Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb’s vulnerability and metabolic adaptation have been carried out.Up to date, pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions . A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports.A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants , phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.The online version of this article (10.1186/s12918-017-0496-z) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis (Mtb), is capable of replicating and surviving for decades within host granulomas, including caseating, fibrotic and cavitating lesions, each providing a diverse array of nutritional microenvironments T; vi are the internal fluxes; bi represents the exchange fluxes in the system; ni is the number of internal metabolites and next is the number of external metabolites in the system. At steady state, dx/dt = S. v = 0. Therefore, the required flux distribution belongs to the null space of S. Since there are more reactions than metabolites, the system becomes underdetermined, thus entailing the formulation of a constrained optimization problem. The critical step is the definition of an objective function that captures the biochemical goal of the system [cTv ; s. t. S. v = 0, wherein c represents the objective function. COBRA Toolbox v2.0 synchronized with MATLAB and the Gurobi optimizer 5.6.3, was used for all FBA calculations.Using the extended model, we performed metabolic flux balance studies under specific conditions and constraints. FBA uses linear optimization to determine the steady-state reaction flux distributions in a metabolic network, by maximizing (or minimizing) an objective function, such as ATP production, growth rate, or metabolite production. The metabolic system is described using the well-known stoichiometric matrix e system , 33. As x, y plane and the optimal flux distribution is computed for all points in the plane. Shadow prices are calculated for the two-parameter space to demarcate regions of constant flux distributions (phenotypic phases). Phenotypic phases are drawn based on changes in these shadow prices. In addition, in the PhPP there is a line called isocline, which demarks zones of constant values of the objective function. The slope of isoclines within each phase of the PhPP is calculated from the shadow prices as follow:PhPP analysis is a method for sensitivity analysis of GEMs , 35. Herπ is the shadow price and x and y refer to the variables on the x- and y-axes. Phases with a negative α value means there is a dual limitation of the substrates (both contribute positively towards the objective function). Phases with a positive α value are termed “futile” phases; in these phases, one of the substrates is inhibitory towards obtaining the objective function, and this substrate will have a positive shadow price. Finally, if a phase has either horizontal or vertical isoclines, there exist single substrate limitations; the α value in this phase will be zero or infinite, respectively. Therefore, in that phase, the shadow price of one of the substrates goes to zero and thus has no value to the cell [Where the cell , 35.fmincon solver. For all runs, the Gurobi feasibility tolerance was set to 10−6, and for the non-linear optimization, the algorithm was terminated when first order optimality was satisfied to within 10−6; the maximum constraint violation was also 10−6. The mathematical formulation of the whole optimization problem is shown in Eqs. (2)–(3). The sensitivity analysis, including the PhPP analysis and the calculation of shadow prices, was carried out by solving the LP problem, used in stage 1. In contrast, stage 2 was utilized for the computation of flux distributions in the phenotypes of interest.For most system biology applications where FBA is used, the analysis lies on the maximization of biomass growth rate; in contrast, in the present study, we used a two-stage optimization approach, as described by Schuetz and colleagues and D’Huys and colleagues , 36. SucΔpfkA) mutant strain, growing in a medium with available glycerol, phosphate, ammonium, and trace elements, and under different oxygen and glucose uptake consumptions. The second case study, explored the metabolic adaptation of an isocitrate lyase (Δicl1 & Δicl2) mutant strain, growing on a medium with available phosphate, ammonium and trace elements, and on various propionate and oxygen availabilities.Simulations using the extended GEM of Mtb were carried out for exploring the metabolic adaptation of the bacteria to different oxygen and substrate availabilities after two-mutation perturbations. The first case study addressed the metabolic adaptation for a phosphofructokinase . Furthermore, shadow prices were calculated for selected important metabolic intermediates that participate in the perturbed pathways. Finally, a pathway utilization analysis was carried out along with the characterization of flux distributions at specific conditions for each phenotype.The identification and analysis of different metabolic phenotypes of the Mtb mutants comprised model formulation, sensitivity analysis, a solution of the optimization problem, analysis of results, and hypothesis generation. The complete strategy is presented in Additional file I.Under the tested environmental conditions, it was identified that some reactions in the β-oxidation pathway within the iOSDD890 model had zero fluxes, and no grow could be achieved on those fatty acids. This condition was caused by the absence of reactions catalyzed by the ATP- dependent fatty acyl-CoA ligases in the iOSDD890 model; these steps are essential for fatty acid activation (conjugation with Coenzyme A) before β-oxidation. Therefore, we added 67 reactions related to fatty acyl-CoA ligases, transport, and exchange of some fatty acids, thus rendering a model able to represent cell growth on fatty acids.II.It was detected that seven transport reactions were missing in the iOSDD890 model, so it cannot use neither acetate nor aminobenzoate, formate, oxoglutarate, oxalate, guanine or urea. Hence, the corresponding transport reactions were added, as some of them can be used by Mtb as sole carbon and nitrogen sources .III. The iOSDD890 model missed the cholesterol degradation pathway. For this, 36 related reactions were added from another published model .The GEM of Mtb iOSDD890 was used as starting point for this analysis . Gene-prAdditional information for new reactions and gene-protein-reaction (GPR) associations are included in Additional file For both case studies, we modified the reaction exchange bounds to allow the Mtb model to run on a minimal culture medium. Then, we identified reactions and pathways that were perturbed, and defined biomass growth rate as the objective function to generate a working constraint-based model to be used in FBA.Findings in humans and the non-human primate model of Mtb infection have indicated that granulomatous lesions are highly heterogeneous . The div2 saturation or O2 tension lower than 10 mmHg), nutrient limitation, low pH and oxidative stress are thought to be present in caseating and necrotic lesions and the phagolysosome of infected macrophages [Granulomatous lesions in humans have different forms: (i) non-necrotizing granulomas characterized by a lymphocyte rim, macrophages and epithelioid histiocytes at the center. In this stage, the granuloma becomes highly vascularized , 41. , functional studies of Phong and Colleagues (2013) suggested that pfkA might be responsible for 100% of the PFK activity under aerobic and hypoxic conditions. In contrast, pfkB overexpression does not lead to detectable PFK activity under the same conditions. Different researchers have argued that a conserved glycolysis pathway in Mtb is a sign of its capacity to use glycolytic substrates during infection [PFKA is a key glycolytic enzyme that catalyzes the irreversible formation of fructose-1,6-biphosphate from fructose-6-phosphate Fig. . Althougnfection –16.Fig. pfkA-mutant of Mtb using the modified iOSDD890 Mtb model. The in silico pfkA-mutant strain was generated by disrupting the glycolytic reaction step involved in the formation of fructose-1,6-biphosphate   analysis for the pfkA-mutant was performed. We generated a three-dimensional graph showing the effect of substrate and oxygen uptake rate on Mtb growth  Fig. . To elucwth Fig. . Four phpfkA-mutant ; each phase has a distinct metabolic pathway utilization pattern  in hypoxic non-replicating mycobacteria, as we observed in our simulations Fig. , and thepfkA and ppgK) during Mtb growth within early stage in-vitro granulomas from individuals. The black point in G4 denote a hypothetic scenario of high oxygen consumption; oxygen consumption rate in aerobic Mycobacterium bovis BCG cultures was calculated to be 0.98 mmol/gDW/h [pfkA-mutant have positive values and therefore an adverse effect on biomass growth rate; in G3 and G4, for these sugar-phosphates, shadow prices near zero were observed  showing a weak inhibition of bacterial growth. In contrast, negative shadow prices are attained for ribose-5-phosphate and xylulose-5-phosphate in G4, indicating the utilization of the pentose phosphate pathway for promoting bacterial growth. Shadow prices for ATP, oxaloacetate, fumarate and succinate are negative in all phases, which suggests that oxidative phosphorylation and TCA cycle are active and promote the survival of Mtb. Similarly, it can be seen that oxygen is important in controlling phenotypic variability in all phases  was exhibited Fig. , which l E. coli –54. ResepfkA-mutant strain, we used the improved GEM to analyze flux distribution through glycolysis, TCA cycle, pentose phosphate pathway, redox, and energy metabolism in all phases  increases towards glucose-6-phosphate in the gluconeogenesis sense  showed a flux decline behavior  and phosphoglycerate mutase (pgmA) remained unchanged in the glycolytic direction  catalyzes the reversible isomerization of glucose-6-phosphate to fructose-6-phosphate. Our results predict an increasing gluconeogenesis activity for this enzyme at low oxygen availability, G1, and G2. The phenotypic phases G2 and G4 are characterized by high glucose-6-phosphate dehydrogenase (fgd1) activity, the first reaction towards pentose phosphate pathway  catalyzes  the first reaction in the pathway of L-serine biosynthesis by converting D-3-phosphoglycerate to hydroxypyruvic acid phosphate utilizing NAD+ as a cofactor . Our results indicate that while the flux through pgmA was unchanged during oxygen depletion (G2), in serA, there is a flux decline  was strongly favored in the TCA cycle  to phosphoenolpyruvate (PEP) reaction for sugar backbone building), in Mtb during hypoxia, this enzyme has demonstrated an anaplerotic function in favor of OAA production [pckA increased in the OAA direction with glucose uptake increments and oxygen depletion (G1 to G2 and G4 to G2)  Fig. .acn) and isocitrate dehydrogenase (icd1/2) decrease noticeably at low oxygen conditions; yet, malate dehydrogenase (mdh), fumarase (fumC), and fumarate reductase (frdA~D) fluxes , increase under oxygen depletion (G4 to G2)  also favors this reductive activity  Fig. . Glucoseity Fig. .