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Thursday, November 15, 2007

Computer Network

A computer network is an interconnection of a group of computers. Networks may be classified by what is called the network layer at which they operate according to basic reference models considered as standards in the industry such as the four-layer Internet Protocol Suite model. While the seven-layer Open Systems Interconnection (OSI) reference model is better known in academia, the majority of networks use the Internet Protocol Suite (IP) as their network model
Computer networks may be classified according to the scale: Personal area network (PAN), Local Area Network (LAN), Campus Area Network (CAN), Metropolitan area network (MAN), or Wide area network (WAN). As Ethernet increasingly is the standard interface to networks, these distinctions are more important to the network administrator than the end user. Network administrators may have to tune the network, based on delay that derives from distance, to achieve the desired Quality of Service (QoS). The primary difference in the networks is the size.

Controller Area Networks are a special niche, as in control of a vehicle's engine, a boat's electronics, or a set of factory robots.
Computer networks may be classified according to the hardware technology that is used to connect the individual devices in the network such as Ethernet, Wireless LAN, HomePNA, or Power line communication.
Ethernets use physical wiring to connect devices. Often, they employ the use of hubs, switches, bridges, and routers.Wireless LAN technology is built to connect devices without wiring. These devices use a radio frequency to connect.
Computer networks may be classified according to the network topology upon which the network is based, such as Bus network, Star network, Ring network, Mesh network, Star-bus network, Tree or Hierarchical topology network, etc.
Network Topology signifies the way in which intelligent devices in the network see their logical relations to one another. The use of the term "logical" here is significant. That is, network topology is independent of the "physical" layout of the network. Even if networked computers are physically placed in a linear arrangement, if they are connected via a hub, the network has a Star topology, rather than a Bus Topology. In this regard the visual and operational characteristics of a network are distinct.

m.sc Biotechnology (Banaras Hindu University )

M.Sc. Biotechnology (4 -Semesters w.e.f. July 2004) OUTLINES OF SYLLABUS (Total Credits-90)
1 SEMESTER
Core Courses Course No. 1 Microbiology Course No. 2 Genetics and Molecular Biology
4 credit Course No. 3 Biochemistry & Biophysics 4 credit
Minor Elective Course No. 4 Cell Biology & Virology 3 credit
PRACTICALS Course No. 5 Based on Course No. 1& 4 2.5 credit
Course No. 6 Based on Course No. 2 & 3 2.5 credit 2 ND SEMESTER
Core Courses Course No. 7 Biology of the Immune System 4 credit Course No. 8
Enzymology & Enzyme Technology 4 credit Course No. 9 Genetic Engineering 4 credit
Course No. 10 Environmental Biotechnology 4 Credit
PRACTICALS -
Course No. 11
Based on Course No. 7 & 8
2.5 credit
Course No. 12
Based on Course No. 9 & 10
2.5 credit
Minor Elective
Course No. 13
Term Paper and Seminar
1.5 credit
3
RD
SEMESTER
Core Courses
Course No. 14
Animal Cell Culture
4 credit
Course No. 15
Plant Biotechnology
4 credit
Course No. 16
Bioprocess Engineering & Technology
4 Credit
( 13 )


Minor Elective
Course No. 17
Computer applications, Bio-Informatics & Biostatistics
3 credit
PRACTICALS -
Course No. 18
Based on Course No. 14 & 15
2 credit
Course No. 19
Based on Course No. 16 & 17
2 credit
Minor Elective
Course No. 20
Term Paper and Seminar
1.5 credit
4
TH
SEMESTER
Major Elective
Course No. 21 Course based on Research project and its Seminar in the area of Animal, Plant and
Microbial Biotechnology.
27 Credit
Course No. 1 :
Microbiology
4 credit
1. History of Microbiology, Discovery of the microbial world
2. Isolation, pure culture techniques, Methods of sterilization and Enrichment culture techniques
3. Bacterial identification, nomenclature and classification, New approaches to bacterial taxonomy /
classification including ribotyping and ribosomal RNA sequencing
4. General structure and features, Brief account of all group of bacteria and cyanobacteria, Rickettsia,
Chlamydia and Mycoplasma
Archaea : Archaebacteria and extremophilic microbes – their biotechnological potentials
5. The definition of growth, growth curve, measurement of growth and growth yields, Culture
collection and maintenance of cultures
6. Different modes of nutrition in bacteria, Sulfate reduction, Nitrogen metabolism – nitrate
reduction, nitrifying and denitrifying bacteria, Nitrogen fixation and Microbes used as biofertilizer
7. Viruses : Classification, morphology and composition of viruses in general,
Bacteriophages : φX 174, cyanophages and retroviruses, Viroids and Prions
Course No. 2 :
Genetics and Molecular Biology
4 credit
1. Introduction to cell division, Mendelian Laws and physical basis of inheritance, dominance and its
molecular basis
2. Basics of gene interaction, cis-trans-test and complementation test, lethal genes, polygenic traits,
linkage and gene maps
3. Double helix: Physico-chemical considerations
( 14 )


Page 3
4. Organization of prokaryotic and eukaryotic genomes, supercoiling, repetitive DNA
5. DNA replication: Mechanism of replication of Prokaryotic & Eukaryotic Chromosome
6. Mutation: Types and molecular mechanisms of mutations, mutagens, DNA Repair
7. Transposition: Mechanisms of transposition, role of transposons in mutation
8. Gene transfer in prokaryotes: Transformation, conjugation, transduction, construction of genetic
maps in bacteria
9. Recombination: Homologous and site - specific recombination
10. Gene expression in bacteria: Transcription and its regulation; operons, attenuation, anti-
termination and anti-sense controls
11. Prokaryotic translation machinery, mechanism and regulation of translation
12. Gene expression in eukaryotes: Transcription, general and specific transcription factors, regulatory
elements and mechanism of regulation, processing of transcripts
Course No. 3 :
Biochemistry & Biophysics
4 credit
1. Carbohydrates; Glycolysis, Gluconeogenesis, Krebs’ Cycle, Electron transport chain, Oxidative
Phosphorylation.
2. Fatty acids; general properties and ß- oxidation
3. Nitrogen metabolism: Amino acids (general properties); Amino acid sequencing and composition;
end group analysis
4. Proteins: Protein structure (primary, secondary, tertiary & quaternary), Globular, Fibrous proteins;
Ramachandran plot, Circular Dichroism, Hydrophobic and hydrophilic interactions. PAGE, SDS-
PAGE, Diagonal Electrophoresis
5. Protein folding (Introduction / Tools to study folding – unfolding phenomenon)
6. DNA - protein interactions; DNA-drug interactions
Course No. 4 :
Cell Biology & Virology
3 credit
1. Principles of Microscopy
2. Structure of Cell (Bacterial, Plant and Animal) - Cell membranes, Composition of Cell Wall
3. Structure and function of organelles (Mitochondria, Chloroplast, Nucleus, Golgi apparatus,
Lysosomes, Ribosomes) and Cytoskeletal elements
4. Cell adhesion
5. Basic concepts of signal transduction
6. Transport across biomembranes: facilitated transport, group translocation, Active transport, Na
+
-K
+
ATPase pump
7. Cell cycle and its control
( 15 )


Page 4
8. Oncogenesis
9. Bacterial Viruses : Bacteriophage lambda and Single stranded DNA Phages (M13)
10. Plant Viruses : TMV, CaMV and Gemini Virus
11. Animal Viruses : Baculoviruses
PRACTICALS
Course No. 5 :
Based on Course No. 1& 4
2.5 credit
Course No. 6 :
Based on Course No. 2 & 3
2.5 credit
2
ND
SEMESTER
Course No. 7 :
Biology of the Immune System
4 credit
1. Introduction
- Innate and acquired immunity
- Clonal nature of the immune response
2. Nature of antigens
3. Antibody structure and function
4. Antigen - antibody reactions
5. Major histocompatibility complex
6. Complement system
7. Hematopoiesis and differentiation
8. Regulation of the immune response: Activation of B and T-lymphocytes, Cytokines, T-cell
regulation, MHC restriction, Immunological tolerance
9. Cell-mediated cytotoxicity : Mechanism of cytotoxic T cells and NK cells mediated target cell
lysis, Antibody dependent cell mediated cytotoxicity, macrophage mediated cytotoxicity
10. Hypersensitivity
11. Autoimmunity
12. Transplantation
13. Immunity to infection and tumours
Course No. 8 :
Enzymology & Enzyme Technology
4 credit
1. Classification and nomenclature of enzymes
2. Isolation, purification and large-scale production of enzymes
3. Coenzymes and Cofactors
( 16 )



4. Steady state kinetics: Methods for estimation of rate of enzyme catalyzed reaction with special
reference to Michaelis-Menten equation. Effects of substrate, temperature, pH and inhibitors on
enzyme activity and stability
5. Mechanism of enzyme action ( active site, chemical modification) and regulation (Zymogens,
Isozymes)
6. Enzyme engineering
7. Applications of enzymes
8. Immobilization of Enzymes
Course No. 9 :
Genetic Engineering
4 credit
1. Restriction endonucleases, Modification methylases and other enzymes needed in genetic
engineering
2. Cloning vectors: Plasmids and plasmid vectors, Phages and Phage Vectors, phagemids, cosmids,
artificial chromosome vectors ( YAC, BAC), Animal virus derived vectors - SV40 and retroviral
vectors
3. Molecular cloning: Recombinant DNA techniques, construction of genomic DNA and cDNA
libraries, screening of recombinants
4. Expression strategies for heterologous genes
5. DNA analysis: labeling of DNA and RNA probes. Southern and fluorescence in situ hybridization,
DNA fingerprinting, chromosome walking
6. Techniques for gene expression: Northern and Western blotting, gel retardation technique, DNA
footprinting, Primer extension, Sl mapping, Reporter assays
7. Sequencing of DNA, chemical synthesis of oligonucleotides; techniques of in vitro mutagenesis,
Site-directed mutagenesis, gene replacement and gene targeting
8. Polymerase chain reaction and its applications
9. Use of transposons in genetic analysis: Transposon tagging and its use in identification and
isolation of genes
10. Applications of genetic engineering: Transgenic animals, production of
recombinant
pharmaceuticals, gene therapy, disease diagnosis
11. Biosafety regulation: Physical and Biological containment
Course No. 10 :
Environmental Biotechnology
4 Credit
1. Environment : Basic concepts and issues
2. Environmental pollution: types of pollution, measurement of pollution; Methodology of
environmental management - the problem solving approach, its limitations
3. Air Pollution and its control through Biotechnology
( 17 )


Page 6
4. Water pollution and its Control : Water as a scarce natural resource; Need for water management;
Measurement of water pollution; Sources of water pollution; Wastewater collection; Wastewater
treatment-physical, chemical and biological treatment processes, Microbiology of waster water
treatments; Aerobic processes: Activated sludge, Oxidation ditches, Trickling filter, Towers,
Rotating discs, Rotating drums, Oxidation ponds; Anaerobic processes: Anaerobic processes;
Anaerobic digestion, Anaerobic filters, Upflow anaerobic sludge blanket reactors; Treatment
schemes for wastewaters of dairy, distillary, sugar, antibiotic industries
5. Degradation of Xenobiotic Compounds in Environment - Decay behaviour & degradative
plasmids; Hydrocarbons, Substituted hydrocarbons, Oil pollution, Surfactants, Bioremediation of
contaminated soils
6. Biopesticides; their roles in pest management
7. Solid wastes; Sources and management: composting, wormiculture and methane production, Food,
feed and energy from solid waste (biomass and agrowastes)
8. Global Environmental Problems: Ozone depletion, UV-B and greenhouse effect, Acid rain, its
impact and biotechnological approaches for management
PRACTICALS
Course No. 11 :
Based on Course No. 7 & 8
2.5 credit
Course No. 12 :
Based on Course No. 9 & 10
2.5 credit
Minor Elective
Course No. 13 :
Term Paper and Seminar
1.5 credit
3
RD
SEMESTER
Course No. 14 :
Animal Cell Culture
4 credit
1. Introduction to the balanced salt solutions and simple growth medium. Brief discussion on the
chemical, physical and metabolic functions of different constituents of culture medium
2. Biology and characterization of the cultured cells
3. Measuring parameters of Growth
4. Basic techniques of mammalian cell cultures in vitro
5. Serum & protein free defined media and their applications
6. Measurement of viability and Cytotoxicity
7. Cell synchronization
8. Cell transformation
9. Applications of animal cell culture, Stem cells and their applications, Hybridoma Technology and
Monoclonal antibodies
10. Organ Culture
( 18 )


Page 7
Course No. 15 :
Plant Biotechnology
4 credit
1. Introduction and history. Tissue culture media, Initiation and maintenance of callus and
suspension cultures; single cell clones
2. Biochemical production
3. Organogenesis; somatic embryogenesis; transfer and establishment of whole plants in soil
4. Rapid clonal propagation and production of virus -free plants
5. In vitro pollination; embryo culture and embryo rescue
6. Protoplast fusion, selection of hybrid cells; symmetric and asymmetric hybrids, cybrids.
7. Nuclear cytology of cultured plant cells and somaclonal variations
8. Production of haploid plants and their utilization
9. Cryopreservation and slow growth for germ plasm conservation
10. Gene transfer in nuclear genome and chloroplasts; Agrobacterium-mediated gene transfer, direct
gene transfer, gene silencing
11. Transgenic plants: insect resistance, virus resistance, resistance to fungal / bacterial diseases,
longer shelf life, male sterility
12. Molecular markers: RFLP, RAPD, AFLP, applications of molecular markers
Course No. 16 :
Bioprocess Engineering & Technology
4 Credit
1. Isolation, Preservation and Maintenance of Industrial Microorganisms
2. Microbial Growth and Death Kinetics
3. Media for Industrial Fermentation
4. Air and Media Sterilization
5. Types of fermentation processes - Analysis of batch, Fed-batch and continuous bioreactions,
stability of microbial reactors, analysis of mixed microbial populations, specialized bioreactors
(pulsed, fluidized, photobioreactors etc.,)
6. Measurement and control of bioprocess parameters
7. Downstream Processing
8. Whole cell Immobilization and their Industrial Applications
9. Industrial Production of Chemicals - Ethanol, Acids (citric, acetic and gluconic), solvents
(glycerol, acetone, butanol), Antibiotics (penicillin, streptomycin, tetracycline), Semisynthetic
antibiotics, Aminoacids (lysine, glutamic acid), Single Cell Protein.
10. Use of microbes in mineral beneficiation and oil recovery
11. Introduction to Food Technology
-
Elementary idea of canning and packing
( 19 )


Page 8
-
Sterilization and Pasteurization of Food Products
-
Technology of Typical Food/Food products (bread, cheese, idli)
Course No. 17:
Computer applications, Bioinformatics & Biostatistics
3 credit
1. Introduction of digital computers : Organization ; low-level and high - level languages; binary
number system, Concept of operating system
2. Flow charts and programming techniques
3. Introduction to programming in C
4. Introduction to data structures and database concepts, Overview of DBMS. Network topologies
and protocols, Internet and its applications. Web-enabled services
5. Introduction to MS-OFFICE software, covering Word Processing, Spreadsheets and presentation
software
6. Computer-oriented statistical techniques : Frequency table of single discrete variable, Bubble sort,
computation of mean, variance and standard deviation; t-test, correlation coefficient
7. Introduction to Bio-informatics :
- Definition and Aims,
- Fundamentals of Database searching,
- Computational gene finding – multiple allignment and sequence search,
- Predicting structure and function,
- Molecular Evolution and phylogenetic trees,
PRACTICALS
Course No. 18 :
Based on Course No. 14 & 15
2 credit
Course No. 19 :
Based on Course No. 16 & 17
2 credit
Minor Elective
Course No. 20 :
Term Paper and Seminar
1.5 credit
4
TH
SEMESTER
Course No. 21:
27 Credit
Course based on project and its Seminar in the area of Animal, Plant and Microbial Biotechnology.

