scholarly journals Systematic Functional Annotation and Visualization of Biological Networks

2015 ◽  
Author(s):  
Anastasia Baryshnikova
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Functional Organization ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Functional Structure ◽  
Cancer Drug ◽  
Functional Connections ◽  
High Level

ABSTRACTLarge-scale biological networks map functional connections between most genes in the genome and can potentially uncover high level organizing principles governing cellular functions. These networks, however, are famously complex and often regarded as disordered masses of tangled interactions (“hairballs”) that are nearly impenetrable to biologists. As a result, our current understanding of network functional organization is very limited. To address this problem, I developed a systematic quantitative approach for annotating biological networks and examining their functional structure. This method, named Spatial Analysis of Functional Enrichment (SAFE), detects network regions that are statistically overrepresented for a functional group or a quantitative phenotype of interest, and provides an intuitive visual representation of their relative positioning within the network. By successfully annotating theSaccharomyces cerevisiaegenetic interaction network with Gene Ontology terms, SAFE proved to be sensitive to functional signals and robust to noise. In addition, SAFE annotated the network with chemical genomic data and uncovered a new potential mechanism of resistance to the anti-cancer drug bortezomib. Finally, SAFE showed that protein-protein interactions, despite their apparent complexity, also have a high level functional structure. These results demonstrate that SAFE is a powerful new tool for examining biological networks and advancing our understanding of the functional organization of the cell.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

2016 ◽  
Author(s):  
Anastasia Baryshnikova
Keyword(s):  
Spatial Analysis ◽  
Biological Networks ◽  
Biological Process ◽  
Genetic Interaction ◽  
Chemical Compounds ◽  
Functional Enrichment ◽  
Relative Positioning ◽  
Chemical Genomic ◽  
Genome Scale ◽  
Novel Drug

Summary/AbstractSpatial Analysis of Functional Enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions are represented in a network, which parts of the network they are associated with and how they are potentially related to one another.Here, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network fromSaccharomyces cerevisiaewith Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes-of-action of chemical compounds and potentially identify novel drug mechanisms.

scholarly journals An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs

2020 ◽  
Vol 9 (3) ◽  
pp. 2943-2949
Keyword(s):  
Biological Networks ◽  
Interconnection Networks ◽  
Large Scale ◽  
Pattern Mining ◽  
Constraint Satisfaction Problem ◽  
Interaction Network ◽  
Frequent Subgraph Mining ◽  
Threshold Policy ◽  
Subgraph Mining ◽  
Frequent Subgraph

Pattern Mining is the key mechanism to manage large scale data element. Frequent subgraph mining (FSM) considers isomorphism which is a subprocess of pattern mining is a well-studied problem in the data mining. Graphs are considered as a standard structure in many domains such as protein-protein interaction network in biological networks, wired or wireless interconnection networks, web data, etc. FSM is the task of finding all frequent subgraphs from a given database i.e. a single big graph or database of many graphs, whose support is greater than the given threshold value. Many databases consider small graphs for solving complex problems. The classification of graph depends upon the application requirement. A good mining architecture may prevent a lot of memory and time. This paper follows the Grami structure for the analysis of frequent subgraph mining and also introduces the 20% threshold policy for the enhancement of the directed pattern graphs. The constraint satisfaction problem (CSP) has been discussed and analyzed using the Grami approach. The proposed model is compared to Grami on twitter dataset based on the evaluation of time and memory consumed. The proposed algorithm shows an improvement of 3-4 % for both the parameters. The results show that the performance of Grami approach has been improved which shows a 6.6% reduction in time and 21% improvement in memory consumption using the proposed approach.

scholarly journals Application of dynamic expansion tree for finding large network motifs in biological networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6917 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Interaction Network ◽  
Motif Finding ◽  
Network Motifs ◽  
Large Network ◽  
Scalable Network

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

scholarly journals Identification of the DNA Replication Regulator MCM Complex Expression and Prognostic Significance in Hepatic Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Ting Cao ◽  
Shi-jie Yi ◽  
Li-xin Wang ◽  
Juan-xia Zhao ◽  
Jiao Xiao ◽  
...  
Keyword(s):  
Cell Lines ◽  
Prognostic Significance ◽  
Interaction Network ◽  
Normal Liver ◽  
Functional Enrichment ◽  
Hepatic Carcinoma ◽  
Positive Correlation ◽  
Normal Hepatocyte ◽  
Mcm Complex ◽  
High Level

