Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism

Metabolic trade-offs between biomass synthesis and photosynthate export at different light intensities in a genome-scale metabolic model of rice

Previously we have used a genome scale model of rice metabolism to describe how metabolism reconfigures at different light intensities in an expanding leaf of rice. Although this established that the metabolism of the leaf was adequately represented, in the model, the scenario was not that of the typical function of the leaf-to provide material for the rest of the plant. Here we extend our analysis to explore the transition to a source leaf as export of photosynthate increases at the expense of making leaf biomass precursors, again as a function of light intensity. In particular we investigate whether, when the leaf is making a smaller range of compounds for export to the phloem, the same changes occur in the interactions between mitochondrial and chloroplast metabolism as seen in biomass synthesis for growth when light intensity increases. Our results show that the same changes occur qualitatively, though there are slight quantitative differences reflecting differences in the energy and redox requirements for the different metabolic outputs.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

Elementary flux modes, flux balance analysis, and their application to plant metabolism

In recent years the number of sequenced and annotated plant genomes has increased significantly, and novel approaches are required to retrieve valuable information from these data sets. The field of systems biology has accelerated the simulation and prediction of phenotypes derived from specific genotypic modifications under defined growth conditions. The biochemical potential of a cell from a specific plant tissue (e.g., seed endosperm) can be derived from its genome in the form of a mathematical model by the method of metabolic network reconstruction. This model can be further analyzed by studying its network properties, analyzing feasible pathway routes through the network, or simulating possible flux distributions of the network . Here, we describe two approaches for identification of all feasible routes through the network (elementary mode analysis) and for simulation of flux distribution in the network based on plant physiological uptake and excretion rates (flux balance analysis).

Application of metabolic flux analysis to plants

This volume compiles a series of chapters that cover the major aspects of plant metabolic flux analysis, such as but not limited to labeling of plant material, acquisition of labeling data, mathematical modeling of metabolic network at the cell, tissue, and plant level. A short revue, including methodological points and applications of flux analysis to plants, is presented in this introductory chapter.

Flux balance analysis as an alternative method to estimate fluxes without labeling

The analysis of plant metabolic networks essentially contributes to the understanding of the efficiency of plant systems in terms of their biotechnological usage. Metabolic fluxes are determined by biochemical parameters such as metabolite concentrations as well as enzyme properties and activities, which in turn are the result of various regulatory events at various levels between control of transcription and posttranslational regulation of enzyme protein activity. Thus, knowledge about metabolic fluxes on a large scale provides an integrated view on the functional state of a metabolically active cell, organ, or system. In this chapter, we introduce flux balance analysis as a constraint-based method for the prediction of optimal metabolic fluxes in a given metabolic network. Furthermore, we provide a step-by-step protocol for metabolic network reconstruction and constraint-based analysis using the COBRA Toolbox.

Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale

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.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

Starch biosynthesis in cassava: a genome-based pathway reconstruction and its exploitation in data integration

Background

Cassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available.

Results

In this work, we exploited the recently released genome information and utilized the post-genomic approaches to reconstruct the metabolic pathway of starch biosynthesis in cassava using multiple plant templates. The quality of pathway reconstruction was assured by the employed parsimonious reconstruction framework and the collective validation steps. Our reconstructed pathway is presented in the form of an informative map, which describes all important information of the pathway, and an interactive map, which facilitates the integration of omics data into the metabolic pathway. Additionally, to demonstrate the advantage of the reconstructed pathways beyond just the schematic presentation, the pathway could be used for incorporating the gene expression data obtained from various developmental stages of cassava roots. Our results exhibited the distinct activities of the starch biosynthesis pathway in different stages of root development at the transcriptional level whereby the activity of the pathway is higher toward the development of mature storage roots.

Conclusions

To expand its applications, the interactive map of the reconstructed starch biosynthesis pathway is available for download at the SBI group’s website (http://sbi.pdti.kmutt.ac.th/?page_id=33). This work is considered a big step in the quantitative modeling pipeline aiming to investigate the dynamic regulation of starch biosynthesis in cassava roots.

