M.TECH. BIOINFORMATICS - (FULL TIME) CURRICULUM & SYLLABUS

2010-11

Faculty of Engineering and Technology SRM University

SRM Nagar, Kattankulathur – 603 203

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M.Tech BIOINFORMATICS (FULL TIME) CCUURRRRIICCUULLUUMM

SEMESTER- I Code Category Course L T P C Theory

BI0501 C Advanced Biochemistry and Immunology 3 0 3 4 BI0503 C Algorithms for Bioinformatics 3 2 0 4 BI0505 C Bioinformatics – Techniques And

Applications 3 0 3 4

MA0539 S Numerical and Bio statistical methods 3 0 0 3 E1 E Elective I 3 0 0 3

Total 15 2 6 18 Total Contact Hours 23 18 SEMESTER- II Code Category Course L T P C Theory

BI0500 C Applications of Matlab in Bioinformatics

3 0 3 4

BI0502 C Functional Genomics and Proteomics 3 0 3 4 BI0504 C Structural Bioinformatics 3 2 0 4 BI0506 S Structural Chemistry of Biomolecules 3 0 0 3

E2 E Elective II 3 0 0 3 Total 15 2 6 18 Total Contact Hours 23 18 SEMESTER- III Code Category Course L T P C Theory

E3 E Elective III 3 0 0 3 E4 E Elective IV 3 0 0 3 E5 E Elective V 0 0 9 3

BI0601 C Seminar 1 0 0 1 BI0603 P Project –Phase I 0 0 12 6

Total 7 0 21 16 Total Contact Hours 28 16 SEMESTER- IV Code Category Course L T P C Theory

BI0602 P Project- Phase II 0 0 36 18 Total Total Contact Hours 36 18 TOTAL CREDITS TO BE EARNED FOR THE AWARD OF DEGREE: 70 CATEGORY OF COURSES: C: Core courses S: Supportive courses E: Elective courses P: Project CONTACT HOUR/CREDIT: L: Lecture hour T: Tutorial P: Practical Hour C: Credit

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ELECTIVE I Code Course L T P C BI0507 Advanced Biology 3 0 0 3 BI0509 Object-Oriented Programming and Database management 3 0 0 3 ELECTIVE II Code Course L T P C BI0508 Metabolic Engineering 3 0 0 3 BI0510 Microarray Bioinformatics 3 0 0 3 ELECTIVE III- ELECTIVE V A student is required to choose a module for Electives 3, 4 &5 The student will not be permitted to choose one course from each module.

MODULE I Code Category Course L T P C BI0611 E3 Systems Biology-Models and Approaches 3 0 0 3 BI0613 E4 Computational Chemistry 3 0 0 3 BI0615 E5 Computer Aided Drug Designing 1 0 6 3

MODULE II Code Category Course L T P C BI0621 E3 Macromolecular Biophysics 3 0 0 3 BI0623 E4 Biomolecular Mechanics And Simulation 3 0 0 3 BI0625 E5 Molecular Dynamics 1 0 6 3

MODULE III Code Category Course L T P C BI0631 E3 UNIX & JAVA 3 0 0 3 BI0633 E4 Python for Bioinformatics 3 0 0 3 BI0635 E5 Perl for Bioinformatics 1 0 6 3 CREDIT HOUR SUMMARY TABLE

Sl. No. Category

Credits %

I Semester II Semester III Semester IV Semester Category total

1 Core courses 12 12 --- --- 24 34.3 2 Optional /

elective courses 03 03 09 --- 15 21.4

3 Supportive courses

03 03 --- --- 06 8.6

4 Seminar --- --- 01 --- 01 1.4 5 Project work --- --- 06 18 24 34.3 6 Total 18 18 16 18 70 100

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CONTACT HOURS SUMMARY TABLE

CATEGORY CONTACT HOURS THEORY 41 PRACTICAL 69 TOTAL 110

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SEMESTER I

BI0501 ADVANCED BIOCHEMISTRY AND IMMUNOLOGY L T P C 3 0 3 4

PURPOSE To learn the advanced features of the biomolecules INSTRUCTIONAL OBJECTIVES

To teach chemistry of biomolecules To teach the structure and metabolism of biomolecules To teach principles of biological techniques used for analysis of biomolecules

Proteins: amino acids- peptides -polypeptide synthesis- Isolation and purification of proteins- Protein folding and dynamics. Sugars and polysaccharides: Monosaccharide- polysaccharides and glycoprotein. Metabolism of carbohydrates- Glycolysis-TCA cycle-gluconeogenesis- glycogen metabolism. Lipids and membrane: Lipid classification-properties of lipid aggregates- Biological membrane-membrane assembly and targeting- Lipid linked proteins and lipoproteins. Nucleic acid: Structure of DNA. Forms of DNA- A, B, Z Structure and classification of RNA’s. Enzymes: Nomenclature- substrate specificity-coenzymes-regulation of enzyme activity. Rate of enzyme reaction- kinetics- inhibition- effect of pH and temperature. Innate vs. Acquired, humoral and cell mediated immunity, Immunity at Body Surfaces. Cells of the immune system: Organs of the immune system – primary and secondary lymphoid organ, B cell (maturation and Function), T cell (maturation and Function) and macrophages, Mononuclear Phagocytic System. Antigens, Antibodies, Antigenicity, Structure, Biological function, synthesis of antibody and secretion, antigen-antibody reaction, Polyclonal and monoclonal antibodies. Major histocompatibility complex, Antigen processing and presentation Pathways. Lymphokines and Cytokines: The complement system, Cell-mediated effectors responses (CTL, NK, DH). Vaccines. Autoimmunity: Breakdown in Self-Tolerance. Transplantation: tissue and organ grafting. LIST OF EXPERIMENTS

1. pH and Buffers 2. Protein, isolation & estimation 3. 2D electrophoresis 4. Carbohydrate assays 5. Lipid assays 6. Nucleotide assays 7. DNA & RNA isolation & estimation 8. Blotting Techniques 9. Enzyme kinetics 10. Immunodiffusion 11. Agglutination 12. Immunoelectrophoresis 13. Western blotting.

