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Part 1 - Networks of prior knowledge as frames to understand complex biological data

De Julio Saez-Rodriguez

Apparaît dans la collection : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire

Modern technologies allow us to profile in high detail biomedical samples at fast decreasing costs. New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for these methods. In addition, they can be converted in dynamic mechanistic models to gain more specific insight. I will give an overview of these approaches showcasing some examples and approaches used in the field.

Informations sur la vidéo

Données de citation

  • DOI 10.24350/CIRM.V.19619503
  • Citer cette vidéo Saez-Rodriguez, Julio (02/03/2020). Part 1 - Networks of prior knowledge as frames to understand complex biological data. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19619503
  • URL https://dx.doi.org/10.24350/CIRM.V.19619503

Bibliographie

  • SAEZ-RODRIGUEZ, Julio, RINSCHEN, Markus M., FLOEGE, Jürgen, et al. Big science and big data in nephrology. Kidney international, 2019. - https://doi.org/10.1016/j.kint.2018.11.048
  • KAWATA, Kentaro, HATANO, Atsushi, YUGI, Katsuyuki, et al. Trans-omic analysis reveals selective responses to induced and basal insulin across signaling, transcriptional, and metabolic networks. iScience, 2018, vol. 7, p. 212-229. - https://doi.org/10.1016/j.isci.2018.07.022
  • TÜREI, Dénes, KORCSMÁROS, Tamás, et SAEZ-RODRIGUEZ, Julio. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature methods, 2016, vol. 13, no 12, p. 966. - https://doi.org/10.1038/nmeth.4077
  • CECCARELLI, Francesco, TUREI, Denes, GABOR, Attila, et al. Bringing data from curated pathway resources to Cytoscape with OmniPath. Bioinformatics, 2019. - https://doi.org/10.1093/bioinformatics/btz968
  • SCHUBERT, Michael, KLINGER, Bertram, KLÜNEMANN, Martina, et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature communications, 2018, vol. 9, no 1, p. 1-11. - https://doi.org/10.1038/s41467-017-02391-6
  • GARCIA-ALONSO, Luz, HOLLAND, Christian H., IBRAHIM, Mahmoud M., et al. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome research, 2019, vol. 29, no 8, p. 1363-1375. - http://dx.doi.org/10.1101/gr.240663.118
  • GARCIA-ALONSO, Luz, IORIO, Francesco, MATCHAN, Angela, et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer research, 2018, vol. 78, no 3, p. 769-780 - http://dx.doi.org/10.1158/0008-5472.CAN-17-1679
  • DUGOURD, Aurelien et SAEZ-RODRIGUEZ, Julio. Footprint-based functional analysis of multi-omic data. Current Opinion in Systems Biology, 2019. - https://doi.org/10.1016/j.coisb.2019.04.002
  • YU, Isseki, MORI, Takaharu, ANDO, Tadashi, et al. Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. Elife, 2016, vol. 5, p. e19274. - https://doi.org/10.7554/eLife.19274
  • MARKOWETZ, Florian. How to understand the cell by breaking it: network analysis of gene perturbation screens. PLoS computational biology, 2010, vol. 6, no 2. - https://dx.doi.org/10.1371%2Fjournal.pcbi.1000655
  • CHUANG, Han‐Yu, LEE, Eunjung, LIU, Yu‐Tsueng, et al. Network‐based classification of breast cancer metastasis. Molecular systems biology, 2007, vol. 3, no 1. - https://doi.org/10.1038/msb4100180
  • KHOLODENKO, Boris N., KIYATKIN, Anatoly, BRUGGEMAN, Frank J., et al. Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. Proceedings of the National Academy of Sciences, 2002, vol. 99, no 20, p. 12841-12846. - https://doi.org/10.1073/pnas.192442699
  • TERFVE, Camille DA, WILKES, Edmund H., CASADO, Pedro, et al. Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nature communications, 2015, vol. 6, p. 8033. - https://doi.org/10.1038/ncomms9033
  • MORRIS, Melody K., SAEZ-RODRIGUEZ, Julio, CLARKE, David C., et al. Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS computational biology, 2011, vol. 7, no 3. - https://dx.doi.org/10.1371%2Fjournal.pcbi.1001099
  • WITTMANN, Dominik M., KRUMSIEK, Jan, SAEZ-RODRIGUEZ, Julio, et al. Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC systems biology, 2009, vol. 3, no 1, p. 98. - http://dx.doi.org/10.1186/1752-0509-3-98

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