News coverage of our latest publication Predicting treatment response more accurately.
We are active in the fields of computational biology, bioinformatics, biostatistics, and systems biology. Our activities include the development of mathematical and statistical models, their efficient implementation in computer programs, and application to biomedical problems.
We are interested in disease-associated biological networks and in pathogen evolution associated with disease progression. Our ultimate goal is to predict the effect and to support the design of medical interventions in complex, rapidly evolving, biological systems, such as virus populations, bacterial colonies, and tumors.
We make use of high-throughput molecular profiling data and apply high-dimensional statistical modeling in order to study intracellular as well as intercellular biological networks and to predict the effect of genetic alterations. We model the evolutionary dynamics of pathogen populations to investigate virulence, disease progression, immune escape, and drug resistance development. Our models aim at understanding evolutionary escape and improving diagnostics, prognostics, and rational treatment design.
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