"Multimodal modeling of tumors"
We now have many modalities to explore the tumor and its environement: many types of omics data, imaging data, clinical records... It is now possible to perform these investigations at single cell level, and also while keeping spatial information, meaning profiling many subregions of a tumor. One burning question is to convert this heaps of data into knowledge useful in biology and clinics. This requires ad hoc algorithms which are capturing the underlying biological nature of the data while being computationally efficient which means keeping a reasonable level of complexity. One key question is to identify trajectories, meaning (potential) evolution over (pseudo-)time of a cell, a tumor or a patient. Another key question is to model explicitly the tumor heterogeneity, its microenvironment and its spatial dimension. These three aspects are key for understanding tumor onset, evolution, drug response and potential fragilities for new therapeutic strategies.
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I will present some methodological aspects of these questions based on principal trees, Boolean modeling and agent-based modeling.
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