Download M. TECH Bio Informatics Syllabus [PDF]
ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS
Subject Code : 14BBI253
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of artificial intelligence and applications of artificial intelligence in bioinformatics.
MODULE 1
Introduction to Artificial Intelligence: Introduction to Artificial Intelligence, Problems, Approaches and tools for Artificial Intelligence. Introduction to search, Search algorithms,
Heuristic search methods, Optimal search strategies. Use of graphs in Bioinformatics. Grammers, Languages and Automata.
Current Techniques of Artificial Intelligence: Probabilistic approaches: Introduction to probability, Bayes’ theorem, Bayesian networks and Markov networks.
MODULE 2
Classification methods: Nearest Neighbour method, Nearest Neighbour approach for secondary structure protein folding prediction, Clustering and Advanced clustering techniques. Identification Trees – Gain criterion, Over fitting and Pruning. Nearest Neighbour and Clustering Approaches for Bioinformatics.
MODULE 3
Applications: Genetic programming, Neural Networks for the study of Gene-Gene interactions. Artificial neural networks for reducing the dimensionality of expression data. Cancer classification with Microarray data using Support Vector Mechanics. Prototype based recognition of splice sites. Analysis of Large-Scale mRNA expression data sets by genetic algorithms. Artificial Immune Systems in Bioinformatics. Evolutionary algorithms for the protein folding problem. Considering Stem-Loops as sequence signals for finding Ribosomal RNA genes. Assisting cancer diagnosis.
MODULE 4
Neural Networks: Methods and Applications. Application of Neural Networks to Bioinformatics. Genetic algorithms and Genetic programming: Single-Objective Genetic algorithm, Multi- Objective Genetic algorithm. Applications of Genetic algorithms to Bioinformatics. Genetic programming – Method, Applications, Guidelines and Bioinformatics applications. Boolean Networks, Bayesian Networks and Fuzzy Neural Networks with case studies.
MODULE 5
Applications of Neural Networks: Introduction, Modeling gene regulatory networks. QSAR and structure prediction with case studies.
COURSE OUTCOMES
i. Students will learn about concepts of artificial intelligence and their applications in bioinformatics.
ii. Students will gain knowledge about neural networks applications of neural networks in bioinformatics.
TEXT / REFERENCE BOOKS
1. Artificial Intelligence Methods and Tools for Systems Biology by Werner Dubitzky, Francisco Azuaje, Published by Springer, 2005.
2. Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems by Edward Keedwell, Ajit Narayanan, published by John Wiley and Sons, 2005.
3. Computational Intelligence in Bioinformatics by Arpad Kelemen, Ajith Abraham, Yuehui Chen, SpringerLink (Online service) Published by Springer, 2008.
4. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications by Tomasz G. Smolinski, Mariofanna G. Milanova, Aboul Ella Hassanien Published by Springer, 2008.
5. Artificial Intelligence: A Modern Approach by Stuart Jonathan Russell, Peter Norvig, John F. Canny, Published by Prentice Hall, 2003.
6. Stuart Jonathan Russell, Peter Norvig. Artificial Intelligence: A Modern Approach, Prentice Hall, 2010
7. Zheng Rong Yang. Machine Learning Approaches to Bioinformatics.World Scientific, 2010
8. Suranjan Panigrahi, K. C. Ting.Artificial intelligence for biology and agriculture.Kluwer Academic Press, 1998.
9. Edward Keedwell, Ajit Narayanan. Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems, John Wiley & Sons,2005.
CHEMOINFORMATICS & COMPUTATIONAL MEDICINAL CHEMISTRY
Subject Code : 14BBI22
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about importance of chemoinformatics in drug discovery and their use in modern biology.
