Therefore engineering C4 feature into C3 plants has been suggested as a feasible way to increase photosynthesis and yield of C3 plants, such as rice, wheat, and potato. To identify the possible transition from C3 to C4 plants, the systematic comparison of C3 and C4 metabolism is necessary.The C4 photosynthetic cycle supercharges photosynthesis by concentrating CO2 fixation. Moreover, C4 plant has better robustness no matter the objective function is biomass synthesis or CO2 fixation. In addition, all the essential reactions in C3 network are also essential for C4, while there are some other reactions specifically essential for C4, which validated that the basic metabolism of C4 plant is similar to C3, but C4 is more complex. We also identified more correlated reaction sets in C4, and demonstrated C4 plants have better modularity with complex mechanism coordinates the reactions and pathways than that of C3 plants. We also found the increase of both biomass production and CO2 fixation with light intensity and CO2 concentration in C4 is faster than that in C3, which reflected more efficient use of light and CO2 in C4 plant. Finally, we explored the contribution of different C4 subtypes to biomass production by setting specific constraints.We compared C3 and C4 metabolic networks using the improved constraint-based models for Arabidopsis and maize. By graph theory, we found the C3 network exhibit more dense topology structure than C4. The simulation of enzyme knockouts demonstrated that both C3 and C4 networks are very robust, especially when optimizing CO2 and radiation use efficiency. In addition, preliminary analysis indicated that the rate of CO2 fixation and biomass production in PCK subtype are superior to NADP-ME and NAD-ME subtypes under enough supply of water and nitrogen.All results are consistent with the actual situation, which indicate that Flux Balance Analysis is a powerful method to study plant metabolism at systems level. We demonstrated that in contrast to C3, C4 plants have less dense topology, higher robustness, better modularity, and higher CO For C4 plants such as maize (NADP-ME subtype), photosynthetic activities are partitioned between mesophyll and bundle sheath cells that are anatomically and biochemically distinct. The initial carbon fixation is catalyzed by phosphoenolpyruvate carboxylase (PEPC) forming oxaloacetate (OAA) from CO2 and phosphoenolpyruvate (PEP). OAA is metabolized into malate, and then diffuses into the BS cell where it is decarboxylated to provide increased concentration of CO2 around Rubisco. Finally, the initial substrate of the C4 cycle, PEP, is regenerated in mesophyll cell by pyruvate orthophosphate dikinase (PPDK) . The simplified model of the C3 network has 494 reactions, 483 metabolites and narrow range on constraints, which can be separated into 65 modules and the largest module consists of 92 reactions. The simplified model of the C4 network has 826 reactions, 806 metabolites and narrow range on constraints, which can be separated into 113 modules and the largest module consists of 169 reactions. There are more correlated reaction sets in C4 than C3 network.The fluxes of reactions in the same module exhibit linear correlation. We found the reactions in Calvin cycle are correlated in both C3 and C4 network, as illustrated in Figure 2 fixation of C3 and C4 models were simulated under different light intensity, as shown in Figure 2 fixation and light intensity when light intensity is less than 1600. Then the CO2 fixation was almost keeping unchanged. The increase of both biomass and CO2 fixation with light intensity in C4 are faster than that in C3, which reflect more efficient use of solar energy in C4 plants [2 fixation under different CO2 concentration, as shown in Figure 2 concentration increases, the more flux of biomass and CO2 fixation, and the increase gradually change slowly until to steady state. The simulated curve was consistent with experiment A-Ci curve [2 fixation with CO2 concentration in C4 are faster than that in C3, which reflect more efficient use of CO2 in C4 plants.The biomass and CO4 plants . In addiCi curve . We foun2 fixation are maximal in PCK subtype. Moreover, when all the three subtypes are assumed to be active in one metabolism system, the PCK subtype is superior to be used for CO2 decarboxylation. These results are consistent with Fravolini's experiments that photosynthetic performance and above-ground biomass production of B.curtipendula, (PCK subtype) are greater than NADP-ME and NAD-ME types [C4 plants can be classified to three subtypes according to decarboxylation modes: NADP-malic enzyme (NADP-ME), NAD-malic enzyme (NAD-ME) and PEP carboxykinase (PCK). We explored the influence of each subtype on biomass synthesis and CO2 fixation, by blocking the flux of other two enzymes and giving enough supply of water and nitrogen. As shown in Table ME types . HoweverME types ,45. For ME types . Therefo2 and radiation use efficiency, which provide important basis for engineering C4 photosynthesis into C3 plants. In addition, preliminary analysis indicated that the rate of CO2 fixation and biomass production in PCK subtype are superior to NADP-ME and NAD-ME subtypes under enough supply of water and nitrogen. All results are consistent with the actual situation, which indicate that Flux Balance Analysis is a useful method to analyze and compare large-scale metabolism systems of plants.There is possibility to engineer C4 photosynthesis into C3 plants, because all C4 key enzymes are also present in C3 plants, although the expression levels are much lower than that in C4 species . Howeverr between carboxylation and oxygenation under specific CO2 and O2 concentration can be calculated by the following (4-6).We improved AraGEM and C4GEM by setting the ratio ofcarboxylation and oxygenation by Rubsico, which has not been conducted in any plant metabolic system. For C3 plants, the ratio 2 with c K= 460μbar and O2 with o K= 330mbar [2 concentration is 210 mbar and the intercellular CO2 concentration is about 70 percent of CO2 in air, which is 380μbar under standard condition.Equation (5) and (6) include mechaelis constants for CO 330mbar . The O2 2 concentrating mechanism. The ratio r of carboxylation to oxygenation can be expressed as equation (7-15) [Unlike C3 plants, C4 photosynthesis requires the coordinated functioning of mesophyll and bundle sheath cells by COn (7-15) :s Cand m Care CO2 partial pressures respectively in bundle sheath and mesophyll cells; s Oand m Oare O2 partial pressures in the two cells; p Vis the rate of PEP carboxylation; pmax V(120μmol·m-2·s-1) is the maximum PEP carboxylation rate; p K(80μbar) is Michaelis constant of PEP carboxylase for CO2; pr V(80μmol·m-2·s-1)is the constant rate of PEP regeneration; s g(3mmol·m-2·s-1) is the physical conductance to CO2 leakage; c Ais Rubisco-limited rate of CO2 assimilation; j Ais electron-transport-limited rate; A is the CO2 assimilation rate; cmax V(60μmol·m-2·s-1) is the maximum Rubisco activity; γ (0.5/2590) is half the reciprocal of Rubisco specificity; d R= 0.01cmax V= 0.6μmol·m-2·s-1 is leaf mitochondrial respiration; m R= 0.5 d R= 0.3μmol·m-2·s-1 is mesophyll mitochondrial respiration; α  is fraction of PSII activity in the bundle sheath; x (x = 0.4) is partitioning factor of electron transport rate. max J(400μmol electron m-2·s-1) is maximal electron transport rate; c K(650μbar) for CO2 and o K(450mbar) for O2 are mechaelis constants of Rubisco. In C4 plants, CO2 concentration in mesophyll cell is only 37 percent of CO2 in air [Where 2 in air  and the 2 in air .The topological properties of metabolic network can be analyzed based on graph theory, which can reflect the structure and robustness of large-scale network. In this study, the reactions are represented as nodes, if the product of reaction A is the substrate of a reaction B, there will be an edge from A to B. We consider some important parameters including degree, clustering coefficient, betweenness centrality and distance (path length). The degree of a node is the number of edges connected with other reactions. Degree centralization of a network is the variation in the degrees of vertices divided by the maximum degree variation which is possible in a network of the same size. Clustering coefficient is used to compute different inherent tendency coefficients in undirected network. Betweenness centralization is the variation in the betweenness centrality of vertices divided by the maximum variation in betweenness centrality possible in a network of the same size. The distance between two nodes is the shortest path length from one to the other. The diameter of network is the maximal distance among all pairs of nodes. All the topology analysis was conducted using the visual software Pajek .S, the flux through all reactions in a network is represented by the vector v, so the system of mass balance equation at steady state is given as Sv = 0. In any realistic large-scale metabolic model, there are more reactions than compounds, so there is no unique solution to this system of equations. Flux Balance Analysis (FBA) can solve the flux distribution by setting a set of upper and lower bounds on v and optimizing some objective function with linear programming, as following:The biochemical reactions can be represented mathematically in the form of a stoichiometric matrix c is a vector of weights indicating how much each reaction contributes to the objective function. In this study, we choose CO2 fixation and biomass synthesis as two objective functions.Where The COBRA toolbox is a free MATLAB toolbox for performing the simulation. The fluxes that are identified at various perturbations can be compared with each other and with experimental data.Uniform random sampling of the solution space in any environmental condition is a rapid and scalable way to characterize the structure of the allowed space of metabolic fluxes. Before the sampling was performed, the effective constraints for each reaction were calculated using the method of Flux Balance Analysis in COBRA toolbox . SpecifiThe authors declare that they have no competing interests.ZW designed the project and analysis methods. CLW conducted the analysis of metabolic network topology and flux distribution. LYG improved the model by setting the ratio between carboxylation and oxygenation by Rubisco. YXL managed the project. CLW and ZW wrote the manuscript.The constraints in the improved models of C3 and C4 metabolic networksClick here for file


