bioinformatics syllabus M.Tech

8/9/2019 bioinformatics syllabus M.Tech

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.0 n--M.Tech. BioinformaticsFirst Semester

mBI-501- Data Structure & Algorithm using C

Lecture 3 Tutorial 1Credits-4

Introduction to data structure, Primitive data structure,Static and Dynamic storage, Sub-algorithm function,

procedure, parameters, parameter passing call by value, call by ref. Introduction to Algorithm analysis for time andspace requirement, Rate of growth and Order of notationBasic time and space analysis of an algorithm, String

Manipulation and Pattern matching Markov AlgorithmPrimitive and composite function, string manipulationApplication - Text handling only, Abstract Data Types.

Non-linear data structures - Concept and Technology of Storage structure of arrays row major column major StacksDefinition, concepts, operation and application of Stacks,Recursion and Polish notations. Queue, Priority Queuedefinition concepts operation and application of Queue,Dqueue.

Linear data structures - Pointers and linked allocationconcepts and operations on singly linked list, circular


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linked list, doubly linked lists Associative list Applicationof linked linear list, Polynomial Manipulation, Multiple

precision arithmetic.

Trees - Definition and concepts storage representation andManipulation of Binary tree conversion of general tree toBinary trees, Threaded Binary tree, Multi-linked structure,sparse matrices, Height balance tree, Multi weight tree, Btree, B+ tree, graph and their representation Matrixrepresentation of graph Breadth first and depth first search,shortest path algorithm.

Internal Sorting External sorting - Selection sort, Bubblesort Merge sort quick sort, radix sort, Tape sorting; Shellsort, Poly phase and Oscillating sorting, (taking bestcase/worst case examples) sorting on Disk, Searching-Sequential search, Binary search, Search trees, Hash tablemethods. BOOKS :

4. Data Structure - Tremble & Sorenson5. Data Structure in `C Language : by Tanenbaum6. Data Structure by Bhagat Singh7. Data Structure by Horowitz & Sohani


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BI-502 - Molecular Biology & Molecular Genetics

Lecture 3 Tutorial 1

Credits-4

Introduction to Molecular biology, central dogma of Molecular biology, kind of data used in Bioinformatics,information molecules, basic structures of nucleotides.DNA, RNA, amino acids, and Proteins, genetic code,introns, exons, open reading frames, functions of variousinformation molecules, different tools and techniques inmolecular biology.

DNA as a genetic material, DNA replication, Biochemicalstructure, Recombination, Polymerase Chain reaction(PCR), DNA fingerprinting. Trancription, synthesis of RNA from template DNA strand, types of RNA, structureand functions of mRNA, tRNA, and rRNA, splicing of

RNA transcript, translation, biochemical aspects of protein biosynthesis, post translational targeting and modifications.

Science of genetics objectives, terminologies, methods,Mendelian principles of inheritance, sex linked inheritance,Concept of linkage, linkage maps and recombination,manipulation of genetic material vectors, detecting targetDNA, techniques involved in gene manipulation.

Mutations gene mutations, chromosomal aberrations, polyploidy, molecular gene/point and chromosomalmutation, Phenotype and genotype relationships, from geneto phenotype, gene interactions, Study of quantitative traits,


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gene transfer, applications of genetic engineering.Regulation of gene expression, role of gene expressionduring cell differentiation, Genetics of populations,

genetics and evolution, role of environment, Genetics anddiseases, Cancer, HIV-AIDS etc.BOOKS:8. Basics Of Molecular Biology - Dr. R . N. Singh9. Cell and molecular biology, Rastogi et al.

10.Molecular evolution:Computer analysis of proteinand nucleic acid sequences Doolittle, Russel,Academic Press.

11.Principles of population genetics Hartl and Clark.

BI-503 - Introduction to Bioinformatics


Introduction, chronological history of Bioinformatics,

evolution of Bioinformatics, Objectives of Bioinformatics,Importance of bioinformatics, Bioinformatics in business,future scope of Bioinformatics.Bioinformatician and bioinformaticist, role, need andimportance of Biology, Computer Science, mathematicsand information technology in bioinformatics, biologicalclassification and nomenclature, life in space and time.Introduction, information networks, protein and genomeinformation resources, DNA sequence analysis, pairwisealignment techniques, multiple alignment techniques,secondary databases, analysis packages.The dawn of sequencing, the biological sequence or structure deficit, human genome project and its status,


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homology and analogy, web browsers, European molecular biology network, national centre for biotechnologicalinformation, specialized genomic resources.

