- Complex Networks
- Modeling and Analysis of Biological Complex Systems
- Computational and Systems Biology
- Computational Laboratory

- Introduction to Graph Theory:
- Introduction to graph theory
- Examples of graphs
- Directed and undirected networks
- Graph theoretical metrics
- Degree distribution
- Clustering
- Adjacency matrix

- Classical random graphs:
- Classical models
- Loopholes in random graphs
- Giant component
- Small and large worlds:
- Diameter of the Web
- Equilibrium versus growing tree
- Fractal nature of giant connected component
- Diversity of networks:
- Internet
- World-wide web
- Cellular networks
- Co-occurrence networks
- Self-organization of networks:
- Random recursive trees
- The Barabasi-Albert model
- General preferential attachment
- Condensation phenomena
- Weighted Networks:
- The strength of weak ties
- World-wide airport network
- Airport network of India
- Modeling weighted networks
- Motifs, cliques, communities:
- Cliques in networks
- Statistics of motifs
- Modularity
- Detecting communities
- Hierarchical architecture
- Applications of complex networks modeling:
- Examples of real-world networks
- Application for biological systems modeling

- 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

### Reference book:

- "The structure of complex networks" by Ernesto Estrada

### Reading materials:

- "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

- 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

- Biological Sequences and Alignment:
- Biological sequences: DNA, RNA, Protein
- Sequence Alignment; phylogeny
- Basics of Dynamic programming
- Needleman and Wunsch Algorithm
- Applications of alignment algorithms
- 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
- Homology Modeling and Clustering Methods:
- Basics of protein structure modeling
- Homology modeling
- Basics of clustering
- K-means clustering
- Microarray- Data and Analysis:
- Basics of microarray technique
- Applications of microarrays
- Data compilation and analysis
- Construction of network models from microarray data
- Introduction to Graph Theory and Systems Biology:
- Introduction to graph theory
- Graph theoretical metrics
- Application of graph theoretical analysis for biological systems modeling
- Systems Biology- Applications:
- Gene regulatory networks
- Protein-protein interactomes
- Anatomical networks

### Reference book:

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

- Introduction to Programming:
- Basics of computation and Programming
- Introduction to Linux Operating systems
- Linux: Concepts, syntax and basic operations

- Introduction to Matlab:
- Basics of MatLab
- Data structures programming constructs
- Functions and scripting
- Examples:
- Sequences
- Random numbers
- File Operations
- Plotting
- Introduction to programming in R:
- Basic of R
- Syntax and data structures
- Scripting and functions
- Library- Bio3d:
- Application of Bio3d for structural analysis
- Library- Bioconductor:
- Application of Bioconductor for bioinformatics analysis
- Sequence alignment; Clustering; Homology modeling
- Network Analysis:
- Network parameters
- Construction of network models of biological systems and analysis
- Introduction to tools of research:
- Mendeley: Reference Management
- Paper writing and literature survey
- Mini Research Computational Project

### Reference Material:

- Christos Xenophontos, "A Beginnerâ€™s Guide to Matlab", (Tutorial)
- Stephan Eglan., "R Programming", (Course Material)
- Martin Dugas and Hans-Ulrich Klein., "Introduction to R and Bioconductor" (Training Material)