Escherichia coli and Saccharomyces cerevisiae are the most widely used heterologous hosts; a direct comparison of both hosts based on experimental data is difficult though. Hence, the terpenoid pathways of E. coli  and S. cerevisiae , the impact of the respective hosts metabolism as well as the impact of different carbon sources were compared in silico by means of elementary mode analysis. The focus was set on the yield of isopentenyl diphosphate (IPP), the general terpenoid precursor, to identify new metabolic engineering strategies for an enhanced terpenoid yield.Heterologous microbial production of rare plant terpenoids of medicinal or industrial interest is attracting more and more attention but terpenoid yields are still low. Starting from the respective precursor metabolites of the terpenoid pathways  and considering only carbon stoichiometry, the two terpenoid pathways are identical with respect to carbon yield. However, with glucose as substrate, the MVA pathway has a lower potential to supply terpenoids in high yields than the DXP pathway if the formation of the required precursors is taken into account, due to the carbon loss in the formation of acetyl-CoA. This maximum yield is further reduced in both hosts when the required energy and reduction equivalents are considered. Moreover, the choice of carbon source  has an effect on terpenoid yield with non-fermentable carbon sources being more promising. Both hosts have deficiencies in energy and redox equivalents for high yield terpenoid production leading to new overexpression strategies (heterologous enzymes/pathways) for an enhanced terpenoid yield. Finally, several knockout strategies are identified using constrained minimal cut sets enforcing a coupling of growth to a terpenoid yield which is higher than any yield published in scientific literature so far.in silico comparison of the most prominent heterologous hosts E. coli and S. cerevisiae as terpenoid factories giving an overview on several promising metabolic engineering strategies paving the way for an enhanced terpenoid yield.This study provides for the first time a comprehensive and detailed  Escherichia coli and Saccharomyces cerevisiae are the most widely used microorganisms for heterologous terpenoid production. They are preferably used due to advanced molecular biology tools, growth speed and their well-established use in industrial biotechnology [E. coli, the trends concerning the production of artemisinin or paclitaxel show a more even distribution between these two hosts with an increasingly more prevalent use of S. cerevisiae[S. cerevisiae as an eukaryotic organism has the advantage of being more suitable than E. coli for the expression of membrane-bound plant cytochrome P450 enzymes along with their respective reductase [Terpenoids are a class of natural products with important medicinal and industrial applications such as artemisinin  or paclitaxel (anticancer) ,2. Howevchnology ,6. Whileerevisiae. This treductase -9. A fureductase . Moreoveeductase  and is nE. coli and S. cerevisiae differ in their metabolic routes to supply these precursors. The 1-deoxy-D-xylulose 5-phosphate (DXP) pathway of E. coli  is fed by pyruvate and glyceraldehyde-3-phosphate whereas in the mevalonate (MVA) pathway of S. cerevisiae IPP derives from acetyl-CoA [The production of terpenoids in both organisms requires a sufficient supply of their common precursor isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP), which are then condensed, cyclized etc. leading to the structural diversity of terpenoids. etyl-CoA ,13.E. coli mainly focused on the combinatorial overexpression of the rate limiting enzymes encoded by dxs, idi, dxr, ispD and ispF with various ranges of expression levels. Those studies stressed the need to carefully balance the expression of the DXP pathway genes with the heterologous terpenoid forming pathway as well as with the host’s overall metabolism [E. coli, circumventing the largely unidentified regulations of its native DXP pathway and also decoupling the MVA pathway from its native control in yeast [Attempts to engineer the DXP pathway in tabolism -21. Altein yeast ,22-25.S. cerevisiae[HMG1), the key regulatory enzyme of the pathway, devoid of feedback inhibition by farnesyl diphosphate (FPP) [ERG9) to reduce the flux draining of FPP to biosynthetically related sterols [upc2-1) to transcriptionally up-regulate several MVA pathway genes [Numerous engineering strategies have been applied especially to the MVA pathway in erevisiae. Three pte (FPP) -28; ii)  sterols ,30; and ay genes ,8,31.in vivo head-to-head comparison of both organisms and terpenoid pathways is difficult to interpret, especially if it is considered that the expression of enzymes of both IPP forming pathways has to be balanced carefully for optimal precursor supply and that E. coli and S. cerevisiae differ in their central metabolic networks. However, in silico methods can reveal their theoretical potential and give further insight into the properties of the underlying metabolic networks, thus providing a rational basis for metabolic engineering.A direct a priori measurements, or an objective function [et al. [Elementary mode analysis (EMA) is a key methodology to study essential properties of a metabolic network. Elementary modes (EMs) characterize the space of feasible steady-state flux distributions; any such flux distribution can be represented as a non-negative (conic) linear combination of elementary modes. Based on the stoichiometric matrix, the steady-state assumption, thermodynamic constraints (reaction reversibilities) and a non-decomposability constraint, elementary modes can be calculated without the need for kinetic data, function -36. EMA function ,37 in orfunction ,38-41. R [et al.  introduc [et al.  that has [et al. .E. coli and S. cerevisiae for the production of the common terpenoid precursor IPP by means of carbon stoichiometry, energy and redox equivalent requirements. Further, the potential of the two hosts’ central metabolic networks to meet those requirements if the MVA, the DXP or both pathways are operating is analyzed. Moreover, the impact of different industrially relevant carbon sources on IPP production is investigated in silico.In this study, EMA is applied to compare the metabolic networks of in silico and knockout targets are computed based on constrained minimal cut sets to give guidelines for metabolic engineering in order to obtain a highly efficient IPP platform organism.While most studies on terpenoid production mainly focused on engineering the DXP and MVA pathway, only a few studies deal with the identification of engineering targets in the two host’s central metabolism for an improved precursor supply ,41,45-50E. coli and S. cerevisiae to produce the precursor IPP is analyzed. For this purpose, models of the central carbon metabolism were built up and analyzed  and pyruvate (PYR) and the overall stoichiometry of this pathway is given by equation 1.The MVA pathway is fed by the carbon precursor acetyl-CoA (AcCoA) and its stoichiometry is given by equation 2.2 per mol IPP (C5); therefore they are identical with respect to carbon yield (5/6 = 0.83) when starting from their precursors GAP (C3) and PYR (C3) or AcCoA (C2). Regarding the demand of ATP and NADPH, they show only minor differences as the DXP pathway needs one mol more NADPH but one mol less ATP than the MVA pathway. Hence, a priori, one could assume that no significant differences in achievable IPP yields occur between E. coli and S. cerevisiae.Both terpenoid pathways lose one mol COE. coli is 5/6 = 0.83 C-mol/C-mol compared to 5/9 = 0.56 C-mol/C-mol in S. cerevisiae if the MVA pathway is used  of one mol CO2. In contrast, the formation of one mol pyruvate and one mol glyceraldehyde-3-phosphate, the precursors of the DXP pathway, does not involve a carbon loss via CO2.Both terpenoid pathways are now analyzed with respect to the metabolic background of their respective host. A first comparison of the maximal IPP yields purely based on carbon stoichiometry, ignoring energy and redox equivalent requirements (i.e. when ATP and NAD(P)H are initially considered to be external metabolites in the model), shows a significant difference. With energy and redox equivalents in excess, the maximum IPP yield on glucose via the DXP pathway in d Figure . This isE. coli and S. cerevisiae as well  in E. coli, the overall stoichiometry is given by equation 3.Besides the different carbon use efficiencies, the energy and redox equivalent requirements for IPP synthesis differ in l Figure . For theS. cerevisiae, the overall stoichiometry for synthesizing one mol IPP with glucose as substrate via the MVA pathway  is given by equation 4.In E. coli, the synthesis of IPP is still accompanied by oxidation of NADPH (equation 3) whereby in S. cerevisiae reduced redox equivalents are generated (equation 4). In both organisms, additional ATP has to be generated from the substrate for IPP synthesis, which leads to a further reduction of the maximum IPP yield.Now, in the context of the metabolic background, significant differences are observed. When the pathways are considered isolated from the network, they can both serve as redox sinks as they require reduced redox equivalents (see equations 1 and 2). However, due to the synthesis of the specific pathway precursors, different energy and redox equivalent requirements have to be balanced by the organism’s metabolism. In E. coli (DXP pathway) and S. cerevisiae (MVA pathway) wild types with glucose as the single carbon source. In total, 36,590 elementary modes were obtained for the E. coli network and 9,844 for the S. cerevisiae network  of E. coli include IPP formation. Computed elementary modes exhibit different yields for IPP and biomass in both organisms . In the optimal case, NADPH is provided by a transhydrogenase, an enzyme which transfers reducing equivalents between two nucleotide acceptors to balance the intracellular redox potential (6 NADH + 6 NADP+ + 2 ATP = 6 NADPH + 6 NAD+ + 2 ADP). However, this leads to an increased requirement for ATP and NADH. Thus, the cell is forced to branch off some carbon flux to the citric acid cycle to generate NADH and ATP as shown in the optimal flux distributions  to about 0.67 C-mol/C-mol in the optimal flux distributions .The limitation in redox equivalents and energy for high yield IPP production can be validated S. cerevisiae, 3 NADH and one NADPH are produced via glycolysis while 6 ATP are needed for the production of one mol IPP from 1.5 moles glucose as described above. Generated NADH can be reoxidized via the respiratory chain to produce ATP. However, not enough ATP can be produced due to the stoichiometry of the respiratory chain. Thus, the cell would have a deficiency in energy for high yield IPP production and would be forced to branch off some carbon flux to the citric acid cycle to generate ATP via NADH. This branch-off is shown in the computed optimal flux distributions  show high product yields as well. However, pure fermentative modes are characterized by a very low theoretical maximum product yield of about 0.1 C-mol/C-mol. High ATP requirements of the MVA pathway cannot be delivered by fermentative metabolism. Additionally, the formation of CO2, the only side-product, is much higher than in optimal flux distributions in E. coli reflecting the high carbon loss in the formation of acetyl-CoA.In s Figure . The maiE. coli and S. cerevisiae, the main flux in computed optimal flux distributions at maximum IPP yield is similar. Key pathways are glycolysis and terpenoid pathway and a portion of the flux is directed to citric acid cycle and respiratory chain. Transhydrogenase is active in E. coli while pyruvate dehydrogenase bypass is active in yeast to supply NADPH. Dispensable reactions include by-product formation except CO2, gluconeogenetic and anaplerotic reactions as well as pentose phosphate pathway if no biomass is produced. Limitations in redox equivalents and energy for high yield IPP production could be validated in silico.Even though theoretical maximal yields differ in S. cerevisiae has been functionally introduced in E. coli and this strain produced substantially more terpenoid than strains with the native or even engineered DXP pathway [in silico into E. coli to analyze its effects on IPP production. The introduction of the MVA and the removal of the DXP pathway  lead to a theoretical maximum value of about 0.56 C-mol/C-mol . No energy or redox equivalent limitations exist for high yield IPP production. E. coli possesses a cytosolic pyruvate dehydrogenase complex instead of a pyruvate dehydrogenase bypass (NADPH source). However, transhydrogenase, Entner-Doudoroff