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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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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