Content (Syllabus outline)
Analysis of omic datasets: introduction of omics technologies (e.g. transcriptomics, Ribo_seq, TRAP-Seq, proteomics, metabolomics) experimental design and FAIR data management, statistical analysis (including time-series, dose-response curves), databases and knowledge-bases in systems biology, from knowledge-bases to knowledge networks, causal networks, multi-omics data integration, visualization of omic data,
Basics of systems biology modeling: development of mathematical models, different approaches to mathematical modeling (deterministic modeling, stochastic modeling, metabolic modeling, Bayesian networks, Boolean models), statistic inference, uncertainty estimation.
Interpretation of omic data: graph theory methods, integration of omic data into knowledge networks, integration of omic data in systems biology models, causal modeling.
Prerequisites
Prerequisites for admittance to final exam:
- presence at at least 80% of practicals,
- seminar presentation.