Deep Hidden Physics Modeling of Cell Signaling Networks
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Published on: February 16, 2017
Martin Seeger1,2, James Longden2, Edda Klipp1,2
1Humboldt-Universitätzu Berlin, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin, Germany.
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.
Insights
Cancer drug development faces low success rates. New computational models are needed to predict molecular signaling network changes in cancer, improving therapeutic development.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Cancer is a leading global cause of death, incurring substantial economic costs.
- Current cancer therapeutics face challenges including low market approval rates and drug resistance.
- Kinase signaling pathways are crucial in cancer, but existing inhibitors cause toxicity and resistance.
Purpose of the Study:
- To address the urgent need for novel cancer therapeutics.
- To improve the understanding and modeling of cancer-related cell signaling networks.
- To develop advanced computational models for predicting molecular signaling alterations in cancer.
Main Methods:
- Utilizing data-driven deep-learning approaches.
- Developing mechanistic computational models.
- Generating in silico probabilistic predictions of molecular signaling network rearrangements.
Main Results:
- The study proposes a framework for sophisticated computational modeling.
- The models aim to predict causal molecular signaling network alterations in cancer.
- This approach facilitates global and mechanistic modeling of cancer signaling.
Conclusions:
- Advanced computational models are essential for understanding cancer signaling.
- Data-driven and mechanistic models can improve the prediction of cancer-related molecular changes.
- This work supports the development of more effective and sustainable cancer therapeutics.
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