Quantitative Biology

Course manual 2024/2025

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, and model formulation and analysis. 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

Syllabus

• Quantitative Biology - Lecture Notes

Practical training material

• Quantitative Biology - Lecture Notes

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 confront the predictions of biological process models with data
• Students can formulate and analyse models of biological dynamics

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

Programme's requirements concerning attendance (OER-B):

• Participation in all practical (computer) sessions, field work and seminars in the curriculum is in principle obligatory. Any additional requirements are described per section in the course manual. Also the possible consequences of not fulfilling this obligation are described.
  

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 two graded projects 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 or modeling project as opposed to collaborating in a group of 3 students.

Assessment

• Students will be required to hand in a written report following 2 specific computer labs detailing their findings and results. These projects will focus on statistical analysis and model analysis, respectively. 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 2 students. For the two graded projects, only a single report needs to be handed in per student pair. 
• The final exam covers the material presented in the entire course. Students will be allowed to use all course hand-outs during the exam, as the assessment is focused on understanding and critical thinking. 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 projects 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 in total 2 specific computer labs. These projects will focus on statistical analysis and model analysis, respectively. 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 2 students. For the two graded projects only a single report needs to be handed in per student pair. 

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

Additional information

• Following the evaluation of previous years, the course schedule has been changed such that students have one more day available prior to the final exam to prepare. This has been achieved by scheduling in week 2 lectures and computer during 5 days instead of 4 days. 

Contact information

Coordinator

• André de Roos

Staff

• dr. Ben Martin
• prof. André de Roos
• Sara Neven
• Lars Koopmans
• Danyang Shi