BioSB

Course date 13-17 December 2021, Wageningen Location: The course will be held at: Forum Building (building nr 102), with number B0409. A Biocafe (on Thursday) will be held in “The Spot” in Building Orion (gebouw nummer 103) in the Wageningen University campus Course coordinator Natal van Riel, Eindhoven University of Technology Lecturers Prof. Natal van Riel, Eindhoven University of Technology… Continue reading →

Living organisms are characterized by an amazing degree of hierarchical complexity. Although our ability to collect measurements at different spatial levels and time-scales has grown dramatically, it has become clear that only measurements cannot provide the answer to unravelling biological complexity. This is because the dynamical behavior of complex systems cannot be reduced to the linear sum of the functions of their… Continue reading →

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;… Continue reading →

From October 5-9, 2020 the next BioSB course on Machine Learning for bioinformatics and systems biology will be organised as an online course. Course overview Modern biology is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels. Interpretation of these large-scale biological data requires the detection of… Continue reading →

Course overview Modern biology is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels. Interpretation of these large-scale biological data requires the detection of statistical dependencies and patterns in order to establish useful models of complex biological systems. Techniques from machine learning are key in this endeavour.… Continue reading →

Course description 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… Continue reading →

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:… Continue reading →

Comparative genomics aims to compare large sets of genomes in order to understand and explain differences in traits of an organism. Contemporary methods are powered by fundamental algorithms and data structures, which are efficient and scale to large data sets. A thorough understanding of these algorithms and data structures is necessary for advanced users and developers in this area. In… Continue reading →

This introductory course gives an overview of many statistical tools to analyse omics data. The course can be followed by researchers with a minimum or elementary background in quantitative data analysis. Participants will learn and practice commonly used tools including: Tools to explore datasets including clustering, principal components and network analysis Models to answer basic statistical questions: differential behaviour (e.g. mRNA… Continue reading →

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… Continue reading →