Kinetic data for modeling the dynamics of the enzymes involved in animal fatty acid synthesis

Chilperic Armel Foko Kuate, Oliver Ebenhöh, Barbara Marleen Bakker, Adélaïde Raguin*

*Corresponding author voor dit werk

    Onderzoeksoutputpeer review

    2 Citaten (Scopus)
    111 Downloads (Pure)

    Samenvatting

    The synthesis and modification of fatty acids (FAs) from carbohydrates are paramount for the production of lipids. Simultaneously, lipids are pivotal energy storage in human health. They are associated with various metabolic diseases and their production pathways are for instance candidate therapeutic targets for cancer treatments. The fatty acid de novo synthesis (FADNS) occurs in the cytoplasm, while the microsomal modification of fatty acids (MMFA) happens at the surface of the endoplasmic reticulum (ER). The kinetics and regulation of these complex processes involve several enzymes. In mammals, the main ones are the acetyl-CoA carboxylase (ACC), the fatty acid synthase (FAS), the very-long-chain fatty acid elongases (ELOVL 1-7), and the desaturases (delta family). Their mechanisms and expression in different organs have been studied for more than 50 years. However, modeling them in the context of complex metabolic pathways is still a challenge. Distinct modeling approaches can be implemented. Here, we focus on dynamic modeling using ordinary differential equations (ODEs) based on kinetic rate laws. This requires a combination of knowledge on the enzymatic mechanisms and their kinetics, as well as the interactions between the metabolites, and between enzymes and metabolites. In the present review, after recalling the modeling framework, we support the development of such a mathematical approach by reviewing the available kinetic information of the enzymes involved.

    Originele taal-2English
    ArtikelnummerBSR20222496
    Aantal pagina's21
    TijdschriftBioscience reports
    Volume43
    Nummer van het tijdschrift7
    Vroegere onlinedatum3-mei-2023
    DOI's
    StatusPublished - jul.-2023

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