Lab of Computational Cell Dynamics

Computational cell dynamics

Yuval is investigating the interplay between cell shape and its intracellular molecular composition and organization

Lion is developing a method for visual interpretation of image-to-image transformation models and applying it to in-silico organelle localization.

Nitai is developing a method to use anomalous cancer cells as an anchor to decipher tumor-microenvironment interactions from multiplexed single cell spatial proteomics data

Mark is developing methods to analyze spatial metallomics data and apply them to triple negative breast cancer patient samples

Orit is mentoring students in multiple diverse projects, advising on computational and technical issues, formulating challenging research questions and keeping contact with collaborators

Esraa is developing methods for quantitative spatiotemporal characterization of heterogeneous collective cell death

Leor is developing methods for analysis and integration of multimodal bioimaging data.

Noy is investigating the coupling between single cell and collective signaling dynamics

Reut is developing methods to analyze fluorescence lifetime imaging microscopy (FLIM) in the context of triple negative breast cancer patient samples

Oded is developing methods for visual interpretability of deep learning models and applying them to biomedical images

Guy is developing network-based representations to measure cell biological state transitions for intracellular and for tissue organization

Ariel is developing data-driven structural inference of biomolecular complexes in cells from super resolution microscopy data.

Leah is investigating the brain vasculature structure in Glioblastoma (GBM) patients data

Gad is developing a method for local assessment of the confidence of image-to-image transformation models and applying it to in-silico organelle localization