Aquifex aeolicus is a deep-branching hyperthermophilic chemoautotrophic bacterium restricted to hydrothermal vents and hot springs. These characteristics make it an excellent model system for studying the early evolution of metabolism. Here we present the whole-genome metabolic network of this organism and examine in detail the driving forces that have shaped it. We make extensive use of phylometabolic analysis, a method we recently introduced that generates trees of metabolic phenotypes by integrating phylogenetic and metabolic constraints. We reconstruct the evolution of a range of metabolic sub-systems, including the reductive citric acid (rTCA) cycle, as well as the biosynthesis and functional roles of several amino acids and cofactors. We show that A. aeolicus uses the reconstructed ancestral pathways within many of these sub-systems, and highlight how the evolutionary interconnections between sub-systems facilitated several key innovations. Our analyses further highlight three general classes of driving forces in metabolic evolution. One is the duplication and divergence of genes for enzymes as these progress from lower to higher substrate specificity, improving the kinetics of certain sub-systems. A second is the kinetic optimization of established pathways through fusion of enzymes, or their organization into larger complexes. The third is the minimization of the ATP unit cost to synthesize biomass, improving thermodynamic efficiency. Quantifying the distribution of these classes of innovations across metabolic sub-systems and across the tree of life will allow us to assess how a tradeoff between maximizing growth rate and growth efficiency has shaped the long-term metabolic evolution of the biosphere. Metabolism lies at the heart of cellular physiology, acting as a chemical transformer between environmental inputs and components of biomass. Identifying rules and principles that underlie metabolic architecture can thus provide important insights into how basic properties of chemistry and physics constrain living systems. Of particular relevance to understanding the chemical history of the biosphere is the foundational layer of autotrophic metabolism, which fixes Aquifex aeolicus. A. aeolicus is a chemoautotroph, deriving both biomass and energy from inorganic chemical compounds, and is one of the deepest-branching and most thermophilic known bacteria The merits of this view A. aeolicus has been the focus of substantial experimental efforts . We recently introduced PMA to reconstruct the evolutionary history of carbon-fixation, relating all extant pathways to a single ancestral form For these reasons we utilize phylometabolic analysis (PMA) A. aeolicus synthesizes a significant fraction of its biomass through metabolic pathways that appear to represent conserved forms of the ancestral pathways to those metabolites. This is relevant in debates on the position of this organism within the tree of life. Initial phylogenetic studies based on 16S rRNA suggested that the Aquificales represent potentially the deepest branch within the bacterial domain A. aeolicusAs we will show, en bloc into metabolism Our analysis highlights three classes of innovations in metabolic evolution. One of these is gene duplication and divergence along a progression from low substrate-specificity to high substrate-specificity enzymes, driven by selection for improved kinetics. It has long been argued that early enzymes had broader substrate affinities than modern enzymes, with greater potential to promiscuously catalyze homologous reactions in earlier times E. coli mutants in which an essential metabolic pathway was knocked out have been observed to recruit an alternate pathway from parts normally used for other functions to maintain growth While selection for improved kinetics has probably lowered the overall occurrence of promiscuous enzymes, metabolism maintains a substantial degree of enzyme promiscuity. For example, In particular one would expect this tradeoff to be different for core processes, where a higher mass flux can significantly amplify the benefits of improved kinetics, versus more peripheral processes that have lower mass flux. In keeping with this expectation, it is found that substrate promiscuity tends to increase toward the metabolic periphery A. aeolicus that provide illustrations of these general principles. We will show that compared with later branching autotrophs A. aeolicus uses a greater abundance of repeated parallel chemical sequences catalyzed by enzymes with high sequence similarity, which could have initially been catalyzed by a smaller number of more promiscuous enzymes. We will further highlight how cost-benefit tradeoffs of improving kinetics have played out differently in core versus peripheral pathways.We will discuss the evolution of several sub-networks in the metabolism of A. aeolicus are catalyzed by multi-subunit enzymes known to be fused in later branching organisms, reflecting Aquifex's more primitive metabolism.A second class of innovations concerns the fusion of enzyme subunits into larger single-subunit enzymes. Enzyme fusion increases the effective concentrations of reactants at the active site for subsequent reactions within a sequence, thus increasing the throughput rate of the sequence A final important class of innovations occurs at the level of pathways. In some cases organisms may have access to multiple different pathways which produce certain metabolites, with different pathways' having different unit costs of required ATP hydrolysis. Recent work has suggested that lowering the ATP cost, thereby increasing overall thermodynamic efficiency, of pathways involved in These three classes of innovations are all different expressions, at different levels, of a more general evolutionary tradeoff, in which either the efficiency or the rate of growth is maximized. For example, for heterotrophic organisms it is observed that in the presence of resource competition cells will use fermentative metabolic modes and maximize the rate of ATP production, while in the absence of competition cells will use respiration to maximize the yield of ATP production A second aspect of metabolism where this growth rate/efficiency tradeoff is expressed, is not in the production of ATP, but in the structure of the biosynthetic machinery driven by its subsequent hydrolysis. For example, improving kinetics of metabolic sequences through increased substrate specificity or gene fusion contributes to improving growth rate, while lowering ATP cost by choosing alternative pathways contributes to improving efficiency. The relative mass contributions of different pathways or reactions are likely a critical determinant in the balance of benefit vs. cost of such innovations in different parts of metabolism.A. aeolicus provides an excellent testbed to start cataloging and exploring these ideas.Cost-benefit tradeoffs in the structure of biosynthesis are likely to be of particular importance in autotrophic metabolism. Because (some) heterotrophs can switch between fermentative and respiratory strategies for given organic inputs, they have access to much larger variability in the rate of ATP production than do autotrophs. Autotrophs generally use purely respiratory metabolic modes to interconvert pairs of inorganic compounds and are thus more constrained on the ATP production side. Since autotrophs form the metabolic basis for ecosystems, the same will tend to hold for aggregate metabolic networks at that level. The biosphere is globally autotrophic, and especially on the longest time-scales of selection we may thus expect that just as new inorganic energy sources were being explored to increase total inputs to ecosystems, both the rate and the efficiency of metabolic processes were simultaneously being optimized where possible. Even if individuals have access to, and can switch between alternate strategies and lifestyles, both those individuals as well as the ecosystem to which they belong would benefit from improved kinetics and efficiency of their metabolisms. The present reconstruction of the metabolism of A. aeolicus.The basic principles of phylometabolic analysis (PMA) The same genomic surveys that help interpret the character of individual phenotypes also produce distributions of metabolic pathways across the tree of life, as shown in panel B. PMA exploits universal topological bottlenecks within metabolism – metabolites through which all pathway alternatives in a given sub-network must pass to allow biosynthesis. Imposing the evolutionary constraint that all extant and ancestral metabolic states supply those bottleneck metabolites allows us to transform the distribution of pathways into specific sequences of pathway innovations. These sequences can be represented as phylometabolic trees, as shown on the right in panel B. As can be seen, the constraint of continuous metabolite production requires evolutionary intermediates in which two pathways are co-present before one may replace the other.Phylometabolic analysis imposes a network-level constraint on constellations of genes, which is not imposed in standard phylogenies using only gene presence/absence or similarity data. However, it is important to recognize that constraints from metabolic stoichiometry alone cannot distinguish whether the genes that make up a network are all contained within the genome of individual organisms, or are distributed within syntrophic communities. Although the distributions of genes that we use as a basis for evolutionary reconstructions come from comparing individual genomes, this does not imply that the gene constellations for reconstructed ancestral states of a given metabolic sub-network were likewise contained within a single species rather than within communities.A. aeolicus), which generate all biomass from inorganics and are in a sense “ecosystems within individuals", and are thus useful model systems for reconstructing the long-term evolution of metabolism.The nodes and links in a phylometabolic tree reflect the serial and parallel dependencies among metabolic innovations at the network level. Whether the serial dependencies, represented by internal nodes, are due to branching among individual lineages, or due to branching among tightly coupled consortia of cells, is not determined by an analysis using only metabolic genes. Such distinctions require more comprehensive analyses involving transporters and perhaps regulatory genes for organisms spanning the tree. We therefore generally withhold interpretations about individuality for internal nodes when reconstructing a phylometabolic tree. Nevertheless, the genomes of individual species may still contain most or all of the network contained within internal nodes of a phylometabolic tree. This is particularly true for autotrophs , comparisons using both sequence similarity and enzyme length will often separate those enzymes into clear groups at the clade level. This in turn increases the chances that individual enzymes within those clade-level groups have been experimentally studied within an individual member of that clade, helping to anchor the functional annotation of each of the enzyme groups.Aquifex aeolicus provides an interesting reference point for minimal deep-branching hyperthermophilic bacterial metabolism. Our reconstruction for this organism contains 756 reactions and 729 metabolites . However, fatty acid biosynthetic machinery is highly flexible and modular, with chains of diverse length and substitution patterns generated by a very small set of proteins T. maritima in fact also has a diverse mixture of chain length and substitution patterns