Dry cell biology

The increasing complexity and volume of microscopy data presents an opportunity for deciphering fundamental biological processes by analyzing image data. Discovery of complex dynamic patterns in such data require deep knowledge of the biological process, the experimental possibilities, the type of information that can be extracted and the computational tools to extract it. The field is desperately in need of computational (or ‘dry’) cell biologists, interdisciplinary scientists who can drive biological inquiry by bridging cell biology and computer science.

We work at the boundaries of quantitative disciplines and cell biology, identify scientific problems, steer (but usually do not perform) experiments, import, adapt and apply quantitative tools, and interpret the data to conceive testable numerical predictions. Read our "dry cell biologist" essay here.


Interpretable deep learning to uncover functional hallmarks of highly-metastatic melanoma 

Deep learning has emerged as a powerful technique to identify hidden patterns in complex cell imaging data, but is criticized as uninterpretable - lacking the ability to provide meaningful explanations for the cell properties that drive the machine’s prediction. We "reverse engineered" a neural network to identify the cellular information that distinguish very aggressive and less aggressive metastatic cells. This was achieved by generating synthetic cell images in a controlled manner to amplify subtle cellular features that critically define the metastatic efficiency of a melanoma tumor. 

Our method, termed “quantitative live cell histology” was highlighted by The Scientist, the preprint is now available in biorxiv, and a twitter summary here.


Defining the building blocks of collective cell behavior 

How multicellular patterns emerge from the heterogeneous behavior and interactions of individual cells? We are developing computational tools to measure cell behavior and cell-cell information-transfer in dynamic multicellular systems and use these tools to decipher emergent collective cell behavior a variety of biological systems: long-range cell-cell mechanical communication (biorxiv, twitter summary), collective cell death, multicellular calcium synchronization and collective cell migration.


Promoting computational cell biology by sharing and reusing cell image data

A key step toward involvement of computational scientists in cell biology is making image cell data publicly available for secondary analysis, and appreciation of such research by the cell biology community. As the first massive datasets are becoming publicly available we are now at the dawn of this revolution (Read our Perspective).

We are applying "new" data science approaches on "old" live cell imaging data to learn new biology. Specific project include protein localization and high content phenotypic screening. 

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 Assaf Zaritsky lab of computational cell dynamics, applying data science to microscopy cell images since 2018