Elucidating rice cell metabolism under flooding and drought stresses using flux-based modeling and analysis

Rice (Oryza sativa) is one of the major food crops in world agriculture, especially in Asia. However, the possibility of subsequent occurrence of flood and drought is a major constraint to its production. Thus, the unique behavior of rice toward flooding and drought stresses has required special attention to understand its metabolic adaptations. However, despite several decades of research investigations, the cellular metabolism of rice remains largely unclear. In this study, in order to elucidate the physiological characteristics in response to such abiotic stresses, we reconstructed what is to our knowledge the first metabolic/regulatory network model of rice, representing two tissue types: germinating seeds and photorespiring leaves. The phenotypic behavior and metabolic states simulated by the model are highly consistent with our suspension culture experiments as well as previous reports. The in silico simulation results of seed-derived rice cells indicated (1) the characteristic metabolic utilization of glycolysis and ethanolic fermentation based on oxygen availability and (2) the efficient sucrose breakdown through sucrose synthase instead of invertase. Similarly, flux analysis on photorespiring leaf cells elucidated the crucial role of plastid-cytosol and mitochondrion-cytosol malate transporters in recycling the ammonia liberated during photorespiration and in exporting the excess redox cofactors, respectively. The model simulations also unraveled the essential role of mitochondrial respiration during drought stress. In the future, the combination of experimental and in silico analyses can serve as a promising approach to understand the complex metabolism of rice and potentially help in identifying engineering targets for improving its productivity as well as enabling stress tolerance.

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo (13)C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast.

Regulation of assimilate import into sink organs: update on molecular drivers of sink strength

Recent developments have altered our view of molecular mechanisms that determine sink strength, defined here as the capacity of non-photosynthetic structures to compete for import of photoassimilates. We review new findings from diverse systems, including stems, seeds, flowers, and fruits. An important advance has been the identification of new transporters and facilitators with major roles in the accumulation and equilibration of sugars at a cellular level. Exactly where each exerts its effect varies among systems. Sugarcane and sweet sorghum stems, for example, both accumulate high levels of sucrose, but may do so via different paths. The distinction is central to strategies for targeted manipulation of sink strength using transporter genes, and shows the importance of system-specific analyses. Another major advance has been the identification of deep hypoxia as a feature of normal grain development. This means that molecular drivers of sink strength in endosperm operate in very low oxygen levels, and under metabolic conditions quite different than previously assumed. Successful enhancement of sink strength has nonetheless been achieved in grains by up-regulating genes for starch biosynthesis. Additionally, our understanding of sink strength is enhanced by awareness of the dual roles played by invertases (INVs), not only in sucrose metabolism, but also in production of the hexose sugar signals that regulate cell cycle and cell division programs. These contributions of INV to cell expansion and division prove to be vital for establishment of young sinks ranging from flowers to fruit. Since INV genes are themselves sugar-responsive "feast genes," they can mediate a feed-forward enhancement of sink strength when assimilates are abundant. Greater overall productivity and yield have thus been attained in key instances, indicating that even broader enhancements may be achievable as we discover the detailed molecular mechanisms that drive sink strength in diverse systems.

Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks

Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases.

Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called ‘stoichiometric capacitance’. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes.

Contact:larhlimi@mpimp-golm.mpg.de, or nikoloski@mpimp-golm.mpg.de

Supplementary Information:Supplementary tables are available at Bioinformatics online.

Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in Arabidopsis

Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.

Metabolic engineering with plants for a sustainable biobased economy

Plants are bona fide sustainable organisms because they accumulate carbon and synthesize beneficial metabolites from photosynthesis. To meet the challenges to food security and health threatened by increasing population growth and depletion of nonrenewable natural resources, recent metabolic engineering efforts have shifted from single pathways to holistic approaches with multiple genes owing to integration of omics technologies. Successful engineering of plants results in the high yield of biomass components for primary food sources and biofuel feedstocks, pharmaceuticals, and platform chemicals through synthetic biology and systems biology strategies. Further discovery of undefined biosynthesis pathways in plants, integrative analysis of discrete omics data, and diversified process developments for production of platform chemicals are essential to overcome the hurdles for sustainable production of value-added biomolecules from plants.

Systematic comparison of C3 and C4 plants based on metabolic network analysis

BACKGROUND

The C4 photosynthetic cycle supercharges photosynthesis by concentrating CO2 around ribulose-1,5-bisphosphate carboxylase and significantly reduces the oxygenation reaction. Therefore engineering C4 feature into C3 plants has been suggested as a feasible way to increase photosynthesis and yield of C3 plants, such as rice, wheat, and potato. To identify the possible transition from C3 to C4 plants, the systematic comparison of C3 and C4 metabolism is necessary.

