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AKTU Bioinformatics II (BBT603) Syllabus PDF – B.Tech 3rd Year
Bioinformatics II BBT603
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Official AKTU syllabus of Bioinformatics II (BBT603) for B.Tech 3rd Year – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Bioinformatics II
Download PDFBBT603 · 4 credits · L-T-P 3-1-0
EXAM DURATION:3 HOURS SEMESTER: VI (EVEN) L: T: P :: 3 : 1 : 0 CREDITS: 4 PREREQUISITE: Elementary knowledge of bioinformatics I , molecular biology and computer
Course Objectives
- To provide knowledge to analyze various computational methods involved in protein modeling, RNA structure prediction and drug designing.
- To teach various concepts of machine learning, Artificial Neural Networks, document clustering.
Course Outcomes
On completion of this course, the students will be able to
- Understand the various tools and techniques related to insilico modeling of biomolecules along with methods of drug designing, protein docking
- Analyze problems related to collection and analysis of biological data
- Develop steady and time dependent solutions along with their limitations
- Apply Machine learning techniques for decision making
- Learning information retrieval using NLP
- Understand molecular dynamics and simulation techniques CO-PO Mapping (1 to 3 scale)
| CO/PO | PO-1 | PO-2 | PO-3 PO-4 PO-5 PO-6 PO-7 PO-8 PO-9 PO- 10 PO- 11 PO- 12 |
| CO-1 | 2 | 3 | 1 |
| CO-2 | 2 | 3 | 1 |
| CO-3 | 2 | 3 | 1 |
| CO-4 | 2 | 3 | |
| CO-5 | 2 | 3 | |
| CO-6 | 2 | 3 | 1 |
Reference Books
| S. No. | Name Of Authors/Books/Publishers | Year of Publication/ Reprint |
| 1. | Computational Methods in Biotechnology – Salzberg S. L. et al., Elsevier Science. | 1998 |
| 2. | D.W.Mount; Bioinformatics- Sequence and genome analysis; Cold Spring HarbourLabpress. | 2004 |
| 3. | Hand Book Of Bioengineering- Skalak R &ShuChien, McGrawHill. | 1986 |
| 4. | Statistical Methods in Bioinformatics-Evens & Grants, Springer- Verlag, NY. | 2006 |
| 5. | Purifing Protein for Proteomics, Richard J. Sinpson, I.K. International Pvt. Ltd. | 2004 |
| 6. | Computational Molecular Biology- Setubal and Meidanis, PWS publishing Co. | 1997 |
Course Details
| Course Details: BIOINFORMATICS II | ||
| Unit | Content | Contact Hours |
| I | Inference problems and techniques for molecular biology. Overview of key inference problems in biology: Homology identification, Genomic sequence annotation (Genes and ORFs identification), Protein structure prediction (Secondary and Tertiary structure prediction), Protein function prediction, Biological network identification, Next generation sequencing, Microarray data analysis | 10 |
| II | Basics of RNA Structure prediction and its limitations, Features of RNA Secondary Structure, RNA structure prediction methods: Based on selfcomplementary regions in RNA sequence, Minimum free energy methods, Suboptimal structure prediction by MFOLD, Prediction based on finding most probable structure and Sequence co-variance method. Application of RNA structure modeling | 10 |
| III | Machine learning: Decision tree induction, Artificial Neural Networks, Hidden Markov Models, Genetic Algorithms, Simulated Annealing, Support vector machines; The relation between statistics and machine learning; Evaluation of prediction methods: Parametric and Nonparametric tests, cross-validation and empirical significance testing (empirical cycle), Clustering (Hierarchical and Kmean). | 8 |
| IV | Basic concept of Force field in molecular modeling (Potential energy calculation); Overview of key computational simulation techniques: Introduction to simulation, Computer simulation techniques, Types of computer simulation (Continuous, Discrete-event and Hybrid simulation), Differential equation solvers, Parameter estimation, and Sensitivity analysis. | 10 |
| V | Overview of key techniques for the management of large document collections and the biological literature: Document clustering, Information retrieval system; Natural Language Processing: Introduction, Major areas of NLP, Natural language information extraction; Insilico Drug Designing: Major steps in Drug Designing, Ligand and Structure based drug designing, Protein-ligand docking, QSAR Modeling, Pharmacodynamics (Efficacy & Potency) &Pharmacokinetics (ADME), Lipinski’s rule of five, Pharmacogenomics. 42 | 6 |
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