regulated by choline concentration, promoting the regulation of phosphocholine synthesis. In Schistosoma, the change in developmental stage could result from the availability of choline, hampering its developmental cycle. There are no structural reports for this protein. In order to inhibit the activity of choline-phosphate cytidyltransferase (CCT), it was modeled by homology modeling using 1COZ as the template from Bacillus subtilis. The transition-state stabilization and catalytic residues were mapped as 'HXGH' and 'RTEGISTT' motif. CCT catalyzes the formation of CDP-choline from phosphocholine in which nucleotidyltransferase adds CTP to phosphocholine. The presence of phosphocholine permits the parasite to survive in an immunologically hostile environment. This feature endeavors development of an inhibitor specific for cytidyltransferase in Schistosoma. Flavonolignans were used to inhibit this activity in which hydnowightin showed the highest affinity as compared to miltefosine. PMID:25149020

  17. MicroRNA-1 properties in cancer regulatory networks and tumor biology.

    Science.gov (United States)

    Weiss, Martin; Brandenburg, Lars-Ove; Burchardt, Martin; Stope, Matthias B

    2016-08-01

    Short non-coding microRNAs have been identified to orchestrate crucial mechanisms in cancer progression and treatment resistance. MicroRNAs are involved in posttranscriptional modulation of gene expression and therefore represent promising targets for anticancer therapy. As mircoRNA-1 (miR-1) exerted to be predominantly downregulated in the majority of examined tumors, miR-1 is classified to be a tumor suppressor with high potential to diminish tumor development and therapy resistance. Here we review the complex functionality of miR-1 in tumor biology. PMID:27286699

  18. Multi-objective mixed integer strategy for the optimisation of biological networks.

    Science.gov (United States)

    Sendín, J O H; Exler, O; Banga, J R

    2010-05-01

    In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation. PMID:20500003

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-07-14

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

  20. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    Gervasio Gomez

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

  1. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

    2011-01-01

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

  2. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

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

  3. Alignment of the magnet and a positioning method

    Science.gov (United States)

    Kim, Dae-Il

    2015-10-01

    The 100-MeV proton linac and magnets for the KOMAC (Korea Multi-purpose Accelerator Complex) were installed in the tunnel and the beamlines. The fiducialization process was accomplished with the measurement of mechanical shape and the transfer of the coordinates to the fiducial points that are used in two laser-trackers based alignments. The reference points called the alignment network were set up on the wall inside tunnel. The linac and the beam transport magnets were aligned based on the survey results of the alignment networks. In this paper, the alignment procedure and the alignment results are presented, and an algorithm that was developed to manipulate the adjusters of the magnetsis introduced.

  4. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2011-01-01

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

  5. Real Interference Alignment

    CERN Document Server

    Motahari, Abolfazl Seyed; Maddah-Ali, Mohammad-Ali; Khandani, Amir Keyvan

    2010-01-01

    In this paper, we show that the total Degrees-Of-Freedoms (DOF) of the $K$-user Gaussian Interference Channel (GIC) can be achieved by incorporating a new alignment technique known as \\emph{real interference alignment}. This technique compared to its ancestor \\emph{vector interference alignment} performs on a single real line and exploits the properties of real numbers to provide optimal signaling. The real interference alignment relies on a new coding scheme in which several data streams having fractional multiplexing gains are sent by transmitters and interfering streams are aligned at receivers. The coding scheme is backed up by a recent result in the field of Diophantine approximation, which states that the convergence part of the Khintchine-Groshev theorem holds for points on non-degenerate manifolds.

  6. Design, Mathematical Modelling, Construction and Testing of Synthetic Gene Network Oscillators to Establish Roseobacter Clade Bacteria and the Protozoan Trypanosoma brucei as Synthetic Biology Chassis.

    OpenAIRE

    Borg, Y.

    2015-01-01

    The aim of this project is to establish Roseobacter marine bacteria and Trypanosoma brucei (T. brucei) protozoa as synthetic biology chassis. This work addresses the gap within synthetic biology resulting from the limited choice of host cells available for use in practice. This was done by developing synthetic bacterial and trypanosomal genetic regulatory networks (GRNs) which function as an oscillator as well as by developing the necessary protocols and set-ups to allow for the analysis of G...

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

    Directory of Open Access Journals (Sweden)

    Reza Azad

    2014-06-01

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

  8. Hybrid coordination-network-engineering for bridging cascaded channels to activate long persistent phosphorescence in the second biological window

    Science.gov (United States)

    Qin, Xixi; Li, Yang; Zhang, Ruili; Ren, Jinjun; Gecevicius, Mindaugas; Wu, Yiling; Sharafudeen, Kaniyarakkal; Dong, Guoping; Zhou, Shifeng; Ma, Zhijun; Qiu, Jianrong

    2016-02-01

    We present a novel “Top-down” strategy to design the long phosphorescent phosphors in the second biological transparency window via energy transfer. Inherence in this approach to material design involves an ingenious engineering for hybridizing the coordination networks of hosts, tailoring the topochemical configuration of dopants, and bridging a cascaded tunnel for transferring the persistent energy from traps, to sensitizers and then to acceptors. Another significance of this endeavour is to highlight a rational scheme for functionally important hosts and dopants, Cr/Nd co-doped Zn1-xCaxGa2O4 solid solutions. Such solid-solution is employed as an optimized host to take advantage of its characteristic trap site level to establish an electron reservoir and network parameters for the precipitation of activators Nd3+ and Cr3+. The results reveal that the strategy employed here has the great potential, as well as opens new opportunities for future new-wavelength, NIR phosphorescent phosphors fabrication with many potential multifunctional bio-imaging applications.

  9. 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

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

    International Nuclear Information System (INIS)

    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

  11. 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

  12. Algorithm engineering for optimal alignment of protein structure distance matrices

    OpenAIRE

    Wohlers, Inken; Andonov, Rumen; Klau, Gunnar W.

    2011-01-01

    International audience Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model...