in its lipid content E. coli was chosen in defining its biomass for reconstruction A. aeolicus and T. maritima may be reasonably representative of minimal bacterial metabolism found near hydrothermal vents. Here we examine in detail the metabolic evolution of A. aeolicus, paving the way for future studies that may help disentangle the evolutionary relatedness between Aquificales and Thermotogales.ites see . In compA. aeolicus. As additional experimental data become available, including in particular detailed data on the biomass composition under different growth conditions, they will provide further benchmarks to make this model increasingly quantitative. Nevertheless, this reconstruction contains a wealth of data on basic principles of metabolic evolution, as we will discuss in detail, starting from core carbon-fixation.Our primary goal was to accurately reconstruct the pathway structure of the metabolism for the purpose of understanding its evolution. We therefore did not significantly modify the biomass vector of the model (with fatty acids as the major exception), or general assignments of genes to enzymes from the initial model obtained from the SEED server. This work should thus be considered a qualitative first step toward building a comprehensive computational organism model for Aquifex aeolicus uses a unique and previously unrecognized form of carbon-fixation. It has been known for more than two decades that A. aeolicus uses the reductive citric acid (rTCA) cycle to fix most of its carbon A. aeolicus is shown in A. aeolicus metabolic phenotype is lacking only the final synthesis of acetyl-CoA within WL, which is catalyzed by one of the most oxygen-sensitive enzymes in the biosphere The complete carbon-fixation strategy reconstructed for A. aeolicus lacks the gene for 10-formyl-THF (tetrahydrofolate) synthetase, which catalyzes the attachment of formate at the A. aeolicusThe specific form of formate uptake by folate remains to be elucidated. A. aeolicus appear to use a partial WL pathway to produce glycine and serine, but which lack the  10-formyl-THF synthetase. 5-formyl-THF cycloligase catalyzes the cyclization of 5-formyl-THF to methenyl-THF and has so far only been suggested to be part of a futile cycle, but its precise physiological role has long remained unclear Formate incorporation in this organism is most likely catalyzed by an unrecognized While it has been recognized that most anabolic pathways originate from TCA intermediates, there exist some variation in this set of intermediates across clades. In particular, many organisms derive pyrroles (precursors to the cofactor family that includes heme and chlorophyll) from Hydrogenobacter thermophilus, which together with A. aeolicus belongs to the Aquificaceae family within Aquificales, identified novel enzymes for several rTCA reactions. The cleavage of citrate to acetyl-CoA and oxaloacetate, and the reductive carboxylation of H. thermophilus both catalyzed in two steps by distinct enzymes also found in the A. aeolicus genome  and two CoA synthase (CS) enzymes. By contrast, all member of the Hydrogenothermaceae and Desulfurobacteriaceae possess only single copies of these enzyme types, except Clear evolutionary driving forces can be identified that connect the different extant forms of the rTCA cycle. The gene fusion within ATP citrate lyase increases the effective concentration of citryl-CoA in the subsequent cleavage reaction, and can thus be understood to improve the kinetics of the rTCA cycle. This gene fusion would appear to have low cost, not requiring evolution of an additional compensatory pathway to release citryl-CoA as a free intermediate, since it is not used elsewhere in metabolism.Detailed thermodynamic analyses have in turn identified two classes of rTCA reactions that in isolation would require ATP hydrolysis to proceed: carboxylation reactions and carboxyl reduction reactions An additional factor that could have further increased the ATP savings of using a single higher-specificity ICDH to generate isocitrate from Sulfurimonas, an Surveys in the genomes of other clades using the rTCA cycle (data not shown) are consistent with these suggested adaptations. Chlorobiales, a later branching photoautotrophic clade in which the rTCA cycle was originally described Together these observations can be used to reconstruct a phylometabolic tree of rTCA variants, which in turn represents a branch of increased resolution on a more general phylometabolic tree of carbon-fixation While not all five homologous enzymes in the symmetric Aquificaceae rTCA variant show the same sequence similarity as the first three reactions (1–3), we argue this is consistent with the suggested evolutionary scenario. Reactions 1–3 represent thermodynamic bottleneck reaction that in isolation would require ATP hydrolysis Having both arcs catalyzed by single sets of promiscuous enzymes in the root node would likely have resulted in slower growth for organisms using such a strategy, but it would also have considerably lowered the overhead for early regulatory machinery. If both rTCA arcs could have been catalyzed by the same set of single enzymes, the whole cycle would required only 7 total enzymes for catalysis: the 5 homologous reactions plus the reduction of malate to fumarate, and the cleavage of citryl-CoA, both of which do not have analogs in the opposite arc. It has previously been argued that a fully connected rTCA+WL network was selected as the root of carbon-fixation, because its topology would have provided the most robust form of network autocatalysis for earlier eras of life Each of the divergences in the tree of rTCA variants can be understood in terms of a cost-benefit tradeoff in metabolic innovations. Duplication and divergence toward greater substrate specificity of the enzymes catalyzing the two arcs would have improved the kinetics of the pathway, and thus the growth rate of organisms using it. While the genome expansion due to these duplications would have had increased cost associated to it, this was apparently more than offset by the benefit of increasing the kinetics of a pathway through which most cellular carbon is incorporated. We will discuss a different case in the biosynthesis of branched chain amino acids in the next section, where a lower mass flux appears to have shifted this balance away from favoring duplication and divergence.For the second divergence, the fusion of the citryl-CoA synthase and citryl-CoA lyase would have improved the kinetics of the rTCA cycle, while the elimination of the ATP dependent S. azorense) has the fusion of the citrate cleavage reaction taken place without elimination of the The distribution of the different innovations governing the evolution of the two extant forms of the rTCA cycle may give some insights into the tradeoffs between optimizing growth rate and growth efficiency. The combined fitness advantage of improving kinetics by fusing the citrate cleavage sequence and improving thermodynamic efficiency through the emergence of the higher-specificity ICDH appears to be significant under most conditions. Only one family (Aquificaceae) within one clade  still uses the ancestral rTCA variant, while the conventional form is distributed across a wide range of bacterial clades. It is further interesting to note that in the vast majority of cases the two innovations occur together. In only one known case  and glutamine (from glutamate). The 3-step sequence from aspartate to homoserine provides the branching point from which the synthesis of threonine, methionine, and lysine  The genome of We note briefly that there is some uncertainty in the annotation of CMS, because of the sequence similarity not only to IPMS, but also to homocitrate synthase of the AAA lysine synthesis pathways Among the archaea in our study, a vast majority  posses a gene for CMS. By contrast, less than half  possess a gene for TDH. Moreover, when TDH is found it is mostly co-present with CMS, while in many cases CMS represents the exclusive pathway to isoleucine. This is relevant, because two versions of TDH are known to exist: an `anabolic' version whose activity is obligatory for cell growth, and a `catabolic' version that only becomes active during salvage of excess threonine Bacteria present a more complex picture than archaea regarding the synthesis of isoleucine. Both TDH and CMS are common in deep-branching bacterial clades, with TDH occurring with higher frequency. However, this balance is dominated by Firmicutes, and among other deep-branching bacteria, the citramalate pathway appears to be the most abundant route to isoleucine. Strikingly, a significant number of deep-branching clades whose members include (hyper)thermophiles, autotrophs, or both, show near exclusive use of the citramalate pathway: Aquificales, Chlorobiales, Nitrospirae, Planctomycetes, and Verrucomicrobia.A closer look within clades where TDH represents the majority pathway provides further context for evolutionary reconstruction. For example, within the large and diverse Firmicute phylum, the aerobic Bacillales nearly all possess the threonine pathway, while the anaerobic Clostridiales exhibit the threonine and citramalate pathways in about equal frequencies. Within the Clostridiales in turn, the Clostridiaceae family, which contains many pathogenic strains Synechococcus and Prochlorococcus. Many of the former and all of the latter are divergent cyanobacterial lineages highly adapted to oligotrophic regions of the world's oceans, often dominating those environments Within the Cyanobacteria in turn, the majority of strains using the threonine pathway belong to the genera E. coli. Thus, all evidence combined leads us to conclude that the citramalate pathway is the ancestral pathway also in bacteria, and thus for all life, with the threonine pathway initially emerging as a salvage pathway.Lastly, it has previously been observed that many deep-branching bacteria that possess both TDH and CMS, appear to possess the catabolic version of TDH A. aeolicus possesses only a single, two-subunit enzyme for both reactions This conclusion is important relative to the previous discussion of a minimal ancestral rTCA cycle. In addition to the enzymes shared between the citramalate pathway and the final sequence in leucine synthesis (reactions 5–7), the enzymes catalyzing the homologous sequences in valine and isoleucine synthesis (reactions 1–3) are similarly shared across pathways, while the final amination reaction (4) is in all three pathways performed by the same enzyme In contrast to the evolution of the rTCA cycle, most enzymes catalyzing homologous reaction sequences in this case have not duplicated and diverged, except for CMS and IMPS. Why this difference? The difference in mass flux between the two pathways would appear to a offer a straightforward explanation. While most cellular carbon passes through the rTCA cycle in autotrophs using that pathway, only 3 out of 20 amino acids are generated through the pathways in A. aeolicus , the dehydration/hydration-isomerization reaction is catalyzed by a single subunit aconitase enzyme (ACO) in rTCA, and a two subunit enzyme (LeuC/D) in the branched chain amino acid pathways. However, the combined length of LeuC and LeuD is similar to that of ACO, while LeuC and LeuD also have high sequence similarity to consecutive, adjacent portions within ACO (data not shown). This suggest the possibility that following duplication and divergence from a common ancestral enzyme the two subunits became fused within the rTCA cycle but not the branched chain amino acid pathways, due to the differences in mass flux