Escherichia coli.To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in  Metabolic engineering aims to enable the production of pharmaceuticals, fine chemicals, and fuels by microbial cell factories, and strives to maximize productivity and profits . In the omics data. Techniques, such as flux balance analysis [From the sheer metabolic and regulatory network complexity a key problem of metabolic engineering approaches falls into place: How does one intervene in those biochemical networks to reach or approach an engineering aim with a reasonable investment of time, money, and materials? The use of computational models of metabolism seeks to answer these and related questions by facilitating the integration of biochemical knowledge and analysis , elementanalysis , or fluxanalysis , helps tanalysis  to suppoThe search for an optimal genetic intervention set typically poses a nested, bilevel-optimization problem. The outer problem optimizes an engineering objective by varying the network structure through gene knockouts, knockdowns, or overexpressions. The inner problem returns the microbial phenotype for a given intervention strategy based on a cellular objective, from which the outer objective function is evaluated. By exploiting basic theorems of linear algebra, such bilevel problems are usually transformed into single-level mixed-integer linear or quadratic programming problems and solved using powerful mathematical programming algorithms ,9,10,11.Solving bilevel optimization problems using gradient search techniques bears two major drawbacks. Firstly, the necessary mathematical transformations increase the complexity of the problem formulation, thus limiting the tractable number of simultaneous interventions per simulation. More importantly, only linear constraints and objective functions can be used in these frameworks, which may not be appropriate for representing biological objectives .The application of metaheuristics as search routines circumvents the formulation and complexity problems of the classical gradient search techniques. Metaheuristics is a general term for optimization techniques, which are to some degree based on random variables to derive optimal solutions to an optimization problem . EvolutiA variety of metaheuristics as optimization algorithms have already been applied for the computation of metabolic engineering strategies ,31,32,33Escherichia coli. We particularly examined the importance of the duality between diversification and intensification  of solution candidates for circumventing premature convergence. Secondly, we demonstrated, and eventually expanded, the GA’s versatility. We included the simultaneous evaluation of multiple cellular objective functions to derive pareto-optimal, robust strain designs. Inspired by the OptStrain framework [Due to the nontransferable behaviors of GAs among different optimization problem classes , we firsramework , we addiIn summary, we investigated and exhausted the behavior and performance capabilities of GAs for metabolic engineering approaches and, beyond that, integrated previously-independent design objectives and methods in one framework. Hence, we promote the use of GAs for sophisticated metabolic models demanding high computing power , as wellA genetic representation of solutions. Here, we employ a binary coding.Populations of individuals as evolutionary communities.A fitness function for evaluating the goodness of individuals.Operators, which generate a new population from an existing one and which can be controlled by parameters that shape the fitness-related or random transformation behavior.The GA is a randomized but directed search and optimization method modeled by the principles of natural selection. It iteratively evolves a set or population of solutions to an optimization problem , i.e., towards better solutions, while eventually converging at optimality. According to Srinivas and Patnaik , the keyThese characteristics have already been shown to be advantageous for in silico metabolic engineering approaches in finding a set of reaction knockouts, which optimizes overproduction ,21,25. TIn terms of a strain design problem, an individual represents a set of reaction or gene deletions. Following characteristic (1), each of the Now consider the problem of assigning Consequently, the maximal difference in the probability of drawing two independent targets is less than 2%. Using this specific binary representation, a maximal, user-defined number of targets per individual is guaranteed.At the start of the GA, a population of E. coli, we obtained our reference state from the experimental results of Ishii et al. [The fitness or goodness d (BPCY) ,23, whic) method , which m) method . MiMBl mi et al. . For morAs a first step in a generation, the best The mating pairs for crossover are assembled using a roulette wheel weighting approach. Therefore, two individuals are randomly selected from the pool of If not noted otherwise, Since during selection the worst individuals are deleted, these Crossover, the creation of offspring individuals from mating pairs, is initialized by a random selection of a crossover point or kinetochore for each mating pair. Kinetochores are restricted to positions in between two neighboring genes. The genes left of the kinetochore of the first parent are merged with those to the right of the kinetochore of the second parent to form the first offspring individual. The second offspring is created complementarily with the remaining parent genes a.The mutation operator drives the exploration of the target space by randomly changing deletion targets within individuals of a population with a given probability. One random number is drawn for every bit of an individual’s binary numbers encoding for the deletion targets. In case of a mutation event, the bit value is inverted. The probability with which a 1 is turned into a 0, or vice versa, is set by the user-defined mutation rate We additionally adapted the concept of elitism and, hence, the best parent individual is not mutated. After mutation, a new generation is propagated by calculating the fitness of the novel individuals.To exploit multi-core and multi-threading processor architectures, several independent generation strands are processed in parallel b. TherefPreviously, auxiliary GA operations have been derived that adapt GA parameters at runtime to simultaneously maintain a sufficient level of diversity of individuals within a population and the convergence capacity of GAs ,36. To tTo make use of the complex gene-protein-reaction (GPR) associations inherent to many metabolic models, we enabled the possibility of computing gene rather than reaction deletion target-sets. Since any fitness function evaluation employing metabolic models demands the specification of reaction network perturbations, we implemented a routine, which translates simultaneous gene deletions to reaction deletions according to the logic operations given by the GPRs.To simultaneously optimize multiple engineering objective functions, the fitness function was expanded by the OptKnock  and gcOpThe GCS has originally been defined as the ratio between the mutant’s total yield space area below the upper yield space bound and the area below the lower yield space bound, which is inaccessible to the mutant . Here, wi according to Equation (8):We incorporated a fitness transformation routine to facilitate the minimization of simultaneous genetic perturbations while evolving overproduction individuals. Our approach is to scale the fitness By applying Equation (8), the fitness of individuals is increased by either improving the objective fitness Inspired by the OptStrain and SimOptStrain frameworks ,11, we eThe average Hamming distance between pairs of individuals can be used to quantify the diversity of a population, which aids in investigating the time convergence of GAs ,45. The jth bit of the first individual of the ith pair in the population. Additionally, HD is normalized by the maximally possible Hamming distance between two individuals. Therefore, we will generally use HD for the normalized, average Hamming distance in this work. Note that Equation (11) represents a formula to calculate the number of possible pairs from In Equation (10), E. coli K-12 MG1655 core [iJO1366 [In this work, the 655 core , as well[iJO1366 , were emAdditionally, the deletion target space was reduced to minimize the complexity of the optimization problem. Partly following the protocol of Feist et al. , reactioE. coli core and the genome-scale iJO1366 model were replicated five and three times, respectively. All data shown is an average of the replicates and given errors denote the correspondent standard deviation. The GA was implemented in MATLAB 2016b  and is freely available on GitHub (https://github.com/Spherotob/GAMO_public). All computations and the evaluation of the results were conducted on a Windows 7 machine with 16 GB of RAM and an AMD FX-8350 Eight-Core (@ 4.00 GHz) processor.All simulations employing the E. coli iAF1260 core metabolic reconstruction. We consistently employed the BPCY as fitness function, which was calculated according to the descriptions given in The performance of GAs on arbitrary optimization problems is strongly dependent on the GA parameters and a sound setting is generally hard to predict. It is, thus, advisable to conduct a thorough parameter sensitivity analysis for a specific problem class to derive the most advantageous parameter ranges. Therefore, we performed a parameter sensitivity analysis for a basic GA cf.  on straiThe arbitrary mutation of individuals is a central operator of GAs driving the exploration of the solution space for globally optimal solutions. At low mutation rates, the search of GAs is narrowed to the local surroundings of a population’s individuals, which is likely to lead to premature convergence. On the contrary, too high mutation rates diminish the fitness intensification in the local area of a population and, thus, convert GAs into random search methods, which results in low convergence speeds. This is illustrated by the maximal fitness and the Hamming distance trends when optimizing for succinate biomass-product coupled yield (BPCY) using a basic GA at different mutation rates . Evolutirate cf. , we condrate cf.  as well rate cf.  and founrate cf.  and 50  and population size  determine how many of