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Abstract Motivation The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Results Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Availability and implementation Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights. 

When isotopes of carbon are fed to photosynthesizing leaves, metabolites of the Calvin–Benson cycle (CBC) are rapidly labeled initially, but then the rate of labeling slows considerably, raising questions about the integration of the CBC within leaf metabolism. We have used 2-h time courses of labeling of Camelina sativa leaf metabolites to test models of 12 C washout when the CO 2 source is rapidly switched to 13 CO 2 . Fitting exponential functions to the time course of CBC metabolites, we found evidence for three temporally distinct processes contributing to the labeling but none for metabolically inactive pools. We next modeled the data of all metabolites by 13 C isotopically nonstationary metabolic flux analysis, testing a variety of flux networks. In the model that best explains measured data, three processes determine CBC metabolite labeling. First is fixation of incoming 13 CO 2 ; second is dilution by weakly labeled carbon in cytosolic glucose reentering the CBC following oxidative pentose phosphate pathway reactions, which forms a shunt bypassing much of the CBC. Third, very weakly labeled carbon from the vacuole further dilutes the labeling. This model predicts the shunt proceeds at about 5% of the rate of net CO 2 fixation and explains the three phases of labeling. In showing the interconnection of three compartments, we have drawn a more complete picture of how carbon moves through photosynthetic metabolism in a way that integrates the CBC, cytosolic sugar pools, glucose-6-phosphate shunt, and vacuolar sugars into a single system. 

Abstract The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.