M.Tech 4th Semester Bio Informatics Syllabus

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NEUROINFORMATICS
Subject Code : 14BBI421

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of modeling and simulation of neurons and brain to understanding the neurological disorders. Students will gain the insights into the applications of neuroinformatics in developing drugs to treat the neurological disorders.

MODULE 1
Linear Response Theory and Single Neuron Models: Properties of a linear system, Convolution and Fourier transforms. Neuron models – Integrate and Fire model, Multi compartment models and Network Models. Neural Encoding: Introduction; Spike Trains and Firing rates, Spike Train Statistics, Neural encoding and decoding – Neural Code, Estimating Firing Rates, Introduction to Receptive Fields, Neural Decoding and Information theory.

MODULE 2
Entropy, Mutual Information, Bayer’s Theorem: Adaptation and learning. Synaptic plasticity rules. Supervised and unsupervised learning. Classical conditioning and Reinforcement learning. Neuroscience Knowledge Management: Managing knowledge in Neuroscience, Interoperability across Neuroscience databases. Database architectures for Neuroscience applications, XML for data representation and Data model specification.

MODULE 3
Computational Neuronal Modeling and Simulation: Tools and methods for simulation of Neurons and Neural Circuits – Model structure analysis in NEURON, Constructing realistic Neural simulations with GENESIS, Simulators for Neural Networks and Action potentials. Data mining through simulation. Computational exploration of Neuron and Neural Network models in Neurobiology.

MODULE 4
Neuroinformatics in Genetics and Neurodegenerative Disorders: Information approach to Systems Neurogenetics. Computational models of dementia and Neurological problems, Application of Systems biology approach to the neuroscience (application to schizophrenia). Brain Image construction, Analysis and Morphometric tools – Brain image Atlases, Databases and Repositories. Tools and databases for Mapping Neural structure and Connectivity Pattern.

MODULE 5
Neuroinforamtics Applications and Models for Neuropsychology: General Neuropsychological assessment – Visuospatial processing, Visual attention and Spatial neglect,
Speech, Language and Aphasia, Phenomics and Neuropsychology. Human Brain Project: Microscale and Macroscale characterization; Basis of Brain mapping; Functional and Cognitive Brain atlas; Interoperable and Federated Brain Map databases.

COURSE OUTCOMES
i. Students will gain knowledge about modeling and simulations of neurons.
ii. Students will learn the importance of neuroinformatics in understanding the genetics and neurodegenerative disorders.
iii. Students will gain knowledge of applications of neuroinformatics.

TEXT / REFERENCE BOOKS
1. Theoretical Neuroscience – Computational and Mathematical Modeling of Neural System by Dayan and Abbot, 1st Edition, The MIT Press, 2001.
2. Neuroinformatics for Neuropsychology by Vinoth Jagaroo, Springer, 2009.
3. Neuroinformatics by Chiquito Joaquim Crasto, Humana Press, 2007.
4. Neuroinformatics: an overview of the Human Brain Project by Stephen H. Koslow, Michael F. Huerta, Routledge, 1997.

HEALTH INFORMATICS
Subject Code : 14BBI423

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of health informatics, tools and techniques used in health informatics. This course will also give insights into applications IT in health informatics to help humans.

MODULE 1
An introduction to Health care informatics: An interaction between health care and information systems. Acquisition, storage, retrieval, and use of information in health and
biomedicine. Tools and techniques. Information systems in Medicine, Dentistry, Nursing, surgery and diagnosis. Future prospects.

MODULE 2
Building blocks of Health care informatics: Standards, types of standards. Modeling – principles of modeling for healthcare. Architecture of Health care system – models, sub
systems, packages and components. Modeling framework for health care. generic health care information model. Unified modeling language. Modeling methodologies in healthcare systems. Databases, types, and applications. Database Architecture; ANSI/SPARC three tier architecture. Data warehousing; architecture.

MODULE 3
Tools and techniques in Health Informatics: Introduction, conditions for telemedicine development, applications, access techniques in telecare and Internet technologies in medical systems: Requirement of Medical systems in the internet environment, internet medical architectures, and internet based telemedical services, next generation point of care information systems, internet access technologies in Telecare. Wireless communication technologies. Electronic Health records (HER): Challenges in clinical care, characteristics of good EHR, Generic EHR representation, EHR Standards and Scope of the HER.

MODULE 4
Decision support systems and Telematic networks in Medicine: Decision support systems, knowledge based and Expert based. Probabilistic and Logical decision systems. Transport layer in telematics networks, health digital data standards, E-health networks services.