the fittest individuals are being selected to the mating pool for breeding new, and eventually superior, offspring individuals. Therefore, both parameters jointly influence the local search behavior of GAs in the vicinity of a population induced by the crossover operator. To assess this influence in terms of convergence characteristics and computational cost minimization, we performed GA runs with varying selection rates and population sizes using succinate BPCY as the engineering objective and limiting the intervention size to seven reaction deletions. For each tested selection rate–population size pair, the progression of the maximal fitness is shown in GA runs employing high selection rates of 0.75 exhibited the slowest convergence towards the maximal observed fitness of s values . With ins values . Hence, s values , which wWith the parallel implementation of the GA, populations are evenly split into sub-populations, which are assigned to multiple separate processing units or workers and evolved independently from each other . After aGenerally, parallelization of generation sequences and fitness function evaluations is applied to cut computation time, particularly when dealing with costly fitness functions . GA runsHowever, the absolute computation time for the overall 900 generations is gradually reduced when decreasing the number of GFEs . This isWe used the basic GA and the optimized GA parameter set to determine strain designs for the overproduction of succinate, ethanol, lactate, and glutamate allowing maximum reaction or gene deletions between three and nine . IndepenInterestingly, the final fitness for five, seven, and nine reaction or gene deletions was the same or in the near range for all products. Hence, individuals representing a high, fixed intervention set size likely included one or more deletions, which did not contribute to the engineering objective. This is explained by our formulation of an individual cf. , which aWe exemplarily applied the intervention set minimization approach to ethanol overproduction using different instances of the trade-off factor eletions a. Howeveeletions b. For exressions , convergTo focus on the robustness of strain designs, we combined laboratory evolution-based objectives, namely gcOpt and OptKnock, as well as BPCY as a non-laboratory evolution objective, in one fitness function and identified reaction deletion strategies for the maximization of succinate, ethanol, lactate and glutamate production. A “perfect” solution would therefore guarantee a high minimal yield at any growth state while predicting an optimal compromise between growth and target synthesis rates for the deletion mutant.Among the investigated target products, convergence characteristics of the population diversity were comparable for the same maximal allowable intervention set size cf. . MoreoveE. coli core model, including novel heterologous reactions  and addition of an ATP-dependent citrate lyase (EC 2.3.3.8) frequently occurred in the best individuals. Simultaneously, formation of acetate and ethanol were inhibited, as well as the malic enzyme knocked out, altogether enforcing metabolic flux through the glyoxylate shunt and the reductive branch of the TCA cycle towards succinate. For the glycolytic product ethanol, switching from the NAD+-dependent to the NADP+-dependent alcohol dehydrogenase (EC 1.1.1.2) and glyceraldehyde-3-phosphate dehydrogenase (phosphorylating), as well as simultaneously deleting the NAD+ transhydrogenase, led to the most promising strategies. Congruently, NADH/NADPH metabolism was the preferred target for glutamate overproduction, which was spurred by the addition of the NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.9) as well as the knockout of NAD+ transhydrogenase. The identified strain designs also suggested to increase flux through the TCA cycle by heterologous expression of the citrate oxaloacetate-lyase (EC 4.1.3.6) to recycle acetate. Interestingly, insertion of the latter in combination with the expression of the non-native NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase and the deletion of various NADH/NAD+-dependent reactions also improved lactate overproduction.For all four target products, the GA was able to further improve the BPCY by adding between one and four novel reactions compared to corresponding quintuple deletion mutants . For laclutamate d, where Genome-scale network reconstructions represent dense information sources of the current knowledge about microbial metabolic functionalities. In combination with constraint-based modeling approaches they can aid in thoroughly predicting the behavior of microbes and their response to genetic perturbations . The sheE. coli GEM iJO1366. Similar to the E. coli core model . All strategies with five gene deletions shared a predicted BPCY of +-dependent glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.13) was inserted. However, insertion of novel functionalities did not significantly improve succinate overproduction as compared to the deletion-only strain designs and in case of the quintuple deletion mutants even showed lowered fitness values  or pyruvate kinase  occurred frequently, presumably due to the elevated recapture of carbon dioxide. For the complete strategies we refer to the One main overproduction principle, however, was the enforcement of flux through the glyoxylate shunt by deleting the fumarase genes ΔBy simultaneously incorporating previously published, as well as novel, engineering approaches into a basic GA framework, we could demonstrate the versatility and broad applicability of GAs for solving strain design problems. The addition of novel reactions and functionalities, in consideration of gene, as well as reaction, deletions, application of multiple optimization objectives, and minimization of necessary network perturbations proved to be simultaneously manageable by the GA. Such an integrative approach allows for an increased level of robustness in terms of overproduction stability and efficiency of mutant strain designs, as well as the consideration of practicability of necessary genetic interventions.In light of ongoing refinements of purely stoichiometric models to enhance their predictive power by introducing novel, kinetics-related protein or enzyme expression constraints ,49, or wThe performance of the GA is, in any case, strongly dependent on the balance between a broad diversity in the genetic pool of consecutive populations and the focusing to the most fit solutions or individuals. An exaggerated concentration on the local search characteristic of the GA, e.g., by applying small mutation rates, led to premature convergence to non-optimal solutions, which became apparent by a drastic drop in the population diversity. On the other hand, constantly high diversities indicated a strong exploration of the solution space, but were accompanied by slow convergence rates and a high computational effort necessary to identify optimal solutions. GA parameters were thus identified by a parameter analysis to blend both exploration, as well as intensification characteristics, and guarantee good optimization performances for any strain design problem. Thus, we reduced the chance for premature convergence and simultaneously minimized the computing time. Above all, the favorable convergence characteristics were not affected when we applied more complex engineering objectives, such as a multi-objective fitness function.Avoiding premature convergence is particularly important if, in future developments, convergence may be detected at runtime to terminate the GA and output the optimal solution instantaneously. A stagnation of the population diversity, which is quantified by the Hamming distance of a population, can potentially serve as a convergence criterion. However, if a population converged to a global or a local optimum can hardly be decided and, thus, premature convergence needs to be circumvented in the first place. Therefore, slight promotion of the GA’s exploration characteristics by careful parameter adaptions can be advantageous. In a previous study we claimed that strong growth-coupling is effectively generated by the perturbation of cofactor balancing and ATP, in particular , which, +-dependent reactions with their NADP+-dependent counterparts, while simultaneously deleting the NADH dehydrogenase, NAD+ transhydrogenase or other NADH-dependent reactions, as was also previously suggested for succinate overproduction by Kim et al. [E. coli iJO1366 GEM and the full capacity of the GA’s features, we identified a rather different strain design compared to other theoretical or experimental studies. Whereas it has been frequently suggested to directly suppress byproduct formation, e.g., by knocking out ackA, ldhA, or pfl [E. coli GEM predicted a redirection of the TCA cycle flux towards the glyoxylate shunt to be most beneficial for succinate production. Moreover, and in line with results from the core metabolic model, reduction of NADH generation in favor of NADPH appeared to be a key design principle. This was pronounced by the suggestion to include the non-native NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase or quinate dehydrogenase, which significantly improved theoretical maximal growth. Since our design suggestions resulted from the simultaneous application of various engineering objectives, a comprehensive metabolic model, and the consideration of actual gene-protein dependencies, as well as detailed wild-type metabolic flux data, it offers the most reliable basis for experimental transfer. Thus, we are looking forward to further investigating or even practically applying the presented designs, which, however, was out of the scope of this work.The addition of novel metabolic reactions taken from a model databank generally targeted the cofactor and, particularly, the NADH/NADPH metabolism, besides the inhibition of byproduct formation. A common strategy was to replace NADm et al.  based on, or pfl , the GA In summary, we could demonstrate that simultaneous application of multiple, complex engineering objectives to genome-scale metabolic models for strain design purposes is indeed feasible using GAs. Moreover, GAs offer the potential to integrate even more complex objectives and methods, and their performance may be tuned according to highlighted characteristics and parameter sensitivities.


Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies. It is assumed that metabolite identification remains a major challenge in untargeted mass spectrometry (MS)-based metabolomics. Is this indeed true? Should there be greater effort to design experiments in a smarter, more streamlined way, and to know how to reduce noise and redundancy in untargeted metabolomics datasets? For example, a meta-analysis comparative strategy can be used, where several pairwise comparisons are performed (with the same control group), followed by second-order or meta-analysis to prioritize the identification of the shared deregulated metabolites ,2. Here,Before starting any metabolomics study, it is important to consider the question(s) being asked. Many metabolomics studies are complex in design and may incorporate several classes, e.g., control subjects versus those receiving low and high dose of a drug , healthy(a)Positive controls, where changes are expected. These can be used to check that experimental methods are working correctly, and could include a group of subjects  with a known disease, or a specific cell line.(b)Negative controls, where no change is expected. These can be used to check that unknown variables are not affecting the experiments, which could result in a false-positive conclusion.(c)Sham controls. These can be used to check effects induced by the procedure or treatment without actual use of the procedure  or substance .(d)Vehicle controls. These can be used to check effects induced by a solution of the experimental compound, e.g., when a drug is administered in dimethyl sulfoxide (DMSO), the effect of DMSO on its own should be studied.(e)Comparative controls. These act as a reference which is commonly accepted or an internal control/disease control. In cases where there is a drug treatment, it is important to test a sample of the drug to assess which (if any) signals observed in the metabolic profile arise from the drug, drug metabolites, or degradation products. Extraction blanks enable artefacts and contaminants to be assigned , and are particularly useful when extracting tissue samples.It is extremely important to design an experiment containing the correct controls, in order to be able to associate observed metabolite changes with the condition being investigated ,8. The mThere are several sources of variation in metabolomics studies. Firstly, there is the biological variation in the samples themselves. Factors which can affect the metabolic profile of individuals include diet, age, medication, underlying disease, and environmental factors . These aThere are several different experimental design types to consider. Amongst the more common are completely randomized, crossover, and factorial designs ,19,20,212O), can be used . T. Tbetweet-test, can be used. Normality can be tested using, e.g., the Shapiro–Wilk test, which is good when the sample size is < 50. Note that it is not possible to assess normality of the distribution if the sample size is small, and some tests do not cope well with small sample sizes. Parametric tests are considered to be more powerful than non-parametric tests, with less risk of a false negative  result than with a non-parametric test. However, when dealing with populations that are non-normally distributed, with unequal variances and/or unequal small sample sizes—all possible in untargeted metabolomics—often a non-parametric test can perform better . B. Bfeaturp-value). It is thus of the utmost importance to interpret identified alterations at the metabolite level within the metabolic networks, especially when it comes to the discovery and understanding of subtle (fold change < 2) but coordinated and physiologically relevant changes, often the case in biomedical and human population studies. Metabolic networks, derived from genome-scale metabolic network models (GSMNM) are the most accurate ways to describe and represent metabolism, as compared to discrete pathways [In general, changes at the metabolite level cannot be looked at independently outside of the context , and meaningful changes can be missed by relying only on the arbitrary significance threshold  Mapping and visualization of metabolite changes in the graphical representation of cell metabolism, i.e., metabolic network; and (2) statistical analysis to determine the overrepresented pathways, known as metabolite set enrichment analysis (MSEA). Most of the open access tools designed for pathway and network analysis provide both of these functionalities, visualization to assess if metabolites are involved in the same pathways and how they are connected within a metabolic network and enrichment analysis to highlight the pathways associated with the examined phenotype. The open access software that provide these functionalities in the interactive fashion are listed in In order to map the identified metabolite changes in the biochemically relevant context, one first needs to convert the metabolite identities into the relevant metabolite identifiers  that can be used for mapping to metabolic networks derived from genome-scale models . The conversion to different metabolite identifiers can be executed in batches using a chemical translation service, provided by UC Davis  or MetabVisualization of metabolite changes in the context of metabolic networks brings together chemical reactions (of which metabolites are the products or substrates), and the genes coding for the enzymes making these reactions possible. MetExplore is among the most comprehensive tools that allows for the construction of tailored networks and collaborative curation and annotation of metabolic models, in addition to the interactive network visualization, from the entire network down to detailed sub-networks (build from selected network elements—a pathway or a set of genes) ,135. In Globaltest [p-value gives the probability that none of the matched compounds in the group of metabolites is associated with the phenotype. A closely related approach to MSEA, an over-representation analysis (ORA), is used to evaluate the probability that the particular set of metabolites  is represented, within a defined list of metabolites of interest, more than expected by random chance. For ORA, a user can provide only a list of metabolite identifiers, corresponding to metabolites that vary significantly between two analyzed conditions. Several probability tests, such as Fisher’s exact test, binomial probability, or hypergeometric distribution test, can be applied, followed by the correction for multiple testing. Here, the reference metabolome should comprise the metabolite sets that can be detected in the analytical conditions used, thus reflecting the analytical method coverage. If the entire library of metabolite sets is used for ORA by default, the observed enrichment may be a consequence of applied analytical platform bias instead of being biologically relevant. ORA and/or MSEA are integrated in many different pathway and/or metabolic network analysis software, such as MetaboAnalyst, MetExplore, Pathvisio, etc. Finally, MetaboAnalyst allows also for the combined MSEA and pathway topology analysis that will display pathway impact values based on centrality measure—local quantitative measure of the position of a node (or a «key» position) relative to the other nodes in the network.As mentioned above, in addition to metabolic (sub)network visualization, metabolite set enrichment analysis (MSEA) as a metabolomic counterpart of the gene set enrichment analysis (GSEA) and/or over-representation analysis (ORA) are used to investigate the metabolic pathways whose activity differs among analyzed conditions . MSEA takes into consideration the quantitative measure associated with each metabolite  . MSEA fiobaltest  to detecobaltest ,121. TheAlthough the tools for metabolic network analysis are being steadily improved by the computational community, there are still a number of challenges, related to metabolome coverage bias of the experiment , scarcity of well-annotated metabolomics data (number of unknowns or non-annotated metabolite(s) (features) remains high), and, finally, the lack of knowledge about network regulation. It is also