pathway as well as malic enzyme can act as NADPH source. Nevertheless, the sole use of the MVA pathway in E. coli is stoichiometrically not efficient as the maximum yield is lower than with the native DXP pathway. The coexistence of both pathways in E. coli  leads to a theoretical maximum IPP yield of 0.67 C-mol/C-mol, which is identical to the value if only the DXP pathway is active. However, the theoretical maximum value in an elementary mode, which includes biomass formation, is slightly enhanced to 0.59 C-mol/C-mol. Thus, the coexistence of both pathways is not only beneficial due to the decoupling of the MVA pathway from its native control in yeast but also in terms of stoichiometry.The MVA pathway from  pathway . Due to In silico, the introduction of the DXP and the removal of the MVA pathway in S. cerevisiae  lead to an increase in the theoretical maximum IPP yield to about 0.64 C-mol/C-mol, and 0.57 C-mol/C-mol including biomass formation. The coexistence of both pathways in yeast  does not lead to a further enhanced IPP yield. Solely the number of elementary modes and thus the flexibility is enhanced. The IPP carbon yield is slightly lower than that one of E. coli as yeast does not possess a transhydrogenase to supply NADPH. Hence, pentose phosphate pathway has to deliver NADPH which involves a carbon loss. Pyruvate dehydrogenase bypass can deliver NADPH in the formation of acetyl-CoA; however, acetyl-CoA is not needed for the DXP pathway. The limitation in energy and redox equivalents can be validated in silico by the introduction of artificial ATP, NADH and especially NADPH sources in the model, which lead to an enhanced theoretical maximum IPP yield of 0.66-0.8 C-mol/C-mol H considered as external metabolites) is identical to that on glucose (0.83 C-mol/C-mol). The overall stoichiometry for the formation of one mol IPP from glycerol is given by equation 5.The maximum IPP yield in E. coli and the native DXP pathway on glycerol  were enhanced in comparison to glucose in silico (0.78 C-mol/C-mol at zero growth and 0.77 C-mol/C-mol in an elementary mode that includes biomass formation). Yields were slightly lower for the heterologous MVA pathway (0.56 C-mol/C-mol at zero growth and 0.45 C-mol/C-mol including biomass formation). Thus, glycerol and the native DXP pathway have the highest potential for E. coli to support high IPP production. The main flux in optimal flux distributions passes lower part of glycolysis and DXP pathway, while only a small portion is diverted to citric acid cycle, respiratory chain and transhydrogenase to generate NADPH, NADH and ATP. The limitation in energy and redox equivalents for high yield IPP production can be validated in silico by the introduction of artificial ATP, NADH or NADPH sources in the model, which lead to the maximum IPP yield of 0.83 C-mol/C-mol  pathway as well as the xylose reductase and xylitol dehydrogenase (XR-XDH) pathway [The carbon as well as the overall stoichiometry for the formation of one mol IPP from galactose or fructose is identical to glucose (see equation 4). Accordingly, the numbers of elementary modes as well as the IPP and biomass yields with galactose and fructose are identical to those with glucose. A heterologous pathway has to be introduced into  pathway . The carThe overall stoichiometry is identical for the XR-XDH pathway if xylose reductase uses NADH (as xylitol dehydrogenase delivers NADH) and slightly changes if the enzyme uses NADPH. The theoretical maximal IPP and biomass yields are identical to the values on glucose for both the XI  and the XR-XDH pathway . However, the IPP yield including biomass formation is enhanced to 0.51 C-mol/C-mol with the XR-XDH pathway. The number of elementary modes and thus the flexibility of the metabolic network is greatly enhanced if the XR-XDH pathway is chosen. The optimal flux distributions were basically identical to those on glucose except that xylose enters glycolysis via the non-oxidative part of pentose phosphate pathway.The maximum IPP yield on the non-fermentable carbon source glycerol (GLYC) purely based on carbon stoichiometry  is with 0.56 C-mol/C-mol identical to glucose. However, the overall stoichiometry is slightly modified (equation 7).+-dependent aldehyde dehydrogenases, second part of citric acid cycle as well as mitochondrial transport systems indicating a certain flexibility in NADH redox balancing which does not influence the IPP yield. The very high number of elementary modes  implies an increased redundancy and thus network flexibility and is due to the activity of the glyoxylate cycle. The glyoxylate cycle showed only a weak activity in yeast cells grown on glycerol in our experiments (data not shown). If the cycle is excluded from the computations, the number of elementary modes decreases  but the theoretical maximum IPP yield remains 0.56 C-mol/C-mol. Nevertheless, the IPP yield including biomass formation is much higher if the cycle is included (0.47 C-mol/C-mol versus 0.35 C-mol/C-mol) indicating that an active glyoxylate cycle could be of benefit.Less energy is required and more NADH is delivered for the production of one mol IPP from glycerol. Glycerol is channeled into glycolysis via glyceraldehyde-3-phosphate and the main flux passes lower part of glycolysis, pyruvate dehydrogenase bypass and the MVA pathway. The carbon flux is not diverted to citric acid cycle as enough energy is produced via NADH and respiratory chain. Thus, the maximum IPP yield of 0.56 C-mol/C-mol is achieved, which is identical to the maximum IPP yield based on carbon stoichiometry only. No limitations in energy or redox equivalent requirements thus exist for high yield IPP production. The optimal flux distributions further reveal that pentose phosphate pathway, side product formation, the first part of citric acid cycle as well as glyoxylate cycle are not active. Some fluxes are flexible namely those of alcohol dehydrogenases, NADE. coli. The overall stoichiometry for the formation of one mol IPP from ethanol (EtOH) is given by equation 8.The maximum IPP yield on the non-fermentable carbon source ethanol purely based on carbon stoichiometry is with 0.83 C-mol/C-mol as high as the IPP yield in 2 (except the carbon loss in the terpenoid pathway itself). In fact, similarly to glycerol and E. coli, ethanol  reveals an increased theoretical maximum product yield of 0.68 C-mol/C-mol at zero growth as well as 0.59 C-mol/C-mol including biomass formation for the MVA pathway. The high number of elementary modes is again due to the activity of the glyoxylate cycle. The optimal flux distributions of yeast (without biomass formation) with only the MVA pathway reveal that the glycolysis, gluconeogenesis, pentose phosphate pathway and side product formation are not active. The flux passes pyruvate dehydrogenase bypass, glyoxylate cycle as well as citric acid cycle and respiratory chain to generate ATP. The energy deficiency preventing high IPP yields can be validated in silico by introducing artificial ATP or NADH sources which lead to the maximum yield of 0.83 C-mol/C-mol. As expected from these computations, the sesquiterpenoid yield of the same yeast strain was increased significantly in vivo by changing the carbon source from glucose (0.0111 C-mol/C-mol) to ethanol (0.1867 C-mol/C-mol) [In contrast to glucose or glycerol as the carbon source, the formation of IPP from ethanol requires more energy but does not involve an additional carbon loss via COin silico and the growth on galactose, fructose or xylose lead to similar yields to those on glucose, except that the theoretical maximal IPP yields including biomass formation are slightly increased on xylose. With ethanol as substrate, the heterologous DXP pathway shows a lower potential compared to the native MVA pathway, with yields of 0.63 C-mol/C-mol at zero growth and 0.57 C-mol/C-mol including biomass formation. Lower yields are due to energy-consuming gluconeogenetic reactions which are essential to provide the precursors of the DXP pathway from ethanol. Nevertheless, the introduction of the DXP pathway could be beneficial for growth on glycerol. The theoretical maximum IPP yield is enhanced to 0.67 C-mol/C-mol at zero growth and 0.6 C-mol/C-mol including biomass formation. The cells would have a limitation in NADPH for high yield IPP production, which can be validated via the introduction of an artificial NADPH source leading to the maximum value of 0.83 C-mol/C-mol.The introduction of the DXP pathway S. cerevisiae while the non-fermentable carbon sources ethanol and glycerol show the highest potential with respect to yield and network flexibility. Similarly, glycerol shows the highest potential in E. coli to supply IPP in high yields.To summarize this subsection, sugars like glucose, galactose, fructose and even xylose lead to identical or similar theoretical maximal IPP yields in A comparison of computed theoretical maximal IPP yields with terpenoid yields published in literature is shown in Table The identified limitations in the generation of sufficient energy and redox equivalents for high yield IPP production with both organisms can be used as starting point for metabolic engineering. Evidently, a reasonable approach would be to implement heterologous enzymes that are more efficient with respect to NAD(P)H or ATP consumption.E. coli, an increase in the maximum yield could be accomplished by the introduction of alternative pathways with less energy requirements or additional NAD(P)H sources. For instance, an additional heterologous NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase enhances the theoretical maximum IPP yield to 0.71 C-mol/C-mol on glucose and to 0.83 C-mol/C-mol on glycerol.For S. cerevisiae, an increase in the maximum IPP yield could be accomplished by the introduction of alternative pathways with less energy requirements. S. cerevisiae possesses a mitochondrial pyruvate dehydrogenase complex and a cytosolic pyruvate dehydrogenase bypass to produce acetyl-CoA. Solely acetyl-CoA produced via the bypass, which requires ATP, can be used for terpenoid production since acetyl-CoA cannot be exported from the mitochondria. In contrast, E. coli possesses a cytosolic pyruvate dehydrogenase complex, which does not require energy in the form of ATP. In silico, a heterologous introduction of cytosolic pyruvate dehydrogenase complex from E. coli into S. cerevisiae leads to the theoretical maximum IPP yield of 0.56 C-mol/C-mol and to a mode enabling biomass formation coupled to an increased IPP yield of 0.51 C-mol/C-mol on glucose. A cytosolic pyruvate dehydrogenase complex has to our knowledge not been introduced into yeast with the aim of enhancing terpenoid production. Though, a patent application indicates that the complex of E. coli  might increase the cytosolic acetyl-CoA pool for butanol production [For oduction .S. cerevisiae into the mitochondria. Likewise, on glucose, the theoretical maximum IPP yield at zero growth is enhanced to 0.56 C-mol/C-mol and the IPP yield including biomass formation is enhanced to 0.51 C-mol/C-mol, if only the MVA in the mitochondria is active, or to 0.56 and 0.53 C-mol/C-mol if the MVA is active in the cytosol and the mitochondria. The mitochondrial IPP and DMAPP as well as the FPP pool have been harnessed successfully for terpenoid production [A different approach would be to transfer the native MVA pathway from oduction . HoweverAlternatively, an ATP-citrate-lyase can be expressed heterologously in the cytosol. The enzyme forms cytosolic acetyl-CoA from citrate that has been exported from the mitochondria and thus circumvents