Chlamydomonas reinhardtii cultured in the presence of acetate perform mixotrophic growth, involving both photosynthesis and organic carbon assimilation. Under such conditions, cells exhibit a reduced capacity for photosynthesis but a higher growth rate, compared to phototrophic cultures. Better understanding of the down regulation of photosynthesis would enable more efficient conversion of carbon into valuable products like biofuels. In this study, Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) have been used with a genome scale model of C. reinhardtii to examine changes in intracellular flux distribution in order to explain their changing physiology. Additionally, a reaction essentiality analysis was performed to identify which reaction subsets are essential for a given growth condition. Our results suggest that exogenous acetate feeds into a modified tricarboxylic acid (TCA) cycle, which bypasses the CO2 evolution steps, explaining increases in biomass, consistent with experimental data. In addition, reactions of the oxidative pentose phosphate and glycolysis pathways, inactive under phototrophic conditions, show substantial flux under mixotrophic conditions. Importantly, acetate addition leads to an increased flux through cyclic electron flow (CEF), but results in a repression of CO2 fixation via Rubisco, explaining the down regulation of photosynthesis. However, although CEF enhances growth on acetate, it is not essential—impairment of CEF results in alternative metabolic pathways being increased. We have demonstrated how the reactions of photosynthesis interconnect with carbon metabolism on a global scale, and how systems approaches play a viable tool in understanding complex relationships at the scale of the organism.Cells of the green alga  Chlamydomonas reinhardtii has been widely used as a model organism to study photosynthesis  of this organism were presented and used for prediction of metabolic fluxes in different growth conditions  and Flux Variability Analysis (FVA) for the prediction of metabolite flux through a metabolic network reconstruction  were subtracted from maximal fluxes under phototrophic conditions (b). Conversely, minimal fluxes under phototrophic conditions (c) were subtracted from maximal fluxes of mixotrophic conditions (d).FVA is an FBA-based method for characterizing all feasible states of genome-scale metabolic models that satisfy a set of given constraints and objective function  environment, used in conjunction with Gurobi optimizer solver (version 5.6) employing a linear programming based optimization algorithm , flux substitutable (where the range of possible fluxes span zero) and flux blocked (reactions with minimum and maximum flux of zero). This method uses FVA results to determine whether a flux is always required for optimal biomass production, is potentially used for optimal biomass production or is never used for optimal biomass production.To determine which reactions and metabolic pathways are active or dormant in a particular condition, a reaction essentiality analysis was performed from the non-overlapping reactions derived from FVA  were used in this study. Cells were cultured in 250 ml conical flasks with 200 ml medium, which were placed in a shaker at 180 rpm and illuminated on a 16 h light/8 h dark cycle with light provided by warm-white LED lamps (color temperature 3000–3200 K), at 140 μmol m−2 s−1, 22°/16°C day/night. Mixotrophic cultures were grown in a Tris-Acetate-Phosphate (TAP), with added potassium phosphate (pH 7.0) and Hutner trace elements  was measured using an Ocean Optics USB4000 spectrophotometer . Specific growth rates (μ) were derived from cells in an exponential phase of growth, from two time points  with associated OD readings used in the equation below.For inoculation, 250 μl phototrophic cultures in exponential growth phase were axenically transferred to 250 ml conical flasks containing 200 ml of the appropriate medium. Following introduction of algae, cell growth was monitored at intervals for 12 days, until a stationary or death phase was reached. The optical density of cultures at 680 nm . Intact C. reinhardtii cells in culture medium were placed into the chamber, maintained at 20°C and continuously stirred. Saturating light was provided by a Led Engin LZ4 warm white LED  driven by a laboratory built constant current power supply. Rates of photosynthesis were normalized to cell number for each sample. Oxygen evolution was determined every 24 h from Day 4 until cells reached a stationary phase, expressed as oxygen evolution on a cellular basis.Measurements of O2 evolution steps of the TCA cycle are bypassed, resulting in retention of organic carbon by the cell. Model simulations are consistent with published experimental data, which have shown that ICL activity is essential for efficient mixotrophic growth  whilst mixotrophic growth was simulated using the phototrophic model as a base model and allowing the uptake of acetate at an experimentally defined rate Hoober, . All othIn phototrophic simulations, pyruvate, originating from carbon fixation is converted into acetyl-CoA and fed into the TCA cycle. ICL activity was redundant and the model chose to export isocitrate to the cytosol in exchange for malate. Cytosolic isocitrate was ultimately converted into biomass and the newly imported malate was converted into oxaloacetate and exported into the cytosol which fed back into pyruvate metabolism.In addition to increasing fluxes through enzymes involved in the TCA cycle and localized in the mitochondria, acetate also increased fluxes associated with the glycolytic pathway. Oxaloacetate, generated in TCA cycle reactions is converted into glycerate 2-phosphate (2PG) and exported into the cytosol, where a flux partition occurs, resulting in the majority of flux continuing through gluconeogenesis leading to the formation of both starch and sucrose, accumulating within biomass. Both starch and sucrose are the main products of photosynthesis , the primary reaction of photosynthetic carbon fixation. The reactions of gluconeogenesis in the mitochondrion are otherwise inactive. The predominant flux from 3PG was branched toward the Calvin-Benson cycle in the chloroplast. A further branching of flux toward erythrose 4-phosphate (E-4P) and sedoheptulose 7-phosphate (S7P) and their immediate removal from gluconeogenesis toward carbon fixation is observed. The resulting flux toward glucose 1-phosphate (G1P) production was reduced due to branching in the network and when compared to mixotrophic simulations, was reduced by a factor of 2.4, and as such, only starch was accumulated within biomass.Phototrophic metabolism Figure  on the o2 into organic compounds. In mixotrophic simulations  and fructose 6-phosphate (F6P) via the reversible action of the enzymes ribulose 5-phosphate epimerase (RPEh) and transketolase (TKT2h) respectively. It is with these reactions that the OPPP combines with the Calvin-Benson cycle and gluconeogenesis and ultimately demonstrates the interconnectivity of photosynthesis with central carbon metabolism.In addition to altering fluxes in central carbon metabolism, acetate is also predicted to modulate fluxes in photosynthesis and the Calvin-Benson cycle. The Calvin-Benson cycle uses the products of photosynthetic electron transport, ATP and NADPH, to convert COs Figure  the Calvb6f from ferredoxin. As a result, ATP production resulting from CEF increased without production of NADPH, as described previously. The activity of CEF was otherwise non-evident with phototrophic simulations  and 1546 (70%) respectively]. Flux essential reactions for phototrophic growth and mixotrophic growth comprised the lowest subset of reactions with 16 essential reactions in each condition, representing less than 1% of reactions, whilst 626 reactions (28.6%) were blocked for phototrophic simulations verses 629 (28.7%) for mixotrophic growth.2 transport into the cytosol (CO2t), the conversion of ribulose 5-phosphate and Xu5P via ribulose-5-Phosphate 3-Epimerase (RPEh), cytochrome b6/f complex (CBFC), ferredoxin-NADP+ reductase (FNORh) and the two photosystems (PSI and PSII). Here, it seems that the activity of CSm, allowing for the entry of acetyl-CoA into the TCA cycle, and PEP carboxykinase suggest that TCA cycle itself is fundamentally important to provide a heightened biomass for mixotrophic growth, as these reactions were all substituble for phototrophic growth. The finding that CEF was substitutable for both growth regimes is important indicating its non-essential role in both mixotrophic and phototrophic metabolism.Out of the 16 essential reactions identified under mixotrophic growth, 10 were also deemed essential for phototrophic growth. These reactions all consisted of photon exchange and uptake. The remaining six reactions were classified as substitutable  whilst acetate uptake (EX_ac(e)) and transport into the cytoplasm ACt were blocked. There were no blocked reactions observed for mixotrophic growth, however, there were seven substitutable reactions that were classed as essential for a phototrophic simulation. These included COiCR1080 to predict primary metabolism with respect to phototrophic and mixotrophic growth. To further asses the reliability of model predictions, growth rate, and oxygen evolution, were measured to compare predicted growth and photosynthetic oxygen evolution to address the initial question of the reported down regulation of photosynthesis with mixotrophic growth.We used the modified C. reinhardtii cell growth occurred following a logistic curve for both phototrophic and mixotrophic cultures  to evaluate the down regulation of photosynthesis associated with a mixotrophic growth regime, in the presence of acetate. Furthermore, model predictions have been validated using experimental results presented here and can be compared to results in the literature (Merchant et al., It is widely accepted that growth in the presence of acetate induces a down regulation of photosynthesis in the model organism Mixotrophic simulations included the uptake of acetate set at an experimentally determined level and allowed for the free exchange of gasses. It has been previously reported that the incorporation of acetate into acetyl CoA might occur via two possible pathways. The first pathway involves a two-step reaction involving ACK, prior to phosphate-acetyltransferase whilst the second pathway involves a single step conversion catalyzed by ACS (Johnson and Alric, C. reinhardtii has the capacity to convert acetate to acetyl-CoA in the cytosol, into the mitochondria through the TCA cycle, into the glyoxylate cycle and in the chloroplast due to localization of the enzyme ACS in these organelles. Our analysis suggests that acetate taken up into the cytoplasm is converted to acetyl-CoA by the enzyme ACS. However, our essentiality analysis did show this reaction to be substitutable for both conditions. Therefore, we are not able to conclusively say which reaction is responsible for acetate assimilation.Different models contain different structures, constraints and different optimization criteria, hence used to address different questions. Based on the annotated genome and metabolic reconstruction, Regardless of the primary acetate assimilatory reaction, we predict that acetyl-CoA feeds directly into a simplified TCA cycle, using the mitochondrial ICL with reducing power being consumed through oxidative phosphorylation. Operation of mitochondrial ICL was deemed essential