  13. The meaning of alignment: lessons from structural diversity

    Directory of Open Access Journals (Sweden)

    Heringa Jaap

    2008-12-01

    Full Text Available Abstract Background Protein structural alignment provides a fundamental basis for deriving principles of functional and evolutionary relationships. It is routinely used for structural classification and functional characterization of proteins and for the construction of sequence alignment benchmarks. However, the available techniques do not fully consider the implications of protein structural diversity and typically generate a single alignment between sequences. Results We have taken alternative protein crystal structures and generated simulation snapshots to explicitly investigate the impact of structural changes on the alignments. We show that structural diversity has a significant effect on structural alignment. Moreover, we observe alignment inconsistencies even for modest spatial divergence, implying that the biological interpretation of alignments is less straightforward than commonly assumed. A salient example is the GroES 'mobile loop' where sub-Ångstrom variations give rise to contradictory sequence alignments. Conclusion A comprehensive treatment of ambiguous alignment regions is crucial for further development of structural alignment applications and for the representation of alignments in general. For this purpose we have developed an on-line database containing our data and new ways of visualizing alignment inconsistencies, which can be found at http://www.ibi.vu.nl/databases/stralivari.

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

    Science.gov (United States)

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

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. PMID:26356597

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

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

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

  16. Discovering Patterns in Biological Sequences by Optimal Segmentation

    OpenAIRE

    Bockhorst, Joseph; Jojic, Nebojsa

    2012-01-01

    Computational methods for discovering patterns of local correlations in sequences are important in computational biology. Here we show how to determine the optimal partitioning of aligned sequences into non-overlapping segments such that positions in the same segment are strongly correlated while positions in different segments are not. Our approach involves discovering the hidden variables of a Bayesian network that interact with observed sequences so as to form a set of independent mixture ...

  17. Galaxy alignments: An overview

    CERN Document Server

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

    2015-01-01

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

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

    DEFF Research Database (Denmark)

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

    2006-01-01

    The discovery of dilute liquid crystalline media to align biological macromolecules has opened many new possibilities to study protein and nucleic acid structures by NMR spectroscopy. We inspect the basic alignment phenomenon for an ensemble of protein conformations to deduce relative contributions...

  19. The Childhood Solid Tumor Network: A new resource for the developmental biology and oncology research communities.

    Science.gov (United States)

    Stewart, Elizabeth; Federico, Sara; Karlstrom, Asa; Shelat, Anang; Sablauer, Andras; Pappo, Alberto; Dyer, Michael A

    2016-03-15

    Significant advances have been made over the past 25 years in our understanding of the most common adult solid tumors such as breast, colon, lung and prostate cancer. Much less is known about childhood solid tumors because they are rare and because they originate in developing organs during fetal development, childhood and adolescence. It can be very difficult to study the cellular origins of pediatric solid tumors in developing organs characterized by rapid proliferative expansion, growth factor signaling, developmental angiogenesis, programmed cell death, tissue reorganization and cell migration. Not only has the etiology of pediatric cancer remained elusive because of their developmental origins, but it also makes it more difficult to treat. Molecular targeted therapeutics that alter developmental pathway signaling may have devastating effects on normal organ development. Therefore, basic research focused on the mechanisms of development provides an essential foundation for pediatric solid tumor translational research. In this article, we describe new resources available for the developmental biology and oncology research communities. In a companion paper, we present the detailed characterization of an orthotopic xenograft of a pediatric solid tumor derived from sympathoadrenal lineage during development. PMID:26068307

  20. Tidal alignment of galaxies

    CERN Document Server

    Blazek, Jonathan; Seljak, Uroš

    2015-01-01

    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 ...

  1. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G.Gomez

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

  2. 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. PMID:27402677

  3. 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. PMID:27402677

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

    Science.gov (United States)

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

    2016-02-01

    Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS. PMID:26276570

  5. Enhanced Dynamic Algorithm of Genome Sequence Alignments

    Directory of Open Access Journals (Sweden)

    Arabi E. keshk

    2014-05-01

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

  6. Creating biological nanomaterials using synthetic biology

    International Nuclear Information System (INIS)

    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)

  7. Creating biological nanomaterials using synthetic biology

    Directory of Open Access Journals (Sweden)

    MaryJoe K Rice

    2014-01-01

    Full Text Available 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.

  8. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    S. Szillasi

    2013-01-01

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

  9. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2012-01-01

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

  10. Comparison of Modules of Wild Type and Mutant Huntingtin and TP53 Protein Interaction Networks: Implications in Biological Processes and Functions

    Science.gov (United States)

    Basu, Mahashweta; Bhattacharyya, Nitai P.; Mohanty, Pradeep K.

    2013-01-01

    Disease-causing mutations usually change the interacting partners of mutant proteins. In this article, we propose that the biological consequences of mutation are directly related to the alteration of corresponding protein protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes Huntington's disease (HD) and mutations to TP53 which is associated with different cancers are studied as two example cases. We construct the PPIN of wild type and mutant proteins separately and identify the structural modules of each of the networks. The functional role of these modules are then assessed by Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find that a large number of significantly enriched () GO terms in mutant PPIN were absent in the wild type PPIN indicating the gain of BPs due to mutation. Similarly some of the GO terms enriched in wild type PPIN cease to exist in the modules of mutant PPIN, representing the loss. GO terms common in modules of mutant and wild type networks indicate both loss and gain of BPs. We further assign relevant biological function(s) to each module by classifying the enriched GO terms associated with it. It turns out that most of these biological functions in HTT networks are already known to be altered in HD and those of TP53 networks are altered in cancers. We argue that gain of BPs, and the corresponding biological functions, are due to new interacting partners acquired by mutant proteins. The methodology we adopt here could be applied to genetic diseases where mutations alter the ability of the protein to interact with other proteins. PMID:23741403

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

    Science.gov (United States)

    McClendon, Mark Trosper

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

  12. An Ant-Based Model for Multiple Sequence Alignment

    CERN Document Server

    Guinand, Frédéric

    2008-01-01

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

  13. Ultra-large alignments using phylogeny-aware profiles.

    Science.gov (United States)

