SYLLABUS MASTER OF TECHNOLOGY PROGRAMME IN BIOINFORMATICS ...files.sathyabamauniversity.ac.in/syllabus/2010 PG... · MASTER OF TECHNOLOGY PROGRAMME IN BIOINFORMATICS ... UNIT III

SATHYABAMA UNIVERSITY(Established under section 3 of UGC Act , 1956)

Jeppiaar Nagar, Rajiv Gandhi Road, Chennai – 119.

SYLLABUS

MASTER OF TECHNOLOGY PROGRAMME

IN

BIOINFORMATICS(4 SEMESTERS)

REGULATIONS 2010

SATHYABAMA UNIVERSITYREGULATIONS – 2010

Effective from the academic year 2010-2011 and applicable to the students admitted to the Master of Engineering/ Technology / Architecture /Science (Four Semesters)

1. Structure of Programme

1.1 Every Programme will have a curriculum with syllabi consisting theory and practical such as:

(i) General core courses like Mathematics

(ii) Core course of Engineering / Technology/Architecture / Science

(iii) Elective course for specialization in related fields

(iv) Workshop practice, Computer Practice, laboratory Work, Industrial Training, SeminarPresentation, Project Work, Educational Tours, Camps etc.

1.2 Each semester curriculum shall normally have a blend of lecture course not exceeding 7 and practicalcourse not exceeding 4.

1.3 The medium of instruction, examinations and project report will be English.

2. Duration of the Programme

A student is normally expected to complete the M.E/M.Tech./M.Arch/M.Sc Programme in 4 semesters but inany case not more than 8 consecutive semesters from the time of commencement of the course. TheHead of the Department shall ensure that every teacher imparts instruction as per the number of hours specifiedin the syllabus and that the teacher teaches the full content of the specified syllabus for the course beingtaught.

3. Requirements for Completion of a Semester

A candidate who has fulfilled the following conditions shall be deemed to have satisfied the requirement forcompletion of a semester.

3.1 He/She secures not less than 90% of overall attendance in that semester.

3.2 Candidates who do not have the requisite attendance for the semester will not be permitted towrite the University Exams.

4. Examinations

The examinations shall normally be conducted between October and December during the odd semesters andbetween March and May in the even semesters. The maximum marks for each theory and practical course(including the project work and Viva Voce examination in the Fourth Semester) shall be 100 with the followingbreakup.

(i) Theory Courses

Internal Assessment : 20 Marks

University Exams : 80 Marks

(ii) Practical courses

Internal Assessment : - -

University Exams : 100 Marks

SATHYABAMA UNIVERSITY FACULTY OF BIOENGINEERING

M.Tech. (BIOINFORMATICS) i REGULATIONS 2010

5. Passing requirements:

(i) A candidate who secures not less than 50% of total marks prescribed for the course (For all coursesincluding Theory, Practicals and Project work) with a minimum of 40 marks out of 80 in the UniversityTheory Examinations, shall be declared to have passed in the Examination.

(ii) If a candidate fails to secure a Pass in a particular course, it is mandatory that he/she shall reappearfor the examination in that course during the next semester when examination is conducted in thatcourse. However the Internal Assessment marks obtained by the candidate in the first attempt shallbe retained and considered valid for all subsequent attempts.

6. Eligibility for the Award of Degree

A student shall be declared to be eligible for the award of the M.E/M.Tech./M.Arch./M.Sc degree provided thestudent has successfully completed the course requirements and has passed all the prescribed examinations inall the 4 semesters within the maximum period specified in clause 2.

7. Award of Credits and Grades:

All assessments of a course will be done on absolute marks basis. However, for the purpose of reporting theperformance of a candidate, Letter Grades will be awarded as per the range of total marks (out of 100) obtainedby the candidate as given below:

RANGE OF MARKS FOR GRADES

Range of Marks Grade Grade Points (GP)

90-100 A++ 10

80-89 A+ 9

70-79 B++ 8

60-69 B+ 7

50-59 C 6

00-49 F 0

ABSENT W 0

CUMULATIVE GRADE POINT AVERAGE CALCULATION

The CGPA calculation on a 10 scale basis is used to describe the overall performance of a student inall courses from first semester to the last semester. F and W grades will be excluded for calculating GPAand CGPA.

CGPA = Σi Ci GPi

Σi Ci

where Ci - Credits for the subject

GPi - Grade Point for the subject

Σi - Sum of all subjects successfully cleared during all the semesters


M.Tech. (BIOINFORMATICS) ii REGULATIONS 2010

8. Classification of the Degree Awarded

1 A candidate who qualifies for the award of the Degree having passed the examination in all thecourses of all the semesters in his/her first appearance within a maximum period of 4 consecutivesemesters after commencement of study securing a CGPA not less than 9.0 shall be declared tohave passed the examination in First Class – Exemplary.

2. A candidate who qualifies for the award of the Degree having passed the examination in all thecourses of all the semesters in his/her first appearance within a maximum period of 4 consecutivesemesters after commencement of study, securing a CGPA not less than 7.5 shall be declared tohave passed the examination in First Class with Distinction.

3. A candidate who qualifies for the award of the Degree having passed the examination in all thecourses of all the semesters in his/her first appearance within a maximum period of 4 consecutivesemesters after commencement of study securing a CGPA not less than 6.0 shall be declared tohave passed the examination in First Class.

4 All other candidates who qualify for the award of the Degree having passed the examination in allthe courses of all the 4 semesters within a maximum period of 8 consecutive semesters after his/hercommencement of study securing a CGPA not less than 5.0 shall be declared to have passedthe examination in Second Class.

5 A candidate who is absent in semester examination in a course/project work after having registeredfor the same, shall be considered to have appeared in that examination for the purpose ofclassification of degree. For all the above mentioned classification of Degree, the break of studyduring the programme, will be counted for the purpose of classification of degree.

6 A candidate can apply for revaluation of his/her semester examination answer paper in a theorycourse, within 1 week from the declaration of results, on payment of a prescribed fee along withprescribed application to the Controller of Examinations through the Head of Department. TheController of Examination will arrange for the revaluation and the result will be intimated to thecandidate concerned through the Head of the Department. Revaluation is not permitted for practicalcourses and for project work.

Final Degree is awarded based on the following :

CGPA ≥ 9.0 - First Class - Exemplary

CGPA ≥ 7.50 < 9.0 - First Class with Distinction

CGPA ≥ 6.00 < 7.50 - First Class

CGPA ≥ 5.00 < 6.00 - Second Class

Minimum CGPA requirements for award of Degree is 5.0 CGPA.

9. Discipline

Every student is required to observe disciplined and decorous behaviour both inside and outside the Universityand not to indulge in any activity which will tend to bring down the prestige of the University. If a studentindulges in malpractice in any of the University theory / practical examination, he/she shall be liable for punitiveaction as prescribed by the University from time to time.

10. Revision of Regulations and Curriculum

The University may revise, amend or change the regulations, scheme of examinations and syllabi from time totime, if found necessary.


M.Tech. (BIOINFORMATICS) iii REGULATIONS 2010

M.TECH. - BIOINFORMATICSREGULATIONS 2010 – CURRICULUM

SEMESTER I

Sl.No. SUBJECT CODE SUBJECT TITLE L T P C Page No.

THEORY

1 SBIX5001 Introduction to Bioinformatics 3 0 0 3 1

2 SBIX5002 Biophysical Chemistry 3 0 0 3 2

3 SBIX5003 Genomics and Proteomics 3 0 0 3 3

4 SBIX5004 Medicinal and Pharmaceutical Chemistry 3 0 0 3 4

5 SMTX5005 Applied Mathematics 3 1 0 4 5

PRACTICALS

6 SBIX6501 PERL Programming and Biological Databases Lab 0 0 4 2 6

TOTAL CREDITS: 18

SEMESTER II


THEORY

1 SBIX5005 Biological Sequence Analysis 3 0 0 3 7

2 SBIX5006 Molecular Modeling and Drug Design 3 1 0 4 8

3 SMTX5006 Bio Statistics 3 1 0 4 9

4 ELECTIVE - I 3 0 0 3

5 ELECTIVE - II 3 0 0 3

PRACTICALS

6 SBIX6502 Sequence Analysis and Molecular Modeling Lab 0 0 4 2 10

TOTAL CREDITS: 19


M.Tech. (BIOINFORMATICS) iv REGULATIONS 2010

SEMESTER III


THEORY

1 SBIX5007 Structural and Functional Genomics 3 0 0 3 11

2 SBIX5008 Systems Biology and Metabolic Engineering 3 1 0 4 12

3 SCSX5032 Advanced Computer Programming 3 0 0 3 13

4 ELECTIVE - III 3 0 0 3

5 ELECTIVE - IV 3 0 0 3

PRACTICALS

6 SBIX6503 Systems Biology and Advanced Computing Lab 0 0 4 2 14

TOTAL CREDITS: 18

SEMESTER IV


1 S33XPROJ Project Work & Viva Voce 0 0 15 15

TOTAL CREDITS: 15

TOTAL CREDITS FOR THE COURSE: 70

LIST OF ELECTIVES SUBJECTS

Sl.No SUBJECT CODE SUBJECT TITLE L T P C Page No.

1 SBIX5009 Microarray- Techniques and Applications 3 0 0 3 15

2 SBIX5010 Data Mining in Bioinformatics 3 0 0 3 16

3 SBIX5011 Cheminformatics 3 0 0 3 17

4 SBIX5012 PERL and CGI Programming 3 0 0 3 18

5 SBIX5013 Machine Learning for Bioinformatics 3 0 0 3 19

6 SBIX5014 High Performance Computing 3 0 0 3 20

7 SBIX5015 Computational Biology 3 0 0 3 21

8 SBIX5016 Applied Bioinformatics 3 0 0 3 22

9 SBIX5017 Clinical Data Management 3 0 0 3 23

10 SBIX5018 Neuroinformatics 3 0 0 3 24

11 SBIX5019 Computational Chemistry 3 0 0 3 25

12 SBIX5020 Immunoinformatics and Computational Vaccinology 3 0 0 3 26

13 SCSX5034 Artificial Neural Networks 3 0 0 3 27

L - Lecture hours; T - Tutorial hours; P - Practical hours; C - Credits


M.Tech. (BIOINFORMATICS) v REGULATIONS 2010

SBIX5001 INTRODUCTION TO BIOINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I HISTORY OF BIOINFORMATICS 10 hrs.

