Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD , a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing  This in silico experimentation can suggest host cell manipulations that can be applied in vitro using synthetic biology techniques, leading to increased production of the target compound  and removal of genes via knockouts. Furthermore, over-expression or inhibition of host genes can either increase or dampen metabolic flux through the reactions that their expressed proteins catalyze. Successful application of such strategies can be used to overproduce host-native targets  characterization of the host metabolic network; (ii) identification of gene additions to bridge native metabolism to the target; (iii) optimization of the modified metabolic network through gene addition, deletion, overexpression or dampening; (iv) trialing successful predictions in the laboratory. This process affords the potential to develop successful strains more cost effectively, and time efficiently. This work focuses on step (iii), which involves elements of network characterization in order to identify suitable optimization strategies.Through modeling approaches, strain predictions can be improved by taking into account full metabolic complexity during the design phase. Designed strains can also be screened To characterize the metabolic network, genome-scale models (GEMs) can be used in conjunction with constraint-based techniques. GEMs are computer-analyzable, structured knowledge bases of genes, proteins, and metabolites present within a given organism , which is a matrix consisting of rows of metabolites (m), and columns of reactions (n). An example based on the toy network in Figure v) through the network ensuring that a steady state is satisfied (Equation i), subject to a set of constraints (Equation ii) and optimizing (Z) a specific function . The minimum solutions of Equation (i) are elementary modes, which are minimal sets of enzymes that can operate at steady state, also known as minimal functional units  allows the computation of fluxes, and cellular growth, by using a set of constraints. FBA uses the stoichiometric matrix . It can be seen in the central network of Figure Z—route A and route B. FVA allows us to garner this additional information by identifying the minimum and maximum flux that each reaction can carry (Equation i). FVA can be implemented at the optimal state whereby y = 1 (Equation ii), subject to flux constraints for each reaction (Equation iii) as demonstrated in the right-hand network in Figure y = 0.95 as used in RobOKoD), which introduces a small amount of flexibility in the system and reduces the chances of optimal pathways being unrealistic when compared in vivo.Flux Variability Analysis (FVA). Box 1 showed an example of FBA, where a single set of fluxes was identified, which can maximize biomass production . RobOKoD takes into consideration metabolite centrality and flux variability in order to comprehensively identify potential knockouts and gene over-/under-expressions, ranked by significance, and follow the schematic presented in Figure Escherichia coli using the reverse β-oxidation cycle. The predictions were validated against a successful, experimentally-validated butanol producing strain developed by Dellomonaco et al.  significantly divert carbon away from target production; and (ii) consume a metabolite known to promote flux loss from target production; are selected preferentially. Once the first knockout is predicted, the model is modified to block this reaction, and the same selection process is used to select the second reaction to delete. This method can be applied iteratively to predict a number of modifications that should enhance target production whilst maintaining growth.All code was developed in Matlab to maintain compatibility with the COBRA Toolbox  per metabolite (m) is calculated, then an MCT-value of 1 is given to all reactions that consume metabolites, denoted by a negative Xm. Xm is calculated according to Equation (1). For each metabolite that is featured in the optimal target producing pathway, for the example network in Figure a, b, e, all producing and consuming reactions are identified. Then per identified reaction, a unitary constant c is calculated which identifies the reaction as a producer (+1) or consumer (−1) of the metabolite during biomass production, thereby indicating whether there is a potential flux loss or gain from that reaction. Each reaction is then weighted (w) according to whether it is vital for both target and biomass (0); or potentially used (1), or not used (0) for biomass production. v is the maximum flux through the reaction during biomass production. All reactions that consume a metabolite m with a negative Xm-value are flagged with a 1 in the corresponding column (see MCT column in Table Metabolite Consumption Test (MCT) identifies metabolites within the optimal target production pathway that are also consumed to produce biomass. The MCT score is given in a two-step process. First ybm) and target product (targety). FVAp is then performed which computes the flux variability of each reaction: (1) at different percentage (0–100%) of ybm whilst optimizing target product; and (2) at different percentage (0–100%) of targety, whilst optimizing biomass. By computing FVAp the flux capacity of each reaction is profiled over a range of target constraints. The key areas of interest are the extremes of target production, and biomass production. It can be seen in Figure 5 that the first and last quartile of the x axis for all examples holds the key information from which beneficial genetic interventions can be inferred.Prior to FVAp, FBA is applied to predict the maximal theoretical yield of both biomass (ybm whilst optimizing target product (red shaded area). Let us denote with (vmax)target|p and (vmin)target|p the maximal and minimal flux, respectively of reaction i obtained through FVAp when requiring a percentage p of ybm to be produced while maximizing for product. Likewise let the maximal and minimal flux of reaction i obtained through FVAp when requiring a percentage p of targety to be produced while maximizing for biomass be defined as (vmax)biomass|p and (vmin)biomass|p, respectively. It must be noted that the percentage p refers to either biomass or target product production requirement depending on the objective function.Knockouts were selected by computing a knockout ranking score. The ranking score is calculated for each reaction using FVAp at different percentage (0–100%) of Figure 5A, where the first quartile of x axis 0-25% of ybm (red shaded area) carries a lower (i)vmax|target, than 75-100% of ybm, which shows that the reaction is required to carry a higher flux to sustain optimal biomass production. This characteristic is captured in Equation (2) (biomass reaction activation). A reduced variability in the fourth quartile also demonstrates a stronger constraint on the flux to produce ybm, this is captured in Equation (3) (product variability area). The final knockout scoring iKOrR for each reaction was computed according to Equation (4), which takes into account the features of both the biomass reaction activation and product variability area.A suitable knockout target displays the key characteristics shown in Biomass reaction activation:Product variability area:iKOrR, are identified as a putative target for knocking out providing it is not a lethal target for the cell. Identified target reactions for knocking out are first ordered by iKOrR, before secondary sorting by MCT flags. An example of this sorting can be seen in Table Reactions that obtain a high x axis in Figure ybm (red shaded area) vi)(min|target has a higher flux capacity than 75-100% of targety (blue shaded area), vi). A lower variability is also desirable for optimizing target subject to 0-25% of ybm  as it ensures that the minimum flux the reaction can carry is close to optimum. The final ranking (iOExR) is determined using Equation (7), where reactions with the highest iOexR are the most likely over-expression targets. An example of a weaker over-expression target (corresponding to a lower iOExR) is shown in Figure and biomass. Negative iOExR represent potential dampening targets  should be knocked out to avoid diversion of carbon and reduced cofactors toward by-products of poor interest. Moreover, by highlighting the competing interests of oxygen uptake pathway between the production of biomass and butanol, RobOKoD was able to indicate an anoxic condition change, similar to the experimental strain which knocked-out fumarate reductase and was grown under microaerobic conditions.Table fadR, atoC(c), crp*, and ΔarcA strains). Moreover, RobOKoD also predicts that a number of transport reactions (or rather genes encoding the relevant transport proteins) should be dampened, hence providing additional modifications that could enhance butanol production. These dampening predictions, less intuitive, were not carried out in the experimental strain and have not been experimentally verified.In addition to the knockout predictions, RobOKoD was also able to predict over-expression and dampening targets. It predicted that enzymes catalyzing the reactions associated with the reverse β-oxidation cycle should be over-expressed, consistent with the experimental strain where the activity of transcriptional inhibitors of this pathway are dampened  solvers. This was not the case for RobustKnock, which required a non-standardized model structure and the use of a specific solver, Tomlab, which has limited free access. An additional goal of designing RobOKoD was therefore to ensure its accessibility and robustness by reusing freely-accessible solvers, extensively validated COBRA Toolbox methods, and standardized model formats such as SBML.gene, rather than, as currently, at the level of the reaction. Due to the presence of both isoenzymes and promiscuous enzymes, it is clear that there is not a 1:1 mapping between gene and reaction. Consequently, manipulation of a given gene is likely to affect a number of reactions. Modification of this method to consider the gene-protein-reaction (GPR) relationships that are present in many genome-scale metabolic models will be a priority for future development.A necessary future direction for both RobOKoD and existing tools such as OptKnock and RobustKnock will be to move to making predictions regarding knockouts, over-expressions, etc. at the level of the To summarize, RobOKoD provides an additional tool to aid the task of designing strains for the (over)production of target products. It is able to predict and rank knockouts, over-expressions, and dampening targets. While predicting an optimized set of gene modifications to implement, unlike other methods, RobOKoD also provides lists of candidate modifications, along with graphical flux variability profiles, allowing the user to manually validate the set of predictions. Such a flexible approach—particularly when used in conjunction with other analysis methods mentioned previously—will allow for sensible gene manipulation approaches to be taken into the laboratory.NJS conceived the study, led the project, developed the method and the code, and wrote and led the writing of the manuscript. PM contributed to the method conception and development, and writing of the manuscript. NS contributed to the code development and the writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation.This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.The online version of this article (doi:10.1186/s12859-015-0809-4) contains supplementary material, which is available to authorized users. Metabol enzymes .i.e. the capacity of enzymes to catalyze one or several types of reactions on more or less different substrates. A recent study in Escherichia coli successfully brings out this enzyme capacity to adapt themselves to new substrates [Subgraphs of these networks are often used to represent metabolic pathways that group sets of connected reactions involved in a same biological process. Several hypotheses on the origin and evolution of metabolic pathways have been proposed, including patchwork evolution by enzyme recruitment in new metabolic pathways , 4, retrbstrates .et al. [et al. made a systematic analysis of the conservation of reaction modules by aligning metabolic pathways from KEGG [et al. [In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on pathway alignment to find similar pathways within or between organisms using the Enzyme Commission (EC) numbers to define reaction similarities –11. Due et al. . They corom KEGG  and usedrom KEGG  to group [et al.  publisheet al. [In the present work, we propose a different formalism for the detection of reaction modules, although we use the same definition of modules as Muto et al. . Insteadet al. . Therebyi.e. metabolites deemed biologically relevant to both reactions in at least one metabolic pathway. Only reactions that belong to a metabolic pathway were taken into account, as only these ones have distinction between main metabolites and co-substrates supporting the reaction such as water, ATP or NAD. Transport reactions, for which translocated substrate remains unchanged, were excluded from the network construction and from further analysis, e.g. ABC transporter ATPase reactions corresponding to 3.6.3.