22145 - Immunological Bioinformatics

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Course Programme


About

  • Course responsible

Carolina Barra Quaglia — Associate professor, course responsible.

Morten Nielsen — Professor, course responsible.


  • Schedule: Every year in the fall 13-weeks period, see DTU Academic Calendar
  • Location: See this year's course program above
  • Exam: Poster presentation + written exam
  • Textbook: No textbook. The learning resources will be stated in the course program
  • Course Description: For full course description, please see the DTU Course Base

General course objectives

  • The course has an applied focus centered around the suite of prediction servers from the IML group
  • Theory and application of computational methods in context with the prediction of immune responses, moreover:
    • The involvement of TCR and BCR and MHC class I/II in inducing an immune response
    • The structural and genetic characteristics of the TCR and BCR and MHC class I/II and corresponding epitopes
    • Computational methods for modeling TCR and BCR and MHC class I/II and respective epitope interactions
    • Application and challenges of the above in disease context, i.e. vaccinology of infectious diseases and cancer
  • General engineering competencies are included in the form of theory in context with concrete application and group-based project work, where the students are responsible for planning, designing, implementing, and communicating a project.

Essential Software and Resources

  • Students should bring their own laptop with internet access to class
  • Students should install PyMol on their laptops. The PyMOL Education License file will be supplied during class. An introduction to PyMOL can be found in a place that does not exist and should be updated.
  • Also, students should install Rstudio on their laptops. Rstudio is free and does not require any license. An introduction can be found here
  • The course focuses on computational methods for proteins and peptides in an immunological context, so it is recommended, that students have a grasp of the biochemical profile of each of the the 20 proteinogenic amino acids
  • A brief introduction to the Immunoinformatics and Machine Learning group can be found here here

Questions?

Please read the detailed course description, for any questions not covered by this, please contact course responsible

Previous versions of the course