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Network Analysis in Biology

Network Analysis in Biology

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What is a network?

A network can be defined as a set of interconnected nodes or vertices that represent entities, and edges or links that represent the connections between these entities. Networks are an abstract mathematical concept that can be used to model and analyze complex systems in various fields, including biology, physics, sociology, and computer science. Due to their intuitive interpretability, networks have become an excellent tool for research process communication.

What is network analysis in bioinformatics?

Network analysis refers to the process of studying the properties and behavior of networks using mathematical and computational methods. This analysis can reveal important structural features of the network, such as the presence of clusters or communities, the degree distribution of nodes, and the importance of individual nodes in the network.

What is network analysis in biology?

In biology, networks are often used to represent chemical interactions between molecules, such as Protein-Protein Interaction (PPI) networks, signaling pathways, transcription factor (TF)-DNA networks, metabolic networks, and genetic interaction networks. 

Network Analysis in BiologyExamples of the five major biological networks.

Experimental procedures can be used to verify these interactions, and the literature can be consulted to provide evidence.

In contrast, in the context of mathematical modeling and statistics, a network is used to provide structure for a model. Nodes may represent different concepts derived from various domains, such as biological, physico-chemical, geographical, or chronological domains. For instance, measured protein concentrations, gene expression, patient age, and the number of patients with a specific condition treated in the hospital can all be included in network representations to predict the outcome of a medical intervention.

What is the importance of studying networks in system biology?

Network analysis in biology is a powerful approach that can provide insights into the organization, function, and regulation of complex biological systems. Here are some applications of biological network analysis that may go some way to answering the question of why network analysis is so important in systems biology:

  • Identifying disease modules in networks analysis: Protein-interaction networks, for example, can provide valuable insights into biological systems and how perturbations to these systems may impact disease. Disease-associated genes are often localized within a specific neighborhood of the network, forming a "disease module," which can implicate other nearby genes in the disease. Identifying these disease modules can help to uncover the underlying mechanisms of disease and potential therapeutic targets.
  • Integration of multi-omics data: Network analysis can be used to integrate multiple types of data, such as genomic, transcriptomic, and proteomic data, to gain a more comprehensive understanding of the molecular interactions involved in disease. This can help to identify new biomarkers and therapeutic targets for complex diseases.
  • Identification of key regulatory pathways: Network analysis can be used to identify key regulatory pathways that are involved in the development and progression of complex diseases. By targeting these pathways, it may be possible to develop more effective treatments for these diseases.
  • Prediction of drug targets: Network analysis can be used to predict potential drug targets based on their proximity to disease-associated genes in the network. This can help to identify new drug targets for complex diseases and accelerate the drug discovery process.

How to choose a graph layout algorithm of network?

The choice of graph layout algorithm depends on the characteristics of the network being analyzed, as well as the desired aesthetic properties of the visualization. Force-directed layouts are often suitable for scale-free networks with distinct communities or high clustering coefficients, like PPIs or highly modular networks with distinct communities or high clustering coefficient. While hierarchical layouts are more appropriate for tree-like graphs such as the Gene Ontology. The use of edge bundling methods can also significantly reduce visual clutter and improve the clarity of the graph.

Network layouts. (A) Grid layout. (B) Circular layout. (C) Hierarchical layout. (D) Force-directed layout (E) Edge-bundling.Network layouts. (A) Grid layout. (B) Circular layout. (C) Hierarchical layout. (D) Force-directed layout (E) Edge-bundling.

It's important to note that the computational complexity of graph layout algorithms can become an issue when dealing with large networks. Therefore, it's essential to balance the tradeoff between computation time and visualization quality when choosing a layout algorithm. Fortunately, many standard visualization tools have a variety of built-in algorithms that can handle networks of different sizes and complexities, and more specialized libraries can be used for custom layouts.

What is the biological network alignment? And how to do it?

