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.
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
The about 20 participants will be PhD students and postdocs that obtained their PhD less than five years ago. They will be selected based on the relevance of the course for their research and their motivation to participate in this workshop.
This course is organised by ERA-Net programme for Systems Biology Applications (ERASysApp, https://www.erasysapp.eu/) and the Dutch systems biology and bioinformatics community (BioSB, http://biosb.nl).