Modeling and Analysis of Biological Complex Systems

Instructor: Dr. Ganesh Bagler

Topics covered in the course:

  1. Notion of a systems, complex system, complexity of biological systems.
  2. Introduction to graph theory
  3. Topological properties of a graph/network
  4. Small-world networks
  5. Watts and Strogatz model
  6. Scale-free networks
  7. Barabasi-Albert strategy for evolution of scale-free networks
  8. Error and Attack tolerance of scale-free networks
  9. Proteins: Structure, function and folding
  10. Residue Interaction Graph (RIG) models of protein structures
  11. Long-range Interaction (LIN) models of protein structures
  12. Properties of RIG and LIN models
  13. Protein-Protein Interaction Networks (PINs)
  14. Topological properties of PINs
  15. Gene Coexpression Networks (GCN)
  16. Gene Regulatory Networks (GRN)
  17. Anatomical Networks
  18. Neuronal connectivity and functional models of brain
  19. Ecological Networks (Food webs and Landscape networks)
  20. Prevalence of regulatory motifs in various networks
  21. Algorithms for generating generic features of RIGs and GRNs

Reference book:

  1. "The structure of complex networks" by Ernesto Estrada

Reading materials:

  1. "Collective dynamics of 'small-world' networks", Duncan J. Watts and Steven H. Strogatz, Nature, 393, 1998.
  2. "Scale-Free Networks", A-L Barabasi and Eric Bonabeau, Scientific American, May 2003, pp 50-59.
  3. PDB-101: Molecular Machinery: A Tour of the Protein Data Bank
  4. PDB-101: What is a protein?

Illustrative research papers discussed in the class:

  1. "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)
  2. "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:

  1. "A map of the brain" by Allan Jones
  2. "The real reasons of the brain" by Daniel Wolpert
  3. "How complexity leads to simplicity" by Eric Burlow