DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce  Saccharomyces cerevisiae and to detect enriched metabolic pathways in four different human cancer cell lines.Understanding deregulations of metabolism based on isolated measures of gene expression or protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for a given data and reconstruction method, there are usually multiple solutions that satisfy the same constraints and with the same quality, but only one solution is returned by the commonly used reconstruction methods. Here, we formalize this problem and we propose and analyze different methods to obtain diverse samples of metabolic sub-networks. We evaluate them by performing an extensive comparison and we show how the different sets of optimal networks discovered by the different methods are biological meaningful by constructing ensembles of networks to improve the prediction of essential genes in  PLOS Computational Biology Methods paper.This is a Metabolism and its regulation is an ensemble of intricate and tightly coordinated processes involving hundreds to thousands of enzymes, reactions, metabolites and genes, whose interactions define complex networks that are unique for each species. This complexity grants organisms the flexibility to adapt their energetic functions and growth requirements to a wide variety of conditions. Changes in nutrient availability, conditions of cellular stress, or any other change in the environment can induce a rapid metabolic reprogramming of cells, rewiring their metabolism to adjust to the requirements of the new situation. Dysfunction of these mechanisms play a central role in the development of many diseases, but most notably in cancer, where cancer cells exploit metabolic reprogramming on their own benefit  to sustaOne of the reasons why this task remains elusive is the complexity of the multiple processes that participate in the regulation of the metabolism . More spGenome-scale metabolic networks (GSMN) are suitable computational models for the integration of these multiple levels of knowledge. These models are automatically built and manually curated networks that encode all reactions with their stoichiometric coefficients, metabolites, enzymes, gene annotations and biochemical constraints that are known for an organism. GSMNs are generic models of an organism, independent of the type of tissue, cell or condition. In order to generate more accurate models for specific tissues or conditions, experimental data such as gene or protein expression can be integrated on top of GSMNs using context-specific network reconstruction methods. Taking into account the different levels of expression of genes between conditions, a sub-network from the GSMN is extracted by finding a steady-state flux most consistent with the experimental data. This process allows the generation of metabolic networks specifically tailored to the condition, to highlight for example differences in metabolism between tissues –9 or to Several methods were proposed in the literature to automatically reconstruct context-specific metabolic networks from gene or protein expression, mostly based on Linear Programming (LP) or Mixed Integer Linear Programming (MILP) models –9, 13–17EXAMO . For example, using a ds value close to one (e.g. ds = 0.99), the distance of the solution obtained at iteration 70 with respect the previous one (obtained in the iteration 69), is going to be 1 − 0.9970 ≈ 0.5, that is, the expected distance of the next optimal solution with respect the previous one is half of the possible maximal distance. At iteration 1,000, this value is 1 − 0.991000 ≈ 1, and so the algorithm is now searching for the most distant solution, as Maxdist does. Using a higher value of ds (e.g. ds = 0.999) makes this transition from near to far solutions slower, since this time the value at iteration 70 is only 1 − 0.99970 ≈ 0.07. It should be noted that if ds = 0, after computing the initial set of solutions, Diversity-enum behaves exactly as Maxdist-enum. By default, the value of this parameter has been set to ds = 0.995, and all experiments performed, unless otherwise indicated, have been done with this value.A detailed version of the algorithm is described in Alg. 1. Reaction-enum method and expand it by progressively looking for more distant solutions, rather than the other way around. The reason is that if we start with the most extreme solutions, as we progressively decrease the distance, the effect we get is to explore solutions that are closer to each other but still located in the extremes of the space, and still far from the initial solutions.Some preliminary experiments that we carried out suggest that it is preferable to start with the initial set of solutions using the Given the unknown volume of the 0/1-polytope comprising the optimal solutions, it is not possible to directly estimate its size without sampling solutions from it. In order to measure how diverse are the set of solutions obtained with different methods, we need to rely instead on indirect measures. Since solutions are indexed by 0/1 coordinates, one reasonable metric to use is the Hamming distance:x, y ∈ {0, 1}n obtained from the set of optimal solutions Ω*, we compute the Hamming distance  and we average across all the distances between any two solutions to obtain the average pairwise distanceFor each pair of solution vectors average nearest neighbor distanceTo discriminate better between these situations, we use also the O ⊆ Ω* obtained with some enumeration method, we measure the distance to all other solutions and we take the distance to its closest solution (nearest neighbor). Then, we average all those distances to have the average nearest neighbor distance.That is, for each optimal solution in the solution set spread the solutions are. We want solutions that are spread to cover a wider range of different solutions and avoid the enumeration of clusters of very similar solutions.The average nearest neighbor distance measures how Considering these two metrics, we can devise four situations when comparing the solution sets obtained by different methods:LowerlowerLargerlowerLowerlargerLargerlargerAlthough simple, these metrics provide an idea of how different the solutions enumerated by the methods are.Saccharomyces cerevisiae (https://www.yeastgenome.org/locus/S000003607) only in the absence of arginine in the medium.Context-specific metabolic networks can be used to make predictions about the metabolism of a cell or tissue in a specific experimental condition. Of a particular interest is the prediction of essential genes. An essential gene is a gene whose totally or partially inactivation prevents the organism to growth or survive. Some genes are absolutely required for survival, whereas other genes are conditionally essential, meaning that they are essential depending on the environmental conditions. For example, the gene ARG2, which encodes glutamate N-acetyltransferase —a mitochondrial enzyme that catalyzes the first step in the biosynthesis of the arginine— is annotated as a essential gene in Many essential genes that are related to metabolism (those related to enzymes) can be predicted using