density. Similarly, the homologous (de)hydrogenation reactions in the two sub-systems are in A. aeolicus also catalyzed by enzymes with high sequence similarity. The enzyme homologies do not extend to the (de)carboxylation reactions across the two sub-systems, which in the direction of decarboxylation is facile, and in the direction of carboxylation requires complex cofactors and ATP hydrolysis (see previous discussions). Phylogenetic reconstructions of the lineages of these enzymes could shed additional light on these hypotheses.Additional overlaps between the rTCA cycle and the branched chain amino acid biosynthetic pathways may provide insights into how the latter emerged. The dehydration/hydration isomerization and dehydrogenation sequences  in the reconstructed ancestral pathways to leucine and isoleucine is homologous to the sequence of local group transformations occurring in the opposite direction in the large-molecule half of rTCA  in the citramalate and leucine pathways are also known as “keto acid elongation” sequences Thus, we suggest that at the substrate level the prior existence of the biosynthetic pathways leading to valine/leucine/isoleucine facilitated the reversal of the TCA cycle from the reductive to the oxidative direction. The additional key innovation appears to have been the recruitment of lipoic acid from the glycine cycle to its interaction with thiamin in the production of acetyl-CoA from pyruvate. Broad affinity of earlier enzymes would have aided the emergence of this novel pathway as promiscuous activity could have allowed this pathway to proceed at lower rates, with duplication and divergence later being favored as respiration came fully online and mass flux through this pathway increased.A. aeolicus, using the reconstruction to provide additional insights into the evolution of their functions.Cofactors are a distinct class of molecules at the substrate level of metabolism, forming a chimeric intermediary layer between monomers and polymers in terms of structure Lipoic acid is a cofactor with very limited, but key metabolic roles. Lipoic acid is central to the “Glycine Cleavage System” (GCS) E. coliB. subtillis, which involves the same basic chemistry, but distinct set of enzymes and an additional intermediate in the transfer of octanoate. In B. subtillis, the distinct octanoyl transferase LipM transfers octanoate from ACP to the H-protein of the GCS, followed by a second transfer  to the E2 subunit of pyruvate dehydrogenase E. coliE. coli mutant in which LipB had been deactivated. In this mutant, lipoate protein ligase (LplA), normally used in the attachment of free lipoic acid to LD enzymes, is recruited in the transfer of free octanoate to an LD enzyme through an AMP-bound intermediate A. aeolicus lacks a gene for LipB and is annotated as having LipA and LplA A. aeolicus use, and what does this teach us about how the biosynthetic pathways and the functionality of lipoic acid evolved?Three pathways are known for the biosynthesis of lipoic acid see . The conB. subtillisB. subtillis than to LplA of either E. coli or the euryarchaeota T. acidophilumB. subtillis variant may be the ancestral pathway to lipoic acid, in most cases we could not find a LipL gene to accompany the putative LipM gene, presenting a puzzle as this gene is absolutely required in lipoic acid synthesis in B. subtillisTo examine this question, we performed broad genomic surveys for each of the enzymes used in lipoic acid metabolism, shown in A. aeolicus, do not use the oxidative TCA cycle nor do they degrade branched chain amino acids. Such organisms should thus not need LipL to transfer octanoate from the H-protein of GCS to other LD enzymes, because they do not possess them. Instead, direct sulfuration of octanoate bound to the H-protein of GCS, a reaction known to be catalyzed by LipA de novo lipoic acid biosynthesis. Note that this proposal further reinforces the conclusion that the reductive TCA cycle preceded the oxidative version.This is where the functional roles of lipoic acid provides critical evolutionary context. As explained, the likely ancestral function of lipoic acid is its role in connecting glycine/serine metabolism to folate-B. subtillis. This would have introduced a redundancy into the lipoic acid system by producing two dedicated octanoyl transferase enzymes. This redundancy then appears to have been removed by replacing the two distinct transferase enzymes with a single all-purpose LipB transferase, as seen in E. coli. The high sequence similarity among LipM, LipL, LplA and LpIJ further suggests that the environmental uptake genes likewise arose through duplication and divergence from octanoyl transferase genes, but that some of this ancestral function was maintained in LplA, making possible its recruitment in E. coli mutants lacking LipB.In this scenario, an additional octanoyl transferase (LipL) emerged for the second subsequent transfer to the E2 domain of PDH, possibly through duplication and divergence of LipM as seen in In contrast to the narrow functionality of lipoic acid, vitamin B6, which refers to pyroxidal 5-phosphate and its substitutes, is one of the most functionally diverse cofactors, with its different forms facilitating a very wide range of reaction classes E. coli, pyridoxine phosphate is derived from 4-erythrophosphate B. subtillis, pyridoxal phosphate is synthesized through the direct condensation of ribulose phosphate and glyceraldehyde phosphate There are two known biosynthetic pathways leading to vitamin B6 in modern metabolism. In the first recognized pathway, described in A. aeolicus possesses the DXP-dependent pathway, prompting us to further examine this hypothesis. Previous analyses found genes for the DXP-independent pathway to be highly conserved and distributed across both archaeal and bacterial domains, while genes for the DXP-dependent pathway were found mainly in the clade level, much more so than we have seen for pathway variants in other sub-systems we have previously analyzed. PdxA is found in a few species within both archaea and Firmicutes, but that enzyme catalyzes a hydrogenation reaction, with the enzyme catalyzing the actual ring condensation reaction (PdxJ) completely absent in those cases. Our analysis further appears to confirm that the DXP-independent pathway represents the ancestral pathway. In addition to nearly all archaea, several deep-branching bacterial clades  also use this pathway.These distributions show some striking patterns. It had previously been noted that the two pathways are mutually exclusive within organisms, which use only one or the other It was previously suggested that the DXP-dependent pathway arose within proteobacteria An alternative explanation would be (possibly repeated) convergent evolution early in the divergences of clades. This explanation has some appeal, as the key enzymes in both pathways, PdxA/J and PdxS/T, are in fact very similar in their 3-D structure and in the sequence of local functional group transformations that make up the respective condensation reactions If the evolutionary sequences and driving forces are not clear, we can at least identify features of the two pathways that would have facilitated the transition between them. The shared fold structure and similarity in reaction mechanisms, but low sequence similarity, between PdxA/J and PdxS/T have been interpreted to mean that they represent convergent discoveries Another common feature between pathways is they both start from intermediates within the pentose phosphate pathway. The key exception is DXP itself, which is the product of the first committed reaction in the DOXP pathway of terpenoid backbone synthesis. Archaea exclusively use the alternate mevalonate (MVA) pathway to synthesize terpenoids, while bacteria use both the MVA and DOXP pathways en bloc into the emergent DXP-dependent pathway. The selective advantage of this adaptation, and the way it might have led to the peculiar distribution of both pathways, remains to be explained, however.Finally, the other reactions in the DXP-dependent pathway that lead up to the condensation sequence catalyzed by PdxA/J represent common and widely used metabolic reactions catalyzed by members of highly diversified enzyme families. The reaction sequence connecting 4-erythrophosphate to phospho-4-hydroxy-threonine, the input to the PdxA/J-catalyzed ring condensation sequence, consists of a hydration/reduction of an aldehyde to a carboxyl group, a subsequent dehydrogenation of an alcohol to a carbonyl group, and finally the reductive amination of that carbonyl group. Especially in an earlier era of more promiscuous catalysts, this pathway could thus well have been recruited A. aeolicus was determined to be 2-demethylmenaquinone-7 (DMK-7) H. thermophilus, had previously been found to use 2-methylthiomenaquinone-7 A. aeolicusThe main component of the quinone pool in A. aeolicus. At the substrate level there is little major variation in the biosynthesis of these compounds A. aeolicus shows complete gene sets for their synthesis Assignment of biosynthetic pathways to pyrimidines and purines was mostly unambiguous in E. coli was found to possess both mechanisms A. aeolicus follows these trends and uses THF as the formyl donor during purine synthesis.There are a few noteworthy details in the biosynthetic pathways of nucleotides. The initial synthesis of the IMP backbone involves several steps in which formyl groups are incorporated, which can proceed either through an ATP mediated addition of free formate, or through donation of the formyl group by A. aeolicus uses a 2-step incorporation of Another minor variation in the synthesis of purines involves the carboxylation of aminoidazole ribunucleotide (AIR). Like other bacteria, A. aeolicus again reflects the primitive nature of its metabolism. In the first reaction in this pathway carbamoyl phosphate is synthesized from glutamine, A. aeolicus this three-part reaction is catalyzed by a heterotrimer enzyme with relatively inefficient coupling between the subunits E. coli two of those subunits are fused together, resulting in a heterodimer enzyme, while in mammals all three subunits plus the enzymes for the subsequent reactions to carbamoyl-aspartate and dihydroorotate are fused together into one large single subunit enzyme A. aeolicus is more closely related to the ancestral enzyme for this reaction A. aeolicus another reason that fusion did not take place may be that the heterotrimer structure may provide additional stability under hyperthermophilic conditions Pyrimidine synthesis in A. aeolicus consists of three main components: phospholipid membranes, a peptidoglycan cell wall, and lipopolysaccharide. A. aeolicus has the full gene complement for the standard diaminopimelate-based variant of peptidoglycan synthesis that is common to gram-negative bacteria A. aeolicus.The cellular encapsulation of A. aeolicus, containing nearly 200 out of ∼760 reactions. As mentioned this is partly due to the fact that we explicitly represent each reaction within this sub-system, and partly due to the fact that A. aeolicus has a complex and diverse lipid composition (see Supplementary Table I) Lipid metabolism represents the single largest sub-system within the reconstructed network of A. aeolicus uses fatty acid chains that contain methyl groups, propyl rings, and unsaturated bonds at different positions In addition, the fatty acid sub-network is further expanded due to the fact that A. aeolicus is unusual among bacteria for containing both phospho-ester and phospho-ether lipids A. aeolicus thus represent a sort of intermediary strategy.Finally, the lipid content