RESULTS

We compared C3 and C4 metabolic networks using the improved constraint-based models for Arabidopsis and maize. By graph theory, we found the C3 network exhibit more dense topology structure than C4. The simulation of enzyme knockouts demonstrated that both C3 and C4 networks are very robust, especially when optimizing CO2 fixation. Moreover, C4 plant has better robustness no matter the objective function is biomass synthesis or CO2 fixation. In addition, all the essential reactions in C3 network are also essential for C4, while there are some other reactions specifically essential for C4, which validated that the basic metabolism of C4 plant is similar to C3, but C4 is more complex. We also identified more correlated reaction sets in C4, and demonstrated C4 plants have better modularity with complex mechanism coordinates the reactions and pathways than that of C3 plants. We also found the increase of both biomass production and CO2 fixation with light intensity and CO2 concentration in C4 is faster than that in C3, which reflected more efficient use of light and CO2 in C4 plant. Finally, we explored the contribution of different C4 subtypes to biomass production by setting specific constraints.

CONCLUSIONS

All results are consistent with the actual situation, which indicate that Flux Balance Analysis is a powerful method to study plant metabolism at systems level. We demonstrated that in contrast to C3, C4 plants have less dense topology, higher robustness, better modularity, and higher CO2 and radiation use efficiency. In addition, preliminary analysis indicated that the rate of CO2 fixation and biomass production in PCK subtype are superior to NADP-ME and NAD-ME subtypes under enough supply of water and nitrogen.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Predictive modeling of biomass component tradeoffs in Brassica napus developing oilseeds based on in silico manipulation of storage metabolism

Seed oil content is a key agronomical trait, while the control of carbon allocation into different seed storage compounds is still poorly understood and hard to manipulate. Using bna572, a large-scale model of cellular metabolism in developing embryos of rapeseed (Brassica napus) oilseeds, we present an in silico approach for the analysis of carbon allocation into seed storage products. Optimal metabolic flux states were obtained by flux variability analysis based on minimization of the uptakes of substrates in the natural environment of the embryo. For a typical embryo biomass composition, flux sensitivities to changes in different storage components were derived. Upper and lower flux bounds of each reaction were categorized as oil or protein responsive. Among the most oil-responsive reactions were glycolytic reactions, while reactions related to mitochondrial ATP production were most protein responsive. To assess different biomass compositions, a tradeoff between the fractions of oil and protein was simulated. Based on flux-bound discontinuities and shadow prices along the tradeoff, three main metabolic phases with distinct pathway usage were identified. Transitions between the phases can be related to changing modes of the tricarboxylic acid cycle, reorganizing the usage of organic carbon and nitrogen sources for protein synthesis and acetyl-coenzyme A for cytosol-localized fatty acid elongation. The phase close to equal oil and protein fractions included an unexpected pathway bypassing α-ketoglutarate-oxidizing steps in the tricarboxylic acid cycle. The in vivo relevance of the findings is discussed based on literature on seed storage metabolism.

Are we ready for genome-scale modeling in plants?

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

Optimizing metabolic pathways by screening for feasible synthetic reactions

BACKGROUND

Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.

RESULTS

Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.

CONCLUSIONS

While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.

FluxMap: a VANTED add-on for the visual exploration of flux distributions in biological networks

BACKGROUND

The quantification of metabolic fluxes is gaining increasing importance in the analysis of the metabolic behavior of biological systems such as organisms, tissues or cells. Various methodologies (wetlab or drylab) result in sets of fluxes which require an appropriate visualization for interpretation by scientists. The visualization of flux distributions is a necessary prerequisite for intuitive flux data exploration in the context of metabolic networks.

RESULTS

We present FluxMap, a tool for the advanced visualization and exploration of flux data in the context of metabolic networks. The template-based flux data import assigns flux values and optional quality parameters (e. g. the confidence interval) to biochemical reactions. It supports the discrimination between mass and substance fluxes, such as C- or N-fluxes. After import, flux data mapping and network-based visualization allow the interactive exploration of the dataset. Various visualization options enable the user to adapt layout and network representation according to individual purposes.

CONCLUSIONS

The Vanted add-on FluxMap comprises a comprehensive set of functionalities for visualization and advanced visual exploration of flux distributions in biological networks. It is available as a Java open source tool from http://www.vanted.org/fluxmap.