History of Bioinformatics-role of Bioinformatics in biological sciences- scope of bioinformatics -introduction tointernet-WWW, network basics, LAN & WAN standards-network topologies and protocols- ftp, http - division ofBioinformatics- Bioinformatics and internet-challenges in Bioinformatics.

UNIT II DATABASES IN BIOINFORMATICS 10 hrs.

Databases in Bioinformatics- Genbank, NCBI, EMBL, DDBJ, UniGene, SGD, EMI Genomes, -proteindatabases-PIR, SWISSPROT, TrEMBL, Prosite, PRINTS -structural databases-PDB, SCOP, CATH, PDB_SELECT,PDBSUM, DSSP, FSSP, DALI, PRODOM, protein families & pattern databases, Pfam, KEGG - sequence storage-sequence accuracy-EST,STS- sequence retrieval systems- Entrez-SRS- sequence query refinement using Booleanoperators, limits, preview, history and index.

UNIT III SEQUENCE SUBMISSION 10 hrs.

Sequence submission tools-BANKIT-SEQUIN-WEBIN-SAKURA-literature databases-PubMed and medline. Datamining and its techniques- data warehousing- Sequence annotation: principles of genome annotation- annotation tools& resources

UNIT IV APPLICATIONS OF BIOINFORMATICS 10 hrs.

Applications of Bioinformatics-phylogenetic analysis-steps in phylogenetic analysis-microarrays-DNA and proteinmicroarrays- Bioinformatics in pharmaceutical industry: informatics & drug- discovery - pharmainformatics resources-drug discovery and designing-SNP.

UNIT V FILE FORMATS 10 hrs.

File formats-raw/plain format-NCBI, Genbank flat file format-ASN.1, GCG, FASTA, EMBL, NBRF, PIR, swissprotsequence formats, PDB format, etc. - introduction to structure prediction methods.

REFERENCE BOOKS:1. Attwood T.K and Parry-Smith, “Introduction to Bioinformatics”, Addison Wesley Longman, 1999.2. David W Mount, “Bioinformatics: Sequence and Genome Analysis”, 2nd edition, CBS publishers, 2004.3. Arun Jagota, “Data Analysis and Classification for Bioinformatics”, Pine Press, 2001.4. Des Higgins and Willie Taylor, “Bioinformatics Sequence, Structures and Databanks”, Oxford University Press, USA, 2000.

WEBSITES:1. www.bioinformatics.org/2. www.biomedcentral.com/bmcbioinformatics/3. www.ebi.ac.uk/4. http://www.ncbi.nlm.nih.gov/5. www.ucmp.berkeley.edu/exhibit/introphylo.html

UNIVERSITY EXAMINATION QUESTION PAPER PATTERN

Max. Marks: 80 Exam Duration: 3 hrs.

Part A: 6 Questions –no choice each carrying 5 marks 30 Marks

Part B: 2 Questions from each unit of internal choice each carrying 10 marks 50 Marks


M.Tech. (BIOINFORMATICS) 1 REGULATIONS 2010

SBIX5002 BIOPHYSICAL CHEMISTRYL T P Credits Total Marks

3 0 0 3 100

UNIT I PRINCIPLES OF BIOPHYSICS 10 hrs.

Principles of biophysics: Methods for separation and characterization of macromolecules: general principles,sedimentation, Centrifugation techniques- principles, types and applications. Ultracentrifugation- types, optical methodsused and applications of preparative and analytical ultracentrifuges.mechanisms of information storage, processing,retrieval in biological system.

UNIT II CHROMATOGRAPHY 10 hrs.

Chromatography- Principles, methodology and applications of chromatography using paper, thin layer, column(gel filtration, ion exchange, affinity), gas and types of HPLC.

Electrophoresis- Principles and types of electrophoresis and their applications for proteins, nucleic acids, includinggradient gel and pulse-filed gel electrophoresis; Agarose gel electrophoresis, PAGE, 2D-PAGE, Capillary electrophoresisand Immunoelectrophoresis and isoelectro focusing, mass spectrometry

UNIT III SPECTROSCOPY 10 hrs.

Spectroscopic Techniques. Principle, Instrument Design, Methods & Applications of UV-Visible Spectra, IR Spectra,Raman Spectra, Fluorescence spectra, NMR and ESR Spectra. Circular dichroism and optical rotatory dispersion

UNIT IV X-RAY DIFFRACTION 10 hrs.

Crystals, Molecular crystal symmetry, X- rays- production and properties of X-ray. X-ray Absorption and diffractionX-ray diffraction by crystals, Bragg’s Law, Laue powder and rotation methods, Calculating electron density and Pattersonmaps (Fourier transform and Structure factors, convolutions), phases, X-ray Absorption and diffraction, X-rayfluorescence, detection and applications Neutron diffraction, Electron diffraction, Application in Biology.

UNIT V MOLECULAR THERMODYNAMICS 10 hrs.

Molecular thermodynamics – complexities in modeling macromolecular structure, molecular mechanics, stabilizinginteractions in macromolecules, simulating macromolecular structure- Statistical thermodynamics: general principles, structuraltransitions in polypeptides and proteins, structural transitions in polynucleotides and DNA, non-regular structures.

REFERENCE BOOKS:1. Upadhyay and Upadhya Nath, “Biophysical Chemistry”, Himalaya Publishing House, 2009.2. Keith Wilson and John Walker, “Practical Biochemistry”, Fifth edition, Cambridge University Press, 2000.3. Douglas A. Skoog, “Instrumentation analysis”, Stanford University Publisher, 2003.4. Cantor R., Schimmel P.R., “Biophysical Chemistry”, Vol. I, II, W.H. Freeman & Co., 1985.

WEBSITES:1. www.mps.lu.se/bpc/2. https://www.annualreviews.org/loi/biophys3. http://www.its.caltech.edu/~ch24/







SBIX5003 GENOMICS AND PROTEOMICSL T P Credits Total Marks

3 0 0 3 100

UNIT I GENOME STRUCTURE 10 hrs.Overview- Structure and organization of Prokaryotic and eukaryotic genome – concept on central dogma,

variations in the general structure and organization in genomes expression profiles. Brief outlook of various genomeprojects and their outcome Genome projects- overview and importance- human genome project - Genome annotations.

UNIT II GENOME SEQUENCING & MAPPING 10 hrs.Various techniques of genome sequencing – short gun, direct short gun, clone contig approach, chromosome

walking, primer walking, chromosome jumping, contig assembled, genome marking and mapping techniques- genomicDNA library, cDNA library- Mapping and Sequence assembly- genetic marker – RFLP, STR, SSLP, VNTR. Physicalmarker, EST, STS, FISH, Radiation hybrid, Sequence marker, SNP’s, Expression analysis, micro array approach.

UNIT III GENOME DATABASES 10 hrs.Genome databases - NCBI, TIGR, KEGG, FLYBASE, MGD, and EMBL, EBI genome reviews, celera, GDB,

GOLD-genomes online database. Ensembl, Genome reference consortium - NCBI - Organelle genome resources -Codon usage Database, vector databases, Genome based tools – map viewer, Genscan, CpG islands, Repeat masker,promoter 2.0, netGene2, RNAmmer 1.2 Server, Netstart, GeneFizz server, NCBI – ORF finder, Univec-Vecscreen,NebcutterV2.0, Webcutter, Pedant, Veil.

UNIT IV PROTEOME ANALYSIS 10 hrs.Proteome analysis- Proteome – Bridging Genomics and Proteomics – Analysis of Proteome – 2D PAGE – Mass

spectrometry – MALDI TOF – Micro array Technology – CD - Application of Proteome analysis – Protein Chips –Drug Development and Toxicology – Proteomic in genetic and breeding - Legal aspects of Genomics & proteomics– Bioethics – IPR & Patents.

UNIT V PROTEOMIC DATABASES AND TOOLS 10 hrs.Proteome databases: protein sequence database, EBI, PRIDE, INTERPRO, INTACT, Expasy, swiss2DPAGE,

SWISS-PROT, TrEMBL, specialized protein sequence databases, PROSITE, BLOCKS, PDB, OMIM, Metabolicdatabases, application of proteomics to medicine, toxicology and Pharmaceuticals- Application of Proteome analysis –Protein biochip technology – Drug Development and Toxicology.

REFERENCE BOOKS:1. Brown T.A, ”Genome”, 2nd Edition, BIOS Scientific Publishers Ltd, 2002.2. Cantor, “Genomics – The Science and Technology behind HGP”, John Wiley & Sons, 1999.3. Pennington SR, Dunn MJ, “Proteomics from Protein Sequence to Function”, 2nd Edition, Viva Books Ltd, 20024. Daniel C. Liebler, “Introduction to Proteomics”, Humana Press, 2001.5. David W Mount, ”Bioinformatics: Sequence and Genome Analysis”, 2nd Edition, CBS Publishers, 2004.

WEBSITES:1. http://www.ncbi.nlm.nih.gov/genbank2. http://sitemaker.umich.edu/microbial_genomics3. http://www.kazusa.or.jp/codon/


Max. Marks: 80 Exam Duration: 3 hrs.Part A: 6 Questions –no choice each carrying 5 marks 30 MarksPart B: 2 Questions from each unit of internal choice each carrying 10 marks 50 Marks



SBIX5004 MEDICINAL AND PHARMACEUTICAL CHEMISTRYL T P Credits Total Marks

3 0 0 3 100

UNIT I OVERVIEW OF MEDICINAL CHEMISTRY 10 hrs.

What is Drug? Drug Targets, Pharmacokinetic issues and medicines - Classification of drugs. Sources ofdrugs-plant, animal, synthetic - Recombinant technology - Nomenclature of drugs- INNs, BAN, USAN - History andtheir role in formulation development & therapeutics and clinical setting - Route of Administration- Oral, parentral,Rectal, Topical administration - Inhalation.

UNIT II PHARMACOKINETICS 10 hrs.

Absorption, distribution, metabolism (biotransformation) and excretion (elimination) of drugs - Physicochemical,biological and pharmaceutical factors altering biopharmaceutical performance of drugs - Passage of drugs acrossbiological barrier (passive diffusion, active transport, facilitated diffusion and pinocytosis) - Factors influencing absorption-physiochemical, physiological and pharmaceutical - Drug distribution in the body, plasma protein binding.

