Actinobacillus succinogenes, which naturally produces succinate by a mixed-acid fermentative pathway. To engineer A. succinogenes to improve succinate yields during mixed acid fermentation, it is important to have a detailed understanding of the metabolic flux distribution in A. succinogenes when grown in suitable media. To this end, we have developed a detailed stoichiometric model of the A. succinogenes central metabolism that includes the biosynthetic pathways for the main components of biomass—namely glycogen, amino acids, DNA, RNA, lipids and UDP-N-Acetyl-α-D-glucosamine. We have validated our model by comparing model predictions generated via flux balance analysis with experimental results on mixed acid fermentation. Moreover, we have used the model to predict single and double reaction knockouts to maximize succinate production while maintaining growth viability. According to our model, succinate production can be maximized by knocking out either of the reactions catalyzed by the PTA (phosphate acetyltransferase) and ACK (acetyl kinase) enzymes, whereas the double knockouts of PEPCK (phosphoenolpyruvate carboxykinase) and PTA or PEPCK and ACK enzymes are the most effective in increasing succinate production.Succinate is a precursor of multiple commodity chemicals and bio-based succinate production is an active area of industrial bioengineering research. One of the most important microbial strains for bio-based production of succinate is the capnophilic gram-negative bacterium  The flux through this pseudo-reaction constitutes the biomass objective function that is optimized for most of the constraint-based analyses discussed in the Results and Discussion section.13C labeling experiments to investigate the fermentative metabolism in A. succinogenes grown in a chemically defined minimal medium AM3 with the medium NaHCO3 concentration varying from 5 to 150 mM. However, the fluxes for the cellular uptake of NaHCO3 and L-glutamate and the non-growth associated ATP requirement (NGAM) for A. succinogenes have not been estimated as part of the above-mentioned studies. We assumed a Michaelis-Menten relation between the medium bicarbonate concentration and the bicarbonate uptake flux into the cell. We thereby estimated the values of the maximal rate (Vmax) and the Michaelis-Menten constant (KM) for bicarbonate uptake, and the L-glutamate and non-growth associated ATP requirement for A. succinogenes in the AM3 medium by fitting model predictions to experimentally determined ratios of fermentation product secretion to glucose uptake fluxes. More precisely, this fitting is achieved by solving the following optimization problem: minimizing the objective function F, given byi ∈ . In the above expression, i consistent with a fraction f of optimal growth, ei and eglc represent the experimentally estimated secretion flux of i and glucose uptake respectively. The flux values A. succinogenes and the objective function F is minimized with respect to these model parameters. For the fitting the glucose uptake into the cell was restricted between 5.9 and 6.2 mmol gDCW-1 hr-1, where the limits are obtained by averaging the lower and upper bounds of the experimentally estimated values of the glucose uptake flux. The value of the fraction f of the optimal growth that we have used to determine the flux ranges for the fitting is 96.5% since the flux ranges for fermentation product secretion corresponding to 96.5% of the maximum growth are the narrowest ones that completely contain the experimentally determined values of the fermentation product secretion fluxes. The values of the model parameters for A. succinogenes fermentative metabolism in AM3 medium are tabulated in Table C in McKinlay et al. have undertaken A. succinogenes.The comparison of the experimental results and the model predictions corresponding to the best fit of the model parameters are presented in 3 concentrations in the medium. Increasing the NaHCO3 concentration in the medium, and hence the CO2 uptake rate, causes the reverse flux through the reaction catalyzed by malic enzyme to preserve mass balance. As a result, the net flux through the malic enzyme reaction decreases. On the basis of their experimental results, McKinlay et al. postulated that decreasing malate decarboxylating flux and constant flux through the PEPCK reaction with increasing medium bicarbonate concentration resulted in increased flux through the C4 pathway and hence increased succinate production [Detailed experimental investigation by McKinlay et al.  indicateA. succinogenes fermentation pathway with increasing medium bicarbonate concentration. Since bicarbonate uptake follows Michaelis-Menten kinetics, increasing medium bicarbonate concentration translates to increasing bicarbonate uptake flux. The model predictions for the reactions PEPCK (phosphoenolpyruvate carboxykinase), MDH , ME , FUM (Fumarase), FR (fumarate reductase), THD2 (transhydrogenase) and ALCD , shown schematically in We have used parsimonious flux balance analysis (pFBA)  and fluxThe gradual decrease of the reaction flux in the forward direction of ME with increasing bicarbonate uptake can be interpreted as increasing flux in the reverse direction of the ME see . This chA. succinogenes in minimal medium AM3 and a rich medium known as Medium A [3 concentration in both AM3 and Medium A was taken to be 150 mM. In medium A, the vitamins, minerals, amino acids, NaCl, and NH4Cl in AM3 are substituted by 5g/liter of yeast extract. We found a detailed account of the molecular composition of yeast extract from Smith et al. [In addition to experiments in the AM3 medium, McKinlay also undertook an experimental investigation to compare the growth trends and fermentation balances of h et al. .A. succinogenes grown in the rich Medium A. The predictions are made for a glucose uptake rate of 8.65 mmol gDCW-1 hr-1 and a medium carbonate concentration of 150 mM in agreement with the experimentally derived fluxes for the rich medium A.Assuming that the parameters in Table C in In We also assessed the effect of deletion of each gene and each reaction in the models for both the minimal (AM3) and rich (medium A) media, for a medium bicarbonate concentration of 150 mM. Both FBA and minimization of metabolic adjustment (MOMA) methods were used for these simulations but the results were identical for both the methods as evident from the Both the numbers of lethal gene and reaction deletions were lower in the rich medium compared to the minimal media. The rich medium (Medium A) contains yeast extract, so it already contains most of the amino acids . Hence, A. succinogenes in the AM3 medium [A. succinogenes on various precursors of L-glutamate.McKinlay et al. have experimentally examined whether glutamate precursors support growth of A. succinogenes at different NaHCO3 and H2 concentrations using 13C metabolic flux analysis. For this investigation, they used a variant of the AM3 medium in which the Cys–HCl, Met, and monosodium glutamate concentrations were changed relative to the minimal AM3 medium [3 in this growth medium, 25 mM and 100 mM, both in the presence and absence of H2. For medium NaHCO3 concentrations of 25 and 100 mM, the H2 oxidation rate were measured as 2.8 mmol gDCW-1 hr-1 and 4.7mmol gDCW-1 hr-1 respectively. The experimentally estimated glucose uptake rate varied between 9 and 10 mmol gDCW-1 hr-1 for the above-mentioned medium. In our model, we have assumed that the H2 input fluxes equal the H2 oxidation rates in McKinlay et al. [2 concentration. Since the energy and redox cofactor yield of different fermentation product pathways are similar, the ranges of predicted product fluxes are large in comparison with the experimental error. These ranges therefore represent all stoichiometrically feasible growth modes capable of yielding a similar biomass growth rate.McKinlay et al.  also exp3 medium . McKinlaA. succinogenes in the presence of H2 results in higher succinate excretion and this effect is accentuated at higher bicarbonate uptake fluxes, which agrees qualitatively with the experimentally derived results [2 is available as an electron source, additional reducing equivalents are available so that the flux through the FR reaction increases, resulting in higher succinate formation. It is evident from 2. Not only that, in accordance with the experimental results [2 is more pronounced at the higher bicarbonate concentration than that at the lower bicarbonate concentration. It should be noted that though our model consistently overestimates the ethanol formation fluxes at various medium bicarbonate concentrations and with/without hydrogen uptake, it qualitatively reproduces the trend for ethanol formation as a function of these media conditions. Experimental papers on A. succinogenes metabolism [A. succinogenes in KEGG and MetaCyc seem to indicate a degree of reversibility of the reaction from acetaldehyde to ethanol in A. succinogenes in MetaCyc, ethanol can be degraded to acetate via acetaldehyde. The presence of such a degradation pathway in vivo, which we do not consider in silico, might be the cause behind the overestimation of the ethanol production flux by our model.As shown in  results . McKinla