scholarly journals Evolutionary significance of metabolic network properties

2011 ◽  
Vol 9 (71) ◽  
pp. 1168-1176 ◽  
Author(s):  
Georg Basler ◽  
Sergio Grimbs ◽  
Oliver Ebenhöh ◽  
Joachim Selbig ◽  
Zoran Nikoloski
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Protein Interactions ◽  
Biological Networks ◽  
Metabolic Networks ◽  
Functional Organization ◽  
Hierarchical Organization ◽  
Clustering Coefficient ◽  
Evolutionary Significance ◽  
System Architectures

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.

scholarly journals Clustering analysis of tumor metabolic networks

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Ichcha Manipur ◽  
Ilaria Granata ◽  
Lucia Maddalena ◽  
Mario R. Guarracino
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Biological Networks ◽  
Clustering Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Computational Time ◽  
Expression Data ◽  
Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

scholarly journals Layer–layer competition in multiplex complex networks

2015 ◽  
Vol 373 (2056) ◽  
pp. 20150117 ◽  
Author(s):  
J. Gómez-Gardeñes ◽  
M. de Domenico ◽  
G. Gutiérrez ◽  
A. Arenas ◽  
S. Gómez
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Structural Properties ◽  
Biological Networks ◽  
Strong Influence ◽  
Communication Channels ◽  
Critical Properties ◽  
Interaction Layer ◽  
Localized State ◽  
Novel Approach

The coexistence of multiple types of interactions within social, technological and biological networks has moved the focus of the physics of complex systems towards a multiplex description of the interactions between their constituents. This novel approach has unveiled that the multiplex nature of complex systems has strong influence in the emergence of collective states and their critical properties. Here we address an important issue that is intrinsic to the coexistence of multiple means of interactions within a network: their competition. To this aim, we study a two-layer multiplex in which the activity of users can be localized in each of the layers or shared between them, favouring that neighbouring nodes within a layer focus their activity on the same layer. This framework mimics the coexistence and competition of multiple communication channels, in a way that the prevalence of a particular communication platform emerges as a result of the localization of user activity in one single interaction layer. Our results indicate that there is a transition from localization (use of a preferred layer) to delocalization (combined usage of both layers) and that the prevalence of a particular layer (in the localized state) depends on the structural properties.

scholarly journals Edge-based analysis of networks: curvatures of graphs and hypergraphs

2020 ◽  
Vol 139 (4) ◽  
pp. 337-348
Author(s):  
Marzieh Eidi ◽  
Amirhossein Farzam ◽  
Wilmer Leal ◽  
Areejit Samal ◽  
Jürgen Jost
Keyword(s):  
Structural Properties ◽  
Protein Interaction ◽  
Biological Networks ◽  
Metabolic Networks ◽  
Systematic Approach ◽  
Directed Networks ◽  
Protein Protein Interaction ◽  
Transcriptional Regulatory ◽  
Edge Based

AbstractThe relations, rather than the elements, constitute the structure of networks. We therefore develop a systematic approach to the analysis of networks, modelled as graphs or hypergraphs, that is based on structural properties of (hyper)edges, instead of vertices. For that purpose, we utilize so-called network curvatures. These curvatures quantify the local structural properties of (hyper)edges, that is, how, and how well, they are connected to others. In the case of directed networks, they assess the input they receive and the output they produce, and relations between them. With those tools, we can investigate biological networks. As examples, we apply our methods here to protein–protein interaction, transcriptional regulatory and metabolic networks.

scholarly journals Flux-based hierarchical organization of Escherichia coli’s metabolic network

2019 ◽  
Author(s):  
Semidán Robaina-Estévez ◽  
Zoran Nikoloski
Keyword(s):  
Steady State ◽  
Biological Networks ◽  
Metabolic Networks ◽  
Growth Conditions ◽  
Hierarchical Organization ◽  
Fine Tuning ◽  
E Coli ◽  
Network Function ◽  
Molecular Phenotypes ◽  
Operational Constraint

AbstractBiological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

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.

Roles of clustering properties for degree-mixing pattern networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750029 ◽  
Author(s):  
Pei Yu ◽  
Qiang Guo ◽  
Ren-De Li ◽  
Jing-Ti Han ◽  
Jian-Guo Liu
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Local Structure ◽  
Clustering Coefficient ◽  
Growth Trend ◽  
Pearson Coefficient ◽  
Comparison Results ◽  
Local Clustering ◽  
Clustering Coefficients ◽  
Structure Properties

The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely [Formula: see text] and [Formula: see text], are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients [Formula: see text] and [Formula: see text] are reported, one can find that the square structures would increase as the degree [Formula: see text] of nodes increasing in disassortative networks. At the same time, the Pearson coefficient [Formula: see text] between the clustering coefficients [Formula: see text] and [Formula: see text] is calculated for networks with different assortative coefficients. The Pearson coefficient [Formula: see text] changes from [Formula: see text] to 0.98 as the assortative coefficient [Formula: see text] increasing from [Formula: see text] to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients [Formula: see text] and [Formula: see text] for networks with tunable assortative coefficients and find that the clustering coefficient [Formula: see text] increases from 0.0038 to 0.5952 while the clustering coefficient [Formula: see text] increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

scholarly journals Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 207
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Elena Sánchez-López ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Complex Networks ◽  
Global Climate ◽  
Spatial Relation ◽  
Clustering Coefficient ◽  
Horizontal Visibility ◽  
Climate Conditions ◽  
Mean Values ◽  
Intrinsic Nature ◽  
Temperature Anomalies ◽  
Mean Temperature

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.

scholarly journals p-adic numbers encode complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Hua ◽  
Ludger Hovestadt
Keyword(s):  
Complex Networks ◽  
Random Graph ◽  
Genetic Interaction ◽  
Hierarchical Structures ◽  
Number System ◽  
Hierarchical Organization ◽  
Natural Representation ◽  
Multimodal Distributions ◽  
Number Sequences ◽  
Er Model

AbstractThe Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.

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