M sc PHYSIOLOGY

PHYSIOLOGY — M Sc
OBJECTIVES
The M.Sc. (Physiology) program has the following broad and intermediate objectives:
Broad Objectives
The candidate qualifying for the award of M.Sc. (Physiology) should be able to:
1. demonstrate comprehensive understanding of physiology as well as that of the applied disciplines;
2. demonstrate adequate knowledge of the current developments in medical sciences as related to
physiology;
3. teach undergraduates and postgraduates in physiology;
4. plan and conduct research;
5. plan educational programs in physiology utilizing modern methods of teaching and evaluation; and
6. organize and equip physiology laboratories.
Intermediate Objectives
The candidate qualifying for the award of M.Sc. (Physiology) should be able to:
1. demonstrate comprehensive understanding of the structure, function and development of the human
body as related to physiology,
2. demonstrate elementary understanding of the clinical applications of physiology,
3. critically evaluate the impact of the recent information on the genesis of current concepts related to
various topics of physiology;
4. recapitulate the information imparted to the undergraduate students in physiology;
5. perform and critically evaluate the practical exercises done by undergraduate students;
6. identify a research problem which could be basic, fundamental or applied in nature; define the
objectives of the problem and give a fair assessment as to what is expected to be achieved at the
completion of the project; design and carry out technical procedures required for the study; record
accurately and systematically the observations and analyze them objectively; effectively use statistical
Course and Curriculum of Physiology 19
methods for analyzing the data; interpret the observations in the light of existing knowledge and
highlight in what way his observations have advanced scientific knowledge; write a scientific paper
on the lines accepted by standard scientific journals;
7. design, fabricate and use indigenous gadgets for experimental purposes;
8. demonstrate familiarity with the principles of medical education including definitions of objectives,
curriculum construction, merits and merits of various tools used in the teaching-learning process;
use of learning aids and learning settings, and methods of evaluation;
9. share learning experiences with the undergraduate and postgraduate students using appropriate
pedagogical skills and methods;
10. draw out meaningful curricula for teaching medical and paramedical courses; give lucid, interactive
lectures, presenting the information in a logical, simple and comprehensive manner; generate interest
and curiosity amongst the students during lectures; give practical demonstrations;
11. organize the laboratories for various practical exercises, substitute and fabricate some of the simpler
equipment for teaching purposes; and
12. handle and order for stores, draw up lists of equipment required to equip physiology laboratories
TEACHING PROGRAMME
To achieve the above objectives in three years, we have the following structured programme.
Semester 1
1. Orientation to the department
2. Choosing the subject of thesis and guide
3. Writing the protocol
4. Recapitulation of undergraduate physiology through attending UG lectures
Semester 2
1. Physiology: theory & practical
2. Thesis work
3. Recapitulation of undergraduate physiology through attending UG lectures
Semester 3
1. Physiology: theory & practical
2. Thesis work
Semester 4
1. Physiology: theory & practical
2. Submission of thesis
Physiology: theory & practical
The theory and practical syllabus is completed in four semesters. The department conducts the
semester-wise programme in a cyclic fashion so that no matter at what point a student joins the programme,
20 Syllabus M Sc / M Biotech — AIIMS
he completes the course in two years. The semester-wise programme is as follows:
I. (a) General & Cellular Physiology
(b) Hematology
(c) Renal Physiology & Fluid Balanace
II. (a) Cardio-vascular Physiology
(b) Respiration
(c) Environmental Physiology
III. (a) Nerve & Muscle Physiology
(b) General, Sensory & Motor Physiology
(c) Special Senses
(d) Limbic System and Higher Nervous System
IV. (a) Nutrition & Metabolism
(b) Gastro-intestinal System
(c) Endocrines & Reproduction
Themes and topics
Semester I
(a) General & Cellular Physiology
• Cell as the living unit of the body
• The internal environment
• Homeostasis
• Control systems
• Organization of a cell
• Physical structure of a cell
• Transport across cell membranes
• Functional systems in the cells
• Genetic code, its expression, and regulation of gene expression
• Cell cycle and its regulation
(b) Hematology
• Erthocytes
– erythropoiesis
– structure & function of RBCs
– formation of hemoglobin
– destruction & fate of RBCs
– anemias
– polycythemias
Course and Curriculum of Physiology 21
• Leucocytes
– general characteristics
– genesis & life span of WBCs
– classification & functions of each type of WBC
– leukopenia
– leukemias
• Blood groups
– classification
– antigenicity
– agglutination
– blood typing
– principles of transfusion medicine
• Hemostasis
– components of hemostasis
– mechanisms of coagulation
– coagulation tests
– anticoagulants
• Immunity
– Innate immunity
– Acquired immunity
– Allergy, hypersensitivity and immunodeficiency
– Psychoneuroimmunology
(c) Renal Physiology & Fluid Balance
• Body fluid compartments
• Water balance; regulation of fluid balance
• Urine formation
• Regulation of extracellular sodium & osmolarity
• Renal mechanisms for the control of blood volume, blood pressure & ionic composition
• Regulation of acid-base balance
• Micturition
• Diuretics
• Renal failure
22 Syllabus M Sc / M Biotech — AIIMS
Semester II
(a) Cardio-vascular Physiology
• Properties of cardiac muscle
• Cardiac cycle
• Heart as a pump
• Cardiac output
• Nutrition & metabolism of heart
• Specialized tissues of the heart
• Generation & conduction of cardiac impulse
• Control of excitation & conduction
• Electrocardiogram
• Arrhythmias
• Principles of Hemodynamics
• Neurohumoral regulation of cardiovascular function
• Microcirculation & lymphatic system
• Regional circulations
• Cardiac failure
• Circulatory shock
(b) Respiration
• Functional anatomy of respiratory system
• Pulmonary ventilation
• Alveolar ventilation
• Mechanics of respiration
• Pulmonary circulation
• Pleural fluid
• Lung edema
• Principles of gas exchange
• Oxygen & carbon-dioxide transport
• Regulation of respiration
• Hypoxia
• Oxygen therapy & toxicity
• Artificial respiration
(c) Environmental Physiology
• Physiology of hot environment
• Physiology of cold environment
• High altitude
• Aviation physiology
• Space physiology
• Deep sea diving & hyperbaric conditions
Course and Curriculum of Physiology 23
Semester III
(a) Nerve & Muscle Physiology
• Resting membrane potential
• Action potential
• Classification of nerve fibres
• Nerve conduction
• Degeneration and regeneration in nerves
• Functional anatomy of skeletal muscle
• Neuro-muscular transmission and blockers
• Excitation-contraction coupling
• Mechanisms of muscle contraction
• Smooth muscle
(b) General, Sensory & Motor Physiology
• General design of nervous system
• Interneuronal communication
• Classification of somatic senses
• Sensory receptors
• Sensory transduction
• Information processing
• Dorsal column & medial lemniscal system
• Thalamus
• Somatosensory cortex
• Somatosensory association areas
• Pain
• Organization of spinal cord for motor function
• Reflexes & reflex arc
• Brain stem & cortical control of motor function
• Cerebellum
• Basal ganglia
• Maintenance of posture and equilibrium
• Motor cortex
(c) Special Senses
• Optics of vision
• Receptors & neural functions of retina
• Colour vision
• Perimetry
24 Syllabus M Sc / M Biotech — AIIMS
• Visual pathways
• Cortical visual function
• Functions of external and middle ear
• Cochlea
• Semicircular canals
• Auditory pathways
• Cortical auditory function
• Deafness & hearing aids
• Primary taste sensations
• Taste buds
• Transduction & transmission of taste signals
• Perception of taste
• Peripheral olfactory mechanisms
• Olfactory pathways
• Olfactory perception
(d) Limbic System and Higher Nervous System
• Autonomic nervous system
• Limbic system and hypothalamus
• EEG
• Sleep
• Emotions & Behaviour
• Learning & Memory
• Yoga
Semester IV
(a) Nutrition & Metabolism
• Carbohydrates
• Fats
• Proteins
• Minerals
• Vitamins
• Dietary fibre
• Recommended Dietary Allowances
• Balanced diet
• Diet for infants, children, pregnant & lactating mothers, and the elderly
• Energy metabolism
• Obesity & Starvation
Course and Curriculum of Physiology 25
(b) Gastro-intestinal System
• General principles of G-I function
• Mastication & swallowing
• Esophageal motility
• Salivary secretion
• Gastric mucosal barrier
• Pancreatic & billiary secretion
• Gastrointestinal motility
• Digestion & absorption
• Functions of Colon
• Pathophysiology of peptic ulcer and diarrheal disease
• Liver functions
(c) Endocrines & Reproduction
• Classification of Hormones
• Mechanism of Hormone action
• Measurement of hormones in Blood
• Endocrine functions of the hypothalamus
• Pituitary
• Thyroid
• Adrenals
• The endocrine pancreas
• Pathophysiology of diabetes
• Parathyroid, calcitonin, Vit D & calcium metabolism
• Pineal gland
• Testosterone & male sex hromones
• Spermatogenesis
• Hyper & hypogonadism
• Menstrual cycle
• Female sex hormones
• Pregnancy & Lactation
• Functions of Placenta
• Parturition
• Lactation
26 Syllabus M Sc / M Biotech — AIIMS
Apart from the above topics in general and systemic physiology, the students are introduced to:
1. Biophysics
2. Biochemistry
3. Biostatistics
4. Molecular Biology
5. Medical Education
6. History of Medicine
The above topics are covered through a mix of self-learning and structured program. The structured
program consists of:
1. Seminars every Saturday
The seminars are on a topic belonging to a system scheduled for the semester. The topic is presented in
depth appropriate for postgraduates by one of the M.Sc or M.D. students and moderated by a faculty
member.
The seminars represent only a small and somewhat arbitrary selection of topics. They are not
intended to cover an entire system. Their aims are to:
(a) introduce the system
(b) tune the students to the system
(c) cover recent advances
(d) give students practice in the art of oral presentation
2. Journal clubs and Faculty presentations, every Tuesday
The journal clubs are on an article belonging to a system scheduled for the semester. The article is
presented by an M.Sc./M.D./Ph.D. student or senior demonstrator, and moderated by a faculty member.
The aims of journal clubs are to:
(a) highlight recent advances
(b) discuss classical papers
(c) inculcate the faculty of critical appreciation of a research article
(d) give students and senior demonstrators practice in the art of oral presentation
(e) Faculty presentations are usually on:
(f) medical education
(g) research methodology
(h) an area of research in which the faculty member is involved
3. Practicals
About 8-10 practical exercises are conducted every semester exclusively for M.Sc. (and M.D.) students
on systems scheduled for the semester. The results obtained in these exercises are presented in teaching
meetings (see below).
Besides specially designed P.G. practicals, M.Sc. students perform all undergraduate practicals and
also teach a few of these practicals to the undergraduates.
Course and Curriculum of Physiology 27
4. Teaching meetings, every Saturday
Since M.Sc. students might opt for a teaching career, they are occasionally involved in teaching
undergraduates. In the teaching meetings, the forthcoming practical exercises are discussed, and feedback
on recently held exercises is obtained. These discussions are designed to tune the M.Sc. students to
teaching and related administrative responsibilities. In addition, teaching meetings are also utilized for
discussion of P.G. practicals, research protocols of new P.G. students, presentation of thesis work by
P.G. students prior to submission of the thesis, and any other items of interest to the teaching and
research staff of the department.
ASSESSMENT
In the first three semesters, an end-semester theory, practical and oral examination is conducted by
the department on the systems scheduled for the semester, and a record of the internal assessment
maintained. In the last (4th) semester, the students take the final M.Sc. examination conducted by the
examination section.

