M. TECH BIOINFORMATICS Syllabus
NUMERICAL METHODS & BIOSTATISTICS
Subject Code : 14BBI11
IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100
COURSE OBJECTIVES
i. The objective of this course is to make students learn basic concepts to solve the numerical and improve their problem solving ability.
ii. To improve their ability to analyze the statistical data to optimize.
MODULE 1
Introduction to statistics and study design: Introduction to statistics, data, variables, types of data, tabular, graphical and pictorial representation of data. Significance of statistics to biological problems, experimental studies; randomized controlled studies, historically controlled studies, cross over, factorial design, cluster design, randomized; complete, block, stratified design, biases, analysis and interpretation.
MODULE 2
Descriptive statistics and Observational study design: Types of variables, measure of spread, logarithmic transformations, multivariate data. Basics of study design, cohort studies, case-control studies, outcomes, odd ratio and relative risks.
Principles of statistical inference: Parameter estimation, hypothesis testing. Statistical inference on categorical variables; categorical data, binomial distribution, normal distribution, sample size estimation
MODULE 3
Comparison of means: Test statistics; t-test, F distribution, independent and dependent sample comparison, Wilcoxon Signed Rank Test, Wilcoxon-Mann-Whitney Test, ANOVA.
Correlation and simple linear regression: Introduction, Karl Pearson correlation coefficient, Spearman Rank correlation coefficient, simple linear regression, regression model fit, inferences from the regression model, ANOVA tables for regression.
Multiple linear regression and linear models: Introduction, Multiple linear regression model, ANOVA table for multiple linear regression model, assessing model fit, polynomials and interactions. One-way and Two-way ANOVA tables, F-tests. Algorithm and implementation using numerical methods with case studies.
MODULE 4
Design and analysis of experiments: Random block design, multiple sources of variation, correlated data and random effects regression, model fitting. Completely randomized design, stratified design. Biological study designs. Optimization strategies with case studies.
MODULE 5
Statistics in microarray, genome mapping and bioinformatics: Types of microarray, objectives of the study, experimental designs for micro array studies, microarray analysis, interpretation, validation and microarray informatics. Genome mapping, discrete sequence matching and programs for mapping sequences.
COURSE OUTCOMES
i. This course will help in understanding of the basic concepts of the statistics.
ii. Design and develop different mathematical skills to design and analyze the experimental models.
iii. Students demonstrate the ability to use numerical and statistical methods to solve life science oriented data.
TEXT/REFERENCE BOOKS
1. Biostatistics by Alvin E. Lewis McGraw-Hill Professional Publishing, 2013
2. J.D. Lee and T.D. Lee. Statistics and Numerical Methods in BASIC for Biologists, Van Nostrand Reinhold Company, 1982.
3. Wolfgang Boehm and Hartmut Prautzsch. Numerical Methods, A K Peters/CRC Press, 1993.
4. Statistical Analysis of Gene Expression Microarray Data, edited by T.P. Chapman & Hall/CRC, Speed. 2003.
5. A Primer of Genome Science by G. Gibson & S.V. Muse., Sinauer Associates, 2001.
6. Bioinformatics and Computational Biology Solutions using R and Bioconductor, edited by R. Gentleman, Springer, 2005.
7. John F. Monahan. Numerical Methods of Statistics (Cambridge Series in Statistical and Probabilistic Mathematics), Cambridge University Press, Edition, 2011.
8. Joe D. Hoffman. Numerical Methods for Engineers and Scientists, CRC Press, 2nd Edition, 2001.
9. Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) by: Warren J. Ewens Gregory Grant, Springer, 2005.
10. The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman. Springer, 2001.
11. Statistical Methods in Bioinformatics by Warren John Ewens, Gregory R. Grant, Gregory Grant, R., Springer, 2005.
ALGORITHMS & SOFTWARE TOOLS
Subject Code : 14BBI153
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 various algorithms that are used in developing softwares. It will help in learning various softwares used in modern biology.
MODULE 1
Basics of Perl. Introduction to BioPerl and BioPerl Objects – Brief descriptions (Seq, PrimarySeq, LocatableSeq, RelSegment, LiveSeq, LargeSeq, RichSeq, SeqWithQuality, SeqI), Location objects, Interface objects and Implementation objects. Sequence Representation: Representing large sequences (LargeSeq), Representing changing sequences (LiveSeq). Accessing Sequence data – Using Bioperl: Accessing sequence data from local and remote databases, Accessing remote databases (Bio::DB::GenBank, etc), Indexing and accessing local databases (Bio::Index::*, bp_index.pl, bp_fetch.pl, Bio::DB::*). Sequence and Alignment format Interconversion – Transforming sequence files (SeqIO), Transforming alignment files (AlignIO). Performing Sequence analysis – Global alignment, Local alignment, Multiple sequence alignment, Parsing BLAST alignment report and Parsing multiple sequence alignment.