REFERENCES

1. D. Voet and J.G. Voet, Biochemistry, Wiley Publications, Second Edition 2005. 2. D.L Nelson and M.M Cox, Lehninger’s Principles of Biochemistry, W.H Freeman

Publications, Fifth edition 2008. 3. Thomas Devlin, Textbook of Biochemistry with clinical correlations, (2nd ed),John Wiley

and sons

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4. Roitt, Essential Immunology, 10th

ed. Blackwell Science, 2005. 5. Richard A. Goldsby, Thomas J. Kindt and Barbara A. Osborne, Kuby Immunology,4

th ed.

W. H. Freeman & Company, 2000. 6. Janeway et al., Immunobiology, 4th Edition, Current Biology publications, 1999. 7. William E. Paul, Fundamental Immunology, 4th edition, Lippencott Raven, 1999.

BI0503 ALGORITHMS FOR BIOINFORMATICS L T P C

3 2 0 4 PURPOSE The purpose of this subject is to study various Algorithm design techniques and applying it in bioinformatics. INSTRUCTIONAL OBJECTIVES

Notation and different types of Algorithms Mapping Algorithms and Greedy approaches Dynamic programming for sequence alignment DNA analysis using graph Algorithms Clustering and trees.

Dynamic Programming, Sequence Alignment: Edit distance, LCS. PAM and BLOSUM Scoring Matrices. Global alignments: Needleman wunsch Algorithm, Local Alignments: Smith Waterman Algorithm, Gap Penalties. Graph Algorithms in Sequencing, SBH and Eulerian Paths, De-novo Peptide Sequencing: Longest Paths and. Space Efficient Alignment Algorithms. Fast LCS using Table Lookup. Exact Pattern Matching: KMP Algorithm, Keyword Trees, Aho-Corasick Algorithm. Clustering Basics: Hierarchical Clustering, Multiple Sequence Alignment: CLUSTAL, Center-based Clustering, Clustering via Cliques. Evolutionary Trees and Ultrametrics, Additive distance trees, Perfect Phylogeny Problem. Small Parsimony Problem, Nearest Neighbor Interchange. Hidden Markov Models: Basics, Forward and Backward (Viterbi) Algorithms REFERENCES

1. Gusfields D, Algorithms on strings, trees and sequences- Computer Science and Computational Biology, Cambridge University Press 1997.

2. Neil C. Jones and Pavel A. Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, First Indian Reprint 2005.

3. Steffen Schulze-Kremer, Molecular Bioinformatics: Algorithms and Applications, Walter de Gruyter, 1996.

4. Gary Benson Roderic page (Eds), Algorithms in Bioinformatics, Springer International Edition, First Indian Reprint 2004.

5. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison. Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acid, Cambridge University Press, 1999.

6. Ron White, Timothy Edward Downs, How computers work, Ziff-Davis Press, 1993.

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BI0505 BIOINFORMATICS – TECHNIQUES AND APPLICATIONS

L T P C

3 0 3 4

PURPOSE To equip the students with the requisite background in areas of modern biology and computer science. INSTRUCTIONAL OBJECTIVES

To launch the students into core areas of Bioinformatics like multiple sequence alignment, phylogenetic trees, genomics, proteomics.

To explore the students to applied areas of Bioinformatics like drug design, metabolic pathway engineering.

File formats, Multiplicity of data and redundancy, Major bioinformatics databases, Alignment of pairs of sequence: Introduction to sequence analysis, Methods of alignment, Application of dot matrices. Tools for sequence Alignment: FASTA, BLAST. Filtered and gapped BLAST, PSI-BLAST, Comparison of running time for various programs. Alignment of Multiple Sequences: methods and Tools for MSA, Application of multiple alignments, Viewing MSA. Phylogenetic analysis: Concepts of trees, Distance matrix methods, Character based methods, Methods of evaluating phylogenetic methods. Gene prediction, Conserved domain analysis, Protein visualization, Prediction of protein structure, Validation of the predicted structure using Ramachandran plot and other steriochemical properties. Protein Function Prediction, Metabolic Pathway analysis, Computer aided drug designing. Pharmacogenomics and Pharmacogenetics. LIST OF EXPERIMENTS

1. Bioinformatics databases 2. Sequence similarity searching for sequences 3. Pairwise sequence alignment 4. Multiple sequence analysis 5. Phylogenetic analysis using distance based methods & character based methods 6. Evaluation of trees 7. Gene prediction tools 8. Prediction Of secondary Structure of proteins 9. Sequence based prediction and Validation of 3d Protein structure 10. Docking studies

REFERENCES

1. S.C. Rastogi et al, Bioinformatics- Concepts, Skills, and Applications, CBS Publishing, 2003.

2. T K Attwood, D J Parry-Smith, Introduction to Bioinformatics, Pearson Education, 2005. 3. C S V Murthy, Bioinformatics, Himalaya Publishing House, 1st Edition 2003. 4. Cynthia Gibas, Per Jambeck, Developing bioinformatics computer skills, O'Reilly Media,

Inc., 2001.

