Acinetobacter baumannii is a clinical threat to human health, causing major infection outbreaks worldwide. As new drugs against Gram-negative bacteria do not seem to be forthcoming, and due to the microbial capability of acquiring multi-resistance, there is an urgent need for novel therapeutic targets. Here we have derived a list of new potential targets by means of metabolic reconstruction and modelling of A. baumannii ATCC 19606. By integrating constraint-based modelling with gene expression data, we simulated microbial growth in normal and stressful conditions (i.e. following antibiotic exposure). This allowed us to describe the metabolic reprogramming that occurs in this bacterium when treated with colistin (the currently adopted last-line treatment) and identify a set of genes that are primary targets for developing new drugs against A. baumannii, including colistin-resistant strains. It can be anticipated that the metabolic model presented herein will represent a solid and reliable resource for the future treatment of A. baumannii infections. Acinetobacter were long considered harmless, environmental organisms, but from the 1960s onward, an increasing number of reports have documented the emergence of Acinetobacter strains of this genus among severely ill, hospitalized patients. These strains showed unusually high levels of resistance to antibiotics that could be used at the time. Also, they gave rise to cross-infections and outbreaks among patients1. Recently, resistance to antibiotics in A. baumannii has risen to worrisome proportions .Bacteria of the genus A. baumannii is colistin, a positively charged molecule that, by interacting with the lipid A moiety of lipopolysaccharide (LPS), causes disorganization of the outer membrane. Unfortunately, colistin resistance in A. baumannii has also been reported, thus highlighting the urgency of finding new molecules to face this threat4. Although careful monitoring, antimicrobial stewardship and measures to prevent spread in health care institutions are important for controlling A. baumannii infections, new antimicrobial agents and/or strategies are urgently required to eradicate antibiotic resistant strains from affected patients.One of the last-line treatments for MDR A. baumannii is required. Metabolic network reconstruction and its conversion to a mathematical framework has become a cornerstone for studying the systems biology of metabolism5, allowing the examination of the connection between phenotype and genotype and driving biological discoveries. In particular, constraint-based tools  enable the estimation of the rate that metabolites’ flow through a metabolic network and to compute cellular phenotypes for various growth conditions6. Interestingly, by identifying those genes whose deletion is predicted to impair cellular growth, this in silico technique can be used to predict essential genes (EGs) at a genome-scale. Following a metabolic modelling approach, several EGs datasets have already been derived for important pathogens such as Helicobacter pylori7, Pseudomonas aeruginosa8, Mycobacterium tuberculosis9, and Staphylococcus aureus10. Usually, such predictions are performed simulating growth in an arbitrarily defined medium, accounting for the main nutrients used by the microbe and without imposing any additional constraint to the model. Indeed, the search space of essential genes predicted can be narrowed by imposing additional constraints on the model. One possibility consists of modulating the flux admissible across each reaction on the basis of the expression values of the corresponding genes. By doing so, it is possible to generate context-specific models that reflect the actual set of reactions employed11. This approach promises to reduce i) the gap between the predicted and real cellular metabolic landscapes, and ii) the number of false positives/negatives in EGs predictions. Additionally, it might reveal hints for the synergistic use of antibiotics and, in particular, to the possible additional targets that might arise from the adaptation/response of a microbe’s metabolism to a single antibiotic. Indeed, changes in gene expression might redirect the cellular metabolic fluxes in such a way that novel and untapped essential reactions may emerge, representing good candidates for a synergic antibiotic. Despite that the use of antibiotics in combination is sometimes questionable, this approach can be considered in cases of severe infections and it has been shown to be effective in the case of Pseudomonas and Acinetobacter spp.13.To address new possible solutions, a system-level study of antibiotic-response in A. baumannii exposure to colistin. We integrated gene expression data during exposure to colistin14 with a newly reconstructed genome scale metabolic model, allowing for constraint-based modelling of the type strain ATCC 19606. Our data revealed the metabolic reprogramming that occurred in this strain following the establishment of a stressful condition such as the presence of an antibiotic. Furthermore, the metabolic reconstruction provided here represents an important resource for the future understanding of A. baumannii metabolism and for the detection and identification of novel drug targets.Here, we explored the system-level metabolic consequences of A. baumannii ATCC 19606 metabolic model was obtained through the Kbase server (http://kbase.us). This was manually curated as described in Methods. Afterwards, we used previously published large-scale phenotypic data15 to validate our reconstruction over a large set of experimental tests. Manual curation was performed by comparing FBA outcomes with such auxotrophies data (determined through Phenotype Microarray (PM) technology). During this process, the capability of our model to represent the observed phenotypes was tested.A preliminary draft reconstruction of the in silico in simulated Simmons minimal medium  under aerobic conditions by iteratively probing each C-source used in PM plates. During these simulations, biomass optimization was selected as the model objective function (O.F.). Results of the simulations  were compared with the activity directly measured during an experimental phenotype microarray experiment, and discrepancies identified between the in silico and experimental data were manually adjusted as possible (such as by filling in missing transport reactions or metabolic gaps).Growth rates were firstly estimated in silico and experimental data: out of the 67 in silico screened metabolites, 24 were correctly found to be carbon and energy sources for A. baumannii ATCC 19606 (true positives) and 32 not (true negatives) while only 10 disagreements remained, 5 false negatives and 5 false positives. All data are briefly summarized in Fig. Following this procedure, we reached an overall agreement of about 84% between the A. baumannii ATCC 19606 genome-scale metabolic model (named iLP844 according to the current naming convention16) contains 1628 reactions (162 exchange reactions), 1509 metabolites, and 844 genes , A. baylyi ADP1 model, iAbaylyi18 (22%), and A. baumanni AYE model, AbyMBEL89119 (17%). A COG classification of all the genes embedded in the model is provided in Table A. baumannii ATCC 19606 model is available as Supplementary Material in SBML validated format and embedding cross-references to several databases , by imposing the necessary constraints to the metabolic reconstruction. Accordingly, screens for EGs were performed for multiple specific conditions: starting by simply changing the set of available nutrients  and then by simulating stressful situations such as antibiotic exposure and mutations (using available expression data in such conditions).As already mentioned, the identification of EGs is one of the key-steps in a drug discovery pipeline. Indeed, both general and condition-specific EGs can be distinguished19. To do so, we set the lower bound of exchange reactions as described in methods. Then we performed in silico gene deletions for each gene in the model. Accordingly, each gene was defined as essential if its elimination destroyed the network’s ability to synthesize at least one key biomass molecules (i.e. the model predicts no-growth); otherwise, the gene was considered to be dispensable. Gene essentiality analysis was performed through both FBA and MOMA approaches (see Methods), which both lead to the identification of 67 EGs. Fig. ratio, see Methods). The complete list of EGs and their functions is reported in Supplementary Material As we were interested in modelling the system in a gradually constrained manner, we initially simulated an arbitrary rich medium, allowing our model to have virtually all the nutrients needed – as likely happens inside of a hosti.e. in Simmons medium, Fig. Next, we repeated EGs prediction by simulating growth in a minimal medium , Wang et al.21 labelled 481 genes as essentials for that strain. However, not all of them were comparable with our predictions since a large fraction was neither metabolic or possessed an orthologous gene in A. buamannii ATCC 19606. In both cases these genes are absent in iLP844. For the same reason, not all the 67 EGs found through our simulation were comparable with the reported experiment. After performing all these necessary restrictions, we reached the result shown in Fig. i.e. 42 genes have been predicted to be essential by both approaches (in silico and wet-lab) for the two A. baumannii strains considered  medium. Specifically, we compared our EGs set to that obtained by Dorsey et al. through insertional mutagenesis experiments with A. baumannii ATCC 1960623, where the metabolic deficiency of insertion derivatives was subsequently confirmed, identifying essentiality of 10 disrupted genes. Repeating the assay in