Fermentation, with its production of organic acids like lactic acid, frequently accounts for the increased acidity in a cell; however, the products of fermentation do not typically accumulate in cells.

The last step in glycolysis is catalyzed by pyruvate kinase. The pyruvate produced can proceed to be catabolized or converted into the amino acid alanine.

If no more energy is needed and alanine is in adequate supply, the enzyme is inhibited. Recall that fructose-1,6-bisphosphate is an intermediate in the first half of glycolysis.

The regulation of pyruvate kinase involves phosphorylation, resulting in a less-active enzyme. Dephosphorylation by a phosphatase reactivates it. Pyruvate kinase is also regulated by ATP a negative allosteric effect.

If more energy is needed, more pyruvate will be converted into acetyl CoA through the action of pyruvate dehydrogenase. If either acetyl groups or NADH accumulates, there is less need for the reaction and the rate decreases.

Pyruvate dehydrogenase is also regulated by phosphorylation: a kinase phosphorylates it to form an inactive enzyme, and a phosphatase reactivates it. The kinase and the phosphatase are also regulated. The gluconeogenesis involves the enzyme fructose 1,6-bisphosphatase that is regulated by the molecule citrate an intermediate in the citric acid cycle.

Increased citrate will increase the activity of this enzyme. Gluconeogenesis needs ATP, so reduced ATP or increased AMP inhibits the enzyme and thus gluconeogenesis. Type 2 diabetes mellitus. Diabetes and hyperglycemia. Fat metabolism deficiencies. Phosphofructokinase : any of a group of kinase enzymes that convert fructose phosphates to biphosphate.

Glycolysis : the cellular metabolic pathway of the simple sugar glucose to yield pyruvic acid and ATP as an energy source.

Kinase : any of a group of enzymes that transfers phosphate groups from high-energy donor molecules, such as ATP, to specific target molecules substrates ; the process is termed phosphorylation. Glucose : a simple monosaccharide sugar with a molecular formula of C 6 H 12 O 6 ; it is a principal source of energy for cellular metabolism.

Hexokinase: an enzyme that phosphorylates hexoses six-carbon sugars , forming hexose phosphate. Pyruvate: a biological molecule that consists of three carbon atoms and two functional groups — a carboxylate and a ketone group. name }} Spark. Next Trial Session:. months }} {{ nextFTS.

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Next Trial: Enter Session. Free Trial Session Enrollment. To accomplish this, a kinetic model of E. coli central carbon and energy metabolism was developed and validated against a large set of existing experimental data. This model includes more mechanistic details than previous ones, and the impact of metabolic regulation on this system was analyzed using local and global methods.

The kinetic model developed in this study represents the central metabolism of Escherichia coli cultivated on glucose under aerobic conditions Fig 1.

This model contains 3 compartments environment, periplasm and cytoplasm , 62 metabolites, and 68 reactions which represent the main central carbon and energy pathways of E. coli , namely: glucose phosphotransferase system PTS , glycolysis and gluconeogenesis EMP , pentose phosphate PPP and Entner-Doudoroff EDP pathways, anaplerotic reactions AR , tricarboxylic acid cycle TCA , glyoxylate shunt GS , acetate metabolism AC , nucleotide interconversion reactions NC and oxidative phosphorylation OP.

A reaction was also included to account for the consumption of metabolic precursors, reducing equivalents, and energy, and thus linking metabolism to cell proliferation. To account for metabolic regulation, a total of metabolite-enzyme interactions i.

where metabolites modulate the reaction rates through thermodynamic or kinetic regulation, such as being substrates, products, allosteric modulators, or other type of inhibitors or activators were included in the model, amongst which 34 are long-range regulatory interactions i. where certain metabolites, which are not reactants, modulate the rates of these reactions.

Metabolites and enzymes are shown in blue and green, respectively. The diagram adopts the conventions of the Systems Biology Graphical Notation process description [ 87 ]. Previously published kinetic models of E.

coli metabolism were used as scaffolds to construct this model [ 18 , 31 , 32 ]. Both the number of pathways and the level of mechanistic detail were increased in the present model S1 Table.

Previous models accounted for the consumption of metabolic precursors for growth in a decoupled way. This may be enough from the point of view of mass balance, but results in artifacts if used for an understanding of dynamics and regulation.

In contrast the present model includes a single reaction to model growth, which ensures that the building blocks are consumed in stoichiometric proportions fixed by the cell composition, and not independently from each other.

The rate of this reaction is a function of the intracellular concentrations of all the building blocks. This represents a significant improvement by satisfying the following growth rate properties: i it monotonically increases with the availability of each building block, ii it is asymptotically independent of each pool above a saturating concentration, and iii it approaches zero if any pool approaches zero [ 21 ].

These properties were not reflected in the previous models [ 33 ]. The present model was calibrated to represent the metabolic state of E. coli cultivated under carbon limitation, a condition frequently experienced by this bacterium in laboratories, in industrial bioprocesses, and likely in its natural environment.