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MA0539 NUMERICAL AND BIO STATISTICAL METHODS L T P C 3 0 0 3 PURPOSE

To provide study of statistical methods and numerical methods

INSTRUCTIONAL OBJECTIVES Numerical calculations Bio statistics Probability calculations

Numerical calculation: introduction and fundamental concepts, numerical methods for linear equation and matrices, Crammers rule, Gaussian elimination method , Crouts method, Similarity transformation, Eigen values and Eigen vectors of a matrix, Numerical solution of differential and integral equations. Biostatistics: Introduction to Biostatistics, Handling univariate, bivariate and multivariate data- Introduction to Probabilities, Interval Estimation. Hypothesis testing: Testing hypothesis, Examining relationships using Correlation & Regression, Analysis of Variance, Multiple Correlation, PCA, Factor analysis, Discriminant functional analysis, Concepts & Methods of Design Experiments, Randomization & Blocking, Analysis OF variance technique, Factorial & Fractional designs, Taguchi’s concepts & Methods and second- order designs. REFERENCE BOOKS

1. George W. Collins, II, Fundamental Numerical Methods and data analysis, George W. Collins, II Press, 2003.

2. Hildebrand. F.B, Introduction to Numerical Analysis, McGraw- Hill book Co, 1956. 3. House holder. A.A, Principles of Numerical Analysis, McGraw Hill Book Co; 1953. 4. Hamming, R.W, Numerical methods for scientists and engineers, McGraw Hill Book Co;

1962. 5. W.W. Daniel, Biostatistics a Foundation for Analysis in the Health Sciences, John Wiley &

sons, 2000. 6. W. Ewans and G, Grant, Statistical method in Bioinformatics- An introduction, prentice

2001.

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SEMESTER II

BI0500 APPLICATION OF MATLAB IN BIOINFORMATICS L T P C 3 0 3 4

PURPOSE This course enables the students to understand various applications of MATLAB in Bioinformatics, Biological image analysis and Systems Biology. INSTRUCTIONAL OBJECTIVES

MATLAB functionalities. Bioinformatics Toolbox and its application. Image processing using MATLAB. SimBiology for modelling Biological system.

Getting started with MATLAB, Basic functionalities of MATLAB. Numerical methods using MATLAB. Bioinformatics toolbox and application: Bioinformatics Toolbox Key Features , Microarray Data Analysis and Visualization, Mass Spectrometry Data Analysis , Graph Theory, Statistical Learning, and Gene Ontology , Sequence Analysis, Importing Data and Deploying Applications. Image processing using MATLAB: Key Features ,Importing and Exporting Images, Pre- and Post-Processing Images, Analyzing Images, Spatial Transformations and Image Registration. SimBiology for modeling Biological System: Modeling, Simulation, Model of the Yeast Hetrotrimeric G Protein Cycle, Kinetic Laws, Kinetic parameters. LIST OF EXPERIMENTS 1. MATLAB basic commands. 2. Sequence analysis tools including functions for pairwise and multiple sequence alignment. 3. Microarray data analysis and visualization 4. Modeling Biological systems. ( SimBiology) 5. Simulation of Biological systems.(SimBiology) REFERENCES 1. G. Alterovitz, M. F. Ramoni, Systems Bioinformatics: An Engineering Case-Based Approach,

Artech House, 2007. 2. Semmlow, Biosignal and Biomedical Image Processing, Marcel Dekker, Inc., 2004. 3. Hoppensteadt, Peskin, Modeling and Simulation in Medicine and Life Sciences, Springer, 2002.

BI0502 FUNCTIONAL GENOMICS AND PROTEOMICS L T P C 3 0 3 4

PURPOSE This course enables the students to understand various applications of genomics and proteomics in bioinformatics INSTRUCTIONAL OBJECTIVES

This course provides a foundation in the following four areas; whole genome analysis; genome sequencing; annotation of genome and proteome analysis.

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Whole genome analysis: Prokaryotes and Eukaryotes, Foundations of genomics. Human genome project. Mapping of genome: linkage mapping, High resolution physical mapping, Marker associated and clone assisted genome mapping: Genome library construction: YAC, BAC and PAC libraries of genome. Genome sequencing, Hierarchical and shot gun sequencing methods, variation in sequencing methods, Pyroseqencing, Automation in genome sequencing, Sequence analysis, Databanks, data mining, The structure, function and evolution of the human genome. Strategies for large-scale sequencing projects, Human disease genes Expression, Bioinformatics for the analysis of sequence data, approaches for determining gene expression patterns and functions. Annotation of genome, experimental and computational approaches, Functional genomics, Experimental and computational approaches, Gene knockouts, yeast two hybrid system, gene expression profiling, microarrays, cDNA and Oligo array, DNA chips, Application of DNA arrays and SNPs. Tools for proteomics : 2D Electrophoresis, Protein digestion techniques and mass spectrometry, MALDI TOF, Analysis of proteins. Proteome analysis, Algorithms for proteomics, Protein expression profiling, protein arrays ,Protein-Protein interactions, protein microarrays. Advantages and disadvantages of DNA and protein microarrays. LIST OF EXPERIMENTS

1. Genome comparison 2. Genome rearrangements 3. Phylogenetic Reconstruction 4. Methods for detecting trans-membrane helices 5. Identification of proteins using database searches 6. Predicting Gene-Gene (Protein-Protein) interactions 7. Primer designing methods: degenerate and general oligonucleotide primers.