silico, our model correctly represented the phenotypes of the A. baumannii mutants, with 8 out of the 10 genes predicted as essential by Dorsey and colleagues also shown to be essential in iLP844  by adding to the minimal medium the metabolite(s) whose production was affected by the mutation.Comparisons were also carried out between our 844 Fig. . Additioscenario into the genome-scale model.Although a large fraction of the predictions was supported by previous experimental data, a possible source of error, using the methodology described above, stems from the observation that not all the reactions of the model will be active during growth in a given physiological condition. In particular, changes in gene expression are likely to influence the activity rate of the corresponding cellular metabolic reactions, leading to the observation that a given reaction can be considered ‘turned on’ or ‘off’ on the basis of the expression levels of the encoding gene(s). Using available computational methodologies, it is possible to modulate the flux across each reaction on the basis of the expression values of the corresponding genes. This permits to take a picture of the current metabolic state and tightening up the predictive capabilities of the model itself. Accordingly, as the dynamic changes of metabolic reprogramming are likely mirrored by changes in gene essentialities, a possible solution for avoiding or reducing false positives is to merge transcriptomics data of the tested A. baumannii strains is the exposure to antibiotics and to colistin in particular14. Importantly, both the (metabolic) consequences and the occurrence of targets to be used in a synergic treatment are, currently, almost untapped. In order to study the dynamic changes of the metabolic network following antibiotic exposure and to derive a more realistic picture of gene essentiality patterns in a real scenario (antibiotic treatment), we used available transcriptomic data for A. baumannii ATCC 19606 in response to colistin treatment14. Up-regulation and down-regulation ratios  of genes were combined with the iLP844 by using MADE 24. Briefly, MADE uses statistically significant changes in gene expression measurements to determine binary expression states (highly and lowly expressed reactions) i.e. reactions are turned on and off depending on the changes in mRNA transcript levels. Thus, by mapping gene expression data into the model, the in silico metabolic predictions are more consistent with the actual physiological state of the cell.Arguably, one of the most interesting physiological conditions of et al.14, A. baumannii was grown in two different media, i.e. with and without 2 mg/L of colistin, and then sampled at 15 and 60 minutes after exposure. Following the described approach, we integrated the available transcriptomic data regarding all the metabolic genes embedded in our in-silico reconstruction (i.e. about 80 genes). Accordingly, we obtained four distinct models, each representing the predicted functional metabolic state of the cell at both 15 and 60 minutes, treated and untreated with colistin. These models differ in that some of their reactions are (completely) ‘turned on’ or ‘off’ according to the measured levels of their corresponding genes. Afterwards, optimization of the four models was performed, allowing the analysis of flux distribution in the network and the occurring metabolic reshape.In the experiment by Henry ratio, see Methods) of treated vs. untreated models, for all the reactions. However, FBA only provides one of the possible optimal solutions out of many alternative (and feasible) cellular flux distributions. Hence, in order to correctly predict metabolic changes following antibiotic exposure we restricted the feasible solution space by performing Flux Variability Analysis (FVA)25. This approach allows estimating the minimum and maximum flux admissible across each reaction (under the same constraints as in FBA) and hence it can be used to estimate the correctness and accuracy of FBA predictions (see Methods).In order to highlight changes in the overall metabolic behaviour and to identify changes on the metabolic rewiring occurring after antibiotic exposure, we compared flux distributions at the two time-points by calculating the flux ratio (RF−1 in some cases), revealing the lack of accuracy in some of FBA-derived predictions. We here used FVA outcomes (as described in Methods) to filter out those reactions whose fluxes display little variation. In other words, each reaction was considered for downstream analyses only if both the maximum and minimum FVA predicted fluxes did not differ from the FBA predicted flux by more than 20%. Consequently, we were left with 901 reactions at 15 minutes and 970 reactions at 60 min. It is worth noting that several intervals of admissible flux ranges were tested and we report in Supplementary Materials vs. treated condition.As shown in Supplementary Material Both qualitative and quantitative flux changes were analysed by dividing the reactions into three categories  at both 15 and 60 minutes after exposure to colistin. As for the analysis involving nutrient availability, shifts in gene essentiality emerged following antibiotic stress. The complete sets of the predicted EGs for each condition have been reported in Supplementary Material A. baumannii ATCC 19606. Interestingly, however, some genes switch from the ‘essential’ condition to the ‘non-essential’ one, following the exposure to colistin.The same outline has been depicted in the second time-point condition (60 minutes): we identified 12 and 5 condition-specific EGs in the absence and in the presence of the antibiotic, respectively, see Table in vivo experiments could impact the set(s) of predicted EGs. Specifically, the robustness of the number of predicted EGs in each of these conditions (i.e. treated vs. untreated samples) in respect to possible variations in the medium composition was assessed via random permutation. We tested up to 1,000 different nutritional compositions as described in detail in Supplementary Material The two new sets of EGs show how changes in gene expression induced by the presence of the antibiotic might influence gene essentiality patterns in the strain ATCC 19606 and provide additional, nontrivial targets for drug design in such an organism. We also performed additional robustness analyses in order to test whether nutrients depletion occurring in treated and untreated samples during the A. baumannii specific or have orthologs in Homo sapiens, meaning that they would not represent good candidates for antibiotic treatment development, the sequences of the identified potential EGs were used to probe the human genome. Based on this BLAST26 search (see Methods), we excluded from further studies those genes presenting more than 30% sequence identity with their human counterparts. Targeting of such genes is non-ideal since they may cause potential side-effects by perturbing critical components in the human body. All BLAST results are reported in Supplementary Material Further, with the aim of discriminating whether the products of all the identified hypothetical EGs are A. baumannii ATCC 19606 infections. However, it is relevant to remark that, while these 46 general EGs could have been detected in several conditions, the other 4 condition-specific EGs are the result of specific constraints integrated in the model (gene expressions data). Hence, as already mentioned, they are nontrivial detections and they could represent a suitable horizon in the field of colistin-coupled treatment. The four genes, named HMPREF0010_00949, HMPREF0010_02972 and HMPREF0010_03445, HMPREF0010_01216 respectively encode a malonate decarboxylase (epsilon subunit), an arginine succinate synthase, a 3-4-dihydroxy-2-butanone-4-phopsphate synthase and a quinolate synthase. Interestingly, malonate decarboxylase epsilon subunit has already been characterized in the closely related organism Pseudomonas putida and labelled as an indispensable component of the enzyme for the cyclic decarboxylation ofmalonate27. However, to the best of our knowledge, no therapies targeting this protein have been developed to date. The product of 3-4-dihydroxy-2-butanone-4-phopsphate synthase is an intermediate in the biosynthesis of riboflavin. The enzyme requires a divalent cation, preferably Mg2+, to be active. The step becomes essential after colistin treatment as the antibiotic is predicted to cause an increase in flux through this pathway, probably following the shutdown of other parts of the network due to the down regulation of the corresponding genes. The arginine succinate synthase, is an enzyme catalysing the penultimate step in arginine biosynthesis (urea-cycle): the ATP-dependent ligation of citrulline to aspartate in order to form arginino-succinate, AMP, and pyrophosphate. The last EGs encodes for a bacterial specific enzyme that catalyses the second step in the de novo biosynthesis of NAD+ from aspartate.Among all the queries, we identified 46 (out of 73) general EGs and 4 (out of 12) condition-specific EGs that do not have any human orthologous. Thus, the 46 EGs represent valuable targets for further development of brand new drugs against Up to now, we have presented how metabolic reconstruction and mathematical modelling can be used to explore the strain’s metabolic response during colistin treatment and how it can lead to the identification of novel potential drug targets. Our last attempt is now to illustrate how, starting from the same available experimental data, the model can be employed as a ready-to use blueprint in order to test new hypothesis.et al.28, the mechanism responsible for colistin resistance is linked to LPS. Specifically, mutations in the lpxA, lpxC, and