Course manual 2020/2021

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 help the student to begin to think critically, quantitatively and statistically about data, and to help the student answer the question 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 question, modern computer-based statistical and mathematical techniques are required. This course serves as an introduction by covering topics like statistical analysis using generalised 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, draw conclusions, and assess the confidence in these conclusions.

Study materials

Software

Objectives

Students will be trained to think critically about biological data and systems
Students will learn how to analyse biological data to infer causality
Students will learn how to confront the predictions of biological process models with data
Students will learn how to formulate and analyse biological process models

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 internalise 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

Activity	Hours
Lectures	24
Q & A meetings	4
Computer labs	48
Self study	92
Total	168	(6 EC x 28 uur)

Academic skills

Critical thinking
Statistical literacy
Computational research methods
Dynamic model analysis

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 focussed on preparing the student for the computer lab immediately following the lecture
Attendance of the computer labs is mandatory. If a student can not attend a specific computer lab, he / she has to discuss this with the course coordinator, who will formulate a substitute assignment. Students will not be able to Missing more than 2 computer labs

Assessment

Item and weight	Details
Final grade
70% Tentamen	Must be ≥ 6
15% Statistical lab report
15% Modelling lab report

Students will be required to hand in a written report following 2 specific computer labs detailing their findings and results. 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 4. Per lab group of 4 only a single report needs to be handed in.
The final exam is organised on-line. Students will be allowed to use all course material during the exam, as assessment is focused on understanding and critical thinking.

Assignments

Assignments consists of handing in a written report on the findings and results of in total 3 specific computer labs. During the compute elands students will work together in groups of 4.

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

Weeknummer	Onderwerpen	Studiestof
1	Linear models, Model fitting, Uncertainty	Statistical Thinking for the 21st century
2	Hypothesis testing, Simulation, More complicated statistical models, Reproducable science	Statistical Thinking for the 21st century
3	Reproducable science, Critical Thinking, Dynamic model analysis	Quantitative Biology - Lecture Notes
4	Dynamics model analysis	Quantitative Biology - Lecture Notes
5
6
7
8

Timetable

The schedule for this course is published on DataNose.

Processed course evaluations

Below you will find the adjustments in the course design in response to the course evaluations.

Owner	Bachelor Biologie
Coordinator	André de Roos
Part of	Bachelor Biologie, year 3 Bachelor Bèta-gamma, major Biologie, year 3

Quantitative Biology