BIOINFO SYlubs

VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELGAUM

SCHEME OF TEACHING AND EXAMINATION FOR

M.TECH. BIOINFORMATICS (BBI)

I Semester

Subject

Code Name of the Subject

Teaching hours/week

Duration of

Exam in Hours

Marks for

Total

Marks Lecture

Practical/

Field Work/

Assignment/

Tutorials

I.A. Exam

12BBI11B

12BBI11C

$Data Structures in C & C++ /

$$ Biomolecules, Molecular Biology and

Genetic Engineering

4 2# 3 50 100 150

12BBI12 Statistical & Probabilistic methods for

Bioinformatics 4 2# 3 50 100 150

12BBI13 Essential Bioinformatics 4 2* 3 50 100 150

12BBI14 Biomolecular Modeling & Simulation 4 2* 3 50 100 150

12BBI15X Elective – I 4 2 3 50 100 150

12BBI16 Seminar -- 3 -- 50 -- 50

Total 20 13 15 300 500 800

Elective – I

12BBI151 DNA Chips & Microarray Data Analysis

12BBI152 Computational Biology

12BBI153 Health Informatics

$ Not for CSE & ISE students $$ Not for Biotechnology Students

M.Tech Full Time Scheme [New] Page 1




II Semester

Subject


Teaching hours/week

Duration of

Exam in Hours

Marks for

Total

Marks Lecture

Practical/

Field Work/

Assignment/

Tutorials

I.A. Exam

12BBI21 Genomics & Proteomics 4 2* 3 50 100 150

12BBI22 Systems Biology 4 2# 3 50 100 150

12BBI23 Data Warehousing & Data Mining 4 2# 3 50 100 150

12BBI24 JAVA & J2EE 4 2* 3 50 100 150

12BBI25X Elective – II 4 2 3 50 100 150

12BBI26 **Project Phase-I(6 week Duration)

12BBI27 Seminar -- 3 -- 50 -- 50

Total 20 13 15 300 500 800

Elective – II

12BBI251 Chemoinformatics

12BBI252 Parallel & Distributed Computing

12BBI253 Cellular Neural Networks & Visual Computing

** Between the II Semester and III Semester. After availing a vocation of 2 weeks.





III Semester

Subject


Teaching hours/week

Duration of

Exam in Hours

Marks for

Total

Marks Lecture

Practical/

Field Work/

Assignment/

Tutorials

I.A. Exam

12BBI31 Research Methodology 4 -- 3 50 100 150

12BBI32X Elective-III 4 2 3 50 100 150

12BBI33X Elective-IV 4 2 3 50 100 150

12BBI34 Project Phase II $

12BBI35 Evaluation of Project Phase I -- 3 -- 50 -- 150

Total 12 07 09 200 300 500

Elective – III Elective - IV

12BBI321 Artificial Intelligence 12BBI331 Database Management & Grid Computing

12BBI322 Neuroinformatics 12BBI332 BioPerl, BioPython & NCBI C++ Toolkit

12BBI323 Java for Bioinformatics & Biomedical Application 12BBI333Bioinformatics in Drug Design & Discovery

$ 3 Days Course work and 3 days for Project work





IV Semester

Subject


Teaching hours/week

Duration of

Exam in Hours

Marks for

Total

Marks Lecture

Practical/

Field Work/

Assignment/

Tutorials

I.A. Exam

12BBI41 Evaluation of Project Phase – II -- -- 3 50 -- 50

12BBI42 Evaluation of Project work – III -- -- 3 50 -- 50

12BBI43 Project work evaluation and

Viva-voce -- 3 3 -- 100+100 200

Total -- 03 15 100 200 300

Grand Total (I to IV Sem.) : 2400

Note: Project work shall be continuously evaluated for phase I, phase II and after completion of the project.


Note:

* Lab Classes for any two core subjects are compulsory (practical will be evaluated for 20 marks and internal assessment for 30

marks. Lab journals should be maintained).

# For the remaining two core subjects, it can be field work, assignment, tutorials.

1) Project Phase – I: 6 weeks duration shall be carried out between II and III Semesters. Candidates in consultation with the guides

shall carryout literature survey / visit to Industries to finalise the topic of dissertation. Evaluation of the same shall be taken up

during beginning of III Semester. Total Marks shall be 50. Colleges have to send the synopsis after Phase – I.

2) Project Phase – II: 16 weeks duration. 3 days for project work in a week during III Semester. Evaluation shall be taken during the

first two weeks of the IV Semester. Total Marks shall be 50.

3) Project Phase – III: 24 weeks duration in IV Semester. Evaluation shall be taken up during the middle of IV Semester. Total Marks

shall be 50. At the end of the Semester Project Work Evaluation and Viva-Voce Examinations shall be conducted. Total Marks

shall be 50 + 50 + 100 = 200 (50 marks for guide, 50 marks for external and 100 for viva-voce).

Marks of Evaluation of Project:

The Marks of Project Phase – I shall be sent to the University along with III Semester I.A. Marks of other subjects.

The I.A. Marks of Project Phase – II & III shall be sent to the University along with Project Work report at the end of the

Semester.

4) During the final viva, students have to submit all the reports.

5) The Project Valuation and Viva-Voce will be conducted by a committee consisting of the following:

a) Head of the Department (Chairman)

b) Guide

c) Two Examiners appointed by the university. (out of two external examiners at least one should be present).


1

Data Structures in C & C++

Subject Code : 12BBI11B IA Marks : 50

No. of Lecture Hrs./Week : 04 Exam Hrs : 03

Total No. of Lecture Hrs. : 52 Exam Marks : 100

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.

Data structures:

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 linked-stack

specification. Linked queues - Basic declarations, Extended linked queues,

Abstract Data Types and Their implementations.

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 sspecifications, 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

2

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.

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.

Graphs: Terminology & Representations, Graphs & Multi-graphs, Directed

Graphs, Sequential representations of graphs - Adjacency matrices,

Traversal, Connected component and Spanning Trees, Minimum Cost

Spanning Trees.

TEXT BOOKS:

1. Nell B. Dale. C++ plus data structures, Jones Learning & Bartlett, 2007

2. Vinu V. Das. Principles Of Data Structures Using C And C++, New Age

International, 2006.

3. Robert Kruse, Alexander Ryba, Data Structures and Program Design in

C++, Prentice Hall, 2001.

REFERENCE BOOKS:

1. S. Lipschutz. Data Structures, Mc-Graw Hill International Editions, 1986.

2. Jean-Paul Tremblay, Paul. G. Soresan. An introduction to data structures

with Applications, Tata Mc-Graw Hill International Editions, 2nd edition,

1984.

3. A. Michael Berman. Data structures via C++, Oxford University Press,

2002.

4. M. Weiss. Data Structures and Algorithm Analysis in C++, Pearson

Education, 2002.

http://gigapedia.com/items:view?eid=Xoved0qV4xCJVovpyJE3wn18lxPLaBa4%2BaAvaSBhIns%3D

http://gigapedia.com/items:view?eid=Xoved0qV4xCJVovpyJE3wn18lxPLaBa4%2BaAvaSBhIns%3D

http://www.indiastudychannel.com/resources/120859-Syllabus-UPTU-M-C-A-st-yr-Semester-II.aspx

3

Biomolecules, Molecular Biology & Genetic Engineering

Subject Code : 12BBI11C IA Marks : 50

No. of Lecture Hrs./ Week : 04 Exam Hrs : 03


Overview of Biomacromolecules – Introduction to Biomacromolecules,

Structure, Characteristics and function of carbohydrates, proteins, lipids and

nucleic acids.

DNA Structure; Replication; Repair & Recombination

Structure of DNA: A, B, Z and triplex DNA; Replication: Enzymes and

accessory proteins, Initiation, elongation and termination in prokaryotes and

eukaryotes; DNA damage and repair: Photoreactivation, Nucleotide excision

repair, Mismatch repair, SOS repair; Recombination: Homologous and non-

homologous, site specific recombination.

Prokaryotic & Eukaryotic Transcription

Prokaryotic Transcription: Transcription unit, Promoters: Constitutive and

Inducible, Operators, Regulatory elements, Initiation, Elongation,

Termination (Rho-dependent and independent); Eukaryotic transcription and

regulation: RNA polymerase I, II and III, Eukaryotic promoters and

enhancers, General Transcription factors, TATA binding proteins (TBP) and

TBP associated factors (TAF), Activators and repressors; Post

Transcriptional Modifications: Processing of mRNA (splicing), 5'-Cap

formation, 3'-end processing and polyadenylation, RNA editing; Nuclear

export of mRNA; mRNA stability; Catalytic RNA.

