609 research outputs found
Network-based approaches to explore complex biological systems towards network medicine
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes
Exploring hierarchical and overlapping modular structure in the yeast protein interaction network
<p>Abstract</p> <p>Background</p> <p>Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement.</p> <p>Results</p> <p>By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications.</p> <p>Conclusions</p> <p>Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.</p
Network Archaeology: Uncovering Ancient Networks from Present-day Interactions
Often questions arise about old or extinct networks. What proteins interacted
in a long-extinct ancestor species of yeast? Who were the central players in
the Last.fm social network 3 years ago? Our ability to answer such questions
has been limited by the unavailability of past versions of networks. To
overcome these limitations, we propose several algorithms for reconstructing a
network's history of growth given only the network as it exists today and a
generative model by which the network is believed to have evolved. Our
likelihood-based method finds a probable previous state of the network by
reversing the forward growth model. This approach retains node identities so
that the history of individual nodes can be tracked. We apply these algorithms
to uncover older, non-extant biological and social networks believed to have
grown via several models, including duplication-mutation with complementarity,
forest fire, and preferential attachment. Through experiments on both synthetic
and real-world data, we find that our algorithms can estimate node arrival
times, identify anchor nodes from which new nodes copy links, and can reveal
significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure
Comparative genomic analysis of novel Acinetobacter symbionts : A combined systems biology and genomics approach
Acknowledgements This work was supported by University of Delhi, Department of Science and Technology- Promotion of University Research and Scientific Excellence (DST-PURSE). V.G., S.H. and U.S. gratefully acknowledge the Council for Scientific and Industrial Research (CSIR), University Grant Commission (UGC) and Department of Biotechnology (DBT) for providing research fellowship.Peer reviewedPublisher PD
SWIM: A computational tool to unveiling crucial nodes in complex biological networks
SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer
Searching for network modules
When analyzing complex networks a key target is to uncover their modular
structure, which means searching for a family of modules, namely node subsets
spanning each a subnetwork more densely connected than the average. This work
proposes a novel type of objective function for graph clustering, in the form
of a multilinear polynomial whose coefficients are determined by network
topology. It may be thought of as a potential function, to be maximized, taking
its values on fuzzy clusterings or families of fuzzy subsets of nodes over
which every node distributes a unit membership. When suitably parametrized,
this potential is shown to attain its maximum when every node concentrates its
all unit membership on some module. The output thus is a partition, while the
original discrete optimization problem is turned into a continuous version
allowing to conceive alternative search strategies. The instance of the problem
being a pseudo-Boolean function assigning real-valued cluster scores to node
subsets, modularity maximization is employed to exemplify a so-called quadratic
form, in that the scores of singletons and pairs also fully determine the
scores of larger clusters, while the resulting multilinear polynomial potential
function has degree 2. After considering further quadratic instances, different
from modularity and obtained by interpreting network topology in alternative
manners, a greedy local-search strategy for the continuous framework is
analytically compared with an existing greedy agglomerative procedure for the
discrete case. Overlapping is finally discussed in terms of multiple runs, i.e.
several local searches with different initializations.Comment: 10 page
Previsão e análise da estrutura e dinâmica de redes biológicas
Increasing knowledge about the biological processes that govern the
dynamics of living organisms has fostered a better understanding of the
origin of many diseases as well as the identification of potential therapeutic
targets. Biological systems can be modeled through biological networks,
allowing to apply and explore methods of graph theory in their investigation
and characterization. This work had as main motivation the inference of
patterns and rules that underlie the organization of biological networks.
Through the integration of different types of data, such as gene expression,
interaction between proteins and other biomedical concepts, computational
methods have been developed so that they can be used to predict and study
diseases.
The first contribution, was the characterization a subsystem of the human
protein interactome through the topological properties of the networks that
model it. As a second contribution, an unsupervised method using biological
criteria and network topology was used to improve the understanding of
the genetic mechanisms and risk factors of a disease through co-expression
networks. As a third contribution, a methodology was developed to remove
noise (denoise) in protein networks, to obtain more accurate models, using
the network topology. As a fourth contribution, a supervised methodology
was proposed to model the protein interactome dynamics, using exclusively
the topology of protein interactions networks that are part of the dynamic
model of the system.
The proposed methodologies contribute to the creation of more precise,
static and dynamic biological models through the identification and use of
topological patterns of protein interaction networks, which can be used to
predict and study diseases.O conhecimento crescente sobre os processos biolĂłgicos que regem a
dinâmica dos organismos vivos tem potenciado uma melhor compreensão da
origem de muitas doenças, assim como a identificação de potenciais alvos
terapêuticos. Os sistemas biológicos podem ser modelados através de redes
biológicas, permitindo aplicar e explorar métodos da teoria de grafos na sua
investigação e caracterização. Este trabalho teve como principal motivação
a inferência de padrões e de regras que estão subjacentes à organização de
redes biolĂłgicas.
Através da integração de diferentes tipos de dados, como a expressão
de genes, interação entre proteĂnas e outros conceitos biomĂ©dicos, foram
desenvolvidos métodos computacionais, para que possam ser usados na
previsão e no estudo de doenças.
Como primeira contribuição, foi proposto um método de caracterização de
um subsistema do interactoma de proteĂnas humano atravĂ©s das propriedades
topológicas das redes que o modelam. Como segunda contribuição, foi
utilizado um método não supervisionado que utiliza critérios biológicos e
topologia de redes para, através de redes de co-expressão, melhorar a
compreensão dos mecanismos genéticos e dos fatores de risco de uma
doença. Como terceira contribuição, foi desenvolvida uma metodologia
para remover ruĂdo (denoise) em redes de proteĂnas, para obter modelos
mais precisos, utilizando a topologia das redes. Como quarta contribuição,
propôs-se uma metodologia supervisionada para modelar a dinâmica do
interactoma de proteĂnas, usando exclusivamente a topologia das redes de
interação de proteĂnas que fazem parte do modelo dinâmico do sistema.
As metodologias propostas contribuem para a criação de modelos biológicos,
estáticos e dinâmicos, mais precisos, através da identificação e uso de
padrões topolĂłgicos das redes de interação de proteĂnas, que podem ser
usados na previsão e no estudo doenças.Programa Doutoral em Engenharia Informátic
A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
The detection and analysis of protein complexes is essential for understanding the functional mechanism and cellular integrity. Recently, several techniques for detecting and analysing protein complexes from Protein–Protein Interaction (PPI) dataset have been developed. Most of those techniques are inefficient in terms of detecting, overlapping complexes, exclusion of attachment protein in complex core, inability to detect inherent structures of underlying complexes, have high false-positive rates and an enrichment analysis. To address these limitations, we introduce a special structural-based weighted network approach for the analysis of protein complexes based on a Weighted Edge, Core-Attachment and Local Modularity structures (WECALM). Experimental results indicate that WECALM performs relatively better than existing algorithms in terms of accuracy, computational time, and p-value. A functional enrichment analysis also shows that WECALM is able to identify a large number of biologically significant protein complexes. Overall, WECALM outperforms other approaches by striking a better balance of accuracy and efficiency in the detection of protein complexes
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