Escherichia coli metabolism in order to (1) determine its maximum theoretical plasmid-producing capacity, and to (2) identify factors that significantly impact plasmid production.Plasmids are being reconsidered as viable vector alternatives to viruses for gene therapies and vaccines because they are safer, non-toxic, and simpler to produce. Accordingly, there has been renewed interest in the production of plasmid DNA itself as the therapeutic end-product of a bioprocess. Improvement to the best current yields and productivities of such emerging processes would help ensure economic feasibility on the industrial scale. Our goal, therefore, was to develop a stoichiometric model of Such a model was developed for the production of a high copy plasmid under conditions of batch aerobic growth on glucose minimal medium. The objective of the model was to maximize plasmid production. By employing certain constraints and examining the resulting flux distributions, several factors were determined that significantly impact plasmid yield. Acetate production and constitutive expression of the plasmid's antibiotic resistance marker exert negative effects, while low pyruvate kinase (Pyk) flux and the generation of NADPH by transhydrogenase activity offer positive effects. The highest theoretical yield (592 mg/g) resulted under conditions of no marker or acetate production, nil Pyk flux, and the maximum allowable transhydrogenase activity. For comparison, when these four fluxes were constrained to wild-type values, yields on the order of tens of mg/g resulted, which are on par with the best experimental yields reported to date.These results suggest that specific plasmid yields can theoretically reach 12 times their current experimental maximum (51 mg/g). Moreover, they imply that abolishing Pyk activity and/or transhydrogenase up-regulation would be useful strategies to implement when designing host strains for plasmid production; mutations that reduce acetate production would also be advantageous. The results further suggest that using some other means for plasmid selection than antibiotic resistance, or at least weakening the marker's expression, would be beneficial because it would allow more precursor metabolites, energy, and reducing power to be put toward plasmid production. Thus far, the impact of eliminating Pyk activity has been explored experimentally, with significantly higher plasmid yields resulting. Escherichia coli are routinely used for producing recombinant proteins. The recombinant gene of interest is usually carried on a high copy number plasmid in order to boost gene dosage and, ultimately, process yield. While the usual view of the plasmid's role is simply as the vehicle for recombinant protein expression, the appeal of plasmids as gene therapy vectors has recently sparked interest in the production of plasmid DNA itself as the process end-product. Once thought inferior to viral vectors, plasmids are being reconsidered as viable vector alternatives for gene therapies and vaccines because they are safer, non-toxic, simpler to produce, and can accommodate larger inserts. In fact, of the 261 gene therapy clinical trials approved from 2006–08, 22% employed a plasmid DNA vector [Microorganisms such as ori) [E. coli, pUC-based plasmids are the norm. A mutation in its RNA primer and elimination of the rop (repressor of primer) gene result in much higher copy numbers than those observed for its originator, pBR322 [To date, efforts aimed at achieving high upstream plasmid yields have focused primarily on the optimization of fermentation conditions , and/or on the usage of a plasmid containing a high copy number, temperature-sensitive origin of replication (ori) -8. Desigori) -7. Reducori) . In E. c, pBR322 . Moreove, pBR322 ,11. The , pBR322 .Unlike other therapeutic products such as streptokinase, vaccines must be produced and administered in population- as opposed to subpopulation-scale quantities. For 100 million doses at 1 mg per dose , and 10 2) to products . This simple calculation for a model pUC-based plasmid  shows that yields of 742 mg/g  might be attained when 30% of glucose carbon is released as CO2 and there is no marker or acetate production (see Appendix). This theoretical yield is 15-fold greater than the best that has been achieved experimentally to date [To estimate what the maximum yield might be for aerobic growth on glucose minimal medium, a simple carbon balance can be performed. The yield determined in such a manner will not, however, consider any constraints that can arise when nested metabolic networks traffic carbon from inputs  flux and the generation of NADPH by nicotinamide nucleotide transhydrogenase activity were found to offer yield-enhancing effects. The highest theoretical yield (592 mg/g) resulted when the optimal flux distribution showed no marker or acetate production, nil Pyk flux, and the maximum allowable transhydrogenase activity. These results suggest that specific plasmid yields can theoretically reach 12 times their current experimental maximum. Moreover, they imply that Pyk deletion and/or transhydrogenase up-regulation would be useful strategies to implement when designing host strains for plasmid production. After presenting these results, we describe the results of published experiments  that werlarge blue arrows) according to the composition specified by Neidhardt et al. [#U62636) from the appropriate numbers of dNTPs, and for the constitutive expression of the antibiotic resistance marker contained on that plasmid . Assuming that 1.36 mol ATP are required per mol dNTP for polymerization [Figure t et al. . Becauserization , the plaThe stoichiometry used for each of the four dNTPs from precursors was that of Neidhardt et al. , with th2 generates 1 mol ATP, and 1 mol NADH generates 2 mol ATP. NADH and NADPH are produced by the central metabolic pathways, and the model also contains a reversible transhydrogenase reaction that allows for their interconversion.The production of biomass, plasmid, and Bla require energy and reducing power. Aside from substrate-level phosphorylation, ATP can be produced via electron transport and oxidative phosphorylation. For P/O ratios, we assumed that 1 mol FADHm reactions and n metabolites of a given reaction network collectively define a set of linear mass balance equations that can be formulated as:At steady state , the S is the n × m stoichiometric matrix, r is an m × 1 vector of the unknown metabolic reaction rates or fluxes, and b is an n × 1 vector of the rates of metabolite consumption or generation.where all optimizations carried out in this work was to maximize plasmid production:The objective function for μ) of 0.74 h-l. The amount of NADPH needed for biomass synthesis was fixed at 18.225 μ mmol/g/h, while the required ATP was defined as a greater-than-or-equal-to constraint (41.223 μ mmol/g/h) [at least the amount of energy needed to produce macromolecular building blocks from precursor metabolites as well as that required for polymerization.The mass balances from Equation (3) were employed as constraints, and bounds were imposed on irreversible fluxes to ensure that their values remained non-negative. The drain on a given precursor metabolite for biomass synthesis  . With th2 . Glucose uptake was limited to an upper bound based on a typical biomass yield of 0.35 g/g glucose for aerobic growth in minimal medium containing glucose concentrations that are high enough to permit acetate excretion [2, acid by-products, plasmid, and/or Bla .Carbon is supplied to the network exclusively from glucose or CO2 Fig. , boxed. xcretion . Becausexcretion . In addiBla Fig. , circledBla production was linearly coupled to plasmid synthesis as follows:α, whose units are mol Bla/mol plasmid, represents the product of transcriptional (number of transcripts per gene) and translational (number of ribosomes per transcript in a polysome) activities. Thus, it reflects the bilinear combination of promoter strength and ribosome binding/content. To put α into a quantitative perspective, consider its value for fully activated LacZ production. A value of 1838 (about 56 transcripts per gene and about 32 ribosomes per transcript) was determined in a modeling study [α and relaxation of Equation (5) to an inequality constraint based on the availability of protein synthesis machinery is described in the text for particular modeling aims. Other key fluxes that were either constrained or unconstrained in order to explore particular flux trafficking scenarios are racetate, rPyk, and rtranshydrogenase . This α-value corresponds to a situation where nil antibiotic resistance marker production is allowed to occur. This case provides the upper bound value of the maximal yield, which represents the horizon for emerging selection techniques that do not rely on high levels of constitutive antibiotic resistance expression [α is considered.In order to determine the optimum flux distribution for maximum plasmid production, the model was solved for pression -26. Latetop) shows the optimal flux