We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at  Metabolism is responsible for creating metabolites with different physicochemical and pharmacological properties compared to those of the original parent molecule. Xenobiotic metabolism in particular is directly relevant for humans, especially as it relates to, for example, the development of drugs, cosmetics, and agrochemicals. In fact, it is supposed that metabolism is the main clearance pathway for the vast majority of all xenobiotics  family of enzymes plays an important role in the metabolism of xenobiotics, especially in the formation of first-generation metabolites, of which roughly 60% are formed by CYPs  , a rule-based metabolite prediction software Few freely available metabolite prediction methods combine information on predicted SoMs with a rule set. MetaTox predicts reaction classes and reacting atoms  separately for each parent molecule, then combines the predictions to generate metabolites. The probability that the metabolite is formed is calculated based on the predicted probabilities of the reaction class and of the SoM predicted with the SOMP method  reduce the false-positive prediction rate while maintaining an acceptable recovery rate and (ii) augment the ranking of predicted metabolites. In order to reduce the false-positive prediction rate, the possibility of utilizing a hard cutoff based on SoM probabilities was explored. This cutoff was used to determine at which atom positions the rules were allowed to be applied. In the context of GLORY, we have called this approach, in which SoM prediction is used as a preliminary filter, MaxEfficiency mode. In contrast, we designate the approach in which SoM probabilities are used for ranking metabolites derived for all positions in a molecule regardless of SoM probability the MaxCoverage mode. The difference in workflow between the two modes is illustrated in To choose a SoM probability cutoff for the MaxEfficiency mode and develop a priority score to rank predicted metabolites, a large reference dataset was generated by combining the CYP metabolism data extracted from DrugBank . For each heavy atom in a molecule, FAME 2 reports a probability between 0 and 1, corresponding to the fraction of trees of the extra trees classifier that predict that a particular atom is a SoM. The decision threshold in FAME 2 for whether or not an atom is considered likely enough to be a SoM to be designated as such was determined by the trained model to be 0.4  false positive predictions, and FN is the number of false negative predictions. In other words, recall measures the portion of known metabolites that were reproduced by the method and precision measures the fraction of all predicted metabolites that are represented in the dataset.Here it is worth noting that the number of false positives, and the designation of a prediction as false positive, is especially dependent on the dataset that is being used for comparison. Many metabolites that are formed in humans have not yet been discovered, or their structures have not yet been exactly elucidated. Since even the highest-quality dataset is limited by the available experimental data, the reality is that the distinction between a real false positive prediction and the true positive prediction of an as yet unknown or unconformed metabolite may not be possible. Nevertheless, with this caveat, we evaluate our method based on the available data, including the putative false positives.The purpose of the MaxEfficiency mode is to use the SoM probability cutoff to predict metabolites with increased precision compared to no cutoff . At the same time, however, we did not want to sacrifice too much in terms of recall, as it is still important to predict a molecule's actual metabolites even while reducing the number of putative false positive predictions.For the purpose of metabolite prediction, we found that using FAME 2's decision threshold of 0.4 as the cutoff for SoM probability resulted in a relatively low recall of 0.65 . Hence, despite the increased precision afforded by a cutoff of 0.4, it was determined that this cutoff too greatly reduced the achieved recall. We therefore additionally tested lower SoM probability cutoffs . ObserviIn order to rank the predicted metabolites for a particular molecule, we developed a priority score for each predicted metabolite based on the SoM probability of the atoms involved in the transformation and whether the reaction type is common or not. Specifically, the SoM probability calculated by FAME 2 for all atoms in the parent molecule that are involved in a reaction as defined by the SMIRKS is considered, and the maximum SoM probability among these atoms is then incorporated into the score, as illustrated in P is the maximum SoM probability out of the atoms in the parent molecule that were matched by the applied transformation and F is the factor according to whether the reaction type was designated as common or uncommon. In case the same predicted metabolite resulted from multiple transformations, the maximum priority score over all transformations leading to that prediction was used. A higher priority score is intended to indicate a higher likelihood of the prediction being true. For all uncommon reaction types, F = 1. The factor F for common reaction types affects the early enrichment of the predictions. Specifically, the early enrichment improves when common reaction types are given more weight in the score than uncommon reaction types, i.e. Fcommon > 1  for the comparison.GLORY's MaxEfficiency mode was designed to address the problem of low precision caused by a high number of putative false positive metabolite predictions. This general problem of an excess of predictions is well-documented for metabolite prediction tools Judson, . HoweverAs described previously, the MaxEfficiency mode uses a cutoff based on the SoM probabilities that FAME 2 predicts for each heavy atom in order to restrict the locations in the molecule at which the reaction rules are allowed to be applied. This SoM probability cutoff was set to 0.2 based on the analysis on the reference dataset; however, we also examine the effect of different SoM probability cutoffs using the high-quality test dataset in order to get a more complete picture of how much can be gained by a cutoff-based approach.As expected, using SoM predictions to confine the application of reaction rules to certain positions does involve a trade-off between precision and recall . Recall Unfortunately, as Based on these results, it appears that using FAME 2's predicted SoM probabilities as a hard cutoff for metabolite prediction may not be sufficient for many use cases. However, the SoM predictions are useful for more than just as a hard cutoff, namely to rank the predicted metabolites, as will be shown in the next section.Neither SyGMa nor BioTransformer uses regioselectivity prediction as a prefilter before applying reaction rules. The same is true of MaxCoverage mode, which only uses SoM prediction in order to score and rank the predicted metabolites. Hence, we compared SyGMa and BioTransformer to GLORY's MaxCoverage mode in terms of recall, precision, and ability to rank the predicted metabolites.A high recall is important for any use case of a metabolite structure predictor, but even more so for applications in which it is of utmost importance to not miss any physically existing metabolites, such as, for example, when attempting to identify metabolites based on MS data. GLORY's MaxCoverage mode performs well in terms of recall, with a recall of 0.83 compared to 0.74 and 0.72 for SyGMa and BioTransformer, respectively, across the entire test dataset . A closePrecision can be a useful metric for measuring how well a method is able to keep the number of putative false positive predictions under control. Precision was low across the board for metabolite prediction on the test dataset, with BioTransformer reaching the highest precision of the three tools at 0.17. SyGMa was close behind at 0.15, and GLORY's MaxCoverage mode lagged further behind at a precision of only 0.08 . Again bIn the case of the low precision observed for SyGMa, it is important to note that SyGMa's rule set is not specific to CYP-mediated metabolism but rather covers phase I metabolism in general. This could indicate that SyGMa might achieve higher precision if only the CYP-specific rules were used.BioTransformer's CYP450 prediction module, which has the highest precision of all three methods, uses isoform prediction as a preliminary filter. Only the relevant reactions for the predicted metabolizing CYP isoform(s) are applied to the parent molecule, which could contribute to the observed precision.Although the precision of MaxCoverage mode (as well as SyGMa and BioTransformer) was found to be low and high rates of false positive predictions are problematic in general, in the case of metabolite structure predictors a low precision is only problematic if there is no way to distinguish between the true positive and putative false positive predicted metabolites. This distinction can be achieved with a well-working ranking of the predicted metabolites, which circumvents the need to reduce the total number of predicted metabolites. Hence it is important that a metabolite prediction tool can rank the predicted metabolites in terms of likelihood of occurrence.GLORY scores its predicted metabolites based partly on the maximum SoM probability of all the atoms involved in the reaction and also takes the type of reaction into account (see above for a more detailed description of the priority score). SyGMa uses empirical probability scores calculated based on the percentage of all predictions for each reaction rule that are found in the training dataset. SyGMa's scoring system thereby relies entirely on the discontinued Metabolite dataset. The scores generated by GLORY or by SyGMa can be used to rank the predicted metabolites for a given parent compound in terms of their likelihood of occurring. The current version of BioTransformer, on the other hand, does not score or rank its predictions.We compared the ranking capability of GLORY's MaxCoverage mode with that of SyGMa. SyGMa was able to predict a known metabolite within the top three ranked positions for 69% of the parent molecules in the test dataset, whereas GLORY's MaxCoverage mode predicted a known metabolite within the top three predictions for 76% of the parent molecules .To look at the overall quality of the scoring as well as the ranking ability of SyGMa compared to GLORY, we generated ROC curves for each method using the score of each predicted metabolite as well as the rank of each predicted metabolite for a given molecule. The rank-based analysis corresponds better to the actual use case, in which it is desired to prioritize the predicted metabolites for a particular parent molecule, as opposed to over an entire dataset . Some transformations may result in multiple products. Products that contain fewer than three heavy atoms are not included in the set of predicted metabolites generated by GLORY.The transformations of parent molecules into predicted metabolites based on the defined SMIRKS strings were performed using Ambit-SMIRKS ) was downloaded from the website. In addition to the database in XML format, the structures of all of the molecules, both parents and metabolites, were downloaded in SD format from the website (drug group “All” for the parent molecules).The DrugBank database  was ignored because its SMILES had two components of which the main component could not be unambiguously identified. All available generations of metabolism reactions were considered, as long as the reaction was annotated as mediated by one or more CYP isozymes. The enzymes for the reactions listed in DrugBank do not have any apparent species information, so all were assumed to be human and thereby relevant for