important to consider that the metabolome cannot be computed directly from the genome, and that many metabolites still need to be integrated into our current metabolic networks, thus making use of the wealth of data generated in metabolomic experiments.Global or untargeted metabolomics provides the opportunity for biomarker discovery and hypothesis generation. Potentially, it can enable the elucidation of the involvement of previously unknown or unsuspected pathways in disease states or in response to therapy. Inherently, this untargeted approach does not bias for or against specific analyte classes and provides a wide view of the metabolome. Sample preparation and analytical methods are somewhat generic and are usually optimized for sample type. However, with this approach comes the bottleneck of metabolite feature annotation and metabolite identification, as described in this review. Therefore, high-coverage targeted assays are becoming more prominent in the field of metabolomics. With targeted assays, tandem or triple quadrupole mass spectrometers are employed, with lower mass resolution than the Orbitrap or Q-ToF mass spectrometers used for untargeted analyses. However, these have the advantages of lower cost, higher sensitivity, linearity, and specificity. By employing isotopically labelled standards of the analytes of interest, which are spiked into each study sample, absolute quantification can be achieved. Furthermore, as the analytes being measured are known upfront, and the chromatographic and mass spectrometric methods are optimized at the start, run times can be much shorter than for untargeted analyses. There are guidelines which can be followed for ensuring accuracy and precision of the assay, such as those laid out by the FDA . SoftwarUntargeted metabolomics is a powerful approach to understanding changes due to disease, drug treatment, or environmental factors in a multitude of human, animal, and cell models. However, as metabolism is complex, so are the data produced in these studies. It is therefore crucial to be vigilant at every stage of the experiment. If the study has not been designed correctly, it will be hard to elucidate biologically relevant information, as confounding factors may overwhelm any biological changes. To maximize the metabolome coverage, it is necessary to acquire data in several chromatographic and ionization modes, ideally HILIC for polar metabolites and RPLC for complex lipids (using non-polar solvents for elution). MS/MS data—of high quality and volume—can be acquired in DDA mode using iterative injections with PC-driven exclusion and/or in DIA mode with sequential mass windows . Furthermore, it is of the utmost importance to pre-process the data correctly, as there will inherently be redundancy in the data. As metabolite identification remains the bottleneck in metabolomics studies, so stringent approaches are needed to ensure that models have been validated and only the strongest candidates are pursued through the identification pipeline. The comprehensiveness of experimentally generated and in silico-derived spectral databases has grown significantly, and their integration into the data processing workflow, together with the improvement of computational approaches (for in silico prediction of MS/MS data), are paving the way towards automated MS/MS data matching to facilitate metabolite annotation. Finally, the advancements in metabolic network analysis tools are enabling more mechanistic insights, beyond the biomarker discovery. Here, metabolite data provide crucial complementary information on “what has indeed happened”, as the phenotype readout at the molecular level, thus representing the “missing piece” of puzzle towards multi-scale omics data integration for more accurate interpretation of biological processes.


Microalgae have emerged as a promising platform for production of carbon- and energy- rich molecules, notably starch and oil. Establishing an economically viable algal biotechnology sector requires a holistic understanding of algal photosynthesis, physiology, cell cycle and metabolism. Starch/oil productivity is a combined effect of their cellular content and cell division activities. Cell growth, starch and fatty acid synthesis all require carbon building blocks and a source of energy in the form of ATP and NADPH, but with a different requirement in ATP/NADPH ratio. Thus, several cellular mechanisms have been developed by microalgae to balance ATP and NADPH supply which are essentially produced by photosynthesis. Major energy management mechanisms include ATP production by the chloroplast-based cyclic electron flow and NADPH removal by water-water cycles. Furthermore, energetic coupling between chloroplast and other cellular compartments, mitochondria and peroxisome, is increasingly recognized as an important process involved in the chloroplast redox poise. Emerging literature suggests that alterations of energy management pathways affect not only cell fitness and survival, but also influence biomass content and composition. These emerging discoveries are important steps towards diverting algal photosynthetic energy to useful products for biotechnological applications. Electrons used for H2 photoproduction both come from PSII activity and a non-photochemical reduction of PQ [Chlamydomonas mutant sta6 showed an impaired H2 photoproduction upon PSII inhibition [2 photoproduction [2 photoproduction may be used as a means to get rid of reductive power produced by carbon storage breakdown, the electron transfer being mediated by an NDA2-dependent pathway [2 production could be obtained under photoautotrophic conditions with a concomitant starch breakdown [2 photoproduction is not yet investigated but probably very minor [Under anaerobic conditions, protons . ATP, at least part of it, can then be shuttled back to chloroplast to sustain chloroplast metabolism. This was first evidenced in Chlamydomonas by Peltier and Thibault [aox mutants of Phaeodactylum tricornutum [Chlamydomonas [Chlamydomonas pgrl1 mutant with an impaired CEF [+, oxidizing malate to oxaloacetate (OAA) in the meantime. Malate or OAA can be shuttled across subcellular membranes by metabolite transporters [Chlamydomonas genome encodes five MDHs [Chlamydomonas genome [Excess NAD(P)H produced in the chloroplast is known to be exported to mitochondria where it can feed the respiratory chain either to produce ATP, or to be used to reduce OThibault , and latcornutum  and Chlaydomonas  and alsoired CEF . Cell meired CEF ,7. MDH csporters ,7. The Cive MDHs , and witive MDHs , and theive MDHs ,66,67,68s genome .The reductant exchange between mitochondria and chloroplasts could be bidirectional. It has also been postulated that NAD(P)H could also be transported from mitochondria to chloroplast . This fufud50 deficient in chloroplast ATP synthase [fud50 mutant cannot grow photoautotrophically, and a suppressor screen identified two independent mutants (fud50su) that have a recovered photoautotrophic growth but without a functional ATP synthase. Further inhibition of mitochondrial activities in the fud50su lines again abolished the growth capacity of the fud50su strains. This is interpreted as the occurrence of ATP transport from mitochondria to chloroplast, that mitochondria-derived ATP is sufficient to allow regain of photoautotrophic growth of the fud50su strains.The presence of an active and functional ATP import pathway from mitochondria to chloroplast has initially been evidenced by the study of the mutant synthase . The fudbckdh mutants deficient in the E1α subunit of the branched-chain ketoacid dehydrogenase (BCKDH), a key enzyme in the catabolism of branched chain amino acids (BCAA) in the mitochondria [bckdh mutants made 30% less oil with a reduced rate of mitochondrial respiration (20–30% less). The reduction in total lipid (including oil content) in the bckdh mutants is considered due to a shortage in the supply of acetyl-CoA as well as ATP (which are both products of BCAA catabolism in mitochondria) to de novo fatty acid synthesis in the chloroplast [Another example of the possible occurrence of an ATP import from mitochondria to chloroplast is the