Transcriptional regulation-Positive and negative gene regulation; Operon

concept: lac, trp operons; Transcriptional control in lambda phage;

Transcriptional and post-transcriptional gene silencing; RNA interference

(Role of miRNA and siRNA in gene regulation) and Antisense RNA.

Translation & Transport

Translation machinery; Ribosomes; Composition and assembly; Universal

genetic code; Degeneracy of codons; Termination codons; Isoaccepting

tRNA; Wobble hypothesis; Mechanism of initiation, elongation and

termination; Co- and post-translational modifications; Genetic code in

mitochondria; Transport of proteins and molecular chaperones; Protein

stability; Protein turnover and degradation.

Basics Concepts of Genetic engineering

Introduction to genetic engineering; Restriction Enzymes; Klenow enzyme;

T4 DNA polymerase; DNA ligase; Cloning Vectors: Plasmids,

4

Bacteriophages, M13 mp vectors, Phagemids, Lambda vectors, Cosmids,

Artificial chromosome vectors (BACs, YACs), Shuttle vectors, Animal Virus

derived vectors-SV-40, Expression vectors: pMal, pET, GST-tag vectors;

Isolation and purification of plasmid and genomic DNA, and total RNA;

Recombinant DNA technology: Cloning, screening of the recombinants,

Protein purification; Isolation and purification of recombinant proteins.

Native and SDS PAGE, His-tag; GST-tag; MBP-tag.

Techniques in Genetic engineering

Construction of genomic and cDNA libraries, Screening of the clones, PCR:

Primer design, technique. Types of PCR: endpoint PCR, real time PCR,

inverse PCR, cloning of PCR products, applications. Blotting techniques

(Southern, Northern and Western) Radio labeled and non-radio labeled

probes, Primer extension, DNA foot printing, EMSA (Electrophoretic

mobility shift assay), In vitro transcription and translation.

TEXT BOOKS:

1. Primrose S.B., Twyman R.M. and R.W. Principles of gene manipulation –

An introduction to genetic engineering, Old, Blackwell Science, 6th

Edition, 2001.

2. Lewin B. Genes IX, Jones and Bartlett Publications, 2008.

3. Lodish et al. Molecular Cell Biology, ? 6th

Edition, 2008.

REFERENCE BOOKS:

1. Alberts B. Johnson A. Lewis J. Raff M., Robert K. and P. Walter.

Molecular Biology of the cell, Garland Science, 2007.

2. Brown T.A. Gene Cloning and DNA Analysis – An Introduction,

Blackwell Science, 5th Edition, 2006.

3. Glick B.R. and J.J. Pasternak. Molecular Biotechnology – Principles and

applications of recombinant DNA, ASM Press, 4th Edition, 2008.

4. Voet D., Voet J.G. and C.W. Prott. Fundamentals of Biochemistry-Life at

the molecular level, John Wiley & Sons, 2nd Edition, 2006.

5. Watson, J.D., Baker, T.A., Bell S.P., Gann A., Levine M and R. Losick.

Molecular Biology of the Gene, Pearson Education, 5th Edition, 2004.

Shashi

Highlight

Shashi

Highlight

Shashi

Highlight

5

Statistical & Probabilistic Methods for Bioinformatics

Subject Code : 12BBI12 IA Marks : 50



Basics of statistics: Types of statistics – Descriptive and Inferential statistics.

Descriptive statistics - Frequency distributions and data presentation,

Relative frequency, Histogram, Measures of central tendency. Inferential

Statistics.

Probability distribution functions: Binomial distribution, Poisson

distribution, Uniform distribution, Normal distribution. Characteristics of a

random variable. Moments of a distribution and Moment generating

functions, Distribution functions of more than one random variable. Joint

distributions, Conditional distributions, Marginal distributions and

Independent random variables.

Statistical Inference: Introduction. Classical and Bayesian methods,

Classical Estimation Methods, Classical Hypothesis testing (few examples),

Likelihood ratios, Hypothesis testing using Maximum as Test Statistics,

Bayesian approach to Hypothesis testing and Estimation. Multiple testing.

Stochastic Processes: Poison processes and Morkov chains, Homogenous

poison processes and Poison distribution, poison and binomial distribution,

poison and gamma distribution, pure birth test, Finite Morkov chains,

Transition Probabilities and Transition Probability Matrix, Morkov chains

with absorbing and no-absorbing states. Graphical representation of Morkov

chains. Morkov modeling. Higher-Order Morkov Dependence. Pattern in

Sequences with First-Order Morkov Dependence. Morkov Chain Monte

Carlo. Continuous-Time Morkov chains.

Analysis of DNA Sequences: Analysis of Single DNA sequences - Shotgun

sequencing, DNA Modeling, Modeling Signals in DNA, long repeats, r-

Scans. Analysis of patterns. Overlaps counted and not-counted. Analysis of

Single Multiple DNA Sequences – Frequency comparison, Sequence

alignment, Simple tests for significant similarity in an alignment. Alignment

algorithms for two sequences. Protein sequences and Substitution matrices.

Multiple sequence alignment.

Estimation and Hypothesis testing theory: Estimation theory -

Introduction, Criteria for “Good estimators”. Methods of estimation -

Maximum Likelihood estimation, Least squares, Multiple regression,

6

Multivariate and Bootstrapping. Hypothesis testing theory – Introduction,

Fixed sample size test, Composite fixed sample size tests, -2 log

approximations. ANNOVA, Multivariate, Bootstrapping Methods. Sequence

analysis.

Statistical approach for sequence alignment and sequence search: Comparison of two aligned, unaligned sequences and Query sequence against

a database. Minimum significance lengths. Gapped BLAST and PSI-BLAST.

Hidden Morkov Models: Introduction. Algorithms –Forward and Backward,

Verterbi and Estimation algorithms. Applications of Hidden Morkov Models.

Statistical approach for Microarray Data Analysis: Application to brain tumor data, Low –level analysis of SNP Chip data,

Genotyping HapMap data, Meta-analysis of genomic data. Demonstration of

the methodology on the breast cancer data. Classification in genomics and

metabolomics - Application to tumor data;

Case study in proteomic mass-spectrometry: Coronary artery disease data;

A statistical framework to infer functional gene associations from multiple

biologically

interrelated microarray experiments. An application to yeast and human data.

Phenotypic characterization of Yersinia pestis. Detecting lineage-specific

evolution of DNA.

Evolutionary models: Models of Nucleotide substitution, Discrete Time

Models, Continuous Time Models. Phylogenic Tree Estimation -

Introduction, Datasets, Tree building methods and Tree evaluation methods.

TEXT BOOKS:

1. Warren J. Ewens Gregory Grant. Statistical Methods in Bioinformatics: An

Introduction (Statistics for Biology and Health), Springer, 2005. 2. Warren John Ewens, Gregory R. Grant, Gregory Grant, R. Statistical

Methods in Bioinformatics, Springer, 2005.

3. T. Hastie, R. Tibshirani, J. H. Friedman. The Elements of Statistical

Learning, Springer, 2001.

REFERENCE BOOKS:

1. Bioinformatics and Computational Biology Solutions using R and

Bioconductor, edited by R. Gentleman, Springer, 2005.

2. Statistical Analysis of Gene Expression Microarray Data, edited by T.P.

Chapman & Hall / CRC, Speed. 2003.

3. G. Gibson & S.V. Muse.A Primer of Genome Science , Sinauer Associates,

2001.

7

Essential Bioinformatics




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.

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.

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

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.

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.

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

8

Methods. Phylogenetic Tree evaluation methods. Phylogenetic analysis

programs.

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.

Microarray Bioinformatics: Sequence databases for Microarrays, Computer

aided design of oligonucleotide probes, Image processing, Measuring and

quantifying microarray variability and Analysis of deferentially expressed

genes.

TEXT BOOKS:

1. Jin Xiong. Essential Bioinformatics, Cambridge University Press, 2006.

2. V. Kothekar. Essentials of Drug Designing, DHRUV Publications, 2005.

3. Paul G. Higgs, Teresa K. Attwood. Bioinformatics and Molecular

Evolution, Blackwell, 2005.

4. Bioinformatics: Sequence and Genome Analysis, CSHL Press, 2004

REFERENCE BOOKS:

1. Lukas. Bioinformatics Basics: Applications in Biological Science and

Medicine, 2005.

2. Pierre Baldi and Søren Brunak. Bioinformatics - The Machine Learning

Approach, 2001.

3. Andreas D. Baxevanis. Current Protocols in Bioinformatics, Published by

Wiley, 2003.