this dataset.For all CYP-mediated reactions, the reactant was considered to be the parent molecule and the product was considered to be a first-generation metabolite of that particular parent molecule. Any metabolite with the same InChI, ignoring stereochemistry information, as its parent molecule was removed from the set of metabolites for that parent molecule. Only those parent molecules with at least one valid metabolite were included in the final dataset.Finally, the six parent molecules that are also present in the manually curated test dataset were removed from the DrugBank dataset prior to any evaluation, along with their corresponding metabolism information. These parent compounds were bupropion, ticlopidine, imipramine, ifosfamide, bosentan, and olanzapine.After preprocessing, including removal of the overlap with the manually curated test dataset, the DrugBank dataset contained 364 parent molecules and 702 metabolites in total, with an average of 1.93 metabolites per parent molecule in the dataset.https://bitbucket.org/djoumbou/biotransformerjar/src/master/database/ ). As the only structural information provided in the MetXBioDB is in the form of InChIs and InChIKeys, any substrate or product without a reported InChI could not be considered. A lacking InChI was only the case for one out of 1468 CYP-mediated, human reactions in MetXBioDB.The human, CYP-mediated reactions were extracted from the MetXBioDB dataset  from the manually curated test dataset were also present in the MetXBioDB dataset, only two of which (metoclopramide and atomoxetine) were not also present in the DrugBank dataset. These parent compounds and the corresponding metabolism data were removed from the MetXBioDB dataset.After preprocessing, including removal of the overlap with the manually curated test dataset, the MetXBioDB dataset contained 743 parent molecules and 1385 metabolites in total, with an average of 1.86 metabolites per parent molecule in the dataset.The DrugBank dataset and the MetXBioDB dataset were combined to form the reference dataset via a straightforward consolidation of the parent and metabolite information. All molecule comparisons occurred using InChIs generated without stereochemistry information. For any parent molecule that was present in both the DrugBank and the MetXBioDB datasets, which was the case for 259 parent molecules, the sets of metabolites from both datasets were combined, disregarding stereochemistry, to yield the final set of metabolites for that parent molecule in the reference dataset.Xenobiotica and Drug Metabolism and Disposition. The time frame considered was from January 2014 to June 2018 for Xenobiotica and from January 2014 to June 2017 for Drug Metabolism and Disposition.A new dataset for testing GLORY was manually assembled from the scientific literature. The data were extracted from publications on metabolism that were found in two journals: Publications were chosen and the metabolism information they contain included in the dataset if the following criteria were fulfilled:The publication must contain a figure that depicts the metabolism scheme and includes the chemical structures of the parent compound and the first-generation metabolites.The metabolism data must have been experimentally determined from a human source . If some but not all of the data were from humans, any non-human metabolites in the metabolism scheme were excluded from the dataset.For at least 75% of all of the first-generation human metabolites depicted in the metabolism scheme (note that any metabolite that is depicted as merely being an intermediate is not considered), the following two criteria must be satisfied. First, the identity of the enzyme(s) responsible for the formation of the metabolite must be known. For this purpose, it is sufficient to know whether or not this metabolite is formed by CYPs. Second, the exact chemical structure, including the connectivity of all atoms, of the metabolite must be known. There is one exception to this rule: If the metabolite is known to not be CYP-formed, then the exact structure is not relevant and the metabolite is counted anyway.Based on these criteria, 29 metabolism schemes containing at least one human, CYP-formed first-generation metabolite with a fully defined structure were found and included in the dataset. For these 29 parent molecules, there are 81 metabolites in total that fulfill the criteria  for inclusion in the dataset. Note that only first-generation metabolites are included in the dataset. Note also that intermediates, as depicted in the metabolism scheme, are not included in the dataset. Instead, the first non-intermediate metabolite in the pathway is used.http://www.chemspider.com/ ). Consistency of stereochemistry information between parents and their metabolites was maintained.The SMILES for the metabolites were generated using ChemSpider .Predicted metabolites were compared to known metabolites from the reference and test datasets using their InChIs. The InChIs used for this comparison were generated without stereochemistry information using CDK , and RDKit . Only the phase I reaction rule set was used and one reaction cycle was applied.https://bitbucket.org/djoumbou/biotransformerjar/src/master/ ) command line tool. BioTransformer was run individually for each parent compound using single SMILES input.The BioTransformer predictions were performed using the CYP450 mode of the BioTransformer . One use case for the MaxCoverage mode could be, for example, identifying metabolites from mass spectrometry data.https://acm.zbh.uni-hamburg.de/glory/.Along with the collection of reaction rules, we provide a new, manually curated test dataset for free use as a benchmark dataset. In addition, GLORY is freely available as a web server at Importantly, the concept of GLORY is such that it can be extended to predict metabolites formed by enzymes not belonging to the CYP family. The enzymes that this approach can be expanded to is limited, in principle, only by the extent of the available data and the coverage of the relevant metabolic reactions by SoM prediction tools.https://bitbucket.org/djoumbou/biotransformerjar/ and https://www.drugbank.ca/.Publicly available datasets were analyzed in this study. This data can be found here: https://acm.zbh.uni-hamburg.de/glory/.The GLORY web service is available at the following address: CdBK and JK: conceptualization; CdBK, CS, and JK: methodology; CdBK, CS, MŠ, NK, and NJ: software development; CdBK: validation; JK and DS: resources; CdBK: data curation; all authors: writing—original draft preparation; CdBK: visualization; DS, NJ, and JK: supervision; JK: project administration; DS and JK: funding acquisition.NJ is a founder and co-owner of Ideaconsult Ltd. and has been the technical manager of the company since 2009. NK works for Ideaconsult Ltd. on a part-time basis. The remaining 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 use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel  This has stimulated the interest to build genome-scale networks, allowing to perform Saccharomyces cerevisiae . These ODEs contain initial values for metabolite concentrations, reaction rate equations and kinetic parameters. These representations were applied to model small-scale central metabolic pathways of well-known organisms, such as This framework, that tackles cell metabolism modeling tasks using a formulation based on the dynamics of metabolic processes, gives detailed and unique solutions in time for the transient and the equilibrium states, from any initial metabolite concentration condition. It is based on kinetic rate laws inferred from biochemical and mechanistic information, while the values of the final fluxes are obtained directly from the rate laws and the metabolite concentrations at equilibrium. However, this type of modeling requires considerable amounts of data that are not always available, such as kinetic parameters or total enzyme concentrations  and assumptions (objective functions) to be able to find an optimal solution  depend both on mathematical models and phenotype prediction methods. We will review phenotype prediction methods that use kinetic rate laws for certain metabolites, and these will be used to support CSOMs for ME purposes. Additionally, hybrid approaches that include the use of kinetic and stoichiometric information will also be revised. Figure Mathematical modeling is used to understand internal and external cell interactions, and how they affect cell metabolism. This is studied through the analysis and modeling of phenotypes, as metabolite concentrations and reaction fluxes over metabolic pathways, regulated by enzymes under different internal and external conditions. The idea is to translate processes to numerical problems with formal representations, aiming a high level of accuracy and detail, since the goal is to reach enough complexity and completeness on the description of the behavior of a metabolic network.The two types of approaches for mathematical representation of biological networks differ on the state at which the system is analyzed. Stationary modeling considers the system working at an equilibrium point, where metabolite concentrations are constant over time. On the other hand, dynamic modeling acknowledges the changes metabolite concentrations suffer over time. Both approaches for phenotype prediction will be discussed and compared in this section.A straightforward approach for the prediction of metabolic phenotypes is using CBM, in the stationary case, which yields predictions usually close to experimental observations, and more importantly, avoiding the hard task of estimating kinetic rate equations and kinetic parameters, while requiring minimal knowledge to infer systemic properties. The most widely used mathematical approach to find an optimal value for the objective function and a relevant flux distribution is Flux Balance Analysis (FBA)  to represent complex biochemical systems or processes have started to create a wide impact in the biotechnology industry for the design of novel cell factories  and environmental conditions   is a vector of dimension i containing the time-dependent state variables; r(·) is a vector with dimension j including: reaction rates that depend on the state variables, a vector with input variables u(t) and a set of kinetic parameters p; and g(·) is a vector to relate model outputs y(t) with x(t). For certain models, this representation is equipped with algebraic equations to complement the ODEs or with further differential equations that include volume changes, such as intracellular dilution for Equation (1) . The mechanisms in this formulation are often very complex and not well-known, and can include nutrients and inhibitors as limiting substances; these considerations and analytical derivation of laws are detailed in Bertolazzi  the activated and inhibited substrates (depending on time and certain parameters), λ is the maximum regulation rate, k is the activation or inhibition coefficient, and x is the substrate concentration. It is noticeable that if n = 1, the Hill function for activation is the same as the Michaelis-Menten equation .where Equation (3) represents an activation, Equation (4) represents an inhibition, In comparison with mechanistic expressions, there are some alternatives to describe the behavior of biological systems in a simpler way, using analytic functions as power series that always converge avoiding unstable systems. Also, a small amount of parameters needs to be determined, since most rate expressions are not known or are difficult to quantify, but a universal formula can be applied to the reaction kinetics , J is the flux for the corresponding reaction, ε is the elasticity coefficient (EC) that measures sensitivities of metabolite concentrations on the reaction rate, and N/N0 is the relative concentration of metabolites for metabolite