metabolic networks. However, conditionally essential genes are particularly hard to predict since they cannot be predicted without integrating experimental data or knowledge related to the condition. Context-specific metabolic networks are able to predict them indirectly, by extracting first the sub-network which is most consistent with the experimental data. After removing all the reactions that are predicted to be inactive in a given context, conditionally essential genes that were not essential in the generic network might be now essential in the reconstructed network.Predictions of essential genes using metabolic networks can be done by comparing the maximum flux through the biomass reaction —an artificial reaction that encodes the minimum requirements of the organism to sustain a basic metabolic activity— using Flux Balance Analysis (FBA)  before aHowever, as explained before in this section, it is common to find more than one optimal context-specific metabolic network for a given condition, each one representing a different hypothesis of the metabolic state. Each network may predict different essential genes. Since all networks fit the experimental data equally well, there is no clear way to decide a priori which of these predictions may be true. In this situation, a reasonable strategy is to consider that if a network predicts a gene to be essential, then the ensemble decides that the gene is essential, in order to maximize the number of true essential genes , similar to what has been done in  with GapsingleGeneDeletion method from the COBRA Toolbox . One of the reasons why there are more pathways that can be enriched is that, with this threshold, far fewer genes are classified as expressed and not expressed, and therefore many reactions of the metabolic network remain unscored . This makes it much more likely to find alternative sub-networks whose flux is consistent for the selected genes. In other words, the fewer genes that are identified as expressed or not expressed (less constraints), the more possible hypotheses about the metabolic state are consistent with the data. This is especially relevant in studies using proteomic or exometabolomic data, where the number of identified proteins or metabolites is lower than in gene expression assays.Overall, the method Diversity-enum detected the metabolic pathways Fatty acid activation, Fatty acid elongation, Fatty acid oxidation and Carnitine shuttle enriched in the reconstructions for the A375 cell line both for thethresholds  , pathway enrichment, network visualization or gene essentiality prediction can be used to get an integrated view of the metabolic behavior.One important limitation with this methodology is that context-specific metabolic network reconstruction is subject to significant variability due to the large number of optimal metabolic networks that can be reconstructed for the same experimental data, among other factors. This variability, which is commonly neglected, can contain relevant information and can offer alternative hypothesis of the metabolic state in terms of different combinations of reactions that are predicted to be active or inactive. Thus, the report of results using only a single optimal context-specific metabolic network can be highly biased and can overlook information relevant to the experiment. While this is an important issue, the analysis of the alternative set metabolic networks is a topic not well explored.In this study we analyze the problem of enumeration of multiple optimal context-specific metabolic networks both from a theoretical and practical perspective. We show how it is common to have multiple different context-specific metabolic networks that optimally explain the same observed experimental data. The set of optimal solutions constitute different hypotheses of the metabolic state and therefore must be taken into account to reduce bias in the interpretation of results.Reaction-enum, Icut-enum, Maxdist-enum and Diversity-enum) that we developed and integrated in an unified open-source library called DEXOM. The first three methods are improvements of previous ideas that we have adapted and improved for the particular case of the enumeration of context-specific metabolic networks, whereas Diversity-enum is a novel method for enumeration of optimal solutions that maximizes incrementally the diversity.We propose four different methods for enumeration of context-specific reconstructions  diversity of the optimal solutions obtained with each method, using two different distance metrics and UMAP plots to evaluate the spreading of the solutions; and 2) the biological relevance of alternative optimal solutions by assessing the predictive capabilities with real data. For this second aspect, we evaluate, on the one hand, the improvement in in-silico predictions of essential genes in Diversity-enum and Maxdist-enum achieve good results, although Diversity-enum explores the solution space in a more homogeneous way than Maxdist-enum, which looks for more solutions in the extremes. Reaction-enum has a limited exploration capacity, focusing on similar solutions that represent a small part of the total solution space. Icut-enum, although capable of enumerating more solutions than Reaction-enum, does so in a much less diverse manner than Diversity-enum and Maxdist-enum, and sometimes even with less diversity than Reaction-enum, as reflected in the simulations using the Yeast 6 model  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. 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Please don't hesitate to contact us if you have any questions or comments.Sincerely,Christoph KaletaAssociate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Reproducibility report has been uploaded as an attachment.Reviewer #2: Overview:In the manuscript, "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks", the authors describe a novel method that create a diversity of context-specific metabolic networks. The issue of alternative metabolic networks not being captured by various different context-specific extraction methods has been noted in the past. Previous papers, for e.g. Robaina-Estevez S et al., 2017, pointed out the effects of alternate optimal context-specific metabolic networks. However, like that paper, the author's results also appear to target iMAT and fastCORE. While I think alternate optimal context-specific metabolic network do contain important results, I haven't seen anyone explicitly show that improvements in context-specific models can be achieved by enumerating alternate optimal states. To this end, the authors have tested alternate optimal context-specific metabolic networks to show that - (a) only small improvements could be achieved over Rxn-enum, and (b) large improvements could be achieved over Maxdist and Integer-cut. Different algorithms are, in a way, different interpretations of the organization of context-specific network; thus, it should depend on the enumeration algorithms. Further, the premise misses the importance of thresholding as an input to the algorithm. Recently, many different thresholding methods have been proposed and reviewed which maybe able to perform better and may