Center for Computational and Theoretical Biology



Five research groups covering different aspects of computational biology form the core of the CCTB. Two affiliated research groups provide a link between the CCTB and the faculty of Biology. Furthermore, there are associated groups from the faculty, and joined projects and students promoting exchange with the Biocenter.

Our common research topic is the development and application of computational and theoretical approaches to generate novel, quantitative biological insight from large-scale experimental data. Currently, the potential of new technologies is often not used due to a lack of resources, time and theoretical concepts necessary to fully explore the generated data. In close collaboration with experimentalists, we use data analysis and quantification as well as data-driven modeling to develop a better understanding of fundamental biological mechanisms on all scales, such as evolution, development and growth, or population dynamics. Furthermore, we adopt open science standards in our research to ensure reproducibility and collaboration in the biological sciences. For these, the CCTB has been recognised by Charm-EU in 2023 for their team effort in the context of Open Science: "... they wished to highlight the applications of team members from the Department of Computational and Theoretical Biology of the University of Würzburg for their outstanding broad implementation of Open Science practices." 

Research Groups at the CCTB

  • Theoretical Biology
  • Evolutionary Genomics

    Natural variation is the prime resource to discover the evolutionary changes associated with adaptation to changing environmental conditions. We are interested in both the development of new statistical tools for association mapping and the identification of causal relationships between genotypes and phenotypes.

  • Computational Image Analysis (moved to HHU)

    Modern imaging methods enable us to capture biological processes in unprecedented detail and with high temporal and spatial resolution. We are working on computational tools to analyze, quantify, and understand biological image data (“Bioimage Bioinformatics”).

  • Ecosystem Modeling (move to U of Birmingham)

    We pursue the understanding of the processes underlying the origin, maintenance and spatiotemporal dynamics of biodiversity across scales, from local to global, and across levels of ecological organization, from individuals to biomes.

  • Supramolecular and Cellular Simulations

    Cell-cell interactions are at the basis of tissue development and maintenance. We develop data-driven computational models to further our understanding of intercellular interactions, e.g during cell differentiation.

  • Computational Evolutionary Biology

    The capability to evolve is a hallmark of all biological systems. Reconstructing evolutionary history and unraveling underlying mechanisms is the main interest of my group. Currently, we are focusing on three different levels, namely proteins, genomes and language.

  • BioMedical Data Science

    We develop and apply machine learning methods in the fields of Molecular Ecology and Medical Imaging. The focus is on multi-modal methods and explainability.

  • Molecular Biodiversity (moved to LMU)

    Our research projects focus on a combination of field ecology with bioinformatics and new sequencing technologies. Conceptually, we are interested in patterns and structuring forces of communities, where organisms are not easily identifiable or distinguishable from each other. This interest applies to various levels, starting with abundance and diversity of taxa, over phylogenetic reconstructions, towards environmental and spatial influences and lastly regarding organisms' molecular interactions with each other on a genomic level. Methodologically, the workgroup is developing computational workflows and databases as well as laboratory protocols to analyse ecological samples with next-generation sequencing technologies.