Teaching

1. Introduction to Graph Theory:
   • Introduction to graph theory
   • Examples of graphs
   • Directed and undirected networks
   • Graph theoretical metrics
   • Degree distribution
   • Clustering
   • Adjacency matrix
2. Classical random graphs:
• Classical models
• Loopholes in random graphs
• Giant component
3. Small and large worlds:
• Diameter of the Web
• Equilibrium versus growing tree
• Fractal nature of giant connected component
4. Diversity of networks:
• Internet
• World-wide web
• Cellular networks
• Co-occurrence networks
5. Self-organization of networks:
• Random recursive trees
• The Barabasi-Albert model
• General preferential attachment
• Condensation phenomena
6. Weighted Networks:
• The strength of weak ties
• World-wide airport network
• Airport network of India
• Modeling weighted networks
7. Motifs, cliques, communities:
• Cliques in networks
• Statistics of motifs
• Modularity
• Detecting communities
• Hierarchical architecture
8. Applications of complex networks modeling:
• Examples of real-world networks
• Application for biological systems modeling

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

1. Introduction to Biological Complex Systems:
   • Definition and notion of system and complexity
   • Natural selection and evolution of biological systems
   • Adaptability; Differences in engineered vs. evolved systems
2. Biological Sequences and Alignment:
• Biological sequences: DNA, RNA, Protein
• Sequence Alignment; phylogeny
• Basics of Dynamic programming
• Needleman and Wunsch Algorithm
• Applications of alignment algorithms
3. Biological Macromolecules: Proteins:
• Introduction to proteins
• Basic ingredients, Ramachandran Plot
• Protein structure, function and folding
• Protein structure organization
• Protein folding models
• First principle and Knowledge-based models
4. Homology Modeling and Clustering Methods:
• Basics of protein structure modeling
• Homology modeling
• Basics of clustering
• K-means clustering
5. Microarray- Data and Analysis:
• Basics of microarray technique
• Applications of microarrays
• Data compilation and analysis
• Construction of network models from microarray data
6. Introduction to Graph Theory and Systems Biology:
• Introduction to graph theory
• Graph theoretical metrics
• Application of graph theoretical analysis for biological systems modeling
7. Systems Biology- Applications:
• Gene regulatory networks
• Protein-protein interactomes
• Anatomical networks

Reference book:

1. Carl Branden and John Tooze, "Introduction to protein structure", Garland Science (2nd Ed), 1999
2. Yaneer Bar-Yam, "Dynamics of Complex Systems", Addison-Wesley, Reading (MA), USA, 1997

1. Introduction to Programming:
   • Basics of computation and Programming
   • Introduction to Linux Operating systems
   • Linux: Concepts, syntax and basic operations
2. Introduction to Matlab:
• Basics of MatLab
• Data structures programming constructs
• Functions and scripting
3. Examples:
• Sequences
• Random numbers
• File Operations
• Plotting
4. Introduction to programming in R:
• Basic of R
• Syntax and data structures
• Scripting and functions
5. Library- Bio3d:
• Application of Bio3d for structural analysis
6. Library- Bioconductor:
• Application of Bioconductor for bioinformatics analysis
• Sequence alignment; Clustering; Homology modeling
7. Network Analysis:
• Network parameters
• Construction of network models of biological systems and analysis
8. Introduction to tools of research:
• Mendeley: Reference Management
• Paper writing and literature survey
9. Mini Research Computational Project

Reference Material:

1. Christos Xenophontos, "A Beginner’s Guide to Matlab", (Tutorial)
2. Stephan Eglan., "R Programming", (Course Material)
3. Martin Dugas and Hans-Ulrich Klein., "Introduction to R and Bioconductor" (Training Material)