Education

The Pattern Recognition & Bioinformatics group contributes to the Bachelor Programmes of the following curricula:

  • Computer Science (BSc CS)
  • Electrical Engineering (BSc EE)
  • Nanobiology (BSc Nanobiology)
  • Clinical Technology (BSc Clinical Technology)
  • Life Science & Technology (LST)
  • and to the following Master Programmes:
  • Computer Science (the Data science and Technology track)
  • Bioinformatics track (with Leiden University)
  • Nanobiology (with Erasmus MC)
  • The group is also involved in a number of postgraduate courses co-organized by the Research Schools: Advanced School for Computing and Imaging (ASCI) and BioSB
    The following core-coursed are (co-)organized by the Bioinformatics lab:
    Course Contents
       
    IN4329 Advanced Bioinformatics Bioinformatics is at the heart of many modern systems biology analyses, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role in finding the genetic origins of various diseases, such as cancer, diabetes or alzheimer.  In this course we will study some key examples of bioinformatics analyses by reading a set of selected papers that present some significant biological conclusions. Instead of the teachers giving lectures about the methodologies, the students are stimulated to read, study and comprehend the available course material.
       
    IN4176 Functional Genomics and Systems Biology 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. 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 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 and for finding active subnetworks.
       
    NB2161 Bioinformatics Bioinformatics is increasingly important to help answer biological questions, to deal with large volumes of measurement data which are now easily generated in the lab in the light of the vast amounts of prior knowledge already present in biological literature and stored in databases. In this course, the student will learn how to use computer methods as tools for biological research. An overview of core application software, databases and online applications will be provided, as well as in-depth knowledge on key algorithms and procedures to process and analyze sequence data and quantitative measurements. The course offers both theoretical insight and practical experience, in lab courses and small projects to be performed.
       
    NB4040 Biology of Cancer In this course the student will learn how cancer is initiated and promoted through a multi-step process. Oncogenes, tumor suppressor genes and viruses, the drivers of cancer, will be discussed. The course will address how key processes for the progression of cancer, signal transduction, cell cycle control, telomere homeostasis, and cell metabolism, are deregulated. The importance of cell-cell interactions and cell migration for tumor metastasis, as will as angiogenesis, resistance mechanisms to apoptosis and immune surveillance escape will be discussed. Classification among tumors based on classical histology will be contrasted to new molecular approaches to detect subgroups within tumors. The working mechanisms of classical anti-cancer treatments will be presented and compared and contrasted to precision medicine approaches made possible by genome-wide molecular characterizations.