IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0087597.html
   My bibliography  Save this article

Modelling Pathways to Rubisco Degradation: A Structural Equation Network Modelling Approach

Author

Listed:
  • Catherine Tétard-Jones
  • Angharad M R Gatehouse
  • Julia Cooper
  • Carlo Leifert
  • Steven Rushton

Abstract

‘Omics analysis (transcriptomics, proteomics) quantifies changes in gene/protein expression, providing a snapshot of changes in biochemical pathways over time. Although tools such as modelling that are needed to investigate the relationships between genes/proteins already exist, they are rarely utilised. We consider the potential for using Structural Equation Modelling to investigate protein-protein interactions in a proposed Rubisco protein degradation pathway using previously published data from 2D electrophoresis and mass spectrometry proteome analysis. These informed the development of a prior model that hypothesised a pathway of Rubisco Large Subunit and Small Subunit degradation, producing both primary and secondary degradation products. While some of the putative pathways were confirmed by the modelling approach, the model also demonstrated features that had not been originally hypothesised. We used Bayesian analysis based on Markov Chain Monte Carlo simulation to generate output statistics suggesting that the model had replicated the variation in the observed data due to protein-protein interactions. This study represents an early step in the development of approaches that seek to enable the full utilisation of information regarding the dynamics of biochemical pathways contained within proteomics data. As these approaches gain attention, they will guide the design and conduct of experiments that enable ‘Omics modelling to become a common place practice within molecular biology.

Suggested Citation

  • Catherine Tétard-Jones & Angharad M R Gatehouse & Julia Cooper & Carlo Leifert & Steven Rushton, 2014. "Modelling Pathways to Rubisco Degradation: A Structural Equation Network Modelling Approach," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-6, February.
  • Handle: RePEc:plo:pone00:0087597
    DOI: 10.1371/journal.pone.0087597
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087597
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0087597&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0087597?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
    ---><---

    References listed on IDEAS

    as
    1. Tianhai Tian & Jiangning Song, 2012. "Mathematical Modelling of the MAP Kinase Pathway Using Proteomic Datasets," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenlong He & Peng Xia & Xinan Zhang & Tianhai Tian, 2022. "Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data," Mathematics, MDPI, vol. 10(24), pages 1-15, December.

    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:plo:pone00:0087597. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.