Non-linear regression was used to estimate parameters α and β from the empirical data described above.The purpose of the present study was to examine the GC content of substituted bases (sbGC) in the core genomes of 35 bacterial species. Each species, or core genome, constituted genomes from at least 10 strains. We also wanted to explore whether sbGC for each strain was associated with the corresponding species’ core genome GC content (cgGC). We present a simple mathematical model that estimates sbGC from cgGC. The model assumes only that the estimated sbGC is a function of cgGC proportional to fixed ATβ = (1.91 ± 0.13) p < 0.001 are approximately (1.91/0.79) = 2.42 times as high, on average, as AT→GC α = (− 0.79 ± 0.25) p < 0.001 mutation rates. Whether the observed sbGC GC-bias for all but the most GC-rich prokaryotic species is due to selection, compensating for the GC → AT mutation bias, and/or selective neutral processes is currently debated. Residual standard error was found to be σ = 0.076 indicating estimated errors of sbGC to be approximately within ±15.2% GC  for the strains of all species in the study.We found that sbGC for each strain showed a non-linear association with the corresponding cgGC with a bias towards higher GC content for most core genomes (66.3% of the strains), assuming as a null-hypothesis that sbGC should be approximately equal to cgGC. The most GC rich core genomes (i.e. approximately %GC > 60), on the other hand, exhibited slightly less GC-biased sbGC than expected. The best fitted regression model indicates that GC → AT mutation rates Not only did our mathematical model give reasonable estimates of sbGC it also provides further support to previous observations that mutation rates in prokaryotes exhibit a universal GC → AT bias that appears to be remarkably consistent between taxa.The online version of this article (10.1186/s12864-018-4984-3) contains supplementary material, which is available to authorized users. Candidatus Zinderia insecticola [Anaeromyxobacter dehalogenans [Rickettsia prowazekii (29% GC) to the GC-rich pathogen Pseudomonas aeruginosa (66.9% GC), were compared with the corresponding core genome GC content (cgGC). Each of the 35 core genomes represented a unique species from altogether 6 different phyla. We developed a first order differential equation model (gcMOD) predicting sbGC based only on cgGC and fixed parameters estimating (α) GC → AT and (β) AT→GC substitutions. The parameters α and β were subsequently estimated using non-linear regression from the empirical data described above and gcMOD.GC content in bacterial genomes varies greatly from, for instance, 13.5% in the intracellular symbiont logenans . This valogenans , selectilogenans , 7 as welogenans –10. Indelogenans . Populatlogenans , presumalogenans . A recenlogenans  showed tlogenans  and/or alogenans . Accessologenans . In addilogenans , 16 may logenans , 17. Sellogenans . The purFGC(cgGC), and that sbGC is proportional to AT→GC and GC → AT mutation rates, termed respectively α and β. Additional file α and β have different universal fixed rates in the sense that they are not exactly equal  with empirical sbGC values, from all strains of each species included in the study, as the response variable against gcMOD, the derived formula for the estimated sbGC function FGC(x):x represents cgGC with arbitrary start values set to α = 2 and β = 1. The nls model converged with σ = 0.076  suggesting that gcMOD’s sbGC estimates are within ±15.2% . On convergence, the nls method estimated that α = (− 0.79 ± 0.25) p < 0.001 and β = (1.91 ± 0.14) p < 0.001, which indicates approximately (1.91/0.79) = 2.42 AT substitutions for each GC substitution on average. Figure α and β are re-estimated accordingly. A similar analysis on bulk sbGC for each core genome  p = 0.003 and β = (2.59 ± 0.61) p < 0.001, which indicates approximately (2.59/1.35) = 1.92 AT substitutions on average for each GC substitution. With residual standard error σ = 0.054, bulk sbGC estimates, which may perhaps be considered as a sort of equilibrium sbGC for each species, are thus within ±11% .To scrutinize the relationship between sbGC and cgGC we created a simple mathematical model of sbGC. The model (gcMOD) assumes, naively, that sbGC is a function of cgGC, i.e. mentclass2pt{minimPropionibacterium acnes, Mycobacterium tuberculosis, Bifidobacterium longum and B. animalis as well as the γ-proteobacterium Pseudomonas aeruginosa. Characteristics shared among these microbes include that they are both free living and host associated  − x gives the gcMOD-estimated ΔsbGC and can be seen in Fig. It is evident from Fig. nstance, ). The maα and β are fixed parameters the model therefore indicates that mutation rates and the GC → AT bias could be universal for prokaryotes as has been suggested by previous studies [In summary, when gcMOD is fitted to empirical sbGC and cgGC values it predicts that there may be, on average, approximately two GC → AT mutations for each AT→GC mutation across the microbial core genomes included in the present study. Since  studies . In this studies , 20, 21.SNP-based phylogeny has fast become the de facto standard for evolutionary analysis of microbial genomes. While recent publications have elucidated a number of mechanisms relating to mutation bias in microbial genomes an examination of mechanisms focusing on the GC content of substituted bases, as well as modelling of AT→GC and GC → AT substitution rates, in microbial core genomes have gained less attention. In the present study, we find a strong non-linear association between sbGC and cgGC where sbGC was more biased towards higher GC content for species with AT-rich core genomes see Fig. . Thus, tIt should be noted that selection is less effective on a shorter time scale . Our estα and β. It also means that, on average, the fixed AT/GC substitution rates in each respective core genome follow something that may resemble a universal trend. On average, similar mutation rates for many different species belonging to vastly different phyla, all living in various environments, may seem peculiar. However, statistical significant estimations of the parameters α and β in gcMOD from empirical sbGC and cgGC values suggests that both similar rates and strongly AT-biased mutations may not be uncommon amongst prokaryotes and this is consistent with previous studies as noted above. Moreover, variable site extraction and removal of recombinant regions are error-prone processes. Indeed, the program Gubbins, used for removal of recombinant sites, is better suited for more closely related isolates with respect to time. Since the substituted bases in each core genome numbered in thousands, errors due to extraction of these substituted bases is expected to be negligible. Furthermore, Gubbins was ran on core genomes of each species, containing both coding and non-coding regions, that are per definition similar with almost identical %GC, together with a core genome-based guide SNP-tree created