REFERENCES

1. Twyman, RM and Primrose, SB, Principle of Genome Analysis, Blackwell Publisher 2003.

2. Brown TA , Genomes 2, Wiley-Liss,2006. 3. T.W. Veenstra, TW and Tates III, JR , Proteomics for biological discovery, Wiley

2006. 4. Daniel C. Liebler, Introduction to Proteomics by. Humana Press, 2001. 5. A.M. Campbell, Discovering Genomics, Proteomics & Bioinfo, C.S.H. Press, 2003.

BI0504 STRUCTURAL BIOINFORMATICS L T P C 3 2 0 4

PURPOSE The structural bioinformatics course, aimed for life-scientists and chemists alike, will present the latest tools available in the field and their usage for derivation of Biological insight INSTRUCTIONAL OBJECTIVES

Structure-function relationships The tools available for the structural bioinformatics including description of their

algorithms

Data representation and databases-PDB, mmCIF and other formats, structure based databases for proteins and nucleic acids. Comparative features-the CATH domain structure Database, Protein Structure Evolution and the SCOP Database, Structural Quality Assurance, Structure Comparison and Alignment. Structure and Functional Assignment-Identifying Structural Domains in Proteins,

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Inferring Protein Function from Structure. Protein- protein interactions. Structural Modeling-Scoring functions: forcefields, surface area based functions, knowledge based potentials, searching procedures: grid based, stochastic methods, building complete protein structures using homology modeling, fold recognition, Ab initio methods. Validation: CASP and CAFASP experiments and their findings, role of structural bioinformatics .Analysis of Folds REFERENCES

1. Philip E. Bourne, Helge Weissig, Structural Bioinformatics, John Wiley & Sons, Inc, 2003. 2. Becker OM., MackKerell AD Jr., Roux B., Watanabe M (Eds.), Computational

Biochemistry and Biophysics, Dekker, 2001. 3. Hinchliffe A., Molecular Modelling for Beginners, Wiley, 2003. 4. Orengo CA., Jones DT., Thornton JM. (Eds.), Bioinformatics - Genes, Proteins and

Computers, Bios Scientific Publishers Ltd. 2003.

BI0506 STRUCTURAL CHEMISTRY OF BIOMOLECULES L T P C 3 0 0 3

PURPOSE This course enables the students to understand the chemical attributes of biological molecules INSTRUCTIONAL OBJECTIVES

Chemistry of Biomolecules Stereochemistry and conformational analysis

Quantum mechanics principle, Photoelectric effect and photos, Angular momentum. Structure and properties of atom: Schrodinger equation Bonding and Molecular structure, Chemical bonds and Lewis structure, Shapes of molecules (VSEPR) theory, hybrid orbital, HMO theory concepts of molecular orbital theory. Stereochemistry, Isomerism: Types, Stereoisomerism, Optical isomerism, Elements of symmetry and chirality, Resolution, Racemisation, Asymmetric synthesis, Walden inversion, Tautomerism. Conformational analysis: Configuration, Geometrical isomerism, optical activity, Specification of configuration of compounds. Methods to study of biomolecular structure: X-ray crystallography, optical, UV and IR spectroscopy, luminescence, fluorescence, magnetic resonance and electron microscopy, Cryo Electronmicroscopy, NMR. REFERENCES

1. Soni P.L, Text book of organic chemistry, 20th edition, Sultan chand and sons, 1997. 2. Morrison and Boyd, Study guide to organic chemistry, Allyn and Bacon, 1983. 3. E.L. Eliel and S.H. Wilen, Steriochemistry, John Wiley and sons 1994. 4. José Luis R. Arrondo, Alicia Alonso, Advanced techniques in biophysics, , Springer,

2006. 5. Charles R. Cantor, Paul Reinhard Schimmel, Biophysical Chemistry: Techniques for

the Study of Biological Structure and Function PART II, W. H. Freeman, 1980.

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SEMESTER III

BI0601 SEMINAR L T P C 1 0 0 1

PURPOSE This course enables the students to study the applications of the core subjects that were introduced in the earlier semesters INSTRUCTIONAL OBJECTIVES

Applications in Bioinformatics Topics Included

1. Synthetic Biology 2. Thermodynamics and Statistical kinetics 3. Pharmacogenomics 4. Immunoinformatics 5. IPR and Bioethics 6. Linux 7. Advances in Bioinformatics 8. Applications of Systems Biology

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ELECTIVE I

BI0507 ADVANCED BIOLOGY L T P C 3 0 0 3

PURPOSE This course enables the students to understand biology in detail INSTRUCTIONAL OBJECTIVES

It enables to survey the biological world which include topics of genetics, Cell biology and molecular biology

Cell Biology: Prokaryotic and eukaryotic cells (plant and animal cells), membranes and cellular compartments of cell organelles, Cell motility, cytoskeletal elements, cilia and flagella, motor proteins, Cell-cell interactions: Intercellular junctions, Photosynthesis, transportation of proteins in cells, signal transduction, Electron transport chain, Cell cycle and its regulation; events during mitosis and meiosis Genetics Molecular Biology: Mendelian principles of inheritance, sex linked inheritance, Concept of linkage, linkage maps and recombination, Mutations, Prokaryotic and eukaryotic genome organization and structure-Basic mechanism of replication, transcription and translation. Gene regulation, Mechanisms by which genome undergoes changes. REFERENCES