of A. aeolicus lipids, including both ester and ether linkages, appears to reflect the “stressed” hyperthermophilic conditions of the hydrothermal vents and springs where it lives.Altogether lipid biosynthesis can be thought of as a compact and highly modular system that distributes 2-carbon units over a set of states of different lengths and substitution patterns that can be varied depending on environmental context. Methyl group side-chains, unsaturated bonds or cyclopropane rings can be used to modify the fluidity of the membrane, while cyclopropane rings may also be used to adapt to lower pH A. aeolicus represents one of its most studied aspects Energy metabolism has the highest mass flux density of all cellular processes, because it generates the global energetic driving forces (both reductants and ATP) for all subsequent metabolic interconversion. The energy metabolism of It is possible that these observations may reflect selection for improved kinetics. In contrast to heterotrophs, which can obtain energy from organics and may use fermentative metabolic modes, autotrophs obtain all energy from inorganic sources. The free energy density available from inorganic redox couples used by chemoautotrophs may further be as much as an order of magnitude lower than that provided by sunlight used by photoautotrophs. Together these effects could create a significant selective advantage for improving the kinetics of energy metabolism in chemoautotrophs.in vitro studies, behaved as freely diffusing entities in intact cells A. aeolicus energy metabolism primarily reflect adaptation to growth at higher temperature. While interactions between components of other known respiratory super-complexes have generally been found to be rather weak, in A. aeolicus super-complexes are found to be exceptionally stable A. aeolicus energy metabolism remains an important and exciting area of research. Because of their complex interconnected nature, the electron transport chains in this organism represent a potential treasure trove for further insights into the ecology and evolution of extremophilic and/or early life forms. We next briefly summarize some of the main findings to date.However, there appears to be some debate as to the functional relevance of protein super-complexes within respiratory electron transport chains (ETC). Recent studies of the yeast mitochondrial ETC concluded that protein complexes found to be part of super-complexes in A. aeolicus has a versatile and diverse energy metabolism. Molecular hydrogen is the universal (and obligate) electron donor, but can in some cases be supplemented by hydrogen sulfide , flavins . If this hypothesis is correct, then comparative genomic and experimental analyses of Aquificale energy metabolisms remains a key area of research for understanding the sequences of innovations that drove their evolutionary diversification.Altogether these observations lead to a plausible evolutionary scenario of Aquificale energy metabolism, from a fully anaerobic ancestral state toward diverse forms that cover the anaerobe-microaerophile spectrum. In this scenario, A. aeolicus was partly meant to highlight that different sub-components and sub-networks within bacterial  lineages may evolve independently from the rest of the system in response to specific selective pressures in the environment. Thus, some organisms may give more insights into the earliest forms of life, because their environments have changed less over the course of Earth history, and because they retain a larger number of sub-components that resemble reconstructed ancestral forms of those sub-components. In this regard we suggest that A. aeolicus is as good as any, and better than most, as a model system for studying the earliest metabolic evolution of life on Earth.There is likely no single organism today that best represents the earliest cells on earth, because all cells alive today are the product of a several billion year evolutionary process. However, the discussion about oxygen use in A. aeolicus, we identified several general driving forces of metabolic evolution. We have highlighted throughout the way selection for improved kinetics and/or improved thermodynamics can shape a metabolic network. Comparing different sub-networks further reveals tradeoffs in the costs versus the benefits of these innovations, which appears to depend strongly on the relative mass flux density of sub-networks. Extending these analyses to other metabolic sub-networks, and to the evolutionary history of other organisms will improve our understanding of how tradeoffs between performance gains and their associated costs generally contributed to fitness in the earliest stages of cellular life.By reconstructing the evolutionary sequences among pathways in Text S1A. aeolicus metabolic network reconstruction and curation.Additional details on the (PDF)Click here for additional data file.File S1A. aeolicus in SBML format The whole-organism metabolic network of (XML)Click here for additional data file.


Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2–4 (optimum 3.5) and a temperature of 75–80°C (optimum 80°C). The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose). Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA), which predicted that 18% of all possible single gene deletions would be lethal for the organism.We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon  Escherichia coli (Bacteria) Methanosarcina acetivorans (Archaea) Arabidopsis thaliana (Eukaryota) in-silico knockout studies and prediction of growth on different carbon sources can give important new insights, which can be used for metabolic engineering. One example is the optimization of L-lysine production in Corynebacterium glutamicumMethanosarcina barkeriMethanosarcina acetivoranshttp://gcrg.ucsd.edu). Both M. barkeri and M. acetivorans are mesophilic anaerobic methanogens, which are very closely related to each other (same genus) and belong to the phylum Euryarchaeota. Our model of S. solfataricus covers the other major branch of Archaea, the Crenarchaeota, and therefore represents a considerable gain of knowledge about the Archaea. The Archaea are of special interest because most culturable species are able to survive in extreme environments. The versatility of archaeal (hyper)thermophiles ranges from survival in the deep sea, in marine or terrestrial environments with temperatures over 100°C (>60°C up to 116°C) to very acidic or alkaline milieu. As an interesting and experimentally characterized representative of the Archaea, Sulfolobus solfataricus was modeled in this work.In recent years an increase of the use of genome-scale computer models for the reconstruction and prediction of cellular metabolic properties can be observed. Metabolic reconstruction starts with a full genome annotation of a particular organism, in particular the prediction of enzymes and transporters, and the addition of enzyme-catalyzed reactions. This allows an in-depth insight into the metabolic capacities of the organism. With growing availability of genomes across the three domains of life, genome-scale reconstructions of metabolic networks are now available for a number of organisms such as S. solfataricus is a hyperthermoacidophilic archaeon and represents one of the best-studied hyperthermoacidophilic organisms within the phylum Crenarchaeota. It colonizes terrestrial volcanic hot springs and was originally isolated from the Pisciarelli solfataric field near Naples, Italy. S. solfataricus has an optimal growth at 80°C (60–92°C) and pH 2–4 Sulfolobus is able to oxidize sulfur, forming sulfate. S. solfataricus is strictly aerobic and grows either heterotrophically or uses cellular respiration with oxygen acting as the final electron acceptor for autotrophic growth. It has a number of transport systems for the uptake of different organic compounds and energy sources such as sugars  and amino acids  S. solfataricus is also able to grow chemolithoautotrophically using bicarbonate fixation Sulfolobus, S. solfataricus P2, was published in 2001 Sulfolobus is of high interest for industry and biotechnology because of its broad physiological versatility – e.g. it exhibits the ability to grow on phenol S. solfataricus is a model organism for research on the mechanisms of DNA replication, DNA processing, chromosomal integration, transcription, translation, and the cell cycle S. solfataricus was based on published annotations of the S. solfataricus P2 genome, different biochemical databases including KEGG Sulfolobus-specific literature, and experimental data generated in our lab. Especially the biosynthetic pathways of the unique coenzymes sulfopterin, caldariellaquinone and sulfolobusquinone could only be obtained from Sulfolobus-specific literature. The model was generated and improved using an iterative model reconstruction process .After the manual model reconstruction process, an automated completion step was performed. In this procedure, any remaining EC number was added to the final model. Only EC numbers with a high reliability were integrated . We included a detailed comparison of our model with M. barkeri, which is the most comparable model organism to S. solfataricus. The percentage of included genes of iTU515 is smaller than in the E. coli model but higher compared to the other published archaeal model of M. barkeri and the model of the extremophile C. salexigens. Furthermore, the species knowledge index (SKI) S. solfataricus  compared to the other reconstructions except for M. barkeri  and C. salexigens (0.008). Compared with the bacterial M. tuberculosis model, our ratio of included genes is very similar, although M. tuberculosis has a much higher SKI value. The number of included transport reactions is lower than in M. barkeri, M. tuberculosis, C. salexigens, and E. coli, indicating a lower versatility of S. solfataricus, smaller genome size, or the lack of information for S. solfataricus due to its low SKI. While the total number of reactions differs strongly between iTU515 and the E. coli model iAF1260, the number of active reactions is similar ). Active reactions are those with a non-zero flux, including metabolic, transport/exchange and biomass reactions. In summary, this comparison indicates that the properties of iTU515 are comparable with other high-quality network reconstructions, especially considering the relative scarcity of available information in the literature.The properties of our metabolic network were compared with the properties of published reconstructions of  iAF692, , Chromoh iOA584, , Mycobac iNJ661,  and EschiAF1260,  . More than one-third of the other pathways are related to the catabolism of various compounds, which reflects the physiological ability of After the reconstruction of S. solfataricus metabolism, the model was checked for the ability to accurately predict the growth yield, which is one of the basic properties of the modeled organism. In order to simulate growth on a single carbon source, four parameters have to be experimentally determined or defined: The nutrient uptake rate, the CO2 production rate (respiration), the P/O ratio, and the maintenance energy demand.2 production rate are necessary for the analysis of the network to predict the efficiency of biomass production, i.e. the ratio between integrated and respirated carbon atoms. In this context it is important to know if any other metabolites are secreted into the medium. The respiration efficiency of the model is described as the P/O ratio. The P/O ratio represents the number of ATP molecules produced from the reduction of one oxygen atom during respiration. The P/O ratio was set to 0.5 because Sulfolobus uses the unusual archaea-specific cytochrome complexes SoxABCD and SoxEFGHIM 2O→ADP+Pi). The GAM value was determined from the genomic summary of S. solfataricus P2 from the Comprehensive Microbial Resource website (http://cmr.jcvi.org) DW−1 is between the values published for different E. coli models and similar to the GAM value of 29.2 mmol ATP gDW−1, which was published for Corynebacterium glutamicumThe nutrient uptake rate and the CODW−1 h−1, because this value resulted in a carbon usage ratio of 25%, which was determined in our laboratory DW−1 h−1 to 2 mmol ATP gDW−1 h−1 decreased the biomass flux by 35%  S. solfataricus has a reverse ribulose-monophosphate pathway instead of the pentose phosphate pathway .Furthermore, e branch . In the The optimal growth rate can often be achieved through a number of alternative flux distributions. Therefore, flux variability analysis (FVA) was used to explore the solution space. This method computes the allowed values for each flux keeping the optimal value of the biomass yield.The analysis showed that 57 of the 352 active reactions during growth on glucose show a variability of more than 100% of their predicted value, and another 1 showed a variability between 30 and 100%. 