lpxD genes have been reported as the main cause of LPS loss, thus abolishing the initial charge-based interaction with the antibiotic. Hence, to simulate an A. baumannii LPS (LPS−) deficient and colistin-resistant strain, we removed this component from the biomass formulation in our genome-scale model. Then, to determine which genes are central for the cell’s survival in such a condition, we used the transcriptomic data of the mutant strain in the presence/absence of colistin at 60 minutes14 and mapped the data onto the new LPS− model. After this, we repeated the EGs prediction pipeline described above.As it was reported by Moffatt The analysis yielded a total of 55 and 72 EGs in the untreated and treated condition, respectively. Even in this case, the two sets share several elements (54 EGs) that remain mandatory for the cell in the two conditions  with the results obtained from experiments found in the literature. Some of these genes, although not yet experimentally validated, might represent primary targets for future research on the treatment of both the wild type and LPS-mutant (i.e. colistin resistant) strains. Our results have practical implications for the identification of new therapeutics as the identified essential genes can be used in drug-design pipelines. Moreover, we showed that the sequences of predicted EGs for the type strain ATCC 19606 are shared by most of the members of A. baumannii species, encouraging further research to check whether they are valuable drug targets for a larger number of strains than currently known. Finally, it can be anticipated that the iLP844 model illustrated herein represents a reliable and solid platform for further developments and the system-level understanding of the physiology of A. baumannii representatives and for the treatment of their infections.In this work, we have reconstructed and validated a genome-scale metabolic model of A. baumannii ATCC 19606 based on the genome annotation using Kbase automated reconstruction method (https://kbase.us/)29. This reconstruction was then thoroughly inspected and refined by integrating data from additional functional databases . Further integration was performed by searching for orthologous genes  in closely related organisms  through a BBH  approach (inParanoid34). Information regarding transport proteins was obtained probing the Transporter Classification Data Base (TCDB35) and transportDB36.We obtained a draft metabolic model of A. baumannii AYE, AbyMBEL89119.In order to predict proper phenotypes, the general biomass producing reaction of Gram negative bacteria automatically generated by Kbase was substituted with a more accurate one that takes into account strain’s specific components, which was recovered from the previously reported model of the related strain 37 and COBRAToolbox-2.038 in MATLAB® R2016a (Mathworks Inc.). Gurobi 6.5.0 (www.gurobi.com) and GLPK 4.32 (http://www.gnu.org/software/glpk/) solvers were used for computational simulations presented. A MATLAB® script to obtain all the results shown in this manuscript is provided as Supplementary Material The reconstructed model was analysed using CoBRApy-0.4.1 COnstraints-Based Reconstruction and Analysis for Pythonin silico simulations:Two growth media were considered during the −1 in order to mimic non-limiting conditions. Carbon sources uptake reactions were set to −100 mmol/g*h−1.Lower bounds of salts uptake reactions were set to −1000 mmol/g*h39  was set to −5.Lower bounds of all uptake reactions accounting for the nutrients present in Simmons mediumi.e. the maximum and minimum values of all the fluxes that satisfy the given constraints) while keeping the same optimal objective value.FVA analysis allows the determination of the span of possible flux variability (FBA), which are notably non-unique. In fact, for any optimal solution found through FBA there may exist alternate flux distribution patterns yielding the same growth rate. Hence, the space of reliable FBA-flux predictions has been restricted by selecting only those that occur in the interval defined as follows:This approach has been used in this work in order to impose bounds to FBA flux predictions  between the simulated knocked out strain growth rate (µKO) and the one predicted for the wild type strain (µWT). Formulated as:Gene essentiality testing was performed by simulating deletion of each gene within the metabolic network and hence setting the associated reactions to carrying no flux (according to the corresponding Gene-Protein-Reaction (GPR) rule). To predict the growth of the mutant strain and determine the set of EGs, we used two different approaches, FBA and MOMAratio = 0, then the knocked out gene is labelled as essential. Conversely, in case GRratio = 1, the removal of the gene has no effect on the growth phenotype. Finally, when 0 < GRratio < 1, the deleted gene was labelled as fitness-contributing gene, i.e. its removal partially affects the capability of the cell to produce biomass.Following this approach, if GR40, we used both approaches to compute essential genes in all the conditions tested in this work. Although no major differences were observed, results obtained with both methods are presented throughout the manuscript.As MOMA and FBA predictions may lead to different essential gene setsvs. the untreated conditions as follows:In order to evaluate the range of the change in the carried flux of each reaction in the model following colistin exposure, we compute the ratio between the predicted flux in the treated ratio equal to one indicate that no changes in the activity of the corresponding reactions were observed when simulating growth in the treated vs. the untreated conditions. Conversely, values of RFratio between 0 and 1 or values greater than 1 will indicate a reduced or increased activity of the corresponding reactions in the treated condition, respectively. Finally, negative values of RFratio will indicate those reactions whose directionality is predicted to change after the treatment.Values of RFA. baumannii ATCC 19606 metabolic model by using MADE 24. The program uses significant statistical changes in gene or gene expression to create functional metabolic models. By adopting an optimization approach that applies Boolean rules, MADE connects reactions to the binary expression states of associated genes. The four arrays of genes to be switched-off yielded by MADE have been reported in Supplementary Material In order to add transcriptional regulatory rules to the metabolic model, we imported the model from COBRA Toolbox into TIGER-1.2.0 0  framework (12). Then, the up- and down-regulation ratios of gene expression were mapped into the A. baumannii ATCC 19606 were probed against the human proteome to test their validity as potential drug target in infections with this pathogen, i.e. to exclude any cross-interactions between the drug used for the treatment and human proteome elements.Protein sequences of the corresponding EGs found in H. sapiens using the default search parameters of the NCBI BLASTP online tool . Results were considered positive (orthologous sequences found) if their sequence identity score value was equal to/greater than 30.Queries were aligned to the protein sequences of A. baumannii species level by probing them against all of sequenced genomes retrieved at NCBI ftp site, i.e. 1099 genomes. Particularly, the focus was centred on EGs found in Simmons medium and in rich medium, as well as for those found after 15 and 60 minutes of colistin exposure. BLAST search parameters and analysis of the results were performed as described above.In addition, the global distribution of EGs was evaluated at the SM1SM2SM3SM4SM5SM6SM7SM8


Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock.To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.The online version of this article (doi:10.1186/s12859-017-1483-5) contains supplementary material, which is available to authorized users. MCSs, was developed by Klamt and Gilles ,,MILP proN and T matrices have been split into reversible (subscript rev) and irreversible submatrices (subscript irr). Similarly, identity submatrices for reversible and irreversible reactions are represented by the matrices Irev and Iirr respectively. cMCS are directly calculated by finding solutions with minimum number of non-zero entries in vp,vn. Additionally binary indicator variables zp and zn are introduced such that zpi=0 if vpi=0 and zpi=1 if vpi>0 and similarly for zn, vn. Only one direction of v (either vpi or vni) can be active, hence Note that the We set up the following optimization problem ri=0 if zpi=1 || zni=1.with the additional constraint that the flux through a reaction is turned off if it is part of a cMCS, i.e., T,D,t and d, the optimization problem can be stated as With this system it is possible to find cMCS which will result in designs satisfying constraints on yields/fluxes specified by , 4). Ho. Ho4). HIn other words, the problem is to find optimal combinations of {target/desired} yields for all reactions to be optimized. This is not easy for a few reasons. In general, this is a non-linear optimization problem. Non-linear optimization is known to be inherently complex with general deterministic solutions being impossible to find. Secondly, slight adjustments in , 4) cou cou4) coPSO is a metaheuristic inspired by the flocking behavior of birds . In PSO,j-dimensional vectors, where j is the dimensionality of the search space. These represent the current position x, its previous best position p which is the position corresponding to the highest fitness achieved by the particle and the velocity v, Fig. Typically, a particle is made up of three g is the position corresponding to the global best fitness of the entire swarm till the current