We are a group of researchers working in different scientific institutions in Dresden, Germany, interested in developing and applying computational and mathematical approaches to solve significant problems in biology and biomedicine. Our research interests comprise a broad spectrum covering Bioinformatics, Systems Biology, Bioimage Informatics, Computational and Evolutionary Genomics, Modelling and Simulating Biological Systems, Engineering, and the Analysis of Regulatory Networks. To learn more about our research interests, please visit the webpages of the individual groups below.
Our groups have openings for PhD, postdoc and staff positions. Our groups are also part of the prestigious Dresden International PhD Program (DIGS-BB & IMPRS-CellDevoSys), which runs two PhD selections (Spring and Fall) every year. We especially encourage students with a background in bioinformatics, computer science or (applied) mathematics to apply to the next PhD Selection. Students can get a PhD in computer science, physics or applied mathematics. The Technical University Dresden offers curricula in Bioinformatics, Computer Science, Mathematical Biology and Mathematics. We are also involved in teaching a Minor in Computational Biology and Bioinformatics to undergraduates at the TU Dresden.
Dresden provides an excellent environment for life sciences and information technology as well as a vibrant and highly international research community with interdisciplinary collaborations between the Technical University, two Max Planck Institutes and other research institutes to solve problems in computational science, molecular, cell and developmental biology, regeneration, biotechnology, image processing, high-performance computing, bioengineering, and biophysics. In 2006, Dresden was awarded the title "City of Science" within Germany.
Dresden, the capital city of the Free State of Saxony, with its scenic historic center on the Elbe river belongs among the most beautiful towns in Germany. Dresden has a rich cultural history and is close to the beautiful national park of Saxon Switzerland.
To learn more about life in Dresden, click here.
|Lutz Brusch||Carlo Vittorio Cannistraci||Andreas Deutsch|
|Michael Hiller||Lars Kaderali||Gene Myers|
|Maria Teresa Pisabarro||Ingo Roeder||Ivo Sbalzarini|
|Michael Schroeder||Pavel Tomancak||Axel Voigt|
In close collaborations with experimentalists, we study by means of mathematical models, analysis and simulation the regulatory mechanisms that underlie spatio-temporal patterning processes inside cells or in developing and regenerating tissues.
We follow a transdisciplinary approach integrating information theory, machine learning and network science to investigate adaptive processes that characterize complex interacting systems in biology and medicine. This knowledge is leveraged to create novel and more efficient artificial intelligence algorithms; and to perform advanced analysis of patterns hidden in biomedical data, signals and images. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (like topology and manifold theory) to characterize many-body interactions in quantitative biology. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network biology, personalized biomedicine and combinatorial drug therapy.
The department "Innovative methods of computing" develops mathematical models and simulation tools for detecting organizational principles of selected biological systems. We focus on collective phenomena of "interacting cell systems". Disorders in cell interaction can imply diseases and malignant pattern formation (e.g. tumor growth). Important insights into function and regulation of biological systems can be gained from the linking of mathematical modeling and computational tools with biological/biomedical (in vitro, in vivo) data.
Our group uses comparative and evolutionary genomics to associate changes in phenotypes between different species to their underlying genomic change. To this end, our research interests include improving methods for genome-wide alignments and ancestral reconstruction, measuring divergence during evolution by incorporating biological knowledge and analyzing large biological data sets, especially genomic sequences.
Our group develops and applies mathematical models and simulation strategies of regulatory processes on the molecular, the cellular, and the tissue level, including aging-related mechanisms, cancer development and treatment, and viral infection. Furthermore, the group works on the development of computational methods to study and understand biological systems based on complex, high-content experimental data using statistical and machine learning tools, e.g. the processing and analysis of RNAi screens or next generation sequencing data.
As part of the new Center for Systems Biology the goal of our group is to develop optical devices, molecular reagents, and analysis software to monitor in as much detail as possible the concentration and localization of proteins, transcripts, and other entities of interest within a developing cohort of cells for the purpose of working toward a biophysical understanding of development at the level of cell communication and force generation. The group primarily specializes in informatics and secondarily optical engineering, working with other groups in the Dresden area on the development of molecular probes, specific biological questions in development and cell biology, and on biophysical and genetic analyses and models.
Our group develops and applies computational strategies for functional genome annotation and rational engineering as strategies for target/drug discovery and biotechnology innovation. By applying structure-based computational methods, molecular modelling and computer simulation techniques we try to decipher the relationships between protein structure and function in order to understand the molecular basis of protein recognition.
Our group develops and applies mathematical models and simulation strategies of regulatory processes on the molecular, the cellular, and the tissue level, including e.g. stem cell organization, aging-related mechanisms, cancer development and treatment. Other research foci are the development and application of statistical/computational methods to study complex experimental data (e.g. cellular genealogies) and of image analysis procedures (e.g. model-based segmentation algorithms in the context of automatic single cell tracking).
The "MOdels, Simulations, and Algorithms for Interdisciplinary Computing" Group develops and applies computational methods for image-based systems biology. This includes bio-image processing, adaptive particle methods for deterministic and stochastic spatiotemporal simulations, optimization, and parallel high-performance computing. The interdisciplinary group combines expertise from computer science, biology, mathematics, physics, and engineering and focuses on challenging problems in systems biology that require novel methods for their solution.
We are interested in predicting biomarkers and drug targets in cancer by integrating expression, interaction, structural, and textual data into drug-target-disease networks. We mine these networks with novel graph-based and text-mining algorithms. We work closely with our spin-off Transinsight.
We are developing molecular, imaging and image analysis tools to probe pattern of gene expression in development with highest possible spatial and temporal resolution. We use these technologies to understand the evolution and constraints acting on gene regulatory networks that underlie embryonic development of fruitflies.
The Institute of Scientific Computing develops continuum models to describe biological systems on the cell scale, especially in the field of cell motility, morphogenesis, polarity and intracellular streaming. We develop numerical methods to solve these equations using adaptive finite element methods and domain decomposition approaches on high performance computers.