highlight power of DEXOM. DEXOM can be a promising method since it did The authors have tried to do some benchmarking but it also requires more testing to fully evaluate the power of DEXOM. How much can I learn more about my data/organism/biology by using DEXOM rather than other methods? Based on this, I recommend major revisions.MINOR CONCERNS:1. Please provide a line numbered version, so reviewers can point out the location of reference easily.2. How prevalent is the usage of DAG in understanding metabolic networks. Perhaps authors could better highlight and justify their use of DAGs in Introduction.3. In regards to TPR and FPR, the authors chose to report the ratios. Could they, please, also show the actual numbers? I am curious how they get influenced from difference between low to high thresholds.4. Are Table 1, Figure 7, and Figure 8 coming from the same set of simulations? If yes, could authors please either concise some of this information into a single figure or put some of it in supplementary information?5. Could the authors, please, provide the link to the method within the paper somewhere as well.MAJOR CONCERNS:1. I want to make sure that by optimal conext-specific model they mean, models which maximizes the addition of active and removal of inactive reactions with experimental evidence. Doesn't this definition assume that the output of the thresholding is accurate? Besides global thresholding used in this paper, recent studies have identified better thresholding approaches that generate more accurate models, also some thresholding approaches have enforced biological significance. Have the authors looked at some of these thresholding approaches to see if DEXOM can do better with better thresholds?2. A lot of the work on how to measure diversity has been done, i.e. the diversity between models has often been reported using Jaccard similarity . The behavior of this distance metric with reaction active with experimental evidence and those of the models has been shown in these papers. The authors can choose to use Hamming distance, but it becomes difficult for the reader to compare results in those papers with those reported by the authors. Could the authors please provide some justification for their choice to use Hamming distance? Alternative authors could also show that these results could be reproduced using Jaccard similarity.3. Robaina-Estevez et al. 2017 found that CORDA, a methods that enforced metabolic functionality rather than the reactions performed better. Does this statement still hold true with DEXOM using gene essentiality as metric?4. This is tied to point 4, how does DEXOM do if it is coupled with metabolic functionality.5. For improving these types of context-specific models, two major ways have been targeted: (1) extraction method, and (2) thresholding. The authors are dealing with a novel extraction method. As a way to point out if this is the way forward, can the authors compare their TPR/FPR with those achieved from changing the thresholding method.6. Different algorithms are enforcing different interepretations of how context-specific metabolic networks are organized. Whether the interpretation is true for only yeast, single-celled organisms, eukaryotes, or fungi, or other organisms too. Yeast is a single cell organism which can be grown in the lab relatively easily compared to mammalian cells. Extensive comparison data and models are available if DEXOM was applied to human tissues, mouse, and/or cancer cell lines. Could the authors show that DEXOM can be extrapolated to other organisms and/or datasets?Reviewer #3: Rodríguez-Mier et al. propose DEXOM, a diversity based enumeration method of optimal context-specific metabolic models. In essence the authors describe advantages and disadvantages of three alternatives for enumerating alternative solutions of optimal context-specific metabolic models.The authors show and describe a thorough understanding of the theoretical aspects of their work.DEXOM takes into consideration the advantages of three alternative MILP methods  and computes increasingly distant (diverse) optimal alternative solutions based on an initial sampling set (computed with Rxn-enum). Diversity and comparison to other methods is quantified by two metrics, hamming and nearest neighbor distance, which are used throughout the manuscript for evaluation across different methods. The capability of DEXOM to predict gene essentiality based on published data and a model for S. cerevisae are demonstrated.Though the authors identified and addressed a very important aspect of constraint based modeling, I see major drawbacks that prevent the laid out DEXOM algorithm to be published in PLOS COMP BIO.Although the diversity seems to be sampled best by DEXOM as visually shown by UMAP based dimension reduction, the advantage of the improved metrics seems to be not always the case as shown in panels E and F of Fig. 5 and S1-S3 and depends to a large quantity on the sampled networks (cf. e.g. S3 against S2 and S1). Hence, it appears that it cannot be guaranteed that DEXOM outperforms Maxdist with respect to the given metrics. In addition, as shown in Table 1 and Figure 7 and 8, the advantage of using DEXOM over Rxn-enum for finding essential genes in a given network is not substantial. Specifically the increased TPR is often achieved at the price of an increased FPR, while the differences in TPR and FPR for Rxn-enum and DEXOM are generally very low. This seems to render Rxn-enum the better algorithm for identifying gene essentiality due to its low runtime , despite providing only a restricted and not diverse set of optimal networks.In summary, the advantages of using and applying DEXOM are not obvious and the authors need to justify why DEXOM should be used instead of Rxn-enum  or Maxdist .With this several further major drawbacks are present:- No usable DEXOM algorithm is provided. This work would greatly benefit from having a ready made software code available that based on any given SBML model allows to compute a diverse set of alternative optimal metabolic models.- Only one model and one dataset on yeast are used for demonstrating and comparing the performance of the proposed algorithm and this only for gene essentiality analysis. Although mentioned in the introduction as potential use case the authors did not include any model analysis to investigate diseases in e.g. human, presumably because human reconstructions like Recon3D possess a large number of reactions, which make it computationally demanding to compute optimal solutions. Nevertheless, it remains unknown how DEXOM performs on other data. At least one further data set analysis, potentially with a different aim than identifying gene essentiality (e.g. enriched function) of another organism would greatly improve the trust in DEXOM, if it can be shown that it outperforms competing methods.- The authors do not discuss the virtually equal quality of Rxn-enum vs DEXOM for gene essentiality analysis. Furthermore, TPR and FPR are not given for the alternative sets presented in S1-S3, which show much different hamming and nearest neighbor values, which presumably influence achievable TPR and FPR values. Code and solutions are not given to allow recapitulating the analysis.