# Introduction to Computational Thinking (ICT)

Vak
2024-2025

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) or the Elective Module ‘DSDT’. This course has the same admission criteria as these programs.

## Description

Computational Thinking is an essential skill for the 21st century. With data gathering and algorithm development by Big Tech in the news regularly, such thinking is not only useful to understand the mechanisms underlying specific observations, but it is also indispensable to develop and apply modern computational approaches to solve the many open problems in the area of (computational) drug research. An important part of computational thinking is the ability to write code that executes well-defined computational tasks.

In this course, we will explore examples of computational thinking in drug research. We will provide an introduction to programming in R. Moreover, we will discuss mathematical equations, limits, derivatives and graphs of functions, which will subsequently be applied in the mathematical/computational modeling of biological networks. Note that a limited amount of calculus skills is thus required to pass for the course. We will also revisit and build upon previous statistics courses and 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 writing your own code in R, with applications in mathematical modeling and using data sets that are relevant in drug research. In addition, computational thinking is highlighted in the context of chemogenomics; we will discuss how machine learning is used in chemogenomics to discover new drugs, introduce techniques from this field, and study peer-reviewed literature. Finally, we will bring these concepts together in a case study, where we will use these techniques on biomedical datasets.

## Course Objectives

The student is able to:

• Solve mathematical equations, calculate limits and derivatives and draw graphs of functions

• Analyze one-dimensional (1D) differential equations by pen and paper and interpret output from such analysis

• Write an R script to analyze mathematical equations including 1D differential equations

• Write an R script to format and analyze a (drug-related) dataset and visualize the data

• Generate artificial data using simulated experiments and analyze the impact of the experimental setup on the resulting statistics

• Choose and perform statistical inference tests using linear models in R on data that often arises in drug research, and draw conclusions from these tests

• Select and discuss the optimal approach from an array of computational methods, for a given chemogenomic use case

## Timetable

In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.

Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.

Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.

Always check the detailed schedule on the Brightspace module of each Course 2-3 weeks before the start of the Course for group-specific meetings, (intermediate) deadlines, etc..

## Mode of instruction

Practical course, consisting of lectures, demonstrations, pen&paper and computer exercises, literature research and a group assignment.

## Assessment method

Students will be assessed on the following modalities:

• A scripting test (30%)

• Group data-analysis assignment (20%)

• Exam (50%)

In the written exam, a total of 60 points can be obtained, and the grade for the exam is determined by dividing the total number of obtained points by 6. Presence and active participation for the hands-on work during the course will be monitored and leads to a maximum of 6 points out of the 60 possible points on the written exam.

Literature will be provided during the course.

## Registration

As a student, you are responsible for enrolling on time through MyStudyMap.

In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.

There are two enrolment periods per year:

• Enrolment for the fall opens in July

• Enrolment for the spring opens in December

Note:

• It is mandatory to enrol for all activities of a course that you are going to follow.

• Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.

• Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.

## Contact

Ms. Dr. G. Callegaro, PhD

## Remarks

Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.