    Nguyen, Nam-Phuong D; Mirarab, Siavash; Kumar, Keerthana; Warnow, Tandy

    2015-01-01

    Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp . PMID:26076734

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

    Science.gov (United States)

    Squires, David

    2012-01-01

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

  15. Partial Alignment for Improvement of Beam Transmission at KOMAC

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-05-15

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

  16. Stress-sensitive neurosignalling in depression: an integrated network biology approach to candidate gene selection for genetic association analysis

    Directory of Open Access Journals (Sweden)

    J. Anke M. van Eekelen

    2012-07-01

    Full Text Available Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors. We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Science.gov (United States)

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

    2010-01-20

    An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance. PMID:20090802

  19. Cell Alignment Driven by Mechanically Induced Collagen Fiber Alignment in Collagen/Alginate Coatings.

    Science.gov (United States)

    Chaubaroux, Christophe; Perrin-Schmitt, Fabienne; Senger, Bernard; Vidal, Loïc; Voegel, Jean-Claude; Schaaf, Pierre; Haikel, Youssef; Boulmedais, Fouzia; Lavalle, Philippe; Hemmerlé, Joseph

    2015-09-01

    For many years it has been a major challenge to regenerate damaged tissues using synthetic or natural materials. To favor the healing processes after tendon, cornea, muscle, or brain injuries, aligned collagen-based architectures are of utmost interest. In this study, we define a novel aligned coating based on a collagen/alginate (COL/ALG) multilayer film. The coating exhibiting a nanofibrillar structure is cross-linked with genipin for stability in physiological conditions. By stretching COL/ALG-coated polydimethylsiloxane substrates, we developed a versatile method to align the collagen fibrils of the polymeric coating. Assays on cell morphology and alignment were performed to investigate the properties of these films. Microscopic assessments revealed that cells align with the stretched collagen fibrils of the coating. The degree of alignment is tuned by the stretching rate (i.e., the strain) of the COL/ALG-coated elastic substrate. Such coatings are of great interest for strategies that require aligned nanofibrillar biological material as a substrate for tissue engineering. PMID:25658028

  20. MaxAlign: maximizing usable data in an alignment

    OpenAIRE

    Pedersen Anders G; Sackett Peter W; Gouveia-Oliveira Rodrigo

    2007-01-01

    Abstract Background The presence of gaps in an alignment of nucleotide or protein sequences is often an inconvenience for bioinformatical studies. In phylogenetic and other analyses, for instance, gapped columns are often discarded entirely from the alignment. Results MaxAlign is a program that optimizes the alignment prior to such analyses. Specifically, it maximizes the number of nucleotide (or amino acid) symbols that are present in gap-free columns – the alignment area – by selecting the ...

  1. Ergodic Secret Alignment

    CERN Document Server

    Bassily, Raef

    2010-01-01

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

  2. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez

    2010-01-01

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

  3. Alignment telescope for Antares

    International Nuclear Information System (INIS)

    The Antares Automatic Alignment System employs a specially designed telescope for alignment of its laser beamlines. There are two telescopes in the system, and since each telescope is a primary alignment reference, stringent boresight accuracy and stability over the focus range were required. Optical and mechanical designs, which meet this requirement as well as that of image quality over a wide wavelength band, are described. Special test techniques for initial assembly and alignment of the telescope are also presented. The telescope, which has a 180-mm aperture FK51-KZF2 type glass doublet objective, requires a boresight accuracy of 2.8 μrad at two focal lengths, and object distances between 11 meters and infinity. Travel of a smaller secondary doublet provides focus from 11 m to infinity with approximately 7.8 m effective focal length. By flipping in a third doublet, the effective focal length is reduced to 2.5 m. Telescope alignment was accomplished by using a rotary air bearing to establish an axis in front of the system and placing the focus of a Laser Unequal Path Interferometer (LUPI) at the image plane

  4. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    Gervasio Gomez

    2012-01-01

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

  5. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    M. Dallavalle

    2013-01-01

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

  6. Three-dimensional cell-dense constructs containing endothelial cell-networks are an effective tool for in vivo and in vitro vascular biology research.

    Science.gov (United States)

    Sekiya, Sachiko; Muraoka, Megumi; Sasagawa, Tadashi; Shimizu, Tatsuya; Yamato, Masayuki; Okano, Teruo

    2010-12-01

    Angiogenesis is a complicated natural process, and understanding the mechanism by which it occurs is important for medical, pharmaceutical, and cell biological sciences. Many techniques for investigating angiogenesis have been reported. In this study, we introduced a novel application of a cell culture technique that can be used in in vitro and in vivo vascular biology research. Cultivated endothelial cells (ECs) were harvested from temperature responsive culture dishes by reducing the temperature, without the need for a proteinase treatment. For this technique, the direct contact of ECs with fibroblasts was important for the formation of a capillary-like network in vitro. Moreover, layered cell sheets containing EC-networks produced lumen and vascular structures in the three-dimensional constructs, as well as in the construct transplanted into a living body. Thus, our culture technique was able to create cell sheets and three-dimensional constructs containing EC-networks, because they preserved normal and intrinsic cell-cell direct contact and various cell adhesive factors. Moreover, the thickness of these three-dimensional (3-D) constructs could be controlled by the number of layered cell sheets. These observations indicated that our novel technology contributed to the progress of vascular biology and lead to a new tool that can be used in in vivo and in vitro vascular biology research. PMID:20696176

  7. Accelerator Technology: Geodesy and Alignment for Particle Accelerators

    CERN Document Server

    Missiaen, D

    2013-01-01

    This document is part of Subvolume C 'Accelerators and Colliders' of Volume 21 'Elementary Particles' of Landolt-Börnstein - Group I 'Elementary Particles, Nuclei and Atoms'. It contains the the Section '8.9 Geodesy and Alignment for Particle Accelerators' of the Chapter '8 Accelerator Technology' with the content: 8.9 Geodesy and Alignment for Particle Accelerators 8.9.1 Introduction 8.9.2 Reference and Co-ordinate Systems 8.9.3 Definition of the Beam Line on the Accelerator Site 8.9.4 Geodetic Network 8.9.5 Tunnel Preliminary Works 8.9.6 The Alignment References 8.9.7 Alignment of Accelerator Components 8.9.8 Permanent Monitoring and Remote Alignment of Low Beta Quadrupoles 8.9.9 Alignment of Detector Components

  8. The CMS Tracker Alignment Strategy

    CERN Document Server

    Weber, Martin

    2006-01-01

    CMS silicon Tracker alignment consists of three key components: Survey during tracker construction, measurements with the Laser Alignment System during operation and track based alignment. Methods and results are explained in detail, with a special focus on track based alignment due to its enormous complexity and numerical challenges.