UNIT III PHARMACODYNAMICS 10 hrs.

Introduction- Enzymes-structure and function - Inhibitors acting at the active site of an enzyme, Inhibitors actingat allosteric binding sites, Uncompetitive and non-competitive inhibitors - Transition-state analogues, suicide substrates- Isozyme selectivity of inhibitors - Medicinal uses of enzyme inhibitors - Enzyme kinetics - Receptors - Structure andfunction - the design of agonists, antagonists, partial agonists, inverse agonists, receptor types & subtypes.

UNIT IV PHARMACOLOGICAL EFFECT OF DRUGS 10 hrs.

Classification- antibacterial agents - Mechanism of antibacterial action - antimetabolites - sulfonamides - applicationof sulfonamides - penicillins - structure and mechanism of action, structure activity relationships of penicillins - antiviralagents, anticancer agents and antiulcer agents - Advantages and disadvantages - structure and mechanism of drugaction.

UNIT V DRUG DISCOVERY 10 hrs.

Physicochemical properties in relation to drug design - structure activity relationship - isoelectric replacement - QSARHansch(property - property relationship) and free Wilson (Structure - property relationship) analysis - SAR vs QSAR -3DQSAR –CoMFA - HINT – CoMSIA – CoMASA – SOMFA – HASL - CoMMA - MS-Whim - Devising a research strategy- challenges in drug discovery, development of new drugs - legal aspects of product protection - getting drug to market.

REFERENCE BOOKS:1. Graham L.Patrick, “An Introduction to Medicinal Chemistry”, Oxford University Press, 2009.2. Camille.G.Wermuth, “The Practice of Medicinal Chemistry”, 3rd Edition, Academic Press, 2008.3. Wilson & Gisvold’s, Lippincott Williams & Wilkins, “Text book of Organic Medicinal and Pharmaceutical Chemistry”, 11th Edition, 2003.4. Frank D King, “Medicinal Chemistry- Principles and Practice”, Royal Society of Chemistry, 1994.5. Tripathi K.D., “Essentials of Medicinal Pharmacology”, 5th Edition, Jaypee Medical Publishers, India, 2004.6. Seth S.D, Vimlesh Seth, “Text book of Pharmacology”, 3rd Edition, Elsevier India, 2009.

WEBSITES:1. www.chem.qmul.ac.uk/iupac/medchem/2. www.chemlin.net/chemistry/medicinal_chemistry.htm3. www.internetchemie.info/chemistry/medicinal_chemistry.htm


Max. Marks: 80 Exam Duration: 3 hrs.Part A: 6 Questions –no choice each carrying 5 marks 30 Marks




SMTX5005 APPLIED MATHEMATICSL T P Credits Total Marks

3 1 0 4 100

UNIT I MATRIX THEORY 10 hrs.

QR decomposition – Eigen values using shifted QR algorithm - Singular Value Decomposition - Pseudo inverse-Least square approximations

UNIT II ORDINARY DIFFERENTIAL EQUATION 10 hrs.

Second order linear differential equation with constant coefficients – Particular Integrals for - Equations reducibleto Linear equations with constant co-efficient using - Method of Variations of Parameters.

UNIT III CALCULUS OF VARIATIONS 10 hrs.

Concept of Functionals - Euler’s equation – functional dependent on first and higher order derivatives – Functionalson several dependent variables – Iso-perimetric problems - Variational problems with moving boundaries

UNIT IV NUMERICAL METHODS – I 10 hrs.

Numerical solution of Ordinary Differential equation – Euler’s method – Modified Euler’s method - Runge-KuttaMethod of fourth order - Runge-Kutta method of simultaneous first order differential equation - Runge-Kutta methodof second order differential equation - Milne’s Predictor Corrector method - Adam-Bashforth predictor-corrrector method.

UNIT V NUMERICAL METHODS – II 10 hrs.

Numerical solution of Partial Differential Equations - Classification - Elliptic equations-Solution of Laplace equationby Liebmann’s Iteration process - Poisson equation - Parabolic equation - Bender Schmidt Method - Crank NicholsonDifference Method - Solution for Hyperbolic equation.

REFERENCE BOOK: 1. Narayanan S., Manicavachagom Pillay T.K., Ramanaiah G., “Advanced Mathematics for Engineering Students”, Volume I, 2nd Edition,

S.Viswanathan Printers and Publishers, 1992. 2. Venkataraman M.K., “Engineering Mathematics – First Year”, 2nd Edition, National Publishing Co., Chennai, 2000. 3. Kreyszig.E, “Advanced Engineering Mathematics”, 8th Edition, John Wiley & Sons, Singapore, 2001. 4. Jain M K, Iyengar S R K & Jain R L, “Numerical Methods for Scientific and Engineering Computation”, Wiley Eastern Ltd., 1987. 5. Balagurusamy E, “Numerical Methods”, Tata McGraw Hill, 2000. 6. Veerarajan T, “Engineering Mathematics for First Year”, 2nd Edition, Tata McGraw Hill Publishers, 2008. 7. Kandasamy P & Co., “Numerical Methods”, S.Chand & Co., New Delhi, 2003.







SBIX6501PERL PROGRAMMING AND BIOLOGICAL

DATABASES LABL T P Credits Total Marks

0 0 4 2 100

PERL PROGRAMMING LAB

1. Perl basics and variables

2. Control and loop statements

3. To find greatest of 3 numbers

4. To check whether a number is prime or not

5. Generating fibonacci series

6. To perform matrix addition & multiplication

7. Functions and subroutines

8. Command line argument

9. Regular expressions

10. Perl web server

11. Modules:LWP

12. Translate DNA to protein by using perl module

13. Program to perform file handling, concatenation& string length

14. Program- shift, unshift, splice & slice

15. Program to perform until, for, for each, while operations

16. Program to perform gc count, chop, chomp, reverse compliment

17. File handling using arrays and scoping of variables

18. BIOPERL: objects in Bioperl, using Bioperl

BIOLOGICAL DATABASES LAB 1. Retrieval of sequences from NCBI

2. ORF Finder

3. Retrieval of sequences from EBI

4. Retrieval of structures from PDB

5. Rasmol, Pymol, Jmol

6. Retrieval of sequences from DDBJ

7. KEGG

8. Retrieval of sequences from PIR

9. OMIM

10. Structural databases, SRS



SBIX5005 BIOLOGICAL SEQUENCE ANALYSISL T P Credits Total Marks

3 0 0 3 100

UNIT I PAIRWISE ALIGNMENT 10 hrs.Pairwise alignment techniques – Global alignment, Local alignment methods, Algorithm and statistics of global

alignment, Algorithm and statistics of local alignment, Scoring matrices, Gap penalty, Dot matrix sequence comparison- Heuristic algorithms – FASTA and BLAST, Gapped BLAST & PSI BLAST, PHI-BLAST, E-value - Significance ofsequence alignment.

UNIT II MULTIPLE SEQUENCE ALIGNMENT 10 hrs.Multiple sequence alignment – Goal of multiple sequence alignment to pairwise alignment, progressive methods

– CLUSTAL W, PILEUP Iterative methods Position – Specific scoring matrices – Hidden Markov Models of multiplesequence alignment.

UNIT III PREDICTIVE METHODS 10 hrs.Predictive methods: Different secondary structure prediction methods – Chou fasman method, GOR method,

Algorithm behind the methods - Tools used for secondary structure prediction - Tertiary structure prediction – homologymodeling tool - Description on software packages - EMBOSS – programs and its usage.

UNIT IV HIDDEN MARKOV MODELS FOR SEQUENCE ANALYSIS 10 hrs.Markov chains, Hidden Markov Models for sequence Analysis –Building an HMM, Viterbi algorithms, Forward,

Backward and EM algorithms, Applications of HMMs (HMMer, PFAM). Predictive methods using DNA sequences,Predictive methods using protein sequences- Prediction of RNA secondary structure- Expressed sequence tags (ESTs).

UNIT V PHYLOGENETICS 10 hrs.Mathematical basis for phylogenetics - Genetic algorithms - Multiple alignment - Construction of phylogenetic

trees - Phylogenetic Analysis – building the data model, extraction of a phylogenetic data set, Tree building method,Distance methods, Character based method, phylogenetic software - Gene prediction methods, Genome analysis andannotation, Large–scale genome analysis and computational tools.

REFERENCE BOOKS: 1. Durbin R., Eddy S., Krogh A.& Mitchison G., “Biological Sequence Analysis: Probabilistic Models of Proteins & Nucleic Acids”,

Cambridge University Press, 1999. 2. Gusfield D., “Algorithms on Strings, Trees & Sequences: Computer Science & Computational Biology”, Cambridge University Press,

1997. 3. Lesk A.M., “Introduction to Bioinformatics”, Oxford University Press, 2002. 4. Pevzner P., “Computational Molecular Biology: An Algorithmic Approach”, MIT Press, 2000. 5. Setubal J. & Meidanis J., “Introduction to Computational Molecular Biology”, PWS Publishing Company, 1997.

WEBSITES:1. www.ebi.ac.uk/emboss/align/2. http://www.cbs.dtu.dk/biolinks/3. www.bioinformatics.oxfordjournals.org/cgi/content/full/btp2284. www.cmgm.stanford.edu/classes/pdf/phylogenetic.pdf5. www.phylogeny.fr/6. www.bmm.icnet.uk/people/rob/CCP11BBS/secstrucpred.html





SBIX5006 MOLECULAR MODELING AND DRUG DESIGNL T P Credits Total Marks

3 1 0 4 100

UNIT I QUANTUM CHEMISTRY & THERMODYNAMICS 10 hrs.

Basic concepts in molecular modeling - Internal parameters - Z-matrix – Introduction to quantum chemistry –basic postulates – Schrodinger wave equation – Derivation – Hydrogen atom – Born-Oppenheimer approximation -Laws of thermodynamics - entropy – enthalpy - free energy calculations-chemical potential.

UNIT II MOLECULAR MECHANICS & VISUALIZATION 10 hrs.

Molecular geometry - Conformational parameters - Potential energy Surface - Molecular mechanics: empiricalforces fields - bond stretching - angle bending - torsional terms – non-bonded and electrostatic interaction - types offorce fields - energy minimization - simplex – sequential univariate method - steepest descent - conjugate gradientmethod - Newton–Raphson method – Conformational analysis - Molecular visualization - Molecular graphics – Rendering- Rasmol.