pyruvate dehydrogenase bypass. The reaction is accompanied by the conversion of one mol ATP to ADP per acetyl-CoA, while the cytosolic pyruvate dehydrogenase bypass converts in the net one mol ATP to AMP. Thus, ATP-citrate-lyase is more energy-efficient and thus promising. The theoretical maximum IPP yield at zero growth is indeed enhanced to 0.55 C-mol/C-mol. Moreover, a patent application indicates that this approach might be promising .Azotobacter vinelandii as well as the introduction of a membrane-bound enzyme from E. coli into S. cerevisiae led to a reduction of the NADPH pool including an increase in glycerol formation [+-dependent glyceraldehyde-3-phosphate dehydrogenase leads in silico to the same improvement in IPP yield on glucose and to a yield of 0.76 C-mol/C-mol on glycerol.If the DXP pathway is introduced into yeast, a heterologous soluble transhydrogenase leads to a higher theoretical maximum IPP yield of 0.67 C-mol/C-mol on glucose. However, the introduction of a heterologous soluble transhydrogenase from ormation -67, whicin vivo validation, it has been shown that adaptive evolution is essential to improve production capabilities from sub-optimal metabolic states to optimal ones as predicted from in silico analysis [The constrained minimal cut sets (cMCSs) approach allows the identification of all possible knockout combinations that eliminate undesired (low-yield) elementary modes and keep desired modes, which (i) have a specified minimum product yield and (ii) allow at least some biomass formation . Hence, analysis ,69.E. coli and S. cerevisiae to present new intervention targets for metabolic engineering.In this study, cMCSs are used to identify combinations of gene deletions for both E. coli wild type metabolic network on glucose  as well as glycerol as carbon source . For glucose, the deletion task was defined by collecting all elementary modes exhibiting an IPP yield lower than 0.25 C-mol/C-mol in the set of target modes n = 1; see Methods), a total of 488 cMCSs accomplishing the specified engineering goal were computed. Cut sets including diffusion processes or parts of respiratory chain were excluded from further considerations due to poor feasibility leaving 28 cMCSs with two or four interventions  has been done in E. coli in vivo and led to an increased flux through pentose phosphate pathway and thus to excess NADPH formation. However, an additional knockout of transhydrogenase (∆pgi ∆udhA) was lethal due to NADPH imbalance in this wild type strain [Another cut set included glucose-6-phosphate-isomerase, transhydrogenase, Entner-Doudoroff pathway as well as one specific part of respiratory chain. The cMCS is not shown in Table e strain . This exn = 1). A total of 769 cMCSs were computed, while cut sets that cannot be realized in practice  were excluded from further considerations leaving only 8 cMCSs with eight interventions each  defining E. coli strains carrying the additional MVA pathway. The IPP and biomass yields of remaining elementary modes are only slightly modified (results not shown).The presented minimal cut set strategies can also be applied to E. coli.The cMCSs approach points to new and promising combinations of metabolic engineering targets. The predicted minimal IPP yields are much higher than the yields that have been published in literature so far. Nevertheless, kinetics and regulation, especially of the DXP pathway, may impose additional challenges to be solved in order to achieve high terpenoid yields with n = 1), a total of 407 cMCSs were computed. Cut sets including mitochondrial transport systems, diffusion, ATP maintenance etc. were excluded from further considerations leaving 8 feasible cMCSs with three to six interventions . Modes exhibiting an IPP yield of not more than 0.25 C-mol/C-mol were collected in the set of target modes 4 EMs. Mo2. Hence, the cell is forced to secrete products like ethanol or acetate. The repression of acetate and ethanol excretion forces the cell to find another sink like IPP. Targets identified for glucose can also be applicable to other sugars like galactose or fructose.Possible knockout combinations were simulated and Table LSC1) was implemented in S. cerevisiae and an enhanced acetate secretion was observed [The knockout of succinyl-CoA ligase  do not oxidize NADPH and malic enzyme reaction seems to be irreversible in vivo[PGI1 gene encoding glucose-6-phosphate-isomerase [cMCSs including a heterologous DXP pathway are basically identical to those of the wild type. Thus, presented strategies can also be applied to a strain carrying an additional DXP pathway if the pathway was active e in vivo,73. Yeassomerase  and no eMinimal cut sets were also computed for the metabolic network with xylose as carbon source including either the XI or the XR-XDH pathway together with the MVA pathway. Identified knockout targets as well as yields after the implementation of a minimal cut set for the XI pathway are identical to those of glucose. Knockout targets for the XR-XDH pathway are different. Minimal cut sets only exist with an additional target, ribulose-5-phosphate-3-epimerase in pentose phosphate pathway. Cut sets including pyruvate dehydrogenase complex do not exist. Moreover, possible IPP yields after the implementation of a minimal cut set are slightly different and can vary between 0.30 and 0.50 C-mol/C-mol.S. cerevisiae. Moreover, the knockout of glucose-6-phosphate-isomerase appears as a very promising strategy to enhance terpenoid production if the DXP pathway can be functionally expressed in yeast.To our knowledge, a disruption of citric acid cycle has not been done with the aim to enhance terpenoid production. Predicted minimal IPP yields are much higher than yields that have been achieved in literature so far. Thus, targets identified by the minimal cut sets approach are completely new and promising for an enhanced terpenoid yield on sugars with E. coli and S. cerevisiae as well as their respective terpenoid pathway, heterologous enzymes and different carbon sources have been analyzed in silico in this study with respect to their potential as terpenoid factories. The DXP pathway of E. coli has a tremendously higher potential for high IPP yields (nearly 50% higher) than the MVA pathway of yeast with respect to carbon stoichiometry due to their different precursors. However, kinetics, regulation and a balanced overexpression have to be considered for in vivo production. Further research should focus on the optimization of the DXP pathway to tap its full potential. E. coli wild type shows a higher theoretical potential to supply IPP in high yields than S. cerevisiae wild type. However, the theoretical maximal yields can be enhanced in both organisms by introducing heterologous enzymes or whole pathways or by simply changing the carbon source. Moreover, new constrained minimal cut set strategies can be applied to both hosts to enforce a coupling of growth to a minimum IPP yield which is higher than any yield published in scientific literature so far.The use and optimization of microorganisms for an ecologically and economically efficient production of plant terpenoids is a contemporary issue in academic research as well as industry. The most widely used model organisms N (with q reactions and p metabolites) and a set Irrev of irreversible reactions. The set of steady-state flux vectors r form the convex (flux) cone reads:Consider a given metabolic reaction network with q × p stoichiometric matrix e ∈ F , i.e. for every vector r ∈ F, r ≠ 0, one has supp(e) ⊆ supp(r), where the support of a vector is defined as the index set of all reactions carrying a non-zero flux (supp(r) = {i | ri ≠ 0}). EMs correspond to minimal functional units (pathways or cycles) of a metabolic network and are useful to study various functional network properties [An Elementary Mode is a non-decomposable vector operties ,36.C of reactions, the removal of which will block (disrupt) a given set M of target flux vectors, M ⊆ F[In contrast, a minimal cut set (MCS) is a (support-) minimal set rs, M ⊆ F,76:One drawback of the original definition of MCSs is that besides the targeted functionalities some desired functionalities can be lost as well. Therefore, a generalization to constrained MCSs was introduced  which cat is represented by its support T = supp(t)).1) Define a set of target modes d is represented by its support D = supp(d)).2) Define a set of desired modes n quantifying the minimal number of EMs from 3) Specify a number n), implemented in the software package CellNetAnalyzer[cMCSs provide a flexible and convenient approach for formulating and solving intervention problems. The approach also allows one to compute interventions that lead to robust coupling of product and biomass formation . To compute the cMCSs, we applied an adapted Berge algorithm . For S. cerevisiae the basic glucose network comprises 69 reactions, thereof 30 reversible, and 60 metabolites . The P/O-ratios (mol ATP produced per 0.5 mol O2 reduced) representing the efficiency of ATP production are assumed to be 1.2 for S. cerevisiae[E. coli 2 from NADH and 1 from quinol [S. cerevisiae and E. coli have been constructed considering the current knowledge from genome-scale models and literature data [A comprehensive analysis of the complete set of elementary modes is still infeasible with genome-scale metabolic models. Therefore, stoichiometric models of the central metabolism of erevisiae,79 and fm quinol ,43,80. Ture data -86. The AcCoA: Acetyl-CoA; ACL: ATP-citrate-lyase; CDW: Cell dry weight; cMCS: Constrained minimal cut set; DH: Dehydrogenase; DMAPP: Dimethylallyl diphosphate; DXP: 1-deoxy-D-xylulose 5-phosphate; EM: Elementary mode; EMA: Elementary mode analysis; EtOH: Ethanol; Ex: Excretion; FPP: Farnesyl diphosphate; GAP: Glyceraldehyde-3-phosphate; GLC: Glucose; GLYC: Glycerol; IPP: Isopentenyl diphosphate; MCS: Minimal cut set; MEP: 2C-methyl-D-erythriol 4-phosphate; MVA: Mevalonate; PDH: Cytosolic pyruvate dehydrogenase complex; PYR: Pyruvate; TH: Soluble and energy-independent transhydrogenase; XI: Xylose isomerase; XR-XDH: Xylose reductase and xylitol dehydrogenase; XYL: Xylose.The authors declare that they have no competing interests.EG did all the computations and interpretations. OH and SK supported the constrained minimal cut sets computations. EG and VS designed the study. EG, VS, OH and SK wrote and drafted the manuscript. OK supervised the research. VS conceived of the study and supervised the research. All authors read and approved the final manuscript.Metabolic networks. Metabolic networks of S. cerevisiae and E. coli including the complete list of genes, heterologous enzymes, alternative carbon sources and considered literature.Click here for file


