2018
DOI: 10.1007/s40626-018-0132-3
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Insights into the spatial and temporal organisation of plant metabolism from network flux analysis

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Cited by 11 publications
(8 citation statements)
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“…Recently, in silico systems approaches have been applied to plant metabolism [10,11], facilitating the development of computational representations of entire metabolic networks from genome sequences. Furthermore, the availability of transcriptomes and whole-genome sequences of algae and earlierdiverging land plants has provided researchers with valuable resources for metabolic modeling [12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, in silico systems approaches have been applied to plant metabolism [10,11], facilitating the development of computational representations of entire metabolic networks from genome sequences. Furthermore, the availability of transcriptomes and whole-genome sequences of algae and earlierdiverging land plants has provided researchers with valuable resources for metabolic modeling [12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Leaf anatomical structure can respond to environmental changes and influence the above-mentioned parameters, creating intricate and complex changes in metabolic cycles (Moreira et al 2019). For instance, stomatal conductance can change accordingly to stomata number, density and shape and also epidermis area (de Boer et al 2016); mesophyll conductance can be altered by cell organization, intercellular space patterns and leaf thickness (Terashima et al 2011); CO 2 availability to Rubisco can be affected by the same factors as mesophyll conductance and by the physical distribution of carbon assimilation specialized cells (Gago et al 2016); and ATP synthesis through light reactions may be altered, for instance, by chloroplast arrangement (Xiao et al 2016).…”
Section: Introductionmentioning
confidence: 99%
“…This approach simplifies the mathematical representation of the system by considering only the stoichiometry of the metabolic reactions and uses experimental constraints and an optimization objective to make predictions about flux distributions in the metabolic network at steady state (Nikoloski et al, 2015). This approach has been proven capable of making quantitatively realistic predictions of plant metabolic behavior (Basler et al, 2018;Moreira et al, 2019). A number of studies have applied FBA to C 3 leaf metabolism (de Oliveira Dal'Molin et al, 2010Poolman et al, 2013Poolman et al, , 2014Arnold and Nikoloski, 2014;Cheung et al, 2014Cheung et al, , 2015Lakshmanan et al, 2015), but the specific questions of the role of mitochondria in the light and the energetic coupling between subcellular compartments have not been explicitly considered.…”
mentioning
confidence: 99%