distribution. The maximum theoretical plasmid yield predicted is 502 mg/g. No input carbon is wasted in the formation of acidic by-products ; hence, all carbon not consumed for biomass synthesis is put towards plasmid production or released as CO2. Note that this yield does not perfectly match that predicted by the simple carbon balance . That number, however, was calculated assuming a value of 30% for the fraction of input carbon given off as CO2, whereas the model's stoichiometry provides a value of 36.8%. Interestingly, the optimal solution results when Pyk flux is nil. Since this flux is nonzero in wild-type E. coli, it may represent a site where targeted mutation could prove beneficial when designing host strains for maximal plasmid production. The impact of Pyk will be further examined below, and experimental results concerning Pyk-deficient E. coli will be described in the Discussion section.Figure Because many current plasmids rely on the constitutive production of an antibiotic resistance marker for selection (e.g. Bla), some fraction of the available precursor metabolites, energy, and reducing power must be consumed to synthesize this protein. Therefore, the more marker produced, the less that these resources are available for plasmid production. Indeed, markers are usually produced at levels far exceeding those that are necessary for plasmid maintenance and selection, with levels at 20% of total cellular protein not uncommon in the case of high copy plasmids ,28. Asidα in Equation (5). Figure black solid line) shows how maximum plasmid yield varies with α and the corresponding amount of Bla produced . As expected, as α and thus Bla production increase, the plasmid yield decreases. The decrease is remarkably steep for the lowest values of α. By α = 250, for example, the marker constitutes roughly 44% of total protein, and its drain on resources diminishes plasmid-producing capacity by over 75% (502 mg/g for α = 0 to 112 mg/g).To gauge the extent to which concomitant marker production negatively impacts plasmid yield, the model was solved for many different values of middle) shows the flux distribution for this α = 250 case. This value of α corresponds to a situation where Bla's promoter strength is about one-third that of the lac promoter (23 transcripts per gene) [α = 0 case, no carbon is wasted in the form of acid by-products, and the Pyk flux is zero. In contrast to the α = 0 case, HMP and Ppc fluxes are greater, whereas glycolytic and TCA cycle fluxes are lessened in order to provide the appropriate balance of precursors, energy, and reducing power for both plasmid and Bla production  ,22, and er gene) . As withα  of total protein, the stoichiometries of protein biomass and Bla can be used to formulate an expression for f. For example, biomass contains 0.488 mmol/g of alanine, meaning that the flux of alanine is 0.488 μ mmol/g/h in our model, and one Bla contains 28 alanine residues. Therefore,Implementing such decoupling in the model is straightforward. When one is interested in limiting Bla production to some fraction  or (6) active. If α is small enough that Bla production does not reach the upper bound established by f, then Equation (5) is active. This leaves Bla production linearly proportional to plasmid production, meaning that by maximizing plasmid production, Bla production is concomitantly being maximized. If, on the other hand, α is large enough such that the limit imposed by f is hit, then Equation (6) is employed in lieu of Equation (5). That is,Depending on the specified values of α* denotes the value of α where Bla production exactly equals f, which can be determined using the results in Fig. black solid line). Equation (7) makes Bla production a fixed drain on resources when α ≥ α*, meaning that Bla is no longer being simultaneously maximized with plasmid production. Sub-maximal Bla production means that a greater amount of resources are available for plasmid production.where Equation (6) has been rearranged, and f = 0.20), a value that, as stated previously, is not uncommon for marker production when copy numbers are high [α* = 24, the 20% limit is reached. Hence, when modeling how the maximum plasmid yield varies with α for this case, Equation (7) can be employed with the appropriate α*. The result is shown in Fig. red dashed line), along with six other cases of f (black dashed lines). In each case, the maximum plasmid yield varies with α along the solid line until its respective value of α *. Thereafter, Bla production is at its imposed limit, and the maximum achievable plasmid yield becomes independent of α. In the f = 0.20 case, this means that E. coli can theoretically yield 377 mg/g of plasmid in addition to expressing the marker at 20% of total protein if α ≥ α*.As an example, assume that the upper limit for Bla production is 20% of total protein  shows the f = 0.20 flux distribution when α = 250 for comparison to the previously described cases where no limit was imposed on Bla production and α = 0 (top) or α = 250 (middle). As mentioned previously, in the case of non-limited Bla production and α = 250, the attainable plasmid yield falls from its absolute maximum of 502 mg/g (α = 0) to 110 mg/g due to the large amount of concomitant marker synthesis . When marker synthesis is instead limited to 20% of total protein, the increased availability of resources that would otherwise go to marker production are instead channelled into additional plasmid synthesis. This allows for a substantially higher maximum plasmid yield of 377 mg/g. Such an effect, as demonstrated here for increased plasmid yield with decreased marker production, has also been observed experimentally for recombinant protein production, where 2-fold greater yields were achieved when marker synthesis was significantly decreased by employing a weakened promoter [Because Bla production becomes a fixed load for a given promoter .bottom). In fact, unless constrained to be nonzero, these two fluxes always adopt a value of zero when the model's objective is to maximize plasmid production, regardless of the value of α and/or the imposition of an upper bound on Bla production.Regarding key fluxes, as with the two previous cases, no acid by-products are formed, and there is nil Pyk flux Fig. , bottom.E. coli produces acidic by-products  [both acetate and Pyk fluxes. To pursue this sensitivity and benchmarking analysis, the following two constraints were added to the model:In practice, wild-type acetate) , and itsacetate) . Thus, aβ and φ are constants that relate glucose uptake to acetate flux and Pyk flux, respectively. Whereas in all previously-discussed models these two fluxes were not constrained, the addition of Equations (8) and (9) enables them to be constrained for any  combination that results in a feasible solution. Figure β, φ) combinations (i.e. feasible combinations of racetate and rPyk) when α = 250. Figure where racetate and rPyk are nil, which is in agreement with earlier results  where the model adopted values of zero for these fluxes when they were left unconstrained. For any  combination where at least one of the fluxes is constrained to be nonzero, the yield decreases from the 112 mg/g value. In fact, the yield results fall into two regions, indicated by Planes A and B  pairs that make up Plane A, this decrease in yield is much steeper when rPyk is increased for fixed racetate than when racetate is increased for fixed rPyk. In fact, the magnitude of the slope in the former case is more than five-times that in the latter, indicating that Pyk exhibits the more dominant effect in this regime. The opposite is true for the  pairs that make up Plane B. There, increasing racetate for fixed rPyk causes a sharp decrease in yield, whereas yield remains nearly constant when rPyk is increased for fixed racetate. The differing trends in Planes A and B arise from how Ppc, malate dehydrogenase (Mdh), and Mez are used in each , and Mez is always inactive (i.e. rMez = 0). In the Plane B distributions, the reverse is true – Mdh is always operated in reverse, and Mez is always active. Here, rPyk is not high enough to provide the necessary pyruvate for meeting the specified racetate. Thus, additional pyruvate is created by a cycle that uses increased rPpc, reverse rMdh, and nonzero rMez. On the edge shared by Planes A and B, rPyk is exactly high enough such that acetate, plasmid, and marker are produced with nil flux through Mez  pairs that constitute Plane B, leaving only Plane A.According to Fig. lts Fig. , middle d B Fig. . In eachach Fig. . For allMez Fig.  and Mdh Mez Fig. . In pracThe preceding results indicate that the highest plasmid yields are attained when the Pyk and acetate fluxes are zero Fig. . MutantsE. coli mutant that has the activity of the two Pyk isozymes abolished has been constructed and denoted as strain PB25 [β = 0.25 and 0.75 for PB25 and JM101, respectively. In terms of a carbon balance, this means that JM101 wastes 25% of input carbon as acetate, whereas PB25 wastes only 8%. At 2 g/L glucose, Ponce [β = 0.33 for