To the extent possible, values of the biochemical parameters were taken from experimental determinations available in the literature. Parameters not available in the literature were estimated to reproduce steady-state and time-course experimental data obtained from a unique E.

This step is critical since both metabolite concentrations and fluxes depend on environmental conditions and differ between strains [ 38 — 40 ]. While results described below are largely in agreement with other experimental observations, the model was not forced to reproduce them , providing an important validation of the model.

Detailed information on the construction and validation of the model is given in the Methods section and Supporting Information S1 Text. The model is included in Supporting Information S1 Model formatted in SBML [ 41 ] and COPASI [ 42 ] formats, and is available from the BioModels database [ 43 ] with accession number MODEL The control properties of E.

coli central metabolism in the reference state see above were investigated under the metabolic control analysis framework [ 10 , 11 ]. Flux and concentration control coefficients quantify the impact of a small change in the rate of each reaction e. through change in the enzyme concentration E , on each flux J and each metabolite concentration M.

Since each metabolic step affects all fluxes and concentrations to some extent, we calculate a metric of its overall control on fluxes and concentrations as the L2 norm of all its flux- and concentration-control coefficients see Methods , respectively.

The overall flux- and concentration-control by each step in the network is displayed in Fig 2. The main control point is the glucose inflow reaction with a control of 8. The system is therefore sensitive to its environment, as expected. Note that this is a direct sensitivity of metabolism to the environment, not through the hierarchical action of signal transduction and gene expression, which is not represented in this model; if it were its effect would thus be overlaid likely with a delay on the direct effect displayed in our model.

Reactions that were identified by previous models as exerting a strong flux control under similar environmental conditions, such as the glucose phosphotransferase reactions or phosphofructokinase [ 13 , 32 , 44 , 45 ], showed low control in our model respectively 0.

Rather, consistently with experimental evidence see for example [ 46 , 47 — 49 ] , the flux control was predicted to be shared between enzymes of all the pathways, amongst which cytochrome bo oxidase reaction CYTBO, with 4.

A similar situation was observed for the control of concentrations, which is widely distributed across the network, and with the environment as the strongest control.

A global sensitivity analysis [ 50 ] shows that these conclusions are robust with regard to parameter uncertainties Fig 2A and 2B. Overall control exerted by each reaction on fluxes A and metabolite concentrations B.

The overall flux and concentration control exerted by each reaction are correlated, as shown in panel C. Gray histograms show the distribution of flux control coefficients of enzymes D and environment glucose supply reaction, E on all the fluxes, and the distribution of concentration control coefficients of enzymes F and environment G on all the intracellular pools.

Red lines D-G represent the cumulative frequency of each distribution. Despite the low control exerted by enzymes over fluxes and concentrations at the network level, a detailed analysis of flux control coefficients reveals generic regulatory patterns between most of the pathways Fig 3.

A general observation is that the control of each pathway resides largely outside of itself. Columns represent the controlling reactions and rows represent the fluxes under control. Red and blue colors represent negative and positive values of flux control coefficients, respectively, and color intensity indicates strong darker to low lighter control.

For example, the control of the partition of carbon through competing pathways is shared between enzymes of each pathway. The glycolytic phosphofructokinase PFK exerts a small negative control on the PPP and ED fluxes and a positive control on the glycolytic flux , while the glucosephosphate dehydrogenase ZWF of the PPP and ED pathways exerts a strong positive control on its own flux and a negative control on the glycolytic flux.

Similar behavior is observed at the main metabolic branch nodes, e. between the TCA cycle and the glyoxylate shunt or between the pentose phosphate and Entner-Doudoroff pathways. It is important to note that the fraction of flux diverted to each branch does not depend only on the local enzyme kinetics, contrary to what is sometimes suggested [ 6 ], but on several enzymes of each of the competing pathways.

Several feedforward and feedback interactions are also observed between the pathways. For instance, the pyruvate kinase PYK controls fluxes through the TCA cycle , and is controlled by some TCA reactions ,. Interestingly, biomass synthesis GROWTH is strongly controlled by the upstream glucose supply , with all other control coefficients lower than 0.

In turn, biomass synthesis exerts a small but global feedback control on most catabolic fluxes. Those several, intertwined feedback and feedforward interactions stress the high degree of functional organization of the central carbon and energy metabolism.

Note that this response is sensed in a very short time scale, rather than the slower response that happens after signal transduction and consequent changes in gene expression. To get a broader picture of the role of metabolic regulation on the coordination of E. coli metabolism, the solution space of this network was explored with and without considering metabolic regulation.

Two versions of the model were used: the kinetic version which accounts for metabolic regulation, and a stoichiometric version of the same model which contains only stoichiometric constraints and is thus similar to a flux balance analysis model. The solution space of each model was explored using a random sampling approach: , flux distributions were uniformly sampled from the solution space using the stoichiometric model, and steady-states were simulated for , sets of random enzyme levels using the kinetic model.