+ were found [Regarding flux distribution through redox and energy metabolism, a detrimental effect was observed under oxygen depletion; still, flux values are favored as glucose uptake increases. These results are comparable to available literature, e.g., Watanabe and colleagues, where a 2.3-fold increase in intracellular ATP levels and an almost 70-fold increase in the ratio of NADH/NADre found .2, during oxidation of any carbon source  on succinyl-CoA  was disfavored which endorses growth inhibition (G2) caused by deprived formation of fatty acids and lipids, as essential components of the Mtb cellular wall , glycine (glyA), tyrosine (tat), tryptophan (trpA), alanine , isoleucine (ilvE), and lysine (lysA) were deprived in phase G2 of the PhPP  and icl2 (Rv1915/Rv1916), which is divided in the genome of Mtb-H37Rv into two individually expressed modules, Rv1915 and Rv1916 (aceAa and aceAb). In this study, we mimicked the metabolic adaptation of icl1 and icl2 mutants (icl-mutant) when fatty acid and oxygen availabilities are changed, using in silico simulations; our extended GEM was also used for this analysis. The model associates a total of 50 pathways including β-oxidation of fatty acids, odd numbered acyl chain fatty acids and of unsaturated fatty acids, that are essential for assimilation of host fatty acids as a carbon source, during Mtb infection [icl-mutant strain lacked the ICL reaction (isocitrate → succinate + glyoxylate) and the MCL reaction (methylisocitrate → succinate)  is a Mtb enzyme that has both isocitrate (ICL) and methylisocitrate lyase (MCL) activities; it is codified by two essential genes for metabolism of even-chain fatty acids , 62, iclnfection . The in te) Fig. . For modicl-mutant is represented in a three-dimensional graph for identifying changes in the slopes of the Phenotypic Planes  are defined by futile phenotypes, which means that either propionate or oxygen are inhibitory towards supporting biomass growth. Shadow prices for propionate, oxygen and other important metabolites are shown for each phase in Table 12 in Mtb pathogenesis remains poorly understood. Currently, it has been demonstrated an enzymatic-dependent role of Vitamin B12 for the activity of methionine synthase (MetH) and methylmalonyl-CoA mutase (mutAB), during methionine biosynthesis and propionate metabolism  [icl-mutant predict an active methylmalonyl pathway, which corresponds to the in-vivo scenario of available vitamin B12. Spontaneous in silico activation of the methylmalonyl pathway occurs because our GEM does not take into account vitamin B12 as a cofactor for methylmalonyl-CoA mutase (mutAB). Consequently, simulations of the icl-mutant metabolism (where glyoxylate shunt and methylcitrate cycle are disrupted) show propionate catabolism by activation of methylmalonyl-CoA mutase (mutAB)  –68. Our AB) Fig. Fig. 5Fluicl-mutant and wild-type . From the TCA cycle, the glycolytic substrates could be replenished by gluconeogenesis, a reaction mediated by phosphoenolpyruvate carboxykinase (pckA). Replenishing of pyruvate takes place by the action of the malic enzyme (mez)  Fig. .icl-mutant phenotypes F1, F3, and F4, were analyzed at a fixed oxygen uptake rate (0.4 mmol gDW−1 h−1) with increasing values of propionate uptake rates . In F1, propionate is essential for growth, but in F3 and F4 (larger propionate uptake), it is inhibitory. Differences in metabolic flux distribution towards amino acid biosynthesis were also observed. From F1 to F2 there is an increase in fluxes towards phenylalanine, histidine, valine, alanine, glycine, lysine, threonine and methionine , isocitrate dehydrogenase (icd1/2), succinate dehydrogenase  and malate dehydrogenase (mdh), were disfavored in comparison with F2 and F5 (broken liquefied granuloma)  in F3 and F4  for Mtb metabolism at low oxygen uptake rates. Shadow prices for NADH/NADPH and FADH2 in our icl-mutant Mtb model were either zero or positive at phenotypes F3 and F4, respectively. Although the magnitude of shadow prices for the wild-type Mtb is very similar to the icl-mutant, a slight difference is found in the values of the shadow prices of metabolites in phase F1, indicating a small positive or negative contribution of those metabolites to Mtb’s growth. The NADH’s shadow prices were zero or negative for all phases in the wild-type strain, which is an indication of a slight improvement of NADH/NAD+ balance (in the wild type) in comparison with the icl-mutant. A possible explanation for this mild improvement in redox balance is the activation of carbon flux by the isocitrate lyase (ICL) and methylisocitrate lyase (MCL) enzymes, in addition to the methylmalonyl pathway activation.The needy activity of the TCA cycle in the F4 Table . In the +/NADP+/FAD+ recovery and ATP synthesis), and vital metabolic pathways as gluconeogenesis, pentose phosphate pathway, synthesis of amino acids, mycolic acids synthesis, and synthesis of methyl-branched lipids  activity , caused apparently by an increase of intracellular H+  led selective corrections in TCA cycle activity and membrane potential.Therefore, we argue that high levels of propionate uptake (Phenotypes F3 and F4) negatively affect both redox metabolism . Additionally, phenotypes F2 and F5  exhibited a noticeable boost in all fluxes towards lipids, amino acids, gluconeogenesis, and pentose phosphate pathway; this is consistent with the increase in Mtb mutant proliferation within broken liquefied granulomas.Although there is not a direct evidence of the effect of hypoxia on the methylmalonyl pathway in vivo, our findings predict that oxygen limitation creates an inability to methylmalonyl pathway to correct TCA cycle activity and membrane potential when the nditions . In otheNitrogen is an essential molecule for Mtb to produce amino acids, proteins, and nucleotides. Metabolite labeling studies have confirmed that Mtb actively imports amino acids such as alanine, aspartate, asparagine, glutamate and glutamine from the host cytoplasm .pfkA- and icl-mutants (G2 and F3/F4 respectively), which corresponds to a caseous granuloma scenario. In the metabolic model, this effect was captured by the relaxation of reaction exchange boundaries, thus allowing asparagine consumption at each punctual phase. The consumption range values showed in Fig. Metabolic profiling during the infection in guinea pig’s lung granulomas infected with Mtb showed accumulation of acetate, glutamate, and aspartate as the infection progressed . It is bAnsP2 (Rv0346c). AnsA (Rv1538c) is the asparaginase enzyme involved in asparagine deamination into aspartate, which releases ammonia; subsequent spontaneous protonation produces ammonium. It is believed that it has a major role in resistance to acid stress, both in vitro and in vivo.Mtb allows asparagine uptake by −1 h−1 and 0.6 mmol gDW−1 h−1 respectively. In contrast, for consumption rates higher than 2 mmol gDW−1 h−1 and 0.6 mmol gDW−1 h−1, a slightly growth inhibition arose. Furthermore, asparagine consumption leads to ammonium secretion in the pfkA- and icl-mutants (as described by Gouzy and coworkers), high fumarate reductase activity and high secretion of succinate, which might be responsible for the minor growth inhibition by carbon loss  catalyzes the synthesis of aspartate, which in turns, allows for the synthesis of oxaloacetate using aspartate oxidase  or the reversible aspartate transaminase . Flux distributions data derived from the additional asparagine consumption on phases G2 and F3/F4 of Mtb-mutants are included in Additional files Our in silico results indicate that the use of asparagine as nitrogen source presented an equilibrated balance between growth and inhibition at different levels of consumption in both Mtb mutants Fig. . For inspfkA-mutant and icl-mutant, the high levels of oxaloacetate should be reduced by the malate dehydrogenase activity, which augments flux in the reductive TCA cycle direction and might favor succinate production. Although recently, succinate production has been associated with elevated carbon flow through the glyoxylate shunt [pfkA-mutant simulations, the glyoxylate shunt could not carry flux with asparagine consumption. We believe that the use of glycolytic substrates like glucose and glycerol could increase succinate production by fumarate reductase activity during hypoxia, but the use of acetate may favor succinate production through the glyoxylate shunt [In both te shunt , in the te shunt .ald (Rv2780), encoding L-alanine dehydrogenase [+ and alanine to recover redox balance during non-replicative states. The use of glycolytic substrates by the pfkA-mutant, when asparagine is used as a nitrogen source, seems to favor L-alanine production.Furthermore, asparagine consumption allowed the production of alanine during the hypoxic caseous granuloma of phase G2. In Mtb, one of the genes upregulated in response to hypoxia is rogenase . This enpfkA-mutant and icl-mutant consumed asparagine in the phases G2 and F3/F4. Acetate accumulation has been detected in guinea-pig granulomas infected with Mtb [(pta) and acetate kinase (ackA), which together can mediate acetate production (when Mtb is consuming pyruvate or fatty acids) to release an excess of carbon units and resumption of acetate as a carbon substrate depending on the available substrates for Mtb [Acetate was produced and spent when both, with Mtb . Though  for Mtb .E. coli: methyl-malonyl-CoA decarboxylase (scpB) and succinate CoA transferase (scpC) [atoD), another E. coli enzyme. These findings point out the need for a further manual curation of the existing GEMs of Mtb; it will contribute to enhance the prediction of metabolic adaptations under a plethora of external perturbations. Indeed, new experimental evidence is needed to prove acetate production by high asparagine uptake rates, adding new insights about the role of the acetate metabolism in Mtb.Nonetheless there is no evidence of propionate production by Mtb; the metabolic model used predicts it using two enzymes that do not have an annotated function in Mtb but in e (scpC) . Similaricl-mutant, by the high activity of the enzyme formate-tetrahydrofolate ligase (fhs1&2), which catalyzes the interconversion between tetrahydrofolate and 10-Formyltetrahydrofolate, the latter acts as a donor of formyl groups in de novo purine biosynthesis pathway.Finally, formate was produced just in the F3/F4 phases of the pfkA- and icl-mutants, when asparagine is used as a nitrogen source, in an environment such as the one of a hypoxic caseous granuloma . This finding shows the potential of genome-scale modeling and shadow pricing for the identification and study of toxic metabolic intermediates.In addition, using shadow price calculations, we predict, for the first time, the appearance of toxic sugar phosphate intermediates (such as glucose-6-phosphate and fructose-6-phosphate) as glucose uptake increases in the icl-mutant was high during the transition from solid to necrotic granuloma, which Mtb population can access to large quantities of fatty acids and hypoxia but not in a liquefied granuloma where oxygen is available. We confirmed the importance of the activity of methyl malonyl pathway and acetate consumption for the detoxification of propionyl-CoA during high propionate uptake rates. We also found a decrease in TCA cycle intermediates that produce a redox imbalance during hypoxia conditions and low ATP synthesis, but methyl malonyl pathway could revert the effect and sustain growth under aerobic conditions, in a liquefied granuloma. Thus, a drug inhibition of icl-mutant seems to be favored in Mtb populations surviving inside hypoxic caseous granulomas. Although previous studies in mouse model showed that a deletion of both icl genes causes a profound attenuation during both the acute and chronic phases of mouse infections, this model does not form clear hypoxic caseous granulomas as in humans [The lethality of an n humans ; therefon humans , 78.Furthermore, in silico results showed that asparagine consumption promotes production of succinate, and alanine for restoring redox balance in both Mtb mutants, as reported in the literature. Besides, acetate resumption seems to have an important role in Mtb metabolic reprogramming at changes in carbon and nitrogen sources.The use of genome-scale modeling and shadow price analysis might help to accelerate our metabolic understanding about the essentiality of Mtb gene knockouts, to generate hypothesis about possible drug targets that could be lethal during all the stages of TB disease, and to prioritize resource-intensive experimental work