- Throughout the manuscript preciseness is missing. Parameters and functions of the presented algorithm are not described  = a^b is not explained). Figures are not described well (subpanel description are not given in captions and are most often not referenced in the main text). Another example is the definition and use of thresholds used in table 1, which need to be briefly introduced in the main text (e.g. at page 20).- A major rewrite is recommended, as much of the methods section is recapitulating methods that are published elsewhere. Instead the focus should be on the DEXOM description, while the extensive text body on alternate methods  leading towards DEXOM should be shortened dramatically in the main text. The detailed description would be suitable for a supplementary text that can be referenced in the methods.Minor==https://doi.org/10.1371/journal.pcbi.1003580 are two references that should be mentioned here as they show performances of methods tailored towards computing context specific metabolic models- page 3: 10.1016/j.cels.2017.01.010 and - page 8: "... extensively exploited in commercial solvers such as IBM CPLEX andGurobi" - both provide academic free academic licenses, which would be great to mention- page 9: Here, a short paragraph on model optimisation by gap filling, detection of orphan reactions or EGCs leading to the fact that this does solve only parts of the problem of network artefacts would be suitable.- page 14: "It starts computing an initial set of solutions using the Rxn-enum method avoiding duplicated solutions. This guarantees that single variations of reactions across all pathways are explored." - This is confusing, as the initial set of solutions will not necessarily include variations across pathways, unless the initial set size is sufficiently high.- page 14: "Using a ds value close to one (e.g. ds = 0.99), the search concentrates at the beginning with more probability in the close vicinity of the selected solution." - Why should it be desirable to start with similar solutions as most diverse solutions are the goal? Of course the formulation allows to start with more distant solutions. It should be made clear here, why similar solutions might be desirable to be computed first.- page 15: "For example, in Saccharomyces Cerevisiae, gene ARG2, which encodes glutamate N-acetyltransferase —a mitochondrial enzyme that catalyzes the first step in the biosynthesis of the arginine— is essential only in theabsence of arginine in the medium." - It should be made clear that this is might be a general theme in arginine metabolism, not just in S cerevisae (lowercase for cerevisiae)- page 15: COBRA toolbox reference should be cited here and elsewhere. Also Yeast 6 is not referenced everywhere (e.g. page 16)- page 17: "The grey points correspond to the 1,024 optimal solutions of the ground truth." - This is very unclear at this point. Please improve clarity of description of the figures here and elsewhere.- page 17: "The Maxdist method shows at the beginning of the search the largest distance, since the solutions are generated by finding extreme differences. After an initial set of 25 optimal solutions, the average distance stops increasing. This is something to expect since the most distant solutions are usually discovered at the beginning of the search." - The question arises, whether having the first most diverse networks with Maxdist is sufficient and fast enough as both hamming and nearest neighbor distance are high. This should be analysed, mentioned and discussed.- page 19: "After DEXOM generates an initial set of around 600 solutions, both the average distance and the average nearest neighbor distance start to grow surpassing the other methods." - It is surprising that there is not a gradual, but sudden improvement by DEXOM . The authors should explain or at least hypothesize on why this is happening abruptly and not monotoically increasing for both hamming and neirest neighboor distance. Regarding particularly investigation in the supplement it appears that for low numbers of sample networks, Maxdist is the better method in terms metric performances. Again, the authors should discuss this point.- page 20: Since [38] lists gene expression under low oxygen levels (at most 2.8%), the authors should discuss briefly, whether there is a difference to be expected for higher oxygen levels and how this might affect gene essentiality.- page 20: "Thus, in total, we generated 16 ensembles per method, one for each threshold." - Thresholds are not defined.- Table 1 would be much easier to investigate as bar plot with four bars (4 methods) for each threshold configuration- methods and results/discussion should be clearly separated (e.g. machine configuration mentioned on page 24 is Methods content)**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesNo: Computed models and essential genes are missing that are the basis for given manuscript and supplement figures and table 1.Reviewer #3: **********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see AttachmentReproducible_report__PCOMPBIOL_D_20_01343 (1).pdfSubmitted filename: Click here for additional data file. 16 Nov 2020AttachmentResponse.pdfSubmitted filename: Click here for additional data file. 3 Dec 2020Dear Dr. Rodríguez Mier,Thank you very much for submitting your manuscript "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations. Please pay particular attention to the comments of reviewer 2 concerning the motivation to include all four methods in the work and the reasoning to include similarly performing methods with large differences in required runtime.Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Christoph KaletaAssociate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Reproducibility report has been uploaded as an attachment.Reviewer #2: OVERVIEW:In the manuscript, "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks", the authors describe a novel method that create a diversity of context-specific metabolic networks. It seems that enumerating multiple context-specific networks is valuable and is known for sometime as authors have pointed out. It is more clear in this version of the manuscript that authors are trying to bring everything under one umbrella called DEXOM and subtly claiming that diversity-enum is more accurate. My first major concern is that Diversity-enum and Rxn-enum seem to have the same accuracy. My second major concern is that current analysis doesn't say anything about networks uniquely captured by Diversity-enum, Rxn-enum, Icut-enum, or Maxdist. What is the problem that authors are solving - time taken to generate the ensemble, diversity in the ensemble, accuracy of the ensemble, or simply a toolbox? Based on the current analysis shown, I am still not clear if additional diversity, i.e. Diversity-enum, was useful in either improving calculation time or accuracy. It is also not clear why a modeler should care for all the 4 approaches that authors have described here, when clearly only Rxn-enum and Diversity-enum are in anyway accurate. Given that analysis presented here needs more fleshing out, I would