Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Steady-state metabolic flux analysis (MFA) is an experimental approach that allows the measurement of multiple fluxes in the core network of primary carbon metabolism. It is based on isotopic labelling experiments, and although well established in the analysis of micro-organisms, and some mammalian systems, the extension of the method to plant cells has been challenging because of the extensive subcellular compartmentation of the metabolic network. Despite this difficulty there has been substantial progress in developing robust protocols for the analysis of heterotrophic plant metabolism by steady-state MFA, and flux maps have now been published that reflect the metabolic phenotypes of excised root tips, developing embryos and cotyledons, hairy root cultures, and cell suspensions under a variety of physiological conditions. There has been a steady improvement in the quality, extent and statistical reliability of these analyses, and new information is emerging on the performance of the plant metabolic network and the contributions of specific pathways. The principles of steady-state MFA are outlined here, the current status of the technique for characterizing primary metabolism in plants is described, and its complementary relationship to metabolomic analysis based on metabolite composition is discussed. It is argued that there is still considerable scope for further development of the technique, either by implementing refinements that have already been adopted in microbial investigations, or by developing techniques that are particularly relevant to the problems posed by plant tissues. If successful, these developments will lead to a more powerful phenotyping tool that will be faster to implement, and which will provide the basis for fully predictive mechanistic models of the network. This in turn will lead to an improved understanding of the regulation of plant metabolic networks, as well as a firm foundation for rational metabolic engineering.

Original publication




Journal article



Publication Date



Department of Plant Sciences, University of Oxford, Oxford, UK.