M.SC PHARMACOLOGY

PHARMACOLOGY — M Sc
OBJECTIVES
The following self-learning sessions for PG students;
• Post graduate lectures in systemic pharmacology to update various aspects basic pharmacology
and applied therapeutics
• Therapeutic club: To critically analyze the day to day development in new drugs
• Journal club: To familiarize research methodologies and application of statistics in experiments
• Seminars: To update newer developments in pharmacology/emerging trends/ novel mechanisms
of drug action etc.
• Practical exercises: Once in a week, under the supervision of a faculty, with/without the help of
animals, various principles/ mode of drug action/ screening of drugs/ drug analysis using various
techniques should be performed to develop practical skills to conduct similar experiments in future.
• Thesis: Each PG student will carry out research work under the supervision of a faculty member
of the Pharmacology Department. The thesis will be submitted to AIIMS and will be analysed by
suitable experts in that field. The acceptance of the thesis by the institute will be a prerequisite for
the candidate to be allowed to appear in the written/practical examination.
MSc EXAMINATION
Theory examination
Paper- I
General pharmacological principles and allied sciences (section -1)
Paper-II
Systemic pharmacology, chemotherapy and therapeutics (section-2)
Paper-III
Experimental pharmacology, bioassay, statistics, pharmacokinetics and recent advances (section-3)
14 Syllabus M Sc / M Biotech — AIIMS
Practical examination ( 2 days)
(1) One exercise on intact animal**
(2) One exercise on isolated organ**
(3) One chemical pharmacology exercise
Oral examination
(1) Thesis presentation and discussion
(2) General viva voce
SECTION 1
1.a. GENERAL PHARMACOLOGICAL PRINCIPLES AND APPLIED SCIENCES
1.b. TOXICOLOGY
Antidotes in the management of poisoning. Applied analytical toxicology and toxicovigilance.
1.c. MOLECULAR BIOLOGY IN PHARMACOLOGY
Gene expression, Pharmacogenomics, Proteomics, techniques involved in studying receptor dynamics.
PCR, Northern blot, Southern blot and Western blot. Protein purification. Mono, poly clonal antibodies.
Molecular biology in receptor identification. Antisense oligonucleotides, molecular targets of drug action.
1.d. ISOLATION OF COMPOUNDS FROM HERBAL SOURCES
Basic constituents of plants (chemical classification). Isolation of active constituent from plant materials.
Percolation and maceration. Qualitative constituent characterisation techniques. Utilisation of HPTLC
for the constituent analysis. Estimation of marker compound in biological fluid after crude plant material
administration.
Practical skills: Isolation of active principles from medicinal plants
1.e. WONDER DISCOVERIES IN PHARMACOLOGY
Nobel laureates in Pharmacology and their revolutionary discoveries
SECTION 2
SYSTEMIC PHARMACOLOGY
Autonomic nervous system
Central nervous system
Autocoids
Drugs affecting kidney function and Cardiovascular system
Drugs affecting gastrointestinal and respiratory system
Drugs affecting uterine motility
Chemotherapy of parasite infections
Course and Curriculum of Pharmacology 15
Chemotherapy of microbial diseases
Antineoplastic agents
Immunomodulators
Drugs acting on blood and blood forming organs
Hormones
Miscellaneous
Vitamins (water soluble and fat soluble vitamins). Heavy metals and heavy metal antagonists. Ocular and
dermato-pharmacology . Recent developments in Pharmacology time to time. Gene therapy. Therapeutic
gases. Free radical biology and antioxidants, pharmacology of biophosphonates, melatonin-therapeutic
potential. Pharmacotherapy of migraine, Drug therapy in Alzheimer’s disease and male sexual dysfuntion.
Hormone replacement therapy.
SECTION 3
3.a. EXPERIMENTAL PHARMACOLOGY, BIOASSAY AND STATISTICS AND
RECENT ADVANCEMENTS
Experimental methodologies involved in the discovery of drugs (in vivo, in vitro, ex vivo). Animal
handling and animal care. Methods of anaesthetising animals and methods of euthanasia. Restraining and
blood collecting methods. Drug screening methods involved in the evaluation of anti-ulcer, antidepressant,
antianginal, antihypertensive, antiarrhythmic, antidiabetic, anticataract, anti-platelet, anticancer, antiinflammatory,
antidiarrhoeal, antiepileptic, analgesic, antithyroid, antipyretic, antiglaucoma,
antihyperlipidemic antiasthmatics drugs and cough suppressants. Drug screening methods used in screening
antifungal, antihelminthic, antibacterial, antiviral agents, drugs for heart failure, posterior pitutary, adrenal
steroid (gluco & mineralo corticoids), testicular, parathyroid, ovarian, thyroid hormones, Methods involved
in testing teratogenicity, carcinogenicity and organ toxicities in animals.
Practical Skills**: Effect of antiinflammatory agents on caraagennan induced rat paw edema.
Evaluation of analgesic activity of morphine using tail flick latency test. Evaluation of cardiotonic drugs
on isolated rabbit heart (Langendroff isolated heart preparation). Demonstration of Dale’s vasomotor
reversal and nicotinic effect of acetylcholine on dog blood pressure. Effect of autonomic drugs on
rabbit intestine. Demonstration of bronchodilation on guinea pig tracheal chain. Effect of sedatives on
rodents (rotarod test).
Four point assay of histamine and acetylcholine on guinea pig ileum. Four point assay of 5HT on rat
uterus. Estimation of PA2 value of atropine. Identification of unknown by evaluating its action on dog
haemodynamic parameters. Assay of acetylcholine using rat fundus. Estimation of pressor agents on rat
blood pressure.
3.b. INSTRUMENTATION IN DRUG ANALYSIS
Qualitative testing, titrimetric analysis. Beer and Lambert’s law. Basis and working principle of colorimeter,
ultraviolet, atomic absorption spectrometers, Fluorescence spectroscopy, NMR and Mass Spectroscopy.
Basics of Chromatography. Partition, adsorption and ionexchange chromatography. column
chromatography, thin layer chromatography, paper chromatography, immunoabsorbant chromatography,
high performance thin layer chromatography, high performance liquid chromatography and gas
16 Syllabus M Sc / M Biotech — AIIMS
Chromatography. Radio immunoassay. Processing of biological materials for drug analysis. Calculations
in drug analysis. Good laboratory practice. Validation of analytical procedure.
Practial skills: Spectrophoto & flurimetric estimations of drugs in biological fluids.
3.c. BIOSTATISTICS
Calculation of basic statistical parameters (mean, median, mode, standard deviation, standard error
etc.). Null hypothesis, parametric and non parametric tests (Student ‘t test, Wilcoxon, ANOVA
etc.).Metaanalysis.
Practical skills: Calculation for statistical significance in the given data for Student paired and unpaired
`t test. Applying ANOVA to the given set of concentration vs time data of two drug formulations to
comment about their bio-equivalence.
3.d. PHARMACOKINETICS
Basics of pharmacokinetics, calculation of pharmacokinetic estimates (C-max, Tmax, T1/2, AUC(0-n),
AUC(0-μ), Vd, Ke, Ka etc.) Compartment models used in pharmacokinetics (oral and intravenous).
Compartment fitting (one comp & two comp). Pharmcodynamic /pharmacokinetic (PK/PD) correlation.
Practical skills: Calculation of Pharmacokinetic estimates from given concentration vs time data
3.e. DRUG REGULATIONS
Drugs and Cosmetics Act, Drug Price Control order, Application for Investigational New Drug (IND),
Application for New Drug Discovery (NDD) according to Indian Control Authority & USFDA guidelines.
Conducting bio-equivalence studies. Ethical considerations in utilizing human subjects for drug discovery
process. Helsinki’s declaration. ICH-GCP Guidelines. Ethical guidelines in utilising animals for experimental
purposes.
Practical skills: Draft an IND and NDD application for the approval of a numbered compound.
3.f. DRUG DEVELOPMENT PROCESS
Methods involved in the development of new drugs. Preclinical toxicological studies. Calculation of
LD50 & ED50. Acute, subacute and chronic toxicity studies. Irwin profile test, Pre-clinilcal pharmacokinetic
and dynamic studies. Lipinski’s rule for drug like molecule, High throughput screening (invitro and
invivo) for pre-clinical pharmacokinetic and pharmacodynamic studies.
3.g. DRUG DEVELOPMENT PROCESS
Methods involved in the development of new drugs. Preclinical toxicological studies. Calculation of
LD50 & ED50. Acute, subacute and chronic toxicity studies. Irwin profile test, Pre-clinilcal pharmacokinetic
and dynamic studies. Lipinski’s rule for drug like molecule, High throughput screening (invitro and
invivo) for pre-clinical pharmacokinetic and pharmacodynamic studies.
3.h. THERAPEUTIC DRUG MONITORING
Basic principles of TDM. Therapeutic index. Trough level monitoring and dosage adjustments.
Drug delivery systems: sustained release, enteric coated formulations and liposome etc.
Pharmacovigilance, Pharmacoeconomics, Pharmacogenetics And Drug Information
Course and Curriculum of Pharmacology 17
Books recommended
1. Goodman Gillman’s The Pharmacological basis of therapeutics. (2001) Ed. Hardman JG, Limbird
LE (Tenth Edition) McGraw Hill press New York.
2. Applied biopharmaceutics and pharmacokinetics (1999) Ed. Sargel L. (IV Edition) Prentice-Hall
International, London.
3. Fundamentals of experimental pharmacology. (1984) Ed.Ghosh MN. Scientific book agency, Calcutta.
4. Text book of receptor pharmacology. Eds. Forman JC, Johansen TJ CRC Press, New York 1996.
5. Drug Discovery and Evaluation –Pharmacological assays. (1997) Ed.Vogel HG & Vogel WH. Springer-
New York.
Journals to be referred
Trends in Pharmacological Sciences, Annual Review of Pharmacology, Pharmacological Reviews, Indian
Journal of pharmacology, Indian Journal of Physiology and Pharmacology, Annals of Pharmacotherapy,
Pharmacology and Experimetnal Therapeutics, Journal of Ethnopharmacology, Nature, Science, European
Journal of Clinical Pharmacology, BJCP and other pharmacology related jounrals
**PRACTICAL EXERCISE USING ANIMAL EXPERIMENTS IS SUBJECT TO ETHICAL
APPROCAL

M.SC BIOPHYSICS

BIOPHYSICS — M Sc
I. Molecular Biology, Radiation Biophysics, Electronics and Dynamics of Nonlinear Processes
II. Molecular Biophysics: X-ray Crystallography, Spectroscopy, Proteins, Viruses, Nucleic Acids And
Membranes.
III. Mathematical Methods, Quantum Chemistry, Theoretical Modeling And Microsocopy.
IV. Laboratory Experiments.
PAPER – I
MOLECULAR BIOLOGY, RADIATION BIOPHYSICS, ELECTRONICS AND
DYNAMICS OF NONLINEAR PROCESSES
CELL AND MOLECULAR BIOLOGY
Central Dogma, Genetic code, gene and operon, Structure of DNA and RNA, extrachromal elements,
plasmids, selectable markers, gel electrophoresis, polymerase chain reaction (PCR), cloning PCR products,
expression vectors, DNA sequence analysis, cDNA libraries, genomic libraries, applications of molecular
biology methods, using internet resources in molecular biology
RADIATION BIOPHYSICS
Interaction of radiation with matter, ionizing radiation,nonionizing radiation, free radicals, ion pairs,
radiation units and dosimetry, dose effect graphs and target theory, direct and indirect radiation action,
radiation on proteins, nucleic acids, carbohydrates, cell and whole organism, genetic effects of radiation,
repair of radiation induced damage, radiation in diagnosis and therapeutics, protection from radiation.
ELECTRONICS
Passive circuit components, series and parallel circuits, circuit theory, power supplies, amplifiers, emitter
followers, oscillators and basic digital circuits.
10 Syllabus M Sc / M Biotech — AIIMS
DYNAMICS OF NONLINEAR PROCESSES
Physico-mathematical foundations of the dynamics of nonlinear processes, phase plane method, different
modes of excitations, nearly sinusoidal oscillations, building up of oscillations, effect of third harmonic
distortion, Liapounov criteria of stability, limit cycles.
PAPER II
MOLECULAR BIOPHYSICS (X-RAY CRYSTALLOGRAPHY,
SPECTROSCOPY, PROTEINS, VIRUSES, NUCLEIC ACIDS AND
MEMBRANES)
X-RAY CRYSTALLOGRAPHY
X-ray Diffraction
Structure factor expression, electron density equation, phase problems, Patterson function, molecular
replacement method, heavy atom method, isomorphous replacement method, refinement procedure and
interpretation of results.
Data Collection
Methods of data collection of crystal containing small molecule and large molecule, factors affecting the
measurement of integrated intensities, photographic methods, diffractometers, area detectors and image
plates.
SPECTROSCOPY
UV, IR, Raman ORD and CD, spectroscopy, basic principles, instrumenation and use. NMR/ESR:
classical description of magnetic resonance in terms of precession moments, relaxation process, Bloch
equation for line width and shape, spin Hamiltonian, ESR spectrometer, spin labelling in biological
molecules, NMR: spectrometer instrumentation, pulsed and Fourier transformed NMR, scalar and dipolar
broadening, line multiplicity, ring current shifts factors affecting relaxation time, Karplus equation and
use of NMR for conformational study, Mössbauer spectroscopy, resonance absorption in biological
samples. Line shape, line width, chemical shifts, quardruple and magnetic splitting in Mössbauer spectra
of biological molecules.
PROTEINS
Stability of protein structures: flexibility, reversible folding and unfolding, pH titration, chemical
denaturation, thermal denaturation solvent perturbation and chemical modification
Prediction of protein structures: circular dichroism, NMR Methods, Structure-function relationship,
catalysis, Study of three dimensional structures of Trypsin, Trypsinogen, Antibody molecules.
NUCLEIC ACIDS
Introduction of nucleic acids, definition of terms for nucleic acids, old nomenclature, IUPAC-IUB
nomenclature. Basis of Watson Cricks original model Different, base- pairing schemes Unsatisfactory
Course and Curriculum of Biophysics 11
nature of Hoogsteen and other base pairing schemes, biological implication of Watson Crick base pairing
scheme refinement of Watson-Crick model by linked- atom least squares, fiber X- ray diffraction studies,
single crystal X-ray diffraction, and NMR studies on mono- and oligo- nucleotides, DNA polymorphism,
parameters for A-, B-, C-, D- and Z-DNA, definitions of roll, tilt and propeller twist, spectroscopic
study of DNA polymorphism, interaction of DNA with proteins, drugs, dyes and carcinogens, experimental
and theoretical studies on base stacking, hydrogen bonding interactions, structure of RNA, basic
differences between DNA and RNA structures, structure of yeast phenylalanine tRNA.
MEMBRANES
Lipid structure and their organization, phase transitions in lipids, polysaccharides, molecular shapes and
the conformation, comparison between different membrane models, diffusions and permeability, carrier
transport, ion transport, active and passive transport, ion pumps, water transport, use of liposomes for
membrane models and drug delivery systems.
PAPER III
MATHEMATICAL METHODS, QUANTUM BIOLOGY AND MICROSCOPY
MATHEMATICAL METHODS
Review of Fourier Series, Laplace transforms, transforms of derivatives, properties of Laplace transform,
solution of linear ordinary differential equation with Laplace transforms, Fourier transforms, solution of
partial differential equation with method of separation of variables.
QUANTUM CHEMISTRY
Atomic orbital models, the wave equation, molecular orbitals, the LCAO method the overlap, Coulomb
and resonance integrals, the hydrogen molecule, charge distributions, approximate methods.
Theoretical modeling
Basic principle of modeling, Modeling by energy minimization technique, Concept of rotation about
bonds, Energy minimization basic technique for small molecules. Ramachandran plot, Torsional space
minimization. Energy minimization in Cartesian space. Molecular mechanics basic principle. Molecular
dynamics basic principles.
MICROSCOPY
Optical Microscopy
Theory and use of light, fluorescence, phase and polarising microscopes, selection of suitable samples,
and observation in different optical systems, study of living cells, principle and techniques of
photomicroscopy, applications and limitations of optical microscopy.
Electron Microscopy
Principle of electron microscopes, preparation of samples, interpretation of ultrastructure and cell function,
confocal microscopy, atomic force microscopy.
12 Syllabus M Sc / M Biotech — AIIMS
LABORATORY EXPERIMENTS
1. Study of peptide/ligand DNA interaction
2. The determination of unit cell constants and space group of a given Crystal using Weissenberg
method.
3. The determination of unit cell constants and space group of given crystal by precession method
using a layer line screened photgraph.
4. Crystallization of Lysozyme and examination of its crystals in the polarizing microscope.
5. Urea Denaturation of protein
6. (a) Conformational energy plot for tripeptide or dinucleotide monophosphates and obtain lowest
energy conformation.
(b) Determine the geometric parameters for the obtained conformation.
7. Molecular weight determination by SDS PAGE.
8. Plasmid isolation.
9. DNA Electrophoresis
10. PCR