4. Dov Stekel, Microarray bioinformatics, Cambridge University Press, 2003.

Bioinformatics Lab

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. Sequence searches using FASTA and BLAST.

5. Phylogenetic analysis.

6. Prediction of secondary structure for given protein and RNA

sequences.

http://books.google.co.in/url?client=ca-print-cambridge&format=googleprint&num=0&channel=BTB-ca-print-cambridge+BTB-ISBN:0521840988&q=http://www.cambridge.org/0521840988&usg=AFQjCNFnz489lr-diHoOZ_9f6Q3ErRUH5w&source=gbs_buy_s&cad=3

http://books.google.co.in/books?q=inpublisher:%22DHRUV+Publications%22&source=gbs_summary_r

http://books.google.co.in/books?q=inpublisher:%22Blackwell%22&source=gbs_summary_r

http://mitpress.mit.edu/catalog/author/default.asp?aid=705


9

7. Retrieval of protein structure from PDB and its visualization and

modification.

8. Primer and Promoter design for a given sequences

9. EST clustering and EST mapping

10. Genome annotation

11. Demonstrating Sequence structure relationship

12. Microarray data analysis- normalization, clustering.

13. Study of Profiles, Patterns and PSSMs

14. Prediction of protein-protein interactions.

10

Biomolecular Modeling & Simulation




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.

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.

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. 3D QSAR

Methods.

Dynamical and Stochastic-Dynamical Foundations for Macromolecular

Modeling: Bimolecular sampling: Algorithms, Test molecules, and metrics.

Approach to thermal equilibrium in Biomolecular simulation, Hybrid Monte

11

Carlo and Newton Raphson methods. Langevin equation for generalized

coordinates, Meta stability and Dominant Eigenvalues of Transfer operators.

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.

Electrostatics and Enhanced Solvation Models: Implicit solvent

electrostatics in Biomolecular Simulation, New distributed multipole

methods.

Quantum-Chemical Models for Macromolecular Simulation: Fast and

Reliable Quantum Chemical Modeling of Macromolecules, Quantum

chemistry simulations of Glycopeptide antibiotics.

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 – Modeling and Simulation of sGC.

TEXT BOOKS:

1. Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, Gerd Folkers

Molecular Modeling, 2008.

2. Jill P. Mesirov, Klaus Schulten, De Witt L. Mathematical Approaches to

Biomolecular Structure and Dynamics, Sumners, 1996.

3. Alberte Pullman, Joshua Jortner. Modeling of Bimolecular Structures and

Mechanisms, 1995.

REFERENCE BOOKS:

1. Tamar Schlick. Molecular Modeling and Simulation: An Interdisciplinary

Guide, Published by Springer, 2nd edition, 2010.

2. Isidore Rigoutsos, G. Stephanopoulos. Systems Biology, Published by

Oxford University Press US, 2006.

3. Timothy J. Barth, Michael Griebel, David E.Keyes, Risto M. Nieminen,

Dirk Roose, Tamar Schlick. New Algorithms for Macromolecular

Simulation, Published by Springer, 2006.

4. Peter T. Cummings, Phillip R. Westmorland, Brice Carnahan. Foundations

of Molecular Modeling and Simulation, Published by American Institute of

Chemical Engineers, 2001.

12

Biomolecular Modeling & Simulation Lab

1. Prediction of 3D structure of unknown protein sequence.

2. Homology Modeling and Docking Evaluation of Aminergic G

Protein-Coupled Receptors

3. Modeling mutations and Single Nucleotide Polymorphisms.

4. Molecular Modeling of Antibodies with Affinity towards

Hydrophobic BINOL Derivatives.

5. Modeling Nanopores for Sequencing DNA

6. Docking small molecules into proteins.

7. Molecular mechanics methods for predicting protein-ligand binding

8. Simulation of lipid bilayer.

9. Simulation of Water Permeation through Nanotubes

10. Simulation of “Forcing Substrates through Channels”

11. Molecular dynamic study on Aggregation of Beta Amyloid 42 in

Alzheimer’s disease.

12. Removing organic contaminants from drinking water- understanding

zeolite water adsorption

13. Hydrogen storage for fuel cells - a density functional theory study of

hydrogen adsorption on aluminum clusters.

14. Design of polymeric membranes - modeling and simulation

diffusion studies of small gas molecules in polymeric materials.

13

DNA Chips & Microarray Data Analysis




Introduction to Biochip and Microarray Construction: Basics of Biochips

and Microarray Technology, Biochip technologies. Types of Biohips - 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. Bioinformatics and microarrays,

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.

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.

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.

DNA Computing: Introduction, Junctions, other shapes, Biochips and large-

scale structures. Strand algebras for DNA computing – Introduction, Strand

14

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.

DNA programming - Deoxyribozyme-Based Logic Gate design processes.

Renewable, Time responsive DNA Logic Gates for scalable digital circuits.

Design of Bimolecular device.

Commercial Aspects of Biochip Technology: Markets for biochip

technologies, Commercial and Government support for biochip development,

Business strategies, and Patent issues.

TEXT BOOKS:

1. DNA Computing: 15th International Meeting on DNA Computing, DNA

15, Fayetteville, AR, USA, June 8-11, 2009, Springer, 2009.

2. Paul F. Predki. Functional Protein Microarrays in Drug Discovery, CRC

Press – Publisher, 2007. 3. Biochips and Microarrays – Technology and Commercial Potential

Published by: Informa Global Pharmaceuticals and Health Care, 2000.

REFERENCE BOOKS:

1. DNA Arrays: Technology and Experimental Strategies, Grigorenko, E.V

(ed), CRC Press, 2002.

2. Mark Schena; J. Microarry Analysis, Wiley & Sons (ed. New York), 2002.

15

Computational Biology




Introduction to Computational biology: Introduction, scope and

applications of Computational biology. Molecular Biology databases.

Statistical approach to DNA and Protein sequence analysis: Analysis of

single DNA sequence: shotgun sequencing, DNA modeling, Scanning long

repeats, Analysis of patterns and Counting of overlaps. Analysis of Multiple

DNA or Protein sequences: Frequency comparisons of two sequences.

Simple tests for significant similarity in an alignment. Alignment algorithms

for two sequences: Gapped global comparisons and Dynamic programming

algorithms, linear gap model for fitting one sequence into another and local

alignment.

Patterns, Motifs and Signals:

Pattern matching - Pattern matching with Consensus sequences Quantitative

& Probabilistic pattern matching. Structural domains and Motifs - Sequence

blocks & Profiles, Protein sequence motifs, Protein structural motifs,

Clustering and Functional analysis of coordinately regulated genes.

Discovering transcriptional regulatory Signals, Ultraconservation in the

Human Genome.

Restriction mapping, Map assembly and Sequencing – Algorithms for

restriction mapping, shotgun sequencing, DNA sequencing, Human Genome

Project. DNA Arrays, Sequence Comparison – sequence alignment, tuples,

antichain. Finding signals in DNA - Gibbs sampling, Viterbi algorithm,

Hidden Markov Models in Bioinformatics, Computational Proteomics (amino

acid, C-terminal, directed acyclic graph), Problems -circular permutation,

interval graph.

Computational Biology and Cancer research: Mathematical modeling of

tumorigenesis - Cellular automaton, tumor, angiogenesis. One hit and two hit

stochastic models - Tumor suppressor gene, Kolmogorov forward equation,

and retinoblastoma. Microsatellite and chromosomal instability in sporadic -

APC gene, colorectal cancer, point mutation. Chromosome loss. DNA

damage and genetic instability - Apoptotic, Fitness landscape, unstable cells.

Tissue aging and the development of cancer - Angiogenesis, Checkpoint

competence, DNA damage. Basic models of tumor inhibition and promotion

- Metastatic, Angiogenic tumor cells, Angiogenesis inhibition. Mechanisms

of tumor neovascularization - vasculogenesis, Cancer and Immune responses

16

- Dendritic cell vaccination, Viruses as antitumor weapons - Tumor load,

Viral replication and Oncolytic viruses.

Computational Immunology: Overview of immune system, Introduction to

computational immunology Immunological databases – IMGT – IMGT-

GENE-DB, – IMGT-HLA, Tools for the prediction binding affinity between

peptide : TAP:MHC:TCR- MHC: Peptide Binding Prediction - SYFPEITHI,

BIMAS, MHC PRED, - Future of computational modeling and prediction

systems in clinical immunology -overview of models- models for HIV

infection.

