Lecture Series "Algorithms in Bioinformatics"
Professor Dr. Marek Karpinski and B-IT Professor Dr. Holger Fröhlich have jointly put together a new Lecture Series in Life Science Informatics that focuses on "Algorithms in Bioinformatics".
"Network Inference with Bayesian Approaches"
Dr. Lars Kaderali Center for “Quantitative Analysis of Molecular and Cellular Biosystems” (BIOQUANT), Heidelberg University, Germany
Venue: Rheinsaal, B-IT, Dahlmannstraße 2
Time: 15:00 hours
The reconstruction of molecular networks directly from experimental high-throughput data is a challenging problem due to the combinatorial explosion of possible network topologies with increasing network size, the typically limited information content of experimental data with insufficient different time points or conditions measured, and high levels of noise in data.
I will discuss different methodological approaches to infer gene regulatory and signal transduction networks from experimental data, based on time-resolved gene expression data. I will particularly focus on two approaches: One to describe small-scale networks at a high level of detail, the second to identify central hubs in large-scale networks using a rather qualitative inference method. A focus of my talk will be on prior distributions to regularize network inference, and drive solutions to networks with specific characteristics such as sparseness or small-world properties. I will show several applications, and conclude by discussing ongoing work and open challenges.
"Operation Research in the Cell: Detecting Functional Module in Protein-Protein Interaction Networks"
Dr. Gunnar Klau Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
Time: 11:00 hours
Venue: Lecture Hall
More information about about the lecturer and his work.
An important topic in systems biology is the identification of functional modules in protein-protein interaction networks by means of detecting jointly differentially expressed network regions. I will discuss an exact integer linear programming solution for this problem, which is based on its connection to the well-known prize-collecting Steiner tree problem from Operations Research.
"Model-based Gene Set Enrichment Analysis"
Dr. Julien Gagneur European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
Time: 14:00 hours
Venue: Room 2.1
More information about the lecturer and his work
The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high overlap between categories. Thus, enrichment analysis performed on one category at a time frequently returns large numbers of correlated categories, leaving the choice of the most relevant ones to the user’s interpretation.
I will present Model-based Gene Set Analysis (Bauer et al. NAR, 2010) in which we tackle the problem by turning the question differently. Instead of searching for all significantly enriched groups, we search for a minimal set of groups that can explain the data. We model the experimental observation by a set of "active" groups. Our model penalizes the number of active groups thus naturally providing parsimonious solutions. Application to a gene expression data set in yeast demonstrates that the method provides high-level, summarized views of core biological processes and correctly eliminates confounding associations.
"Identification of prognostic gene signatures in cancer patients from high-dimensional data"
Prof. Dr. Tim Beißbarth University of Göttingen, Group Head Statistical Bioinformatics, Göttingen, Germany
Time: 15:00 hours
One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.
In this talk we will present our experience from the development of prognostic signatures from practically relevant data sets on cancer prognosis from rectal cancer for the clinical research group 179 as well as from prostate cancer from the NGFN consortium IG-Prostate cancer. We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer data set comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes (Johannes et al, Bioinformatics, 2010). We compare our results with results produced using the method PathBoost (Binder and Schumacher, BMC Bioinformatics, 2009; Porzelius et al, Biometrical Journal, 2010).
"Evolution towards disease"
Professor Dr. Niko Beerenwinkel , Computational Biology Group, ETH Zürich, Zurich, Switzerland
Time: 15:00 hours
Many human diseases are the result of evolutionary processes on time scales much shorter than the human lifetime. Prominent examples of pathogenic, measurably evolving populations are cancer cells in a tumor and infectious parasites, such as bacteria and viruses. Treatment of these constantly changing ensembles of individuals is complicated by evolutionary escape from the selective pressure of drugs and immune responses. I will present several statistical models for the evolutionary dynamics of escape and discuss applications to the genetic progression of cancer and to the development of drug resistance in HIV.
"Fixed point characterization of biological networks with complex graph topology"
Professor Dr. Nicole Radde Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany
Time: 15:00 hours
Feedback circuits are important motifs in biological networks and part of virtually all regulation processes that are needed for a reliable functioning of the cell. Mathematically, feedback is connected to complex behavior of the systems, which is often related to bifurcations of fixed points. Therefore, several approaches for the investigation of fixed points in biological networks have been developed in recent years. Many of them assume the fixed point coordinates to be known, and an efficient way to calculate the entire set of fixed points for interrelated feedback structures is highly desirable. In this talk I consider regulatory network models, which are differential equations with an underlying directed graph that illustrates dependencies among variables. I introduce the circuit-breaking algorithm (CBA), a method that constructs one-dimensional characteristics for these network models which inherit important information about the system. In particular, fixed points are related to the zeros of these characteristics. The CBA operates on the graph topology, and results from graph theory are used in order to make calculations efficient. Our framework provides a general scheme for analyzing network models in terms of interrelated feedback circuits. The efficiency of the approach is demonstrated on a model for calcium oscillations based on experiments in hepatocytes.