k.where superscripts denote steady-state, Log-lin kinetics. Similar to lin-log, this formulation allows to get analytic solutions through linear and logarithmic expressions on reactants and effectors to approximate reaction rates  equations, which can be derived from corresponding deterministic partial differential equations for the kinetics of the probability distribution of the molecules . The formulation also includes a master differential equation that holds the time evolution of the state probabilities, which can be described by a non-trivial stochastic simulation algorithm  are illustrated in Figure The measurements obtained from experiments help to determine the values of individual parameters for kinetic rate expressions, initial conditions and outputs. These values can be found in data repositories that compile this information such as BRENDA, which gives a collection of enzyme and metabolic information , M data points in a vector p = (p1…pM) measured at times (t1…tM), θ as the set of parameters to be estimated, and an objective function Φ(θ) defined by the distance of the vector residuals , that usually is seen as the weighted sum of squares. The formulation of the objective function is described as follows:The classical approach for the objective function, when performing the estimation of parameters, consists in minimizing the difference between the model output and the experimental data   + εj, with which the observed output in Eq. (1) is modified to y(t) = g(x(t), u(t), θ) + ε. Assuming independent and normally distributed deviations, the likelihood Λ of observed data points p with the rest of variables defined in Equation (8) is:Furthermore, the parameter estimation can be seen as a geometrical problem, as stated previously, or also as a statistical formulation , which leads to rewrite the problem as the minimization of the negative logarithm of the likelihood function, that is equivalent to the geometrical formulation. Likelihood theory is equivalent to least squares theory, and it yields identical estimators of the structural parameters (except for the variance) for linear and nonlinear models when the error terms are assumed to be independent and normally distributed  to evaluate the local structural identifiability  when the mode of action is simple, not metabolic regulated or when no experimental data is available to evaluate the effect of parameters (Michaelis-Menten equations); (2) when different quantitative data is available, and enzymes have complex catalytic or regulatory mechanisms, but without any allosteric properties ; (3) when enzymes have allosteric properties ; and, (4) when reaction rates of enzymes cannot be expressed by a single equation, rather with a separated ODE system, considering effect of pH on enzyme activity, and using E. coli, introducing an ensemble modeling procedure to address the challenge of identifying kinetic parameter values and kinetic rate laws. The model integrates all the reactions used in previous discussed models . The values of parameters are estimated using a constrained evolutionary search method to be able to predict allosteric effectors and gene expressions. The values estimated are fixed for all the mutant cases, an improvement with respect to previous models that use different parameter values for each mutant. The dynamic model uses the structure of the batch or continuous culture based on mass balance equations in a system of ODEs, and a cell growth rate estimation connected to the flux of production of adenosine triphosphate  encompassing the biological objectives of the problem, and another where the bioengineering objective is tackled in the form of an optimization problem. The objective is to find the optimal set of genetic modifications applied to an organism to achieve a desirable goal  gene deletion, by canceling fluxes related to specific genes making their reaction rates equal to zero; (2) heterologous insertion, by adding new genes or pathways, and redirecting fluxes in the desired directions; (3) gene modulation, tuning the level of contribution of enzymes by over or under expressing the activity of their reaction rates; and (4) cofactor binding specificity modulation, by exchanging the cofactor specificities of certain reactions in a network. These tasks can also be used in combination with each other to find more complex ME strategies. The solutions are translated into modifications in the network applied to the flux constraints or kinetic parameters, for constraint-based and kinetics-based models, respectively. A complete description and discussion regarding application of CSOMs for constraint-based models, with a special classification as exact bilevel mixed-integer, metaheuristic and elementary-mode analysis-based programming methods, is provided in  and the degree of modulation to optimize a set of pre-defined performance metrics, subject to process constraints . These quantitative methods are able to describe, predict and optimize the behavior of a biological system, in comparison with the approach that CBM offers for pathway-oriented analysis that involves only prediction of the steady state fluxes.E. coli, by reprogramming the metabolic network and studying the transience of metabolism . This was possible by analyzing an optimal temporal flux profile of a manipulated reaction. They also examined growth inhibition due to variations in the end production, concluding that genetic alterations are critical from the standpoint of productivity  ϕkin, that contains the reactions for which kinetic information is known, such as enzyme activity, kinetic parameters and metabolite concentrations, representing them as a system of nonlinear ODEs.One outstanding effort to build CSO using hybrid models is k-OptForce  the problem is formulated similarly to the one described by the predecessor CSOM OptForce  the wild-type behavior is defined by using flux variability analysis mathematical modeling frameworks. Systems biology provides strategies for ME to take advantage of the best simulation and optimization method features, and to deal with the most remarkable limitation regarding the lack of available experimental information, which affects accuracy and feasibility of solutions (Machado et al., Moreover, current research trends point to the inclusion of parameter uncertainty to increase the level of flexibility, using kinetic models built with stochastic optimization methods, and also improvement of phenotype predictions by using complementary data, such as various types of omics data, particularly gene expressions (Jahan et al., E. coli or S. cerevisiae (Rocha et al., The revised dynamic modeling approaches are supported by the use of optimization methods for two main identified tasks. On one hand, we have the development of models for phenotype prediction, particularly for parameter estimation. This task requires model calibration through the minimization of differences between predicted and experimental values Banga, . On the Kinetic models can also be studied within hybrid models for phenotype prediction, which allows the integration of available kinetic relations with genome-scale constraint-based model formalisms. This idea results in more detailed descriptions of large-scale models, since the effect of metabolite concentrations and substrate-level enzyme regulation cannot be captured with stoichiometry-only metabolic models and analysis methods (Chowdhury et al., The mentioned CSOMs using hybrid models, such as the one used by Chowdhury and coworkers, attempt to identify a minimal set of interventions on enzymatic parameter changes and reaction flux changes, such that less rearrangements of the flux distribution are required, and concentration bounds are not violated (Chowdhury et al., We emphasize as one of the major outcomes from the revision of phenotype prediction and strain optimization methods, the fact that the selection of a specific dynamic modeling approach is always subject to the prospective application, as well as the amount and type of experimental data available. While and adequate path is difficult to define for all cases, Table Furthermore, stochastic approaches are able to capture variability in the described species, for very well-known structural features, such as stiff systems. The dynamics are simulated by knowing the probabilities of transitions between every possible state. However, the formulation can become a non-trivial stochastic simulation algorithm for networks when the number of reacting molecules is large per reactant, which is common for most of realistic kinetic models. The aim will be always to establish a trade-off between the size of the model and the level of accuracy of the solutions, depending on the type of experimental data, which is not always available in the literature. Finally, efforts for enforcing the inclusion of experimental data as Supplementary Material when publishing new findings should be taken by publishers, a measure that would greatly improve the development of these type of methods.a priori provided with the quantitative description and as many as possible regulatory switches in response to genetic or environmental perturbations. The quality of mechanistic information enables a detailed description of metabolism such as dynamics, enzyme activities, and metabolite concentrations but can result in erroneous predictions since some modeling assumptions can be missing or incorrect. Nevertheless, by studying failure modes of kinetic models, valuable information can be uncovered for restoring prediction consistency for new phenotypes (Khodayari et al., Also, studying kinetic models, especially used within hybrid models, has revealed strengths and limitations of model-driven strain design, and indicated that kinetic models have the potential to substantially over-perform FBA-based predictions when parameterized under similar conditions, but may perform worse than FBA when predicting a significantly different metabolic phenotype. Studies have also demonstrated the need to perform model parameterization for a diverse range of genetic or environmental perturbations, and the tight integration of transcriptional level along with substrate-level regulatory interactions. At a fundamental level, kinetic models must be Additional studies, not covered in detail in this review, point to the use of artificial intelligence as a different approach to analyze mathematical models for ME purposes. Novel technologies applied to metabolomics can substantially improve search algorithms to increase the dynamic range, number of carbon-carrying metabolites and possible pathways to transform a given source metabolite into a given target metabolite Kell, . One exaE. coli and S. cerevisiae. Thus, the advancement of the field will always be dependent on incremental investments in fundamental research geared toward the deeper understanding of biological mechanisms, including local phenomena. For this purpose, the use of hybrid models as flexible vessels for the cumulative inclusion of knowledge will be of major importance in the coming years.In this review, we analyzed the main mathematical formalisms used for dynamic/hybrid modeling of microbial metabolism, scrutinized their inclusion into strain optimization applications and made a critical evaluation of future steps in this research topic. The ladder toward more realistic strain engineering strategies seems to be undeniably limited by more detailed representation of the dynamics of the biological systems. However, as was thoroughly discussed in this work, these are still hindered by the lack of appropriate parameters even for the most studied organisms such as All authors participated in the preparation of this work. OK had a major role in writing and editing the manuscript. All authors contributed to manuscript revision, read 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.


Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods.Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype.The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.The online version of this article (10.1186/s12864-019-5979-4) contains supplementary material, which is available to authorized users. Cancer is influenced by genetic and environmental factors and is one of the leading causes of death worldwide . The mulGenome-scale metabolic models (GSMM) have emerged as a potential tool to decipher the molecular mechanisms underlying cancer metabolism associated with tumor progression and malignancy acquisition . GSMMs aGPRs enable the integration of gene or protein expression data from different high-throughput techniques which enhances the predictive capabilities of GSMM-based analysis. A large number of computational approaches have been developed to integrate expression data through GPRs into constraint-based methods . Howeverp < 0,01), among which only 1,6% are metabolic genes , in DU145 prostate cancer (PC) cells. This endocrine disruptor can be found at low concentrations in the environment . While mIn the current study, we evaluated the improvements in transcriptomics-based model-driven analysis, when the stoichiometry is incorporated to the classical GPR formulation (S-GPR), to study the metabolism of Aldrin-exposed and non-exposed DU145 PC cells. It was achieved by integrating the transcriptomic data through GPR and S-GPR rules into one of the latest human GSMM: HMR2 . It was Significant improvements in the predictive capabilities of the GSMM have been observed when S-GPRs were implemented, correcting some wrong predictions provided by GPR-based approaches.Furthermore, all the analyses performed in this study revealed important alterations in key metabolic pathways associated to the chronic exposure to Aldrin. Thus, prostaglandin biosynthesis was predicted to be over-activated in Aldrin-exposed cells, which is associated with malignancy acquisition , 14. CreThe approach presented here represents an important step forward in the state-of-the-art of the current transcriptomic-based model-driven methods and opens new avenues in the study of the molecular mechanisms underlying multi-factorial diseases in complex scenarios such as the case study presented here, with potential clinical and environmental applications.The computational analysis performed in this work was based in one of the latest reconstruction of human metabolism (HMR2) , with 13The gene-to-reaction associations included in the HMR2 were automatically build by using an in-house developed algorithm that gathers information retrieved from a number of databases to generate both GPRs and S-GPRs  and by Recon 2 (which incorporates GPRs) . To thisp-value. The parameters and the procedure used to evaluate the chosen thresholds allowed us to determine which threshold was better to discriminate between active and inactive reactions taking into account the transcriptomic data sets. From these analyses, a set of fluxes were obtained from which the exchange of the different species could be inferred. Specifically, we analyzed the activity state of the exchange reactions for the species experimentally measured  and qualitatively compared with the experimental measurements  to determine the reliability of the model predictions. The exchanges predicted from each method were qualitatively compared with experimental measurements of metabolic and lipidomic data and its significance was determined by Fisher’s Exact test using α = 0.05  enable us to consider the copy number needed to generate the active catalytic unit of an enzyme. In order to evaluate the improvement in GSMMs at predicting metabolism when S-GPRs are implemented, different methods for integrating transcriptomic data were used on HMR2 model incorporating either GPRs or S-GPRs: Gimme, iMat, Gonçalves et al. 2012 and MADE  see Met and 9. TIn all the tested methods, the S-GPR implementation corrects some metabolic uptakes/secretions predicted by classical GPR-based analyses that were not supported by experimental measurements Table . For insFrom each of the four transcriptomics-based model-driven analyses performed in this study, active and inactive reactions were predicted. Based on these results we determined which pathways were over-activated in one condition compared to the other. Pathways showing a significantly higher number of active reactions in Aldrin-exposed analysis compared to non-exposed were tracked down as indicated in the Methods section. Nine overactive pathways were identified in at least three of the four methods, including two predicted to be over-activated in Aldrin-exposed cells by all four analyses: Prostaglandin Pathway and Carnitine Shuttle. In Fig. High-throughput technologies have transformed molecular biology into a data-rich discipline. In order to extract knowledge from this large amount of data, a number of approaches have been developed. Among these methods, those integrating transcriptomic data into GSMM have emerged as potent tools, since they enable us to determine molecular processes involved in a specific case from one of the most widely used and cost-effective high-throughput techniques. However, these approaches present important limitations when trying to deal with complex scenarios as the case studied here.The present work aimed to improve the predictive capabilities of the current transcriptomic-based model-driven approaches by incorporating stoichiometry to the GPR rules embedded in GSMM. For illustration, we have studied the effects of the chronic exposure to Aldrin on DU145 PC cells metabolism. The multi-factorial nature of cancer disease together with the complexity of this study, which goes beyond the classical dose-effect studies, increases the difficulty of determining metabolic changes associated with an increase of tumor malignancy in Aldrin-exposed cells. While the human genome microarray platform HG U-219 used for transcriptomics analysis covers more than 36,000 transcripts and variants , comparing gene expression data obtained from Aldrin-exposed and non-exposed cells identified only 0.35% of difference between conditions. In such complex scenarios, the implementation of S-GPRs has improved model performance at predicting metabolite and lipids exchange in all the methods tested (from 1.34% up to 6.53%). This improvement has been accompanied by changes in the overall activity state of the metabolic network  . This novel approach has been tested with different methods to integrate transcriptomic data into GSMM reconstruction analyses with the same result: S-GPR incorporation improves model analysis performance. In addition, this novel concept has been tested on a very complex scenario involving a multifactorial disease, activation of secondary metabolism triggered by very low concentrations of pollutant and two highly isogenic populations which make more relevant the improvements provided by S-GPR based analyses. Despite it does not exist a lineal relationship between individual gene expression and specific metabolic flux, the incorporation of stoichiometry into the gene-to-reaction formulations has enhanced the predictive capabilities of the transcriptome-based model-driven approaches used in this study at evaluating the effects of the chronic exposure to Aldrin in DU145 PC cells. The approach presented here has the potential to be extrapolated to the study of other cancer types like the NCI-60 cancer cell lines of which consumption/release metabolic profiles and gene expression can be retrieved from the CORE database . Thus, tNon-targeted metabolomic data, lipidomic data and cDNA samples for transcriptomics analysis were obtained from a previous study  which ta-frozen cells were extracted using cold methanol/chloroform (90:10). After vortexing, mixtures were centrifuged 5 min at 16000 g. The supernatants were evaporated and further dissolved in 150 μl of LC–MS mobile phase [Metabolomic and lipidomic data analysis: In this work, lipid and metabolite measurements from non-targeted LC–MS analysis at time 0 and after 5 h of incubation were used. Lipidomic data was generated by following the same protocol of lipid extraction and analysis as Bedia et al., 2015. Metabolomic data was obtained as follows: metabolites from snaple phase . The LC–The resulting chromatogram matrix was compressed by applying regio-of-interest (ROI) strategy  togetherNext, the relevant molecular masses and retention times of the lipids and metabolites annotated in the model were identified by using both home-made and external on-line databases , after 50 days of exposure to sub-lethal concentration of Aldrin that did not affect the proliferation rate of tumoral cells. Microarray data was normalized by using RMA method  Additio  genome-scale model . GSMMs aEnabling GSMM for lipidomic and metabolic data integration: The model was modified to account for the consumption/production of the lipids and metabolites of interest. Important alterations in the lipidomic and metabolomic profile of DU145 cells have been associated to the chronic exposure to Aldrin . Thus, tReduction and Refinement: To eliminate those reactions in the model that cannot carry a flux different to zero (blocked reactions)  a reductIn this study we used one of the latest reconstructions of human metabolism , which dFlux Balance Analysis (FBA)  is one oIncorporating stoichiometry into gene-to-reaction associations: GPR and S-GPR design: GPR rules enable the association between gene expression levels and the activity state of the biochemical reactions in a GSMM. In brief, genes annotated in the gene-to-reaction associations are replaced by their corresponding expression levels (absolute or relative to a control depending on the used method). Next, the logical “OR”, representing different isoenzymes, is replaced by either “mean” or “maximum” operators, which varies depending on the approach, while the logical “AND”, representing an enzymatic complex, is replaced by the operator “minimum”. Here, we introduce a new state-of-the-art GPR: S-GPR, which take into account the number of transcript copies required to generate a fully functional protein that catalyzes a given reaction Fig. . Thus, fTranscriptomic data integration through GPR and S-GPR associations: The integration of transcriptomic data into model-driven methods is based on the inference of metabolic reactions values by using the expression of the associated genes. Next, the reaction values are used to determine the activity state of a reconstructed metabolic network in a specific case/tissue/organism. The way in which transcriptomic data is propagated from genes to reactions and how reaction values are used to perform a metabolic network reconstruction analysis vary among the different integration methods. In the case of GPRs, the normalized gene expression is integrated with no previous modification, whereas in S-GPR, prior to the integration, the expression of