TEXT BOOKS:

1. Darren Flower. In Silico Immunology, Springer, 2006.

2. Dominik Wodarz, Natalia L. Komarova, Computational Biology of

Cancer, , Published by World Scientific, 2005.

3. Dominik Wodarz, Natalia L. Komarova,. Computational Biology of

Cancer, World Scientific, 2005.

4. Neil C.Jones and Pavel .A Pevzner. An introduction to Bioinformatics

Algorithms (Computational Molecular Biology), MIT Press, 2004.

REFERENCE BOOKS:

1. Lukas. Bioinformatics Basics: Applications in Biological Science and

Medicine, 2005.

2. Pierre Baldi and Søren Brunak. Bioinformatics - The Machine Learning

Approach, 2001.



17

Health Informatics




An introduction to Health care informatics: An interaction between health

care and information systems. Acquisition, storage, retrieval, and use of

information in health and biomedicine. Tools and techniques. Information

systems in Medicine, Dentistry, Nursing, surgery and diagnosis. Future

prospects.

Building blocks of Health care informatics: Standards, types of standards.

Modeling – principles of modeling for healthcare. Architecture of Health care

system – models, sub systems, packages and components. Modeling

framework for health care. generic health care information model. Unified

modeling language. Modeling methodologies in healthcare systems.

Databases, types, and applications. Database Architecture; ANSI/SPARC

three tier architecture. Data warehousing; architecture.

Tools and techniques in Telecare and E-Health: Introduction, conditions

for telemedicine development, applications, access techniques in telecare,

Internet technologies in medical systems: Requirement of Medical systems

in the internet environment, internet medical architectures, and internet based

telemedical services, next generation point of care information systems,

internet access technologies in Telecare. Wireless communication

technologies.

Electronic Health records(HER): Challenges in clinical care,

characteristics of good EHR, Generic EHR representation, EHR Standards

and Scope of the HER.

Decision support systems and Telematic networks in Medicine: Decision

support systems, knowledge based and Expert based. Probabilistic and

Logical decision systems. Transport layer in telematics networks, health

digital data standards, E-health networks services.

Applications of IT in hearing and chronic problems: Methodology of

hearing screening, computer aided adjustment of hearing aids, diagnosis,

tinnitus treatment. Application of IT to diagnose chronic conditions pateint

centered symptom monitoring.

Computer aided techniques in Medicine: Laproscopic surgery navigation,

Introoperative imaging, multimodel imaging, Biosignal processing and

algorithms. Biosignal databases.

TEXT BOOKS:

18

1. Naakesh A. Dewan, John Luo, Nancy M. Lorenz. Information Technology

Essentials for Behavioral Health Clinicians, 2010.

2. Krzysztof Zielinski, Mariusz Duplaga. Technology Solutions for

Healthcare (Hardcover), 2006.

3. Moya Conrick, Health Informatics, 2006.

REFERENCE BOOKS:

1. Frank Sullivan, Jeremy Wyatt. ABC of Health Informatics, 2009

2. Moya Conrick. Health Informatics, 2006.

http://www.amazon.com/dp/1852339780/ref=rdr_ext_tmb






http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Frank+Sullivan%22

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Jeremy+Wyatt%22

19

Seminar


Field work/Assignment

Hrs./Week

: 03

Seminar Mechanism

1. A list of contemporary topics will be offered by the faculty members of

the department.

2. Student can opt for a topic of their own choice and indicate their option

to the department at the beginning of the semester.

3. Students have to do a literature survey of the selected topic from journals

and web resources.

4. A draft copy of the report should be submitted one week before the

presentation, to the seminar coordinator.

5. Students have to give a presentation in power point for about 30 minutes

followed by the Q/A session.

6. The Evaluation will be done by committee constituted by the

department.

7. The final copy of the report should be submitted after incorporating any

modifications suggested by the evaluation committee.

Guidelines for Evaluation

The following are the weightages given for the various stages of the seminar:

1. Selection of the topic. 05 Marks.

(20%)

2. Literature survey. 05 Marks.

(20%)

3. Understanding and presentation of the given topic. 05 Marks.

(20%)

4. Reporting and Documentation. 10 Marks.

(40%)

20

Genomics & Proteomics




Introduction: Introduction to Genomics & Proteomics. Structure,

Organization and features of Prokaryotic & Eukaryotic genomes. Vectors.

Genome 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, Gene discovery and

Gene Ontology. Haplotyping and Diplotyping.

Genome Sequencing: Early sequencing efforts. DNA sequencing methods -

Maxam-Gilbert Method, Sanger Dideoxy method, Fluorescence method,

shot-gun approach and ultra-high-throughput DNA Sequencing using

Microarray technology. Genome sequencing projects on E.coli. Arabidopsis

and rice; Human-genome project and the genetic map. Recent developments

and next generation sequencing.

Raw genome sequence data, expressed sequenced tags (ESTs), Gene

variation and associated diseases, diagnostic genes and drug targets.

Genotyping - DNA Chips, diagnostic assays, diagnostic services.

Comparative genomics and Functional Genomics - Studies with model

systems such as Yeast, Drosophila, C. elegans, Arabidopsis. SAGE.

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

proteins - high throughput screening, engineering novel proteins, Mass-

Spectroscopy based protein expression and post-translational modification

analysis. Bioinformatics analysis - clustering methods, Analysis of proteome

functional information.

Genome management in eukaryotes: Regulation of transcription,

transcription factors and the co-ordination of gene expression, Regulation of

translation and post-translational modification in eukaryotes, mitochondrial

and chloroplast genome.

Functional genomics: C-Values of eukaryotic genomes. Organization of

microbial, plant and animal genomes, repetitive and coding sequences.

Identification and tagging of markers for important traits, T-DNA &

trasposon tagging. Cloning of genes by map-based cloning. Construction &

Screening of cDNA libraries, differential display via RT-PCR. Micro-array

in functional genomics.

21

Genome analysis: Methods in mapping plant and animal genomes, Genome

mapping in Plant and animal breeding, Yeast Artificial Chromosome (YAC)

libraries and their uses in genome mapping. General features of mapping

microbial genomes – Bacterial and Fungal genomes.

Genome and Proteome Annotation:

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.

Pharmacogenomics:

Genetic Haplotyping in Natural Populations Introduction – Introduction

to Pharmacogenomics. Notion and definitions – Notation and Definitions,

Likelihoods, The EM Algorithm, Sampling variances of parameter estimates,

Model selection, Hypothesis tests, Haplotyping with multiple SNP and R-

SNP Model,

Functional Mapping – Strategies for Genomic mapping of Drug Response -

QTL, to QTN and Functional Mapping of Drug Response. Dynamic Genetic

Control, Structure of functional mapping, Estimation of functional Mapping,

Hypothesis fests of functional mapping, Transform-Both-Sides model of

Functional mapping, Structured ante dependence model of Functional

mapping, Optimal strategy of structuring the covariance.

Functional Mapping of Pharmacokinetics and Pharmacodynamics -

Mathematical modeling of Pharmacokinetics and Pharmacodynamics,

Functional mapping of Pharmacokinetics, and Pharmacodynamics,

Sequencing Pharmacodynamics.

Haplotyping Drug Response by Linking Pharmacokinetics and

Pharmacodynamics: Unifying model for Functional mapping Algorithms

and determination of risk haplotypes, Hypothesis tests, Computer simulation,

Genetic and Statistical Considerations for haplotyping of drug response.

22

TEXT BOOKS:

1. Dmitrij Frishman, Alfonso Valencia, Modern genome annotation: the

BioSapiens Network, Springer, 2008.

2. Rongling Wu, Min Linen. Statistical and Computational

Pharmacogenomics (Interdisciplinary Statistics), Chapman & Hall/CRC,

2008.

3. Benjamin Lewis. GeneVIII, Jones and Bartlett Publisher, 2006.

4. Werner Kalow, Urs A. Meyer, Rachel F. Tyndale. Pharmacogenomics,

Informa Healthcare, 2005.

5. Sándor Suhai, Genomics and Proteomics, Springer – Publisher, 2000.

REFERENCE BOOKS:

1. A. Malcolm Campbell, Laurie J. Heyer. Discovering genomics, proteomics

and bioinformatics, Published by Pearson/Benjamin Cummings, 2006.

2. Ann Batiza, Ann Finney Batiza. Bioinformatics, Genomics, and

Proteomics, Chelsea House Publishers, 2005.

