[BBC] Register now for BioSB course: Algorithms for Biological Networks - June 25-29, Wageningen

[Femke Francissen]

BioSB Course: Algorithms for Biological Networks (5th edition)Course
coordinators: dr. Maria Suarez Diez (WUR), prof. dr. Dick de Ridder (WUR),
dr. Jeroen de Ridder (UMCU)

*Date:* June 25-29, 2018
*Location:* Wageningen University & Research
*Website:* link to course page
<https://www.biosb.nl/education/course-portfolio/course-algorithms-for-biological-networks/>
*Registration:* link to registration form
<https://www.biosb.nl/education/course-portfolio/course-algorithms-for-biological-networks/enrollment-afbn-course-5th-edition/>

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 interaction and 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.

Course programme
The tentative course programme and course materials can be found here
<http://www.bioinformatics.nl/courses/BioSB-AfBN/>.

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”
<https://www.biosb.nl/education/course-portfolio/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.

BioSB research school

This course is part of the Education Programme of BioSB
<http://biosb.nl/education/course-portfolio-2/>, 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.
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