The development of high-throughput ‘omic techniques has sparked a rising interest in genome-scale metabolic models, with applications ranging from disease diagnostics to crop adaptation. Efficient and accurate methods are required to analyze large metabolic networks. Flux sampling can be used to explore the feasible flux solutions in metabolic networks by generating probability distributions of steady-state reaction fluxes. Unlike other methods, flux sampling can be used without assuming a particular cellular objective. We have undertaken a rigorous comparison of several sampling algorithms and concluded that the coordinate hit-and-run with rounding (CHRR) algorithm is the most efficient based on both run-time and multiple convergence diagnostics. We demonstrate the power of CHRR by using it to study the metabolic changes that underlie photosynthetic acclimation to cold ofArabidopsis thalianaplant leaves. In combination with experimental measurements, we show how the regulated interplay between diurnal starch and organic acid accumulation defines the plant acclimation process. We confirm fumarate accumulation as a requirement for cold acclimation and further predictγ–aminobutyric acid to have a key role in metabolic signaling under cold conditions. These results demonstrate how flux sampling can be used to analyze the feasible flux solutions across changing environmental conditions, whereas eliminating the need to make assumptions which introduce observer bias.

Light response curves (LRCs) describe how the rate of photosynthesis varies as a function of light. They provide information on the maximum photosynthetic capacity, quantum yield, light compensation point and leaf radiation use efficiency of leaves. Light response curves are widely used to capture photosynthetic phenotypes in response to changing environmental conditions. However, models describing these are predominantly empirical and do not attempt to explain behaviour at a mechanistic level. Here, we use modelling to understand the metabolic changes required for photosynthetic acclimation to changing environmental conditions. Using a simple kinetic model, we predicted LRCs across the physiological temperature range ofArabidopsis thalianaand confirm these using experimental data. We use our validated metabolic model to make novel predictions about the metabolic changes of temperature acclimation. We demonstrate that NADPH utilization are enhanced in warm-acclimated plants, whereas both NADPH and CO₂utilization is enhanced in cold-acclimated plants. We demonstrate how different metabolic acclimation strategies may lead to the same photosynthetic response across environmental change. We further identify that certain metabolic acclimation strategies, such as NADPH utilization, are only triggered when plants are moved beyond a threshold high or low temperature.

The global spread of coronavirus disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. The majority of existing models assume random diffusion but do not take into account differences in the amount of interactions between individuals, i.e. the underlying human interaction network, whose structure is known to be scale-free. Here, we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Using stochastic simulations in a scale-free network, we show that the epidemic can propagate for a long time at a low level before the number of infected individuals suddenly increases markedly, and that this increase occurs shortly after the first hub is infected. We further demonstrate that mitigation strategies that target hubs are far more effective than strategies that randomly decrease the number of connections between individuals. Although applicable to infectious disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models.

Plants adjust their photosynthetic capacity in response to their environment in a way that optimizes their yield and fitness. There is growing evidence that this acclimation is a response to changes in the leaf metabolome, but the extent to which these are linked and how this is optimized remain poorly understood. Using as an example the metabolic perturbations occurring in response to cold, we define the different stages required for acclimation, discuss the evidence for a metabolic temperature sensor, and suggest further work towards designing climate-smart crops. In particular, we discuss how constraint-based and kinetic metabolic modelling approaches can be used to generate targeted hypotheses about relevant pathways, and argue that a stronger integration of experimental and in silico studies will help us to understand the tightly regulated interplay of carbon partitioning and resource allocation required for photosynthetic acclimation to different environmental conditions.

Photosynthesis is especially sensitive to environmental conditions, and the composition of the photosynthetic apparatus can be modulated in response to environmental change, a process termed photosynthetic acclimation. Previously, we identified a role for a cytosolic fumarase, FUM2 in acclimation to low temperature in Arabidopsis thaliana. Mutant lines lacking FUM2 were unable to acclimate their photosynthetic apparatus to cold. Here, using gas exchange measurements and metabolite assays of acclimating and non‐acclimating plants, we show that acclimation to low temperature results in a change in the distribution of photosynthetically fixed carbon to different storage pools during the day. Proteomic analysis of wild‐type Col‐0 Arabidopsis and of a fum2 mutant, which was unable to acclimate to cold, indicates that extensive changes occurring in response to cold are affected in the mutant. Metabolic and proteomic data were used to parameterize metabolic models. Using an approach called flux sampling, we show how the relative export of triose phosphate and 3‐phosphoglycerate provides a signal of the chloroplast redox state that could underlie photosynthetic acclimation to cold.

Plants acclimate their photosynthetic capacity (P_max) in response to changing environmental conditions. In Arabidopsis thaliana, photosynthetic acclimation to cold requires the accumulation of the organic acid fumarate, catalysed by a cytosolically localized fumarase, FUM2. However, the role of this accumulation is currently unknown. Here, we use an integrated experimental and modelling approach to examine the role of FUM2 and fumarate across the physiological temperature range. We have studied three genotypes: Col-0; a fum2 mutant in a Col-0 background; and C24, an accession with reduced FUM2 expression. While low temperature causes an increase in P_max in the Col-0 plants, this parameter decreases following exposure of plants to 30 °C for 7 d. Plants in which fumarate accumulation is partially (C24) or completely (fum2) abolished show a reduced acclimation of P_max across the physiological temperature range (i.e. P_max changes less in response to changing temperature). To understand the role of fumarate accumulation, we have adapted a reliability engineering technique, Failure Mode and Effect Analysis (FMEA), to formalize a rigorous approach for ranking metabolites according to the potential risk that they pose to the metabolic system. FMEA identifies fumarate as a low-risk metabolite, while its precursor, malate, is shown to be high risk and liable to cause system instability. We propose that the role of FUM2 is to provide a fail-safe in order to control malate concentration, maintaining system stability in a changing environment. We suggest that FMEA is a technique that is not only useful in understanding plant metabolism but can also be used to study reliability in other systems and synthetic pathways.