Frontiers in metabolic reconstruction and modeling of plant genomes

A major goal of post-genomic biology is to reconstruct and model in silico the metabolic networks of entire organisms. Work on bacteria is well advanced, and is now under way for plants and other eukaryotes. Genome-scale modelling in plants is much more challenging than in bacteria. The challenges come from features characteristic of higher organisms (subcellular compartmentation, tissue differentiation) and also from the particular severity in plants of a general problem: genome content whose functions remain undiscovered. This problem results in thousands of genes for which no function is known ('undiscovered genome content') and hundreds of enzymatic and transport functions for which no gene is yet identified. The severity of the undiscovered genome content problem in plants reflects their genome size and complexity. To bring the challenges of plant genome-scale modelling into focus, we first summarize the current status of plant genome-scale models. We then highlight the challenges - and ways to address them - in three areas: identifying genes for missing processes, modelling tissues as opposed to single cells, and finding metabolic functions encoded by undiscovered genome content. We also discuss the emerging view that a significant fraction of undiscovered genome content encodes functions that counter damage to metabolites inflicted by spontaneous chemical reactions or enzymatic mistakes.

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.

Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies

For the past decade, flux maps have provided researchers with an in-depth perspective on plant metabolism. As a rapidly developing field, significant headway has been made recently in computation, experimentation, and overall understanding of metabolic flux analysis. These advances are particularly applicable to the study of plant metabolism. New dynamic computational methods such as non-stationary metabolic flux analysis are finding their place in the toolbox of metabolic engineering, allowing more organisms to be studied and decreasing the time necessary for experimentation, thereby opening new avenues by which to explore the vast diversity of plant metabolism. Also, improved methods of metabolite detection and measurement have been developed, enabling increasingly greater resolution of flux measurements and the analysis of a greater number of the multitude of plant metabolic pathways. Methods to deconvolute organelle-specific metabolism are employed with increasing effectiveness, elucidating the compartmental specificity inherent in plant metabolism. Advances in metabolite measurements have also enabled new types of experiments, such as the calculation of metabolic fluxes based on (13)CO(2) dynamic labelling data, and will continue to direct plant metabolic engineering. Newly calculated metabolic flux maps reveal surprising and useful information about plant metabolism, guiding future genetic engineering of crops to higher yields. Due to the significant level of complexity in plants, these methods in combination with other systems biology measurements are necessary to guide plant metabolic engineering in the future.

Model-based confirmation of alternative substrates of mitochondrial electron transport chain

Discrimination of metabolic models based on high throughput metabolomics data, reflecting various internal and external perturbations, is essential for identifying the components that contribute to the emerging behavior of metabolic processes. Here, we investigate 12 different models of the mitochondrial electron transport chain (ETC) in Arabidopsis thaliana during dark-induced senescence in order to elucidate the alternative substrates to this metabolic pathway. Our findings demonstrate that the coupling of the proposed computational approach, based on dynamic flux balance analysis, with time-resolved metabolomics data results in model-based confirmations of the hypotheses that, during dark-induced senescence in Arabidopsis, (i) under conditions where the main substrate for the ETC are not fully available, isovaleryl-CoA dehydrogenase and 2-hydroxyglutarate dehydrogenase are able to donate electrons to the ETC, (ii) phytanoyl-CoA does not act even as an indirect substrate of the electron transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase complex, and (iii) the mitochondrial γ-aminobutyric acid transporter has functional significance in maintaining mitochondrial metabolism. Our study provides a basic framework for future in silico studies of alternative pathways in mitochondrial metabolism under extended darkness whereby the role of its components can be computationally discriminated based on available molecular profile data.

Functional genomics tools applied to plant metabolism: a survey on plant respiration, its connections and the annotation of complex gene functions

The application of post-genomic techniques in plant respiration studies has greatly improved our ability to assign functions to gene products. In addition it has also revealed previously unappreciated interactions between distal elements of metabolism. Such results have reinforced the need to consider plant respiratory metabolism as part of a complex network and making sense of such interactions will ultimately require the construction of predictive and mechanistic models. Transcriptomics, proteomics, metabolomics, and the quantification of metabolic flux will be of great value in creating such models both by facilitating the annotation of complex gene function, determining their structure and by furnishing the quantitative data required to test them. In this review, we highlight how these experimental approaches have contributed to our current understanding of plant respiratory metabolism and its interplay with associated process (e.g., photosynthesis, photorespiration, and nitrogen metabolism). We also discuss how data from these techniques may be integrated, with the ultimate aim of identifying mechanisms that control and regulate plant respiration and discovering novel gene functions with potential biotechnological implications.

