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Department of Biological and Medical Sciences
Faculty of Health and Life Sciences
After studying biochemistry at Oxford University, followed by a DPhil on the physical biochemistry of yeast pyruvate kinase, David started lecturing at Oxford Polytechnic. His research gradually moved from experimental biochemistry into computer simulation and theoretical analysis of metabolic control, and he has written the only textbook on metabolic control analysis, Understanding the Control of Metabolism. In 2001, he helped to found and became part-time Chief Scientific Officer of the Oxford company, Physiomics plc, which is using computer simulation of cellular systems for the development and analysis of therapeutic strategies for the pharmaceutical industry.
Physiomics has firmly established itself as a leading light in systems biology approaches to drug discovery and latterly in therapy design, demonstrable through contracts with three major international pharmaceutical companies. Through its strong advocacy of this approach the sector has invested in and adopted new computational biology processes. As Physiomics has continued to grow, it has expanded its own specialist research team, in many cases recruiting scientists trained within Fell’s Brookes-based research group at Brookes.
David is chairman of the Policy Committee of the Biochemical Society, and has been a member of several panels and committees of the Biotechnology and Biological Sciences Research Council.
David’s group formed nearly thirty years ago with initial interests in computer simulation of metabolism and the theory of metabolic control. To these it has since added interests in modelling signal transduction, in various different approaches to network analysis of metabolism, and in reconstructing metabolic networks from genomic data. In the course of this research, he has addressed problems in microbial, plant and mammalian metabolism, often in conjunction with collaborators who have contributed experimental results.
His work forms part of the emerging field of Systems Biology, in that we are concerned with understanding how biological function arises from the interactions between many components, and with building predictive models. Potential applications of our work include the design of changes in cellular metabolism to improve the output of product such as antibiotics, detecting vulnerable sites in cellular networks that could be targets for drugs to control disease-causing organisms, and improved understanding of how organisms manage to adjust their metabolism in response to environmental changes and other signals.
Lysophosphatidate acyltransferase (LPAAT) catalyses the second step of the Kennedy
pathway for triacylglycerol (TAG) synthesis. Here we express Trapaeolum majus LPAAT in
Brassica napus (B. napus) cv 12075 to evaluate the effects on lipid synthesis and estimate
the flux control coefficient for LPAAT.
• We estimate the flux control coefficient of LPAAT in a whole plant context by deriving a
relationship between it and overall lipid accumulation, given that this is an exponential
• Increasing LPAAT activity resulted in greater TAG accumulation in seeds of between 25 and
29 %; altered fatty acid distributions in seed lipids (particularly those of the Kennedy
pathway); and a redistribution of label from 14C-glycerol between phosphoglycerides.
• Greater LPAAT activity in seeds led to an increase in TAG content despite its low intrinsic flux
control coefficient on account of the exponential nature of lipid accumulation that amplifies
the effect of the small flux increment achieved by increasing its activity. We have also
developed a novel application of metabolic control analysis likely to have broad application
as it determines the in planta flux control that a single component has upon accumulation of
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.
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.
Produce rich in phytochemicals may alter postprandial glucose and insulin responses by interacting with the pathways that regulate glucose uptake and insulin secretion in humans. The aims of the present study were to assess the phytochemical constituents of red beetroot juice and to measure the postprandial glucose and insulin responses elicited by either 225 ml beetroot juice (BEET), a control beverage matched for macronutrient content (MCON) or a glucose beverage in healthy adults. Beetroot juice was a particularly rich source of betalain degradation compounds. The orange/yellow pigment neobetanin was measured in particularly high quantities (providing 1·3 g in the 225 ml). A total of sixteen healthy individuals were recruited, and consumed the test meals in a controlled single-blind cross-over design. Results revealed a significant lowering of the postprandial insulin response in the early phase (0–60 min) (P < 0·05) and a significantly lower glucose response in the 0–30 min phase (P < 0·05) in the BEET treatment compared with MCON. Betalains, polyphenols and dietary nitrate found in the beetroot juice may each contribute to the observed differences in the postprandial insulin concentration.
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 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.