Building a sequence search protocol, practical approach for structural and functional interpretation, introduction toanalysis package, commercial databases, softwares andcomprehensive packages, internet packages specializing inDNA and protein analysis.BOOKS:

12.Introduction to Bioinformatics T.K. Attwood andParry Smith

13.Introduction to Bioinformatics Arthur M. Lesk 14.Fundamental Concepts in Bioinformatics Krane and

Raymer BI-504 - Numerical Techniques and Biostatistics


Newton forward and backward formulae, Everett andBessel Interpolation formulae, Lagranges, Hermite andspline interpolation. Implementation of these method in C.

Integration: Weddle, Gauss-Legendre, Monte Carlomethods of integration, Differential Equations: ModifiedEuler, Runge-Kutta methods, Predictor-Corrector methods-Milne Adams-Bashforth methods, Accuracy of Runge-Kutta and Milens methods.

Solution of algebraic and transcendental equation: Newton-Raphsan method, N0ewtons method for multiple roots,


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Lin-Barstows and Graffes method for complex roots,Solution of Non-linear equations.

Non-linear Equations: Solving simple non-linear equations.Programs of simultaneous and non-linear equations,Simultaneous Equations: Gauss elimination and Pivot,Gauss-Siedel and Gauss-Jacobi iteration techniques.Invertion of Matrix by Choleskys method.

Introduction to Statistics and Biostatistics, graphical datarepresentation, descriptive statistics, probability and

probability distribution, Central tendency, regression,correlation and perception of statistics in biology,inferential statistics, health statistics, and vital statistics.

BOOKS:

1. B.S. Grewal : Numerical Algorithms, KhannaPublications.

2. Krishnamutrhy & Sen : Numerical Algorithms EWP.

3. M.K. Jain & Iyengar: Numerical Methods for Scientists & Engineers4. Biostatistics: a foundation for analysis in the healthsciences - Wayne W. Daniel

BI-511 - Introduction to Genomics & Proteomics



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Genes, genomes, proteins , proteomes, eavesdropping onthe transmission of genetic information, picking out genes

in the genomes, genomes of Prokaryotes, genomes of Eukaryotes, the genome of Homo sapiens (human genome),genome analysis.

Genome information resources, specialized genomicresources, the genome of the bacterium Escherichia coli ,the genome of the archaeon Methanococcus jannaschii , thegenome of the simplest organism Mycoplasma genitalium ,mapping between the maps, high-resolution maps.

Single nucleotide polymorphisms (SNPs), protein codinggenes, repeat sequences, genetic diversity in anthropology,genetic diversity and personal identification, geneticanalysis of cattle domestication, Evolution of genomes,horizontal gene transfer, comparative genomics of

Eukaryotes, genomes of Saccharomyces cervisiae (baker'syeast), Caenorhabditis elegans , Drossophila melanogaster (Fruit fly), Arabidopsis thaliana etc.

Genome rearrangements, the breakpoint graph, expectedreversal distance, signed permutations, interleaving graphsand hurdles, algorithm for sorting by reversals,transforming men in to mice, duality theorem and genomicdistance.

Different types of structures of proteins, proteinclassification, tools for proteome analysis, differentstructural proteins, methods of structure prediction (known


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folds and unknown folds), prediction of protein functions,Proteome analysis, computational proteomics, peptidesequencing, peptide identification problem, spectral

alignment of peptides, spectral convolution.

BOOKS:15.Bioinformatics A practical guide to the analysis of

Genes and Proteins Baxevanis and Fancis Ouellette,Wiley Interscience, New York.

16.Computational Molecular Biology An algorithmicapproach Pavel A. Pevzner

17.Modern Genetic Analysis :Integrating Genes andGenomes - AnthonyJ.F. Griffiths,William M.Gelbart,Jeffery H. Miller

BI-512 - Proteomics and Transcriptomics Lecture 3 Tutorial 1

Credits-4

Protein classification, alpha-helix, beta-sheet, loops andcoils, classes of protein structure, viewing protein structure,

protein structure classification databases, alignment of protein structures, dynamic programming, distance matrixalignment (DALI), fast structure similarity and significanceof alignments.

Evolution of protein structures, Protein structure prediction,use of sequence patterns for structure prediction, proteinstability and folding, the side chains, Ramachandran plot,

protein stability and denaturation, hydrophobicity and itsapplications.