each gene is divided by the number of needed copies described in the corresponding gene-to-reaction association. To determine the improvement at integrating stoichiometry into the classical GPR (S-GPR), transcriptomic data from Aldrin-exposed and non-exposed was integrated into a GSMM reconstruction analysis. To this aim four different algorithms were applied, each of them being representative of one of the main four approaches for integrating transcriptomic data into a constraint-based method (CBM) analysis Fig. .Gimme algorithm  integratiMat method  assigns The method proposed by Gonçalves et al. 2012 , uses rep-value. Next, the algorithm finds a solution that is consistent with maximum number of relative discrete levels while is constrained by the stoichiometry and thermodynamics imposed by the model. In other words, if a given reaction is up-regulated in Aldrin-exposed cell the algorithm will be penalized if provides a solution in which the reaction is active in control and inactive in Aldrin-treated cells . In order to assess the reliability of the model predictions and to determine the improvement associated to the use of S-GPR, a qualitative comparison is performed between the predicted metabolic consumption/productions and the metabolomic and lipidomic experimental measurements. A Fisher exact test is applied for this purpose  Fig. 1 .Metabolic pathway analysis to unveil the metabolic alterations associated to the chronic exposure to endocrine disruptor in Prostate Cancer.The phenotype differences associated to the chronic exposure to Aldrin in DU145 cells should be reflected in a different activity state of the metabolic reactions between Aldrin-exposed and non-exposed cells. Thus, these reactions are of interest to understand the metabolic reprogramming underlying the enhancing of malignancy associated to the chronic exposure to ED in PC. To this aim, a pathway analysis is performed based on the reactions that are consistently predicted to have different activity state between control (or non-exposed cells) and Aldrin-exposed cells with all the methods used in this work. More specifically, each metabolic reaction is associated to a given metabolic pathway in the GSMM, which enables to determine whether a particular pathway is over or under-active between conditions using the number of active reactions in each condition. To this aim, the following procedure was used: All the reactions which were stated as differentially active in both experimental conditions from the results of most of the different transcriptomics-based model-driven analysis were selected. Next, all pathways from these reactions were tracked down to have a list of the most active pathways (pathways with more active reactions). This analysis was performed in each integration analysis for both experimental conditions. The significance was determined by a t-test analysis. Finally, a bibliographic research was performed in order to find evidences that support these findings.Additional file 1:Metabolomic and Fluxomic experimental data. Metabolomic data and ROI analysis result from Aldrin-exposed and non-exposed DU145 cells at time 0 and after 5 h. (XLSX 1253 kb)Additional file 2:Mapping metabolomic and lipidomics on GSMMs: Metabolomic and Lipidomic data mapped on HMR2 and Recon2 models. (XLSX 296 kb)Additional file 3:Transcriptomic data. Transcriptomic data of Aldrin-exposed and non-exposed DU149 PC cells and gene expression analysis. (XLSX 4835 kb)Additional file 4:GSMM readjustments/refinement process. This file includes; i) Definition of Metabolites included in the biomass reaction, ii) Required Metabolic Functionalities (RMF) necessary to apply GIMME algorithm, iii) Experimentally measured metabolites annotated in HMR2 and iv) automatically generated GPR and S-GPR of each catalytic reaction in HMR2 . (ZIP 2623 kb)Additional file 6:GSMMs: Genome-scale metabolic models used in the analysis. (ZIP 2743 kb)Additional file 7:Comparison of the reliability of model predictions between HMR2 + algorithm-based GPRs and Recon2. (XLSX 501 kb)Additional file 8:Results of the analyses comparing predictive capabilities of GPR-based and S-GPR-based approaches using iMat, Gimme, Gonçalves et al. 2012 and MADE algorithms. (XLSX 309 kb)Additional file 9Supplementary methods and further biological interpretation. (DOCX 15 kb)


Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph,  The methanol is converted to formaldehyde, via the ribulose monophosphate (RuMP) in type I methanotrophs, or serine or Calvin, Benson, Bassham cycle (CBB) cycle in type II methanotrophs . Methylotrations . All theMethylomicrobium buryatense strain 5GB1 is available  is a collection of most of the annotated metabolic reactions in form of a model which makes it possible to simulate cellular metabolic behavior under different conditions. The GSMM are reconstructed and analyzed by flux balance analysis (FBA) using tools such as Constraint-Based Reconstruction and Analysis (COBRA) Toolbox . At presvailable . For Mcaapproach  and was Methylococcus capsulatus Bath (Mcap) was carried out using its annotated genome sequence obtained from NCBI. The complete reaction network was built using primary and review literature, biochemical databases such as KEGG (http://www.genome.jp/kegg/) and MetaCyc (http://metacyc.org/), and automated GSMM pipelines such as ModelSEED , references, and corresponding coefficients in biomass equation are summarized in tus Bath . The aminse 5GB1 . As litense 5GB1 , Methyloilum 20Z , Methyloethanica , Methylouens AM1  or Eschehia coli , in propnse 5GB1  , n is the number of reactions (columns), and each element as the coefficient of a metabolite in a reaction. A negative coefficient corresponds to consumption or intake of a metabolite, whereas a positive coefficient corresponds to its production. This stoichiometric matrix (S) is a linear transformation of the flux vector X) with respect to time A network can be converted into a mathematical form that can be read by software like COBRA toolbox. This mathematical representation is a matrix of order Where, With the psuedo-steady state assumption, this differential equation changes to a linear equation of the form −1 . In this work, we separately minimized and maximized flux through each reaction while keeping the growth rate constant at 0.37 hwth rate ) and the−6) growth rate. Essential reactions for Mcap in different conditions like optimal, oxygen stress, different nitrogen sources, and under deleted Embden–Meyerhof–Parnas (EMP) cycle were determined using these deletion studies. In addition to a genome-scale single reaction deletion study, a genome-scale double-deletion study was also performed using the doubleGeneDeletion function of the COBRA Toolbox.In silico single and double gene knockout studies were performed on the model. For the single gene deletion studies, each reaction was deleted one at a time by constraining the flux bounds to zero, and the metabolic network was tested for growth using FBA. A reaction was defined as essential if the mutant showed  and cellular maintenance. Depending on the growth rate of cells, growth associated ATP maintenance could vary, hence in this study, two different conditions were tested (i) ATP maintenance value of 23 mmol/gDCW , and wa(E. coli ). To acc(E. coli . The ATP−1) unless mentioned otherwise in the result section. Similarly, in the case of different nitrogen sources, the flux value for the uptake of the default nitrogen source, that is, nitrate, was set to zero and the lower bound of the reaction flux for the respective nitrogen source was set equal to the uptake rate of nitrate in optimal growth conditions (0.37 h−1).To evaluate the metabolic versatility of Mcap, different nitrogen and carbon sources were tested and the growth rate was observed. For testing different carbon sources, the methane uptake was set to zero and the flux for the respective carbon source was set to 28.10 mmol/gDCW/h  to 1.0 for the sampling to be used for “identifyCorrelSets.”Correlated sets (Co-sets) are the sets of reactions whose fluxes remains functionally correlated, that is, remain correlated in all possible states of the network under a condition. The Co-sets for Mcap model were calculated by the COBRA Toolbox function “identifyCorrelSets.” We used uniform random sampling using the Artificial Centering Hit-and-Run sampler while keeping the maximum correlation (Methylococcus capsulatus Bath (Mcap) “iMC535” was carried out based on its annotated genome sequence, available literature, information from biochemical databases such as KEGG and BRENDA, and automated GSMM pipelines such as ModelSEED and BioModels (see Methods section for more details). The final constructed model contained 899 reactions corresponding to 535 genes with 865 metabolites distributed over two different cellular compartments: cytoplasm and periplasm  (−1 (similar to Mb5GB1), and the oxygen uptake at this growth was 23.26 mmol/gDCW/h. The methane uptake rate of 28.10 mmol/gDCW/h was predicted at the optimal growth rate of 0.37 h−1, and was used for all the downstream analysis. Therefore, the methane intake rates of Mcap are expected to be higher than that for Methylomicrobium buryatense 5GB1. As the FBA maximized the biomass yield on methane, in reality the biomass yield is expected to be lower. That is, the methane intake rate is very likely higher than that suggested by FBA. The model-calculated ratio of uptake values of oxygen and methane corroborates with the available experimental values which suggests that almost an equal or a slightly higher amount of oxygen is required as compared to methane for the optimum growth of Mcap  , the gro of Mcap .−1 and 0.37 h−1 were observed in the case of pMMO knockout (low copper) and sMMO knockout (high copper) simulated conditions, respectively , Tetrahydrofolate (THF)-linked carbon transfer pathways, RuMP cycle, or Pentose phosphate pathway (PPP). In Mcap, formaldehyde is oxidized to CO pathway . Mcap ha pathway . All the pathway . ED path pathway . The mod pathway .