1. Thomas J. Kindt; Barbara A. Osborne; Richard A. Goldsby, Kuby Immunology, W.H. Freeman, 2007.

2. Bruce Alberts, Essential cell biology, Taylor & Francis, 1998. 3. Harvey F. Lodish, Arnold Berk, Molecular cell biology, W.H. Freeman, 2008. 4. Geoffrey M. Cooper, Robert E. Hausman, The cell: a molecular approach, Edn 4, ASM

Press, 2007. 5. Terence A. Brown, Genetics: a molecular approach, Chapman and Hall, 1990. 6. Peter J. Russell, Genetics, Edition 5, Benjamin/Cummings, 1998.

BI0509 OBJECT-ORIENTED PROGRAMMING AND DATABASE MANAGEMENT

L T P C

3 0 0 3 PURPOSE Designing database for different applications is an important area of program development and C++ as tools for solving problems INSTRUCTIONAL OBJECTIVES

C++ important concepts Design of database for any given problem

OOP concepts: data hiding, encapsulation, inheritance, overloading, polymorphism; classes and objects; constructor and destructor; Inheritance: single, multiple, multilevel- overloading: Function overloading, Operator overloading DBMS Architecture & Data Abstraction, DBMS Languages, DBMS System Structure, ER Model: Objects, Attributes and its Type, Entity and Entity Set, Relationship & Relationship Set. Design Issues in choosing attributes or entity set or relationship set: Constraints, Super Key, Candidate

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Keys, Primary Key, ER Diagram Notations. SQL: Overview, the Form of Basic SQL Query, UNION, INTERSECT, and EXCEPT- Nested Queries- Aggregate Functions- Null Values. REFERENCES

1. Herbert Schildt, The Complete Reference C++, Tata McGraw Hill, 2001. 2. Robert Lafore, Object Oriented Programming in Microsoft C++, Galgotia

Publications, 2002. 3. Abraham Silberschatz, Henry F. Korth, S. Sudarshan,, Database System Concepts,

McGraw-Hill , 4th Edition , 2002. 4. Elmashri & Navathe, Fundamentals of Database System, Addison-Wesley Publishing,

3rd Edition, 2000.

ELECTIVE II

BI0508 METABOLIC ENGINEERING L T P C 3 0 0 3

PURPOSE This course provides the fundamental knowledge on upcoming field of Metabolomics and the metabolic engineering in post genomic era INSTRUCTIONAL OBJECTIVES

Metabolome and its study Applications of Metabolomics Comprehensive models cellular reactions Metabolic flux analysis and its applications

Overview: Background and definitions of Metabolomics, importance of Metabolomics. Technologies: Mass spectrometry: principles, definitions, nomenclature, Metabolite isolation and analysis by Mass Spectrometry, Sample preparation, metabolite library, HPLC, capillary electrophoresis coupled with Mass spectrometry. Applications of Metabolomics to biology: examples and case studies, Metabolome informatics- data integration and mining. Metabolic engineering: introduction, mass balance, black box, metabolic flux analysis, stochiometry. Principles of metabolic engineering: flux balance analysis: flux balance methods, group based flux balance, metabolic control analysis: overview, control coefficients, methods of measuring control. Flux analysis of networks- top down approach, bottom up approach. REFERENCES

1. M. Tomita, T. Nishioka, Metabolomics- the Frontier of Systems Biology, Springer publications, 2003.

2. Gregory N. Stephanopoulos, Metabolic Engineering- Principles and Methodologies, Academic press, First Edition, 1998.

3. Wolfram Weckwerth, Metabolomics: Methods and Protocols, Humana Press, 2007. 4. Sang Yup Lee, E. Terry Papoutsakis, Metabolic engineering, , CRC Press, 1999. 5. William J. Griffiths, Metabolomics, metabonomics and metabolite profiling, Royal

Society of Chemistry, 2008.

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BI0510 MICROARRAY BIOINFORMATICS L T P C 3 0 0 3

PURPOSE This course gives the technical knowledge on Microarray techniques and data analysis. INSTRUCTIONAL OBJECTIVES

DNA Microarray and its statistical analysis Analysis of RNA data Statistical computing and Statistical Genetics

DNA MicroArray: The Technical Foundations, Why are MicroArray Important? What is a DNA MicroArray, Designing a MicroArray Experiment: The Basic steps, Types of MicroArray, NCBI and MicroArray Data Management, GEO (Gene Expression Omnibus), MAML, The benefits of GEO and MAML, The Promise of MicroArray Technology in Treating Disease, MicroArray Data, Preprocessing the Data, Data normalization, Measuring Dissimilarity of Expression Pattern, Distance Motifs and Dissimilarity measures, Visualizing MicroArray Data, Principal Component Analysis, PCA and MicroArray Data. KMeans Clustering, Hierarchical Clustering, Self Organization Maps (SOM). Identifying Genes: Expressed usually in a sample, Expressed significantly in population, Expressed differently in two populations. Classifying Samples from two populations using Multilayer Perceptron, Support Vector Machines and their applications, Using genetic algorithm and Perceptron for feature selection and supervised classification. Gene Ontology and pathway analysis, Promoter analysis and gene regulatory network, Coexpression analysis, CGH & Genotyping chips, Chromosome aberration and polymorphism via genome-wide scanning, Future direction of microarray approach, Pharmacogenomics, Toxicogenomics, Data mining.