M.SC BIOCHEMISTRY

BIOCHEMISTRY — M Sc
Goal
The program is designed to enable a student acquire sound knowledge in the subject and develop
practical skills to contribute effectively in academics and health sciences research.
OBJECTIVES
1. At the end of the 2 years training in Biochemistry, the PG student is expected to demonstrate sound
knowledge of general concepts and principles of Biochemistry. Evolutionary perspectives of
biomolecules, cell organelles and diversity provided at the molecular level, discuss various aspects
of nutrition and metabolism under different physiological conditions, explain the occurrence, regulation
and interrelationship of metabolic events, identify the molecular/metabolic basis of a disease, explain
concepts of body defense/immunology and detoxification, molecular and cell biology, describe the
principles of various biochemical techniques and instrumentations and analyze and interpret the
data.
2. Plan & conduct lecture, practical demonstrations & tutorial classes.
3. Critically review & comment on research papers
4. Prepare research protocols to conduct experimental studies, analyse, interpret diseases, experimental
results of generate hypothesis.
5. Be familiar with literature survey / computer skills, basic knowledge of biostatistics.
METHODOLOGY
Following methods are used to facilitate learning and training of the students.
A. Theory
1. Tutorials: will be held for 1 hour duration, at least twice a month. The objective is to provide an
opportunity to the students to have interaction with the teachers and gain maximum coordinated
information on the subject.
6 Syllabus M Sc / M Biotech — AIIMS
2. Seminars: will be held once a month. The topics will be chosen from the latest advances in the
subject and also from areas of general biological / biochemical interest. One student will take up a
topic for one seminar, prepare and speak on it. Presentation and discussion will be for 1 hour. By
this exercise, the students will know the advanced developments in the fields & also learn, comprehend
and explain the information they have obtained.
3. Journal club: To develop a) skills of analysis, evaluation and presentation of research papers b)
familiarity with approaches and methodologies of research and c) to update on new development/
emerging trends in biochemistry.
4. Invited lectures: to gain access to recent work by an expert in an area and opportunity for free
interactions with scientists of eminence.
5. Relevant lectures in biotechnology
B. Practicals
Core- Biochemistry.
These will include some of the Biochemistry practicals from the undergraduate course.
Advanced practicals- Biochemistry, cell biology, molecular biology, immunology.
Some of the biochemistry practicals from the undergraduate course and from cell biology and immunology.
Students will attend this weekly biochemistry practicals. Other practicals will be arranged in the Deptt.
laboratories.
Advanced : Biochemistry, cell biology, molecular biology, immunology.
1. Study of the cell – (i) Cell culture, lymphocyte isolation & culture, growth rate studies, staining
techniques (ii) Cell fractionation, homogenization of the tissue, centrifugation, marker enzyme assays
(iii) Microscopy and microphotography.
2. Quantitative assays – (i) Enzyme assays (ii) RIA (iii) ELISA iv) DNA, RNA & proteins
3. Protein fractionation – (i) Salting in and out, gel filtration, electrophoretic separation (ii) Gel
filtration affinity based techniques (iii) SDS-PAGE (iv) Electrophoretic separation of LDH isoenzymes
4. Enzymology – purification of enzyme & its kinetics
5. DNA – (i) Genomic and plasmid DNA isolation (ii) Restriction enzyme digestion (iii) Electrophoresis
(iv) PCR (v) RT-PCR
6. Southern blotting
7. Western blotting
8. Tissue culture
9. Absorption & fluorescence spectroscopy
10. Chromatographic techniques – HPLC, Gel filtration, ion exchange, affinity chromatography
These practicals will give an exposure to the students on the basic techniques as well as advanced
techniques. A researcher uses in his/her studies.
C. Thesis
A small topic based on-going work will well established / standardized parameters.
Course and Curriculum of Biochemistry 7
Oral examination
1. Thesis work presentation and discussion.
2. General viva voce and practical bench viva.
Paper I: Essentials of biochemistry and advances in intermediary metabolism. Duration- 3 hrs, Marks-
100.
(Section 1)
Paper II: Nutrition, vitamins, hormones and immunology. Duration- 3 hrs, Marks-100.
(Section 2)
Paper III: Molecular biology, Biostatistics, Clinical Biochemistry and techniques in Biochemistry. Duration-
3 hrs, Marks-100.
(Section 3)
Practicals: Duration- 2 days, Marks-300.
SECTION 1
PAPER I : Biomolecular, chemical bonding, structure and function of carbohydrates, proteins and lipid,
water and its properties, cell composition, architecture and function, enzymes coenzymes, metabolism
of carbohydrate, amino acids and lipids, in born of metabolism. Principles of thermodynamics, Bioenergetics
and oxidative phosphorylation. Blood clotting – biochemistry, body fluids – pH and acid base
balance and their importance in clinical biochemistry, muscle contraction. Techniques in the study of
proteins, carbohydrates and lipids.
SECTION 2
PAPER II : Hormones – chemistry, mechanism of action and physiological effects. Nutrition and food
assimilation, macronutrients and micronutrients, vitamins and trace elements, chemistry and metabolism
of purines and pyrimidines. Immunology.
SECTION 3
PAPER III : Nucleic acids - structure synthesis, regulation. Molecular biology and recombinant DNA
technology, genomics and proteomics, DNA microarrays, biochemical genetics, environmental
biochemistry. Oncogenes, group factors, biochemistry of cancer, clinical biochemistry. Experimental
techniques in biochemical research and study of cell. Current topics, biostatistics and its application in
research and clinical chemistry, Journal club seminars.
Books recommended
1. Biochemistry Ed Lubert Stryer. W.H. Freeman and Company, New York.
2. Principles of Biochemistry. Ed Lehninger, Nelson and Cox. CBS publishers and distributors.
3. Harper’s Biochemistry. Ed. R.K. Murray, D.K. Granner, P.A. Mayes and V.W. Rodwell. Appleton
and Lange, Stamford, Connecticut.
4. Textbook of Biochemstry with Clinical Correlations. Ed. Thomas M. Devlin.Wiley-Liss Publishers.
8 Syllabus M Sc / M Biotech — AIIMS
5. Genes VI. Ed Benjamin Lewin. Oxford University Press.
6. Tietz Textbook of Clinical Chemistry. Ed Burtis and Ashwood. W.B. Saunders Company.
7. Principles and techniques of practical biochemistry. Ed Keith Wilson and John Walker. Cambridge
University Press.
8. Biochemistry. Ed Donald Voet and Judith G. Voet. John Wiley & sons, Inc.
9. Molecular Cloning- A Laboratory Manual. J. Sambrook, E.F.Fritsch and T.Maniatis. Cold Spring
Harbor Laboratory Press.
10. Molecular Cell Biology, H. Lodish, A. Berk, S.L.Zipursky, P. Matsudaira, D. Baltimore, J. Darnell

M.SC ANATOMY

ANATOMY — M Sc
OBJECTIVES
At the end of the two years of training programme in Master of Science in Anatomy the student should
be able to:-
1. Acquire comprehensive knowledge of structure and functions of human body, ontogeny of human
development and genetic mechanisms involved in normal and abnormal development, knowledge of
light microscopic and ultrastructure of human body. Knowledge of structure and functional correlation
of nervous system and be able to communicate the same clearly and with precision.
2. Inculcate habit of scientific enquiry and be able to identify lacunae in the existing knowledge in a
given area. Acquire knowledge of modern research techniques and be familiar with the recent
advances in human biology.
Learning Activities, Training And Evaluation
During the course students have formal teaching and are trained for teaching and research
I. Didactic teaching:
Topics in gross anatomy, microanatomy, embryology, neuroanatomy, histochemistry, and genetics,
along with related practical sessions.
II Training
Communication skills – journal club, seminars
Hands on experience – techniques in micro, neuro, gross anatomy, embryology, histochemistry,
genetics, electron and confocal microscopy.
Teaching experience – taking UG classes : demonstrations and practicals for one semesters
(six months)
Educational technology – preparation of AV aids for teaching, posters/manuscripts for presentation
in conferences/workshops and publication in journals. Setting objective
questions – SAQs, MCQs and OSPE. Prepare teaching modules &
museum specimens, casts. Participation in organization of symposia/
workshops.
2 Syllabus M Sc / M Biotech — AIIMS
III. Research
Thesis – progress monitoring every semester.
Presenting paper/poster at conferences/Preparing manuscripts for documentation.
Thesis work presentation at regular intervals.
Thesis submission at the end of 1 &1/2 yrs.
IV. Evaluation of training
Written/practical assessment every semester. Feedback on teaching/training programme.
M.Sc. Anatomy Examinations
Final examination at the end of the course has theory, practical and viva-voce.
Theory
Paper-I : Gross Anatomy with evolution and Comparative Anatomy. Gross Anatomy will include
functional Anatomy. (Section-1)
Paper-II : Microscopic Anatomy, Developmental Anatomy and Genetics. (Section-2)
Paper-III : Neuroanatomy including development and microscopic structure (Section-3)
Practical and Viva
1. Histological techniques, identification light and electron microscopic structure of tissues of body.
2. Slides, specimens of developmental anatomy, genetics, neuroanatomy to assess comprehensive
knowledge in these areas.
Viva-voce on gross anatomy, living anatomy, sectional anatomy and neuroanatomy, developmental
anatomy and genetics.
SECTION – 1
GROSS ANATOMY
COURSE CONTENT
Structure of whole human body in detail, including functional, sectional and radiological anatomy.
PRACTICAL
Dissection of entire body. Preparation of museum specimens, casts, plastination, fixation and preservations
of human body.
SECTION – 2
HISTOLOGY AND HITOCHEMISTRY
COURSE CONTENT
1. Cell Biology: Cytoplasm – Cytoplasmic matrix, cell membrane, cell organelles, cytoskeleton, cell
inclusions, cilia and flagella.
Course and Curriculum of Anatomy 3
Nucleus – nuclear envelope, nuclear matrix, DNA and other components of chromatin, protein
synthesis, nucleolus, nuclear changes indicating cell death.
Cell cycle, mitosis, meiosis, cell renewal. Cellular differentiation and proliferation.
2. Tissues of Body: Light and electron microscopic details and structural basis of function, regeneration
and degeneration.
3. The systems/organs of body – Cellular organization, light and electron microscopic features, structurefunction
correlation, and cellular organization.
PRACTICAL
Preparation of histological sections, light microscopy and its applications, electron microscopy and its
applications, confocal microscopy, histological staining -routine and special stains, identification of
normal and abnormal organelles in electron micrographs , three dimensional interpretation, artifacts
identification.
GENETICS
COURSE CONTENT
Normal and abnormal chromosomes, Molecular genetics, developmental genetics, immunogenetics,
population genetics and genetic counselling.
1. Human Chromosomes - Structure, number and classification, methods of chromosome preparation,
banding patterns. Chromosome abnormalities, Autosomal abnormalities – syndromes, Sex
chromosomal abnormalities – syndromes, Molecular and Cytogenetics.
2. Single gene pattern inheritance, Autosomal and Sex chromosomal patterns of inheritance, Intermediate
pattern and multiple alleles, Mutations, Non Mendelian inheritance, Mitochondrial inheritance, Genomic
imprinting, parental disomy.
3. Multifactorial pattern of inheritance: Criteria for multifactorial inheritance, Teratology, Structure of
gene, Molecular Screening, Cancer Genetics – Haematological malignancies, Cancer Genetics,
Pharmacogenetics.
4. Reproduction Genetics- Male infertility, Female Infertility, assisted reproduction, Preimplanation
genetics, Prenatal diagnosis, Genetic Counselling Ethics and Genetics.
PRACTICAL
DNA Isolation from peripheral blood lymphocytes, Polymerase Chain Reaction (PCR), Fluorescence
In-Situ Hybridization (FISH), Chromosomal Analysis
DEVELOPMENTAL ANATOMY
COURSE CONTENT
Gametogenesis, early human development, general and systemic embryology,environmental and genetic
influences on normal growth and development, teratogenesis.
PRACTICAL
Models, specimens of early human development and slides of chick and pig embryos to correlate avian
and mammalian early development with human development. Specimens of congenital malformations.
4 Syllabus M Sc / M Biotech — AIIMS
IMMUNOLOGY
COURSE CONTENT
Immune system and the cell types involved in defense mechanisms of the body. Gross features,
cytoarchitecture, functions, development and histogenesis of various primary and secondary lymphoid
organs in the body. Biological and clinical significance of the major histocompatibility complex of man
including its role in transplantation, disease susceptibility/resistance and genetic control of the immune
response. Common techniques employed in cellular immunology and histocompatibility testing. Molecular
hybridization and PCR technology in immunology research particularly mechanism of antigen presentation,
structural and functional relevance of the T cell receptor, genetic control of the immune response,
molecular basis of susceptibility to disease.
PRACTICAL
Techniques of DNA preparation, electrophoresis and southern blot hybridization.
SECTION – 3
NEUROANATOMY
COURSE CONTENT
Brain and its environment, Development of the nervous system, Neuron and Neuroglia, Somatic sensory
system, Olfactory and optic pathways, Cochleovestibular and gustatory pathways, Motor pathways,
Central autonomic pathways, Hypothalamo-hypophyseal system, Cross sectional anatomy of brain and
spinal cord.
PRACTICAL
Identification of structures in sections of brain stem and spinal cord at different levels. Staining nervous
tissue using Nissl’s staining and other special stains.
RECOMMENDED BOOKS
1. Gray’s Anatomy 38th edition, 1995 reprint in 2000 Williams et al
Churchill Livingstone
2. Wheaters Functional Histology 4th ed.(2000) B. Young and J.Heath
Churchill Livingstone www.med.uc.edu.embryology
3. Histology: A text & atlas 3rd edition (1995) M.H.Ross, E.& L.J.
Williams & Wilkins
4. Medical Embryology 8th edition Jan Langman
William and Wilkins
5. Genetics in medicine 6th edition, 2001 J.S.Thompson &
W.B. Saunders & Co.Philadelphia, London M.W. Thompson
6. Human Neuroanatomy 9th edition, 1996 Stuin J and Carpenter MB
7. Clinical Neuroanatomy for Medical Students Richard S. Snell
Willian and Wilkins