results  have sug results , our modtabolism , 4, 6, 8-1 and the non-growth associated ATP maintenance flux to be at 1.082 mmol gDCW-1 hr-1. A subset of model reactions was selected for knocking out that excluded the ATP synthase, NGAM, and the uptake and excretion reactions.We used the OptKnock algorithm to predict both single and double reaction knockouts for maximizing succinate production in the minimal AM3 medium while restraining the growth to be at least 0.1 gDCW hrin silico double mutants, instead of running the FVA for all possible pairs of reactions from the set selected for knockouts, we created a set of reactions corresponding to the top FVA predictions for single reaction deletions. We then chose all possible reactions pairs from this latter set and ran the FVA after in silico deletion of each possible reaction pair.Depending on whether we predicted single or double reaction knockouts, we compared the OptKnock results with results from flux variability analysis, after knocking out one or a pair of reactions from the above-mentioned subset. However, in the case of -1 hr-1. However, the L-glutamate flux for knockout strains might vary over a range and we chose the range of L-glutamate fluxes from the robustness analysis of A. succinogenes growth with respect to L-glutamate uptake flux. Hence, the extended range of L-glutamate uptake fluxes that we considered based on robustness analyses was 0–0.5 mmol gDCW-1 hr-1. Similarly, the range of bicarbonate fluxes chosen for the reaction knockout study was chosen as 0–5.0 mmol gDCW-1 hr-1. The single knockouts that are most effective in maximizing succinate production involve knocking out the reactions catalyzed by the PTA (Phosphate acetyltransferase) and ACK (Acetyl kinase) enzymes. The double knockouts that are most effective in increasing succinate production are the  and  knockouts.For the minimal AM3 medium, our estimate for the L-glutamate flux was about 0.439 mmol gDCWKnocking out the reaction catalyzed by PTA or ACK reduces the carbon flux from pyruvate to acetyl-CoA since the conversion of acetyl-CoA to acetate is favored over the conversion of acetyl-CoA to ethanol, as the latter route requires more reducing equivalents . For thein silico single reaction knockouts. Additional NADPH required for the increased flux in the reverse direction of the malic enzyme reaction is made available by splitting the flux from β-glucose-6-phosphate to both the oxidative branch of the pentose phosphate pathway and to β-glucose-6-phosphate via the PGI reaction  and , the reaction flux in the PEPCK reaction from phosphoenolpyruvate to oxaloacetate in the wild type and single knockout mutants is diverted to the PK (pyruvate kinase) reaction . There ireaction , with a reaction . Insteadin silico WT strain, whereas for the double knockouts  the predicted growth flux is 74.9% of the predicted growth flux for the in silico WT strain. This observation is indicative of the robustness of our model system.It should be noted that both the single and double reaction knockouts cause increase in succinate flux without stalling the biomass production or growth flux. Thus, for the single ACK or PTA knockouts, the model predicted growth flux is reduced to about 84.5% of the predicted growth flux for the A. succinogenes. This model contains most of the pathways relevant to the biosynthesis of the main components of biomass, namely glycogen, proteins, lipids, DNA and RNA and UDP-GlcNAc. We have validated this model by comparing the model predictions with results and postulations from published reports on experimental investigation of the mixed acid fermentation in A. succinogenes. We then use variants of this model corresponding to different growth media to make predictions about single and double reaction knockouts to optimize succinate production.We have developed a detailed metabolic model for the gram-negative succinogenic bacterium, On the modeling and computational front, future work using this model would involve extending the model to the genome scale and examine if the model extension causes model predictions to vary. In a different direction, it would be worthwhile to work towards experimentally executing the knockouts predicted by the model. In an ideal situation, these two directions would go hand in hand and form different steps of a combined and iterative computational and experimental workflow where the model helps to predict knockouts and the experimental validation or invalidation of the predicted knockouts helps to refine or extend the model.S1 File(DOCX)Click here for additional data file.S1 Model(XML)Click here for additional data file.S2 Model(XML)Click here for additional data file.S1 Data Set(XLSX)Click here for additional data file.S2 Data Set(XLSX)Click here for additional data file.


Escherichia coli central metabolism that links growth to environment. Metabolic control analyses confirm that the control is widely distributed across the network and highlight strong interconnections between all the pathways. Exploration of the model solution space reveals that several robust properties emerge from metabolic regulation, from the molecular level  to the overall cellular physiology , while allowing a large degree of flexibility at most individual metabolic steps. These properties have important physiological implications for E. coli and significantly expand the self-regulating capacities of its metabolism.The metabolism of microorganisms is regulated through two main mechanisms: changes of enzyme capacities as a consequence of gene expression modulation  and changes of enzyme activities through metabolite-enzyme interactions. An increasing body of evidence indicates that hierarchical control is insufficient to explain metabolic behaviors, but the system-wide impact of metabolic regulation remains largely uncharacterized. To clarify its role, we developed and validated a detailed kinetic model of  Escherichia coli, a model organism in Systems and Synthetic biology. Model simulations indicate that kinetic considerations of metabolism alone can explain data from hundreds of experiments, without needing to invoke regulation of gene expression. In particular, metabolic regulation is sufficient to coordinate carbon utilization, redox and energy production, and growth, while maintaining local flexibility at individual metabolic steps. These findings indicate that the self-regulating capacities of E. coli metabolism are far more significant than previously expected, and improve our understanding on how cells work.Metabolism is a fundamental biochemical process that enables cells to operate and grow by converting nutrients into ‘building blocks’ and energy. Metabolism happens through the work of enzymes, which are encoded by genes. Thus, genes and their regulation are often thought of controlling metabolism, somewhat at the top of a hierarchical control system. However, an increasing body of evidence indicates that metabolism plays an active role in the control of its own operation via a dense network of metabolite-enzyme interactions. The system-wide role of metabolic regulation is hard to dissect and so far remains largely uncharacterized. To better understand its role, we constructed a detailed kinetic model of the carbon and energy metabolism of the bacterium  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. While the topology of metabolic networks is fairly well known, understanding how metabolic behaviours emerge from the dynamic interactions of their molecular components remains one of the main challenges faced by systems biology and is crucial for the development of synthetic biology .i.e. the metabolic fluxes, represents the ultimate output of several regulatory mechanisms. Metabolic fluxes are functions of enzyme activities and of the concentrations of reactants, products, and other effectors. While the enzyme activities are the ultimate outcome of gene expression through the hierarchy of transcriptional, post-transcriptional, translational and post-translational regulatory mechanisms, the reactant and effector concentrations are directly regulated at the metabolic level by enzyme activities themselves. Hierarchical regulation, and in particular transcriptional regulation, has attracted much attention because of mature experimental methods, but also because of early examples of flux increase with enzyme induction [13C-flux analyses revealed that flux distributions in Saccharomyces cerevisiae and Escherichia coli are incredibly robust to the deletion of global transcriptional regulators [The operation of metabolic networks, nduction , 3. Thesgulators , 5. Integulators –9. Theree.g. Mycoplasma genitalium [Mathematical frameworks such as metabolic control analysis , 11 werenitalium , but unfE. coli, which constitutes the backbone of its metabolism by providing macromolecular precursors, reducing equivalents, and energy for growth and maintenance. While previous studies typically focused on the role of particular regulatory interactions, we attempt to determine whether more global and generic properties arise from the interplay of the many regulatory interactions that compose metabolic regulation. 