scholarly journals MTopGO: a tool for module identification in PPI Networks

2017 ◽  
Author(s):  
Danila Vella ◽  
Simone Marini ◽  
Francesca Vitali ◽  
Riccardo Bellazzi
Keyword(s):  
Single Step ◽  
Ppi Network ◽  
Topological Information ◽  
Network Partition ◽  
Ppi Networks ◽  
Module Detection ◽  
Informatics Tools ◽  
Go Terms ◽  
Selection Of

The increasing amount of -omics data leads to development of models to interpret and analyse them. A common approach consists in representing data as PPI Networks. These models can be very complex and informatics tools are needed to analyse them. In this abstract, we present MTopGO, an algorithm of module detection specific for PPI Network, exploiting both the network topological information and the Gene Ontology (GO) knowledge about network proteins. MTopGO output consists in a network partition, where each obtained cluster is labelled with a specific GO term describing its biological nature. In a single step, MTopGO performs a double PPI network analysis; from a topological perspective, through the individuation of a meaningful network partition and, from a biological perspective, through the selection of significant GO terms describing the biological role of network proteins.

scholarly journals MTopGO: a tool for module identification in PPI Networks

2017 ◽  
Author(s):  
Danila Vella ◽  
Simone Marini ◽  
Francesca Vitali ◽  
Riccardo Bellazzi
Keyword(s):  
Single Step ◽  
Ppi Network ◽  
Topological Information ◽  
Network Partition ◽  
Ppi Networks ◽  
Module Detection ◽  
Informatics Tools ◽  
Go Terms ◽  
Selection Of

The increasing amount of -omics data leads to development of models to interpret and analyse them. A common approach consists in representing data as PPI Networks. These models can be very complex and informatics tools are needed to analyse them. In this abstract, we present MTopGO, an algorithm of module detection specific for PPI Network, exploiting both the network topological information and the Gene Ontology (GO) knowledge about network proteins. MTopGO output consists in a network partition, where each obtained cluster is labelled with a specific GO term describing its biological nature. In a single step, MTopGO performs a double PPI network analysis; from a topological perspective, through the individuation of a meaningful network partition and, from a biological perspective, through the selection of significant GO terms describing the biological role of network proteins.

scholarly journals The Critical Role of PTEN Mutation in Cellular Process and Drug Selection of Endometrial Cancer

2020 ◽  
Author(s):  
Ying Liu ◽  
Jintuo Zhou ◽  
Peiguang Niu ◽  
Fanxiang Zeng ◽  
Ruihong Cai ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Cell Lines ◽  
Mrna Levels ◽  
Drug Selection ◽  
Ppi Network ◽  
Kegg Analysis ◽  
Pten Mutation ◽  
Mannose Metabolism ◽  
Selection Of

Abstract Background: Phosphatase and tensin homolog (PTEN) is a frequently mutated genes found in endometrial cancer (EC), making it a potential biomarker for individualized treatment opinions. In this study, a method was designed to evaluate the role of the PTEN mutation in the prognosis and drug selection of EC. We identified the potential alterations in pathways and genes related to the mechanism. Methods: cBioPortal database was used to analyze the PTEN mutation status for EC patients. Kaplan-Meier was used to analyze the prognosis of PTEN mutation in EC patients. GDSC dataset was used to identified the drugs that sensitive to cell lines with PTEN mutation. DEGs between PTEN mutation and wide type group were identified using the edgeR package. GO and KEGG analysis were carried out using the DAVID database. GSEA v3.0 were used to dig out the differences in gene mRNA levels of biological function annotation and pathways between PTEN mutation and wide type patients. PPI network of DEGs was performed using STRING and then visualized using Cytoscope software (3.7.2).Results: Our results showed that PTEN mutation was carried in 68% of EC patients. The mRNA expression level of PTEN was lower in patients with PTEN mutation than that with wide type. Prognosis analysis showed that there were favorable overall survival and progression free survival in EC patients with PTEN mutation. Moreover, it is more sensitive to AKT inhibitor (Afuresertib and AZD5363), and Mcl-1 inhibitor (MIM1) on EC cell lines with PTEN mutation than that with wide type. A total of 216 genes were identified as DEGs. GO analysis showed that DEGs significantly enriched in chemical synaptic transmission, extracellular region, etc.. KEGG analysis suggested that DEGs significantly enriched in categories associated with metabolic progression. GSEA analysis identified signaling pathways including fatty acid metabolism, fructose and mannose metabolism, etc.. PPI network analysis identified top 10 genes and top three clusters.Conclusions: Multiple genes and pathways may play an important role in EC patients with PTEN mutation. These results provide a potential target and therapeutic strategies for patients with PTEN mutation.

scholarly journals Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Altaf-Ul-Amin ◽  
Masayoshi Wada ◽  
Shigehiko Kanaya
Keyword(s):  
Gene Ontology ◽  
Clustering Algorithm ◽  
Biological Significance ◽  
General Purpose ◽  
Simple Graph ◽  
High Density ◽  
Ppi Network ◽  
Simple Graphs ◽  
Ppi Networks ◽  
Go Terms