with the program parSNP. We therefore assume that the variance of sbGC between the strains of the different species is mainly due to life-style, environment and selective pressures. Since we have used data submitted to public databases and these genomes have been sequenced for a reason there could be some kind of implicit selection bias. We have applied strict criteria for inclusion making sure that there are enough strains in each core genome (10 or more) and that the species represent a wide range of genomic GC content found in microbes. Finally, there is obviously also a possibility that our mathematical model is too simple. But its simplicity, we believe, is also its strength as gcMOD significantly fits (p < 0.001) the empirical data.While gcMOD appears to give reasonable descriptions of the empirical results, there are some cautions that must be raised. First, the model does not assume anything about the core genomes except that the function estimating sbGC should be a continuously differentiable function of cgGC proportional to mutation rates Brucella spp., representing a unique species. The model only takes core genome GC content (cgGC) as an independent variable and return estimates of sbGC. gcMOD sbGC predictions are based on parameter estimates of GC → AT and AT→GC substitutions  obtained using non-linear regression on empirical data. We find that sbGC, taken from 35 core genomes, each comprising a unique species, with GC content ranging from 29 to 65% GC, is biased towards higher GC compared with cgGC, for all but the most GC-rich microbial species. This GC-bias could be associated with natural selection to counter the higher GC → AT than AT→GC mutation rates observed universally for microbial genomes. sbGC decreases with respect to increasing cgGC and becomes more AT-biased as core genome %GC increases. It is well known that selection and environmental factors may influence genomic %GC and thus also the sbGC differences between the different species observed here. However, our findings suggest that, given long enough time for selection to operate, the universal higher GC → AT than AT→GC mutation rates may also influence how base composition in microbial core genomes evolves regardless of taxa. Finally, our model support previous observations that the AT-biased mutation rates in bacteria may be pervasive throughout the prokaryotic kingdom and that mutation rates in general are remarkably similar between microbial species from different taxa.We present a mathematical model gcMOD that models substituted bases in microbial core genomes (sbGC) each, except for Sulfolobus islandicus was removed) and have been thoroughly described there [Brucella spp., having more than 10 fully sequenced strains with closed genomes. It was decided on strict criteria as variant site calling is sensitive to both methods and algorithms used to identify them. Gubbins [All 716 genomes used were taken from a previous study  with a null-hypothesis of being zero Additional file 2:A detailed explanation of how sbGC and cgGC were derived in pdf-format. (PDF 190 kb)Additional file 3:An Excel file containing data used for strain-wise sbGC/cgGC analyses (XLSX 44 kb)Additional file 4:An Excel file containing data used for bulk sbGC/cgGC analyses (XLSX 11 kb)Additional file 5:The graph shows bulk sbGC on the y-axis plotted against corresponding cgGC on the x-axis for the core genomes of 35 different species each coloured according to phyla. The dashed line designates sbGC = cgGC while the blue points represent gcMOD fitted to the data using non-linear regression. (PDF 8 kb)Additional file 6:The figure shows the difference between bulk sbGC and corresponding cgGC (i.e. cgGC subtracted from sbGC) plotted against cgGC  as well as the estimated values from (gcMOD-cgGC) (blue line). (PDF 8 kb)


Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism. Klebsiella pneumoniae, originally discovered in the lung samples of pneumonia patients, is a gram-negative, facultative anaerobic bacterium within the Enterobacteriaceae family , and conventional medicine has failed to eradicate such infections  is the most widely used in silico analysis method to predict intracellular flux distributions from GMNs at steady-state, which solves an optimization problem satisfying a predefined objective function  Kp13 strain was developed  models are commonly used to decipher pathogen and host metabolisms since they offer a systems-wide approach  was reported as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Using molecular docking, we attempted to elucidate the interaction dynamics of the KdsA enzyme and to identify possible inhibitors. We identified various potential anti-infectious agents interacting with the enzyme. Taken together, we suggested the putative targets and the KdsA inhibitors through the comprehensive bioinformatic analyses in this work. These findings can provide crucial insights to guide experimentalists for the development of new drugs.Here, we aim to provide a better insight into K. pneumoniae MGH 78578 (GenBank accession number: CP000647), iYL1228 . In addition, the coefficient of FMN was not available in the E. coli model and it was taken as 1 × 10−5 in this study. A low coefficient value ensured the necessity of cofactors for growth, without disturbing carbon flows in the metabolic network. The coefficients for the new biomass formation reaction is available in Based on a recent work that drew attention to the importance of inclusion of essential cofactors in biomass formation reactions, we improved the biomass formulation of the original model through integration of universally essential cofactors of prokaryotes    to zero for each gene. Compensatory functions of isoenzymes were also considered during the gene deletions. The effect of each single-gene knockout on the biomass production was evaluated based on a cut-off of 1% of the maximum wild-type growth rate  of −4 .Through a systematic workflow, the non-homologous drug target candidates identified by the gene-centric approach were prioritized to discover more effective therapeutic targets. To this aim, −4; Yu et al., E. coli K12 proteome by Lopez-Campistrous and colleagues  to find its crystal structure. The sequence identity and similarity from multiple sequence alignments were further confirmed by ClustalW  was prepared as a receptor via UCSF chimera to perform the structure based analyses  of K. pneumoniae and human were compiled using the human genome-scale metabolic reconstruction, Recon 2  known as the metabolites involved in many reactions in a metabolic network . The enzymes in the outgoing reactions of EMs that passed the cut-offs were suggested as the putative drug targets.Identification of the EMs was followed by a similar filtering method of Kim et al.  to narroK. pneumoniae MGH 78578 metabolism through the GMN of iYL1228 for the computational prediction of effective drug targets. The available infection models are predominantly based on the simulation of the bacterial