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Computational proteomics, the peptide sequencing problem, learning Ion types, scoring paths in spectrumgraphs, peptide sequencing and antisymmetric paths,

peptide identification problem, spectral alignment andconvolution.

Approaches from proteomics to genomics, importance of transcription, large scale protein analysis, tools andtechniques in proteomics and transcriptomics, protein-

protein interaction, post-translational modifications, Ab Initio approaches for protein prediction, protein function prediction, protein prediction from a DNA sequence.

BOOKS:1. Introduction to computational molecular biology

Setubal and Meidanis.2. Genes Lewin B., Oxford University Press.

BI-513 - Modelling Gene Mapping & Sequencing


Computational gene mapping and gene hunting, geneticmapping, physical mapping, sequencing similarity search,gene prediction, mutational analysis, comparativegenomics, introduction to restriction mapping and mapassembly, gene prediction methods, gene prediction tools,gene expression.

DNA double digest problem, multiple solutions to double


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digest problem, alternating cycles in colored graphs, physical maps and alternating Eulerian cycles,transformations in Eulerian cycles, partial digest problem,

probed partial digest problem, homometric sets.

Gene mapping, mapping with unique and non-unique probes, optical mapping, interval graphs, mapping withrestriction fragment fingerprints, Lander-Watermanstatistics, screening clone libraries, radiation hybridmapping.Introduction to sequencing, overlap, layout and consensussequencing concepts, double-barreled shotgun sequencing,shortest superstring problem, some other problems andapproaches, finishing phase of DNA sequencing.DNA arrays, sequencing by hybridization (SBH), positionalsequencing by hybridization, SBH and the shortestsuperstring problem, SBH and the Eulerian path problem,

probability of unique sequence rearrangements, string

rearrangements, design of DNA microarrays, fidelity probes for DNA arrays.BOOKS:

18.Techniques in Quantification and Localization of GeneExpression - Bruce K. Patterson.

19.DNA Sequencing by Brown T.A., IRL press Oxford.20.Mathematics of Genome analysis Percus, Jerome K.,

Cambridge Univ. Press.

M. Tech. Second Semester

BI-551 - Languages, Algorithms & Tools forBioinformatics


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Sequence comparison, Sequence similarity search,computational models for sequence analysis, sequencealignment, longest common subsequence problem,alignment with gap penalties, space efficient sequencealignment, Scoring matrices for similarity search PAM,BLOSUM etc.

Comparing a sequence against a database, method of sequence database similarity search, BLAST, FASTA, andother methods for comparing databases of sequence and

patterns- PSI-BLAST, PHI-BLAST & PROBE etc.

Methods of optimal alignment, distance and similarity,motifs & patterns, evolutionary basis for sequence

alignment, Smith Waterman, Needleman and Wunschdynamic programming algorithms, progressive alignmentmethods, alignments presentation methods, multiplesequence alignment (MSA), practical aspects of MSA,tools and applications of MSA.Programming languages Perl, Bioperl, Java, Biojava,HTML, BioXML etc.,Bioinformatic Tools Genscan, Rasmol, Cn3D, Phylip,Oligo, Clustal W, ALSCRIPT, MOLSCRIPT etc.BOOKS:

21.Perl for Bioinformatics Tisdall and James.22.Introduction to Bioperl Tata Mc Graw Hill.23.The comlete reference Java2, Patrick Naughton et


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al.

BI-552 - Biological Databases and their Management

Lecture 3 Tutorial 1Credits-4Introduction to database: Data Abstraction, Data `Models,Basic concepts of database: Data Independence DML,DCL, DDL and structure of Data Base ManagementSystem, Entity relationship diagram: Basic and Advanceconcepts Application of ER diagram in designing databasesystem. Relational Algebra, Tuple Relational Calculus

Database design issues, Normalization 1NF, 2NF, 3NF,4NF, BCNF and 5NF, database design problem. Securityand Integrity: Use of SQL for specifying Security andintegrity. Authorization, view, Encryption. Storage

structure indexing and hashing. Different type of fileorganization.

Transaction & concurrency control, Schedules, testing,serializability, Lock based Protocol, Time stamp protocol,validation technique, Multiple granularity, Multi-versionscheme Insert and delete operation, Crash recovery, Log

based recovery, buffer management checkpoints, shadow paging. Object oriented databases.

