Metabolism across the cell cycle
The cell cycle is the process where a cell builds a copy of itself, which builds a copy of itself, which builds a copy of itself … Self-replication requires synthesizing an incredible variety of substances and structures that make up a new cell. Metabolism is of course at the heart of this process, yet we know little about how metabolism is orchestrated over the cell cycle, besides the obvious need for deoxynucleotides during DNA replication.
A major challenge is to measure metabolism at different points in the cell cycle without disturbing the system. In this project, we are developing noninvasive methods to sort cells according to their cell cycle stage and capture their metabolome. For more information, contact Irena Roci.
Mitochondrial folate metabolism and MTHFD2
Mitochondrial metabolism of folate-carried “one carbon units” has emerged as an important process that supports cell proliferation. We are particularly interested in a bifunctional dehydrogenase/cyclohydrolase in this pathway encoded by the MTHFD2 gene. This enzyme is overexpressed in tumors but low or absent in most normal cells (even proliferating ones), and appears to function during embryonic development. Why embryonic and transformed cells prefer this enzyme is not clear, but loss of the enzyme kills a variety of cancer cell types. We are investigating the biology of this fascinating metabolic pathway with the hope that it will facilitate development of a new generation of antifolate drugs that may be used to treat cancer and other disorders of cell proliferation.
Isotope tracing methods for mammalian cells
We are actively developing methods for measuring the activity of (flux through) various metabolic pathways of interest. This is a large, long-term cross-disciplinary undertaking, aiming to provide better tools with which to learn about the fascinating variety of metabolic phenotypes displayed by human cells.
Potential Master’s thesis projects
Are you up for a challenging project for your master’s thesis?
Statistical methods for isotope tracing
Error models and statistical inference methods for isotope tracing data are still in their infancy. Multivariate (spherical) normal distributions are commonly used to represent mass isotopomer data, even though the data is embedded in an n-simplex. In this project we suggest to model isotope data with Dirichlet distributions, and explore the effects on metabolic network model fitting and inference.
This project is suitable for a candidate with solid mathematical statistics / applied math training and a keen interest in computational biology. For more information, contact Roland.