The environment around cancer stem cells (CSCs) plays an essential role in how tumors grow and spread, but figuring out this environment is challenging due to the complexity of tissue structures. To tackle this, we propose developing a new computational tool that uses machine learning (ML) to digitally recreate these tissues. Specifically, the master's student will create an algorithm to simulate how cells are spatially arranged, enabling us to rebuild these tissue environments with high accuracy. By doing this, we aim to identify and study the specific areas, or "niches," where CSCs thrive, based on how they interact with their surrounding cells in the reconstructed tissue. We believe that this approach will uncover important information about the factors in these environments that help CSCs survive, which could lead to new cancer treatments. This project combines computational modeling, ML, and cancer research, offering the master's student an opportunity to work at the intersection of these fields and contribute to our understanding of how tumors vary and how CSCs contribute to cancer progression.
This project lies at the exciting intersection of computational science and biology, offering the student the opportunity to contribute to both fields. The main supervisor has extensive experience in developing computational tools for understanding tissue heterogeneity, as demonstrated by key publications in Cell (2021), Cancer Cell (2021), and Nature Biotechnology (2023).
Prerequisites:
- The student will be trained in understanding the data, and how to apply existing tools.
- The student needs to know some programming (for example Python and/or R) and must have a genuine interest in computational analyses.
Contact:
Main supervisor: Chloé B. Steen chloebs@uio.no (https://www.ous-research.no/cbs)