Dynamic ¹³C/¹ H NMR imaging uncovers sugar allocation in the living seed

Seed growth and accumulation of storage products relies on the delivery of sucrose from the maternal to the filial tissues. The transport route is hidden inside the seed and has never been visualized in vivo. Our approach, based on high-field nuclear magnetic resonance and a custom made (13)C/(1) H double resonant coil, allows the non-invasive imaging and monitoring of sucrose allocation within the seed. The new technique visualizes the main stream of sucrose and determines its velocity during the grain filling in barley (Hordeum vulgare L.). Quantifiable dynamic images are provided, which allow observing movement of (13)C-sucrose at a sub-millimetre level of resolution. The analysis of genetically modified barley grains (Jekyll transgenic lines, seg8 and Risø13 mutants) demonstrated that sucrose release via the nucellar projection towards the endosperm provides an essential mean for the control of seed growth by maternal organism. The sucrose allocation was further determined by structural and metabolic features of endosperm. Sucrose monitoring was integrated with an in silico flux balance analysis, representing a powerful platform for non-invasive study of seed filling in crops.

MetaCrop 2.0: managing and exploring information about crop plant metabolism

MetaCrop is a manually curated repository of high-quality data about plant metabolism, providing different levels of detail from overview maps of primary metabolism to kinetic data of enzymes. It contains information about seven major crop plants with high agronomical importance and two model plants. MetaCrop is intended to support research aimed at the improvement of crops for both nutrition and industrial use. It can be accessed via web, web services and an add-on to the Vanted software. Here, we present several novel developments of the MetaCrop system and the extended database content. MetaCrop is now available in version 2.0 at http://metacrop.ipk-gatersleben.de.

Experimental flux measurements on a network scale

Metabolic flux is a fundamental property of living organisms. In recent years, methods for measuring metabolic flux in plants on a network scale have evolved further. One major challenge in studying flux in plants is the complexity of the plant's metabolism. In particular, in the presence of parallel pathways in multiple cellular compartments, the core of plant central metabolism constitutes a complex network. Hence, a common problem with the reliability of the contemporary results of (13)C-Metabolic Flux Analysis in plants is the substantial reduction in complexity that must be included in the simulated networks; this omission partly is due to limitations in computational simulations. Here, I discuss recent emerging strategies that will better address these shortcomings.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to ¹³C metabolic flux analysis

Plant oils are an important renewable resource, and seed oil content is a key agronomical trait that is in part controlled by the metabolic processes within developing seeds. A large-scale model of cellular metabolism in developing embryos of Brassica napus (bna572) was used to predict biomass formation and to analyze metabolic steady states by flux variability analysis under different physiological conditions. Predicted flux patterns are highly correlated with results from prior ¹³C metabolic flux analysis of B. napus developing embryos. Minor differences from the experimental results arose because bna572 always selected only one sugar and one nitrogen source from the available alternatives, and failed to predict the use of the oxidative pentose phosphate pathway. Flux variability, indicative of alternative optimal solutions, revealed alternative pathways that can provide pyruvate and NADPH to plastidic fatty acid synthesis. The nutritional values of different medium substrates were compared based on the overall carbon conversion efficiency (CCE) for the biosynthesis of biomass. Although bna572 has a functional nitrogen assimilation pathway via glutamate synthase, the simulations predict an unexpected role of glycine decarboxylase operating in the direction of NH₄⁺ assimilation. Analysis of the light-dependent improvement of carbon economy predicted two metabolic phases. At very low light levels small reductions in CO₂ efflux can be attributed to enzymes of the tricarboxylic acid cycle (oxoglutarate dehydrogenase, isocitrate dehydrogenase) and glycine decarboxylase. At higher light levels relevant to the ¹³C flux studies, ribulose-1,5-bisphosphate carboxylase activity is predicted to account fully for the light-dependent changes in carbon balance.