M.SC ANATOMY

ANATOMY — M Sc
OBJECTIVES
At the end of the two years of training programme in Master of Science in Anatomy the student should
be able to:-
1. Acquire comprehensive knowledge of structure and functions of human body, ontogeny of human
development and genetic mechanisms involved in normal and abnormal development, knowledge of
light microscopic and ultrastructure of human body. Knowledge of structure and functional correlation
of nervous system and be able to communicate the same clearly and with precision.
2. Inculcate habit of scientific enquiry and be able to identify lacunae in the existing knowledge in a
given area. Acquire knowledge of modern research techniques and be familiar with the recent
advances in human biology.
Learning Activities, Training And Evaluation
During the course students have formal teaching and are trained for teaching and research
I. Didactic teaching:
Topics in gross anatomy, microanatomy, embryology, neuroanatomy, histochemistry, and genetics,
along with related practical sessions.
II Training
Communication skills – journal club, seminars
Hands on experience – techniques in micro, neuro, gross anatomy, embryology, histochemistry,
genetics, electron and confocal microscopy.
Teaching experience – taking UG classes : demonstrations and practicals for one semesters
(six months)
Educational technology – preparation of AV aids for teaching, posters/manuscripts for presentation
in conferences/workshops and publication in journals. Setting objective
questions – SAQs, MCQs and OSPE. Prepare teaching modules &
museum specimens, casts. Participation in organization of symposia/
workshops.
2 Syllabus M Sc / M Biotech — AIIMS
III. Research
Thesis – progress monitoring every semester.
Presenting paper/poster at conferences/Preparing manuscripts for documentation.
Thesis work presentation at regular intervals.
Thesis submission at the end of 1 &1/2 yrs.
IV. Evaluation of training
Written/practical assessment every semester. Feedback on teaching/training programme.
M.Sc. Anatomy Examinations
Final examination at the end of the course has theory, practical and viva-voce.
Theory
Paper-I : Gross Anatomy with evolution and Comparative Anatomy. Gross Anatomy will include
functional Anatomy. (Section-1)
Paper-II : Microscopic Anatomy, Developmental Anatomy and Genetics. (Section-2)
Paper-III : Neuroanatomy including development and microscopic structure (Section-3)
Practical and Viva
1. Histological techniques, identification light and electron microscopic structure of tissues of body.
2. Slides, specimens of developmental anatomy, genetics, neuroanatomy to assess comprehensive
knowledge in these areas.
Viva-voce on gross anatomy, living anatomy, sectional anatomy and neuroanatomy, developmental
anatomy and genetics.
SECTION – 1
GROSS ANATOMY
COURSE CONTENT
Structure of whole human body in detail, including functional, sectional and radiological anatomy.
PRACTICAL
Dissection of entire body. Preparation of museum specimens, casts, plastination, fixation and preservations
of human body.
SECTION – 2
HISTOLOGY AND HITOCHEMISTRY
COURSE CONTENT
1. Cell Biology: Cytoplasm – Cytoplasmic matrix, cell membrane, cell organelles, cytoskeleton, cell
inclusions, cilia and flagella.
Course and Curriculum of Anatomy 3
Nucleus – nuclear envelope, nuclear matrix, DNA and other components of chromatin, protein
synthesis, nucleolus, nuclear changes indicating cell death.
Cell cycle, mitosis, meiosis, cell renewal. Cellular differentiation and proliferation.
2. Tissues of Body: Light and electron microscopic details and structural basis of function, regeneration
and degeneration.
3. The systems/organs of body – Cellular organization, light and electron microscopic features, structurefunction
correlation, and cellular organization.
PRACTICAL
Preparation of histological sections, light microscopy and its applications, electron microscopy and its
applications, confocal microscopy, histological staining -routine and special stains, identification of
normal and abnormal organelles in electron micrographs , three dimensional interpretation, artifacts
identification.
GENETICS
COURSE CONTENT
Normal and abnormal chromosomes, Molecular genetics, developmental genetics, immunogenetics,
population genetics and genetic counselling.
1. Human Chromosomes - Structure, number and classification, methods of chromosome preparation,
banding patterns. Chromosome abnormalities, Autosomal abnormalities – syndromes, Sex
chromosomal abnormalities – syndromes, Molecular and Cytogenetics.
2. Single gene pattern inheritance, Autosomal and Sex chromosomal patterns of inheritance, Intermediate
pattern and multiple alleles, Mutations, Non Mendelian inheritance, Mitochondrial inheritance, Genomic
imprinting, parental disomy.
3. Multifactorial pattern of inheritance: Criteria for multifactorial inheritance, Teratology, Structure of
gene, Molecular Screening, Cancer Genetics – Haematological malignancies, Cancer Genetics,
Pharmacogenetics.
4. Reproduction Genetics- Male infertility, Female Infertility, assisted reproduction, Preimplanation
genetics, Prenatal diagnosis, Genetic Counselling Ethics and Genetics.
PRACTICAL
DNA Isolation from peripheral blood lymphocytes, Polymerase Chain Reaction (PCR), Fluorescence
In-Situ Hybridization (FISH), Chromosomal Analysis
DEVELOPMENTAL ANATOMY
COURSE CONTENT
Gametogenesis, early human development, general and systemic embryology,environmental and genetic
influences on normal growth and development, teratogenesis.
PRACTICAL
Models, specimens of early human development and slides of chick and pig embryos to correlate avian
and mammalian early development with human development. Specimens of congenital malformations.
4 Syllabus M Sc / M Biotech — AIIMS
IMMUNOLOGY
COURSE CONTENT
Immune system and the cell types involved in defense mechanisms of the body. Gross features,
cytoarchitecture, functions, development and histogenesis of various primary and secondary lymphoid
organs in the body. Biological and clinical significance of the major histocompatibility complex of man
including its role in transplantation, disease susceptibility/resistance and genetic control of the immune
response. Common techniques employed in cellular immunology and histocompatibility testing. Molecular
hybridization and PCR technology in immunology research particularly mechanism of antigen presentation,
structural and functional relevance of the T cell receptor, genetic control of the immune response,
molecular basis of susceptibility to disease.
PRACTICAL
Techniques of DNA preparation, electrophoresis and southern blot hybridization.
SECTION – 3
NEUROANATOMY
COURSE CONTENT
Brain and its environment, Development of the nervous system, Neuron and Neuroglia, Somatic sensory
system, Olfactory and optic pathways, Cochleovestibular and gustatory pathways, Motor pathways,
Central autonomic pathways, Hypothalamo-hypophyseal system, Cross sectional anatomy of brain and
spinal cord.
PRACTICAL
Identification of structures in sections of brain stem and spinal cord at different levels. Staining nervous
tissue using Nissl’s staining and other special stains.
RECOMMENDED BOOKS
1. Gray’s Anatomy 38th edition, 1995 reprint in 2000 Williams et al
Churchill Livingstone
2. Wheaters Functional Histology 4th ed.(2000) B. Young and J.Heath
Churchill Livingstone www.med.uc.edu.embryology
3. Histology: A text & atlas 3rd edition (1995) M.H.Ross, E.& L.J.
Williams & Wilkins
4. Medical Embryology 8th edition Jan Langman
William and Wilkins
5. Genetics in medicine 6th edition, 2001 J.S.Thompson &
W.B. Saunders & Co.Philadelphia, London M.W. Thompson
6. Human Neuroanatomy 9th edition, 1996 Stuin J and Carpenter MB
7. Clinical Neuroanatomy for Medical Students Richard S. Snell
Willian and Wilkins

M.sc Biotechnology AIIMS

M BIOTECH (MASTER OF BIOTECHNOLOGY)
Course No. Subjects
Course 1 Techniques: Instrumentation & Principles
Course 2 Cell Biology
Course 3 Biochemistry
Course 4 Immunology
Course 5 Molecular Biology
Course 6 Computers
Course 7 Structural Biology
Course 8 Dissertation
Course 9 Biostatistics
Course 10 Applied Biotechnology
Course 11 Seminar Series
(I) Immunology Seminars
(II) Mol. Biology, Recombinant DNA technology Seminars
COURSE – 1
TECHNIQUES: INSTRUMENTATION AND PRINCIPLES
THEORY Hours - 25
Spectrophotometry and Colorimetry; Electrophoretic techniques: Proteins Carbohydrates and
Elecrophoretic techniques: Nucleic Acids; Adsorption chromatography partition chromatography &
Affinity chromatography; Ion-exchange chromatography, Gel filtration chromatography and High
performance (Pressure) Liquid chromatography; Radioisotope techniques – Nature of radioactivity,
detection, measurements counters and safety aspects; Laser Confocal Microscopy and Digital Image
Analysis; Centrifugation & Ultracentrifugation; Biosensors in Diagnostics; Animal Tissue Culture;
Course and Curriculum of M Biotech (Master of Biotechnology) 29
Decontamination, Sterilization and disinfection; Radioimmunoassay; Chemical synthesis of nucleic acids;
Enzyme purification and assay techniques; Techniques in cytogenetics; DNA sequencing; PCR Human
Genome Project; DNA microarray; Proteomics; Nanotechnology; Cell separation techniques; Immobilized
enzymes
COURSE – 2
CELL BIOLOGY
THEORY Hours - 16
Protein synthesis; Protein sorting, transport and secretion; Cell to cell communication: Hormones and
receptors; Transport across membranes; Endocytosis and Protein trafficking-I; Cell Structure, Function
and subcellular compartments; cells to molecules to atoms; the inter-disciplinary approach; Cell-interaction:
growth factors, transformation and oncogenes; Energy oriented organelles; mitochondria, cytoplasmic
matrix and cytoskeleton; Endocytosis & protein trafficking-II; Cell fusion, cellular dynamics, Movements
of macromolecules, organelles and whole cells; Biophysical methods of study; Endocytosis & protein
trafficking-III
PRACTICALS Hours - 30
Sample preparation for TEM; Fixation dehydration infiltration embedding and block making; Preparation
of glass knife, Block-trimming and ultramicrotomy; Preparation of coated grids, negative staining and
viewing; Staining of ultrathin sections and TEM viewing; Sample preparation for SEM; Critical point
drying of SEM samples; Sputter coating of SEM samples; SEM viewing; Immunoelectron Microscopy
COURSE – 3
BIOCHEMISTRY
THEORY Hours - 24
Thermodynamics: concept of free energy, entropy, High energy Molecules and their significance; Structure
and function of Biomembranes; Liposomes and their applications; Chemistry, biosynthesis and catabolism
of purines; Chemistry, biosynthesis and catabolism of pyrimidines; Transport mechanisms; Cytoskeletonstructure
and function; Kinetics of reactions and factors that determine that rate of reactions; Protein
structure & function; Factors determining that rate of enzyme catalyzed reactions; Inhibitors and activators
of enzyme catalyzed reactions; Enzyme Kinetics; Methods of regulation of enzyme activity; Regulation
of carbohydrate metabolism; Regulation of Lipid Metabolism; Regulation of Amino acid metabolism and
inborn errors of metabolism; Metabolic Interrelationship; Inborn errors of metabolism clinical disorders
associated with purine and pyrimidine metabolism; Environmental pollution
COURSE 4
IMMUNOLOGY
THEORY Hours - 60
Innate immunity, cell, CD nomenclature; Experimental Systems used in the Immunology; Acquired
immunity, T-cells, B-cells; Immunoglobulins, class, sub-class and structure; Super Antigens and T-Cell
30 Syllabus M Sc / M Biotech — AIIMS
Activation; Antibody combining sites, conformational changes; H.L.A. Part I; Immunoglobulin superfamily,
affinity, avidity; Complement Part I; Antigenecity, Antigenecity & Immunogenecity; Complement Part
II; Antigen Processing & Presentation; Cell-cell interaction, adhesion molecules; Antigen Processing &
Presentation (Contd.); B-cell activation and differentiation – generation of humoral response; Antibody
mediated Effector mechanisms; Cell mediated Effector mechanism; Immunization strategies; Cytokines,
characteristics and function; Monoclonal antibody production; Antibody screening Assays; Cytokine
classes and their biological activities; Characterization of monoclonal antibodies; Human Monoclonal
Antibodies; Cytokine Receptor and Network; T-cell Hybridomas; Chemokines and chemokine receptor;
Cytokine assays-I; Immunoglobulin gene organization; Immunoglobulin gene rearrangement and
expression; T-cell receptors: molecular structure & gene organization of CD2, CD3, CD4 & CD8; MHC
gene organization; New generation antibodies; MHC gene expression and regulation; Immunoregulation
and Apoptosis; Flowcytometry: Principle & Instrumentation; Applications of Flowcytometry; Cell migration
and Homing; Mucosal Immunity; Tumour Immunity; Immunity to Bacteria; Immunity to viruses;
Transplantation Immunity; Immunity to Parasites; Autoimmunity; Strategies of vaccine development
PRACTICALS Hours - 120
Hybridoma Technology; Antibody Purification & Conjugation; Immunofluorescence and Flowcytometry;
CMI; Gel Techniques; ELISA; SDS PAGE/Western blot
COURSE 5
MOLECULAR BIOLOGY
THEORY Hours - 30
mRNA structure and relation to function; Mechanisms of DNA replication; tRNA structure and relation
to function; DNA Repair; Ribosomes and rRNA; Prokaryotic transcription-initiation mechanisms and
sigma factors; Mutations and mutants; Prokaryotic Regulation of gene expression in prokaryotes-I
transcription-elongation and termination mechanisms; Transcription initiation in Eukaryotes-protein coding
genes; Regulation of gene expression in prokaryotes-II; Protein biosynthesis, genetic code; Eukaryotic
transcription of tRNA and rRNA genes; Post transcriptional processing of rRNA; Post transcriptional
processing of tRNA; Components of Translation; Post transcriptional processing of nuclear RNA;
Mechanism of translation
RECOMBINANT DNA TECHNOLOGY
THEORY Hours - 30
Eukaryotic vectors- C3 1 lifecycle and gene regulation; Post-translational modifications; Comparison of
transcription in Prokaryokes, Eukaryokes and Archaea; Introduction of recombinant DNA technology;
Attenuation and Antitermination mechanisms in Bacteria; Enzymes used in recombinent technology I;
Bacterial plasmids; Bacteriophage lambda-I structure & assay; Plasmids: replication and copy number
control; Bacteriophage lambda-II life cycle and gene regulation; Plasmid and Cosmid vectors; Restriction
modification systems in Bacteria; F factor and conjugation; Transformation; Viruses-I; Generalized and
Specialized transduction; Bacteiophage lambda vectors; M-13 based vector; Transposable elements;
Yeast Vectors; E.coli expression systems; Cloning Strategies I; Viruses-II; Cloning Strategies II; Strategies
for Screening DNA libraries; Analysis of Recombinants Part I; Viruses III; Gene Therapy Pt. II; Analysis
Course and Curriculum of M Biotech (Master of Biotechnology) 31
of Recombinants Part II; Molecular genetics in clinical practice; Genetic Counselling
PRACTICALS Hours - 120
1.a. Preparation of buffers, reagents and media etc.; b. Laboratory equipment handling and safety
guidelines, Radiation safety guidelines; 2.a. Isolation and characterization of genomic DNA for E.Coli; b.
Unit determination of restriction enzyme activity; 3.a.Cutting of DNA and clean up of DNA for ligation;
b. Setting up ligation; c. Preparation of culture media, pouring Plates and streaking of E.Coli; d. Evaluation
of transformants and preparation of glycerol stocks; e. Demonstration of electorporation; 4.a. Preparation
of radiolabelled DNA probe (random primer labeling); b. Hybridization, washing & autoradiography
(Cleaning and monitoring work bench for radioactive spill); 5. Induction of Lac operon; 6. Demonstration
of PCR; a. Setting up PCR reaction; b. Analysis of amplified product; 7.a. Minipreparation & digestion
of plasmid DNA; b. Southern transfer of plasmid DNA digest & baking of membrane; 8.a. Phage
unification, titration and preparation of stocks; b. Isolation of phage DNA; 9. Demonstration of DNA
sequencing; a. Setting up sequencing reactions; b. Casting sequencing gel; c. Gel electrophoresis &
autoradiography. d. Reading sequencing from X-ray film
COURSE 6
COMPUTERS
THEORY Hours - 24
Introduction to Computers Science; Introduction to Data-Base; Introduction to Windows; Windows
Application (Word, Excel, PowerPoint and Multimedia); ntroduction to INTERNET & use of Electronic
Mail; Introduction to Medical Informatics & use of Statistical Package; Introduction to UNIX & C;
Computer Aided Teaching & testing
PRACTICALS Hours - 30
Medline, Medlar Search; Usage of statistics for data analysis; Creation of DataBase; Slide Presentation;
Computer Aided Learning
COURSE 7
STRUCTURAL BIOLOGY
THEORY Hours - 24
Introduction to DNA-Structure of Historic and current view; Polymorphism of DNA; Basic principles
of NMR-Vector description; Structural feature of protein; Structural features of protein-DNA and drug-
DNA complexes; Pulse Fourier NMR & relaxation Phenomena; Peptide bonds, non covalent forces in
proteins; Chemical shift and coupling constraints; Chemistry of building blocks, various structural
organization in proteins; Introduction to protein structure: Principle of folding; Hydrophilicity,
hydrophobicity & amphipathicity in proteins; Building blocks: amino acids-L and D configurations;
Peptide bonds, conformation and dihedral angles; Secondary structures of proteins: b pleated sheets and
helical structures; b-turns/hairpin loops/-blends and other non repetitive structures; Tertiary structure
with one example of a globular protein; Basic principles of 2D NMR; Applications of NMR in the study
of Biomolecules; UV-VIS Absorption Spectroscopy; NMR imaging and invivo NMR spectromicroscopy
32 Syllabus M Sc / M Biotech — AIIMS
PRACTICALS Hours - 16
Simulation of A, B and Z forms of DNA using packages; Simulation of a helix, b sheet and turn
conformation of protein; Molecular dynamics simulation of a peptide fragment with known structures
using AMBER; Homology-based modeling of proteins; Simulation of A, B and Z forms of DNA;
Applications of NMR in the study of Biomolecules; Fluorescence Spectroscopy;
COURSE 8
DISSERTATION
DURATION : One Year
Topics of the thesis submitted by M.Biotech students under
various guides for the batch (2000-2002)
1. Evaluation of the utility of the Mycobacterium smegmatis Dormancy model in the assessment of the
M.tuberculosis devR Gene activity.
2. Studies on DevS Protein and devS gene of Mycobacterium tuberculosis.
3. Study of Innate immune response elicited by Natural Killer cells in tuberculosis.
4. Study of histone like protein in mycobacteria.
5. Production and characterization of monoclonal antibodies against Plasmodium vivax recombinant
antigen PvHSP28.
6. Detection of chloroquine resistance in Plasmodium falciparum isolates by cg10 gene based mutation
– specific polymerase chain reaction.
7. Study of cytokine protein expression in oral cancer patients.
8. The role of in-vitro nuclear magnetics resonance spectroscopy (NMR) in the study of breast cancer.
9. A pilot study for detection of CMV DNA from saliva of neonates by polymerase chain reaction.
10. Genetic Polymorphisms of drug metabolizing enzymes CYP2D6 and GST M1 in epileptic patients
undergoing therapy showing idiosyncratic and adverse drug reactions.
11. Immunolocalization of transcription of factors (c-Fos & c-Jun) during development of auditory
brain stem nuclei in domestic chick by immunolectronmicroscopy.
Course 9
Biostatistics
THEORY Hours - 25
Definition of selected terms Scale of measurements Related to statistics; Methods of collecting date;
Presentation of date statistical Tables; Need for reduction of data measures of averages and location;
Measures of dispersion Range quartile deviation mean deviation & relative deviation; Concepts of statistical
population and sample need for sampling studies; Simple procedures of random sampling; Methods of
sampling; Probability : Basic concepts; Basic theorems of probability addition and multiplication theorems;
Conditional probability of Bayes Theorems; Probability distribution definition & applications; Bionominal
distribution and its application; Poisson distribution and its application; Normal distribution and its
Course and Curriculum of M Biotech (Master of Biotechnology) 33
application; Logic of statistical standard error estimation testing of hypothesis; Tests of significance :
Normal deviate tests (Ztest); Student’s “t” test; Chi-Squared test; F. test and analysis of variance;
Correlation concept and applications; Regression concept and application; Statistics in Genetics-; Statistics
in Genetics – II
PRACTICALS Hours - 25
Dia-grams and graphs; Measures of averages and location; Measures of dispersion; Probability; Bionominal
distribution; Poisson distribution; Normal distribution; Normal deviates and students “T” test; Chi-
Squared test; Analysis of variance; Correlation analysis; Regression analysis
COURSE 10
APPLIED BIOTECHNOLOGY
SEMINAR TOPICS Hours - 42
1. Concept to Industry (Biotechnology Industry)
2. Intellectual Property Rights (Biotechnology Industry)
3. Challenging problem in Biology in the new Millenium (Bioinformatics)
4. Molecular technologies for diagnosis of genetic disorders (Clinical Disease)
5. Molecular genetics in clinical practice (Clinical Diagnosis)
6. New generation viral vectors for Gene Therapy (Clinical Therapy)
7. In vivo targeted gene delivery (Clinical Therapy)
8. Ribozymes for therapeutic use in viral infection (Clinical Therapy)
9. Biology of Nitric oxide implications in diagnosis and therapeutics (Therapy)
10. Characterization of a retrotransposable element in Entamoeba histolytica (Mol. Biology)
11. Designing of endothelin receptor antagonists (Mol. Biology)
12. Immuno-Gene therapy in cancer (Immunology)
13. Oncoviruses and Immunity in cervical cancer (Immunology)
14. Applications of immuno flowcytometry in cell death processes (Immunology)
15. Lymphocyte homeostasis (Immunology)
16. Molecular charaterisation of immunodominant allergens and antigens of Aspergillus fumigatus, an
opportunistic fungal pathogen Diagnostic application (Immunology)
17. Molecular mechanism in fungal allergies (Immunology)
18. Viral induced modulation of host immune response (Immunology)
19. Immunological memory (Immunology)
20. Human leukocyte antigen (HLA) Polymorphism in health and disease (Immunology)
21. Induction and maturation of B cells (Immunology)
22. Homing & cytokine polarization in T cells of pulmonary TB patients (Immunology)
23. Phage Display for Antibody Generation-I (Immunology)
34 Syllabus M Sc / M Biotech — AIIMS
24. Phage Display for Antibody Generation-II (Immunology)
25. Molecular mechanism in mammalian fertilization (Cell Biology)
26. Ligand-Receptor Interaction (Cell Biology)
27. Molecular aspects of apoptosis (Cell Biology)
28. Erythrocyte invasion and cyto adherence by Malaria parasite (Cell Biology)
29. How are safe limits for radiation determined? (Radiation)
30. Radiation-carcinogenesis (Radiation)
COURSE 11
SEMINAR SERIES
I : IMMUNOLOGY
SEMINAR TOPICS Hours - 15
Autoimmunity through infection/immunization; B7 superfamily of Co-stimulating molecules; Role of
Toll receptors in innate immunity; Chemokine regulation of immune response; Functional regulation of
lymphocytes by apoptosis; Dendritic cell regulation of Th1 - Th2 development; Genomic views of the
Immune System; NK Cell Receptor Complexes & Signalling pathways; Immunological Basis of Celiac
Disease; Lymphocyte Migration, Homing & Trafficking; Granulysin mediated anti-microbial activity of
T cells.
II : MOLECULAR BIOLOGY, RECOMBINANT DNA TECHNOLOGY
SEMINAR TOPICS Hours - 15
Malaria Genome Project; Interaction transcriptomes in the study of heat pathogen interaction RNA as
enzymes; Molecular basis of Cystic Fibrosis; Cancer genomics and cell cycle; DNA microarrays :
Analysing genome wide expression; Single nucleotide polymorphism and future of molecular medicine;
Eukaryotic transcription factors: DNA binding domains; Protein translocation; TB & Leprosy genomes;
Salient features of human genome.