This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell.The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and Aquilegia and; ii) the impact of another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent.Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation correspond with existing experimental observations. Two examples are: i) two MCSs which require the simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational results of the same genes being co-regulated for eliminating floral pigmentation in in silico analyses methods in the design of experiments. The methods could be used to prioritize target enzymes for different objectives to achieve desired outcomes, especially for less understood pathways.From the various MCSs identified for eliminating colour pigmentation, several correlate to existing experimental observations, indicating that different MCSs are suitable for different plants, different cells, and different conditions and could also be related to regulatory genes. Being able to correlate the predictions with experimental results gives credence to the use of these mathematical and  Flavonoids ,2 are ubAquilegia, the particular subset of genes that were suppressed, was strongly correlated. They also observed that the genes concerned (DFR and ANS) were co-regulated, one of similar findings of co-regulation in the anthocyanin biosynthetic pathway (ABP) genes, another example of which is made by Durbin et al. [Because of their role in pigmentation, changes in the expression of anthocyanins are easily observed. One example is where flowers have been found to lose their colour in response to different insect pollinators . It is rn et al.  who showin silico perspective. Rather than observing genes and inferring network properties, we start from a known self-contained metabolic network containing all reactions necessary for the synthesis of flavonoid compounds. As reactions are catalyzed by enzymes which are coded by genes, the reactions can be correlated to their corresponding genes; without needing knowledge about the genes involved, metabolic pathway analysis (MPA) [This study complements the biological investigations from a mathematical and is (MPA) ,8 tools In their work, Whittal et al remarked that the DFR and ANS enzymes corresponded to steps that were late in the ABP. This led them to interpret their finding as a consequence of pleiotropic functions for enzymes such as CHS and CHI that occur earlier in the ABP. While this explanation seems plausible enough in terms of a linear pathway, it needs to be recognized that, like all empirical pathways, the ABP is in fact embedded in a complex biochemical network. This raises additional questions, for example, even if the early enzymes perform other functions, would their loss destroy those functions or are there alternative paths available? Instead of blocking a "late" gene, would it not be more efficient to block an "early" gene and eliminate the subsequent reactions? Is the number of reactions in which an enzyme participate an adequate measure of whether it is essential, or can more refined measures be employed?In order to answer such questions, a more quantitative approach based on the biochemical network analysis methods is clearly needed. The MPA methods are based on elementary modes (EMs) which are non-decomposable pathways or routes that lead from an external substrate to an external product with the network maintained at steady state. The EMs consist of minimal sets of reactions that allow the system to operate at steady state ,10; theyEMs can be used to compute minimal cut sets (MCSs) . MCSs arBy performing a comprehensive mathematical analysis of the extracted subnetwork structure, the study enables the prediction of all possible EMs and MCSs compatible with the network structure. Such exhaustive characterization is very hard to achieve experimentally because, regardless of how many examples of a phenomenon one has observed, there might always be others not yet observed. However, the mathematical analysis provides a complete set of all possible EMs and MCSs. It is this aspect of completeness, subject only to a complete knowledge of the network itself, that makes it possible to make quantitative assessments e.g. of the relative importance of reactions and their corresponding enzymes/genes.Hence, the study provides an interesting look at all the possible target genes for anthocyanin suppression and how they fit into different situations. For example, although it is possible to target one single enzyme in order to suppress anthocyanin production, the question is whether that would be best? In nature, the absence of more than one enzyme is often observed and the reasons for pigmentation loss are numerous: it can be a single mutation in CHS, as described for a specific white flowering Matthiola line  or petunArabidopsis network. Next, the set of EMs for it are calculated. For the main focus of the study, which is the role of anthocyanin genes in the loss of colour pigmentation, the EMs that directly lead to the formation of anthocyanin compounds are identified and taken to constitute the ABP. MCSs and other methods designed for EM-based network structural analysis are then applied to identify and compare candidate genes (through their associated reactions) that could be targeted for eliminating colour pigmentation. Finally, we also use flux balance analysis (FBA) to investigate how each MCS candidate would affect the rate of production of associated metabolites. Where possible, the analysis results are compared and related to results from biological studies.The study proceeds through a number of stages: First, a self-contained flavonoid subnetwork is extracted from the full Arabidopsis, is used for the study because of its fully sequenced genome and the actively curated metabolic data (AraCyc) [Arabidopsis network formed by collecting all known metabolic reactions and enzymes from the AraCyc database version 8.0 [AC8_subnet.sbml, metabolites, prelim_constr_influx.val, and reactions, constitute input files for CNA).The standard model plant, (AraCyc)  availablsion 8.0 . A set osion 8.0  scripts sion 8.0  performssion 8.0  network sion 8.0 , a softwFrom the MPA perspective, a metabolic network is a (hyper-) graph where the nodes represent compounds and the edges chemical reactions. The boundary nodes are external biochemical inputs (substrates) or outputs (products) while the internal nodes are intermediate compounds with associated stoichiometric constraints to guarantee mass conservation. Full specification of the network also requires identification of the compounds to be taken as external. As part of the parsing process, a preliminary classification of 'external' is applied to all compounds that consistently appear as either substrates or products in all reactions.While many previous MPA studies, particularly on microbes, have been directly applied to genome scale networks, our approach is to first isolate a self-contained functional subnetwork to which FBA methods are then applied. This is motivated, both by:Arabidopsis network (2719 compounds in 2418 reactions), which would entail a combinatorial explosion of the number of EMs [i) the large size of the r of EMs  and timer of EMs ,27; andii) the fact that only a limited subset of the secondary metabolism is relevant to flavonoid production.For the approach to be viable, the subnetwork needs to be both self-contained  and complete .To achieve that, we note that a subnetwork is created by cutting through some internal network nodes, in effect removing the corresponding stoichiometric constraints. That is justifiable for compounds that are known to be of sufficient supply or that are buffered by an external reservoir in the cell, such as carrier compounds like ATP and ADP. The fact that this maintains the integrity of all EMs involving such compounds, is intuitively obvious but has been more formally demonstrated using null space analysis in .The following heuristic approach  has beenFirst, a list of external compounds plausibly buffered for the purposes of flavonoid metabolism is drawn up. Guidelines for this is taken from the catabolism-anabolism interface as described, for example, by Palsson : fig thrThe preliminary classification of nodes in the network mentioned above, is augmented by taking all those that correspond to compounds on this list, as external as well. The connectivity (i.e. the number of reactions in which it participates) for each node in the network is also recorded.Next, a prototype subnetwork for flavonoid production is compiled from all reactions participating in empirically known pathways explicitly listed in the AraCyc pathways table as flavonoid related, for example, anthocyanin, flavonol, and flavanone biosynthetic pathways.This prototype is then extended by comparing node classification and connectivity for the full and prototype networks:Arabidobsis network, where it is involved, on the one hand, in the formation of cyanidin and 2,3-trans-catechin, but itself is formed by dihydroquercetin. The related compounds and their corresponding reactions are added to the prototype and leucocyanidin becomes internal in both the revised prototype and the full network.1. If a node is external in the prototype but internal in the full network, its links in the full network are traced back and forwards to an acceptable external and the node becomes internal in the prototype. For example, taking the anthocyanin biosynthetic pathway as one of the starting pathways for the prototype, consider one of its starting substrates- the compound leucocyanidin. Leucocyanidin is external in the anthocyanin pathway but internal in the full AraCyc f in the full network than its connectivity Cs in the subnetwork, then:2. If a node is internal in both, but has larger connectivity Cf >>Cs, the node is be reclassified as external in the prototype because there are many processes in the full network outside the subnetwork to account for buffering it. In effect, this refers to ubiquitous compounds and if we had not already recognized compounds like malonyl coA, Erythrose-4P as ubiquitous earlier in the process, they would have been classified here as external in the prototype, although they are internal, but highly connected, in the full network. A highly connected node in the full network qualifies the node to be external in the prototype.• If C• Otherwise, all its connections are traced back as in 1 above.3. All reactions encountered in the traceback are added to the prototype, thus transforming a node initially not in the prototype, into an internal or an external node according to its status in the full network.O-sophoroside and malonylshisonin, which are external or highly connected in the full network.4. Classification, comparison and traceback steps 1- 3 are repeated until no reaction is added and no node is reclassified. For example, starting from our initial compound, leucocyanidin, the iterated traceback steps eventually lead to external nodes that include substrates like cinnamyl-alc or products like 2,3-trans-catechin, 2,3-cis-epicatechin, cyanidin 3-The prototype becomes a self contained subnetwork when the nodes connecting it to the rest of the full network are either external nodes in the full network, or boundary nodes that represent plausible reservoirs. Furthermore, the set of links emanating from each internal node in the subnetwork is identical in both the full network and the subnetwork. This is termed a coherent subnetwork.Arabidopsis flavonoid subnetwork, convergence was obtained for a subnet of 180 compounds and 164 reactions . However, these methods do not claim any knowledge of enzymes/genes or situations in which reactions occur. So, although it is necessary to discuss MCS in terms of enzymes for relating to experimental observations, the MCSs are based on the reactions themselves which are considered independently of their corresponding enzymes/genes. A consequence of this is that the same enzymes are considered separately in relation to their different reactions. This is especially important in the MCS study and will be explained further under that section and the explanations for the third table.Three different criteria were used to prioritize enzymes targeted for suppression of colour pigmentation:The pleiotropic considerations such as those used by Whittal et al. can be quantified by counting the number of pathways or EMs in which each reaction and its associated