characterization of the chondria . The bckoroplast .Chlamydomonas [How do ATP produced by mitochondria-based reactions pass through cytosol and finally get into chloroplast remain unknown. A direct transport is possible since mitochondria are often observed located right next to the chloroplast in some transmission electron micrographs . Candidaydomonas , but nonChlamydomonas has started to be appreciated [Chlamydomonas, it has been evidenced that NADH issued by fatty acid β-oxidation reactions is communicated to the chloroplast. This exchange of reducing equivalent between peroxisome and chloroplast is mediated by MDH2. In its absence, the Chlamydomonas mdh2 mutants exhibited better sustained LEF rate and resulted in higher production of NADPH under N deprivation. The increased capacity in production of NADPH led to an increased level of fatty acids and starch in the mutants [2O2 in this communication has been proposed [Thus far, in the landscape of subcellular energetics, most literature has focused on chloroplast-based process or its interaction with mitochondria ,27,72. Ireciated ,68. NADHreciated ,73,74. T2 and water into organic compounds that can be integrated into other subcellular metabolisms subsequently. The key enzyme of the cycle is the ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). The carbohydrate products of the CBB cycle are three-carbon phosphate molecules, or “triose phosphate” namely glyceraldehyde-3-phosphate (GA3P). GA3P is a central metabolite participating in many biosynthetic reactions, for example it can be used either by chloroplast-based reactions or exported to other subcellular compartments to drive anabolic reactions elsewhere. Thanks to their capacity to grow heterotrophically, mutants deficient in the large subunit of RuBisCO - the rbcL mutant - have been isolated and characterized [rbcL mutants displayed an aberrant LEF but with enhanced CEF activities [CBB cycle connects energetic metabolism to carbon metabolism. The CBB cycle uses the energy from NADPH and ATP to convert COcterized . Consisttivities .For unicellular organisms, biomass accumulation is directly linked to cell division and growth activities. Indeed, cell division is known to be one of the most energy-consuming events during the life of a cell . This isChlamydomonas is starch, a glucose polymer stored in the chloroplast of land plants and most green algae [sta6 mutant, have been observed to exhibit reduced photosynthetic performance and poor growth [rbcL mutant, CEF and other alternative electron pathways are enhanced in the bafJ5 mutant [During photoautotrophic growth, the major outputs of CBB cycle is used partly for cell division and growth, and partly as storage for night-time use when carbon and energy levels are low. The major form of carbon storage during a standard growth in en algae . Starch en algae ,80. Thisr growth . Similar5 mutant .2, acetate). While starch and oil are often considered two similar “sinks” for storing carbon and energy [Competition between starch and oil accumulation is therefore at least at three levels i.e., C, ATP and NAD(P)H, which might explain the contrasting findings present in the literature ,81. In ad energy , it shouArabidopsis thaliana [Chlamydomonas [In algae, similar to land plants, de novo fatty acid synthesis occurs in the chloroplast where the redox state is highly dependent on photosynthetic activities and where lipid metabolism intersects starch metabolism. It has been observed that the expression pattern for many genes encoding components of de novo fatty acid synthesis is similar to that of genes encoding photosynthetic peptides in developing seeds of thaliana , suggestydomonas . Among wChlamydomonas [Chlamydomonas. Nevertheless, Arabidopsis mutants deficient in an enoyl-acyl carrier protein reductase (ER), catalyzing the last reaction in the FAS complex, are defected in fatty acid synthesis and showed programmed cell death as a result of ROS over-production associated with the mitochondrial electron transport [The first and committed step of fatty acid synthesis is the formation of malonyl-CoA from acetyl-CoA catalyzed by acetyl-CoA carboxylase (ACCase). This reaction requires one molecule of ATP . One playdomonas . The playdomonas ,85, and ydomonas , therebyransport . Later oransport . These s2 molecule while generating a NADH. Chlamydomonas lines with reduced amount of PDH showed a dramatic decline in photosynthesis under photoautotrophic growth [Chlamydomonas genome encodes three genes encoding acetyl-CoA synthetase (ACS) [Chlorella desiccata [Chlamydomonas cells to grow under mixotrophic/heterotrophic conditions, and also the production of acetate by fermentative reactions in the cell, ACS should play an important role in Chlamydomonas.In addition to above key steps of de novo fatty acid synthesis, precursor supply i.e., acetyl-CoA has been increasingly shown to play a critical role in determining cellular oil content ,70,89. Uc growth . A shortse (ACS) . ACS catesiccata . ConsideFatty acids are precursors to all acyl-lipid synthesis, including triacylglycerols . Beyond the steps of de novo fatty acid synthesis, ATP or NADPH contributes to lipid synthesis at four other major areas: i) the activation of acyl chains, for example the reactions catalyzed by long chain acyl-CoA synthetase (LACS) ,93,94; iChlamydomonas have started to emerge. One notable example to elucidate the importance of the connection between photosynthesis and reserve formation is the study on the std1 mutant. The std1 mutant is deficient in a gene encoding a DYRK  kinase, and has a better photosynthetic CO2 fixation during N starvation, and as a result, higher amount of biomass, oil and starch is made in this mutant [Due to two major urgent societal issues, i.e., the need for renewable energies and global warming, research on microalgae has flourished in the last 10–15 years. Large international efforts have since been put in place. Oil, starch, and growth are major electron sinks for a cell, and the diversion of their metabolism to one form or another is governed essentially by the cellular redox poise, which is a collective response of cells’ energy production and management pathways to a fluctuating environment. Novel insights into the energetic requirements and constraints in carbon fixation and storage in s mutant . It is hConsidering the complex nature of algal biology, an integrated understanding of algal photosynthesis, physiology and metabolism is required. A systematic understanding of the interaction between redox and carbon reserve metabolism is still to come. Such a question becomes possible now to be addressed due to the development of biophysical techniques in NADPH measurement and due to the availability of large number of well characterized genetic mutants defected either in photosynthetic activities , in mito


Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study.Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.The online version of this article (10.1186/s12859-019-2685-9) contains supplementary material, which is available to authorized users. Nij represents the stoichiometric coefficient of metabolite i in reaction j [A vast majority of diseases is classified as complex, implying that environmental and lifestyle factors, alongside with genetics, might play a crucial role in the onset and progression of the disease itself. Complex diseases infrequently follow the Mendelian laws of monogenic inheritance. Instead, they are caused by a combination of multiple genetic and environmental components, with low heritability , 2. The action j . GEMs praction j –20. Usuaaction j , researcaction j –29. The action j . Here weaction j , which uThe Genome-scale metabolic model of the human adipocyte, “iAdipocytes1809”, reconstructed and curated by Adil Mardinoglu et al. , was dowRaw data, in CEL format, were imported, corrected, transformed and normalized through “GEOquery”  and “AffThe algorithm by Lee and colleagues , to whic2O, CO 2, ATP, NADH, etc.) were excluded to avoid an unrealistic definition of the paths [To investigate the topological properties of the metabolic networks, metabolic fluxes obtained by the model simulations