Title:Deep Hidden Physics Modeling of Cell Signaling Networks
Volume: 22
Issue: 4
Author(s): Martin Seeger, James Longden, Edda Klipp and Rune Linding*
Affiliation:
- Rewire Tx, Humboldt- Universitätzu Berlin, Invalidenstr. 42, 10115 Berlin,Germany
Keywords:
Phosphorylation sites, kinase signaling networks, inhibitors, deep-learning, computational models, cell signaling
networks.
Abstract: 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.