The active, yet energetically inefficient electron transport chain of the ethanologenic bacterium Zymomonas mobilis could be used in metabolic engineering for redox-balancing purposes during synthesis of certain products. Although several reconstructions of Z. mobilis metabolism have been published, important aspects of redox balance and aerobic catabolism have not previously been considered. Here, annotated genome sequences and metabolic reconstructions have been combined with existing biochemical evidence to yield a medium-scale model of Z. mobilis central metabolism in the form of COBRA Toolbox model files for flux balance analysis (FBA). The stoichiometric analysis presented here suggests the feasibility of several metabolic engineering strategies for obtaining high-value products, such as glycerate, succinate, and glutamate that would use the electron transport chain to oxidize the excess NAD(P)H, generated during synthesis of these metabolites. Oxidation of the excess NAD(P)H would also be needed for synthesis of ethanol from glycerol. Maximum product yields and the byproduct spectra have been estimated for each product, with glucose, xylose, or glycerol as the carbon substrates. These novel pathways represent targets for future metabolic engineering, as they would exploit both the rapid Entner–Doudoroff glycolysis, and the energetically uncoupled electron transport of Z. mobilis.
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.
Organisms share a common core to their metabolic networks. But what determined this: chance, chemical necessity, or evolutionary optimization? In this issue of Molecular Cell, Noor et al. (2010) provide new evidence for selection of a network with optimal features from a broader set of possibilities.
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.
Motivation: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis. Results: Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA.
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 184.108.40.206) (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.
The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with ‘mathematical modelling’ the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term ‘computational model’ when mathematical models are complemented with information generated from bioinformatics resources. Hence, ‘the model’ is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases: 1. Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking. 2. Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems. 3. Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action: 4. While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead. 5. To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered. 6. The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed. 7. Leading medical journals should be encouraged to promote mathematical modelling.
Stoichiometric analysis of metabolic networks allows the calculation of possible metabolic flux distributions in the absence of kinetic data. In order to predict which of the possible fluxes are present under certain conditions, additional constraints and optimization principles can be applied. One approach of calculating unknown fluxes (frequently called flux balance analysis) is based on the optimality principle of maximizing the molar yield of biotransformations. Here, the relevance and applicability of that approach are examined, and it is compared with the principle of maximizing pathway flux. We discuss diverse experimental evidence showing that, often, those biochemical pathways are operative that allow fast but low-yield synthesis of important products, such as fermentation in Saccharomyces cerevisiae and several other yeast species. Together with arguments based on evolutionary game theory, this leads us to the conclusion that maximization of molar yield is by no means a universal principle.
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.
Motivation: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis.
Of all the molecular-interaction networks in plants, metabolism is by far and away the most completely described both in terms of network topology and in terms of the properties of its molecular components. Moreover, experimental tools exist that allow systematic measurement of its behaviour and there is now a wealth of data describing metabolic systems at the molecular level. Nevertheless, we remain some way off being able to exploit this knowledge to rationally engineer metabolism and we are unable to predict the nature of metabolic change under different conditions or in response to genetic intervention. One of the great problems is the persistence of the metabolic pathway paradigm when in reality, metabolism is a highly interconnected network and fixed pathways do not exist. To generate a more realistic view of metabolism, one must encapsulate information about metabolic behaviour into a model of the network as a whole. The construction of a mathematical model that describes the metabolic network of plants at ‘genome scale’ is now a plausible goal (especially in the case of model plants such as Arabidopsis). We have begun the construction of such a model and in my talk I will outline our current progress.
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, Ï†, 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.
Despite dramatic increases in glucose influx during the transition from fasting to fed states, plasma glucose concentration remains tightly controlled. This constancy is in large part due to the capacity of skeletal muscle to absorb excess glucose and store it as glycogen. The magnitude of this capacity is controlled by insulin by way of regulated insertion of glucose transporters into the muscle cell membrane. Here, we examine the mechanism by which muscle cells are able to tolerate large flux increases across their transporters without significantly changing their own metabolite pools. MCA was used to probe data sets that measured the effects of changing plasma glucose and/or insulin concentrations on the rates of glycogen synthesis and the concentrations of metabolites, particularly glucose-6-phosphate. We find that homeostasis is achieved by insulin-dependent phosphorylation changes in GSase sensitivity to the upstream metabolite glucose-6-phosphate. The centrality of GSase to homeostasis resolves the paradox of its sensitivity to allosteric and covalent regulation despite its minimal role in flux control. The importance of this role for enzymatic phosphorylation to diabetes pathology is discussed, and its general applicability is suggested.