ABP enzyme/gene participates. The result quantitatively shows the importance of the genes in terms of their link with other reactions and processes.Escherichia coli [Arabidopsis.MPA provides a more specific tool for investigating loss of function than mere participation counting: this is the concept of minimal cut sets (MCSs). The concept of MCSs was introduced in 2004  and has hia coli  using thThe MCSs are calculated using the concept  where a Although recent studies  have shoThe "objective reactions" for our study are those that directly form the anthocyanin compounds responsible for the function of colour pigmentation. In effect, these MCSs provide the full set of ABP reactions to block, which are related to the subsequent gene candidates for genetic changes for loss-of-function, thereby preventing anthocyanin production. Because a complex network provides many alternate pathways, there are generally several different MCSs for a single collection of objective reaction(s). All of these MCSs would be effective; however, their efficiency would differ.Because the mathematics guarantees that the collection of MCSs is complete, we can use quantitative analysis to compare and investigate the effect that each MCS has on the remaining non-target set of EMs. Along with other different MPA methods, these effects are utilized in exploring the MCSs that would achieve loss-of-function most efficiently, and whether this was related to the position ("early" versus "late") of the genes in the ABP.fc) provide a measure of how essential reactions are for a specific objective. The number of EMs that a gene is involved in is a good indication of its importance but it is also conceivable that a gene involved in only a small number of EMs may nevertheless be crucial for those modes. In effect, if a gene is part of a larger MCS, its fc will be lower as its malfunction will be less crucial for the objective reaction(s).Another method used in considering the best candidates for loss of colour pigmentation is the fragility coefficient calculated using the MCSs. The fragility coefficients  is often used to identify the most realistic representation of the metabolic state of a cell within the constrained region of flux space. This is not applicable for flavonoids because they are secondary metabolites not needed for basic life functions in a cell. Fortunately, however, the chosen subnetwork also supplies the ingredients for synthesizing the lignin macromolecule, which is a significant constituent of plant biomass. This allows us to relate some flux values to the well-measured overall biomass growth rate.• Literature values for lignin monomer ratios show some variation according to plant species, tissue, developmental state and environmental stress, and similarly the lignin fraction of the biomass is variable. We make the following assumptions based on a survey of the literature -37:• The lignin composition is taken as:370 CONIFERYL-ALCOHOL + 238 SINAPYL-ALCOHOL + 60 COUMARYL-ALCOHOL-1 [-1 of the primary growth of the plant.• Taking the lignin biomass constituent as 15% ,35 of th-1 , the lig• In the absence of measured rates for flavonoid reactions, we estimate values based on measured concentrations. We assume that the rates are proportional to the concentrations; as we only evaluate ratios between rates with and without an MCS imposed, the unknown proportionality constant cancels.• Rates are compiled in this way for one type of plant: grapes ,40. InfoTable Taking these constraints as well as the fixed lignin biomass production rate, CNA is allowed to calculate a feasible state of the network and this is taken as the reference state. The values of all input fluxes calculated in this state are listed in Table Due to the varying origins of the constraints used and the arbitrary selection of a single feasible state, results based on this reference state is only expected to be indicative, not quantitatively significant. Some reassurance is obtained from the fact that the resulting flux balance system does have feasible solutions, so that the constraints are at least not mutually contradictive. A further measure to reduce reliance on details of the reference state is that only flux ratios are considered, not absolute values. It is also noted that the reference state is not meant to represent any particular physiological state, but rather a generic representation of all the capabilities of the flavonoid subnetwork. In the final analysis, the results themselves as reported in the next section gives further support to the appropriateness of this approach.The MCS states are simulated by constraining the fluxes through the reactions constituting each MCS to zero, while maintaining the initial constraints and external inputs at the same values as for the reference state. It is conceivable that blocking the reactions in a MCS might change the fluxes exchanged with the full network, provided that such changes are compatible with flux balances in the full network. If we did allow changes to these inputs, we could not test their feasibility with just the subnet. So while we have only studied a specific case, to extend that would necessitate dealing with the complete network which does not seem practical. By keeping the influx into the network constant, the reaction rates for each MCS can be compared against the reference state. The MCS simulated fluxes are normalized to their values in the reference state by dividing each MCS reaction rate by its rate from the reference state. This cancels out the units so we are looking at the relative impact of the MCSs on a typical (reference) state of a plant cell. A similar analysis was done without the constraints, to check that the relative impact is not just specific to the chosen reference state; it was found that the flux ratios were generally the same whether the preliminary constraints were used or not.To represent the results the external metabolite products are grouped into seven flavonoid groups and four non-flavonoid groups. The flavanoid groups are flavanols, flavanones, flavones, proanthocyanidins, leucoanthocaynidins, anthocyanins and flavonols which is further split into two groups: flavonols1 consisting of kaempferol derivatives and flavonols2 consisting of quercetin derivatives. The average relative fluxes through the end reactions of the EMs producing these external products enable us to study the impact that the MCSs have on other processes.The extracted flavonoid subnetwork shown in Figure The flavonoid subnetwork has more metabolites than reactions; it consists of 180 metabolites and 164 reactions. This illustrates a common feature of plant secondary metabolites where "decorating"  enzymes are of low substrate specificity, enabling a single reaction to utilize multiple substrates to produce different products ,17.In addition, the 359 EMs calculated from the flavonoid network is quite low compared to previous studies that concentrated on the primary metabolism of microbes ,9. TheseFrom the 359 EMs calculated from the subnetwork, 203 lead to non-flavonoid products, whilst 156 lead to flavonoid compounds of which there are seven subclasses. These are: i) anthocyanins; (ii) flavanols; iii) flavanones; iv) flavones; v) flavonols; vi) leucoanthocyanidin and vii) proanthocyanidins. Details of these flavonoids can be seen in -47. FourFigure The focus of this study is on the anthocyanin compounds and their role in plant colour pigmentation. As shown in Figure A detailed part of the subnetwork showing the subset of nodes or metabolites involved in the 24 EMs relevant for the ABP is shown in Figure Arabidopsis contains two anthocyanin compounds responsible for colour pigmentation: pelargonidin-3-O-glucoside (Pg) and cyanidin-3-O-glucoside (Cy). The number of EMs (routes) is higher for Cy-type anthocyanin than Pg-type anthocyanin because EM calculation depends on the structure of the metabolic network and how the compounds are linked by reactions. As shown in Figure O-glucoside than there are post pelargonidin-3-O-glucoside.As shown in Figure Further modification of the anthocyanin compounds  is illustrated in Figure For the metabolic reactions in the ABP, the main related genes/enzymes to be studied are: CHS, CHI, F3H, F3'H, DFR, LDOX/ANS ,60, and Genes such as FLS  are not The first network property looked at is the connectivity of the anthocyanin genes as shown in Figure The results in Figure Table The reactions directly forming the anthocyanin compounds (PELUDP-RXN and RXN1F-775) are identified as the objective functions for which the MCSs are determined.The 22 MCSs shown in Table At the top of the list is MCS15 which does not affect any other mode except the 24 anthocyanin EMs. As shown in Figure The MCS result illustrated in Figure On the other hand, it could be more efficient to block an 'early' gene, which argues for MCSs 11 (ANS(cy) & 3-UGT(pg)) and 14 (DFR(pg) & 3-UGT(cy)) with 135 non-affected EMs. While these eliminate 3 additional modes, they may compensate for it by moving the cut higher up the pathway, although these MCSs contain at least one objective function gene. The next five MCSs that do not contain any objective function genes but still make good candidates are: MCS8 (ANS(pg) & ANS(cy)) with 132 non-affected EMs, MCS9 (DFR(pg) & ANS(cy)) and MCS10 (DFR(cy) & 3-UGT(pg)) with 129 non-affected EMs, MCS5 (ANS(pg) & DFR(cy)) with 126 non-affected EMs, and MCS6 (DFR(pg) & DFR(cy)) with 123 non-affected EMs. The number of non-affected EMs then drops to the 60 s or lower.fc) could provide further information on target genes by looking at how essential they are to the pathway.The above analysis gives independent insight into the reasons for the observed molecular convergence in the evolution of colour pigmentation losses by providing a complete listing of all options open to the plant for suppressing anthocyanin production, as well as characterization of the impact each mutation has on the production of other compounds. It shows a more quantitative and therefore more differentiated characterization of the enzyme roles through the exhaustive nature of the mathematical analysis of network structure. Fragility coefficients  than other earlier genes. This supports the observation by Whittall et al [Aquilegia.Considering all the issues highlighted in the analyses so far, the genes ANS/LDOX and DFR corresponding to MCSs 5 and 9 could be considered as having favourable characteristics: they participate in a low number of EMs so don't affect the production of too many compounds; they occur earlier in the pathway and; they are more essential for anthocyanin production  but increase fluxes of kaempferol related flavonols.In general, the reaction rates show that non-flavonoid compounds are not much affected by the elimination of anthocyanins. Even for MCSs 1 and 2 which require the elimination of the early ABP genes that result in the elimination of most of the flavonoids, benzenoid production is not affected. This provides a quantified explanation of experimental observations such as that by Spitzer et al  where thThe above FBA provides a quantified measure of the impact that the different MCSs would have on the production and processes of other flavonoids and related compounds. They provide a more informative way of considering favourable target genes for eliminating colour pigmentation, and show that MCS options that appear similar in terms of structural measures, can have opposite quantitative impacts. For example, looking at genes ANS & DFR (MCSs 5 & 9) that were considered favourable target genes for eliminating colour pigmentation from