For each set, enzyme levels i. Vmax were sampled from a log uniform distribution between 0. It is important to mention that cells do not express enzymes levels according to the distribution generated, therefore the distribution of the variables is not expected to provide any information on the probability for a cell to reach a specific state in vivo [ 50 ].

Rather, uniformity is used to clearly grasp the functional implications of applying metabolic regulation to the network. We first investigated the relationship between supply glucose uptake and demand growth , which provides information on the allocation of resources by the metabolic network [ 52 ].

Direct sampling of the solution space Fig 4A revealed that most of the metabolic states are not efficient in term of resource allocation: most of them correspond to a high glucose uptake rate, but with a low growth rate, because this situation significantly increases the attainable intracellular flux states.

Interestingly, the opposite picture is observed when metabolic regulation is applied on this network Fig 4B : a smaller region of the solution space is reached, where the growth rate is now coupled to the glucose uptake rate.

Density of scatter plots between glucose uptake and growth rates sampled using the stoichiometric model A , without metabolic regulation or the kinetic model B , with metabolic regulation. Shades of white to blue denote null to high frequency, respectively.

Red dots are measurements obtained from independent cultivations of wild-type and mutant E. coli strains on glucose, carried out under a wide range of cultivation conditions. To evaluate this prediction quantitatively, we gathered from the literature experimental data obtained from growth experiments carried out under similar environmental conditions glucose as sole carbon source in aerobic conditions [ 5 , 27 , 34 , 53 — 67 ] S2 Dataset.

Therefore, these data represent a very broad range of the metabolic states that can be expressed by E. coli growing on glucose. Importantly, these data were not used for parameter estimation, thereby they constitute an independent validation and provide a robust assessment of the predictive ability of the model.

These experimental observations correspond to the region of the solution space less frequently sampled using the stoichiometric model, but they closely match the region sampled by the kinetic model Fig 4B.

This means that the observed physiology of E. coli is closer to the metabolic model that is regulated by metabolite-enzyme interactions the kinetic model than it is to a metabolic model that would be regulated by gene expression alone the stoichiometric model. Hence, metabolic regulation alone, without needing to invoke coordinated expression of genes , seems to be sufficient to explain the emergence of a coupling between anabolic growth and catabolic glucose uptake fluxes, and thereby appears to be a major determinant of the overall cellular physiology by ensuring an efficient and robust allocation of nutrients towards growth.

We extended the above analysis to determine whether additional couplings emerge from metabolic regulation. Several variables representative of the physiological state of E. coli were calculated for each steady-state reached by the kinetic model, namely: growth and glucose uptake rates, ATP, NADH and NADPH production rates, sum of all intracellular fluxes, sum of all intracellular metabolite concentrations and cost of enzymes defined as the product of enzyme concentration and number of amino acids of the corresponding enzyme, summed over all reactions, as detailed in Methods.

Additional variables derived thereof were also computed: biomass, ATP, NADH and NADPH yields, enzyme cost and ATP production rate per sum of fluxes, and sum of fluxes per glucose consumed. Pairwise relationships between systemic variables and absolute and relative fluxes through the main pathways were examined using Spearman correlation and mutual information.

The outcome is a correlation matrix which maps the degree of functional coupling between all the variables Fig 5A. The same patterns were highlighted by both methods, which indicate that these couplings are monotonic since mutual information, but not Spearman correlation, would identify non-monotonic relations.

Steady-states were simulated for , sets of random enzyme levels, and the relationships between various systemic variables, absolute fluxes, and relative fluxes through the different pathways were identified using Spearman correlation test above diagonal and mutual information below diagonal. For Spearman correlation test, red and blue colors represent negative and positive correlations, respectively, and color intensity and circle size indicates high darker, larger to low lighter, smaller correlation coefficient.

For mutual information, color and circle size denote low white, smaller to high blue, larger mutual information, respectively. The density of scatter plots between particular steady-state variables glucose and oxygen uptake, growth rate, biomass yield, and relative fluxes through the TCA cycle sampled using the kinetic model B-D , with metabolic regulation or the stoichiometric model E-G , without metabolic regulation.

Red dots are measurements obtained from a total of independent cultivations , 65 and in panels B , C and D , respectively of wild-type and mutant E. The outcome of predictive analyses based on this assumption such as the minimization of the sum of fluxes in FBA according to the hypothesis that cells minimize their enzyme levels should therefore be interpreted with caution.

In general, systemic variables correlated poorly with relative and absolute fluxes from most of the pathways. This is interesting as it shows that while there is coordination between several processes, there is nevertheless a significant degree of flexibility in the intracellular flux distribution.

Thus, the partition of carbon between energy production ATP and NADPH and growth via the synthesis of many anabolic precursors is predicted to be realized primarily at the level of the TCA cycle and appears to be largely controlled at the metabolic level.

To evaluate these model predictions, additional experimental data on extracellular and intracellular fluxes growth rate, glucose and oxygen uptake rates, and TCA cycle fluxes through the citrate synthase were collected from the literature [ 5 , 27 , 34 , 53 , 54 , 56 , 58 — 60 , 62 — 65 , 67 ] S2 Dataset.

