Training course on ‘Data Integration in the Life Sciences’

An ERASysAPP training course on ‘Data Integration in the Life Sciences’ is in preparation for the autumn of 2014.

The Netherlands systems biology platform (SB@NL) will organise an international ERASysAPP training course on ‘Data Integration in the Life Sciences’ in the autumn of 2014. The FP7 ERA‐net programme for Applied Systems Biology (ERASysAPP) is a Europe-wide initiative in which 16 funding organisations cooperate, and that aims at funding transnational research, networking, and sharing existing resources (

Understanding biological systems requires combining measurements of all components and their interactions at relevant time‐ and length-scales. This integration process is hampered by the fact that in modern technologies mostly one specific component of the system is measured, as in (epi)genomics, proteomics, metabolomics, transcriptomics and modern microscopy. To overcome this hurdle, novel approaches are being developed enabling the integration of diverse data sets in ways that are biologically sound and that provide insight into the architecture and dynamics of the biological system under study.

The basic concept of this course is that diverse data sets can be integrated in predictive and quantitative
computational models. Depending on the type of data and the research aim, optimal integration and
modelling approaches must be selected.

The course creates a solid basis for:

  • in silico exploration of the architecture and dynamic behaviour of complex biological systems,
  • identification of critical experiments for model validation and improvement, and thereby deepening our understanding of the system,
  • identification of effective and realistic routes towards biotechnological, pharmaceutical and biomedical applications.

The five days course aims at providing students with:

  • 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.

For didactic reasons the course will focus on metabolic systems, with futher attention for signal transduction systems and gene regulatory networks. Lectures and case studies will address different types of genome‐wide data sets (e.g. transcriptomics, proteomics, metabolomics), as well as small scale dynamic biochemical data sets (e.g. from microscopy), and published data, models and maps.