A 2-day introduction to methods and applications - December 12&13, 2013

Department of Knowledge Engineering, Maastricht University

Maastricht, The Netherlands

Systems biology is a interdisciplinary research field that focusses on complex interactions within biological systems of varying magnitudes, ranging from single cells to full organisms or even eco-systems.  One of the goals of the systems biology is to automatically discover and model properties, interactions and behavior of/in biological systems.  This is where systems biology meets the field of machine learning.  


This seminar will introduce machine learning approaches most relevant for applications in systems biology, including methods for analyzing structured data, particularly those that deal with structured output prediction, and methods for learning the full dynamics of biological networks, including the network structure and the kinetics of the corresponding reactions.  Example applications will include gene function prediction, predicting the response of genes to stress and analyzing image data from genome screens, as well as the dynamics of endocytosis.

The seminar will consist of 4 half-day blocks.

  1. Thursday morning will be mainly aimed at aligning the vocabularies of the participants and will introduce the audience to a few key concepts from systems biology and machine learning needed in the rest of the course.  Concepts covered will include experimental techniques and modeling in systems biology and machine learning topics such as predictive modeling and clustering. 
  2. Thursday afternoon will cover networks in systems biology, focussing on techniques for network analysis and network reconstruction.  The session will include a demo of (and possibly some practical experience with) “CytoScape”.
  3. Friday morning will  focus on automated modeling of dynamic systems through equation discovery and inductive process modeling.  It will also highlight applications of these techniques in modeling glycolysis and endocytosis and in the design of biocircuits that adapt or exhibit complex behavior.  A demo of (and possibly some practical experience with) ProBMoT developed during the European SUMO project will be used as an illustration.
  4. Friday afternoon will offer a lecture on the prediction of structured outputs using predictive clustering trees and ensemble methods and it’s applications in systems biology such as prediction gene function and gene reaction to stress and analyzing data from genomic and compound screens.  For this session, the CLUS system will be used for demonstrations.

An exact schedule can be found here.


The courses will take place at the Department of Knowledge Engineering, in the Bouillonstraat 8-10 in Maastricht.


The main speaker for this seminar is Saso Dzeroski, one of the world’s leading researchers on machine learning for systems biology.  Saso Dzeroski is Senior Scientific Associate at the Jozef Stefan Institute in Ljubljana, Slovenia.  His main research expertise lies in knowledge discovery methods that use both data and domain knowledge and applications of such methods in environmental and life sciences.  He teaches a course on “Computational Systems Biology” at the Jozef Stefan International Graduate School and has presented a number of tutorials on the subject at various international conferences.

Intended Audience
This seminar is intended for anyone with an interest in systems biology.  A background in biology, mathematical modeling or machine learning will be useful, but is certainly not required.

The seminar is free, but registration is required

To register for the seminar please send an email to:

Kurt Driessenskurt.driessens@maastrichtuniversity.nl and 

Evgueni Smirnov: smirnov@maastrichtuniversity.nl

In the e-mail please specify:

  • Name
  • University / Organisation
  • Address
  • Phone
  • E-Mail

Registration Deadline: December 6, 2013 

Coffee will be available during the breaks. The local cafeteria will be available for lunch (not included).

Kurt Driessens and Evgueni Smirnov 
Department of Knowledge Engineering
Faculty of Humanities and Sciences
Maastricht University
P.O.Box 616
6200 MD Maastricht
The Netherlands
Phone: +31 (0) 43 38 82023
Fax: +31 (0) 43 38 84897

E-mails: kurt.driessens@maastrichtuniversity.nl and



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