Biological systems function through the dynamic interplay of large numbers of components. At the molecular level these include genes, transcripts, proteins and metabolites. At higher organisational levels the main players are cells, tissues, organs and organisms. Understanding biological systems, for instance in the context of biomedical and industrial applications, requires combining multiple diverse data sets on all components and their interactions. This integration process is hampered by the fact that in modern data acquisition technologies each concentrates on one specific component of the system, e.g. a specific type of molecule. Proteomics, metabolomics and transcriptomics are examples. To overcome this hurdle novel approaches are being developed, enabling the integration of disparate data sets in ways that are biologically sound and that provide insight into the architecture and dynamics of biological systems.
The basic concept in this course is that diverse data sets can be integrated in predictive and quantitative computational models. Depending on the types of data and the research aim, optimal integration and modelling approaches must be selected.
Date: Feb 2-6, 2015
Target audience: PhD students and postdocs that obtained their PhD less than five years ago.
Program: Aim of the five days course is to provide students with the following knowledge:
- an overview of different types of data sets and data integration approaches
- hands-on training in applying such approaches in selected case studies
- insight into how such approaches may affect their individual research project