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Computational Analysis of Biological networks
Full text linkCaratterizzare, descrivere ed estrarre informazioni da un network, è sicuramente uno dei principali obbiettivi della scienza, dato che lo studio dei network interessa differenti campi della ricerca, come la biologia, l'economia, le scienze sociali, l'informatica e così via. Ciò che si vuole è riuscire ad estrarre le proprietà fondamentali dei network e comprenderne la funzionalità. Questa tesi riguarda sia l'analisi topologica che l' analisi dinamica dei network biologici, anche se i risultati possono essere applicati a diversi campi. Per quanto riguarda l'analisi topologica viene utilizzato un approccio orientato ai nodi, utilizzando le centralità per individuare i nodi più rilevanti e integrando tali risultati con dati da laboratorio. Viene inoltre descritto CentiScaPe, un software implementato per effettuare tale tipo di analisi. Vengono inoltre introdotti i concetti di "interference" e "robustness" che permettono di comprendere come un network si riarrangia in seguito alla rimozione o all'aggiunta di nodi. Per quanto riguarda l'analisi dinamica, si mostra come l'abstract interpretation può essere utilizzata nella simulazione di pathways per ottenere i risultati di migliaia di simulazioni in breve tempo e come possibile soluzione del problema della stima dei parametri mancanti.This thesis, treating both topological and dynamic points of view,
concerns several aspects of biological networks analysis.
Regarding the topological analysis of biological networks, the main
contribution is the node-oriented point of view of the analysis. It means
that instead of concentrating on global properties of the networks, we
analyze them in order to extract properties of single nodes. An excellent
method to face this problem is to use node centralities. Node centralities
allow to identify nodes in a network having a relevant role in the network
structure. This can not be enough if we are dealing with a biological
network, since the role of a protein depends also on its biological activity
that can be detected with lab experiments.
Our approach is to integrate centralities analysis and data from biological
experiments. A protocol of analysis have been produced, and the CentiScaPe tool for
computing network centralities and integrating topological analysis with
biological data have been designed and implemented. CentiScaPe have been
applied to a human kino-phosphatome network and according to our protocol,
kinases and phosphatases with highest centralities values have been
extracted creating a new subnetwork of most central kinases and
phosphatases. A lab experiment established which of this proteins presented
high activation level and through CentiScaPe the proteins with both high
centrality values and high activation level have been easily identified.
The notion of node centralities interference have also been introduced to deal with central role of nodes in a
biological network. It allow to identify which are the nodes that are more
affected by the remotion of a particular node measuring the variation on
their centralities values when such a node is removed from the
network. The application of node centralities interference to the human
kino-phosphatome revealed that different proteins affect centralities values
of different nodes. Similarly to node centralities
interference, the notion of centrality robustness of a node is introduced.
This notion reveals if the central role of a node depends on other
particular nodes in the network or if the node is ``robust'' in the sense
that even if we remove or add other nodes the central role of the
node remains almost unchanged.
The dynamic aspects of biological networks analysis have been treated from an
abstract interpretation point of view. Abstract interpretation is a powerful
framework for the analysis of software and is excellent in deriving
numerical properties of programs. Dealing with pathways, abstract
interpretation have been adapted to the analysis of pathways simulation.
Intervals domain and constants domain have been succesfully used to
automatically extract information about reactants concentration. The
intervals domain allow to determine the range of concentration of the proteins, and
the constants domain have been used to know if a protein concentration
become constant after a certain time. The other domain of analysis used is
the congruences domain that, if applied to pathways simulation can easily
identify regular oscillating behaviour in reactants concentration.
The use of abstract interpretation allows to execute thousands of simulation
and to completely and automatically characterize the behaviour of the
pathways. In such a way it can be used also to solve the problem of
parameters estimation where missing parameters can be detected with a brute
force algorithm combined with the abstract interpretation analysis.
The abstract interpretation approach have been succesfully applied to the
mitotic oscillator pathway, characterizing the behaviour of the pathway
depending on some reactants.
To help the analysis of relation between reactants in the network,
the notions of variables interference and variables abstract interference
have been introduced and adapted to biological pathways simulation.
They allow to find relations
between properties of different reactants of the pathway. Using the abstract interference
techniques we can say, for instance, which range of concentration of a protein
can induce an oscillating behaviour of the pathway
Identifying critical traffic jam areas with node centralities interference and robustness
No full textWe introduce the notions of centrality interference and centrality robustness, as measures of variation of centrality values when the structure of a network is modified by removing or adding individual nodes from/to a network. Centrality analysis allows categorizing nodes according to their topological relevance in a network. Thus,centrality in terf er en c e analysis allows understanding which parts of a network are mostly influenced by a node and, conversely, centrality robustness allows quantifying the functional dependency of a node from other nodes in the network. We examine the theoretical significance of these measures and apply them to classify nodes in a road network to predict the effects on the traffic jam due to variations in the structure of the network. In these case the interference analysis allows to predict which are the distinct regions of the network affected by the function of different nodes. Such notions, when applied to a variety of different contexts, opens new perspectives in network analysis since they allow predicting the effects of local network modifications on single node as well as global network functionalit
Biological network analysis with CentiScaPe: centralities and experimental dataset integration
Full text linkThe growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dynamic modeling and simulation of leukocyte integrin activation through an electronic design automation framework
Full text linkModel development and analysis of biological systems is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulations of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Several characteristics and issues of biological system modeling are common to the electronics system modeling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during verification. This paper proposes a modeling and simulation framework for discrete event-based execution of biochemical systems based on SystemC. SystemC is the reference language in the electronic design automation (EDA) field for modeling and verifying complex systems at different abstraction levels. SystemC-based verification is the de-facto an alternative to model checking when such a formal verification technique cannot deal with the state space complexity of the model. The paper presents how the framework has been applied to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues, by handling the solution space complexity through different levels of simulation accuracy
Node interference and robustness: performing virtual knock-out experiments on biological networks: the case of leukocyte integrin activation network.
No full textThe increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node interference and robustness as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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