Metabolic modeling reveals distinct roles of sugars and carboxylic acids in stomatal opening as well as unexpected carbon fluxes.

Guard cell metabolism is crucial for stomatal dynamics, but a full understanding of its role is hampered by experimental limitations and the flexible nature of the metabolic network. To tackle this challenge, we constructed a time-resolved stoichiometric model of guard cell metabolism that accounts for energy and osmolyte requirements and which is integrated with the mesophyll. The model resolved distinct roles for starch, sugars, and malate in guard cell metabolism and revealed several unexpected flux patterns in central metabolism.

A role for fermentation in aerobic conditions as revealed by computational analysis of maize root metabolism during growth by cell elongation.

The root is a well-studied example of cell specialisation, yet little is known about the metabolism that supports the transport functions and growth of different root cell types. To address this, we used computational modelling to study metabolism in the elongation zone of a maize lateral root. A functional-structural model captured the cell-anatomical features of the root and modelled how they changed as the root elongated.

Isotopically non-stationary metabolic flux analysis of heterotrophic cell cultures.

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation.

A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments.

While flux balance analysis (FBA) provides a framework for predicting steady-state leaf metabolic network fluxes, it does not readily capture the response to environmental variables without being coupled to other modelling formulations. To address this, we coupled an FBA model of 903 reactions of soybean (Glycine max) leaf metabolism with e-photosynthesis, a dynamic model that captures the kinetics of 126 reactions of photosynthesis and associated chloroplast carbon metabolism. Successful coupling was achieved in an iterative formulation in which fluxes from e-photosynthesis were used to constrain the FBA model and then, in turn, fluxes computed from the FBA model used to update parameters in e-photosynthesis.