in the development of new anti-TB drugs.Although in this study we explored the metabolic reprogramming of only two Mtb knockouts, future efforts will be made to provide a web server for displaying the in silico metabolic phenotypes reached by a large number of mutants (known metabolic-based drug targets) under diverse nutritional conditions. This tool will accelerate the study of metabolic reprogramming of Mtb mutants under a wide range of intracellular constraints.Additional file 1:Flowchart of the methodology adopted in this in silico study. (PDF 41 kb)Additional file 2:New reactions included into the iOSDD890 model. (XLSX 24 kb)Additional file 3:Improved genome-scale model of Mtb from iOSDD890. (XML 2218 kb)Additional file 4:a) 3D top view of the PhPP. (b) 2D top view of the PhPP highlighting isoclines with negative slopes. (PDF 457 kb)Phenotypic phase planes of the Wild-Type Mtb during shifts of glucose and oxygen. Flux values and shadow prices for all the explored nutrient conditions of the Additional file 6:pfkA-mutant in phases G1, G2, G3, and G4. ppgK: polyphosphate glucokinase, pgmA: phosphoglucomutase, pgk: phosphoglycerate kinase, atpFH: ATP synthase, nuoA∼N: NADH dehydrogenase, cydD: cytochrome c oxido-reductase. (PDF 1119 kb)Carbon flux distributions depicting other enzymes for the Additional file 7:pfkA-mutant. arcA: arginine deiminase, lysA: diaminopimelate decarboxylase, ilvE: branched-chain-amino-acid aminotransferase, trpA: tryptophan synthase, tat: tyrosine aminotransferase, glyA: serine hydroxymethyltransferase, alaT: alanine aminotransferase, aspB: aspartate aminotransferase, gdh: glutamate dehydrogenase, serB2: phosphoserine phosphatase, proC: pyrroline-5-carboxylate reductase. (PDF 384 kb)Flux towards amino acid biosynthesis in phases G1, G2, G3 and G4 of the Additional file 8:a) 3D top view of the PhPP. (b) 2D top view of the PhPP highlighting isoclines. (PDF 623 kb)Phenotypic phase planes of the Wild-Type Mtb during shifts of propionate and oxygen. Flux values and shadow prices for all the explored conditions of the Additional file 10:icl-mutant in phases F1, F2, F3, F4, and F5. (PDF 200 kb)Carbon flux distributions depicting additional enzymes for the Additional file 11:pfkA-mutant. (PDF 265 kb)Flux towards amino acid biosynthesis in phases F1, F2, F3, F4 and F5 of the Additional file 12:pfkA-mutant during asparagine consumption. (XLS 287 kb)Flux values for the phase G2 of the Additional file 13:icl-mutant during asparagine consumption. (XLS 1716 kb)Flux values for the phases F3 and F4 of the Additional file 14:pfkA-mutant in a caseous granuloma (G2) during the consumption of asparagine. Blue boxes represent up taken and produced metabolites. (PDF 52 kb)Model scheme of fluxes during the metabolic adaptation the Additional file 15:icl-mutant in a caseous granuloma (F3/F4), during the consumption of asparagine. Blue boxes represent up taken and produced metabolites. (PDF 58 kb)Model scheme of fluxes during the metabolic adaptation of the


Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models. PBMCs are comprised of several classes of immune cells, including T cells (~70%), B cells (~15%), monocytes (~5%), dendritic cells (~1%) and natural killer (NK) cells (~10%)  metabolism. Plasma metabolomics validated the identity of glycoceramide as a marker of emphysema  were also found to be altered , while functional analysis inspired by these transcriptomes suggested differences in the control of cell cycle regulation  obtained from 197 subjects were performed to assess the impact of cis-genetic and epigenetic factors. The major outcome of this study was the identification of 345 molecular trait QTLs (quantitative trait loci) which co-localized with immune disease specific loci  to identify protein biomarker(s) associated with specific pathophysiological processes, and (b) to characterize different subsets of immune cells based on their proteomes. Recently, comparative proteomics using tandem mass spectrometry (MS) was applied to PBMC samples obtained from kidney biopsies of 40 kidney allograft recipients, either with healthy transplants or those suffering acute rejection. A total of 344 proteins were identified, cataloged and mapped to 2905 proteoforms  based profiling of cell surface proteome was used to identify differentially expressed proteins between human Th1 and Th2 cells. Among the differentially expressed proteins, BST2  and TRIM (T cell receptor interacting molecule) were found to be significantly differently regulated  was used to identify 42 IL-12 regulated genes, among which 22 were up- and 20 were down-regulated. Functional characterization of the up-regulated proteins helped to identify a multifunctional cytokine macrophage migration inhibitory factor and a novel IL-12 target gene  based targeted metabolomics was used to quantify glucose derived metabolites in PBMCs of healthy controls, schizophrenia and major depressions. Most of these metabolites were found to be significantly altered particularly in schizophrenic subjects. In addition, ribose 5-phosphate showed a high diagnostic performance for first-episode drug-naïve schizophrenia subjects  in PBMC phospholipids were positively correlated with phagocytosis by neutrophils and monocytes, neutrophil oxidative burst, lymphocyte proliferation, and interferon-γ production. The study also suggested that variations in the fatty acid composition of PBMCs phospholipids might induce subtle variations in immune cell functions as seen in healthy individuals  is a constraint-based mathematical modeling approach that integrates biochemical, genetic and genomic informations within a computational framework  was built with a vision to integrate and analyze biological datasets      algorithm uses cell specific protein abundances to generate genome-scale active metabolic networks  that includes semi-automatically reconstructed GEMs of 773 human gut bacteria . The reconstruction can accommodate metagenomics or 16S rRNA sequencing datasets that can be used to study metabolic diversities among microbial communities  was designed to study the effect of diet on microbial communities , i.e., reactions marked by significant and coordinated changes in the surrounding metabolites following the environmental/genetic perturbations. By combining transcriptome data, it is possible to infer whether the reactions are hierarchically or metabolically regulated  that modulate immune responses. They could also be used to identify resident microbe(s) which perform specialized metabolic functions. Moreover, GEMs can provide mechanistic overview of substrate allocation, microbe-microbe competition for resources and microbe-assisted modulation of the host immune responses. Modeling metabolic interactions among cells- and tissue-specific GEMs using a cellular compartment and/or metabolic intermediates have been previously possible (Bordbar et al., While GEMs mechanistically link metabolic genotypes and phenotypes, at the same time they handle multitude of constraints and variables which could in turn enhance uncertainty of predictions. Therefore, clear standards for GEM reconstruction, solver integration and usability has to be decided prior to the modeling (Orth et al., As indicated in this review, transcriptome, proteome, epigenome and signaling of PBMCs and Th subsets, have been well studied. In comparison, the metabolism of Th subsets and its underlying regulations is so far poorly studied. It is known that metabolism of circulating T cell undergoes dramatic changes under the environmental stress which drives the immunity (Gerriets and Rathmell, PS: drafted the manuscript; EK and MO: provided critical comments and edits to the manuscript; All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Completion of genome-scale flux-balance models using computational reaction gap-filling is a widely used approach, but its accuracy is not well known.We report on computational experiments of reaction gap filling in which we generated degraded versions of the EcoCyc-20.0-GEM model by randomly removing flux-carrying reactions from a growing model. We gap-filled the degraded models and compared the resulting gap-filled models with the original model. Gap-filling was performed by the Pathway Tools MetaFlux software using its General Development Mode (GenDev) and its Fast Development Mode (FastDev). We explored 12 GenDev variants including two linear solvers (SCIP and CPLEX) for solving the Mixed Integer Linear Programming (MILP) problems for gap filling; three different sets of linear constraints were applied; and two MILP methods were implemented. We compared these 13 variants according to accuracy, speed, and amount of information returned to the user.We observed large variation among the performance of the 13 gap-filling variants. Although no variant was best in all dimensions, we found one variant that was fast, accurate, and returned more information to the user. Some gap-filling variants were inaccurate, producing solutions that were non-minimum or invalid (did not enable model growth). The best GenDev variant showed a best average precision of 87% and a best average recall of 61%. FastDev showed an average precision of 71% and an average recall of 59%. Thus, using the most accurate variant, approximately 13% of the gap-filled reactions were incorrect (were not the reactions removed from the model), and 39% of gap-filled reactions were not found, suggesting that curation is still an important aspect of metabolic-model development. The plethora of genomes sequenced in recent years has given rise to a large number of genome-scale metabolic models. Flux Balance Analysis (FBA) , 2 is a Reaction gap-filling, applied to a non-growing FBA model given a specific growth condition , consists of adding reactions to the reaction network to enable growth . Automatic gap-filling uses an algorithm, a “gap-filler,” to find these reactions to add. But how effective are such gap-fillers? Past publications on reaction gap-filling have performed very limited evaluations, and they have not directly asked how accurately gap-fillers can reconstruct a known metabolic network.