b-it Life Science Informatics Lecture Series 2011

This year's Lecture Series is organised by B-IT Professor Holger Fröhlich and B-IT Professor Martin Hofmann-Apitius.
The talks will be held in B-IT Lecture Hall at 5 p.m.

Quantification and modelling of stem cell fate decisions - A conceptual perspective

 17 March 2011 

Professor Dr. Ingo Röder Institute of Medical Informatics and Biometry, Dresden Technical University, Germany  
More information about the lecturer and his work

Mapping gene regulatory networks in /Drosophila/ species by transcriptomics and cis-regulatory sequence analysis

 07 April 2011 

Professor Dr. Stein Aerts Center for Human Genetics (CME), Laboratory of Computational Biology, Katholieke Universiteit Leuven, Belgium More information about the lecturer and his work 


Perhaps the most fundamental aspect of development is the acquisition by a cell of a terminal and stable fate because it marks the usually irreversible commitment to a specific morphology and function. Understanding the molecular mechanisms that control this process has profound implications for stem cell biology and cancer research. Because cell fate commitment is regulated by transcription factors acting as master switches, a mechanistic understanding of the process requires revealing the gene regulatory networks downstream of these factors. We discuss the recent progress on computational predictions of cis-regulatory elements and transcriptional targets in Drosophila. We illustrate these methods to map a gene regulatory network underlying retinal determination in Drosophila. Finally, we discuss the use of next-generation sequencing to measure gene expression across different Drosophila species.

Boolean networks for modeling gene regulation.

14 April 2011 

Dr. Hans Kestler Institute of Neuroinformatics, Ulm University, Germany More information about the lecturer and his work 


At the core of systems biology research lies the identification of biomolecular networks from experimental data via reverse-engineering methods. In this context, Boolean networks provide a model for analysis of gene-regulatory networks. In a Boolean net, a gene is modeled as a Boolean variable over discrete time that can attain two alternative levels, expressed (1) or not expressed (0). 

Several methods have been developed for reverse-engineering these networks from 0/1 time series. However, a general problem in reconstructing these networks from time series data is the large number of different genes compared to a relatively low number of temporal measurement points. Furthermore, even this task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. In this talk I will present two strategies addressing the complexity of the Boolean network reconstruction process. One is centered on the binarization process the other takes advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene.

Bioactivity mapping of chemical space

05 May 2011 

Professor Dr. Jordi Mestres IMIM Institut Municipal d'Investigació Mèdica, Barcelona, Spain 
More information about the lecturer and his work

Integrative analysis of breast cancer: Dissecting heterogeneity in samples and signals

12 May 2011 

Dr. Florian Markowetz Cancer Research UK, Cambridge Research Institute, Cambridge University, Cambridge, UK
More information about the lecturer and his work 


I will talk about computational methods to address heterogeneity of breast cancer at different levels:

  • At the *sample* level we often find cancer cells mixed with immune cells, stromal cells and others. This mixture of cells leads to a mixture of signals when DNA, RNA, or proteins are measured in these samples. I will present an automated and quantitative approach to estimate cell mixtures and devolute molecular signals. 
  • On the level of *patients*, different data types (like copy number alterations and gene expression) can offer complementary perspectives on drivers of disease. When integrating these data to identify homogeneous subpopulations, it is important to distinguish cases where signals are concordant from cases where they are contradictory. I will describe how patient-specific data fusion based on the hierarchical Dirichlet process can reveal prognostic cancer subtypes. 
  • On the *population* level, there exist different distinct sub-types of breast cancer and genetic architecture differs between them. When inferring copy-number hotspots and regulatory networks, these sub-types have to be taken into account. In the last part of my talk, I will discuss how penalized regression can elucidate aberration hotspots mediating subtype-specific transcriptional responses in breast cancer.

Systems Biology of the Erythropoietin Receptor

19 May 2011 

Professor Dr. Jens Timmer Institute of Physics, University of Freiburg, Germany More information about the lecturer and his work  


Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. By quantitative and predictive mathematical models, systems biology promises deeper understanding of cellular processes. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). These receptor characteristics facilitate the temporal resolution of extracellular ligand dynamics while preventing an absolute refractory state of the responsive cell. The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. The linearity of this relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

Nested Effects Models

14 July 2011 

Professor Dr. Rainer Spang Computational Diagnostics Group, Institute of Functional Genomics, University of Heidelberg, Germany More information about the lecturer and his work 


Functional genomics has a long tradition of inferring the inner working of a cell through analysis of its response to various perturbations. Observing cellular features after knocking out or silencing a gene reveals which genes are essential for an organism or for a particular pathway. A key obstacle to inferring genetic networks from perturbation screens is that phenotypic profiles generally offer only indirect information on how genes interact. 

I will discuss a network inference method that we called Nested Effects Models (NEM). It can be used to model the flow of information in cells based on the nested structure of downstream effects of perturbations like RNAi mediated gene knockdowns. Special attention will be given to strategies for controlling network complexity. I will demonstrate the power of our method in the context of modelling disrupted Wnt signalling in colorectal cancers. 

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