t. φ1,2 are called “acceleration constants” and determine the relative influence of the particle’s own knowledge and that of the group, both of which are commonly set to 2 [β1,2 are uniformly generated random numbers within the range  which we had previously developed . The modented in . FinallyConsider the network given in Fig. E. coli core network which will ensure high yield of ethanol even in the face of high growth. This network was previously used to design a high yield ethanol producing strain in [We apply PSOMCS to generate designs in an train in . This motrain in , particutrain in . Here weE. coli presented in [zp, zn variables to zero. The machine we used for this test had 24 CPUs, 396GB RAM, Intel Xeon E5-2667 2.90 GHz processor, running on Ubuntu 14.4 LTS. The cMCS cardinality was limited to 5. With 4 particles being processed in parallel, the program was run for 40 iterations. It took 14 iterations (∼ 74 hours) to find the optimal design. One of the designs is shown in Fig. FVA [To test the capabilities of our method we applied it to the genome-scale model of ented in . Our aimented in , 9 to reHere we have presented a method, PSOMCS, to design strains with high minimal product yield using knockouts of minimal possible size. To do this, we employ a PSO together with the direct enumeration of cMCS developed in –9. This There have been other methods with a similar strategy as ours, which is the use of a metaheuristic in combination with another method like linear programming. Most methods have relied on genetic algorithms , 12, 27,As shown by the comparison with OptKnock and RobustKnock in Fig. In heuristic search algorithms, performance comes at the cost of being too specific to the problem being solved . By virtMILPs are more difficult to solve than LPs and may consume large amounts of time as well as memory . During Improvements in run time can be made by forcing PSOMCS to explore only a part of the flux space leading to a smaller solution space to be explored. For instance lets consider the design in Fig. Here we have dealt only with knockout strategies to design better strains. It can easily be extended to include the concept of regulatory MCS introduced in  which coPSOMCS finds the best possible design in metabolic networks given multiple objectives with the corresponding cMCS. We have demonstrated its capability in finding optimal knockouts and designs in genome-scale metabolic networks. It finds competitive designs compared to standard tools and is orders of magnitude faster than EFM based tools in finding the optimal solution. PSOMCS could be used to predict minimal knockouts resulting in optimal yields in industrially important microorganisms. As the size and quality of metabolic models increase, methods like the one presented here will be even more useful.


What does it take to convert a living organism into a truly productive biofactory? Apart from optimizing biosynthesis pathways as standalone units, a successful bioengineering approach must bend the endogenous metabolic network of the host, and especially its central metabolism, to support the bioproduction process. In practice, this usually involves three complementary strategies which include tuning-down or abolishing competing metabolic pathways, increasing the availability of precursors of the desired biosynthesis pathway, and ensuring high availability of energetic resources such as ATP and NADPH. In this review, we explore these strategies, focusing on key metabolic pathways and processes, such as glycolysis, anaplerosis, the TCA (tricarboxylic acid) cycle, and NADPH production. We show that only a holistic approach for bioengineering — considering the metabolic network of the host organism as a whole, rather than focusing on the production pathway alone — can truly mold microorganisms into efficient biofactories. The bioproduction of value-added chemicals is gaining momentum, slowly but surely replacing environmentally unsustainable fossil-carbon-based chemical processes. Metabolic engineering of microbes now supports the production of food additives , plasticYet, apart from the properties of the bioproduction pathway itself, as a standalone unit, the endogenous metabolic network of the host organism plays a key role in determining biosynthesis efficiency. There are three primary means by which central metabolism can be modified to enhance a bioproduction process: decreasing and abolishing metabolic flux toward competing pathways that lead to waste byproducts, increasing the availability of the direct precursors of the desired biosynthesis routes, and ensuring high availability of cellular energetic resources, mainly ATP and NADPH. These metabolic strategies are the focus of this review. We argue that engineering of the cell as a whole is crucial for the optimization of any metabolic process. We find the term holistic bioengineering most appropriate to describe this approach.Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae — are overlaid . Key pathways are marked by specific colors. Enzymes to which we directly refer below are marked with a yellow background. We divide this review into several sections, each discussing the modifications made to a different central pathway or process with the aim of enhancing a particular biosynthetic flux. As the topic is quite extensive, our review focuses on several primary examples which, we believe, demonstrate the key aspects.E. coli, ethanol by yeasts, and lactate by cancerous cells. This ‘overflow metabolism’ phenomenon is the center of a wide range of research efforts , produce different combinations of compounds. While it is clear that fermentation, in the absence of electron acceptors, results in the conversion of most carbon input into fermentation products, it is important to emphasize that many organisms also produce large amounts of such products under aerobic conditions, e.g. acetate production by ts e.g. –18). For. ForE. cts  lactate dehydrogenase (LDH), to eliminate lactate production; (ii) alcohol dehydrogenase, to eliminate ethanol biosynthesis; (iii) acetate kinase and/or phosphate acetyltransferase (PTA), to eliminate acetate production; (iv) pyruvate formate-lyase (PFL), to eliminate formate production; and (v) fumarate reductase (FRD), to eliminate succinate production. Deletion of the genes encoding these enzymes, or a subset of them, was shown to dramatically increase the production of hydrogen , pyruvat-butanol , branche-butanol , tryptop-butanol , citrama-butanol , and man-butanol ,25,28,29E. coli has not only led to the accumulation of pyruvate but also resulted in the increased activity of the alternative anaplerotic enzyme PEP carboxykinase , as well as an enhanced oxidative phosphorylation, which was required to balance the increased cellular NADH/NAD+ ratio . Si. SiS. celutarate . Intereslutarate .Figure E. coli )In most cases, the rewiring of cellular metabolism was rationally designed based on the knowledge and experience of the authors. In other cases, a computational strategy was taken to systematically explore all possible alternations of central metabolism that could lead to beneficial results e.g. –141). Wh. Wh141])E. coli [We emphasize that while we focused on central metabolism in this review, a truly holistic bioengineering encompasses other aspects of cellular physiology, including amino acid, nucleotide and fatty acid metabolism, assimilation of inorganic elements such as nitrogen and sulfur, passive and active cellular transport, as well as transcription and translation. Each of these cellular processes can be — and should be — modified or rewired as to support the most efficient conversion of feedstock into product. We would like to finish by noting that even if we keep focusing on central metabolism, the challenge of wholly redrawing it — as opposed to mildly rewiring it, as is commonly done — is still open. To what extent can we reinvent key metabolic pathways? Can such dramatic changes be useful for biotechnological applications? Some pioneering studies, e.g. engineering an active Calvin Cycle in E. coli  — may st


Oenococcus oeni is the main responsible agent for malolactic fermentation in wine, an unpredictable and erratic process in winemaking. To address this, we have constructed and exhaustively curated the first genome-scale metabolic model of Oenococcus oeni, comprising 660 reactions, 536 metabolites and 454 genes. In silico experiments revealed that nutritional requirements are predicted with an accuracy of 93%, while 14 amino acids were found to be essential for the growth of this bacterial species. When the model was applied to determine the non-growth associated maintenance, results showed that O. oeni grown at 12% ethanol concentration spent 30 times more ATP to stay alive than in the absence of ethanol. Most of this ATP is employed for extruding protons outside of the cell. A positive relationship was also found between specific consumption rates of fructose, amino acids, oxygen, and malic acid and the specific production rates of erythritol, lactate, and acetate, according to the ethanol content of the medium. The metabolic model reconstructed here represents a unique tool to predict the successful completion of wine malolactic fermentation carried out either by different strains of Oenococcus oeni, as well as at any particular physico-chemical composition of wine. It will also allow the development of consortium metabolic models that could be applied to winemaking to simulate and understand the interactions between O. oeni and other microorganisms that share this ecological niche. This process is primarily responsible for lowering the acidity of wine, and also generates other benefits, such as improving aroma and flavor complexity; as well as increasing the biological stability of the resulting wines , low pH (<3.5), limited nutrient availability and high sulphite concentration (<50 ppm)  content of 38%. Borneman et al.  to provide a tool for simulating the metabolism, nutritional requirements, and specific growth rate of this microorganism under the harsh conditions of winemaking. Here we report the general features of the model, as well as its prediction performance. The resulting metabolic model was employed to assess the metabolic capabilities, limitations and potential of this LAB to successfully accomplish malolactic fermentation in wine.In this work, we constructed the first genome-scale metabolic model of an O. oeni PSU-1. The model was then manually curated consulting scientific literature and the online databases KEGG™Lactococcus lactis and Lactobacillus plantarum respectively, was conducted in order to find missing reactions . Gurobi 6.5We employed FBA to calculate the optimal distribution of metabolic fluxes of an underdetermined system of stoichiometric equations , true negatives (no growth observed neither in vivo nor in silico), false positives (growth in silico but not in vivo) and false negatives (growth in vivo but not in silico).For both sets of experiments, results were classified as true positives  to represent non-growth associated maintenance (NGAM).