Systems Theory and Automatic Control

Systems and Control Seminars for the summer semester 2009

Statistical approaches for the inference of biological networks


Nicole Radde

Time and Place

The lecture will be given on June 25, 2009, 5 p.m. in the lecture hall V005-2 at the Max-Planck-Institute Magdeburg.

The problem of reverse engineering intracellular networks from experimental data often results in a highly nonlinear and underdetermined optimization problem. Data are usually noisy and sparse, and the systems are intrinsically stochastic. Therefore, a stochastic modeling framework and statistical approaches for network inference are appropriate in this setting, since they naturally take uncertainties and measurement errors into account. In my talk I compare likelihood functions of different time-discrete stochastic models which have been suggested to capture stochastic effects in biological network models. I propose to classify those models into three groups, according to the interpretation of the origin of stochasticity. General expressions for likelihoods are developed, and a comparison of those across the groups is provided. This method also suggests a way to separate noise in biological systems, which is illustrated on a small sample network.

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Information about the Speaker

Since several years my research focuses on the investigation of intracellular network dynamics at a molecular level.This was a complete change after studying physics and writing my diploma thesis in nuclear astrophysics at the University of Darmstadt. In 2007 I finished my PhD thesis entitled 'Nonlinear dynamic phenomena in biochemical networks' at the Institute for Applied Computer Science at the University of Cologne. After this, I went to Leipzig to work as a postdoc in the Statistics and Computational Biology group at the Institute for Medical Informatics, Statistics and Epidemiology. Since October 2008 I am working in as a Junior-Professor the Institute for Systems Theory and Automatic Control at the University of Stuttgart. My research interests include: Graph-based approaches for analyzing feedback mechanisms in biological networks, Statistical approaches for parameter estimation from experimental data, Modeling the dynamics of intracellular networks, in particular, signalling pathways and secretory pathways

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