  9. RNA Structural Alignments, Part I

    DEFF Research Database (Denmark)

    Havgaard, Jakob Hull; Gorodkin, Jan

    Simultaneous alignment and secondary structure prediction of RNA sequences is often referred to as "RNA structural alignment." A class of the methods for structural alignment is based on the principles proposed by Sankoff more than 25 years ago. The Sankoff algorithm simultaneously folds and aligns...

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

    Science.gov (United States)

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

    2006-01-01

    A network constitutes an abstract description of the relationships among entities, respectively termed links and nodes. If a power law describes the probability distribution of the number of links per node, the network is said to be scale-free. Scale-free networks feature link clustering around certain hubs based on preferential attachments that emerge due either to merit or legacy. Biologic systems ranging from sub-atomic to ecosystems represent scale-free networks in which energy efficiency forms the basis of preferential attachments. This paradigm engenders a novel scale-free network theory of evolution based on energy efficiency. As environmental flux induces fitness dislocations and compels a new meritocracy, new merit-based hubs emerge, previously merit-based hubs become legacy hubs, and network recalibration occurs to achieve system optimization. To date, Darwinian evolution, characterized by innovation sampling, variation, and selection through filtered termination, has enabled biologic progress through optimization of energy efficiency. However, as humans remodel their environment, increasing the level of unanticipated fitness dislocations and inducing evolutionary stress, the tendency of networks to exhibit inertia and retain legacy hubs engender maladaptations. Many modern diseases may fundamentally derive from these evolutionary displacements. Death itself may constitute a programmed adaptation, terminating individuals who represent legacy hubs and recalibrating the network. As memes replace genes as the basis of innovation, death itself has become a legacy hub. Post-Darwinian evolution may favor indefinite persistence to optimize energy efficiency. We describe strategies to reprogram or decommission legacy hubs that participate in human disease and death. PMID:16580786

  11. Contextual alignment of cognitive and neural dynamics.

    Science.gov (United States)

    Ames, Daniel L; Honey, Christopher J; Chow, Michael A; Todorov, Alexander; Hasson, Uri

    2015-04-01

    Effective real-world communication requires the alignment of multiple individuals to a common perspective or mental framework. To study how this alignment occurs at the level of the brain, we measured BOLD response during fMRI while participants (n = 24) listened to a series of vignettes either in the presence or absence of a valid contextual cue. The valid contextual cue was necessary to understand the information in each vignette. We then examined where and to what extent the shared valid context led to greater intersubject similarity of neural processing. Regions of the default mode network including posterior cingulate cortex and medial pFC became more aligned when participants shared a valid contextual framework, whereas other regions, including primary sensory cortices, responded to the stimuli reliably regardless of contextual factors. Taken in conjunction with previous research, the present results suggest that default mode regions help the brain to organize incoming verbal information in the context of previous knowledge. PMID:25244122

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

    OpenAIRE

    Jafar, Syed A.

    2012-01-01

    We explore degrees of freedom (DoF) characterizations of partially connected wireless networks, especially cellular networks, with no channel state information at the transmitters. Specifically, we introduce three fundamental elements --- aligned frequency reuse, wireless index coding and interference diversity --- through a series of examples, focusing first on infinite regular arrays, then on finite clusters with arbitrary connectivity and message sets, and finally on heterogeneous settings...

  13. Improving Multiple Sequence Alignments by Revising Sequence Families with Alignment Scoring Approaches

    OpenAIRE

    Levchuk, Aleksandr O.

    2011-01-01

    Characterizing the functional, structural, and evolutionary relationships of biological sequences is an important task in modern genomics and computational biology. Most of these applications involve the assembly of sequence families by similarity searching, subsequent formation of multiple sequence alignments (MSAs) and downstream phylogenetic analyses. Especially, MSAs play a central role in this modeling workflow. Thus, the quality of the MSAs is of critical importance for its success. In ...

  14. MUSE optical alignment procedure

    Science.gov (United States)

    Laurent, Florence; Renault, Edgard; Loupias, Magali; Kosmalski, Johan; Anwand, Heiko; Bacon, Roland; Boudon, Didier; Caillier, Patrick; Daguisé, Eric; Dubois, Jean-Pierre; Dupuy, Christophe; Kelz, Andreas; Lizon, Jean-Louis; Nicklas, Harald; Parès, Laurent; Remillieux, Alban; Seifert, Walter; Valentin, Hervé; Xu, Wenli

    2012-09-01

    MUSE (Multi Unit Spectroscopic Explorer) is a second generation VLT integral field spectrograph (1x1arcmin² Field of View) developed for the European Southern Observatory (ESO), operating in the visible wavelength range (0.465-0.93 μm). A consortium of seven institutes is currently assembling and testing MUSE in the Integration Hall of the Observatoire de Lyon for the Preliminary Acceptance in Europe, scheduled for 2013. MUSE is composed of several subsystems which are under the responsibility of each institute. The Fore Optics derotates and anamorphoses the image at the focal plane. A Splitting and Relay Optics feed the 24 identical Integral Field Units (IFU), that are mounted within a large monolithic instrument mechanical structure. Each IFU incorporates an image slicer, a fully refractive spectrograph with VPH-grating and a detector system connected to a global vacuum and cryogenic system. During 2011, all MUSE subsystems were integrated, aligned and tested independently in each institute. After validations, the systems were shipped to the P.I. institute at Lyon and were assembled in the Integration Hall This paper describes the end-to-end optical alignment procedure of the MUSE instrument. The design strategy, mixing an optical alignment by manufacturing (plug and play approach) and few adjustments on key components, is presented. We depict the alignment method for identifying the optical axis using several references located in pupil and image planes. All tools required to perform the global alignment between each subsystem are described. The success of this alignment approach is demonstrated by the good results for the MUSE image quality. MUSE commissioning at the VLT (Very Large Telescope) is planned for 2013.