−1 h−1. At each iteration, the growth rate was maximized and the error between the experimental and predicted growth rate was calculated. The value that minimized the error between the experimental and predicted growth rate was chosen as the ATP required for maintenance of cellular processes.The values of NGAM were determined from the model for each experimental condition. For this purpose, we first fixed the consumption and production rates of different metabolites and then we progressively increased the NGAM from 0 to 5 mmol gDWThe experimental rates included in the model were specific consumption rates of glucose, fructose, citrate, L-malate, L-cysteine, L-serine, L-threonine; it also included specific production rates of D-mannitol, L-lactate, D-lactate, acetate, erythritol, and ethanol. They were calculated from experimental data of two batch cultures containing 0 and 12% ethanol, respectively, run in duplicate (see below).O. oeni PSU-1 preculture was prepared from a frozen stock by inoculating 100-ml Erlenmeyer flasks containing 75 ml MRS  medium  to reach 0 or 12% ethanol concentration (v/v) in each culture.An The adapted cells were inoculated in 50 mL flasks containing 35 mL of a chemically defined, wine-like, culture medium to achieve an initial optical density at 600 nm (OD600) of approximately 0.2. We employed the modified culture medium described by Terrade and Mira de Orduña , at an iThe flasks were incubated at 25°C, without stirring. OD600 was periodically measured to calculate the specific growth rate. At the same time, the content from each flask was centrifuged, the supernatant was collected and an aliquot was injected in a Lachrom L-700 HPLC system  equipped with a Diode Array and a Refractive Index detectors . Organic acids, alcohols and sugars were separated using an Aminex HPX-87H ion exchange carbohydrate-organic acid column  and quantified, as described previously  were inoculated in 50 mL flasks containing 35 mL of the same chemically defined, wine-like, culture medium, but lacking the amino acids evaluated for essentiality , one amino acid per flask, in duplicate. The flasks were incubated at 25°C during 13 days, without stirring, and OD600 was periodically measured to calculate the specific growth rate.An For each optimization using experimental data, non-zero reduced costs were extracted from the solver solution and employed for quantifying the impact of changing a capacity constraint on the objective flux. Scaled reduced costs were calculated as follows:wi represents the reduced cost, qi the flux through exchange reaction i, and μ the specific growth rate. Wi, the scaled reduced cost of the exchange reaction i, represents the fractional change in biomass obtained by a fractional variation in compound i. Reactions that showed both, a non-zero reduced cost and a non-zero scaled reduced cost, were further analyzed.Where FVA was carried out by minimizing and maximizing the flux through each reaction, under either unconstrained or constrained conditions. Span range was sorted by magnitude and plotted.Reactions unable to carry flux were considered blocked. For each of these, the cause of the obstruction was investigated by finding dead-end metabolites; these were determined searching for those metabolites that were only consumed or produced in the stoichiometric matrix. Additionally, we determined dead-ends by adding a demand (maximizing the flux) or a sink (minimizing the flux) reaction for each metabolite. Metabolites were considered dead-ends if the model was unable to produce, nor consume them.hit and run , IL1403 (Lc. Lactis), and Yeast 7 (S. cerevisiae) models  . We only left pathway I in the model because it was the most complete, i.e., 9 out of the 10 reactions of this pathway were associated with genes.The iSM454 model contains the whole biosynthetic pathways for 6 amino acids , as arisen from genome annotation , unsaturated fatty acids  and cyclopropane fatty acids (lactobacillate and dihydrosterculate)  associated with a generic acyl fatty acid. We therefore manually included these genes and reactions for the canonical catabolism of the above-mentioned saturated fatty acids.In relation to β-oxidation of fatty acids, Pathway Tools assigned two genes, L. lactis , codified by the l Figure  , and that the 6 remaining ones  were not.The O. oeni. On the other hand, nicotinic acid and pantothenate were predicted to be essential nutrients. Moreover, several vitamins, such as biotin, folic acid, pyridoxine, riboflavin, and thiamin were not essential.Single nucleotide omission experiments of iSM454 showed that these metabolites were not essential for O. oeni was able to grow in 91% of the cases in which growth has been observed experimentally (sensitivity); whereas it identified correctly 96% of the cases where O. oeni did not grow (specificity). Furthermore, 97% of the experiments in which O. oeni was predicted to grow, O. oeni actually grew (precision). Additionally, 90% of the experiments in which O. oeni was not predicted to grow, O. oeni actually did not grow (NPV). The accuracy of the model was 93%, i.e., the proportion of correct results to total predictions. By comparison, the GEM model of L. plantarum presents an accuracy of 86% , pyruvate kinase (EC 2.7.1.40), and phosphoglycerate kinase (EC 2.7.2.3); and subtracting the fluxes through hexokinase (EC 2.7.1.1/E.C 2.7.1.2) and fructokinase (EC 2.7.1.4). On the other hand, by using the usual method for ATP determination through heterolactic fermentation, which consists of adding the total D-lactate and acetate produced, the resulting ATP production rates were 1.38 and 2.12 mmol gDW−1 h−1, for 0 and 12% ethanol-containing cultures, respectively.As expected from the higher carbon flow through the heterolactic pathway in ethanol-containing cultures, the corresponding ATP specific production rate was 3-fold faster than in the absence of ethanol, passing from 0.74 to 1.98 mmol gDWTotal ATP production increases as the concentration of ethanol in the medium increases Figure . The modO. oeni, by conducting a sensitivity analysis  as well as the transport of acids -malate or citrate uptake- , and the export of acetate, L- and D-lactate . The specific uptake rate of cysteine, serine and threonine also showed a positive effect on the specific growth rate . On the contrary, the production rate of D-mannitol and D-erythritol had a negative effect on the specific growth rate, which showed scaled reduced cost of −2.67 and −1.48, respectively.To evaluate the robustness of our results, we employed random sampling and FVA to identify and explore the different phenotypes achieved at optimal specific growth rate.−1 h−1, i.e., a negative value for the logarithm of the flux range in Figure −1 h−1 in the unconstrained network; meanwhile, this value increased to 370 in the constrained network. Moreover, 99% of the reactions in the constrained network showed a flux range lower than 3 mmol gDW−1 h−1, suggesting that the constraints applied (uptake and production rates) strongly delimit the solution space. Thus, applying these constraints, the phenotype is well defined.FVA revealed some important differences between unconstrained and constrained networks Figure . 