Dr Mark Poolman

Clostridium autoethanogenum is an industrial microbe used for the commercial-scale production of ethanol from carbon monoxide. While significant progress has been made in the attempted diversification of this bioprocess, further improvements are desirable, particularly in the formation of the high-value platform chemicals, such as 2,3-butanediol. A new, experimentally parameterised genome scale model of C. autoethanogenum predicts dramatically increased 2,3-butanediol production under non-carbon-limited conditions when thermodynamic constraints on hydrogen production are considered.

Analysis of the impact of photorespiration on plant metabolism is usually based on manual inspection of small network diagrams. Here we create a structural metabolic model that contains the reactions that participate in photorespiration in the plastid, peroxisome, mitochondrion and cytosol and the metabolite exchanges between them. This model was subjected to elementary flux modes analysis, a technique that enumerates all the component, minimal pathways of a network. Any feasible photorespiratory metabolism in the plant will be some combination of the elementary flux modes (EFMs) that contain the Rubisco oxygenase reaction. Amongst the EFMs we obtained was the classic photorespiratory cycle, but there were also modes that involve photorespiration coupled with mitochondrial metabolism and ATP production, the glutathione‐ascorbate (GSH‐ASC) cycle and nitrate reduction to ammonia. The modes analysis demonstrated the underlying basis of the metabolic linkages with photorespiration that have been inferred experimentally. The set of reactions common to all the elementary modes showed good agreement with the gene products of mutants that have been reported to have a defective phenotype in photorespiratory conditions. Finally, the set of modes provided a formal demonstration that photorespiration itself does not impact on the CO2:O2 ratio (assimilation quotient, AQ), except in those modes associated with concomitant nitrate reduction.

Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.

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.

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.

Organisms are often exposed to environmental pressures that affect homeostasis, so it is important to understand the biological basis of stress-response. Various biological mechanisms have evolved to help cells cope with potentially cytotoxic changes in their environment. miRNAs are small non-coding RNAs which are able to regulate mRNA stability. It has been suggested that miRNAs may tip the balance between continued cytorepair and induction of apoptosis in response to stress. There is a wealth of data in the literature showing the effect of environmental stress on miRNAs, but it is scattered in a large number of disparate publications. Meta-analyses of this data would produce added insight into the molecular mechanisms of stress-response. To facilitate this we created and manually curated the miRStress database, which describes the changes in miRNA levels following an array of stress types in eukaryotic cells. Here we describe this database and validate the miRStress tool for analysing miRNAs that are regulated by stress. To validate the database we performed a cross-species analysis to identify miRNAs that respond to radiation. The analysis tool confirms miR-21 and miR-34a as frequently deregulated in response to radiation, but also identifies novel candidates as potentially important players in this stress response, including miR-15b, miR-19b, and miR-106a. Similarly, we used the miRStress tool to analyse hypoxia-responsive miRNAs. The most frequently deregulated miRNAs were miR-210 and miR-21, as expected. Several other miRNAs were also found to be associated with hypoxia, including miR-181b, miR-26a/b, miR-106a, miR-213 and miR-192. Therefore the miRStress tool has identified miRNAs with hitherto unknown or under-appreciated roles in the response to specific stress types. The miRStress tool, which can be used to uncover new insight into the biological roles of miRNAs, and also has the potential to unearth potential biomarkers for therapeutic response, is freely available at http://mudshark.brookes.ac.uk/MirStress.

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.

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 13C-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.

Reconstructing a model of the metabolic network of an organism from its annotated genome sequence would seem, at first sight, to be one of the most straightforward tasks in functional genomics, even if the various data sources required were never designed with this application in mind. The number of genome-scale metabolic models is, however, lagging far behind the number of sequenced genomes and is likely to continue to do so unless the model-building process can be accelerated. Two aspects that could usefully be improved are the ability to find the sources of error in a nascent model rapidly, and the generation of tenable hypotheses concerning solutions that would improve a model. We will illustrate these issues with approaches we have developed in the course of building metabolic models of Streptococcus agalactiae and Arabidopsis thaliana.

We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast.

A decrease in retinoic acid levels due to alcohol consumption has been proposed as a contributor to such conditions as fetal alcohol spectrum diseases and ethanol-induced cancers. One molecular mechanism, competitive inhibition by ethanol of the catalytic activity of human alcohol dehydrogenase (EC 1.1.1.1) (ADH) on all-trans-retinol oxidation has been shown for the ADH7 isoform. Ethanol metabolism also causes an increase in the free reduced nicotinamide adenine dinucleotide (NADH) in cells, which might reasonably be expected to decrease the retinol oxidation rate by product inhibition of ADH isoforms. To understand the relative importance of these two mechanisms by which ethanol decreases the retinol oxidation in vivo we need to assess them quantitatively. We have built a model system of 4 reactions: (1) ADH oxidation of ethanol and NAD(+), (2) ADH oxidation of retinol and NAD(+), (3) oxidation of ethanol by a generalized Ethanol(oxidase) that uses NAD(+), (4) NADH(oxidase) which carries out NADH turnover. Using the metabolic modeling package ScrumPy, we have shown that the ethanol-induced increase in NADH contributes from 0% to 90% of the inhibition by ethanol, depending on (ethanol) and ADH isoform. Furthermore, while the majority of flux control of retinaldehyde production is exerted by ADH, Ethanol(oxidase) and the NADH(oxidase) contribute as well. Our results show that the ethanol-induced increase in NADH makes a contribution of comparable importance to the ethanol competitive inhibition throughout the range of conditions likely to occur in vivo, and must be considered in the assessment of the in vivo mechanism of ethanol interference with fetal development and other diseases.

