🧬Systems Biology Unit 9 – Biological Networks: Robustness & Modularity

Key Concepts in Biological Networks

• Biological networks represent complex interactions between various components in living systems (genes, proteins, metabolites)
• Networks consist of nodes (components) and edges (interactions or relationships between nodes)
• Network topology refers to the arrangement and connectivity of nodes and edges in a network
  • Common topologies include random, scale-free, and hierarchical networks
• Hubs are highly connected nodes that play crucial roles in network stability and function (p53 protein in cell signaling networks)
• Centrality measures (degree, betweenness, closeness) quantify the importance of nodes in a network
• Network robustness is the ability to maintain functionality despite perturbations or failures
• Modularity describes the presence of densely connected groups of nodes (modules) with sparse connections between them

Network Structure and Properties

• Degree distribution is the probability distribution of node degrees in a network
  • Scale-free networks exhibit a power-law degree distribution with a few high-degree hubs and many low-degree nodes
• Clustering coefficient measures the tendency of nodes to form tightly connected groups (cliques)
• Average path length is the mean shortest distance between any two nodes in a network
• Small-world property combines high clustering coefficients with short average path lengths (C. elegans neural network)
• Assortativity refers to the tendency of nodes with similar properties to connect with each other
• Community structure reflects the presence of densely connected groups of nodes with sparse connections between them
• Network resilience is the ability to maintain functionality under node or edge removal

Robustness in Biological Systems

• Robustness is the capacity of a system to maintain its functions despite internal and external perturbations
• Redundancy contributes to robustness by providing backup components that can compensate for failures (gene duplication)
• Feedback loops (positive and negative) help maintain homeostasis and confer robustness
  • Negative feedback loops stabilize systems by counteracting perturbations (thermoregulation)
  • Positive feedback loops amplify signals and enable switch-like behaviors (blood clotting cascade)
• Distributed robustness emerges from the collective behavior of many components rather than relying on a single component
• Robustness-performance tradeoff suggests that highly optimized systems may be less robust to unexpected perturbations
• Evolvability is the capacity to generate heritable phenotypic variation, which is essential for adaptation and robustness
• Robustness facilitates evolvability by allowing the accumulation of genetic variation without immediate fitness consequences

Modularity: Building Blocks of Life

• Modularity refers to the organization of a system into functionally distinct and semi-autonomous subsystems (modules)
• Modules are characterized by dense intra-module connections and sparse inter-module connections
• Hierarchical modularity involves the recursive organization of modules into higher-level modules (cell → tissue → organ)
• Modularity facilitates evolvability by allowing the independent evolution of modules without disrupting the entire system
• Modular organization reduces the propagation of perturbations across the system, enhancing robustness
• Modules can be identified using network clustering algorithms (Louvain, Infomap) based on connectivity patterns
• Functional modules in biological networks correspond to specific biological processes or pathways (metabolic modules, signaling modules)

Network Motifs and Patterns

• Network motifs are small, recurring subgraphs that appear more frequently than expected by chance
• Motifs represent basic building blocks of complex networks and may perform specific functions
• Feed-forward loops (FFLs) are common motifs in gene regulatory networks and can generate temporal gene expression patterns
  • Coherent FFLs have the same overall effect (activation or repression) through both direct and indirect paths
  • Incoherent FFLs have opposite effects through direct and indirect paths, enabling pulse-like responses
• Bi-fan motifs consist of two input nodes regulating two output nodes and can process multiple signals
• Single-input modules (SIMs) have a single regulator controlling multiple targets, allowing coordinated gene expression
• Motif analysis involves comparing the frequency of subgraphs in a network to randomized networks to identify overrepresented patterns
• Motifs can be used as signatures to classify networks and infer their functional properties

Analyzing Network Dynamics

• Network dynamics describe the temporal evolution of node states and edge weights in a network
• Boolean networks model gene regulatory dynamics using binary node states (on/off) and logical update rules
• Continuous models (ordinary differential equations) capture more detailed dynamics by representing node states as continuous variables
• Attractors are stable states or limit cycles that the network dynamics converge to over time
  • Point attractors represent steady states, while limit cycle attractors represent oscillatory behavior
• Bifurcation analysis examines how changes in model parameters affect the qualitative behavior of the system (bistability, oscillations)
• Sensitivity analysis assesses the impact of parameter variations on network dynamics and identifies critical components
• Stochastic models incorporate random fluctuations and can capture noise-induced phenomena (gene expression bursts)
• Synchronization and entrainment occur when coupled oscillators adjust their rhythms to match each other or an external signal

Tools and Techniques for Network Study

• High-throughput experimental techniques (RNA-seq, proteomics, metabolomics) generate large-scale data for network reconstruction
• Network inference algorithms (correlation-based, mutual information, Bayesian networks) infer interactions from experimental data
• Network visualization tools (Cytoscape, Gephi) enable the exploration and analysis of network structure and properties
• Pathway databases (KEGG, Reactome) curate and annotate biological pathways and facilitate network analysis
• Network alignment methods identify conserved subnetworks across species or conditions, revealing evolutionary relationships and functional similarities
• Network-based biomarker discovery aims to identify network components or patterns associated with specific diseases or phenotypes
• Network pharmacology approaches target multiple nodes or edges in a network to achieve desired therapeutic outcomes
• Network-guided CRISPR screens combine network information with CRISPR-based perturbations to identify functionally related genes

Real-World Applications and Examples

• Protein-protein interaction networks help understand cellular organization and identify disease-associated modules (Alzheimer's disease network)
• Gene regulatory networks unravel the complex control of gene expression in development and disease (embryonic stem cell differentiation network)
• Metabolic networks elucidate the flow of metabolites and identify potential drug targets (cancer metabolism network)
• Signaling networks decode the transmission of signals and their role in cellular decision-making (MAPK signaling network)
• Ecological networks model the interactions between species in an ecosystem (plant-pollinator networks, food webs)
• Social networks analyze the structure and dynamics of human interactions and information spread (Twitter retweet network)
• Transportation networks optimize the flow of people, goods, and resources (airline route network, urban traffic network)
• Brain connectivity networks map the structural and functional connections between brain regions (human connectome)