Instructor: Dr. Ganesh Bagler
Topics covered in the course:
- Notion of a systems, complex system, complexity of biological systems.
- Introduction to graph theory
- Topological properties of a graph/network
- Small-world networks
- Watts and Strogatz model
- Scale-free networks
- Barabasi-Albert strategy for evolution of scale-free networks
- Error and Attack tolerance of scale-free networks
- Proteins: Structure, function and folding
- Residue Interaction Graph (RIG) models of protein structures
- Long-range Interaction (LIN) models of protein structures
- Properties of RIG and LIN models
- Protein-Protein Interaction Networks (PINs)
- Topological properties of PINs
- Gene Coexpression Networks (GCN)
- Gene Regulatory Networks (GRN)
- Anatomical Networks
- Neuronal connectivity and functional models of brain
- Ecological Networks (Food webs and Landscape networks)
- Prevalence of regulatory motifs in various networks
- Algorithms for generating generic features of RIGs and GRNs
- "The structure of complex networks" by Ernesto Estrada
- "Collective dynamics of 'small-world' networks", Duncan J. Watts and Steven H. Strogatz, Nature, 393, 1998.
- "Scale-Free Networks", A-L Barabasi and Eric Bonabeau, Scientific American, May 2003, pp 50-59.
- PDB-101: Molecular Machinery: A Tour of the Protein Data Bank
- PDB-101: What is a protein?
Illustrative research papers discussed in the class:
- "Protein-Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks", Javier De Las Rivas and Celia Fontanillo, PLoS Computational Biology, 6(6), e1000807 (2010)
- "A Protein-Protein Interaction Network for Human Inherited Ataxias and Disorders of Purkinje Cell Degeneration", J Lim et al., Cell 125, 801-814 (2006).
Educational videos (TED-talks) referred in the class:
- "A map of the brain" by Allan Jones
- "The real reasons of the brain" by Daniel Wolpert
- "How complexity leads to simplicity" by Eric Burlow