growth in laboratory media. Although these conditions are also informative, they are insufficient to represent the host-cell nutrient environment. Therefore, we employed growth simulations in different host-mimicking conditions including SM, HBF, and generic host media.Here, we simulated the K. pneumoniae induces production of thick jelly sputum  could survive inside alveolar macrophages by blocking phagosome maturation . It is used to discover the putative drug targets. In addition to sputum, body fluid cultures  are frequently used for the detection of K. pneumoniae  is available in the prioritized gene list given in panBCD gene cluster is crucial to manage pantothenate synthesis pathway, which is crucial for CoA production  dedicated to the transportation of pantothenate. Since HBF medium includes pantothenate unlike SM medium , enoyl-(acyl carrier protein) reductase (FabI), and UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA)]. Druggability analysis identified 29 targets from HBF simulations as druggable , lipopolysaccharide (LPS), fimbriae, outer membrane proteins, and siderophores) and ARGs considering that they share some common characteristics , galU, kdsA, and gmhA (formerly lpcA) were predicted  and ADP-L-glycero-D-manno-heptose-6-epimerase (encoded by hldD) catalyze the biosynthesis of the heptose precursors. On the other hand, 2-dehydro-3-deoxyphosphooctonate aldolase (encoded by kdsA) is responsible for the synthesis of the Kdo, which links lipid A and core oligosaccharides , UDP-3-O-acyl-N-acetylglucosamine deacetylase (encoded by lpxC), and UDP-3-O-(3-hydroxymyristoyl) glucosamine N-acyltransferase (encoded by lpxD) are involved in the lipid A biosynthesis  is responsible for the regulation of the intracellular UDP-galactose and UDP-glucose concentrations  and this compound is used as a substrate for synthesis of different surface structures  is one of the major components of the outer membrane in gram-negative bacteria, and it contributes to protection against antimicrobial molecules in addition to membrane integrity  in this prioritized target list were identified by the three prioritization approaches. That is, those proteins were predicted to be druggable, broad-spectrum, and virulence factors  to survive in the host .Based on the prioritization criteria discussed above, the KdsA was defined as the highest-ranked drug target among the target candidates satisfying all criteria defined for the virulence, druggability, and broad-spectrum analyses . It is tK. pneumoniae CG43  in the target prioritization process. However, the targets identified by most of the prioritization approaches may be also attractive. For instance, Ugd is a promising drug target though it was not prioritized in the broad-spectrum analysis. It is crucial for the biosynthesis of both CPS and LPS , which sccharide . Hence, in silico techniques. To our knowledge, this is the first attempt for the K. pneumoniae KdsA enzyme. A comparative structure prediction method was firstly used since the 3D structure of K. pneumoniae KdsA enzyme was not available. Upon similarity search via BLAST, the 3D structure of KDO8P synthase from E. coli was selected (PDB code: 1D9E). The E. coli enzyme showed 95.42% sequence identity, 100% query coverage, and had a high resolution with 2.4 Å. Computer-aided drug design is a useful approach to reduce time and cost. Therefore, we investigated putative inhibitors against the high-ranked potential target (KdsA) using K. pneumoniae  are associated with the remaining EMs, involved in the corresponding outgoing reactions , and enoyl-(acyl carrier protein) reductase (FabI)] are involved in several outgoing reactions, which are associated with the riboflavin, peptidoglycan, or fatty acid metabolism. Of these enzymes, RibC, RibH, and FabI were found to be independently essential, and the PBP 1A-C enzymes were identified as synthetic lethal. Characteristics of the final EMs and the associated genes are summarized in Firstly, EMs were determined by constraining the flux through the associated outgoing reaction(s) of each metabolite in the model to zero. In this manner, the effect of the absence of a metabolite on the growth rate of the organism was simulated. Forty-one EMs were predicted via the SM simulation. Of these, 23 metabolites were found to be essential for the bacterial growth in the HBF. The generic host medium simulation led to identification of the same EM list as HBF simulation. These metabolites were filtered to eliminate the compounds also involved in the human metabolism. Removal of the common metabolites is crucial to decrease the risk of drug-related side effects. In this regard, the metabolites found in both eactions . TherefoBacterial fatty acid synthesis is an essential process supporting formation of the cell membrane. Even if each step in this pathway is vital for the bacteria, FabI has received a particular interest owing to its rate-controlling role in the fatty acid synthesis. It catalyzes the last step in the elongation cycle of fatty acid synthesis including NAD(P)H dependent reduction of the trans-2-enoyl-ACP to acyl-ACP  were predicted using both gene- and metabolic-centric approaches. They are essential in riboflavin (vitamin B2) synthesis. Riboflavin is an essential precursor of FMN and FAD. Its synthesis is essential in the gram-negative bacteria lacking a transport system for riboflavin uptake while the ribC is conditionally essential in the bacteria with a riboflavin transporter. Disruption of the ribC was reported as lethal in the mutant H. influenzae cells cultured in a riboflavin-deficient medium (Saeed-kothe et al., RibC and RibH (encoded by K. pneumoniae strains have been developed so far (Liao et al., K. pneumoniae comprehensively via a network-based metabolism-centered approach. We identified the K. pneumoniae enzymes that are crucially involved in the bacterial survival. In all simulations, different host-mimicking environments and an improved biomass formation reaction were included in the GMN. Upon gene-centric approach, KdsA enzyme was found to be the high-ranked putative target highly satisfying most of the target prioritization criteria. This result encouraged us to investigate the potential KdsA inhibitors. We identified a list of compounds including ZINC95543764 and ZINC20057784, which can efficiently bind to the active site of the enzyme. It is important to note that a scarce number of KdsA inhibitors have so far been reported (Du et al., K. pneumoniae metabolism has enabled better understanding of the pathogenic phenotype and elucidation of the putative targets. Future studies can provide more evidence for the targets and inhibitors suggested in this study.Genome-scale metabolic networks of different The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.MC performed metabolism-based simulations and analyses. BS employed structure-based analyses. SD, RU, TÇ conceived and designed the study.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.


