🧬Systems Biology Unit 5 – Network Biology: Graph Theory & Analysis

Key Concepts in Network Biology

• Network biology applies graph theory to study complex biological systems (gene regulatory networks, protein-protein interaction networks, metabolic networks)
• Represents biological entities as nodes and their interactions as edges
• Enables understanding of the structure, function, and dynamics of biological systems
• Provides insights into the organization and behavior of complex biological networks
  • Helps identify key players (hubs) and functional modules
  • Allows for the prediction of network perturbations and their effects
• Facilitates the integration of multi-omics data (genomics, proteomics, metabolomics) for a systems-level understanding of biological processes
• Contributes to the development of targeted therapies and personalized medicine by identifying disease-associated network alterations

Graph Theory Fundamentals

• Graphs consist of nodes (vertices) connected by edges (links)
• Nodes represent entities (genes, proteins, metabolites) while edges represent interactions or relationships between them
• Edges can be directed (one-way interaction) or undirected (bidirectional interaction)
• Degree of a node refers to the number of edges connected to it
  • In-degree: number of incoming edges
  • Out-degree: number of outgoing edges
• Path is a sequence of nodes connected by edges without repeating any node
• Shortest path between two nodes is the path with the minimum number of edges
• Connected component is a subgraph in which any two nodes are connected by a path
• Cliques are complete subgraphs where every node is connected to every other node

Types of Biological Networks

• Gene regulatory networks represent interactions between transcription factors and target genes
  • Nodes: genes
  • Edges: regulatory relationships (activation or repression)
• Protein-protein interaction (PPI) networks depict physical interactions between proteins
  • Nodes: proteins
  • Edges: physical interactions (binding, complex formation)
• Metabolic networks illustrate the flow of metabolites through biochemical reactions
  • Nodes: metabolites and enzymes
  • Edges: substrate-product relationships and enzyme-catalyzed reactions
• Signaling networks represent the transmission of signals through molecular interactions
  • Nodes: signaling molecules (receptors, kinases, transcription factors)
  • Edges: activation or inhibition of downstream targets
• Disease networks connect genes, proteins, and other molecular entities associated with a specific disease
  • Nodes: disease-associated molecules
  • Edges: known or predicted interactions relevant to the disease

Network Visualization Tools

• Cytoscape: open-source software for visualizing and analyzing complex networks
  • Supports various layout algorithms and customizable visual properties
  • Allows for the integration of additional data (expression levels, functional annotations)
• Gephi: open-source network analysis and visualization software
  • Provides interactive exploration and manipulation of large graphs
  • Offers advanced layout algorithms and metrics calculation
• Graphviz: open-source graph visualization software
  • Generates high-quality static images of graphs
  • Supports various graph layout algorithms (hierarchical, circular, spring-based)
• igraph: R package for creating and analyzing graphs and networks
  • Provides a wide range of graph algorithms and visualization functions
  • Enables the manipulation and analysis of large-scale networks
• NetworkX: Python package for the creation, manipulation, and study of complex networks
  • Offers a variety of graph algorithms and network analysis tools
  • Allows for easy integration with other Python libraries for data analysis and visualization

Network Analysis Techniques

• Degree distribution analysis examines the distribution of node degrees in a network
  • Scale-free networks exhibit a power-law degree distribution with a few high-degree nodes (hubs) and many low-degree nodes
  • Random networks follow a Poisson degree distribution with most nodes having similar degrees
• Clustering coefficient measures the tendency of nodes to form clusters or groups
  • Local clustering coefficient: proportion of a node's neighbors that are also connected to each other
  • Global clustering coefficient: average of local clustering coefficients across the network
• Community detection identifies densely connected subgroups (modules) within a network
  • Modules often correspond to functional units or biological processes
  • Algorithms: Louvain method, Girvan-Newman algorithm, Infomap
• Network robustness assesses the resilience of a network to node or edge removal
  • Helps identify critical nodes or edges whose removal significantly disrupts the network structure
  • Percolation analysis: studying the effect of random or targeted node/edge removal on network connectivity
• Network comparison evaluates the similarity or difference between multiple networks
  • Comparing network topology, node/edge overlap, or functional enrichment
  • Helps identify conserved or divergent network properties across species or conditions

Centrality Measures and Hub Identification

• Degree centrality ranks nodes based on their number of connections
  • Nodes with high degree centrality are considered hubs and are likely to be essential for network function
• Betweenness centrality quantifies the extent to which a node lies on the shortest paths between other nodes
  • Nodes with high betweenness centrality are important for information flow and network cohesion
• Closeness centrality measures the average shortest path distance from a node to all other nodes
  • Nodes with high closeness centrality can quickly reach or influence other nodes in the network
• Eigenvector centrality assigns higher scores to nodes connected to other high-scoring nodes
  • Identifies nodes that are influential by virtue of their connections to other important nodes
• PageRank is a variant of eigenvector centrality that considers the importance of incoming links
  • Originally developed for ranking web pages in search engine results
  • Adapted for identifying important nodes in biological networks
• Hubs are nodes with high centrality scores that play crucial roles in network organization and function
  • Date hubs: transiently interact with multiple partners at different times
  • Party hubs: simultaneously interact with multiple partners as part of a complex

Network Motifs and Modules

• Network motifs are small, recurring subgraphs that appear more frequently than expected by chance
  • Represent basic building blocks of complex networks
  • Examples: feed-forward loops, bi-fan motifs, single-input modules
• Motif detection algorithms identify overrepresented subgraphs in a network
  • Mfinder: enumerates all possible subgraphs and compares their frequencies to randomized networks
  • FANMOD: fast network motif detection tool using sampling techniques
• Motifs often have specific functional roles in biological networks
  • Feed-forward loops: signal processing, noise filtering, response acceleration
  • Bi-fan motifs: coordinated regulation, switch-like behavior
• Network modules are densely connected subgraphs that represent functional units
  • Modules often correspond to biological processes, pathways, or protein complexes
  • Identification methods: hierarchical clustering, modularity optimization, clique percolation
• Modular organization of biological networks provides insights into the organization and regulation of cellular functions
  • Allows for the identification of disease-associated modules and potential drug targets
  • Enables the study of module conservation and divergence across species

Applications in Systems Biology

• Network-based drug discovery identifies potential drug targets by analyzing disease-associated networks
  • Prioritizes targets based on their centrality, connectivity, or functional role
  • Helps in the development of targeted therapies and drug repurposing
• Network pharmacology studies the effects of drugs on biological networks
  • Predicts drug-target interactions and potential side effects
  • Facilitates the design of multi-target therapies and drug combinations
• Network biomarkers are subnetworks or modules that are differentially expressed or regulated in disease states
  • Provide more robust and reliable diagnostic or prognostic markers compared to individual genes or proteins
  • Enable the stratification of patients based on network signatures for personalized medicine
• Network-based data integration combines multiple omics datasets to gain a systems-level understanding of biological processes
  • Integrates gene expression, protein interaction, metabolomics, and other data types
  • Identifies key regulators, pathways, and functional modules associated with specific conditions
• Network analysis in evolutionary biology studies the evolution of biological networks across species
  • Identifies conserved and divergent network properties and functional modules
  • Provides insights into the evolutionary mechanisms shaping biological systems
• Network-based approaches in synthetic biology guide the design and optimization of engineered biological systems
  • Helps in the rational design of metabolic pathways and gene regulatory circuits
  • Enables the prediction and optimization of system behavior and productivity