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 effect of externally applied L-cysteine and glutathione (GSH) on ATP sulphurylase and adenosine 5'-phosphosulphate reductase (APR), two key enzymes of assimilatory sulphate reduction, was examined in Arabidopsis thaliana root cultures. Addition of increasing L-cysteine to the nutrient solution increased internal cysteine, gamma-glutamylcysteine and GSH concentrations, and decreased APR mRNA, protein and extractable activity. An effect on APR could already be detected at 0.2 mm L-cysteine, whereas ATP sulphurylase was significantly affected only at 2 mm L-cysteine. APR mRNA, protein and activity were also decreased by GSH at 0.2 mm and higher concentrations. In the presence of L-buthionine-S, R-sulphoximine (BSO), an inhibitor of GSH synthesis, 0.2 mm L-cysteine had no effect on APR activity, indicating that GSH formed from cysteine was the regulating substance. Simultaneous addition of BSO and 0.5 mm GSH to the culture medium decreased APR mRNA, enzyme protein and activity. ATP sulphurylase activity was not affected by this treatment. Tracer experiments using (35)SO(4)(2-) in the presence of 0.5 mm L-cysteine or GSH showed that both thiols decreased sulphate uptake, APR activity and the flux of label into cysteine, GSH and protein, but had no effect on the activity of all other enzymes of assimilatory sulphate reduction and serine acetyltransferase. These results are consistent with the hypothesis that thiols regulate the flux through sulphate assimilation at the uptake and the APR step. Analysis of radioactive labelling indicates that the flux control coefficient of APR is more than 0.5 for the intracellular pathway of sulphate assimilation. This analysis also shows that the uptake of external sulphate is inhibited by GSH to a greater extent than the flux through the pathway, and that the flux control coefficient of APR for the pathway, including the transport step, is proportionately less, with a significant share of the control exerted by the transport step.
Elementary mode analysis has been used to study a metabolic pathway model of a recombinant Saccharomyces cerevisiae system that was genetically engineered to produce the bacterial storage compound poly-β-hydroxybutyrate (PHB). The model includes biochemical reactions from the intermediary metabolism and takes into account cellular compartmentalization as well as the reversibility/irreversibility of the reactions. The reaction network connects the production and/or consumption of eight external metabolites including glucose, acetate, glycerol, ethanol, PHB, CO2, succinate, and adenosine triphosphate (ATP). Elementary mode analysis of the wild-type S. cerevisiae system reveals 241 unique reaction combinations that balance the eight external metabolites. When the recombinant PHB pathway is included, and when the reaction model is altered to simulate the experimental conditions when PHB accumulates, the analysis reveals 20 unique elementary modes. Of these 20 modes, 7 produce PHB with the optimal mode having a theoretical PHB carbon yield of 0.67. Elementary mode analysis was also used to analyze the possible effects of biochemical network modifications and altered culturing conditions. When the natively absent ATP citrate-lyase activity is added to the recombinant reaction network, the number of unique modes increases from 20 to 496, with 314 of these modes producing PHB. With this topological modification, the maximum theoretical PHB carbon yield increases from 0.67 to 0.83. Adding a transhydrogenase reaction to the model also improves the theoretical conversion of substrate into PHB. The recombinant system with the transhydrogenase reaction but without the ATP citrate-lyase reaction has an increase in PHB carbon yield from 0.67 to 0.71. When the model includes both the ATP citrate-lyase reaction and the transhydrogenase reaction, the maximum theoretical carbon yield increases to 0.84. The reaction model was also used to explore the possibility of producing PHB under anaerobic conditions. In the absence of oxygen, the recombinant reaction network possesses two elementary modes capable of producing PHB. Interestingly, both modes also produce ethanol. Elementary mode analysis provides a means of deconstructing complex metabolic networks into their basic functional units. This information can be used for analyzing existing pathways and for the rational design of further modifications that could improve the system's conversion of substrate into product.
We have determined the kinetic parameters of the individual steps of the threonine pathway from aspartate inEscherichia coli under a single set of experimental conditions chosen to be physiologically relevant. Our aim was to summarize the kinetic behaviour of each enzyme in a single tractable equation that takes into account the effect of the products as competitive inhibitors of the substrates in the forward reaction and also, when appropriate (e.g. near-equilibrium reactions), as substrates of the reverse reactions. Co-operative feedback inhibition by threonine and lysine was also included as necessary. We derived the simplest rate equations that describe the salient features of the enzymes in the physiological range of metabolite concentrations in order to incorporate them ultimately into a complete model of the threonine pathway, able to predict quantitatively the behaviour of the pathway under natural or engineered conditions.