UNIT III MOLECULAR DYNAMICS 10 hrs.

Molecular Dynamics(MD) simulation of biopolymers - time steps - Setting up MD - energy conservation in MD Simulation-continuous potentials and constraint dynamics - MD at constant temperature and pressure - incorporating solvent effects- examples-random number generator - Monte Carlo simulation of biological macromolecules - MD softwares.

UNIT IV STRUCTURE PREDICTION 10 hrs.

Prediction of secondary structure - membrane prediction – Comparative modeling - Sequence Alignment Homologs- analogs - Homology modeling - steps in homology modeling – side chain modeling – loop modeling - fold recognition– ab initio prediction – Predicting protein structures by threading protein folding – active site/binding site prediction –tools – databases - CASP.

UNIT V DRUG DESIGN 10 hrs.

Drug Design: Role of Bioinformatics in drug design - Drug discovery cycle - Physicochemical principles of drugaction - lead discovery - lead modification - optimization - Docking –docking algorithms - Structure Based Drug Design-Rational Design - pharmacophores - QSAR - ADME/T - drug delivery.

REFERENCE BOOKS:1. Leach A.R. , “Molecular Modelling - Principles and Applications”, 2nd Edition, Prentice Hall, 2001.2. Prasad R.K., “Quantum Chemistry”, Halsted Press, 1992.3. Ramachandran K. I., Deepa G., Namboori K., “Computational Chemistry and Molecular Modeling: Principles and Applications”, Springer, 2008.4. McCammon, J.A. and Harvey, S.C., “Dynamics of Proteins and Nucleic Acids”, Cambridge University Press, Cambridge, 1987.5. Young, D.C., “Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems”, Wiley-Interscience, 2001.

WEBSITES:1. http://www.msg.ameslab.gov/tutorials/tutorials.html2. http://www.charmm.org/3. http://www.biochem.vt.edu/modeling/index.html4. http://www.netsci.org/Science/Compchem/feature01.html5. http://cmm.info.nih.gov/modeling/







SMTX5006 BIO STATISTICSL T P Credits Total Marks

3 1 0 4 100

UNIT I SAMPLING THEORY AND TESTS OF SIGNIFICANCE 10 hrs.

Sampling Concepts: Methods of sampling – simple random sampling, stratified and systematic sampling(description only) - Concept of sampling distributions and standard error – tests of hypothesis critical region, two typesof errors – level of significance - Student’s t test for single mean, two sample mean, paired t test - z test for singlemean, two sample mean, single proportion and two sample proportion.

UNIT II CHI SQUARE TEST AND DESIGN OF EXPERIMENT 10 hrs.

Chi square test of goodness of fit and independence of attributes, Completely randomized Design, RandomizedBlock Design, Latin Square Design and their analysis of Variance.

UNIT III MULTIVARIATE ANALYSIS 10 hrs.

Multiple regression analysis, Principal Component analysis, Canonical Correlation analysis, Factor Analysis,Descriminant Analysis, Cluster Analysis (No derivations, theory only).

UNIT IV NON PARAMETRIC TESTS 10 hrs.

Sign test, Wilcoxon signed rank test, One sample Run Test, Median test, Kruskal Wallis H test, KolmogorovSmirnov test, Mann Whitney U test.

UNIT V MARKOV CHAIN MODELS 10 hrs.

Definition, Classification, Markov Chain,Transition Probability Matrix, chapman Kolmogrov theorem, Classificationof states and chains, Poisson Process, Postulates, Properties. Hidden Markov Models Basic Algorithms, ViterbiAlgorithm, Estimation, Forward and Backward Algorithm, Applications - Multiple Sequence Alignment, Pfam and Genefinding.

REFERENCE BOOKS: 1. Veerarajan T, Probability, “Statistics and Random Process”, 4th Edition, Tata Mcgraw Hill, 2008. 2. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability & Statistics for Engineers & Scientists”, 8 th

Edition, Prentice Hall, 2006. 3. Stephen Bernstein, “Schaum’s Outline of Elements of Statistics I: Descriptive Statistics and Probability”, McGraw-Hill, 1998. 4. Jerrold H zar, “Biostatistical Analysis”, 5th Edition, Prentice Hall, 2010. 5. Irivin Miller, John E Freund, “Probability and Statistics for Engineers”, Prentice Hall, 1977.







SBIX6502SEQUENCE ANALYSIS AND MOLECULAR

MODELING LABL T P Credits Total Marks

0 0 4 2 100

SEQUENCE ANALYSIS BY GCG

1. Sequence Retrieval - Fetch

2. Importing and Exporting – Reformat

3. Pairwise comparison – Gap, Bestfit, Compare, Frame Align

4. Multiple comparison - Pileup, Pretty, Profile Make, Profile Gap

5. Sequence Searching – BLAST, FASTA

6. Evolution – Distances, Diverge

7. Gene Finding and Pattern Recognition – FindPatterns

8. HMMer – HMMer Build, HMMer Align

9. Mapping – Map, Mapsort

10. Protein Analysis – Isoelectric, Helical Wheel

11. Sequence analysis by online tools

BLAST, FASTA, CLUSTALW, PHYLIP etc.,

MOLECULAR MODELING AND DRUG DESIGNING LAB

1. Construction of molecules

2. Secondary structure prediction

3. Molecular visualization – JMOL, VMD

4. Homology modelling – Insight II, Modeller

5. Docking- Autodock, Discovery Studio, Schrodinger

6. Molecular dynamics - AMBER

7. QSAR



SBIX5007 STRUCTURAL AND FUNCTIONAL GENOMICSL T P Credits Total Marks

3 0 0 3 100

UNIT I INTRODUCTION 10 hrs.Structure & Organization of Prokaryotic & Eukaryotic genome - Nucleotide & protein sequencing methods –

Chemical, enzymatic, high through put method – Automated sequencing methods – shotgun – chromosome walking,Contig assembly - Levels of structures in Biological macromolecules.

UNIT II CONFORMATIONAL ANALYSIS 10 hrs.Biomolecules and their interactions - Forces that determine protein and nucleic acid structure, basic problems,

polypeptide chains geometrics, potential energy calculations, observed values for rotation angles, hydrogen bonding,hydrophobic interactions and ionic interactions, disulphide bonds - Prediction of proteins structure - Nucleic acids,general characteristics of nucleic acid structure, geometrics, glycosidic bond rotational isomers - Ribose puckering -forces stabilising ordered forms, base pairing, base stacking- tertiary structure of nucleic acids.

UNIT III STRUCTURAL ANALYSIS OF MACROMOLECULES 10 hrs.Size and shape of macromolecules - methods of direct visualisation - X-ray crystallography – X–ray diffraction,

determination of molecular structures, electron microscopy, NMR.

Protein structure databases - Protein Data Bank - SCOP - CATH - structure superposition - RMSD - TM-score- structure alignment - Different structure alignment algorithms - DALI, CE, VAST, TM-align - protein folds in PDB.

UNIT IV STRUCTURE-FUNCTION RELATIONSHIP 10 hrs.DNA binding proteins, Prokaryotic and Eukaryotic transcription factors - DNA polymerases, Helix-turn-Helix motif

in DNA binding, Trp repressor, Zn fingers, helix-turn helix motifs in homeodomain, Leucine zippers - DNA polymerases,Membrane proteins and receptors, Trans-membrane segments, bacterio rhodopsin, photosynthetic centres, epidermalgrowth factor, insullin and PGDGF receptors and their interaction with effectors, protein phosphorylation,immunoglobulins, Serine proteases, ribonuclese, lysozyme.

UNIT V FUNCTIONAL GENOMICS 10 hrs.Introduction to Functional Genomics - The Relationship of Genotype and Phenotype - Eight Model Organisms

for Functional Genomics- Proteomics approaches to Functional Genomics - Protein–Protein Interactions - The YeastTwo-Hybrid System - Human Genetic Disease - Garrod’s View of Disease - Classification of Disease - NIH DiseaseClassification - Neurological Disorders - Genetic basis of neurological disorders.

REFERENCE BOOKS:1. Cantor R., Schimmel P.R., “Biophysical Chemistry”, Vol. I, II, W.H. Freeman & Co., 1985.2. Kensal E. van Holde, W. Curtis Johnson and P. Shing Ho, “Principle of Physical Biochemistry”, Prentice Hall, New York, 1998.3. Brown T.A, “Genome”, 2nd Edition, BIOS Scientific Publishers Ltd., UK. 2002.4. Pennington SR, Dunn MJ, “Proteomics from Protein Sequence to Function”, Viva Books Ltd, 20025. David W Mount, “Bioinformatics: Sequence and Genome Analysis”, 2nd Edition, CBS Publishers, 2004.6. Creighton TE, “Proteins”, Freeman WH, 2nd Edition, 1993.

WEBSITES:1. www.sgc.ox.ac.uk/2. www.structuralgenomics.org/3. www.isgo.org/

UNIVERSITY EXAMINATION QUESTION PAPER PATTERNMax. Marks: 80 Exam Duration: 3 hrs.Part A: 6 Questions –no choice each carrying 5 marks 30 MarksPart B: 2 Questions from each unit of internal choice each carrying 10 marks 50 Marks



SBIX5008 SYSTEMS BIOLOGY & METABOLIC ENGINEERINGL T P Credits Total Marks

3 0 0 3 100

UNIT I TERMINOLOGY & PRINCIPLES 10 hrs.Basic Terminology & Principles – The Biology – Modeling – Properties of Models - Advantages of Computational

Modeling - Typical Aspects of Biological Systems and Corresponding Models - Network Versus Elements – Modularity- Robustness and Sensitivity - Data integration – Living Science - The human genome landscape - Genetic regulationand disease – Differential Gene expression & Developmental Biology.

UNIT II STANDARD MODELS AND APPROACHES 10 hrs.Standard Models and Approaches - Metabolic Engineering - Metabolic Networks - Translating biochemical

networks into linear algebra - Systems Equations - Information Contained in the Stoichiometric Matrix N - ElementaryFlux Modes and Extreme Pathways - Flux Balance Analysis - Approximations Based on Timescale Separation –Metabolic Control Analysis - The Coefficients of Control Analysis - The Theorems of Metabolic Control Theory -Extensions of Metabolic Control Analysis - Selected Biological Processes - Cell Cycle - Aging.