for mixotrophic growth, but substitutable for phototrophic growth. Our model simulations are thus consistent with the experimental observation that acetate induces the expression of ICL and that ICL is required for growth on acetate (Plancke et al., 2 fixation within the chloroplast, yet still exhibiting a greater biomass than phototrophically growing cells. The suppression of CO2 fixation by acetate has been predicted under a mixotrophic growth regime at low light (Boyle and Morgan, C. reinhardtii mutants devoid of active Rubisco are still able to grow in the presence of acetate, albeit sub-optimally, compared to wild type (Pinto et al., 2 fixation indicating an overestimation of the inhibitory effect of acetate.Acetyl-CoA was then fed into the mitochondria with the export of succinate toward mitochondrial respiration and increasing flux toward ubiquinol. In addition, acetate increased fluxes along the pathway of gluconeogenesis whilst suppressing CO6fb complex. Previously, De Oliveira Dal'Molin et al. (6fb complex and feredoxin NADPH oxidoreductase has been characterized (Iwai et al., 2. As such, our mixotrophic simulations suggest C. reinhardtii prefers to assimilate acetate rather than CO2 by way of suppressing the CO2 fixation step, thereby alleviating the requirement for NADPH. Acetate assimilation via ACS however still requires ATP (Morales-Sanchez et al., We observe an increased flux through the CEF reactions, consistent with the suggestion that CEF around PSI explains the down-regulation of photosynthesis (Johnson and Alric, n et al.  reportedn et al. . Consistn et al. . The genn et al. . The wayn et al. . CEF alsn et al. . HoweverHere we have presented a GEM which is able to predict and explain the role of CEF in acetate metabolism while demonstrating the non-essentiality of CEF. It is well known that acetate metabolism leads to an increased growth rate and biomass. Model simulations are able to reproduce this and suggest that an enhanced biomass is achieved in mixotrophic cells by increased fluxes into starch and sucrose metabolism resulting from increased gluconeogenesis. In addition, the OPPP was active resulting in the incorporation of 6PGC into R5P. R5P is used in the synthesis of nucleotides and nucleic acids, supporting greater cell profilation and increased biomass (Bar-Peled and O'Neill, As a tool, flux balance models are becoming increasingly popular for explaining biological phenomena. As we have shown here, FBA is a powerful tool for data qualification. Of course, assumptions are made in constraint based modeling approaches and, as such, the questions investigated need to be applicable to the methodology being used. To accurately predict the fate of acetate using constraint based modeling techniques presents a challenge due to the presence of isozymes. To fully constrain the solution space requires integration of targeted omics data.We have modeled acetate metabolism in the light and as such, in a photosynthetic organism, metabolic pathways that are specifically active in the night are ignored. For example, starch degradation was constrained to zero to reflect the suppression of this reaction in the light. As a result, although starch contributed to the biomass, its accumulation was not specifically optimized account for low synthesis of starch. Experimental studies have shown that phototrophic growth favors starch accumulation when compared to mixotrophic cultures (Singh et al., Just as the ratio of ATP and NADPH have been shown to modulate the reactions of photosynthesis, elemental ratios of carbon and nitrogen have also been shown to affect cellular metabolism of photosynthetic microalgae (Talmy et al., Chlamydomonas reinhardtii, with an accurate representation of CEF, which is now able to explain the down regulation of photosynthesis observed under mixotrophic growth conditions in the presence of acetate. The predictive capabilities of the model were used to highlight changes in metabolism that were essential for this trait. We can conclude that acetate incorporation into metabolism is achieved via reactions of the TCA cycle before entering central metabolism requiring CEF. CEF serves to support the production of a proton motive force that allows ATP generation without the net formation of NADPH, serving as a balance to meet metabolic demands imposed on the system. The functioning of CEF is however non-essential. In doing so, we have demonstrated how the reactions of photosynthesis interconnect with carbon metabolism on a global scale, and how systems approaches play a viable tool in understanding complex relationships at the cell-wide scale.In conclusion, we have produced a curated version of a GEM of 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.


E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the network's size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of  Based on a steady-state analysis of a stoichiometric matrix  these methods allow for phenotypic predictions from genotype dataConstraint-based reconstruction and analysis methods have been proven to be valuable tools in gaining system wide understanding of cellular metabolism12EFMA decomposes the stoichiometric matrix into non-decomposable, non-zero steady-state pathways, called EFMset al.Regardless of the theoretical challenges, several software tools are available and allow the calculation of the full set of EFMs at least in small or medium scale (metabolic) models11in vivo as they are in opposition to other constraints that have not been accounted for, like known regulatory mechanisms15EFMA utilizes stoichiometric information only. Yet, many of the topologically feasible EFMs are infeasible E. coli under glucose saturated conditions.Here we present a novel computational tool – thermodynamic EFMA (tEFMA) – which integrates the cellular metabolome into the EFMA. This allows us to verify the thermodynamic feasibility of EFMs already during the runtime of the EFMA and curbs the explosion of the number of EFMs without losing any biologically relevant EFMs. Computationally, our new approach successfully tackles the major bottleneck of double description based EFMA by strongly reducing computational costs, both in terms of runtime and resource consumption. Biologically, tEFMA allows the identification of infeasible pathways based on an unbiased analysis derived from first principles. More specifically, tEFMA correctly predicts the inactivity of the glutamate dehydrogenase (GDH) in m  metabolites and r reactions can be represented by an m × r matrix, S. At steady-state all flux distributions, v, obey Sv = 0 and virrev ≥ 0, where virrev is a sub-vector of v containing only irreversible reactions. We assume that the network contains only irreversible reactions, as any reversible reaction can be split into an irreversible forward reaction and an irreversible backward reaction. Of particular interest are so called EFMs, ieiv > 0) is omitted, the remaining reactions can no longer carry a steady-state flux. Geometrically, the EFMs (in a network of irreversible reactions) can be regarded as extreme rays, i.e. edges, in a convex polyhedral coneThe stoichiometry of a metabolic network with S) each row of this matrix is processed and converted to binary form. For each row (i.e. reaction) intermediate EFMs (that are the columns of the matrix) are combined such that their fluxes are nonnegative and therefore convertible to a bit representation and added to the matrix. New intermediates are added to the quickly growing list of intermediate EFMs if they are not a superset of any other intermediate EFMs. The iteration stops if all reactions are processed and the intermediate EFMs are fully converted into binary format. The remaining intermediate EFMs are then, in fact the EFMs. A step by step example can be found in the The binary (null-space) approach represents EFMs as binary bit vectors of the supporting reactions. These bit patterns are generated iteratively. Starting from an initial solution matrix . That is why the limits in Eq. (8) were expressed in terms of logarithms. The LP checks whether all reactions contributing to an EFM, ke, are simultaneously feasible [Eq. (5)] and consistent with a metabolome within the given error bounds riG, while we are only interested in the feasibility of Eqs. (4-8). Therefore, any  solution suffices, which poses a computationally less challenging problem.Eqs. (1) and (2) identify isolated, thermodynamically infeasible reactions based on (measured) metabolite concentrations. However, NET analysis does not only study a reaction in isolation, but rather considers a reaction's feasibility in the context of pathways. NET analysis utilizes the thermodynamic interdependencies between reactions and verifies if a given network structure is consistent with a (measured) metabolome. To this end NET analysis is solved by the linear program (LP) given by22The basic feature of NET analysis is illustrated in i we may find that 18, 41, and 12 intermediate EFMs have positive, zero, and negative flux values in reaction i. This gives rise to 18 × 12 = 216 potentially new intermediate EFMs of which only 22 EFMs are actually added to the list of new intermediates as only these pass the (tree-based) adjacency and superset testing of the EFM enumeration procedure. In tEFMA we check the feasibility of the original 18 + 12 intermediate EFMs and remove infeasible EFMs there. Suppose that 8 out of the 18 positive intermediate EFMs are infeasible and can be removed instantly. Rather than 216 potentially new intermediates we now just get 10 × 12 = 120 potentially new intermediates of which only 17 EFMs are actually added to the list of new intermediate EFMs as these pass the (tree-based) adjacency and superset testing. (The numbers for this example were taken from line i = 10 in In tEFMA every intermediate EFM is checked at the beginning of each iteration against a given metabolome according to Eqs. (4-8) and immediately removed if infeasible. The efficiency of this approach is illustrated in T = 310.15 K (37°C).In the remainder we assume that efmtool, which was originally developed by Terzer and Stellingefmtool. The new classes are responsible for reading the additional information, call CPLEX  and handle infeasible EFMs. To invoke the new functionality we modified two already existing Java classes and the XML file that handles command line arguments  based on experimental metabolite concentrations measured by Bennett et al.We calculated thermodynamically feasible EFMs in medium scale metabolic models of We compared tEFMA against an ordinary EFMA followed by NET analysis. The sets of thermodynamically feasible EFMs were identical in both analyses. For growth on glucose about one third of all EFMs were thermodynamically feasible. The reduction in the number of feasible EFMs is highly condition specific as on glycerol and acetate the numbers of feasible EFMs are roughly cut in half. These comparisons were based on the full metabolic model, M1, without any other adaptations, i.e. also unused uptake reactions were subject to the analysis. If all unused uptake reactions were removed from the models, then the changes in the number of feasible EFMs was even more pronounced.On glucose minimal medium only 19% of the EFMs were thermodynamically feasible, while on acetate minimal media 76% were feasible (using model M2). However, in the case of glucose 19% corresponded to more than 30,000 feasible EFMs, while on acetate roughly 900 EFMs remained