Δ from a metabolic network R to produce a modified network R′. We then run a gap-filler on R′ and ask: to what degree do the reactions suggested by the gap-filler match Δ? We call solutions that exactly match Δideal solutions because they exactly recover the original network R — that is, had a gap-filler encountered R′ in a real-world run, it would have perfectly reconstructed the biologically correct starting network R. For our experiments, R is the EcoCyc-20.0-GEM metabolic model for Escherichia coli derived from the EcoCyc database. EcoCyc is likely to contain the most accurate genome annotation and metabolic network of any free-living organism because EcoCyc has been curated from 32,000 publications. Starting with an accurate model means we have more confidence in evaluating a gap-filler’s solutions than if we started with an inaccurate model, if all other factors are kept the same.The central idea behind our approach to testing gap-filler accuracy is to randomly remove a set of reactions We also compute the precision and recall metrics on the results obtained. Precision tells us what fraction of the reactions predicted by the algorithm were in the set of reactions removed. Recall tells us what fraction of the reactions removed were recovered by the algorithm.development modes of MetaFlux. One gap-filler uses mixed-integer linear programming (MILP) and is called General Development Mode (GenDev); the second uses linear programming (LP) and is called Fast Development Mode (FastDev). We implemented 12 variants of GenDev during the course of this work using two linear solvers (SCIP and CPLEX) for solving the Mixed Integer Linear Programming (MILP) problems for gap filling; three different objectives were applied; and two MILP methods were implemented. We found large variation among the performance of these 12 variants in terms of speed, accuracy, and value of the information returned to the user.MetaFlux is the metabolic modeling component of the Pathway Tools software , 8. MetaAlthough the gap-filling algorithm is an important determinant of gap-filling performance, the accuracy of a gap-filler will also be highly dependent on both the size and the quality of the reaction database from which it draws. GenDev and FastDev both draw their reactions from the MetaCyc database . MetaCycE. coli. Previous gap-filling algorithms are as follows.We note that although a number of other algorithms are described as performing gap filling, they actually solve a range of different problems from enabling flux of just the biomass metabolites in a model to enabling flux through all metabolites in a model. Although enabling flux through all metabolites in a model is in general a worthy scientific goal, we see it as a problematic goal for evaluation purposes because we do not yet have any gold-standard, highly curated model for which all metabolites are unblocked — even for ModelSEED  uses a tThe GapFind/GapFill  applicatFastGapFill  gap-fillMirage  gap-fillPonce de Leo et al. use gap filling to unblock the blocked reactions in many metabolic models  using thE. coli metabolic model (the iJR904 model [A recent paper by Prigent  is most 04 model ). SeveraIn the following sections, we present the two MetaFlux gap-fillers, our computational gap-filling experiments, and a discussion of the results.Given a metabolic network, a set of nutrients for growth, and a set of biomass metabolites, the GenDev gap-filler  is callenon-producible biomass metabolites.We implemented three GenDev techniques, and for each technique we studied four variations of it using CPLEX or SCIP as the solver, and using indicators or Big M. In Technique A, each biomass metabolite participates independently in the objective function and no biomass reaction is involved. Technique A  reports all the biomass metabolites. In Technique B, the biomass metabolites do not directly participate in the objective function of the MILP formulation, but the biomass reaction, in which all biomass metabolites participate, is constrained to have a flux greater than 10−3. This value is constant, that is, it does not depend on the model or any of the parameters specified in the model. This value has been selected such that a model is considered growing if and only if the model can produce a flux greater than this value. Technique B was implemented during this work because, as we will show, it yields more solutions that show growth when the model is solved than does Technique A. This increase in valid solutions is mostly due to constraining the biomass reaction to have a flux of at least 10−3. Technique B does not identify the non-producible biomass metabolites to the user.Technique B determines the minimum-cost set of reactions that must be added to the network to produce −3 (as in Technique B), and the biomass metabolites participate in the objective function. Technique C does identify the non-producible biomass metabolites to the user.Finally, Technique C is a combination of Technique A and Technique B, that is, a biomass reaction exists, which is constrained to have at least a flux of 10For the three techniques, the candidate reactions are all the metabolic reactions of MetaCyc version 20.5. In addition to gap-filling reactions, GenDev can also  gap-fill10,000 is assigned to any biomass metabolite (used only in Technique A)E. coli)30 is assigned to candidate reactions in the taxonomic range of the organism being gap-filled , the candidate reaction r is added; if not ), the candidate reaction r is not added. Similarly, for Technique A, variables sb, one per biomass metabolite b to produce, control whether b can be produced or not.In the GenDev MILP implementation, a Boolean variable ar controls the flux vr of each candidate reaction r. To do so, two methods are well-known, one called “Big M”, the other using the more direct notation of indicator constraints, which we often simply called the “indicators method” (discussed below). For Big M, two constraints are applied to each candidate reaction: Moreover, in GenDev, each boolean variable ar has value 0 or 1 to control the activation of candidate reaction r; continuous variable vr is for the flux of candidate reaction r; Bl and Bu are the lower and upper bounds, respectively, for the flux of any reaction considered to be active. Notice that when ar is 1, the variable vr has a value between Bl and Bu; whereas when ar is 0, vr has value 0ar is 1, the flux of a candidate reaction is forced to be equal to or greater than Bl. In our computational experiments, the values used for Bl and Bu were 10−5 and 30,000, respectively . The bound Bl ensures that the reactions have a minimum acceptable flux to be considered active, whereas Bu limits the flux of any reaction when it is active to avoid numerical overflow.where the Boolean variable ar over the flux vr of each candidate reaction r. This notation has the form The indicators notation accepted by most MILP solvers is a more direct way to express the control of each boolean variable ar, vr, Bl, and Bu have the same meaning as for the Big M method. The symbol → means “implies”. The indicators are such that when ar=0 then the flux of reaction r is zero, whereas if ar=1 then the flux of r is between Bl and Bu. As can be seen, no numerical operations, such as multiplication or addition, are involved in these constraints. That approach reduces explicit numerical errors. But more importantly, there is an explicit indication to the solver that variable ar directly controls the values of variable vr, which is hidden in the Big M method. Solvers will typically treat these indicators in such a way that when a solution is found, these implications are true. On the other hand, as will be seen in the experimental results of “where The objective function of the MILP formulation in Technique A and Technique C is sb controls the inclusion of the biomass metabolite b and wr is one of the three reaction weights . With such a high value of 10,000, the objective is to synthesize as many biomass metabolites as possible by using a minimum weighted set of candidate reactions.where the Boolean variable The objective function in Technique B is wr and ar are the same as in Technique A. A constraint vbiomass>10−3 is also added to the formulation to ensure that the biomass reaction, involving all biomass metabolites, is sufficiently above zero. This constraint is also applied by Technique C, but where the number of biomass metabolites in the biomass reaction is variable (see next paragraph).where the variables dynamic behavior of the biomass reaction. Using indicators, such a constraint for a metabolite M is formed by two conditional constraints of the form In Technique C, the constraints to enforce the FBA steady state for the biomass metabolites are handled differently than the other two Techniques. Recall that an FBA LP (or MILP) formulation constrains each metabolite to be flux balanced to obtain a steady state, that is, the sum of all production fluxes are constrained to be equal to the sum of all consumption fluxes for that metabolite. Such a constraint is also applied to the biomass metabolites. However, Technique C allows a variable number of biomass metabolites to participate in the biomass reaction, that is, once the MILP formulation is solved, the solution may contain a subset of the original list of biomass metabolites provided (such a solution points to the impossibility of producing some biomass metabolites even when considering candidate reactions to add to the reaction network). Therefore, a steady state constraint must be written to allow this sM is a binary variable controling the inclusion or exclusion of metabolite M in the biomass reaction, cr is the stoichiometric coefficient of M in reaction r producing or using metabolite M (with the appropriate positive or negative sign according to whether M is used or produced by r) and cb is the coefficient of M in the biomass reaction. As can be seen, when M is included in the biomass reaction , the constraint includes the biomass reaction flux, but when M is excluded , the biomass reaction flux is not constrained by the production of M.where sM value , that is, we use: When using Big M, the previous two constraints are instead written using four constraints, two constraints per sM=1, that is, when sM=1 the first two constraints translate to sM=0 by a similar reasoning. The value 10 used in this Big M method was experimentaly chosen. If that value is too large, it appears that numerical imprecision of the solver cannot enforce properly the equality to zero.The first two constraints implement the case when MetaFlux’s FastDev mode uses LP instead of MILP to gap-fill a reaction network. The running time of FastDev is therefore more predictable and, in most cases, faster than GenDev. The running time of FastDev is more predictable, because it always solves a limited number of linear programs; whereas GenDev, using MILP, might try to solve a very large number or even an unlimited number of linear programs. The complete algorithm of FastDev is described in , but theThe main objective of FastDev is not finding the minimum-cost set of reactions to enable production of all biomass metabolites; rather, it is maximizing flux through the biomass reaction. In some cases, fulfilling that objective is equivalent to enabling production of all biomass metabolites; but in other cases, FastDev will find solutions that increase biomass flux beyond a solution that GenDev (Technique A) would find.The objective function of the FastDev LP formulation is g is a numerical parameter, integer or not, selected by the FastDev algorithm; vbiomass is the flux of the biomass; the weights wr are the same as in GenDev; and vr is the flux of the candidate reaction r. Using binary search, the FastDev algorithm tries several values of g between 0 and n, where n is the number of candidate reactions. That means that ⌈logn⌉ values are tried, which is currently 14  and will unlikely be more than 17 for the foreseeable future, because that would require more than ⌊217.5⌋=185,364 reactions in MetaCyc. All the constraints of FastDev have the same form as in the LP formulation, but more such constraints exist in FastDev because the candidate reactions are also in the LP formulation.where vr for the flux of the reaction r have a similar role. After this LP formulation is solved, if the flux vr is set to a non-zero value, the candidate reaction r is active and suggested to be added to the model.The objective function above includes all candidate reactions as in GenDev, but no integer variables are used to turn the reactions on or off; instead, the non-integer variables R such that, once added to the model, the biomass flux vbiomass becomes a non-zero value . We can show that objective function . A minimal set is not necessarily a set with the smallest number of reactions that can make the model grow , because another minimal set may have a smaller number of reactions that also does so. On the other hand, keeping g smaller than all weights wr and finding a set of suggested reactions showing growth are not always possible. This is typically the case when setting g to a large enough value such that enough candidate reactions become active and setting vbiomass to a non-zero value are necessary.If two sets In summary, FastDev may find the smallest set of reactions to enable production of all biomass metabolites, but such an outcome is not guaranteed.restricted active reactions as the subset of the active reactions that are not transport reactions and where instantiated reactions are replaced by their generic reactions. There were 400 restricted active reactions.We