Motivation: Metabolic modelling provides a mathematically rigorous basis for system-level analysis of biochemical networks. However, the growing sizes of metabolic models can lead to serious problems in their construction and validation. In this work, we describe a relatively poorly investigated type of modelling error, called stoichiometric inconsistencies. These errors are caused by incorrect definitions of reaction stoichiometries and result in conflicts between two fundamental physical constraints to be satisfied by any valid metabolic model: positivity of molecular masses of all metabolites and mass conservation in all interconversions. Results: We introduce formal definitions of stoichiometric inconsistencies, inconsistent net stoichiometries, elementary leakage modes and other important fundamental properties of incorrectly defined biomolecular networks. Algorithms are described for the verification of stoichiometric consistency of a model, detection of unconserved metabolites and inconsistent minimal net stoichiometries. The usefulness of these algorithms for effective resolving of inconsistencies and for detection of input errors is demonstrated on a published genome-scale metabolic model of Saccharomyces cerevisiae and one of Streptococcus agalactiae constructed using the KEGG database.

We describe a method by which the reactions in a metabolic system may be grouped hierarchically into sets of modules to form a metabolic reaction tree. In contrast to previous approaches, the method described here takes into account the fact that, in a viable network, reactions must be capable of sustaining a steady-state flux. In order to achieve this decomposition we introduce a new concept-”the reaction correlation coefficient, φ, and show that this is a logical extension of the concept of enzyme (or reaction) subsets. In addition to their application to modular decomposition, reaction correlation coefficients have a number of other interesting properties, including a convenient means for identifying disconnected subnetworks in a system and potential applications to metabolic engineering. The method computes reaction correlation coefficients from an orthonormal basis of the null-space of the stoichiometry matrix. We show that reaction correlation coefficients are uniquely defined, even though the basis of the null-space is not. Once a complete set of reaction correlation coefficients is calculated, a metabolic reaction tree can be determined through the application of standard programming techniques. Computation of the reaction correlation coefficients, and the subsequent construction of the metabolic reaction tree is readily achievable for genome-scale models using a commodity desk-top PC.

In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database.

In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.

Myocardial hibernation represents an adaptation to sustained ischemia to maintain tissue vitality during severe supply–demand imbalance which is characterized by an increased glucose uptake. To elucidate this adaptive protective mechanism, the regulation of anaerobic glycolysis was investigated using human biopsies. In hibernating myocardium showing an increase in anaerobic glycolytic flux metabolizing exogenous glucose, the adjustment of flux through this pathway was analyzed by flux:metabolite co-responses. By this means, a previously unknown pattern of regulation using multisite modulation was found which largely differs from traditional concepts of metabolic control of the Embden–Meyerhof pathway in normal and diseased myocardium.

Exact adjustment of the Embden-Meyerhof pathway (EMP) is an important issue in ischemic preconditioning (IP) because an attenuated ischemic lactate accumulation contributes to myocardial protection. However, precise mechanisms of glycolytic flux and its regulation in IP remain to be elucidated. In open chest pigs, IP was achieved by two cycles of 10-min coronary artery occlusion and 30-min reperfusion prior to a 45-min index ischemia and 120-min reperfusion. Myocardial contents in glycolytic intermediates were assessed by high performance liquid chromatographic analysis of serial myocardial biopsies under control conditions and IP. Detailed time courses of metabolite contents allow an in-depth description of EMP regulation during index ischemia using metabolic control analysis. IP reduced myocardial infarct size (control, 90.0 ± 3.1 versus 5.05 ± 2.1%;p < 0.001) and attenuated myocardial lactate accumulation (end-ischemic contents, 31.9 ± 4.47versus 10.3 ± 1.26 μmol/wet weight,p < 0.0001), whereby a decrease in anaerobic glycolytic flux by at least 70% could constantly be observed throughout index ischemia. By calculation of flux:metabolite co-responses, the mechanisms of glycolytic regulation were investigated. The continuous deceleration of EMP flux in control myocadium could neither be explained on the basis of substrate availability nor be attributed to regulatory “key enzymes,” as multisite regulation was employed for flux adjustment. In myocardium subjected to IP, an even pronounced deceleration of EMP flux during index ischemia was observed. Again, the adjustment of EMP flux was because of multisite modulation without any evidence for flux limitation by substrate availability or a key enzyme. However, IP changed the regulatory properties of most EMP enzymes, and some of these patterns could not be explained on the basis of substrate kinetics. Instead, other regulatory mechanisms, which have previously not yet been described for EMP enzymes, must be considered. These altered biochemical properties of the EMP enzymes have not yet been described.

The dynamic and steady‐state behaviour of a computer simulation of the Calvin cycle reactions of the chloroplast, including starch synthesis and degradation, and triose phosphate export have been investigated. A major difference compared with previous models is that none of the reversible reactions are assumed to be at equilibrium. The model can exhibit alternate steady states of low or high carbon assimilation flux, with hysteresis in the transitions between the steady states induced by environmental factors such as phosphate and light intensity. The enzymes which have the greatest influence on the flux have been investigated by calculation of their flux control coefficients. Different patterns of control are exhibited over the assimilation flux, the flux to starch and the flux to cytosolic triose phosphate. The assimilation flux is mostly sensitive to sedoheptulose bisphosphatase and Rubisco, with the exact distribution depending on their relative activities. Other enzymes, particularly the triose phosphate translocator, become more influential when other fluxes are considered. These results are shown to be broadly consistent with observations on transgenic plants.

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