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.In this study, we aim at investigating the role of metabolic regulation on the central metabolic network of Escherichia coli cultivated on glucose under aerobic conditions , 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 255 metabolite-enzyme interactions  were included in the model, amongst which 34 are long-range regulatory interactions .The kinetic model developed in this study represents the central metabolism of nditions . This moE. coli metabolism were used as scaffolds to construct this model [2)) and simulate energy and redox metabolism. 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 [Previously published kinetic models of is model , 31, 32.hes zero . These phes zero .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. coli strain (the model strain K-12 MG1655) grown under a unique reference condition  [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  , 34–37. 2 > 20%) –40. Whilormation . The modormation  formatteE. coli central metabolism in the reference state (see above) were investigated under the metabolic control analysis framework [e.g. 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  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 [bo oxidase , glucose-6-phosphate dehydrogenase , glyceraldehyde-3-phosphate dehydrogenase , citrate synthase  and the anabolic machinery  are the ones with the largest share. 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. In fact, a significant correlation  can be observed between the overall flux- and concentration-control exerted by each step ,ach step , indicatach step .Further analysis confirmed the wide distribution of flux control across all the enzymes, with 97% of the individual control coefficients between -0.3 and 0.3 . These oDespite 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 . A genere.g. 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 [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  negative control on the PPP and ED fluxes  controls fluxes through the TCA cycle . The solution space of each model was explored using a random sampling approach: 600,000 flux distributions were uniformly sampled from the solution space using the stoichiometric model, and steady-states were simulated for 600,000 sets of random enzyme levels using the kinetic model. For each set, enzyme levels (i.e. Vmax) were sampled from a log uniform distribution  to ensure each order of magnitude to be sampled in similar proportions. 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 [To get a broader picture of the role of metabolic regulation on the coordination of  in vivo . Rather,We first investigated the relationship between supply (glucose uptake) and demand (growth), which provides information on the allocation of resources by the metabolic network . Direct 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 (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.To evaluate this prediction quantitatively, we gathered from the literature experimental data obtained from 254 growth experiments carried out under similar environmental conditions (glucose as sole carbon source in aerobic conditions) , 53–67 . These dic model . This meE. 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 , reflecting the important role of oxidative phosphorylation in energy production under aerobic conditions.A systematic positive correlation was predicted between some anabolic and catabolic fluxes and yields: glucose uptake rate, growth rate, NADPH production rate, biomass yield and NADPH yield (with ρ > 0.85), indicating a considerable degree of coordination in the metabolic operation. These variables are negatively correlated with the energetic (ATP and NADH) yields (ρ < -0.85), which is consistent with the fact that the single carbon source, glucose, is used by two competing metabolic processes: energy production and biomass synthesis, reflected in an increase in Yper se. 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.The low correlation coefficient between the sum of fluxes and the cost of enzymes (ρ = 0.11) indicates that the sum of fluxes cannot be considered as a proxy for enzyme investment O2_uptake, vGlc_uptake, vNADH_production, vATP_production, with ρ > 0.84), and the relative contribution of this pathway negatively correlated with anabolic rates and yields  and positively correlated with energy yields (ρ > 0.90 for ATP and NADH yields). 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.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. A notable exception was observed for the TCA cycle: its absolute flux is positively correlated with catabolic and energy fluxes  were collected from the literature –65, 67 . These dic model . The resin vivo in both prokaryotic (E. coli) and eukaryotic (S. cerevisiae) microorganisms [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.Interestingly, additional couplings predicted by the model were recently observed rganisms : betweenE. 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 . InE. 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 778 independent flux data from some 266 experiments, allowed the identification of several properties which emerge from metabolic regulation and explain many experimental observations of E. coli’s physiology. 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.In this study, we investigated the contribution of metabolic regulation on the operation of the central metabolism of E. 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.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 . This siE. coli, as was previously believed [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.Exploration of the solution space indicated that metabolic regulation does not significantly restrict the metabolic capabilities of believed , 69. WhiE. coli metabolism [E. coli metabolism displays remarkably robust yet simple emergent properties, and these properties have major implications on its overall cellular physiology, e.g. by preventing unnecessary osmotic stress, maintaining the coordination between key processes, and optimizing the allocation of resources towards particular functions such as growth.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 tabolism . DespiteE. coli central metabolism reflect the evolutionary selection that has been exerted on the ensemble of enzymes  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 [The self-regulating capabilities of rganisms .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 [Of course we do not suggest that hierarchical regulation does not play an important role in the metabolic operation of al level , and oural level , 75, 88.Escherichia coli K-12 MG1655 , two reactions were included to represent the continuous glucose inflow into the environment and acetate wash out .The model contains three compartments: the environment and the two cellular compartments: periplasm and cytoplasm. The periplasmic volume is set to be 20% of the total cell volume . Since tBiological systems are open systems, three exchange reactions were therefore included to enable the transport of extracellular glucose (XCH_GLC), acetate (XCH_ACE) and phosphate (XCH_P) between the environment and the periplasm. Their rate was modelled as a saturable, porin-facilitated diffusion process through the outer membrane , using aDetailed description of the glucose phosphotransferase system was taken from . ReactioE. coli uses oxidative phosphorylation to produce ATP. The main components of this pathway are two NADH dehydrogenases (reactions NDHI and NDHII), the succinate dehydrogenase complex (reactions SDH and SQR), the cytochrome bo oxidase (CYTBO) and the ATP synthase (ATP_SYN) [+ from cytoplasm to periplasm, with an H+/e- ratio of 2 [2. Further transfer of electrons from FADH2 to Q (reaction SQR) occurs via the three other proteins. CYTBO couples the two-electron oxidation of ubiquinol (QH2) with the four-electron reduction of molecular oxygen to water. This enzyme also functions as a proton pump, with an H+/e- ratio of 2 [+/ATP ratio of 4 [+ pumps (NDHI and CYTBO) and ATP synthase on pH gradient across the membrane was considered using the relation proposed by [In aerobic conditions, ATP_SYN) . NDHI antio of 2 . SQR is tio of 2 . The protio of 4 . Oxidatiposed by . The rati) non-growth associated processes which consume ATP are lumped in the reaction ATP_NGAM, ii) adenylate kinase (reaction ADK), which catalyzes the reversible conversion of AMP and ATP into two molecules of ADP, iii) adenylate cyclase (CYA), which catalyzes the synthesis of cAMP from ATP, and iv) cAMP phosphodiesterase (DOS), which hydrolyses cAMP into AMP. The reversible reduction of NADP by NADH is catalyzed by two transhydrogenases [Several processes strongly impact the balance of ATP/ADP/AMP/cAMP pools and had to be considered to fit the experimental data: ogenases  lumped iSi, the rate of this reaction was modeled using the following equation: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 iAF1260 . Assumine.g. Michaelis constants of the biomass function), or for which biochemical measurements are generally not representative of intracellular conditions (e.g. Vmax) were estimated to