These data, which were not used to calibrate the model, covered the particular regions highlighted by the kinetic model Fig 5B—5D. The excellent agreement between the spread of simulated and experimental data strongly supports the existence of the functional couplings predicted by the model.

It is important to mention that these couplings are not caused by stoichiometric constraints since they are not observed when the solution space is uniformly sampled using the stoichiometric model Fig 5E—5G.

The results also show that the coordination of gene expression by hierarchical regulatory mechanisms is not an important factor in these couplings since they are still maintained when enzyme levels are changed randomly. In contrast, metabolic regulation brought about by metabolite-enzyme interactions is sufficient to explain their emergence; therefore they represent intrinsic properties of the central metabolism of E.

Interestingly, additional couplings predicted by the model were recently observed in vivo in both prokaryotic E. coli and eukaryotic S. Since the central metabolic networks of E.

coli and S. cerevisiae are highly conserved, the present results may explain why similar properties are observed in both microorganisms, though this hypothesis requires further investigation.

Predicted couplings between the ATP and NADH production rates A , between the sum of fluxes per glucose uptake rate and the ATP yield B , and between the growth rate per sum of fluxes and the sum of fluxes per glucose uptake rate C.

The red line in panel A corresponds to the linear correlation proposed by [ 73 ] from energy production fluxes estimated using 13 C-flux data.

The results presented above support the view that metabolic regulation reduces the solution space defined by the stoichiometric constraints, as previously suggested [ 68 , 69 ]. However, the very low probability regions of the solution space might not be captured by random sampling approaches [ 50 ].

To test further if metabolic regulation actually shrinks the solution space of E. coli central metabolism, its boundaries were determined with and without considering metabolic regulation by using the kinetic and the stoichiometric models, respectively.

Unexpectedly, the boundaries were similar for both models Fig 7. This indicates that metabolic regulation does not shrink the solution space of the system—and thus does not restrict the metabolic capabilities of E. coli —, at least for the variables considered here.

The solution space defined only by stoichiometric constraints blue area was computed using the stoichiometric model. The solution space defined when metabolic regulation is taken into account orange area was estimated using the kinetic model, by optimizing enzyme levels with particular metabolic states orange dots as objective functions.

The two solution spaces are similar, indicating that metabolic regulation does not significantly shrink the solution space, at least for the variables investigated here A , glucose uptake rate vs.

growth rate; B , biomass yield vs. glucose uptake rate; C , oxygen uptake rate vs. glucose uptake rate; D , TCA cycle flux—relative to glucose uptake—vs. growth rate. It has been shown that metabolic regulation plays an important role in metabolite homeostasis, which prevents osmotic stress and disadvantageous spontaneous reactions by avoiding large changes in metabolite concentrations for example see [ 20 , 70 ].

This narrow range of predicted intracellular concentrations is physiologically relevant [ 63 , 71 ]. Since no constraints on metabolite concentrations were included in the model, we conclude that metabolic regulation alone may explain global metabolite homeostasis, while still allowing significant changes in fluxes.

Distribution blue bars and cumulative frequency red line of the total concentrations of intracellular metabolites for steady-states simulated from , random enzyme levels. In this study, we investigated the contribution of metabolic regulation on the operation of the central metabolism of E.

coli , which provides building blocks, cofactors, and energy for growth and maintenance. We developed, to our knowledge, the first detailed kinetic model of this system that links metabolism to environment and cell proliferation through intracellular metabolites levels.

This model, validated by independent flux data from some experiments, allowed the identification of several properties which emerge from metabolic regulation and explain many experimental observations of E. The intrinsic, self-regulating capacities of E.

coli central metabolism appear to be far more significant than previously expected. The results presented here imply that gene regulation is not required to explain these properties.

Metabolic control analysis showed that the flux and concentration control exerted by single enzymes is low and largely distributed across the network, confirming again the insights of Kacser and Burns [ 51 ]. This significantly contrasts with the outcome of previous kinetic models [ 13 , 32 , 44 , 45 ], where a few enzymes were predicted to exert most of the flux control, but is in line with much experimental evidence [ 7 , 21 , 46 , 47 , 72 ].

coli metabolism, and likely not to the metabolism of other organisms. Its persistence in the literature is a major handicap to understanding metabolism.

In fact, the central metabolism is not even self-contained in terms of control due to a large portion of control being exerted by the environment, making E. coli responsive to environmental changes.

One of the most striking examples of this phenomenon is manifested in growth controlling most fluxes but being controlled virtually by glucose availability alone.

The low control exerted by single enzymes on the system makes the metabolic operation of E. coli robust to fluctuations of enzyme levels that may arise from noise in gene expression or other factors.

Moreover, the majority of control resides not within but outside the controlled pathways. The dense, yet highly organized, interactions between pathways allow a rapid and coordinated response of the entire system to perturbations.