- EC class.Metabolic data was extracted from MetaCyc public database version 19.0 . MetaCychttp://molsig.sourceforge.net). These molecular signatures are encoded using SMILES-like strings [https://github.com/mSorok/createRMS.git). The RMS method has been chosen in this work as it guarantees that all reactions described by the same signature perform the same chemical transformation, making manual post-process unnecessary.Reaction Molecular Signatures (RMS) were computed for all MetaCyc reactions, belonging or not to a metabolic pathway, as described in . These s strings  and the  strings . Then, a strings  and the i.e. self-loops are avoided). This transformation was made for the two RMS heights and we obtained two networks called RMS-H1 and RMS-H2 networks. Furthermore, this graph reduction, which aggregates reaction nodes and edges, allowed us to define Markov chains transition probabilities of order 1 between connected RMS. RMSi to RMSj among the total number of outgoing edges from reactions signed by RMSi.The reaction network was reduced in a directed network of chemical transformations represented by RMS. As shown in Fig. wRea, corresponds to the number of MetaCyc reactions associated to a given RMS, whether they are present or not in the initial reaction network. It gives a quantitative measure of the diversity of reactions represented by a RMS.Several weights, reflecting different biological conservation meanings, have been computed on nodes of the RMS networks. The first weight, wPageRank, is computed using PageRank algorithm [A second weight, lgorithm  implemenlgorithm . This towProt, is an estimation of the number of proteins associated to a given RMS. Known protein/reaction associations were extracted directly from MetaCyc and from Swiss-Prot using EC numbers [Np(p∈Domj), for d2r ratio, or by the total number of known proteins associated to the RMS Np(p∈RMSi), for r2d ratio.The last weight,  numbers . These a numbers  and assoscore, was computed as the harmonic mean of d2r and r2d values. This score represents a trade-off between sensitivity and specificity to associate protein domains to chemical transformations and tends to be very low when domains or RMS are very frequent.Next, the association score, wProt is, for each protein domain associated to the given RMS, the geometric mean of the total number of UniProt proteins associated to a domain multiplied by the score. Only proteins from UniProt reference proteomes [Finally, roteomes  . To make them comparable, metabolic pathways from MetaCyc were translated in overlapping RMS paths of the same length. In addition, a Pathway Conservation Index (PCI) was computed for each RMS path and represents the number of distinct corresponding reaction paths that are present in at least one MetaCyc pathway.An enumeration of all paths of length 1 (one edge and two RMS nodes) to 4 (four edges and five nodes) was made in both RMS networks using the Grph Java library . In thisscoreRea, scorePageRank and scoreProt, were calculated as the geometrical means of path node weights multiplied by their probability of transition to the next node of the path. As an illustration, the formula of scoreRea is given in which RMSi and RMSi+1 are two consecutive nodes and n is the path length.According to previously defined RMS weights, path conservation scores, named ScorePageRank and scoreProt are computed in the same way using wPageRank and wProt, respectively.Among the 12,377 MetaCyc reactions, RMS of of height 1 (RMS-H1) and 2 (RMS-H2) have been computed for 9,001 reactions excluding transport reactions and reactions without proper compound structures as described in the Methods section. As shown in Table e.g. the in vivo reaction directionality). These ambiguities, that are quite common between lyases and hydrolases or transferases, were also previously reported in other chemical classifications of reactions like MOLMAP [This fully automated chemical classification of reactions was compared with the Enzyme Commission (EC) classification which is a human expertise classification of enzymatic activities . Even ife MOLMAP .Fig. 2EFinally, an initial reaction network was established using metabolic pathway information from MetaCyc. It is made of 5,830 reaction nodes and 11,197 directed edges with an average node degree of 2.6. This graph was reduced in two RMS networks using RMS-H1 and H2 signatures. As summarized in Table et al. obtained similar results but with a higher number of detected conserved paths (between 338 and 928 for the same path lengths). Although our results are not directly comparable to those of Muto et al. by the usage of different primary data sources , the RMS paths detected by our method can be directly considered as conserved modules whereas the paths obtained by Muto et al. need a manual examination to obtain conserved modules from them. In fact, they adopted a looser definition of chemical conservation without taking into account side compounds and using fingerprint similarities to group reactions without the constraint that the reactions perform the same chemical transformation. Only 34 reaction modules were finally confirmed by the authors [β-oxidation pathway, that is well-known for fatty acid degradation, is also conserved for other molecule types , has four reaction variants in its first step. As another example, we detected a new three-step module for the biosynthesis of aldoximes from amino acids, which are notably precursors of several secondary metabolites produced by plants  to 4 . To evaluate their conservation in the light of known metabolic pathways, a Pathway Conservation Index (PCI) was computed for each RMS path and corresponds to the number of distinct reaction paths present in MetaCyc pathways. The number of RMS paths with a PCI ≥2 is reported in Table  authors . Among tscoreRea reflects the diversity of reactions performing the same chemical transformations on different substrates, scoreProt represents the conservation of enzymes performing these chemical transformations across the tree of life and scorePageRank shows the topological importance of the module in the network by highlighting chemical hubs. These scores were computed for all paths and analyzed more precisely for paths of length 2 in the RMS-H2 network . Similar results were obtained for RMS-H1 network  and no correlation between other pairs of scores. Therefore, the proposed scores can be considered as rather independent and then used conjointly to explore the RMS network.To go further, our method proposes an evaluation of chemical module conservation in the metabolism using three scores corresponding to different biological points of view. Indeed, rk Table . It shoui.e. Kruskal-Wallis rank sum tests completed by post-hoc Tukey’s HSD tests, see Additional file p-values <0.05) among all pathway types for at least one of the three conservation scores. These results presume that pathway types could be predicted by machine learning using a combination of the three scores. Thus, pathway assignment rules were generated with the NNge algorithm [e.g. the “biosynthesis” class contains almost twice the number of paths than other types), classes were virtually balanced using resampling function of Weka. We successfully obtained rules that correctly classify RMS paths in pathway types with an accuracy greater than 89 %  were introduced to evaluate module conservation according to different biological meanings. We show that known metabolic paths present higher score values than random ones and that the scores, used conjointly, may predict module pathway types. In terms of improvement of the graph reduction method, it may be of interest to dynamically adapt the precision of the reaction signatures when merging reaction nodes to take into account the local graph topology. This could be achieved taking inspiration from the method proposed by Xu et al. [We present here a novel metabolic network representation where nodes are chemical transformations depicted by reaction molecular signatures. This data model is particularly useful for finding conserved chemical transformation modules in metabolic pathways as they correspond to paths in the RMS network. An important number of modules was detected and could be integrated in metabolic databases, like KEGG  or MetaCu et al.  in whiche.g. chromosomal clusters, co-occurrence profiles) that include candidate proteins and known enzymes. As a perspective, one of the possible improvements of these methods could be the use of a RMS network instead of a reaction network as it may be easier to find proper genomic contexts using relaxed notions of metabolic context. This enhancement may also be applied in the discovery of gene clusters corresponding to new metabolic pathways.Although its construction is based on an initial reaction network, the RMS network offers new insights into metabolism as it could capture relevant metabolic contexts even without precise definition of initial reaction sets or metabolite structures. Indeed, more than two thousand reactions lacking a metabolic pathway were integrated in the RMS network and now share common contexts with reactions from known pathways. Furthermore, considering that many orphan enzymes have network neighbours that are orphans themselves , computa