UNIT III MODELING OF GENE EXPRESSION 10 hrs.Modeling of Gene Expression – Modules of Gene Expression - Promoter Identification - Modeling Specific

Processes in Eukaryotic Gene Expression - Description with Ordinary Differential Equations - Representation of GeneNetwork as Directed and Undirected Graphs - Bayesian Networks - Boolean Networks - Gene Expression Modelingwith Stochastic Equations - Analysis of Gene Expression Data - Data Capture - Clustering Algorithms - ReverseEngineering Genetic Networks - Stochastic simulation methods and constraint based models – E-cell Project.

UNIT IV EVOLUTION AND SELF - ORGANIZATION 10 hrs.Evolution and Self-organization - Quasispecies and Hypercycles - Other Mathematical Models of Evolution -

Prediction of Biological Systems from Optimality Principles - Data Integration - Database Networks - InformationMeasurement in Heterogeneous Data - The Human interactome - Protein-DNA and Protein-Protein Interactions – Scalefree networks in Cell Biology.

UNIT V INFORMATION RETRIEVAL AND EXAMINATION 10 hrs.Computer-based Information Retrieval and Examination – Systems Biology Databases and Tools on the Internet

- Gene Ontology – Reactome - TRANSFAC and EPD - Genome Matrix - Modeling Tools - Modeling and Visualization- Mathematica and Matlab – Gepasi - E-Cell – PyBioS - Systems Biology Workbench – CellDesigner - Petri Nets -Model Exchange Languages, Data Formats - Systems Biology Markup Language - MathML.

REFERENCE BOOKS: 1. Edda Klipp, Ralf Herwig, Axel Kowald, Christoph Wierling, Hans Lehrach., “Systems Biology in Practice: Concepts, Implementation

and Application”, Wiley-VCH, 2005. 2. Choi, Sangdun, “Introduction to Systems Biology”, Humana Press, 2007. 3. Alberghina, L. and Westerhoff, H, “Systems Biology : Definitions and Perspectives”, Springer, 2005.

WEBSITES:1. www.systemsbiology.org/2. http://www.sbi.jp/





SCSX5032 ADVANCED COMPUTER PROGRAMMINGL T P Credits Total Marks

3 0 0 3 100

Unit I INTRODUCTION 10 hrs.

Introduction to HTML- introduction to World Wide Web- Hyper Text Transfer Protocol – HTML Elements – Webpage layout – HTML Tags – basic & Formatting Tags – Links and Frames – Anchor A, Image G, Frames- Forms– Input Tags

Java Script – Advantages – Object hierarchy – Script Tag – Document Object Model - Embedding JavaScriptin HTML – Literals, values, variables and Operators- Functions- Objects and Arrays- Windows & Frames – Forms.

UNIT II JAVA 10 hrs.

Java – An overview of Java- Data Types, Variables, Arrays, Operators – Control Statements – Introduction toclasses – Methods and Classes - Inheritance – packages -interfaces- Exception handling –Multithreading – Appletprogramming- Event Handling- Swings - JDBC.

UNIT III SERVLETS 10 hrs.

Servlets – Introduction to Server Side Scripting – Servlet Lifecycle – Servlets Basics- Servlet API Basics –Servlet Chaining – Server Side Includes (SSI) – JDBC in Servlets – JDBC ODBC Bridge, Driver Types - Updatingthe Database – Using Prepared Statements - Using Callable Statements.

UNIT IV JAVA BEANS, J2EE 10 hrs.

Writing Thread – Safe Servlets – Http Redirects – Applet Servlet Communication Session Tracking – Interservletcommunication – Servlet Tags – Introduction to Java Beans, J2EE - Java Server Pages - Introduction – JSP lifecycle– Elements – directives – Scripting Elements – Standard Actions – Session Tracking – Form data Parsing – JSPSessions – JDBC in JSP – Tag Extensions and Libraries.

UNIT V BIOJAVA 10 hrs.

BioJava: symbols & Symbol Lists, sequences & features, sequence I/O basics - Change Event overview, ChangeEvent example using distribution objects, - implementing Changeable, Blast-like parsing (NCBI Blast, WU-Blast, HMMER)- Dynamic Programming examples.

REFERENCE BOOKS:1. Thomas A.Powell, “HTML & XHTML: The Complete Reference”, 4th Edition Tata McGraw Publications, 2003.2. Powell, “JavaScript: The Complete Reference”, Tata McGraw Publications, 2004.3. Herbert Schildt, “Java Complete Reference”, Tata McGraw Publications, 2006.4. Dustin R. Callaway, “Inside Servlets”, Addison Wesley, 2001.5. Phil Hanna, “JSP Complete Reference”, Tata McGraw Publications, 2003.

WEBSITES:1. www.sun.java.com2. http://www.biojava.org/tutorials/index.html







SBIX6503SYSTEMS BIOLOGY AND ADVANCED

COMPUTING LABL T P Credits Total Marks

0 0 4 2 100

SYSTEMS BIOLOGY LAB

1. Modeling and Visualization

2. Mathematica and Matlab

3. Gepasi

4. E-Cell

5. PyBioS

6. Systems Biology Workbench

7. CellDesigner

8. Petri Nets

9. Model Exchange Languages

10. Data Formats

11. Systems Biology Markup Language

12. MathML

CLUSTER LAB

1. Parallel programming

2. MPI

3. MPI-BLAST

4. MPI-CLUSTALW

5. Running AMBER in Cluster

JAVA LAB

1. HTML program

2. Prime numbers

3. Floyds triangle

4. String search & String count

5. Inheritance

6. Interface

7. Exception handling

8. Applet program



SBIX5009 MICROARRAY - TECHNIQUES AND APPLICATIONSL T P Credits Total Marks

3 0 0 3 100

UNIT I GENE EXPRESSION 10 hrs.Basics of Gene expression - definition - gene expression studies, gene expression patterns - Applications of gene

expression studies - Microarrays – definition, discovery, technique, making microarrays, spotted microarrays, in situ synthesizedoligonucleotide arrays, inkjet array synthesis, affymetrix techniques, DNA Chip technology, photolithography, spot quality,sample preparation and labeling, washing, image acquisition - Sequencing by Hybridization Arrays - DNA MassArrayTechnology - Printing DNA Microarrays -Types of Micorarrays - Designing a microarray experiment.

UNIT II IMAGE PROCESSING 10 hrs.Image processing, feature extraction, identifying positions of features - Normalisation – data cleaning and

transformation, within array normalization, between array normalization, measuring and quantifying microarray variability– variability between replicate features on an array, variability between hybridizations to different arrays - Analysis ofdifferentially expressed genes - significance analysis of microarrays.

UNIT III PREDICTION 10 hrs.Prediction of cross hybridization to related genes, thermodynamics of nucleic acid duplexes, prediction of Tm

probe secondary structure - Analysis of relationships between genes, tissues or treatments - similarity of gene orsample profiles – dimensionality reduction, principal component analysis, hierarchial clustering, machine learning methodsfor cluster analysis.classification of tissues and samples – validation.

UNIT IV CARBOHYDRATE MICROARRAYS 10 hrs.Carbohydrate microarrays - Carbohydrate sources - Synthesis of oligosaccharides - Isolation of oligosaccharides

from natural sources- Arrays of monosaccharides and disaccharides - Arrays of polysaccharides - Arrays ofoligosaccharides - Perspectives - DNA microarray analysis - clustering - immunological applications.

UNIT V DATABASES AND TOOLS FOR MICROARRAYS 10 hrs.Bioinformatics in Arrays - Databases and tools for microarrays - Bioconductor, expression profiler, EST databases

- Assessing levels of gene expression using EST’s, TIGR gene indices, STACK, SAGE, CGAP, Xprofiler, ARRAY DB,cluster, treeview, Scanalyze, genecluster, informatics aspects of microarray production - MGED and gene-ontology,description of MIAME (Minimum Information About a Microarray Experiment), Business Aspects of Biochip Technologies- Microarray Technology in Treating Disease.

REFERENCE BOOKS: 1. Dov Stekel, “Microarray Bioinformatics”, Cambridge, 2003. 2. Steen Knudsen, “A Biologist’s Guide to Analysis of DNA Microarray Data”, 2nd Edition, John Wiley & Sons, 2004. 3. Andreas D Baxevanis and BF Francis Ouelette, “Bioinformatics: A Practical Guide to the Analysis of Gene and Proteins”,

Wiley-Interscience, 1998. 4. David W. Mount, “Bioinformatics: Sequences and Genome Analyses”, Cold Spring Harbor Laboratory Press, 2000.

WEBSITES:1. www.arrayit.com/.../Microarray.../Carbohydrate_Microarray2. www.microarrayworld.com/3. www.ebi.ac.uk/microarray/





SBIX5010 DATA MINING IN BIOINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I DATA MINING 10 hrs.

Relational Databases – DBMS versus Data Mining – KDD and Data Mining - Data Warehouses – TransactionalDatabases – Advanced Data and Information System and Advanced Applications – Data Mining Functionalities:Concept/Class Description – Classification and Prediction – Cluster Analysis – Outlier Analysis – Evolution Analysis –Classification of Data Mining Systems.

UNIT II DATA PREPROCESSING 10 hrs.

Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, DataDiscretization and Concept Hierarchy Generation, Data Warehouse and OLAP technology – A Multidimensional DataModel – From Data Warehousing to Data Mining – Efficient Methods for Data Cube Computation – Further Developmentof Data Cube and OLAP Technology.

UNIT III CLASSIFICATION AND PREDICTION 10 hrs.

Issues regarding Classification and Prediction – Classification by Decision Tree Induction – Bayesian Classification– Rule-Based Classification - Classification by Backpropagation - Support Vector Machines - Associative Classification:Classification by Association Rule Analysis – Lazy Learners (or Learning from Your Neighbors) - Other ClassificationMethods – Prediction- Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – EnsembleMethods – Increasing the Accuracy – Model Selection.

UNIT IV CLUSTER ANALYSIS 10 hrs.

Types of Data in Cluster Analysis – Partitioning Methods-Hierarchical Methods – Density Based Methods-Grid-Based Methods- Model Based Clustering Methods- Clustering High Dimensional Data - Constraint-Based ClusterAnalysis - Clustering paradigms - K-medoid algorithm - Heirarchial Clustering – Adittive Properties of Cluster features– Categorical clustering algorithms.