Combined noninvasive imaging and modeling approaches reveal metabolic compartmentation in the barley endosperm

The starchy endosperm of cereals is a priori taken as a metabolically uniform tissue. By applying a noninvasive assay based on (13)C/(1)H-magnetic resonance imaging (MRI) to barley (Hordeum vulgare) grains, we uncovered metabolic compartmentation in the endosperm. (13)C-Suc feeding during grain filling showed that the primary site of Ala synthesis was the central region of the endosperm, the part of the caryopsis experiencing the highest level of hypoxia. Region-specific metabolism in the endosperm was characterized by flux balance analysis (FBA) and metabolite profiling. FBA predicts that in the central region of the endosperm, the tricarboxylic acid cycle shifts to a noncyclic mode, accompanied by elevated glycolytic flux and the accumulation of Ala. The metabolic compartmentation within the endosperm is advantageous for the grain's carbon and energy economy, with a prominent role being played by Ala aminotransferase. An investigation of caryopses with a genetically perturbed tissue pattern demonstrated that Ala accumulation is a consequence of oxygen status, rather than being either tissue specific or dependent on the supply of Suc. Hence the (13)C-Ala gradient can be used as an in vivo marker for hypoxia. The combination of MRI and metabolic modeling offers opportunities for the noninvasive analysis of metabolic compartmentation in plants.

Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

Modelling the size and composition of fruit, grain and seed by process-based simulation models

Understanding what determines the size and composition of fruit, grain and seed in response to the environment and genotype is challenging, as these traits result from several linked processes controlled at different levels of organization, from the subcellular to the crop level, with subtle interactions occurring at or between the levels of organization. Process-based simulation models (PBSMs) implement algorithms to simulate metabolic and biophysical aspects of cell, tissue and organ behaviour. In this review, fruit, grain and seed PBSMs describing the main phases of growth, development and storage metabolism are discussed. From this concurrent work, it is possible to identify generic storage organ processes which can be modelled similarly for fruit, grain and seed. Spatial heterogeneity at the tissue and whole-plant level is found to be a key consideration in modelling the effects of the environment and genotype on fruit, grain and seed end-use value. In the future, PBSMs may well become the main link between studies at the molecular and whole-plant levels. To bridge this phenotype-to-genotype gap, future models need to remain plastic without becoming overparameterized.

Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.

Modeling synergistic drug inhibition of Mycobacterium tuberculosis growth in murine macrophages

We developed a metabolism-based systems biology framework to model drug-induced growth inhibition of Mycobacterium tuberculosis in murine macrophage cells. We used it to simulate ex vivo bacterial growth inhibition due to 3-nitropropionate (3-NP) and calculated the corresponding time- and drug concentration-dependent dose-response curves. 3-NP targets the isocitrate lyase 1 (ICL1) and ICL2 enzymes in the glyoxylate shunt, an essential component in carbon metabolism of many important prokaryotic organisms. We used the framework to in silico mimic drugging additional enzymes in combination with 3-NP to understand how synergy can arise among metabolic enzyme targets. In particular, we focused on exploring additional targets among the central carbon metabolism pathways and ascertaining the impact of jointly inhibiting these targets and the ICL1/ICL2 enzymes. Thus, additionally inhibiting the malate synthase (MS) enzyme in the glyoxylate shunt did not produce synergistic effects, whereas additional inhibition of the glycerol-3-phosphate dehydrogenase (G3PD) enzyme showed a reduction in bacterial growth beyond what each single inhibition could achieve. Whereas the ICL1/ICL2-MS pair essentially works on the same branch of the metabolic pathway processing lipids as carbon sources (the glyoxylate shunt), the ICL1/ICL2-G3PD pair inhibition targets different branches among the lipid utilization pathways. This allowed the ICL1/ICL2-G3PD drug combination to synergistically inhibit carbon processing and ultimately affect cellular growth. Our previously developed model for in vitro conditions failed to capture these effects, highlighting the importance of constructing accurate representations of the experimental ex vivo macrophage system.

The remarkable diversity of plant PEPC (phosphoenolpyruvate carboxylase): recent insights into the physiological functions and post-translational controls of non-photosynthetic PEPCs