197 reactions with a predicted flux of zero could assume non-zero values in an alternative optimal solution. The fluxes of 294 reactions are fixed by the optimal conditions, representing 83% of the network. Such a high value indicates a well-defined network. None of the active reactions had a higher carbon flux than the glucose uptake rate.The analysis of flux variabilities showed candidates for reactions involved in stoichiometrically balanced cycles S. solfataricus. The FVA calculations show no flexibility for the reverse ribulose-monophosphate pathway, which reflects the importance of this part of the network. In contrast, the TCA cycle shows a greater variability. FVA calculations suggest that the glyoxylate shunt could be used instead of the normal TCA cycle without any loss of growth yield. According to the literature, the glyoxylate shunt is inactivated via catabolic repression in many bacteria grown on glucose S. solfataricusThe highest flux variabilities were observed for the semi-phosphorylative and the non-phosphorylative branches of the Entner-Doudoroff pathway. The yield of energy carriers from the semi-phosphorylative and from the non-phosphorylative branch is one ATP for each branch metabolizing one molecule of glucose to two molecules of pyruvate. Because both branches are energetically equivalent, they can be used alternatively by DW−1 h−1 (glucose uptake rate 0.55 mmol gDW−1 h−1) with 471 reactions having a flexibility between 10−6 and 10−22 mmol gDW−1 h−1 and 284 reactions having zero variability.79 of 834 reactions showed a flux variability of more than 0.0001 mmol gIn a second analysis, the influence of suboptimal adaption to the environment on flux variability was assessed. To this goal an extended flux variability analysis was performed with the requirement that at least 95% of the optimal biomass production be achieved.The results showed an increase of the variability of more than 100% for 126  of the 352 active reactions. Another 17 reactions  had a variability between 30 and 100%. 197 reactions with a predicted flux of zero  could assume non-zero values in an alternative suboptimal solution, and 209 reactions  have fixed flux values in this case.DW−1 h−1. The flux variability of the transport and degradation reactions remained unaffected. In summary, increasing the tolerance of the optimal FBA solutions by 5% increased the flux variability of the whole network on average by 345%. The comparison of the optimal and suboptimal results indicates that there exist multiple suboptimal solutions across the network.The results show a very similar flexibility of the Entner-Doudoroff pathway as described for optimal FVA before. In contrast to this, the reverse ribulose-monophosphate pathway shows a slightly higher variability than before. The overall flexibility of the network increased from 79 to 352 reactions with a flux variability of more than 0.0001 mmol gS. solfataricus uses between 17% and 28% of the imported carbon for the production of exopolysaccharides under certain conditions 2O DW−1 h−1 corresponding to 22.5% EPS of the imported carbon atoms from 0.55 mmol gDW−1 h−1 glucose. The NGAM value and the glucose uptake rate were kept at the same values as during glucose growth without EPS production .As described before, there is evidence that −1 as compared to 0.0222 h−1 without EPS). The loss of energy, caused by the formation of energy-rich EPS also leads to a decrease of the ratio between biomass and CO2 formation (1∶3 in the non-EPS scenario vs. approximately 1∶4 in the EPS scenario).Only those flux differences were counted to be significant where the two variation intervals as determined by optimal FVA do not overlap . The only significant differences can be found in the pathways leading to the production of the EPS compounds glucose, mannose, N-sulfo-D-glucosamine, and galactose (results not shown). The predicted biomass flux was decreased during growth on glucose producing EPS  and E-values of 10−11 and 10−24 for the other two , which gives additional theoretical support. In particular, the key enzymes acetyl-CoA carboxylase  in our simulation of autotrophic carbon fixation (CO2-fixation scenario). To ensure the same carbon uptake as during growth on glucose, the hydrogen carbonate uptake rate was set to 3.3 mmol gDW−1 h−1.2-fixation scenario). The only significant differences are the sulfur metabolism with the oxidation of hydrogen sulfide (2-fixation scenario. Other significant differences are the decreased fluxes through the first three reactions of the TCA cycle (2) is essential for the CO2 scenario, whereas it is dispensable for growth on glucose. On the other hand, the anaplerotic reaction  shown in the figures is indispensable for growth on glucose.As expected, the Entner-Doudoroff pathway was inactive, and the hydroxypropionate-hydroxybutyrate cycle was active , with fl sulfide  in the C−1 and thus much higher than the biomass flux calculated for growth on glucose (0.0222 h−1)  , highlighting the importance of this parameter.Since the uptake rate for hydrogen sulfide is not known during bicarbonate fixation, no constraint was placed on this reaction. This had the effect that most of the energy necessary for growth is taken from the oxidation of hydrogen sulfide in the optimal solution, because energy production by hydrogen sulfide oxidation is not associated with loss of carbon atoms. The calculated biomass flux is 0.0745 hS. solfataricus is known to grow on phenol as sole carbon source S. solfataricus), phenol is converted to catechol by phenol 2-monooxygenase (Aromatic compounds such as phenol are frequently found in polluted soil. DW−1 h−1 (the same carbon uptake as during growth on glucose), and the NGAM value was kept at the same value as during glucose growth.In this study, we analyzed the capability of iTU515 to predict aerobic growth on phenol. For comparability, the phenol uptake rate was set to 0.55 mmol g2)  DW−1 h−1, which corresponds to a carbon usage ratio of 25% for biomass and 75% for respiration for glucose as sole carbon source. This offers a direct comparison of the theoretical efficiency of each carbon source metabolized in iTU515 in contrast to glucose.As described above, The model is able to produce biomass utilizing 35 different carbon sources. For 13 of them the model predicts an increase in biomass production of more than 10% compared to glucose .The biomass flux of iTU515 using dextrin and fructose as carbon source showed identical behavior for the fluxes and predicted biomass compared to glucose as carbon source. In addition, 9 other carbon sources showed a biomass yield that differs by less than 10% from that of glucose.The predicted biomass flux for the remaining 11 carbon sources was decreased on average by 35%. One of the lowest yields (25% of that in the glucose scenario) was observed for phenol. To get a direct comparison of the efficiencies of each carbon source, the energy yield resulting from the conversion of these carbon sources to pyruvate was calculated. Whereas the conversion of phenol to pyruvate needs three NAD(P)H molecules, corresponding to 1.5 ATP, the pathway from glucose to pyruvate, on the other hand, provides a gain of one ATP.For a better overview, all carbon sources were grouped by their entry point into the central metabolism. The different points of entry are glucose/galactose at the beginning of the Entner-Doudoroff pathway , glyceraldehyde in the non-phosphorylating branch of the Entner-Doudoroff pathway (glycerol), acetyl-CoA at the link between the Entner-Doudoroff pathway and TCA cycle , and the following TCA cycle intermediates: 2-oxoglutarate , succinyl-CoA (propanol), succinate (succinate), malate , and oxaloacetate (tartrate).In summary, most carbon sources that enter the central metabolism through the TCA cycle but not via 2-oxoglutarate  had a significantly lower biomass flux compared to glucose as carbon source. A comparable biomass flux to glucose was achieved by monosaccharides as carbon source, which enter the central metabolism through the Entner-Doudoroff pathway  in a similar way as glucose.S. solfataricus to degrade these carbon sources +. In contrast, glucose metabolism reduces 2 oxidized ferredoxin and 2 NAD(P)+ but also consumes 1 ATP for TCA cycle refilling. In terms of energy, 1 ATP is equivalent to 2 NAD(P)H because of the P/O ratio of 0.5. Furthermore, the activity of ferredoxin-NADP+ reductase converts 2 reduced ferredoxin to 1 NAD(P)H. From this it follows that the yield from metabolizing arabinan, D-arabinose, xylan, and xylose to 2-oxoglutarate is 2 NAD(P)H in contrast to only 1 NAD(P)H from metabolizing glucose. The difference in energy yield is reduced by comparing the energy needed for the import of one carbon atom of glucose, arabinan, D-arabinose, xylan, or xylose. The import of arabinan, D-arabinose, xylan, or xylose consumes 0.2 ATP per carbon atom, which is slightly higher than the import cost of glucose (0.1667 ATP per carbon atom). A similar biomass flux increase, compared to glucose as carbon source, had already been described for D-arabinose and xylose A slightly higher biomass yield (110%) compared to growth on glucose could be observed for arabinan, D-arabinose, xylan, and xylose. Two possibilities are described for S. solfataricus takes up mono-, di- and oligosaccharides exclusively via ABC transporters The last group represents oligosaccharides, which are metabolized via the Entner-Doudoroff pathway like glucose. This group consists of melibiose, sucrose, lactose, cellobiose, trehalose, maltodextrin, raffinose, and maltose. They show an increased biomass flux by 12–22% compared to growth on glucose, although these carbon sources use for the most part the same catabolic reactions like glucose. The explanation is that The highest increase in biomass was predicted for glycerol as carbon source. During growth on glycerol the biomass flux increased by 75% in comparison to glucose. Metabolizing glycerol to pyruvate generates two ATP per six carbon atoms in contrast to one ATP gained using glucose as carbon source. In addition, the uptake of glycerol does not consume any ATP, unlike the uptake of glucose via an ABC transporter. This makes glycerol the energetically most efficient carbon source per carbon atom in our study.In conclusion, compared to glucose most of the carbon sources metabolized via the TCA cycle