This paper presents an algorithm called DPClusO for partitioning simple graphs into overlapping modules, that is, clusters constrained by density and periphery tracking. The major advantages of DPClusO over the related and previously published algorithm DPClus are shorter running time and ensuring coverage, that is, each node goes to at least one module. DPClusO is a general-purpose clustering algorithm and useful for finding overlapping cohesive groups in a simple graph for any type of application. This work shows that the modules generated by DPClusO from several PPI networks of yeast with high-density constraint match with more known complexes compared to some other recently published complex generating algorithms. Furthermore, the biological significance of the high density modules has been demonstrated by comparing their P values in the context of Gene Ontology (GO) terms with those of the randomly generated modules having the same size, distribution, and zero density. As a consequence, it was also learnt that a PPI network is a combination of mainly high-density and star-like modules.

scholarly journals Resilience and evolvability of protein-protein interaction networks

2020 ◽  
Author(s):  
Brennan Klein ◽  
Ludvig Holmér ◽  
Keith M. Smith ◽  
Mackenzie M. Johnson ◽  
Anshuman Swain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Preferential Attachment ◽  
Network Resilience ◽  
Biological Noise ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

scholarly journals Inferring the selection window in antimicrobial resistance using deep mutational scanning data and biophysics-based fitness models

2017 ◽  
Author(s):  
Pouria Dasmeh ◽  
Anh-Tien Ton ◽  
Caroline Quach ◽  
Adrian W.R. Serohijos
Keyword(s):  
Antimicrobial Resistance ◽  
Genetic Parameters ◽  
Catalytic Efficiency ◽  
Single Step ◽  
Mutant Selection ◽  
Selection Window ◽  
Low Levels ◽  
Lower Limits ◽  
Selection Of

AbstractMutant-selection window (MSW) hypothesis in antimicrobial resistance implies a range for antimicrobial concentration that promotes selection of single-step resistant mutants. Since the inception and experimental verification, MSW has been at the forefront of strategies to minimize development of antimicrobial resistance (AR). Setting the upper and lower limits of MSW requires an understanding of the dependence of selection coefficient of arising mutations to antimicrobial concentration. In this work, we employed a biophysics-based and experimentally calibrated fitness model to estimate MSW in the case of Ampicillin and Cefotaxime resistance in E.coli TEM-1 beta lactamase. In line with experimental observations, we show that selection is active at very low levels of antimicrobials. Furthermore, we elucidate the dependence of MSW to catalytic efficiency of mutants, fraction of mutants in the population and discuss the role of population genetic parameters such as population size and mutation rate. Altogether, our analysis and formalism provide a predictive model of MSW with direct implications in the design of dosage strategies.

scholarly journals FMFinder: A Functional Module Detector for PPI Networks

2017 ◽  
Vol 7 (5) ◽  
pp. 2022-2025
Author(s):  
M. Modi ◽  
N. G. Jadeja ◽  
K. Zala
Keyword(s):  
Detection Method ◽  
Biological Process ◽  
Functional Module ◽  
Large Network ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Module Detection ◽  
Function Module ◽  
Biological Actions

Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define the limits for module detection as well as clarify the construction of a PPI network. For understating the bio-mechanism of various living beings, a detailed study of FMFinder detection by clustering process is called for.

Faculty selection of textbooks: Key variables and the role of teaching experience

2010 ◽  
Author(s):  
Aja Taitano ◽  
Bradley Smith ◽  
Cade Hulbert ◽  
Kristin Batten ◽  
Lalania Woodstrom ◽  
...  
Keyword(s):  
Teaching Experience ◽  
Key Variables ◽  
Faculty Selection ◽  
Selection Of

Posttransplant CMV infection and the role of immunosuppression (Sponsor: Novartis Pharma GmbH, Nürnberg)

2016 ◽  
Vol 04 (01) ◽  
pp. 4-10
Keyword(s):  
Lower Incidence ◽  
Paradigm Shift ◽  
Mtor Inhibitor ◽  
De Novo ◽  
Expert Panel ◽  
Risk Of Infection ◽  
Cmv Infection ◽  
Expert Meeting ◽  
Selection Of

AbstractImmunosuppression permits graft survival after transplantation and consequently a longer and better life. On the other hand, it increases the risk of infection, for instance with cytomegalovirus (CMV). However, the various available immunosuppressive therapies differ in this regard. One of the first clinical trials using de novo everolimus after kidney transplantation [1] already revealed a considerably lower incidence of CMV infection in the everolimus arms than in the mycophenolate mofetil (MMF) arm. This result was repeatedly confirmed in later studies [2–4]. Everolimus is now considered a substance with antiviral properties. This article is based on the expert meeting “Posttransplant CMV infection and the role of immunosuppression”. The expert panel called for a paradigm shift: In a CMV prevention strategy the targeted selection of the immunosuppressive therapy is also a key element. For patients with elevated risk of CMV, mTOR inhibitor-based immunosuppression is advantageous as it is associated with a significantly lower incidence of CMV events.

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