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 course “Pattern recognition” and the ASCI course “Advanced pattern recognition” (a1) discuss 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 at the course website and should be read by all students before the course starts.

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 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 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 sub networks.

Course programme
The course website, including full course programme, course material and, can be found here.

More information about BioSB can be found at www.biosb.nl.

On April 25, 2015, BioSB organises the fourth edition of the Kick Start R course in Amsterdam.

The goal of this kick-start course is to:

1. Introduce the structure and philosophy behind the R-language.
2. Introduce a number of tools specific to the R-language.

This is an introductory course to speed up the study of R.

Target audience
Primarily targeting researchers from academia, but participants from the private sector are also welcome.

The course is part of the Education Programme of BioSB, the Netherlands Bioinformatics and Systems Biology Research School, which offers training and education for in bioinformatics and systems biology. More information about BioSB can be found at www.biosb.nl.

More information about BioSB can be found at www.biosb.nl.

Description
Constraint-based modelling is a powerful modelling 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 modelling is the use of a genome-scale reconstructions (GSR’s) that maps out the metabolism of a cell as a biochemical reaction network.

In this course you will be introduced to:

– techniques for the construction of a working GSR: tools, tricks and pitfalls
– the underlying mathematical definition and description of a constraint-based model
– methods for interrogating models and interpreting results, such as Flux Balance Analysis and Flux Variability Analysis
– software and standards

The preliminary course structure will be to divide each day into both lectures and practical sessions so that learned theory can immediately be applied in silico.

Target Audience
This course is primarily targeted at academic researchers such as PhD students and Postdocs in Life Sciences, Biotechnology, Bioinformatics, Biomedical Engineering, Microbiology and Systems Biology. Participants from the private sector are welcome to apply. Including both introductory and advanced components, participants would benefit from some knowledge of either biological modelling, metabolism or microbial physiology.

More information about BioSB can be found at www.biosb.nl.