have performed the following computational experiments using MetaFlux version 21.5 to study the strengths and weaknesses of GenDev  and FastDev. The publicly released version of MetaFlux uses Technique A with SCIP and Big M; all other gap-filler variations are not in the public release. We used version 20.0 of the genome-scale metabolic model [R of the model, a set of randomly selected restricted active reactions to create a perturbed reaction network R′. We then separately used GenDev  and FastDev to gap-fill the resulting networks R′. We ran 100 cases for each of six different numbers of removed reactions,  for a total of 600 cases. Gap-filling was performed separately on those 600 cases by GenDev  and FastDev. The sets of nutrients, secreted metabolites, and biomass metabolites were held constant for all these cases.Each computational experiment consisted of removing, from the reaction network For each of the 600 GenDev cases for each of Techniques A, B and C, we ran both Big M and the indicators method on each of two solvers, SCIP (version 2.0.1) and CPLEX (version 12.6.3.0), for a total of 7200 computational experimentsR′ to obtain growth), because the randomly deleted active reactions were among the 13,469 metabolic reactions of MetaCyc.All the 7200 gap-filler experiments for GenDev  and the 600 experiments of FastDev  considered all the metabolic reactions of MetaCyc (version 20.5) as candidate reactions for gap-filling, which includes all the reactions of EcoCyc. Therefore, we expected to find a solution with growth for all 7800 experiments , because these cases are given in row 3. The numbers are high when removing two and five reactions but quickly fall to zero when 20 or more reactions are removed. This quick fall occurs because, in many cases, only one or two suggested reactions are outside the set of removed reactions.Row 5 shows the number of cases where the number of reactions suggested is the same as the numbers of reactions removed, but the actual reactions differ. This difference is acceptable because other reactions than the one removed can enable growth of the model. GenDev computes only one optimal solution, but several optimal solutions may exist.Row 7 shows the number of cases where the number of reactions suggested is larger than the number of reactions removed. We would expect that each set of suggested reactions has at most the number of removed reactions, because GenDev searches for a minimum cost set of reactions to add to obtain growth, and because the reactions removed exist in the set of candidate reactions. For many cases, though, the number of reactions suggested to be added to obtain growth is larger than the number of removed reactions.Row 8 shows the number of cases where the solver could not find any solution. It rarely happens. We explain in “The rows on precisions and recalls give an overall evalution of the results.Finally, the last rows of the tables give the number of cases where model growth was in fact not obtained once the suggested solutions were applied to the model. For Technique A the results are very high suggesting that this Technique has a major issue. For Technique B the results are close to zero and for Technique C, for only one case was no growth obtained, due to the time limit reached by the CPLEX solver using indicators.For FastDev, the results are presented in Table E. coli metabolic network have that make it an appropriate subject for this study? The network is above average in size for a bacterium: EcoCyc 20.0 contains 1637 reactions versus the average network size of 1065 reactions across the 11,000 organisms in BioCyc; EcoCyc contains 4506 genes versus the average genome size of 3488 genes across all of BioCyc. The E. coli network has been very highly studied, and has been the subject of multiple modeling efforts [What properties does the  efforts , 23. TheGenDev should theoretically always find solutions with a number of reactions suggested to be added that is not greater than the number of reactions removed. In practice this is not the case as row seven of Tables When a small number of reactions were removed, GenDev would propose some solutions where no reactions were suggested to be added. These null solutions mean that even when some active reactions are removed, the model still grows. This is not necessarily an error, because some reactions are not essential as the fluxes of other reactions of the model can compensate for the removed reactions. For example, when the reaction RXN0-5461 was removed, no reaction was suggested to be added, and indeed this reaction was inessential. According to Table Some of these cases are possibly in error . Note that solving a model from which some reactions were removed is different than gap-filling the same model with the same reactions removed because the former used LP, whereas the latter used MILP. These errors occurred for Technique A, but not for Techniques B and C.Table Similarly as in the previous section, the correct solutions rerouted the fluxes of removed reactions to possibly other reactions, but this time through the suggested reactions to be added, to obtain growth. These errors occurred for Technique A, but not for Techniques B and C.Tables 10, which is more than 59,000 solutions, a very large number. On the other hand, the found solution likely overlaps to some degree with the reactions within the “correct solution.”On the other hand, how serious is the gap-filler’s solution being not exactly the same as the set of reactions removed? MetaFlux gap-filling is based on the nearly 14,000 candidate reactions present in MetaCyc version 20.5. In general, MetaFlux solutions are not unique — multiple subsets of reactions with the same cost may exist. The number of alternative solutions tends to increase as the number of reactions needed to enable model growth increases. For example, if there is a solution with 10 reactions to gap-fill the network, and each reaction has two equivalent reactions that can substitute for it, the number of solutions would be 3To estimate these overlaps, the recall and precision of the results were calculated as shown in Tables The best average precisions are obtained by CPLEX using indicators either for Technique A (82%), Technique B (87%) or Technique C (83%). FastDev has an average precision of 71%. This means that for all four techniques, on average, at least 71% of the reactions suggested were in the correct solution. Conversely, this result means that about one quarter of the reactions suggested were incorrect. We were surprised to see that for Techniques A and B precision  were essentially constant no matter how many reactions were initially removed from the model.The best average recalls are obtained by CPLEX using indicators for GenDev Technique A (56%), and B (61%), but for Technique C, SCIP using Big M has the best average recall (60%). FastDev has an average recall of 59%. That means that more than half of the correct reactions were recovered by the four gap-fillers. These percentages would have been higher if the sets of reactions removed had been essential for growth, rather than being active reactions. However, we feel these results are representative because presumably the genome-annotation errors that cause reaction gaps do not preferentially target essential reactions.Tables The most important aspect that differentiates the applicability of Technique A and Technique B is the number of solutions that do not show growth  for Technique A to be a viable approach; Technique B has a large precision using indicators but lacks the flexibility of Technique A; and Technique C using indicators provides a good average precision, avoids errors  and has the flexibility of Technique A.Considering the computational experiences described here, what are the different strengths and weaknesses of GenDev and FastDev? One general difference between the two approaches is their objectives. For GenDev, if no numerical errors occurred, it outputs a minimum-cost set of reactions to add to obtain growth, but no such guarantee exists for FastDev because its method is based on a heuristic. GenDev uses MILP, which in general is an NP-hard optimization problem, whereas FastDev uses LP, which is solvable in polynomial time. These are two very different computational complexity classes, where GenDev could take a large amount of time  to find its answer compared to FastDev . The computational time, though, also depends on the methods used, that is, either Big M or indicators. If the Big M method is used the computational time of GenDev and FastDev are similar.As given in Tables We performed an empirical study of the two gap-filling tools — called GenDev and FastDev — within MetaFlux, the metabolic-modeling component of the Pathway Tools software. We compared FastDev and 12 different variations of GenDev based on three techniques, A, B and C; using two solvers, SCIP and CPLEX; and using two methods for MILP formulation, called Big M and indicators. We randomly removed reactions from the EcoCyc-20.0-GEM metabolic model, ran each of the 13 gap-filler variations on the resulting reaction network, and assessed how closely the resulting set of reactions matched the removed set of reactions, which we take to be biologically correct given the large amount of curation and experimental knowledge behind EcoCyc.Technique A was shown to have a major accuracy issue, because a large percentage (over 24%) of solutions found, using either solvers or methods, did not show growth when the solutions were tested on the model using FBA. However, Technique A provides high flexibility because it identifies to the user the non-producible biomass metabolites.Technique B was shown to be very accurate — its solutions show growth 99% of the time. On the other, hand, this technique assumes that all the metabolites can be produced with some candidate reactions, which is not always the case when trying to gap-fill a model. Technique A can find a solution even when not all biomass metabolites can be produced.Technique C produces almost no errors, and has the flexibility of Technique A to identify to the user the non-producible biomass metabolites. However, its accuracy is less than that of Technique B. We intend to use Technique C, most likely with Big M, in the next MetaFlux release in 2018.Using indicators resulted in better average accuracy when using the CPLEX solver, but the computational cost increased substantially compared to the Big M method. This cost might not always be worth the time to wait for a solution, which makes the Big M method viable. On a solver such as SCIP, the computational cost of indicators becomes prohibitive and is not practically usable.In general, metabolic models that have been purely automatically gap-filled with no subsequent curation should be treated with caution because of the precisions and recalls found in our study. The best MILP-based GenDev gap-filler using Technique B (CPLEX using indicators) had an average precision of 87% (82% for Technique A), and the LP-based FastDev gap-filler had an average precision of 71%. The best average recall was 61% for GenDev Technique B (CPLEX using indicators) (56% for Technique A) versus 59% for FastDev, meaning that both gap-fillers recovered more than half of the correct reactions.Many gap-fillers are based on a minimality criterion inspired by the Occam’s razor principle: “More things should not be used than are necessary.” Yet we found that this minimality criterion is sometimes too strong because the GenDev minimality-based gap-filler sometimes recovers only a subset of the reactions deleted in our experiments. That subset is the subset necessary to enable growth of the model, not the set necessary to restore all the deleted reactions. Similarly, real-world gap-filling probably restores only a fraction of the reactions omitted by incomplete genome annotations.