A computer simulation of the threonine-synthesis pathway in Escherichia coli Tir-8 has been developed based on our previous measurements of the kinetics of the pathway enzymes under near-physiological conditions. The model successfully simulates the main features of the time courses of threonine synthesis previously observed in a cell-free extract without alteration of the experimentally determined parameters, although improved quantitative fits can be obtained with small parameter adjustments. At the concentrations of enzymes, precursors and products present in cells, the model predicts a threonine-synthesis flux close to that required to support cell growth. Furthermore, the first two enzymes operate close to equilibrium, providing an example of a near-equilibrium feedback-inhibited enzyme. The predicted flux control coefficients of the pathway enzymes under physiological conditions show that the control of flux is shared between the first three enzymes: aspartate kinase, aspartate semialdehyde dehydrogenase and homoserine dehydrogenase, with no single activity dominating the control. The response of the model to the external metabolites shows that the sharing of control between the three enzymes holds across a wide range of conditions, but that the pathway flux is sensitive to the aspartate concentration. When the model was embedded in a larger model to simulate the variable demands for threonine at different growth rates, it showed the accumulation of free threonine that is typical of the Tir-8 strain at low growth rates. At low growth rates, the control of threonine flux remains largely with the pathway enzymes. As an example of the predictive power of the model, we studied the consequences of over-expressing different enzymes in the pathway.
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.
Although epidermal growth factor (EGF) induces transient activation of Ras and the mitogen-activated protein kinase (MAPK) cascade in PC12 cells, whereas nerve growth factor (NGF) stimulates sustained activation, the basis for these contrasting responses is not known. We have developed a computer simulation of EGF-induced MAPK cascade activation, which provides quantitative evidence that feedback inhibition of the MAPK cascade is the most important factor in determining the duration of cascade activation. Hence, we propose that the observed quantitative differences in EGF and NGF signalling can be accounted for by differential feedback regulation of the MAPK cascade.
A theoretical metabolic-control-analysis approach has been used to study aspects of glycolytic-flux control and carbon-metabolite regulation, particularly the role of ATP demand (ATPase), in order to determine what general features of the regulation of energy metabolism would be consistent with good carbon-metabolite homeostasis in the face of large changes in carbon flux. On the basis of a semi-quantitative control-analysis model, incorporating estimates of substrate, product and effector actions on the enzymes, the experimentally observed characteristics of glycolytic-flux changes prove to impose constraints on the feasible ranges of these estimates. This leads to the identification of several features of energy metabolism, each of which is necessary but not sufficient to explain the observations; although most of these have been advocated previously (such as AMP activation of phosphofructokinase (PFK), ADP inhibition of ATPase and the role of energy charge or ATP/ADP ratio), our analysis allows their relative importance to be assessed. In the model, the distribution of flux control depends primarily on ADP inhibition of ATPase, and on the activation of PFK by AMP; increase in ADP inhibition of ATPase increases the control on PFK; increase in AMP activation of PFK increases control on ATPase. PFK exerts greater flux control than does ATPase over approximately 50 % of the ranges (parameter space) studied, but its control is sufficiently high to achieve sizeable flux increases over less than 20 % of the space. Furthermore, control by alteration in PFK activity is shown to result in poor glycolytic metabolite homeostasis over the entire parameter space studied. However, over a large proportion of the parameter space, control by activation of ATPase can lead to large flux changes, i.e. high flux control, coupled with excellent glycolytic-metabolite homeostasis, similar to that observed in working muscle. As well as altering the relative degrees of flux control invested in PFK and ATPase, ADP inhibition of ATPase and AMP activation of PFK have pronounced effects on the homeostatic properties of the system. Stronger ADP inhibition of ATPase results in improved homeostasis of glycolytic metabolites, ATP and ADP in response to PFK activation, whereas stronger activation of PFK by AMP improves the homeostasis of these three quantities in response to ATPase activation. The results are further evidence of the potential for physiological ATP demand to exert control over glycolytic flux, but additionally show that the known effector interactions, in addition to their previously known role in ATP regulation, could contribute to the remarkable homeostasis of glycolytic-metabolite levels observed in vivo. They further indicate that quantitative characterisation of likely domains of behaviour of metabolic systems can be achieved by an algebraic analysis that is not highly dependent on a full and precise knowledge of the molecular details of the kinetic/regulatory properties of the enzymes, but that still allows an assessment of whether hypotheses regarding the system are feasible and sufficient to account for the observations.