MODULE 5
Applications of IT in hearing and chronic problems: Methodology of hearing screening, computer aided adjustment of hearing aids, diagnosis, tinnitus treatment. Application of IT to diagnose chronic conditions patient-centered symptom monitoring. Computer aided techniques in Medicine: Laproscopic surgery navigation, Introoperative imaging, multimodel imaging, Biosignal processing and algorithms. Biosignal databases.

COURSE OUTCOMES
i. Students will gain knowledge about concepts and building blocks of health informatics.
ii. Students will learn about tools and techniques used in health informatics.
iii. Students will gain insights into the applications of IT in health informatics.

TEXT / REFERENCE BOOKS
1. Naakesh A. Dewan, John Luo, Nancy M. Lorenz. Information Technology Essentials for Behavioral Health Clinicians, 2010.
2. Krzysztof Zielinski, Mariusz Duplaga. Technology Solutions for Healthcare, 2006.
3. Moya Conrick, Health Informatics, 2006.
4. Frank Sullivan, Jeremy Wyatt. ABC of Health Informatics, 2009

COMPUTER-AIDED DRUG DISCOVERY
Subject Code : 14BBI422

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of drug design process, methods used for the drug design and role of bioinformatics in drug discovery.

MODULE 1
Drug Design Process: Drug design – Compound searching, Target Identification, ADMET Studies and Study of drug resistance. Drug design process for a known protein target – Structure based drug design process, finding initial hits, Compound refinement, ADMET Studies and Study of drug resistance. Drug design process for unknown protein target – Ligand based drug design process, finding initial hits, Compound refinement, ADMET Studies and Study of drug resistance. Compound Library Design: Target library vs diverse libraries, Non-Enumerative techniques, Drug likeliness and Synthetic accessibility, Analyzing diversity and Spanning known chemistries. Compound selection techniques.

MODULE 2
Homology Modeling and Drug Design: Structure Generation, Retrieval, Structure Visualization. Homology modeling – Constructing an initial model, Refining the model, Manipulating the model, Navigation of the model. Model evaluation – Model evaluation techniques, Concept of energy minimization and Energy minimization techniques. Conformation generation, Deriving bioactive conformations, Molecular superposition and alignment, deriving the Pharmacophoric pattern, receptor mapping and estimating biological activities. Molecular Mimicry and Chemical Intuition – important key and the role of the Molecular Modeling, limitations of Chemical Intuition.

MODULE 3
Molecular Mechanics and Docking: Introduction to Molecular mechanics, Force fields for drug design. Study of protein folding: Algorithms, Conformation analysis. Quantum Mechanics in Drug Design: Quantum Mechanics algorithms in Drug design – Modeling Systems with metal atoms, computing reaction paths and computing spectra. Docking: Introduction, Search algorithms, Scoring functions, Docking Process – Protein Preparation, Building the ligand, setting the bounding box, running the docking calculations. Molecular docking softwares and their utilities in drug design.

MODULE 4
Building the Pharmacophore Models: Components of Pharmacophore model, creating a Pharmacophore model from active compounds, Creating Pharmacophore model from Active site and Searching compound databases. QSAR: Conventional QSAR vs 3D-QSAR, QSAR Process, Molecular descriptors, Automated QSAR Programs. 3D-QSAR – 3D-QSAR Process. ADMET Studies: Oral bioavailability of compound, Finding Drug Half life in the Blood stream, Blood- Brain Barrier permeability and Toxicity studies.

MODULE 5
Computer-Assisted Drug Discovery: Drug Discovery and Development process, New Lead Discovery Strategies. Composition of Drug Discovery teams, Current Practice of CADD in the Pharmaceutical industry, Management structures of CADD groups, Contributions and achievements of CADD groups, Limitations of CADD support, Inherent Limitations of CADD support. State of Current Computational Models, Software and Hardware constraints

COURSE OUTCOMES
i. Students will gain knowledge about drug design process and methods and tools used for the drug discovery.
ii. Students will learn about the computer-assisted drug discovery and various tools used.

