Network Biology: An Overview

Introduction to Network Biology

• Network biology studies biological systems as networks instead of isolated molecules, using computational and systems approaches.
• The purpose is to understand biological processes at a higher level than individual genes.
• Network biology helps visualize biomolecular interactions in pathways and networks.
• It is useful for identifying functional relationships among molecules.

Types of Biological Networks

• Protein-Protein Interaction (PPI) Networks represent how proteins interact in cellular functions, such as enzyme-substrate interactions in metabolism.
• Gene Regulatory Networks show how genes regulate each other's expression via transcription factors, such as Hox genes in developmental biology.
• Metabolic Pathways map biochemical reactions in metabolism, such as glycolysis and the Citric Acid Cycle.
• Disease-Gene Networks illustrate the association between genetic mutations and diseases, like TP53 mutations linked to cancer.

Importance of Network Biology

• Goes beyond studying individual genes to understand biological outcomes.
• It acknowledges that communication between biomolecules determines these outcomes.
• Cellular processes are understood through the study of signal transduction pathways.
• Network biology allows for broader, unbiased analysis of molecular interactions
• Cell Cycle Pathways were first studied in yeast genetics, and are interconnected with metabolism, growth, and differentiation.
• Insights are provided into cancer progression and the development of treatment targets.

Transition from Mendelian Genetics to Systems Biology

• Traditional genetics focused on single-gene mutations and phenotypic effects relating to the Mendelian view (one gene - one phenotype)
• Waddington's Epigenetic Landscape (1957) presents a visual model for how genes interact to influence cell fate.
• Cellular states are depicted in valleys, with differentiation shown as a ball rolling down the landscape.
• Modern use of gene expression profiling help study proliferation, differentiation, and disease states.

Molecular Networks & Functional Relationships

• Nodes are biomolecules such as genes, proteins, metabolites, or miRNAs.
• Edges are functional relationships, including transcriptional regulation (e.g., TF binding to DNA)
• Other functional relationships include protein-protein interactions (e.g., enzyme-substrate complexes), metabolic reactions (e.g., glycolysis intermediates), and genetic interactions (e.g., synthetic lethality).
• Networks are dynamic and change with time and space
• Cellular differentiation involves dynamic shifts in gene expression networks.

Transcriptomics and Network Biology

• Early Transcriptomics (1980s-1990s) used gel-based approaches.
• Northern Blotting detected single gene expression but its limitation was it could not analyze multiple genes simultaneously.
• Microarrays (1990s-2000s) include thousands of gene-specific probes on a chip
• Microarrays detect relative gene expression through fluorescence signals.
• Microarrays enabled co-expression network analysis, gene clustering based on expression patterns, and integration with PPI networks and gene regulation studies.
• Transcriptomics shifted analysis from single-gene to system-wide understanding
• Data integration led to complex gene regulatory and co-expression networks.

Cytoscape

• Cytoscape is an open-source bioinformatics software for visualizing and analyzing molecular networks.
• It integrates gene expression data with networks, analyzes protein-protein interaction networks, and organizes metabolic pathways and signaling networks.
• It has a highly interactive interface that allows zooming and filtering and is a graph-based visualization of networks.
• It is compatible with pathway databases for deeper analysis.
• Nodes are genes, proteins, or metabolites and edges are molecular interactions (functional or physical).
• Common node attributes include gene/protein name, GO annotations (Gene Ontology), expression levels, and cellular localization.
• Common edge attributes include interaction type (activation/inhibition).

Cell Survival & Apoptosis Network: Key Pathway Components

• Survival signals are transmitted from the membrane to the nucleus via EGFR → RAS → ERK1/2 activation
• Cell survival via ERK1/2 → BCL2 Activation enhances BCL2 activity
• Apoptosis is prevented via ERK1/2 → BAX Inhibition as suppresses BAX.
• The balance toward apoptosis is tipped by BAX → BCL2 Inhibition by BAX inhibiting BCL2.

Biological Significance of Cell Survival & Apoptosis Network

• Cancer is characterized by overactive survival signaling that leads to uncontrolled cell proliferation.
• Excessive apoptosis leads to cell loss in diseases like Parkinson’s and Alzheimer’s in Neurodegeneration.

Importance of Networks in Biology

• Networks reduce complexity by converting large datasets into understandable models.
• Graphs efficiently show interactions clearly.
• They allow data integration by combining different types of biological information.
• Visualizations are intuitive, and help researchers interpret systems biology data.

Applications of Network Biology

• Drug discovery involves identifying key nodes in disease networks for targeted therapies.
• Studying oncogenic pathways and tumor suppressor networks leads to Cancer research.
• Engineering biological circuits for specific functions describes Synthetic biology.
• Mapping neuronal interactions and synaptic connections is Neuroscience.

Final Thoughts

• Network biology shifts focus from single-gene studies to systems-level understanding.
• Tools such as Cytoscape facilitate efficient analysis of vast molecular interaction networks.
• Understanding cellular networks helps develop therapies for cancer, genetic disorders, and metabolic diseases.