Arthrospiraplatensis has increasingly gained attention as a source for bioethanol production. To study the metabolic capabilities of glycogen production in A. platensis C1, a genome-scale metabolic model (GEM) could be a useful tool for predicting cellular behavior and suggesting strategies for glycogen overproduction. New experimentally validated GEM of A. platensis C1 namely iAK888, which has improved metabolic coverage and functionality was employed in this research. The iAK888 is a fully functional compartmentalized GEM consisting of 888 genes, 1,096 reactions, and 994 metabolites. This model was demonstrated to reasonably predict growth and glycogen fluxes under different growth conditions. In addition, iAK888 was further employed to predict the effect of deficiencies of NO3−, PO43−, or SO42− on the growth and glycogen production in A. platensis C1. The simulation results showed that these nutrient limitations led to a decrease in growth flux and an increase in glycogen flux. The experiment of A. platensis C1 confirmed the enhancement of glycogen fluxes after the cells being transferred from normal Zarrouk’s medium to either NO3−, PO43−, or SO42−-free Zarrouk’s media. Therefore, iAK888 could be served as a predictive model for glycogen overproduction and a valuable multidisciplinary tool for further studies of this important academic and industrial organism.Glycogen-enriched biomass of  Due to the environmental concerns of production and utilization of fossil fuels, researches towards renewable energy are currently of great interest. Conversion of carbohydrate-enriched biomass of microalgae has become one of the promising approaches for sustainable clean energy generation . Since tArthrospira (Spirulina) platensis, a filamentous non-nitrogen-fixing cyanobacterium, is an attractive candidate which has certain properties to be used as promising feedstocks for bioethanol production. For example, it has the capacity to accumulate large amounts of glycogen during cultivation under nutrient or environmental stresses [−1 d−1 was reported under NO3− depletion and high light intensity of 700 μmol photons/m2/s [Arthrospira biomass by yeast fermentation [Arthrospira to generate bioethanol, however, the glycogen productivity is still not high enough to profitably produce bioethanol at the commercial scale. To overcome this challenge, strategies for enhancement of glycogen content in A. platensis is needed. Among Arthrospira, A. platensis strain C1 is a good candidate for bioethanol production because it has distinct advantages over the others in term of strain improvement, non-gliding property [A. platensis C1 has not been investigated.stresses . The higons/m2/s . It alsoons/m2/s  and a faons/m2/s . Moreoveproperty , genome property , transfoproperty , and theproperty ,14 proteproperty ,16,17 anproperty . HoweverA. platensis C1, the first GEM of A. platensis C1, iAK692, was developed in 2012 [Driven by advancements in high-throughput biological and computational technologies, genome-scale metabolic model (GEM), a mathematical form of the cellular metabolic network, is currently being the indispensable tool for understanding cell phenotypes and providing rational strategies to maximize production of a desired metabolic product . GEMs of in 2012 . This moA. platensis C1 named iAK888 was employed. iAK888 was built based on information of iAK692 as well as new annotated and curated genomic and biochemical knowledge. The iAK888, a fully compartmentalized GEM, is the most up-to-date comprehensive model for A. platensis. The model was demonstrated herein to be a helpful tool for proposing the rational strategies for improvement of glycogen production in A. platensis C1.Since bioinformatics tools, pathway/genome databases, and literatures have now been updated, newly curated genes and biochemical knowledge have become available. In this research, an updated GEM of iAK888 was reconstructed by refining and updating the previously reconstructed genome-scale model for A. platensis C1, iAK692 [A. platensis C1  [iAK692, RAST [The , iAK692 . The firM001632)  was retrM001632)  supporteM001632)  and (ii)M001632) . To incr92, RAST , and KAA92, RAST  were com92, RAST  against 92, RAST . A. platensis C1 based on various information sources including biochemical databases [Synechocystis sp. PCC6803 [Escherichia coli [A. platensis C1 (accession E-MTAB-2714) [A. platensis C1 was cultured in Zarrouk’s medium at 35 °C under a light intensity of 100 µmol photons/m2/s, were preprocessed and normalized. Then, the average of the log intensities between a housekeeping gene, 16S rRNA, and all genes were compared. Genes whose expression were equal to or greater than the housekeeping gene indicated the evidence for the existence of these genes and the associated reactions. Furthermore, all reactions were charge and mass balanced. The reaction directions were also assigned based on the standard Gibbs free energy of reaction (ΔrGo) provided by the SEED [A. platensis C1 was verified using organism-related species literature. Cellular compartments of candidate genes and reactions were determined based on the literature and the GEM of Synechocystis sp. PCC6803 [iAK692, but absent in the annotation results of RAST [Subsequently, a list of GPR associations was assembled and manually curated to reflect actual physiology of atabases ,29,30, latabases ,32,33,34 PCC6803  and Eschhia coli . Manual hia coli . A confihia coli  was emplthe SEED . Formulathe SEED . Subsequ PCC6803  were con of RAST  and KAAS of RAST  were rem2 and photon uptake fluxes were set to zero. For NAD(P), an artificial reaction NAD(P)H → NAD(P) + H was added in the reconstructed network and optimized when CO2 and photon uptakes were not available. If either ATP or NAD(P) could be produced without nutrient uptake, the reactions related to the production of these energy metabolite were checked manually.Additionally, energy-generating cycles (type-II pathways) or internal cycles (type-III pathways) were checked during the reconstruction process to ensure that ATP and NAD(P) could not be produced without nutrient consumption. For ATP, the ATP maintenance flux was optimized when COiAK888, compared to the