MODULE 2
Introduction to python. Python basics – Variables, Operators, Data types and Assignments. Statements – Input/output statements, flow control – IF…THEN….ELSE, SWITCH, FOR, MAP, FILTER and WHILE, goto statements. Names, Functions and Modules. Object Oriented Programming in Python: Introduction to object oriented programming in python. Classes and objects. Inheritance, Polymorphism. Constructors and Destructors. Exception handling. Biopython and Bioinformatics: Parsing DNA data files, Image manipulation, Sequence analysis – Sequence alignment (pair wise and multiple sequence alignment), Dynamic Programming, Detecting tandem repeats and generating Hidden Marko Models, Simulation of EST Clustering. Data mining – Text mining, Simulating Genetic algorithm. Analysis of Microarray data – Spot finding and Measurement.
MODULE 3
Introduction to the NCBI C++ Toolkit: Introduction to C++ modules – CORELIB, ALGORITHM, CGI, CONNECT, CTOOL, DBAPI, GUI, HTML, OBJECT MANAGER, SERIAL and UTIL module. C++ Toolkit Library Reference: CORELIB Module – Writing simple applications, Namespaces, CNcbiRegistry Class, Portable Stream Wrappers. Working with diagnostic streams – Debug Macros, Handling exceptions, CObject and CRef Classes and Atomic counters. Executing commands and Spawining processes using CExec class, working with files and directories using CFile and CDir, Input /Output utility class.
MODULE 4
Introduction to MatLab and molecular forces; Bioinformatics ToolBox, Statistics ToolBox, Distributed computing server, Signal Processing ToolBox. The Matlab working environment.Variables, constants and reserved words. Arrays and matrices. Scripts. The debugger. Generating 2D and 3D Graphics. Simple statistical analysis. String manipulation. Boolean logic and if statements. Loops (while, for). Functions & Files. Program design. Matlab structures. Complexity.
MODULE 5
Overview of the R language: Defining the R project, Obtaining R, Generating R codes, Scripts, Text editors for R, Graphical User Interfaces (GUIs) for R, Packages. R Objects and data structures: Variable classes, Vectors and matrices, Data frames and lists, Data sets included in R packages, Summarizing and exploring data, Reading data from external files, Storing data to external files, Creating and storing R workspaces. Manipulating objects in R: Mathematical operations (recycling rules, propagation of names, dimensional attributes, NA handling), Basic matrix computation (element-wise multiplication, matrix multiplication, outer product, transpose, eigenvalues, eigenvectors), Textual operations, Basic graphics (high-level plotting, low-level plotting, interacting with graphics.
COURSE OUTCOMES
i. Students will learn about various algorithms used in software development.
ii. Students will gain knowledge about various softwares and their applications.
TEXT / REFERENCE BOOKS
1. Java Foundations by John Lewis, Peter Joseph DePasquale, Joseph Chase, Joe Chase, Addison- Wesley, 2010.
2. Perl Programming for Biologists by D. Curtis Jamison, Wiley-IEEE, 2003.
3. Bioinformatics Programming Using Python by Mitchell L Model, O’Reilly Media, Inc., 2009.
4. Alain F. Zuur, Elena N. Ieno, and Erik Meesters. A Beginner’s Guide to R. Use R. Springer, 2009.
5. Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon. Bioconductor case studies. Springer, 2008
6. Robert Gentleman. Bioinformatics with R. Chapman & Hall/CRC, Boca Raton, FL, 2008.
7. Robert Gentleman. R Programming for Bioinformatics. Computer Science & Data Analysis. Chapman & Hall/CRC, Boca Raton, FL, 2008.
8. Peter Dalgaard. Introductory Statistics with R. Springer, 2nd edition, 2008.
9. Python for Bioinformatics (Chapman & Hall/CRC), Sebastian Bassi, 2009.
10. BioJava: A Programming Guide by Kaladhar D S V G K, 2012.
11. Python for bioinformatics by Jason M. Kinser, Jones & Bartlett Learning, 2009.
12. Mastering Perl for Bioinformatics by James T Tisdall, 2007.
13. D. Curtis Jamison. Perl Programming for Biologists,John Wiley & Sons,2003
14. James Tisdall. Mastering Perl for Bioinformatics, O’Reilly Media, Inc,2003.
DATA STRUCTURES IN C & C++
Subject Code : 14BBI12B
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 basic principles of problem solving, implementing through C programming language and to design and develop programming
skills. To know about data structures and their applications.
