ゲノム情報科学研究教育機構  アブストラクト
Date 14:00-15:00 Sep 06, 2024
Speaker Rune Linding
Humboldt-Universität zu Berlin
Germany
Title Systematic Discovery of Mechanistic Cell Signaling Networks in Cancers with Theory Informed Machine Learning
Abstract
 Signaling systems in multicellular organisms are vital for cell-cell communication, tissue organization and disease. Cancer genomics has unraveled a surprisingly large set of novel gene lesions from tumors. Our previous studies have globally explored the rewiring of cell signaling networks underlying malignant transformation caused by kinases and other signaling proteins. We are now working to employ Sci-ML (Scientific ML) and BPINNs (Biophysics Informed Neural Networks) based machine learning with big data to discover the underlying causal and mechanistic systems structure and dynamics (e.g. ODEs with partially unknown functions and/or parameters). We aim to use such models to predict novel treatment and diagnostic strategies for tumors harboring different genetic lesions. To this end our lab has generated systems-scale quantitative time- and state-series multi-omics data and subsequently used these as input for computational algorithms aiming to model the principal changes in the genome, cell signaling and phenotypes of cells harboring cancer mutations. We have validated this approach by forward prediction of experimentally observed phenotypic responses to drug and genetic perturbations. We are currently attempting to deploy biophysics-informed Sci-ML models to forecast how cell signaling networks are mechanistically, dynamically and differentially utilized in TNBC (Triple Negative Breast Cancer) cells during metastasis. Our studies aim to unravel the fundamental rewiring of cell signaling networks in cancer and its impact on the disease, paving the way for future clinical applications and tumor specific cancer therapy.

Selected References:

  1. Linding et al., Cell 2007.
  2. Bakal et al., Science 2008.
  3. Jørgensen et al., Science 2009.
  4. Miller et al., Science Signaling 2008.
  5. Tan et al., Science Signaling 2009.
  6. Tan et al., Science 2009.
  7. Tan et al., Science 2011.
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  9. Creixell et al., CELL 2015 I & II.
  10. Koplev et al., CELL Reports 2017.
  11. Van de Kooij et al., Elife 2019.
  12. Miller et al. PLoS Biology 2019.
  13. Longden et al. CELL Reports 2021.
  14. Seeger et al. Curr Genomics 2021.
  15. Klipp & Linding Current Opinion in Systems Biology 2021.
  16. Johnson et al. Nature 2023.

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