scholarly journals Statistical properties of the MetaCore network of protein-protein interactions

2021 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus Michael Frahm ◽  
José Lages ◽  
Dima L Shepelyansky
Keyword(s):  
Protein Interactions ◽  
Statistical Properties ◽  
Matrix Analysis ◽  
Directed Network ◽  
Protein Protein Interactions ◽  
Google Matrix ◽  
The World ◽  
Functional Nature ◽  
Average Action ◽  
Effective Network

The MetaCore commercial database describes interactions of proteins and other chemical molecules and clusters in the form of directed network between these elements, viewed as nodes. The number of nodes goes beyond 40 thousands with almost 300 thousands links between them. The links have essentially bi-functional nature describing either activation or inhibition actions between proteins. We present here the analysis of statistical properties of this complex network applying the methods of the Google matrix, PageRank and CheiRank algorithms broadly used in the frame of the World Wide Web, Wikipedia, the world trade and other directed networks. We specifically describe the Ising PageRank approach which allows to treat the bi-functional type of protein-protein interactions. We also show that the developed reduced Google matrix algorithm allows to obtain an effective network of interactions inside a specific group of selected proteins. This method takes into account not only direct protein-protein interactions but also recover their indirect nontrivial couplings appearing due to summation over all the pathways passing via the global bi-functional network. The developed analysis allows to espablish an average action of each protein being more oriented to activation or inhibition. We argue that the described Google matrix analysis represents an efficient tool for investigation of influence of specific groups of proteins related to specific diseases.

scholarly journals Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

2021 ◽  
Vol 23 (1) ◽  
pp. 67
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky ◽  
Oksana Kunduzova
Keyword(s):  
Heart Failure ◽  
Protein Interactions ◽  
Cardiac Fibrosis ◽  
Matrix Analysis ◽  
Great Promise ◽  
Protein Protein Interactions ◽  
Matrix Algorithms ◽  
Google Matrix ◽  
End Stage Heart Failure ◽  
End Stage

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

scholarly journals Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions

2019 ◽  
Author(s):  
Klaus M. Frahm ◽  
Dima L. Shepelyansky
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Linear Response Theory ◽  
Matrix Analysis ◽  
Global Network ◽  
Protein Protein Interactions ◽  
Effective Interactions ◽  
Ising Spin ◽  
Google Matrix ◽  
Frame Work

AbstractMotivationDirected protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning.ResultsWe develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions. We also apply a recently developed linear response theory for the Google matrix which highlights a pathway of proteins whose PageRank probabilities are most sensitive with respect to two proteins selected for the analysis. This group of proteins is analyzed by the reduced Google matrix algorithm which allows to determine the effective interactions between them due to direct and indirect pathways in the global network. We show that the dominating activation or inhibition function of each protein can be characterized by its magnetization. The results of this Google matrix analysis are presented for three examples of selected pairs of proteins. The developed methods work rapidly and efficiently even for networks with several million of nodes and can be applied to various biological networks.AvailabilityThe Google matrix data and executive code of described algorithms are available at http://www.quantware.ups-tlse.fr/QWLIB/google4signornet/

scholarly journals Fibrosis protein-protein interactions from Google matrix analysis of MetaCore network

2021 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky ◽  
Oksana Kunduzova
Keyword(s):  
Heart Failure ◽  
Protein Interactions ◽  
Cardiac Fibrosis ◽  
Matrix Analysis ◽  
Great Promise ◽  
Protein Protein Interactions ◽  
Matrix Algorithms ◽  
Google Matrix ◽  
End Stage Heart Failure ◽  
End Stage

Protein-protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here we use the MetaCore network and the Google matrix algorithms for prediction of protein-protein interactions dictating cardiac fibrosis, a primary causes of end-stage heart failure. The developed algorithms allow to identify interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

scholarly journals Google matrix analysis of worldwide football mercato

2018 ◽  
Author(s):  
José Lages ◽  
Justin Loye ◽  
Célestin Coquidé ◽  
Guillaume Rollin
Keyword(s):  
Large Scale ◽  
World Wide ◽  
Matrix Analysis ◽  
Two Dimensional ◽  
Network Nodes ◽  
Pagerank Algorithm ◽  
Google Matrix ◽  
Football Clubs ◽  
The World ◽  
Different Types

The worldwide football transfer market is analyzed as a directed complex network: the football clubs are the network nodes and the directed edges are weighted by the total amount of money transferred from a club to another. The Google matrix description allows to treat every club independently of their richness and allows to measure for a given club the efficiency of player sales and player acquisitions. The PageRank algorithm, developed initially for the World Wide Web, naturally characterizes the ability of a club to import players. The CheiRank algorithm, also developed to analyze large scale directed complex networks, characterizes the ability of a club to export players. The analysis in the two-dimensional PageRank-CheiRank plan permits to determine the transfer balance of the clubs in a more subtle manner than the traditional import-export scheme. We investigate the 2017-2018 mercato concerning 2296 clubs, 6698 player transfers, and 147 player nationalities. The transfer balance is determined globally for different types of player trades (defender, midfielder, forward, …) and for different national football leagues. Although, on average, the network transfer flows from and to clubs are balanced, the discrimination by player type draws a specific portrait of each football club.

scholarly journals Post-Brexit Power of European Union From the World Trade Network Analysis

2021 ◽  
pp. 39-47
Author(s):  
Justin Loye ◽  
Katia Jaffrès-Runser ◽  
Dima L. Shepelyansky
Keyword(s):  
European Union ◽  
World Trade ◽  
Dominant Role ◽  
Matrix Analysis ◽  
The European Union ◽  
Trade Network ◽  
Google Matrix ◽  
The World ◽  
Leading Position

We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.

scholarly journals World Influence of Infectious Diseases from Wikipedia Network Analysis

2018 ◽  
Author(s):  
Guillaume Rollin ◽  
José Lages ◽  
Dima L. Shepelyansky
Keyword(s):  
Network Analysis ◽  
Infectious Diseases ◽  
Global Network ◽  
World Health ◽  
Directed Network ◽  
Ancient Egyptian ◽  
Google Matrix ◽  
The World ◽  
The Times ◽  
Health Organization

AbstractWe consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method we construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix we determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the Wikipedia network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.

scholarly journals Crisis contagion in the world trade network

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Célestin Coquidé ◽  
José Lages ◽  
Dima L. Shepelyansky
Keyword(s):  
World Trade ◽  
Trade Balance ◽  
Matrix Analysis ◽  
Western World ◽  
Asian Countries ◽  
Trade Network ◽  
Global Crisis ◽  
Google Matrix ◽  
The World ◽  
Contagion Process

Abstract We present a model of worldwide crisis contagion based on the Google matrix analysis of the world trade network obtained from the UN Comtrade database. The fraction of bankrupted countries exhibits an on-off phase transition governed by a bankruptcy threshold κ related to the trade balance of the countries. For κ>κc, the contagion is circumscribed to less than 10% of the countries, whereas, for κ<κc, the crisis is global with about 90% of the countries going to bankruptcy. We measure the total cost of the crisis during the contagion process. In addition to providing contagion scenarios, our model allows to probe the structural trading dependencies between countries. For different networks extracted from the world trade exchanges of the last two decades, the global crisis comes from the Western world. In particular, the source of the global crisis is systematically the Old Continent and The Americas (mainly US and Mexico). Besides the economy of Australia, those of Asian countries, such as China, India, Indonesia, Malaysia and Thailand, are the last to fall during the contagion. Also, the four BRIC are among the most robust countries to the world trade crisis.

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