reproduce in the best possible way 276 experimental data obtained from E. coli K-12 MG1655 grown on glucose, under aerobic condition, at a dilution rate of 0.1 h-1. These data were steady state fluxes and metabolite concentrations [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:xi is the experimental value of the data point i, with experimental standard deviation σi, and yi(p) is the corresponding simulated value. Constraints were defined on estimated parameters  to ensure they are kept within a biologically reasonable range. The objective function was minimized with the Particle Swarm Optimization algorithm [Values of 56% of the parameters  were directly taken from the literature. Parameters not available in the literature, which do not have a real biochemical estimate  after converting the model into Fortran. All the scripts are provided in Supporting Information . Absolute and relative error tolerances were set to 10−8 and 10−6, respectively.Steady-states were calculated using the J and metabolite M in response to a fractional change in the rate of the step E coefficients, were calculated as follows:Scaled flux (ECJ) and concentration (ECC) controls exerted on the system by the step E were calculated as the L2-norm of all its control coefficients:The overall flux . For each set, enzyme levels were sampled over two orders of magnitude  from a log uniform distribution, which ensures each order of magnitude to be sampled in similar proportions.A total of 600,000 flux distributions were uniformly sampled within the solution space of a stoichiometric version of the kinetic model, using the Cobra Toolbox (v2.0.5) and Matlab 2013. In parallel, the cor function of R stats package) and mutual information (mutinformation function of infotheo R package v1.2.0).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 , 86. EnzE. coli K-12 strains  were grown in different conditions  . 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 778 intracellular and extracellular flux data from some 266 experiments where several wild-type and mutant emostat)  [5, 27, E. coli growing on glucose. 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.The good agreement between simulated and experimental validation data Figs –6 indicaS1 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 Model(ZIP)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.S2 Dataset(XLSX)Click here for additional data file.S1 Code(ZIP)Click here for additional data file.


Plants produce a diverse repertoire of specialized metabolites that have multiple roles throughout their life cycle. Some of these metabolites are essential components of the aroma and flavor of flowers and fruits. Unfortunately, attempts to increase the yield and prolong the shelf life of crops have generally been associated with reduced levels of volatile specialized metabolites and hence with decreased aroma and flavor. Thus, there is a need for the development of new varieties that will retain their desired traits while gaining enhanced scent and flavor. Metabolic engineering holds great promise as a tool for improving the profile of emitted volatiles of domesticated crops. This mini review discusses recent attempts to utilize metabolic engineering of the phenylpropanoid and its primary precursor pathway to enhance the aroma and flavor of flowers and fruits. These volatile metabolites can attract pollinators and seed dispersals, stimulate or suppress signaling cascades and protect against harsh environmental conditions, herbivores or pathogens [Plants synthesize a diverse repertoire of specialized metabolites that play important roles in various aspects of plant life such as growth, development, reproduction, defense and environmental responses ,2. Volatathogens ,4,5. Theathogens ,7,8. Theathogens ,9,10.Throughout the years, crop-breeding schemes were largely focused on attaining traits such as rapid growth, high yields and long post-harvesting shelf life. Less attention was devoted to maintaining or improving desirable aroma and flavor, partially because growers are generally paid for yield rather then for taste or aroma. Furthermore, aroma and flavor are complex traits controlled by multiple genes, and unlike yield or shelf life, are difficult to screen for. Hence, modern breeding schemes often lead to domesticated crops that produce less volatile metabolites, resulting in the loss of some of their desired aroma and flavor ,9. In reArabidopsis Production Of Anthocyanin Pigment1 (PAP1) transcription factor into rose flowers resulted in increased levels of phenylpropanoid-derived color and scent compounds, as well as higher levels of terpenoid scent compounds. Olfactory assays revealed that the enhanced levels of scent volatiles could be identified by both bees and humans [Several attempts have been made to improve the aroma and flavor of flowers and fruits by metabolic engineering of AAA-derived pathways . One d humans . Howeverd humans ,18,19 ord humans . Furtherd humans .A different approach, taken by our lab in the last few years, is to augment the amount of the primary metabolites—the AAAs—in an effort to increase the carbon flux towards the metabolic synthesis of specialized metabolites. This approach can be used to study the regulatory interactions between pathways of primary and specialized metabolism associated with the AAAs, to identify bottlenecks in the biosynthetic pathways and to look at cross regulation between different pathway branches. Moreover, elucidation of the secondary metabolites produced by various plant species, particularly crop plants, is hindered by the fact that many of these metabolites accumulate to very low steady state levels that are below detection threshold. Enhancing the production, and hence the levels of AAA-derived specialized metabolites in transgenic lines, can serve as a valuable tool for the detection of these low abundance compounds.Arabidopsis thaliana [AroG gene (AroG*), encoding a feedback-insensitive DAHPS , condenses these two products to form 3-deoxy-di-arabino-heptulosonate 7-phosphate (DAHP). DAHP is then directed via the shikimate and the AAAs biosynthesis pathways to form AAA, with chorismate serving as an intermediate metabolite . Thus, tthaliana ,23, and thaliana  and a tothaliana ,26. Recethaliana . One hetve DAHPS . As DAHPolecules . These colecules . Similarabolites . The traabolites . In a grenhanced . Taken tPheA gene (PheA*), encoding a feedback-insensitive bi-functional chorismate mutase/prephenate dehydratase gene. The PheA gene converts chorismate via prephentate into phenylpyruvate, directing the carbon flux to the phenylpyruvate branch of the AAAs pathway leading to phenylalanine synthesis (PheA* was expected to direct the flux more specifically towards phenylalanine and the specialized metabolites that are derived from it [PheA* gene in Arabidopsis seedlings resulted in increased levels of phenylalanine [PheA* overexpressing lines had increased levels of several specialized metabolites derived from phenylalanine and tyrosine, but reduced levels of several specialized metabolites derived from tryptophan. Though the mechanistic reason for these changes is still unclear, these results imply regulatory cross-interactions between the flux of AAA biosynthesis from chorismate and their metabolism into various specialized metabolites [PheA* in tomato fruits led only to minor changes in the levels of the AAAs or any of the identified volatile metabolites [AroG* and the PheA* genes had a metabolic profile that was predominantly impacted by the expression of the AroG* gene. However, the aroma attributes of the tomato fruits expressing both genes were unique and different from those of the lines expressing a single gene, suggesting a contribution of the PheA* gene to the overall metabolic profile [Arabidopsis transcription factor AtMYB12 in tomato fruits [Arabidopsis. Expression of AtMYB12 in tomato fruits induced the expression of genes encoding enzymes of flavonol and hydroxycinnamic ester biosynthesis, resulting in exceptionally high levels of phenylpropanoids, specifically flavonols and caffeoyl qunic acids [A second bacterial gene heterologously expressed in transgenic plants is a mutated ynthesis . Thus, e from it . Expresslalanine . Intriguabolites . In contabolites , and tom profile . Taken to fruits . AtMYB12ic acids . Interesic acids . It was In conclusion, the use of metabolic engineering can provide new information on the regulation of the channeling of primary metabolism to specialized metabolism, the cross-interaction of different metabolic pathways and the repertoire of specialized secondary metabolites in different plant species. In turn, gaining information on the various secondary metabolites and the metabolic pathways involving their production can contribute to the development of knowledge-based breeding strategies. Furthermore, metabolic engineering opens a new venue to stimulate the production of specialized metabolites and to enhance the quality of various plant organs. In particular, it can overcome the general negative correlation between flavor and aroma and long shelf life in different crops, an issue that has so far been difficult to solve by classical breeding.


Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes. As one of the key inventions in evolution, oxygenic photosynthesis has transformed life on Earth and dominates the Earth’s primary productivity today . Electrons, derived from water, are provided to the electron transport chain (ETC) and molecular oxygen is released as the byproduct of photosynthesis.Survival and growth of (most) cyanobacteria begins with the absorption of photons facilitated by large light-harvesting antennae, the phycobilisomes, and chlorophyll The ETC consists of a number of large protein complexes, mostly located in the thylakoid membrane. Electrons are transferred along the ETC, ultimately resulting in the regeneration of adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) as energy carrier and reductant, respectively. The functioning of the photosystems and the ETC are complex biophysical processes and objects of intense research. The respective processes are characterized by fast time scales and transitions between a large number of possible states. While a number of detailed computational models of these processes are available, often with a focus on photosystem II, the respective models typically do not describe regeneration of ATP and NADPH, and hence are not straightforwardly connected to other cellular functions.2). Cyanobacteria possess mechanisms to concentrate inorganic carbon in the vicinity of the CO2-fixing enzyme, the ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), making use of bacterial microcompartments known as carboxysomes. Compared to the light reactions, the relevant time scales of the so-called dark reactions are significantly slower. Modeling of CO2-concentrating mechanisms (CCMs) typically involves consideration of diffusion and spatial structure.The ATP and NADPH regenerated by the photosynthetic light reactions play a crucial role for almost all other cellular processes. Beyond their role as energy donor and reductant, they also serve as important signaling compounds to convey information about the intracellular state. Regenerated ATP and NADPH are utilized to assimilate atmospheric carbon dioxide .While current kinetic models of metabolism rarely involve more than a few dozen compounds, cellular metabolism is increasingly analyzed using large-scale metabolic reconstructions and constraint-based computational methods. Metabolic network reconstructions are based upon the predicted gene content deduced from genomic DNA and aim to provide an unbiased and comprehensive account of all interconversions of small molecules inside a single cell or a compartment. Unlike kinetic models, metabolic reconstructions only make use of the stoichiometric properties of the respective interconversions, and manually curated reconstructions have been reported for a number of cyanobacteria , Photosystem I (PSI), and ATP synthase (ATPase). See Figure In most cyanobacteria, light harvesting is facilitated by large antenna complexes, the phycobilisomes. Phycobilisomes are attached to the cytoplasmic surface of the thylakoid membrane  or cytochrome-c (cyt-c). At PSI, electrons are transferred to ferredoxin and eventually to NAPDH using light-induced excitation of the PSI reaction center . Alternatively, electrons from the excited PSI state can be transferred back to PQ and Cytb6f , details of CET are still under debate and insufficiently understood. Photosynthetic electron flow results in a protein gradient across the thylakoid membrane that drives regeneration of ATP by the ATPase.PSII splits water and reduces the plastoquinone (PQ) pool. The latter mediates the transport of electrons from PSII to Cytb6f complex, and PC (or cyt-c) as soluble electron carrier are involved in respiratory as well as photosynthetic electron transport. While photosynthesis exclusively takes place in the thylakoid membrane, a rudimentary respiratory chain is also present in the plasma membrane  curve of cyanobacteria and microalgae. Early models were derived by Crill , Megard Beyond overall bulk models of photosynthesis, there is a significant history of biophysical models to understand oxygen evolution and chlorophyll fluorescence transients, often with a focus on PSII, as well as to understand specific properties, such as energy distribution in the photosynthetic apparatus , assimilation of CO2 by RuBisCO, and the subsequent synthesis of cellular building blocks mediated by a network of metabolic reactions. Computational concepts used to describe cyanobacterial metabolism have been discussed previously  genome of Synechococcus elongatus PCC 7942 may correspond to a shift of metabolic regulatory strategies from transcriptional control in latter toward post-transcriptional control in the former . Rate equations are derived from enzyme kinetic mechanisms, and implemented using  non-linear Michaelis–Menten type functions. The rate equations consider allosteric regulations, as well as other post-translational mechanisms, as far as such interactions are known. The light reactions are typically highly simplified. In the model of Pettersson and Ryde-Pettersson  and its +) as dynamic variables that allow coupling to the ETC. In this respect, first steps have been made for plant metabolism. The models of Laisk et al.  and redox state , as signaling compounds  to facilitate the uptake and acquisition of inorganic carbon. CCMs allow cyanobacteria to raise the local concentration of CO2 in the vicinity of the carboxylating enzyme RuBisCO, and thereby overcome the comparatively low affinity of RuBisCO for CO2 and depress the competitive oxygenation reaction (photorespiration). Cyanobacterial CCMs typically make use of dedicated microcompartments, the carboxysomes, that separate the assimilation of CO2 by RuBisCO from the rest of the cell. The CCMs of cyanobacteria relies on a number of components. The respective mechanisms are reasonably well understood  and is computationally feasible also for networks consisting of several thousands of reactions. The strength of stoichiometric models and constraint-based analysis are questions such as the following: What is the maximal growth yield for a given light or carbon input? Which set of enzymes is essential for the synthesis of certain biomass components? How many distinct biochemical paths exist for the synthesis of certain biomass components and how do these pathways differ with respect to cellular energy expenditure and cofactor utilization? Due to the specific computational methodology, however, a direct integration of large-scale stoichiometric models into kinetic models of metabolism remains challenging. Various extensions toward incorporating dynamics have been proposed .In addition to the biochemistry of metabolism, phototrophic growth is highly dependent on regulatory networks to coordinate growth and to relay environmental information. To this end, of particular relevance is the cyanobacterial circadian clock—discovered in the late 1980s and unique among prokaryotes  and Table in vivo) for selected models of the cyanobacterial circadian clock.The KaiC phosphorylation rhythm has widely been studied in systems biology, and hence, a variety of mathematical models have been put forward. The first models were rather minimal to explain how sustained oscillations in phosphorylation of KaiC occur, including no intermediate steps of phosphorylation, introducing feedbacks on KaiC phosphorylation or assuming hypothetical states of KaiA, KaiB, and KaiC  to the 24-h period of the environment and to transmit temporal information to downstream processes resulting in circadian rhythms in cellular physiology. Clock input cues involve the cellular ATP/ADP ratio, which has a direct effect on the core clock by modulating the KaiCs kinase activity  and RpaA (regulator of phycobilisome association A)  and therefore require careful balance between different electron transport pathways and alternative electron “valves”. CCMs use energy and have implications for the efficiency of carbon assimilation. The cellular ATP/ADP ratio and the oxidized PQ pool relays information to the circadian clock, which affects transcriptional output and hence metabolic activity. Metabolism itself depends on cellular energy and redox potential—and must be appropriately coordinated to synthesize the right metabolites at the right time. The