MODULE 1
Basic concepts: Variables, Operators, Statements, Functions and Pointers. Introduction to Classes, Objects and Object oriented design, C++ string classes. Features of Object Oriented Programming – Encapsulation, Inheritance and Polymorphism. Introduction to C++ modules – CORELIB, ALGORITHM, CGI, CONNECT, CTOOL, DBAPI, GUI, HTML, OBJECT MANAGER, SERIAL and UTIL module.
MODULE 2
Stacks: Stack specifications, Lists and Arrays. Reversing a list, Information hiding, Standard template library, Implementation of Stacks, Specification of methods for Stacks. Class Specification, Pushing, Popping, and Other Methods. Encapsulation, Abstract Data Types and Their Implementations.
Queues: Definitions, Queue Operations, Extended Queue Operations, Implementations of Queues – Circular Implementation of Queues, Demonstration and Testing. Application of Queues –
Simulation, Functions and Methods of the Simulation.
Linked Stacks and Queues: Pointers and Linked structures, Introduction and Survey, Pointers and Dynamic memory in C++. Basics of linked structures – Linked stacks, Linked stacks with safeguards, Destructor, Overloading Assignment Operator, Copy Constructor, Modified linkedstack specification. Linked queues – Basic declarations, Extended linked queues, Abstract Data Types and Their implementations.
MODULE 3
Recursion: Introduction to Recursion, Stack Frames for Subprograms, Tree of Subprogram Calls,
Factorials: A Recursive Definition, Divide and Conquer (Towers of Hanoi). Principles of Recursion – Designing recursive algorithms. Tail Recursion, Refinement.
Lists and Strings: List definition, Method specifications, Implementation of lists, Class templates, Contiguous implementation, Simply linked implementation. Variation: Keeping the Current Position, Doubly Linked Lists, Comparison of Implementations. Strings – Strings in C++, Implementation of strings, String operations. Linked lists in Arrays.
Searching: Searching: Introduction Basic search types – Sequential search, Binary search, Ordered lists. Algorithm Development. Asymptotics – Introduction, Orders of Magnitude, Big-O and
Related Notations.
Sorting: Introduction, Storable Lists. Sort types – Bubble sort, Insertion sort, Merge sort, Selection sort, Shell sort, Divide-and-Conquer sorting, Merge sort for linked lists, Ordered insertion. Linked version. Analysis – Algorithm, Contiguous implementation and Comparisons. Analysis of Merge sort. Quick sort for Contiguous lists, Partitioning the list, Analysis of Quicksort, Comparison with Merge sort. Heaps and Heapsort, Analysis of Heapsort. Two-Way trees as lists. Priority Queues.
MODULE 4
Tables and Information Retrieval: Introduction. Tables of various shapes, Triangular tables, Rectangular tables Jagged tables, Inverted tables. Tables: New Abstract Data Type, Hashing, Sparse tables. Collision resolution with Open Addressing, Collision Resolution by Chaining, Analysis of Hashing.
Trees: Basic terminology. Binary trees – Binary tree representation, algebraic Expressions, Complete binary tree, Extended binary tree, Array and Linked representation of Binary trees. Traversing binary trees, threaded binary trees. Traversing Threaded binary trees, Huffman algorithm.
Searching and Hashing: Sequential search, binary search, comparison and analysis. Hash table, Hash functions, Collision resolution strategies. Hash table implementation. Binary search trees,
Ordered lists and implementations, Tree search, Insertion into a Binary search tree, Tree sort, Removal from a Binary search tree, Building a binary search tree. Random search trees and Optimality splay trees. Self-Adjusting Data Structure.
MODULE 5
Multiway Trees: Orchards and Binary trees, On the Classification of Species, Ordered Trees, Forests and Orchards. Lexicographic search trees. Searching for a Key. Insertion and Deletion
from a tree. Assessment of trees, External Searching – B-Trees, Access time multiway search trees, Balanced multiway trees, Insertion into a B-Tree.
Graphs: Terminology & Representations, Graphs & Multi-graphs, Directed Graphs, Sequential representations of graphs – Adjacency matrices, Traversal, Connected component and Spanning
Trees, Minimum Cost Spanning Trees.
File Structures: Physical Storage Media File Organization, Organization of records into blocks, Sequential files, Indexing and Hashing – Primary indices, Secondary indices, B+ Tree index files, B tree index files, Indexing and Hashing Comparisons.
COURSE OUTCOMES
i. Achieve knowledge of design and development of problem solving skills
ii. Understand the basic principles of programming in C and C++
iii. Understanding the basic concepts of data structures
iv. Developing the ability to write programs applicable to the field of life science.
