Network inference using steady-state data and goldbeter-koshland kinetics

Chris J. Oates, Bryan T. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee

    Research output: Contribution to journalArticle

    7 Scopus citations

    Abstract

    Motivation: Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference. Results: Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.

    Original languageEnglish (US)
    Article numberbts459
    Pages (from-to)2342-2348
    Number of pages7
    JournalBioinformatics
    Volume28
    Issue number18
    DOIs
    StatePublished - Sep 1 2012

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

    Fingerprint Dive into the research topics of 'Network inference using steady-state data and goldbeter-koshland kinetics'. Together they form a unique fingerprint.

  • Cite this