  15. Strategic Alignment of Business Intelligence

    OpenAIRE

    Cederberg, Niclas

    2010-01-01

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

  16. RHIC survey and alignment

    International Nuclear Information System (INIS)

    The Relativistic Heavy Ion Collider consists of two interlaced plane rings, a pair of mirror-symmetric beam injection arcs, a spatially curved beam transfer line from the Alternating Gradient Synchrotron, and a collection of precisely positioned and aligned magnets, on appropriately positioned support stands, threaded on those arcs. RHIC geometry is defined by six beam crossing points exactly in a plane, lying precisely at the vertices of a regular hexagon of specified size position and orientation of this hexagon are defined geodetically. Survey control and alignment procedures, currently in use to construct RHIC, are described

  17. Alignment and commissioning of the APS beamline front ends

    International Nuclear Information System (INIS)

    Fifteen out of forty initial beamline front ends have been installed in the storage-ring tunnel at the 7-GeV Advanced Photon Source (APS). For the front-end installation, a four-step alignment process was designed and consists of (1) prealigning the front-end components with support tables in the preassembly area, (2) installing the components with tables in the storage-ring tunnel and aligning relative to the APS global telescope survey network, (3) confirming the alignment using a tooling laser alignment system, and (4) performing adjustments with the synchrotron-radiation beam during commissioning. The laser alignment system and the prealignment database have been of great importance for the expedient maintenance of front-end components. These tools are very important to a large synchrotron radiation facility, such as the APS, since they make a quick alignment setup possible and minimize alignment time inside the tunnel. This paper will present the four-step alignment process, the laser alignment system, and discuss the alignment confirmation results. copyright 1996 American Institute of Physics

  18. BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC

    Directory of Open Access Journals (Sweden)

    Miklós István

    2009-08-01

    Full Text Available Abstract Background We have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences. Results We present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the α-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques. Conclusion BigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from http://www.stats.ox.ac.uk/~satija/BigFoot/

  19. A Brief Introduction to Chinese Biological Biological

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

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

  20. Discriminative Shape Alignment

    DEFF Research Database (Denmark)

    Loog, M.; de Bruijne, M.

    taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two...

  1. MUON DETECTORS: ALIGNMENT

    CERN Multimedia

    G. Gomez and Y. Pakhotin

    2012-01-01

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

  2. Aligning Mental Representations

    DEFF Research Database (Denmark)

    Kano Glückstad, Fumiko

    2013-01-01

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

  3. Interference Alignment with Analog Channel State Feedback

    OpenAIRE

    Ayach, Omar El; Heath Jr, Robert W.

    2010-01-01

    Interference alignment (IA) is a multiplexing gain optimal transmission strategy for the interference channel. While the achieved sum rate with IA is much higher than previously thought possible, the improvement often comes at the cost of requiring network channel state information at the transmitters. This can be achieved by explicit feedback, a flexible yet potentially costly approach that incurs large overhead. In this paper we propose analog feedback as an alternative to limited feedback ...

  4. Methotrexate monotherapy and methotrexate combination therapy with traditional and biologic disease modifying antirheumatic drugs for rheumatoid arthritis: abridged Cochrane systematic review and network meta-analysis

    OpenAIRE

    Hazlewood, Glen S; Barnabe, Cheryl; Tomlinson, George; Marshall, Deborah; Devoe, Dan; Bombardier, Claire

    2016-01-01

    Objective To compare methotrexate based disease modifying antirheumatic drug (DMARD) treatments for rheumatoid arthritis in patients naive to or with an inadequate response to methotrexate. Design Systematic review and Bayesian random effects network meta-analysis of trials assessing methotrexate used alone or in combination with other conventional synthetic DMARDs, biologic drugs, or tofacitinib in adult patients with rheumatoid arthritis. Data sources Trials were identified from Medline, Em...

  5. An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm.

    Science.gov (United States)

    Kumar, Manish

    2015-01-01

    One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770

  6. Analysis of the text of a textbook of biology oriented by actor-network theory: a study on the theme “biological evolution”

    Directory of Open Access Journals (Sweden)

    Francisco Ângelo Coutinho

    2014-12-01

    Full Text Available In this paper we present a strategy for analyzing the text of the textbook based on principles of Actor-Network Theory. This theory seeks to understand central claims about knowledge, subjectivity, society and nature, as effects of an interaction network. By analyzing the argumentative strategies employed by the authors of the text analyzed, we found that there is a process of strengthening the scientific explanation that construct a conception of science and which leaves religious conceptions isolated. In this process, the authors establish a network that exclude religious explanation of the field of rationality and interdict the rationality of religion. We wondered whether this is the best attitude when considering the diversity present in school.