TEXT / REFERENCE BOOKS
1. C++ plus data structures by Nell B. Dale, Jones & Bartlett Learning, 2007.
2. Data Structures and Program Design in C++ by Robert Kruse, Alexander Ryba, Prentice Hall, 2001
3. Principles Of Data Structures Using C And C++ by Vinu V. Das, New Age International, 2006
4. Data Structures by S. Lipschutz, Mc-Graw Hill International Editions, 1986.
5. An introduction to data structures with Applications by Jean-Paul Tremblay, Paul. G. Soresan, Tata Mc-Graw Hill International Editions, 2nd edition 1984.
6. Data structures via C++ by A. Michael Berman, Oxford University Press, 2002.
7. Data Structures and Algorithm Analysis in C++ by M. Weiss, Pearson Education, 2002.
ESSENTIAL BIOINFORMATICS
Subject Code : 14BBI13
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 basic concepts of bioinformatics and the importance of biological databases and tools.
MODULE 1
Bioinformatics & Biological Databases: Introduction to Bioinformatics, Goals, Scope, Applications in biological science and medicine and Limitations,
a) Sequence Databases
b) Structure Databases
c) Special Databases and applications: Genome, Microarray, Metabolic pathway, motif, multiple sequence alignment and domain databases. Mapping databases – genome wide maps.
Chromosome specific human maps. Applications of these databases. Database Similarity Searching: Unique Requirements of Database Searching. Heuristic Database searching, Basic Local Alignment Search Tool (BLAST), FASTA, Comparison of FASTA and BLAST, Database Searching with the Smith–Waterman Method.
MODULE 2
Sequence Alignment: Evolutionary basis, Homology vs Similarity, Similarity vs Identity. Types of Sequence alignment – Pairwise and Multiple sequence alignment, Alignment algorithms, Scoring
matrices, Statistical significance of sequence alignment.
Multiple Sequence Alignment: Scoring function, Exhaustive algorithms, Heuristic algorithms, Practical issues.
Profiles and Hidden Markov Models: Position-Specific scoring matrices, Profiles, Markov Model and Hidden Markov Model.
MODULE 3
Prediction Motifs and Domains: Motif and Domain databases, Identification of Motifs and Domains in Multiple Sequence Alignment using Regular expressions, Motif and Domain databases statistical models, Protein Family databases, Motif Discovery in unaligned sequences. Sequence logos.
Gene and Promoter Prediction: Promoter and Regulatory elements in Prokaryotes and Eukaryotes. Promoter and Regulatory element prediction – algorithms. Gene prediction. Gene prediction in
Prokaryotes and Eukaryotes. Categories of Gene Prediction Programs. Prediction algorithms. Discussions with case studies.
MODULE 4
Predictive Methods: Predictive methods using Nucleic acid sequence – DNA framework, Masking of repetitive DNA, predicting RNA secondary structure, Finding RNA genes, Detection of functional sites and Codon bias in the DNA. Predictive methods using protein sequence – Protein identity and Physical properties. Structure prediction – Prediction of secondary structure of protein, Antigenic sites, Active sites, Folding classes, specialized structures and Tertiary structures. Discussions with case studies. Concepts involved in insilico Primer Designing and developing Restriction Maps.
MODULE 5
Molecular Phylogenetics: Phylogenetics Basics. Molecular Evolution and Molecular
Phylogenetics – Terminology, Gene Phylogeny vs Species Phylogeny, Forms of Tree Representation. Phylogenetic Tree Construction Methods and Programs – Distance-Based
Methods, Character-Based Methods. Phylogenetic Tree evaluation methods. Phylogenetic analysis software and algorithms. Bootstrap methods.
COURSE OUTCOMES
i. Understanding the importance of different biological databases.
ii. Students will be able to use the different software’s and tools.
TEXT / REFERENCE BOOKS
1. Essential Bioinformatics by Jin Xiong, Cambridge University Press, 2006.
2. Essentials of Drug Designing by V. Kothekar, DHRUV Publications, 2005.
3. Systems Biology: Applications and Perspectives by Bringmann, Springer, 2007.
4. Bioinformatics and Molecular Evolution by Paul G. Higgs, Teresa K. Attwood, Blackwell, 2005.
5. Bioinformatics Basics: Applications in Biological Science and Medicine by Lukas, 2005.
6. Bioinformatics – The Machine Learning Approach, Pierre Baldi and Søren Brunak, 2001.
7. Current Protocols in Bioinformatics by Andreas D. Baxevanis, Published by Wiley, 2003
8. Bioinformatics: Sequence and Genome Analysis By David Mount, 2004
9. Andreas D Baxevanis, B. F Francis Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. III Edition. Wiley John & sons, 2005.