REFERENCES

1. Arun Jogota , Microarray Data Analysis and Visualization, The Bay Press, 2001. 2. Ernst Wit and John McClure, Statistics for Microarrays Design, Analysis and Inference,

John Wiley & Sons, 2004. 3. Steen Knudsen, Guide to analysis of DNA Microarray data, John Wiley & Sons, 2004. 4. Dov Stekel , Microarray Bioinformatics, Cambridge University Press, 2003. 5. S. Draghic, Chapman, Data Analysis tools for DNA Microarray, Hall/ CRC Press, 2002.

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ELECTIVE III- ELECTIVE V MODULE I

BI0611 SYSTEMS BIOLOGY- MODELS AND APPROACHES L T P C

3 0 0 3 PURPOSE The course aims at introducing various concepts of systems biology, required for modeling and simulation of biological systems INSTRUCTIONAL OBJECTIVES

Basic Principle involved in Systems Biology. Standard Models and approaches in System Biology. Signal Transduction. Modeling of selected Biological process. Data Integration. Database and Modeling tools. Future of Systems Biology.

Principles of Systems Biology, Experimental techniques: Elementary Technique, Advanced High throughput Techniques: Protein chips, DNA chip Yeast two hybrid system, RNAi. Standard Models and approaches in Systems Biology: Metabolism, Enzyme Kinetics and thermodynamics, Law of mass action, Michaelis–Menten kinetics model and Monod-wyman-changeux model, Hills Equation. Other Biological Process: Signal transduction, Quantitative measures of properties of signaling pathway. Selected Biological process: Glycolytic oscillation, coupling of oscillator, cell cycle, Minimal cascade model, models of budding yeast cell cycle, ageing, Evolution of ageing process, Accumulation of defective mitochondria, Dilution of membrane damage, choice of parameters and simulation. Modeling of Gene expression, Bayesian networks, Boolean Networks Analysis of Gene expression data. The Model according to Griffith, The model according to Nicolis and Prigogine. Evolution and self organization: Quasispecies and Hypercycles, The Genetic Algorithm Spin-glass Model of Evolution, Boolean Network Models. Data integration: Basic Concepts of database integration and data management, Biclustering and data integration. Future applications of Systems Biology REFERENCES

1. Edda Klipp, Ralf Herwig, Systems Biology in Practice-Concepts, Implementation and Application, Wiley VCH, I Edition, 2005.

2. Bernhard Ø. Palsson, Systems Biology: Properties of reconstructed network, Cambridge university press, 2006.

BI0613 COMPUTATIONAL CHEMISTRY L T P C 3 0 0 3

PURPOSE This course provides knowledge on upcoming field of chemoinformatics. INSTRUCTIONAL OBJECTIVES

Representation and Manipulation of 2D ,3D Molecular Structures, Molecular descriptors High throughput screening Virtual screening

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QSAR Analysis Applications

Representation and Manipulation of 2D Molecular Structures, Representation and Manipulation of 3D Molecular Structures, Representations of Chemical Reaction, Databases and data sources in chemistry, Calculation of structure descriptors: structure keys and 1D fingerprints, topological descriptors, 3D Descriptors, Chirality descriptors, Further descriptors, Descriptors that are not structure based, properties of structure descriptors. Methods for data analysis: introduction, machine learning techniques, decision trees, Chemometrices, Neural Networks, Data mining, Computational Models: deriving a QSAR Equation: Simple and Multiple Linear Regression, Designing a QSAR "Experiment", Analysis of High-Throughput Screening Data, Virtual Screening, Applications: Predictions of properties of compounds, structure- spectra correlations, chemical reactions and synthesis design, Drug designing: molecular docking- De-novo ligand designing- and structure-based methods. REFERENCES

1. Andrew R Leach, Valerie J Gillet, An Introduction to Chemoinformatics, Kluwer academic publishers, 2003.

2. Johann Gasteiger, Thomas Engel, Chemoinformatics: A Textbook, Wiley-VCH, 2003, 3. Hugo Kubinyi ,3D QSAR in drug design: theory, methods and applications , , Springer,

1993. 4. Smith and Williams, Introduction to the principles of drug design and action, CRC Press,

2006 5. Barry A. Bunin, Brian Siesel, Chemoinformatics: theory, practice, & products, Springer,

2007.

BI0615 COMPUTER AIDED DRUG DESIGNING L T P C 1 0 6 3

PURPOSE To design a novel drug through in silico approach. INSTRUCTIONAL OBJECTIVES

In silico docking/scoring ADME and Toxicity prediction Pharmacophore modeling

Computer aided drug design, methods of computer aided drug design, ligand design methods, docking algorithms and programs, drug design approaches, absorption, distribution, metabolism, and excretion (ADME) property prediction, computer based tools for drug design. Pharmacophoric approach, Pharmacophore based ligand design, Pharmacophore concept, Pharmacophore elements and representation, active conformation, molecular superimposition, examples of 3D Pharmacophore models and their uses. LIST OF EXPERIMENTS

1. Homology modeling of a protein 2. Evaluate the 3D structure of a protein 3. Active site/cavity in a receptor 4. Building small molecules 5. ADME Predictions 6. Protein-ligand docking

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7. Protein-protein docking 8. Combinatorial library generation 9. Pharmacophore modeling 10. Virtual screening: Structure based designing and ligand based designing

REFERENCES

1. A. R. Leach, Molecular Modeling- Principles and applications, Prentice hall, II edition, 1996.

2. Paul S Charifson. , Practical application of CADD, Informa Health Care, 1997. 3. T.J. Perun and C.L. Propst, Computer Aided Drug Design, Informa Health Care, 1992. 4. Rastogi et al, Bioinformatics – Genomics, proteomics, and drug discovery, PHI Publishing,

2008. 5. Lab Manual.

