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.
Keywords: Phosphorylation sites, kinase signaling networks, inhibitors, deep-learning, computational models, cell signaling networks.
Current Genomics
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.
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Cite this article as:
Seeger Martin, Longden James, Klipp Edda and Linding Rune*, Deep Hidden Physics Modeling of Cell Signaling Networks, Current Genomics 2021; 22 (4) . https://dx.doi.org/10.2174/1389202922666210614131236
DOI https://dx.doi.org/10.2174/1389202922666210614131236 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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