In biology and the medical sciences, complexity and detail prevail. Many branches of mathematics may be employed to great effect to provide biological insight.

Many modern medical applications require sophisticated statistical approaches. For instance, we are developing new methods to find differences between healthy and diseased brains. This involves reconstructing neural activity from non-invasive scanning measurements such as EEG, MEG and fRMI. In this way we can identify networks of cortical activity, and find persistent differences between these. Second, we develop new statistical methods to find relevant genes involved in diseases from omics data. For example, we have investigated finding the genes for a disease that evolves over time, such as cancer. Modern gene array experiments yield incredibly large datasets on the activity of the whole genome, but finding the relevant genes is one of today’s most prominent fields of biostatistics. A third example involves estimating the parameters controlling stochastic gene expression from time series data of mRNA and protein copy numbers.
Lastly, if cellular or other physical processes of interest can be described in terms of countable event occurences, a portion of which is typically unobservable, then estimation of (non)parametric model components is achieved through survival analytic techniques. Epidemiologists use these methods to assess, for instance, how individual patient characteristics affect a medicine's effectiveness or the patient's chances of survival.

The department also has a long-standing collaboration with systems biologists. Here, we try to develop dynamical models of cellular processes and behaviour. How does a single cell sense its environment and deal with stochastic information? How does it decide whether to invest into growth processes or instead mount stress responses? Understanding such broad questions, relevant for all of unicellular life, requires both stochastic and deterministic modelling, formal analysis of ODE and PDE models, and optimization and control theory. Statistics in Life Sciences Amsterdam focusses on statistics in the area of life science.

In all our research, we strive for the results to be maximally applicable in the original biomedical fields from which the problems originated, by cooperating closely with biologists and medical research staff.


Dr. Daniele Avitabile
Dr. Dennis Dobler
Prof. dr. Mathisca de Gunst
Dr. Rikkert Hindriks
Prof. dr. Joost Hulshof
Dr. Bob Planqué
Dr. Wessel van Wieringen