Resolution of the link between micro- and macroevolution calls for comparing both processes on the same deterministic landscape, such as genomic, metabolic or fitness networks. We apply this perspective to the evolution of carotenoid pigmentation that produces spectacular diversity in avian colors and show that basic structural properties of the underlying carotenoid metabolic network are reflected in global patterns of elaboration and diversification in color displays. Birds color themselves by consuming and metabolizing several dietary carotenoids from the environment. Such fundamental dependency on the most upstream external compounds should intrinsically constrain sustained evolutionary elongation of multi-step metabolic pathways needed for color elaboration unless the metabolic network gains robustness – the ability to synthesize the same carotenoid from an additional dietary starting point.We found that gains and losses of metabolic robustness were associated with evolutionary cycles of elaboration and stasis in expressed carotenoids in birds. Lack of metabolic robustness constrained lineage’s metabolic explorations to the immediate biochemical vicinity of their ecologically distinct dietary carotenoids, whereas gains of robustness repeatedly resulted in sustained elongation of metabolic pathways on evolutionary time scales and corresponding color elaboration.The structural link between length and robustness in metabolic pathways may explain periodic convergence of phylogenetically distant and ecologically distinct species in expressed carotenoid pigmentation; account for stasis in carotenoid colors in some ecological lineages; and show how the connectivity of the underlying metabolic network provides a mechanistic link between microevolutionary elaboration and macroevolutionary diversification.This article was reviewed by Junhyong Kim, Eugene Koonin, and Fyodor Kondrashov. For complete reports, see the Reviewers’ reports section.The online version of this article (doi:10.1186/s13062-015-0073-6) contains supplementary material, which is available to authorized users. What determines evolutionary pathways of phenotypic diversification? Historically, the emphasis was placed on whether structural consideration of trait development or environment of trait functioning are more influential at predicting and directing evolutionary change . One insA combination of known biochemical reactions among naturally occurring carotenoids  is a connected network . Like mion Fig. . For exaion Fig. . Such poCarotenoids play a multitude of functions in birds, and carotenoid-based pigmentation of plumage has evolved multiple times e.g., , 23). He. He23]).Second, we examine whether diversification in carotenoid metabolism in general, and periodic species convergence in particular, are quantitatively predictable from the structure of biochemical network on which birds diversify. We present evidence for strong structural trade-off between robustness – ability to sustain gain or loss of compounds without extinction – and elongation of carotenoid metabolic pathways in species that depend on external (dietary) starting points for their carotenoid production. We test empirical predictions of this trade-off in extant bird species, explore its likely evolutionary trajectories, and use it to calibrate potential for metabolic diversification across avian taxa that utilize diverse dietary carotenoids.r = 0.18, P < 10−4), although the relationship was weakened by frequent convergence of distantly related taxa in plumage carotenoids  increased with the phylogenetic distance between species , and “expressed” carotenoids and partitioned these correlations by periods of 20 million years Fig. . We founQuelea erythrops) and Cream-backed woodpecker (Campephilus leucopogon) have identical carotenoids in their plumage  despite more than 90 million years since divergence , likely by lessening the probability of compound’s loss during evolution of long metabolic pathways  compounds within reconstructed networks at each phylogenetic internal node and found that a gain of a pathway from a new dietary compound subsequent to elongation  resulted in continuing elongation , such that dietary gains, in essence, “rescue” ongoing elongation of pathways . However, when a link to a novel dietary compound was not encountered, the descendant network either shrunk or remained static  . Encounters of a compound that is connected to a new dietary entry point produced alternation of “boom” and “bust” events in the occupation of the avian network space  compounds from purely structural considerations, we used a metric called “biochemical scope”  that refsole dietary precursor  and Pallas’s Rosefinch (Carpodacus roseus) combine exceptional metabolic elongation (>6 metabolic steps from a dietary entry) with strong robustness (>4 different dietary pathways sustaining such elongation); reconstructing metabolic networks on internal phylogenetic nodes leading to these taxa revealed that these lineages consistently gained a new dietary compound at each of the last 4 and 5 phylogenetic transitions, correspondingly  does not strongly accelerate metabolic evolution as evidenced by relatively short branches in some seed-eater finch lineages that start with several dietary compounds because of their carotenoid-diverse diet, but do not consistently gain new ones Fig. . Indeed gly Fig. .Carotenoids are ancient compounds, involved in a multitude of biological functions in addition to coloration . Most eIn this study we did not explore correspondence between carotenoid producing metabolic network and carotenoid-based color space in birds. Three factors can affect such correspondence. First, plumage colors and metabolic elongation can differ across network areas – in some enzymatic sequences a single reaction step produces a different color, whereas in others more steps are required for the same color change . SimilaOur results have several implications. First, the structure of underlying metabolic networks mechanistically links microevolutionary elaboration and macroevolutionary diversification in avian carotenoid coloration. Such linkages do not depend on specific evolutionary force: for example, metabolic elaboration within lineages can be driven by sexual selection , 48 whisb (cytB) sequences for 181 species in the dataset from GenBank [Fregata minor and Zosterops japonicas sequences were trimmed to include only cytB gene before alignment using MUSCLE [We retrieved cytochrome  GenBank   [To quantify the number of clades found in our supertree that are not found in the strict consensus tree based on both our supertree and the  supertre 1.00-7) . The RF Further, both trees are based on the same backbone phylogeny for avian orders  and the To further assess the validity of the use of the Hackett et al.  phylogenAlthough complete and experimentally verified carotenoid biosynthetic networks are known for only a relatively small number of species , includdietary if they are known to be consumed with diet, intermediate (metabolized) if (i) they are not expressed in the plumage, but found in plasma, liver, gut, fat, or (ii) known to be metabolized on the path from dietary to plumage compounds, and expressed if they are found in plumage  [Ancestral network reconstruction in an explicitly phylogenetic context requires evaluations of structural relationships and biochemical rules by which the compounds and enzymes are linked in the networks of closely-related species and reconstruction of ancestral networks by either parsimony –91 or maion 1.8) , 97 to oTo quantify structural distance between species’ metabolic networks we used a modified version of the metabolic distance  that meaphangorn (version 1.99-7) [n taxa is 2(n-3) [n = 152 species using the phytools (version 0.3–93) [To determine the topological similarity between the metabolic pathway-based phylogeny and the molecular-based supertree we used the Robinson-Foulds metric (RF) , 102, in 1.99-7) . Phyloges 2(n-3) . The lik 0.3–93) .In this paper, the authors address a classic question in evolutionary biology: if some distinct trait distribution of organisms is seen, how much of the pattern is due to the distribution of fitness peaks and historical contingency and how much is due to constraints like the set of developmentally reachable phenotypes. We might imagine a spread of people in a landscape of multiple hills and mountains, trying to climb up; but, the landscape also has cliffs and gorges that constrain travel routes. If we take a snapshot of the people, how much of their distribution pattern is due to the location of the hills, their ability to walk, and the location of the forbidden obstacle? Some authors might argue that excluding constraints of physics and chemistry, such questions are really a question of time scale. What is reachable by certain developmental processes can change as the mechanisms of development itself can evolve – that is, the constraints themselves can change over time. Nevertheless, the question, originally made famous by late S.J. Gould, is both conceptually interesting and significant for proper inference of empirical phenotypic distributions.The current paper has some very strong points in its attempt to address evolutionary questions arising from the above framework: (1) it addresses a deep and important question in macroevolution using approaches that leverage a unique information set; (2) this information set, consisting of biochemical reaction network of carotenoid, is marvelously researched and it is truly fascinating in terms of both its appropriateness and relevance to an phenotype (coloration) of great interest; and (3) the many numerical methods applied throughout the paper are carried out with extremely high degree of technical competency and attention to detail. I believe this paper should be published but with some major revisions.While the paper has many excellent points of strength, unfortunately, I found the paper very difficult to read and in many parts how the results tie to the models or hypotheses were cryptic. With respect to reading difficulty, I believe some of this is perhaps due to the manuscript addressing a specialized audience that might be already familiar with some terms or concepts but not a more general audience. For example, the paper uses the term “diversification” and “elaboration” – presumably in a specific manner, but to a general reader it is not clear what pattern of data would be considered diversification versus elaboration. I suspect such a reader would stumble at phrases like “…structural consideration of trait construction and development or selective environment of trait functioning over historical time.” It would greatly help the manuscript to revise the text for more general audience. I personally stumble on phrases like “components of current adaptations”, as I do not know whether “components” refers to a specific technical term or whether it simply means things other than genes. The manuscript has a large number of such ambiguous terms: e.g., “structural opportunities”, “elongation of pathways”, “metabolic elaboration”, “biochemical scope”, “biochemical space”, etc. I think each such term needs to be defined for the reader.We took this opportunity to edit and clarify the text and eliminate excessive jargon. We made consistent the use of “metabolic elongation” and “plumage elaboration” and now define several terms in the text more explicitly and delete others. Established measures from network biology and biochemistry are listed in Additional file: Table S1.Authors’ response: As mentioned, some of the results are cryptic or it wasn’t entirely clear what hypothesis was being tested by the analyses. For example, on page 5, it states that the hypothesis being tested is whether the biochemical pathway context explains the patterns wherein “closely related species are often strongly diverged…distant and ecologically distinct taxa are often highly convergent.” I am not sure which of the results are directly addressing this problem. Some of this problem may again be due to the writing and my confusion.We now rewrote this introductory paragraph to emphasize the general approach of the study: whereas rapid divergence among related species (and frequent convergence among distant species) in plumage carotenoids is