Algorithms for Biological Networks

a biennial BioSB course

Lecturers: prof. dr. ir. Dick de Ridder, Wageningen University
dr. Aalt-Jan van Dijk, Wageningen University
Contact: prof. dr. ir. Dick de Ridder
telephone: +31 317 484074

Time and location

The next course will be given on-line, February 1-5, 2021.

Target audience

The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. The BioSB fundamental course "Machine Learning for Bioinformatics & Systems Biology" discusses many of the tools used in this course, but it is not required to have followed these. Prior knowledge of molecular biology is a bonus, but also not strictly required.

Preparation material on probability theory, linear algebra and molecular biology can be found below and should be read by all students before the course starts.


Molecular biology is concerned with the study of the presence of and interactions between molecules, at the cellular and sub-cellular level. In bioinformatics and systems biology, algorithms and tools are developed to model these interactions, with various goals: predicting yet unobserved interactions, assigning functions to yet unknown molecules through their relations with known molecules; predicting certain phenotypes such as diseases; or just to build up biological knowledge in a structured way.

Such interaction models are often best modelled as networks or graphs, which opens up the possibility of using a large number of readily available algorithms for inferring networks, performing simulations of biology, optimising paths or flows through networks, graph-based data integration and graph mining. Many of these algorithms can be applied (sometimes with slight alterations) to solve a particular biological problem, such as modeling transcriptional regulation or predicting protein interaction/complex formation, but also to derive systems behaviour by breaking down networks into modules or motifs with certain characteristics.

In this course, we will first give a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model all this. We will highlight a number of peculiar features of biological networks. Next, a number of basic network models (linear, Boolean, Bayesian) will be discussed, as well as methods of inferring these from observed measurement data. A number of alternative network models more suited for high-level simulation of cellular behaviour will also be introduced. Building on the network inference methods, a number of ways of integrating various data sources and databases to refine biological networks will be discussed, with specific attention to the use of sequence information to refine interaction and transcription regulation networks. Finally, we will give some examples of algorithms exploiting the networks found to learn about biology, specifically for inspecting protein interaction networks.


In preparation for the course, please read the following primers on

Not all topics discussed in these primers will be used extensively in the course, but if you find yourself severely lacking in a certain area it may be wise to look up additional texts.

A folder containing handouts of the slides, papers to be discussed and a lab course manual will be distributed at the start of the course. Electronic copies of the course material can be found here later.


After the course, for an additional 1.5 EC you can write a short research proposal (e.g. for an MSc student) on the application of one or more of the algorithms discussed during the course to a problem you encounter in your own research. The proposal should clearly state the background, the motivation, the problem statement and a proposed solution. Most importantly, it should be realistic, i.e. not require many years of work, large investments or magic to finish. You can discuss your idea for the proposal with the lecturers during the course. An example proposal (by Peter van Nes) and a set of guidelines are available for download.

Alternatively, you can apply one of the methods discussed in the course to a dataset or network in your own research. In this case, you should write a report in which you clearly introduce the background of your work, describe the data and network involved and the approach you take, discuss results and give conclusions.


You can register for this course through the BioSB website. The maximum number of participants is 25, so register soon to be sure of a course seat! Should the course be overbooked, BioSB PhD student members will be allowed access first.

Please refer to the BioSB site for fees.

The fee includes:


One full week, followed by a final assignment. Most days are laid out uniformly, roughly as follows:

09.30 - 12.30 Lectures and labwork
12.30 - 13.30 Lunch
13.30 - 15.00 Hands-on computer lab work
15.00 - 17.30 Participants read a scientific paper on the topics of the day, in small groups, and prepare and deliver short presentations. A couple of salient keywords is distributed among the participants.

Global schedule

A detailed schedule will be provided later.