previously published model, iAK692, is shown in A. platensis C1 [A. platensis C1 [iAK888 also included pigments, vitamins and minerals, of which the coefficients were calculated based on the published information of the closest species [An organism-specific biomass equation, representing cell growth in silico, was formulated and used as an objective function for simulating growth phenotypes through FBA . The majensis C1 . The stoensis C1 . Moreove species ,43,44,45S, in which each column and row represented one unique reaction and metabolite, respectively. The entries of S are the stoichiometric coefficients of metabolites participating in a reaction. By applying the mass balance constraints and steady state assumption, the particular metabolite’s concentration change per unit time is equal to zero (Sv = 0), where v represents the vector of reaction fluxes. Then, other constraints such as thermodynamic and enzyme capacity were accounted, thereby, determining reaction directions  [rGo while, reaction reversibility is a kinetic property of enzymes which are able to catalyze the reactions in the forward and backward directions. Hence, thermodynamic constraints help to decide the reaction directions in FBA. Finally, a linear programming was applied to maximize or minimize an objective function, usually the biomass reaction flux. This optimization results in optimal objective flux and optimal flux distribution of the metabolic network. In this work, the COBRA toolbox version 2 [Flux balance analysis (FBA) is a widely used constraint-based optimization approach for predicting specific reaction rates (fluxes) of a large-scale network based on stoichiometry and steady-state assumption . Brieflyibility) . Reactioersion 2  with MATA. platensis, iAK888 incorporates 4 glycogen associated reactions, i.e., (i) glycogen synthesis reaction, (ii) glycogen utilization reaction for biomass growth, (iii) glycogen transport reaction for carbon storage and (iv) glycogen degradation reaction. In this work, glycogen was treated as a monomer  in the glycogen synthesis reaction where one mole of glycogen was synthesized from one mole of glucose-1-phosphate by glycogen synthase . In addition, glycogen phosphorylase  that is responsible for the glycogen degradation was assumed to be inactive under autotrophic growth. Thus, the glycogen production flux was simply determined from the flux of the glycogen synthesis reaction.To represent the glycogen metabolism in To evaluate the accuracy of the reconstructed metabolic model, comparisons between predicted phenotypes and experimental data, including a maximal growth rate under different growth conditions and maximal carbohydrate production flux under nitrogen depletion condition were performed. A. platensis C1 was grown under autotroph, heterotroph, and mixotroph were calculated and used as the input parameters. Cells grown under autotrophic conditions were simulated and compared, including two independent sets of A. platensis C1 experiments, (i) setting the photon uptake rate to 100 μmol photons/m2/s and HCO3− uptake rate to 0.2 mmol/gDCW/h [2/s and HCO3− uptake rate to 0.25 mmol/gDCW/h [2/s, HCO3− uptake rate to 0.2 mmol/gDCW/h, and glucose uptake rate to 0.017 mmol/gDCW/h [For prediction of the maximum specific growth rate, the experimental data sets in which l/gDCW/h  and (ii)l/gDCW/h . Cells gl/gDCW/h . For mixl/gDCW/h .3− uptake rate were set at 100 μmol photons/m2/s and 0.2 mmol/gDCW/h, respectively. NO3− uptake rate and specific growth rate were set to zero as observed in the experiment [To further assess the reliability of the model, the maximum total carbohydrate production flux under nitrogen depletion condition was predicted. The photon and HCOperiment . The obj2, O2, and H2O were left unconstrained. In addition, all photons were assumed to be absorbed and used for driving photosynthesis without the influence of photoinhibition.For all the simulations, the uptake rates of all nutrients present in the Zarrouk’s medium  were con3−, PO43−, and SO42− on growth and glycogen production were simulated by FBA [3− was fixed at 1.6 mmol/gDCW/h for all simulations to guarantee an excess carbon condition. The maximum photon uptake flux was set to 100 μmol photons/m2/s. The effect of each nutrient on specific growth rate and total glycogen production flux was analyzed by varying the uptake flux values of each nutrient, NO3−, PO43−, and SO42−. The objective function was set to maximize the flux of the glycogen synthesis reaction. Additionally, flux variability analysis (FVA) [The effect of NOd by FBA  under this (FVA)  was perfis (FVA)  was alsoiAK888, A. platensis C1 (PCC9438) was cultured in 1 L Erlenmeyer flasks containing 500 mL of Zarrouk’s medium [2/s until mid-logarithmic phase. Then, the cells were transferred to three different Zarrouk’s media, lacking either nitrate (NaNO3 and Co(NO3)·6H2O), or phosphate (K2HPO4), or sulfur , and K2Cr2(SO4)4·24H2O), and then re-incubated under the same incubation conditions as mentioned above. The control experiments were carried out in normal Zarrouk’s medium formulation [To evaluate the validity of A. platensis C1, the cell concentration was quantified by turbidity based on the optical density at 560 nm (OD560) using a Genesys 20 spectrophotometer  and measured for dry cell weight (DCW). The correlation between OD560 and DCW (DCW in g/L = 1.0888xOD560) was calculated based on triplicated experiments. Glycogen content was measured using iodine-glycogen assay [To analyze growth of en assay . BrieflyA. platensis C1 (iAK692) [iAK692 [iAK692 [iAK692 [A. platensis C1. Subsequently, the GPR of all candidate genes were then manually curated based upon various information such as physiological evidence in literature, gene expression [Genome annotation showed that the rapid annotation of microbial genomes using Subsystems Technology (RAST) annotation servers  predicte(iAK692)  as a ref [iAK692  and KAAS [iAK692 ; 45 gene [iAK692  and RAST [iAK692 ; 275 gen [iAK692  and KAAS [iAK692   was consistent with the experimental production (0.081 mmol/gDCW/h) [iAK888 could predict the growth rates and carbohydrate production of A. platensis C1 reasonably well.Furthermore, an