availability of ribosomes and amino acids, which are itself products of metabolism, affects the rates of translation of new proteins—which must be coordinated to account for damage, stability, and turnover times of proteins. In particular, the components of PSII complexes themselves are dependent on environmental conditions due to photodamage caused by ROS  periods of darkness (Hood et al., Mycoplasma genitalium—based on a subdivision of cell functionality into modules (Karr et al., Overall, there is increasing interest in whole-cell models to understand cellular trade-offs and functions in the context of a living cells. The construction of integrative computational models to predict phenotype from genotype has gained momentum with a first whole-cell model of the life cycle of the human pathogen Distinct from other approaches to whole-cell models, however, we argue that it is unlikely that a single universal model—a model that spans all scales from intracellular to intercellular to properties of ecosystems—will fulfill all requirements needed to describe cellular growth. Rather, we envision a modular approach. Depending on the research question, different temporal and spatial scales must be considered. The task is then to derive an appropriate representation of cellular processes that accounts for the spatial and temporal scales involved. The derived submodels should be consistent with more fine-grained representation and their construction must be informed by knowledge how processes interact and which trade-offs are relevant for a specific research question. We note that, besides the biological challenges, such a strategy also entails major computational challenges. As yet, the annotation of computational models is often poor. That is, the biochemical identity of model variables is not defined in a computer-readable format, and hence, merging of models typically requires extensive manual curation (Krause et al., e-cyanobacterium.org (Klement et al., Notwithstanding these challenges in computational methodology, we expect a growing library of models related to cyanobacterial growth that can inform and guide research how phototrophic organisms, such as cyanobacteria, adapt to complex environments. These models must increasingly adhere to common standards and should be made available on open platforms, such as the BioModels database (Li et al., SW and RS conducted the research and wrote the article. Both authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite,  Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Our results support and extend the available experimental evidence on the essential genes and nutritional requirements of this organism. Besides, we identify metabolites that give rise to thermodynamic bottlenecks and suggest substrate channeling. Overall, these results provide novel insight into the metabolism of P. falciparum and may guide experimental studies to develop a better characterization of the parasite metabolism and the identification of antimalarial drug targets.Almost half of the world population is at risk of infection by malaria parasites. The rise in drug-resistant parasites requires better understanding and targeting of their metabolism. In this study, we present a genome-scale metabolic reconstruction (iPfa) of the deadliest malaria parasite,  Plasmodium species capable of infecting humans, P. falciparum is responsible for most malaria-related deaths. The current increase in parasites with resistance to most of the clinically used antimalarial drugs, including artemisinin, renders the treatment of this disease more challenging [Malaria remains a major global health care concern, with almost half of the world population at risk of infection that ultimately results in over half a million deaths each year . Of the llenging . The devllenging , 3) thatP. falciparum, iTH366 [P. falciparum metabolism that updates, among other important features, the functional annotation of the genome, the localization of the enzymes and the definition of the available substrates according to the currently existing data.Genome-scale metabolic models (GEMs) represent an invaluable platform for the integrative analysis of cell metabolism . Current, iTH366  and Plas, iTH366  or in th, iTH366 . HoweverThe standard approach for analyzing different phenotypes using GEMs is flux balance analysis (FBA) , 10. FBAThermodynamic bottlenecks arise when alternative metabolic pathways are thermodynamically impeded although, based on network topology, they could have theoretically served a metabolic function. Unfavorable thermodynamics imposed by bulk-phase metabolite concentrations can be circumvented with substrate channeling. Substrate channeling involves the coupling of two or more reactions, and the common intermediate is transferred from the first enzyme to the second without escaping into the bulk phase. Such process has a major effect on the thermodynamics and kinetics of the involved catalytic functions and might determine specific responses to regulatory mechanisms. Techniques such as isotope dilution or enrichment, competing reaction or enzyme buffering have been traditionally used to detect and characterize substrate channeling for an enzyme pair or larger metabolon . Such meP. falciparum that can reveal potential targets in its metabolism for efficient intervention. We also seek to identify metabolites whose intracellular concentrations give rise to thermodynamic bottlenecks and pathways where substrate channeling may exist. These analyses can guide metabolomics and biochemical studies on the metabolism of the parasite. For this purpose, we developed a GEM of P. falciparum (iPfa) and performed thermodynamically consistent studies using TFA and integrating the metabolite concentration ranges previously measured in intraerythrocytic P. falciparum [P. falciparum.The primary goal of this study is to provide new insight into the essential metabolic capabilities and the nutritional requirements of lciparum –22. We pP. falciparum metabolism to reconstruct iPfa. A detailed description of the steps followed for the reconstruction process is provided in the We combined semi-automated approaches with a manual curation process based on the available literature on Plasmodium species  to the parasite’s cytosol, and 155 transports between intracellular compartments . Due to tability . These an orphan .The metabolic enzymes included in iPfa are distributed in six classes according to the E.C. identifiers. The majority of the metabolic enzymes in iPfa are transferases (43%), followed by oxidoreductases (24%), ligases (12%), hydrolases (12%), lyases (7%) and isomerases (2%) . RegardiP. falciparum metabolism. For example, based on recent findings [Toxoplasma gondii, ToxoNet1 [P. falciparum, iPfa is capable of growing on an in silico rich medium composed of 236 substrates that allow less restrictive conditions in the analyses. iPfa includes 245 common genes with iTH366 and 80 additional genes. Regarding metabolic tasks, iPfa accounts for 73 biomass building blocks of which 40 are common with iTH366 [P. falciparum. A detailed comparison of iPfa with the previous reconstructions iTH366 [The GEM iPfa presents an unbiased bottom-up reconstruction and an updated database of findings , the pyrfindings , PlasmoNfindings  and theifindings , 8 .We used iPfa to study single and double gene essentiality n silico . To undeP. falciparum, whereas the remaining 24 genes have not been examined empirically, to our knowledge. We grouped the 24 non-validated genes in two classes: the first class is composed of 14 genes that participate in metabolic pathways where other genes have been experimentally defined as essential, and the second class contains 10 genes that are associated with functions or pathways where other genes have not been reported as indispensable. Therefore, we could not hypothesize the latter class of genes to be essential based on previous context-dependent information. For example, the MEP/DOXP pathway in the apicoplast is essential in P. falciparum for the production of isopentenyl diphosphate and isoprenoid derivatives [We first performed essentiality studies without accounting for thermodynamic constraints and identified 55 genes that are essential for growth . The genivatives . There aivatives –33, whilrG’) and