MODULE II

BI0621 MACROMOLECULAR BIOPHYSICS L T P C 3 0 0 3

PURPOSE To understand the structural properties of various biomolecules and the energetics involved in various biological process. INSTRUCTIONAL OBJECTIVES

Understand the various structural properties of biomolecules The various physical techniques to view the molecule Understanding signal transmission in neurons Understanding the energetics involved in various biochemical processes

Structural Properties of Proteins And Nucleic Acids: Dissociation Characteristics of Amino acids, Ramachandran plot, Water and its properties, Collagen, Keratin, Elastin, Resilin, Ribose-phosphate backbone, B and Z family of DNA. Physics Of Biomolecules: Molecular Forces, Strong Force, Inter-molecular weak forces, Van der Waals Force, Lenard-Jones Potential, Hydrogen Bond, Hydrophobic-Hydrophilic Force, Principle of Molecular recognition. Physical Techniques In Structure Determination: Optical Rotary Dispersion, Circular Dichroism, Absorption Spectroscopy, Absorption by oriented molecules, X-ray absorption Spectroscopy, Flow Cytometry, Ultraviolet Spectroscopy, Infrared Spectroscopy. Neurobiophysics: Concepts of membrane transport, Membrane-pore diffusion, Active transport, Action potential, Signal transmission, Signal reception, Photoreceptors. Bioenergetics: Bioenergetics and ATP Molecules, Energetics of cellular respiration, Chemiosmotic Theory, Photosynthesis, Emersion Effect, Mechanism and energetics of muscle contraction. REFERENCES

1. P. Narayanan, Essentials of Biophysics, New Age International Ltd., II edition, 2007. 2. P.K. Srivastava, Elementary Biophysics an Introduction, Narosa Publishing House, 2005. 3. Charles R. Cantor, Paul Reinhard Schimmel, Biophysical Chemistry: The conformation of

biological macromolecule PART I, W. H. Freeman, 1980.

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BI0623 BIOMOLECULAR MECHANICS AND SIMULATION L T P C 3 0 0 3

PURPOSE This course tries to understand the various mechanical properties of biomolecules INSTRUCTIONAL OBJECTIVES

Basic concepts in Molecular Mechanics Empirical Force Field Models Computer Simulation Techniques Conformational analysis

Useful Concepts In Molecular Mechanics: Introduction, Coordinate systems, Units of Length and Energy, Potential Energy surfaces, other surfaces, Molecular Graphics. Computational Quantum Mechanics: One-electron atoms, Polyelectron atoms and molecules, Molecular orbitals, Hartree-Fock Equations, Molecular Properties using ab initio methods, Semi-empirical methods, Huckel Theory. Empirical Force Field Methods: Bond Stretching, Angle Bending, Torsional Terms, Non-bonded and electrostatic interactions, Van der Waals Interaction, Hydrogen bonding parameterization, United atom force field representation, Force field parameterization. Computer Simulation Methods: Simple Thermodynamic properties, Phase space, Practical aspects of Computer simulation, Boundaries, Truncating the potential, Minimum Image convention, Long-range forces. Conformational Analysis: Systematic methods for exploring conformational space, Random search methods, Evolutionary algorithms, Simulated Annealing, Restrained molecular methods, Molecular fitting, Clustering algorithm, Reducing dimensionality of data set, Pooling. Monte Carlo Simulations: Calculating properties by integration- metropolis methods- metropolis Monte Carlo methods- simulations of molecules- models- biased methods- different ensembles- calculating chemical potentials- Gibbs ensemble methods REFERENCES

1. Andrew R. Leach, Molecular Modeling- Principles and applications, Prentice hall, II edition, 1996.

2. Alan Hinchliffe, Modelling Molecular Structures, John Wiley, 2000. 3. K. I. Ramachandran, G. Deepa, Krishnan Namboor , Computational Chemistry and

Molecular Modeling: Principles and Applications, Springer, 2008. 4. Charles R. Cantor, Paul Reinhard Schimmel, Biophysical Chemistry: The Behavior of

Biological Macromolecules PART III, W. H. Freeman, 1980.

BI0625 MOLECULAR DYNAMICS L T P C 1 0 6 3

PURPOSE This course gives an insight into the kinetics and dynamics of the biomolecules INSTRUCTIONAL OBJECTIVES

Introduction to the dynamics of biomolecules Understanding the structural properties Energy Minimization in the folding process

Introduction to molecular dynamics (MD), Statistical mechanics for MD, Energy minimization, Equations of motion-finite difference methods, Constant energy MD simulations , Constant

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temperature and pressure MD simulations ,Brownian dynamics MD simulations, Molecular dynamics packages--CHARMM, AMBER, Energy calculations for complex interaction potentials, Energy minimization for complex interaction potentials, Molecular dynamics simulation of macromolecules, Free energy calculations ,Molecular dynamics trajectories for activated processes LIST OF EXPERIMENTS

