In Silico Modelling

photo credits: Copyright Medical University Graz

Experimental Design

• Unbiased, adequately powered, with a wide range of applicability, amenable to statistical analysis, simple and efficient designs 
• Proper sample size calculation (in accordance with the 3Rs) to produce reliable and predictive results
• Power analysis for given sample sizes and effects of variation in retrospective studies
• Project support from the first concept until the interpretation of your data

Data Analysis

• State-of-the art know how for bioinformatics and statistical data analysis
• Broad range courses focused on bioinformatics and biostatistics (zmf.medunigraz.at/merag)
• Growing number of tools for data processing and analysis
• Access to a platform for reproducible, and transparent computational biological research (galaxy. medunigraz.at)
• Access to a High Performance Computing Cluster (MedBioNode) for comprehensive data analysis

CONTACT
Andrea Groselj-Strele
Medical University of Graz
andrea.groselj-strele@medunigraz.at

photo credits: Copyright Medical University Graz

The design development and evaluation of algorithms, methods and tools support to understand the underlying principles of diseases, particularly cancer. One of the keys to understand the concepts of cancer lies within an integrative translation and multi-dimensional connection of open data sets. AI/machine learning approaches to biomedical analysis and simulation involve several techniques such as validation, classification, inference, prediction and modeling. Existing well-maintained databases provide, integrate and annotate information on various diseases and are increasingly being used to generate predictive models, which in turn will inform and guide biomedical experiments.

CONTACT
Andreas Holzinger
Medical University of Graz
andreas.holzinger@medunigraz.at

photo credits: Copyright Medical University Graz

Anatomically accurate and biophysically detailed in silico models of the heart are able to replicate cardiac function under a broad range of experimental or clinical conditions. They are used to complement or even replace experimental models such as single cell patch clamp, tissue monolayers, Langendorff or working heart models.

Capabilities

• Advanced model building workflows comprising image based finite element meshing and cardiac navigation systems for local feature control
• Multiphysics simulation engine (cardiac electrophysiology, biomechanics and hemodynamics)
• Cellular dynamics and biomaterial models for various species
• Advanced closed loop hemodynamic models
• Cardiovascular blood flow simulations
• Data analysis tools for biomarker extraction
• Models of pacing, defibrillation, cardiac resynchronization and valve therapies
• Parameterization and data assimilation techniques to match simulation with experimental or clinical data

CONTACT
Gernot Plank
Medical University of Graz
gernot.plank@medunigraz.at