Systems biology is introduced as studying biological systems at multiple levels of organization to understand properties that emerge from interactions between components. Experimental methods like genomics, proteomics, and metabolomics are used to generate large data sets, which are then analyzed using mathematical and statistical tools to model biological networks and systems. Systems approaches are shown to have medical applications in identifying diagnostic markers, personalizing therapy, and predicting comorbidities by analyzing whole disease networks. The future of medicine is proposed to involve predictive, preventive, personalized, and participatory approaches based on systems biology insights.

1. Systems biology for medical students/Systems medicine
Lecture 1: Introduction to systems biology/Introduction to systems medicine
Definition and vocabulary generated for system studies: examples of omics (from real world studies
to computational models).
The major premises behind systems studies: level of organization generates new properties of whole
system like robustness and switching behaviour; increasing level of organization is behind upgrading
in scale or time (bigger, more efficient and more durable); from hypothesis driven studies to
hypothesis generating studies.
Lecture 2: Experimental methods and data sets
Genomics - sequencing and chips.
Proteomics – mass-spectrometry.
Metabolomics – liquid chromatography and mass-spectrometry.
Freely available data sets resources for bioinformatics study: GEO, Target Scan, Swiss-Prot, DbGAP,
OMIM, Pharm GKB.
Lecture 3: Mathematical and statistical tools for systems analysis (How to analyse large data sets)
How using mathematical tools can solve biological problems; finding reliable data and analysing
signalling networks with ordinary and partial differential equations.
Basic terms used in graph theory: nodes, edges, node degree, degree distribution, power low...
Building computational models from data sets using programs like Genes2Networks and
List2networks.
Lecture 4: Medical applications
Examples of system biology approach for finding key diagnostic markers, personalizing therapy or
predicting co-morbidity from whole human disease network.
Future medicine based on systems biology: P4 (predictive, preventive, personalized, participatory).