notes for presentation

1. “A cross-border region where rivers connect, not divide” –
Interreg V-A Hungary-Croatia Co-operation Programme 2014-2020
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).