were used to build specific graphs and analyse their graph-theoretic measures. For each of the four conditions , the reactions having zero flux rate were removed, and the directions of the reversible reactions were forced by the sign assigned at the flux rate values during the simulation. Furthermore, recurring metabolites  subtype [The differential expression analysis was carried out on microarray data from GSE78958 between lean (BMI < 25) and obese (BMI > 30) subjects affected by Lum A breast cancer. Five genes resulted significantly differentially expressed Table  with fou subtype , and GO  subtype –46. A ro subtype . DLX2 ge subtype , 49. The subtype , 51. AltThe output flux rate values from the four performed simulations were analysed. In particular, reversible reactions having opposite directions, in lean and obese groups, were identified. Most of them, at both time points, were associated with transport reactions driven by SLC (Solute Carrier) gene family  of lean and obese using preprandial and postprandial experimental fluxes were analysed. The connections  among the reactions  were defined by the presence of shared metabolites, with the direction going from the reaction producing a metabolite to the one consuming it. The obtained directed graphs were then imported into the Cytoscape environment, and their topological properties were analysed. Figure To perform network comparison and visualise the results, the Cytoscape application DyNet was used. Comparison of networks in terms of nodes presence for each time point highlighted a high number of nodes specific to the lean condition which are repressed in obese networks Fig. a-b. In pThe nodes were ranked based on the Dn-score, and the top twenty were further investigated. At both time points, almost all these reactions involved the amino acid transport  investigate the relationships among genes associated with metabolic reactions; ii) identify the key regulators of the cellular functions involved, and their putative alteration in obese cancer patients. The top 50 rewired nodes from the previous networks comparison were extracted, and the associated genes were enriched in terms of Gene Ontology Biological Processes Additional file 2R scripts and the relative input files to create the reaction-based graphs. (ZIP 368 kb)Additional file 32FC) ≥ 1 in lean and obese metabolic network. For each table of fluxes, we also provided the table with the expression values (mean and SD) of the associated genes. (XLSX 304 kb)Tables of the flux rates output having either opposite direction or same direction with absAdditional file 5Enrichment analysis of top 50 rewired nodes of tp4 and tp5 central reference gene-based networks, obtained by the union and comparison between lean and obese networks. The list of the rewired genes and the relative Dn-scores is also provided. (XLSX 17.6 kb)


Corynebacterium glutamicum serves as case study to demonstrate its practical relevance.A biorefinery comprises a variety of process steps to synthesize products from sustainable natural resources. Dynamic plant‐wide simulation enhances the process understanding, leads to improved cost efficiency and enables model‐based operation and control. It is thereby important for an increased competitiveness to conventional processes. To this end, we developed a Modelica library with replaceable building blocks that allow dynamic modeling of an entire biorefinery. For the microbial conversion step, we built on the dynamic flux balance analysis (DFBA) approach to formulate process models for the simulation of cellular metabolism under changing environmental conditions. The resulting system of differential‐algebraic equations with embedded optimization criteria (DAEO) is solved by a tailor‐made toolbox. In summary, our modeling framework comprises three major pillars: A Modelica library of dynamic unit operations, an easy‐to‐use interface to formulate DFBA process models and a DAEO toolbox that allows simulation with standard environments based on the Modelica modeling language. A biorefinery model for dynamic simulation of the OrganoCat pretreatment process and microbial conversion of the resulting feedstock by  Techniques from computer‐aided process design play an important role in increasing competitiveness of bio‐based processes compared with existing conventional processes  models were used to approximate the reaction kinetics of microbial transformations.However, this class of models is not suitable to describe and predict the complex intracellular metabolism of microorganisms cultivated in bioreactors for the purpose of bio‐based production. Clearly, to understand these multilevel interactions in a real quantitative manner, mechanistic pathway modeling in combination with multi‐omics analytics would be required  enables the simulation of consecutive, steady‐state metabolic flux distributions under changing environmental conditions , dynamic optimization approach (DOA), and direct approach (DA). SOA divides the time horizon into intervals, solves the embedded optimization problem at the beginning of each interval and performs integration assuming a fixed flux distribution for the respective interval batch operation. Therefore, we chose the multiscale modeling approach by embedding a DFBA model into a plantwide biorefinery simulation. Mathematically, this leads to a system of differential‐algebraic equations with embedded optimization criteria (DAEO).Corynebacterium glutamicum.In this study, we present a framework that enables dynamic plant‐wide biorefinery modeling and simulation. The framework builds on Modelica as powerful equation‐based modeling language and specific libraries for easy implementation of different submodels describing biomass treatment and microbial transformation processes. The integration of a detailed DFBA model into a biorefinery design process enables conclusions regarding the interaction of individual process steps, design of potential feedback loops, and development of model‐based control systems. To demonstrate the practical relevance of the approach a two‐step biorefinery model is implemented, describing the dynamics of the OrganoCat pretreatment process and the microbial conversion of lignocellulose‐derived sugars by the model organism 22.1We implemented a library of dynamic models for unit operations that are widely used in chemical processes, for example, flash unit, decanter, and distillation column, using the object‐oriented modeling language Modelica . The parameter record also defines the models to be used for calculation of thermodynamic properties such as specific enthalpy or molar volume. The property equations were implemented as Modelica functions.The respective unit operation model defines either single‐ or multiphase equations. For multiphase unit operations a quasi‐homogeneous modeling approach was used. Consequently, equilibrium was assumed for the respective phases. Examples for the most relevant phase equilibria and the associated unit operations are vapor‐liquid equilibrium (VLE) for flash unit and distillation column or liquid–liquid equilibrium (LLE) for decanter and extraction column. An equation‐oriented formulation for phase calculation that allows dynamic simulation in all phase regimes, that is, in single‐ and two‐phase region, was implemented based on literature for VLE Biegler,  and LLE 2.2The dynamic model for the microbial conversion step is based on the DFBA approach given byx are the nx differential states describing the extracellular environment with initial conditions given by x0. The differential equations f depend on the optimal solution v of an embedded optimization problem. Note that a typical DFBA process model does not include any algebraic equations outside the embedded optimization problem. Therefore, we refer to equation system N with dimensions nm×nv and the vector vˆ covering nv reaction fluxes. The equations are derived from nm steady‐state mass balances for the nm intracellular metabolites assuming that the metabolite pools adjust infinitely fast to changing environmental conditions , that is, the constrained linear system of equations given by v. The objective functions used in