PB25 and 0.46 for JM101. Additionally, Flores et al. [β = 0.55 for JM101 when grown on 3 g/L glucose.An ain PB25 . Many ofain PB25 ,20,33-37ain PB25  found the, Ponce  reportedφ = 0 for PB25, as its Pyk activity has been eliminated [φ = 0.41. Also, Emmerling et al. [φ = 0.19 for JM101 grown in a chemostat operated at a dilution rate of 0.40 h-1 with 3.6 g/L glucose.Concerning Pyk, iminated . For JM1iminated  measuredg et al.  found φ racetate, rPyk) flux regimes for each strain have been highlighted in the results of Fig. α = 250). As this sensitivity analysis shows, even if racetate and rPyk are allowed to vary somewhat, the deletion of the two Pyk isozymes in PB25 permits it to attain substantially higher plasmid yields than JM101  lotio Fig. .bottom) and JM101 (top) in greater detail. Here, values of  were selected for each strain that fell within their respective experimental ranges. That is,  =  for PB25 and  for JM101, hereafter referred to as the PB25 model and the JM101 model, respectively. In addition, the earlier version of the model, where the acetate and Pyk fluxes were left unconstrained, will be referred to as the unconstrained model. As shown in Fig. rHMP/rglycolysis flux ratio (3.3 vs. 0.81) . The higher CO2 levels of JM101 arise partially due less Ppc flux (30 vs. 21) .Figure α = 250), the PB25 and JM101 models can be solved at various values of α, as was done already for the unconstrained model , the maximum capacity of the mutant is about 4-fold greater (443 vs. 110 mg/g) than the wild-type. When Bla production is limited, the advantage of PB25 over JM101 widens further. For example, when  in Equation (7) for the PB25 model, the maximum yield is 318 mg/g for all α ≥ 28. In contrast, the JM101 model is unable to produce Bla at 20% of total protein even at infinite α. In fact, even when α = 1000 , plared font). These reactions must generate enough NADPH to meet not only that required for basal biomass demand, but also that required for plasmid and marker synthesis. As a result, the NADPH constraint (see Methods) has a significant impact on the resulting flux distributions. Indeed, a characteristic of high plasmid-producing solutions is high HMP flux  two reactions in the oxidative HMP pathway, (ii) one reaction in the TCA cycle, and (iii) a small additional contribution if there is flux through the NADP-dependent Mez , specific plasmid yield reaches 200 and 533 mg/g in the JM101 and PB25 models, respectively. In the unconstrained model, it reaches 592 mg/g, which is the highest value found in this analysis.This relaxed transhydrogenase constraint was incorporated into the PB25 and JM101 models, as well as into the unconstrained model. For all ive Fig. . In the left column) for α = 250 alongside their respective distributions that resulted when the transhydrogenase was constrained to be inactive , less NADPH needs to be produced elsewhere. Consequently, lower fluxes should be expected through the oxidative HMP pathway and/or isocitrate dehydrogenase. Indeed, while each model shows about the same flux through isocitrate dehydrogenase relative to their respective inactive transhydrogenase case, each exhibits significantly decreased oxidative HMP pathway usage . This allows a significantly greater fraction of input carbon to directly enter glycolysis, with the ultimate effect being a greater supply of precursor metabolites downstream of GAP that can be directed to plasmid and Bla synthesis. Moreover, decreased oxidative HMP flux, increased Ppc flux , and less-than-or-equal-to pyruvate dehydrogenase and TCA fluxes collectively mean that less carbon is lost as CO2, which further favors plasmid and Bla production.How transhydrogenase activity improves plasmid yield can be gleaned from contrasting the flux distributions for the PB25, JM101, and unconstrained cases. These are shown in Figure E. coli for producing plasmid was investigated. Acetate production and the constitutive expression of the plasmid's antibiotic resistance marker were found to exert negative effects, while low Pyk flux and the generation of NADPH by transhydrogenase activity enhance yield. The highest theoretical yield (592 mg/g) resulted under conditions of no marker or acetate production, nil Pyk flux, and the maximum allowable transhydrogenase activity . Such a yield is 12-fold higher than the best reported value [Through flux analysis, the capacity of ed value , which sed value .These results and conclusions, however, should be considered in terms of the limitations of flux models and contrasted further to existing experimental results. When one conservatively views the predictions of flux models, they can be viewed as indicating the best a cell can do given (i) the constraints imposed, (ii) the nature of the nested metabolic reactions, and (iii) the competing demands on resources that occur given that some beneficial redundancy exists, such as multiple points for NADPH generation. Regulation and other limitations are typically not considered. In this case, one major assumption was that plasmid replication was constrained more by precursor supply than by the negative control over replication frequency or DNA polymerase activity.We suggest that such a limitation does not necessarily negate the value of predictions from flux models. Rather, if the aim is to alter cells through extensive metabolic engineering, then it seems useful to have the best yield and target flux distribution identified ahead of time. Knowing whether current yields already approach the theoretical yield is worthwhile because it indicates that research resources can be better directed elsewhere. On the other hand, when the yield horizon is promising, the optimal flux distribution provides the ultimate target for the mutagenesis tasks.B. subtilis for the purpose of producing more folic acid. Flux analysis predicted that suppressing Pyk activity was amongst the attributes of an optimal flux distribution. Subsequent experimentation determined that this mutation, in conjunction with others, increased the folic acid titer by 700% [Prior work also suggests that informative and implementable results are often attained. One example is provided by the metabolic engineering of  by 700% .E. coli expressing high copy number plasmids can be made. Here, how adaptations compare to the trends in the optimal flux distributions can provide an indication of the reasonableness of the optimal trends. In one study, E. coli JM101 grown in M9 minimal medium containing glucose (i.e. the same strain and growth conditions modeled in this study) exhibited less growth rate burden from plasmid maintenance when the HMP pathway gateway enzyme, glucose-6-phosphate dehydrogenase (Zwf) was over-expressed [-1; the growth rate recovered to 0.57 and 0.64 h-1, however, when Zwf activity increased 9- and 12-fold, respectively [E. coli DH5α harbored a high copy plasmid, expression from zwf increased by 1.2-fold, while expression from phosphoglucose isomerase decreased by 1.8-fold. Provided that flux correlates with expression level, their results suggest that one adaptation to plasmid presence entails increasing the rHMP/rglycolysis ratio by about a factor of 2. Lastly, Birnbaum and Bailey [E. coli contained significantly less Pyk and significantly more Ppc when copy number increased.To scrutinize further the predictions reported here, a comparison to prior work on xpressed . Transfoectively . In anotectively  found thd Bailey  reportedrHMP/rglycolysis ratio, in turn, was predicted to range from roughly 1 to 2, where the particular value depends on the transhydrogenase activity . This optimal value of the ratio is 3.5- to 7-fold greater than the experimentally observed wild-type JM101 value of 0.29 [black lines). Indeed, the best plasmid-producing solutions show nil Pyk activity. In contrast, Ppc flux has been measured to be only 60% that of Pyk for wild-type JM101 [top; 70%). Overall, the trends in the optimal flux solutions thus both track and combine the HMP/glycolysis, Pyk, Ppc, and other flux adaptations reported to occur when high copy number plasmids are maintained.The optimal  of 0.29 . In the pe JM101 , a featuThus, the match between the trends in the optimal flux distributions and experimental results raises the prospect that the particular flux values can serve as useful guideposts. Furthermore, suppressing Pyk activity appears to be a key facet of the optimal solutions. Abolishing Pyk provides a hard constraint that alters cellular stoichiometry such that Ppc and HMP fluxes are forced to increase, while acetate formation is suppressed. These changes provide improved precursor supply for plasmid and concomitant marker synthesis, as well as ensure that the necessary NADPH production occurs. These features also can be viewed as "bundling" the individually observed adaptations described above for maintaining high copy number plasmids.ori, the copy numbers were measured in cells growing isothermally at 37°C as well as in cells that were shifted from 37 to 42°C, where copy number typically increases 2- to 3-fold