Computational Thinking is an essential skill for the 21st-century. It is a method for problem solving, a prerequisite for learning to write computer scripts and to conduct computational drug research.
In this course, we will explore examples of computational thinking in drug research. We will start with a basic introduction to programming in R and follow this up with a computational view on mathematical equations, limits, partial derivatives and graphs of functions. We will then explore the use of linear models for statistical inference testing and how to apply this in R. A substantial part of the course will be hands-on-training in scripting your own computer programs in R using data sets that are relevant in drug research.
The student is able to:
Solve mathematical equations, calculate limits and partial derivatives and draw graphs of functions
Analyse one-dimensional (1D) differential equations by pen and paper and interpret output from such analysis
Program an R-script to analyse mathematical equations including 1D differential equations
Program an R-script to format and analyze a (drug related) dataset and visualize the data
Peform statistical inference tests using linear models in R on data that often arises in drug research
Literature will be provided during the course.
Dhr. G. Burger, MSc
Mode of instruction
Practical course, consisting of: lectures, demonstrations, computer exercises, literature research and a group assignment.
Students will be assessed on the following modalities:
Weekly scripting tests (20%)
Group data-analysis assignment (20%)
Total grade = 100%
Admission requirements & Registration
This course is mandatory for and restricted to students who do the Minor ‘Computational approach to Disease Signaling and Drug Targets’ (CADSDT; the entire Minor or only Part 1), the Minor ‘Modern Drug Discovery’ (MDD) and the Elective Module ‘DSDT’. The same admission criteria apply to this course as for the respective afore mentioned programs. Registration for the lectures and exam via uSis is mandatory.