showed a reduced growth, except for those entering through 2-oxoglutarate. Almost all di- and oligosaccharides showed a slightly higher biomass flux, because of their lower ratio of ATP consumption per imported carbon atom. This information could be used for optimizing the medium composition with respect to biomass gain, but additional comparisons of uptake rates would be necessary. For example, as no experimental value for the ethanol consumption was known and we normalized the simulations to equal carbon atom influx, a decreased biomass flux on ethanol by 50% resulted, which could be easily corrected by assuming a higher ethanol consumption rate S. solfataricus on glucose were determined using minimization of metabolic adjustment (MOMA) . The gene-associated reactions were deleted one gene at a time by constraining the flux through the corresponding reactions to zero. MOMA minimizes the Euclidean distance to the wild type flux vector (FBA). The results of such calculations as presented in the following paragraphs have to be interpreted with caution as the calculations are based on the – necessarily wrong – assumption that all enzymes are expressed. Hence the number of essential genes is underestimated. Transcriptome or proteome data would improve the results.The essential genes and reactions needed for growth of For 68% of the single-gene deletions, 90–100% of the original biomass flux could be achieved. About 18% of the gene-associated reactions in iTU515 with glucose as carbon source were predicted to be essential. They showed less than 2% of the original biomass flux. About 14% of the tested gene deletions showed a decline in growth to 89–2% of the initial growth. Especially gene deletions in the central metabolism had a highly negative impact on growth .Thus, for example, each gene deletion in the reverse ribulose-monophosphate pathway was lethal Most of the genes related to gluconeogenesis are essential because it leads to fructose 6-phosphate, which is an important precursor leading to the reverse ribulose-monophosphate pathway and the biosynthesis of glycogen and trehalose, which are energy storage molecules.However, only three of the deletions (genes) in the Entner-Doudoroff pathway were lethal, and two led to a strong decline in biomass formation. In particular, the deletions of gluconate dehydratase (+)  when reactions cannot be circumvented via other pathways  the flux distribution through the network in different scenarios and compared distributions between scenarios, (ii) the active reactions and pathways, (iii) the variability of the network under the constraint of (sub)optimal biomass yield and, (iv) the robustness of the network against gene deletions.To our knowledge, iTU515 is the first genome-scale reconstruction of S. solfataricus model was done manually  and all cofactors could be reached and all artificial transporters could be removed. Organism specificity of the reactions was achieved by manually including unique metabolites and pathways known to be present in S. solfataricus, such as the unusual branched Entner-Doudoroff pathway Manual model building started with the core of the network, composed of the central carbon metabolism (CCM) and essential transporters, namely those for glucose, water, COS. solfataricus. Evidence for the postulated enzyme classes was gathered from literature using a hierarchical approach starting at the species level. If no species-specific evidence was found, the search was extended to the genus and finally to the domain level.Transport reactions were added from the database TransportDB Finally, the model was extended automatically. If an enzyme function was predicted by the annotation, the EC number and the respective reaction were automatically included. Reaction stoichiometry and reversibility were checked before any reaction was added to the network. Information on reaction reversibility was primarily gained from literature and further from BRENDA. Additionally, thermodynamic considerations were used for determining reaction reversibility and direction in case of irreversibility. For example, reactions consuming high-energy metabolites such as ATP and carbon-dioxide-producing reactions are generally irreversible, with the exception of ATP-producing and anaplerotic reactions.Methanosarcina barkeriS. solfataricus genome (http://cmr.jcvi.org) DW−1 h−1). The molar contribution of the other components such as carbohydrates, lipids, and the contents of the soluble pool  of the S. solfataricus biomass reaction were taken from the biomass reaction of M. barkeriThe biomass reaction was used as the objective function. Maximization of the flux through the biomass reaction simulates a cell which has been optimized by evolution for optimal usage of resources and efficient growth. A detailed biomass composition for cells growing in the log phase was not available. Therefore, the relative biomass composition of Sulfolobus solfataricus P2 Sulfolobus solfataricus P2 complete genome sequencing project” S. solfataricus or found in the literature (included in BRENDA and/or AMENDA).A weighted combined annotation of the S. solfataricus as predicted by EnzymeDetector. The rating system is represented by a self-defined confidence score (1–4), which was used in this work. Those reactions and EC numbers that have been biochemically proven to be present in S. solfataricus received a confidence score of 4, including the EC numbers found in BRENDA for S. solfataricus. A score of 3 marked an EC number when the prediction is based on high BLAST (EnzymeDetector) evidence  and the function was also predicted by most of the main annotation databases. Reactions were assigned a confidence score of 2 when the BLAST search found a homologous enzyme with an E-value>10−40 for this function and when this EC number was also predicted by some of the main annotation databases. During the model reconstruction phase some reactions had to be added to the network to fill gaps. For these enzymes no genetic or experimental evidence was found, or they were found only with a very bad E-value by the BLAST search. They were marked with a confidence score of 1. The final reconstructed network has an average confidence score of 2.75. 35% of all S. solfataricus reactions are experimentally verified using the data from BRENDA and AMENDA (evidence score of 4). Over 21% of the reactions in the network have an evidence score of 3. Enzymes of these reactions have very good genome-based evidence. 28% of the reactions are based on a good genome annotation (2). Only the remaining 16% are based on a low genome annotation quality or had to be included without genome evidence (1). These reactions belong to pathways for the degradation of some carbon sources  or biosynthesis of coenzymes like vitamin B12. The explanation is that there are published results that S. solfataricus is able to grow on several carbon sources or to produce the coenzymes by itself, but there is no evidence in the genome for the necessary enzymes.A rating system for the quality of enzymes included in the model was employed to rate each reaction in the network with regard to its presence in metano  was used for the computations in this work. metano natively uses a human-readable ASCII format for model input, but is also able to import SBML files S (m×n) is used, where each row corresponds to a metabolite and each column to a reaction. A positive entry iSj, indicates that compound i appears on the right-hand side in reaction j, while a negative value means that it appears on the left-hand side. The change in metabolite concentrations is described by this equation:v = T to a vector of time derivatives of the concentration vector x = T. In steady state, all metabolite concentrations in the system under consideration are constant:v that satisfies iv,min≤iv≤iv,max for each reaction i, where iv,min and iv,max are lower and upper bounds of the reaction flux. Reversibility and irreversibility of reactions are defined by the constraints −∞≤iv≤∞ and 0≤iv≤∞, respectively. Fluxes can be further constrained by experimental results. The uptake of a metabolite was defined as 0≤iv≤∞, and secretion of a metabolite was defined as −∞≤iv≤0 for every exchange reaction i. These reactions define an in-silico bioreactor where the exchange metabolite is continuously added to a virtual medium, and secreted products are continuously removed. A metabolic network typically has more reactions than metabolites (n>m). This implies that the system of linear The open-source software toolbox Finally, the use of constraints corresponding to observed environmental conditions  or different genetic conditions (e.g. knockout mutants) leads to an organism-specific model.iv such that the biomass flux is still optimal. Flux variability analysis (FVA) iv under the added constraint that the biomass flux be above some threshold. Artificial constraints |iv|<100,000 are added for unconstrained fluxes. The difference iv,max,FVA – iv,min,FVA is a measure for the flexibility of iv with respect to the goal of maximum biomass production.Metabolic networks of living organisms are usually underdetermined, as described above. Because of the linear nature of the FBA problem, it is easy to determine a minimum and a maximum value for each constrained flux variable Because of the artificial assumption of a fully optimized biomass production, the incompleteness of the network, and the fact that not all enzymes coded on the genome are expressed in a given condition, there is no perfect fit between real organisms and computed results. Suboptimal FVA can be applied where instead of fixing the biomass reaction flux to its optimum, the biomass reaction flux is allowed to vary within a certain range. The lower limit accepted for the biomass reaction flux used in this work was set to 95% of the optimum. The threshold of 95% was chosen to show that a slight change towards achieving an optimal solution can have a big effect on the variability of the fluxes in the network.An FVA calculation provides numerical values  for each of the predicted fluxes signifying how well a flux value is defined under the given constraints, i.e. the size of the solution space in the direction of this flux. Comparison of the variability intervals of the same reaction computed by FVA in different growth scenarios, e.g. growth on glucose vs. growth on phenol, allows the identification of significant differences. Variability intervals that do not overlap identify significant differences because the corresponding reactions will never assume the same flux.in-silico knockout study was performed in order to determine the effects of all single-gene deletions on the model. A simple approach is to sequentially set the flux through each gene-associated reaction to zero and perform an FBA calculation with this additional constraint. This method only determines if a gene deletion is lethal or not. If no positive biomass flux is obtained, the deletion of the reaction is by definition lethal.An in-silico prediction of the fluxes in the metabolic network of a mutant.To find out how the network behaves to perturbations by single-reaction deletion, the MOMA method (Minimization of Metabolic Adjustment) Figure S1The iterative and automatic model building procedure used to generate iTU515. The genome annotation was used as a scaffold for the genome-scale model. The reactions added to the model were taken from both biochemical databases and published data. FBA simulations under steady-state conditions were used to determine the reaction flux distribution in the network and to improve the model.(TIFF)Click here for additional data file.


Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant disease with a varying range of phenotypes involving abnormal vasculature primarily manifested as arteriovenous malformations in various organs, including the nose, brain, liver, and lungs. The varied presentation and involvement of different organ systems makes the choice of potential treatment medications difficult.A patient with a mixed-clinical presentation and presumed diagnosis of HHT, severe exertional dyspnea, and diffuse pulmonary shunting at the microscopic level presented for treatment. We sought to analyze her metabolomic plasma profile to assist with pharmacologic treatment selection. Fasting serum samples from 5 individuals  were metabolomically profiled.A global metabolic network reconstruction, Recon 1, was used to help guide the choice of medication via analysis of the differential metabolism between the patient and healthy controls using metabolomic data. Flux Balance Analysis highlighted changes in metabolic pathway activity, notably in nitric oxide synthase (NOS), which suggested a potential link between changes in vascular endothelial function and metabolism. This finding supported the use of an already approved medication, bevacizumab (Avastin). Following 2 months of treatment, the patient's metabolic profile shifted, becoming more similar to the control subject profiles, suggesting that the treatment was addressing at least part of the pathophysiological state.In this 'individualized case study' of personalized medicine, we carry out untargeted metabolomic profiling of a patient and healthy controls. Rather than filtering the data down to a single value, these data are analyzed in the context of a network model of metabolism, in order to simulate the biochemical phenotypic differences between healthy and disease states; the results then guide the therapy. This presents one approach to achieving the goals of individualized medicine through Systems Biology and causal models analysis. Complex diseases with multi-factorial etiologies often have multiple alternative pathways leading to a particular pathophysiological state, with a wide range of resulting phenotypes. Such diseases provide a significant diagnostic and treatment challenge, and will require individual-specific, personalized treatments. Hereditary Hemorrhagic Telangiectasia (HHT) is an example of a Mendelian genetic disease with broad variability in presentation and involvement of different organs. Arteriovenous malformations (AVM) can occur in multiple beds, including the brain, liver, lungs, and nose . A challThe patient in this study is a female who began experiencing syncopal episodes at rest and during exertion at 21 years of age. She was found to have a 28 mm atrial septal defect (ASD), for which percutaneous closure was attempted, but failed, ultimately requiring open heart surgery. The ASD repair resolved the syncopal episodes, however her dyspnea continued unabated to the point where she could not walk her dog without getting chest pain and expectorating small amounts of blood streaked sputum. Further investigation with contrast echocardiography showed she had a large right to left shunt at the pulmonary capillary level but had no treatable AVMs on computed tomography (CT) scan of the chest. Her resting oxygen saturations were 97-99% but with effort could decrease to the high 80's. Her pulse was regular but varied between 90 and 120 beats per minute and variations in blood pressure were also noted and treated medically when elevated. Because of a strong family history cancer of the colon and some occasional blood streaks in her stool, a colonic endoscopy was done revealing several small polyps in her small intestine and telengiectasias in her large intestine that were ablated with laser therapy. Her Endoglin and ALK-1 gene analysis were negative including SMAD-4. Based upon these findings as well as a family history of familial adenomatous polyposis and a sibling with similar diffuse AVMs, a presumed diagnosis of HHT was given based on the Curacao Criteria .In this individual case study, untargeted, quantitative plasma metabolomic profiling is carried out in a patient and healthy controls. We sought to use constraint-based modeling of metabolism on an organism scale to identify potential differences in metabolism between the non-HHT and HHT patient through identification of biomarker signature profiles (as opposed to single biomarkers) that can be linked to different functional states. These differences were then used to support the use of particular drug treatments; post-treatment profiling was carried out to assess whether the treatment revised the biochemical pathways accordingly.Fasting plasma samples drawn from 5 healthy individuals  and 1 patient with HHT (age 24) . The blood from the healthy volunteers was taken as part of a general protocol for internal standardization/normalization at UCSD. A set of samples from individuals in the study were used and are currently part of an ongoing study that allows acquisition and analysis of blood from healthy individuals as well as HHT patients . Blood draws from two different time points had been previously obtained from two of the healthy individuals and for the patient, resulting in a total of 9 samples. Additionally one individual provided a fasting as well as non-fasting blood sample, which served as a non-fasting positive control. Written informed consent was obtained from the patient for publication of this report. A copy of the written consent is available for review by the Editor-in-Chief. All of the experimental research with volunteers and the patient were performed with the approval of the ethics committees of the institutions where the studies were done.Blood samples were obtained by a licensed health care practitioner and spun down for 10 minutes at approximately 3000 rpm at 20 C. The supernatant was stored in a freezer, and after all samples were obtained, were sent for processing and profiling . The samples were filtered using 3 kDa molecular weight cut-off filters (Nanosep 3K Omega microcentrifuge filter tubes). Internal standard solution was added to each sample solution, and the resulting mixture was vortexed for 30s. 600 μL of the mixed solution was transferred to an NMR tube for data acquisition. Spectra were acquired on a 600 MHz Varian INOVA spectrometer with 32 scans per sample at 298 K. Spectra were processed and CNX files were generated using the Processor module in Chenomx NMR Suite 6.0. Metabolites were identified and quantified using the Profiler and Library Manager modules in Chenomx NMR Suite 6.0, using a metabolite reference library of 297 metabolites.Unsupervised hierarchical clustering showed clear separation between the healthy individuals and the patient. There were not enough replicates to apply conventional statistical tests (seven measurements in one group and two in another), however two-way hierarchical clustering with unweighted average distance linkage of the metabolite profiles demonstrated a clear separation between the patient and healthy individuals, which also drove the separation in the clustergram Figure . The disDifferences in metabolism between non-HHT individuals and the HHT patient were assessed using the plasma metabolomic profiles. Specific metabolites that were quantitatively and qualitatively different between the non-HHT and HHT patient were determined by identifying those metabolites whose maximum concentration in one group was less than the minimum of the other group or vice versa. This resulted in two sets of metabolites that were different in the two conditions; a high set and a low set. The two general classes of metabolites were labeled the as 'ketone group' and the 'amino acid/carbon rich group' and used to define qualitative metabolic pseudo-reactions.Recon 1, a global human metabolic network reconstruction , with elWe predicated our approach on the assumptions that following 8 hour fasting overnight, the body is at or moving towards a homeostatic state, the full content of Recon 1 represents the set of metabolic interactions in the human body, and that changes in plasma profile reflect net changes in uptake and/or secretion of different metabolites with the set of all organs. We briefly describe the constraint-based analysis approach for Flux Balance Analysis, noting there is a rich literature on the subject that the interested reader can pursue in greater detail ,20,21.S is the mxn stoichiometric matrix with m metabolites, n reactions, with each column representing a metabolic (or transport) reaction and v is a vector of fluxes corresponding to each reaction in the network.In which Constraints area applied to the network as upper and lower bounds on the fluxes,α vector specifies reaction flux lower bounds and the β vector specifies reaction flux upper bounds.In which the The next step requires specification of an objective function which will then be minimized or maximized. We consider linear objective functions only in this study, thus,c is a signed, binary vector. Flux Variability Analysis (FVA) [are the objectives, in which is (FVA)  is a metis (FVA) ,24.The intravascular space is available for uptake and secretion of metabolites with all organs of the body, thus changes in metabolomic profiles may be interpreted in the context of a global human metabolic network. Direct flux data for particular reactions were not available, so the two qualitatively different profiles, derived from the quantitative differences in the metabolomic profiles of plasma were used to define two sets of different transport constraints. Changes in the simulation conditions provided information about the changes in the metabolite profiles, and were used to define constraints  for the linear optimization steps, using Recon 1 [The COBRA toolbox  was used Recon 1 . The cyt Recon 1 ,27  largely consisted of ketones and were dubbed the 'ketone group'. Conversely, the set of metabolites that were decreased in the HHT patient  primarily were amino acids and were dubbed the 'amino acid group'. The production (i.e. exchange) maxima for the 'ketone group' and 'amino acid group' metabolites were used to specify coefficients for two different pseudo-objective reaction constraints, one representing the HHT patient and the other representing the non-HHT profiles. The 'ketone group' objective was scaled by one-half in order to be able to maintain a feasible solution in the null space . These objectives represent the different non-biomass demands for each condition . Fasting blood samples from 4 non-HHT individuals, was obtained and metabolomic profiling carried out as well. Two of the non-HHT individuals provided multiple fasting samples (on two different occasions) and another individual provided a non-fasting sample. The serum samples were analyzed using NMR spectroscopy with subsequent quantification and identification of 30 metabolites.Clustering of the samples demonstrated clear separation of the non-HHT individuals fasting sample, the HTT patient (pre-treatment), and the single non-HHT individual non-fasting sample Figure . The treA global metabolic human network, Recon 1 , was useComparison of the non-HHT and HHT patient networks using flux span ratios (see METHODS) demonstrated decreased energy production in the HHT patient, reflective of a 'starvation-like' state. Interestingly, there were noted increased flux potentials in nitrogen handling and disposition pathways in the HHT patient, notably with nitric oxide synthase (NOS) Figure . This obAn individualized case-study is described for a rare disease in which we demonstrate how it is possible to progress from untargeted metabolomic profiling to identify metabolic profiles that can be then used to constrain a mechanistic network model which is then used to direct therapy decisions. Metabolomic data has been recognized as an important 'omic' data type, as it represents a quantitative biochemical phenotype and has potential to serve as a source of diagnostic and therapeutic biomarkers. As with other high-throughput data types however, a challenge with data interpretation remains. In particular, in order to achieve any practical realization of individual specific, personalized therapies, it will be important to move away from strictly statistically driven models towards more mechanistic models.We employed the use of a global human metabolic network to interpret plasma metabolite profiling and help direct the therapy of a patient with a complex disease. Following treatment, the patient's metabolomic profile became more similar to a non-HHT individual's, suggesting the treatment was effective, at least in addressing the metabolic derangements associated with HHT. While the disease stabilized during this treatment, the patient did not undergo further cycles of treatment due to gastrointestinal intolerance of bevacizumab and long-term assessment of disease status could not be carried out. While deeper analysis with more patients are needed to further elucidate the directionality of causation, this 'individual clinical trial' illustrates how metabolomic data analyzed in the context of a network reconstruction can be potentially used to help direct therapy for complex disease states, using complicated metabolomic profiles as opposed to individual biomarkers.One goal of personalized medicine is to analyze the data from a particular individual and determine the appropriate medication or therapeutic intervention. In this individualized case study of personalized medicine, we carry out untargeted metabolomic profiling of a patient and healthy controls. Rather than filtering the data down to a single value, these data are analyzed in the context of a network model of metabolism, in order to simulate the biochemical phenotypic differences between healthy and disease states; the results then guide the therapy. Thus, we have found that one approach to achieving the goals of individualized medicine is to use Systems Biology and causal models to drive the analysis and interpretation of data.The authors declare that they have no competing interests. FJM, MDK, and NJ conceived of the study. FJM, JM, TE, and MDK provided patient care and treatment management. NJ carried out the data analysis, simulations, and drafted the manuscript. NJ and MDK analyze the results. FJM, MDK, and NJ carried out extensive revisions to the manuscript. All authors read and approved the final content.a summary of the PCA results from the metabolomic concentration measurements.Click here for fileAn Excel spreadsheet with 5 worksheets including the metabolomic data measurements ('metabolomic_profiles'), FVA results ('simulation_FVA'), FVA summary measures ('simulation_summaries'), the reaction upper and lower bounds ('reactions'), the objective coefficients ('metabolites').Click here for filean archive with the Recon 1 stoichiometric matrix (as a text file) used in the analyses.Click here for file