PEPC [PEP (phosphoenolpyruvate) carboxylase] is a tightly controlled enzyme located at the core of plant C-metabolism that catalyses the irreversible β-carboxylation of PEP to form oxaloacetate and Pi. The critical role of PEPC in assimilating atmospheric CO2 during C4 and Crassulacean acid metabolism photosynthesis has been studied extensively. PEPC also fulfils a broad spectrum of non-photosynthetic functions, particularly the anaplerotic replenishment of tricarboxylic acid cycle intermediates consumed during biosynthesis and nitrogen assimilation. An impressive array of strategies has evolved to co-ordinate in vivo PEPC activity with cellular demands for C4–C6 carboxylic acids. To achieve its diverse roles and complex regulation, PEPC belongs to a small multigene family encoding several closely related PTPCs (plant-type PEPCs), along with a distantly related BTPC (bacterial-type PEPC). PTPC genes encode ~110-kDa polypeptides containing conserved serine-phosphorylation and lysine-mono-ubiquitination sites, and typically exist as homotetrameric Class-1 PEPCs. In contrast, BTPC genes encode larger ~117-kDa polypeptides owing to a unique intrinsically disordered domain that mediates BTPC's tight interaction with co-expressed PTPC subunits. This association results in the formation of unusual ~900-kDa Class-2 PEPC hetero-octameric complexes that are desensitized to allosteric effectors. BTPC is a catalytic and regulatory subunit of Class-2 PEPC that is subject to multi-site regulatory phosphorylation in vivo. The interaction between divergent PEPC polypeptides within Class-2 PEPCs adds another layer of complexity to the evolution, physiological functions and metabolic control of this essential CO2-fixing plant enzyme. The present review summarizes exciting developments concerning the functions, post-translational controls and subcellular location of plant PTPC and BTPC isoenzymes.

Towards the creation of a systems tip growth model for a pollen tube

Essential features of pollen tube growth are polarization of extracellular ion fluxes, intracellular ion gradients, and oscillating dynamics. These features in pollen tube growth are regulated by a wide range of spatiotemporally organized functions such as exocytosis and endocytosis, actin cytoskeleton reorganization, cell wall deposition and assembly, intracellular signalling, fertilization, and self-incompatibility. Recently, by developing a compartmental model, we have demonstrated that the tip and shank in a pollen tube combine in an integrative and self-regulatory system of ion and growth dynamics. Recent developments in modelling and the wealth of experimental data can be used to develop a systems model that provides an integrative view of the the interactions between the different functions that affect pollen tube growth.

An in silico compartmentalized metabolic model of Brassica napus enables the systemic study of regulatory aspects of plant central metabolism

Biochemical network reconstructions represent valuable tools for the computational metabolic modeling of organisms that present a great biotechnological interest. An in silico multi-compartmental model of the central metabolism of the plant Brassica napus (Rapeseed) was constructed, aiming to investigate the metabolic properties of the Brassicaceae family. This family comprises many plants with major importance for the energy and nutrition sector, including the model plant Arabidopsis thaliana. The model utilized as objective function to be subsequently optimized, the biomass production of rapeseed developing embryos, which are characterized by a very high, oil content, up to 60% of biomass weight. In order to study global network properties of seed metabolism, various methods were employed, like Flux Balance Analysis, Principal Component Analysis of the flux space and reaction deletion studies, which simulate the effect of gene knock-out experiments. The model successfully simulated seed growth during the stage of oil accumulation and provided insight, regarding certain aspects of network plasticity, with the emphasis given in lipid biosynthesis regulation.

Phosphorylation of bacterial-type phosphoenolpyruvate carboxylase at Ser425 provides a further tier of enzyme control in developing castor oil seeds

PEPC [PEP (phosphoenolpyruvate) carboxylase] is a tightly controlled anaplerotic enzyme situated at a pivotal branch point of plant carbohydrate metabolism. Two distinct oligomeric PEPC classes were discovered in developing COS (castor oil seeds). Class-1 PEPC is a typical homotetramer of 107 kDa PTPC (plant-type PEPC) subunits, whereas the novel 910-kDa Class-2 PEPC hetero-octamer arises from a tight interaction between Class-1 PEPC and 118 kDa BTPC (bacterial-type PEPC) subunits. Mass spectrometric analysis of immunopurified COS BTPC indicated that it is subject to in vivo proline-directed phosphorylation at Ser425. We show that immunoblots probed with phosphorylation site-specific antibodies demonstrated that Ser425 phosphorylation is promoted during COS development, becoming maximal at stage IX (maturation phase) or in response to depodding. Kinetic analyses of a recombinant, chimaeric Class-2 PEPC containing phosphomimetic BTPC mutant subunits (S425D) indicated that Ser425 phosphorylation results in significant BTPC inhibition by: (i) increasing its Km(PEP) 3-fold, (ii) reducing its I50 (L-malate and L-aspartate) values by 4.5- and 2.5-fold respectively, while (iii) decreasing its activity within the physiological pH range. The developmental pattern and kinetic influence of Ser425 BTPC phosphorylation is very distinct from the in vivo phosphorylation/activation of COS Class-1 PEPC's PTPC subunits at Ser11. Collectively, the results establish that BTPC's phospho-Ser425 content depends upon COS developmental and physiological status and that Ser425 phosphorylation attenuates the catalytic activity of BTPC subunits within a Class-2 PEPC complex. To the best of our knowledge, this study provides the first evidence for protein phosphorylation as a mechanism for the in vivo control of vascular plant BTPC activity.