3. Christopher A. Cullis. Plant Genomics and Proteomics, Wiley-Liss, 2004.

Genomics & Proteomics Lab

1. Virtual sequencing (base calling, Sequence assembly, Mapping

assembly, Contig mapping)

2. Analysis of next generation sequencing data

3. Genome annotation.

4. Structure and sequence detection.

5. Study of vector and virtual subcloning

6. Chromatographic data analysis.

7. Chromatogram sequence alignment and editing.

8. CGH and Genotype Array Analysis.

9. Analysis of X-Ray data and spectroscopic data.

10. 2D PAGE image analysis.

11. Prediction of secondary and tertiary structure of unknown proteins.

12. Protein annotation.

13. Prediction of protein functional sites in Protein

14. Microarray data analysis.

http://gigapedia.com/items:view?eid=6yDCGaB2H2QtAhgA2xDg97HUwA9aeak%2FNxh7WYCiozY%3D

http://gigapedia.com/items:view?eid=6yDCGaB2H2QtAhgA2xDg97HUwA9aeak%2FNxh7WYCiozY%3D

23

Systems Biology




Introduction to Systems Biology: Scope, Concepts, Implementation and

Applications.

Information and Integration Technologies for Systems Biology:

Databases for Systems Biology, Natural Language Processing and Ontology

Enhanced Biomedical Data Mining, Text Mining. Integrated Imaging

Informatics. Modeling tools - SBML, MathML, CellML, Petri Nets,

Standard Platforms and applications in Systems Biology.

Models and Applications: Metabolic Control Analysis, Glycolysis,

Michaelis-Menten Kinetics, and Flux Balance Analysis. Signal Transduction

- Phosphorylation, JAK-STAT Pathway, MAP Kinase. Biological Processes -

Mitochondria, Cyclin, CDC2. Evolution and Self organization - Hypercycle,

Quasispecies model, Self Replication.

Integrated Regulatory and Metabolic Models:Metabolic Network,

Reconstruction of Metabolic Network from Genome Information. Mapping

Genotype – Phenotype relationship in Cellular Networks. Estimation

Modeling and Simulation – Computational Models of Circadian Rhythm.

,Gene Regulatory Networks, Attractor, and Boolean functions.

Modeling of Gene Expression: Modeling of Gene Expression - Lactose, Lac

Operon, tRNA. Analysis of Gene Expression Data - Support Vector

Machines, Identifying Gene Regulatory Networks from Gene Expression

Data. Modeling and Analysis of Gene Networks using Feedback Control.

Global Gene Expression Assays.

Multiscale representations of cells and Emerging phenotypes:

Multistability and Multicellurarity, Spatio-Temporal Systems Biology, Mass

Spectrometry and Systems Biology, Cytomics – from cell state to predictive

medicine, The Human Interactome - Protein-DNA and Protein-Protein

Interactions.

TEXT BOOKS:

1. Andres Kriete, Roland Eils. Computational Systems Biology, Academic

Press, 2006.

2. Andrzej K. Konopka. Systems Biology, CRC, 2006.

3. Edda Klipp. Systems biology in practice: concepts, implementation and

application, Wiley-VCH, 2005.

24

REFERENCE BOOKS:

1. Corrado Priami. Transactions on Computational Systems Biology I.

Springer – Publisher, 2009.

2. Fred C. Boogerd, H.V. Westerhoff. Systems Biology, Elsevier – Publisher,

2007.

3. Glenn Rowe. Theoretical Models in Biology , Oxford University Press –

Publisher, 2004.

25

Data Warehousing & Data Mining




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.

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.

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.

Data Mining Algorithms: One-R, Naïve Bayes Classifier, Decision trees,

Decision rules, Association Rules, Regression, K-Nearest Neighbour

Classifiers.

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.

26

TEXT BOOKS:

1. J. Han and M. Kamber. Data Mining: Concepts and Techniques, Morgan

Kaufman, 2000.

2. Fundamentals of Data Warehouses, M. Jarke, M. Lenzerini, Y. Vassiliou,

P. Vassiliadis (ed.), Springer-Verlag, 1999.

3. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools

and Techniques with Java Implementations, Morgan Kaufman, 1999.

4. Ralph Kimbal. The Data Warehouse Toolkit, Wiley, 1996.

REFERENCE BOOKS:

1. M. H. Dunham. Data Mining: Introductory and Advanced Topic, Prentice

Hall, 2003.

2. Zhengxin Chen. Intelligent Data Warehousing, CRC Press, 2002.

3. D. Hand, H. Mannila and P. Smyth. Principles of Data Mining, MIT Press,

2001.

27

JAVA & J2EE




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.

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, Event Handling: 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.

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.

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.

28

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.

TEXT BOOKS:

1. Herbert Schildt. Java - The Complete Reference, Tata McGraw Hill,

7th Edition, 2007.

2. Jim Keogh, The Complete Reference, Tata McGraw Hill, 2007.

3. Jonathan Knudsen. Java 2D Graphics, O'Reilly, 1999.

REFERENCE BOOKS:

1. Y. Daniel Liang. Introduction to Java Programming Comprehensive

Version,), Pearson Prentice Hall – Publisher, 7th Edition, 2010.

2. Y. Daniel Liang. Introduction to JAVA Programming, Pearson Education,

6th Edition, 2007.

3. Stephanie Bodoff et al. The J2EE Tutorial, Pearson Education,

2nd Edition, 2004.

JAVA & J2EE Lab

1. Design, Write and Execute Java Program that illustrate Constructor

and Method overloading.

2. Design, Write and Execute Java Program that implement inner class

and demonstrate its Access Protections.

3. Write a Java Program to create an interface and implement it in a

class.

4. Write a Java program that prints all real solutions to the quadratic

equation ax2 + bx + c = 0. Read in a, b, c and use the quadratic

formula. If the discriminant b2-4ac is negative, display a message

stating that there are no real solutions.

5. Write a JAVA program to implement Client Server(Client requests a

file, Server responds to client with contents of that file which is then

display on the screen by Client – Socket Programming)

6. Write a program to insert Protein information into ProteinDB

database and retrieve the list of Protein sequences based on

particular queries Using JDBC (Design Front end using Swings).

7. Design, Write and Execute Java Program that illustrate Exception

Handling (Using Nested try catch and finally).

8. Write a Program to construct the phylogenetic tree using sequential

clustering by reading input distance matrix.

9. Write a Java program to Implement Dynamic Programming for

sequence alignment.

http://labmanual.blogspot.in/2011/10/write-java-program-that-prints-all-real.html




http://labmanual.blogspot.in/2011/10/write-java-program-to-multiply-two.html

http://labmanual.blogspot.in/2011/10/write-java-program-to-multiply-two.html

29

10. Write a JAVA Servlet program to implement a dynamic HTML

using Servlet username and password should be accepted using

HTML and displayed using a Servelet).

11. Write a Java program that correctly implements producer consumer

problem using the concept of inter thread communication.

12. Develop an applet that receives an integer in one text field, and

computes its factorial Value and returns it in another text field, when

the button named “Compute” is clicked.

13. Write a java program that simulates a traffic light

14. Write a Java program using AWT to demonstrate Choice

implementation.

15. Create a slideshow which has three slides. Which includes only text,

program should change to the new slide after 5 seconds. After the

third slide program returns to the First Slide.

30

Chemoinformatics




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.

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.

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.

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.

Quantitative Structure-Activity Relationaaship Analysis: Model building,

Model evaluation, 4D-QSAR. Methods of QSAR analysis - Monte Carlo

methods, Simulated annealing, Molecular dynamics and Probabilistic

methods. Virtual screening and Compound filtering.

Virtual Screening: Introduction. "Drug-Likeness" and Compound

filters. Structure-based Virtual screening and Prediction of ADMET

Properties.

Combinatorial Chemistry and Library Design: Introduction. Diverse and

Focussed libraries. Library enumeration. Combinatorial library design

strategies.

31

TEXT BOOKS:

1. Rongling Wu, Min Linen. Statistical and Computational

Pharmacogenomics (Interdisciplinary Statistics) Chapman & Hall/CRC,

2008.

1. Andrew R and Valerie J. Gillet. Leach. An Introduction to

Chemoinformatics, Springer, 2007.

2. Barry A. Bunin, Jürgen Bajorath, Brian Siesel, Guillermo Morales.

Chemoinformatics: Theory, Practice, & Products, 2005.

REFERENCE BOOKS:

1. Alexandre Varnek, Alex Tropsha. Chemoinformatics Approaches to

Virtual Screening, Royal Society of Chemistry, 2008.