MODULE 1
Introduction to Chemoinformatics: Fundamental concepts – molecular descriptors and chemical spaces, chemical spaces and molecular similarity, modification and simplification of chemical spaces. Compound classification and selection – cluster analysis, partitioning, support vectors machines. Predicting reactivity of biologically important molecules, combining screening and structure – ‘SAR by NMR’, computer storage of chemical information, data formats, OLE, XML, web design and delivery. Representing intermolecular forces: ab initio potentials, statistical potentials, force fields, molecular mechanics.
MODULE 2
Chemoinformatics Databases: Compound availability databases, SAR databases, chemical reaction databases, patent databases and other compound and drug discover databases. Database search methods: Chemical indexing, Proximity searching, 2D and 3D Structure and Substructure searching.
Similarity Searching: Structural queries and Graphs, Pharmacophores, Fingerprints. Topological analysis. Machine learning methods for similarity search – Generic and Neural networks. Library design – Diverse libraries, Diversity estimation, Multi-objective design and Focused libraries.
MODULE 3
Computational Models: Introduction, Historical Overview, Deriving a QSAR Equation. Simple and Multiple Linear Regression. Designing a QSAR “Experiment”. Principal Components Regression, Partial Least Squares. Molecular Field Analysis and Partial Least Squares.
Quantitative Structure-Activity Relationaaship Analysis: Model building, Model evaluation, 3DQSAR, 4D-QSAR. Methods of QSAR analysis – Monte Carlo methods, Simulated annealing, Molecular dynamics and Probabilistic methods. Virtual screening and Compound filtering.
MODULE 4
Virtual Screening: Introduction. “Drug-Likeness” and Compound filters. Structure-based virtual screening and Prediction of ADMET Properties. Discussions with case studies.
Combinatorial Chemistry and Library Design: Introduction. Diverse and Focused libraries. Library enumeration. Combinatorial library design strategies. Discussions with case
studies.
MODULE 5
Interaction of ‘receptors’ with agonists and antagonists. Receptor structure prediction methods. Enzyme kinetics and Interaction of enzymes with inhibitors (competitive, non-competitive). Drug discovery pipeline. Optimisation of lead compound, SAR (structure-activity relationships), Physicochemical and ADME properties of drugs and Prodrugs. QSAR (Quantitative structureactivity relationships), Combinatorial synthesis. Case studies (e.g. G-coupled protein receptor agonists and antagonists, antibacterial agents etc).
COURSE OUTCOMES
i. Students will learn about various chemoinformatics databases and their importance in drug discovery process.
ii. Students will gain knowledge about chemistry of medicinal compounds.
TEXT / REFERENCE BOOKS
1. Chemoinformatics: Theory, Practice, & Products by Barry A. Bunin, Jürgen Bajorath, Brian Siesel, Guillermo Morales, 2005.
2. Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics) by Rongling Wu, Min Linen, Chapman & Hall/CRC, 2008.
3. An Introduction to Chemoinformatics by Andrew R. Leach, Valerie J. Gillet, Springer, 2007.
4. Chemoinformatics: Theory, Practice, & Products by Barry A. Bunin, Jürgen Bajorath, Brian Siesel, Guillermo Morales, Royal Society of Chemistry, 2006.
5. Chemoinformatics Approaches to Virtual Screening by Alexandre Varnek, Alex Tropsha. Royal Society of Chemistry, 2008.
6. Chemoinformatics by Johann Gasteiger Wiley-VCH, 2003.
7. “An introduction to medicinal chemistry”, 5th edition, G. L. Patrick, Oxford University Press, New York.
8. Young D. C., Computational Drug Design: A Guide for Computational and Medicinal Chemists, John Wiley & Sons, 2009.
9. Peter Bladon, John E. Gorton, Robert B. Hammond. Molecular Modelling: Computational Chemistry Demystified.RSC Publishing, 2012.
10. Lee Banting, Tim Clark, David E. Thurston, Drug Design Strategies: Computational Techniques and Applications. RSC Publishing, 2012.