Exploration of the solution space indicated that metabolic regulation does not significantly restrict the metabolic capabilities of E. coli , as was previously believed [ 68 , 69 ]. While the observed behavior of many different E. coli strains and mutants are confined to a small region of the solution space, this is not due to kinetic constraints as it is possible to simulate other behaviors simply by changing parameter values.

This apparent paradox can be resolved, of course, if the action of natural selection had favored these behaviors. The systematic mapping of the relationships between various systemic variables revealed that metabolic regulation is sufficient to explain the emergence of several functional couplings, which are independent from gene regulation since they are conserved when enzyme levels are changed randomly by orders of magnitude and cannot be explained by stoichiometric constraints.

An important finding is that metabolic regulation alone may be responsible for the coordination of major catabolic, energetic and anabolic processes at the cellular level to optimize growth. Metabolic regulation thus appears to be sufficient to maintain multi-dimensional optimality of E.

coli metabolism [ 65 ]. Despite this overall coordination, there is a large degree of flexibility at most individual metabolic steps. The role of metabolic regulation in maintaining global homeostasis of intracellular metabolite pools under a broad range of flux states was also verified by the present model.

The modeling results were in excellent agreement with experimental data, even quantitatively. coli metabolism displays remarkably robust yet simple emergent properties, and these properties have major implications on its overall cellular physiology, e. by preventing unnecessary osmotic stress, maintaining the coordination between key processes, and optimizing the allocation of resources towards particular functions such as growth.

The self-regulating capabilities of E. coli central metabolism reflect the evolutionary selection that has been exerted on the ensemble of enzymes in terms of kinetic and regulatory properties, but not necessarily of expression levels to realize a network with these properties.

Since central metabolism is essential in most organisms and is highly conserved across the three domains of life, it is tempting to speculate that metabolic regulation is responsible for the very similar operation principles observed in different organisms [ 73 ].

Of course we do not suggest that hierarchical regulation does not play an important role in the metabolic operation of E. coli , but it is in addition to the properties observed here, since these can operate without it. For instance, the robustness of the flux partition to the deletion of global transcriptional regulators was interpreted as a low control of this partition at the hierarchical level [ 5 ], and our results confirm that this robustness lies, to some extent, in metabolic regulation, given the low control exerted by enzymes.

However, this conclusion is valid only for moderate changes of enzyme levels with the notable exception of the flux through the TCA cycle , and other mechanisms such as hierarchical regulation are required to explain the robust flux partition.

Expanding the kinetic model to incorporate regulation of gene expression will be needed ultimately to understand the interplay between these two regulatory levels [ 19 , 74 , 75 , 88 ].

The kinetic model of the central carbon and energy metabolism of Escherichia coli K MG Fig 1 was developed with the software COPASI build 45 [ 42 ]. This model is briefly described in this section, and additional information can be found in Supporting Information S1 Text.

The model is available in SBML and COPASI formats in Supporting Information S1 Model , as well as from the Biomodels database [ 43 ] with identifier MODEL The model contains three compartments: the environment and the two cellular compartments: periplasm and cytoplasm.

Since the calibrated model simulates the metabolic operation of E. Their rate was modelled as a saturable, porin-facilitated diffusion process through the outer membrane [ 77 ], using a reversible Michaelis-Menten kinetics. Detailed description of the glucose phosphotransferase system was taken from [ 18 ].

Reactions of glycolysis, TCA cycle, glyoxylate shunt, and pentose phosphate and Entner-Doudoroff pathways were taken from [ 32 ]. Inhibition of 6-phosphogluconate dehydratase GND by phosphoenolpyruvate PEP was removed due to lack of experimental evidence.

Inhibition of glucosephosphate isomerase PGI by 6-phosphogluconate PGN [ 78 ] was added, and an error in the kinetic rate law of Entner-Doudoroff aldolase EDA in [ 32 ] was corrected.

Reactions involved in acetate metabolism acetyl-CoA synthetase, ACS; phosphotransacetylase, PTA; acetate kinase, ACK were taken from [ 31 ]. The rate law of ACS was modified to be function of the concentration of the cofactors that are actually involved in this reaction ATP and CoA instead of NADP.

In aerobic conditions, E. coli uses oxidative phosphorylation to produce ATP. NDHI and NDHII catalyze the transfer of electrons from NADH to the quinone pool Q in the cytoplasmic membrane.

SQR is a complex of four proteins SdhA, B, C and D. SdhA is a part of the TCA cycle reaction SDH and oxidizes succinate to fumarate by reducing FAD to FADH 2. Further transfer of electrons from FADH 2 to Q reaction SQR occurs via the three other proteins.

CYTBO couples the two-electron oxidation of ubiquinol QH 2 with the four-electron reduction of molecular oxygen to water. Oxidative phosphorylation was modelled using mass action kinetics. The rate of these reactions was modeled using the following rate laws: 1 2 3 4 5 with 6.

