Part 1 - Networks of prior knowledge as frames to understand complex biological data | Vidéo | Carmin.tv

00:00:00 / 00:00:00

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

By Julio Saez-Rodriguez

Appears in 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.

Information about the video

Date of recording 02/03/2020
Date of publication 23/03/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19619503
Cite this video 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

Domain(s)

Bibliography

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

MSC codes

Last related questions on MathOverflow

You have to connect your Carmin.tv account with mathoverflow to add question

Ask a question on MathOverflow

Copyright Carmin.tv 2026

Give feedback