COMPUTATIONAL SYSTEMS BIOLOGY
Subject Code : 14BBI23
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of modeling of biological processes and their representation.
MODULE 1
Introduction to Systems Biology: Scope, Applications. Concepts, implementation and application. Databases for Systems Biology, Mass Spectrometry and systems Biology. Bioinformatics databases supporting systems biology approaches.
MODULE 2
Network Models and Applications: Natural Language Processing and Ontology enhanced Biomedical data mining, text mining. Integrated Imaging Informatics – ntegrin, centroid, cell culture. Standard platforms and applications – metabolic control analysis, glycolysis, metabolic network, Michaelis-Menten kinetics, and flux balance analysis. Signal Transduction – phosphorylation, Jak-Stat pathway, MAP kinase. Biological Processes – mitochondria, cyclin, Cdc2. Modeling of Gene Expression – lactose, lac operon, tRNA. Analysis of Gene ExpressionData – support vector machines, cDNA microarray. Evolution and Self organization – hypercycle, quasispecies model, self-replication. Reconstruction of metabolic network from Genome Information.
MODULE 3
Integrated Regulatory and Metabolic Models – Phosphorylation, Gene expression, and Metabolites. Estimation Modeling and Simulation – Circadian rhythms, Petri net, mRNA.
Deterministic – Circadian rhythms, mRNA, Circadian oscillations. Multi scale representations of Cells and Emerging Phenotypes – Gene Regulatory Networks, attractor, and Boolean functions. Mathematical models and Optimization methods for De Novo Protein design. Global Gene expression assays. Mapping Genotype – Phenotype relationship in cellular networks.
MODULE 4
Multiscale representations of cells and Emerging phenotypes: Multistability and Multicellurarity, Spatio-Temporal systems biology, Interactomics, Cytomics – from cell state to predictive medicine.
MODULE 5
Modeling Tools: SBML, MathML CellML, Petri Nets and Boinformatics with case studies.
COURSE OUTCOMES
i. Students will learn about modeling and simulation of various biological processes using bioinformatics tools.
ii. Students will gain knowledge about importance of modeling and simulation of biological processes.
TEXT / REFERENCE BOOKS
1. Computational Systems Biology by Andres Kriete, Roland Eils. Academic Press, 2006.
2. Systems Biology by Andrzej K. Konopka, CRC, 2006.
3. Systems biology in practice: concepts, implementation and application by Edda Klipp, Wiley- VCH, 2005.
4. Systems Biology by Isidore Rigoutsos, G. Stephanopoulos, Published by Oxford University Press US, 2006.
5. Theoretical Models in Biology by Glenn Rowe, Oxford University Press – Publisher, 2004.
6. Transactions on Computational Systems Biology I by Corrado Priami, Springer – Publisher, 2009.
7. Systems Biology by Fred C. Boogerd, H.V. Westerhoff, Elsevier – Publisher, 2007.
8. Sangdun Choi. Introduction to Systems Biology, Humana Press.2007.
9. Michael G. Katze.Systems Biology. Springer, 2013.
10. Konopka A.K. Systems Biology: Principles, Methods, and Concepts. CRC Press, Tailor & Francis.2007.
11. Robert A. Meyers. Systems Biology, Wiley Blackwell. 2012.
ADVANCED DBMS
Subject Code : 14BBI24
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of databases, database management data warehousing and security.
MODULE 1
Overview: PL/SQL – Introduction to PL/SQL – Declare, begin statements, Variables, Control Structure, PL/SQL Transactions – Save point, Cursor, PL/SQL Database
Objects – Procedures, Functions, Packages, Triggers. Programmatic SQL – Embedded SQL, Dynamic SQL, and ODBC Standard.
MODULE 2
Transaction processing and concurrency control: Definition of Transaction and ACID properties. Transaction Processing – Transaction-processing monitors, transactional
workflows, main-memory databases, real-time transaction systems, long-duration transactions, transaction management in multi-databases. Concurrency Control – Locks, Optimistic Concurrency Control (Backward and Forward validations), Timestamping Concurrency Control.
