Open projects at Niels Bohr Institute
Project 12
Main supervisor
Associate Professor Ala Trusina
Niels Bohr Institute, University of Copenhagen
Interdisciplinary co-supervisor
Clinical Professor Bjarne Winther Kristensen
Biotech Research and Innovation Centre, University of Copenhagen
- https://nbi.ku.dk/english/research/biocomplexity/uni-bio-lab/
- The group is interested in how populations of cells self-organize to achieve complex behaviours. One direction in our group is on understanding inter- and intra-cellular coordination to respond to stresses (unfolded proteins, reactive oxygen species, antibiotics, inflammation). Another direction is how complex forms and shapes emerge during the development of organs and organoids.
- https://www.bric.ku.dk/research-groups/Research/kristensen-group/
- Glioblastoma is the most frequent and malignant type of brain cancer with poor survival outcomes for most patients. Novel therapeutic targets and strategies that overcome the efficient mechanisms of resistance are urgently needed. Our aim is to identify novel targets and to understand and overcome mechanisms of resistance in the microenvironment of glioblastomas. This is obtained by patient tissue and model systems combined with spatial transcriptomics and bioinformatics.
- What?
- This PhD project aims to develop quantitative in silico models that capture the emergence of 3D morphologies in in vitro differentiating stem cells and cancer organoids. Our lab has previously developed computational approaches capable of simulating the dynamics of up to 100,000 interacting cells. These models account for a range of cellular behaviors, such as cell division, migration, differential adhesion, and cell-cell signaling. By leveraging these approaches, we have successfully simulated complex morphological transitions, including the during sea urchin and drosophila gastrulation, mammalian neurulation, the emergence of branched vascular structures, and 3D patterning during early pancreas development.
- This PhD project aims to develop quantitative in silico models that capture the emergence of 3D morphologies in in vitro differentiating stem cells and cancer organoids. Our lab has previously developed computational approaches capable of simulating the dynamics of up to 100,000 interacting cells. These models account for a range of cellular behaviors, such as cell division, migration, differential adhesion, and cell-cell signaling. By leveraging these approaches, we have successfully simulated complex morphological transitions, including the during sea urchin and drosophila gastrulation, mammalian neurulation, the emergence of branched vascular structures, and 3D patterning during early pancreas development.
- How?
- The methodology we employ is based on an agent-based modeling approach that simulates both mechanical and biochemical interactions between cells. The interactions are governed by discrete, logical, or continuous rules, which can be represented by differential equations. These rules are derived from current literature or inferred from data provided by experimental collaborators. This multi-scale approach enables the modeling of intricate cell behaviors, bridging experimental observations and theoretical predictions.
- The methodology we employ is based on an agent-based modeling approach that simulates both mechanical and biochemical interactions between cells. The interactions are governed by discrete, logical, or continuous rules, which can be represented by differential equations. These rules are derived from current literature or inferred from data provided by experimental collaborators. This multi-scale approach enables the modeling of intricate cell behaviors, bridging experimental observations and theoretical predictions.
- Why?
- This project has the potential to significantly enhance our understanding of organoid development and cancer progression. By applying our validated in silico modeling tools to organoid systems—such as gastruloids and cancer organoids—we aim to uncover the underlying mechanisms governing these processes. Ultimately, this work may inform targeted perturbations to culture conditions, such as introducing specific drugs or signaling molecules, to limit cancer invasiveness or optimize organoid development for potential use in organ replacement therapies.
- This project has the potential to significantly enhance our understanding of organoid development and cancer progression. By applying our validated in silico modeling tools to organoid systems—such as gastruloids and cancer organoids—we aim to uncover the underlying mechanisms governing these processes. Ultimately, this work may inform targeted perturbations to culture conditions, such as introducing specific drugs or signaling molecules, to limit cancer invasiveness or optimize organoid development for potential use in organ replacement therapies.
Together with Kristensen lab, we aim to develop quantitative predictive models of glioblastoma progression. We envision that these models will help us to interpret observed experimental data on cell proliferation and invasion in glioblastoma organoids and patient data (spatial single cell profiling). We envision that these data-driven models will provide us with the better understanding of the drug resistance in these highly malignant cancers. We expect the PhD student in this project to be involved in both modeling and data analysis (spatial transcriptomics, image processing) with the possibility of taking on minor experimental activities.