the previous network structural analysis, the FBA shows that the effect of their MCSs on reaction fluxes is mainly to decrease PA. MCSs 13 & 15, on the other hand, lead to an increase in fluxes through the reactions mainly producing PAs and flavonols. Both effects have been observed in different systems. This additional information suggests that different MCSs suit different cell types or environmental conditions and could be utilized to identify which MCS would work best to achieve a certain outcome.While the reference state used for the reaction rate analysis is admittedly only one out of a possible range of feasible states, the general principle demonstrated by the results is that various MCSs that are by construction equivalent regarding a set of target products, can have quite different repercussions on the flux distribution in the rest of the network.In the main focus of the study, three methods  are employed to consider target ABP reactions and their corresponding genes for eliminating colour pigmentation. On their own, gene participation and reaction rates cannot be used as a basis for identifying the genes to target but they do provide an idea of the importance of, and interplay between, genes from a whole cell perspective. This information, along with MCSs, proves very useful in considering target genes for blocking colour pigmentation.The MCS method is the most important because it offers a list of the set of loss-of-function target genes that would eliminate anthocyanin production. By using the other methods to investigate these MCSs, we are able to analyze the properties of the corresponding genes and the impact that their suppression would have on other processes in the network. The fact that the impacts we demonstrate do coincide with actually observed distinct patterns of flavonoid production supports the simplifying assumption about our reference state and the analysis using reaction rates.The first two methods analyze the structure of the network to determine EMs and make predictions on the capability of the genes in terms of the network structure. When considering the MCS results and initially looking at the impact that each MCS had on other EMs, MCS 15 stands out as containing the best set of genes to target. However, looking at the different MCSs in relation to gene participation and fragility coefficients, the case arises that it might not be so good in certain circumstances and the argument develops for the possibility of other MCSs making better candidates for different situations related to loss of colour pigmentation in plants.Aquilegia [The third method takes the analysis a step further by looking at the functionality of the genes in terms of simulated reaction rates for different MCSs. By using all the three methods together, we are able to consider different possibilities and relate these to experimental observations that have been made. For example, MCSs 5 and 9 that respectively require the simultaneous suppression of genes DFR(cy)/ANS(pg) and DFR(pg)/ANS(cy) to eliminate colour pigmentation, correspond to observational results of the co-regulation of the DFR and ANS genes for eliminating floral pigmentation in quilegia  that shoquilegia  in whichThe results of this study provide a comprehensive list of all the different MCSs possible, according to metabolic network information available to date. It is obvious that these different MCSs would be best for different plants, different cells, and different conditions and may correspond to regulatory genes. From the results we are also able to understand more about the interplay between the MCSs and other metabolic processes in the cell; knowledge which enables us to relate which MCS would correspond best with which plant, cell or condition. Being able to relate the different MCSs to experimental observations, not only provides credence to the mathematical approach, but could also assist with the design of experiments that would more quickly achieve the required results. It also gives credibility to use MCSs to analyse and predict the metabolic functionality of less understood pathways.f: Connectivity in the full network; Cs: Connectivity in the subnetwork; CHI: Chalcone isomerise; CHS: Chalcone synthase; CNA: CellNetAnalyzer; Cy, cy: cyanidin-3-O-glucoside; DFR: Dihydroflavonol reductase; EM: Elementary Mode; F3H: Flavanone 3-hydroxylase; F3'H: Flavanone 3'-hydroxylase; FBA: Flux Balance Analysis; fc: fragility coefficient; FLS: Flavonol synthase; LDOX: Leucoanthocyanidin dioxygenase; MCS: Minimal Cut Set; MPA: Metabolic Pathway Analysis; Pg, pg: pelargonidin-3-O-glucoside; UDPG: UDP-glycosyltransferase.3-UGT: Anthocyanidin 3-O-glucosyltransferase; ABP: Anthocyanin Biosynthetic Pathway; ANS: Anthocyanidin synthase; CThe authors declare that they have no competing interests.STC participated in the design of the study, carried out the analysis and drafted the manuscript. WSV helped in the design of the study, the analysis, and the draft of the manuscript. All authors read and approved the final manuscript.AC8_subnet.sbml. This is an sbml [ an sbml  file repClick here for filemetabolites. A file containing the metabolites and their attributes, downloaded from the AraCyc database [database . Each roClick here for fileprelim_constr_influx.val. A file specifying the preliminary constraints. This can be uploaded and displayed in the CellNetAnalyzer flavonoid network project using the 'Scenario' feature under the 'CellNetAnalyzer' menu. Click here for filereactions. A file containing details of the reactions in the flavonoid subnetwork, downloaded from AraCyc. Each row in the file defines a reaction and its properties: <reaction identifier> <reaction equation> | <default value: # if unknown> <Par1: rate minimum> <Par2: rate maximum> <Par3: objective function coefficient 1- exclude, 0- include> <x-Pos: pixel coordinates> <y-Pos: pixel coordinates> <map number: network map where the box appears> <text box type: 1- editable, 2- noneditable >. Click here for file


Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite’s essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential.The search for new drug targets for antibiotics against Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations.Using the well-curated metabolic networks PlasmoNet of the parasite Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug. An ideal drug should tackle the disease-causing processes in the most selective way, i.e. with no harm for the healthy cells. To our knowledge, such a perfectly selective drug does not exist. In fact, administrated chemicals can trigger at least secondary consequences (off-target effects) in the organism or in host cells. In casePlasmodium falciparum and its human host are studied to identify essential enzymes[Mycobacterium tubercolosis is integrated with a human alveolar macrophage to describe three degrees of infection [Plasmodium falciparum is embedded in a red blood cell model to simulate its intra-erythrocytic developmental stage[Plasmodium falciparum (PlasmoNet) and the human hepatocyte (HepatoNet1)[in silico gene deletions of homologous enzymes were performed. Alternatively, the concept of reduced fitness was applied to homologous essential enzymes to assess inhibitions in relative terms. While in silico gene deletions predicted scenarios where the enzyme is fully disrupted (e.g. with gene-excision experiments), the second approach better described the possibility of residual enzymatic activity. The latter is more appropriate for the administration of enzymatic inhibitors that only gradually reduce the enzyme activity.Genome-scale metabolic models of pathogens are extensively exploited to predict putative drug targets with FBA frameworks-11. Furt enzymes. The metculosis). Similaral stage. Furtheral stage. Thus, iatoNet1),16. AlthatoNet1). To predPlasmodium falciparum metabolic homeostasis and growth[in silico gene deletions of the enzymatic set were simulated. Twenty-four enzymes were found to be essential only for the parasite and did not compromise the host metabolic performance. Twelve enzymes were essential for both models and were investigated with the concept of reduced fitness[in silico perturbations had a larger impact on the achievement of host metabolic objectives  enzThe gene-deletion approach predicted that 12 enzymes are essential in both networks . The essentiality of this enzyme is experimentally demonstrated in human lymphoblast-like cells[It was predicted that only ke cells.thymidylate synthase was equally detrimental for both models (selectivity score=1). This enzyme is a possible therapeutic drug target that is efficient against a wide spectrum of human tumors[The fitness approach predicted that n tumors,23 but oOrotidine-5-phosphate decarboxylase and orotate phosphoribosyltransferase form a multienzymatic complex, but for this analysis the two enzymatic activities were assessed separately. Nevertheless, the results obtained with the fitness approach indicated the same degrees of biomass impairment. The cellular toxicity caused by the inhibition of orotidine-5-phosphate decarboxylase was demonstrated in human leukemia cell lines[aspartate carbamoyltransferase was predicted to inhibit the same biomass components  or glutamine (EC 6.3.5.5). Here, it was not discriminated between the ammonia-donors (NH4+ or glutamine) because the inhibition of one reaction was able to be fully compensated by the other. The essentiality of carbamoyl phosphate synthetase (EC 6.3.5.5) was demonstrated in mouse liver[se liver.glycerol-3-phosphate acyltransferase, that is essential for human hepatocyte cultures[The last enzyme of this set was cultures.The gene deletions of this antimalarial drug target set were confirmed by the aforementioned experimental data. Moreover, the reduced fitness approach correctly predicted the specific case that was triggered by the inhibition of acetyl-CoA carboxylase: while in the reference state the triglycerides are part of the maintenance function, thus they are exported into the blood, under the perturbation there was an inversion of the boundary flux and they were instead imported.The behavior of the predicted fitness functions, that were simulated for this enzymatic set, are depicted in FigureFor the 12 essential enzymes, we applied the reduced fitness concept to assess in a more subtle way their impact on the two metabolic networks , while in PlasmoNet the inhibition severely destroyed the production of sphingomyelin (–97.07%). HepatoNet1 mitochondrial cardiolipin was produced by the condensation of glycerol-3-phosphate and CDP-diacylglycerol, that was obtained by mitochondrial phosphatidate. This latter metabolite was synthesized from acyl-CoA mitochondrial pool . Cytosolic acyl-CoA syntethase was responsible for the formation of these activated fatty acids, thus an enzymatic impairment can affect the cardiolipin synthesis. This is experimentally demonstrated in human tumoral cells[Plasmodium falciparum.The impairment of al cells. Acyl-Coal cells. During al cells. The foraspartate carbamoyltransferase whose inhibition caused an impairment of UDP-glucose production in HepatoNet1 (–80.57%) and mRNA in PlasmoNet (–57.44%). It is reported that inhibitors of aspartate carbamoyltransferase cause a 10% reduction of the UTP intracellular pools in hepatoma cell culture[The second enzyme is  culture.glycerol-3-phosphate acyltrans- ferase, that caused dramatic effects in the hepatocyte metabolism, hindering the production of phosphatidylethanolamine (–55.77%), phosphatidylinositol (–50%), phosphatidylcholine (–50%), cardiolipin (–50%) and