The reversible reduction of NADP by NADH is catalyzed by two transhydrogenases [ 56 ] lumped into the reaction PNT. These reactions were modeled using mass action kinetics. An overall pseudo-reaction was defined to describe cellular growth in terms of all the required metabolic precursors, with stoichiometric coefficients taken from the biomass function of iAF [ 36 ].

Assuming the growth rate μ is controlled by intracellular concentration of all the cell building blocks S i , the rate of this reaction was modeled using the following equation: 7.

Parameters not available in the literature, which do not have a real biochemical estimate e. Michaelis constants of the biomass function , or for which biochemical measurements are generally not representative of intracellular conditions e. Vmax were estimated to reproduce in the best possible way experimental data obtained from E.

coli K MG grown on glucose, under aerobic condition, at a dilution rate of 0. These data were steady state fluxes and metabolite concentrations [ 13 , 34 , 36 , 37 , 82 ] and time-course concentrations of intracellular metabolites in response to a glucose pulse [ 35 ] S1 Dataset.

Parameter estimation was formulated as a constrained optimization problem: where p is the parameter vector, f is the objective function which evaluates the deviation between the simulated and measured data, g p is the constraint function, and c is the constraint vector.

The objective function f was defined as the sum of squared weighted errors: where x i is the experimental value of the data point i , with experimental standard deviation σ i , and y i p is the corresponding simulated value.

The objective function was minimized with the Particle Swarm Optimization algorithm [ 83 ], using the Condor-COPASI system [ 84 ] on a pool of CPU cores. The experimental and fitted data are provided in Supporting Information S1 Dataset.

Values of all the parameters and the corresponding references for those values taken from the literature are given in Supporting Information S1 Text. Analyses described below were performed using R v3. org after converting the model into Fortran.

All the scripts are provided in Supporting Information S1 Code. Steady-states were calculated using the runsteady function of the rootSolve R package v1. Scaled flux and concentration controls, which represent the fractional change in the steady-state flux J and metabolite M in response to a fractional change in the rate of the step E coefficients, were calculated as follows: 8 and 9.

The overall flux CJ E and concentration CC E controls exerted on the system by the step E were calculated as the L2-norm of all its control coefficients: 10 and A total of , flux distributions were uniformly sampled within the solution space of a stoichiometric version of the kinetic model, using the Cobra Toolbox v2.

In parallel, the runif function of R was used to generate , random sets of enzyme levels, and the corresponding steady-states were simulated under excess glucose concentration fixed at 10 mM.

For each set, enzyme levels were sampled over two orders of magnitude between 0. For each steady state, the following variables were calculated: growth rate, glucose and oxygen uptake rates, ATP, NADH and NADPH production rates and yields, sum of fluxes, cost of enzymes, biomass yield, total intracellular metabolites pool, ATP production rate and enzyme cost per sum of fluxes, and sum of fluxes per glucose consumed.

Enzyme cost, defined as the total amount of amino acids present in central metabolic enzymes, was calculated by summing for each reaction the product of enzyme concentration by the number of amino acids in the corresponding enzyme, similarly to [ 85 , 86 ]. Enzyme concentrations were taken from [ 63 ], those not available were assumed to be the average concentration of other enzymes 18 μM.

Pairwise relationships between steady-state systemic variables and absolute and relative intracellular fluxes through the main pathways were evaluated using Spearman correlation test cor function of R stats package and mutual information mutinformation function of infotheo R package v1.

Exploration of the model solution space suggested the existence of robust couplings between several metabolic processes carbon uptake, catabolism, energy and redox production, and growth.

We independently validated these model predictions based on a test set of experimentally observed phenotypes that were not used during model construction. We collected from the literature a total of intracellular and extracellular flux data from some experiments where several wild-type and mutant E.

coli K strains MG and its close derivatives JM, BW, W and TG1 were grown in different conditions deep well plate, bioreactor, shake flask, and chemostat S2 Dataset [ 5 , 27 , 34 , 53 — 67 ]. This data set therefore represents a very broad range of the metabolic states that can be expressed by E.

The testing data set is very different from the training data set, which contains experimental data from a single wild-type E.

coli strain grown in a unique condition. This provides a robust assessment of the predictive ability of the model. The good agreement between simulated and experimental validation data Figs 4 — 6 indicates the model yielded fairly accurate predictions of the metabolic states that can be expressed by E.

Glucose uptake, catabolism, energy and redox production and growth were predicted to be strongly coupled despite large, random changes of gene expression.

All the experimental data support the model-driven hypothesis that metabolic regulation is sufficient to maintain the tight coordination between these key metabolic processes. We thank Ed Kent MCISB, University of Manchester for help with running the Condor-COPASI system, and Jean-Charles Portais and Brice Enjalbert MetaSys team, LISBP, INSA Toulouse for helpful comments and discussion on this work.

Conceptualization: PMi PMe KS. Data curation: PMi. Formal analysis: PMi PMe KS. Funding acquisition: PMi PMe KS. Investigation: PMi. Methodology: PMi KS PMe. Project administration: PMi PMe. Resources: PMi PMe KS.

