BBT603

Bioinformatics II

L-T-P3-1-0 Credits4 Session2024-25 TypeTheory
Appears in 1 semester

B.Tech Biotechnology · Sem 6 (Core)

Syllabus

5 units · 44 lecture hours
Unit1

Unit 1

10 hrs

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

Unit2

Unit 2

10 hrs

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

Unit3

Unit 3: Machine learning

8 hrs

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).

Unit4

Unit 4

10 hrs

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.

Unit5

Unit 5

6 hrs

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. B.Tech. (Biotechnology) 3rd Year Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh, Lucknow

Course Objectives2 points
  • 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 (COs)6 outcomes

After completion of the course student will be able to:

  1. CO1Understand the various tools and techniques related to insilico modeling of biomolecules along with methods of drug designing, protein docking
  2. CO2Analyze problems related to collection and analysis of biological data
  3. CO3Develop steady and time dependent solutions along with their limitations
  4. CO4Apply Machine learning techniques for decision making
  5. CO5Learning information retrieval using NLP
  6. CO6Understand molecular dynamics and simulation techniques

Reference Book

  1. S. No.

Books

  1. Year of Publication/ Reprint
  2. Computational Methods in Biotechnology – Salzberg S. L. et al., Elsevier Science. 1998
  3. D.W.Mount; Bioinformatics- Sequence and genome analysis; Cold Spring HarbourLabpress. 2004
  4. Hand Book Of Bioengineering- Skalak R &ShuChien, McGrawHill. 1986
  5. Statistical Methods in Bioinformatics-Evens & Grants, Springer- Verlag, NY. 2006 B.Tech. (Biotechnology) 3rd Year Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh, Lucknow
  6. Purifing Protein for Proteomics, Richard J. Sinpson, I.K. International Pvt. Ltd. 2004
  7. Computational Molecular Biology- Setubal and Meidanis, PWS publishing Co. 1997

Official AKTU syllabus

This subject is on page 39, 40, 41 of the original AKTU PDF.

Official AKTU syllabus PDF ↗