UNIT V MINING STREAM, TIME-SERIES AND SEQUENCE DATA 10 hrs.

Mining Data Streams, Mining Time-Series Data, Mining Sequence Patterns in Transactional Databases, MiningSequence Patterns in Biological Data: Alignment of Biological Sequences – Hidden Markov Model - Graph Mining -Multirelational Data Mining – Multimedia Data Mining - Data Mining Applications – Trends in Data Mining - Web mining– Web content miming text mining - Challenging Problems in Data Mining Research – Case Study.

REFERENCE BOOKS:1. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, 2nd Edition, Morgan Kauffman-Elsevier, 2006.2. Pieter Adriaans and Dolf Zantinge, “Data Mining”, Addison-Wesley Longman Publishing Co., 1997.3. Arun K. Pujari, “Data Mining Techniques”, Universities Press, 2001.4. BPB Editorial Board, “Data Mining”, BPB Publications, 1st Edition, 2004.

WEBSITES:1. http://datamining.typepad.com2. http://dataminingarticles.com3. www.sciencedaily.com/articles/d/data_mining.htm4. www.exforsys.com/tutorials/data-mining.html5. infolab.stanford.edu/~ullman/mining/mining.html6. www.kdnuggets.com/data_mining_course/x1-intro-to-data-mining-notes.html

UNIVERSITY EXAMINATION QUESTION PAPER PATTERNMax. Marks: 80 Exam Duration: 3 hrs.





SBIX5011 CHEMINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I INTRODUCTION TO CHEMINFORMATICS 10 hrs.

Introduction to Cheminformatics - History and evolution of Cheminformatics - Chemical representation - Sequence,2D, 3D structure, Types of chemical representation - linear notation, tabular storage, graphical representation - Chemicaldata management - Chemical mark up languages.

UNIT II CHEMICAL DATABASES 10 hrs.

Chemical Databases - CHEMDB, KEGG LIGAND, CSD, CAS REGISTRY, BIOMETA DB, National Cancer InstituteDatabase(NCI) - Chemical searching methods - exact searching, sub structure searching, similarity searching, reactionsearching.

UNIT III COMBINATORIAL CHEMISTRY 10 hrs.

Combinatorial chemistry: Introduction, Liquid Phase synthesis, Solid phase synthesis, Designing CombinatorialSynthesis, High through put screening.

UNIT IV COMBINATORIAL LIBRARY DESIGN 10 hrs.

Combinatorial library design - Rational principles of compound selection for combinatorial library design andoptimization approach, Descriptor Analysis, Modeling toxicity, Computer Assisted Synthesis design and structure basedlibrary design.

UNIT V APPLICATION OF CHEMINFORMATICS 10 hrs.

Application of cheminformatics - QSPR Drug design - Target identification and Validation, lead finding andoptimization - Pharmacophore-Based Drug Design - Structure-Based Drug design - Application of Cheminformatics inDrug Design.

REFERENCE BOOKS:1. Andrew R. Leach & Valerie J.Gillet, “An Introduction to Cheminformatics”, Revised Edition, Springer Publication, 2007.2. Johann Gasteiger, Dr. Thomas Engel, “Cheminformatics”, Wiley-VCH Press, 2003.3. Jurgen Bajorath, “Cheminformatics: Concepts, Methods and Tools for Drug Discovery”, Humana Press, 2004.4. Tudor. L.Oprea, “Cheminformatics in Drug Discovery”, Wiley-VCH Press, 2005.

WEBSITES:1. http://cdb.ics.uci.edu/2. http://www.cas.org/.3. http://www.combichemistry.com/4. http://www.wiziq.com/tutorial/2967-Combinatorial-Chemistry-and-Library-Design.5. http://www.vuw.ac.nz/staff/paul_teesdale-spittle/peptide-synthesis/pep-syn-files/solid-phase.htm







SBIX5012 PERL AND CGI PROGRAMMINGL T P Credits Total Marks

3 0 0 3 100

UNIT I DATA TYPES 10 hrs.Introduction to Perl – a functional approach – data types - strings - text formatting – printf – Introduction to

arrays – qw function - split function – for each loop – advanced array operation – copying and creating arrays –populating arrays with sequential data – determination of the size of an array – counting arrays – accessing lastelement in an array – adding elements – removing elements – altering elements – array slices – splicing array –sorting arrays – reversing arrays – arrays from strings - constructing atgc.pl.

UNIT II PERL CONTROL STATEMENTS 10 hrs.Perl control statements – control structures – if statements – if-else – if-elsif – if-elsif-else – while loop – until

loop – for loop – foreach loop – scoping of variables.

Perl regular expressions – special characters (+, *,?, [ ]) – regex operator – pattern modifier operator – conditionalmatching operator – range operator – match quantifiers – Pattern comments. – the s/// operator – the chop andchomp operators- tr/// function.

UNIT III FILES 10 hrs.Files – Operating files – file modes – file test operators – accessing files with @ARGV variable – accessing

file with perl modules – extracting files – deleting files.

Introduction to Perl modules – the getOpt::Long module – accessing directories – Cwd module – system function– perl subroutines and functions - trapping errors at run time.

UNIT IV CGI 10 hrs.Common Gateway Interface - HTML form elements, GET, POST & HEAD Method, CGI Environment Variables,

Handling forms, Passing Parameters via CGI, Debugging CGI programs - The LWP::Simple module - HTMLSpecifications and Syntax.

UNIT V : BIOPERL 10 hrs.General Bioperl Classes, Sequences, Sequence Manipulation, Features and Location Classes - Alignments:

AlignIO - Analysis: Blast, Genscan - Databases: Database Classes, Accessing a local database - accessing remotedatabases - Bioperl modules - parsing blast reports – align sequences – clustalw.pm – Tcofee.pm.

REFERENCE BOOKS:1. Harshawardhan P. Bal, “Perl programming for Bioinformatics”, Tata McGraw-Hill, 2004.2. Larry Wall, Tom Christiansen & John Orwant, “Programming Perl”, 3rd Edition, O’Reilly, 2000.3. James D. Tisdall, “Beginning Perl for Bioinformatics”, O’Reilly, 2001.4. Eric Ladd, J.O’Donnell, “Using HTML 4, XML and JAVA”, Prentice Hall of India, 1999.5. Brown, “Perl - The Complete Reference”, Tata McGraw-Hill, New Delhi, 2004.

WEBSITES:1. http://www.perl.org/2. http://doc.bioperl.org/3. http://www.bioperl.org/





SBIX5013 MACHINE LEARNING FOR BIOINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I MACHINE-LEARNING 10 hrs.

Machine-Learning Foundations: The Probabilistic Framework - Bayesian modeling - The Cox Jaynes axioms -Bayesian inference & induction - Model structures: graphical models & other tricks - Probabilistic Modeling & Inference:Examples - The simplest sequence models - Statistical mechanics Machine Learning Algorithms - Dynamic programming-Gradient descent - EM/GEM algorithms – Markov chain Monte - Carlo methods - Simulated annealing – Evolutionary& genetic algorithms. Learning algorithms: miscellaneous aspects.

UNIT II SVM 10 hrs.SVM: introduction – architecture – kernel - ROC – feature selection – sensitivity – specificity – accuracy –

implementation - SVM applications in sequence analysis, structure prediction, drug design – SVM light - LIBSVM –Weka.

UNIT III NEURAL NETWORKS 10 hrs.

Neural Networks: The Theory - Introduction - Universal approximation properties – Priors & likelihoods - Learningalgorithms: backpropagation - Neural Networks: Applications - Sequence encoding & output interpretation - Sequencecorrelations & neural networks – Prediction of protein secondary structure - Prediction of signal peptides & their cleavagesites - Applications for DNA & RNA nucleotide sequences - Prediction performance evaluation - Different performancemeasures -Perceptrons and Multilayer Perceptrons -Neural Networks in Drug Design.

UNIT IV HIDDEN MARKOV MODELS 10 hrs.Hidden Markov Models: The Theory - Introduction -Prior information & initialization - Likelihood & basic algorithms

- Learning algorithms - Applications of HMMs: general aspects - Protein applications - DNA & RNA applications -Advantages & limitations of HMMs – tools.

UNIT V PROBABILISTIC GRAPHICAL MODELS 10 hrs.Probabilistic Graphical Models in Bioinformatics - Markov Models & DNA symmetries - Markov Models & gene

finders - Hybrid models & neural network parameterization of graphical models -The single-model case - Bi-directionalrecurrent neural networks for protein secondary structure prediction.

REFERENCE BOOKS:1. Pierre Baldi and Søren Brunak, “Bioinformatics: The Machine Learning Approach”, MIT Press, 1998.2. David W Mount, “Bioinformatics: Sequence and Genome Analysis”, 2nd Edition, CBS Publishers, 2004.3. Zupan J., Gasteiger J., “Neural Networks in Chemistry and Drug Design”, Wiley-VCH, 2000.

WEBSITES:1. http://svmlight.joachims.org/2. www.support-vector.net/3. www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html4. www.learnartificialneuralnetworks.com/5. http://ai.stanford.edu/~nilsson/mlbook.html





SBIX5014 HIGH PERFORMANCE COMPUTINGL T P Credits Total Marks

3 0 0 3 100

UNIT I INTRODUCTION 10 hrs.

High-Performance Computing Architectures: Vector, Distributed memory, Shared memory - parallel computermodels - Parallel processing - pipeline - Overview of Recent Supercomputers – Grid computing - Meta computing -Cloud computing.

UNIT II CLUSTER & HPC 10 hrs.

Cluster computing at a glance – cluster classifications - cluster middleware – cluster applications – cluster setupand administration – multi path communication – distributed shared memory - representative cluster system: Beowulf– RWC PC cluster II - Parallel Processing on Linux Clusters - Java for HPC: java and different flavors of parallelprogramming models.

UNIT III PARALLEL PROGRAMMING 10 hrs.

Fundamental concepts – Designing for threads – scheduling - Threading and parallel programming constructs– Synchronization – Critical sections – Deadlock. Threading APIs - OPENMP PROGRAMMING: OpenMP- Non-blockingalgorithms – Memory and cache related issues - MPI PROGRAMMING: MPI Model – collective communication – datadecomposition – communicators and topologies – point-to-point communication – MPI Library- parallel virtual machine.