−1), our model showed flux through ED and EMP pathways at a similar ratio of 3.4:1 without any external constraints, and hence corroborates very well with the available literature evidence. Additionally, in the robustness analysis, it was observed that the ED:EMP ratio can go up to a maximum of 10.9:1 for the optimal growth of 0.37 h−1. All these observations are consistent with the biochemical studies of gammaproteobacteria, which point toward the role of ED pathway as a major route for C-1 assimilation even though the energy requirement of this pathway is greater  and EMP pathway  has been reported as 3.3:1 . With me greater . The fluiMC535, it was observed that only one reaction of this cycle was essential for the growth of Mcap, and had a role in glycerate synthesis and purine metabolism. These observations of flux distribution are consistent with the fact that Methylococcus capsulatus bath is type I methanotroph and a member of the gammaproteobacteria family which utilizes RuMP pathway instead of the serine pathway for formaldehyde assimilation.An intriguing property which makes this bacterium unique is the presence of genes of both RuMP cycle and serine cycle. While the former plays a major role in biomass production, the role of the serine cycle is still unclear. It has also been hypothesized in earlier studies that serine cycle cannot operate as the major assimilatory pathway in Mcap unless glyoxylate, which is depleted during the cycle, is regenerated. However, no genes were identified in the Mcap genome which can convert acetyl-CoA to glyoxylate . In our 2, were tested on iMC535. The model showed growth on reported downstream oxidation products of methane such as methanol and formaldehyde  values are not known for Mcap. Therefore, we tested the response of the model to different mATP values. At low growth-associated maintenance ATP demand (GAM) value of 23 mmol/gDCW, the growth rate and the CCE were higher while reduced O network .iMC535 was tested on different nitrogen sources such as nitrate, nitrite, and ammonia. It was observed that using ammonia as the nitrogen source, the flux through biomass was higher as compared to nitrate/nitrite, and the CCE of Mcap was also increased  genes was predicted to be lethal (biomass flux less than 10−6)  ; Fig. 4.e lethal . The gene lethal .−4 and 10−1 mmol/gDCW/h. A total of 158 enzymatic reactions with zero flux could potentially carry flux in an alternate optimal solution space. On the other hand, 269 reactions had fixed flux (less than 10−4 mmol/gDCW/h variation) at optimal growth value and represented 70.23% of the active network. High variability was observed for reactions related to glucose, fructose, respective phosphates and bisphosphates because alpha-D, beta-D, and D compounds of above-mentioned species were considered as different entities. Glycolysis, EMP, EDD, serine cycle, fatty acid synthesis, etc., showed high variability due to alternate reactions or presence of the above entities.In a metabolic network, optimal flux through objective function can be achieved through a number of alternative flux distributions if the alternate routes for the same function are available in the bacterium. Such variability in the solution space can be explored using FVA, which computes the range of allowed flux values for each reaction while keeping the optimum value of growth rate. This analysis revealed that 99 out of 383 active enzymatic reactions could show more than 0.1 mmol/gDCW/h flux change without affecting the growth rate. Other 15 enzymatic reactions showed variability between 10iMC535 model are shown in  to the central metabolism. Top 35 highly connected metabolites out of 827 unique metabolites in the shown in . Rankingshown in . As appashown in .In a metabolic model, reactions with linearly correlated fluxes form the correlated reaction set (Co-set) . EarlieriMC535 could not simulate growth in the absence of ED pathway. It is because, 6-phospho-D-gluconate, an intermediate of ED pathway, is crucial for the growth of Mcap, and the gene (gluconokinase) for its alternate source was absent in the Mcap genome. Similarly, the analysis identified that NH3 could be a better N-source than NO3− and that Mcap could grow on amino acids.The model analysis revealed several interesting features about this organism. iMC535 model were also compared with iMb5GB1 (Methylomicrobium buryatense strain 5GB1) model, which is the only available model for a methanotroph. The iMb5GB1 network is available in two formats: a network of 841 reactions that cannot be simulated, and a smaller COBRA toolbox-compatible model containing 402 reactions . Hence, the latter was used for this analysis. The number of reactions in the Mcap model (898 reactions) is more than twice of the working (that can be simulated) 5GB1 model (402 reactions). The models were compared using pathways and the number of reactions in 12 different subsystems  genes were found to be essential in the 5GB1 model, only 30% (161 out of the 535) genes were found essential in the Mcap model.The properties of bsystems . The reaM. capsulatus str. Bath according to KEGG database and therefore, this reaction was not included in the model. In our model ubiquinone-8 has been used as the general electron acceptor as has been done in some prior GSMMs  was not identified in the genome sequence of or GSMMs , in addiSolanum lycopersicum L.) uses Non-growth associated ATP maintenance value of Arabidopsis model, and metabolic model of Thermus thermophilius uses growth-associated maintenance ATP demand (GAM) value of E. coli. (iMC535 to predict cellular phenotypes. We anticipate that iMC535 model will serve as a framework for improved metabolic reconstructions, and as an analysis platform for the engineering of methanotrophs for methane mitigation and industrial applications.Although the model developed in this study utilizes some parameters from the available published metabolic models, which might add some bias in the results. However, it is not uncommon for models to use information from closely-related organisms, if such information is not available for the target organism, and nonetheless makes it possible to conduct some initial qualitative analyses at the genome scale. For example, the metabolic model of Tomato (E. coli. . The avaMethylococcus capsulatus is one of the two important experimental model systems to understand the biochemistry of methanotrophy. Thus, the first genome-scale metabolic reconstruction of Methylococcus capsulatus str. Bath, and constraint-based analysis of its metabolic network are the major steps in understanding the potential capabilities of these obligate methanotrophs and understanding methanotrophy in general. Improvements in the carbon assimilation pathways and channeling the metabolic flux of these pathways will be desired to fine tune its metabolic capabilities to various products of interest. Hence, the GSMM constructed in this study would play a pivotal role in designing and accelerating the metabolic engineering approaches to harness the potential of this methanotroph.10.7717/peerj.6685/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.6685/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.6685/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.6685/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.6685/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.6685/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.6685/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.6685/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.6685/supp-9Supplemental Information 9Click here for additional data file.10.7717/peerj.6685/supp-10Supplemental Information 10Click here for additional data file.


Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440T has been built despite the role of Salinispora strains in drug discovery.There is little published regarding metabolism of Salinispora tropica CNB-440T, and GSMs were constructed for two sequenced type strains covering the three-identified species. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains and a few non-conserved genes associated with essential reactions. The models predicted no auxotrophies for essential amino acids, which was corroborated experimentally using a defined minimal medium (DMM). Experimental observations suggest possible sulfur accumulation. The Core metabolic content shows that the biosynthesis of specialised metabolites is the less conserved subsystem. Sets of reactions were analyzed to explore the differences between the reconstructions. Unique reactions associated to each GSM were mainly due to genome sequence data except for the ST-CNB440 reconstruction. In this case, additional reactions were added from experimental evidence. This reveals that by reaction content the ST-CNB440 model is different from the other species models. The differences identified in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species in DMM. Furthermore, models were used to evaluate in silico single gene knockouts under DMM and complex medium. Cluster analysis of these results shows that ST-CNB440, and SP-CNR114 models are more similar when considering predicted essential genes.Here, we updated, and expanded the scope of the model of Salinispora species, and a core model representing the conserved metabolic capabilities of Salinispora was constructed. Models will allow in silico metabolism studies of Salinispora strains, and help researchers to guide and increase the production of specialised metabolites. Also, models can be used as templates to build GSMs models of closely related organisms with high biotechnology potential.Models were built for each of the three currently identified The online version of this article (10.1186/s12918-019-0683-1) contains supplementary material, which is available to authorized users. Actinomycetes are a well known source of specialised metabolites including many that possess pharmacological properties such as antibiotics and anticancer agents. The marine actinomycete genus Salinispora is no exception [Salinispora strains are heterotrophic, obligately aerobic, Gram-positive bacteria belonging to the family Micromonosporaceae [S. tropica, S. arenicola, and S. pacifica [Salinispora strains are widely distributed in tropical and subtropical marine environments with a distinct geographical pattern [S. tropica and S. arenicola which could represent ecotype defining traits [S. pacifica [Salinispora strains are of particular interest because of their role as specialised metabolite producers [S. pacifica, the anticancer agent Salinosporamide A produced by S. tropica and sioxanthin responsable for the characteristic orange pigmentation of colonies [Salinispora species [Salinispora species. Also, the identification of unique nutrient sources can aid the development of fermentation methods to increase the production of specific metabolites, and by optimizing fermentation conditions, the expression of dormant loci could be triggered.xception . Salinisporaceae . Membersporaceae . Currentpacifica , 5. Sali pattern –8. Co-oc pattern . Based og traits , 11. Howroducers –14. Speccolonies –17. Althcolonies –20, ther species , 22. GroGenome-scale metabolic reconstructions enable determination of functional capabilities of an organism , 24. TheSalinispora tropica strain CNB-440T was constructed [iCC908 model was based on physiological and biochemical information of primary and specialised metabolism pathways. The reconstruction enabled characterization of the metabolic capabilities for understanding and modeling the cellular physiology of this actinobacterium through integration and validation of experimental observations. The validation gave some insight about the missing context-specific information. Moreover, this model provides a starting point to produce Genome-scale models (GSMs) of closely related organisms such as other Salinispora species [Salinispora, this study is focused on new insights into the metabolism of the three-identified species using constraints-based modeling. Here, we updated the model of Salinispora tropica strain CNB-440T (from iCC908 to iCC926) where new genes and novel reactions have been added as more information about the genome and its annotation has become available [S. arenicola, and S. pacifica, strains CNH643T and CNR-114T, respectively. These are the first manually curated genome-scale metabolic reconstructions for these Salinispora strains. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains. Therefore, the GSMs cover the three-identified species and the core metabolic capabilities among Salinispora strains. Condition-specific models were used to predict and analyze metabolic capabilities of the three species under a minimal growth-supporting environment. Also, functional differences between the developed metabolic networks were identified to have a glimpse into the unique metabolic differences attributable to each Salinispora species.Few manually curated actinomycete reconstructions are currently available despite their important role in drug discovery. Recently, the first manually curated genome-scale metabolic model for structed . The iCC species –32. In cvailable . GSMs weSalinispora were tested for their ability to grow under a minimal growth-supporting environment as part of this study. S. tropica strain CNB-440T was kindly provided by Dr. Paul Jensen . S. arenicola strain CNH-643T, and S. pacifica strains CNH-732, CNS-143, CNY-231, and CNS-055 were kindly provided by Dr. Michael Goodfellow . The strains were maintained on medium M1 [v/v) at − 80 °C. M1 composition per liter of salt formulation: 10 g of starch (Sigma), 2 g of peptone (Sigma), and 4 g of yeast extract (Sigma). Medium M1 is also referred to as A1 [Six strains of to as A1 , 34. In Salinispora tropica CNB-440T was used as positive control to determine growth in DMM. DMM was modified to remove the sulfur source to determine if it supported growth, and if sulfur was accumulated as observed previously for S. tropica CNB-440T. Negative controls were done to discard toxicity problems. All experiments were done in duplicate. DMM formulation has been previously described in detail by Contador et al. (2015).A defined minimum medium (DMM) based on salt formulation SF2  and mari230) – (75.8 x A260).A protein extraction protocol was selected to determine the growth of the cultures over time . Ultra-vSalinispora species, the gene sequence from the metabolic model for Salinispora tropica CNB-440T was used to identify orthologs. The InParanoid algorithm version 8.0 [Salinispora strains with high quality draft genomes together with the type strains were selected to build an ortholog table. The ortholog table, and a heatmap of InParanoid scores are available in Additional file To create metabolic reconstructions to represent each sion 8.0  was selesion 8.0 . We studiCC926 were added to the new models according to the ortholog table or if no genes were associated to the reaction. Reactions associated to missing orthologs were not considered. Gene-protein-reaction (GPR) associations were updated for each reaction based on the ortholog table. GPR associations provide information about which gene encodes which proteins and links the functional proteins to one or more enzymatic reaction. Additional reaction, and gene content was added from KBase [S. arenicola CNH-643T and S. pacifica CNR-114T against S. arenicola CNS-205 were identified comparing two proteome from KBase’s methods [S. arenicola CNS-205, and Salinispora tropica CNB-440T strains. In addition, reactions from the KBase automatic model generator were evaluated and added to the reconstructions. All draft reconstructions were manually inspected, curated and validated to include essential biomass components, specialised metabolic pathways, and to permit computation of steady-state properties at a broad range of growth conditions such as minimal media. A well-established reconstruction protocol and curation tutorial was followed through the process [iCC908 was used to standardize metabolites and reaction identifiers and annotations. A list of the reactions and metabolites included in each reconstruction with corresponding references, associated genes, E.C. numbers and notes is available in Additional file Reactions from om KBase , and KEGom KBase . For tha methods . A minimPhylogenetic estimation was carried out using the amino acid sequences of the proteins encoded by the core genes. Protein sequences were aligned at the amino acid level using MUSCLE . Then, tiCC926 was updated based on the macromolecular composition of each species to reflect specific physiology [− 1 [Biomass equations are critical components to predict growth rates. The biomass reaction from ysiology , 5. Esseysiology . The bioysiology . The bioogy [− 1 .2+, Cl−, Co2+, Cu2+, Fe2+, H+, H2O, Mg2+, Mn2+, K+, Na+, Mo+ 7 and Zn2+. The lower bounds of the respective exchange reactions were set to − 100 mmol gDw− 1 h− 1. The default carbon, nitrogen, phosphorous and sulfur sources were glucose, ammonium, phosphate, and sulphate. Default lower bounds were set to − 20 mmol gDw− 1 h− 1. To identify growth-supporting nutrients, the default carbon, nitrogen, phosphorous, and sulfur sources were removed one at a time by setting their lower bound to 0 mmol gDw− 1 h− 1. FBA was used to determine growth rates for each condition one at the time. Aerobic conditions were simulated by limiting the oxygen uptake rate to 20 mmol gDw− 1 h− 1 [Growth rates and metabolite production were predicted using Flux balance analysis (FBA)  from COB− 1 h− 1 . Viabili− 1 h− 1 .Salinispora strains, salinosporamide and sporolide by S. tropica, and lomaitivicin A by S. pacifica, growth rate was maximized and then used as an additional constraint to maximize the production of the specialised metabolites since they are produced alongside biomass. For simulation of in silico single gene knockouts, constraints were added by setting the bounds of the corresponding reaction(s), as defined by the GPR association(s), to 0 mmol gDw− 1 h− 1. The function analyzeSingleGeneDeletion [− 1 h− 1 [For production of sioxanthin by Deletion  implemen− 1 h− 1 . This re− 1 h− 1 . Uptake To visualize relationships between the GSMs, binary matrices were generated to indicate: (a) whether a metabolic reaction is present or absent in a particular model, and (b) whether a gene is essential or not under a given growth environment. UpSet R package  was usedSalinispora models under DMM growth conditions and models were only able to produce sioxanthin as specialized metabolite since minimal conditions were evaluated. Models without blocked reactions were used as inputs. A threshold was set to 10e-6 for defining the active reaction set of each model. Reactions with fluxes less than this threshold were removed. The ACHR sampler implemented in the COBRA Toolbox [The Metabotools Toolbox protocol  was used Toolbox  was usedSalinispora tropica strain CNB- 440T, iCC908 [Salinispora strains and Core model. iCC926 is larger in scope than iCC908, containing 18 additional open reading frames (ORFs), and 52 additional reactions. The additional reactions are up to date with the content in KEGG [Salinispora cultures has been identified and named Sioxanthin. Sioxanthin is a glycosylated compound derived from lycopene, and a biosynthetic pathway has been proposed [iCC908 because they were found to be duplicates, and 146 reactions were modified. These modifications were related to EC number, subsystem, and/or GPR associations. In iCC926, twenty-one non-GPR reactions are present in the model related to amino acid metabolism since no auxotrophies for essential amino acids have been identified [iCC926, we compared model predictions to a recent study where the ability of S. tropica to use sole growth supporting sources on a defined minimum medium was evaluated [iCC908. Growth conditions and constraints have been previously described in detail [The content of the genome-scale metabolic model for , iCC908 , was exp in KEGG , and litproposed . Howeverproposed . Thus, Bentified . To assevaluated  and by cvaluated , 57. Ninn detail .Fig. 1OvSalinispora models developed in this study were constructed manually using iCC926 as a template. Type strains were selected to build GSMs since they define the main characteristics of each species. To define the minimum growth requirements, the strains were examined for their capacity to grow in a minimal growth-supporting environment. No strain-specific auxotrophies were identified experimentally on DMM. As observed previously [Salinispora pacifica CNR-114T is able to grow on M1 (data not shown). However, the strain was not tested in DMM, and S. pacifica strain results were used to support growth in DMM. Pathways for amino acid biosynthesis and metabolism were gap filled in each specific case according to the experimental data available. Physiological and biochemical information of primary and specialised metabolism pathways was added to the Salinispora models. For Salinispora arenicola strain CNH-643T model, iSACNH643, reactions were added to allow growth on L-proline, L-threonine, L-tyrosine, and D-salicin as sole carbon sources under aerobic conditions [S. tropica, different transport systems were added to allow interchange with the environment. The sioxanthin biosynthesis pathway was gap filled since orange pigmentation has been observed, and production was predicted on M1 medium. The biomass reaction was updated to reflect the physiology of the organism. However, details about biomass composition are scarse. Macromolecular content was calculated assuming a growth rate of 0.109 h− 1 [iCC926.The eviously , growth nditions , and gronditions . Additio109 h− 1  as in iCSalinispora pacifica strain CNR-114T model, iSPCNR114, predictes growth on ISP5, and ISP7 media since transport reactions were added for asparagine and L-tyrosine [iSPCNR114, seven reactions were added without a GPR association related to amino acid pathways excluding transport and exchange reactions. Macromolecular content was calculated assuming a growth rate of 0.048 h− 1 [S. pacifica strains compared to the other two Salinispora species in the complex medium M1. As stated above, the biomass reaction was updated to reflect the physiology of this strain. In this case, the phospholipid biosynthesis equation was modified based on the chemotaxonomic analysis done by Ahmed et al. [iSPCNR114 contains an additional specialised metabolite, Lomaiviticin A. Lomaiviticin A is a member of the angucycline family of aromatic polyketides, and exhibit a potent antiproliferative antimicrobial activity [048 h− 1  since loactivity . Biosyntactivity , 60. As S. arenicola and S. pacifica with accuracies of 96 and 95%, respectively. The predicted growth rates and specialised metabolite production for each condition can be found in Additional file