MODULE 3
Object-based databases and xml: Object-based databases – Complex data types, structured types and inheritance in SQL, table inheritance, array and multiset types in SQL, object identity and reference types in SQL, implementing O-R features, Persistent programming languages, OO vs OR. XML – Structure of XML, Document Schema, Querying and Transformation, API in XML, XML applications.
MODULE 4
Data warehousing: Introduction to Data Warehousing – Concepts, Benefits and Problems, DW Architecture – Operational Data, load manager, meta data, DW Data flows – inflow, upflow, meta flow, DW tools and technologies – Extraction, cleansing and transformation tools, DW DBMS, admin and management tools, data marts – reasons and issues, Data Warehousing using Oracle.
Data Warehousing Design – Designing, Dimensionality modeling, Design methodology, DW deign using Oracle.
Olap and data mining: On-line Analytical Processing – OLAP BenchMarks, applications, benefits, tools, categories, extensions to SQL, Data mining – introduction, techniques, predictive modeling, tools. Data mining algorithms – Apriori, Decision tree, k-means, Bayesian classifier.
MODULE 5
Database security: Security and integrity threats, Defence mechanisms, Statistical database auditing & control. Security issue based on granting/revoking of privileges, Introduction to statistical database security. PL/SQL Security – Locks – Implicit locking, types and levels of locks, explicit locking, Oracles’ named Exception Handlers.
COURSE OUTCOMES
i. Students will learn about structure of databases and different types of databases.
ii. Students will gain knowledge about database management, warehousing and security related issues.
TEXT / REFERENCE BOOKS
1. Advanced DBMS by Rini Chakrabarti, Shilbhadra Dasgupta, Wiley.
2. Avi Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts, McGraw-Hill.
3. C. J. Date, An Introduction to Database Systems, Addison-Wesley Longman Publishing Co.
4. Advance Database Management System by Arihant Khicha, Neeti Kapoor.
PROTEIN ENGINEERING & DESIGN
Subject Code : 14BBI251
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of engineering of proteins using various techniques for the biological applications.
MODULE 1
Amino acids (the students should be thorough with three and single letter codes) and their molecular properties (size, solubility, charge, pKa), Chemical reactivity in relation to posttranslational modification (involving amino, carboxyl, hydroxyl, thiol, imidazole groups) and peptide synthesis.
MODULE 2
Primary structure: peptide mapping, peptide sequencing – automated Edman method and Mass Spectrometry. High-throughput protein sequencing setup Secondary structure: Alpha, beta and loop structures and methods to determine Super-secondary structure: Alpha-turn-alpha, beta-turnbeta (hairpin), beta-sheets, alpha-beta-alpha, topology diagrams, up and down & TIM barrel structures nucleotide binding folds, sites Tertiary structure: Domains, denaturation and renaturation, protein folding pathways, overview of methods to determine 3D structures, Interaction with electromagnetic radiation (radio, micro, infrared, visible, ultraviolet, X-ray) and elucidation of protein structure. Quaternary associations: Modular nature, formation of complexes.
MODULE 3
Overview of protein structure, PDB, structure based classification, databases, visualization tools, structure alignment, domain architecture databases, protein-ligand interactions. Covalent, Ionic, Hydrogen, Coordinate, hydrophobic and Vander walls interactions in protein structure. Bioinformatics Approaches: Secondary structure prediction and determination of motifs, profiles, patterns, fingerprints, super secondary structures, prediction of substrate binding sites, tertiary structure, quaternary structure, methods to determine tertiary and quaternary structure, post translational modification.