10. Introduction to Bioinformatics: Anna Tremonton, CRC Press, Taylor & Francis, 2006.
11. Introduction to Bioinformatics: Arthur Lesk, III edition, Oxford Publications.2009.
12. Stan Tsai. An introduction to computational Biochemistry. Wiley John & sons, inc., publication, 2002.
BIOMOLECULAR MODELING & SIMULATION
Subject Code : 14BBI14
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 basic concepts of structural features of proteins, the modeling tools and their use in modern biology.
MODULE 1
Biomolecular Structure and Modeling: Historical Perspective, Introduction to Molecular Modeling, Roots of Molecular modeling in Molecular mechanics. Introduction to X-Ray crystallography and NMR spectroscopy. Introduction to PDB and 3D Structure data, Structure of PDB and other 3D Structure record.
Protein Structure Hierarchy: Structure Hierarchy. Helices – Classic α-Helix and π Helices, Left- Handed α-Helix and Collagen Helix. β-Sheets – Turns and Loops. Supersecondary and Tertiary structure. Complex 3D Networks. Classes in Protein Architecture – Folds, α-Class, Bundles, Folded leaves, Hairpin arrays. β-Class folds, Anti-parallel β domains, parallel and Anti-parallel Combinations. α/β and α+β-Class, α/β Barrels, Open twisted α/β folds, Leucine-rich α/β folds. α+β folds. Quaternary structure. Discussions with case studies.
MODULE 2
Force Fields: Formulation of the Model and Energy, Quantifying Characteristic Motions, Complex Biomolecular Spectra, Spectra as force constant sources, In-Plane and Out-of-Plane Bending.
Bond Length Potentials – Harmonic term, Morse term, Cubic and Quadratic terms. Bond Angle Potentials – Harmonic and Trigonometric terms, Cross bond stretch / Angle bend terms. Torsional
potentials – Origin of rotational barriers, Fourier terms, Torsional parameter Assignment, Improper torsion, Cross dihedral/Bond angle, Dihedral terms. Van der Waals potentials. Rapidly decaying potential. Parameter fitting from experiment. Two parameter calculation protocols. Coulomb potential – Coulomb’s Law. Slowly decaying potential, Dielectric function and Partial charges. Discussions with case studies.
MODULE 3
Molecular modeling: Modeling basics. Generation of 3D Coordinates Crystal data, Fragment libraries, and conversion of 2D Structural data into 3D form. Force fields, and Geometry optimization. Energy minimizing procedures – Use of Charges, Solvent effects and Quantum Mechanical methods. Computational tools for Molecular modeling. Methods of Conformational analysis – Systematic search procedures, Monte Carlo and molecular dynamics methods.
Determining features of proteins – Interaction potential, Molecular electrostatic potential, molecular interaction fields, Properties on molecular surface and Pharmacophore identification.
MODULE 4
3D QSAR Methods. Comparative protein modeling – Conformational properties of protein structure, Types of secondary structural elements, Homologous proteins. Procedures for sequence
Alignments, Determination and generation of structurally conserved regions, Construction of structurally variable regions, Side-Chain modeling, Secondary structure prediction, Threading methods. Optimization and Validation of Protein Models with suitable case studies. Computation of the Free Energy: Free energy calculations in Biological Systems – Drug design, Signal
transduction, Peptide folding, Membrane protein association, Numerical methods for calculating the potential of mean force, Replica-Exchange-Based Free-Energy Methods.
MODULE 5
Membrane Protein Simulations: Membrane proteins and their importance, Membrane protein environments in vivo and in vitro. Modeling a complex environment – Simulation methods for
membranes, Membrane protein systems, Complex solvents, Detergent micelles, Lipid bilayers, Self-Assembly and Complex systems. Modeling and Simulation of Allosteric regulation in
enzymes – Discussions with case studies.
Electrostatics and Enhanced Solvation Models: Implicit solvent electrostatics in Biomolecular Simulation, New distributed multipole methods. Quantum mechanical principles and applications to force field development with case studies.
COURSE OUTCOMES
i. Students will learn about structural features of proteins.
ii. Students will gain insights into the various tools used for modeling of small molecules, lipids and proteins.
TEXT / REFERENCE BOOKS
1. Molecular Modeling by Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, Gerd Folkers, 2008.
2. Modeling of Bimolecular Structures and Mechanisms by Alberte Pullman, Joshua Jortner, 1995.
3. Mathematical Approaches to Biomolecular Structure and Dynamics by Jill P. Mesirov, Klaus Schulten, De Witt L. Sumners, 1996.
4. Foundations of Molecular Modeling and Simulation by Peter T. Cummings, Phillip R. Westmorland, Brice Carnahan, Published by American Institute of Chemical Engineers, 2001.