Ralstonia solanacearum connecting a genome-scale metabolic network, a virulence macromolecule network, and a virulence regulatory network, which includes 63 regulatory components. We develop in silico methods to quantify phenotypic robustness under a broad set of conditions in high-throughput simulation analyses. This approach reveals that the virulence regulatory network exerts a control of the primary metabolism to promote robustness upon infection. The virulence regulatory network plugs into the primary metabolism mainly through the control of genes likely acquired via horizontal gene transfer, which results in a functional overlay with ancestral genes. These results support the view that robustness may be a selected trait that promotes pathogenic fitness upon infection.Robustness is a key system-level property of living organisms to maintain their functions while tolerating perturbations. We investigate here how a regulatory network controlling multiple virulence factors impacts phenotypic robustness of a bacterial plant pathogen. We reconstruct a cell-scale model of  R. solanacearum to a model of its metabolic network, and find that the VRN activates functionally redundant primary metabolism genes to promote phenotypic robustness during infection.How pathogens maintain phenotypic robustness during infection is poorly understood. Here the authors couple the virulence regulatory network (VRN) of the pathogen  Depending on the biological function to be maintained and the kinds of perturbations considered, robustness can arise from various types of molecular mechanisms  functional redundancy, which includes genetic redundancy6 and fail-safe alternatives , and (iii) the system control, i.e., the capacity of the system to sense and compensate appropriately the undergone perturbation to maintain its homeostasis. This variety of mechanisms and their tight imbrications make robustness challenging to study. Nevertheless, as these mechanisms carry a buffering capacity, studying robustness is key to understand accommodation capacity as well as evolvability of biological systems7. For instance, genetic redundancy is recognized as a main source of robustness with respect to genetic  perturbation8. The adaptive backup capacity provided by genetic redundancy was also proposed to be a transient byproduct during the functional divergence of gene duplicates9, which eventually could promote evolvability1.Living organisms have to face changing environments and are subject to accumulation of molecular damages during their growth and development. Therefore, they need to exhibit a certain degree of robustness to sustain their resilience in response to these perturbations. Robustness is commonly defined as the capacity of a biological system to maintain its function(s) despite environmental or internal perturbations; internal perturbations referring here to deleterious mutations or stochastic gene expressionsms Fig. . Among t1. Robustness provided by the metabolic network has been extensively studied13. One challenge in studying properties of complex networks like metabolism, which harbors a high number of pathway combinations, remains in the computational capacity to explore the large solution space of the system. The development of flux balance analysis (FBA) methods14, which rely on constraint-based modeling and steady-state assumption has provided the capacity to investigate the sources of robustness at genome-scale level17. Modeling and experimental approaches revealed that robustness of metabolic networks challenged with genetic perturbations relies on pathway redundancy or genetic redundancy, but their extent can vary depending of the organisms15.Phenotypic robustness of living organisms is recognized as a network-scale property20. Various types of regulation via feed-back loops within the metabolism have been discovered, most of them being aimed to maintain consistency between the biosynthesis of metabolites and their consumption upon perturbation23. There is evidence that an increase of fitness in various organisms was obtained through modifications of the regulatory network structure27. Indeed, inappropriate metabolic transcriptional reprogramming can lead to lower fitness with regard to the kind of environmental perturbation encountered by an organism29. Interactions between the regulatory and metabolic networks therefore determine the properties of the global network and the distribution of robustness or fragility points.Robustness provided by regulatory networks received attention because it determines broad adjustments of cellular processes through the genetic regulation of metabolism, signal transduction, and cell differentiation processes13. During interaction, the pathogen subverts the host cell machinery to collect host resources30, while the host reacts to protect its own physiological functions31. Each one will induce perturbations to the other’s system, with the aim to lessen the system’s robustness of the confronting organism32. Virulence functions of pathogens are controlled by sophisticated, multicomponent, interconnected regulatory networks that are responsive to environmental and internal signaling34. Reconstruction of metabolic models of pathogens and studies of their robustness with regard to internal perturbations were conducted with the aim to discover new drug targets36 but only one virulence regulatory network has been reconstructed so far37. To our knowledge, robustness analysis at the system level of a pathogen combining both metabolic and virulence regulatory networks has not yet been investigated. This information should provide new insights about how phenotypic robustness is associated to the evolution of virulence determinants.One highly complex biological system in which robustness is a crucial property is the host-pathogen interactionRalstonia solanacearum. This bacterium is one of the most destructive plant pathogenic bacterium worldwide due to its unusual wide host range and its broad geographical distribution40. Several reports have already highlighted that many pathogenicity functions display a high level of robustness as evidenced by genetic or functional redundancies44. We recently found that key VRN components can impair the versatility of the pathogen due to a resource allocation trade-off between virulence function and bacterial growth (hereafter referred as proliferation)45. Using a reconstructed cell-scale model of R. solanacearum and modeling studies, we predicted the robustness of phenotypes under environmental as well as internal perturbations. We evaluated the functional interplay between the metabolic network and the VRN and revealed the strong impact of this VRN on various sources of robustness such as functional redundancy. Results indicate that the VRN evolved to control functionally redundant metabolic genes, which overlap some primary metabolic pathways, thus leading to increased robustness capacities under specific host environment.In this study, we investigated impact of the virulence regulatory network (VRN) on the phenotypic robustness of the plant pathogen R. solanacearum comprising three interconnected modules: a genome-scale metabolic network, a macromolecule network dedicated to virulence functions and a VRN , including three transcriptomic studies49. The reconstructed R. solanacearum VRN comprises 712 genes including 29 genes coding for transcription factors and 34 proteins involved in signal transduction. This VRN perceives 86 signals and controls the expression of 606 genes. Among those genes, 444 are present in the biochemical reaction network. Overall, 86 reactions of the macromolecule network are also regulated by the VRN, and correspond to the induction of macromolecule secretion via the control of secretion systems. In total, the VRN iRP1443REG encompasses 1443 interactions generating 705 logical rules  nitrate availability. We concluded from this analysis that the model reliably predicted the transcriptional responses controlled by the VRN and the major signals or constraints it perceives in its environment.To validate the capacity of the reconstructed VRN to predict correctly gene expression in response to environmental changes, we compared the differential gene expression from an experimental transcriptomic data-set with the model predictions. These experimental data were extracted from a study using 53, which controls many biological functions including production of virulence determinants or motility repression.We tested the capacity of the hybrid model to predict the modulation of the phenotypic plasticity by the VRN. We performed a simulation of various phenotypes in different environmental conditions and compared them with experimental observations  of the phenotype (φ) facing the set of perturbations (π) as the number  of perturbations for which the phenotype is feasible, i.e., the value of the phenotype is strictly superior to a chosen threshold (α), divided by the number (nπ) of the faced perturbations.R) ranges from 0, when each perturbation leads to a loss of function, to 1 when the phenotype is always maintained in presence of any perturbation tested. This definition is prone to each considered phenotype and perturbation. It has also the advantage to allow comparison of robustness of different phenotypes with respect to a similar set of perturbations as well as comparing the phenotypic robustness of different organisms encountering a similar set of perturbations. To perform in silico robustness quantification, we designed a global robustness analysis, called BECO for Biological Entity Clustering by their contribution to the Objective function. This algorithm performs multiple regulatory steady-state analyses51 followed by FBA of a set of phenotypes (ϕ), used as objective functions, under a number n(πe) of environmental conditions (πe) , i.e., an objective function, is possible . An environment refers to different compositions and concentrations of compounds available in the medium and to physical parameters . An environmental perturbation is simulated by changing composition or concentration of any compound in an environment or any physical parameter. An internal perturbation is simulated by removing a metabolic gene, a regulator or a reaction.In this study, the BECO analysis that was conducted referred to the following definitions: a phenotype is considered to be feasible if a flux through its specific reaction or defined set of reactions .Then, BECO systematically tests the contribution of each biological entity , i.e., internal perturbations, on the phenotypes under several environments that correspond to environmental perturbations. This is realized by simulating series of gene and reaction knockout (KO) analyses and flux variability analyses, which leads to a classification of these entities similarly to the classification proposed by Wang and ZhangR. solanacearum has a level of phenotypic robustness comparable to what is observed for other microorganisms. Hence, we determined the level of genetic redundancy in R. solanacearum and 10 other representative microorganisms. We also performed a BECO analysis on orthologs shared between R. solanacearum, E. coli, and P. aeruginosa. We found that both levels of functional and genetic redundancy ensuring robustness of the proliferation trait were similar in R. solanacearum and the other tested bacteria, see Supplementary Figs. As a first analysis, we assessed if R. solanacearum VRN on phenotypic robustness when bacteria face an environmental perturbation. We selected a range of 14 phenotypes . The apparent decrease of robustness for the virulence-associated phenotypes indicated that essential genes required for virulence were not induced or repressed by the VRN in some specific environments. When comparing conditions that activate virulence functions, we found that the robustness of the virulence phenotypes is higher in planta (0.741 ± 0.313) compared to in vitro conditions (0.208 ± 0.140), p-value 0.0006. On the other side, housekeeping functions were almost insensitive to the VRN control with respect to environmental perturbations, thus showing that