Distributed database structure, Design transparency andAutonomy, Distributed Query processing Recovery,commit protocol Deadlock handling, Multidatabase system,


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Parallel database concept and related issues, Web interfaceto database, Database System Architecture

Introduction to biological databases, Nucleic acid sequencedata banks: Genbank, EMBL, DDBJ, TrEMBL, GenPept,nucleotide sequence databank, cDNA databanks, AIDSVirus sequence data bank, rRNA data bank, Proteinsequence data banks: NBRF-PIR, SWISSPROT, Signal

peptide data bank etc.REFERENCE BOOKS:

(i) Database System Concept By C.J.Date.

(ii) Database System By Aho.Ullman.

24. Database Systems By Rob, Coronel.

BI-553 - Modelling and Simulation


System Models : Concept, environment, stochasticactivities, continuous and discrete simulation, Model Type-static, dynamic and probabilistic models. Growth andDecay model examples. Principles of Mathematicalmodeling, static physical model, system modelling.

Continuous systems models, differential equations, analogcomputers, analog methods, hybrid computer, analogsimulations, continuous system simulation, languases,CSMP III hybrid simulation, feedback system, simulationof an interactive system, real time simulation.


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Discrete system simulation, probability concepts insimulation, random number generations and their testing,

stochastic variable generation, fixed time stop versus eventto event model.

Simulation of queuing systems, arrival pattern, Poissonarrival pattern, exponential distributions, hyper-exponentialdistribution, service times, simulation of a single-server queue, normal distribution, measures of queues.Simulation languages, continuous and discrete simulationlanguages, block-structured continuous simulationlanguages, GPSS, SIMSCRIPT, SIMULA, factor inselection of a discrete simulation language.BOOKS:1. Gordan Simulation and Modeling - PHI.2. System simulation and modelling - Narsingh Dev - PHIBI-554 - Optimization Techniques and Graph theory


Linear Programming, Mathematical Model, Assumptionsof Linear Programming,simplex Method, Degeneracy, Applications, Duality, Dual

Simplex Method, & Algorithm Assignment Problem.Hungarian Method & its Algorithm.

Transportation Problem, Integer Programming :-Gomorras method, Branch and Bound techniques. Integer Programming Algorithm, Dynamic Programming:-


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Bellmans Principle of optimality, Dynamic ProgrammingApproach, optimal subdivision problem, Decomposition,Applications in linear programming.DPP Algorithms.

Queuing Theory : Queuing problem and system,Transient and steady state distributions in queuingsystem, Poisson process, Exponential process, classificationof queuing models, Model I (M/M/1) : ( /FCFS), Model-II General Erlang Queuing model, Model - III (M/M/1) :(N/FCFS), Model - IV (M/M/S) : ( /FCFS), Algorithms.

Inventory Theory :- Basic concepts, classification of Inventory systems & models, Economic order quantity,Deterministic Inventory models :- EoQ Models withoutshortages, EoQ Models with shortages, ProbabilisticInventory Models with instantaneous demand, no set upcost model, Discrete and continuous cases.

Games Theory : solution of games with saddle points,Minimax-Maxmin principle for Mixed strategy games,Dominance, to reduce size of game, Graphical method,solution of (mxn) game by simplex method & Algorithms,Job sequencing : Processing n jobs through 2 machines,Processing n jobs through 3 machines & Algorithms, PERT

CPM: introduction, applications, network diagram

representation, Determination of the critical path, updating.

TEXT BOOKS :

(i) S.D. Sharma Operations Research, Kedarnath


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Ramnath & Co. Meerut

(ii) P.K.Gupta & D.S.Hira Operations Research,

S.Chand & Co.(iii) Kantiswaroop Operations Research, S.Chand & Sons.

(iv) Gillet, B.E. Introduction to operations Research - A

Computer Algorithm Approach, McGraw Hill.

(v) Introduction to operations Research, 7/e by Hillier.

TMH.Elective-IIBI-561. Biomolecular Structure Interaction andDynamics


Prokaryotic gene expression, operons positive & negativeregulation, sigmaFactors, Initiation, elongation and termination of transcription template & enzymeProperties, Eukaryotic RNA pdymerase I, II & IIItranscribed genes, promoter & regulatory sequences,transcription factors, Techniques foot printing, Reporter genes. Organisation of globin, immunoglobulin, HLA, rRNA and5 sRNA genes, processing of RNA and Proteins - Transportand Stability, stress & hormones regulated gene expression


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Organization of human genome. RFLP, Fingerprinting,RAPDs, Microarrays, ESTs.