  7. Fast global sequence alignment technique

    KAUST Repository

    Bonny, Mohamed Talal

    2011-11-01

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

  8. Dynamics of carbon nanotube alignment by electric fields

    International Nuclear Information System (INIS)

    The dynamics of multiwall carbon nanotube (MWCNT) alignment inside viscous media using electric fields is investigated. Electrical current measurements were performed in situ during the application of an electric field to liquid solutions of deionized water or dissolved polymer containing MWCNTs. The variation of electrical current over time was associated to the dynamics of the MWCNT network formation. The influence of the electric field magnitude and frequency on the MWCNT network formation was studied. MWCNT migration towards the negative electrode was observed when a direct current electric field was applied, whereas formation of an aligned MWCNT network was achieved for an alternating current electric field. The increase of the electric field frequency promotes a faster formation of an aligned MWCNT network and thinner MWCNT bundles. A higher viscosity of the liquid medium yields slower MWCNT alignment evidenced by a slower change of electrical current through the viscous system. An analytical model based on the dielectrophoresis-induced torque, which considers the viscosity of the medium, is also proposed to explain the dynamics of MWCNT alignment. Furthermore, aligned MWCNT/polysulfone solid composites were fabricated and electrically characterized. The solid composites presented anisotropic electrical conductivity, which was more evident for low MWCNT concentrations (0.1–0.2 wt%). (paper)

  9. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; de Fries, Louise Skovlund

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social...

  10. MaxAlign: maximizing usable data in an alignment

    Directory of Open Access Journals (Sweden)

    Pedersen Anders G

    2007-08-01

    Full Text Available Abstract Background The presence of gaps in an alignment of nucleotide or protein sequences is often an inconvenience for bioinformatical studies. In phylogenetic and other analyses, for instance, gapped columns are often discarded entirely from the alignment. Results MaxAlign is a program that optimizes the alignment prior to such analyses. Specifically, it maximizes the number of nucleotide (or amino acid symbols that are present in gap-free columns – the alignment area – by selecting the optimal subset of sequences to exclude from the alignment. MaxAlign can be used prior to phylogenetic and bioinformatical analyses as well as in other situations where this form of alignment improvement is useful. In this work we test MaxAlign's performance in these tasks and compare the accuracy of phylogenetic estimates including and excluding gapped columns from the analysis, with and without processing with MaxAlign. In this paper we also introduce a new simple measure of tree similarity, Normalized Symmetric Similarity (NSS that we consider useful for comparing tree topologies. Conclusion We demonstrate how MaxAlign is helpful in detecting misaligned or defective sequences without requiring manual inspection. We also show that it is not advisable to exclude gapped columns from phylogenetic analyses unless MaxAlign is used first. Finally, we find that the sequences removed by MaxAlign from an alignment tend to be those that would otherwise be associated with low phylogenetic accuracy, and that the presence of gaps in any given sequence does not seem to disturb the phylogenetic estimates of other sequences. The MaxAlign web-server is freely available online at http://www.cbs.dtu.dk/services/MaxAlign where supplementary information can also be found. The program is also freely available as a Perl stand-alone package.

  11. A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

    Science.gov (United States)

    Eissing, Thomas; Kuepfer, Lars; Becker, Corina; Block, Michael; Coboeken, Katrin; Gaub, Thomas; Goerlitz, Linus; Jaeger, Juergen; Loosen, Roland; Ludewig, Bernd; Meyer, Michaela; Niederalt, Christoph; Sevestre, Michael; Siegmund, Hans-Ulrich; Solodenko, Juri; Thelen, Kirstin; Telle, Ulrich; Weiss, Wolfgang; Wendl, Thomas; Willmann, Stefan; Lippert, Joerg

    2011-01-01

    Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach. PMID:21483730

  12. A computational systems biology software platform for multiscale modeling and simulation: Integrating whole-body physiology, disease biology, and molecular reaction networks

    Directory of Open Access Journals (Sweden)

    Thomas eEissing

    2011-02-01

    Full Text Available Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

  13. Aligned and suspended fiber force probes for drug testing at single cell resolution

    International Nuclear Information System (INIS)

    The role of physical forces in disease onset and progression is widely accepted and this knowledge presents an alternative route to investigating disease models. Recently, numerous force measurement techniques have been developed to probe single and multi-cell behavior. While these methods have yielded fundamental insights, they are yet unable to capture the fibrous extra-cellular matrix biophysical interactions, involving parameters of curvature, structural stiffness (N m−1), alignment and hierarchy, which have been shown to play key roles in disease and developmental biology. Using a highly aggressive glioma model (DBTRG-05MG), we present a platform technology to quantify single cell force modulation (both inside-out and outside-in) with and without the presence of a cytoskeleton altering drug (cytochalasin D) using suspended and aligned fiber networks (nanonets) beginning to represent the aligned glioma environment. The nanonets fused in crisscross patterns were manufactured using the non-electrospinning spinneret based tunable engineering parameters technique. We demonstrate the ability to measure contractile single cell forces exerted by glioma cells attached to and migrating along the fiber axis (inside-out). This is followed by a study of force response of glioma cells attached to two parallel fibers using a probe deflecting the leading fiber (outside-in). The forces are calculated using beam deflection within the elastic limit. Our data shows that cytochalasin D compromises the spreading area of single glioma cells, eventually decreasing their ‘inside-out’ contractile forces, and ‘outside-in’ force response to external strain. Most notably, for the first time, we demonstrate the feasibility of using physiologically relevant aligned fiber networks as ultra-sensitive force (∼nanoNewtons) probes for investigating drug response and efficacy in disease models at the single cell resolution. (paper)

  14. Jet activity versus alignment

    CERN Document Server

    Lokhtin, I P; Sarycheva, L I; Snigirev, A M

    2005-01-01

    The hypothesis about the relation between the observed alignment of spots in the x-ray film in cosmic ray emulsion experiments and the features of events in which jets prevail at super high energies is tested. Due to strong correlation between jet axis directions and that between momenta (almost collinearity) of jet particles, the evaluated degree of alignment is considerably larger than that at randomly selected chaoticly located spots in the x-ray film. It appears comparable with experimental data provided that the height of primary interaction, the collision energy and the total energy of selected clusters meet certain conditions. The Monte Carlo generator PYTHIA, which basically well describes jet events in hadron-hadron interactions, was used for the analysis.

  15. Inflation by alignment

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-06-08

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

  16. LAF: Logic Alignment Free and its application to bacterial genomes classification

    OpenAIRE

    Weitschek, Emanuel; Cunial, Fabio; Felici, Giovanni

    2015-01-01

    Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples ...