expression of essential genes required for housekeeping functions is not repressed by the VRN in any of the tested environments.To assess the significance of the observations made through the BECO analysis, we developed a 2D robustness analysis (ROBA), which simulates robustness of the phenotypes when considering a broader range of 1000 random environmental perturbations , i.e., when occurs a loss of function of each gene or reaction included in the model, in a defined set of 14 different environments . A tendency to an enrichment of VRN-regulated genes in conditionally dependent redundant pathways was observed  differs from T3SS effector production (virulence-associated function) , see Fig. We verified that the connection of the VRN on metabolic pathways inferred from the BECO analysis was significant and therefore statistically different from a random connection. We first defined eight different classes of genes, based on how they contribute to robustness of the metabolic network Fig. , see Met54 and the quorum sensing signal53. The simulations predicted that the robustness of both phenotypes increased when the VRN was activated, either by the plant cell wall or the quorum sensing signal . We determined the Robustness under three conditions: an environment not activating the VRN used as control, and two environments with activating signals encountered by the bacteria during infection: the presence of plant cell wall componentsR. solanacearum) and some gamma and alpha-class species as outgroups  belongs to the highly conserved tryptophan biosynthesis operon found in most bacterial species and is likely the most ancestral gene  is suspected to have been acquired through horizontal gene transfer as it is not found in others beta-Proteobacteria outside of the R. solanacearum species and a blast analysis first identified an homologous gene in alpha-Proteobacteria (62% identity at the protein level with trpD2) instead of R. solanacearum trpD1 (45% identity). This observation was supported by a phylogenetic analysis of trpD sequences, which revealed incongruence on taxonomy for trpD2  also reveals incongruence of the phylogeny with the taxonomy  and gamma-Proteobacterium  rather than the R. solanacearum ancestral gene trpC1 . These data were again suggestive of a horizontal acquisition of trpC2. A similar situation was also observed for other VRN-regulated genes in the l-proline (RSp0418), l-Lysine (RSp0424), l-cysteine (RSp0417), l-aspartate (RSp0943), and l-methionine (RSp0781) biosynthesis pathways, see Supplementary Fig. l-methionine biosynthesis pathway, which illustrates a case of pathway redundancy.Our analysis revealed that several VRN-regulated genes within the primary metabolism provide robustness to proliferation as well as virulence functions. To get evolutionary insights on how phenotypic robustness was shaped by the VRN, we performed phylogenetic analyses using homology groups containing the genes from 28 representatives of the beta-Proteobacteria  opened an unprecedented possibility to evaluate how metabolic network components from a pathogen are specifically controlled by the VRN. Our results reveal that the R. solanacearum VRN controls phenotypic robustness through two different means: (i) as expected, by activating essential reactions required to produce virulence phenotypes in specific environment, (ii) more surprisingly, by activating functionally redundant reactions within primary metabolism, presumably with the aim to specifically sustain virulence  is not yet reconstructed, the impact of this network could not be evaluated. However, this study revealed the direct effect of the VRN on phenotypic robustness as the analyses were conducted in a straight comparison between activated/inactivated VRN states independently of the global regulatory network; future investigations are required to undercover potential cross talks between the R. solanacearum VRN and the global regulatory network.The large repertoire of genes regulated by the see Fig. . Obvious60, and our report suggests that some primary metabolic pathways are specifically relevant for virulence purposes in R. solanacearum.The resource allocation trade-off between virulence and proliferation traits may also generate perturbations on the regulation of some primary metabolic pathways as the corresponding genes are subjected to the basal metabolic regulation dedicated to the homeostasis of biomass precursors, but also to regulation by the VRN. Interestingly, the VRN plugs on different genes that apparently overlap genes or operons involved in amino-acids biosynthesis. This control therefore provides phenotypic robustness to internal perturbations during infection and could also be a potent way to increase some metabolic fluxes required for the efficient synthesis of virulence factors. There is indeed increasing evidence for the contribution of primary metabolism genes to support both virulence and growth traits in various bacterial pathogensR. solanacearum. Another situation is observed in the case of the methionine biosynthetic pathway: here the metE gene encoding the cobalamin-independent methionine synthase was recruited by the VRN, whereas the functionally redundant metH gene encoding the cobalamin-dependent enzyme was insensitive to VRN regulation . Such apparent redundancy allows uncoupling the VRN control from the basal homeostatic control on these amino-acid biosynthetic pathways. This probably provides an advantage for the pathogen and may compensate the cost of possessing two functionally redundant genes. This process of metabolic gene acquisition and recruitment in the virulence regulon thus appears to be a key mechanism contributing to the phenotypic robustness of 13 and the identification of sources of robustness of pathogens will be a major issue for medical or agricultural applications. The R. solanacearum cell model is a valuable hypothesis-testing tool for further functional approaches in a biological system where phenotypic robustness potentially hampers the identification of virulence/plant adaptation determinants32.The robustness of biological systems implies that a limited number of internal perturbations can substantially disrupt the systemR. solanacearum mutant database was generated through random insertion of the transposon EZ-Tn5<KAN-2> (EPICENTER® Biotechnologies) in strain GMI100039. The EZ-Tn5<KAN-2> was electroporated in strain GMI1000 competent cells following the protocol of the supplier and mutants were isolated on BG medium supplemented with Kanamycin. Localization of each individual insertion in the genome was determined by sequencing of its flanking region (which may differ from the mutated gene) using an adaptation of the TAIL -PCR protocol from Qian et al.62 and automated BlastN comparison with the genome sequence. A total of 4046 transposon insertions in the GMI1000 genome were accurately mapped  involving one regulator and one target such as the binding of transcription factor to target gene promoters, the activation of a receptor (protein) by metabolites, protein-protein interactions, etc…. These various kinds of interactions were annotated following Systems Biology Ontology recommendations64. The interactions also contain information of the state of the regulator leading to the change of the target status. Indeed, when a quantitative value is available, like affinity of a receptor for a metabolite or quantitative response of the target to a transcription factor, the value is converted into discrete thresholds . The interactions were then converted in logical rules describing for each target the rule of activation, i.e., state of the regulators and their combination leading to the change of the target state. Then, they were assembled to generate a file in sbml-qual format .The workflow to combine the see Fig. . Each ty51. Briefly, the network component status was assessed by computing synchronous update of the component status following their logical rules until an attractor was reached. The attractor, called steady-state, could be a point attractor or a cyclic attractor. The initial state of network components used to initiate the simulation were either (i) determined from the environment, (ii) set to 0 in case of gene KO mutants or internal network component disruption analysis, (iii) set using the default network component state  which is based on multi-state logical modelingSimulation of the biochemical reaction network state containing the genome-scale metabolic network module plus the macromolecule network module was performed using constraint- based modeling. Flux distributions were simulated by FBA and lower and upper values of the fluxes were determined using Flux Variability Analysis. Various constraints were applied on simulations: i) environmental conditions were defined by constraining lower and/or upper bound of exchange fluxes depending on the availability of the metabolite/macromolecule, or experimentally measured fluxes, ii) gene KO mutants or internal network component disruption were simulated by setting flux (or the gene involved in a GPR) value to 0, iii) the regulatory network constraints were taken into account by setting a gene involved in a GPR or reaction flux (lower bound and upper bound) to appropriate value depending on the regulatory network states solved previously. A broad diversity of phenotypes were simulated depending on the biological function (reaction flux) optimized in FBA.27 The gene categories depending on their contribution to the optimality of the objective function value are  method has been designed to classify the biological entities  depending on their contribution to the optimization of different cellular objectives in different conditions. For each pair condition/objective, a classification of the metabolic genes depending on their contribution to the objective function  greater than the absolute minimum number of enzymes required for objective function optimality.The same categories (with the suffix _c) are extended to condition-dependent categories. For instance, a gene in the category ess-c is essential for the objective function only in specific conditions.α = 0) the phenotype is considered as performed by the bacteria and thus the prediction is classified as active phenotype (1), or if the reaction flux ≤ α the prediction is classified as not active phenotype (0). Then a binary matrix (i×j) containing the contingency of the experimental phenotype (i) and network prediction phenotypes (j) with the two classes  are built and the network performance metrics are calculated as following. The true positive (TP) phenotypes correspond to identical value of 1 for i and j. The false positive (FP) phenotypes correspond to a value of 0 for i and 1 for j. The true negative (TN) phenotypes correspond to identical value of 0 for i and j, and the false negative (FN) phenotypes correspond to a value of 1 for i and 0 for j. The sensitivity (Sn) of the model prediction capacity, which corresponds to the true positive rate is calculated as following, where (P) is the number of experimentally active results (i = 1):Qualitative evaluations of the cell scale model performance in predicting the bacterial phenotypes were performed by comparing the model predictions with experimental measurements. Phenotype predictions in various environments were performed by in silico simulation as described in the previous section by optimizing the biochemical reaction corresponding to the investigated phenotype. If the reaction flux > α , differentially repressed (−1), not changed (0), activated (1). For the experimental data analysis, transcriptomics data were discretized in three similar classes (l)  considering differential gene expression by using a threshold of 2, i.e., twice repressed or twice more expressed in planta vs complete medium. Only genes harboring a differential expression with a p-value <0.01 were considered. Then, metrics of the model prediction were computed for each class. Subsequently the global mean prediction (M) was calculated by averaging the metrics of the three classes, see Supplementary Note F1 score (mean) (F1(M)).M) is the mean precision calculated as the average precision of the prediction for the three classes, where n is the number of classes:M) is the mean sensitivity, or true positive rate, calculated as the average sensitivity of the network prediction for the three classes:Evaluation of the VRN performance in predicting differential gene expression was done by comparing the model prediction with the experimental transcriptomic data-set produced by Jacobs et al.http://lipm-bioinfo.toulouse.inra.fr/systemsbiology/models/rsolanacearum).Detail of the algorithms for in silico analyses can be found in the Supplementary Note 67 via Family-Companion (http://family-companion.toulouse.inra.fr). Sequence clustering was performed using a p-value cut-off of 1 × 10−5, and a percent match cut-off of 80. Sequences were aligned with Mafft v7.27168. Columns with more than 50% of gaps were removed. Phylogenetic trees were computed with IQ-tree (automatic mode)69 and drawn with Itol (https://itol.embl.de/).Homology groups of proteins sequences in 29 bacterial genomes downloaded on NCBI database (see Supplementary Table http://lipm-bioinfo.toulouse.inra.fr/flexflux/documentation.html#beco). Codes for VRN validation are available from the corresponding author upon request.The BECO method is available in the FlexFlux library . The metabolic network is available in BioModels70 with the identifier MODEL1612020000. The VRN model was deposited also in BioModels and assigned the identifier MODEL1710170000.The data that support the findings of this study are available in the website dedicated to the cell model of Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Software 1Supplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