Over recent years, the concept that flux through a metabolic pathway is controlled by a single rate-limiting enzyme has been challenged from a number of quarters. We have presented three lines of evidence that the control of pathway flux by activation of multiple several steps, including steps responsible for consumption of pathway products, is an important feature of physiological flux control in response to external stimmuli. Theoretical tools exist that can be used to analyze the distribution of flux control between the steps involved in signal transduction and enzyme activation.
We have applied Metabolic Control Analysis (MCA) in an attempt to determine the distribution of glycolytic flux control between the steps of glycolysis in aged disks of potato tuber under aerobic conditions, using concentrations of glycolytic metabolites in tuber tissue from a range of transgenic potato plants and published enzyme kinetic data. We modelled the substrate and effector kinetics of potato tuber phosphofructokinase (PFK) by reanalysing published results. Despite the scarcity of reliable kinetic data, our results are in agreement with experimental findings namely that, under the conditions described, PFK has little control over glycolytic flux. Furthermore our analysis predicts that under these conditions far more control lies in the dephosphorylation of phosphoenolpyruvate and/or in the steps beyond. We have validated the results of our analysis in two ways. First, predictions based on calculated concentration control coefficients from the analysis show generally good agreement with observed metabolite deviation indices discussed in the preceding paper [Thomas, Mooney, Burrell, and Fell (1997) Biochem. J. 322, 111-117]. Second, sensitivity analysis of our results shows that the calculated control coefficients are robust to errors in the elasticities used in the analysis, of which relatively few need to be known accurately. Experimental and control analysis results agree with previous predictions of MCA that strong co-operative feedback inhibition of enzymes serves to move flux control downstream of the inhibiting metabolite. We conclude that MCA can successfully model the outcome of experiments in the genetic manipulation of enzyme amounts.
Genetically engineered organisms overexpressing phosphofructokinase (PFK), a supposed 'regulatory' step of glycolysis, often show little or no measurable change in glycolytic or respiratory flux, although the concentrations of glycolytic intermediates may change. We have used the finite change theory of Metabolic Control Analysis (MCA) to analyse the concentrations of glycolytic metabolites in aged disks of tuber tissue from four lines of transgenic potatoes expressing different amounts of PFK that, under aerobic conditions, showed statistically indistinguishable rates of respiration. The constancy of the metabolites' concentration deviation indices for different increases in PFK expression indicated that the metabolite changes from a graded series, excluding the possibility of anomalous behaviour that might be observed in a single transgenic line. Consequently we were able to use the finite change method to validate the results of an MCA model of tuber glycolysis [Thomas, Mooney, Burrell and Fell (1997) Biochem. J. 322, 119-127]. Furthermore the metabolite changes with PFK activity are evidence that near-equilibrium steps do not transmit increased substrate concentrations down the pathway without attenuation. Our results support the view that flux increase by activation of a single enzyme early in the pathway will, contrary to expectations, be of limited effectiveness in achieving flux increases.
Metabolic Control Analysis has invalidated many traditional biochemical concepts of control, in particular the rate-limiting step. However, it has not been used to question the mechanisms by which pathway flux is thought to be controlled, such as the action of allosteric effectors or of covalent modification mechanisms. Here we use Control Analysis and computer simulation to examine the response of pathway segments to change in flux imposed by action on an enzyme outside the segment. Whether these segments contain near-equilibrium enzyme-catalysed reactions, cooperative enzymes, feedforward activation loops or feedback inhibition loops, their responses are significantly different from those observedin vivo. In particular, they do not exhibit the remarkable degrees of metabolite homoeostasis during large flux changes that have frequently been observed experimentally. On the other hand, near-constant levels of metabolites in spite of large changes of flux are consistent with our recent proposal thatmulti-site modulation—simultaneous activation of many pathway steps—is the normal method by which metabolism is controlled.
Your article describes a proposal by Japanese researchers to improve the desert tolerance of plants by giving them the gene for a more efficient form of the CO2-fixing enzyme RuBisCo (Technology, 20 January). This gives credence to a naive proposal that is very unlikely to work.
