Deep Hidden Physics Modeling of Cell Signaling Networks
According to the WHO, cancer is the second most common cause of death worldwide.
The social and economic damage caused by cancer is high and rising. In Europe, the annual direct
medical expenses alone amount to more than €129 billion. This results in an urgent need for new
and sustainable therapeutics, which has currently not been met by the pharmaceutical industry; only
3.4% of cancer drugs entering Phase I clinical trials get to market. Phosphorylation sites are
parts of the core machinery of kinase signaling networks, which are known to be dysfunctional in
all types of cancer. Indeed, kinases are the second most common drug target yet. However, these inhibitors
block all functions of a protein, and they commonly lead to the development of resistance
and increased toxicity. To facilitate global and mechanistic modeling of cancer and clinically relevant
cell signaling networks, the community will have to develop sophisticated data-driven deep-
-learning and mechanistic computational models that generate in silico probabilistic predictions of
molecular signaling network rearrangements causally implicated in cancer.
Journal Title: Current Genomics