485 and−1 h−1.Additionally, random sampling in the constrained network revealed that the solution space was tight and alternative pathways were limited. Except in the case of reactions of isomer interconversions which could result in a futile cycle, none of the reaction rates analyzed changed more than 1.1 mmol gDWO. oeni requires oxygen to achieve growth at all ethanol levels. Moreover, the oxygen consumption rates needed for growth increase as the concentration of ethanol increases, ranging from 0.8–1.2 to 4.1–4.2 mmol gDW−1 h−1 for 0 and 12% ethanol, respectively.Interestingly, when constrained by experimental rates, FVA shows that O. oeni were predicted by in silico simulations of reaction knockouts—inhibiting the activity of the enzyme(s) carrying away the respective reaction. This was conducted by further constraining the model, i.e., fixing the flux of the corresponding reaction to zero.The essential reactions of 132 essential reactions were found by reaction deletion analysis, which represent 20% of the 660 total reactions of the model; of these, 28% correspond to fatty acid biosynthesis, and 14% to unsaturated fatty acid biosynthesis. The other main essential pathways include the biosynthesis of peptidoglycan (10%), glycerolipids (8%), and amino acid biosynthesis (8%)  were thoroughly curated in the model.In this work, we reconstructed, curated and validated the first genome-scale metabolic model of OEOE_1176, a gene related to a generic reaction of cyclization of unsaturated fatty acids. We identified dihydrosterculic acid, which derives from oleic acid under stress conditions , cis-vaccenic acid (C18:1 Δ11), lactobacillic acid (C19:0 cyΔ11), and dihydrosterculic acid (C19:0 cyΔ9). Moreover, we discovered that the reactions for generating cyclopropane bounds were associated with O. oeni under stress conditions, such as those prevailing in wine during malolactic fermentation (MLF). Moreover, these polysaccharides have been implicated in ropiness, a wine spoilage process . Likewise, a lactic acid transporter that allows this compound to cross the cytoplasmic membrane was added. Citrate metabolism, another important system of energy generation in O. oeni, was also included. Both processes allow proton extrusion, which relates to ATP synthase, allowing more ATP to be synthesized. These three added metabolic processes were critical for accurate model performance, especially for the prediction of specific growth rates. Thus, simulations strongly suggest that proton transport is the most important process for the survival of O. oeni under these harsh cultural conditions.Another important feature of the model is the representation of different proton extrusion/energy generation pathways employed by O. oeni phenotype by iSM455 model. For this purpose, we ensure that the only reactions for extrusion of protons were those related to heterolactic fermentation (efflux of lactate and carbon dioxide); in those cases, the network properly described the proton motive force needed to generate energy from the ATP synthase located in the cell membrane. Additionally, a careful revision of the metabolism was performed to account for proper cofactor utilization. This feature was recently reported as a critical step in improving phenotypic predictions by genome-scale metabolic models  was on average 0.25 mmol ATP gDW−1, pointing out that under ethanol stress (12%v/v), O. oeni spends in total almost nine times more energy in cell maintenance than under non-stress conditions, i.e., absence of ethanol in the medium.In general, microorganisms colonizing extreme ecological niches need higher energetic requirements for cell maintenance, which is not reflected in biomass formation  to those determined by Aceituno et al. , threonine degradation by aminoacetone:oxygen oxidoreductase (E.C. 1.4.3.21) and spontaneous diacetyl formation by acetoin oxidation.Moreover, a redistribution of intracellular fluxes occurred when ethanol content increased in the culture medium. FVA revealed that o et al.  for the O. oeni is still scarce; the physiological as well as proteomic and transcriptomic responses of the bacterium grown under different environmental perturbations are necessary to further improve the model. These might include determination of biomass composition, exhaustive electronic and proton balancing of stoichiometric equations and integration of transcriptomic and proteomic data. On the other hand, dynamic flux balance analysis (dFBA) has emerged as a promising strategy to study batch cultures of several strains (Sainz et al., Saccharomyces cerevisiae strains in wine-like medium (Vargas et al., O. oeni could be useful to simulate the kinetics of growth and industrial MLF extension.The overall good performance of the metabolic model evidences the correct distribution of metabolic fluxes, with or without 12% ethanol content and pH 4.8. However, it is worthy to mention that the information about E. coli's GEM has been successfully employed as a platform to model metabolite exchange between different organism under different environmental conditions (Jain and Srivastava, O. oeni and other microorganisms that share this ecological niche, as S. cerevisiae and other LAB, like Lb. plantarum, Lb. kunkeii, Pediococcus pentosaceus; and even undesirable and detrimental wine microorganisms, like Brettanomyces spp. or Acetobacter aceti, responsible for acetic acid production spoilage. Consortium metabolic models might predict how each organism develops in a shared, not isolated, scenario (Tzamali et al., O. oeni in coexistence with other species.Finally, a future challenge is the development of a more extended platform, based on the iSM454 model, allowing the simulation and prediction of the biological interactions occurring within the wine microbiome. For example, EA designed the research and provided guide throughout the investigation. EA, SM, and PC coordinated the project. SM and PC are first-authorships and developed and refined the model, and PC developed the OMIX version of the reconstruction. SM and MR performed the model validation and evaluation. AC designed, performed, and coordinated the batch experiments as well as processed the HPLC data. All the authors wrote the paper, read, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering. Growth-coupled designs for chemical production are limited by native metabolic networks' optimality for growth. Here, the authors introduce pathway orthogonality as a measure of the independence of biomass and chemical production pathways, identify metabolic valves that allow substrate utilization to be switched between the two, and demonstrate advantages of orthogonal designs. C-methyl-D-erythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate (MEP/DOX) pathway than the mevalonate pathway. Simultaneously, the expression of chemical production pathways is not sufficient to overproduce chemicals because cellular objectives are in competition with a chemical production objective34It is well established that pathway selection for chemical production plays an important role in metabolic engineering, for example, isopentenol has a higher pathway efficiency on a basis of yieldHowever, growth-coupled production has many biological challenges owing to the complex nature of metabolism. From an evolutionary perspective, metabolic pathways in cells have been optimized to convert sugar to biomass. The design of these pathways may, partially at least, be explained by optimality principles relating their structure to their function. Examples of such descriptions can be found in literature4678910Since structure is inexorably linked to function, it follows that a network function supporting chemical production and satisfying the key metrics stated above should exhibit a different structure from a wild-type cell and therefore also obey different principles of optimality. For example, in one recent study, the structure of the central metabolism was described as a ‘minimal walk' between the input substrate and the 12 requisite precursors for biomassHere we argue that to optimally convert the input substrate to the target chemical, one has to analogously generate a biosynthetic network that is largely independent of the natural metabolism but still capable of synthesizing biomass. We define such pathways as orthogonal pathways and examine the orthogonal properties of natural and synthetic metabolic networks that are designed for chemical production. This approach is in contrast to the approach taken in contemporary metabolic engineering design where a network optimized for biomass is augmented by deletions. Instead, we seek to determine the design characteristics of an orthogonal network that is optimal for the production of a target chemical as opposed to biomass. In doing so, we also present an algorithmic approach to engineer orthogonal pathways and develop a method based on cut sets to identify metabolic control reactions  that can be manipulated to allow or disallow cell growth. A metric for optimality that we developed helps to identify pathways that are optimal in the context of a set of minimal cellular interactions.Our analysis also leads us to consider substrates beyond the sugars that are naturally used by organisms, and to identify substrates such as ethylene glycol that are inherently better suited to produce target chemicals. We show that reworking the metabolic network structure to meet design specifications and designing networks by considering substrate–product pairs has implications for two-stage fermentation design. The tradeoff between growth and metabolite production in such orthogonal strategies can be effectively addressed through the design and identification of enzymes, such as phosphoenolpyruvate carboxykinase, to act as control valves between product and biomass production. Finally, we believe that the approach provides a new paradigm of metabolic engineering strategies for chemicals, in contrast to the existing growth-coupled strategies, which can be difficult to implement in practice. In doing so, it provides an improved framework for industrial strain design and the selection of substrate utilization pathways and strongly suggests that orthogonal strategies can lead to reduced cellular interactions when compared to native pathways that are instead optimized for growth.Orthogonal pathways are growth-independent pathways optimized for the production of a target chemical. These pathways are characterized by the minimization of interactions between the chemical-producing pathways and the biomass-producing pathways. Physiologically, this means in perfect orthogonal networks (i) the product pathway shares no enzymatic steps with cellular pathways that are responsible for the production of precursors required for biomass and, (ii) only a single metabolite serves as a branch point from which product and biomass pathways diverge. Hence, by design these pathways not only minimize interactions between the cell's