literature are either linear, for example, maximization of cell growth =−cTvˆ, where c is the cost vector.For a typical biochemical network, the number of metabolic fluxes is larger than the number of metabolites .2.For the microbial transformation step, we apply the visualization tool Omix .4.The coupling of different biorefinery submodels, for example, biomass pretreatment and microbial transformation, leads to a multiscale biorefinery model. Mathematically, this model is a DAEO and a tailor‐made solution method is required to allow process simulation in standard software such as Dymola5.Finally, simulation studies with a compiled version of the DAEO model can be performed in MATLAB The Modelica library, the Omix converter and the DAEO toolbox are available for interested users.Corynebacterium glutamicum in different scenarios.In the following we applied this framework to simulate the dynamics of the OrganoCat pretreatment process and the subsequent conversion of the resulting sugar streams by 3.2Plant‐derived biomass as feedstock has to be pretreated to make the native biomolecules accessible for subsequent chemical or microbial transformation. The effectiveness of a pretreatment depends on many factors such as type of biomass, process conditions, formation of unwanted degradation products, recyclability of catalysts, energy and catalyst costs, and many more. An overview of various pretreatment processes for lignocellulosic biomass, their advantages and drawbacks as well as an economic assessment can be found elsewhere  and C6 (mainly d‐glucose) sugars with defined ratios that are envisioned to be processed in microbial transformation steps to yield high‐value products. To discuss the impact of different sugar ratios we define the fraction of total carbon atoms stemming from d‐xylose ascC5 and cC6 denoting the concentrations of d‐xylose and d‐glucose, respectively. Note that ϕC5=0 refers to a pure d‐glucose solution, while ϕC5=1 indicates pure d‐xylose.In this study, we considered the OrganoCat process  is exposed to a biphasic reaction medium consisting of water with oxalic acid and 2‐MTHF for a fixed reaction time in a batch reactor Figure , R1. AftϕC5=0.09 is obtained  and d‐xylose (XYL), the biomass (X) as well as the model target product succinate (SUCC) and by‐products lactate (LAC) and acetate (ACE). Uptake rates for d‐glucose and d‐xylose were modeled by classical Michaelis–Menten kinetics. In addition, an upper limit for the overall uptake rate of carbon sources was imposed to account for potential transport limitations:μ was used as optimization criterion in all simulations. The full DFBA model including kinetic parameter values is given in Appendix The balanced extracellular species are the carbon sources lactate LC and ace3.3.1C. glutamicum for aerobic growth on two different sugar streams as expected outcome of the OrganoCat pretreatment process  as feedstock, biomass growth is achieved via simultaneous metabolization of both carbon sources without any by‐product formation −1. The decrease of d‐glucose concentration (after approx. 9 hr) leads to an increase of the d‐xylose uptake rate before its depletion. In Figure t=1 hr is shown. Under these conditions, d‐xylose is expected to be exclusively metabolized via the Weimberg pathway to synthesize the essential biomass precursor α‐ketoglutarate.Using the OrganoCat medium with high ontent ϕC=0.09 as ontent ϕC=0.09 as ontent ϕC=0.09 as ϕC5=0.86), both carbon sources are again simultaneously consumed and similar amounts of biomass are formed  resulting in a high growth rate of μ=0.49hr−1. Compared with growth on higher d‐glucose contents, no additional d‐glucose is channeled into this precursor. After depletion of d‐glucose, the sole carbon source d‐xylose is metabolized similarly via Weimberg and isomerase pathway (μ=0.34hr−1).For the other OrganoCat medium ϕC=0.86, boϕC5=0.09 because the higher content of the preferred carbon source d‐glucose leads to a higher growth rate. Consequently, growth on this medium is preferable from a process perspective.The simulation results indicate that growth on both OrganoCat media yields almost equal biomass titer. The space‐time yield is higher for 3.3.2STYX=cX(tB)tB, where tB is the final batch time defined by depletion of carbon sources and cX(tB) is the biomass concentration at that timepoint. The maximum oxygen uptake rate was again not constrained to model aerobic growth conditions. In this scenario we looked into different strain designs regarding the activity of the four different d‐xylose metabolic pathways. In the first design, all of the four different pathways are potentially available and the solution of the optimization problem decides upon activity or inactivity of the respective pathway. In the other designs, only one of the four metabolic pathways is considered to be active by constraining the corresponding other reaction fluxes to be zero.Following this outcome, we performed a second simulation study to investigate the influence of different sugar ratios on cell growth under aerobic batch operation. The main objective during growth in batch operation is high biomass titer in short batch time, that is, maximization of space‐time yield with respect to biomass defined by d‐glucose (ϕC5=0) than on pure d‐xylose (ϕC5=1), independent on the activity or inactivity of the different d‐xylose assimilation pathways , d‐xylose is exclusively metabolized via the Weimberg pathway to synthesize biomass growth precursor α‐ketoglutarate  the operation of the isomerase pathway leads to significantly higher space‐time yields compared to all other pathways. This can be explained by the favorable energetics because reducing equivalents such as NAD(H) and NADP(H) are better balanced.The results of this simulation study indicate that the STY is higher for growth on pure s Figure . Howevers Figure , the hig3.3.3ϕC5=0.2, cf. Figure ϕC5,in in the feed and the maximum oxygen uptake rate vO2,max during the feeding phase. Noteworthy, during feeding phase, substrates may accumulate in the reactor. To facilitate the comparison of different conditions, the process simulation was terminated when carbon sources were depleted . We define the space‐time yield with respect to the fed‐batch phase according to STYSUCC=cSUCC(tF)ΔtFB, where tF is the final process time, cSUCC(tF) the concentration of succinate at that timepoint and ΔtFB the period of the feeding phase  plus the duration until all carbon sources were depleted.In our third simulation study, we were interested in an optimal production scenario for succinate under microaerobic fed‐batch conditions. Therefore we coupled the optimal media design for biomass growth in a first batch phase  due to simultaneous formation of acetate. Consequently, based on the assumed strain design , a high growth rate is obtained by metabolization of d‐glucose at the highest possible rate. In addition, d‐xylose is metabolized via the Weimberg pathway to synthesize biomass precursor α‐ketoglutarate. Both carbon sources are depleted almost at the same time. During production phase, pure d‐xylose is fed at a low concentration such that only small amounts accumulate in the reactor. d‐xylose is metabolized via the isomerase pathway while all other pathways are inactive. Finally, oxygen limitation enforces the downregulation of the oxidative part of the TCA cycle and reverse operation of its reductive part leading to combined formation of succinate and acetate.The combination of optimal batch and fed‐batch operation with highest product titer is shown on the right‐hand side of Figure 4C. glutamicum.We presented a multiscale and multidisciplinary modeling framework that allows dynamic modeling and simulation of complex biorefinery processes. The developed Modelica library of (bio)chemical unit operations permits easy adaptation to describe different subprocesses for the conversion of renewable feedstock into value‐added products or to replace (sub)models when new insights and more detailed models become available. The DFBA approach enables realistic modeling and simulation of microbial transformation steps and accounts for changing environmental conditions during dynamic