TEXT / REFERENCE BOOKS
1. Cancer Drug Design and Discovery by Stephen Neidle, Academic Press – Publisher, 2008.
2. Bioinformatics Technologies by Yi-Ping Phoebe Chen, Springer – Publisher, 2005.
3. Textbook of drug design and discovery by Povl Krogsgaard-Larsen, Tommy Liljefors, Ulf Madsen, Published by Taylor & Francis, 2002.
4. Computational Drug Design: A Guide for Computational and Medicinal Chemists by D. C. Young, Wiley-Interscience, 2009.
5. Moody P.C.E and A.J. Wilkinson. Protein Engineering, IRL Press, Oxford University Press.
6. Protein Science by Arthur M Lesk, Oxford University Press.
7. The Molecular Modeling Perspective in Drug Design by N Claude Cohen, Academic Press.
8. Bioinformatics Methods & Applications: Genomics, Proteomics & Drug Discovery by SC Rastogi, N Mendiratta & P Rastogi, PHI.
9. Drug Discovery Strategies and Methods by Alexandros Makriyannis, Diane Biegel, Marcel Dekker, 2004.
10. Modern Methods of Drug Discovery by Alexander Hillisch, Rolf Hilgenfeld, Birkhäuser, 2003.
11. Wilson and Gisvold’s Textbook of Organic Medicinal and Pharmaceutical Chemistry by Charles Owens Wilson, John H. Block, Ole Gisvold, John Marlowe Beale, Lippincott Williams & Wilkins, 2010.
12. Structure- based drug design by Veerapandian, Pandi Veerapandian, Marcel Dekker, 1997.
13. 3D QSAR in Drug Design by Hugo Kubinyi, Gerd Folkers, Yvonne Connolly Martin, Springer – Publisher, 1998.

RESEARCH METHODOLOGY, BIOSAFETY& IPR
Subject Code : 14BBI41

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
Students enabled to understand and apply the different methodologies of scientific research. Students capable of applying the principles of biosafety guidelines in the use of genetically modified organisms. Students able to define and distinguish between the various types of intellectual property protection in their respective context.

MODULE 1
Concept of Research: Types & classification, steps involved. Identification of the research question and justification for the topic.
Literature Collection: Review of literature, review process and bibliography. Research Objectives and hypothesis.
Research Design: Detailed discussion of the conceptualization and operationalization of variables. Research methods and materials. Research action. Data collection and analysis plan: data gathering – thorough description of methods of data gathering and sources. Scientific writing: Organization and writing of a research papers, short communications, review articles, technical and survey reports, dissertations and books. Organization of reference material, bibliography. Endnote to be discussed with case studies. Research budget and resources.

MODULE 2
Introduction to Intellectual Property Rights: Types of IPR: Patents, Trademarks, Copyright & Related Rights, Issues related to plagiarism in research, copyright laws, acknowledging the sources etc to be discussed with case studies. Basics of Patents and Concept of Prior Art; Introduction to Patents; Types of patent applications: Ordinary, PCT, Conventional, Divisional and Patent of Addition; Specifications: Provisional and complete; Forms and fees Invention in context of “prior art”; Patent databases; Searching International Databases; Country-wise patent searches (USPTO, EPO, PATENTScope, WIPO, IPO, etc.).

MODULE 3
IPR in Research: Traditional Knowledge, Geographical Indications, Protection of GMOs, IP as a factor in R&D; IPs of relevance to Biotechnology and few Case Studies. Patent filing procedures; National & PCT filing procedure; Time frame and cost; Status of the patent applications filed; Precautions while patenting – disclosure/non-disclosure; Financial assistance for patenting – introduction to existing schemes Patent licensing and agreement Patent infringement- meaning, scope, litigation, case studies.

MODULE 4
Biosafety: Introduction & historical background; Primary Containment for Biohazards; Biosafety Levels for Microbes, Plants & Animals; Biosafety guidelines – Government of
India; Definition of GMOs & LMOs: RCGM, GEAC etc. for GMO applications in food and agriculture; Environmental release of GMOs; Risk Analysis; Risk Assessment; Risk
management and communication. Roles of Institutional Biosafety Committees.

MODULE5
Patent laws: History, broad account & latest amendments (if any) of the provisions of Indian Patent Act 1970 & recent amendments, GATT & TRIPS Agreement, Madrid Agreement, Hague Agreement, WIPO Treaties, Budapest Treaty, PCT.

COURSE OUTCOMES
i. Students demonstrate the ability to use research methodology tools in a stepwise, logical fashion.
ii. Students capable of examination and evaluation of biosafety requirements while designing experiments.
iii. Students endowed with the ability to discriminate between different types of IPR safeguards and their mode of application.