5. New Algorithms for Macromolecular Simulation by Timothy J. Barth, Michael Griebel, David E.Keyes, Risto M. Nieminen, Dirk Roose, Tamar Schlick, Published by Springer, 2006.
6. Nicolas Claude Cohen, Guidbook on molecular modeling in drug design Academic Press. Elsevier, 1996.
7. Tamar Schlick. Molecular Modeling and Simulation: An Interdisciplinary Guide: An Interdisciplinary Guide. Second Edition, Springer. 2010.
8. Tamar Schlick, Innovations in Biomolecular Modeling and Simulations, Volume 2, RSC Publishing. 2012.
DNA CHIPS & MICROARRAY DATA ANALYSIS
Subject Code : 14BBI151
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 concepts of DNA chips and microarray technology. This course will also deal with microarray data analysis and interpretation.
MODULE 1
Introduction to Biochip and Microarray Construction: Basics of Biochips and Microarray Technology, Biochip technologies. Types of Biochips – DNA Microarrays, Oligonucleotide, cDNA and genomic microarrays, Integrated biochip system. Biochip versus gel-based methods.
Limitations of biochip technology. Biochip construction -Megac10ne technology for fluid microarrays, Microarray labels, Microarray scanners, Microarray robotics. Microfluidics systems,
Chips and Mass Spectrometry. Electrical detection methods for microarrays. Applications of Biochips – Tissue Chip, RNA Chip, Protein Chip Technology, Glycochips, Biochip assays, Combination of microarray and biosensor technology.
MODULE 2
Microarray Data analysis: Introduction, Image Acquisition and Analysis, Detection of differential gene expression. Pathway analysis tools. Data validation.
Genomic Signal Processing: Introduction, Mathematical models, and Modeling DNA Microarray data – Singular Value Decomposition algorithm. Online Analysis of Microarray Data Using
Artificial Neural Networks – Introduction, Methods. Signal Processing and the Design of Microarray. Time-Series Experiments.
MODULE 3
Predictive Models of Gene Regulation: Introduction, Regression approach to cis-regulatory element analysis, cooperativity. Spline models of cooperative gene regulation. Statistical framework for gene expression data analysis – Materials and methods. Analysis of comparative genomic hybridization data on cDNA microarrays – Introduction, materials and methods. Interpreting microarray results with gene ontology and MeSH – Introduction, materials and methods. Incorporation of gene ontology annotations to enhance microarray data analysis – Materials and methods.
MODULE 4
DNA Computing: Introduction, Junctions, other shapes, Biochips and large-scale structures. Strand algebras for DNA computing – Introduction, Strand Algebras. Discussion of Robinson and Kallenbach’s methods for designing DNA shapes, DNA cube, computing with DNA, Electrical analogies for biological circuits, Challenges, Future Trends. Discussions with case studies. DNA programming – Deoxyribozyme-Based Logic Gate design processes. Renewable, Time responsive DNA Logic Gates for scalable digital circuits. Design of Bimolecular device.
MODULE 5
Applications of Biochip Technology: Molecular diagnostics, Pharmacogenomics, application of microarray technology in drug discovery and development, Use of DNA chip technology for drug safety, drug delivery, population genetics and epidemiology. Applications of Microarray technology in Forensics. DNA chip technology for water quality management, Application of micro arrays in the agro-industry; use of microarrays in Genetic disease monitoring.
Commercial Aspects of Biochip Technology: Markets for biochip technologies, Commercial and Government support for biochip development, Business strategies, and Patent issues.
COURSE OUTCOMES
i. Students will learn about various DNA chips and microarray techniques.
ii. Students will gain insights into the methods used to analyse and interpret the microarray data.
iii. Students will learn the applications of DNA chips and microarray technology in modern biology.
TEXT / REFERENCE BOOKS
1. Biochips and Microarrays – Technology and Commercial Potential Published by: Informa Global Pharmaceuticals and Health Care.
2. Functional Protein Microarrays in Drug Discovery by Paul F. Predki, CRC Press – Publisher
3. DNA Computing: 15th International Meeting on DNA Computing, DNA 15, Fayetteville, AR, USA, June 8-11, 2009, Springer, 2009.
4. DNA Arrays: Technology and Experimental Strategies by Grigorenko, E.V (ed), CRC Press, 2002.
5. Microarry Analysis by Mark Schena; J. Wiley & Sons, New York, 2002.
6.Andreas D Baxevanis, B. F Francis Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. III Edition. Wiley John & sons, 2005.
7.Aidong Zhang. Advanced analysis of gene expression microarray data, World Scientific, 2006.
8.Pierre Baldi, G. Wesley Hatfield. DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling.Cambridge University Press.2002.
