Mathematical Modeling of Plant Metabolic Pathways

Morgan, John A.; Rhodes, David

doi:10.1006/mben.2001.0211

Cited by 104 publications

(55 citation statements)

References 89 publications

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“…Plant metabolism has repeatedly been the subject of kinetic modeling, for reviews see Morgan and Rhodes (2002) and Lange (2006). Here, we present a kinetic model of the synthesis of aliphatic glucosinolates in Arabidopsis thaliana leaves (in particular, its Columbia ecotype).…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

et al. 2008

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Aliphatic glucosinolates are a major class of defensive secondary metabolites in plants that are mostly derived from methionine. Occurring in different chain lengths, they show a structural diversity arising from the variable number of chain elongation cycles taking place during their biosynthesis. The key enzymes in determining glucosinolate chain length are the methylthioalkylmalate (MAM) synthases, MAM1 and MAM3, with MAM3 showing a broader substrate specificity than MAM1. A comparison of the measurements of wild type and MAM1 knockout mutant plants shows the following distinct changes in glucosinolate chain length profiles:(1) a reversal of the relative proportions of the two shortest glucosinolates, (2) a significant increase in the concentration of the longest glucosinolate, (3) an increase in total glucosinolate content in the mutant.MAM3 knockout mutants on the contrary differ from wild type plants by a pronounced abundance of the second shortest glucosinolate and the depletion of the two longest glucosinolates. To clarify the contribution of the multifunctional enzymes MAM1 and MAM3 to the glucosinolate profile of Arabidopsis thaliana leaves, we simulated glucosinolate biosynthesis in a kinetic model, taking into account the structure of the pathway and measured enzymatic properties. The predicted glucosinolate profiles show all characteristics of the actual differences between wild-type and MAM1 mutants or MAM3 mutants, respectively. The model strongly supports experimental indications that the two MAM activities are not independent of each other. In particular, it showed that an elevated expression of MAM3 in the MAM1 mutant is critical in determining the glucosinolate profile of this plant line. The simulation was critical for this finding since it allowed us to assess the individual effects of two processes-the knocking out of MAM1 and the overexpression of MAM3-that are difficult to separate experimentally.

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Section: Introductionmentioning

confidence: 99%

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

et al. 2008

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“…In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000;Morgan and Rhodes, 2002;Poolman et al, 2004;Rios-Estepa and Lange, 2007).…”

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Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

et al. 2013

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Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.

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“…However, the kinetic parameters of individual enzymes do not by themselves provide much insight into the behaviour of an intact, functioning metabolic pathway. Cellular network models, such as those applied in the approach of computational systems biology, extend the usefulness of kinetic data on individual enzymes immensely and can have both explanatory and predictive value.Several papers that give an overview of different approaches for studying and modelling metabolism, such as metabolic flux analysis, metabolic control analysis (MCA) and positional isotopic labelling combined with NMR or MS, have been published recently [1][2][3]. Of these approaches, MCA [4,5] is particularly useful in studies of metabolic pathways, as it quantifies the degree of control of individual reaction steps on the steady-state pathway flux or metabolite concentrations.…”

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A kinetic study of sugarcane sucrose synthase

Schäfer¹,

Rohwer

Botha

2004

European Journal of Biochemistry

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The kinetic data on sugarcane (Saccharum spp. hybrids) sucrose synthase (SuSy, UDP-glucose: D-fructose 2-a-Dglucosyltransferase, EC 2.4.1.13) are limited. We characterized kinetically a SuSy activity partially purified from sugarcane variety N19 leaf roll tissue. Primary plot analysis and product inhibition studies showed that a compulsory order ternary complex mechanism is followed, with UDP binding first and UDP-glucose dissociating last from the enzyme. Product inhibition studies showed that UDP-glucose is a competitive inhibitor with respect to UDP and a mixed inhibitor with respect to sucrose. Fructose is a mixed inhibitor with regard to both sucrose and UDP. Kinetic constants are as follows: K m values (mM, ± SE) were, for sucrose, 35.9 ± 2.3; for UDP, 0.00191 ± 0.00019; for UDP-glucose, 0.234 ± 0.025 and for fructose, 6.49 ± 0.61. K S i values were, for sucrose, 227 mM; for UDP, 0.086 mM; for UDP-glucose, 0.104; and for fructose, 2.23 mM.Replacing estimated kinetic parameters of SuSy in a kinetic model of sucrose accumulation with experimentally determined parameters of the partially purified isoform had significant effects on model outputs, with a 41% increase in sucrose concentration and 7.5-fold reduction in fructose the most notable. Of the metabolites included in the model, fructose concentration was most affected by changes in SuSy activity: doubling and halving of SuSy activity reduced and increased the steady-state fructose concentration by about 42 and 140%, respectively. It is concluded that different isoforms of SuSy could have significant differential effects on metabolite concentrations in vivo, therefore impacting on metabolic regulation.Keywords: metabolic control analysis; sugarcane; sucrose synthase; kinetic modelling.The kinetic parameters of enzymes provide important information about their interactions with substrates, products and effectors. Typically, substrate K m values are interpreted to give an indication of the affinity of enzymes for their substrates, and conclusions about enzymes' physiological roles are often based on these values. However, the kinetic parameters of individual enzymes do not by themselves provide much insight into the behaviour of an intact, functioning metabolic pathway. Cellular network models, such as those applied in the approach of computational systems biology, extend the usefulness of kinetic data on individual enzymes immensely and can have both explanatory and predictive value.Several papers that give an overview of different approaches for studying and modelling metabolism, such as metabolic flux analysis, metabolic control analysis (MCA) and positional isotopic labelling combined with NMR or MS, have been published recently [1][2][3]. Of these approaches, MCA [4,5] is particularly useful in studies of metabolic pathways, as it quantifies the degree of control of individual reaction steps on the steady-state pathway flux or metabolite concentrations. Hence, MCA can be a great help in determining potential target steps for metabolic engineering, beca...

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Mathematical Modeling of Plant Metabolic Pathways

Cited by 104 publications

References 89 publications

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

A kinetic study of sugarcane sucrose synthase

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