Computational methods for pathways and systems biology,databases of metabolic pathways, Kyoto Encyclopedia of Genes and Genomes (KEGG), analysis of pathways,Glycolysis, signaling pathways, genetic pathways.

Metabolic network properties, metabolic control analysis,simulation of cellular activities, modelling and simulationenvironment for biochemical and genetic processes, E-cell,general framework for spatial modelling and simulation of cellular physiology, V-cell.BOOKS:

25.Biological sequence analysis: Probabilistic modelsof proteins and nucleic acids Durbin R., et al.

26.Genetic Data Analysis Weir B.S.27.Molecular Modelling by Andrew R. Leach

BI-562 - AI and Soft Computing techniques


Introduction to AI various types of production system.Analysis of problem for developing an AI system. Problemcharacteristics, Production system and its characteristics.BFS, DFS, and Different heuristic search techniques- Hillclimbing, Ascent Hill climbing, Constraint satisfaction. A*algorithm AO* Algorithm,


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Knowledge Representation : Representation andmappings, Approaches to knowledge representation, Issue

in knowledge representation, Knowledge representationusing predicate logic resolution, and unification algorithm.Knowledge representation using rules procedural versusdeclarative knowledge logic programming, Forward and

back ward reasoning .Symbolic reasoning under uncertainty monotonic and non monotonic reasoning.Semantic nets, Frames Conceptual dependency.

Natural language processing and study of its different phases, Game planning Minimax Search procedure,Adding Alpha Beta cut-offs, Iterative deepening Planningcomponents of planning system Goal stack planning

Nonlinear and Hierarchical planning.Statistical reasoning Probability and Bayes TheoremCertaininty factor and rule base system Baysiean Networks

Dempster Shafer Theory Fuzzy Logic. UnderstandingParallel and distributed AI Psychological Modeling,Parallelism in reasoning system.Introduction to learning, various learning techniquesIntroduction to Expert System. Connectionist ModelHopfield networks leraning in Neural NetworksApplication Common Sense Common Sense ontologies andMemory organizations.

TEXT BOOKS :28.AI by Rich & Knight.29.AI by Norwing.

BI-563 - Phylogenetics


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Introduction to trees, concept of evolutionary trees, phylogenetic trees, relationship of Phylogenetic analysis tosequence alignment, genome complexity and Phylogeneticanalysis, sequence alignment based on evolutionary model.

Methods of phylogenetic analysis like distance matrix,Fitch and Margoliash method, maximum parsimonymethod, maximum likely hood method, unweighted pair group method with arithmetic mean (UPGMA), neighbour-

joining method and character based methods, evaluation of phylogenetic methods.Converting sequence similarity to distance scores, choosingan outgroup, correcting of distances between nucleic acidsequences for multiple changes and reversions, comparison

of protein sequences and protein encoding genes,comparison of open reading frames by distance methods.Reliability of Phylogenetic predictions, problems andcomplications from Phylogenetic analysis, automated toolsfor Phylogenetic analysis, bootstrapping and jack knifereplicates, jumbling sequences.Books:

30.Bioinformatics Sequence and Genome analysisBy David W. Mount

31.Molecular Evolution and Phylogenetics Nei andKumar.


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BI-564 - Genetic Engineering:


Introduction to genetic engineering, enzymes involved inmanipulation of genetic material, restriction endonucleases,DNA polymerase, DNA ligases, Kinases, vectors used ingenetic engineering, plasmids and cosmids, phages,transposons, yeast artificial chromosome.

Detection of target DNA, labeling of nucleic acids, labelingtechniques, nick translation, end labeling, primer extension,random priming methods, concept and use of linkers and

adapters, radioactive and non-radioactive phosphatases,reverse transcriptase.

Chemical synthesis of oligonucleotides, exonucleases,ribonucleases and proteases, genomic and cDNA library,DNA foot printing, polymerase chain reaction (PCR) andits applications, site directed mutagenesis, cell fusiontechniques, southern and northern blotting, in vivoexpression techniques.

Applications of genetic engineering, gene transfer in animaland plant cells, generations of novel plants and animals,diagnostics of genetic expressions, protein and metabolic


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engineering, ethical issues and patenting of geneticallymodified objects.

BOOKS:32.Biological sequence analysis: Probabilistic modelsof proteins and nucleic acids Durbin R., et al.