TEXT/REFERENCE BOOKS
1. Research Methodology by C R Kothari. New Age International (P) Ltd. 2008.
2. Research Methodology by Kumar. Pearson Education India, 2005.
3. Research Methodology: An Introduction: by Wayne Goddard, Stuart Melville. Juta and Company Ltd, 2004.
4. Research methodology: Techniques and trends by Y.K. Singh. APH Publishing Corporation, 2007.
5. Research Methodology by Sharma KR. National Publishing House, 2004.
6. Research Methodology by D K Bhattacharyya, Excel Publishing Company Ltd. 2007.
7. Indian Patent Act 1970 Acts & Rules by BAREACT, Universal Law.
8. Genetic Patent Law & Strategy by Kankanala C., 1st Edition, Manupatra Information Solution Pvt. Ltd., 2007.
9. Biosafety in Industrial Biotechnology by P. Hambleton, J. Melling, T.T. Salusbury – Springer, 1994.
10. Laboratory biosafety manual by World Health Organization – 2004.
11. Biosafety and Bioethics by Rajmohan Joshi. Isha Books publisher – 2006.
12. Bioethics and Biosafety by M.K. Sateesh. IK International – 2008.

DATABASE MANAGEMENT AND GRID COMPUTING
Subject Code : 14BBI424

IA Marks : 50
No. of Lecture Hrs./ Week : 04 Exam Hrs : 03
Total No. of Lecture Hrs. : 50 Exam Marks : 100

COURSE OBJECTIVES
The objective of this course is to make students learn about concepts of database management systems, issues related to database design and structure. Students will also learn about grid computing and grid architecture.

MODULE 1
Database Management System: Introduction to Nucleic acid and Protein sequence data banks. Data Abstraction and Data Models. Basic concepts of database: Data Independence DML, DCL, DDL and Architecture of DBMS. Entity Relationship diagram. Application of ER diagram in designing database system. Relational Algebra and Tuple Relational Calculus.

MODULE 2
Database Design Issues: Normalization 1NF, 2NF, 3NF, 4NF, BCNF and 5NF and database design problem. Security and Integrity: Use of SQL for specifying Security and integrity. Authorization, View, Encryption. Storage structure indexing and hashing. Different type of file organization. Transaction & Concurrency control – Schedules, Testing, Serializability, Protocols – Lock based Protocol, Time Stamp protocol. Validation technique – Multiple granularity, Multiversion scheme Insert and delete operation, Crash recovery, Log based recovery, Buffer management checkpoints, Shadow paging. Object oriented databases.

MODULE 3
Distributed Database Structure: Design transparency and Autonomy. Distributed Query Processing Recovery – Commit protocol Deadlock handling, Multidatabase system. Parallel database concept and related issues, Web interface to database and Database System Architecture. Distributed Database Structure Implementation: Implementation of networks, Programme Environment. Implementation of Hierarchical database – Hierarchical Data Manipulation Language. Relational database model – Basic principles in relational algebra, Relational Calculus, Domain relational calculus. Introduction, ISBL, SQUARE, SEQUEL, Query by Example, Commercial database systems.

MODULE 4
Grid Computing: Introduction. Grid computing concepts: Exploiting underutilized resources, Parallel CPU Capacity, Virtual resources and Virtual organizations for collaboration. Resource management – Access to additional resources, Resource balancing and resource reliability.

MODULE 5
Grid Architecture: Application Considerations; CPU considerations; Data considerations. Design – Building Grid architecture, Solution objectives, Grid architecture models: Computational Grid and Data grid. Grid Topologies – Intragrid, Extragrid. Conceptual Architecture – Infrastructure, Conceptual Components. Schedulers; Condor. Data sharing; Distributed File Systems: Security. Service Oriented Architecture – Web Services, Convergence of Web Services and Grid Services. Introduction to Globus Toolkit (Open Standards based). Case Studies on Grid Implementation for Life Sciences projects.

COURSE OUTCOMES
i. Students will gain knowledge about concepts database management systems, database design and structure.
ii. Students will learn about grid computing and grid architecture.

TEXT / REFERENCE BOOKS
1. An introduction to database systems by C. J. Date, Addison-Wesley, 2000
2. Database System Concepts by Abraham Silberschatz, Henry F. Korth, S. Sudarshan, McGraw- Hill, 2010.
3. Database systems: design, implementation, and management by Peter Rob, Carlos Coronel, Cengage Learning, 2009.
4. Enterprise grid computing with Oracle by Brajesh Goyal, Shilpa Lawande, McGraw-Hill Professional, 2006.
5. Grid computing: infrastructure, service, and applications by Lizhe Wang, Wei Jie, Jinjun Chen, CRC Press, 2009.

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