The Digital Revolution effects and changes the Life Sciences (biology and medicine) thoroughly. An important aspect of this is the generation and management of digital biological and medical data..
Data acquisition methods from molecular biology (e.g. Next Generation Sequencing) yield such amounts of data (omics-data) that their analysis and interpretation requires sophisticated software tools. Bio-medical sensors (as for example glucose sensors with diabetes patients) generate raw data which have to be mathematically filtered and cleaned from noise and disturbances. ECG und EEG measurements yield complex data signals with their characteristics and patterns being indicators of health or illness of the patients.
Typically, the biological system and the measurement device are connected via sensors and software. Mathematical methods are important mediators between all of these components.
The disciplines of systems biology and systems medicine establish bridges between biology, medicine, information technology and mathematics. They aim at a comprehensive understanding of biological processes on all levels. With this knowledge, causes and mechanisms of diseases are better understood, and new diagnostic and therapeutic tools can be developed.
We use mathematical methods to develop software tools for analysis, interpretation, and visualization of bio-medical data. Our methods include:
Model Based Approaches:
- Stochastic differential equations
- Hierarchical Bayes models
- Population models
Data Based Approaches:
- Big Data
- Machine Learning
- Deep Learning