feasible. Thus, growth on glucose still opened dramatically more metabolic possibilities (counted by the number of feasible EFMs) than growth on any other carbon source.We verified the stability of the feasible EFMs by randomly perturbing the metabolite concentrations (see methods section for details). For all tested perturbations  the median number of feasible EFMs remained constant see the . MoreovefG′-values (see methods section for details). Up to a perturbation magnitude of 1 kJ/mol our results stayed essentially constant  oxidizes malate to generate oxaloacetate as part of the tricarboxylic acid cycle. tEFMA correctly identified the reverse reaction to be infeasible. Similarly acetaldehyde dehydrogenase (AdhE), which catalyzes the reduction of acetyl coenzyme A to acetaldehyde, was identified to be infeasible under the three tested growth conditions in accordance with well established knowledge. Both conclusions could have been made without the help of tEFMA, as evaluating Eqs. (2) and (3) for Mdh and AdhE unambiguously identified the reactions' directions without considering the network structure of metabolism.E. coli grown on glucose. The latter could not have been concluded without a NET analysis or tEFMA. For example, within the error bounds of the measured metabolite concentrations the reactions phosphoglycerate kinase (Pgk), glyceraldehyde 3-phosphate dehydrogenase (Gapd), and fructose bisphosphate aldolase (Fba) were found to be reversible if analyzed individually. Only together tEFMA identified them to be infeasible in direction of gluconeogenesis  pathway produces glutamate in two steps: (i) glutamate is used to produce glutamine by the energy dependent glutamine synthase and (ii) the amide group is then transferred reductively from glutamine to α-ketoglutarate to form glutamate. Both pathways were identified in an ordinary EFMA and produce 1 mole of glutamate net. For growth on glucose, however, tEFMA identified inconsistencies between GDH and the lower part of the glycolysis as well as between reactions GDH and aconitate hydratase (ACONTb). We found that on glucose no thermodynamically feasible EFM was supported by an active GDH feasible EFMs is again infeasibleCurrently DDM is the most common approach for calculating EFMs9To curb the explosion of the number of (intermediate) EFMs, we solved many LPs to determine their feasibility. In our application LPs are uncritical in terms of memory consumption. Overall we saved memory by removing infeasible EFMs at the price of an increased computational load to evaluate the LPs. Fewer (intermediate) EFMs meant a shorter list of (intermediate) EFMs, too. This reduced the time to perform the adjacency and superset tests on the EFMs. In the tested cases, the overall runtime decreased at least by 50%. Note that the scaling and efficiency of the DDM critically depends on the order in which constraints are processed31E. coli on our computer infrastructure. More (omics-)data, like regulatory constraintsIt is known that out of all EFMs in large networks few are physiologically significanta posteriori. tEFMA will aid such studies in providing better computational performance and allowing larger systems to be analyzed.Although tEFMA utilizes an optimization principle to fit the metabolic profile, it still retains the ability to unbiasedly characterize all metabolic capabilities of an organism. However, tEFMA cannot predict individual metabolic fluxes. In fact, even the combination of two thermodynamically feasible EFMs might result in an infeasible flux distribution2937404142fG, for each metabolite. Our analysis showed that an error in ΔfG of up to 1 kJ/mol did not cause alterations. Such accuracy is achievable with current (reactant contribution) methods for the estimation of the Gibbs energyThe success of tEFMA is dependent on the availability of a measured metabolome. Measurement errors in the concentrations were taken into account, and tEFMA was found to be robust against further perturbations. More critical for tEFMA is the requirement for accurate data on the Gibbs free energy of formation, Δet al.tEFMA is inherently condition specific and in principle has to be repeated upon any change in the environment. In practice, however, that might not be necessary as Ishii et al.P. tricornutumet al.tEFMA's condition specificity is in strong contrast to the approach taken by Hunt tEFMA retains the ability to allow for a fully unbiased analysis of metabolism. In fact, the predicted inactivity of GDH under growth on glucose was completely derived from first principles. This allows to draw very general statements of biological relevance without relying on optimality criteria or particular flux distributions. The inactivity of GDH for instance, allows glutamate synthesis only via the ATP consuming GOGAT pathway. The increased energy demand for glutamate synthesis might cause problems during recombinant protein production, which induces additional energy requirements in the host. Thus by activating GDH rather than GOGAT the metabolic burden is reduced.ad hoc assumptions are required to estimate the missing compartment specific concentration dataCurrently an assumption-free tEFMA can only be performed on prokaryotes. tEFMA on eukaryotes would require compartment specific concentration data. A theory to describe the thermodynamics of inter-compartmental transport is availableIn summary, we developed tEFMA, a tool that presents an important step forward to the analysis of genome-scale metabolic networks. tEFMA integrates NET analysis into EFMA and succeeds in calculating only EFMs, that are thermodynamically consistent with a given metabolome. By doing so, it dramatically reduces the hardware requirements for such an analysis to be carried out and paves the way to enumerate EFMs in large-scale metabolic networks. This is possible as the calculated set of EFMs is reduced by the large number of thermodynamically infeasible EFMs. To show the accuracy of the tool we presented the correct identification of several infeasible pathways without making wrong predictions. Furthermore, we pointed out that tEFMA correctly distinguishes between the GDH and GOGAT pathways to produce glutamate. Additionally, we verified that the patterns, and therefore pathways, which were found to be infeasible in the smaller model remained infeasible in the larger model.M.P.G., C.J. and J.Z. conceived and designed the study; M.P.G. and C.J. developed the software; M.P.G. and J.Z. developed the models; M.P.G. run the experiments; M.P.G. and D.E.R. contributed analysis tools and performed the analysis; M.P.G., D.M., C.J. and J.Z. wrote the manuscript.Supplementary file 1Supplementary file 2Supplementary file 3


A central unsolved problem in early evolution concerns self-organization towards higher complexity in chemical reaction networks. In theory, autocatalytic sets have useful properties to help model such transitions. Autocatalytic sets are chemical reaction systems in which molecules belonging to the set catalyze the synthesis of other members of the set. Given an external supply of starting molecules – the food set – and the conditions that (i) all reactions are catalyzed by at least one molecule, and (ii) each molecule can be constructed from the food set by a sequence of reactions, the system becomes a reflexively autocatalytic food-generated network (RAF set). Autocatalytic networks and RAFs have been studied extensively as mathematical models for understanding the properties and parameters that influence self-organizational tendencies. However, despite their appeal, the relevance of RAFs for real biochemical networks that exist in nature has, so far, remained virtually unexplored.Escherichia coli, for the existence of RAFs. We find that the largest RAF encompasses almost the entire E. coli cytosolic reaction network. We systematically study its structure by considering the impact of removing catalysts or reactions. We show that, without biological knowledge, finding the minimum food set that maintains a given RAF is NP-complete. We apply a randomized algorithm to find (approximately) smallest subsets of the food set that suffice to sustain the original RAF.Here we investigate the best-studied metabolic network, that of The existence of RAF sets within a microbial metabolic network indicates that RAFs capture properties germane to biological organization at the level of single cells. Moreover, the interdependency between the different metabolic modules, especially concerning cofactor biosynthesis, points to the important role of spontaneous (non-enzymatic) reactions in the context of early evolution.The online version of this article (doi:10.1186/s13322-015-0009-7) contains supplementary material, which is available to authorized users. E. coli.Autocatalytic sets were initially proposed as chemical reaction networks with intrinsic properties that could promote a natural rudimentary selection process en route towards self organization in chemical evolution -4. UntilE. coli metabolic network data ” in the original data set, plus the ones produced in the periplasm, the introduced general catalysts, and a few essential species such as ATP, to make a total of 438 food molecules. The resulting reaction network, consists of 1199 distinct molecules types (including the 438 food molecules), 1826 reactions  and 42 catalysts. We then applied the RAF framework to analyze this CRS for the existence and structure of autocatalytic sets.E. coli growth on glucose-6-phosphate and glucose as in [E. coli Kegg pathways [A second food set was created based on the laboratory conditions given for se as in . The reapathways . HierarcE. coli metabolic network results in a maximal RAF set (maxRAF) consisting of 1787 reactions. This corresponds to 98% of the full 1826-reaction metabolic network, that is, only 39 reactions of the full network are not part of the maxRAF. When ATP or an equivalent compound such as ADP is available in this food set, the resulting RAF set is also a so-called “constructible” autocatalytic set (CAF set). This is in agreement with previous results from [Applying the RAF algorithm to the lts from  where ATAn outline of the number of reactions per functional category in the maxRAF is presented in Table E. coli metabolic network consists of 438 molecule types, 324 of these being chemical species that are exchanged with the environment in [A crucial determinant for the existence (and size) of RAF sets is the composition of the food set. As described above, the initial food set for the nment in . A largenment in  where itnment in -46.However, this initial food set can be reduced without diminishing the size of the maximal RAF set. Out of the 438 initial food molecules, there are 117 “essential” ones: removing any one of these individually reduces the size of the maximal RAF set. In the majority of cases (103) the RAF set is reduced by less than 30 reactions, but there are 7 molecules that reduce the size of the RAF set by more than 1000 reactions when removed from the food set, in particular the generic catalysts.Removing the remaining 321 molecules from the food set, i.e., using only the 117 essential food molecules, also results in a reduced maxRAF. So, at least some subset of these 321 molecules needs to remain in the food set to maintain the maxRAF. This brings up the question of whether it is possible to (efficiently) find a minimum food set that maintains a given RAF set in a reaction network. Unfortunately, this problem turns out to be