Overall, 23 and 19 different conditions including DMM experiments and published data were tested during the validation process for the models of Salinispora metabolic model is a subset of the genome-scale metabolic reconstruction iCC926. The subset was selected according to the genes that were conserved across 93 sequenced Salinispora strains defining the core metabolic capabilities among all of the strains. The genome sequences were derived from 10 S. tropica, 45 S. arenicola, and 38 S. pacifica strains. Isolation and genome metadata are available in . The most conserved subsystems were lipid metabolism (79%), energy metabolism (70%), amino acid metabolism (62%), and nucleotide metabolism (61%). These reactions are associated to oxidative phosphorylation, and essential components for cell growth as was defined by the biomass reactions and the revised literature. As was expected, the biosynthesis of specialised metabolites was the less conserved subsystem in the core reactome (30%). Specialised metabolites have been mentioned as unique traits that help to differentiate each species [S. pacifica clade is a sister group to S. tropica. These clades are consistent with information from literature [S. arenicola CNR-416 which is placed in the S. tropica clade.The content of the core model was compared to the pan metabolic capabilities among the three ple GSMs , 32. Tabtabolism %. These iCC926, iSACNH643, and iSPCNR114 will be referred to hereafter as ST-CNB440, SA-CNH643, and SP-CNR114, respectively. From the graph, 848 reactions are shared between the four models. These reactions include both GPR and non-GPR associated reactions. Furthermore, it is observed that 191 reactions are absent in the Core model, and shared by the rest of the models. The 7 unique reactions of the Core model are related to the biomass reaction. On the other hand, ST-CNB440 presents the higher number of unique reactions among the four models while SA-CNH643 is the less distinct strain.As previously observed, unique sets of reactions and genes are responsible for the extra metabolic capabilities specific to each strain. To visualize the shared and distinct content between models, an Upset plot was generated . As in VSalinispora was not available at the time of the analysis. In the case of SA-CNH643, 32% of the non-GPR associated reactions are related to biomass and transport. The unique set of SP-CNR114 shows the highest number of GPR associations with 88%. Moreover, the distribution of the unique, and shared reactions by the functional subsystem was found not to be similar. The majority of these reactions are involved in transport (48%), amino acid metabolism (30%), and carbohydrate metabolism for ST-CNB440, SA-CNH643, and SP-CNR114, respectively. Amino acid and carbohydrate metabolism accounted for 31 and 21% of the shared reactions between ST-CNB440, and SA-CNH643. Twenty five percent of the shared reactions by ST-CNB440, and SP-CNR114 are involved in the metabolism of vitamins and cofactors. Meanwhile, most of the reactions shared between SA-CNH643, and SP-CNR114 are related to lipid metabolism (29%). These shared sets defined common features between species. The distribution of sets by subsystems can be found in Additional file Since not only the magnitude of the sets is interesting, unique sets of reactions and intersections were analyzed to explore differences between reconstructions. The first step was to verify that these distinctive sets were not a major consequence of the gap filling associated to the biomass reactions, and the validation process. Reactions were classified according their GPR associations (GPR and non-GPR reactions). GPR associations determine the set of metabolic reactions encoded in the genome. From Fig. Salinispora strains and core model were converted into computational models to allow the simulation of growth in minimum medium. Condition-specific models were derived from the active reactions of these computational models in DMM. Reactions unable to carry flux were identified through flux variability analysis (FVA) [Next, condition-specific models were used to determine if the identified differences in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species. For this purpose, the genome-scale metabolic reconstructions for is (FVA) , and remis (FVA)  and thisis (FVA) . To inteSalinispora models. Genes that were absent in models were assigned a growth ratio value of − 1. The analysis yielded 96 and 42 shared lethal gene deletions for the models in DMM and M1, respectively. A unique set of essential genes was identified for ST-CNB440 (4 genes), SA-CNH643 (31 genes), and SP-CNR114 (10 genes) in DMM growth conditions. Three of the unique essential genes of the ST-CNB440 model were only present in this model. For complex medium, 3, 36 and 22 distinct sets of essential genes were identified for ST-CNB440, SA-CNH643, and SP-CNR114, respectively. Clustering of species by the effect of single gene deletions in the models revealed that the SA-CNH643 model is more different from the other models. ST-CNB440, and SP-CNR114 models are still more metabolically similar when considering predicted essential genes. Phylogenetic studies have shown that S. pacifica strains form a distinct phylogenetic lineage that is a sister to S. tropica [1.Finally, gene content was evaluated to identify genetic differences between species. Models were used to identify essential genes in DMM and complex medium M1. A gene was classified as essential if one or more reactions associated to this gene need to carry a flux to satisfy a particular objective function, i.e., the gene is necessary for in silico growth in the given conditions. Results of simulations were categorized according to the effect of the deletion compared to the wild-type strain . Strain-specific auxotrophies were not identified. Additionally, the phylogeny generated using the core genes was consistent with the species phylogeny in the literature. However, one exception was observed with S. arenicola CNR-416 that was included in the S. tropica clade. Then, analysis and selection of phylogenetic markers are important to avoid incongruent associations.In continuation with efforts regarding the study of the metabolism of omarin A . Also, aSalinispora strains. Functional differences between the developed metabolic networks were identified to have a glimpse into the cell metabolism that are different in each Salinispora species. Disagreements with experimental results indicate potential errors in the reconstructions or missing regulatory constraints. Additional knowledge is necessary to better represent metabolite consumption and byproduct release as in the case of the specialised metabolites. Transport systems are not well documented, and energy costs could be underestimated. Since knowledge about Salinispora species is continually evolving, new data can be incorporated into the reconstructions to better represent the metabolism of each species. The overall accuracies of ST-CNB440, SA-CNH643, and SP-CNR114 models were 92.8, 96 and 95%, respectively. Additional phenotypic profiling is required to test different nutrient requirements. Phenotypic microarrays are a good alternative to study the ability of strains to use different nutrient sources [Salinispora strains may be able to store sulfur. The ability of the strains to use different sulfur sources must take into consideration these findings.GSMs were validated and analyzed to explore the differences between the reconstructions by growth phenotypes. Despite the structural similarities between the models, results suggest that models are able to describe different metabolic states of  sources , 66. Exp sources  suggestsSalinispora models after analyzing the metabolic capabilities of the networks. These applications cover from integration of omic data  to the study of microbial communities. Specialised metabolites are involved in the microbial interactions with the environment and other microorganisms. The study of Salinispora metabolism is still in the early stages, however the use of metabolic models could help to gain new knowledge of this marine actinomycete and the production of specialised metabolites.Condition-specific models were used to predict and analyze metabolic capabilities of the three-species using a qualitative approach. Models allow assessment of the metabolic characteristics of the cell when integrating environment conditions. The contextualized models predicted different usage of essential nutrients on DMM. This could explain the differences observed in growth rates on DMM . Table S2. contains a heat map of InParanoid scores (worksheet2). Table S3. contains genome and available isolation metadata of Salinispora strains in Core (worksheet3). References used to extract data related to isolation metadata are listed in the table. Table S4. contains list of conserved and not conserved reactions in each mode (worksheet4). Tables S5, S6 and S7. contain reactions associated to essential genes in ST-CNB440, SA-CNH643 and SP-CNR114 models under DMM growth conditions, respectively (worksheet5). Table S8, S9 and S10. contain reactions associated to essential genes in ST-CNB440, SA-CNH643 and SP-CNR114 models under CM growth conditions, respectively (worksheet6). Table S11. contains growth rate ratio between deletion strain and wild type strains for each model under DMM and CM growth conditions (worksheet7). (XLSX 2620 kb)Additional file 2:Salinispora model built in this work. (MAT). (ZIP 1303 kb)Excel files with list of reactions, metabolites, and genes included in each reconstruction with corresponding references, associated GPR, E.C. numbers and notes. Mat files for each Additional file 3:Salinispora arenicola (worksheet2) and Salinispora pacifica (worksheet3) models. References are included in the file. (XLSX 67 kb)Details of biomass equations used for Core (worksheet1), Additional file 4:Figure S4a. Growth curves of Salinispora strains in DMM without sulfur sources. Sulfur source was not included in the medium to examine the ability of strains to store sulfur as was observed in the case of S. tropica CNB-440T by Contador et al. 2015. Sulfur must be supplemented to the medium after the depletion of the accumulated sulfur. All experiments were done in duplicate. Error bars represent standard deviation. Figure S4b. Growth curves of Salinispora strains in DMM without carbon sources. (ZIP 214 kb)Additional file 5:Table S1 to S8. Predicted growth rates and specialised metabolite production used in the validation process of the models. Uptake constraints for non-defined media were set to match the experimental conditions as closely as possible. References to published studies used in the validation are included in the file. Figure S5. Distribution of unique sets of reactions by subsystems. (a) ST-CNB440; (b) SA-CNH643; (c) SP-CNR114; (d) ST-CNB440/SA-CNH643; (e) ST-CNB440/SA-CNH643. Sets with 20 or more reactions were represented. (DOCX 492 kb)