Escherichia coli, that may be suboptimal for a required function. In this review, we discuss some of the properties desirable in a versatile chassis and summarize some examples of alternative hosts for synthetic biology amenable for engineering. These properties include a suitable life style, a robust cell wall, good knowledge of its regulatory network as well as of the interplay of the host components with the exogenous circuits, and the possibility of developing whole-cell models and tuneable metabolic fluxes that could allow a better distribution of cellular resources . We highlight Pseudomonas putida, widely used in many different biotechnological applications as a prominent organism for synthetic biology due to its metabolic diversity, robustness and ease of manipulation.The chassis is the cellular host used as a recipient of engineered biological systems in synthetic biology. They are required to propagate the genetic information and to express the genes encoded in it. Despite being an essential element for the appropriate function of genetic circuits, the chassis is rarely considered in their design phase. Consequently, the circuits are transferred to model organisms commonly used in the laboratory, such as  The goal of synthetic biology (SB) is to design and build organisms endowed with a set of pre-defined technical specifications. Great computational and molecular efforts take place to assemble the components of genetic circuits and metabolic pathways, typically encoded in DNA vectors, which are delivered to a suitable cellular host for expression known as the chassis. There are a number of potential hosts for the carefully crafted genetic material, and these organisms are selected according to the task that needs to be accomplished considering a variety of factors. These factors include the ability of the chassis to survive the environmental conditions of the final application, its metabolic properties, and the availability of a versatile array of molecular tools for manipulation. Although genetic circuits are designed following engineering principles such as those of modularity and orthogonality — lack of potential interactions between the heterologous material with other components of the chassis — the chassis itself is normally considered a mere vessel and it is not taken into consideration in the design phase .http:://www.projectara.com).In this review, we explore some additional properties that could allow the chassis to become an integral part of SB designs. Deep engineering of a chassis is of utmost importance in other disciplines, such as those related to transport of goods and people, in which the chassis is devised to serve not one, but multiple applications. For instance, in the aerospace industry, the versatility of the chassis is a key feature, and the same model of aeroplane must allow for accomplishing several and sometimes unrelated tasks: transport of passengers and goods in different configurations, surveillance, fire-extinguishing, support of communications, etc. A good example of a chassis being designed to accommodate a subset of modules is the project Ara for building smartphones. The project aims to generate an open platform for the production of phones with longer lifespans and adaptable to new hardware developments, which is also likely to reduce waste generation since the modules are replaceable  to the production of fuel in a process known as microbial electrosynthesis [The lifestyle of a particular organism is one of the main reasons for selecting it as a chassis. Chemoheterotrophs, such as olecules  and mateolecules  because olecules . Photoauhemicals . They arhemicals . A diffestridium  and Geobeobacter  species.ynthesis ,18.Mycoplasma pneumoniae (http:://www.mycosynvac.eu).The cell envelope is another important factor. Micro-organisms are often exposed to harsh environments where they must cope with high shear forces and other physical stresses , therefoE. coli, further work is required to determine whether the same distribution laws apply to all organisms and what will be the interplay between circuits and cellular machinery in other chassis.An important factor contributing to the performance of exogenous genetic circuits is their interplay with the host . A numbeSimilarly, in the case of genes encoding metabolic pathways, the correct implementation of an external pathway should consider the interactions with the endogenous metabolic network. This can be achieved by generating whole metabolic reconstructions based on genome information data . These rE. coli [In order to produce more accurate predictions beyond the static view of the network, dedicated methods of analysis are then required to constrain FBA models to known parameters of the chassis, for example, the levels of gene expression or the availability of cellular resources for transcription and translation ,26. The E. coli  among otE. coli when glucose consumption exceeds a threshold). Overflows are avoided in metabolic engineering and they can be overcome by incorporating the appropriate activities as illustrated by the decrease in acetate production in E. coli when expressing a recombinant NADH oxidase to tune the redox ratio [E. coli [+-ATPase and lactate dehydrogenase can direct an excess of pyruvate synthesis into alanine production through the expression of a recombinant alanine dehydrogenase from Geobacillus stearothermophilus.Related to this, accurate dynamic predictions will help to unveil other metabolic properties that could be of interest in a chassis. The so-called ‘overflow metabolism’ ,29 is onox ratio . Metabol[E. coli . In thisE. coli accounting for the consumption of 74% of ATP. In addition to the protein production burden, extremophiles have to spend vast amounts of energy to cope with the harsh environments that they colonize. For example, organisms living in environments with high concentrations of salts need to synthesize ‘compatible solutes’ [Chromohalobacter salexigens  [P. putida KT2440 [In addition,  biology . Pseudomus drugs . P. putiompounds . The comP cycle) . This mea KT2440 ,49.These metabolic properties have been exploited in a series of applications. The strain KT2440 is still a major agent used in the mineralization of toxic aromatic compounds such as toluene and naphthalene through plasmid-encoded enzymes ,51. By aP. putida is also used as a cell factory in SB. A major breakthrough is the expression of antibodies which production seems to be restricted to only heavily engineered E. coli strains. In that sense, the synthesis of single-chain Fv fragments [P. putida with good yields and proper folding. Apart from proteins, P. putida has been used to produce a wide number of small molecules such as rhamnolipids, terpenoids, polyketides and non-ribosomal peptides .P. putida KT2440 can also naturally accumulate polyhydroxyalkanoates (PHAs) to high yields [P. putida can use for PHA production. Some strains of P. putida can synthesize PHAs using polystyrene [P. putida for plastic recycling, contributing to solve the problem of the environmental fate of plastics (http:://www.p4sb.eu) [h yields , even unh yields , making h yields . Anotherystyrene  or vegetystyrene  as substp4sb.eu) .P. putida KT2440 discussed above, there is room for its improvement as a superior chassis for SB. By carrying out directed genome minimization, it has been possible to obtain versions of the strain with a reduced metabolic burden that can achieve greater heterologous gene expression [P. putida KT2440 is not a good producer of biofilms, it is possible to manipulate the intracellular concentration of messengers such as cyclic-diGMP to increase biofilm production resulting in the generation of artificial consortia with enhanced biodegradative properties [Besides the many applications of pression . Finallyoperties . This shE. coli to others, depending on the biotechnological application sought, has allowed the identification of key features that make a good chassis. These properties include a suitable lifestyle, fast growth and a versatile envelope, well-characterized regulatory events, the possibility of engineering social interactions, and an accurate map of its metabolic network. By modifying all of these properties, the streamlining of organisms such as P. putida, which is now considered a reliable chassis for SB applications, has been possible. In addition to desired technical specifications, we have identified the need for obtaining reliable predictions of the physiological status of the cell by combining metabolic models with models accounting for availability of resources and their investment. The dynamic descriptions of the cell obtained this way could then be used to select the chassis more efficiently and also to highlight important metabolic modules needed for a certain application.The expansion of SB from traditional laboratory organisms such as E. coli continues to be the preferred bacterial chassis for SB projects. In fact, recent developments such as the modification of the E. coli lifestyle to express the Calvin cycle [E. coli exclusively, a different organism or a collection of them, is part of the current discussion in the field and will be determined by technological advances and future applications of SB.Despite these features having been identified in many organisms, in cycle  are closOrganisms used as workhorses in synthetic biology, typically referred to using the term “chassis”, are not taken in consideration in the design phase of applications.Escherichia coli, are often selected for these applications, mainly due to a good understanding of the biology and the availability of molecular tools.Despite being suboptimal, model host organisms, such as The concept of modularity can also be applied to the design of an efficient and versatile chassis by endowing a suitable host with certain properties.Properties that need to be considered are lifestyle, endurance to environmental stress and well-characterized regulatory and metabolic networks. The knowledge about the physiology of the organism must allow for the use of predictive global models accounting for the usage of resources  and potential interactions with exogenous circuits.Pseudomonas putida is an organism that possesses many of these properties. It is a robust and versatile biocatalyst that can be easily manipulated, constituting an ideal candidate for numerous applications in synthetic biology.