33.Genomics: the science and technology behindhuman genome project, Cantor and Smith

M. Tech. Third Semester:

BI-601 - Data Mining and Data Warehousing


Need for data warehouse, definition, goals of datawarehouse, Data Mart, Data warehouse architecture, extractand load process, clean and transform data, star, snowflake

and galaxy schemas for multidimensional databases, factand dimension data, Designing fact tables. partitioning,

partitioning strategy horizontal partitioning, vertical partitioning,

Data warehouse and OLAP technology, multidimensionaldata models and different OLAP operations, OLAP Server:ROLAP, MOLAP and HOLAP. Data warehouseimplementation, efficient computation of data cubes,

processing of OLAP queries, indexing OLAP data.

Data preprocessing, data integration and transformation,data reduction, Discretization and concept Hierarchy


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Generation, Data mining primitives, Types of DataMining, Data Mining query language, Architectures of datamining. data generation & Summarization based

characterization, Analytical characterization, Mining classcomparisons, Mining descriptive statistical measures inlarge data bases.

Mining Association Rules in large databases: Associationrule mining, single dimensional Bookan association rulesfrom Transactional DBS, Multi level association rules fromtransaction DBS, multidimensional association rules fromrelational DBS and DWS, Correlation analysis, Constraint

based association mining.

Classification and Prediction : Classification by decisiontree induction, Back propagation, Bayesian classification,classification based in association rules, Prediction,classifier accuracy, Cluster analysis, partitioning and

hierarchical methods, Denrity based methods Grid basedmethods, web mining, Temporal and spatial data mining.

Text Books:

34.Building Data Ware House by W.H.Inmon, JohnWiley & Sons

35.Data warehousing by S . Anahory and D.Murray,Pearson Education, ASIA

36.Data Mining Concepts & Techniques by Jiawei Han& Micheline Kamber; Harcourt India PVT Ltd.

37.TMH Oracle 8i Building Data Ware Housing byMichall Corey, M.Abbey, I Azramson & Ben Taub.


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38.Data Mining, Practical Machine Cearing tools &techniques with Java by I.H. Whiffen (MorganKanffmen)

39.Data Ware Housing with oracle by Sima Yazdanri &Shirky S. Wong40.Data Mining Techniques by A.K. Pujari , University

Press.

BI-602 - Biomathematics


Growth and Decay Models in Biological; Populationin Natural and Laboratory Environments. Intoxicants and

Nutrients. Stability Analysis Interacting Population withPredation; Basic Models and Their solutions.

Epidemic Models; Deterministic models with and

without Removal, General Deterministic Models with

removal and Immigration. Control of an Epidemic,

Stochastic Epidemic Model without removal.

Models in Genetics; Basic models for Inheritance,

Further Discussion of Basic Model for Inheritance of

Genetic Characteristics, Models for Genetic

Improvement: Selection and Mutation, Models for


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Genetic Inbreeding

Pharmaco-Kinetics, Compartmental Models in terms

of System of Differential Equations. Bio-Diffusion.Diffusion of Drugs. Trans-Capillary Exchange.Oxygenation and Deoxygenating of Blood. CardioVascular Flow Patterns. Temperature regulation in HumanSubjects.

Curve Fitting and Biological Modeling; Fitting curvesto Data, The Method of Least Squares, Polynomial curveFitting.Text Books:

41.Mathematical Biology- J. D. Murray42.Mathematical Models in Biology and Medicine. By

J. N. Kapur 43.Mathematical Models in Biology; An Introduction.

By Elizabeth S. Allman and John A. Rhodes44.Linear Models in Biology (Pharmacy): By Cullen45.Bio-Physics46.Bio-FluidDynamics : By F. C. Fung 47.Bio-Fluid Mechanics: By F. C. Fung 48.Introduction to Mathematical Biology: By

Rubinov.


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Elective IIIBI-611 - Advanced algorithms for Bioinformatics

Lecture 3 Tutorial 1Credits-4Introduction: Analyzing algorithm, growth of functions,asymptotic notation, standard notation and commonfunctions. Divide and Conquer approach: Performanceanalysis of binary search, quick sort, merge sort, heap sort.

Recurrences: Substitution method, iteration method,master method, proof of master theorem. Red-Black Trees: Properties of red-black trees, Rotations, insertion,deletion. Augmenting Data Structures: Dynamic order statistics, How to augment a data structure. Dynamic Programming: Elements of DynamicProgramming, Matrix chain multiplication, optimal binarysearch tree. Greedy Algorithm: Elements of the greedystrategy, Huffman codes, A task scheduling problem,Amortized Analysis: Aggregate method, accountingmethod, potential method.

Introduction: Graph and Minimum Spanning Trees,Kruskals and prims algorithm. Single-Source ShortestPaths: The Bellman-Ford algorithm, Single-SourceShortest Paths in directed acyclic graphs, Dijkstrasalgorithm. All-Pairs Shortest Paths: Shortest paths and matrix

representation, The Floyd-Warshall


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algorithm.

Sorting Networks: A bitonic sorting network, merging

network, sorting network.Matrix Operations: Properties of matrices, Strassensalgorithm for matrix multiplication. Linear Programming:Stander and slack forms, Formulating problems as linear

programs, The Simplex algorithm. Introduction to NP-Completeness.TEXT BOOKS :(i) Fundamentals of Computer Algorithms Ellis

Horowitz, Sartaj sehni galgotia publications.(ii) Introduction to Algorithms Cormen, Leiserson, Rivets,PHI.

BI-612 - Pattern Recognition and Machine Learning


Credits-4Foundation of pattern recognition, Biological data in digitalsymbol sequences, genomes diversity, size and structure,information content recognition of biological sequences,

probabilistic modelling and inferences, statisticalmechanics.

Machine learning foundation, Bayesian modelling,Bayesian inference and induction, machine learningalgorithms, dynamic programming, gradient descent,Markov chain, evolutionary and genetic algorithms.

Introduction to neural networks, backpropagation, sequence


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encoding and output interpretations, sequence correlations, prediction performance evaluation, Hidden Markovmodels, likelihood and basic algorithms, applications of

HMMs.

Probabilistic graphical models in bioinformatics, probabilistic models of evolution, substitution probabilitiesand evolutionary rates, optimal trees and learning,stochastic grammars and linguistics, applications of grammars to biological sequences.BOOKS:

49. Bioinformatics Machine learning approach byBaldi & Brunak

50.Statistical methods in Bioinformatics: anintroduction Ewens and Grant.

BI-613 - Software Engineering


Credits-4

Software characteristics, components, S/W process models, prototyping, RAD, evolutionary models, formal methods,models, management spectrum. Software Project Planning

objectives, scope, resources, project estimation,decomposition techniques, empirical estimation models,make buy decision. Risk Management S/W risks, risk identification, risk projection, monitoring and management

Project Scheduling and Tracking Basic concepts, task setfor s/w project, selecting tasks, refinement, scheduling,

project plan. Quality assurance meaning, movement,


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reviews, measures of reliability, SQA plan, Systemengineering : Hierarchy, strategy planning, area analysis,

product engineering, three feasibilities. Analysis Concepts

requirement analysis, principles, prototyping,specification, review Design Concepts and Principles- Software design andengineering, design process, principles, concepts. Effectivemodular design. Design Methods- Data design,architectural design & process, transform mapping. Designsteps, transaction mapping, interface design and guidelines. Analysis Modeling elements of analysis model,data modeling, functional modeling, behavioral modeling,data dictionary.

Software Testing Methods- Fundamentals, Test casedesign, white box testing, basic path testing, control testing,

black-box testing. Testing strategies-strategic approach,

criteria for completion testing, unit testing, validationtesting, system testing. Software quality- McCalls qualityfactor, FURPS, Framework for software matrices.

Object-Oriented software engineering- Management of Object-oriented software projects. Object-oriented analysis,domain analysis, object-oriented design- issues, landscape,system design process, task data and resource management.Object-oriented testing- Testing OOA and OOD models,unit and integration testing. Software Reuse managementissues, reuse process, domain engineering building reusablecomponents, economics of reuse.


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TEXT BOOKS :1. R. Pressman- Software Engineering- A

Practitioners Approach Mc GrawHill.

2. Soummerville- Software Engineering, PHI3. P. Jalote- Software Engineering, Narosa.4. A Leon & M Leon- Fundamentals of s/w

engineering, Vikas.


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BI-614 - Chemoinformatics & Drug Design


Basics of biochemistry, biochemical structure of proteinmolecules, Introduction to cheminformatics, history of cheminformatics, use of cheminformatics, evolution of cheminformatics, future scope of cheminformatics,chemical information source.

Introduction to drug discovery, areas influencing drugdiscovery, important parameters in drug discovery,technologies and strategies in drug discovery, cell cycle key to drug discovery, introduction to pharmacogeneticsand pharmacogenomics, applications of pharmacogeneticsand pharmacogenomics.

Drug life cycle, drug development time lines, definition of drug discovery, stages of drug discovery, strategic issues indrug discovery, emerging approaches to drug design anddiscovery, strategy to identify possible drug targetmolecules.

