Systems biology - medical statistics - bioinformatics


There is a simple reason why we make modules mandatory that are heavy on the stats and maths: You cannot do biomedical research or conduct scientific studies in- or outside academia without a good grasp of statistics, systems biology or bioinformatics, its tools and programming languages. Vast masses of data are created when using high-throughput and high-content research methods in biomedicine such as Next Generation Sequencing, automated digital flow cytometry or various mass-spectrometry techniques, to name only a few. Those data have to be processed and analysed to yield results and make progress in biomedical research possible.

In Medical Life Sciences, you start with the basics of Systems biology and Medical statistics to continue with Bioinformatics.



Systems biology provides you with a basic understanding how to use computer models of biological systems to understand and predict biological phenomena.  When modelling systems, you will often use networks. Those networks can represent metabolic networks of individual cells, for instance. Those networks can also integrate into larger networks, for example when considering metabolic exchanges between organs or within microbial communities. Using computer models in systems biology allows you to interpret data in the context of knowledge you already have about a system:  you can propose as well as validate molecular mechanisms that underlie biological phenomena; outcomes of wet lab experiments can be predicted to reduce the experimental effort you would need without employing models.
To build and use models you work with lots of data from many disciplines (e.g. genetics, cell biology, biochemistry, pharmacology): It is demanding and requires knowledge of biology, thinking in concepts and connections, a good head for numbers and equations and wielding tools for computer-based data analysis confidently.

Medical statistics provides you with the toolkit to analyse the data you gain in research, which in today’s biomedicine are mostly BIG DATA. Depending on the scientific hypothesis you want to test, you need to be able to determine which kind of data sets you need; the data sets determine which statistical methods you have to use to analyse your data correctly in terms of scale, validity, their relation to other data sets. Data sets that are worked on with the wrong statistical approaches lead to wrong results and wrong conclusions – always very bad in science.

You will be introduced to the most important statistical methods and tests for medical sciences and writing basic R scripts. You will need R again and again, it is so fundamentally important that you cannot do without it.

programmingBioinformatics builds on your knowledge of systems biology, your understanding of statistics and your ability to use computational tools to analyse large data sets. Learning to program and to develop software pipelines will allow you to take full advantage of bio-computing. It is an integral part of biomedicine because genetics, proteomics and metabolomics are big drivers in biomedicine; all three yield vast amounts of data. From elucidating the genetics of pathogens (take current research on mutations of the SARS-CoV-2 virus) to throwing more light on disease mechanisms of non-communicable diseases such as diabetes or cancer: working without huge data sets is already next to impossible. It will be part of tomorrow’s medical research in almost every field. No bioinformatics – no biomedicine.

Bioinformatics, Medical statistics and Systems biology will definitely play an important role in your studies and careers. Getting your head around how to analyse data sets and interpret your data in context is a challenge you have to face – in your thesis work and in jobs in biomedicine, medical statistics and bioinformatics are part of the parcel. If that is not your cup of tea, you won’t get joy out of biomedical research, no matter how fascinating you find Oncology, Evolutionary Medicine or Inflammation: you can’t escape data processing and analysis.