Sunday, November 11, 2007

Artificial intelligence Section C

Expert Systems
Learning Objectives
After reading this unit you should appreciate the following:
Need and Justification of Expert Systems
Knowledge Acquisition
Case Studies
MYCIN
RI
Top
Need and Justification of Expert Systems
This unit describes the basic architecture of knowledge-based systems with emphasis placed on expert systems. Expert systems are recent product of artificial intelligence. They began to emerge as university research systems during the early 1970s. They have now become one of the most important innovations of AI, since they have been shown to be successful commercial products as well as interesting research tools.
Expert systems have been proven to be effective in a number of problem domains, which normally require the kind of intelligence possessed by a human expert. The areas of application are almost endless. Wherever human expertise is needed to solve a problem, expert systems are most likely of the options sought. Application domain includes law, chemistry, biology engineering, manufacturing, aerospace military operations, finance, banking, meteorology, geology, geophysics and more .The list goes on and on.
In this chapter we explore expert system architectures and related building tools. We also look at a few of the more important application areas as well. The material is intended to acquaint the reader with the basic concepts underlying expert system and to provide enough of the fundamentals needed to build basic systems to pursue further studies and conduct research in the area.
An expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise. An expert system’s knowledge is obtained from expert sources and coded in a form suitable for the system to use in inference or reasoning processes. The expert knowledge must be obtained from specialists or other sources of expertise, such as texts, journal articles, and databases. This type of knowledge usually requires much of training and experience in specialized fields such as medicine, geology, system configuration, or engineering design. Once a sufficient body of expert knowledge has been acquired, it must be encoded in some form, into a knowledge base, then tested, and refined continually throughout the life of the system.
Characteristic Features of Expert Systems
Expert systems differ from conventional computer systems in several important ways.
1. Expert systems use knowledge rather than data to control the solution process. “In the knowledge lies the power” is a theme repeatedly followed and supported through this book. Much of the knowledge used is heuristic in nature rather than algorithmic.
2. The knowledge is encoded and maintained as an entity separate from the control program. As such, it is not compiled together with the control program itself. This permits the incremental addition and modification (refinement) of the knowledge base without recompilation of the control programs. Furthermore, it is possible in some cases to use different knowledge bases with the same control programs to produce different types of expert systems. Such systems are known as Expert System Shell, as they may be loaded with different knowledge bases.
3. Expert systems are capable to explain how a particular conclusion was achieved, and why requested information is needed during a consultation. This is important as it gives the user a chance to assess and understand the system’s reasoning ability, thereby improving the user’s confidence in the system.
4. Expert systems use symbolic representations for knowledge (rules, networks, or frames) and perform their inference through symbolic computations that closely resemble manipulations of natural language. (An exception to this is the expert system based on neutral network architectures.)
5. Expert systems often reason with metaknowledge (knowledge about knowledge) also, their own knowledge limits it’s capabilities.
Top
MYCIN
The development of MYCIN began at Stanford University. MYCIN is an expert system, which diagnoses infectious blood diseases and determines a recommended list of therapies for the patient. As part of the Heuristic Programming Project at Stanford, several projects directly related to MYCIN were also completed including a knowledge acquisition component called THEIRESIUS, a tutorial component called GUIDON, and a shell component called EMYCIN (for Essential MYCIN). EMYCIN was used to build other diagnostic systems including PUFF, a diagnostic expert for pulmonary diseases. EMYCIN also became the design model for several commercial expert system building tools.
MYCIN’s performance improved significantly over a period of several year as additional knowledge was added. Tests indicate that MYCIN’ performance now equals or exceeds that of experienced physicians. The initial MYCIN knowledge base contained about only 200 rules. This number was gradually increased to more than 600 rules by the early 1980s. The added rules significantly improved MYCIN’s performance leading to a 65% success record that compared favorably with experienced physicians who demonstrated only an average 60% success rate.

Subgoaling in MYCIN
MYCIN is a heterogeneous program, consisting of many different modules. There is a part of MYCIN's control structure that performs a quasi-diagnostic function. But the goals to be achieved are not physical goals, involving the movement of objects in space, but reasoning goals that involve the establishment of diagnostic hypothesis.
This section concentrates upon the diagnostic module of MYCIN, giving a simplified account of its function, structure and runtime behavior.
Treating blood infections
Firstly, we need to give a brief description of MYCIN's domain: treatment of blood infections. This description pre-supposes no specialized medical knowledge on the part of the reader. But, as with any expert system, having some understanding of the domain is crucial to understand what the program does.
An 'anti-microbial agent' is any drug designed to kill bacteria or arrest their growth. Some agents are too toxic for therapeutic purposes, and there is no single agent effective against all bacteria. The selection of therapy for bacterial infection can be viewed as a four-part decision process:
Deciding if the patient has a significant infection;
Determining the (possible) organism(s) involved;
Selecting a set of drugs that might be appropriate;
Choosing the most appropriate drug or combination of drugs.
Samples taken from the site of infection are sent to a microbiology laboratory for culture, that is, an attempt to grow organisms from the sample in a suitable medium.
Early evidence of growth may allow a report of the morphological or staining characteristics of the organism. However, even if an organism is identified, the range of drugs it is sensitive to, may be unknown or uncertain.
MYCIN is often described as a diagnostic program, but this is not so. Its purpose is to assist a physician who is not an expert in the field of antibiotics with the treatment of blood infections. In doing so, it develops diagnostic hypotheses and weights them, but it need not necessarily choose between them. Work on MYCIN began in 1972 as collaboration between the medical and AI communities at Stanford University. The most complete single account of this work is Short-life (1976).
There have been a number of extensions, revisions and abstractions of MYCIN since 1976, but the basic version has five components shown in the fig. 8.1 which shows the basic pattern of information flow between the modules.
(1} A-knowledgebase which contains factual and judgmental knowledge about the domain.
(2) A dynamic patient database containing information about a particular case.
(3) A consultation program, which asks questions, draws conclusions, and gives advice about a particular case based on the patient data and the static knowledge.
(4) An explanation program, which answers questions and justifies its advice, using static knowledge and a trace of the program’s execution.
(5) A knowledge acquisition program for adding new rules and changing existing ones.
The system consisting of components (l)-(3) is the problem solving pan of MYCIN, which generates hypotheses with respect to the offending organisms, and makes therapy recommendations based on these hypotheses.

Figure 8.1: Organization of MYCIN
MYCIN's knowledge base
MYCIN's knowledge base is organized around a set of rules of the general form
if condition1 and ... and conditionm hold
then draw conclusion1 and... and conclusionn
encoded as data structures of the LISP programming language
Figure 8.2 shows the English translation of a typical MYCIN rule for inferring class of an organism. This translation is provided by the program itself. Such rules are called ORGRULES and they attempt to cover such organisms as streptococcus, pseudomonas, and entero-bacteria.
The rule says that if an isolated organism appears rod-shaped, stains in a certain way, and grows in the presence of oxygen, then it is more likely to be in the class entero-bacteria. The number 0.8 is called the tally of the rule, which says how certain conclusion is given, that the conditions are satisfied. The use of the tally is explained below. Each rule of this kind can be thought of as encoding a piece of human knowledge whose applicability depends only upon the context established by the conditions of the rule.
The conditions of a rule can also be satisfied with varying degrees of certainty, the import of such rules roughly is as follows:
if condition1 holds with certainty x1 ... and conditionm holds with certainty xm
then draw conclusion1 with certainty y1 and... and conclusionn with certainty yn
where the certainty associated with each conclusion is a function of the combined certainties of the conditions and the tally, which is meant to reflect our degree of confidence in the application of the rule.
In summary, a rule is a premise-action pair and such rules are sometimes called ‘productions' for purely historical reasons. Premises are conjunction of conditions, and their certainty is a function of the certainty of these conditions. Conditions are either proposition, which evaluate the truth or falsehood with some degree of certainty, (for example 'the organism is rod-shaped') or disjunctions of such conditions. Actions are either conclusions to be drawn with some appropriate degree of certainty, for example the identity of some organism, or instructions to be carried out, for example compiling a list of therapies.
We will explore the details of how rules are interpreted and scheduled for application in the following sections, but first we must look at MYCIN's other structures for representing medical knowledge.
IF 1) The stain of the organism is gramneg, and
2) The morphology of the organism is rod, and
3) The aerobicity of the organism is aerobic
THEN There is strongly suggestive evidence (.8) that
the class of the organism is entero-bacteria
A MYCIN ORGRULE for drawing the conclusion enterobacteriaaceae
In addition to rules, the knowledge base also stores facts and definitions in various forms:
simple lists, for example the list of all organisms known to the system;
knowledge tables, which contain records of certain clinical parameters and the values they take under various circumstances, for example the morphology (structural shape) of every bacterium known to the system;
a classification system for clinical parameters according to the context in which they apply, for example whether they are attributes of patients or organisms.
Much of the knowledge not contained in the rules resides in the properties associated with the 65 clinical parameters known to MYCIN. For example, shape is an attribute of organisms which can take on various values, such as 'rod' and 'coccus.' Parameters are also assigned properties by the system for its own purposes. The main ones either (i) help to monitor the interaction with the user, or (ii) provide indexes which guides the application of rules.
Patient information is stored in a structure called the context tree, which serves to organize case data. Figure on next page shows a context tree representing a particular patient, PATIENT-1, with three associated cultures (samples, such as blood samples, from which organisms may be isolated) and a recent operative procedure that may need to be taken into account (for example, because drugs were involved, or because the procedure involves particular risks of infection). Associated with cultures are organisms that are suggested by laboratory data, and associated with organisms are drugs that are effective against them.
Imagine that we have the following data stored in a record structure associated with the node for ORGANISM-1:
GRAM = (GRAMNEG 1.0)
MORPH = (ROD .8) (COCCUS .2)
AIR = (AEROBIC .6)
with the following meaning:
the Gram stain of ORGANISM-1 is definitely Gram negative;
ORGANISM-1 has a rod morphology with certainty 0.8 and a coccus morphology with certainty 0.2;
ORGANISM-1 is aerobic (grows in air) with certainty 0.6.