recommend a major revision. Please see details below.MAJOR CONCERNS:1. Why is there a need for 4 different approaches which need to be under one umbrella? Why can't just the best method prevail? Are there any advantages of Icut-enum and Maxdist-enum? These two have very poor accuracy and why do they need to be included in the paper or in the toolbox. Can the authors show, systemically, cases where enumeration using one method is better than other. The authors can do this by finding which essential genes are captured differentially among networks calculated using different methods. I think this is an important benchmarking step for the authors to justify the need of multiple enumeration methods within this toolbox for context-specific networks.2. To me it appears Rxn-enum and Diversity-enum have similar accuracy. Diversity-enum and Maxdist give the most diversity within the models and they both take a long time to run. So authors generated a method which produces a diverse ensemble but it takes a long time. However, this long time is not producing better results than Rxn-enum, seems pretty similar to me. I would use Rxn-enum because it goes really fast and produces that same accuracy as Diversity-enum. I am not sure if Diversity-enum produces valuable diversity within models. Unless authors can systematically show that extra context-specific metabolic networks that are being differentially captured by Diversity-enum but not by Rxn-enum are valuable. Authors can simply see which of the networks captured uniquely by Diversity-enum captured gene essentiality not explained by Rxn-enum.3. If Diversity-enum is better, could the authors just do a statistical test for difference in distributions of TPR from different methods? These are basically p-values from comparing TPR distributions between pairs of methods.Reviewer #3: The authors underwent substantial labor to improve their manuscript.The focus of the paper has been improved and shifted away from focusing on the new DEXOM formulation itself (now termed Diversity-enum) to presenting a suite of readily usable algorithms for enumerating alternative optimal metabolic network solutions. These include Diversity-enum as one of four methods for which no COBRA compatible toolbox extension was available so far. The precision in phrasing has been improved throughout the manuscript. More importantly the authors succeeded in adding another use-case, by adding an enrichment analysis of four human cancer cell lines. The authors showed that a set of differing context specific metabolic models with equal optimality can possess a diverse set of enriched metabolic pathways. This outcome is a valuable addition to the manuscript, but misses one aspect to be complete (see below).The authors show that equally probable context specific networks can possess a diverse set of enriched functions. In consequence pathway enrichment analysis needs to be treated with care. The authors do not show, however, how frequent any of these pathways occur, e.g. is there a single pathway that is enriched in most, if not in all, sets of networks? This way enriched pathways occurring only a few times, might be considered artifacts of the respective networks, yet pathways that are enriched in most networks hint towards metabolic function that translated into the context specific metabolic networks and thus would be of value. Here, it would also be of interest how the frequency of enriched pathways differs after applying the four different methods. Such an analysis should be easy to add, would improve the provided insights into enriched pathway analysis and complete the revision.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease. Non-genetic heterogeneity is a hallmark of cell physiology. Isogenic cells can display markedly different phenotypes as a result of the stochasticity of intracellular processes and fluctuations in environmental conditions. Gene expression variability, in particular, has received substantial attention thanks to robust experimental techniques for measuring transcripts and proteins at a single-cell resolution  mixture weights that can be computed from models for gene expression or single-cell expression data, and (ii) mixture components that are directly computable from deterministic pathway models. The resulting mixture model can be employed to explore the impact of biochemical parameters on metabolite variability. We illustrate the power of the method in two exemplar systems that are core building blocks of large metabolic networks. Our theory provides a quantitative basis to draw testable hypotheses on the sources of metabolite heterogeneity, which together with the ongoing efforts in single-cell metabolite measurements, will help to re-evaluate the role of metabolism as an active source of phenotypic variation.M metabolites Pi with i ∈ {1, 2, …, M} and N catalytic enzymes Ej with j ∈ {1, 2, …, N}. A typical enzymatic reaction has the formWe consider metabolic pathways composed of enzymatic reactions interconnected by sharing of metabolites as substrates or products. In general, we consider models with Pi and Pk are metabolites, and Ej and Cj are the free and substrate-bound forms of the enzyme. The parameters  and  are positive rate constants specific to the enzyme. In contrast to traditional metabolic models, where the number of enzyme molecules is assumed constant, here we explicitly model enzyme expression and enzyme catalysis as stochastic processes. Our models also account for dilution of molecular species by cell growth and consumption of the metabolite products by downstream processes.where P given the pathway stoichiometry, analytical solutions of the CME are tractable only in few special cases. To overcome this challenge, we propose a method for approximating metabolite distributions that can be applied in a wide range of metabolic models. We first note that using the Law of Total Probability, the marginal distribution P can be generally written as:Though in principle one can readily write a Chemical Master Equation (CME) for the marginal distribution P =  and E =  are the vectors of metabolite and enzyme abundances, respectively. The equation in (2) describes the metabolite distribution in terms of fluctuations in gene expression, comprised in the distribution P(E), and fluctuations in reaction catalysis, described by conditional distribution P(P|E).where P(E) and mixture components P(P|E). To compute the mixture weights and components, we make use of the timescale separation between gene expression and metabolism. Gene expression operates on a much slower timescale than catalysis  corresponds to a mixture model with weights Ejt,  is the total number of enzymes Ej. Note that since our models integrate enzyme kinetics with enzyme expression, the variables Ejt,  follow their own, independent stochastic dynamics. It is important to note that in our approach, the conservation relation in (3) holds only in the fast timescale of catalysis. This contrasts with classic deterministic models for metabolic reactions, which typically focus on the fast catalytic timescale and assume enzymes as constant model parameters  can be obtained as solutions of a