Advances in Statistical Modeling of High Dimensional Data:
Variable selection, and Challenges in Image Analysis

Tim Beissbarth, Uni Göttingen

Reconstruction of signaling networks from gene intervention data

Signalling processes are the key to understand the functions of a living cell. These processes are often complex and little understood. In Systems Biology different levels of quantitative and qualitative modeling of these processes are proposed in order to predict the response of cellular systems or drug treatments for complex diseases. Here, we focus on methods that reconstruct the architecture of such networks. Mere correlation analysis is usually not enough to understand the complex modes of action in a living cell. Active interventions into the system, for example by up- or downregulating certain genes, are crucial to reconstruct signalling networks. RNAi has become a useful tool to quickly produce such interventions. Modern techniques for gene or protein expression analysis are efficient tools to monitor the effects of such interventions. We are working on the development of several methods to reconstruct signalling networks from interventional data: Nested Effects Models are designed to reconstruct signalling networks of few interventions based measurement of high-dimensional effects. Deterministic Effects Propagation Networks on the other hand are designed to reconstruct signalling networks from high-dimensional interventions with direct effects measurements. Here we will give a short overview of these methods and demonstrate their application on example datasets connected to the medical treatment of cancer.

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