over several generations [To experimentally explore the prediction that abolishing Pyk activity leads to flux changes that can increase plasmid yield, we recently compared and reported the plasmid copy numbers attained by JM101 and PB25 . These cerations , as mentWhen grown at 37°C, PB25 maintained a 4-fold higher copy number than JM101. After being temperature-shifted, PB25 contained over 9-fold more plasmid than isothermally-grown JM101. Furthermore, when the strains are compared on a temperature-shifted basis, the copy number attained by JM101 was less than half that attained by PB25, whose copy number at 42°C reached over 1200. Overall, PB25 was found to contain considerably more plasmid than JM101, while PB25 maintained 78 and 92% of the specific growth rate of JM101 at 37 and 42°C, respectively. The comparable growth rates indicate that growth rate changes alone cannot account for the significantly different plasmid contents. Moreover, after the temperature shift, new plateau levels of plasmid/cell were attained after about three generations, indicating that balanced growth was achieved by each strain and that each exhibited significantly different rates of plasmid synthesis.ori mutations that might further weaken negative control beyond the level already provided by the rop and pUC mutations, bearing in mind that additional non-ori sequence-specific effects may also prove beneficial [Based on the modeling results, we interpret the experimental results described above as follows. Increasing temperature increased the copy number in both JM101 and PB25 to new plateaus; hence, negative control over plasmid replication remains a factor in pUC-type plasmids. However, when grown at 42°C, PB25 maintained a growth rate similar to JM101 despite possessing much more plasmid and comparable or more marker synthesis. Thus, as suggested by the modeling, PB25 possesses a flux distribution that enables extra DNA and marker synthesis to be better tolerated while significant cell growth still occurs. These conclusions then suggest that diminishing negative control further in a metabolic background provided by PB25 may further increase the copy number. An approach such as that described by Tomizawa [neficial . Additioneficial  have demE. coli for producing plasmid DNA was examined using metabolic flux analysis, and factors were identified that significantly influence specific yield. Both the production of acetate and the plasmid-encoded antibiotic resistance marker negatively impact plasmid production, whereas transhydrogenase activity and low/nil Pyk flux offer positive effects. The highest theoretical yield (592 mg/g) resulted under conditions of no acetate or marker production, nil Pyk flux, and maximum transhydrogenase activity. As this yield is 12-fold higher than the best reported to date, it implies that metabolic engineering of the host strain background might aid in narrowing the gap between the theoretical yield and the achievable yield. One model-based mutation, the deletion of Pyk, has been explored experimentally. In glucose minimal media, it produced substantially more plasmid DNA than the wild-type with only a modest penalty to growth rate, thus corroborating the model predictions.The capacity of Additional strain engineering strategies suggested by the model include transhydrogenase up-regulation and minimization of marker expression, or perhaps a shift away from selection via antibiotic resistance all together. A precedent has already been established in the former case, where transhydrogenase over-expression has helped to increase the productivity and yield of poly(3-hydroxybutyrate) . Weakeni2. This carbon is used to produce biomass, and it is also given off as CO2 and excreted as acid by-products . When replicating a plasmid and constitutively producing its antibiotic resistance marker, carbon must also be used for these two species. This gives the following overall carbon balance reaction:For the case of growth on minimal glucose medium, carbon enters the cell as either glucose or CO2 is located on the right-hand side to reflect its net production.where CO2, this leaves 25%. If we further assume (i) that all of this remaining carbon is put toward plasmid production, instead of lost as acid by-products, and (ii) that we neglect marker production, then the maximum attainable plasmid yield for a model plasmid, pGFPuv, is 742 mg/g. This number was arrived at using the chemical formula of pGFPuv (C65131H75142O40044N24968P6674) and its corresponding molecular weight. For the 3337-bp pGPFuv plasmid, this yield corresponds to roughly 33,300 copies/cell. This plasmid was chosen because it is pUC-based, commercially available, and contains a gfpuv gene that acts as a dummy therapeutic gene sequence.The amount of input carbon (mmol carbon/g) from glucose equals 95.2 based on its molecular weight, the fact that there are six carbons per glucose, and that 0.35 g/g glucose is a typical biomass yield for aerobic growth in glucose minimal medium . The amo-1); 2PG: 2-phosphoglycerate; 3PG: 3-phosphoglycerate; 6PG: 6-phosphogluconate; 6PGL: 6-phosphogluconolactone; AcCoA: acetyl-coenzyme A; Ac-P: acetyl-phosphate; Bla: β-lactamase; BPG: 1,3-bisphosphoglycerate; CIT: citrate; dATP: deoxyadenosine triphosphate; dCTP: deoxycytosine triphosphate; dGTP: deoxyguanosine triphosphate; DHAP: dihydroxyacetone phosphate; dNTP: deoxyribonucleoside triphosphate; dTTP: deoxythymidine triphosphate; F6P: fructose-6-phosphate; FBP: fructose-1,6-bisphosphate; FUM: fumarate; G6P: glucose-6-phosphate; GAP: glyceraldehyde phosphate; HMP: hexose monophosphate pathway; IC: isocitrate; LacZ: β-galactosidase; MAL: malate; Mdh: malate dehydrogenase; Mez: malic enzyme(s); OAA: oxaloacetate; ori: origin of replication; PEP: phosphoenolpyruvate; Ppc: phosphoenolpyruvate carboxylase; PTS: phosphotransferase system; Pyk: pyruvate kinase; PYR: pyruvate; R5P: ribose-5-phosphate; ri: flux of i (e.g. rPyk is the flux of pyruvate kinase); Ru5P: ribulose-5-phosphate; S7P: sedoheptulose-7-phosphate; SUC: succinate; Succ-CoA: succinyl-coenzyme A; TCA: tricarboxylic acid cycle; X5P: xylulose-5-phosphate.αKG: α-ketoglutarate; μ: specific growth rate , as well as participated in the interpretation of the results and the drafting of the manuscript. RRK provided advice on the experiments and participated in the interpretation of the results and editing of the manuscript. MMA was involved in the modeling design and participated in the interpretation of the results and editing of the manuscript. MMD conceived the study, oversaw the modeling, and participated in the interpretation of the results and the drafting of the manuscript. All authors read and approved the final manuscript.


Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell.Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection.We reconstructed the metabolism of This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. Salmonella. Even when their potential for global pandemics or for being used as biothreat agents is recognized, in-depth data cannot be easily generated in a timely fashion. While high-throughput data such as full genome sequences, microarray gene expression profiles, or proteomic data can be obtained quite efficiently for the pathogen in question, the context for proper interpretation of such data is often missing. One such case is represented by Francisella tularensis, the causative agent of tularemia [Francisella had been recognized has been focused on vaccine development, virulence factors, and whole genomic sequencing of Francisella isolates. There are few data sets for the physiological characterization of this bacterium during infection. Its central metabolic pathways still remain largely uncharacterized. However, growth of Francisella within macrophages or other potential host cells depends on its ability to utilize available nutrients and exploit this niche by presumably having to adjust its metabolism [Francisella as an intracellular pathogen. At the same time, critical bacterial metabolic pathways represent the most likely targets for novel antibacterial strategies. In this setting computational systems biology approaches may facilitate the integration of high-throughput data, exclude redundant possibilities, and suggest novel hypotheses.Emerging or re-emerging infectious agents are rarely subject of extensive preceding experimental investigations by sheer implication of their definition, as they would, for example, exist for model pathogens such as ularemia . This paularemia . Extensitabolism . This isin silico gene deletion analysis, robustness analysis, and the successive application of other suitable constraints. Such analysis has already been implemented for organisms from all the three classes  including E. coli [Salmonella [Haemophilus [Saccharomyces [Leishmania [Francisella tularensis with the goal of providing a framework for the integration of existing high-throughput data that might be used for interrogating pathogenesis and for identifying novel antibacterial targets.Constraints-based