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 and our perspective on data science in cell imaging here.
Interpretable deep learning
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 (paper, The Scientist highlight, twitter summary).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. We are now applying similar ideas to other domains such as in vitro fertilization, organelle-organelle interactions and intracellular organization.
Bottom-up modular characterization of tissue state
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 such as long-range cell-cell mechanical communication (biorxiv, twitter summary), collective cell death (paper, twitter summary), multicellular calcium synchronization (biorxiv, twitter summary).
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.