commonly documented, here we proposed a novel explanation: recurrent occupancy  of a small biochemical space. We specifically address the reason and calibrate the rate for such recurrence for different ecological groups.Authors’ response: For example, referring to Fig. We now expanded both the figure legends and associated text explanation and more explicitly refer to comparisons of panels in Fig.1. The conclusion mentioned here refers to comparisons of the upper (dietary subnetwork), middle (metabolized subnetwork) and lower (expressed subnetwork) panel of Fig.1c for the same time period . Figure shows that network divergence of species separated by <20 million years is the largest in metabolized compounds (middle panel), but the smallest in dietary compounds (upper panel). We further clarified presentation of results and made it less abbreviated by simplifying Figs.3and4(panels are now presented as separate figures) and brining two figures from Additional Files to the main text to streamline the presentation.Authors’ response: There are some concepts or measures that are not explained sufficiently: How is “robustness” defined? What is meant by robustness to fluctuations? How is evolutionary persistence defined?Robustness is now defined in both the Background and also in figure legends. Evolutionary persistence was referring to occurrence across distant species and is now rephrased and deleted from figures.Authors’ response: How does Additional file We now show former Additional file: Figure S3 and Fig.3a as panels to facilitate comparison. New Fig.6b shows that most nodes (not only dietary ones) are represented in many species and most nodes are occupied by species under this study. Frequencies of gain and loss in relation to distance from dietary entries in Fig.3a shows that long pathways are unlikely to persist unless, as predicted in Fig.1a and3a (insert), metabolic pathways acquire additional link to dietary compounds. When such evolutionary acquisition is possible, given local enzymatic connectivity, pathway can elongate and more nodes get inflated in Fig.6b. When such acquisition is not possible, nodes get smaller. Figure6b is necessarily a qualitative illustration of outcome of such expansion-contraction process. Quantitative results for the depicted process are presented in Figs.8and9.Authors’ response: In sum, this is a paper of unusual depth, technical sophistication, and scholarly breadth. However, the presentation is difficult and at places the models and hypotheses are hard for the reader to derive from the presented results. That is, the conclusions drawn from each figure or results table may be obvious to the authors but a reader is likely to have much harder time. Given the potential impact of the paper it would be a great service to the readership to clarify the presentation. Lastly, given that the results from this paper is supposed to explain, at least in part, the spectacular diversity and distribution of bird coloration, it would really help draw interest to the paper to graphically show to the reader the implications from the models to the macrophenotype of popular interest.We greatly appreciate Dr. Kim’s assessment of this work and did our best to clarify the presentation. Some of the results of this work lend themselves for extrapolation into actual color distribution in birds. For example we mention why yellow and green are the colors of stasis in avian pigmentation evolution and also an ancestral state in many lineages. Further, we suggest that prevalence of losses over gains in avian history can be related to a combination of selection for exaggeration of color displays (and associated selection for efficiency of pigment production and their modularity) and birds’ dependence on upstream carotenoids they cannot themselves convert from non-carotenoids. This combination leads to formation of isolated modules that are frequently lost in avian evolution. However, as we discuss in the paper, direct correspondence between enzymatic pathways and expressed plumage color varies across the network.Authors’ response: In my previous review, I noted the significance of this paper and the unique and novel analyses applied to a singularly interesting dataset. The paper addresses a topic of great conceptual depth and overall conclusions seem to be supported by the data. However, I still had great trouble understanding individual pieces of the analyses and how the presented figures relate to the verbal descriptions given in the paper. Here, I list in more detail the particular problematic sections. I hope the authors will address and clarify these sections such that the reader can appreciate the results.Line 125: “Metabolic divergence”—it would really help the reader here to state in a few words how the authors measured metabolic divergence rather than only in the methods section. Line 126: “Periodic convergence” – Fig. Line 221: The legend for Fig. Line 280: What is “proportion of gained biochemical scope”? Is this the proportion of the metabolic network realized versus reachable in X number of generations?We now clarified our references to the figures and made necessary corrections.Authors’ response: Badyaev and colleagues explore a truly interesting evolutionary landscape where evolution of a metabolic network is intimately linked to the evolution of a highly visible phenotypic trait, the avian plumage coloration. The analysis is quite elaborate, and the conclusions, in particular, the explanation for convergent evolution of carotenoid pigmentation, seem important.Yet, I have to confess that, at least to me, the manuscript is difficult to read, and I was unable to follow all the steps that led the authors to these conclusions. I wonder whether it might be possible to explain the connection between metabolic robustness and elaboration in simpler terms and/or in greater detail, perhaps using some diagrams to facilitate understanding. Also, it is not immediately clear to me how and where does this paper address the (dis)connect between microevolution and macroevolution.The diagram showing how elongation of metabolic pathways is structurally linked to robustness is shown in Fig.1a. We now more explicitly state that it is birds’ dependence on dietary  carotenoids to initiate their metabolism that results in this property. Importantly, the link between metabolic elaboration and dietary robustness does not depend crucially on fluctuations in availability of different dietary carotenoids in environment, but rather refers to the fact that these compounds are external to the organism and thus have different evolutionary dynamic.Authors’ response: The link between micro- and macroevolution in this study comes from bringing together the mechanisms behind changes in metabolic pathways within taxa or lineage  with the patterns of interspecific diversity in these pathways . We bring these micro- and macroevolutionary processes together by considering them on the same structural landscape – the enzymatic connectivity in global carotenoid network. Diversity of avian species, their long history, and the relatively small size of carotenoid network on which they diversify also enabled robust ancestral reconstruction of their metabolic networks. This enabled us to consider ecological and biochemical processes within the same phylogenetic framework; for example, comparing contribution of ecological and metabolic components to the evolution of avian carotenoid pigmentation .tion Fig.c.Furthermore, would it be possible to explain, in specific terms, how do metabolic networks expand in birds. Given that birds do not appear to be prone to horizontal gene transfer, I suppose that this expansion involves gene duplication followed by neofunctionalization. Is this correct? Would it be possible to present specific cases? I think this would clarify the entire story. The situation with losses is more obvious but again, a specific description at the level of genes and enzymes would be helpful.In short this is quite a substantial paper on an interesting subject but I think it could gain a lot from a more complete and specific presentation.It is very good point and this, indeed, would clarify the entire story. From a companion study involving larger sample size  we know that, structurally, carotenoid-producing networks in birds expand at most connected enzymes – these nodes are the “hotspots” of phenotypic diversification in carotenoid network [. But there are currently no empirical data to test the correspondence between genomic and metabolic networks for carotenoid production across birds. Avian gene duplication rate per se seems to insufficient [to account for the fast rate of convergence in carotenoid enzymes across distantly related bird species documented in this study , although it is very likely that neofunctionalization at genomic or metabolic levels plays a major role. The finding of strongly reductive evolution of carotenoid-producing pathways  corroborates this assumption. The hypothesis advanced in this paper also explains the prevalence of reaction losses during evolution of avian lineages.Authors’ response: species, ) we know network . But thefficient  to accouways Fig. corroborThis reviewer provided no comments on this version for publication.


Arabidopsis thaliana. To quantify these costs in terms of the energy equivalent ATP, we introduce an improved cost measure based on flux balance analysis and apply it to three state-of-the-art metabolic reconstructions to ensure robust results. We present the first systematic in silico analysis of the effect of nitrogen supply (nitrate/ammonium) on individual amino acid synthesis costs as well as of the effect of photoautotrophic and heterotrophic growth conditions, integrating day/night-specific regulation. Our results identify nitrogen supply as a key determinant of amino acid costs, in agreement with experimental evidence. In addition, the association of the determined costs with experimentally observed growth patterns suggests that metabolite synthesis costs are involved in shaping regulation of plant growth. Finally, we find that simultaneous uptake of both nitrogen sources can lead to efficient utilization of energy source, which may be the result of evolutionary optimization.Plants as sessile organisms cannot escape their environment and have to adapt to any changes in the availability of sunlight and nutrients. The quantification of synthesis costs of metabolites, in terms of consumed energy, is a prerequisite to understand trade-offs arising from energetic limitations. Here, we examine the energy consumption of amino acid synthesis in  Metabolism can be regarded as an integrated network of biochemical reactions through which simple chemical building blocks are assembled into larger molecules supporting cellular tasks which ensure viability and reproduction. As a result, metabolism does not operate in isolation from the other levels of cellular organization, and its state is tightly regulated via the participating proteins that enable most of the underlying reactions . In addie.g., growth rate. Therefore, systematic evaluation of resource allocation strategies has the potential of elucidating principles underlying metabolic regulation. However, the cost-benefit analysis requires condition-specific quantification of metabolic costs.Metabolic resource allocation strategies usually lead to beneficial adaptations of the metabolic state, which can be explained by a cost-benefit principle: resources are diverted towards other metabolic processes as long as they result in larger benefit, de novo) synthesis requirement for resources, which then cannot