Despite the support given in many biochemistry textbooks to the supposed role of RuBisCo in limiting the rate of photosynthesis, it is unlikely that the enzyme would pose a serious limitation in desert conditions. Mark Stitt and his colleagues, then at the University of Bayreuth, Germany, quantified the effect of varying the activity of RuBisCo in tobacco plants under a variety of environmental conditions. They found that RuBisCo alone does not limit the rate, except when photosynthesis is being driven at high rates by above normal light intensity, carbon dioxide levels and humidity. Other factors are at least as important under conditions of reduced ...
No abstract: reproduced with minor modifications from the obituary published in the The Biochemist 17 (3) 26-27
Since Blackman  proposed the concept of the 'rate-limiting
step' in 1905, it has dominated the approach to understanding
the control of metabolic pathways. For example, it was endorsed
in Krebs' concept of 'pacemaker' enzymes , which he saw as
the target sites for hormone and drug action on metabolism.
Even though the theory of Metabolic Control Analysis [3,4] has
since shown that control can be distributed over many steps in a
pathway, and that the degree of control of any given step can be
quantified by its flux control coefficient, qualitative explanations
of how a pathway can be controlled have not been greatly
affected. Indeed, although Metabolic Control Analysis has been
increasingly adopted in metabolic biochemistry, and experiments
have confirmed both that control is generally distributed  and
that genuinely rate-limiting enzymes are rare, it has also
legitimized the concept that an enzyme that responds to some
external controlling factor can be an agent of metabolic control
provided the enzyme has a finite flux control coefficient. However,
we shall cite arguments that such mechanisms cannot be responsible
for large changes in metabolic flux. On the other hand,
recent theoretical developments arising from Metabolic Control
Analysis do allow us to characterize how large changes in
metabolic flux could be implemented; they can only be achieved
with minimal disturbance ofmetabolite concentrations and fluxes
in other pathways by co-ordinated changes in the activities of
many of the enzymes in the pathway, and this can be shown to
be a common mechanism of control.
Related experimental and theoretical evidence also contradicts
the view that regulatory enzymes exhibiting allosteric properties
are effective agents for control of metabolic flux. Our conclusion
is that their more significant role is in homoeostasis. In consequence,
different approaches are needed to both the study and
explanation of metabolic control.
Metabolic control analyses of glucose utilization were performed for four groups of working rat hearts perfused with Krebs-Henseleit buffer containing 10 mM glucose only, or with the addition of 4 mM D-beta-hydroxybutyrate/1 mM acetoacetate, 100 nM insulin (0.05 unit/ml), or both. Net glycogen breakdown occurred in the glucose group only and was converted to net glycogen synthesis in the presence of all additions. The flux of [2-3H]glucose through P-glucoisomerase (EC 220.127.116.11) was reduced with ketones, elevated with insulin, and unchanged with the combination. Net glycolytic flux was reduced in the presence of ketones and the combination. The flux control coefficients were determined for the portion of the pathway involving glucose transport to the branches of glycogen synthesis and glycolysis. Major control was divided between the glucose transporter and hexokinase (EC 18.104.22.168) in the glucose group. The distribution of the control was slightly shifted to hexokinase with ketones, and control at the glucose transport step was abolished in the presence of insulin. Analysis of the pathway from 3-P-glycerate to pyruvate determined that the major control was shared by enolase (EC 22.214.171.124) and pyruvate kinase (EC 126.96.36.199) in the glucose group. Addition of ketones, insulin, or the combination shifted the control to P-glycerate mutase (EC 188.8.131.52) and pyruvate kinase. These results illustrate that the control of the metabolic flux in glucose metabolism of rat heart is not exerted by a single enzyme but variably distributed among enzymes depending upon substrate availability, hormonal stimulation, or other changes of conditions.
Metabolic Control Analysis has provided tools for understanding and quantifying the regulation of biochemical systems, but the values of control coefficients in non-trivial systems can depend on the values of a large number of quantities: metabolite concentrations, fluxes and elasticity coefficients. This poses two related questions. One concerns the accuracy and precision of the values obtained for the control coefficients, depending as they do on a large number of experimentally determined values. The second concerns the importance of the variables in determining the values of the control coefficients: as these variables represent components of the biochemical system under consideration, this corresponds to the question of how do the components determine the regulatory properties of the system? This paper describes an extension to an earlier sensitivity analysis of the control coefficients that can be used to answer both of these quest