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9-13 December 2019, WUR, Wageningen

In this course the participants learn how to describe the dynamic behavior of biological systems, and how to use optimization techniques to solve a variety of problems in bioinformatics and systems biology. Concepts of modelling in terms of differential equations are introduced via a great variety of case studies taken from diverse practices. This course also provides you with the main ideas underlying classical optimization, such as steepest descent methods, Levenberg-Marquardt approach, genetic algorithms, global and local search methods,  and to get you acquainted with the optimization techniques that are nowadays available and widely used. Examples of optimization problems in life sciences will be presented and discussed.

The course is a mixture of theory sessions and computer practicals. During the practicals we will work with the easy-to-use package PPlane for phaseplane analysis and most of the time with Matlab. Participants not acquainted with Matlab will get the opportunity to follow an introduction module. The course offers a math refresher to help those participants who are not (yet) involved in modelling on a daily basis. The course is completed with assignments in the form of practical exercises as homework afterwards.

More information?

See BioSB website:

https://www.biosb.nl/archive-courses/modeling-and-optimization-2019/

At the end of November, there is a BioSB course that is still up for registration! Grab your chance and join the course at Leiden University:

Constraint-based modelling: introduction and advanced topics

25-29 November 2019, Leiden University

Constraint-based modelling 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 modelling 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:

• the principles of constraint-based reconstruction and analysis (COBRA)
• the underlying mathematical foundations of constraint-based modeling
• methods for integration of omics data with constraint-based models
• basic and advanced methods for interrogating models and interpreting results
• open source COBRA software, including the COBRA Toolbox
• standards for reconstruction and model sharing
• example applications to biomedicine and biotechnology

For more information, see BioSB website:

https://www.biosb.nl/archive-courses/constraint-based-modelling-introduction-and-advanced-topics-2019/

Date

June 25-29, 2018

Location

Wageningen University & Research (rooms TBA)

Course coordinators

• dr. Maria Suarez Diez (WUR)
• prof. dr. Dick de Ridder (WUR)
• dr. Jeroen de Ridder (UMCU)

Lecturers

• dr. Maria Suarez Diez, Wageningen University
• prof. dr. ir. Dick de Ridder, Wageningen University
• dr. ir. Jeroen de Ridder, University Medical Center Utrecht
• dr. K. Anton Feenstra, Vrije Universiteit, Amsterdam
• dr. Aalt-Jan van Dijk, Wageningen University
• prof. dr. Jaap Heringa, Vrije Universiteit, Amsterdam

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 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 material can be found on the course website.

Registration

You can now register via the enrollment form.