Recent technological advances in high-throughput methods (such as NGS) and quantitative proteomics, allow us to analyse each individual all the way down to his/her proteins and chemicals and his/her own DNA sequences in just a few hours. In near future we may expect that a specifically trained physician will be able to use this type of data and quickly find support for deciding on individualised therapy, on the basis of analyses made on these data: which drugs, what health risks, what consequences of lifestyle changes, which diets or rehabilitation measures are appropriate for a patient with such an individualised profile.
This training course we will provide hands-on experience on the analysis and modeling of biological systems from several angles. First of all, we will introduce systems biology and modeling from an interaction- and network-based perspective. We will familiarise the audience with publicly available data resources, pathways databases, Reactome, KEGG, TRANSPATH, TRANSFAC and ConsensusPathDB. Next, we will provide a soft introduction to mathematical modeling of biological systems, and we will look at different mathematical modeling stategies using simple Boolean networks, Petri Nets and ordinary differential equation systems (ODEs). We will train the participants in the use of computational tools, such as Cytoscape and CellDesigner, for the set-up and development of model prototypes. We will then move into the usage of Copasi, BioUML and PyBioS, that can handle parameter-fitting and sensitivity analysis.
We will then look at methods for reconstructing gene regulatory networks from gene expression data. It will introduce methods of analysis of topological properties of biochemical and regulatory networks. This will lead us to the application of such methods to revealing key nodes in networks as potential biomarkers or drug targets. The participants will be developing group work towards the validation of such drug targets, with the help of dynamic modeling of the network systems. We will work with real case examples of application of these methods for identification of disease related biomarkers, drug discovery and personalized medicine. In some of our hands-on training sessions we will be constructing workflows “From genome to drug targets” using the flexible geneXplain platform.