Software: PMi. Supervision: PMe. Validation: PMi. Visualization: PMi. Writing — original draft: PMi. Article Authors Metrics Comments Media Coverage Reader Comments Figures.

Maranas, The Pennsylvania State University, UNITED STATES Received: September 13, ; Accepted: February 3, ; Published: February 10, Copyright: © Millard et al. Introduction Metabolism is a fundamental biochemical process that converts nutrients into energy and biomass precursors, thus enabling cells to maintain their structures, grow, and respond to their environment.

Results Model development The kinetic model developed in this study represents the central metabolism of Escherichia coli cultivated on glucose under aerobic conditions Fig 1.

Download: PPT. Fig 1. Representation of the central carbon and energy network of Escherichia coli. Metabolic regulation ensures robustness to fluctuations in enzyme levels and a high sensitivity to the environment The control properties of E.

The distributed control results in strong interconnections between all the pathways Despite the low control exerted by enzymes over fluxes and concentrations at the network level, a detailed analysis of flux control coefficients reveals generic regulatory patterns between most of the pathways Fig 3.

Metabolic regulation couples growth to glucose uptake To get a broader picture of the role of metabolic regulation on the coordination of E. Fig 4. Exploration of the solution space of E.

coli metabolism with and without metabolic regulation. Metabolic regulation coordinates several processes at the cellular level while maintaining flexibility We extended the above analysis to determine whether additional couplings emerge from metabolic regulation.

Fig 5. Identification of the functional couplings that are independent of enzyme levels. Fig 6. Some functional couplings observed in different microorganisms are predicted by the model.

Metabolic regulation does not significantly shrink the solution space defined by the stoichiometric constraints The results presented above support the view that metabolic regulation reduces the solution space defined by the stoichiometric constraints, as previously suggested [ 68 , 69 ].

Fig 7. Boundaries of the solution space with and without metabolic regulation. Metabolic regulation maintains global metabolite homeostasis It has been shown that metabolic regulation plays an important role in metabolite homeostasis, which prevents osmotic stress and disadvantageous spontaneous reactions by avoiding large changes in metabolite concentrations for example see [ 20 , 70 ].

Fig 8. Metabolite homeostasis is largely driven my metabolic regulation. Discussion In this study, we investigated the contribution of metabolic regulation on the operation of the central metabolism of E. Materials and methods Model construction The kinetic model of the central carbon and energy metabolism of Escherichia coli K MG Fig 1 was developed with the software COPASI build 45 [ 42 ].

Exchange reactions. Central carbon metabolism. Oxidative phosphorylation. Interconversion of nucleotides and reduced cofactors. Model analysis and validation Analyses described below were performed using R v3.

Metabolic control analysis. Sampling of the solution space. Identification of functional couplings. Model validation. Supporting information. S1 Table. Comparison of available detailed kinetic models of Escherichia coli metabolism.

s XLSX. S1 Text. Extended information on the kinetic model of Escherichia coli metabolism. s PDF. S1 Model.

Topics Bioenerg. Rosen, B. Simon, W. Acta , Wilson, D. Cecchini, G. Purdy, D. West, I. C, Mitchell, P. Collins, S. Hirata, H. Haddock, B.

Knöpfel, H. Thesis, Diss. Zurich Dietzler, D. Saier, M. Winkler, H. HI, Wilson, T. Morris, D. Mugharbil, U. Dills, S. Ashcroft, J. Jones, C. Shipp, W. Castor, L. Pudek, M. Lawford, H. Brice, J.

Downie, A. Cox, G. Jurtshuk, P. Blangy, D. Mavis, R. Nissler, K. Bohme, H. Uyeda, K. Sokatch, J. Hudson, P. Atkinson, D. Griffin, C. Sapico, V. Lowry, O. Ferdinandus, J. Grillo, J. P , Fraenkel, D. Gordon, G. Monod, J. Yoshida, M. Cass, K. Kopperschlager, G. Lindell, T. Tamaki, N. Diezel, W.

Laurent, M. Plietz, P. Taucher, M. Tijane, M. Wilgus, H. Hengartner, H. Kotlarz, D. Acta , 35 Thornburg, B. Ewings, K. Thesis, Dept. Evans, P. Freyer, R. Morissey, A. Babul, J. Peters, W. Gray, C. Acta , 22 Hess, B. Vinopal, R. O'Sullivan, W. Akkerman, J. Knowles, C. A synthetic analog of human amylin that binds to the amylin receptor, an amylinomimetic agent, is in development.

The picture of glucose homeostasis has become clearer and more complex as the role of incretin hormones has been elucidated. Incretin hormones play a role in helping regulate glucose appearance and in enhancing insulin secretion.

Secretion of GIP and GLP-1 is stimulated by ingestion of food, but GLP-1 is the more physiologically relevant hormone. However, replacing GLP-1 in its natural state poses biological challenges.