systems analysis after reconstruction of genome scale metabolic networks has emerged as a suitable tool for such tasks . This ty E. coli , Salmonelmonella , Haemophmophilus , Sacchararomyces , and Leiishmania . For theFrancisella tularensis subspecies holarctica vaccine strain (F. tularensis LVS) is described in detail in Material and Methods and followed previously described procedures [iRS605, which has 683 genes, 547 proteins, and 605 intra-system reactions, are summarized in Figure iRS605 refers to the metabolic reconstruction of Francisella LVS. The low number of reactions in the reconstruction as compared to similar models like E. coli and Salmonella is the result of a reduced set of metabolic pathways in this organism and of limited legacy data to confirm the annotation of its genome. The reconstruction includes 481 single gene protein reaction relationships (GPR) and 66 multigene GPRs . The genes included in the model represent those for which genomic, transcriptomic or physiological data exist in the literature in the form of gene-protein specific biochemistry or large-scale screens. Genes with high sequence homology to genes in other Francisella strains such as Schu4 or OSU118 are also included in the model. There are 55 non-gene associated reactions included in the model that lack experimental evidence but are essential for validation and prediction of a physiological phenotype in silico .The reconstruction process for building a metabolic network for ocedures ,3. The nFrancisella has several pathways that are disrupted or that miss genes [Francisella network . The reactions catalyzed by these genes form a set of reactions whose presence or absence provide important hypotheses on possible connecting reactions that can be further experimentally investigated and can thus lead to efficient annotation of the genome. With this, a more functionally complete network can be obtained.Gap analysis of the model identified several gaps in the network. There are gaps in a network when certain metabolites can only be consumed or produced. Since ss genes , there aThe distribution of pathway functions are summarized in Figure Francisella to build up complex carbohydrates by gluconeogenesis. This suggests the use of the Embden-Meyerhof-Parnas (EMP) pathway for gluconeogenesis rather than glycolysis. The pathogen is then able to synthesize glucose from pyruvate, since it encodes phosphoenolpyruvate (PEP) carboxylase. This allows gluconeogenesis starting from oxaloacetate. Despite an incomplete glycolytic pathway and only an oxidative branch of the pentose phosphate pathway, genes predicted to encode sugar uptake systems might at least ensure partial energy generation via the oxidization of hexoses. The retention of transketolase and transaldolase genes of the nonoxidative branch of the pentose phosphate pathway indicates the ability to regenerate glucose-6-phosphate and oxidize hexoses to pyruvate, although without an energy yield. For energy generation, Francisella may thus prefer to oxidize amino acids or other organic compounds derived from the host. Thus network reconstruction and constraints-based analysis identify condition-dependent operational pathways that throw light on adjustments the organism is capable of making in different environments.Pathways identified to be incomplete by gap analysis and confirmed using gene sequence data are discussed below. Central metabolic pathways that are incomplete include the pentose phosphate pathway and glycolysis. The genome encodes all glycolytic genes but lacks the phosphofructokinase (PfkA) gene. Constraints based analysis suggests that the presence of fructose bisphosphatase (Fbp), allows Francisella are virtually identical to those in E.coli. Several glycerol and phospholipid biosynthesis pathways are present in Francisella including phosphatidyl serine. However, there is no evidence for cardiolipin synthesis [Pyrimidine and purine metabolism  and fattynthesis . OrtholoFrancisella is able to reduce sulfate to sulfide for macromolecule synthesis via the APS-PAPS pathway and eventually fix as cysteine. This may mean broader specificities of the enzymes involved and further emphasizes the need to test experimentally the predicted gaps in Francisella metabolism.Although PAPS reductase is missing, Francisella are secondary carriers [Francisella has retained several transport systems for amino acids [Francisella seems to posses uptake systems for several minerals and salts including a low and high affinity transport system for potassium [Francisella also encodes an ABC transporter for manganese and zinc.The majority of transport systems present in carriers . The remno acids , includiotassium ,16, a seiRS605 of the Francisella metabolic network. It emphasizes evidence of metabolic function for different pathways although many intermediate reactions are found missing. This does not reduce the significance of the pathway or reaction in the growth and survival of Francisella, and suggests dependence on the host for that particular function. Francisella seems to have kept redundant enzyme function to a minimum (lower number of isozymes) with metabolic analysis suggesting a broader specificity for enzymes.Figure F. tularensis. The biomass objective function was defined using experimental data and literature for chemical composition studies of F. tularensis and includes metabolites in addition to experimentally determined growth associated ATP maintenance costs. We accounted for absence of phospholipids like cardiolipin that form integral parts of biomass in other organisms like E.coli and Salmonella. The reconstruction of the biomass function is critical to the success of the model in predicting physiological behaviors  since it defines the drain on several metabolites to make macromolecules that determine the biomass composition.A constraints based flux balance model was derived from this metabolic reconstruction that can be used to predict growth both qualitatively and quantitatively under specified conditions. Flux balance analysis is set up as a linear optimization problem with maximization of the biomass function as the objective. This biomass objective function was defined by conducting experimental measurements for macromolecular composition of iRS605 was grown in silico by specifying appropriate exchange fluxes  in defined Chamberlain media and using Flux Balance Analysis (FBA) to maximize the defined biomass function (described in detail in Methods). Calculated physiological states of F. tularensis indicate that amino acids can provide all the bulk carbon requirements for growth. Biomass production changed on varying amino acid concentration, which correlates with experimental data. Thirty-six percent of the total metabolic reactions were estimated to be operational  during growth of F. tularensis on Chamberlain's minimal media supplemented with glucose. This is higher than found in other bacteria such as E. coli and Salmonella [Francisella than for Salmonella. Ammonia exchange was estimated using flux balance analysis. Amino acids were not only incorporated into biomass but also used for energy generation in Francisella. The molar fraction of the amino acids in biomass was experimentally determined (data not shown) and used to specify coefficients in the biomass equation. Different ATP requirements for growth and maintenance were thus incorporated in the Francisella model by combining specific growth rates, substrate uptake data, and flux balance analysis [analysis . The acciRS605 was used to perform in silico gene deletion studies that carry the potential to identify antibiotic drug targets. The model was initially used to determine condition dependent essential metabolic genes  needed for growth and survival. The conditions chosen were Chamberlain media with no additional carbon/energy source, Chamberlain media with six different primary carbon/energy sources , a rich media designed to simulate conditions found in macrophages, and complex Mueller-Hinton broth. Gene essentiality was assessed by deleting single reactions in silico and testing for growth. The essentiality of a given gene was determined by calculating the maximal growth rate using FBA when fluxes through associated reactions were constrained to be zero. This type of analysis identifies lethal gene deletions (where maximum growth rate is zero) and non-lethal gene deletions  are limited, more metabolic genes are essential than in the rich medium. Hence, gene deletion studies with minimal media predicted 25% more essential genes than in the case of enriched environments. These genes are condition-dependent lethals [iRS605 at the intersection of the sets of such essential genes in six environmental conditions including a simulated macrophage environment. lethals  and are Francisella, we used data on virulence genes associated with metabolism to determine if our predictions matched any existing literature [Francisella that are essential for the growth phenotype and whose deletion may cause attenuation in pathogens.Based