Metabolic Engineering has undertaken a rapid transformation in the last ten years making real progress towards the production of a wide range of molecules and fine chemicals using a designed cellular host. However, the maximization of product yields through pathway optimization is a constant and central challenge of this field. Traditional methods used to improve the production of target compounds from engineered biosynthetic pathways in non-native hosts include: codon usage optimization, elimination of the accumulation of toxic intermediates or byproducts, enhanced production of rate-limiting enzymes, selection of appropriate promoter and ribosome binding sites, application of directed evolution of enzymes, and chassis re-circuit. Overall, these approaches tend to be specific for each engineering project rather than a systematic practice based on a more generalizable strategy. In this mini-review, we highlight some novel and extensive approaches and tools intended to address the improvement of a target product formation, founded in sophisticated principles such as dynamic control, pathway genes modularization, and flux modeling. The utilization of industrial biotechnology for the conversion of energy, chemicals and materials to value-added products is centuries-old. Traditional methods of strain development based on evolution, random mutagenesis, mating and selection strategies were successfully used for the optimization of production hosts. However, early applications of microbial production of chemicals were limited to native metabolites, such as amino acids, alcohols, organic acids, fatty acids or antibiotics. The foundational development of genetic and metabolic engineering made possible the production of a wide and diverse range of molecules including biofuels, pharmaceuticals, biopolymers, precursors and specialty chemicals . By defiEscherichia coli [E. coli enabled the conversion of 6dEB into the bioactive erythromycin analogs Ery C and Ery D. Further chassis optimization provided a robust proof of principle that mature erythromycin analogs can be efficiently produced in a single heterologous expression system [The potential of metabolic engineering could be exemplified by the early successful heterologous production of polyketide compounds. Polyketides are an important class of natural products with complex chemical structures widely used as antibiotics, immunosuppressant, antitumor, antifungal and antiparasitic agents . These bhia coli . This wahia coli , a polykhia coli . These rn system , 10.Nevertheless, metabolic engineering is not only restricted to “compound manufacture”. Nielsen J. grouped different examples of metabolic engineering into the following categories : 1) heteMaximization of product yields through pathway optimization is a central challenge for every metabolic engineering project . NaturalIn essence, pathway optimization is a multivariate problem and the answer of this challenge is not universal and depends on the pathway to be expressed, the compound to be produced, the host, the availability of genetic tools, and the substrates utilized. In this context, the ability of the metabolic engineer to identify potential bottlenecks and eliminate them is critical and has a decisive impact on project success. This mini-review will dig into the existing novel approaches intended to improve the efficiency of metabolite-compounds production in designed microbial cells.The simple overexpression of a biosynthetic pathway often is not enough to achieve high yield production of the desired compound. In this context, the depletion of essential cellular precursors  and the accumulation of intermediates can cause toxic effects on the host and lead therefore to a decrease in productivity –15. On tE. coli strain [tac promoter [E. coli [E. coli FadR transcription factor, capable of binding fatty acyl-CoA molecules, and on hybrid promoters created by the combination of FadR and LacI responsive elements [E. coli fatty acid biosynthetic pathway and a thioesterase, TesA, to produce free fatty acids. Module B is an ethanol biosynthetic pathway that converts pyruvate into ethanol. Module C contains an acyl-CoA synthase (FadD) and a wax-ester synthase (AtfA). AtfA condenses the fatty acyl-CoA product (generated from module A plus FadD activity) and ethanol (module B) to form FAEEs  an aollutant . A clearollutant . Future ad hoc for each new metabolic engineering project, making this discipline costly and very time-consuming [As we already mentioned, classical optimization of metabolic pathways is achieved by modification of promoters, RBS, codon composition, copy number, construction of operons, etc . Howeveronsuming .In order to achieve the same flexibility of chemical engineering for the production of structurally diverse molecules, metabolic engineering need to establish principles of systematization to achieve their goals in terms of production and optimization. An excellent application of this concept was applied to the design and development of engineered pathways for the production of terpenoid derived moieties.E. coli resulted in innovative and interesting advances not only for their particular yield improvement, but also because they laid down strategies capable to systematize metabolic engineering approaches, generating universal principles capable of been transferred to other pathways and/or hosts [Studies addressing the heterologous pathway optimization of isoprenoid-derived molecules on or hosts . Chronolor hosts .Artemisia annua, in E. coli [First, amorphadiene production was achieved by heterologous expression of the MVA pathway from yeast and a codon-optimized amorphadiene synthase gene from  E. coli . Althoug E. coli . Importa E. coli ; and theE. coli by expressing the native host MEP pathway, the geranylgeranyl diphosphate (GGPP) synthase and the taxadiene synthase. The MEP pathway has the advantage over the MEV pathway in being more balanced and efficient in producing isoprenoids from sugars. The MMME approach is based on grouping enzymes with similar turnovers in modules, and then doing a combinatorial screening varying the expression of the different modules. For this, operons are constructed and the toolbox of promoters, RBS and plasmid vector can be used to achieve different expression levels which will maximize the product titers of the system. The combinatorial approach can easily circumvent the non-linear interactions between enzymes in the pathway and offer the opportunity to broadly sample parameter space [E. coli MPE pathway, which generates IPP (isopentenyl pyrophosphate) and DMAPP ; and the “downstream module”, a GGPP synthase and taxadiene synthase biscistronic transcriptional unit. Thus, instead of dealing with six genes, the problem is reduced to two modules, considerably simplifying the system, making it more tractable and easy to handle. In this way, the carbon flux balance along the pathway is achieved by changing the expression levels (plasmid copy number or promoter strength) of each module, and doing a combinatorial screening of all the possible combinations  . They deer space . In thisinations . Using tDespite its outstanding medical and industrial relevance, the progress in metabolic engineering of this family of products is laying down the basis for systematization of metabolic engineering in general. Principles of codon-adjustment, identification of pathway bottlenecks and, more recently, modularization and combinatorial experimentation can be transferred and reused to perform the heterologous production of completely different products. These principles, together with the ever declining costs of DNA synthesis and sequencing, and the development of computational software for analyzing and predicting the behavior of these heterologous systems will lead microbial metabolic engineering to be a systematic and flexible tool for production of an expanding array of molecules for pharmaceutical and other industrial uses.The last two decades encompassed what is called “post-genomics era” of metabolic engineering, an overwhelming compendium of data coming from genomics, transcriptomics, proteomics and more recently, metabolomics fields. In this context, the paradigm of metabolic engineering has clearly shifted away from one focused in perturbing individual pathways, to one which considers cell reactions in their entirety, and which integrates metabolic (native and heterologous) pathways in a genome-scale metabolic network.in vivo conditions in order to maximize product formation [Metabolic engineering makes particular emphasis in the metabolic fluxes and their control under ormation . Since eormation . In the ormation , 33. Oncormation . By alteormation , 36.While it is generally accepted that microorganisms metabolism is suited to achieve optimal growth, this may not be the case of a mutant organism obtained under laboratory conditions. Frequently, when a mutation is introduced, the topology and flux distribution of a metabolic network is so perturbed that the mutant organism does not follow the optimal growth principle. In such cases, the objective of “minimization of metabolic adjustment” (MOMA) is better suited for predicting growth and product yields, since it is not based on an optimal growth objective function, but in the concept that the mutant metabolism remains initially as close as possible to the wild type optimum in terms of flux values . In thisin silico predict increasing product titers. For example, Brochado et al., have successfully improved vanillin production in yeast by knocking out genes of central carbon metabolism [in silico analysis of carbon metabolism in E. coli for redirecting carbon flux to malonyl-CoA, in order to optimize the synthesis of the flavanone naringenin [These two different approaches to analyze metabolic networks are frequently complementary, and understanding the general principles and the biological interpretation of each method provide an extremely powerful advantage to the metabolic engineer . Algorittabolism . More retabolism . Malonyltabolism , 42 and tabolism , and alstabolism . Since mtabolism . Many retabolism . Howeverringenin . They usIn silico simulation tools open the field of metabolic engineering possibilities that can be explored to circumvent the problems that arise when expressing a foreign pathway, contributing to accelerate the design cycle of metabolic engineering and leading to the cost-effective production of industrial and medical important molecules.Competition with native pathways and metabolites, accumulation of side products and consequent growth inhibition are only some of the possible effects that a heterologous pathway can have on cell metabolism. These examples are a clear demonstration of how metabolic networks and metabolic flux analysis can be used to identify non-trivial targets for genetic interventions that lead to further optimization of product titers. The rapid rate of fossil resources consumption has drawn the attention towards the development of an economy based on renewable biological raw materials. In this context, the term “Biorefineries” is used to describe biological entities capable of generating high value-aggregated directly from biomass, in an analogy with petroleum-based refineries . MetabolThe intersection of metabolic engineering with other emerging areas of systems and synthetic biology presents exciting opportunities to develop solutions to many of the global challenges we face nowadays. In particular, these disciplines will be of fundamental importance to promote sustainable energy, reduce the environmental impact of several industrial processes and to improve the cell factory production of natural and semisynthetic drugs that are currently synthesized at very low yields, hopefully having an impact in the costs and availability of medicines.