UNIT IV GRID COMPUTING 10 hrs.

Introduction: What is a grid? -Infrastructure of hardware and software -Main Projects and Applications - TheOpen Grid Forum -International Grid Trust Federation. Grid Architecture - Overview of Resource Managers - Overviewof Grid Systems - Application Management - Grid Application Description Languages - Application Partitioning - Meta- scheduling –Mapping – Monitoring - Web Services - Grid Portals - Clouds.

UNIT V APPLICATIONS & CASE STUDY 10 hrs.

Applications and Algorithms - Globus tool kit - Object oriented implementation of parallel genetic algorithm -http://openmp.org - MPI-BLAST, MPI-CLUSTALW.

REFERENCE BOOKS: 1. Rajkumar Buyya, “High Performance Cluster Computing: Programming and Applications”, Prentice Hall, 1999. 2. Michael J Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw-Hill, 2003. 3. Kevin Dowd, “High Performance Computing”, O’Reilly, 1993. 4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture: A Hardware/Software Approach”, Morgan Kaufmann/Elsevier

Publishers, 1999. 5. Ahmar Abbas, “Grid Computing: A Practical Guide to Technology and Applications”, Charles River Media, 2003.

WEBSITES:1. http://www.gridbus.org/~raj/cluster/2. http://www.globus.org3. http://www.phys.uu.nl/~steen/web03/overview.html







SBIX5015 COMPUTATIONAL BIOLOGYL T P Credits Total Marks

3 0 0 3 100

UNIT I INTRODUCTION 10 hrs.Introduction to Bioinformatics and Computational Biology with historical background, major developments - Strings

& Graphs-Native string search, Suffix tree, KMP Algorithms, Boyer-Moore Algorithms, Sequence alignment- AlignmentAlgorithms for two sequences - Advance sequence alignment techniques- linear space, Affine gap, Time warping -Topics based on basic string matching.

UNIT II BASIC PROBABILITY 10 hrs.Basic probability and statistics theory: Random Variables, Probability distribution, Combinatorial Optimization.

Markov processes, Entropy - application - Restriction maps: graphs, interval graphs, measuring fragment sizes - Matrices- PAM & BLOSUM.

UNIT III DATABASES & GRAPHIC SEQUENCE ANALYSIS 10 hrs.Databases & Graphic Sequence Analysis: DNA and Protein sequence databases - Sequence alignment, Dynamic

programming alignment -global distance alignment(indel functions, position dependent weights) algorithm - localalignment algorithm (self comparision, tandem repeats) - Multiple sequence alignment algorithm - Sequence alignmentwith scores, Markov Model. Hidden Markov Model – CpG - Gene finding – Gibbs sampling.

UNIT IV STRUCTURE PREDICTION 10 hrs.Structure Prediction: secondary structure prediction -GOR, CHOU-FASMEN, RNA structure prediction –

combinatorics, consensus folding. amino acid pair potential, lattice model of protein- Hart & initial approximate algorithm,tertiary structure prediction: homology modeling - ab-initio method - protein threading.

UNIT V PHYLOGENETIC ANALYSIS 10 hrs.Phylogenetic Analysis:Introduction - basic steps in phylogenetic analysis - Introduction to systematics –

Classification systems – Phylocode and Biocode - phenetic method-UPGMA, cladistic – clustering method - Maximumlikelihood, maximum parsimony - Estimating sampling errors using Bootstrap - Transformed Distance, Neighbors-Relation, Neighbor-Joining - Evolutionary analysis: use of the PHYLIP package - tree construction - Models of sequenceEvolution - Molecular Clock - Application of Phylogenies.

REFERENCE BOOKS:1. Michael Waterman, “Introduction to Computational Biology: Maps, Sequences & Genomes”, Chapman & Hall, 1995.2. Hugo O. Villar, “Advances in Computational Biology”, Jai Press Inc, 1996.3. Dan Gusifield, “Algorithm on Strings, Trees, & Sequences: Computer Science & Computational Biology”, Cambridge University Press, 1997.4. Pavel. A. Pevzner, “Computational Molecular Biology”, MIT Press, 2000.5. Peter Clote and Rolf Backofen, “Computational Molecular Biology: An Introduction”, Wiley, 2000.

WEBSITES:1. http://www.compbio.cmu.edu2. http://www.iscb.org/3. http://www.cs.bc.edu/~clote/ComputationalMolecularBiology/4. http://www.nature.com/nature/computationalbiology/5. http://www.liebertpub.com/products/product.aspx?pid=316. http://www.ploscompbiol.org/home.action





SBIX5016 APPLIED BIOINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I MODEL ORGANISMS 10 hrs.

Model organisms – structural properties of model genomes – characteristic & features – genome and proteome(Streptomyce sp., mouse, rat) – synteny – linkage disequilibrium – ’All against all’ – and ’between’ - analysis.

UNIT II GENE MODULES 10 hrs.

Computational aspects – gene modules – gene regulatory networks - gene history – functional annotation –genome wide analysis of functional linkages – high resolution expression analysis.

UNIT III HAPMAP 10 hrs.

HapMap – Quantitative trait loci – homogeneity and heterogeneity – Haplotype – multilocus genotype – dataanalysis – Human population analysis – statistical method – Hardy-Weinberg proportion – Ewen watterson test.

UNIT IV CLUSTERS 10 hrs.

Clusters – Heuristic programming - cluster analysis – comparative analysis – HUPO data bases – lead moleculeand ligand characterization – Markov chains - Monte Carlo approaches of analysis.

UNIT V PREDICTIVE SPECIATION 10 hrs.

Predictive speciation – Cladistics – evolutionary models – mammalian evolution – populational variations –personalized medicine and pharmacogenomics – mendelian diseases – complex diseases – genetic drug resistancein Candida albicum.

REFERENCE BOOKS:1. David W. Mount, “Bioinformatics: Sequences and Genome Analyses”, Cold Spring Harbor Laboratory Press, 2000.2. Michael Waterman, “Introduction to Computational Biology: Maps, Sequences & Genomes”, Chapman & Hall, 1995.

WEBSITES:1. http://www.compbio.cmu.edu2. http://www.cs.bc.edu/~clote/ComputationalMolecularBiology/3. http://www.nature.com/nature/computationalbiology/







SBIX5017 CLINICAL DATA MANAGEMENTL T P Credits Total Marks

3 0 0 3 100

UNIT I CLINICAL TRIAL 10 hrs.

Clinical Trial – definition – ethical considerations - phases - regulatory requirements for clinical trials - role ofregulatory authorities - gene therapy clinical trial - case report form design - function of the crf - the life history ofthe crf, data capture - fax-based data capture - automated data acquisition from optical images - remote data entry.

UNIT II DATA VALIDATION 10 hrs.

Data validation - steps, data presentation, coding of data - MedDRA and other medical terminologies - datadefinition, forms, and database design - defining data items to be collected - design of case report forms - databasedesign, computer systems for data management and data entry - software for data management and data entry -data management software - software selection - screen layout - distributed computing.

UNIT III LOCAL DATA MANAGEMENT SYSTEMS 10 hrs.

Local Data Management Systems - selection of patients- eligibility check and patient entry- regulatory compliance-patient registration - maintaining files for the trial- protocol compliance - patient’s role - forms scheduling- calendarsystem - card system - submission of materials - monitoring trial inventories - documentation of procedures.

UNIT IV CENTRAL QUALITY CONTROL OF DATA 10 hrs.

Central Quality Control of Data - eligibility checking - checking for correct identifiers - queries to the institutions-requests for overdue data - quality control at reference centers, data management and good clinical practice - guidelinesof good clinical practice - good clinical practice responsibilities - good clinical practice responsibilities of thesponsor/coordinating center - implementation of good clinical practice at participating sites.

UNIT V SOFTWARE TOOLS FOR TRIALS MANAGEMENT 10 hrs.

Software tools for trials management - database update - calculated variables - logical/edit checks - electronicqueries - overdue data requests - standard reports- interface with statistical software- online data lookup , follow - upand close - out phase - trials with a long follow - up phase- collecting follow - up data- preparing for analysis- close- out of trial.

REFERENCE BOOKS:1. Richard K. Rondel, Sheila A. Varley, Colin F. Webb, “Clinical Data Management”, 2nd Edition, John Wiley & Sons Ltd., 2000.2. Eleanor Mcfadden, “Management of Data in Clinical Trials”, 2nd Edition, John Wiley & Sons, 2007.3. Rick Ng, “Drugs from Discovery to Approval”, Wiley-Liss Ltd., 2004.

WEBSITES:1. www.clinpage.com/article/the_clinical_data_life_cycle2. www.librarything.com/author/rondelrk3. www.clinicalrm.com/4. www.pharmanet.com/pdf/whitepapers/QCQA.pdf5. www.capterra.com/clinical-trial-management-software







SBIX5018 NEUROINFORMATICSL T P Credits Total Marks

3 0 0 3 100

UNIT I NEUROSCIENTIFIC INFORMATION 10 hrs.

Integration of neuroscientific information the genome level to the level of human behavior - Major goal - producedigital capabilities for a web-based information management system in the form of databases and associated datamanagement tools.

UNIT II DATABASES AND SOFTWARE TOOLS 10 hrs.

Databases and software tools - for the benefit of neuroscientists, behavioral scientists, clinicians - databases forNeuroinformatics - Surface Management System (SuMS), The fMRIDC, BrainMap, BrainInfo, X-Anat, The BrainArchitecture Management System (BAMS), The Ligand Gated Ion Channel database (LGICdb), ModelDB -Neuroinformatic software tools - GENESIS, NEURON, Catacomb, Channelab, HHsim, NEOSIM, NANS, SNNAP, etc -data sharing in neuroscience.

UNIT III COMPUTATIONAL MODELING 10 hrs.

The computational modeling of ion channels - various parts of neurons - full neurons and - neural networks -modeling - overlaps with systems biology - benefit from bioinformatics databases - Indian Neuroinformatics research- National Brain Research Centre.

UNIT IV BRAIN FUNCTIONS 10 hrs.

Computational modeling - understanding of brain functions in normal and various disorder states.

UNIT V NEURODEGENERATIVE DISORDERS 10 hrs.