be allocated to other metabolic processes. Various definitions of metabolic costs have been proposed, reflecting the variety of possible resources required for synthesis, e.g., carbon  generate[d] from light ”; to ensure The model of Oliveira Dal’Molin et al. which, as stated above, do not capture the competition of carbon fixation and As mentioned above, the costs of amino acid synthesis increase with the rom zero . The reaThere is biological evidence for the various effects of nitrogen supply on metabolite levels, depending on the diurnal cycle and relations to the carbon supply. For instance, it was demonstrated for tobacco that the application of wth rate , 34, 35.spartate . That saIn this study, we have introduced an improved methodology to determine amino acid costs in terms of the energy equivalent ATP and have discussed the variation of individual costs due to the influence of external  perturbations. We have shown that the integration of environmental conditions has serious impact on the synthesis costs of amino acids in terms of energy: average weighted amino acid synthesis costs exhibited large dependence on the trophic level and on the nitrogen supply. Therefore, integration of environmental conditions is crucial in the analysis of the energy-balance related regulation in plants in order to arrive at meaningful interpretation.e.g., light intensity or on the amount of sugar storage): a limitation of resource availability imposes constraints on the energy budget. The effect of metabolite synthesis costs then depends on their ratio to the energy budget. Consequently, an environmental shift accompanied with a change in resource limitation may not only alter the metabolite synthesis costs but also the cellular energy budget and, hence, the effect of costs. The shift of the total energetic budget may then have important implications on resource reallocations.The majority of amino acid costs for night conditions are higher than those for heterotrophic day conditions. In addition, the costs for heterotrophic day conditions are distinctly exceeded by the costs for autotrophic conditions. While amino acid synthesis costs were systematically affected by the alternation of day and night, as well as of the trophic level , positinThe supply of nitrogen source was shown to significantly affect amino acid costs, exhibiting lowest costs for sole − supply , Table 2nditions . This is to NO3− .We found enhanced efficiency of energy source utilization for mixed uptake of supplied , 33. ThiThe utilization of resources often limits growth, even under optimal conditions . Thus, rM metabolites Xi  and N reactions. The change of the concentration xi of a metabolite Xi can be described as sij is the stoichiometric coefficient associated with the flux fj through reaction j and bi is the net transport flux of metabolite Xi. The mass conservation relation under steady-state conditions, i.e., S is the stoichiometric matrix (with M rows and N columns), f is the vector of metabolic fluxes of the N reactions and b is the vector representing consumption/production fluxes of the M metabolites. The consumption/production fluxes are set to zero for internal metabolites. In contrast, external metabolites constitute an interface to the environment and do not have to obey the steady-state condition. A metabolic flux crossing the system boundary is normally realized by a transporter reaction which converts an internal metabolite into an external metabolite. Constraints on the fluxes of the transporter reactions importing or exporting metabolites across the system boundary are utilized to establish environmental conditions, e.g., determining the availability of nutrients. As the system of equations described in (N > M), there exist multiple solutions corresponding to feasible flux distributions, each representing a particular metabolic state (with respect to fluxes) satisfying the stoichiometric constraints. The question usually addressed by FBA is that of determining which of the feasible metabolic states is manifested in the studied metabolic network.Flux balance analysis (FBA) is a structural modeling framework developed to characterize the synthesizing capabilities of metabolic networks at steady state . A metabS·f-b=0,where S f, which leads to a linear programming problem:z representing the phenotypic property to be optimized, and c being a vector of coefficients quantifying the contribution of each flux to this objective. The bounds fmin(i) and fmax(i), represent constraints on the fluxes, i.e., the minimum and maximum values for the fluxes and, thus, determine reaction reversibility.FBA relies on the assumption that the metabolic system exhibits a state that is optimal with respect to some objective. Usually, the objective is expressed as a linear combination of fluxes in et al. . The ATP production efficiency is a property of the network and the initial constraints.The amino acid cost calculation proceeds in three steps. We assume that nutrient import, amino acid export and the maintenance reaction(s) are such that they carry nonnegative flux. First, we determine the ATP production efficiency, as described in the algorithm shown in The amino acid cost calculation is then executed according to the two following steps, which are repeated for every amino acid.After the initial constraints have been restored, the total ATP cost, In the third step, we determine the surplus ATP cost, The amino acid synthesis cost then is given as the difference of the total and the surplus ATP cost.e.g., by utilizing high-throughput data [It should be noted that the amino acid costs depend on the energy source uptake or on the amino acid export flux, if nonzero boundaries of other reactions limit the outcome of the optimization in any of the three steps. This is usually not the case in the original setup of metabolic reconstructions (like in this study), but can occur when additional boundary conditions are incorporated, put data .We provide the models with the denoted modifications in the SBML format in the Supplementary S1 Fig(TIF)Click here for additional data file.S1 Tableet al. [et al. [lb and ub, respectively.Modified model constraints compared to the published versions of the models of Poolman et al.  and de O [et al.  which ar(PDF)Click here for additional data file.S2 Tableet al..The reactions marked with ‘>’ were detected by the Algorithm shown in (PDF)Click here for additional data file.S3 Tableet al. [et al. [et al. [2S importer is implemented for the model of Poolman et al. (a), and the b). The unnecessary import of Pi for amino acid synthesis is deactivated (c).As the model of Poolman et al.  is only  [et al. , and Arn [et al. . Due to  [et al.  to utili(PDF)Click here for additional data file.S1 Dataset(XLS)Click here for additional data file.S2 Dataset(ZIP)Click here for additional data file.


Escherichia coli was investigated by the modification of central metabolism based on a genome-scale metabolic model and experimental validation.3-hydroxypropionic acid (3HP) is an important chemical precursor for the production of bioplastics. Microbial production of 3HP from glycerol has previously been developed through the optimization of culture conditions and the 3HP biosynthesis pathway. In this study, a novel strategy for improving 3HP production in tpiA and zwf as a candidate for improving 3HP production. A 3HP-producing strain was constructed by the expression of glycerol dehydratase and aldehyde dehydrogenase. The double knockout of tpiA and zwf increased the percentage carbon-molar yield (C-mol%) of 3HP on consumed glycerol 4.4-fold (20.1 ± 9.2 C-mol%), compared to the parental strain. Increased extracellular methylglyoxal concentrations in the ΔtpiA Δzwf strain indicated that glycerol catabolism was occurring through the methylglyoxal pathway, which converts dihydroxyacetone phosphate to pyruvate, as predicted by the metabolic model. Since the ΔtpiA Δzwf strain produced abundant 1,3-propanediol as a major byproduct (37.7 ± 13.2 C-mol%), yqhD, which encodes an enzyme involved in the production of 1,3-propanediol, was disrupted in the ΔtpiA Δzwf strain. The 3HP yield of the ΔtpiA Δzwf ΔyqhD strain (33.9 ± 1.2 C-mol%) was increased 1.7-fold further compared to the ΔtpiA Δzwf strain and by 7.4-fold compared to the parental strain.Metabolic simulation identified the double knockout of tpiA Δzwf ΔyqhD E. coli strain by the modification of the central metabolism, based on metabolic simulation and experimental validation of engineered strains.This study successfully increased 3HP production by 7.4-fold in the Δ Its pKlebsiella pneumoniae[Pseudomonas denitrificans[Escherichia coli[E. coli[dhaT and yqhD to reduce the production of byproducts in K. pneumoniae[E. coli and developed fed-batch cultivation with simultaneous feeding of glycerol and glucose [The microbial production of 3HP from glycerol has been developed using a natural 3HP producer, neumoniae-13. Exprtrificans,15 and Echia coli-19. To dchia coli-19. For i[E. coli. Ashok eneumoniae. Kim et  glucose ,20.in silico metabolic simulation has been developed to consider whole metabolic networks. A genome-scale metabolic model, which includes most of the metabolic reactions of the cell [Recently, the cell -23, can the cell ,25 by asthe cell ,27. Thisthe cell -30. Succthe cell -33.The introduction of heterologous genes and gene manipulations does not always produce the expected growth behavior and target production in engineered strains. Experimental validation of engineered strains provides useful information about the actual metabolic state of a cell such as bottleneck reactions, growth inhibition factors, and redox imbalance -36. A stE. coli by focusing on the whole metabolic network. Metabolic pathway modification was designed by integrating in silico gene-knockout simulation with the experimental validation of engineered strains. The genome-scale metabolic model of E. coli was used to design a metabolic network for increased 3HP production. Metabolic simulation identified two candidate genes to be deleted, tpiA and zwf, which are involved in central metabolism. Based on the simulation, knockout strains were constructed and 3HP production was successfully increased, as predicted. In addition, a gene, yqhD, related to the biosynthesis pathway of a major byproduct, 1,3-propanediol , was deleted to further increase 3HP production.The current study aimed to enhance 3HP production from glycerol in . Since E. coli does not possess the 3HP biosynthetic pathway, the 3HP-producing strain (TK52) was constructed by the overexpression of dhaB and gdrAB, which encode for glycerol dehydratase and glycerol dehydratase reactivase (from K. pneumoniae), respectively, and aldH, which encodes for aldehyde dehydrogenase (from E. coli), as described in a previous study [E. coli does not possess 1,3-PDO biosynthetic pathways, the introduction of dhaB for 3HP production enabled production of 1,3-PDO as follows: glycerol dehydratase converts glycerol to 3HPA, and an endogenous alcohol dehydrogenase (encoded for by yqhD) further converts 3HPA to 1,3-PDO [The 3HP biosynthetic pathway from glycerol consists of two reactions: the dehydration of glycerol to 3-hydroxypropionaldehyde (3HPA), catalyzed by glycerol dehydratase, and the oxidation of 3HPA to 3HP, catalyzed by aldehyde dehydrogenase . Since Eus study . TK52 wa 1,3-PDO ,32.E. coli, iAF1260, which includes 2,077 metabolic and transport reactions and 1,038 unique metabolites [E. coli. Since the 3HP biosynthesis pathway does not exist in E. coli, seven reactions involved in the 3HP biosynthetic pathway were added to the iAF1260 model . In this