2. Barry A. Bunin, Jürgen Bajorath, Brian Siesel, Guillermo Morales.

Chemoinformatics: Theory, Practice, & Products, Royal Society of

Chemistry, 2006

3. Johann Gasteiger. Chemoinformatics: A Textbook, Wiley-VCH, 2003.

http://gigapedia.com/items:view?eid=3WgtrNrRWt1RV3Pk4dgteqXy%2B%2BYQObdTVTy46rj34Pc%3D

http://gigapedia.com/items:view?eid=rUcdOXSSDJEBZAw3ge4jYKS7Qbrzk9aXwnJDSJhGaF0%3D

http://gigapedia.com/items:view?eid=rUcdOXSSDJEBZAw3ge4jYKS7Qbrzk9aXwnJDSJhGaF0%3D

http://gigapedia.com/items:view?eid=3WgtrNrRWt1RV3Pk4dgteqXy%2B%2BYQObdTVTy46rj34Pc%3D

http://gigapedia.com/items:view?eid=SAT9f9vFUDz5aw9re4sDUxbHQsh76ZDoDTtZxrNUlhw%3D

32

Parallel & Distributed Computing




Parallel Algorithms and Models: Need for parallel computing, Overview of

different parallel computing architectures (OpenMP, MPI and CUDA) and

Parallel and Evolutionary approaches to Computational Biology. Parallel

Monte Carlo Simulation of HIV Molecular evolution in response to Immune

surveillance. Differential evolutionary algorithms for In Vivo Dynamic

analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli.

Compute-Intensive Simulations for cellular models. Parallel Computation in

Simulating diffusion and deformation in Human brain.

Sequence Analysis and Microarrays: Special-Purpose Computing for

Biological Sequence Analysis. Multiple Sequence Alignment in Parallel on a

Cluster of Workstations. Searching sequence databases using High-

Performance BLASTs. Parallel Implementations of Local sequence

alignment: Hardware and Software. Parallel Computing in the analysis of

Gene expression Relationships. Assembling DNA Fragments with a

Distributed Genetic Algorithm. Cooperative Genetic Algorithm for

Knowledge discovery in Microarray Experiments.

Phylogenetics: Parallel and Distributed computation of large Phylogenetic

trees. Phylogenetic parameter estimation on COWs. High-Performance

Phylogeny reconstruction under Maximum Parsimony.

Protein folding: Protein folding with the Parallel Replica Exchange

Molecular Dynamics Method. High-Performance alignment methods for

Protein Threading. Parallel evolutionary computations in discerning Protein

structures.

Platforms and enabling technologies: A Brief Overview of Grid Activities

for Bioinformatics and Health Applications. Parallel Algorithms for

Bioinformatics. Cluster and Grid Infrastructure for Computational Chemistry

and Biochemistry. Distributed Workflows in Bioinformatics. Molecular

Structure Determination on a Computational systems and Data Grid.

Software framework for Parallel Bioinformatics on Computational grids.

FPGA Computing in Modern Bioinformatics. Virtual Microscopy:

Distributed image storage, Retrieval, Analysis, and Visualization. Parallel

Computing for Bioinformatics and Computational Biology:

TEXT BOOKS:

1. El-Ghazali Talbi. Grid Computing for Bioinformatics and Computational

Biology, Wiley-Interscience – Publisher, 2008.

http://www.wiley.com/remtitle.cgi?ISBN=0471784095

33

2. Kim-Meow Liew, Hong Shen, Simon See, Wentong Cai, Pingzhi Fan,

Susumu Horiguchi. Parallel and Distributed Computing , Published by

Springer, 2004.

3. Michel Cosnard, Afonso Ferreira, Joseph Peters, Parallel and Distributed

Computing, Springer – Publisher, 1994.

REFERENCE BOOKS:

1. Albert Y. Zomaya. Parallel Computing for Bioinformatics and

Computational Biology, Published by Wiley-Interscience, 2006.

2. Lynn Arthur Steen, Math and Bio 2010, MAA, 2005.

3. Albert Y. Zomaya, Parallel and Distributed Computing Handbook,

McGraw-Hill Professional – Publisher, 1996.

4. Chi-hau Chen, Fuzzy logic and neural network handbook McGraw-Hill,

1996.

34

Cellular Neural Networks & Visual Computing




Introduction: Notations, Definitions and Mathematical foundation.

Characteristics and analysis of simple CNN templates – Case studies: EDGE

and EDGEGRAY templates

Simulation of the CNN Dynamics: Integration of the standard CNN

differential equation, Software simulation, Digital hardware accelerators,

Analog CNN implementations, Scaling the signals, Discrete-time CNN

(DTCNN).

Binary CNN Characterization via Boolean Functions: Binary and

Universal truth table, Boolean and Compressed local rules, Optimizing the

truth table.

Uncoupled CNNs: Unified Theory and Applications: Explicit CNN output

formula, CNN theorem, Primary CNN mosaic, Explisit formula for transient

waveform and settling time, local Boolean functions, Designing uncoupled

CNNs.

Introduction to the CNN Universal Machine: Global clock and global

wire, Set inclusion, translation of sets and binary images. Opening, Closing

and Implementation of morphological operator. Analog-to-digital array

converter.

CNN Universal Machine (CNN - UM): Architecture of CNN – UM).

Examples of CNN – UM. Language, compiler and operating system.

Template Design Tools: Design techniques. Binary representation, linear

separability and simple decomposition, Template optimization, Template

decomposition techniques.

CNNs for Linear Image Processing:

Coupled CNN with Linear Synaptic Weights: Active and inactive cells,

dynamic local rules. Binary activation pattern and template format, A simple

propagating type example.

Uncoupled Standard CNNs with Nonlinear Synaptic Weights: Dynamic

equations and DP plot.

Standard CNNs with Delayed Synaptic Weights and Motion Analysis: Dynamic equations, Motion analysis – discrete time and continuous time

image acquisition.

35

Visual Microprocessors - Analog and Digital VLSI Implementation of

the CNN Universal Machine: Analog CNN core, Analogic CNN – UM cell,

Emulated digital implementation, Visual microprocessor.

CNN Models in the Visual Pathway and The ‘bionic eye’: Receptive field

organization, synaptic weights and Cloning templates, CNN models of visual

pathway, CNN model of Vertebrate retina, Bionic Eye implemented on a

CNN – UM.

TEXT BOOKS:

1. Angela Slavova, Valeri Mladenov. Cellular Neural Networks: Theory and

Applications , Nova Publishers, 2004.

2. Leon O. Chua, Tamas Roska, Cellular Neural Networks and Visual

Computing, Cambridge University Press, 2002.

REFERENCE BOOKS:

1. Klaus Mainzer. Thinking in Complexity, Springer – Publisher, 2007.

2. Angela Slavova. Cellular Neural Networks: dynamics and modeling,

Springer – Publisher, 2003.

http://www.cambridge.org/catalogue/searchResult.asp?ipcode=226968&sort=Y

http://www.cambridge.org/catalogue/searchResult.asp?ipcode=226969&sort=Y

36

Seminar


Field work/Assignment

Hrs./Week

: 03

Seminar Mechanism

1. A list of contemporary topics will be offered by the faculty members of

the department.

2. Student can opt for a topic of their own choice and indicate their option

to the department at the beginning of the semester.

3. Students have to do a literature survey of the selected topic from journals

and web resources.

4. A draft copy of the report should be submitted one week before the

presentation, to the seminar coordinator.

5. Students have to give a presentation in power point for about 30 minutes

followed by the Q/A session.

6. The Evaluation will be done by committee constituted by the

department.

7. The final copy of the report should be submitted after incorporating any

modifications suggested by the evaluation committee.

Guidelines for Evaluation

The following are the weightages given for the various stages of the seminar:

1. Selection of the topic. 05 Marks.

(20%)

2. Literature survey. 05 Marks.

(20%)

3. Understanding and presentation of the given topic. 05 Marks.

(20%)

4. Reporting and Documentation. 10 Marks.

(40%)

37

Artificial Intelligence




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.

Nearst Neighbour and Clustering Approaches: Nearst Neighbour method,

Nearst Neighbour approach for secondary structure protein folding

prediction, Clustering and Advanced clustering techniques. Identification

Trees - Gain criterion, Over fitting and Pruning. Nearst Neighbour and

Clustering Approaches for Bioinformatics.

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.

Applications of Artificial Intelligence: 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

Inferring Gene Regulatory Networks from Expression Data: Introduction, Modeling gene regulatory networks. Boolean Networks and

Bayesian Networks and Fuzzy Neural Networks.

TEXT BOOKS:

1. Werner Dubitzky, Francisco Azuaje. Artificial Intelligence Methods and

Tools for Systems Biology Published by Springer, 2005.