MODULE 4
Methods of protein isolation, purification and quantification; large scale synthesis of engineered proteins, design and synthesis of peptides; methods of detection and analysis of proteins. Protein database analysis, methods to alter primary structure of proteins, examples of engineered proteins, protein design, principles and examples. Advantages and purpose, overview of methods, underlying principles with specific examples: thermal stability T4-lysozyme, recombinant insulin to reduce aggregation and inactivation, de novo protein design.
MODULE 5
DNA-binding proteins: prokaryotic transcription factors, Helix-turn-Helix motif in DNA binding, Trp repressor, Eukaryotic transcription factors, Zn fingers, helix-turn helix motifs in
homeodomain, Leucine zippers, Membrane proteins: General characteristics, Trans- membrane segments, prediction, bacteriorhodopsin and Photosynthetic reaction center. Immunoglobulins: IgG Light chain and heavy chain architecture, abzymes and Enzymes: Serine proteases, understanding catalytic design by engineering trypsin, chymotrypsin and elastase, substrateassisted catalysis other commercial applications.
COURSE OUTCOMES
i. Students will learn about proteins and engineering of proteins for biological applications.
ii. Students will gain knowledge about isolation of proteins, examples of important proteins that are used for engineering.
TEXT / REFERENCE BOOKS
1. Moody P.C.E and A.J Wilkinson. Protein Engineering, IRL Press, Oxford University Press.
2. Protein Science by Arthur M Lesk, Oxford University Press.
3. Protein Structure by Creighton, Oxford University Press.
4. Introduction of protein structure by Branden C and Tooze R., Garland.
5. The molecular modeling perspective in drug design by N Claude Cohen, Academic Press.
6. Bioinformatics Methods & Applications: Genomics, Proteomics & Drug Discovery, S C Rastogi, N Mendiratta & P Rastogi, PHI.
7. Young D. C., Computational Drug Design: A Guide for Computational and Medicinal Chemists, John Wiley & Sons, 2009.
8. Jeffrey L. Cleland, Charles S. Craik. Protein engineering: principles and practice, Wiley-Liss, 1996.
9. Paul R. Carey. Protein Engineering and Design, Acdemic Press Inc., 1996.
10. In Silico Lead Discovery. Maria A. Miteva, Bentham Books, 2011.
11. Kenneth M. Merz, Jr, Dagmar Ringe, Charles H. Reynolds. Drug Design: Structure- and Ligand-Based Approaches,Cambridge University Press, 2010.
DATA WAREHOUSING & DATA MINING
Subject Code : 14BBI252
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of data warehousing and issues related with data warehouse design. Students will also learn about concepts of data mining, algorithms and evaluation of data mining results.
MODULE 1
Introduction to Data Warehousing: Heterogeneous information, Integration problem. Warehouse architecture. Data warehousing, Warehouse vs DBMS.
Aggregations: SQL and Aggregations, Aggregation functions and Grouping.
Data Warehouse Models and OLAP Operations: Decision support; Data Marts, OLAP vs OLTP. Multi-Dimensional data model. Dimensional Modelling. ROLAP vs MOLAP; Star and snowflake schemas; the MOLAP cube; roll-up, slicing, and pivoting.
MODULE 2
Issues in Data Warehouse Design: Design issues – Monitoring, Wrappers, Integration, Data cleaning, Data loading, Materialised views, Warehouse maintenance, OLAP servers and Metadata. Building Data Warehouses: Conceptual data modeling, Entity-Relationship (ER) modeling and Dimension modeling. Data warehouse design using ER approach. Aspects of building data warehouses.
MODULE 3
Introducing Data Mining: KDD Process, Problems and Techniques, Data Mining Applications, Prospects for the Technology.
CRISP-DM Methodology: Approach, Objectives, Documents, Structure, Binding to Contexts, Phases, Task, and Outputs.
Data Mining Inputs and Outputs: Concepts, Instances, Attributes. Kinds of Learning, Kinds of Attributes and Preparing Inputs. Knowledge representations – Decision tables and Decision trees, Classification rules, Association rules, Regression trees & Model trees and Instance-Level representations.