NP-complete, as the following theorem states.Consider the following combinatorial optimization problems.min-F RAF:F⊆X, with k.INSTANCE: A CRS F′ of F of size at most k for which QUESTION: Is there a subset min-F generation:F−generated, and a positive integer k.INSTANCE: A set of reactions F′ of F of size at most k for which F′−generated?QUESTION: Is there a subset F−generated means that each reactant in F or can be created from F by using a series of reactions only from Recall that The min-F RAF and min-F generation problems are NP-complete.See the This seems a rather technical result, but it implies that there is no hope of constructing an efficient  algorithm for finding a minimum food set to maintain the same maxRAF set. This, therefore, presents a limit to our ability to study a given reaction network analytically.However, we can still take a heuristic approach and construct a randomized search algorithm to sample food subsets, and then take the smallest set from the resulting samples as an approximate solution to the min-F problem. This randomized algorithm is similar to that for sampling irreducible RAF sets as described in section “min-F search algorithmF.Randomly reorder the list of molecule types the original food set fi∈F do: fi from F.Remove Apply the RAF algorithm.fi to F, otherwise leave it out.If the resulting  RAF set is smaller than before, return For each next element F.Return the (reduced) food set E. coli data set always returns the same smallest size for the food set of 123 molecules. This includes the 117 essential molecules, plus some combination of six molecule types from the remaining 321 compounds. This combination of six molecule types is not always the same, though, but they do come from a very small subset. Table Repeating this algorithm any number of times on the E. coli network it seems very likely that the minimum food set is of size 123, but that it is not unique.Table With such a minimum food set, however, the maxRAF set is not “constructible” anymore , although there is still a CAF subset of 434 reactions within the maximal RAF. For the remainder of our analysis, we use the essential 117 molecules plus the first set of six additional molecules from Table This large number of “essential” molecules in the food set is to some extent an artifact arisen from how the initial metabolic network was constructed  and fromE. coli. As recently summarized by Mackie et al. [E. coli can grow under different conditions, for example, under aerobic and anaerobic conditions, or using different carbon sources. The extended food set allows for all of these alternative pathways to be functioning within the RAF network. This raises the question of why to use E. coli as a model organism for probing questions regarding self-organization of primordial metabolism. The reason is simple: even though other metabolic networks exist [E. coli’s is the best studied and annotated biological network available, even if not perfect  act as catalysts, either by themselves or as part of a catalyst compound. Thus, in the E. coli RAF network, at least 65% of the reactions are catalyzed by a cofactor and this number would be larger were complete annotations for the genes available  is only a catalyst in the conversion of uracil to UMP (and is a substrate in only 13 reactions), but the removal of this conversion reaction reduces the RAF size by 1377 reactions, even though UMP can be produced by seven other distinct reactions. This shows the central role of PRPP in biological networks. In The extensive biological action of the different cofactors can be observed by the uneven distribution of the catalysts according to their functional annotations, see Figure E. coli metabolic network. An obvious question arises: “Is there any modularity in this RAF set?”The maxRAF contains 98% of the reactions in the i is i reaction steps away from the food set. Since the maxRAF (with the minimum food set) is not a CAF, obviously there will be some reactions that are not in any of these levels. These are the “non-CAF” reactions.First, we looked at hierarchical levels of reactions. Starting with the full maxRAF and only the food molecules, we considered all reactions that can proceed catalyzed, i.e., all reactions in the maxRAF that have all their reactants and at least one of their catalysts in the molecule set. This represents the “level 0” reactions. Next we added all the products of these level 0 reactions to the molecule set, and considered all additional reactions that then proceed catalyzed. This represents level 1 . We repeated this procedure for subsequent levels, until the number of reactions that can proceed catalyzed does not further increase. In short, a reaction in level E. coli maxRAF results in a number of reactions in each level as shown in Table Applying this analysis to the size of such a smallest subset is NP-hard). Therefore, determining the distance (number of reaction steps) from the food set of these non-CAF reactions appears to be intractable.Finding a minimum subset of reactions that, when allowed to proceed uncatalyzed at least once, realizes the full maxRAF turns out to be an NP-hard problem   always results in an irrRAF of size one. This is not surprising, as our hierarchical levels analysis above resulted in 63 reactions in level 0. Since all of these reactions have their reactants and at least one catalyst in the food set, they are by definition RAF sets by themselves . We refer to these as “trivial” (irr)RAFs. Removing these trivial irrRAFs from the network, however, breaks the original maxRAF, as the resulting RAF set now contains only 17 reactions (with irrRAFs of sizes two and three). So, the “trivial” RAFs are also “essential” for the full maxRAF.Recall that an irreducible RAF set (irrRAF) is an RAF set from which no reactions can be removed without losing the RAF property. Applying the irrRAF sampling algorithm  removal of a reaction. Figure E. coli network we use here [E. coli metabolism [The reactions whose removal reduces the size of the RAF the most involve nitrogen assimilation, glutamine synthase (GS) and mureine recycling. The latter seems surprising but is easily explained, because in the use here , a mureitabolism , and witE. coli RAF. Of the 76 reactions that reduce the size by more that 100 reactions, 43 (57%) fall in the functional category synthesis of cofactor and prosthetic groups. This is intuitively understandable because cofactors are catalysts. It furthermore underscores the importance of cofactors as mediators of metabolism [Also of interest is the central role that cofactor biosynthesis assumes in the tabolism ,54,55. FFinally, there are clear intermediate levels in the (reducing) RAF sets. As Figure The irrRAF search algorithm gives insight into the importance of individual reactions on the size of the RAF set. We can perform a similar procedure for the molecules. Figure E. coli metabolism. But if this is the periphery, it would leave only 128 compounds in the core. This might seem like an unrealistically small core, but we note that there are only 303 essential genes in E. coli and the metabolic network model of E. coli needs just 250 genes to be able to produce biomass [Obviously the molecules that are involved in many reactions  from the food set, since its removal also does not affect the size of the RAF. In this case, the source of carbon becomes ATP itself, by its conversion into adenosine in the nucleotide salvage pathway. This captures an aspect of E. coli’s versatility since E. coli can grow aerobically or anaerobically, both in silico and in vivo, using adenosine as the sole carbon and nitrogen source [However, not all of these 39 food molecules are essential to maintain a similar RAF size Table  and thisn source .E. coli KEGG metabolic pathways. Based on the relationship between the different E. coli metabolic pathways with the 53 hierarchical levels retrieved, it is possible to represent the CAF network in a tree-based structure containing several grouped pathways  formation of N-carboxymethanofuran [Among the caveats to the present analyses, the underlying databases are not complete. As one example, biotin does not occur as a cofactor in the present annotations from the UNIPROT database for rboxyase . Anotheranofuran . In modeanofuran  and sponanofuran . For thiE. coli, the full extent to which gene products can substitute for each other is not known, although in one classical study, 620 genes in E. coli were found to be essential in rich medium (263 of which had no known function), while 3126 were dispensable [E. coli genes were essential (37 of which had no known function), and 3985 were dispensable [E. coli metabolism and that there is still much to be learned about its map.Another caveat is promiscuity (or messiness) in enzyme function, that is, the inherent ability of enzymes to catalyze several different selectable reactions , wherebypensable . A more pensable . This inpensable  built inlargest irrRAF within the E. coli metabolism network. However, at present it is not clear whether this can be computed efficiently.Finally, although we can easily find RAFs, including CAFs and irreducible RAFs, there are also limits to what can be calculated. For example, we have shown here that finding the minimal food set needed to maintain a given RAF is a computationally intractable (NP-complete) problem in general. So too is finding a smallest irrRAF , but her2 and CO 2 to yield organic products. Of interest, Kauffman’s speculations entailed the synthesis of large peptides from small ones, and early work showed that the synthesis of both amino acids and peptides under hydrothermal vent conditions are exergonic processes [The origin and initial interest in RAFs stem from early speculation about chemical evolution  and the rocesses , wherebyrocesses ,69.E. coli is a heterotroph that can live anaerobically but can also, like human mitochondria, use O 2 as the terminal electron acceptor in its ATP-generating electron transport chain. In that sense the E. coli metabolic network is hardly an ideal model for early chemical evolution. In addition, during evolution abiotic catalysts and metals in primordial RAF sets have been replaced by sophisticated chemical catalysts, as studies of metal and cofactor gains and losses across protein families have shown [ve shown -72. Compve shown . Moreoveve shown , such thve shown -77 whereve shown .E. coli to recycle pentoses to hexoses [et al expanded contemporary central carbon metabolism by assuming a relaxed specificity of enzymes [E. coli connects input sugars and the key precursors metabolites essential for biomass and energy production by the minimal number of enzymes, suggesting that contemporary metabolism is a small subset of the original possibilities.Finally, early enzymes probably had a more relaxed substrate specificity than in modern metabolism. This reasoning is the basis of “The Game of the Pentose Phosphate Cycle” study where Meleindez-Hevia and Isidoro showed that generic aldolases and ketolases could generate a large set of sugar phosphate interconversions and the subsequent growing specificity of the enzymes lead to a minimal solution, that in fact is equivalent to the naturally occurring pathway in  hexoses . Similar enzymes . With thE. coli represents the result of billions of years of catalytic refinement through natural variation and natural selection. However some of the properties germane to early evolution are common to all life forms and should still