38

2. Edward Keedwell, Ajit Narayanan, Intelligent Bioinformatics: The

Application of Artificial Intelligence Techniques to Bioinformatics

Problems, published by John Wiley and Sons, 2005.

REFERENCE BOOKS:

1. Arpad Kelemen, Ajith Abraham, Yuehui Chen. Computational Intelligence

in Bioinformatics, SpringerLink (Online service) Published by Springer,

2008.

2. Tomasz G. Smolinski, Mariofanna G. Milanova, Aboul Ella Hassanien.

Computational Intelligence in Biomedicine and Bioinformatics: Current

Trends and Applications, Published by Springer, 2008.

3. Stuart Jonathan Russell, Peter Norvig, John F. Canny, Artificial

Intelligence: A Modern Approach, Published by Prentice Hall, 2003.

39

Neuroinformatics




Introduction: Introduction to Neuroinformatics, Scope, Applications, Brief

introduction about Neuron, Glial Cells, Neurophysiology - action potential,

resting potential, Chemical control of Brain, Learning and Memory.

Linear Response Theory and Single Neuron Models: Properties of a linear

system, Convolution and Fourier transforms. Neuron models - Integrate and

Fire model, Multi compartment models and Network Models.

Neural Encoding: Introduction; Spike Trains and Firing rates, Spike Train

Statistics, Neural encoding and decoding - Neural Code, Estimating Firing

Rates, Introduction to Receptive Fields, Neural Decoding and Information

theory.

Entropy, Mutual Information, Bayer’s Theorem: Adaptation and learning.

Synaptic plasticity rules. Supervised and unsupervised learning. Classical

conditioning and Reinforcement learning.

Neuroscience Knowledge Management: Managing knowledge in

Neuroscience, Interoperability across Neuroscience databases. Database

architectures for Neuroscience applications, XML for data representation and

Data model specification.

Computational Neuronal Modeling and Simulation: Tools and methods

for simulation of Neurons and Neural Circuits - Model structure analysis in

NEURON, Constructing realistic Neural simulations with GENESIS,

Simulators for Neural Networks and Action potentials. Data mining through

simulation. Computational exploration of Neuron and Neural Network

models in Neurobiology.

Neuroinforamtics Applications and Infrastructure:

Neuroinformatics in Genetics and Neurodegenerative Disorders:

Information approach to Systems Neurogenetics. Computational models of

dementia and Neurological problems, Application of Systems biology

approach to the neuroscience (application to schizophrenia). Brain Image

construction, Analysis and Morphometric tools - Brain image Atlases,

Databases and Repositories. Tools and databases for Mapping Neural

structure and Connectivity Pattern.

Neuroinforamtics Applications and Models for Neuropsychology: General Neuropsychological assessment - Visuospatial processing, Visual

attention and Spatial neglect, Speech, Language and Aphasia, Phenomics and

40

Neuropsychology.

Human Brain Project: Microscale and Macroscale characterization; Basis

of Brain mapping; Functional and Cognitive Brain atlas; Interoperable and

Federated Brain Map databases.

TEXT BOOKS:

1. Vinoth Jagaroo. Neuroinformatics for Neuropsychology, Springer, 2009.

2. Dayan and Abbot. Theoretical Neuroscience – Computational and

Mathematical Modeling of Neural System, The MIT Press, 1st Edition,

2001.

REFERENCE BOOKS:

1. Chiquito Joaquim Crasto. Neuroinformatics, Humana Press, 2007.

2. Stephen H. Koslow, Michael F. Huerta. Neuroinformatics: an overview of

the Human Brain Project, Routledge, 1997.

41

Java for Bioinformatics & Biomedical Application




Introduction to Java: Basics, Introduction to Java Applications, The Java

Programming Environment, Fundamental Programming Structures in Java,

Objects and Classes in Java, Polymorphism and Abstract Classes,

Inheritance, Interfaces and Inner Classes, Program Design & Considerations ,

Graphical Components & Visual Design ,Java Utilities , Exception Handling,

Exception Handling, File I/O, Applets, Linked Data Structures,

Multithreading.

Bioinformatics and Java: Current state of biomedical research. Cancer

biomedical informatics and Grid program. caBIG organization and

architecture. Model-View-Controller framework. Web services and service

oriented architecture, caGrid.

Sequence Search: Peforming BLAST analysis, Developing SwingBlast

application. Designing SwingBlast Java application - adding events to

applications, Designing SwingBlast GUI, Coding SwingBlast GUI.

Description of Blast Classes, Implementing JQBlast. Enhacing SwingBlast

application – Retrieving sequence from GenBank using BioJava and without

using BioJava.

Facilitating PubMed Searches: JavaServer pages and Java Servelets –

HTTP and CGI, HTTP Protocol, GET and POST methods. Servelets and

JavaServer pages technologies - Java API for Servelets and JSPs, JavaServer

pages Standard Tag Library (JSTL), Apache Tomcat server. The NCBI

PubMed literature search and retrieval service. Accessing biomedical

literature through Entrez. Development of web application with Servelets and

JSPs.

Creating a Gene Prediction and BLAST Analysis Pipeline: Gene

prediction programs. Performing Gene prediction with Genscan - Running

genscan analysis, Analyzing Genscan output. Craeting SwingGenscan,

Coding for SwingGenScan, SwingGenScan user Iunterface and Running

SwingGenScan.

Cancer Biomedical Informatics Grid (caBIG): Cancer Biomedical

informatics Grid: Structure and Organisation of caBIG, Data Integration and

ETL, Cancer Common Ontology Representation Environment (caCORE).

Cancer Bioinformatics and Infrastructure Object (caBIO). Downloading and

configuring caBIO. Creating JcaBIO application - JcaBIO classes and

Application, Coding the SwingCaBIO application and Running JcaBIO

application.

http://www.edugrid.ac.in/iiitmk/java/java_syllabus.htm#top7



42

TEXT BOOKS:

1. Harshawardhan Bal, Johnny Hujol. Java for Bioinformatics, Published by

Pearson Education, Limited, 2007.

2. Harshawardhan Bal, Johnny Hujol. Java for Bioinformatics and

Biomedical Applications, Published by Springer, 2007.

3. Cay S. Horstmann, Gary Cornell. Java 2: Fundamentals: Fundamentals,

Published by Prentice Hall PTR, 2001.

REFERENCE BOOKS:

1. Harshawardhan Bal, Johnny Hujol. Java for Bioinformatics, Published by

Pearson Education Limited, 2007.

2. Cynthia Gibas, Per Jambeck. Developing bioinformatics computer skills,

Published by O'Reilly, 2001.

43

Database Management & Grid Computing




Introduction to Biological Databases: Nucleic acid and Protein sequence

data banks: Genbank, EMBL, DDBJ, cDNA databanks, AIDS Virus

sequence data bank, rRNA data bank, Protein sequence data banks: NBRF-

PIR, SWISSPROT, Signal peptide data bank, TrEMBL, GenPept, PRINTS,

CATH, SCOPE, BLOCKS. Structural databases – PDB, exPDB, MMDB and

PDBsum,

Data Base Management System: Data Abstraction and Data Models. Basic

concepts of database: Data Independence DML, DCL, DDL and Architecture

of DBMS. Entity Relationship diagram. Application of ER diagram in

designing database system. Relational Algebra and Tuple Relational

Calculus.

Database Design Issues: Normalization 1NF, 2NF, 3NF, 4NF, BCNF and

5NF and database design problem. Security and Integrity: Use of SQL for

specifying Security and integrity. Authorization, View, Encryption. Storage

structure indexing and hashing. Different type of file organization.

Transaction & Concurrency control - Schedules, Testing, Serializability,

Protocols - Lock based Protocol, Time Stamp protocol. Validation technique

- Multiple granularity, Multi-version scheme Insert and delete operation,

Crash recovery, Log based recovery, Buffer management checkpoints,

Shadow paging. Object oriented databases.

Distributed Database Structure: Design transparency and Autonomy.

Distributed Query Processing Recovery - Commit protocol Deadlock

handling, Multidatabase system. Parallel database concept and related issues,

Web interface to database and Database System Architecture.

Distributed Database Structure Implementation: Implementation of

networks, Programme Environment. Implementation of Hierarchical database

- Hierarchical Data Manipulation Language. Relational database model -

Basic principles in relational algebra, Relational Calculus, Domain relational

calculus. Introduction, ISBL, SQUARE, SEQUEL, Query by Example,

Commercial database systems.

Grid Computing: Introduction. Grid computing concepts: Exploiting

underutilized resources, Parallel CPU Capacity, Virtual resources and Virtual

organizations for collaboration. Resource management - Access to additional

resources, Resource balancing and resource reliability.

