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…

The students will be provided with a theoretical basis, a variety of methods, and a computational hands-on experience to set-up systems biology models and handle numerical optimization.

After having followed this course, the student has a good understanding of a wide range of machine learning techniques and is able to recognize what method is most applicable to data analysis problems (s)he encounters in bioinformatics and systems biology applications.

Constraint-based modeling is a powerful modeling methodology that is being used to model a diverse range of biological phenomena. These include both fundamental and applied questions relevant to biotechnology, microbiology and medicine. Central to constraint-based modeling is the use of genome-scale reconstructions that represent particular cellular functions as a biochemical reaction network. In this course, you will be introduced to …..

This course brings together contributions from different disciplines, i.e. bioprocess technology, applied physics, transport phenomena, molecular biology and biomedical sciences. They all address different elements on the coupling between several time and length scales in the simulation of different bioprocesses (e.g. bioreactor and bioprocess operation, microbial strain improvement, tissue and organ cultivation, plant design and integration). Computational methods will deepen the understanding of the connecting principles between different scales. The industrial relevance is underlined by the venue being the Biotech Campus Delft.

Did you ever wonder how to improve your data handling in your daily research practice? Are you thinking of a career change or starting a career as a data steward in the life sciences in academia or industry? Join this introductory course to FAIR data stewardship in the life sciences!

In this course, we will cover genome analysis, variant analysis, and pangenomics. Core concepts, applications, and future trends will be discussed, with a focus on the algorithms and data structures underlying state-of-the-art methods. The course offers an engaging mix of lectures, paper discussions, hands-on tutorials, and a do-it-yourself project.

In this one-week course, the foundations of machine learning will be laid out and commonly used methods for unsupervised (clustering, dimensionality reduction, visualization) and supervised (mainly classification) learning will be explained in detail. Methods will be illustrated using recent examples from the fields of systems biology and bioinformatics. Methods discussed in the morning lectures will be put into practice during the afternoon computer lab sessions.

The aim of the course is to give a broad overview of the different applications of NGS with a focus on different ways to analyse the data. Presentations will discuss specifically the different choices available for data analysis, why a specific approach was selected and which alternatives are available. The course will, besides discussing and demonstrating some generally applicable tools, not include practical data analysis nor discuss in detail specific algorithms used.