1. Simulation of A, B and Z forms of DNA 2. Constant energy and constant temperature simulations of macromolecules. 3. Molecular dynamics simulation of a peptide fragment with known structures using AMBER 4. Energy minimization 5. Non-polarizable and polarizable rigid models and Flexible models in water and small

organic molecules 6. Structural and dielectric properties of a polar medium 7. Calculation of structure, energy and free energy through simulations. 8. Concept of hydrophobic and hydrophilic interactions

REFERENCES

1. Andrew R. Leach, Molecular Modelling- Principles and applications, Prentice hall, 1996. 2. Carl Branden and John Tooze, Introduction to protein structures, Garland publishing Inc.,

1999. 3. D.W. Heerman, Computer Simulation Methods, Springer- Verlag, 1990 . 4. J.A. McCammon and Stephen C. Harvey, Dynamics of proteins and nucleic acids,

Cambridge U. Press, 1987. 5. Lab Manual.

MODULE III

BI0631 UNIX & JAVA L T P C 3 0 0 3

PURPOSE An introduction to the UNIX operating system primarily focused on command line usage. It also covers the fundamentals of java. INSTRUCTIONAL OBJECTIVES

Evaluate, understand and execute fundamental UNIX operations Discuss and employ concepts in JAVA

File system, KDE, Gnome, Bourne shells, manipulating files and directories, Text editors, managing user groups, Shell programming, variables, flow control, File security and permissions, file name and substitutions, shell input and output ,Regular expressions merge and divide, login environment, creating screen input and output, Software development, source code management, revision control system, version control systems, GNU utility, GDB debugger JAVA: data types with variables and constants, Program control statements including if else, while, for and the switch as well as debugging techniques and recursion. Classes and objects (Object Oriented Programming OOP), Interfaces, Event handling, AWT, Applets, multithreading, Java Library. REFERENCES 1. Katherine Wrightson, Joseph Merlino, Mastering UNIX, Sybex publishers, 2000.

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2. Jerry Peek, Grace Todino & John Strang, Learning the Unix operating systems, Reilly Publications, 1997.

3. Bruce Eckel, Thinking in Java, Prentice Hall, 1999. 4. Herbert Schildt & Fatrick Nanghton, The complete reference Java 2.0, Tata McGraw-Hill,

2002.

BI0633 PYTHON FOR BIOINFORMATICS L T P C 3 0 0 3

PURPOSE To apply python for bioinformatics applications INSTRUCTIONAL OBJECTIVES

Data types and operations in python Python in HMM , SOMS, PCA and other applications

Running programs, types and operations, Functions, modules, classes, Exceptions, Object Oriented Programming, Threads, process, synchronization, databases and persistence, NumPy, SciPy, image manipulation, Akando and Dancer modules, Parsing DNA data files, Sequence Alignment, Dynamic programming, Hidden Markov Model, Genetic algorithms, Multiple Sequence Alignment , gapped alignment, trees, text mining, clustering, Self Organizing Map, Principal Component Analysis, Fourier transforms, Numerical Sequence Alignment, gene expression array analysis, Spot finding and Measurement, Spreadsheet Arrays and Data Displays, Applications with Expression Arrays REFERENCES 1. Jason Kinser , Python for Bioinformatics, Jones & Bartlett Publishers, 2008. 2. Mark Lutz, Learning Python, Edition: 3, O'Reilly, 2007. 3. Alex Martelli, David Ascher, Python cookbook, O'Reilly, 2002.

BI0635 PERL FOR BIOINFORMATICS L T P C 1 0 6 3

PURPOSE To understand the applications of perl in Bioinformatics. INSTRUCTIONAL OBJECTIVES

Basics of Perl Perl in Bioinformatics Bioperl

Perl in Bioinformatics: Basic concepts, Scalar data, Arrays and list data, Control structures, Hashes, Regular expressions: Concepts about regular expressions, simple uses of regular expressions, patterns, matching operator, substitutions, the split and join functions, Subroutines: System and user functions, the local operator, variable-length parameter lists, lexical variables. Filehandles and file tests: Opening and closing a filehandle, using pathnames and filenames, die, using Filehandles. Other data transformation: Finding a substring, extracting and replacing a substring. Formatting data: Sorting, Transliteration

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CGI programming: The CGI.pm Module, CGI program in context, simple CGI programs, passing parameters via CGI, Perl and the Web. Bioperl: Introduction, Installation procedures, Architecture, Uses of bioperl LIST OF EXPERIMENTS

1. Hello World: an introduction to Perl programming. How to run a Perl program. Variables and data types and the importance of syntax.

2. Input/output and more syntax 3. Algorithms, control statements and loops 4. Files and filehandles 5. Subroutines 6. Comparing files. Combining and extracting data from different files 7. BioPerl and object-orientation. Assignment: Using ready-made modules 8. Searching for conserved motifs in a multiple alignment. 9. Using hash tables to store and sort motifs. 10. CGI- Perl Programs.

REFERENCES

1. James D. Tisdall, Mastering Perl for bioinformatics, O' Reilly, 2003. 2. Cynthia Gibas & Per Jamesbeck, Developing ioinformatics Compuer skills, O’ Rilley

& Associates, 2000. 3. Rex A.Dawyer, Genomic Perl, Cambridge University Press, 2003. 4. Randal L.Schawrtz and Tom Phoneix, Learning Perl, 3rdEdition, O’ Reilly, 2008. 5. Lab Manual.