stochastic model for enzyme expression , on the other hand, can be estimated with suitable approximation techniques. For simplicity, here we choose to employ the Linear Noise Approximation (LNA), which provides a Gaussian estimate of the stationary distribution of a stochastic chemical system  can be used if required  can be computed separately. Specifically, the mixture weights We thus propose the following procedure for computing single-cell metabolite distributions:P(E) from a stochastic model for gene expression, either analytically (if possible) or numerically with Gillespie's algorithm.1. Starting from the mixture model in Equation (2), compute the enzyme distribution P(P|E) with the LNA, compute the steady state solution E:2. To approximate the mixture components S is the stoichiometric matrix and v(·) is the vector of deterministic reaction rates; for ease of notation we have assumed a unit cell volume, and hence the deterministic rates are equal to the propensities of the stochastic model. Note that due to the timescale separation, Equation (4) must be solved assuming constant enzymes E, and its solution depends on the enzyme abundance, i.e., where E, compute the solution to the Lyapunov equation  evaluated at the steady state and BBT = Sdiag{v}ST. Note that, as in (4), the solution of the Lyapunov equation depends on the enzyme state, i.e., Σ = Σ(E).where P(P|E) as a multivariate Gaussian distribution with mean 4. Following the LNA, approximate the mixture components P(E) and Gaussian components P(P|E) through the mixture model in (2).5. Combine the weights In the next sections we illustrate the effectiveness of our method in two exemplar systems.S, a free enzyme E, a substrate-enzyme complex C and a metabolic product P. To model enzyme expression, we use the well-known two-stage scheme for transcription and translation  correspond to a reversible Michaelis-Menten reaction as in (1), while reactions in (7) are the two-stage model for gene expression. We include four additional first-order reactions (8) and (9) to model consumption of the metabolite product with rate constant Since on the fast timescale of the catalytic reaction, the total number of enzymes can be assumed in quasi-stationary state  can be written as:P can be computed from the stochastic model for gene expression in (7). Under the standard assumption that mRNAs are degraded much faster than proteins  with the LNA, we write the full system of deterministic rate equations [see (35) in section 6] for the three species E, C, and P. Note that in this case, we can further reduce the rate equations by (i) using the conservation law in (10), and (ii) assuming that the binding and unbinding reactions between S and E reach equilibrium faster than the product P, a condition that generally holds in metabolic reactions. After algebraic manipulations, the reduced ODE can be written as:To compute the mixture components whereKSm = (kb+kcat)/kf and KPm = (kb+kcat)/krev.and the parameters are Etotal, the variance Σ of each Gaussian component is given by the solution to the Lyapunov equation in (5):The mean of each mixture component is simply given by the steady state solution of (13), which we denote as f′ and g′ are first-order derivatives. Combining the negative binomial in (12) with the Gaussian components, we can rewrite Equation (11) to get a Gaussian mixture model for the metabolite:where x) must be computed for each value of x = Etotal in the summation. The normalization constant in (16) iswhere both In A common regulatory motif in metabolism is end-product inhibition, in which a pathway enzyme can bind to its own substrate as well as the pathway product see . The proP1 as a product and substrate, respectively. The end-product inhibition in (20) consists of reversible binding between h molecules of P2 and the first enzyme E1 into a catalytically-inactive complex E*. The remaining model reactions in (21)–(25) are analogous to the previous example in section 3: reactions in (21) and (22) describe the two-stage model for expression of both enzymes, and with reactions (23)–(25) we model first-order mRNA degradation, product consumption, and dilution by cell growth. For simplicity we also assume that both enzymes are independently expressed, but in general our method can also account for cases in which enzymes are co-expressed or co-regulated  and (19) are reversible Michaelis-Menten kinetics, sharing the intermediate metabolite and therefore the mixture model in (2) becomesEt, 1, Et, 2) pairs. Since both enzymes are expressed independently, the enzyme distribution is the product of two negative binomials P = P × P, each one analogous to the distribution in (12).where the summation goes through all –(23); the full set of ODEs is listed in Equation (36) in the Methods. As in the first example, by employing the conservation laws in (26) and assuming rapid equilibrium of the complexes Et, 1 and Et, 2. The nonlinear functions in (28) arewhere for ease of notation we have omitted the dependency on ksq/krsq is the product-enzyme binding constant and the remaining parameters are defined as κS = kcat, 1kf, 1/, κ1 = kb, 1krev, 1/, κ2 = kcat, 2kf, 2/, κ3 = kb, 2krev, 2/, Km, S = kcat, 1/κS, Km, 1 = kb, 1/κ1, Km, 2 = kcat, 2/κ2, and Km, 3 = kb, 2/κ3.where θ = AΣ+ΣAT+BBT = 0 with A and BBT given byAs in the previous example, the ODEs in (28) correspond to the full model (36) rewritten in terms of both metabolites assuming that the enzyme-substrate reactions reach equilibrium in a faster timescale than catalysis. This reduced model can be readily employed to obtain approximations for the mixture components with the LNA. If we denote as f(·), g(·), and their derivatives are evaluated at the steady state solution where where x, y) need to computed numerically for each pair  =  in the summation. The burst frequencies ai = kitx, /δ and burst sizes bi = kitl, /kideg,  are specific to each enzyme, and the normalization constant is given bywhere To test the quality of the approximation, we numerically computed the mixture model in (33) for various combinations of parameter values, shown in P(P1) for various combinations of average enzyme expression levels. The results suggest that expression levels can have a strong impact on the mean and coefficient of variation of the intermediate metabolite. Moreover, in P for combinations of bursting parameters. The results show that uncorrelated enzyme fluctuations can result in correlated metabolite distributions due to the coupling introduced by the pathway  can be approximated through stochastic simulations , for a broad range of metabolic pathways. Yet in cases where its assumptions do not hold, e.g., low abundance of metabolites, the LNA step in our method can be replaced by more accurate approximations. Such alternative methods include, for example, the conditional system size expansion including terms beyond the LNA, maximum entropy reconstructions using the method of conditional moments, or the finite state projection algorithm  corresponding to thousands of cell cycles. The ODE models and Lyapunov equations were solved in Matlab. In all examples, the negative binomial distribution for gene expression in (12) was computed with its continuum approximation (Gamma distribution).The full set of rate equations for the reversible reaction in (6)–(8) is:C = Etotal−E, and use the fact that the substrate-enzyme complex (C) typically equilibrates much faster than the product P, which means that dC/dt ≈ 0 in the timescale of catalysis.To further reduce the above system of ODEs to Equation (13) in the main text, we can substitute the conservation relation in Equation (10), i.e. The full set of rate equations for the reactions in (18)–(23) is:Et, 1 = E1+E*+C1 and Et, 2 = E2+C2, to simplify the 7-dimensional ODE in (28) to the 2-dimensional system in (28) of the main text.As in the previous example, we can use the rapid equilibrium assumption and the conservation relations in (26), i.e., The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.MT carried out research, model simulation, model analysis, and wrote the paper. PT and MB contributed to model analysis and paper writing. DO designed the research, model analysis, and wrote the paper. All authors contributed to the article and approved the submitted version.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.


A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website ( MetaNetX/MNXref provides computed cross-references between metabolites as well as biochemical reactions, to reconcile major public biochemical databases and a selection of public genome-scale metabolic networks (GSMN) from a few dedicated resources . The preGSMN reconstruction is one of the foundations of Systems Biology. The development of such reconstruction involves the integration of knowledge about reactions and metabolites from the scientific literature, public databases and previously published GSMNs. Historically, many GSMNs were formulated using metabolites represented as ‘symbols’, i.e. without explicit reference to a molecular structure. The initial motivation for creating this resource ten years ago was to add molecular structures to existing GSMNs. More generally, the goal was to establish cross-links between symbols in GSMNs published by different groups and the molecules found in the major biochemical databases. This problem is trivial as long as one-to-one mappings can be established between the different resources, but this is not always the case. Merging metabolites in a metabolic network may lead to the merging of reactions, possibly altering the model properties, and hence the predictions that could be made using it.https://www.perl.org/) and R 3.6.1 (https://www.R-project.org) scripts. These components include InChI 1.0.5 (https://chemaxon.com/), Gurobi 9.0.2 (https://www.gurobi.com/), Blast 2.7.1+ (https://ch.mathworks.com/products/matlab.html), COBRA Toolbox 2.0.0 (https://httpd.apache.org/) and Virtuoso (https://virtuoso.openlinksw.com/). The frontend uses jQuery (https://jquery.com/) and DataTables (https://datatables.net/) with some of their plugins to ease browsing.The computation of the reconciliation was performed using various different software components glued together by Bash, Perl 5.24.0  first refers to a set of metabolites in external databases, which are merged together because they are assumed to be the same biochemical entity, i.e. distinctions in protonation states, tautomeric forms or isotopes are typically ignored. Secondly, for every set of metabolites, a single external database identifier is selected as the (best) reference, from which a molecular structure is possibly retrieved. ChEBI identifiers that appear in Rhea reactions are preferentially selected as reference.For MetaNetX/MNXref, a metabolite identifier (e.g. MNXR456) designates a set of reactions in external databases that are assumed to be the same biochemical entity, because they were merged together. A single external identifier is selected as the reference for this set, preferably a reaction from Rhea when feasible. However, a reaction rewritten with MNXref identifiers may differ from the original equation, for example, with respect to proton balancing. In addition, since release 2.0, a distinction was introduced between transported protons that account for the proton motive force (MNXM01) and those introduced to balance chemical equations (MNXM1). Such a distinction is also made in some recently published models  have also been used as a trigger for the manual inspection of a particular reconciliation.Two metabolites to be merged that appear in the same reaction, in the left and right terms for example, also deserve scrutiny. Pairs of tautomers or acid-bases can be validly merged, possibly yielding an The errors we have detected have been systematically reported to the resource from which they originated. Most of them have since been corrected, thanks to the diligence of their curators.properties. We have attempted to summarize the latter by attributing a status to every reaction in a GSMN  reactions were set to bi-directional; b, the reaction can carry a flux after all reactions in the GSMN were set to bi-directional; B, the reaction cannot carry any flux because of the network topology, for example because of a dead-end metabolite. Computation is performed using a variant of the flux variability algorithm ) and 1tdecg3p (1-tetradecanoyl-sn-glycerol 3-phosphate). In other GSMNs, the merging of metabolites may cause the status of a few reactions to change, the latter not necessarily involving the merged metabolites and possibly augmenting the number of reactions that can carry a non-zero flux. Taking control of such indirect effects has been crucial for testing and improving the reconciliation. More public GSMNs will be added in the future to further challenge and improve the quality of the reconciliation.For example, the iAF1260 model for E. coli  containsTables https://www.metanetx.org) provides access to the full database and online services. Most notably, the service that permits a user to upload his or her own GSMN and map it to MNXref has been greatly improved to produce diagnostic messages about the model content. The database can be queried through the website user interface, as well as through a SPARQL endpoint (https://rdf.metanetx.org).The MetaNetX website . The MNXref reconciliation is available in TAB-delimited and RDF/Turtle formats. The mapped GSMNs are available in SBML level 2 and 3 formats from the web site and as part of the RDF graph.Data is distributed under a Creative Commons Attribution 4.0 International License (