be at least partially conserved and, it is generally of interest to know whether the best-studied metabolic system is an RAF. With a few restrictions, for example the introduction of generic catalysts where no cofactors are involved, it indeed is. This is an encouraging result for future studies on the metabolic networks of ostensibly more primitive organisms such as acetogens and methanogens [E. coli, but also more similar to chemistry at hydrothermal vents, and whose metabolism furthermore involves a more prominent role of catalysis by metals [The metabolic network of hanogens ,81 whosey metals ,82.E. coli RAFs might point to an interesting aspect of early chemical evolution. We see here that the size, hence in some respects the complexity, of RAFs within the E. coli metabolic network are dependent upon cofactors: a small number of catalysts that promote a large number of reactions each. Regardless of where life arose, in the very earliest phases of chemical evolution, there must have been both thermodynamically controlled reactions (the most stable compounds accumulate) and kinetically controlled reactions (the most rapidly synthesized products accumulate). By lowering the activation energy of a reaction, cofactors influence the latter class of reactions more than the former as seen in the PLP example before mentioned [E. coli metabolic map as harboring some relics from early evolution, or not. In our opinion, some of these original imprints may still be present in the metabolism of modern organisms, as seen by their recurrent use of the same set of metals and organic cofactors as catalysts of biologic reactions, a set much smaller than the number of protein families that have evolved to catalyze them. The directives of the continuity principle in evolution demand that complex biochemistries had to be preceded by simpler chemistries. Thus, the initial RAF set would certainly be much simpler than the one analyzed in this paper but migth have already manifest the principle of cofactor and metal dependencies as recurrently is observed across studies showing their central role in modern metabolism [The critical role of cofactors in the entioned . Hence ttabolism ,59,83.E. coli reaction network can produce useful insights into primordial RAFs by identifying the essential role of metals and molecules such as ATP, CoA or thiamin in metabolism. This suggests the existence of some sort of abiotic autocatalysis at the onset of primordial metabolic networks. We have also shown here that maximal RAFs can be efficiently detected in real biological data, but the identification of large irreducible RAFs and the minimal food sets required to support a maximal RAF remain challenging problems.We have shown that the F′−generated and/or an RAF, for any given set F′.First note that both problems are in the complexity class NP, since we can determine in polynomial time (in the size of the input) whether a set of reactions is I of ‘min-F generation’ there is a corresponding instance I′ of ‘min-F RAF’ obtained by (i) making every molecule in X catalyze every reaction in , and (ii) taking X to be the molecules in the support of I′ has an affirmative answer if and only if I does.Next, notice that it suffices to show that the simpler problem ‘min-F generation’ is NP-complete, since for any instance We will show that ‘min-F generation’ is NP-complete by exhibiting a  reduction from the following set-theoretic decision problem.Exact cover by 3-sets (X3C)Y with |Y|=3q, q an integer; collection S of 3-element subset of X.INSTANCE: Finite set S contain an exact cover for Y, i.e., a subcollection S′ of S such that every element of Y occurs in exactly one member of S′?QUESTION: Does Y occurs in at least one element (3-element subset) of S. As an example of X3C, consider the set Y={a,b,c,d,e,f} and The decision problem X3C was one of the early ones to be shown NP-complete by Karp . We may S′={{a,b,c},{d,e,f}} is the (unique) subset of S that provides an exact 3-cover for Y.In this case Y,S) of X3C, we will construct an associated set X involved in these reactions is the (disjoint) union Y∪S∪W, where W:={ws:s∈S}. Thus |X|=|Y|+|S|+|W|=3q+2|S|. Moreover, we will take the food set F to consist of all of X.Given an arbitrary instance  has an exact 3-cover, if and only if F′−generated for some subset F′ of F of size at most q=|Y|.S′ is an exact 3-cover for Y. Then |S′|=q and every element of Y is generated by a reaction that has its (sole) reactant in S′. It follows that for every s∈S  the associated reaction y+y′→ws has reactants that can be generated from S′. This ensures, in turn, that the other associated reaction ws+y″→s can proceed. Consequently, all of S can be constructed, and so each of the reactions s→a+b+c for all s={a,b,c}∈S can also now proceed. In summary, the reactants of all reactions in S′. Thus F′−generated for any subset F′=S′ of F(=X) of size q that provides an exact 3-cover for Y.First, suppose that F′−generated for some subset F′ of F(=X) of size at most q=|Y| (we will show that this implies that S contains an exact 3-cover). For each molecule m in F′ for which either: m=y∈Y, orm=ws∈W.Conversely, suppose that y by s for any s∈S that contains y ; in case (ii) we replace m by s (i.e. the same s appearing in ws). Then s generates y in case (i), and s generates the reactants required to generate ws in case (ii). In this way we can replace F′ by a subset S′ of S, with we proceed as follows. In case (i) select replace S′−generated. Thus, it is possible to generate each element of Y by some series of reactions from S′.and for which Y are those that have a single reactant in S, and any s∈S that is the product of any sequence of reactions from s are either present or produced earlier in that sequence of reactions. It follows by an inductive argument that each molecule in Y must be able to be generated by just a single reaction from S′, and so S′ is a 3-cover for Y. This requires that 3|S′|≥|Y|, and so |S′|≥q,which, combined with Inequality  S′ is an exact 3-cover for Y, as required.Now, the only reactions that generate molecules in mentclass2pt{minim


Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations.Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.Availability and implementation: R code is available from the authors upon request.Contact:j.norkunaite@imperial.ac.uk; m.stumpf@imperial.ac.ukSupplementary information:Supplementary data are available at Bioinformatics online. In gene expression assays, for example, we typically measure messenger RNA expression, but not the activity of transcription factors and/or the occupancy of transcription-factor binding sites. Similarly, in metabolomic analyses , key met13C and 15N metabolic flux analysis) metabolites through the metabolic network  belong to the armoury of non–parametric Bayesian methods and have been widely used to describe dynamical processes  and to ix (or concentrations) and fluxes Here we develop a more general framework that uses so-called derivative GPs (Gaussian process regression (GPR) can be applied to recover an underlying dynamical process from noisy observations. A GP defines a prior distribution over all possible functions, and to specify a GP, we need expressions for the mean and covariance function that describe the behaviour of the system output over time . Below wx and outputs z via functions, n-dimensional real-valued vectors. The observed values of the dependent variable, z, can be related to the independent variables, ε is a noise term, which is here assumed to be independent and identically distributed according to a Gaussian distribution, K,In a typical regression problem, we connect inputs ace a GP  prior ovI the identity matrix. The unknown set of hyper-parameters, θ, can be estimated from the data by evaluating the following log-likelihood function,Different functional forms can be chosen for the covariance function , either  methods .For any finite number of input (test) points, f evaluated at inputs y are jointly distributed as whey, it remains unclear how to deal with several variables simultaneously: if outputs are correlated then the standard GPR framework may fail in providing an adequate description.Although h(t), and the output z(t) can be expressed via convolution integral,h(t) must be absolutely integrable, i.e.Dealing with linear filters is central to signal processing where such filters describe a physical systems that can generate an output signal in response to a given input signal . Linear X(t) is specified to be a Gaussian white noise process, the output process, Z(t), will also be a GP.Then if the input M white noise processes as inputs, K outputs, kth output. As discussed previously, the observed variables might differ from expected variables owing to the measurement noise, and we thus considerSpecifying a stable linear time-invariant filter with GP model . A multiijC permits the definition of a positive definite symmetric covariance matrix C between K variables,Multiple-input multiple-output filters are able to capture the relationships between several variables iN the number of observations of variable i. Having defined the covariance matrix, we can use the log-likelihood, which has the form (1) for the inference of the hyper-parameters i, predictions can be made using the appropriate marginal distribution, which is Gaussian, with mean Here X(t) is a white noise GP, h(t) is a kernel function and W(t) is an additive noise, it is easy to formulate the expression of a derivative process. Taking a derivative of Y with respect to t, it is possible to obtain a new process U that is also a GP  with a derivative kernel function g(t). This definition enables us to consider derivative processes and the corresponding original processes as a collection of dependent GPs. This is true because the derivative processes and the original processes are derived from exactly the same input, X(t).Thus, it is possible to construct the derivative process by convolving a white noise GP iUAutocovariance function of derivative process iU and jUCross-covariance function between two derivative processes iY and corresponding derivative process iUCovariance between original process iY and derivative process jUCovariance between original process To construct a dependent model for several related variables R denote a block matrix,H denoteZ1 and Z2 evaluated at a finite set of test points ijDDC consists of auto/cross-correlations between derivative variables U1 and U2. The matrices H are building components of the overall covariance matrix K, which is symmetric and positive definite,Let Y, functions Z and derivatives U,At a finite number of input points i and its derivative process at any input point Evaluating a GP posteriorRwww.r-project.org.To demonstrate the performance of derivative processes, we consider two simulation examples—a system of two oscillating signals and a simple model of linear metabolic pathway—before turning to a more complicated metabolic process and, finally, some real metabolic network data. The derivative processes can be used to address the flux estimation problem from time course data. Here GPs describe the dynamics of metabolites, and the corresponding derivative processes capture the functional forms of the associated fluxes. Below, all examples were implemented using the free statistical computing platform A is the amplitude, iA of each Gaussian kernel to be A simple oscillating signal can be expressed as lgorithm . DependeSupplementary Material for