study, we focused on the double knockout of tpiA and zwf for further analysis. tpiA encodes for triosephosphate isomerase, which converts dihydroxyacetone phosphate (DHAP) to glyceraldehyde-3-phosphate (GAP). zwf codes for glucose-6-phosphate-1-dehydrogenase, which converts glucose-6-phosphate to 6-phospho-glucono-1,5-lactone.In single-gene knockout simulations, solutions in which the metabolic flux of 3HP production was higher than zero were not obtained. Double-gene knockout simulations identified some candidate genes that when deleted together could enhance 3HP production  and entered into gluconeogenesis and the TCA cycle. The flux into glycolysis was blocked by the inability to generate GAP from DHAP because the amount of GAP consumed by the fructose 6-phosphate aldolase reaction, which converts DHA and GAP to fructose 6-phosphate, was equal to the amount of GAP produced by the Entner-Doudoroff pathway   in TK52tz. Increased 3HP production might lead to the accumulation of intracellular 3HPA, a precursor of 3HP as well as 1,3-PDO, and result in the overflow of flux toward 1,3-PDO production. We speculated that changing the aldehyde dehydrogenase to the superior enzyme , or disrtpiA and zwf, TK52t and TK52z, respectively, were constructed to analyze the effects of the knockout of each gene. Metabolic simulation predicted that the zwf knockout would not affect metabolism, but the tpiA knockout would alter metabolic flux into the Entner-Doudoroff pathway and decrease the growth rate. Similar culture results between TK52z and TK52  in TK52t contained significant variation, thus it was difficult to compare the experimental results with the results from the metabolic simulation.The single knockout strains of 2 Figure  suggest r Figure . The 3HPtpiA knockout, which was not predicted by metabolic simulation, might be due to the conversion of glycerol to glycerol 3-phosphate by glycerol kinase, which may have had a higher activity than the glycerol dehydrogenase that converts glycerol to DHA [The increased 3HP yield in the l to DHA . This woyqhD, which is involved in 1,3-PDO biosynthesis, was deleted in TK52tz, generating the strain TK52tzy, in order to decrease 1,3-PDO production and further increase 3HP production. As a result, the 3HP yield of TK52tzy was increased 1.7-fold (33.9 ± 1.2 C-mol%) and the 1,3-PDO yield was drastically reduced (5.9 ± 0.5 C-mol%) relative to TK52tz , likely due to the presence of other alcohol dehydrogenases that might convert 3HPA to 1,3-PDO. The deletion of yqhD increased the maximum concentration of extracellular methylglyoxal in TK52tzy (0.60 mM at maximum) relative to TK52tz (0.22 mM at maximum), since YqhD also utilizes methylglyoxal as a substrate [yqhD in TK52tz decreased the magnitude of the error, suggesting that the high production of 1,3-PDO in TK52t and TK52tz might cause redox imbalance, resulting in the large variations in the measurements.3HP production was successfully increased in TK52tz, however a high yield of 1,3-PDO was also produced (37.7 ± 13.2 C-mol%). ubstrate . The oth12, a coenzyme for glycerol dehydratase [E. coli strain by knocking out ackA, pta, and yqhD to reduce byproduct generation and knocking out glpR and overexpressing glpF to enhance glycerol metabolism [tpiA and zwf prevented flux into glycolysis, reducing excess NADH production, as predicted by metabolic simulation.The 3HP yield of TK52tzy (33.9 ± 1.2 C-mol%) was comparable to previous studies producing 3HP from glycerol via flask cultivation. Mohan et al. achieved 3HP yield of 39.0 ± 0.01 C-mol% by the optimization of culture conditions such as the initial culture medium pH . Rathnasydratase . Jung ettabolism . They actabolism ,18, acetLa, of a shaking flask is 10–100 1/h), the metabolic simulation was used effectively for improving 3HP production. Further increases in 3HP production can be expected by the optimization of aeration conditions using a bioreactor.In this study, metabolic simulation was performed under the conditions of OUR/GUR = 0.67 (OUR = 10 mmol/(g DC●h) and GUR = 15 mmol/(g DC●h)), and additional simulations were also performed under other various OUR/GUR conditions (data not shown). Given the 3HP yield of TK52tzy (33.9 ± 1.2 C-mol%), the metabolic state of the cell in this study was estimated under the condition of OUR/GUR ≈ 1, by the metabolic simulation. Although the adjustment of oxygen supply to the simulation result was difficult using flask cultivation  relative to TK52. The double knockout of tpiA and zwf contributed to the reduced production of byproducts such as succinate and lactate that are associated with NADH oxidation, likely due to reduced NADH production by the inhibition of glycolysis. The successful increase in 3HP production, based on metabolic simulation, demonstrated the effectiveness of metabolic modeling in designing a metabolic engineering strategy. Moreover, experimental validation of the engineered strains and comparison with the simulation results provided additional modifications to the engineering strategy to increase target production.In conclusion, the production of 3HP from glycerol in E. coli MG1655, using the λDE3 Lysogenization Kit . The 3HP-producing strain, TK52, was constructed as published previously [dhaB1, dhaB2, and dhaB3, encoding for components of glycerol dehydratase, and the gdrA and gdrB genes, encoding for glycerol dehydratase reactivase, were amplified from the genomic DNA of K. pneumoniae subsp. pneumoniae (NBRC 14940), which was purchased from the National Institute of Technology and Evaluation . Fragments were generated by PCR using the primer pair 5′-CCGGAATTCATGAAAAGATCAAAACGATTTGCAGTACT-3′ and 5′-GTTAAGCTTGATCTCCCACTGACCAAAGCTGG-3′ for dhaB  and gdrA and the primer pair 5′-GAAAAGCTTGAGGGGGACCGTCATGTCGCTTTCACCGCCAG-3′ and 5′-GCGCTTAAGTCAGTTTCTCTCACTTAACGGC-3′ for gdrB. The aldH gene was amplified from the genomic DNA of E. coli MG1655 with the primer pair 5′-GGAGGATCCATGAATTTTCATCATCTGGC-3′ and 5′-TCGAAGCTTTCAGGCCTCCAGGCTTAT-3′. PCR was performed using KOD FX Neo . Each amplified fragment was treated with A-attachment mix , and then cloned into pGEM-T easy , followed by sequence confirmation by the BigDye Terminator v3.1 Cycle Sequencing Kit , and the 3130 Genetic Analyzer (Applied Biosystems). The EcoRI-HindIII and HindIII-AflII fragments carrying dhaB-gdrA and gdrB, respectively, on pGEM-T easy were cloned into the same restriction sites in pCDFDuet-1 (Merck KGaA), generating pCDFDuet-1/dhaB-gdrAB. In addition, the BamHI-HindIII fragment carrying aldH on pGEM-T easy was cloned into the pTrc99A expression vector , generating pTrc99A/aldH.The strains used in this study are summarized in Table eviously . Fragmenkc-mediated phage transduction [yqhD, the disruption cassette, including the tetracycline resistance gene and homologous regions upstream and downstream of yqhD, was amplified from pKD13tet [yqhD::tet. To delete zwf, tpiA, and yqhD in E. coli MG1655(DE3), P1 transduction was performed using P1 phage obtained from JW3890 [zwf::cat and BW25113ΔyqhD::tet strains, respectively. Finally, the plasmids pTrc99A/aldH and pCDFDuet-1/dhaB-gdrAB, were introduced into MG1655(DE3) and the knockout strains to construct the 3HP-producing strains  of 0.02. For the main culture, M9 medium  supplemented with 2 g/L yeast extract, 0.02 mM cyanocobalamin, 0.1 mM IPTG, 0.05 g/L ampicillin, and 0.05 g/L streptomycin was used. Cells were cultured in 500 mL Sakaguchi flasks containing 50 mL of the M9 medium at 37°C in a shaking incubator at 120 rpm .Pre-cultures were grown aerobically at 37°C overnight in Lennox medium  containing 0.05 g/L ampicillin and 0.05 g/L streptomycin. Pre-cultures were transferred to the main culture with an initial optical density at 600 nm . Concentrations of glycerol, 3HP, 1,3-PDO, succinate, lactate, acetate, formate, and ethanol present in the supernatant of the culture broth were determined by an HPLC system  equipped with an Aminex HPX-87H column , a UV/vis detector (SPD-20A), and a refractive index detector (RID-10A). The column temperature was set to 65°C, and 2 mM H2SO4 was used as the mobile phase with a flow rate of 0.5 mL/min. The flow cell temperature of the refractive index detector was set to 35°C. The supernatant of the culture broth was obtained by centrifugation at 21,500 × g for 5 min at 4°C, and then filtered through a Millex HV 0.45-μm filter (Merck KGaA).Cell growth was monitored by the measurement of ODMethylglyoxal in the supernatant was quantified colorimetrically with 2,4-dinitrophenylhydrazine  . The reaE. coli K-12, named iAF1260 [E. coli, seven reactions required for 3HP biosynthesis from glycerol were added to the iAF1260 model , occurs through a proton symporter in the iAF1260 model. In the same manner, the 1,3-PDO transport reaction was added as a diffusion reaction, which is considered to utilize the 1,2-propandiol transporter in the iAF1260 model.The genome-scale metabolic model of  iAF1260 , was useFBA was used to simulate the metabolic flux distribution in the genome-scale metabolic model ,25. BrieS represents the stoichiometric matrix of metabolites in metabolic reactions, and v indicates a vector of flux for each metabolic reaction. The values vmin and vmax represent the minimum and maximum constraints of flux for each reaction, and c is a vector that represents the objective function to be maximized or minimized. In this study, biomass production was used as the objective function to be maximized, with the assumption that cellular metabolism is self-organized to maximize growth rate. After the maximal biomass production flux was obtained, the 3HP production flux was maximized under the fixed biomass production flux, on the maximal value, to avoid undetermined 3HP production flux [where ion flux .2 and NH3 were allowed to transport freely through the cell membrane. For gene knockout simulations, minimum and maximum fluxes of the corresponding reactions were set to zero. All simulations were performed using Matlab  with a solver for linear programming, GLPK .For metabolic simulation, glycerol was used as the sole carbon source, and the uptake rate was set to 15 mmol/(g DC●h). The oxygen uptake rate was set to 10 mmol/(g DC●h). Other external metabolites such as CO3HP: 3-hydroxypropionic acid; FBA: Flux balance analysis; 1,3-propanediol: 1,3-PDO; 3HPA: 3-hydroxypropionaldehyde; GUR: Glycerol uptake rate; DC: Dry cell; OUR: Oxygen uptake rate; C-mol%: Carbon-molar yield; DHAP: Dihydroxyacetone phosphate; GAP: Glyceraldehyde-3-phosphate; DHA: Dihydroxyacetone; 2,4-DNPH: 2,4-dinitrophenylhydrazine; G3P: Glycerol 3-phosphate; G6P: Glucose 6-phosphate; 6PG: 6-phospho-gluconate; 6PGL: 6-phospho-glucono-1,5-lactone; F6P: Fructose 6-phosphate; FBP: Fructose 1,6-bisphosphate; Ru5P: Ribulose 5-phosphate; R5P: Ribose 5-phosphate; S7P: Sedoheptulose 7-phosphate; X5P: Xylulose 5-phosphate; E4P: Erythrose 4-phosphate; MGO: Methylglyoxal; PEP: Phosphoenolpyruvate; Pyr: Pyruvate; AcCoA: Acetyl-CoA; AcetP: Acetyl phosphate; EtOH: Ethanol; Oxa: Oxaloacetate; Cit: Citrate; IsoCit: Isocitrate; α-KG: α-ketoglutarate; Suc: Succinate; Fum: Fumarate; Mal: Malate; Glyox: Glyoxylate; Q: Ubiquinone; QH2: Ubiquinol.The authors have no conflicts of interest to declare.KT carried out the strain construction and the culture experiments and drafted the manuscript. SO performed the metabolic simulation. KY participated in the design of the study, and drafted the manuscript. TH participated in the design of the study. ST carried out the strain construction. CF participated in the design of the study. HS conceived and supervised the study. All authors read and approved the final manuscript.