on this intersection set we found 105 condition independent lethal genes . Given the scarcity of experimentally confirmed essential genes during infection with terature ,23,22,35Salmonella, for a pathogen it is the ability to achieve this during infection [Francisella could exploit in order to survive and replicate.The metabolic capacity of an organism is determined by all available alternate routes it can use to achieve its growth objective. As demonstrated for nfection . Flux viRS605) spans a non-zero integer flux for optimal biomass production. This is less than half of that we found in silico for Salmonella iRR1083 in a similar environment [Francisella potentially minimizes utilization of its own pathways during infection. The larger reactome  during optimal growth in a minimal medium (Chamberlain with glucose) supports this hypothesis. The condition-independent reactome for optimal growth identified 59 reactions .In defined medium (Chamberlain with glucose) 32% of the network reactions that span a range of flux values when biomass production was optimal were operational. The constraining conditions that represent the environment during infection in the macrophage  were derived by literature mining. Based on this nutrient composition, FVA identified only 103 reactions that can be utilized for growth via alternate routes. This suggests that only 6% of the whole genome . Proteomic data [in silico . The prediction of metabolic capacity as a function of the reactome is lower for Francisella than for Salmonella [iRS605. The lower predictive capability of the model in comparison to Salmonella is in part due to the incomplete knowledge of exchange fluxes that represent the environment during intracellular replication and infection and in part a result of Francisella's unique potential to exploit of the host niche for intracellular replication and growth.We compared the transcriptomic data from literature  and founmic data  identifiin silico using robustness analysis [in vivo and in vitro conditions. Robustness diagrams (RD) consist of trajectories describing the linear or non-linear relation between a specific metabolic enzyme concentration (correlated here to mRNA abundance) and growth.The effect of reducing expression levels of metabolic genes on growth was tested analysis . Pair-wiin-silico simulation is the investigation of pH homeostasis on cell behavior. In the case of Francisella grown in Chamberlain medium the presence of amino acids and their consumption could compensate for excess protons by production and exchange of ammonium (NH4+) ions in the opposite direction. Thus media might serve as pools supplying and dissipating both acidic and basic ions as needed. The calculated ammonium flux out of iRS605 is four times that of Salmonella. The model was used to correlate the effect of changing H+ and NH4+ ion flux on growth rate in different environments using robustness analysis  that might neutralize the environment to promote growth. The RDs for both ions show a threshold above which there is a sharp decrease in growth rates. Thus there exists a limiting concentration of these ions that results in a buffered environment ideally suited for growth of Francisella. In the presence of glucose (2 mg/ml) in Chamberlain media both NH4+ and H+ ions lower the growth rate gradually. During infection of host cells the growth seems to be independent of the NH4+or H+ ion fluxes. This type of analysis demonstrates the power of our systems approach to generate important new hypotheses that can then be addressed experimentally.In the absence of any added carbohydrate source in Chamberlain media, the RDs for NH4Francisella was done to identify carbon and nitrogen sources (C/N) that it can utilize for respiration. Different growth states of Francisella were tested to identify changes in metabolic capacity based on age and growth cycle. Cells were pre-cultured on rich chocolate agar plates to confluence and minimal Chamberlain media with glucose to exponential, mid-log, and stationary phase.Metabolic profiling of Francisella as reported in literature [Francisella cells can utilize 10 compounds including some hexoses and amino acids completely and 8 compounds including glycoconjugate and pyrimidine salvage intermediates partially for respiration  and maximum growth rate (μmax0.4 hr-1) on glucose were estimated using the experimental data. Using the model to vary initial uptake rates of glucose and to estimate growth rate resulted in a good correlation between experimental and in silico results.Chamberlain's chemically defined medium with the addition of different sugars was used to experimentally determine the growth and metabolism of s Figure . Growth reported ,41. HoweGrowth under microaerophillic conditions resulted in specific growth rates lower by an order of magnitude Figure . Oxygen milieu for Francisella. Variations in gene expression of metabolic pathway genes reflect changes in components of this pool as the pathogen adapts to the host environment. We selected a core set of 69 metabolic genes for quantitative analysis , which included genes that may be required for adaptation to the host cell by driving metabolism, energy and pH homeostasis. These genes included some house-keeping genes and genes with putative predicted function.The global metabolite pool in the macrophage cytosol defines the substrate availability and nutrient Francisella's intracellular growth. Gene expression was measured quantitatively by using the Genomelab Expression profiler (GeXP) as described in Methods. The media and other physical parameters were kept identical except for carbohydrates added to the medium. During growth on all carbohydrates, the citrate synthase gene, glucose-6-p isomerase, NAD dependent malic gene, pyruvate kinase, isocitrate dehydrogenase, NADH dehydrogenase, chorismate synthase, fructose 1,6 bisphosphatase, glucose kinase, purine/pyrimidine phosphotransferase, glutamine synthase were all active indicating similar metabolic function and up-regulation of central metabolic, amino acid, and nucleotide pathways. However, some of the specific substrate utilizing, amino acid, nucleotide metabolism, and transport genes had different expression profiles, depending on the added carbon source. In the case of growth on pentoses and glycerol, glycolysis and TCA cycle genes  were low (two-fold) compared to growth on hexoses. During growth on pentoses and glycerol, the expression level of the glycine cleavage system (H-protein) was 2-fold that found for hexoses. There was also a 2-fold increase in PEP carboxylase  expression. Relatively higher rates (2 fold increase) of aromatic amino acid synthesis  also took place when Francisella was growing at the expense of fructose and glycerol as carbon-sources. PRPP synthesis was similar in all cases, suggesting similar rates of nucleotide biosynthesis. However, during growth on glycerol adenine phosphoribosyl transferase was very low, suggesting the de-repression by FBP. On comparison with the data where no carbohydrate was added to Chamberlain media, amino acid pathway genes were expressed at higher levels than genes involved in the metabolism of fatty acid oxidation (FAX), oxidative stress response (OX), and gluconeogenesis (GNS).Initially we analyzed the expression of selected metabolic genes during growth under defined culture conditions Figure  in orderFrancisella recovered from macrophages after one or twenty-four hours of infection  and was required for acid resistance and is critical to survival in the host cell. The pathogen respires oxidatively in the initial stages as indicated by increased ubiquinol oxidase activity. Expression of ubiquinone/menaquinone biosynthesis gene in later stages was detected, suggesting a switch to more microaerophillic environments. Co-expression of glycolysis/gluconeogenesis  and fatty acid oxidation genes at this time indicated the switch in carbon source utilization for growth and survival in the macrophage. The gene encoding PEP carboxykinase was highly expressed at 24 hr post infection. Our data also show the utilization of fatty acids as growth substrates, similar to what has been observed for Mycobacteria [Francisella, and also by increased expression of several other genes involved in oxaloacetate synthesis. Detection of catalase and peroxidase early on in Francisella suggests a redox active phenotype for the macrophage. Amido ribosyltransferase (AMPRT), which commits pentose phosphate pathway metabolite PRPP to nucleotide biosynthesis, had increased mRNA abundance but there were lowered transcript levels for adenine phosphoribosyltransferase (APRT) at 24 hours post infection. This might result in AICAR accumulation, which is an allosteric regulator for many pathways. These data demonstrate the metabolic adaptations Francisella undergoes when replicating in macrophages.Several genes of the citric acid cycle were expressed, including succinate dehydrogenase and cytochrome bdII. The data showed a glutamate decarboxylase mediated pH homeostasis in bacteria . SeveralWe