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Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.

Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework.

Understanding whether the functionality of a biological system can be characterized by measuring few selected components is key to targeted phenotyping techniques in systems biology. Methods from observability theory have proven useful in identifying sensor components that have to be measured to obtain information about the entire system. Yet, the extent to which the data profiles reflect the role of components in the observability of the system remains unexplored. Here we first identify the sensor metabolites in the model plant Arabidopsis (Arabidopsis thaliana) by employing state-of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed with in silico generated metabolic profiles from a medium-size kinetic model of plant central carbon metabolism. Altogether, due to the small number of identified sensors, our study implies that targeted metabolite analyses may provide the vast majority of relevant information about plant metabolic systems.

Trade‐offs between traits arise and reflect constraints imposed by the environment and physicochemical laws. Trade‐off situations are expected to be highly relevant for sessile plants, which have to respond to changes in the environment to ensure survival. Despite increasing interest in determining the genetic and molecular basis of plant trade‐offs, there are still gaps and differences with respect to how trade‐offs are defined, how they are measured, and how their genetic architecture is dissected. The first step to fill these gaps is to establish what is meant by trade‐offs. In this review we provide a classification of the existing definitions of trade‐offs according to: (1) the measures used for their quantification, (2) the dependence of trade‐offs on environment, and (3) experimental designed used (i.e. a single individual across different environments or a population of individuals in single or multiple environments). We then compare the approaches for quantification of trade‐offs based on phenotypic, between‐individual, and genetic correlations, and stress the need for developing further quantification indices particularly for trade‐offs between multiple traits. Lastly, we highlight the genetic mechanisms underpinning trade‐offs and experimental designs that facilitate their discovery in plants, with focus on usage of natural variability. This review also offers a perspective for future research aimed at identification of plant trade‐offs, dissection of their genetic architecture, and development of strategies to overcome trade‐offs, with applications in crop breeding. [ABSTRACT FROM AUTHOR]

Accumulating evidence for trade‐offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade‐offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade‐offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade‐offs obtained from constraint‐based metabolic modelling, we emphasize the relationship of metabolic trade‐offs to the classical black box Y‐model that provides a conceptual explanation for resource acquisition–allocation trade‐offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade‐offs by relying on the toolbox provided by constraint‐based metabolic modelling for single organisms and microbial communities. [ABSTRACT FROM AUTHOR]