b-it Lecture Series in Life Science Informatics: Statistical and Algorithmic Bioinformatics Winter Semester 2012/2013
b-it Professor Holger Fröhlich organises as in the previous years - a lecture lecture series in this winter semester. This winter semester the focus is: Statistical and Algorithmic Bioinformatics. All lectures will be held in b-it Lecture hall at 17.00 hours (5.00 pm).
Modeling heterogeneities in gene expression during cellular decisions
Prof. Dr. Dr. Fabian Theis German Research Center for Environmental Health, Technical University Munich, Germany
Cell-to-cell variations in gene expression underlie many biological processes. Currently more and more experimental tools are becoming available in order to observe these variations, and to draw conclusions on underlying processes - for instance Munsky et al [MSB 2009] have shown that such information can be used for reducing model indeterminacies. However, given these experimental advances, we are now facing a series on computational questions dealing with these data, since classical analysis tools are often tailored to population averages.
Here I will present three such analyses and models on different scales: I will start with a genome-scale mixture model for the analysis of microarray data from stochastic profiling, first proposed by Janes et al [Nat Meth 2010]. Then I will discuss the analysis of single-cell PCR expressions using nonlinear dimension reduction with an application to data from embryonic stem cell differentiation. I will finish with a molecular model of blood cell differentiation trained from time-lapse microscopy data.
Network learning for disease biology
Dr. Sach Mukherjee The Nederlandse Cancer Institute, Division of Biochemistry, Amsterdam, the Netherlands
More information about the speaker
An emerging approach in systems biology and personalized medicine is that of relating molecular networks to disease outcomes and treatment. In a nutshell, the idea is that networks that describe biology relevant to disease phenotypes may differ between patients, or between patient subpopulations. A major computational challenge is to develop algorithms and protocols by which to characterise such network variation in a systematic manner and thereby better understand drug response or disease progression.
Using cancer signalling as an paradigmatic example, I will discuss our ongoing efforts to develop network inference approaches for this problem, including scalable tools for time-course data, non-linear models and validation frameworks that can be applied in the complex mammalian settings relevant to disease biology. Along the way I will discuss some of the caveats and fundamental concerns in this general area.
Machine Learning and the Missing Heritability of Complex Diseases
Prof. Dr. Karsten Borgwardt Max Planck Institute for Developmental Biology and for Intelligent Systems, Tübingen, Germany
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Around the world, large genetics consortia are collecting genotypic and phenotypic data to discover the genes underlying complex, heritable diseases. However, the genes discovered to date only explain an unsatisfactory fraction of the heritability observed. This phenomenon is referred to as the problem of "missing heritability".
In my talk, I will describe how machine learning can help to discover the missing heritability of complex diseases and will report findings and results of our current work in this direction.