also observed upregulation of amino acid genes , sugar metabolism genes  and genes related to fatty acid metabolism (phospholipase D and acid phosphatase precursor). This suggests a possible intra-macrophage environment for pathogen metabolism during infection. The up-regulation of both acetyl-coA carboxylase subunits indicates regulation of fatty acid oxidation by synthesis of malonyl-coA. The down-regulation of PEP carboxykinase indicates that there may be other mechanisms of mediating anaplerosis during infection.Francisella in the macrophage and in vitro. Hierarchical clustering analysis (based on Pearson coefficients) showed similarity when Francisella was grown on the pentoses (xylose and ribose) or on the hexoses (fructose and glucose) studied. Expression profiles on glycerol resembled that on pentoses. Gene expression levels during growth on arabinose were distinctly different. Gene expression profiles during infection resembled those during growth on defined media with pentoses as carbon sources. Clustering based on genes also showed that genes were expressed similarly based on functional category.Figure in vivo and in vitro. Among the genes whose expression levels do not affect the growth differently based on environment, are the nodes of metabolic network that are highly connected, like ATP synthase and NADH dehydrogenase  the addition of a carbohydrate changed the robustness diagram trajectory. If the flux through the GABA/Glutamate antiporter is equated to the observed mRNA abundance in vitro, the growth rate is in the maximum range in silico, indicating the time of harvest in exponential growth. The homolog of this gene in E.coli forms a part of the glutamate based acid resistance system that protects E.coli from the deleterious effects of high proton concentration environments [The model predictions of robustness analysis matched the trends in differential gene expression both e Figure . The rearonments .F. tularensis has been linked to the ability of the pathogen to escape from the phagosome and replicate in the cytosol [F. tularensis during infection. This is the first comprehensive study that uses a systems biology approach to define the metabolic capacity of an experimentally ill-characterized pathogen, F. tularensis, in multiple environments including the macrophage cell. In this study we designed an in silico strain iRS605 of Francisella tularensis LVS and used it to probe the functional state of the cell.Metabolism of the pathogen during infection of its host is one of the most fundamental aspects of the host-pathogen relationship. Emphasis on mechanisms and survival strategies aimed at evading the immune responses of the host often ignore that the host cell determines the habitat for the pathogen by limiting concentrations of certain nutrients and other essential metabolic factors. The pathogenicity of  cytosol . Little Francisella represents a pathogen with limited experimental datasets and has recently garnered increased attention because of its potential as a biothreat agent. Our systems approach presented here illustrates how constraints-based computational analysis can be used under these circumstances in an efficient way to provide an analysis of the pathogen's metabolic potential during infection and provide a framework for integration of high-throughput datasets. Such an approach is an important testing ground when novel therapeutics or vaccines have to be developed for an emerging pathogen for which initially only high-throughput data such as genomic and transcriptomic data are available.Francisella has a reduced genome although about a quarter of its genome codes for metabolic functions. Only the penultimate or final steps of many metabolic pathways, such as those of amino acid biosynthesis, are maintained in the pathogen. It has a broad range of host specificity, which suggests that its metabolism is specifically tailored to exploit conserved eukaryotic metabolic niches. Thus there may have been a certain selection pressure during the evolution of Francisella to select or preserve enzymes with a broader specificity and subsequently reduce genome size and still be an inimitable intracellular pathogen. This is supported by flux balance analysis of iRS605, which demonstrates very stringent growth requirements, and by in silico gene deletion analysis, which predicts a high number of 105 condition-independent single essential genes.Francisella. The significance of amino acid metabolism is also supported by our experimental data that demonstrate that all amino acids in Chamberlain media are consumed in the presence of a carbon source. Asparagine, tryptophan and glutamine synthase genes are essential probably because only their precursors are available to Francisella in the host cell. Amino acid decarboxylases may coordinate pH homeostasis along with their corresponding antiporter. Nucleotide metabolism genes are indeed critical to the survival of the pathogen with PRPP being supplied by the pentose phosphate pathway. The production of several intermediates in purine and pyrimidine synthesis is essential and suggests a role as substrates for other networked pathways. Interconversion of nucleotide kinases is important since all the four nucleotide kinases are predicted to be essential. Several fatty acid genes are also essential and suggest the use of fatty acids as gluconeogenic substrates. Combined with limited validation by transcriptomic profiling as presented in this study this approach can thus generate testable hypotheses. These will have to be validated by further experimentation, but our approach significantly reduces the possible solution space. We have also illustrated this approach by highlighting how the in silico analysis of pH homeostasis affects the metabolism and subsequently growth of Francisella, which correlates well with observed experimental results.Experimental single gene deletion assays have become a principal established tool for studying cell behaviour ,47,18. SIn silico analysis of iRS605 can be used to identify nodes in metabolic networks that act as potential controllers of overall metabolism. One such example is AICAR. The adenylyosuccinate lyase gene that catalyzes its formation, is a condition independent lethal gene, essential for survival. Robustness analysis showed that flux through the adenylosuccinate lyase and AICAR formyltransferase genes need to be in equilibrium for optimum growth (production and consumption of AICAR must be at steady state). AICAR is a known allosteric regulator of several enzymes in carbohydrate and amino acid metabolic pathways [(fbp) gene in Francisella. Thus, the predicted accumulation of AICAR in the macrophage suggests a role as a potential master regulator during infection. Such hypotheses are not readily intuitive and further demonstrate the usefulness of a systems biology approach  is comprised of genes required for growth and macromolecule formation including nucleotides and fatty acids. The microbe tends to utilize preferentially specific amino acids for energy and fatty acids as gluconeogenic substrates rather than relying on carbohydrate sources like glucose and fructose during infection. These predictions have been validated by selective quantitative gene expression profiling as discussed earlier. The significance of these findings remains to be further elucidated, especially in conjunction with further research as to what particular metabolic niche the host provides and how it might deal with intracellular pathogens by restricting the availability of certain nutrients. Previous metabolic analysis of the model pathogen Salmonella [Our Mycobacterium tuberculosis constraints-based modeling was also used to predict potential targets for antibacterial therapy in the mycolic acid pathways, which has always been the main attention of antituberculous research [Constraints-based modeling approaches have now been used for a variety of prokaryotic and eukaryotic organisms, but usually for those with a significant body of experimental data. In most of these cases the modeling efforts could predict experimental findings with some accuracy and correlated with prevailing hypotheses about pathogenesis. In research . The chaFrancisella tularensis (iRS605). This model has been validated by legacy data, experimental phenotypic arrays, selective determination of metabolites, and growth profiles on defined minimal media. We demonstrated an accuracy of almost 80% in predicting growth and virulence phenotypes.We have presented here the reconstruction of a genome-scale metabolic model for the biothreat agent Analysis of the model showed significant changes of metabolism during Francisella's intracellular growth in its host cell, the macrophage. This was evident in a switch from oxidative metabolism (TCA cycle) in the initial stages of infection to glycolysis, fatty acid oxidation, and gluconeogenesis during the later stages. Computational analysis also demonstrated a limited set of metabolic genes that are likely to be operational during infection. These