44

Grid Architecture: Application Considerations; CPU considerations; Data

considerations. Design – Building Grid architecture, Solution objectives,

Grid architecture models: Computational Grid and Data grid. Grid

Topologies – Intragrid, Extragrid. Conceptual Architecture – Infrastructure,

Conceptual Components. Schedulers; Condor. Data sharing; Distributed File

Systems: Security. Service Oriented Architecture – Web Services,

Convergence of Web Services and Grid Services. Introduction to Globus

Toolkit (Open Standards based). Case Studies on Grid Implementation for

Life Sciences projects.

TEXT BOOKS:

1. Abraham Silberschatz, Henry F. Korth, S. Sudarshan. Database System

Concept, McGraw-Hill, 2010.

2. Database Systems: A Practical Approach To Design, Implementation And

Management, 4/E, Pearson Education India , 2008.

3. C. J. Date, An introduction to database systems, Addison-Wesley, 2000.

REFERENCE BOOKS:

1. Peter Rob, Carlos Coronel. Database systems: design, implementation, and

management, Cengage Learning, 2009.

2. Lizhe Wang, Wei Jie, Jinjun Chen. Grid computing: infrastructure, service,

and applications, CRC Press, 2009.

3. Brajesh Goyal, Shilpa Lawande. Enterprise grid computing with Oracle,

McGraw-Hill Professional, 2006.

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22Abraham+Silberschatz%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22Henry+F.+Korth%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22S.+Sudarshan%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22C.+J.+Date%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22Lizhe+Wang%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22Wei+Jie%22

http://www.google.co.in/search?tbs=bks:1&tbo=p&q=+inauthor:%22Jinjun+Chen%22

45

BioPerl, Biopython & NCBI C++ Toolkit




BioPerl:

Perl - Introduction to Perl, writing and executing a Perl program. Data Types

– Scalar, Arrays and Associative arrays. Operators, Variables and Special

variables. Regular expressions, Subroutines. Packages – writing and calling

packages. Modules – writing and calling modules.

Programming Applications:

Motifs and Loops - Finding Motifs, Counting Nucleotides, Exploding Strings

into Arrays, Operating on Strings, Writing to Files. Mutations and

Randomizations - A program to simulate DNA mutation, Generating random

DNA, Analyzing DNA. The Genetic Code - Hashes, Data Structures and

Algorithms for Biology, Translating DNA into Proteins, Reading DNA from

Files in FASTA format, Reading Frames. Restriction Maps and Regular

Expressions - Restriction Maps and Restriction Enzymes, Generation of

Restriction maps of Nucleic acid sequence.

Database access and searching: GenBank Files, GenBank Libraries,

Separating Sequence and Annotation, Parsing Annotations, Indexing

GenBank with DBM. Protein Data Bank - PDB Files, Parsing PDB Files.

BLAST - Obtaining BLAST, String Matching and Homology, Parsing

BLAST Output.

BioPerl: Introduction to BioPerl, BioPerl Modules, Applications of BioPerl –

Sequence retrieval and Sequence submission, Pair wise and Multiple

sequence alignment, Parsing BLAST/FASTA results, Phylogenetic analysis.

Retrieval and Parsing PDB Files. Creating graphics for Sequence display and

Annotation.

BioPython:

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.

46

Biopython and Bioinformatics: Parsing DNA data files, Image

manipulation, Sequence analysis - 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.

NCBI C++ Toolkit:

Introduction: Introduction, Applications and Feutures of NCBI C++ Tool

kit. 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. Working with diagnostic streams - Debug

Macros, Handling exceptions. Working with files and directories.

TEXT BOOKS:

1. John Lewis, Peter Joseph DePasquale, Joseph Chase, Joe Chase. Java

Foundations, Addison-Wesley, 2010.

2. Mitchell L Model. Bioinformatics Programming Using Python , O'Reilly

Media, Inc., 2009.

3. D. Curtis Jamison. Perl Programming for Biologists, Wiley-IEEE, 2003.

REFERENCES/REFERENCE BOOKS:

1. Todd Greanier, Jason M. Kinser, Jones & Bartlett Learning. Python for

bioinformatics , 2009.

2. Java foundations, John Wiley and Sons, 2004

3. http://www.bioperl.org

4. http://biojava.org/

5. http://biojava.org/wiki/BioJava:BioJavaInside

http://www.bioperl.org/

http://biojava.org/

http://biojava.org/wiki/BioJava:BioJavaInside

47

Bioinformatics in Drug Design & Discovery




Drug Design Process: Drug design - Compound searching, Target

Identification, Target characterisation, Study of molecular interactions

between target and compound (docking), ADMET Studies and Study of drug

resistance. Drug design process for a known protein target – Structure based

drug design process, Finding initial hits, Compound refinement, ADMET

Studies and Study of drug resistance. Drug design process for unknown

protein target – Ligand based drug design process, Finding initial hits,

Compound refinement, ADMET Studies and Study of drug resistance. Case

studies.

Compound Library Design: Target library vs Diverse libraries, Non-

Enumerative techniques, Drug likeliness and Synthetic accessibility,

Analysing diversity and Spanning known chemistries. Compound selection

techniques.

Homology Modeling and Drug Design: Structure Generation, Retrieval,

Structure Visualization. Homology modeling - Constructing an initial model,

Refining the model, Manipulating the model, Navigation of the model.

Model evaluation – Model evaluation techniques, Concept of energy

minimization and Energy minimization techniques. Conformation generation,

Deriving bioactive conformations, Molecular superposition and alignment,

Deriving the Pharmacophoric pattern, Receptor mapping and estimating

biological activities. Structural similarities and Superimposition techniques.

Rational Drug Design and Chemical Intuition, Important Key and the Role of

the Molecular Model, Limitations of Chemical Intuition.

Molecular Mechanics: Introduction to Molecular mechanics, Force fields

for drug design. Study of protein folding: Algorithms, Conformation analysis.

Docking: Introduction, Search algorithms, Scoring functions, Docking

Process – Protein Preparation, Building the ligand, Setting the bounding box,

Running the docking calculations.

Building the Pharmacophore Models: Components of Pharmacophore

model, Creating a Pharmacophore model from active compounds, Creating

Pharmacophore model from Active site and Searching compound databases.

QSAR: Conventional QSAR vs 3D-QSAR, QSAR Process, Molecular

descriptors, Automated QSAR Programs. 3D-QSAR – 3D-QSAR Process.

Quantum Mechanics in Drug Design: Quantum Mechanics algorithms in

Drug design - Modeling Systems with metal atoms, Computing reaction

48

paths and Computing spectra.

ADMET Studies: Oral bioavailability of compound, Finding Drug Half life

in the Blood stream, Blood- Brain Barrier permeability and Toxicity studies,

Computer - Assisted Drug Discovery: Drug Discovery and Development

process, New Lead Discovery Strategies. Composition of Drug Discovery

teams, Current Practice of CADD in the Pharmaceutical industry,

Management structures of CADD groups, Contributions and achievements of

CADD groups, Limitations of CADD support, Inherent Limitations of

CADD support. State of Current Computational Models, Software and

Hardware constraints

TEXT BOOKS:

1. D. C. Young. Computational Drug Design: A Guide for Computational and

Medicinal Chemists, Wiley-Interscience, 2009.

2. Stephen Neidle. Cancer Drug Design and Discovery , Academic Press –

Publisher, 2008.

3. Yi-Ping Phoebe Chen. Bioinformatics Technologies , Springer – Publisher,

2005.

4. Povl Krogsgaard-Larsen, Tommy Liljefors, Ulf Madsen. Textbook of drug

design and discovery, Published by Taylor & Francis, 2002.

REFERENCE BOOKS:

1. Charles Owens Wilson, John H. Block, Ole Gisvold, John Marlowe Beale

Lippincott . Wilson and Gisvold's Textbook of Organic Medicinal and

Pharmaceutical Chemistry, Williams & Wilkins, 2010.

2. Alexandros Makriyannis, Diane Biegel, Marcel Dekker. Drug Discovery

Strategies and Methods, 2004.

3. Alexander Hillisch, Rolf Hilgenfeld, Birkhäuser. Modern Methods of Drug

Discovery, 2003.

4. Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin. 3D QSAR in Drug

Design: Ligand-protein interactions and molecular similarity, Springer,

1998

5. Veerapandian, Pandi Veerapandian, Marcel Dekker. Structure-based drug

design 1997.

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Hugo+Kubinyi%22

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Gerd+Folkers%22

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Yvonne+C.+Martin%22