Combining metabolomics data with physico-chemical, thermo-dynamical and biochemical knowledge

The aim of this project is to model dynamic measured metabolomic profiles, and to combine this with quantitative and qualitative a priori knowledge. It is the intention to infer from this dynamic information the hidden (non-measurable) dynamic processes of a system.
Using dynamic endocrine data from multiple subjects we want to obtain a mechanistic understanding of the dynamics of regulation. Our working hypothesis is that these features are most pronounced in the dynamics of perturbed or diseased subjects. To formally investigate this hypothesis, a set of statistical dynamic measures was developed, each of them focusing on specific aspects of the dynamic signal. With the introduction of some additional concepts of the interacting agents, the obtained results are probed for hidden drivers that influence the regulatory and control mechanisms.

Main project title: 
Incorporating a priori information
AIO 01-09-10
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