Single-Course Engelsk 5 ECTS

Immunological Bioinformatics

Overall Course Objectives

The student will be able to outline the theoretical background, apply, and analyze the output of computational methods related to the prediction of immune responses.
Moreover, upon completion of the course the student will be able to:
– Apply computational methods for modeling genetic, structural, and functional characteristics of the immune receptors and their corresponding epitopes in inducing immune responses.
– Design a pipeline and execute a project applying and combining computational methods in an immune response context, i.e. vaccinology of infectious diseases, allergy, autoimmunity, cancer neo-epitope prioritization, protein drug-deimmunization.
General engineering competencies are included in context with concrete application in a group-based project work, where the students are responsible for planning, designing, implementing, and communicating a project.

See course description in Danish

Identify and query relevant immunological databases, and integrate multiple tools and data sources to determine allele frequencies and estimate population coverage
Compare and analyze the structural and functional characteristics of MHC-I and MHC-II molecules, including their respective antigen processing and presentation pathways
Apply publicly available tools to process and analyze data from proteomics and immunopeptidomics experiments
Apply and interpret predictive tools for MHC-I and MHC-II peptide binding and presentation
Explain and construct a Position Specific Scoring Matrix (PSSM) and demonstrate how it is used to generate sequence logos from peptide datasets
Explain the architecture, training process, and prediction mechanisms of artificial neural networks, and illustrate their application in immune prediction models
Compare, evaluate, and justify the use of different performance metrics in the assessment of immune prediction models
Analyze TCR and BCR sequence data using state-of-the-art tools, identify germline gene usage, and locate relevant sequence repositories
Compare the structural and functional differences between antibodies/BCRs and TCRs, and apply computational methods to predict their structure
Select and justify appropriate computational tools for predicting T-cell epitopes, B-cell epitopes, and TCR–pMHC interactions, and evaluate the advantages and limitations of immune prediction methods
Design, conduct, and present a research project using in silico methods to investigate immune responses
Design relevant queries and critically evaluate AI-generated outputs, assessing their correctness, limitations, validity, reproducibility, and biological relevance

The course aims to introduce students to state-of-the-art methods in computational immunology.

The theoretical foundations of these methods are introduced through lectures and are followed by practical exercises, enabling students to independently perform computational analyses.

The course is structured in two parts. Part 1 consists of lectures and group-based exercises. Part 2 focuses on group-based project work aimed at developing and implementing a complete project workflow.

22111/22117/27070, -or equivalent. The listed prerequisite courses are recommended but not required. However, you are expected to already know the material covered in those courses.
If you do not have this background knowledge, you are responsible for learning the missing material on your own. If you are unsure whether you have the necessary background, please contact the course responsible.

Lectures, exercises and project work