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Deep Hidden Physics Modeling of Cell Signaling Networks
Martin Seeger1,2, James Longden2, Edda Klipp1,2
1Humboldt-Universitätzu Berlin, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin, Germany.
Current Genomics
|March 11, 2022
Summary
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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