Research focus
We study how mitochondria and other organelles sense, remodel, and adapt their physical state. We combine fluorescent probes, microscopy, genetics, biochemistry, and quantitative image analysis to connect membrane organization with organelle function and cellular metabolism. We use yeast and mammalian cell models to answer these questions, but we are not tied to any single model system. Over time, we hope to extend these ideas and tools to more complex biological systems.
1. How do cells sense and regulate mitochondrial membrane fluidity?
The inner mitochondrial membrane is where respiration and energy production happen. For this membrane to work well, it must maintain the right physical state - not too rigid, not too fluid. Our previous work showed that cells actively regulate the fluidity of this membrane in response to respiratory demand. We now ask how cells sense this membrane state, how they restore it after stress, and which lipids, proteins, and signalling pathways regulate it.
2. How does membrane physical state shape mitochondrial behaviour?
Our recent work showed that the way electrons enter the respiratory chain can strongly affect mitochondrial ROS production and network morphology. This raises a central question: does the physical state of the inner mitochondrial membrane influence how mitochondria handle electron flow, generate ROS, and remodel their networks? One possibility is that a more tightly packed membrane changes the movement or local availability of mobile electron carriers such as ubiquinone. This could create "electron-traffic" constraints and alter how electrons move through the respiratory chain. To test this, we generate defined mitochondrial membrane states using genetic, chemical, lipid, metabolic, and stress-based perturbations, including models such as ferroptosis. We then ask how changes in inner-membrane order and fluidity affect core mitochondrial outputs such as respiration, electron transfer, ROS production, mitochondrial morphology, and cell state.
3. Using machine learning to understand and design biological tools
We are interested in using protein language models and machine-learning methods to design better biosensors, fluorescent proteins, and molecular tools for cell biology. We are also interested in using AI/ML pipelines to automate image/data analysis, derive quantitative biophysical features from multi-dimensional imaging, and build interpretable predictors of mitochondrial shape and function.