Contact persons: Jonas Paulsen, Rein Aasland
Keywords: Hi-C, 3D genome, epigenomics, machine learning, AI
Research group: Genetics and Evolutionary Biology (EVOGENE)
Department of Biosciences
The eukaryotic genome is folded into a three-dimensional (3D) architecture within the cell's nucleus, a configuration that is essential for the cell's proper functioning. Disruption of this complex spatial organization can lead to various diseases, including cancer. To gain a deeper understanding of the 3D genome's structure and its relationship with disease, computational methodologies are becoming pivotal. This research theme focuses on employing advanced computational modeling, simulation, and machine learning techniques to unravel principles underlying the multi-scale organization of the 3D genome.
Topics from methodological research:
- Direct or indirect modeling of 3D genome organization
- Stochastic or molecular dynamics simulation of 3D genome dynamics
- Machine learning for dimensionality reduction, clustering or prediction, to capture relationships between 3D genome structure, epigenetics, and gene regulation
- Developing user-friendly software related to any of the topics above
Topics from natural sciences or technology:
- Hi-C
- 3D genome structure
External partners:
- Oslo University Hospital (OUH)
- Akershus University Hospital (AHUS)
- The Norwegian Computing Centre (NR)