operation. Our solution strategy for the embedded optimization problem is easy to use and allows problem formulation and modification in Modelica language. Finally, our case study showed that sugar mixtures from OrganoCat pretreatment are well‐suited carbon sources for bio‐based production with the platform organism The direct approach is suitable for solving the embedded optimization problem during dynamic simulation of DFBA models. However, it is important to note that numerical difficulties may arise because active set changes of the embedded optimization problem are hidden from the integrator. In addition, the direct solution approach requires the simulation software to use a numerical approximation technique  to obtain derivative information for the embedded optimization problem inducing an truncation error. For the case studies in this contribution, none of these difficulties were observed. Nevertheless, a more advanced solution method to handle these drawbacks is desirable. The method of Höffner et al.  determinThe promising simulation results presented in this study need to be confirmed experimentally and the biorefinery model may be extended with respect to downstream processing to allow plant‐wide evaluation of the different production scenarios discussed in this study. In addition, the substitution of some (sub)models of the pretreatment process by more detailed models is necessary for in‐depth analysis of the influence between the various process steps and identification of reasonable feedback loops. For instance, a more detailed model of the enzymatic hydrolysis step would enable a feedback loop regarding the required sugar ratios for microbial transformation and appears to be a promising handle to improve the overall process performance. In addition, the presented tool may be used to support scale‐up of biorefinery processes, for instance, in terms of plant‐wide model‐predictive control, sizing of equipment, and identification of potential bottlenecks .All authors declare no competing interests.T. P. implemented the Modelica models and the DAEO toolbox, conducted the case studies, and prepared the manuscript. X. Z. implemented the interface between OMIX and Modelica. J. H. helped to finalize the manuscript. Ev. L. guided the research and edited the manuscript. R. H. implemented the parser of the DAEO toolbox and conceived the research. U. N. reviewed and improved the manuscript. W. W. guided the research and edited the manuscript. A. M. conceived the research, defined the case study and edited the manuscript. S. N. created the cell model, guided the research and helped to prepare the manuscript. All authors read and approved the submitted manuscript.The Modelica library, the Omix converter, and the DAEO toolbox will be made available for interested users.


S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus’ metabolic response to its environment. S. aureus molecular biology with previous annotations resulting in the first quantitatively and qualitatively validated S. aureus GEM. GEM guided predictions obtained from model analysis provided insights into the effects of medium composition on metabolic flux distribution and gene essentiality. The model can also serve as a platform to guide network reconstructions for other Staphylococci as well as direct hypothesis generation following the integration of omics data sets, including transcriptomics, proteomics, metabolomics, and multi-strain genomic data.Environmental perturbations  induce systems-level perturbations of bacterial cells that vary depending on the growth environment. The generation of omics data is aimed at capturing a complete view of the organism’s response under different conditions. Genome-scale models (GEMs) of metabolism represent a knowledge-based platform for the contextualization and integration of multi-omic measurements and can serve to offer valuable insights of system-level responses. This work provides the most up to date reconstruction effort integrating recent advances in the knowledge of  Staphylococcus aureus (MRSA) USA300 strains have emerged as the predominant cause of community-associated infections in the United States, Canada, and Europe  is the set of concentration measurements across t in mmol/L, gdW is the gram dry weight in g/L, t is the time in hours, For each condition, we verified that the uptake and secretion rates were mass balanced and that the overall flux of elements going towards biomass production  is larger than the total flux of elements needed to support biomass production at the experimentally measured growth rate.Ne,i is the base ratio for element e in the metabolic structure for nutrient i, SURi is the substrate uptake rate for nutrient i, bk is the relative coefficient for the biomass precursor k, Pe,k is the base ratio of element e in the metabolic structure of the biomass precursor k.Where S15-S17 Tables in S2 Appendix). We subsequently ran flux balance analysis (FBA) to simulate maximal biomass production.We proceeded to build two condition-specific GEMs by constraining the reactions exchanging the extracellular nutrients to +/- 10% of the measured corresponding calculated uptake and secretion rates . The maximal flux through the ATP maintenance reaction obtained was 1.93 mmol/gDW/h for the CDM-constrained GEM and was 12.64 mmol/gDW for the CDMG-constrained GEM. As a result, we obtained a growth associated maintenance of 9.59 mmol/gDW/h  and a non-growth associated maintenance of −5.87 mmol/gDW. Since non-growth associated maintenance should be positive, we hypothesize that data-sets {vATPM,μ} covering a larger range of conditions  coupled with measurements of CO2 secretion rates is needed to lead to more accurate values. We instead used the GAM and NGAM values experimentally obtained for E. coli and iteratively computed maximal growth for decreasing percentages of the initial value. For each condition, the GAM and NGAM value is chosen when the simulated maximal growth corresponds to the observed experimental growth rate .We calculated a theoretical growth associated maintenance by changing the objective for both condition-specific GEMs to ATP production  and glucose + chemically defined medium (CDMG).1. Modification of the network content. 2. MRSA-specific additions reveal metabolic knowledge gaps. 3. False negatives highlight gaps of knowledge in metabolism. 4. Gene essentiality highlights cases of non-essential protein complex subunits. 5. Protein structures coupled with gene essentiality uncover false and real isozymes. 6. Mutants with partially affected growth corroborate the presence of functional isozymes in (PDF)Click here for additional data file.S2 AppendixTable S1: iYS854 metabolic reactions. Table S2: iYS854 metabolic genes. Table S3: iYS854 metabolites. Table S4: Energy dissipation reactions tested. Table S5: iYS854 protein structures, mapped to PDB crystal structures using the ssbio package. Table S6: iYS854 protein structure homology models generated using the ssbio package. Table S7: Biomass objective function. Table S8: Simulated chemically defined media type. Table S9: Growth predictions and supplementation on defined minimal media. Table S10: High throughput microarray growth phenotypes for aerobic conditions aligned with experimental predictions. Table S11: Prediction and validation of transposon mutant phenotypes. Table S12: Gene essentiality prediction for 854 gene knockout mutants simulated on rich medium. Table S13: Gene essentiality prediction for 854 gene knockout mutants on chemically defined minimal media. Table S14: S. aureus dry cell weight measurements. Table S15: Uptake rates calculated from the absolute quantitative exo-metabolomics measurements for growth of S. aureus strain LAC on chemically defined medium (CDM) and glucose + chemically defined medium (CDMG). Table S16: Elemental mass balance for the two sets of exo-metabolomics. Table S17: Upper and lower bounds in the condition-specific genome scale models. We allowed for a variation in +/- 15% of the computed uptake rate.(7Z)Click here for additional data file.S1 Data(JSON)Click here for additional data file.S2 Data(JSON)Click here for additional data file.