Arabidopsis thaliana.Plants assimilate sulfate from the environment to synthesize biologically active sulfur-containing compounds required for growth and cellular development. The primary steps of sulfur metabolism involve sequential enzymatic reactions synthesizing adenosine 5′-phosphosulfate (APS) and 3′-phosphoadenosine 5’-phosphosulfate (PAPS). Recent finding suggests that an adenosine nucleotide transport system facilitating the exchange of PAPS and 3′-phosphoadenosine 5′-phosphate across the plastid envelope is essential for establishing an intimate connection between the plastidic and cytosolic sulfate assimilation pathways in plants. Subcellular partitioning and integration of metabolic pathways provide focal points for investigating metabolic flux regulations. This perspective article presents an integrative view of sulfur metabolic flux control mechanisms with an emphasis on subcellular partitioning of APS/PAPS biosynthetic pathways in  Figure 1; reviewed in Figure 1).Sulfate assimilation occurs in both plastids and cytosol in vascular plants  where it serves as sulfate donor for synthesizing sulfated metabolites including glucosinolates .The plastidic and cytosolic pathways merge following PAPS biosynthesis as PAPS is mainly utilized in the cytosol . ATPS and APK are predominant in plastids, as evident from the localization of individual isoforms, distribution of enzyme activities, and phenotypes of corresponding mutants , increase in PAPS synthesis in the cytosol would thus prevent PAP/PAPS shuttling and may lead to accumulation of PAP in the cytosol.In this framework of metabolic pathway connections, abundance and functions of ATPS and APK in plastids and cytosol, together with efficiency of PAPS transport, appear pivotal for controlling subcellular distributions of PAPS and PAP  in bidopsis . In suppriations . Furtherriations . In contbidopsis . It has s leaves , suggestSulfur metabolic enzymes are not equally expressed in all plant cell types and organelles. ATPS, APK, APR, and PAPST focused on in this article, represent a sub-network of sulfur assimilation pathway, in which partitioning between cytosol and plastids is particularly important. Details of the subcellular localizations of individual isoforms have only recently been acquired and although the identity of at least one additional PAPST1 is still not known, this knowledge will facilitate the dissection of isoform-dependent and compartment-specific functions of these enzymes and transporters and provide new insights into their contributions to control of flux through sulfate assimilation. Given the biological significance of metabolites synthesized in this pathway, the flux regulation of APS/PAPS biosynthesis in specific cell-types and compartments may be associated with physiological adaptations.The Reviewer Stephan Krueger declares that, despite having collaborated in the past with the author Stanislav Kopriva, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns.The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down mannerhttp://mimoza.bordeaux.inria.fr/, or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives.Mimoza can be installed standalone, or used on-line at The online version of this article (doi:10.1186/s12918-015-0151-5) contains supplementary material, which is available to authorized users. For example, in a generalized model we might group all hexoses, and thus group together most hexose transporters, for a study where the differences between these transporters is not pertinent. Generalization is a kind of dimension reduction in complex models. It can also be used on several models simultaneously: a challenge in comparing models of related organisms, or in reconciling two models of the same organism, is that different curation standards may have been applied to the different models. Generalization can bring disparate models to the same level of abstraction so that they can be compared. To explore the opportunities of model generalization, we implement it here as a practical tool that can be easily adopted and easily integrated into existing workflows.Semantic generalization of metabolic network models  is a thezooming user interface (ZUI) [The ce (ZUI)  paradigmce (ZUI) , explorace (ZUI) , collaboce (ZUI)  and biolce (ZUI) ,7. The cE. coli [yeast 7 metabolic network model of S. cerevisiae [recon 2, a global human metabolism reconstruction [There is a conflict between the level of detail of metabolic models needed for computer simulation and the one that can be easily analyzed by a human curator: Genome-scale metabolic models include thousands of reactions that may participate in organism’s metabolism , while truction ,12.Metabolic network reconstruction can address various objectives. Examples include creation of a model for a new organism from its genomic data and a reference model for a similar organism; creation of a larger-scale model by combining several models of different aspects of organism’s metabolism; improving an existing model by incorporating new data and new expertise. To accomplish these objectives the following tasks are used  require the intervention of a human expert, and thus require methods of dealing with the complexity of the models, e.g., by splitting them into smaller modules, by defining different levels of abstraction, and by visualization.There exist various modeling tools for metabolic networks that also support visualization. Desktop tools include CellDesigner , VANTED Web-based tools allowing for metabolic network visualization are also available. JWS online , for exasubsystem annotation of reactions) and duplication of minor metabolites. Metabolite duplication reduces clutter, but the huge number of reactions in the compartments of some models and missing subsystem annotations, makes the visualization consume too much space and do not allow a user to grasp the essential structure of the network.MetDraw  is an ongeometric zooming, in which a region of the network is enlarged; and semantic zooming, in which additional properties are introduced with enlargement [Due to the huge numbers of reactions and of metabolites participating in multiple reactions, we have an uncomfortable choice between either many edge crossings in an automatic visualization of a genome-scale network, or over-duplication of various metabolites making the essential parts of the network disconnected and the visualization too large to grasp. Therefore an approach different to a simple graph layout algorithm is necessary. ZUIs, which can change the size and nature of the content displayed at different zoom levels, provide a pertinent alternative. Two main types of magnification can be considered: argement .Semantic zooming was first introduced for biological data visualization in 1988 with Zomit , a generThere exist several web-based tools that include a zoomable representation of metabolic networks. Genome Projector  is a zooNaviCell  is a webAnother web-based tool, the Cellular Overview  creates The Reactome pathway database ,24 browsNone of the ZUI tools for metabolic map representation described above, except for NaviCell, allow users to input their own models. Moreover, as these examples show, not only geometric zoom but also model decomposition and semantic zoom are important for multi-level visualization of huge models. At the general level, the network needs to be decomposed into several meaningful modules . If after such a decomposition the model remains complicated . The generalized representation reveals the overall structure of the network while hiding the details.If no similar metabolites/reactions can be detected by the generalization method (due to the model structure or to missing ChEBI metabolite annotations), this level will be missing.The most detailed level is intended for computer simulation and represents the inner structure of each of the modules with all the metabolites, reactions and their kinetics, stoichiometries and constraints.Our method places similar metabolites and reactions (detected at level 2) next to each other, thus simplifying the analysis of their presence.We address the problem of large-scale metabolic model visualization by combining meaningful decomposition into modules with automatic multi-level abstraction. Decomposition is performed in the following way: The network is first split into compartments; then the model generalization method is applied to each compartment to detect the generalized modules. Thereby, the most appropriate is to adopt 3 levels of semantic zooming: β-oxidation of fatty acids [Y. lipolytica. The first level (bottom) shows the peroxisome compartment, and the transport reactions; the second level (middle) shows the generalized structure of the peroxisome; the most detailed level (top) represents the complete model, placing semantically similar metabolites and reactions next to each other.Figure ty acids  in the pThe metabolic model generalization method , which wChEBI ontology, as it is the de facto standard for metabolite annotation in metabolic networks. If a ChEBI annotation for a metabolite is not present in the model, the method attempts to automatically deduce it by comparing metabolite’s name to ChEBI terms’ names and synonyms.To make metabolite grouping semantically meaningful, an ontology describing hierarchical relationships between biochemical entities is used. Each metabolite can be generalized up to one of its ancestors in the ontology. We use the Reactions that share the same generalized reactants and the same generalized products, are considered equivalent and are factored together into a generalized reaction.Stoichiometry preserving restriction: metabolites that participate in the same reaction cannot be grouped together;Metabolite diversity restriction: metabolites that do not participate in any pair of similar reactions are not grouped together (as there is no evidence of their similarity in the network).The appropriate level of abstraction for metabolites and reactions is defined by the network itself as the most general one that satisfies two restrictions: Aggressive reaction grouping based on the most general metabolite grouping (defined by ChEBI), in order to generate reaction grouping candidates;Ungrouping of some metabolites and reactions to correct for violation of the stoichiometry preserving restriction;Ungrouping of some metabolites (while keeping the reaction grouping intact) to correct for violation of the metabolite diversity restriction.Overall, the generalization method is composed of three modules: (S)-3-hydroxydecanoyl-CoA, (S)-3-hydroxylauroyl-CoA and (S)-3-hydroxytetradecanoyl-CoA have a common ancestor hydroxy fatty acyl-CoA in ChEBI. They can be grouped and generalized into hydroxy fatty acyl-CoA, if in the network there is no reaction whose stoichiometry would be changed by such a generalization (stoichiometry preserving restriction), and exist similar reactions that consume or produce them (metabolite diversity restriction).For instance, SBML [The method is available as a python library  that opeSBML  format. SBML  that conr is 1.14: The compression that can be achieved with the model generalization method depends on the model structure and on how well the model is annotated with the ChEBI ontology . Additional file To visualize a metabolic network we first represent it as a bipartite graph  with twoWhile layout of large graphs is widely studied , the corConnected Component Packing algorithm (provided by Tulip), which places the components close