MODULE 4
Data Mining Algorithms: One-R, Naïve Bayes Classifier, Decision trees, Decision rules, Association Rules, Regression, K-Nearest Neighbour Classifiers.
MODULE 5
Evaluating Data Mining Results: Issues in Evaluation; Training and Testing Principles; Error Measures, Holdout, Cross Validation. Comparing Algorithms; Taking costs into account and Trade-Offs in the Confusion Matrix.
COURSE OUTCOMES
i. Students will learn about data warehouse design and concepts of data warehousing.
ii. Students will gain knowledge about data mining algorithms and evaluation of data mining results.
TEXT / REFERENCE BOOKS
1. Fundamentals of Data Warehouses by M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis (ed.), Springer-Verlag, 1999.
2. The Data Warehouse Toolkit by Ralph Kimball, Wiley 1996.
3. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by I. Witten and E. Frank, Morgan Kaufman, 1999.
4. Data Mining: Concepts and Techniques by J. Han and M. Kamber, Morgan Kaufman, 2000.
5. Principles of Data Mining by D. Hand, H. Mannila and P. Smyth., MIT Press, 2001.
6. Data Mining: Introductory and Advanced Topic by M. H. Dunham, Prentice Hall, 2003.
7. Intelligent Data Warehousing by Zhengxin Chen, CRC Press, 2002.
8. Heuristics and optimization for knowledge discovery by Ruhul A. Sarker, Hussein A. Abbass, Charles Sinclair Newton, Charles Newton. Idea Group Inc (IGI), 2002.
GENOMICS & PROTEOMICS
Subject Code : 14BBI21
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
The objective of this course is to make students learn about genomes, regulation gene expression and their effect on cell. This course also gives insights of proteins and their role in biology of cell.
MODULE 1
Introduction: Introduction to Genomics & Proteomics. Structure, Organization and features of Prokaryotic & Eukaryotic genomes. C-values of eukaryotic genomes – coding, noncoding and repetitive sequences. Organisation of genome within nucleus, mitochondria and chloroplast.
Genome mapping: Genetic and physical mapping. Polymorphisms. Molecular markers – RFLP, AFLP, RAPD, SCAR, SNP, ISSR, and Protein markers – Allozymes and Isozymes, Telomerase. FISH – DNA amplification markers and Cancer biomarkers. Genome sequences databases and Genome annotation and Gene Ontology.
MODULE 2
Genome Sequencing: Recent developments and next generation sequencing, ultra-highthroughput DNA Sequencing using Microarray technology. Genome sequencing projects on H. Influenzae, E. coli, Oriza sativum and Neem. Human-genome project. Raw genome sequence data, Gene variation and associated diseases, diagnostic genes and drug targets. Genotyping-DNA Chips.
Comparative and Functional Genomics: Studies with model systems such as Yeast, Drosophila, C. elegans, Arabidopsis. Approaches to analyze global gene expression –
transcriptome, Serial Analysis of Gene Expression (SAGE), Expressed Sequence Tags (ESTs), Massively Parallel Signature Sequencing (MPSS), microarray and its applications, gene tagging.
MODULE 3
Genome annotation: Extrinsic, Intrinsic (Signals and Content), Conservative information used in gene prediction. Frameworks for Information integration – Exon chaining. Generative models: Hidden Morkov Models, Discriminative learning and Combiners. Evaluation of Gene prediction methods – Basic tools, Systematic evaluation and Community experiments (GASP, EGASP and NGASP).
Functional annotation of Proteins: Introduction, Protein sequence databases, UniProt, UniProtKB – Sequence curation, Sequence annotation, Functional annotation, annotation of protein structure, post-translational modification, protein-protein interactions and pathways, annotation of human sequences and diseases in UniProt and UniProtKB. Protein family classification for functional annotation – Protein signature methods and Databases, InterPro, InterProScan for sequence classification and functional annotation. Annotation from Genes and Protein to Genome and Proteome.