Plants produce a diverse array of natural products, many of which have high pharmaceutical value or therapeutic potential. However, these compounds often occur at low concentrations in uncultivated species. Producing phytochemicals in heterologous systems has the potential to address the bioavailability issues related to obtaining these molecules from their natural source. Plants are suitable heterologous systems for the production of valuable phytochemicals as they are autotrophic, derive energy and carbon from photosynthesis, and have similar cellular context to native producer plants. In this review we highlight the methods that are used to elucidate natural product biosynthetic pathways, including the approaches leading to proposing the sequence of enzymatic steps, selecting enzyme candidates and characterizing gene function. We will also discuss the advantages of using plant chasses as production platforms for high value phytochemicals. In addition, through this report we will assess the emerging metabolic engineering strategies that have been developed to enhance and optimize the production of natural and novel bioactive phytochemicals in heterologous plant systems. Plants have a remarkable capacity to produce a wide array of specialized metabolites , to support their defense and ecological adaptation. These phytochemicals exhibit a variety of bioactivities and many of them are of considerable pharmaceutical importance. Based on their biosynthetic origins, plant secondary metabolites can be divided into three major groups: terpenoids, alkaloids, and phenolics.Artemisia annua) and the anticancer drug Taxol (Taxus brevifolia) (Catharanthus roseus), camptothecin (anticancer) from Camptotheca acuminata and morphine  from opium poppy (Podophyllum peltatum), and are widely used in chemotherapies. Alongside alkaloids, terpenoids, and phenolics, plants also produce other types of secondary metabolites, such as polyketides, cyanogenic glycosides and glucosinolates, of which many have pharmaceutical relevance.Terpenoids or isoprenoids are a structurally diverse class of plant specialized metabolites. They are derived by the repetitive fusion of branched five-carbon isopentanes, usually referred to as isoprene units. Many terpenoids have biological activities and are utilized as valuable pharmaceuticals. The most renowned terpenoid-based drugs include the antimalarial medicine artemisinin (vifolia) . Anothervifolia) . Numerouum poppy . Phenolium poppy . These cSourcing bioactive natural products from plants has many challenges. These metabolites are often found in low abundance or are produced in rare or slow-growing plant species, which limits the availability and accessibility of the active pharmaceutical agent and impacts market prices. In many cases, these molecules are very complex and are recalcitrant to chemical synthesis, as they contain many chiral centers or polycyclic rings , while circumventing the obstacles routinely associated with the commercial growth of dedifferentiated plant cells  or microbes . The advantages of using microbes as production platforms for valuable chemicals include fast growth cycles, ease of genetic modification, and the fact that they offer a simplified product purification pipeline. The synthetic biology approaches used to produce plant metabolites in microbial organisms have been recently reviewed , can be fed to the plant. The target specialized metabolite can then be assessed for isotope labeling by mass spectrometry (MS) or nuclear magnetic resonance (NMR)  from The next step is to assign possible enzyme classes to each hypothesized reaction based on characterized enzyme activities. For instance, a hydroxylation step will probably involve a cytochrome P450 enzyme or a 2-oxoglutarate dependent oxygenase , different developmental stages or in elicitor-treated material. Candidate genes involved in biosynthesis can be identified through correlation of expression with metabolite accumulation, or through identification of co-expressed clusters in which genes involved in the same pathway share expression patterns across tissues .Tripterygium wilfordii, was identified through an integrative gene prioritization approach  is known to elicit various species-specific specialized metabolic pathways. This feature has often been used to identify genes involved in the biosynthesis of various natural products. Cytochrome P450 (CYP728B70) catalyzing the oxidation step in triptolide biosynthesis, an abietane-type diterpenoid from Artemisia annua   led to the discovery of the unusual nepetalactol-related short-chain dehydrogenase enzymes (NEPS), which are enriched in the trichome and involved in the biosynthesis of volatile nepetalactones -salutaridinol 7-O-acetate. However, functional assays with total soluble protein isolated from opium poppy latex resulted in a 10-fold increase in the formation of thebaine. After protein fractionation and subjecting the active protein fraction to LC-MS/MS proteomics analysis, candidate genes were identified and were then tested through enzymatic assays, leading to the functional characterization of thebaine synthase  of interest. This plant also provides a cellular context that is similar to the native producer plant. This characteristic allows the expressed proteins to be properly folded and targeted to the correct subcellular compartment, as well as provides a natural supply of chemical precursors; coenzymes, and cofactors.pathways . It is hN. benthama has led to the discovery of a variety of enzymes underlying the biosynthesis of diverse high value plant-derived compounds   is a reverse genetics approach commonly employed for the N. benthamiana represents a valuable tool for the heterologous production of phytochemicals. For example, 16 biosynthetic genes from Himalayan mayapple (Podophyllum hexandrum) were transferred to N. benthamiana to produce precursors of the chemotherapeutic drug etoposide at milligram-scale  , in N. tabacum   , it is possible to synthesize novel molecules with enhanced proprieties (Calgaro-Kozina et al., The generation of costunolide and parthenolide derivatives was achieved by applying a combinatorial metabolic engineering approach expressing different enzymes involved in sesquiterpenoids biosynthesis from ia annua  (Kashkooia annua . In a rePhytochemicals were foundational to the emergence of organic chemistry and the early development of pharmaceuticals. The structural complexity and bioactivity of phytochemicals will ensure their continued relevance in synthetic chemistry and drug discovery. With the maturity of genetic and biochemical tools for gene discovery, characterization and engineering, we are entering into a new synthetic era of phytochemistry, in which biosynthetic pathways are elucidated and high-value phytochemicals can be produced in synthetic biological chasses.in vitro assays, coupled with in vivo validation through RNAi or VIGS, subsequently enables verification of candidate gene function. Once the pathway is uncovered, the heterologous in planta expression of the biosynthetic pathway is made possible, with many emerging engineering strategies available to enhance or diversify the production of the target compounds. These advances open the door to a future where diverse phytochemicals with new bioactivities and unusual structures can be routinely produced and purified from heterologous plant systems, aiding drug discovery efforts and leading to the scalable and sustainable production of valuable phytochemicals.An integrative approach to gene prioritization, combining genomic transcriptomic and metabolomics, has become an efficient method for the identification of candidate biosynthetic genes. Characterization of genes through KE conceived of the manuscript and wrote the first draft. Both authors prepared figures, edited text, and approved the manuscript for submission.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.