Informatics of Immunity<br />
Immunity: Defense against Disease<br />A cancer cell under attack by T cells <br />of the immune system<br />Cancer cell k...
Immunology as a Convergence of Sciences<br />
From Genomics to Immunomics<br />
What does the Future Hold??<br />
Immune system is Complex and Diverse<br />
Different Tissues<br />
Different Cells, Defense Mechanisms<br />
Extensive Receptor Diversity<br />
Role of Informatics<br />
Key Questions in Vaccine Design<br />Which immune response should the vaccine elicit?<br />     - T-helper cell mediated i...
Prediction of B-cell Epitopes<br />Recognized by antibodies<br />Linear B- cell Epitopes<br />Conformational B-cell <br />...
Prediction of T-cell Epitopes<br /><ul><li>Each stage in the antigen processing and presentation pathway occurs non-randomly
T-cell epitopes are linear and short (8-11 amino acids for class I, slightly more for class II)
Prediction of T-cell epitopes is easier than conformational B-cell epitopes</li></li></ul><li>Prediction of MHC class I Ep...
In silico Vaccine Discovery<br />A wide range of software are available<br />How do you choose the best?<br />   - Predict...
Databases<br />
Other Resources<br />
Informatics Of Immunity
Informatics Of Immunity
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Informatics Of Immunity

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The immune system is one of the most complex systems consisting of more than a billion unique receptors on T-cells, B-cells and even other cell types. As such, our success in comprehending immunological reactions can greatly benefit from computer science. I made this presentation at the First Young Kenyan Scientists meeting in held at the Kenya Medical Research Institute (KEMRI) in Nairobi, Kenya.

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Informatics Of Immunity

  1. 1. Informatics of Immunity<br />
  2. 2. Immunity: Defense against Disease<br />A cancer cell under attack by T cells <br />of the immune system<br />Cancer cell killed<br />
  3. 3.
  4. 4. Immunology as a Convergence of Sciences<br />
  5. 5. From Genomics to Immunomics<br />
  6. 6. What does the Future Hold??<br />
  7. 7. Immune system is Complex and Diverse<br />
  8. 8. Different Tissues<br />
  9. 9. Different Cells, Defense Mechanisms<br />
  10. 10. Extensive Receptor Diversity<br />
  11. 11. Role of Informatics<br />
  12. 12. Key Questions in Vaccine Design<br />Which immune response should the vaccine elicit?<br /> - T-helper cell mediated immunity<br /> - Cytotoxic T cell mediated immunity<br /> - Humoral immune responses<br />
  13. 13. Prediction of B-cell Epitopes<br />Recognized by antibodies<br />Linear B- cell Epitopes<br />Conformational B-cell <br />Epitopes<br />
  14. 14. Prediction of T-cell Epitopes<br /><ul><li>Each stage in the antigen processing and presentation pathway occurs non-randomly
  15. 15. T-cell epitopes are linear and short (8-11 amino acids for class I, slightly more for class II)
  16. 16. Prediction of T-cell epitopes is easier than conformational B-cell epitopes</li></li></ul><li>Prediction of MHC class I Epitopes<br />
  17. 17. In silico Vaccine Discovery<br />A wide range of software are available<br />How do you choose the best?<br /> - Prediction accuracy<br /> - HLA allele population coverage<br /> - Promiscousepitope prediction<br /> - T-cell epitope Hotspots<br /> - Conserved epitope prediction<br />
  18. 18. Databases<br />
  19. 19.
  20. 20.
  21. 21. Other Resources<br />

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