Computer aided drug design, methods of computer aideddrug design, ligand design methods, docking algorithmsand programs, drug design approaches, absorption,distribution, metabolism, and excretion (ADME) property

prediction, computer based tools for drug design.


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BOOK:51.Introduction to Chemoinformatics by Andrew R.

Leach, Valerie J. Gillet

52.Introduction to Computational Chemistry by Frank Jensen53.Targeted and Controlled Drug Discovery -

S.P.VYAS and R.K.Khar 54.Bioinformatics Genomics, proteomics, and drug

discovery, Rastogi et al.

BI-615 - Medical InformaticsR4


Introduction, history of medical informatics, prospects of medical informatics, hospital information and managementsystem, computerized physician order entry, health and

disaster management plan,

Medical expert systems, computer based patient records, polyclinic module, computer assisted medical and patienteducation, three dimensional imaging, virtual endoscopy

Three dimensional navigation systems, surgicalsimulations, volume image data file, computer assistedsurgery (CAS), merits and demerits of CAS, virtualenvironment, materials and methods for virtualenvironment

Telecommunication based medical systems, telemedicime


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and tele surgery, internet and its relay chat, internet gratefulmed (IGM).

BOOK:55.Medical Informatics A primer By Mohan Bansal56.Bioinformatics basics applications in biological

science and medicine, Rashidi et al.57.Biomedical Signal Analysis - Rangaraj M.

Rangayyan.

BI-616 - Computer Graphics


Graphic Systems : Refresh CRT, raster-scan displays,

DVST, plasma displays, LCD, Input devices : functions provided by input devices, basic working of these inputdevices, image scanners, touch panels, Input modes,request mode, event mode.

Output Primitives : Points, lines, DDA, Bresenham live &circle drawing algorithm, ellipse generation. Filled areaPrimitives : scan line polygon fill algorithm, inside outsidetests, boundary fill algorithm. Line width, color, color andgray levels, character attributes.

Two-dimensional Geometric Transformation: Basictransformation, translation, rotation, scaling, fixed point


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scaling, general scaling direction. The viewing pipeline,window viewpoint transformation. Line clippingalgorithms.

Three Dimensional concepts : Polygon surfaces, planeequation, curved lines and surfaces, quadric surfaces, splinerepresentation, spline specification. Bezier curves andsurfaces, B-splines and surfaces, Displaying spline curvesand surfaces, Horners rule.

Three Dimensional transformation and viewing methods :3 D transformations, viewing co-ordinates, transformationfrom world to viewing co-ordinates, parallel and

perspective projections, Back face detection, depth-buffer and A-buffer methods.

TEXT BOOK :

1. Computer Graphics by D.Hearn and M.P. Baker 2 Computer Graphics - A programming approach byHarriyton.3. Procedural Elements of Computer Graphics by Roger.4. Three Dimensional Computer Graphics by Folleyand Dam

BI-617 - Neuroinformatics


Introduction: Structure and functions of Neurons, passive electrical properties of neurons, signal spread,


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principles of electrical circuits, the cable equation,electrical models of real neurons, ion permeability andmembrane potential at equilibrium: the Nernest equation,

the Nernest plank equation.Ion pumps and homeostasis: sodium and potassium,the sodium pumps, the potassium pumps, calcium, anions,the regulation of pH, action potential, Hadgkin Huxleyexperiments, Hadgkin Huxley model, the mechanism of action potential.

The structure and function of voltage gated channels:structure of sodium and potassium channels, the channel

pore, mechanism of activation and inactivation, thediversity of voltage gated channels: sodium channel,

potassium channel, calcium channel.Pre Synaptic mechanism of synaptic transmission, gap

junctions, electrical synapses, modulation of electricalsynapses, chemical synapses and the calcium hypothesis.

Excitatory transmission, Acetyl choline and

cholinergic transmission, glutamate and glutametergictransmission, inhibitory transmissions, structure of GABAreceptor and Glycine receptor.Books references:

58.Molecular and cellular physiology of neurons byGordon L. Fain.

59.Brain facts by Candle.

M. Tech. Fourth Semester:

60.Project and Dissertation


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61.Projects for Bioinformatics Software Development62.Projects in Molecular Modeling and Drug Design

63.Projects in Computational Biology64.Other projects relevant to Bioinformatics

bioinformatics syllabus M.Tech

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Transcript of bioinformatics syllabus M.Tech