Neurodegenerative disorders – Alzheimer’s disease – targets - Neuropsychiatric disorders – Alzheimer’s disease- Parkinson’s disease - Huntington’s disease - Amyotrophic Lateral Sclerosis (ALS) - Genetic basis of disease – threemajor classes of genetic disease-OMIM – Programmed cell death - Apoptosis (type I) - Autophagic (type II) -Cytoplasmic (type III) - PCD and neurodegeneration - amyloid disease – prion disease.

REFERENCE BOOKS:1. Michael A. Arbib (Editor), Jeffrey S. Grethe (Editor), “Computing the Brain: A Guide to Neuroinformatics”, San Diego, 2001.2. Steven H. Koslow, “Databasing the Brain: From Data to Knowledge (Neuroinformatics)”, John Wiley & Sons, 2005.3. Rolf Kotter, “Neuroscience Databases: A Practical Guide”, Springer Verlag, New York, 2002.4. Stephen H.Koslow (Editor), “Neuroinformatics: An Overview of the Human Brain Project”, Lawrence Erlbaum Associates, 1997.

WEBSITES:1. http://www.pharmtao.com/Neuroinfo/index.htm2. http://www.hirnforschung.net/cneuro/3. http://www.neurogems.org/







SBIX5019 COMPUTATIONAL CHEMISTRYL T P Credits Total Marks

3 0 0 3 100

UNIT I FOUNDATIONS OF MOLECULAR ORBITAL THEORY 10 hrs.Quantum Mechanics and the Wave Function - The Hamiltonian Operator - General Features- The Variational

Principle - The Born–Oppenheimer Approximation - Construction of Trial Wave Functions - The LCAO Basis SetApproach - The Secular Equation

UNIT II HUCKEL THEORY 10 hrs.Fundamental Principles - Application to the Allyl System Many-electron Wave Functions - Hartree-product Wave

Functions -The Hartree Hamiltonian - Electron Spin and Antisymmetry - Slater Determinants - The Hartree-FockSelf-consistent Field Method.

UNIT III SEMIEMPIRICAL METHODS 10 hrs.Introduction - The basic principles of SCF SE methods - the Neglect of DifferentialOverlapMethod -Complete

Neglect of Differential Overlap Method- Modified Neglect of the Diatomic Overlap Method- AustinModel 1 MethodParametric Method 3 Model - The Pairwize Distance Directed Gaussian Method - The Zero Differential OverlapApproximation Method- Software Used for Semiempirical Calculations.

UNIT IV BASIS SETS 10 hrs.Introduction -The Energy Calculation from the STO Function - The Energy Calculation of Multielectron - Gaussian

-Differences Between STOs and GTOs -Classification of Basis Sets-Minimal Basis -A Comparison of Energy Calculationsof the Hydrogen Atom Based on STO-nGBasis Sets -STO-2G - STO-3G - STO-6G – Contracted Gaussian TypeOrbitals - Double- and Triple-Zeta Basis Sets and the Split-Valence Basis Sets -Polarized Basis Sets -Basis SetTruncation Errors - Basis Set Superposition Error - Methods to Overcome -The Chemical Hamiltonian Approach -TheCounterpoise Method -The Intermolecular Interaction Energy of Ion Water Clusters - A List of Commonly AvailableBasis Sets -Internet Resources for Generating Basis Sets.

UNIT V DENSITY FUNCTIONAL THEORY 10 hrs.Introduction - The basic principles of density functional theory – electron Density -Pair Density -The Development

of DFT- The Functional - The Hohenberg and Kohn Theorem -The Kohn and Sham Method - Density Functionals-The Dirac-Slater Exchange Energy Functional and the Potential -DFT Methods - Applications of -The Performance ofDFT - Advantages of DFT in Biological Chemistry.

REFERENCE BOOKS: 1. Ramachandran K. I., Deepa G., Namboori K, “Computational Chemistry and Molecular Modeling Principles and Applications”,

Springer-Verlag Berlin Heidelberg, 2008. 2. Jensen F., “Introduction to Computational Chemistry”, Wiley, New York, 1999. 3. Errol Lewars, “Computational Chemistry”, Kluwer Academic Publishers, 2003.

WEBSITES:1. www.computational-chemistry.co.uk/2. http://www.nvidia.com/object/computational_chemistry.html3. www.netsci.org/Science/Compchem/index.html





SBIX5020IMMUNOINFORMATICS AND COMPUTATIONAL

VACCINOLOGYL T P Credits Total Marks

3 0 0 3 100

UNIT I INTRODUCTION TO IMMUNOINFORMATICS 10 hrs.

Definition of Immunoinformatics – overview of the Immune system- Bioinformatics strategies for betterunderstanding of immune function - structural features of MHC peptides-MHC peptide interaction parameters: a) interfacearea between peptide and MHC, b) intermolecular hydrogen bonds, Immunological databases – IMGT – IMGT-GENE-DB– IMGT-HLA IMGT/LIGM-DB - HaptenDB – EPITOME – dbMHC – JenPep.

UNIT II DNA MICROARRAY AND IMMUNOLOGY 10 hrs.

DNA micro array analysis-protocol for immnunological applications-clustering- applications:Identification of potentialgenes responsible for pathogenesis-case study of DNA microarray applications – Immunogenomics.

UNIT III ALLERGEN BIOINFORMATICS 10 hrs.

Introduction- allergen database-need for allergen database-existing allergen database :IUIS, SWISS PROT, SDAP,ALLERGOME, Allergenicity prediction-sequence similarity search, FAO and WHO guidelines, current status ofallergenicity prediction.

UNIT IV COMPUTATIONAL VACCINOLOGY 10 hrs.

Vaccines –types- Preparation of vaccines -From immunome to vaccine- epitope mapping,-vaccine design tools-Prediction of cytotoxic T cells (MHC CLASS I) epitopes-Antigen processing in the MHC –I pathway, Prediction of Thelper cells(MHC-II) epitopes-processing of MHC-II epitopes. B cell Epitopes. Tools in immunoinformatics for theprediction binding affinity between peptide : TAP:MHC:TCR- MHC:Peptide Binding Prediction - SYFPEITHI, BIMAS,MHCPred, TEPITOPE, NetMHC, BLEEP Proteasomal Cleavage Prediction : PAProC, NetChop - TAP Binding:TAPPredT-cell Binding: EpiJen.

UNIT V COMPUTER AIDED MODELING 10 hrs.

Introduction computational immunology- Future of computational modeling and prediction systems in clinicalimmunology -overview of discrete models-discrete models for HIV infection-simulation of HIV-1 infections-simulation ofHIV -1 Molecular evolution in response to chemokine receptors and antibodies- Integration of immune models usingPetri Nets.

REFERENCES BOOKS: 1. Richard A. Goldsby, Thomas J. Kindt, Janis Kuby, and Barbara A. Osborne, “Immunology”, 5th Edition, WH Freeman, 2002. 2. Kannan I., “Immunology”, MJP Publishers, 2007. 3. Gregory Bock, Jamie Goode, “Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function”, No. 254,

Novartis Foundation Symposium 254, John Wiley & Sons, 2003. 4. Darren Flower, “In Silico Immunology”, Springer, 2006. 5. Shoba Ranganathan (Editor), Vladimir Brusic (Editor), Christian Schonbach (Editor), “Immunoinformatics”, New York: Springer, 2008. 6. Ole Lund, Morten Nielsen, Claus Lundegaard, Can Kemir and Søren Brunak, “Immunological Bioinformatics”, MIT Press, 2005 .

WEBSITES:1. http://www.cbs.dtu.dk/researchgroups/immunobioinf.php2. http://www.vaccines.org/3. http://www.worldwidevaccines.com/4. http://www.bt.cdc.gov/agent/tularemia/

UNIVERSITY EXAMINATION QUESTION PAPER PATTERNMax. Marks: 80 Exam Duration: 3 hrs.





SCSX5034 ARTIFICIAL NEURAL NETWORKSL T P Credits Total Marks

3 0 0 3 100

UNIT I PATTERN RECOGNITION 10 hrs.

Humans and computers, The structure of the brain, Learning in machines, the differences - Pattern recognitionin perspective, Pattern recognition – a definition, Feature vectors and Feature space, Discriminant functions,Classification techniques, Linear classifiers, Statistical techniques, Pattern recognition – a summary.

UNIT II THE BASIC NEURON 10 hrs.

Introduction; Modelling the single neuron, Learning in simple neurons,The Perceptron: a vectorial perspective,the Perceptron learning rule: proof, Limitations of perceptrons, Single layer ANN, Multilayer ANN - The MultilayerPerceptron - The new model, The new learning rule (Back propagation rule), The Multilayer Perceptron algorithm, theXOR problem revisited Visualizing network behaviour, multilayer perceptron as classifiers – generalization.

UNIT III KOHENEN SELF-ORGANISING NETWORKS 10 hrs.

Introduction, the Kohenen algorithm, Weight training, Neighbourhoods, Reducing the neighbourhood, Learningvector quantisation(LVQ), The phonetic typewriter.

UNIT IV HOPFIELD NETWORKS 10 hrs.The Hopfield model, The Energy lanscape, The Boltzmann machine, Constraint satisfaction - Adaptive Resonance

Memory: Adaptive resonance theory, Architecture and Operation, ART algorithm, Training the ART Network,Classification, Conclusion, Summary of ART.

UNIT V ASSOCIATIVE MEMORY 10 hrs.

Standard computer memory, Implementing associative memory, Implementation in RAMs, RAMs & N-tupling,Willshaw’s associative networks.

REFERENCE BOOKS:1. Beale R.& Jackson T., “Neural Computing: An Introduction”, Adam Hilger, 1990.2. Pao Y.H., “Adaptive Pattern Recognition and Neural Networks”, Addison-Wesley, 1989.

WEBSITES:1. www.learnartificialneuralnetworks.com/2. www.imtech.res.in/raghava/betatpred2/neural.html


Max. Marks: 80 Exam Duration: 3 hrs.Part A: 6 Questions –no choice each carrying 5 marks 30 Marks




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SYLLABUS MASTER OF TECHNOLOGY PROGRAMME IN BIOINFORMATICS ...files.sathyabamauniversity.ac.in/syllabus/2010 PG... · MASTER OF TECHNOLOGY PROGRAMME IN BIOINFORMATICS ... UNIT III