biomass-producing pathways and the chemical-producing pathways, but also obey a minimal-walk optimality principle for product formation. The ideal structure of this type of network is shown in Escherichia coli. These pathways, the Embden–Meyerhof–Parnas (EMP) pathway, Entner–Doudoroff (ED) pathway variant and the methylglyoxal (MG) bypass shown in Glycolysis, including its many variants, consumes glucose through a highly connected metabolic network. We hypothesized that these points of connectivity, often described as redundancies that make cells robust to perturbations in their environment12Since analysis of pathways requires a substrate–product pair, we used succinic acid production from these pathways as a case study for our analysis as succinic acid is a well-studied, industrially produced biochemical made from sugars. Using our orthogonality analysis framework , we showThe orthogonality score for succinate production for each of the natural pathways is shown in St, We find that, within the natural pathways, the difference in orthogonality arises from the degree to which the glucose utilization pathways overlap with elements of the metabolism that support biomass. Both the MG shunt and the less connected ED provide routes that bypass several biomass precursors. This can be determined by analysing the elementary flux modes (EFMs) that only produce the target chemical set St,  and calcWhere possible, the orthogonality scores of A second observation was that orthogonality and redundancy are negatively correlated. Since orthogonality quantifies the shared nature of the biomass and the chemical-producing pathways within a metabolic network, we find that increasing the redundancy of the network decreases its underlying orthogonality. As an example, the inclusion of phosphofructokinase, which is typically unique to the EMP pathway, in the network of the ED pathway, reduces the orthogonality score (0.43). Hence, increasing the number of redundant reactions common to product and biomass synthesis decreases the orthogonality of the two objective functions. This result is expected as the goal of orthogonal networks was to share the least number of reactions and thereby reduce redundancies in the network. Hence, as hypothesized, by eliminating shared redundant pathways between the product and biomass precursors, well-designed synthetic pathways can reduce the complexity of supporting two distinct production objectives relative to the wild-type network. Next, given the popularity of growth-coupled strategies, we analysed the impact of growth-coupled strain design on the orthogonality between production and growth.Sx in Sx), the orthogonality score is undefined (as there are no biomass production modes left) and it can no longer be calculated using In growth-coupled strain design, cell growth is linked to product formation by identifying reactions such that biomass-producing EFMs  that eliminates the biomass production pathways and enforces production above a threshold yield.The MCS algorithm allows for the identification of designs with a non-zero production of the target chemical that lead to orthogonality by removing all the growth-dependent pathways (whether coupled or not), resulting in zero growth. We use this algorithm and develop a novel approach  to identify valve reactions that permit orthogonal pathway design. Specifically, the MCS algorithm is used to search for cut sets that guarantee theoretically viable product yields when the growth rate is zero. By applying these cut sets to the metabolic network, and then searching for reactions in the cut set that, when active, can restore growth rates above a desired threshold , it is possible to identify metabolic valves. If the reaction can restore biomass growth, then the cut set can be considered as a candidate for a branched structure. By calculating its orthogonality score when the valve is considered on, permitting flux , the cut sets' suitability can be assessed from the perspective of orthogonality. This approach necessitates a dynamic metabolic engineering (DME) strategy consisting of a growth phase and a subsequent production phase. We provide a detailed explanation of the approach in Methods section.First, since earlier results indicated that glycolytic pathways for consuming glucose and producing biomass were not orthogonal to succinic acid production pathways, we tested the ValveFind methodology on the natural glycolytic metabolism. In this case, we sought to determine whether a branched structure for succinate production could be derived through a set of gene deletions in a network characterized by a low orthogonality score, thereby effectively raising its score. This is akin to the method used to obtain strain designs for metabolite production albeit without demanding growth as is common in these methods. We identified 99 cut set strategies capable of producing succinic acid independent of growth. Of these, only 38 contained a reaction that could serve as a metabolic valve not linked to nutrient limitation . TCA cycle reactions such as isocitrate dehydrogenase and fumarase reactions were among the most commonly identified valve reactions. An example of one of those designs is the deletion set encompassing reactions phosphoenolpyruvate carboxykinase, a transketolase and both malic enzymes. Restoring isocitrate dehydrogenase could restore growth above 90% of wild type . This saWe then calculated the orthogonality scores for all 38 sets and manually verified that none exhibited an obvious branched structure. We initially expected all cut sets to be lower than the wild-type score of 0.41 since we expected that branched structures would be absent from the natural metabolism. Instead, we found that the scores varied considerably between cut sets, and the maximum was 0.43 and the minimum was 0.28. In hindsight, it is intuitive that orthogonality scores can be both greater and less than the unmodified network score. Removing reactions that contribute to redundancy in biomass space increases the orthogonality score if those reactions support biomass synthesis, as shown earlier. In contrast, if those reactions disproportionately remove EFMs that support product formation, it is possible for the score to decrease. Hence, the results suggest that cut set-based design strategies can be selected rationally to minimize the interactions between biomass and chemical production EFMs, even in natural metabolism where branched structures are not apparent. Low scores can be disregarded as they are not suitable for the primary objective of orthogonal metabolism, which is the minimization of interactions.High scores, however, do not necessarily guarantee branched structures, although branched structures, as we will describe below, do result in high scores. In the example, controlling isocitrate dehydrogenase as a metabolic valve prevents cell growth, but a zero flux through that reaction does not preclude the synthesis of most of the individual component metabolites of biomass. Specifically, synthesis of 11 of the 12 biomass precursors is possible even when biomass as a whole cannot be synthesized. The orthogonality score captures this dependence that the individual components of biomass have on network interactions, which can be indiscernible by simply examining individual valve reactions. The relatively lower-orthogonality score for this case (0.41) compared to the synthetic pathways in ldhA, adhE, ackA). These five consist of the gluconeogenic enzymes phosphoenolpyruvate synthase, both malic enzymes as well as succinyl-coa ligase . Whereas, for the synthetic pathways, the same reduction in yield can be achieved with a valve flux that is 10–25% of the wild-type flux. These results further highlight the value of orthogonal design for modulating flux and the differences in the orthogonality between the native and the synthetic substrate utilization pathways. We extend this analysis to the xylose metabolism of E. coli as well to show that these principles appear to be consistent . However, conversion of xylose to succinic acid was highly orthogonal for the non-native Weimberg pathway  it provides non-phosphorylated routes to assimilation, which bypass regulation in the metabolism and, (2) it allows the substrate's entry point into the metabolism in a way that bypasses highly connected nodes of natural metabolism. Both xylose to succinate and ethylene glycol to succinate are examples of these types of pairings. For example, in the context of orthogonality, glucose conversion is not as well suited to succinate production compared to ethylene glycol, which has a substantially higher score. In addition, we wanted to evaluate whether the orthogonality results obtained for succinate production could be extended to other products. Hence, we examined a total of nine different pathways and five additional products  from four substrates to support the generality of the findings in this case study. See Flux through metabolic pathways for biomass and product synthesis are determined by, among many factors, hierarchical cellular regulation that favours biomass synthesis over product synthesis. However, this flux is also a function of the driving force available through that pathway, expressed as changes in the Gibbs free energy and the kinetic parameters of the enzymes in the pathway. Coupling product formation to growth overrides the cell's regulation, that in the presence of a driving force, permits product synthesis and provides a basis for rational engineering to increase that flux.Since orthogonal pathways exist outside any regulatory framework, only a driving force is required to support flux through them. By examining the energetics of their path, we can understand how cell factories can support flux