IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v461y2009i7261d10.1038_nature08454.html
   My bibliography  Save this article

Molecular networks as sensors and drivers of common human diseases

Author

Listed:
  • Eric E. Schadt

    (Pacific Biosciences)

Abstract

The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.

Suggested Citation

  • Eric E. Schadt, 2009. "Molecular networks as sensors and drivers of common human diseases," Nature, Nature, vol. 461(7261), pages 218-223, September.
    • Handle: RePEc:nat:nature:v:461:y:2009:i:7261:d:10.1038_nature08454
    DOI: 10.1038/nature08454
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature08454
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature08454?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guiqiong Xu & Chen Dong & Lei Meng, 2022. "Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    2. Frank Emmert-Streib & Galina V Glazko, 2011. "Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-6, May.
    3. Seyed Yahya Anvar & Allan Tucker & Veronica Vinciotti & Andrea Venema & Gert-Jan B van Ommen & Silvere M van der Maarel & Vered Raz & Peter A C ‘t Hoen, 2011. "Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-14, November.
    4. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    5. Susan Dina Ghiassian & Jörg Menche & Albert-László Barabási, 2015. "A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-21, April.
    6. Pi-Jing Wei & Di Zhang & Hai-Tao Li & Junfeng Xia & Chun-Hou Zheng, 2017. "DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes," Complexity, Hindawi, vol. 2017, pages 1-10, August.
    7. Xue Jiang & Han Zhang & Xiongwen Quan & Zhandong Liu & Yanbin Yin, 2017. "Disease-related gene module detection based on a multi-label propagation clustering algorithm," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    8. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    9. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    10. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    11. Valur Emilsson & Elias F. Gudmundsson & Thorarinn Jonmundsson & Brynjolfur G. Jonsson & Michael Twarog & Valborg Gudmundsdottir & Zhiguang Li & Nancy Finkel & Stephen Poor & Xin Liu & Robert Esterberg, 2022. "A proteogenomic signature of age-related macular degeneration in blood," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    12. Calvin McCarter & Judie Howrylak & Seyoung Kim, 2020. "Learning gene networks underlying clinical phenotypes using SNP perturbation," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-24, October.
    13. Andrea Pinna & Nicola Soranzo & Alberto de la Fuente, 2010. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-8, October.
    14. Alexander Gudjonsson & Valborg Gudmundsdottir & Gisli T. Axelsson & Elias F. Gudmundsson & Brynjolfur G. Jonsson & Lenore J. Launer & John R. Lamb & Lori L. Jennings & Thor Aspelund & Valur Emilsson &, 2022. "A genome-wide association study of serum proteins reveals shared loci with common diseases," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    15. Luis P Fernandes & Alessia Annibale & Jens Kleinjung & Anthony C C Coolen & Franca Fraternali, 2010. "Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-14, August.
    16. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    17. Lingfei Wang & Tom Michoel, 2017. "Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-26, August.
    18. Peng Wei & Hongwei Tang & Donghui Li, 2012. "Insights into Pancreatic Cancer Etiology from Pathway Analysis of Genome-Wide Association Study Data," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-10, October.
    19. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    20. Darcy A Davis & Nitesh V Chawla, 2011. "Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-9, July.
    21. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    22. Eric P Xing & Ross E Curtis & Georg Schoenherr & Seunghak Lee & Junming Yin & Kriti Puniyani & Wei Wu & Peter Kinnaird, 2014. "GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-19, June.
    23. Matt Silver & Peng Chen & Ruoying Li & Ching-Yu Cheng & Tien-Yin Wong & E-Shyong Tai & Yik-Ying Teo & Giovanni Montana, 2013. "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts," PLOS Genetics, Public Library of Science, vol. 9(11), pages 1-28, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:461:y:2009:i:7261:d:10.1038_nature08454. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.