MODULE 4
Proteomics: Scope, Experimental methods for studying proteomics, methods of protein isolation, purification and quantification. Methods for large scale synthesis of proteins.
Applications of peptides in biology. Analysis of proteome – High throughput screening – Yeast two hybrid system and Protein chips, engineering novel proteins, Mass Spectroscopy based protein expression and post-translational modification analysis. Bioinformatics analysis – clustering methods. Analysis of proteome functional information.
MODULE 5
Applications of Computational Tools towards Proteomics studies (to be discussed with appropriate case studies) – Applications of proteome analysis to drug development and
toxicity, phage antibodies as tools for proteomics, glycoanalysis in proteomics, proteomics as tools disease diagnostics and plant genetics. Chromatographic data analysis. Chromatogram sequence alignment and editing. CGH and Genotype Array Analysis. X-Ray data and spectroscopic data analysis. 2D PAGE image analysis. MS data analysis.
COURSE OUTCOMES
i. Students will learn about genome organization, gene regulation & their role in biology of cell.
ii. Students will gain knowledge about protein and role in biology.
TEXT / REFERENCE BOOKS
1. Pharmacogenomics by Werner Kalow, Urs A. Meyer, Rachel F. Tyndale, Informa Healthcare, 2005.
2. Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics) by Rongling Wu, Min Linen, Chapman & Hall/CRC, 2008.
3. Genes VIII by Benjamin Lewis, Jones and Bartlett Publisher, 2006.
4. Genomics and Proteomics by Sándor Suhai, Springer, 2000.
5. Modern genome annotation: the BioSapiens Network by Dmitrij Frishman, Alfonso Valencia, Springer, 2008.
6. Discovering genomics, proteomics and bioinformatics by A. Malcolm Campbell, Laurie J. Heyer, Published by Pearson/Benjamin Cummings, 2006.
7. Bioinformatics Genomics, and Proteomics by Ann Batiza, Ann Finney Batiza, Published by Chelsea House Publishers, 2005.
8. Plant Genomics and Proteomics by: Christopher A. Cullis, Wiley-Liss 2004.
9. Stephen R. Pennington, Michael J. Dunn. Proteomics: From Protein Sequence to Function. Garland Science, 2001
10. Darius M. Dziuda. Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data. John Wiley & Sons, 2010.
11. Christopher A. Cullis. Plant Genomics & Proteomics, John Wiley & Sons, 2004.
12. Ann Finney Batiza Bioinformatics, Genomics, and Proteomics: Getting the Big Picture, Infobase Publishing,2006.
MODELING & SIMULATIONS LAB
Subject Code : 14BBI26
IA Marks : 25
No. of Hrs./ Week : 03 Exam Hrs : 03
Total No. of Lecture Hrs. : 36 Exam Marks : 50
COURSE OBJECTIVES
The objective of this course is to make the students learn about developing bench skills through lab exercises, oriented towards utilizing various web based tools for bioinformatics projects.
1. Homology Modeling of Receptors
2. Docking of small molecules into Receptors active sites.
3. Modeling Protein-Protein Interactions
4. Modeling mutations and Single Nucleotide Polymorphisms
5. Modeling Nanopores for Sequencing DNA
6. Simulation of lipid bilayer.
7. Simulation of Water Permeation through Nanotubes
8. Simulation of “Forcing Substrates through Channels”
9. Design of polymeric membranes – modeling and simulation diffusion studies of small gas molecules in polymeric materials.
10. Virtual sequencing (base calling, Sequence assembly, Mapping assembly, Contig mapping)
11. Analysis of NGS (next generation sequencing) data
12. Genome annotation and Comparative Genomics studies
COURSE OUTCOMES
i. Students would learn to appreciate the various algorithms used for diverse exercises.
ii. Students would gain knowledge about various softwares and their multitude of applications.