findings were corroborated by quantitative gene-expression profiling of selected genes that code for key metabolic enzymes. Prediction of synthetic lethals also identified a set of potential drug targets.We have thus demonstrated how such an integrated systems approach can be used for pathogens with limited extended datasets to elucidate key metabolic processes during pathogenesis and identify potential novel drug targets.iRS605 are summarized in Additional File S1. The reconstruction process was done using SimPheny™ , a platform for cellular model development, which includes an interface for references, annotation, and confidence levels of annotation. The sequence based genome annotation of F. tularensis subsp. holarctica ( GenBank Accesion AM233362.1) was downloaded and served as the framework for the initial draft of reconstruction. Charge and elementally balanced reactions were added individually based on this annotation and legacy data when available. Biohealthbase and FranTCyc were used as ancillary tools. Following the initial reconstruction, the gaps were evaluated individually by searching for direct evidence in the literature for their metabolism. Due to the relative paucity of literature for F. tularensis, many of these gaps could not be filled. Legacy data in the form of primary articles, review articles, and textbooks were employed in addition to the database resources during the reconstruction and model building phases. If an annotation was made to an ORF that supplemented the annotation found in the genome file or that led to additional functional assignments, this was noted in the reaction database for the model in Simpheny. Also, high sequence homology of an ORF in the LVS strain, compared to genes in other species of francisella with strong experimental evidence were included in the model. A comprehensive map of iRS605 metabolic network was visualized by creating a map of the network organized by lumped subsystems of metabolism. After the debugging process, when the cell could grow (i.e. produce biomass) on different media, the remaining intracellular gaps were evaluated by searching the literature for evidence of metabolic reactions involving that particular metabolite in different species of the bacterium. If no evidence of transport or biochemical transformations of the metabolite in F. tularensis was found, no additional reactions or transporters were added.Reconstruction and model development of the metabolic network followed established methods ,3. The rF. tularensis data from the literature was then used to determine the amount of lipids (and their phospholipid composition), lipopolysaccharide, peptidoglycan, and glycogen. The stoichiometric ratios of all the components in biomass are given in the non-gene associated reaction in Additional Files.A reaction that represents biomass production is included in the model to account for the drain of precursors and macromolecular building blocks into biomass. Biomass was represented as a linear combination of all the macromolecular components  or monomers of macromolecules (amino acids and nucleotides). Amino acid and fatty acid composition were experimentally measured (data not shown) using standard methods . The rel-1 gDW-1: 95), and the slope gives the amount of ATP (in mmol gDW-1) required for assembly. The high ATP value is indicative of less energetically efficient pathway usage. The model will always use the reactions that give the highest P/O ratio. If the organism uses instead, a less energetic enzyme systems, then the ATP terms would be lower because of a forced lower P/O ratio. This is similar to organisms like Lactobacillus and Shewanella species . The biomass reaction thus represents the weighted combination of components forming the dry weight of the cell and the amount of ATP hydrolysis, needed for energy during growth and cellular maintenance as discussed.The growth associated ATP maintenance was calculated using data from Figure i vi ßi, where i and ßi represented the lower and upper limits, respectively. i was set to zero for irreversible reactions, whereas ßi was set to measured uptake rates for transport reactions or the Vmax of the corresponding enzymes.The reconstructed metabolic network was represented by a stoichiometric matrix, S (m × n), where m is the number of metabolites and n is the number of reactions. Reactions within the network were mass balanced such that Sv = 0, where v was a steady-state flux vector. Additional constraints  on each reaction had the form The biomass reaction thus represents the weighted combination of components forming the dry weight of the cell and the amount of ATP hydrolysis, needed for energy during growth and cellular maintenance as discussed elsewhere.E. coli  [Fluxes through metabolic reactions across the network can be calculated using flux balance analysis (FBA). With FBA, the biological system is assumed to be at steady-state so that all intracellular metabolite concentrations and fluxes are assumed to be constant. The steady-state assumption makes it possible to compare the simulation results directly to data obtained from cells growing at a fixed growth rate. FBA is formulated as an optimization problem, where constraints are imposed that limit flux values . These constraints define the range of values that fluxes can take. An objective function is also used to compare flux distributions (v) that satisfy all the constraints in order to find optimal flux distributions. Flux through the biomass production reaction has successfully been used as an objective function for nctions)  and was Robustness analysis is performed by varying a particular flux through a pre-defined range and recalculating the objective function. The slope of the curve describes the sensitivity of the objective function on that particular flux . The flux through several reactions in the metabolic network was varied and the growth rate was calculated.Two different environments were used to provide constraints for simulations: (i) Chamberlain minimal medium  with a variable carbon source ; (ii) hoF. tularensis LVS strain was used for all experiments. LVS was grown in Chamberlain media. Chamberlain media was supplemented with 4 mg/ml of the following sugars: glucose, glycerol, fructose, arabinose, ribose, and xylose. All in vitro growth experiments were done in an incubator shaker at 37°C and 150 rpm. Samples were withdrawn every 2 hours and the optical density (OD) measured. OD was correlated to dry weight using a previously determined calibration curve (y = 0.74 ×). Samples were further syringe filtered and centrifuged in order to obtain supernatants for HPLC analysis. Infection of the macrophage-like cell line RAW264.7 was performed as described previously [eviously .+ column (Biorad). A 5 mM Sulfuric acid mobile phase at a flow rate of 0.5 ml/min at 65°C was used for separation. Both UV and RI (refractive index) detector were employed. Small molecules were quantified using previously determined calibration curves.Organic acid and sugar analysis was done using an ion exchange Aminex 87HAmino acid analysis was performed using a Waters AccQTag column. Samples were derivatized using the kit and the protocol recommended to form a 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate derivative of the amino acid. The derivatized amino acids were then separated and detected using the C18 reverse phase AccQ-Tag column, with a flow rate of 1 ml/min at 37°C using a 12.5 mM sodium phosphate: Acetonitrile mobile phase with a previously published gradient profile. UV detection was employed and the amino acids were quantified using previously determined calibration curves.Francisella was harvested at different growth phases. Early exponential phase was defined in Francisella by an optical density (OD) of 0.32 AU, while ODs of 0.45 AU and 1.2 AU defined mid logarithmic and stationary phase cells.Metabolic profiling was performed using GN2 plates. . Protocols provided by the manufacturer were followed. For experiments determining the effect of growth state on metabolic phenotype, F. tularensis. GeXP employs eXpress Profiling (XP-PCR), a patented technology for multiplex gene expression profiling analysis by which up to 30 genes can be easily multiplexed in the same reaction. We used a protocol that comes with GeXP Reagent kits that involved five basic steps: 1) Primer design; 2) cDNAsynthesis; 3) PCR; 4) Separation on the GenomeLab GeXP Genetic Analysis System; 5) Fragment Analysis and Expression Profiling. The data was then analyzed using the GeXP analysis proprietary software , Excel  and Matlab . Primers were designed using proprietary software provided by Beckman .Infection of the macrophage like cell line (RAW246.7) was carried out as described . RNA wasAR conducted the reconstruction process and performed all the analyses. AR and SS obtained all the experimental data. AR and SD planned and designed the study, analyzed, and interpreted all the data. AR and SD drafted the manuscript. All authors approve the content of this manuscript.