In clinical trials, continuous subcutaneous or intravenous infusion was superior to single or repeated injections of GLP-1 because of the rapid degradation of GLP-1 by DPP-IV. To circumvent this intensive and expensive mode of treatment, clinical development of compounds that elicit similar glucoregulatory effects to those of GLP-1 are being investigated.

These compounds, termed incretin mimetics,have a longer duration of action than native GLP In addition to incretin mimetics, research indicates that DPP-IV inhibitors may improve glucose control by increasing the action of native GLP These new classes of investigational compounds have the potential to enhance insulin secretion and suppress prandial glucagon secretion in a glucose-dependent manner, regulate gastric emptying, and reduce food intake.

Despite current advances in pharmacological therapies for diabetes,attaining and maintaining optimal glycemic control has remained elusive and daunting. Intensified management clearly has been associated with decreased risk of complications. Glucose regulation is an exquisite orchestration of many hormones, both pancreatic and gut, that exert effect on multiple target tissues, such as muscle, brain, liver, and adipocyte.

While health care practitioners and patients have had multiple therapeutic options for the past 10 years, both continue to struggle to achieve and maintain good glycemic control. There remains a need for new interventions that complement our current therapeutic armamentarium without some of their clinical short-comings such as the risk of hypoglycemia and weight gain.

These evolving therapies offer the potential for more effective management of diabetes from a multi-hormonal perspective Figure 3 and are now under clinical development. Aronoff, MD, FACP, FACE, is a partner and clinical endocrinologist at Endocrine Associates of Dallas and director at the Research Institute of Dallas in Dallas, Tex.

Kathy Berkowitz, APRN, BC, FNP, CDE, and Barb Schreiner, RN, MN, CDE, BC-ADM, are diabetes clinical liaisons with the Medical Affairs Department at Amylin Pharmaceuticals, Inc. Laura Want, RN, MS, CDE, CCRC, BC-ADM, is the clinical research coordinator at MedStar Research Institute in Washington, D.

Note of disclosure: Dr. Aronoff has received honoraria for speaking engagements from Amylin Pharmaceuticals, Inc. Berkowitz and Ms. Schreiner are employed by Amylin. Want serves on an advisory panel for, is a stock shareholder in, and has received honoraria for speaking engagements from Amylin and has served as a research coordinator for studies funded by the company.

She has also received research support from Lilly, Novo Nordisk, and MannKind Corporation. Amylin Pharmaceuticals, Inc.

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User Tools Dropdown. Sign In. Skip Nav Destination Close navigation menu Article navigation. Volume 17, Issue 3. Previous Article. β-CELL HORMONES. α-CELL HORMONE: GLUCAGON. INCRETIN HORMONES GLP-1 AND GIP. AMYLIN ACTIONS. GLP-1 ACTIONS. Article Navigation. Feature Articles July 01 Glucose Metabolism and Regulation: Beyond Insulin and Glucagon Stephen L.

Aronoff, MD, FACP, FACE ; Stephen L. Aronoff, MD, FACP, FACE. This Site. Google Scholar. Kathy Berkowitz, APRN, BC, FNP, CDE ; Kathy Berkowitz, APRN, BC, FNP, CDE. Barb Shreiner, RN, MN, CDE, BC-ADM ; Barb Shreiner, RN, MN, CDE, BC-ADM.

Laura Want, RN, MS, CDE, CCRC, BC-ADM Laura Want, RN, MS, CDE, CCRC, BC-ADM. Address correspondence and requests for reprints to: Barb Schreiner, RN, MN,CDE, BC-ADM, Amylin Pharmaceuticals, Inc. Diabetes Spectr ;17 3 — Get Permissions.

toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Figure 1. View large Download slide. Table 1. Effects of Primary Glucoregulatory Hormones.

View large. View Large. Figure 2. Figure 3. Figure 4. Figure 5. American Diabetes Association: Clinical Practice Recommendations Diabetes Care.

Am Fam Physician. DCCT Research Group: Hypoglycemia in the Diabetes Control and Complications Trial. DCCT Research Group: Weight gain associated with intensive therapy in the Diabetes Control and Complications Trial. UKPDS Study Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes.

Clinical Diabetes. Biochem Biophys Res Commun. Am J Physiol. Proc Natl Acad Sci U S A. In International Textbook of Diabetes Mellitus. In William's Textbook of Endocrinology. Baillieres Best Pract Res Clin Endocrinol Metab. J Clin Endocrinol Metab. J Clin Invest. Data on file, Amylin Pharmaceuticals, Inc.

Curr Pharm Des. normal controls Abstract. Curr Opin Endocrinol Diab. Diabetes Educ. Physiol Behav. Crit Revs Neurobiol. Expert Opin Therapeut Patents. J Pharmacol Exp Ther. Glucagon or Adrenaline binds to the membrane of hepatocytes, which regulates stimulates or inhibits certain enzymes through the cAMP pathway Figure 1.

While the kinases are inactivating the glycogen synthase , they activate the glycogen phosphorylase. The generated glucosep is transported via the bloodstream to the peripheral tissues, later it is used in glycolysis.

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