triglycerides (–50%). Phosphatidate is a common precursor of these metabolites and is formed in the human in the linear chain from 1-acyl-glycerol-3-phosphate, a product of glycerol-3-phosphate acyltransferase. A specific inhibitor of this enzyme (FSG67) causes in obese rats the reduction of triglycerides and phosphatidylcholine[in silico inhibition of the enzyme impaired the production of phosphatidylcholine (–94.55%) in PlasmoNet. The plasmodial gene sequence that encodes glycerol-3-phosphate acyltransferase is expressed in double yeast mutants and biochemically characterized[Plasmodium falciparum, that requires high amount of phospholipids for membrane synthesis. This hypothesis is sustained by another work, whose biochemical analyses show that parasites in trophozoite and schizont stages have an high acyltransferase activity[The last enzyme is lcholine and thiscterized. The autactivity.Although it was not possible to assess the quantitative aspect of the selectivity score with experimental evidence, there was a good agreement among the obtained results and the literature.in vitro medium compositions and restriction of indicated inbound fluxes .Twelve drug targets showed up to be non-essential in both networks Table, althougaldolase, for example, binds AMA1 effector to initiate host invasion[Hexokinase is found to be essential for the intra-erythrocytic stage of the protozoan[The remaining discrepancies may be due to possible molecular interactions that were not considered in the network reconstruction, e.g. off-target effects. For example, Velanker and coworkers find that plasmodial glycolytic enzymes associate with membranes and cytoskeleton components and drain their substrates near to the invasion machineries, making contact with host microtubules. The autinvasion. Furtherrotozoan. On the rotozoan. It is trotozoan.triose-phosphate isomerase, that is expressed on the membrane of infected erythrocytes where it triggers antibody selection and prolonged hemolytic anemia[The last enzyme of this little set is c anemia.Because of its specific extra-metabolic functions (infection/immune system activation), it is very likely that this enzyme is connected to off-target effects.ornithine decarboxylase was found to be essential under the restriction of polyamines (as putrescine and spermidine) and under the block of agmatinase. This enzyme synthesizes the first polyamine: putrescine. In PlasmoNet, a secondary bypass through agmatinase (EC 3.5.1.53) made this enzyme dispensable. Agmatinase was identified in Plasmodium berghei and hypothesized in Plasmodium falciparum but never characterized here, so this bypass may not be present. Here, a double enzymatic knock-down was simulated and both enzymes resulted to be essential for PlasmoNet. Recently, it has been reported that a full perturbation of ornithine decarboxylase triggers sophisticated compensatory mechanisms on the transcriptome, proteome and metabolome of the parasite[per se essential for the parasite and that the “rescue” mechanism is a evolutionary survival strategy. The literature indicates that the same enzyme is dispensable for the human host, suggesting a similar underlying conserved mechanism of regulation[phosphoethanolamine methyltransferase was found to become essential under depletion of external choline and phosphatidylcholine. This enzyme had a replenishing function in the phospholipid synthetic pathway, transferring 3 methyl groups on the ethanolamine to form the choline head (in case of choline depletion). In Plasmodium berghei it has two different substrates, ethanolamine phosphate and phosphatidylethanolamine[bc1. Inhibitors of NADH dehydrogenase (as atovaquone) target the mitochondrial electron transport chain but also affect Complex bc1[bc1was applied, whose outcome indicated that the synergistic activity of both enzymes was essential for the parasite.Among the enzymes that are conditionally essential, parasite. It is tgulation. In the nolamine. In Plasmplex bc1. Thus, aTo increase the selectivity of an anti-pathogenic drug that could target also host enzymes, a conventional way is to exploit structural differences between homologous proteins. On the other hand, network-based modeling methods are alternative and complementary strategies to assess enzymatic inhibitions. In fact, genome-scale networks can detect enzymes that are likely to be essential and selective. Ideally, these approaches identify potential drug targets that are specific to the parasite.Plasmodium falciparum metabolism.While this idea may be impressive in its simplicity, the amount of parasite-specific enzymes can be restricted. In fact, out of the merged list of 96 “gold standards” only 18 enzymes are specific to Plasmodium falciparum, when exposed to drug-induced selective pressure, develops drug-resistance (e.g. pyrimethamine and chloroquine)[It should also be considered that roquine). In thisroquine). Furtherroquine).To apply FBA-based methods, the main requirement is the availability of curated genome-scale metabolic networks. Furthermore, to achieve a realistic flux distribution, each model should be simulated with a literature-based set of metabolic objectives. This set allows the simulation of an anabolic physiological state of the metabolic model, in similar way to the biomass objective function. The appOverall, this work predicted that a large drug target set was non-essential for the hepatocyte model (24/48) and that 12 enzymes were instead essential for both models. For the first set gene deletions were simulated, while for the second set the concept of reduced fitness was applied. This last method can analyze more in detail the drug-induced impairments and their consequences on the network performance.a priori disrupted and the resulting metabolic consequences are then investigated. On the other hand, it is rare that an administrated drug can achieve 100% enzymatic inhibition. To simulate this last scenario, the concept of reduced fitness was applied. In this case, 12 enzymes, that gene deletions predicted to be essential in both models, were studied in the context of reduced fitness. This method allows to compare the relative enzymatic essentiality and to understand which model is more sensitive to a chosen enzymatic restriction. Unfortunately, the predicted selectivity score could not be validated with the available pharmacological data (e.g. drug binding constant for the target and cytotoxic IC50 index) for infected and non-infected hepatocyte cultures. Our work had clearly some limitations, that were due to lacking pharmacological data on antimalarials and approximations of the metabolic objectives. Then, genome-scale metabolic networks do not usually integrate regulatory feed-back loops and, thus, they may be not feasible to predict consequences of enzyme inhibition that are triggered by negative regulation. As mentioned above for the case of thymidylate synthase, enzymatic drug-induced inhibition may cause enzymatic over-expression and this aspect can not be exhaustively predicted with the methods that are here applied.Gene-deletion simulations are a mean to assess the importance of a given biochemical reaction for the functionality of the network. This type of simulations are comparable to experimental gene-excision methods: the enzyme sequence is Plasmodium falciparum, as drug resistance and sensitivity may largely vary among different strains. This is the rationale why anti-pathogenic drug discovery is now focusing on drug combinations, that are effective against a wide spectrum of pathogenic strains in low doses[Despite the applied approximations and the missing data, the obtained results were in agreement with the available literature. Thus, this framework may be useful to detect putative selective drug targets, that gene-deletion simulations may discard. Further analyses on the selectivity of antimalarial targets  are therefore required to validate the predicted selectivity score. RNA interference, for example, allows to tune the degree of inhibition, avoiding off-target effects due to unspecific protein binding. Alternaow doses.Plasmodium falciparum. Three enzymes are targets of approved drugs and only two of them are common in all sets . For 18 targets, no homologous enzymes are present in HepatoNet1. These pathogen-specific enzymes are suitable as drug targets and require no further in silico flux-based investigation. Thirty other enzymes belong to genetic functions and are only remotely intertwined with metabolism . Thus, they were discarded, since they are not represented in the networks. The remaining 48 enzymatic drug targets were present in both models. In FigureThree literature-based datasets of validated antimalarial targets,18,19 wePlasmodium falciparum, whose reconstruction is based on information from several databases and extensive literature search[HepatoNet1 is a primarily literature-based metabolic network of a human hepatocyte, whose reactions are individually curated and functionally tested. It compe search. While te search, were ine search. Previoue search indicatee search. The PlaThe metabolic objectives of the two networks were formulated in terms of the so-called biomass reactions which: (i) yield building blocks for the cellular composition, (ii) remove potentially harmful metabolites (e.g. toxins) and (iii) are exported by the cell in the context of systemic physiological functions. For HepatoNet1, literature search collected concentration shares of 98 metabolites, that are cellular building blocks or that can be exported into the blood. The list of applied HepatoNet1 metabolic functions is included as Additional fileTo test the essentiality of drug targets, gene deletions were performed under the flux minimization framework, in simifitness. A detailed explanation of this concept together with an exemplary application are given in Additional fileThis approach has been applied to an erythrocyte model to simulate the impact of enzyme deficiencies on network performance. The iniTo assess the network performance under an enzymatic impairment, the initial reference state is obtained with flux minimization method. This framework does not predict a unique solution but this aspect is common to many FBA methods that are based on optimization problems. To calculate the reduced fitness for an enzymatic knock-down, the enzyme-catalyzed null fluxes are blocked. The non-null fluxes, instead, are subjected to a progressive reduction of 1% of the reference flux values for each simulation run. For each decremental step of the non-null fluxes, a single value of reduced fitness is calculated. In this way, a single fitness curve is obtained for each enzymatic knock-down (FigureThen, the selectivity score of a drug target is defined as Athe possible extra-metabolic role of the biomass components.The authors declare that they have no competing interests.SB developed the original idea, carried out all computational analyses and drafted the manuscript. AH provided tools for the knock-out simulations and together with HGH and SB participated in the design and evaluation of the analyses. All authors contributed to and approved the final manuscript.Merged set of validated antimalarial targets.Click here for fileUpdated version of PlasmoNet. The network is available on Biomodels database under the ID MODEL1206070000.Click here for fileHepatoNet1 applied metabolic objectives.Click here for filePlasmoNet applied metabolic objectives.Click here for fileAppendix A. Flux minimization optimization framework.Click here for fileAppendix B. Reduced Fitness optimization framework and example.Click here for file