  17. Alignment of concerns

    DEFF Research Database (Denmark)

    Andersen, Tariq Osman; Bansler, Jørgen P.; Kensing, Finn; Moll, Jonas; Nielsen, Karen Dam

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

  18. Nuclear reactor alignment plate configuration

    Science.gov (United States)

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

    2014-01-28

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

  19. Seeking the perfect alignment

    CERN Multimedia

    2002-01-01

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

  20. Alignment at the ESRF

    International Nuclear Information System (INIS)

    The ESRF Survey and Alignment group is responsible for the installation, control and periodic realignment of the accelerators and experiments which produce high quality x-rays used by scientists from Europe and around the world. Alignment tolerances are typically less than one millimetre and often in the order of several micrometers. The group is composed of one engineer, five highly trained survey technicians, one electronic and one computer technician. This team is fortified during peak periods by technicians from an external survey company. First an overview and comparative study of the main large-scale survey instrumentation and methods used by the group is made. Secondly a discussion of long term deformation on the ESRF site is presented. This is followed by presentation of the methods used in the realignment of the various machines. Two important aspects of our work, beamline and front-end alignment, and the so-called machine exotic devices are briefly discussed. Finally, the ESRF calibration bench is presented. (authors)

  1. Semiautomated improvement of RNA alignments

    DEFF Research Database (Denmark)

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

    2007-01-01

    We have developed a semiautomated RNA sequence editor (SARSE) that integrates tools for analyzing RNA alignments. The editor highlights different properties of the alignment by color, and its integrated analysis tools prevent the introduction of errors when doing alignment editing. SARSE readily...... the SARSE editor makes it a flexible tool to improve all RNA alignments with relatively little human intervention. Online documentation and software are available at (http://sarse.ku.dk)....

  2. A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

    OpenAIRE

    ThomasEissing

    2011-01-01

    Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform co...

  3. Alignment as a Teacher Variable

    Science.gov (United States)

    Porter, Andrew C.; Smithson, John; Blank, Rolf; Zeidner, Timothy

    2007-01-01

    With the exception of the procedures developed by Porter and colleagues (Porter, 2002), other methods of defining and measuring alignment are essentially limited to alignment between tests and standards. Porter's procedures have been generalized to investigating the alignment between content standards, tests, textbooks, and even classroom…

  4. Improving your target-template alignment with MODalign.

    KAUST Repository

    Barbato, Alessandro

    2012-02-04

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

  5. Survey, alignment, and beam stability at the Advanced Light Source

    International Nuclear Information System (INIS)

    This paper describes survey and alignment at the Lawrence Berkeley Laboratories Advanced Light Source (ALS) accelerators from 1993 to 1997. The ALS is a third generation light source requiring magnet alignment to within 150 microns. To accomplish this, a network of monuments was established and maintained. Monthly elevation surveys show the movement of the floor over time. Inclinometers have recently been employed to give real time information about magnet, vacuum tank and magnet girder motion in the ALS storage ring

  6. Specification of a benchmarking methodology for alignment techniques

    OpenAIRE

    Euzenat, Jérôme; García Castro, Raúl; Ehrig, Marc

    2004-01-01

    This document considers potential strategies for evaluating ontology alignment algorithms. It identifies various goals for such an evaluation. In the context of the Knowledge web network of excellence, the most important objective is the improvement of existing methods. We examine general evaluation strategies as well as efforts that have already been undergone in the specific field of ontology alignment. We then put forward some methodological and practical guidelines for running such an eva...

  7. Epitaxial growth of aligned GaN nanowires and nanobridges

    OpenAIRE

    Kim, Kyungkon; Henry, Tania; Cui, George; Han, Jung; Song, Yoon-Kyu; Nurmikko, Arto V.; Tang, Hong

    2007-01-01

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

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

    CERN Document Server

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

    2010-01-01

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

  9. Survey and alignment at the ALS

    International Nuclear Information System (INIS)

    This paper describes survey and alignment at the Lawrence Berkeley National Laboratory's Advanced Light Source (ALS) accelerators from 1993 to 1995. The ALS is a 1.0 - 1.9 GeV electron accelerator producing extremely bright synchrotron light in the UV and soft-X-ray wavelengths. At the ALS, electrons are accelerated in a LINAC to 50 MeV, injected into a booster ring for further acceleration and finally injected into the storage ring. This is shown schematically in Figure 1. The storage ring, some 200 m in circumference, has been run with electron currents above 400 mA with lifetimes as high as 24 hours. The ALS is a third generation light source and requires for efficient storage ring operation, magnets aligned to within 150 mm of their ideal position. To accomplish this a network of monuments was established and their positions measured with respect to one another. The data was reduced using GEONET'' and STAR*NET'' software. Using the monuments as reference points, magnet positions were measured and alignment confirmed using the Kem Electronic Coordinate Determination System (ECDS''). A number of other papers dealing with survey and alignment (S ampersand A) at the ALS have been written that may further elucidate some details of the methods and systems described in this paper

  10. Alignment analysis of urban railways based on passenger travel demand

    DEFF Research Database (Denmark)

    Andersen, Jonas Lohmann Elkjær; Landex, Alex

    2010-01-01

    , this article presents a computerised GIS based methodology that can be used as decision support for selecting the best alignment. The methodology calculates travel potential within defined buffers surrounding the alignment. The methodology has three different approaches depending on the desired level of detail......: the simple but straight-forward to implement line potential approach that perform corridor analysis, the detailed catchment area analysis based on stops on the alignment and the refined service area analysis that uses search distances in street networks. All three approaches produce trustworthy results...

  11. Aligning graphs and finding substructures by a cavity approach

    Science.gov (United States)

    Bradde, S.; Braunstein, A.; Mahmoudi, H.; Tria, F.; Weigt, M.; Zecchina, R.