9. Wan-Li Xing, Jing Cheng. Biochips: Technology and Applications, Springer. 2003.
DEVELOPMENT OF WEB BASED TOOLS
Subject Code : 14BBI152
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 developing life science oriented web based tools and their use in bioinformatics.
MODULE 1
Introduction to Java: Java and Java applications. Java Development Kit (JDK). Byte Code, JVM; Object-oriented programming. Simple Java programs. Data types and Tokens: Boolean variables, int, long, char, operators, arrays, white spaces, literals, assigning values. Creating and destroying objects. Access specifiers. Operators and Expressions: Arithmetic Operators, Bitwise operators,
Relational operators, Assignment Operator, The ? Operator; Operator Precedence. Logical expression. Type casting, Strings. Control Statements: Selection statements, iteration statements, Jump Statements.
MODULE 2
Classes, Inheritance, Exceptions: Classes. Classes in Java – Declaring a class, Class name, Super classes, Constructors. Creating instances of class. Inner classes. Inheritance: Simple, multiple, and multilevel inheritance; Overriding, overloading. Exception handling: Exception handling in Java. Multi Threaded Programming: Multi Programming: Extending threads; Implementing rentable.
Synchronization, Changing state of the thread. Bounded buffer problems, Read-write problem, Producer-Consumer problems.
Event Handling: Two event handling mechanisms, Delegation event model, Event classes; Sources of events; Event listener interfaces. Delegation event model; Adapter classes; Inner classes.
MODULE 3
Applets: The Applet Class: Two types of Applets, Applet basics, Applet Architecture, An Applet skeleton; The HTML APPLET tag; Passing parameters to Applets, Simple Applet display
methods; Requesting repainting; Using the Status Window. getDocumentbase() and getCodebase(); ApletContext and showDocument(); The AudioClip Interface; The AppletStub Interface;
Drawing Lines; Drawing Other Stuff; Color; Mouse Input; Keyboard Input and Output to the Console. Threads and Animation, Backbuffers, Graphics, and Painting; Clocks. Playing with
text: Introduction to 2D arrays and hyperlinks, 3D Graphics – Basic classes.
MODULE 4
Java 2 Enterprise Edition Overview, Database Access: Overview of J2EE and J2SE. The Concept of JDBC; JDBC Driver Types; JDBC Packages; A Brief Overview of the JDBC process; Database Connection; Associating the JDBC/ODBC Bridge with the Database; Statement Objects; ResultSet; Transaction Processing; Metadata, Data types; Exceptions.
MODULE 5
Servlets: Background; The Life Cycle of a Servlet; Using Tomcat for Servlet Development; Simple Servlet; The Servlet API. The Javax.servlet Package. Reading Servlet Parameter, Javax.servlet.http package, Handling HTTP Requests and Responses. Cookies and Session Tracking.
COURSE OUTCOMES
i. Students will learn about various developing web based tools and their applications.
ii. Students will gain knowledge about various web based tools and their applications.
TEXT / REFERENCE BOOKS
1. Java – The Complete Reference, 7th Edition by Herbert Schildt, Tata McGraw Hill, 2007.
2. J2EE – The Complete Reference by Jim Keogh, Tata McGraw Hill, 2007.
3. Java 2D Graphics by Jonathan Knudsen, O’Reilly, 1999.
4. Introduction to JAVA Programming, 6th Edition by Y. Daniel Liang, Pearson Education, 2007.
5. The J2EE Tutorial, 2nd Edition by Stephanie Bodoff et al, Pearson Education, 2004.
6. Introduction to Java Programming Comprehensive Version (7th Edition) by Y. Daniel Liang, Pearson Prentice Hall – Publisher, 2010.
7. Java foundations by Todd Greanier, John Wiley and Sons, 2004.
CONCEPTS IN BIOTECHNOLOGY
Subject Code : 14BBI12A
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 basic concepts life science and different cellular processes.
MODULE 1
Introduction to Biology; Macromolecules; Carbon chemistry; Proteins: Structure, folding, catalysis; Nucleic acids: DNA & RNA; storage and transfer of genetic information; Lipids: membranes, structure & function; Carbohydrate chemistry, energy storage, building blocks.
MODULE 2
Cell Structure: Eukaryotic and Prokaryotic cells, plant and animal cells, structure of nucleus, mitochondria, ribosomes, Golgi bodies, lysosomes, endoplasmic reticulum, chloroplast, vacuoles;
Cell cycle and cell division: Different phases of cell cycle, cell division: Mitosis and meiosis.