Modelling dynamic plant cells

A major challenge in plant biology is to understand how functions in plant cells emerge from interactions between molecular components. Computational and mathematical modelling can encapsulate the relationships between molecular components and reveal how biological functions emerge. We review recent progress in modelling in metabolism, growth, signalling and circadian rhythms in plant cells. We discuss challenges and opportunities for future directions.

A novel approach for determining environment-specific protein costs: the case of Arabidopsis thaliana

Motivation: Comprehensive understanding of cellular processes requires development of approaches which consider the energetic balances in the cell. The existing approaches that address this problem are based on defining energy-equivalent costs which do not include the effects of a changing environment. By incorporating these effects, one could provide a framework for integrating ‘omics’ data from various levels of the system in order to provide interpretations with respect to the energy state and to elicit conclusions about putative global energy-related response mechanisms in the cell.

Results: Here we define a cost measure for amino acid synthesis based on flux balance analysis of a genome-scale metabolic network, and develop methods for its integration with proteomics and metabolomics data. This is a first measure which accounts for the effect of different environmental conditions. We applied this approach to a genome-scale network of Arabidopsis thaliana and calculated the costs for all amino acids and proteins present in the network under light and dark conditions. Integration of function and process ontology terms in the analysis of protein abundances and their costs indicates that, during the night, the cell favors cheaper proteins compared with the light environment. However, this does not imply that there is squandering of resources during the day. The results from the association analysis between the costs, levels and well-defined expenses of amino acid synthesis, indicate that our approach not only captures the adjustment made at the switch of conditions, but also could explain the anticipation of resource usage via a global energy-related regulatory mechanism of amino acid and protein synthesis.

Contact:nikoloski@mpimp-golm.mpg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Flux coupling analysis of metabolic networks is sensitive to missing reactions

Genome-scale metabolic reconstructions are comprehensive, yet incomplete, models of real-world metabolic networks. While flux coupling analysis (FCA) has proved an appropriate method for analyzing metabolic relationships and for detecting functionally related reactions in such models, little is known about the impact of missing reactions on the accuracy of FCA. Based on an alternative characterization of flux coupling relations using elementary flux modes, this paper studies the changes that flux coupling relations may undergo due to missing reactions. In particular, we show that two uncoupled reactions in a metabolic network may be detected as directionally, partially or fully coupled in an incomplete version of the same network. Even a single missing reaction can cause significant changes in flux coupling relations. In case of two consecutive Escherichia coli genome-scale networks many fully coupled reaction pairs in the incomplete network become directionally coupled or even uncoupled in the more complete reconstruction. In this context, we found gene expression correlation values being significantly higher for the pairs that remained fully coupled than for the uncoupled or directionally coupled pairs. Our study clearly suggests that FCA results are indeed sensitive to missing reactions. Since the currently available genome-scale metabolic models are incomplete, we advise to use FCA results with care.

The metabolic network of Synechocystis sp. PCC 6803: systemic properties of autotrophic growth

Unicellular cyanobacteria have attracted growing attention as potential host organisms for the production of valuable organic products and provide an ideal model to understand oxygenic photosynthesis and phototrophic metabolism. To obtain insight into the functional properties of phototrophic growth, we present a detailed reconstruction of the primary metabolic network of the autotrophic prokaryote Synechocystis sp. PCC 6803. The reconstruction is based on multiple data sources and extensive manual curation and significantly extends currently available repositories of cyanobacterial metabolism. A systematic functional analysis, utilizing the framework of flux-balance analysis, allows the prediction of essential metabolic pathways and reactions and allows the identification of inconsistencies in the current annotation. As a counterintuitive result, our computational model indicates that photorespiration is beneficial to achieve optimal growth rates. The reconstruction process highlights several obstacles currently encountered in the context of large-scale reconstructions of metabolic networks.

A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13)C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.