    2010-02-01

    We introduce a new distributed algorithm for aligning graphs or finding substructures within a given graph. It is based on the cavity method and is used to study the maximum-clique and the graph-alignment problems in random graphs. The algorithm allows to analyze large graphs and may find applications in fields such as computational biology. As a proof of concept we use our algorithm to align the similarity graphs of two interacting protein families involved in bacterial signal transduction, and to predict actually interacting protein partners between these families.

  12. A network biology approach evaluating the anticancer effects of bortezomib identifies SPARC as a therapeutic target in adult T-cell leukemia cells

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2008-10-01

    Full Text Available Junko H Ohyashiki1, Ryoko Hamamura2, Chiaki Kobayashi2, Yu Zhang2, Kazuma Ohyashiki21Intractable Immune System Disease Research Center, Tokyo Medical University, Tokyo, Japan; 2First Department of Internal Medicine, Tokyo Medical University, Tokyo, JapanAbstract: There is a need to identify the regulatory gene interaction of anticancer drugs on target cancer cells. Whole genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by hundreds to thousands of genes that induce changes in expression. A proteasome inhibitor, bortezomib, could be a potential therapeutic agent in treating adult T-cell leukemia (ATL patients, however, the underlying mechanism by which bortezomib induces cell death in ATL cells via gene regulatory network has not been fully elucidated. Here we show that a Bayesian statistical framework by VoyaGene® identified a secreted protein acidic and rich in cysteine (SPARC gene, a tumor-invasiveness related gene, as a possible modulator of bortezomib-induced cell death in ATL cells. Functional analysis using RNAi experiments revealed that inhibition of the expression SPARC by siRNA enhanced the apoptotic effect of bortezomib on ATL cells in accordance with an increase of cleaved caspase 3. Targeting SPARC may help to treat ATL patients in combination with bortezomib. This work shows that a network biology approach can be used advantageously to identify the genetic interaction related to anticancer effects.Keywords: network biology, adult T cell leukemia, bortezomib, SPARC

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

    International Nuclear Information System (INIS)

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

  14. Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

    Science.gov (United States)

    Tkačik, Gašper

    2016-07-01

    The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to "randomized" ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

  15. The CMS Muon System Alignment

    CERN Document Server

    Martinez Ruiz-Del-Arbol, P

    2009-01-01

    The alignment of the muon system of CMS is performed using different techniques: photogrammetry measurements, optical alignment and alignment with tracks. For track-based alignment, several methods are employed, ranging from a hit and impact point (HIP) algorithm and a procedure exploiting chamber overlaps to a global fit method based on the Millepede approach. For start-up alignment as long as available integrated luminosity is still significantly limiting the size of the muon sample from collisions, cosmic muon and beam halo signatures play a very strong role. During the last commissioning runs in 2008 the first aligned geometries have been produced and validated with data. The CMS offline computing infrastructure has been used in order to perform improved reconstructions. We present the computational aspects related to the calculation of alignment constants at the CERN Analysis Facility (CAF), the production and population of databases and the validation and performance in the official reconstruction. Also...

  16. Beam alignment system

    International Nuclear Information System (INIS)

    A patent is claimed for the invention of a beam alignment system. The aim of the invention is the obtention of an accurate monitoring of the beam position and direction. It is of great interest in the nuclear industry. The invention can be applied in an infrared laser beam for welding operations. An auxiliar radiation source is incorporated to the device. The system's configuration allows a simultaneous and separated utilisation of two beams. The description and the design of the proposed system are provided

  17. Alignment in hadronic interactions

    CERN Document Server

    Wibig, T

    2000-01-01

    The alignment of the products of very high energy interactions seen in mountain altitude experiments is one of the most puzzling phenomena in cosmic ray physics for quite a long time. The observations of the Pamir and Chacaltaya emulsion chamber groups and by the Tien-Shan extensive air shower experiment, together with a very clear event seen in the Concorde French-Japanese experiment in the stratosphere, makes the experimental basis very substantial. In the present paper a novel possible explanation is put forward.

  18. Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

    OpenAIRE

    Lee, Younghee; Yang, Xinan; Huang, Yong; FAN, HANLI; Zhang, Qingbei; Wu, Youngfei; Li, Jianrong; Hasina, Rifat; Cheng, Chao; Lingen, Mark W.; Gerstein, Mark B.; Weichselbaum, Ralph R.; Xing, H. Rosie; Lussier, Yves A.

    2010-01-01

    Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. ...

  19. DatAlign - implementation of the new APS survey and alignment database

    International Nuclear Information System (INIS)

    The Advanced Photon Source (APS) at Argonne National Laboratory is a national 3rd-generation synchrotron-radiation light source research facility located about twenty-five miles southwest of Chicago. The APS accelerator systems and X-ray beamlines are approximately six kilometers in length with more than four thousand precisely aligned components. The APS Survey and Alignment Group (SAG) is responsible for the correct positioning of beamline components for the APS accelerator systems. SAG fiducializes beamline components, aligns components in the accelerator tunnel and in the experiment hall, and maintains all necessary geodetic control networks to achieve required placement tolerances. All these tasks generate large amounts of data that have to be stored and readily accessible to the SAG members. As a result, the utilization of a database system is inevitable. During the 1990s, the SAG depended on Geonet software (1) for its data storage and management needs. Geonet was originally developed under the DOS environment at SLAC in the 1980s as an all-inclusive software package for the accelerator alignment community. After a decade of a reliable service, Geonet unavoidably became obsolete. First, some Geonet utilities, like data collection and data reduction programs, were phased out or replaced by new tools to keep up with advancements in survey and alignment technology. By the year 2002, a decision was made to also replace the database portion of Geonet with a new relational database. The conceptual design of the new APS Survey and Alignment Database (2) was completed in 2002. After a review of commercially available database management systems (DBMSs), Microsoft(regsign) Access 2000 was chosen for the implementation of our database design. The implementation of the new database (DB), under the name 'DatAlign,' commenced the following year.

  20. Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks.

    Science.gov (United States)

    Chang, Xiao; Wang, Zhuo; Hao, Pei; Li, Yuan-Yuan; Li, Yi-Xue

    2010-06-01

    The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level. PMID:20298776