Gönomix: Emerging genetic model systems Cross-species comparison of developmental gene function and gene regulatory networks (RTG 3074)

               Aims of the project

New insights by studying emerging model organisms Most knowledge of gene function comes from a few traditional model organisms. Recent advances now allow functional genetic studies in a wider range of animals, expanding research scope and enabling novel applications. This requires interdisciplinary expertise—from morphology and biodiversity to RNAi, genome editing and bioinformatics. As a group we cover all these skills, working with diverse emerging model systems across the protostome clade. The function and evolution of the gene regulatory network of head development In this RTG, we aim to uncover how animal diversity evolves through changes in developmental gene regulatory networks (GRNs). Focusing on anterior head patterning, we study Wnt signaling initiation, gradient dynamics, and target gene interactions in the anterior GRN. Using protostomes and a Cnidarian outgroup, we conduct cross-species experiments to generate comparable data and develop new bioinformatics and machine learning tools to reconstruct and analyze gene regulatory networks. To explore evolutionary novelties, we investigate intriguing traits such as beetle horns, annelid regeneration, and molluscan shells. The Göttingen Zoo of model systems and the Göttingen Spirit This RTG is based on the unique “Göttingen Zoo” of model systems and the “Göttingen Spirit” with strong scientific interactions between University, Max-Planck-Institutes and University Medical Center.

Contribution of the Department of Medical Bioinformatics

 Integrative Bioinformatics for Gene Regulatory Network Reconstruction

Our department develops integrative bioinformatics approaches for the reconstruction and cross-species comparison of gene regulatory networks (GRNs) in emerging model organisms. A particular focus lies on regulatory networks associated with Wnt signaling and their evolutionary conservation and diversification across species.

Within GönomiX, the group combines different types of molecular data, including RNA-seq, ATAC-seq and single-cell sequencing data, to infer regulatory interactions and reconstruct GRN architecture. Existing methods and knowledge derived from well-established model organisms are adapted and extended for application to less extensively studied species. Machine-learning approaches are used to predict regulatory elements and gene expression from genomic and open-chromatin information, while probabilistic network reconstruction methods integrate expression profiles, time-series data and experimental perturbations. These approaches aim to capture dynamic regulatory relationships and improve the identification of interactions within GRNs. Georg-August Universität Göttingen

The developed methods are initially established and validated using existing and publicly available datasets and are subsequently refined with data generated within the GönomiX consortium. By incorporating evolutionary relationships between species, genes and regulatory pathways, the group aims to identify conserved and divergent regulatory mechanisms across different model systems.

Synergy and Collaborations

The Beißbarth group closely collaborates with the experimental and computational partners within GönomiX. Its bioinformatics expertise contributes to several collaborative projects, including the identification and comparison of direct Wnt target genes, the reconstruction of evolving anterior gene regulatory networks, and the integrative analysis of omics data generated across different model systems. Antrag_B_Project Description Gö…

Together with the Hauschild group, the Beißbarth group contributes to the development of new computational tools for cross-species analyses of gene regulatory networks. These tools are designed to integrate data from different species and omics modalities and to support the comparison of conserved and divergent regulatory mechanisms. The close interaction between bioinformatics and experimental groups allows computationally generated hypotheses to be tested experimentally, while experimental results feed back into the further development and refinement of the computational methods. Antrag_B_Project Description Gö…

Publications

1. Sahrhage M, Paul NB, Beißbarth T, Haubrock M. The importance of DNA sequence for nucleosome positioning in transcriptional regulation. Life Sci Alliance, 2024, 7(8):e202302380. 

2. Bayerlová M, …, Beißbarth T, Bleckmann A. Newly Constructed Network Models of Different WNT Signaling Cascades Applied to Breast Cancer Expression Data. PloS One, 2015, 10:e0144014. 

3. Paul NB, …, Beißbarth T, Haubrock M. Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization. Bioinformatics, 2025, accepted. 

4. Schrod S, …, Beißbarth T, …, Altenbuchinger M. Spatial Cellular Networks from omics data with SpaCeNet. Genome Res, 2024, 34(9):1371-1383. 

5. Schrod S, … Beißbarth T, Hauschild AC, Altenbuchinger M. CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations. Bioinformatics, 2024, 40(Suppl 1):i91-i99. 

6. Sitte M, …, Beißbarth T. Reconstruction of Different Modes of WNT Dependent Protein Networks from Time Series Protein Quantification. Stud Health Technol Inform, 2019, 267:175-180.

Coordination and Project partners