effort was made to investigate the total carbohydrate production flux under NOnditions  with the/gDCW/h) . The modA. platensis C1. iAK888 was simulated by varying the uptake flux of NO3−, PO43−, and SO42− under autotrophic conditions with the following parameters: HCO3− uptake flux, 1.6 mmol/gDCW/h; photon uptake flux, 100 μmol photons/m2/s. In overall, limitation of NO3−, PO43−, and SO42− uptakes and the excess HCO3− resulted in the negative effect on growth, while revealed positive effect on glycogen production  SOT medium [A. platensis NIES-39 cultured under nitrate-limited condition produced pyruvate, acetate, and lactate in the culture medium [43−, and SO42− starvation, the simulation results showed that the changes in flux patterns of both nutrients were very similar. Obviously, limitation of these nutrients greatly affected the fluxes through phosphoglycerate mutase, enolase, pyruvate kinase, and malate dehydrogenase reactions in comparison to the normal growth and NO3−-limited conditions. These much lower fluxes occurred at the branch points where the precursor metabolites were drained for the synthesis of amino acid. However, future inclusion of such experimental data could be expected to verify the prediction of flux distribution under each nutrient limitation.The internal metabolic fluxes throughout the central metabolism were determined using geometric FBA . HCO3− uFlux map  showed tT medium . The time medium . RegardiiAK888 suggested that NO3−, PO43−, and SO42− starvation enhance glycogen content in A. platensis C1, -growth rate and glycogen production under NO3−, PO43−, and SO42− depletion was experimentally determined. A. platensis C1 was cultured with standard Zarrouk’s medium [A. platensis C1 were investigated for 96 h. Regarding to growth in term of dry weight (3− and SO42− starvation decreased significantly (p < 0.05) whereas the dry weight under PO43− starvation gradually increased, even though the growth rate was lower than that of the control. An increase in growth observed during the first 18 h might be due to the results from availability of endogenous nitrogen, phosphorus, and sulfur. In terms of glycogen content (43− and SO42− depletion showed a significant increase in glycogen content and production flux (p < 0.01) in the first 18 h. In contrast, the significant increase in glycogen content and production flux were observed in the NO3−-free medium after 24 h (p < 0.01).Since s medium  and cells medium . Changesy weight A and groy weight A, the re content B and gly content B, cells 3−, PO43−, and SO42− depletion induced an increase of glycogen production in A. platensis C1. Notably, only the growth rate under PO43− starvation between the experimental and iAK888-simulated fluxes had no significant difference. It should be noted that despite FBA [Compared to the results predicted by FBA simulations, glycogen flux was considered to be consistent with those obtained by experiments, suggesting that NOpite FBA  being wipite FBA  was unabA. platensis C1, iAK888, was performed. The iAK888 model displayed a highly detailed reconstruction capturing the fundamental knowledge and the significantly biotechnological capabilities as compared to the previous model, iAK692. The iAK888 model was demonstrated to be a suitable model for prediction of growth and carbohydrate production flux of A. platensis C1 under various conditions. Moreover, it was demonstrated that the iAK888 model could suggest rational cultivation strategies for overproduction of glycogen in A. platensis C1. In the future, this new model shall be a versatile platform for further studies towards glycogen-enriched A. platensis C1 as an alternative feedstock for bioethanol production. In this study, an improved genome-scale model for


Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs.Saccharomyces cerevisiae. We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering.In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo.The online version of this article (10.1186/s12859-019-2934-y) contains supplementary material, which is available to authorized users. Indeed, the wide availability of whole-genome sequencing and annotation tools have enabled the reconstruction of a multitude of metabolic networks for various organisms [Genome-scale metabolic models (GSMM) are well proven tools for the rganisms . Constrarganisms  and its rganisms –5, as werganisms  are capaDesigning optimized microbial strains for compound overproduction, however, is achieved through computational strain optimization methods (CSOMs), providing a rational approach for finding intervention strategies, as opposed to trial-and-error experiments , 8. The CSOMs can be branched in two main categories: simulation-based (SB) and EMA-based methods. For simplification purposes we group bi-level mixed integer programming (MIP) and metaheuristic methods as SB methods, due to their similarities in evaluating candidate strategies, although with different methods for generating them  approach.MCS enumeration algorithms were mostly reliant upon complete elementary mode (EM) enumeration, rendering it an infeasible task for models of greater scale and complexity. Several methods have been developed to tackle the computational limitations of EMA-based CSOMs by allowing partial enumeration of EMs through the use of sampling approaches , evolutiM method  is a relEscherichia coli [Escherichia coli, albeit using a smaller scale model [The MCSEnumerator approach recently proposed by von Kamp et al.  successfhia coli . Erdrichhia coli  and a rele model .Despite the existence of some studies highlighting the importance of EMA-based CSOMs to solve ME problems , 25, theThe comparison between EMA and SB CSOM derived strategies is also often overlooked. Most SB CSOMs are subject to optimality bias since they require an objective function which is often misleading and hard to define in certain organisms or metabolic systems. EMA-based CSOMs can now provide a suitable alternative to obtain highly robust design strategies with a low number of modifications, but there is a general lack of information regarding how well these perform against state-of-the-art SB CSOMs and whether their strategies produce