Mendelian law of inheritance: Monohybrid and dihybrid inheritance, law of segregation and independent assortment; Gene Interaction; Multiple alleles, supplementary and complementary genes, epistasis. Identification of genetic material: classical experiments; chromosome structure and organization, chemical composition of chromatin, structural organization of nucleosomes, heterochromatin, polytene and lamp-brush chromosomes, human chromosomes, chromosomal disorders.
MODULE 3
Scope and History of microbiology, Introduction to the structure and functions of microorganism: Bacteria, Viruses, Fungi and Protozoan’s. Microscopy and microbial techniques: Study of microscopes; sterilization techniques: Heat, steam, Radiation, Filtration and chemical methods; Pure culture techniques: Serial Dilution, Streak, Spread, Pour Plate. Immune System, Innate and adaptive immunity, antigens and antibodies; types of immune response, hypersensitivity. Humoral immunity: B-lymphocytes, Immunoglobulin classes, Major Histocompatibility Complex (MHC). Cell mediated immunity. Thymus derived lymphocytes (Tcells), Antigen presenting cells (APC); Immunity to infection, Cytokines.
MODULE 4
Scope of agricultural biotechnology, Role of Micorbes in agriculture, Biopesticides, Bio fertilizers (Nitrogen fixing microbes), GM crops. Plant metabolic engineering and industrial products:
Molecular farming for the production of industrial enzymes, biodegradable plastics, antibodies, edible vaccines. Metabolic engineering of plants for the production of fatty acids, industrial oils, flavonoids etc. Basic aspects of Food & Nutrition.
MODULE 5
Industrially important Microorganisms, Preservation techniques, Different media for fermentation, basic structure of fermentor and different types. Types of fermentation processes (surface,
submerged, and solid state) and their products (ethanol, citric acid, lactic acid, enzymes, antibiotics). Biological treatment of waste water, primary, secondary and tertiary treatments. Bio indicators, Bioremediation of xenobiotic compounds, Bioleaching of minerals from ores, Bio-sorption of toxic metals. Solid waste management. Biofuel production from agricultural wastes.
COURSE OUTCOMES
i. Obtain the knowledge of the biomolecules present in the cell.
ii. Study carious biological pathways in bacteria, animals and plants.
iii. Understanding the concepts to solve the environmental related problems.
TEXT/REFERENCE BOOKS
1. De Robertis EDP and De Robertis Jr. EMF (2001) Cell and Molecular Biology, Wippincott Williams and Wiilkins publisher
2. Strickburger M W, Principles of Genetics, 3rd edition, Prentice Hall Publication, India
3. Gardner, Simmonns and Snustad, Principles of Genetics 8edition 2005
4. Salisbury F B and Ross C W (1991) Plant Physiology, CBS
5. P S Verma, V R Agarwal, Cell Biology, Genetics, Evolution and Ecology, New Publisher Delhi, 2007
6. Hopkins WG (2008) Introduction to Plant Physiology, 4th edition, Wiley
7. Concise Medical Physiology- Sujit K Chaudhari, 5th Ed. New Central Book Agency. Pvt Ltd
8. Tizard. Immunology an Introduction, Thomas. 2004
9. Plant biotechnology in Agriculture by K. Lindsey and M.G.K. Jones, Prentice hall, New Jersey.
10. Plant Biotechnology, Prakash and Perk, Oxford & IBH Publishers Co.
11. Plant Biotechnology by J Hammond, P McGarvey and V Yusibov, Springer Verlag.
12. Biotechnology in Crop Improvement by HS Chawla, Intl Book Distributing Company.
13. Biodegradation and Detoxification of Environmental Pollutants by Chakrabarthy AM. CRC Press.
14. Tortora, Funke, Case, Microbiology: An Introduction (2012). 11 edition. Benjamin Cummings
15. Ananthanarayan and Paniker. Textbook of Microbiology (2005). 7 edition. Orient Blackswan.
16. Wulf Cruegar and Anneliese Cruegar, A textbook of Industrial Microbiology, Panima Publishing Corporation.
BIOINFORMATICS LAB
Subject Code : 14BBI16
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. Sequence retrieval from nucleic acid and protein databases.
2. Retrieval of information about structure, bioassay, physical and Chemical properties of chemical compounds (such as Drugs and naturally occurring compounds).
3. Gene sequence assembly and contig mapping and identification of Gene.
4. Primer and Promoter design for a given sequences
5. Sequence searches using FASTA and BLAST, and Phylogenetic analysis.
6. Prediction of secondary structure for given protein and RNA sequences.
7. Retrieval of protein structure from PDB and its visualization and modification.
8. Prediction of 3D structure of unknown protein sequence.
9. Prediction of protein-protein interactions.
10. EST clustering and EST mapping, and Genome annotation
11. Microarray data analysis- normalization, clustering.
12. Study of Profiles, Patterns and PSSMs
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.