details), which is summarized in x1 to x2 is negatively regulated by metabolite x3, and x3 increases the transformation of x2 into x3. A set of ordinary differential equations (ODEs) can be used to describe the dynamics of these two metabolites, x2 and x3 ,Next we consider a linear metabolic pathway with two regulatory signals ; x3 inhibits the conversions of x1 into x2, and x2 positively regulates reaction v4. The following ODE model describes the dynamics of the metabolites that are involved in this pathway,v4,We now turn to an example of metabolic pathway that was originally proposed by  pathway b, the chv1 and v4. In this example, we will focus only on estimation of fluxes v2 and v3 from available data rather than try to address a uniqueness problem of v1 and v4.These expressions define a system of linear equations that is underdetermined, as we have more fluxes to estimate than available equations, and it cannot be solved using standard Gaussian elimination techniques. For this reason, additional information is required to uniquely determine fluxes x2 and x3 with added random Gaussian noise x2 and x3 dynamics; the green lines describe the derivatives of the same metabolites and can be understood as a slope estimates. In v2 and v3 under consideration of pathway The above ODE model enables us to generate simulated time course data using the initial conditions E. coli, where we have measurements of the abundance of several key metabolites involved in nitrogen assimilation. Nitrogen is one of the key chemical elements that acts as a nutrient for the cells; ammonium is a preferred source of nitrogen for E. coli growth  that operates during cell growth in ammonium-rich environments, and glutamine synthetase-glutamate synthase (GS-GOGAT) that operates during cell growth in low-ammonium conditions (α-ketoglutarade (glutamate (GLU) and glutamine (GLN) metabolites over the time after ammonium spike; red dots correspond to a wild-type (WT) E. coli metabolic measurements, and in squares—isogenic glnG deletion (ΔglnG) measurements. Below we focus on the pathway summarized in v3 and v4 can be summarized by the overall flux v3 that describes the flow from GLU to GLN, as there is not enough information to discriminate between them. From the pathway, we can construct a system of linear equations that describe the dependencies between fluxes and metabolites,Finally, we apply our technique to the experimental data from GLU and the third one describes additive noise; GLN is modelled similarly. However, GLU is modelled as the sum of four GPs, where the first three describe GLU; the dependence between GLU and GLU and GLN; and the fourth is an additive noise. Choosing kernel functions to be Gaussian E. coli, and green lines for ΔglnG. Using the relationship v1, v2 and v3 . In E. coli, glnG encodes the transcription factor, NtrC (nitrogen regulator) that controls GS expression levels, and in its active form, GS catalyses glutamine synthesis . Because there is less enzyme available to catalyse the reaction in ΔglnG, the flux v3 in the mutant will be noticeably reduced compared with the WT flux v3 .To evaluate our predictions, we can compare flux Flux estimation has become central to many analyses into the metabolic processes and mechanisms. Typically, the estimates for a set of fluxes are obtained in a point-wise manner at discrete time points. It is clear that this fails to capture the temporal behaviour of the fluxes and additional consideration of parametric models is compulsory to fully explain the fluxes; further, this approach is susceptible to noise that is present in experimentally measured metabolite data.Here we have addressed these problems and proposed a novel non-parametric Bayesian approach to modelling metabolic fluxes. This is based on MGPs that enable the construction of derivative processes. Because the derivative processes and original processes share the same input source, we can complement the dependent GP model and make joint predictions about original and derivative processes at any finite number of input points. Such derivative processes can be applied to characterize the temporal behaviour of metabolic fluxes from time course data—without having to make reference, e.g. transcriptomic data, to explain temporal variation—and here we have demonstrated the applicability on simple models and a real-world example.GPs, including our approach, propagate uncertainty in line with the assumed covariance structures. This can lead to large confidence intervals, especially if the dependencies among different observations are not considered explicitly. With increasing number of metabolic species within the pathway, the derivative process approach might become computationally costly due to the inference of a large number of hyper-parameters and a matrix inversion step; however, this limitation potentially might be addressed by considering a sparse approximation for the full covariance matrix of all metabolic species . These c


Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair. Toxoplasma gondii, which chronically infects about 30% of humans worldwide. Through a semi-automatic reconstruction we have developed ToxoNet1, a comprehensive metabolic model of Toxoplasma gondii, and we have performed an extensive computational analysis to explore the properties of Toxoplasma metabolism. In particular, we have identified and classified the minimal set of substrates the parasite utilizes for growth, along with the genes and pairs of genes that are essential for cellular functions such as growth and energy metabolism. We have validated our computational predictions that the genes encoding acetyl-CoA synthase and ATP-citrate lyase enzymes serve complementary function but their simultaneous disruption does not allow cell growth. This study presents a number of hypotheses generated using ToxoNet1, which can lead to the discovery of novel antiparasitic drug targets.Understanding the metabolism of disease-causing microorganisms can guide drug design through the identification of metabolic enzymes whose activity is indispensable for important cellular functions. Such understanding can come from the reconstruction and computational analysis of metabolic networks. In this study we have focused on the metabolism of an opportunistic human pathogen,  Toxoplasma gondii, is a ubiquitous opportunistic pathogen responsible for one of the most common parasitic infections in humans and warm-blooded animals. It is estimated that up to 30% of the human population is chronically infected [The phylum of Apicomplexa comprises a large number of obligate intracellular parasites that can infect organisms across the whole animal kingdom. An important member of this phylum, infected . Toxoplainfected .T. gondii possesses a complex life cycle, which is composed of an asexual replicative stage in the intermediate host and a sexually replicative stage within the definitive feline host. During the asexual phase, T. gondii can switch from a fast-replicative tachyzoite form, which causes acute disease, to a slow-growing bradyzoite stage, which forms cysts that are characteristic of chronic infection. The encysted, slow growing form is resistant to commonly used drugs and immune system attack. Few efficient medicines are available to treat toxoplasmosis and they mainly treat the acute phase of the disease. Furthermore, poor tolerance of these drugs promotes the search for novel drug targets.Plasmodium species, T. gondii is able to invade and asexually replicate within virtually any nucleated cell of warm-blooded animals. The broad range of cells amenable to infection by T. gondii reflects the plasticity of the parasite’s metabolism and versatility in accessing and utilising nutrients to support its intracellular growth [T. gondii.Unlike other notorious apicomplexan parasites that infect a narrow range of host cell types, such as the r growth ,4. The cT. gondii replication and which of these are dispensable; 2) in which intracellular compartments do the enzymatic activities annotated at the genome level occur; 3) which of these enzymatic activities are indispensable for replication or other vital processes of the parasite. While achievable, the application of high-throughput gene knockout or knockdown strategies to globally determine gene essentiality in T. gondii remains a major undertaking.Currently, no adequate experimental approaches exist to answer comprehensively the following important questions: 1) what substrates are available within the host cell that are necessary for in silico) metabolic modelling coupled with systematic analyses facilitates the study of biological systems. This modern approach of systems biology has been extensively exerted to predict gene essentiality in various bacteria, including numerous pathogenic species [In silico metabolic models offer a cost-effective pipeline to identify putatively indispensable metabolic processes that, in the case of pathogens, represent potential targets for therapeutic intervention [Computational  database. The databases used for identification of “gap” reactions were KEGG [For the compartmentalization process we consulted the models of lciparum ,13 and ulciparum –16 as weere KEGG  and LLAMere KEGG . Recon 2ere KEGG  was usedReconstruction of the metabolic network was achieved by combination of the state-of-the-art algorithm for a semi-automated generation of metabolic networks  and a coThe reconstructed metabolic network accounted for 527 open-reading frames (ORFs) that were linked to 867 unique metabolic reactions present in the KEGG  databaseIn ToxoNet1, the majority of reactions were associated with genes that encode corresponding metabolic enzymes . IntraceT. gondii and present within ToxoNet1, the transferases (class 2) and oxidoreductases (class 1) were the most numerous . Hydrolases (class 3), lyases (class 4) and ligases were significantly less represented , experimental data was available only for a limited number of proteins: 60 out of the 527 included in ToxoNet1. Thus the only reasonable option for building a compartmentalized metabolic model was to make use of sequence-based localization predictors. To define putative localizations of the enzymes with no experimental evidence we generated sequence-based predictions using three software algorithms: TargetP [Relatively straightforward experimental methods exist to determine the localization of proteins in . gondii , however TargetP , MitoPro TargetP , and Api TargetP . We thenComparison of these predictions with 60 experimentally established subcellular localizations available from ApiLoc uncovered two issues: (i) not all the computational predictions matched their experimental data . This enzyme interconverts IPP into its more reactive isomer—DMAPP. In T. gondii, as well as in Plasmodium spp. this enzyme is absent [In ToxoNet1 a majority of the reactions (c.a. 69%) occur in the cytosolic compartment. We also considered this compartment as a default for enzymes with a subcellular localization remaining unclear from the rganelle . We connrganelle ; (ii) exs absent . Thus, bcheckTasks and fitTask functions), which verifies that specific tasks are fulfilled and, if necessary, it fills the gaps in the metabolic pathways to meet the required functionality.A major limitation of all algorithms for automatic reconstruction of metabolic models is the number of missing reactions  that disrupt metabolic pathways. The RAVEN Toolbox offers a functionality called metabolic tasks (de