allows the integration of metabolomics data . We applied two different approaches within the TFA framework. In the first approach, we allowed the concentration of every intracellular metabolite to vary between 1 μM and 50 mM. Besides, the concentration of extracellular metabolites  varied between 0.01 μM and 100 mM. Such concentration limits are similar to the physiological ranges used in previous TFA studies [Next, we performed essentiality analysis using TFA, which includes information about the Gibbs free energy of reaction  with antimalarial inhibitors . DespiteP. falciparum trophozoites [In the second approach within the TFA framework, we integrated ten available metabolomics data sets, i.e., absolute concentrations measured in mature hozoites –22  were allowed to vary from 0.1 fM to 500 mM . While increasing the upper limit of the concentration range did not impact the essentiality of the three genes, decreasing the lower bound was critical for their essential function .KM, provides a measure of the substrate concentration required for effective catalysis to occur. The KM of the enzymes usually varies between 10−1 and 10−7 M. The KM values found in the literature indicate that metabolite concentrations of 1 fM are not physiologically relevant, which further supports the essential function of the P. falciparum genes predicted with TFA and generic concentration ranges.When the intracellular concentrations were decreased and allowed to vary between 0.01 μM and 50 mM, the gene encoding a serine palmitoyl transferase (E.C. 2.3.1.50) was incorrectly predicted as non-essential . The esscell / mol/Lcell based on the absolute minimum and maximum experimentally measured concentrations [We next sought the bottleneck metabolites and the associated reactions in iPfa whose directionalities were constrained and led to the essential function of the genes . The analysis of bottleneck metabolites suggests that AMP and ATP constitute a single minimal set of metabolites that is responsible for rendering three genes essential . The thrtrations –22  from ethanolamine through the Kennedy pathway . The botP. falciparum involved in the Kennedy pathway are expressed in the blood stages [Plasmodium berghei [TFA with metabolomics data rendered the Kennedy pathway thermodynamically infeasible. However, the genes of d stages , and evi berghei , which f berghei . When weP. falciparum, depend on the availability of a broad array of metabolites provided by the host cell and its surroundings. To characterize the nutritional requirements of P. falciparum, we followed and extended the approach previously applied to the related pathogen T. gondii to study the in silico minimal medium (IMM) [Obligate intracellular parasites, such as um (IMM) .in silico rich medium composed of 236 substrates . This result demonstrated that the non-appearance of two substrates in the same IMM is a necessary but not sufficient condition to define the substrate substitutability.backbone moieties) among the non-constitutive substrates, which allowed us to cluster the 36 non-constitutive substrates of the IMM  enzymes [Orotate is an intermediate metabolite in the canonical and highly conserved biosynthetic pathway of UMP, a common precursor of pyrimidine nucleotides. TFA of iPfa suggested that the biosynthetic pathway of pyrimidines is thermodynamically infeasible and hence the orotate backbone moiety, provided by orotate and (S)-dihydroorotate, needs to be taken up. However, the biosynthetic pathway of UMP, which involves six enzymes, is known to be functional  and esseimidines , 52. We  enzymes . Previou enzymes  and comp enzymes  studies  enzymes . The pre enzymes .P. falciparum, we evaluated single gene essentiality in iPfa for each of the alternative IMMs (in silico rich medium (less restrictive condition)   remainedndition) , and thendition) .P. falciparum and performed thermodynamics-based flux analysis to study the essential metabolic capabilities and the nutritional requirements of the parasite. Besides, we identified thermodynamic bottlenecks and hypothesized substrate channeling in the metabolic pathways. The results from this work, which have been compared with available experimental evidence, demonstrate that iPfa and its TFA can be used to fill in the knowledge gaps regarding the critical aspects of P. falciparum metabolism.In this work, we reconstructed a GEM for The highest accuracy score (59%) in the prediction of essential genes for growth was obtained using TFA with metabolomics data. Such approach revealed up to 63 lethal genes and 26 synthetic lethal pairs in iPfa . ExistinP. falciparum.The analysis of metabolic tasks identifies the effect of the gene knockouts on the parasite’s growth and suggests that the disruption of the 63 essential genes impair the production of up to 30 biomass precursors . When thP. falciparum a thermodynamic dependency between the production of phospholipids, phosphorylated nucleotides, and sugar nucleotides through the concentrations of the bottleneck metabolites. The knowledge of metabolic subsystems that are thermodynamically dependent can be used to design drugs that synergistically target these parts of the metabolism and prevent their metabolic regulation and rise of drug-resistant parasites.We identify eighteen bottleneck metabolites in iPfa, whose intracellular concentrations determine the directionality of a set of reactions and render eight genes essential . The botP. falciparum [P. falciparum requires the experimental characterization of the enzymatic mechanisms using techniques such as isotope dilution [Through the analysis of thermodynamic bottlenecks, we identify two thermodynamically infeasible pathways, i.e. the Kennedy and the pyrimidine biosynthesis pathway, that are functional in lciparum . The prelciparum , 54, 56.lciparum , 16. Thedilution .P. falciparum. The IMM analysis suggests that P. falciparum requires at least 23 substrates for growth and 13 of them are indispensable . These constraints in iPfa accounted for the intracellular conditions, like the pH, the membrane potential and the intracellular concentration ranges of the metabolites  , 61, 62,abolites . TFA furP. falciparum trophozoite-infected red blood cells [We created a thermodynamically curated version of iPfa where we allowed the concentration of every intracellular metabolite to vary between 1 μM and 50 mM, which is the physiological range used in similar TFA studies , 13. Theod cells  and one od cells . The remod cells . The phyod cells –22, in wod cells –22.cell. The concentration ranges are integrated within TFA and constrain the allowable ΔrG’ range of the reactions in which the metabolites participate and with it the flux ranges of the neighboring reactions. We integrated the metabolomics data sets one-by-one and simultaneously to study the number of bidirectional reactions and the predictions of essential genes. The essential genes varied among data sets, and the data set measured with LC-MS [We translated the reported values and measurement errors into ranges of concentration expressed in mol/Lth LC-MS  and the in silico growth reducing phenotypes. No additional filtering was applied to identify essential genes and pairs of genes.This analysis allowed the identification of the metabolic tasks or biomass building blocks that could not be performed or produced, respectively, upon knockout of the essential genes. The analysis involved two steps: first, the essential genes for biomass growth were identified following standard procedures . Second,P. falciparum (in vitro) available in the literature. Information for other Plasmodium spp. was also used for comparison. An accuracy score was calculated as (TP+TN)/(TP+TN+FP+FN), where TP (true positive) and TN (true negative) define predictions that correctly simulate growth and non-growth, respectively, based on the available experimental data. While FP  and FN  describe predictions that incorrectly simulate growth and non-growth, respectively. The accuracy score served to evaluate the prediction of essential genes. For the predictions with TFA and metabolomics data integrated, an accuracy score of 59% is obtained based on the data available for P. falciparum in the literature (references reported in P. berghei in the blood stages (PlasmoGEM data) [The genes predicted as essential in iPfa with FBA and TFA were compared with experimental information available in the literature. We used primarily experimental data for EM data) , 58. Thesimilarity between the essential enzymes in iPfa and the human proteome when there exists at least one matching protein in non-redundant human proteome (txid9606) with the standard protein BLAST settings in the bioinformatics software blastp version 2.6.0+ [We further tested whether the enzymes predicted as essential in iPfa are similar to any human protein. We define n 2.6.0+ : cutoff n 2.6.0+ .