better in vivo candidates.Saccharomyces cerevisiae. To assess the feasibility of these methods, outputs from both categories are compared and different formulations of both approaches are also analysed. The aim is to bridge the gap between EMA and SB CSOMs by assessing performance metrics for each set of strategies and understanding the advantages and limitations of each method regarding their usage for ME tasks with different production and growth demands.The purpose of this work is to employ EMA-based and SB CSOMs in strain design applications using a case study involving the identification of knockout strategies for the production of succinic acid with Java library developed in-house. Two additional problem formulations are also presented to allow greater flexibility of growth-coupled design strategies based on MCSs. A graphical user interface for the MCS enumeration algorithm was also made available as a plugin for the OptFlux metabolic engineering platform [We present a pipeline including strain optimization, filtering and analysis of design strategies based on previous developments and packaged it as part of the Metabolic Engineering Workbench (MEW),platform .The methodological pipeline used for this work can be divided in three key steps as illustrated in Fig. The primary goal with our workflow is to explore growth-coupled phenotypes through design strategies obtained with EMA-based and SB CSOMs. Five different strain design strategies, described in more detail in the Methods section, were obtained with SB CSOMs  and EMA-based CSOMs (with MCSEnumerator) as part of our experimental setup.EAw) and product minimum maximization (EAm) were tested, allowing different trade-offs between biomass and target compound production. EAw includes maximum cell growth and production flux at maximum cell growth as two separate objectives, while EAm uses the maximum cell growth rate and minimum product flux at maximum growth as objectives.Using SPEA2, biomass-product coupling maximization  cover previously used and novel approaches that are more or less strict regarding robustness of the solutions by different definitions of the undesired spaces. In MCSe the undesired space contains low product yield flux vectors with a maximum substrate uptake rate and maintenance ATP rate above a defined value. In MCSf, low product yield phenotypes are blocked, assuming a fixed substrate uptake rate. In MCSw, low product flux phenotypes are blocked only when biomass fluxes are above a fraction of the maximum growth rate, aiming to reach strategies with less strict demands regarding product synthesis and its coupling with growth.For MCSEnumerator, three strategies . SPEA2 and MCSw strategies lead to lower product flux values and only guarantee product synthesis at higher growth rates. It is worth noting that a considerable amount of MCSw strategies do not allow growth-coupling at all, or do not meet our demand for production at 90% of the maximum mutant growth rate. This explains the need for a filtering step prior to our analysis.Strategies from the EAm strategies lead to moderately robust phenotypes with higher product rates across different cell growth thresholds, with some of these strategies leading to strongly growth-coupled production phenotypes. These results are expected, considering the constraints imposed upon the various formulations.MCSe formulation seems to negatively affect production robustness and rates, as this set underperforms the MCSf regarding these properties.The maintenance ATP constraint imposed on the MCSf and MCSe strategies appear to be the worst performing strategies when considering cell growth. BPCY values for these sets are similar and considerably lower than for EA solutions. Despite the increased product flux values, strongly coupled MCSs (MCSe and MCSf) lead to much lower growth rates, which negatively impacts BPCY.MCSw solution set, with lower production rates and robustness, leads to similar BPCY values since the maximum cell growth is much higher. EAw solutions reach the highest average BPCY figures, surpassing the EAm set, which, despite the increased product flux values, leads to lower maximum growth rates.Conversely, the MCSe and MCSf solutions lead to higher carbon yields, followed by the EAm and EAw set. MCSw strategies have noticeably lower production rates, also leading to lower carbon yields.Regarding carbon yields, all strategy sets predict maximum glucose uptake, leading to carbon yields that are directly dependent upon the product flux. Thus, The MCSe set appears to lead to slightly higher maximum growth rates and BPCY than the MCSf set. Maximum carbon yield, however, is lower (due to lower product rates).The size of the analysed strategies is not homogeneous across all groups. MCS derived formulations are guaranteed to deliver the smallest solutions for the specified problem. SPEA2, being a heuristic approach, may lead to bigger solutions. Despite the large range of solution sizes for formulations based on this algorithm, EA-derived strategies with the highest BPCY values contain less than 10 knockouts. Additionally, the smallest strategies for succinate production are found in these solution sets (less than 5 knockouts), even though the average solution size across the entire sets is higher.MCSw solutions were limited to sizes 3 through 5, while MCSe and MCSf were achieved with sizes up to 9 deletions. Productivity appears to increase with solution size in this particular case. However, the aforementioned changes in robustness and productivity metrics can be verified even when comparing similar solution sizes.MCS derived strategies have low ranges as the number of solutions increases heavily with the proposed size, which imposes a computational limit. The main results of the pathway distribution approach used in this work are depicted on Fig. EAw and both MCS solution sets. Flux on the non-oxidative branch is also generally decreased, although some reactions leading up to the amino-acid biosynthetic pathways remain active, which can be observed on Fig. EAm set.Across the solutions from all strategies, there is a decreased usage of the oxidative branch of the pentose phosphate pathway (PPP), as compared