SIIK24002U Computational Immunology

Volume 2024/2025

Msc in Immunology and Inflammation


Computational biology and analyses are part of most basic and clinical immunology and inflammation research activities, facilitating the interpretation and integration of large and diverse data sets to gain new insights into the cellular and molecular mechanisms of immune response and disease.

Through this course, students will gain practical experience with methods of computational immunology. In weekly practical exercises, students will analyze, visualize, and interpret data related to topics of immunology and inflammation.

Lectures leading up to each exercise will provide students with a basic understanding of the principles underlying computational methods used in immunological research. The lectures will incorporate examples of use in current research from both academic and industry settings, providing students with insights to real-world applications of these methods.

The course includes analysis of bulk and single cell RNA seq and epigenetic seq data, cytometry data analysis, integration of multi-omics data and network analyses, data visualization, B/T cell receptor analysis, protein structure and interaction prediction.

The methods and tools learned will be integrated in study exercises and coursework of other course of the MSc program.

Learning Outcome

After completing the course the student is expected to be able to:


  • Locate publicly available bioinformatics databases
  • Locate publicly available computational biology tools and software resources
  • Understand the basic principles of key bioinformatics methods



  • Identify appropriate computational tools and methods for the analysis of Omics and network data
  • Identify appropriate computational tools and methods for analysis and prediction of protein folding and interactions
  • Utilize publicly available bioinformatics databases
  • Manipulate, analyze, visualize and present data relating to immunological research
  • Interpret and extract meaningful insights from large-scale immunological datasets, network models of the immune system and models of molecular interactions.



  • Understand and evaluate computational biology papers published in peer-reviewed immunological journals
  • Critically analyze and discuss experimental data in the field of computational immunology
  • Conceptually develop and initiate small computational analyses within the field of immunology
  • Selected introduction papers to bioinformatics methods
  • Selected scientific papers
  • Experimental course material


Practical exercise course including lectures, journal club, student oral presentation of data analyses.
  • Category
  • Hours
  • Lectures
  • 7
  • Preparation
  • 132,5
  • Exercises
  • 21
  • Project work
  • 35
  • Exam Preparation
  • 10
  • Exam
  • 0,5
  • Total
  • 206,0
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral exam on basis of previous submission, 20 minutes, under invigilation
Type of assessment details
The exam consists of a group-project report including extraction, analysis, and interpretation of biological data sets, as well as an individual oral examination of the analyses performed.
Exam registration requirements


All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Internal examiners
Criteria for exam assesment

To pass, the student must be able to, at an acceptable level:


  • Demonstrate knowledge of strengths and limitations of diverse computational biology methods for the interpretation of disease and biology. 

  • Demonstrate knowledge of databases and tools for computational biology.



  • Search, extract, analyse and visualize data from publicly available databases

  • Apply computational biology software for analysis of Omics, network and protein structure analyses

  • Visualize, present data and conclusions drawn from analysis of immunological research data.