Mathematical models are essential in answering medical and biological questions. Such models typically involve many parameters that need to be estimated from data. How sure can we be about these estimated parameters? That is one of the questions in statistics for life sciences.
The human brain is one of the best examples of complex organized systems in biology. In collaboration with other groups within the Neurocampus Amsterdam, statistical methods are developed to interpret the data of several imaging methods (EEG, MEG, MRI, fMRI, PET). Moreover, statistical techniques are developed to find differences between healthy and diseased brains. Also, the complex process of neuron outgrowth and connections between neurons is studied, leading to new measures for quantifying the connectivity in such networks. So-called “small world networks” are efficient networks that quickly transfer information using relatively few connections. How small a world is our human brain? A third example in stochastics arises in finding genes that play a role in susceptibility to diseases. 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.
The department also develops and analyses deterministic models of biological and medical problems. Examples include collective behaviour and social structure in ant colonies, cell growth models, and glycolysis in yeast cells. Our expertise spans from model building, derivation of differential equation models, to formal analysis of ODE and PDE systems, including geometric and topological approaches.
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.