through these pathways. Hence, we calculated the various components of the protein cost: the kinetic, thermodynamic and saturation cost19−1. The synthetic glucose pathway was the most expensive—two orders of magnitude greater than the natural EMP pathway. This difference occurs as a result of poor kinetics of a single enzyme that dehydrates glycerate to pyruvate. In general, these results show that the difference in the cost of supporting flux can vary depending on the pathway. However, not all orthogonal pathways have high protein costs. For example, the orthogonal ethylene glycol pathway has costs of 47 g s mol−1 and the Weimberg pathway has a cost of 110 g s mol−1.We calculated the protein costs of the synthetic pathways and the natural glycolytic pathway . The proWe find an interesting observation when looking at the three components that make up the protein cost. A feature of orthogonal pathways is that they involve non-phosphorylative reaction steps that are orthogonal to the phosphorylative steps that are typically found in the metabolism of biomass pathways. The synthetic pathways have an overall higher cost because the thermodynamic advantage from non-phosphorylating reactions is detracted by a higher kinetic penalty for using inefficient enzymes. For example, the synthetic pathway has almost no thermodynamic penalty while the glycolytic pathway has a 10% penalty. This suggests the possibility that successful enzyme engineering or screening of non-phosphorylating enzymes with better kinetic parameters might lead to orthogonal pathways capable of supporting a higher flux than natural pathways due to their thermodynamic advantage. Taken together, these results reasonably suggest that cell factories that utilize non-natural pathways for substrate utilization may be able to more efficiently support flux for chemical production.de novo strain design. This idea of orthogonality is closely related to modularity, which has been well studied for metabolic networks21222425E. coli is highly connected and robust, elements of it do behave as modular subsystems. Amino-acid biosynthesis control is one such example that allows the cells to be stable in the presence of varying environmental conditionsIn this paper, we provide an alternative perspective to the problem of designing pathways and strains for metabolic engineering. In contrast to the prevalent approach of growth-coupled designs, we suggest that orthogonal pathway design coupled with DME might be effective for When traditional metabolic engineering aims to repurpose cellular metabolism for chemical production, it does so within the evolutionary disposition for growth known as growth coupling. However, the organization of this network structure follows principles of optimality different from those that metabolic engineers would attribute to be optimal for chemical production. We have shown efficient chemical production requires an optimality principle outside the scope of a cellular growth objective, which, akin to elements of metabolism such as amino-acid biosynthesis, require modular and independent subsystems in the cell, and a robust control mechanism over them. In this work, these subsystems can be measured by the ability of the metabolic network to perform two separate tasks . The orthogonality score measures this ability by calculating a ‘distance' metric in the metabolic flux space for these two tasks.A determinant of orthogonality is the overlap of the reactions that support biomass production and the chemical production pathways. A key finding of our work is that native glucose utilization pathways are not orthogonal for succinate and several other products  due to this overlap. Further analysis reveals that this non-orthogonality is largely due to the generation of phosphorylated metabolites and the individual biomass precursor metabolites in these native pathways that are valuable for biomass production, but are not essential for substrate utilization in the chemical production modes. In the We found that, by contrast, the catabolism of most orthogonal pathways lacked phosphorylation reactions. We found both glucose and xylose to be structurally more efficient for product formation when they were not phosphorylated. These types of non-phosphorylated pathways are sometimes observed naturally in microbes, although they are not common. These pathways typically do not involve substrate-level phosphorylation, are less energy-efficient and dissipate more free energy, thereby providing a higher thermodynamic driving force than conventional pathways. This is an important aspect of the flux capacity of metabolic pathways.zwf. (2) Orthogonal pathways implicitly bypass regulation as the biomass precursors tend to be highly regulated. For example, fructose-1-6-bisphosphate has been demonstrated to be a metabolic ‘flux sensor' important to the control of glycolytic fluxThere are two significant benefits for bypassing biomass precursors: (1) Pathways produce higher yields because they avoid carbon losses associated with precursor synthesis. For example, the generation of metabolites of the pentose phosphate pathway results in carbon loss through The significance of a flux sensor in natural metabolism is an important consequence for metabolic engineering. Glycolytic flux during stationary phase often ceases due to the accumulation or draining of intracellular metabolites, which are recognized by these flux sensors, and play a role in reducing glycolytic fluxde novo for a given substrate–product pair is a better approach to metabolic engineering than depending on pathways that consume glucose for any and all biochemical products. The diversification of feedstocks away from glucose, syngas, methane, methanol and glycerol, supports our idea30313233We also uncovered that orthogonality principles rest on the pairing of an input substrate and the product. Accordingly, engineering pathways Our work also has important applications for DME. Conceptually, DME has gained quite a bit of attention353738394041424339It is worthwhile noting that nutrient-based valves can exist, and there have been demonstrations of such valves including the use of oxygenThe recent focus in metabolic engineering has been the design and use of complex synthetic circuits to control gene expression . Each branch should contain reactions entirely devoted to the production of either the product or biomass. The common metabolite serves as an intermediate compound from which biomass precursors as well as the desired biochemical can be produced in the two different branches. We provide a metric, the orthogonality score, that quantifies the network's ability to convert a substrate to a product with as few shared nodes as possible, between the reactions that are responsible for producing biomass and those that are responsible for the conversion of the substrate to the product.St and Sx, respectively, the orthogonality score measures the degree of separation of Sx and St. It also captures the complexity of moving between the St and Sx subspaces as a function of the average number of reactions that need to be turned ‘on' or ‘off' in EFMs to move between subspaces. This measurement is akin to the Euclidean Norm measuring the distance between any two points in n-dimensional space. Geometrically, the score characterizes the complexity of separating the reactions that contribute to product production from the reactions that contribute to biomass formation. Accordingly, by measuring the average similarity (AS) between two shared parts of the same network, orthogonality enables one to make decisions regarding the ability to uncouple biomass from product production.If the stoichiometric solution subspace of reactions contributing to product production and biomass production can be represented by Sx and St containing their respective EFMS, The calculation of the orthogonality score uses EFMs55Sx and St  is designated as a candidate for the metabolic valve. The cut set is then identified as a possible candidate for having a branched network topology since it contains metabolic valve reaction. Its orthogonality score is then calculated. The scores calculated for the resulting network(s) can help confirm its orthogonality and eventually rank them as such. When the MCS algorithm is used in combination with the orthogonality score, the output is a strain metric that attempts to maximize growth-independent chemical production, and a set of genetic interventions required to establish it. This metric allows ranking the designs to determine the best combination of cut sets and metabolic valve reactions, which can then be systematically evaluated for the presence of a branched network topology as shown in et al.We used the methodology described by Flamholz kcat and Km obtained from in vitro enzyme assays data in the literature.The estimated cost accounts for the thermodynamics and the kinetics of the enzyme with respect to its interaction with the substrates and products of the reaction, and is also a function of the forward flux flowing through the reaction. We used the linear programming (LP) formulationThe data that support the conclusions of this study are available from the authors on request.http://www.labs.chem-eng.utoronto.ca/mahadevan/the-lab/downloads/All relevant codes to run the algorithms are publicly available at How to cite this article: Pandit, A. V. et al. Redesigning metabolism based on orthogonality principles. Nat. Commun.8, 15188 doi: 10.1038/ncomms15188 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures, supplementary tables, supplementary notes, supplementary methods and supplementary references.This table contains over 168 strain designs and metabolic control valves for the succinate production in Escherichia coli.