Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference

Fei He, Michael P H Stumpf

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)
    27 Downloads (Pure)


    One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.
    Original languageEnglish
    Pages (from-to)2035-2046
    Number of pages12
    JournalBiophysical Journal
    Issue number10
    Early online date19 Apr 2019
    Publication statusPublished - 21 May 2019

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    ASJC Scopus subject areas

    • Biophysics


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