WueBiT: Research and Teaching on all Scales

Who we are

Prof Dr Sabine Fischer

Chair of Computational and Theoretical Biology (CCTB)

Are there general mechanisms that drive biological processes across scales and species? This question has been puzzling me since my PhD. The focus of my group is on the link between the morphology of multicellular systems and their functionality. To investigate this connection, we use data-driven, spatially explicit, mechanistic modelling. Our list of projects spans the subcellular, multicellular and multitissue scale. All projects are conducted in close collaboration with experimental experts to ensure that the modelling is relevant to the biology.

Email

  • A Kuhn, T Krüger, M Schüttler et al. (2024). Quantification of Trypanosoma brucei social motility indicates different colony growth phases. J R Soc Interface DOI: 10.109ß/rsif.2024.0469
  • K Schmid, AL Olivares, O Camara et al (2024). Inference of alveolar capillary network connectivity from blood flow dynamics. Am J Physiol Lung Cell Mol Physiol. DOI: 10.1152/ajplung.00025.2024
  • R Dirk, JL Fischer, S Schardt et al. (2023). Recognition and reconstruction of cell differentiation patterns with deep learning. PLoS Comput Biol. DOI: 10.1371/journal.pcbi.1011582

Prof Dr Kathi Zarnack

Chair of Bioinformatics II - Bioinformatics of RNA Regulation And Gene Expression

The Zarnack group studies the molecular mechanisms that control RNA regulation and genome stability in healthy human cell function and disease. We integrate computational and statistical methods with high-throughput sequencing data and functional genomics to investigate how RNA- and DNA-binding proteins regulate gene expression at multiple levels.

Email

  • Y Zhou*, M Ćorović*, P Hoch-Kraft* et al. (2024). m6A sites in the coding region trigger translation-dependent mRNA decay. MolCell DOI: 10.1016/j.molcel.2024.10.033 *shared first authors
  • A Pacholewska*, M Lienhard*, M Brüggemann* et al. (2024). Long-read transcriptome sequencing of CLL and MDS patients uncovers molecular effects of SF3B1 mutations. Genome Res DOI: 10.1101/gr.279327.124 *shared first authors
  • L Molitor*, M Klostermann*, S Bacher et al. (2023). Depletion of the RNA-binding protein PURA triggers changes in posttranscriptional gene regulation and loss of P-bodies. Nucleic Acids Res DOI: 10.1093/nar/gkac1237 *shared first authors

Prof Dr Thomas Dandekar

Chair of Bioinformatics

We investigate regulatory and metabolic networks in human cells, animals, plants, bacteria and viruses. Individual RNA and protein molecules are analysed for structure and function. Next, in regulatory networks the Boolean logical connectivity is modelled and on this the dynamic signalling simulated and iteratively refined using experimental data. Metabolic pathways are calculated for metabolic networks and their flux strength under different conditions using expression data. 

Email

  • R Salihoglu, J Balkenhol, G Dandekar et al. (2024). Cat-E: A comprehensive web tool for exploring cancer targeting strate gies. Comput Struct Biotechnol J. DOI: 10.1016/j.csbj.2024.03.024
  • A Akash, E Bencurova, T Dandekar (2024). How to make DNA data storage more applicable. Trends Biotechnol DOI: 10.1016/j.tibtech.2023.07.006
  • Ö Osmanoglu, M Khaled AlSeiari, HA AlKhoori et al. (2021). Topological Analysis of the Carbon-Concentrating CETCH Cycle and a Photorespiratory Bypass Reveals Boosted CO2 Sequestration by Plants. Front Bioeng Biotechnol DOI: 10.3389/fbioe.2021.708417

Prof Dr Chaitanya Gokhale

Theoretical Biology at the CCTB

Dynamics of living systems unfold across multiple physical and temporal scales — from subcellular processes to societies, and from microseconds to millennia. We seek to understand how interaction-driven evolutionary, ecological and informational processes generate complexity across these scales, using theoretical insight to reveal general principles and responsibly inform real-world challenges. Our work focuses on processes within and between hierarchical levels of biological organisation, spanning from mathematical models of endosymbiosis to behavioural ecology.

Email

Dr Markus Ankenbrand

BioMedical Data Science at the CCTB

The BioMedical Data Science (BioMeDS) group develops, evaluates, and applies bioinformatic methods and algorithms to extract biological insights from large-scale data. We focus on sequencing and biomedical imaging data, with particular emphasis on integrating both modalities to advance our understanding of complex biological systems.

Email

 

  • Ahmad, ..., Ankenbrand, and Smyth (2025). Visualizing the Transcription and Replication of Influenza A Viral RNAs in Cells by Multiple Direct RNA Padlock Probing and in Situ Sequencing (mudRapp-Seq). Nucleic Acids Research  DOI: 10.1093/nar/gkaf461 
  • Hackl, Laurenceau, Ankenbrand, et al. (2023). Novel Integrative Elements and Genomic Plasticity in Ocean Ecosystems. Cell DOI: 10.1016/j.cell.2022.12.006
  • Ankenbrand et al. (2021). Deep Learning-Based Cardiac Cine Segmentation: Transfer Learning Application to 7T Ultrahigh-Field MRI. Magnetic Resonance in Medicine DOI: 10.1002/mrm.28822

Dr Arthur Korte

Evolutinary Genomics at the CCTB

How does evolution act on the genotype–phenotype map? This question is at the core of my research: I want to understand how genetic variation is translated into phenotypic diversity, how gene–gene and gene–environment interactions shape this mapping, and how these processes drive adaptation to abiotic stress. To answer this question I apply and extend quantative genetic approaches as well as single-cell transcriptomics.

Email

  • Lopéz Arboleda WA, Reinert S, Nordborg M and Korte A (2021). Global genetic heterogeneity in adaptive traits. MBE DOI: 10.1093/molbev/msab208
  • The 1001 genomes consortium (2016). 1,135 Genomes Reveal the Global Pattern of Polymprphism in Arabidopsis thaliana. Cell DOI: 10.1016/j.cell.2016.05.063
  • Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q, Nordborg M (2012): A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nature genetics DOI: 10.1038/ng.2376