NOTE: Registration closes on April 16 at 16:00!
This course presents algorithms for studying biomolecular networks, e.g. for inferring such networks, optimising flows through networks, graph-based data integration and graph mining. These can be used to solve a variety of biological problems, such as modeling transcriptional regulation or predicting protein interactions, but also to derive systems behaviour by breaking down networks into modules or motifs with certain characteristics.
Co-organisers
- University of Amsterdam
- Vrije Universiteit Amsterdam
- Wageningen University
Course coordinator
- Prof. dr. Aalt-Jan van Dijk, University of Amsterdam
Course credits
1.5 ECTS for following the course. An additional 1.5 ECTS will be earned for successful completion of the supplemental assignment.
Course description
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 (“executable models”) 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 transcriptomics, transcription factor and pathway information. Finally, we will discuss how these networks can be exploited in various settings: clustering, to derive biologically relevant subnetworks (e.g. protein complexes, regulatory modules) and classification, using networks or network neighbourhoods as features in machine learning applications.
Learning objectives
In this course the students will learn to:
- Understand algorithms for the analysis of biomolecular networks;
- Apply such algorithms during hands-on sessions on specific datasets;
- Interpret the results of network analysis in order to answer relevant biological questions.
Target audience
The course is aimed at PhD students with a background in bioinformatics, systems biology, 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 this course. Prior knowledge of molecular biology is a bonus, but also not strictly required.
Preparation material on probability theory, linear algebra and molecular biology will be provided before the start of the course and should be read by all students before the course starts.
Lecturers
- Prof. dr. Aalt-Jan van Dijk, University of Amsterdam
- Prof. dr. ir. Dick de Ridder, Wageningen University
- Dr. Edoardo Saccenti, Wageningen University
- Dr. Pariya Behrouzi, Wageningen University
- Prof. dr. Jaap Heringa, Vrije Universiteit Amsterdam
- Dr. Anton Feenstra, Vrije Universiteit Amsterdam
Additional information
The course will be held at the Amsterdam Science park, building Lab42.
Software required for the lab course will be available online and (if needed) for installation.
For more information about the course you can contact Aalt-Jan van Dijk
Wildcards
If you would like to join this course using a wildcard please contact the community manager, Petra Aarnoutse.
Until the maximum number of wildcard spots is reached, you can register with a wildcard until 17 March 2025.
Always mention in your application from which academic group the wildcard comes. Your registration is only valid after confirmation from the community manager.
Registration
Registration is open until April 16 (16:00):
- € 480 (excl. VAT) for PhD/MSc students
- € 720 (excl. VAT) for academic researchers (non-profit)
- € 1080 (excl. VAT) industry participants (for profit)
The course fee includes course materials and catering (coffee, tea and lunch)
You can register for the course by filling out this registration form
There is room for max. 25 participants.
Find general enrollment information here.
