Quantitative Biology

Course manual 2025/2026

Course content

How can causal relations be inferred from data collected on a particular biological system and how can observations provide insight about the processes in such a system that result in the observed patterns? Quantitative Biology is a methodological course that centers around these two questions.

Biological data, whether from biomedical research or from ecology and environmental science, are quantitative and noisy. The goal of this course is to teach students to think critically, quantitatively, and statistically about data, and to help the student answer the question of what the data actually reveal about what causal relations underly these data, and what the data can tell about the biological processes that produce them.

To help answer these questions, modern computer-based statistical and mathematical techniques are required. This course serves as an introduction by covering topics like statistical analysis using generalized linear models, parameter estimation and hypothesis testing. By the end of the course, the student will have a set of tools that should allow attacking any quantitative problem, drawing conclusions, and assessing the confidence in these conclusions.

Study materials

Literature

• Background literature: Statistical Thinking for the 21st Century

Software

• R (at least version 4.1.2 or later)

• Rstudio (at least version 2021.09.1 Build 372 or later)

Objectives

• Students can think critically about biological data and systems
• Students can analyse biological data to infer causality
• Students can program in R to statistically analyse data
• Students can use simulation techniques for testing hypothesis and parameter estimation

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study
• Working independently on e.g. a project or thesis

• Lectures are intended to introduce the students to the basic concepts and methods for modern statistical analysis, critical thinking, and computational analysis of dynamical models in biology
• Computer labs are focused on students' training to apply the methods discussed in the lectures and to internalize the concepts
• Self-study is focused on reading specific parts of the course literature prior to the lectures and the computer labs
• Projects are intended to assess the level of understanding acquired by the students

Learning activities

Attendance

Additional requirements for this course:

• Attendance of the lectures is highly advisable as they are focused on preparing the student for the computer lab immediately following the lecture.
• Attendance of the computer labs is mandatory, because during these computer labs students train and acquire the necessary skills that they require to successfully complete the graded project and to pass the final exam. In particular, the training during the computer labs is necessary to achieve the course objectives 2 and 4, listed above.
• If a student can not attend a specific computer lab, this should be discussed with the course coordinator. Students are not allowed to miss more than 2 computer labs. If a student misses more than these 2 computer labs, the student will have to individually complete the graded statistical project as opposed to collaborating in a group of 3 students.

Assessment

• Students will be required to hand in a written report following a specific computer lab detailing their findings and results. This project will focus on statistical analysis. During computer labs, students will work on their own computers to solve assignments, but collaborate, discuss, and give support and feedback to each other in groups of 3 students. For the graded project, only a single report needs to be handed in per group of 3 students. 
• The final exam covers the material presented in the entire course. The final exam will take place in an examination hall with pre-installed university computers. NOTICE: THIS EXAMINATION HALL IS AT THE SITE OF THE ACADEMIC HOSPITAL.
• For grading the projects and the final exam, the rules in the Teaching and Examination Regulations are followed, more specifically section B-4.7 in part B of these regulations. This implies that the grades for the projects and the final exam should all be equal to or higher than 5.0 and that the final grade should be equal to or higher than 5.5.

Inspection of assessed work

• To inspect the graded project students can make an appointment with one of the course assistants
• To inspect the final exam a session will be organised about 2 weeks after the course during which students can inspect their work. This session will be announced via Canvas

Assignments

• Assignments consists of handing in a written report on the findings and results of the statistical project. During computer labs students will work on their own computer to solve assignments, but collaborate, discuss and give support and feedback to each other in groups of 3 students. For the graded project only a single report needs to be handed in per group of 3 students. 

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Processed student feedback

Changes have been made in the course design following the student evaluations of previous years.

1. A component of the course has been removed, and students only have one statistical project this year.
2. The number of lectures has been cut to 4 lectures per week in weeks 1,2 and 3, with no lectures in week 4.  

Contact information

Coordinator

• dr. Yael Artzy-Randrup

Staff

• Prof. André de Roos
• Dr. Ben Martin
• Dr. Yael Artzy-Randrup
• Danyang Shi
• Dr. Peter Assink
• Shreyas Gadge
• Hesam Farhadi