Background. The microliposome maintenance (MCM) complex, MCM2-7, is revealed to be involved in multiple cellular processes and plays a key role in the development and progression of human cancers. However, the MCM complex remains poorly elaborated in hepatic carcinoma (HCC). Methods. In the study, we found the mRNA and protein level by bioinformatics. We also explored the prognostic value, genetic alteration, interaction network, and functional enrichment of MCM2-7. The MCM expression and correlation among these MCMs in HCC cell lines were identified by western blot. Results. MCM2-7 was significantly increased in HCC tissues compared to normal liver tissues. The high level of MCM2-7 had a positive correlation with poor prognosis. However, MCM2-7 alterations were not correlated with poor OS. MCMs were both increased in HCC cell lines compared to the normal hepatocyte cell line. Furthermore, the positive correlation was found among MCMs in HCC cell lines. Conclusions. The MCM complex was increased in HCC tissues and cell lines and negatively correlated with prognosis, which might be important biomarkers for HCC.

scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
Vol 7 (8) ◽  
pp. 921-929 ◽  
Author(s):  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
Cell Fate ◽  
Biological Networks ◽  
Decision Model ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Cell Fate Decision ◽  
Boolean Models ◽  
Genetic Interaction Network ◽  
Fate Decision

The network representation of the cell fate decision model (Calzoneet al., 2010) is used to generate a genetic interaction network for the apoptosis phenotype. Most genetic interactions are epistatic, single nonmonotonic, and additive (Dreeset al., 2005).

scholarly journals Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

2019 ◽  
Author(s):  
Christopher J. Lord ◽  
Niall Quinn ◽  
Colm J. Ryan
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Molecular Heterogeneity ◽  
Loss Of Function ◽  
Driver Genes ◽  
Lethal Effects ◽  
Synthetic Lethal

AbstractGenetic interactions, such as synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Understanding which genetic interactions are robust in the face of the molecular heterogeneity observed in tumours and what factors influence this robustness could streamline the identification of therapeutic targets. Here, we develop a computational approach to identify robust genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. We used this approach to evaluate >140,000 potential genetic interactions involving cancer driver genes and identified 1,520 that are significant in at least one study but only 220 that reproduce across multiple studies. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions in cancer are enriched for gene pairs whose protein products physically interact. This suggests that protein-protein interactions can be used not only to understand the mechanistic basis of genetic interaction effects, but also to prioritise robust targets for further development. To explore the utility of this approach, we used a protein-protein interaction network to guide the search for robust synthetic lethal interactions associated with passenger gene alterations and validated two novel robust synthetic lethalities.

scholarly journals A large-scale chemical-genetic strategy to design antimicrobial combination chemotherapy forMycobacterium tuberculosis

2019 ◽  
Author(s):  
Eachan O Johnson ◽  
Emma Office ◽  
Tomohiko Kawate ◽  
Marek Orzechowski ◽  
Deborah T Hung
Keyword(s):  
Small Molecule ◽  
Combination Chemotherapy ◽  
Large Scale ◽  
Genetic Interaction ◽  
Resistance Mechanisms ◽  
Screening Strategy ◽  
Chemical Genetic ◽  
Collateral Sensitivity ◽  
Novel Target ◽  
High Level

The efficacies of all antibiotics against tuberculosis are eventually eroded by resistance. New strategies to discover drugs or drug combinations with higher barriers to resistance are needed. Previously, we reported the application of a large-scale chemical-genetic interaction screening strategy called PROSPECT to the discovery of newMycobacterium tuberculosisinhibitors, which resulted in identification of the small molecule BRD-8000, an inhibitor of a novel target, EfpA. Leveraging the chemical genetic interaction profile of BRD-8000, we identified BRD-9327, another, structurally distinct small molecule EfpA inhibitor. We show that the two compounds are synergistic and display collateral sensitivity because of their distinct modes of action and resistance mechanisms. High-level resistance to one increases the sensitivity to and reduces the emergence of resistance to the other. Thus, the combination of BRD-9327 and BRD-8000 represents a proof-of-concept for the novel strategy of leveraging chemical-genetics in the design of antimicrobial combination chemotherapy in which mutual collateral sensitivity is exploited.