Network alignment is a technique used in biological network analysis to compare and integrate multiple biological networks, such as protein-protein interaction networks or gene regulatory networks, from different organisms or different conditions. The goal of network alignment is to identify the corresponding nodes (proteins or genes) in different networks that share similar functions or have similar topological properties, despite being labeled differently in each network.

Network alignment can be useful in a number of ways, such as identifying conserved biological processes across species, predicting protein or gene function, and identifying potential drug targets. There are several approaches to network alignment, but one common strategy involves finding a mapping between the nodes in the networks that maximizes a certain objective function, such as the number of conserved edges or the similarity of the topological features of the aligned nodes.

There are several tools available for network alignment in biological network analysis, such as NetAlign, IsoRankN, and MAGNA++, each with their own strengths and weaknesses. The choice of tool will depend on the specific needs and characteristics of the networks being aligned.

A topology-based network alignment example.A topology-based network alignment example.

Publicly Available Tools For Network Analysis

There are many publicly available tools for network analysis in biology, including Cytoscape, Gephi, and NetworkX, among others. These tools provide a user-friendly interface for network construction, analysis, and visualization, and allow researchers to explore complex biological systems in a more intuitive and accessible way. Some tools also offer specific functionalities for certain types of networks or analysis methods, such as STRING for protein-protein interaction networks or MCODE for identifying network modules.

Tool Features
Cytoscape Network visualization and annotation, hosting of >200 plugins for network analysis, highly interactive allowing for node/edge editing (color, size, labeling, annotations, filtering, etc.)
Cytoscape.js JavaScript library for network visualization and analysis
Gephi Interactive visualization for large-scale networks, with editing features (node/edge attributes, layout algorithms, filtering) and network analysis plugins
Pajek Network analysis software with features for visualization, clustering, and centrality measures
Ondex Integrates heterogeneous data from various sources, allowing for pathway analysis and network visualization
Proviz Tool for visualizing protein-protein interactions
VisANT Web-based tool for network visualization and analysis
Medusa Tool for network visualization and analysis, with features for clustering and pathway analysis
Osprey Cytoscape plugin for the visualization and analysis of biological pathways
Arena3D Visualization tool for 3D networks
Graphia Interactive network visualization and analysis tool, with features for clustering, layout algorithms, and filtering
BioLayout Express Tool for the visualization and analysis of large-scale biological networks, with 3D network visualization
Network Analyzer Plugin for Cytoscape for topological analysis of networks
ZoomOut Web-based tool for topological analysis of networks
Network Analysis Toolkit (NEAT) Open source tool for network analysis
NAP Tool for the analysis of protein interaction networks
Stanford Network Analysis Project (SNAP) Open source library for the analysis of large-scale networks
Large Graph Layout (LGL) Outdated command line application for the visualization of large-scale networks
NetworkX Python package for the creation, manipulation, and study of complex networks
GraphViz Open source graph visualization software
Pathview Tool for pathway analysis and visualization
BioTapestry Tool for the visualization of gene regulatory networks
PathVisio Tool for pathway visualization and analysis
Interactive Pathways Explorer (iPath) Web-based tool for pathway visualization and analysis
MapMan Tool for the visualization and analysis of high-throughput data
WikiPathways Open source platform for the curation and dissemination of biological pathways
Pathway Commons Public database of biological pathways
Dendroscope Tool for the visualization and analysis of phylogenetic trees
iTOL Tool for the visualization of phylogenetic trees
GeneMANIA Tool for the detection of genes related to a set of input genes using functional association data (protein and genetic interactions, co-expression, co-localization, and protein domain similarity)

Note: This table only provides a brief overview of the tools and their features. For more detailed information, please refer to the original sources mentioned earlier.

Reference

  1. Koutrouli M, Karatzas E, Paez-Espino D, et al. A guide to conquer the biological network era using graph theory[J]. Frontiers in bioengineering and biotechnology, 2020, 8: 34.
* For Research Use Only. Not for use in diagnostic procedures.
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