ΔrG’ and tested the gene essentiality of the three genes. These are the three genes that were further identified as essential with TFA using the generic concentration ranges (1 μM—50 mM) with reference to the FBA prediction (We reduced the lower bound ten orders of magnitudes (minimum intracellular concentration of 0.1 fM) and increased the upper bound ten fold (maximum intracellular concentration of 500 mM). We then recalculated the feasible range of ediction . The minThis study identified the metabolites responsible for the thermodynamic bottlenecks, which determined the directionality of a set of reactions and allowed the identification of eight genes as essential . iPfa waΔrG’) is a measure of its thermodynamic potential and based on the second law of thermodynamics, defines the thermodynamically feasible direction under which the reaction can operate, defined as reaction directionality [ΔrG’ accounts for the standard Gibbs free energy of a reaction (ΔrG0’), which was calculated using the group contribution method (GCM) [ΔrG0’ and the metabolite concentrations is expressed within TFA in the form of ranges that ultimately define the feasible range of ΔrG’ associated with each reaction. The range of ΔrG’ was known with Thermodynamic Variability Analysis (TVA) [ΔrG’ is negative, then we define the reaction to be unidirectional in the forward direction. When the range of ΔrG’ is positive and negative, then we define the reaction as bidirectional.The Gibbs free energy of a reaction  , 62, andis (TVA) , which fis (TVA) . We appl1 and E2) was simulated within the TFA framework. The reactions R1 and R2 catalyzed by E1 and E2 were lumped to define an overall reaction (L = R1+R2). The overall reaction eliminates the common intermediates and recycled metabolites since they are produced in the first reaction R1 and consumed in the second reaction R2 and vice versa, respectively. For example, if R1 involves the transformation A + B → I + C and R2 is defined as I + D → P + B, the final lumped or overall reaction L is defined as A + D → P + C. The overall reaction L does not involve the intermediate metabolite I and the recycled metabolite B. It is important to note that reactions R1 and R2 should be first defined in the direction that satisfies mass balances and allows flux through the pathway of study. Then the lumped reaction can be formed. For a pathway of study that contains reactions R1 and R2 the starting and product metabolites are usually A and P, respectively. Then, the reactions R1 and R2 should be defined as A + B → I + C and I + D → P + B. Other definitions, such as A + B → I + C and P + B → I + D, do not satisfy mass balances. In this study, the reactions of the pyrimidine biosynthesis pathway and the Kennedy pathway were defined to allow production of UMP and PE, respectively. The overall reaction was then allowed to be bidirectional. Thermodynamic properties were calculated for the overall reactions and TFA and Thermodynamic Variability Analysis (TVA) [ΔrG’ obtained from TVA.The presence of substrate channeling between two enzymes (Eis (TVA)  were perThe IMM analysis was performed following the strategy defined before . Here, Tin silico medium of 52 metabolites identified in the IMM. Second, we tested that under such conditions, the inclusion of each substrate of the group individually allowed simulated growth in iPfa. This analysis was also performed in the rich medium of 236 substrates to validate the groups of substrates that contained the three backbone moieties .This study was performed on the substrates of iPfa to validate their ability to substitute for each other and support growth. Groups of substrates were created based on the IMM analysis. Substrates were grouped if they never appeared in the same IMM and presented a common molecular substructure or backbone moiety. We validated these groups in two steps. First, we tested that iPfa could not grow when all substrates in a group were removed from the The tools and methods used are described in more detail in S1 Methods(PDF)Click here for additional data file.S1 Table1Gap-filling reactions are defined with their R-5 digit identifier as obtained from the KEGG database. Transport reactions are marked with T_. Cellular compartments are defined with the reaction name: _r, endoplasmic reticulum; _c, cytosol; _m, mitochondrion; _a, apicoplast. Note that the same gap-filling reaction can be suggested for more than one metabolic task.(XLSX)Click here for additional data file.S2 Table1Genes predicted as essential for iPfa growth in the in silico rich medium  essential; (gr) growth reducing; (ne) non-essential. 3Impact of the gene knockout on the metabolic tasks of iPfa is explainable based on the annotated function of the gene. 4This gene is involved in an essential pathway and is thus presumed to be essential. 5None of the reactions associated with this gene is single essential; the gene is essential when two or more associated reactions are knocked out. 6We define similarity between the essential enzymes in iPfa and the human proteome when there exists at least one matching protein in non-redundant human proteome (txid9606) with the standard protein BLAST settings in the bioinformatics software blastp version 2.6.0+ [n 2.6.0+ : cutoff n 2.6.0+ .(XLSX)Click here for additional data file.S3 Table1Pairs of genes predicted as essential for iPfa growth with TFA and the MS data  in the in silico rich medium Click here for additional data file.S4 Table1Reaction I.D. in iPfa. Cellular compartments are defined after “_” in the reaction I.D.: (_c) cytosol, (_m) mitochondrion, (_r) endoplasmic reticulum, (_e) extracellular. Transport reactions are marked with T_. 2Values of the standard Gibbs free energy of reaction (ΔG0') calculated with the group contribution method (GCM), as explained in the ΔG0' does not determine the reaction directionality. 3Reaction directionalities determined with Thermodynamic Variability Analysis (TVA). 4The reactions R02057_r, R01468_r, R02038_r, and T_c_to_r_C00189 are unidirectional (forwards) using TVA and generic concentration ranges, or blocked using TVA and metabolomics data (see data see .(XLSX)Click here for additional data file.S5 Table1Genes predicted as essential for iPfa growth with TFA and the MS data  in the (XLSX)Click here for additional data file.S6 Table1Gene IDs were obtained from PlasmoDB. 2K-ID or Orthology groups were obtained from the KEGG database. 3The E-value denotes the goodness of the annotation to the assigned metabolic function. The annotation process is described in detail in (XLSX)Click here for additional data file.S7 Table1Localizations from the ApiLoc database. Intracellular compartments are defined by letters, such as c, cytosol; m, mitochondrion; a, apicoplast; er, endoplasmic reticulum; fv, food vacuole; e, extracellular space/parasitophorous vacuole; n, nucleus; ag, Golgi apparatus.(XLSX)Click here for additional data file.S1 DatasetΔrG’ was calculated in both approaches. The reactions in iPfa were ranked based on two ranking criteria: the number of reactions impacted by each metabolite (sheet S1 A and sheet S1 C in S1 Dataset), and the global RoU (sheet S1 B and sheet S1 D in S1 Dataset) as defined in the ΔrG’ is higher than 0.1%.The concentration range of each metabolite was integrated separately in iPfa following two approaches. In the first approach, the concentration of the metabolite was integrated at-a-time in all compartments of iPfa where the metabolite appears (sheet S1 A and sheet S1 B in S1 Dataset). In the second approach, the concentration of the metabolite was integrated separately in each compartment of iPfa where the metabolite appears (sheet S1 C and sheet S1 D in S1 Dataset). The RoU in the (XLSX)Click here for additional data file.