Figure 8.2: A typical MYCIN context tree
Suppose now that the rule of conclusion above is applied. We want to compute the certainty that all three conditions of the rule
IF 1) the stain of the organism is gramneg, and
2) the morphology of the organism is rod, and
3) the aerobicity of the organism is aerobic
THEN there is strongly suggestive evidence (0.8) that the class of the organism is entero-bacteria.
are satisfied by the data. The certainty of the individual conditions is 1.0, 0.8 and 0.6 respectively, and the certainty of their conjunction is taken to be the minimum of their individual certainties, hence 0.6.
The idea behind taking the minimum is that we are only confident in a conjunction of conditions to the extent that we are confident in its least inspiring element. This is rather like saying that a chain is only as strong as its weakest link. By an inverse argument, we argue that our confidence in a disjunction of conditions is as strong as the strongest alternative, that is, we take the maximum. This convention forms part of a style of inexact reasoning called fuzzy logic.
In the case, we draw the conclusion that the class of the organism is entero-bacteria with a degree of certainty equal to
0.6 x 0.8 = 0.48
The 0.6 represents our degree of certainty in the conjoined conditions, while the 0.8 stands for our degree of certainty in the rule application. These degrees of certainty are called certainty factors (CFs). Thus, in the general case,
CF(action) x CF(premise) x CF(rule).
Where we revisit the whole topic of how to represent uncertainty. It turns out that the CF model is not always in agreement with the theory of probability; in other words, it is not always correct from a mathematical point of view. However, the computation of certainty factors is much more tractable than the computation of the right probabilities, and the deviation does not appear to be very great in the MYCIN application.
MYCIN’s control structure
MYCIN has a top-level goal rule which define the whole task of the consultation system, which is paraphrased below:
IF 1) there is an organism which requires therapy and
2) consideration has been given to any other organisms requiring therapy
THEN compile a list of possible therapies, and determine the best one in this list.
A consultation session follows a simple two-step procedure:
• create the patient context as the top node in the context tree;
• attempt to apply the goal rule to this patient context.
Applying the rule involves evaluating its premise, which involves finding out if there is indeed an organism which requires therapy. In order to find this out, it must first find out if there is indeed an organism present which is associated with a significant disease. This information can either be obtained from the user directly, or via some chain of inference based on symptoms and laboratory data provided by the user.
The consultation is essentially a search through a tree of goals. The top goal at the root of the tree is the action part of the goal rule, that is, the recommendation of a drug therapy. Subgoals further down the tree include determining the organism involved and seeing if it is significant. Many of these subgoals have subgoals of their own, such as finding out the stain properties and morphology of an organism. The leaves of the tree are fact goals, such as laboratory data, which cannot be deduced.
A special kind of structure, called an AND/OR tree, is very useful for representing the way in which goals can be expanded into subgoals by a program. The basic idea is that root node of the tree represents the main goal, terminal nodes represent primitive actions that can be carried out, while non-terminal nodes represent subgoals that are susceptible to further analysis. There is a simple correspondence between this kind of analysis and the analysis of rule sets.
Consider the following set of condition-action rules:
if X has BADGE and X has GUN, then X is POLICE
if X has REVOI.VER or X as PISTOL or X has RIFLE, then X has GUN
if X has SHIELD, then X has BADGE
We can represent this rule set in terms of a tree of goals, so long as we maintain the distinction between conjunctions and disjunctions of subgoals. Thus, we draw an arc between the links connecting the nodes BADGE and GUN with the node POLICE, to signify that both subgoals BADGE and GUN must be satisfied in order to satisfy the goal POLICE. However, there is no arc between the links connecting REVOLVER and PISTOL and RIFLE with GUN, because satisfying either of these will satisfy GUN. Subgoals as BADGE can have a single child, SHIELD, signifying that a shield counts as a badge.
The AND/OR tree in Figure 8.3 can be thought of as a way of representing the search space for POLICE, by enumerating the ways in which different operators can be applied in order to establish POLICE as true.

Figure 8.3: Representing a rule set as an AND/OR tree
This kind of control structure is called backward chaining, since the program reasons backward from what it wants to prove towards the facts that it needs, rather than reasoning forward from the facts that it possesses. In MYCIN, goals were achieved by breaking them down into subgoals to which operators could be applied. Searching for a solution by backward reasoning is generally more focused than forward chaining, as we saw earlier, since one only considers potentially relevant facts.
MYCIN's control structure uses an AND/OR tree, and is quite simple as AI programs go;
(1) Each subgoal set up is always a generalized form of the original goal. So, if the subgoal is to prove the proposition that the identity of the organism is E. Coli, then the subgoal actually set up is to determine the identity of the organism. This initiates an exhaustive search on a given topic, which collects all of the available evidence about organisms.
(2) Every rule relevant to the goal is used, unless one of them succeeds with certainty. If more than one rule suggest a conclusion about a parameter, such as the nature of the organism, then their results are combined. If the evidence about a hypothesis falls between -0.2 and +0.2, it is regarded as inconclusive, and the answer is treated as unknown.
(3) If the current subgoal is a leaf node, then attempt to satisfy the goal by asking the user for data. Else set up the subgoal for further inference, and go to (1).
The selection of therapy takes place after this diagnostic process has run its course. It consists of two phases: selecting candidate drugs, and then choosing a preferred drug, or combination of drugs, from this list.
Evidence Combination
In MYCIN, two or more rules might draw conclusions about a parameter with different Weights of evidence. Thus one rule might conclude that the organism is E. Coli with a certainty of 0.8, while another might conclude from other data that it is E. Coli with a certainty of 0.5 or – 0.8. In the case of a certainty less than zero, the evidence is actually against the hypothesis.
Let X and Y be the weights derived from the application of different rules. MYCIN combines these weights using the following formula to yield the single certainty factor.

where |X| denotes the absolute value of X.
One can see what is happening on an intuitive basis. If the two pieces of evidence both confirm (or disconfirm) the hypothesis, then confidence in the hypothesis goes up (or down). If the two pieces of evidence are in conflict, then the denominator dampens the effect.
This formula can be applied more than once, if several rules draw conclusions about the same parameter. It is commutative, so it does not matter in what order weights are combined.
IF the identity of the organism is pseudomonas
THEN I recommend therapy from among the following drugs:
1 CCLISTIN (.98)
2 POLYMYXIN (.96)
3 QENTAMICIN (.96)
4 CARBENICILLIN (.65)
5 SULFISOXAZOLE (.64)

A MYCIN therapy rule
The special goal rule at the top of the AND/OR tree does not lead to a conclusion, but instigates actions, assuming that the conditions in the premise are satisfied. At this point, MYCIN's therapy rules for selecting drug treatments come into play; they contain sensitivities information for the various organisms known to the system. A sample therapy rule is given above.
The numbers associated with the drug are the probabilities that a pseudomonas will be sensitive to the indicated drug according to medical statistics. The preferred drug is selected from the list according to criteria, which attempts to screen for contra-indications of the drug and minimize the number of drugs administered, in addition to maximizing sensitivity. The user can go on asking for alternative therapies until MYCIN runs out of options, so the pronouncements of the program are not definitive.
Applications of Expert System
Since the introduction of these early expert systems, the range and depth of applications has broadened dramatically. Applications can now be found in almost all areas of business and government. They include such areas as
Different types of medical diagnoses (internal medicine, pulmonary diseases, infectious, blood diseases, and so on)
Diagnosis of complex electronic and electromechanical system
Diagnosis of diesel electric locomotion systems
Diagnosis of software development projects.
Planning experiments in biology, chemistry, and molecular genetics
Forecasting crop damage
Identification of chemical compound structures and chemical compounds.
Location of faults in computer and communications systems
Scheduling of customer orders, job shop production operations, computer resources for operating system, and various manufacturing tasks.
Evaluation of loan applicants for lending institutions
Assessment of geologic structures from dip meter logs.
Analysis of structural systems for design or as a result of earthquake damage
The optimal configuration of components to meet given specifications for a complex system (like computers or manufacturing facilities)
Estate planning for minimal taxation and other specified goals.
Stock and bond portfolio selection and management
The design of very large scale integration (VLSI) systems
Numerous military applications ranging battlefield assessment to ocean surveillance.
Numerous applications related to space planning and exploration
Numerous areas of law including civil case evaluation, product liability, assault and battery, and general assistance in locating different law precedents.
Planning curricula for students.
Teaching students specialized tasks (like trouble-shooting equipment faults)
Importance of Expert Systems
The value of expert systems was well established by the early 1980s. A number of successful applications had been completed by then and they proved to be cost effective. An example, which illustrates this point well is the diagnostic system developed by the Campbell Soup Company.
Campbell Soup use large sterilizers or cookers to cook soups and other canned products at eight plants located throughout the country. Some of the larger cookers hold up to 68,000 cane of food for short periods of cooking time. When difficult maintenance problems occur with the cookers, the fault must be found and corrected quickly or the batch of foods being prepared will spoil. Until recently, the company had been depending on a single expert to diagnose and cure the more difficult problems, flying him to the site when necessary. Since this individual will retire in a few years taking his expertise with him, the company decided to develop an expert system to diagnose these difficult problems.
After some months of development with assistance from Texas Instruments, the company developed an expert system, which ran on a PC. The system has about 150 rules in its knowledge base to diagnose the more complex cooker problems. The system has also been used to provide training to new maintenance personnel. Cloning multiple copies for each of the eight locations cost the company only a few pennies per copy. Furthermore, the system cannot retire, and its performance can continue to be improved with the addition of more rules. It has already proven to be a real asset to the company. Similar cases now abound in many diverse organizations.
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Representing and Using Domain Knowledge
Expert systems are complex AI programs. However, the most widely used way of representing domain knowledge in expert systems is, as a set of production rules, which are often coupled with a frame system that defines the objects that occur in the rules. Let's look at a few additional examples drawn from some other representative expert systems. All the rules we show are English versions of the actual rules that the systems use. Differences among these rules illustrate some of the important differences in the ways that expert systems operate.
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RI
RI (sometimes also called XCON) is a program that configures DEC VAX systems. Its rules look like this:
If: The most current active context is distributing massbus devices, and
There is a single-port disk drive that has not been' assigned to a massbus, and
The number of devices that each massbus should support is known, and
There is a massbus that has been assigned at least
One disk drive and that should support additional disk drives and
The type of cable needed to connect the disk drive to the previous device on the massbus is known
then
Assign the disk drive to the massbus.
Notice that Rl's rules, unlike MYCIN's, contain no numeric measures of certainty. In the task domain with which RI deals, it is possible to state exactly the correct thing to be done in each particular set of circumstances (although it may require a relatively complex set of antecedents to do so). One reason for this is that there exists a good deal of human expertise in this area. Another is that since RI is doing a design task (in contrast to the diagnosis task performed by MYCIN), it is not necessary to consider all possible alternatives; one good one is enough. As a result, probabilistic information is not necessary in RI.
PROSPECTOR is a program that provides advice on mineral exploration. Its rules look like this:
If: Magnetite or pyrite in disseminated or vein let form is present
then (2, -4) there is favourable mineralization and texture for the propylitic stage.
In PROSPECTOR, each rule contains two confidence estimates. The first indicates the extent to which the presence of the evidence described in the condition part of the rule suggests the validity of the rule's conclusion. In the PROSPECTOR rule shown above, the number 2 indicates that the presence of the evidence is mildly encouraging. The second-confidence estimate measures the extent to which the evidence is necessary to the validity of the conclusion, or stated another way, the extent to which the lack of the evidence indicates that the conclusion is not valid. In the example rule shown above, the number -4 indicates that the absence of the evidence is strongly discouraging for the conclusion.
DESIGN ADVISOR is a system that critiques chip designs. Its rules look like:
If The sequential 'level count of ELEMENT is greater than 2, UNLESS the signal of ELEMENT is resetable
then Critique for poor resetability
DEFEAT Poor resetability of ELEMENT
due to Sequential level count of ELEMENT greater than 2
by ELEMENT is directly resetable
The DESIGN ADVISOR gives advice to a chip designer, who can accept or reject the advice. If the advice is rejected, then system can exploit a justification-based truth maintenance system to revise its model of the circuit. The first rule shown here says that an element should be criticized for poor resetability if its sequential level count is greater than two, unless its signal is currently believed to be resetable. Resetability is a fairly common condition, so it is mentioned explicitly in this first rule. But there is also a much less common condition, called direct resetability. The DESIGN ADVISOR does not even bother to consider that condition unless it gets in trouble with its advice. At that point, it can exploit the second of the rules shown above. Specifically, if the chip designer rejects a critique about resetability and if that critique was based on a high level count, then the system will attempt to discover (possibly by asking the designer) whether the element is directly resetable. If it is, then the original rule is defeated and the conclusion withdrawn.

