Introduction to Biological Network Analysis I: Network Basics and Properties
summary
Graph representations map molecules or patients to nodes and physical, regulatory or co-expression ties to edges, and show that clustering coefficient correlates with essentiality while betweenness centrality predicts bottlenecks that matter for information flow. Random‑graph comparisons use Erdos‑Rényi, Watts‑Strogatz small‑world and power‑law growth models; viral host interactions either target scale‑free hubs vulnerable to targeted attack or many low‑degree partners. Simulations (R) and shortest‑path algorithms (Floyd‑Warshall) are used, and crucially biological noise breaks engineering models.
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chapters (29)
- 0:00Intro
- 1:09Molecular biology is evolving
- 3:45Biological systems often represented by graphs
- 7:10Representation and Abstraction
- 7:53Network representations
- 8:44Bipartite graphs
- 9:52Heterogeneous networks
- 10:48Network Evolution
- 12:03Network properties and biological implications
- 13:52Diameter
- 15:15Clustering coefficient
- 19:08Degree-centrality examples
- 20:36Eigenvector Centrality
- 23:50Betweenness Centrality and Bottlenecks in Molecular Networks
- 26:23Disease gene centrality?
- 30:26Random Graph Models
- 32:24Erdös-Renyi model
- 32:59E-R phase transitions
- 35:28Do biological networks look like E-R?
- 36:05Power law degree distributions
- 36:32Random graphs vs. scale-free
- 38:31Biological networks often have hubs
- 39:16Degree, vulnerability characterize virus-host interactions
- 42:12Small-world models
- 43:46Example: ring model
- 44:52Small-world biological networks
- 46:14Power law networks in biology
- 49:12Geometric Random Graph Model
- 51:20Local motifs or structure
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