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Course module: BMB513321
BMB513321
Computational Immunology
Course info
Course codeBMB513321
EC4.5
Course goals
At the end of the course, students should:
  • be able to choose the appropriate statistical test for different circumstances
  • understand the principles underlying hypothesis-driven and hypothesis-generating bioinformatic analysis
  • be able to explain which computational techniques should be used in which circumstances
  • be able to apply different computational methods for the analysis of biological big data
  • be able to make a prediction model and judge its quality
  • be able to judge papers using computational techniques
  • understand how a biological phenomenon can be translated into a mathematical model
  • be able to translate unexpected modelling results into intuitive understanding
Content
Period (from-till): 20 November - 8 December 2023

Lecturers:
Julia Drylewicz, CTI, UCMU, 50%
Terry Huisman, CTI, UMCU 100%
José Borghans, CTI, UMCU, 50%
Alex Yermanos, CTI, UMCU, 50%
3 Guest lecturers

Assisting PhD students and postdocs, CTI, UMCU, % depending on number of students

Course description:
This course will focus on different computational techniques that are currently used to study the immune system.
It will cover a range of biostatistical techniques, computational techniques to handle big data, and mathematical models.
With these concepts, you will be able to design a study, analyze and interpret the resulting data, and critically read immunological papers using computational techniques.
Each day, introductory lectures on specific topics will be followed by computer practical sessions in which participants will put into practice the introduced techniques.
 
Literature/Study material:
  • The primary study material will consist of handouts, specifically made for this course
  • Immunology:  Abbas, Abdul K., Cellular and Molecular Immunology, 10th edition, Elsevier. Electronically available via University Library. 
  • (highly recommended) Introduction to R: Even though knowledge of R is not a prerequisite, it is very useful to know the basics of R while attending the course. Under the Tab “0.6 Pre-Intro R Test” you will find some questions to test your knowledge on R, to help you decide if you should join the "Introduction to R" in the morning of Day 1. If you want to have a bit more knowledge on R, we highly recommend completing the E-learning module called "Preparatory E-learning: Introduction to R” (which you can find in ULearning, at the end of Day 0), before the course starts. The module explains the very basics of R and contains some practical exercises with model answers. It will take a novice user around 5-6 hours to complete.  
Basic knowledge of immunology 
The following two short weblectures are a good representation of the prerequisite knowledge in Immunology
https://hstalks.com/t/4058/the-immune-system-an-overview-innate-immunity/?biosci
https://hstalks.com/t/4059/the-immune-system-an-overview-adaptive-immunity/?biosci

 Course content:
  • Introduction to R (2 hours on d1)
  • Brief biostatistical recap (half a day on d1)
  • How to determine sample size for experiments?
  • Basic statistical tests and statistical analysis
  • Distributions. What is a p-value? When to correct for multiple testing?
  • Linear regression
  • More advanced Biostatics (1.5 days on d1-d2) [Bridge to deliver tools for next part of the course]
o   Non-linear regression, GLM
o   Model prediction (example on biomarkers), training and test sets
o   Dimension reduction (PCA, MDS)
  • Big Data Part I (d3-d4)
o   Analysis of RNAseq data and big data (luminex, Olink, FACS data):
  • Data analysis (Volcano plots, MA plot, how/why/when to normalize data?)
  • Visualising omics data: Heatmaps, tSNE (including the paper comparing them)
o   Clustering: Non-supervised versus supervised (k-means, Lasso, cross-validation, PLS, UMAP)
o   Case studies, other applications of NGS data
  • T-cell receptor repertoires, and/or
  • HIV sequence analysis: Virus diversity
  • Big Data Part II (d5-d6)
  • Single-cell sequencing data and regulatory networks (hands-on)
  • Basics of networks
  • Gene regulation
  • Design of omics experiments
  • Mathematical modeling (2.5 days on d7-d8)
o   Measuring cell dynamics (counter-intuition: telomeres, TRECs), including introduction into ODEs
o   ​Host-pathogen interactions (HIV)
  • Question hour (d8)
  • Self-study (d8-d9)
  • Exam (d10)
Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site.
Max number of students is 25.

Contact:
José Borghans (course content); j.borghans@umcutrecht.nl
I&I Secretariat (Course registration & Osiris); secretary.iimaster@umcutrecht.nl

Mandatory for students in own Master’s programme:
No
 
Optional for students in other GSLS Master’s programme:
Yes
 
Prerequisite knowledge:
This course is open for students with and without a computational background. It assumes basic knowledge of I&I.
 
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