novo synthesis was possible in the model). The second group contains the metabolites whose synthesis required a precursor with a specific moiety (e.g. hypoxanthine or other source of purine moiety for purine nucleotides and their derivatives). The third group are the biomass constituents that could not be produced even with the maximum set of host-supplied substrates, and therefore they were directly taken up from the host .We created metabolic tasks for the synthesis of every metabolite included in the biomass reaction  from the list of host-supplied molecules, enabled production of the necessary biomass precursors in ToxoNet1. Nevertheless, several biomass building blocks could not be produced in the model even when all the 230 host-supplied substrates were provided simultaneously, indicating that the parasite is likely auxotrophic for these molecules.According to ToxoNet1, no acids . When tafillGaps) and manual gap-filling based on the information from the LLAMP database [In the cases when metabolic tasks could not be accomplished despite literature evidence (mainly acquired from the LLAMP database ), we perdatabase . For insdatabase . ToxoNetT. gondii. We manually built such a model prior to this study based on the genome annotation from ToxoDB and refined it according to the relevant primary literature. This model contained a number of recent experimental findings as well as manually assembled complex gene-reaction associations. Among these were the pyruvate dehydrogenase activity that can be carried out by the branched-chain keto-acid dehydrogenase complex (BCKDH) in the mitochondrion [Importantly, some of the information necessary for a metabolic model could not be easily deduced from the genome sequences alone. This is why we merged the draft output metabolic network from the RAVEN Toolbox with a small-scale model of central carbon metabolism in hondrion , mitochohondrion –30 and tin silico is the unknown range of metabolites available for the parasite within the host cell. As extracellular replication (axenic cultivation) of T. gondii is not possible, it remains undefined which of the substrates are dispensable for the parasite and which are not.One of the major challenges in simulation of parasite metabolism in silico, we developed a method to enumerate all of the smallest/ minimal metabolite sets , which could enable simulation of growth in ToxoNet1. In brief, the algorithm applied iterative rounds of biomass production using the fewest number of substrates possible. After each of the iterations we added a constraint to ensure that the next IMM included at least one substrate uncommon from the preceding ones .To address this issue T. gondii replication in ToxoNet1. We also observed a relatively large number (2592) of alternative sets of 19 substrates with 10 metabolites being constitutively present in all the IMMs. The other 9 substrates could be substituted by at least one other host-supplied metabolite  are listed in Gene essentiality predictions depend on the following important aspects of metabolism as represented in the model: (1) range of substrates that can enter the model (i.e. molecules that the parasite can take up from the infected host cell); (2) composition of the parasite cell represented as a set of biomass precursor molecules (the concept of biomass objective functions is explained in ); (3) thT. gondii observed experimentally in tissue culture, we chose to assess the importance of the two independent routes of cytosolic acetyl-CoA production in the rapidly dividing tachyzoite stage  by introducing a 3xTy-epitope tag at the C-terminal end of both genes in the RHku80ko (Ku80ko) background strain, which limits random integration in the genome hence facilitating recovery of homologous recombination events  monolayers. Indeed, both ACLko and ACSko parasites formed lysis plaques of similar sizes when compared to wild type (Ku80ko) parasites  in Ku80ko . For botarasites . Moreovearasites . FinallyT. gondii as suggested by ToxoNet1, we first attempted to disrupt ACL by single homologous recombination in the middle of the ORF in the ACSko parasite background , the signal was lost immediately after the first passage of these cultures (P1 and P2)  indicatiT. gondii FASI was disrupted in wild type RH strain parasites by CRISPR/Cas9 mediated genome editing [To determine whether the deleterious phenotype and loss of pellicle integrity could be attributed to a block in type I fatty acid synthesis following depletion in cytosolic acetyl-CoA, the gene coding for  editing . TrangenT. gondii, which can simulate growth of the parasite in silico and infer essentiality of its genes. It considerably extends the scope of previous work [T. gondii, in the present study we reconstructed an independent model that represents the potential scope of the metabolic capabilities of the parasite independently of the life-stage and strain-type. Aiming at the most reliable gene essentiality predictions we used many alternative assumptions on the range of accessible substrates and transportability of metabolites. We have also achieved more comprehensive coverage of all the metabolic capabilities of the parasite and implemented functional gene-protein-reaction associations enabling rigorous gene deletions studies. Hereafter we provide a more in depth discussion on the key aspects of our approach.ToxoNet1 constitutes a full genome-scale reconstruction of metabolism within ous work  and contP. falciparum [There are two distinct approaches for model reconstruction process that are commonly referred to as top-down and bottom-up . The toplciparum  used as lciparum ) as wellT. gondii protein-coding genes, which is comparable but greater than in the genome-scale models of P. falciparum (7%) [L. major (6.7%) [Cryptosporidium hominis (5.5%) [T. gondii [P. falciparum [T. gondii possesses broader metabolic capabilities  of the rum 7%) , L. major (6.7%)  and Cryp% , L. ma. gondii . Comparilciparum ,9,13 con% of the bilities . These d-141 and bit-score 479). Accordingly, an orthologous gene (PF3D7_0621200) is annotated as pyridoxine biosynthesis protein (PDX1) in P. falciparum, yet without an E.C. number assigned. There was also a set of genes with low confidence in their function, despite their annotation in ToxoDB . These two examples demonstrate the potential of the RAVEN Toolbox to improve genome annotation and produce models with confidence estimates that enable evaluation and, potentially, future corrections of the existing models.ToxoNet1 contains confidence estimates  for each gene annotated as encoding certain metabolic activities. This has allowed us to suggest a number of metabolic functions for genes that did not have annotations with E.C. numbers in the ToxoDB database . For exaToxoNet1 contains 260 unique dead-end metabolites, which currently can be only produced or only consumed in the network. We kept them in the model in order to allow future developments and expansion of the scope of the model. Further definition of the metabolic routes can utilise these dead-end metabolites and contribute to a more complete understanding of the metabolic capabilities of the parasite. For instance, the tRNAs loaded with the respective amino acids were included to enable future extension of the model to the representation of protein synthesis.T. gondii. Our objective of this work is to provide ToxoNet1 as a resource for the community and therefore we avoided imposing constraints and hypotheses, which are not well-tested and confirmed that could contaminate the model and our results.We chose to impose relaxed constraints in terms of subcellular compartments due to rather high uncertainty in subcellular localisation of the enzymes as well as largely unknown capabilities of the parasite to transport metabolites across its organellar membranes. We assigned the enzymes with the corresponding reactions to their putative compartments and allowed a broad range of metabolites to be transported across the compartment boundaries. This approach to compartmentalisation ensures minimal bias in our results due to underestimated transport capabilities or incorrect assignment of enzymes to subcellular compartments. We expect subsequent refinements of ToxoNet1 with more stringent compartmentalisation as more reliable, high-throughput computational and experimental methods are becoming available for subcellular localisation of enzymes and functional annotation of transporter proteins in T. gondii, we implemented an algorithm that identifies and ranks the minimal number of substrates required for growth in ToxoNet1. The results indicate that uptake of as few as 19 of 230 substrates allows the model to produce all biomass building blocks. Moreover, we identified 2592 sets of 19 substrates that can allow growth, and 10 substrates common to all these sets . Among the alternative substrates we identified 7 carbon sources: five hexoses and two pentoses (ribose and deoxyribose), which can be incorporated into the metabolism by a ribokinase enzyme. Uptake of pentoses through a hexose transporter has been reported in the protozoan parasite L. major and may potentially represent an additional level of versatility in meeting the need of a carbon source, similarly to the previously observed in T. gondii utilization of glutamine [To understand the nutritional requirements of ese sets . These 1lutamine ,46. We cde novo biomass building blocks from glucose and inorganic substances. Interestingly, almost all the cofactors of metabolic enzymes, with the exception of pyridoxal phosphate cannot be produced de novo. Their synthesis required precursors that contained certain chemical moieties . Notably, T. gondii lacks the pathway for arginine synthesis, which can be produced by human cells, however arginine is growth limiting for human cells because its de novo synthesis is insufficient and thus supplemental uptake is required [Validation of genome-scale models using metabolic tasks is an approach developed to evaluate metabolic capabilities of the models in a systematic manner. To date it has been applied to several models ,47,48 fomoieties . This surequired . This suT. gondii can potentially produce. However, this pathway includes the enzyme homoserine dehydrogenase (E.C. 1.1.1.3), the encoding gene for which is not identified to date in the genome of the parasite. Data from ToxoDB suggests that the genes encoding the enzymes of the threonine biosynthesis pathway, namely aspartokinase (TGME49_227090), threonine synthase (TGME49_220840), homoserine kinase (TGME49_216640) and aspartate-semialdehyde dehydrogenase (TGME49_205420), are expressed at low levels in the tachyzoite stage. Homoserine kinase has been detected in the proteome of oocysts [T. gondii could be a conditional threonine auxotroph. Should threonine biosynthesis also be active in the other life stages it could represent a selective drug target for inhibition of parasite replication without affecting the mammalian host cells. Maintenance of some metabolite pools in T. gondii may be a result of both uptake and de novo production. Hence, if the complete set of genes of a metabolic pathway is present in the genome of the parasite, this pathway is not necessarily active and will not meet the demands of the parasite regarding the products of the pathway. Fox et al. showed that salvage of pyrimidine nucleotides from the host cell can support in vitro but not in vivo survival of T. gondii mutants with disrupted de novo pyrimidine synthesis pathways [T. gondii.Threonine is the only amino acid essential for human cells and which  oocysts , the inf oocysts . Taken tpathways . TherefoT. gondii. The earlier efforts to infer essentiality of genes in this parasite were reported by Gautam et al. [Our study represents the first model-based gene essentiality predictions for m et al. . Their am et al. ,54. NeveP. falciparum and 32 of them were predicted as essential in the existing metabolic models [T. gondii is currently scarce, thus, we could not test the majority of our essentiality predictions  have orthologs in the malaria parasite c models . Evidencdictions . However. gondii . The datWe predicted synthetic essentiality of 20 pairs of genes, which represent two distinct cases. In the first case the pair of genes encode two isoenzymes that catalyse the same reaction in the same compartment. For example, the genes TGME49_318580 and TGME49_285980 encode isoenzymes, which catalyse the phosphoglucomutase reaction (E.C. 5.4.2.2) and both were experimentally localised to the cytosol . This reT. gondii acetyl-CoA transporter (AT1) is localized in the membrane of the ER and is anticipated to deliver acetyl-CoA to the secretory pathway. It would be interesting to examine the importance AT1 for parasite survival. In contrast, the mitochondrion and the apicoplast should not be affected since these compartments have their own routes for production of acetyl-CoA: (i) in the apicoplast it is produced by the pyruvate dehydrogenase (PDH) complex [Moreover given that deletion of the genes coding for elongases previously reported  did not  complex , and , the enzymatic reactions associated with the corresponding KEGG orthology ID were added to the draft metabolic network. As a result we have obtained a set of metabolic reactions linked to genetic loci of T. gondii. At this stage the model did not contain information about either subcellular compartments, or about transport of metabolites. We next merged this model with a manually curated, small-scale metabolic reconstruction that we previously built based on the P. falciparum model [The reconstruction process started with generation of a draft metabolic network of database . Within database  orthologdatabase . In the um model .et al [Similarly to Huthmatcher et al ., we remT. gondii from the host cell cytosol, is the outermost compartment represented in ToxoNet1. Extracellular compartment of the model corresponds to the PV space. A eukaryotic organization of intracellular space of the parasite also includes a number of compartments, among which we chose to represent in ToxoNet1 the most relevant to metabolism, namely: mitochondrion, apicoplast and cytosol. The following sequence-based localization predictors were used to suggest putative subcellular localization of the enzymes: TargetP [T. gondii and P. falciparum in the cases when the latter were available (see The parasitophorous vacuole (PV) space, which secludes  TargetP   or Coenzyme A (CoA) moiety attached .de novo synthesis of ATP” was formulated as following: given unlimited uptake of glucose, oxygen, inorganic phosphate, sulfate and ammonium, can the model produce ATP. The model failed to accomplish this task, which is consistent with the literature knowledge about auxotrophy of T. gondii for purines [Testing metabolic tasks is a built-in functionality of the RAVEN Toolbox  meant fo purines . Thus, tWe simulated metabolism of the host cell using the most recent tissue-unspecific model of human metabolism . The Recde novo production in the parasite and sequestered towards biomass from the cytosol.In order to simulate replication of the parasite and assess the essentiality of genes, reactions and substrates, we assumed maximisation of flux through the biomass reaction to be the objective function of ToxoNet1. It represents cellular replication as a reaction that consumes pre-defined amounts of metabolites defined as small-molecule biomass precursors as well as energy in the form of ATP. We used the biomass reaction from the previous study  as a temFBA is a standard computational approach for exploration of the metabolic capabilities represented in constraint-based models; principles, computational implementation of FBA as well as the key assumptions are extensively described elsewhere .In the absence of clear knowledge on the scope of substrates that the parasite can take up from the host we made the following assumption: all the metabolites present in the host cell cytosol are potentially accessible for the parasite except those that are phosphorylated, bound to coenzyme A, acyl-carrier protein or carnitine. A list of molecules that satisfy these criteria was generated using the recent tissue-unspecific model of human metabolism Recon2  the lis.Minimal sets of metabolites necessary for the production of biomass were explored using an in-house developed mixed-integer linear programming algorithm. For each of the exchange reactions that allowed uptake of a substrate into the model (i.e. 230 host-supplied metabolites) we added one binary variable that denoted its utilization. Our algorithm solved the model subject to minimization of the sum of the binary variables thus yielding a minimal set of uptakes that allowed the model to simulate growth. After each iteration the algorithm added one new constraint to the model to assure that the following set would include at least one different uptake reaction compared to all the previously generated ones. The iterations were repeated until no more alternative sets of the same length could be found.-1). Similar approaches were used for double gene deletion studies—simultaneously blocking reactions associated with all pairs of genes that were not predicted as singularly essential. In reaction deletion simulations we blocked flux through every single reaction in ToxoNet1 one at a time. Subsequent attempts at achieving the wild-type doubling time in the absence of the reaction indicated whether the gene was dispensable or not.We performed simulation of gene deletion using a conventional approach  that impT. gondii tachyzoites (RHku80-ko (Ku80ko), RHku80-ko/ACS-ko (ACSko), RHku80-ko/ACL-ko (ACLko), RHku80-ko/ACL3Ty-LoxP3’UTRLoxP-U1 (ACL-lox), RHku80-ko/ACS-ko/ACL3Ty-LoxP3’UTRLoxP-U1 (ACSko/ACL-lox), RH/FASIko (FASIko)) were grown in confluent Human Foreskin Fibroblasts (HFF) and maintained in Dulbecco’s Modified Eagle’s Medium  supplemented with 5% foetal calf serum, 2 mM glutamine and 25 μg/ml gentamicin in a humidified incubator at 37°C with 5% CO2.All amplifications were performed with LA Taq (TaKaRa) polymerase and primers used are listed in Knock-in of ACS: (pKI-ACS-3Ty) The genomic fragment of ACS (TGME49_266640) was amplified using primers ACS-1 and ACS-2 prior to digestion with with KpnI and SbfI and subsequent cloning in the same sites of pTub8MIC13-3Ty-HXGPRT [SnaBI.y-HXGPRT  to introKnock-in of AT1: (pKI-AT1-3Ty) The genomic fragment of AT-1 (TGME49_215940) was amplified using primers AT1-1 and AT1-2 prior to digestion with KpnI and NsiI and subsequent cloning in the same sites of pTub8MIC13-3Ty-HXGPRT [HindIII.y-HXGPRT  to introKnock-in and knock-down of ACL: (pKI-ACL-3Ty and pKI-ACL-3Ty-LoxP-3’UTR-LoxP-U1) Genomic fragment of ACL (TGME49_223840) was amplified using primers ACL-1/ACL-2 and subsequently digested with KpnI and NsiI prior to cloning in the same sites of the pTub8MIC13-3Ty-HXGPRT for 3Ty-tag knock-in [XhoI.knock-in , or the knock-in . Prior tKnockout of ACS: (pTub-CAT-ACS-ko) around 1.5kb of the 5’ and 3’ flanking regions of ACS were amplified using primers ACS-3/ACS-4 and ACS-5/ACS-6 respectively. The 5’ flanking region was then cloned between KpnI and XhoI restriction sites of the pTub5-CAT and the 3’ flanking region between the BamHI and NotI sites. The plasmid was cut with KpnI and NotI prior to transfection.Knockout of ACL: (pTub-HXGPRT-ACL-ko) around 1.5kb of the 5’ and 3’ flanking regions of ACL were amplified using primers ACL-3/ACL-4 and ACL-5/ACL-6 respectively. The 5’ flanking region was then cloned between the KpnI and XhoI sites of the p2855-HXGPRT plasmid and the 3’ flanking region between the BamHI and NotI sites. The plasmid was cut with KpnI and NotI restriction enzymes prior to transfection.Frameshift knockout of FASI using CRISPR/CAS9 plasmid : This vector has been generated using the Q5 site-directed mutagenesis kit (New England Biolabs) with the vector pSAG1::CAS9-U6::sgUPRT as template (a gift from Dr. L.D. Sibley). The UPRT-targeting gRNA was replaced by the FASI (TGME49_294820) specific gRNA using the primer pairs gRNA-FASI/gRNA-rev (gRNA highlighted in red in hxgprt gene was used as a positive selectable marker in the presence of mycophenolic acid (25 μg/mL) and xanthine (50 μg/mL) for pKI-ACS-3Ty, pKI-AT1-3Ty, pKI-ACL-3Ty, pKI-ACL-3Ty-LoxP-3’UTR-LoxP-U1 and pTub-HXGPRT-ACL-ko vectors transfected in Ku80ko or ACSko, as previously described [Parasite transfections were performed by electroporation as previously described .The hxgpescribed .Ku80ko was transfected with pTub-CAT-ACS-ko and 20μM chloramphenicol was used to select resistant parasites.Resistant parasites were cloned by limiting dilution in 96 well plates and clones were assessed by genomic PCR.FASI locus, 30 ug of the FASI gRNA-specific CRISPR/CAS9 vector was transfected into wild type RH parasites. 48 hours after transfection, GFP positive parasites were sorted by flow cytometry and cloned into 96-well plates using a Moflo Astrios (Beckman Coulter). Individual clones were then analysed by sequencing.To efficiently disrupt the Genomic DNA was prepared from tachyzoites using the Wizard SV genomic DNA purification system (Promega). Correct integration of the different constructs into the genome of the various strains was determined by genomic PCR using GoTaq Green Master Mix (Promega).The antibodies used in this study were described previously as follows: polyclonal rabbit anti-GAP45, rabbit anti-TgProfilin , monocloParasite-infected HFF cells seeded on cover slips were fixed with 4% paraformaldehyde/0.05% glutaraldehyde (PFA/Glu) in PBS. Fixed cells were then processed as previously described . ConfocaParasites were lysed in PBS-1% Triton X-100 and mixed with SDS–PAGE loading buffer under reducing conditions. The suspension was subjected to sonication on ice. SDS-PAGE was performed using standard methods. Separated proteins were transferred to nitrocellulose membranes and probed with appropriate antibodies in 5% non-fat milk in PBS-0.05% Tween20. Bound secondary peroxidase conjugated antibodies were visualized using the SuperSignal (Pierce).Plaque assays: HFF monolayers were infected with parasites and let to develop for 7 days before fixation with PFA/Glu and Crystal Violet staining to visualize plaques.Intracellular growth assays: HFFs were inoculated with parasites, washed 4h post infection and coverslips were fixed at 24 h post-infection with 4% PFA/Glu and stained by IFA with rabbit anti-TgGAP45. Number of parasites per vacuole was counted in triplicate for each condition (n = 3). More than 200 vacuoles were counted per replicate.http://129.16.106.142/tools.php?c=raven). Input data was taken from KEGG database , ToxoDB , ApiLoc database , and LLAMP portal .Flux-balance analysis was performed using MATLAB  with CPLEX  and Mosek (version 7) solvers; RAVEN Toolbox was used within Matlab environment  PCR for sequencing was done using primers FAS-1/FAS-2 . The gRN(EPS)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(PDF)Click here for additional data file.