Reasoning with the Knowledge
As these example rules have shown, expert systems exploit many of the representation and reasoning mechanisms that we have discussed. Because these programs are usually, written primarily as rule-based systems, forward chaining, backward chaining, or some combination of the two, is usually used. For example, MYCIN used backward chaining to discover what organisms were present; then it used forward chaining to reason from the organisms to a treatment regime. RI, on the other hand, used forward chaining. As the field of expert systems matures, more systems that exploit other kinds of reasoning mechanisms are being developed. The DESIGN ADVISOR is an example of such a system; in addition to exploiting rules, it makes extensive use of a justification-based truth maintenance system.
Expert System Shells
Initially, each expert system that was built was created from scratch, usually in LISP. But, after several systems had been built this way, it became clear that these systems often had a lot in common. In particular, since the systems were constructed as a set of declarative representations (mostly rules) combined with an interpreter for those representations, it was possible to separate the interpreter from the domain-specific knowledge and thus to create a system that could be used to construct new expert systems by adding new knowledge corresponding to the new problem domain. The resulting interpreters are called shells. One influential example of such a shell is EMYCIN (for Empty MYCIN), which was derived from MYCIN.
There are now several commercially available shells that serve as the basis for many of the expert systems currently being built. These shells provide much greater flexibility in representing knowledge and in reasoning with it than MYCIN did. They typically support rules, frames, truth maintenance systems, and a variety of other reasoning mechanisms.
Early expert system shells provided mechanisms for knowledge representation, reasoning, and explanation. Later, tools for knowledge acquisition were added. Expert system shells needed to do something else as well. They needed to make it easy to integrate expert systems with other kinds of programs. Expert systems cannot operate in a vacuum, any more than their human counterparts can. They need access to corporate databases, and access to them needs to be controlled just as it does for other systems. They are often embedded within larger application programs that use primarily conventional programming techniques. So one of the important features that a shell must provide is an easy-to-use interface between an expert system that is written with the shell and a larger, probably more conventional, programming environment.
Student Activity 8.1
Before reading next section, answer the following questions.
What problems does an expert system solve?
Discuss the characteristics and applications of Expert Systems.
Distinguish between RI and MYCIN.
If your answers are correct, then proceed to the next section.
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Explanation
In order for an expert system to be an effective tool, people must be able to interact with it easily. To facilitate this interaction, the expert system must have the following two capabilities in addition to the ability to perform its underlying task:
1. Explain its reasoning. In many of the domains in which expert systems operate, people will not accept results unless they have been convinced of the accuracy of the reasoning process that produced those results. This is particularly true, for example, in medicine, where a doctor must accept ultimate responsibility for a diagnosis, even if that diagnosis was arrived at with considerable help from a program. Thus it is important that the reasoning process used in such programs proceed in understandable steps and that enough meta-knowledge (knowledge about the reasoning process) be available so the explanations of those steps can be generated.
2. Acquire new knowledge and modifications of old knowledge. Since expert systems derive their power from the richness of the knowledge bases they exploit, it is extremely important that those knowledge bases be as complete and as accurate as possible. But often there exists no standard codification of that knowledge; rather it exists only inside the heads of human experts. One way to get this knowledge into a program is through interaction with the human expert. Another way is to have the program learn expert behavior from raw data.
TEIRESIAS was the first program to support explanation and knowledge acquisition. TEIRESIAS served as a front-end for the MYCIN expert system. A fragment of a TEIRESIAS-MYCIN conversation with a user (a doctor) is shown in figure 8.4. The program has asked for a piece of information that it needs in order to continue its reasoning. The doctor wants to know why the program wants the information, and later asks how the program arrived at a conclusion that it claimed it had reached.

Is the patient’s illness with Organism-1 a hospital-acquired infection?
**Why
[i.e., Why it is important to determine whether or not the infection with Organism-1 was acquired while the patient was hospitalized?]
[2.0] This will aid in determining the category of
Organism-1. It has already been established that
[2.1] the gram strain of Organism-1 is gramneg, and
[2.2] the morphology of Organism-1 is rod, and
[2.3] the aerobicity of Organism-1 is facultative Therefore if
[2.4] the infection with Organism-1 was not acquired while the patient was hospitalized, then

There is strongly suggestive evidence (.8) that the category of Organism-1 is entero-bacteria.

[Also: there is weakly suggestive evidence (.1) that the identity of Organism-1 is pseudomonas-aeruginosa] [Rule037]
** How [2.3]
[i.e., How was it established that the aerobicity of Organism-1 is facultative?]
The following were used:
[3.1] Rule027 indicated there is strongly suggestive evidence (.8) that the aerobicity of Organism-1 is facultative.
[3.2] Rule033 indicated there is weakly suggestive evidence (.2) that the aerobicity of Organism-1 is not facultative.

Since this gave a cummulative CF of (.6) for facultative, the evidence indicates that the aerobicity of Organism-1 is facultative.
Figure 8.4: A Portion of a Dialogue with TEIRESIAS
An important premise underlying TEIRESIAS's approach to explanation is that the behavior of a program can be explained simply by referring to a trace of the program's execution. There are ways in which this assumption limits the kinds of explanations that can be produced, but it does minimize the overhead involved in generating each explanation. To understand how TEIRESIAS generates explanations of MYCIN's behavior, we need to know how that behavior is structured.
MYCIN attempts to solve its goal of recommending a therapy for a particular patient by first finding the cause of the patient's illness. It uses its production rules to reason backward from goals to clinical observations. To solve the top-level diagnostic goal, it looks for rules whose right sides suggest diseases. It then uses the left sides of those rules (the preconditions) to set up subgoals whose success would enable the rules to be invoked. These subgoals are again matched against rules, and their preconditions are used to set up additional subgoals. Whenever a precondition describes a specific piece of clinical evidence, MYCIN uses that evidence if it already has access to it. Otherwise, it asks the user to provide the information. In order that MYCIN's requests for information will appear coherent to the user, the actual goals that MYCIN sets up are often more general than they need be to satisfy the preconditions of an individual rule. For example, if a precondition specifies that the identity of an organism is X, MYCIN will set up the goal "infer identity." This approach also means that if another rule mentions the organism-1's identity, no further work will be required, since the identity will be known.
We can now return to the trace of TEIRESIAS-MYCIN's behavior shown in Figure above. The first question that the user asks is a "WHY" question, which is assumed to mean, "Why do you need to know that?" Particularly for clinical tests that are either expensive or dangerous, it is important for the doctor to be convinced that the information is really needed before ordering the test. (Requests for sensitive or confidential information present similar difficulties.) Because MYCIN is reasoning backward, the question can easily be answered by examining the goal tree. Doing so provides two kinds of information:
1. What higher-level question might the system be able to answer if it had the requested piece of information? (In this case, it could help determine the category of ORGANISM-1.)
2. What other information does the system already have that makes it think that the requested piece of knowledge would help? (In this case, facts [2.1] to [2.4].)
When TEIRESIAS provides the answer to the first of these questions, the user may be satisfied or may want to follow the reasoning process back even further. The user can do that by asking additional "WHY" questions.
When TEIRESIAS provides the answer to the second of these questions and tells the user what it already believes, the user may want to know the basis for those beliefs. The user can ask this with a "HOW" question, which TEIRESIAS will interpret as "How did you know that?" This question can also be answered by looking at the goal tree and chaining backward from the stated fact to the evidence that allowed a rule that determined the fact to fire. Thus we see that by reasoning backward from its top-level goal and by keeping track of the entire tree that it traverses in the process, TEIRESIAS- MYCIN can do a fairly good job of justifying its reasoning to a human user.
The production system model is very general, and without some restrictions, it is hard to support all the kinds of explanations that a human might want. If we focus on a particular type of problem solving, we can ask more probing questions. For example, SALT is a knowledge acquisition program used to build expert systems that design artifacts through a propose-and-revise strategy. SALT is capable of answering questions like WHY-NOT ("why didn't you assign value x to this parameter?") and WHAT-IF ("what would happen if you did?"). A human might ask" these questions in order to locate incorrect or missing knowledge in the system as a precursor to correcting it. We now turn to ways in which a program such as SALT can support the process of building and refining knowledge.
Student Activity 8.2
Before reading next section, answer the following questions.
What is the role of Expert System shells?
What are the chance TERISTIES of a knowledge acquisition system?
Contrast expert system and neural networks in terms of knowledge representation and knowledge acquisition. Give one domain in which the expert system approach would be more promising and one domain in which the neural network approach is more promising.
If your answers are correct, then proceed to the next section.
Knowledge Acquisition
How are expert systems built? Typically, a knowledge engineer interviews a domain expert to elucidate expert knowledge, which is then translated into rules. After the initial system is built, it must be iteratively refined until it approximates expert-level performance. This process is expensive and time-consuming, so it is worthwhile to look for more automatic ways of constructing expert knowledge bases. While no totally automatic knowledge acquisition systems yet exist, there are many programs that interact with domain experts to extract expert knowledge efficiently. These programs provide support for the following activities:
1. Entering knowledge
2. Maintaining knowledge base consistency
3. Ensuring knowledge base completeness
The most useful knowledge acquisition programs are those that are restricted to a particular problem-solving paradigm, e.g., diagnosis or design. It is important to be able to enumerate the roles that knowledge can play in the problem-solving process. For example, if the paradigm is diagnosis, then the program can structure its knowledge base around symptoms, hypotheses, and causes. It can identify symptoms for which the expert has not yet provided causes. Since one symptom may have multiple causes, the program can ask for knowledge about how to decide when one hypothesis is better than another. If we move to another type of problem solving, say designing artifacts, then these acquisition strategies no longer apply, and we must look for other ways of profitably interacting with an expert. We now examine two knowledge acquisition systems in detail.
MOLE is a knowledge acquisition system for heuristic classification problems, such as diagnosing diseases. In particular, it is used in conjunction with the cover-and-differentiate problem-solving method. An expert system produced by MOLE accepts input data, comes up with a set of candidate explanations or classifications that cover (or explain) the data, then uses differentiating knowledge to determine which one is best. The process is iterative, since explanations must themselves be justified, until ultimate causes are ascertained.
MOLE interacts with a domain expert to produce a knowledge base that a system called MOLE-p (for MOLE-performance) uses to solve problems. The acquisition proceeds through several steps:
1. Initial knowledge base construction. MOLE asks the expert to list common symptoms or complaints that might require diagnosis. For each symptom, MOLE prompts for a list of possible explanations. MOLE then iteratively seeks out higher-level explanations until it comes up with a set of ultimate causes. Whenever an event has multiple explanations, MOLE tries to determine the conditions under which one explanation is correct. The expert provides covering knowledge, that is, the knowledge that a hypothesized event might be the cause of a certain symptom. MOLE then tries to infer anticipatory knowledge, which says that if the hypothesized event does occur, then the symptom will definitely appear. This knowledge allows the system to rule out certain hypotheses on the basis that specific symptoms are absent.
2. Refinement of the knowledge base. MOLE now tries to identify the weaknesses of the knowledge base. One approach is to find holes and prompt the expert to fill them. It is difficult in general, to know whether a knowledge base is complete, so instead MOLE lets the expert watch MOLE-p solving sample problems. Whenever MOLE-p makes an incorrect diagnosis, the expert adds new knowledge. There are several ways in which MOLE-p can reach the wrong conclusion. It may incorrectly reject a hypothesis because it does not feel that the hypothesis is needed to explain any symptom. It may advance a hypothesis because it is needed to explain some otherwise inexplicable hypothesis. Or it may lack differentiating knowledge for choosing between alternative hypotheses.
For example, suppose we have a patient with symptoms A and B. Further suppose that symptom A could be caused by events X and ¥, and that symptom B can be caused by Y and Z. MOLE-p might conclude Y, since it explains both A and B. If the expert indicates that this decision was incorrect, then MOLE will ask what evidence should be used to prefer X and/or Z over Y.
MOLE has been used to build systems that diagnose problems with car engines, problems in steel-rolling mills, and inefficiencies in coal-burning power plants. For MOLE to be applicable, however, it must be possible to preenumerate solutions or classifications. It must also be practical to encode the knowledge in terms of covering and differentiating.
But suppose our task is to design an artifact, for example, an elevator system. It is no longer possible to pre-enumerate all solutions. Instead, we must assign values to a large number of parameters, such as the width of the platform, the type of door, the cable weight, and the Cable strength. These parameters must be consistent with each other, and they must result in a design that satisfies external constraints imposed by cost factors, the type of building involved, and expected payloads.
One problem-solving method useful for design tasks is called propose-and-revise. Propose-and-revise systems build up solutions incrementally. First, the system proposes an extension to the current design. Then it checks whether the extension violates any global or local constraints. Constraint violations are then fixed, and the process repeats. It turns out that domain experts are good at listing overall design constraints and at providing local constraints on individual parameters, but not so good at explaining how to arrive at global solutions. The SALT program provides mechanisms for elucidating this knowledge from the expert.
Like MOLE, SALT builds a dependency network as it converses with the expert. Each node stands for a value of a parameter that must be acquired or generated. There are three kinds of links: contributes-to, constrains, and suggests-revision-of. Associated with the first type of link are procedures that allow SALT to generate a value for one parameter based on the value of another. The second type of link, constrains, rules out certain parameter values. The third link, suggests-revision-of, points to ways in which a constraint violation can be fixed. SALT uses the following heuristics to guide the acquisition process:
1. Every noninput node in the network needs at least one contributes-to link coming into it. If links are missing, the expert is prompted to fill them in.
2. No contributes-to loops are allowed in the network. Without a value for at least one parameter in the loop, it is impossible to compute values for any parameter in that loop. If a loop exists, SALT tries to transform one of the contributes-to links into a constraint link.
3. Constraining links should have suggests-revision-of links associated with them. These include constrains links that are created when dependency loops are broken.
Control knowledge is also important. It is critical that the system propose extensions and revisions that lead toward a design solution. SALT allows the expert to rate revisions in terms of how much trouble they tend to produce.
SALT compiles its dependency network into a set of production rules. As with MOLE, an expert can watch the production system, solve problems and can override the system's decision. At that point, the knowledge base can be changed or the override can be logged for future inspection.
The process of interviewing a human expert to extract expertise presents a number of difficulties, regardless of whether the interview is conducted by a human or by a machine. Experts are surprisingly inarticulate when it comes to how they solve problems. They do not seem to have access to the low-level details of what they do and are especially inadequate suppliers of any type of statistical information. There is, therefore, a great deal of interest in building systems that automatically induce their own rules by looking at sample problems and solutions. With inductive techniques, an expert needs only to provide the conceptual framework for a problem and a set of useful examples.
For example, consider a bank's problem in deciding whether to approve a loan. One approach to automating this task is to interview loan officers in an attempt to extract their domain knowledge. Another approach is to inspect the record of loans the bank has made in the past and then try to generate automatically rules that will maximize the number of good loans and minimize the number of bad ones in the future.
META-DENDRAL was the first program to use learning techniques to construct rules for an expert system automatically. It built rules to be used by DENDRAL, whose job was to determine the structure of complex chemical compounds. META-DENDRAL was able to induce its rules based on a set of mass spectrometry data; it was then able to identify molecular structures with very high accuracy. META-DENDRAL used the version space learning algorithm. Another popular method for automatically constructing expert systems is the induction of decision trees. Decision tree expert systems have been built for assessing consumer credit applications, analyzing hypothyroid conditions, and diagnosing soybean diseases, among many other applications.
Statistical techniques, such as multivariate analysis, provide an alternative approach to building expert-level systems. Unfortunately, statistical methods do not produce concise rules that humans can understand. Therefore it is difficult for them to explain their decisions.
For highly structured problems that require deep causal chains of reasoning, learning techniques are presently inadequate. There is, however, a great deal of research activity in this area.
Summary
l Expert systems use symbolic representations for knowledge (rules, networks, or frames) and perform their inference through symbolic computations that closely resemble manipulations of natural language. An expert system is usually built with the aid of one or more experts, who must be willing to spend a great deal of effort transferring their expertise to the system.
l Expert systems are complex AI programs. However, the most widely used by way of representing domain knowledge in expert systems is, as a set of production rules, which are often coupled with a frame system that defines the objects that occur in the rules
l The most useful knowledge acquisition programs are those that are restricted to a particular problem-solving paradigm, e.g., diagnosis or design
l Transfer of knowledge takes place gradually through many interactions between the expert and the system, The expert will never get the knowledge right or complete the first time.
l The amount of knowledge that is required depends on the task. It may range from forty rules to thousands.
l The choice of control structure for a particular system depends on specific characteristics of the system.
l It is possible to extract the nondomain-specific parts from existing expert systems and use them as tools for building new systems in new domains.
l MYCIN is an expert system, which diagnoses infectious blood diseases and determines a recommended list of therapies for the patient.
l RI (sometimes also called XCON) is a program that configures DEC VAX systems
 
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