Immunoinformatics

After completion of the course student will be able to:

1. CO1Understand sequence analysis methods including DNA alignments, molecular evolution, viral evolution, and prediction of functions.K2
2. CO2Apply computational methods in immunological bioinformatics (sequence weighting, clustering, Gibbs Sampling, HMM, neural networks, microarray applications).K3
3. CO3Apply prediction methods for cytotoxic T cell (MHC Class I) epitopes and explain antigen processing pathways.K3
4. CO4Apply prediction methods for helper T cell (MHC Class II) epitopes and describe their processing mechanisms.K3
5. CO5Apply B cell epitope prediction approaches and web-based tools for vaccine design.K3

1. / REFERENCES
2. Rammensee, “Immunoinformatics- Bioinformatics Strategies for Better Understanding of Immune Function”, Wiley, 2003.
3. Ole Lund, “Immunological Bioinformatics”, MIT Press, September 2005.
4. Darren Flower, “In Silico Immunology”, Springer, 2006. Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh
5. Darren R Flower, “Immunoinformatics: Predicting Immunogenicity in Silico”, Humana Press, 2007. Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh