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CIM-School of Computer Science Seminar Series

Computational methods to analyze large-scale and high-dimensional gene perturbation screens

Florian Markowetz
Princeton University

March 26, 2008 at  9:30 AM
George Zames Room MC437

Recent advances in biotechnology have produced a wealth of genomic data, which capture a variety of complementary cellular features.

One of the key sources of data stems from observing phenotypes after perturbing the activity of target genes in the cell. While these data promise to yield key insights into molecular biology and medicine, much of the information present remains underutilized because of the lack of scalable approaches for detecting signals across large, diverse data sets. In my talk I will introduce computational methods to analyze gene perturbation screens from different perspectives and in combination with other sources of data to gain a detailed understanding of cellular mechanisms. In particular, my talk covers three different approaches:

First, I will describe Nested Effects Models, a novel methodology to efficiently infer features of pathways from the nested structure of observed perturbation effects. Second, I will introduce a probabilistic model that integrates high-throughput data with gene perturbation experiments and existing hand-curated biological knowledge-bases. Third, I will show results of a study on the effects of knocking down Nanog, one of the key transcription factors regulating self-renewal and differentiation in embryonic stem cells. Our computational results reveal a central part of the regulatory network that revolves around Nanog and guide further research on Nanog's role in differentiation of mouse ES cells.

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Florian Markowetz holds degrees in Mathematics and Philosophy from the University of Heidelberg, and a Ph.D. in Computational Biology from the Free University Berlin. His Ph.D. thesis was honored by the Max Planck Society with an Otto-Hahn medal. He is presently pursuing postdoctoral research at Princeton University.