scholarly journals POLYMORPHISMS OF EXTRACELLULAR CONNECTIVE TISSUE REMODELING PROTEINASES AND MMP2, MMP3, MMP9 GENES, AND NEOANGIGENESIS VEGF GENE IN RETINAL MICROANGIOPATHY IN THE PATIENTS WITH TYPE 2 DIABETES MELLITUS

2019 ◽  
Vol 21 (3) ◽  
pp. 441-450 ◽  
Author(s):  
A. V. Shevchenko ◽  
V. F. Prokofiev ◽  
V. I. Konenkov ◽  
V. V. Klimontov ◽  
N. V. Tyan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Functional Organization ◽  
Topological Analysis ◽  
Diabetic Patients ◽  
Integral Parameter ◽  
Type 2 Diabetic Patients ◽  
Visual Reconstruction ◽  
High Level

The aim of our study was to perform an association analysis betweenMMP2,MMP3,MMP9,VEGFgene polymorphisms and development of non-proliferative diabetic retinopathy (DR) in the type 2 diabetic patients (DM).201 DM patients: 90 cases of DR and 111 subjects without DR features were included into the study. Polymorphic variants ofMMP2(rs2438650),MMP3(rs3025058),MMP9(rs3918242), andVEGF(rs699947andrs3025039) genes were assayed. The genetic typing was carried out by restriction fragment length polymorphism and TaqMan methods.The analysis of complex genotypes at the five polymorphic positions has revealed some significant findings in positive and negatively incorporated complexes. Increased frequencies ofMMP2-1306 CCgenotype in the group of patients with “early” development of complication, and more frequent combination of high-level HbA1c withMMP2-1306CCandMMP9-1562CTgenotypes were shown in DR patients. Computerassisted modelling with visual reconstruction of network interactions between the genotypes involved into the destruction events and angiogenesis, as well as altered HbA1с levels (an integral parameter of glycemia), has revealed some differences in structural and functional organization of gene-gene and gene- protein interactions between the groups of patients with DRversusthose without this disorder. Сonclusion. A design of interactome biological networks based on transcription regulation and metabolic pathways, as well as their topological analysis allows to build and study interactions of genes and proteins, with reference to pathogenetic studies of DM2 complications aiming for development of approaches to personalized prevention and therapy in future times.

Social inequality in the evolution of human societies

2019 ◽  
pp. 98-120
Author(s):  
Georgi Derluguian
Keyword(s):  
Social Inequality ◽  
Large Scale ◽  
Human Beings ◽  
Mass Violence ◽  
Public And Private ◽  
Tangible Assets ◽  
Transition To Agriculture ◽  
New Institutions ◽  
High Level ◽  
Political Economic

The author develops ideas about the origin of social inequality during the evolution of human societies and reflects on the possibilities of its overcoming. What makes human beings different from other primates is a high level of egalitarianism and altruism, which contributed to more successful adaptability of human collectives at early stages of the development of society. The transition to agriculture, coupled with substantially increasing population density, was marked by the emergence and institutionalisation of social inequality based on the inequality of tangible assets and symbolic wealth. Then, new institutions of warfare came into existence, and they were aimed at conquering and enslaving the neighbours engaged in productive labour. While exercising control over nature, people also established and strengthened their power over other people. Chiefdom as a new type of polity came into being. Elementary forms of power (political, economic and ideological) served as a basis for the formation of early states. The societies in those states were characterised by social inequality and cruelties, including slavery, mass violence and numerous victims. Nowadays, the old elementary forms of power that are inherent in personalistic chiefdom are still functioning along with modern institutions of public and private bureaucracy. This constitutes the key contradiction of our time, which is the juxtaposition of individual despotic power and public infrastructural one. However, society is evolving towards an ever more efficient combination of social initiatives with the sustainability and viability of large-scale organisations.

Sign in / Sign up

Export Citation Format

Share Document