to each other while removing the overlaps between them.In order to lay out the sub-networks corresponding to each of the compartments after the generalization, we use a combination of standard layout algorithms provided by Tulip. We divide the compartment graph into connected components , using a method provided by Tulip. We then apply an appropriate layout algorithm on each of them. The results are combined together using the Circular Layout for the strongly connected components with less than 20 cycles (Circular (OGDF) [O(|E|2) time and space complexity);r (OGDF) , with O(Force-Directed Layout (FM3(OGDF) [O(|V|log|V|+|E|) with linear memory requirements) to reduce the number of edge intersections.For components with more cycles we use M3(OGDF) , that haRegarding each of the connected component subgraphs as a directed graph , we detect their strongly connected components  using path-based depth-first search algorithm . DependiHierarchical Layout (Sugiyama (OGDF) [O(|V||E|) in time and of O(|V|+|E|) in space) on the initial connected component subgraph (that now contains no cycles), and then open the meta-nodes.We then represent each strongly connected component as a meta-node , apply aa (OGDF)  algorithminor metabolites  before applying the layout algorithms, so that there is a copy of a minor metabolite for each reaction in which it is used. We then extract a subgraph, containing all but the minor metabolites, apply the combined layout on it, and then place the minor metabolites next to the reactions in which they participate.To avoid clutter we duplicate all the The layout for the full model is based on the corresponding generalized model’s layout. To allow zooming into the generalized model, we keep the same coordinates as in the generalized model for the minor metabolites and the ungeneralized metabolites and reactions, and place similar metabolites or reactions next to each other inside the space used by the corresponding generalized metabolites or reactions in the generalized model.An edge in the generalized view might expand into several edges in the full-model view, for example, if it is a generalized edge connecting a generalized metabolite to a generalized reaction. The positions of the edges after such an expansion might slightly differ from the corresponding generalized one.A different color is assigned to each generalized metabolite/reaction; and is propagated to the corresponding metabolites/reactions of the full model. Minor metabolites are colored grey. Mimoza’s interface includes a checkbox that permits to hide/show minor metabolites.The size of the nodes depends on their nature: minor metabolites are smaller than the other ones; a radius of a generalized metabolite/reaction is calculated as a sum of radiuses of the elements that it groups; compartment sizes are defined by the layouts of the elements inside them, so that the compartments are represented as minimal rectangles containing all the corresponding elements. All major specific  metabolites are of the same size; as well as all specific reactions.peroxisome compartment is surrounded by its membrane, and contained in cytoplasm; the cytoplasm is part of the cell, which is surrounded by the cell envelope.Metabolic models may include several compartments, nested into each other. For example, the outside tag. However, it is not available in all SBML levels, nor is widely used.SBML allows to represent relative positions of the compartments in the model with an optional cellular component branch of GO by using annotations in the model if they are present, or matching the compartments’ names otherwise. We then use the part_of and is_a relationships between the terms in GO to infer relative compartment positions. If no term for a compartment could be found, it is placed on the outer-most level.To be able to visualize the compartments correctly even for the SBML models lacking this information, we infer their relative positions from the Gene Ontology (GO) . We assoTo store the calculated layout of the model elements we use the layout extension  of SBML.The zoomable interactive representation is achieved using Leaflet , a JavaSWe export elements of the network graph  as map features in GeoJSON format  in orderThe GeoJSON objects are then added as layers to the map and rendered by Leaflet into clickable elements at corresponding zoom levels. We follow SBGN Process Description language convention  to choosAfter the visualization with Mimoza is done, we provide a link for embedding the view in another web page.As a standalone application. All Mimoza code is open-source and can be downloaded from the project web page  and instOn the Mimoza web server. Mimoza web server  lets oneAs a Galaxy  project One can use Mimoza in three different ways: The user submits a model in SBML format  via a web form.If the model does not yet contain groups, it is generalized using the model generalization method, and the resulting SBML file (level 3 version 1 with groups extension) is made available to the user.The SBML file with groups of similar metabolites and reactions is converted into a Tulip graph: metabolite nodes are connected by edges to the nodes of the reactions in which they participate. The generalized metabolites and reactions form quotient nodes. The Tulip graph is split into sub-graphs corresponding to different compartments, and layout algorithms are applied to them.The compartment sub-graphs are exported in GeoJSON format and rendered by the Leaflet library into an interactive map that is represented to the user.The result can either be browsed on the Mimoza web page directly, or downloaded as a COMBINE archive and embedded into a different website.The overall Mimoza pipeline contains 5 steps: http://mimoza.bordeaux.inria.fr/yeast4. Mimoza automatically split the network into compartments and created a 3-level visualization for each of them.To illustrate the use of Mimoza and compare it with other available ZUI tools, we visualized the yeast consensus genome-scale metabolic network model . The resa has only one zoom level with lots of clutter, that does not allow one to see the structure of the network.We visualized the same model using MetDraw with no manual adjustments. The resulting SVG fileSaccharomyces cerevisiae.b It has a clear non-overlapping representation of various pathways present in the model, but does not show the compartmentalization. It is not automatic and is pathway-oriented, thus is not suitable for models having no pathway metadata. The zoom-in shows additional labels but all the metabolites and reactions are present at all the levels, making the elements at the most general level very small and hard to analyze.Cellular Overview does not allow one to visualize a model provided by a user, but has a map of metabolism of NaviCell does not allow to visualize an SBML model automatically. Genome Projector only contains maps for bacterial genomes and does not permit user’s model input.Saccharomyces cerevisiae.c It has two semantic zoom levels: a visualization of a list of pathways present in the model, and submaps corresponding to each of them. The representation of each pathways is very clear, and has several geometric zoom levels. However, it is not always space-efficient as it contains gaps due to reactions present in other organisms but absent in S. cerevisiae. Another particularity is that while the positions of elements common to different organisms are conserved within a pathway, their positions might differ between different pathways of the same organism. In Mimoza, on the contrary, the positions of the reactions and metabolites are conserved between the compartments of the same organisms; but the layout of common processes  in different organisms’ visualizations might differ in the current implementation.Neither Reactome allows users to visualize their own models, but it contains a pathway map for Table β-oxidation of fatty acids as a 4-step cycle, with standard positions for the generalized metabolites common for all the networks that incorporate β-oxidation. The third strategy, which we are in the process of testing, is to learn a common layout by generalizing the union of the two networks. The idea is to combine the reactions into one set, run the generalization procedure on the union to fix the positions of the common features, then to build each of the layouts using only its own set of nodes. Each network layout only contains its own nodes, but the common nodes of the two networks will be in common positions.Using generalization to compare two metabolic networks makes most sense if they have equivalent generalized nodes that can be placed in corresponding positions in the two layouts. Mimoza currently handles this correspondence between zoom levels of the same network, but does not guarantee such correspondence when two networks are laid out independently. To meet this challenge, three strategies can be explored. The first is to use constrained layout , to impoFinally, the API of the Leaflet framework used for the interactive navigation can be used to integrate the maps with other web-based tools, such as annotation editors or simulation software.Mimoza is currently targeted to metabolic networks. While it can provide a geometric zooming visualization of a generic SBML model , the knowledge-based generalization, and therefore semantic zooming, depends on the ChEBI ontology and is intended for metabolic models. A domain-specific adaptation of the generalization method  might allow Mimoza to assist in modeling of other kinds of biological networks.model generalization, Mimoza reduces the dimension of the model’s network at outer zoom levels, and intelligently co-localizes equivalent reactions and molecular species at inner zoom levels. Consequently the biological user may efficiently navigate the high-level structure of the model; whether the goal is to understand the model or to search for errors, Mimoza exposes the important features at out zoom levels and and hides the specific details in the inner ones. We provide an efficient, useful tool that is easy to adopt and, through the use of standards such as SBML and the ChEBI ontology, is easy to integrate into existing expert-centered modeling pipelines. By carefully combining model generalization with adaptive layout and open-source cartographic software, the Mimoza web server requires just a browser with Javascript. Mimoza is open source and can also be installed locally, as described on the web page, and depends on libSBML, Tulip, and Python.We have implemented Mimoza, a novel software tool for automatically constructing zooming user interfaces for genome-scale metabolic models. By exploiting Project name: MimozaProject home page:http://mimoza.bordeaux.inria.frOperating system(s): Platform independentProgramming language: Python, JavaScriptOther requirements: JavaScript should be enabled in the web browser. The standalone Mimoza application requires Python 2.7; libSBML-experimental ≥ 5.9 for Python with groups and layout extensions; Leaflet 0.7.3; jQuery 2.1.1 and jQuery-ui 1.10.4; Tulip ≥ 4.0 for python; and model generalization libraryd.License: CeCILL (GPL compatible)Any restrictions to use by non-academics: no restrictionsa MetDraw – http://www.metdraw.com/metdraw/bc7df60221ba314c383b1bf6e7dad4c3056f92bb.b Cellular Overview – http://biocyc.org/overviewsWeb/celOv.shtml.c Reactome – http://www.reactome.org/PathwayBrowser/\#SPECIES=68322\&DIAGRAM=5686439.d Model Generalization – http://metamogen.gforge.inria.fr.