to the wild-type. This correlates with the knockouts themselves on the pathway, found in EAw set, leading to higher growth rates, maintain flux in TCA cycle reactions, whereas the solutions from the remaining strategies do not. This could be explained by the high usage of fumarate reductase and the glyoxylate shunt  which is the primary mechanism found for succinate production in EAw solutions, in contrast with 4-aminobutyrate (GABA) degradation pathways used in strongly-coupled solutions.The tricarboxylic acid (TCA) cycle is also disrupted in most of the proposed designs, leading to less active reactions in this pathway. This also leads to a lower activity in the electron transfer chain. Interestingly, the solutions from the MCSf and MCSe solutions and in a smaller proportion in EAm. Increased usage of this pathway seems to directly correlate with higher production robustness. Similarly, usage of the methylglyoxal catabolic pathway is also increased in solutions from MCSf, MCSe and EAm strategies. Through further analysis of the admissible flux ranges using FVA we were also able to find that GABA shunt fluxes become essential for the feasibility of most MCSf and MCSe solutions, which also shows that growth and production are coupled to this pathway.The GABA degradation pathway plays a key role in the solutions obtained from strongly-coupled strategies. This pathway, otherwise known as the GABA shunt, leads to succinate production through degradation of GABA in the cytosol and serves as the main production mechanism in all MCSf solutions, the NADPH/NADP balance is disrupted through deletions that force glucose uptake through NADPH dependent pathways or flux through certain pathways dependent on these cofactors, such as folate interconversions, or methylglyoxal metabolism. Flux through the GABA shunt is then required to maintain a steady-state. These phenomena involving cofactor pools have already been described as possible mechanisms for growth-coupled synthesis by Erdrich et al. [It is worth noting that the strongly growth-coupled phenotypes we found were dependent on the imbalance of at least one cofactor. This finding is supported by the fact that the fixed maintenance ATP demand is essential to impose strong coupling – only weak coupling is achieved if the maintenance ATP pseudo-reaction bounds are lower. Additionally, in many h et al.  and Hädih et al. , althougAnalysis of the specific knockouts included in the obtained strategies is a key feature to determine which are the most important deletions leading to weak or strong coupling phenotypes. Figure A common disruption across the solutions from all strategies is the succinate dehydrogenase complex, a TCA cycle mitochondrial protein complex supplying electrons for the transfer chain and catalysing the conversion of succinate to fumarate. Subunit 2 (EC 1.3.5.1) of this complex is present in every solution, with subunits 3 and 1 being less present , in decreasing order of frequency. The importance of this disruption may come from the fact that this is the only available irreversible conversion of succinate to fumarate.EAm, MCSe and MCSf), with higher frequency of knockouts in kinases involved in glucose phosphorylation, such as hexokinase or glucokinase. The presence of PGI (EC 5.3.1.9) knockouts in most strategies may help explain the increased flux in the PPP and alternative glucose catabolic pathways.Glycolysis appears as a target for disruption in strategies with higher production robustness , which force the usage of alternative pathways, such as methylglyoxal catabolism or serine biosynthesis.Due to the decreased rates in the oxidative phosphorylation, fermentation appears as an alternative for pyruvate catabolism for many solutions. As such, succinate producing strategies with higher titres tend to include knockouts in PYRDC (EC 4.1.1.1), ALDD (EC 1.2.1.4) and ACOAH (3.1.2.1) reactions to reduce the number of viable alternative pathways that could drive carbon away from succinate production.MCSw. Regarding oxidative-phase knockouts, G6PDH (EC 1.1.1.49) appears in strongly-coupled MCSs with relatively high frequencies, especially in the MCSf set and in the EAw set.Knockouts in the PPP are somewhat prevalent across all strategy sets except MCSf solutions, which leads to strongly coupled succinate production through forced usage of the TCA cycle producing 2-oxoglutarate in the mitochondria and then transported outside to feed the GABA shunt for cytosolic succinate to be synthesized.Finally, in the TCA cycle, the cytosolic IDH (EC 1.1.1.42) knockout seems to be a relatively common occurrence in Two metabolic routes for succinate overproduction from the TCA cycle have been described in the literature, respectively, the oxidative and reductive branches of the TCA cycle . The oxiSaccharomyces cerevisiae [Early efforts using yeasts as succinate-overproducing microbes have focused on the enhancement of sake brewing processes with revisiae . This strevisiae .Yarrowia lipolytica), resulting in production at a minimum of 56% of the theoretical maximum [Additionally, it is shown that growth and substrate uptake are not significantly altered in strains with disrupted pairs (SDH1 and SDH2) . The str maximum . The resFurther studies found improved strategies for aerobic succinate production in yeast. The addition of a isocitrate dehydrogenase (IDH) knockout to the SDH1/2 pair was studied as a means of diverting citric acid cycle intermediates into the glyoxylate cycle, resulting in succinate overproduction . This muiMM904 metabolic model) and at subunit 3 of the SDH complex. Carbon flux is redirected towards glyoxylate to allow serine production, with succinate as a by-product of using this metabolic route. This strategy was developed using in silico tools and then experimentally tested. Not surprisingly, some of the solutions obtained in the present work include those strategies.An alternative strategy for glyoxylate shunt carbon redirection was presented by Otero et al. , involviSaccharomyces cerevisiae from the GABA shunt is residual in normal fermentation conditions (with oxygen limitation) [There is in vivo evidence that succinate production in itation) . It is sRegarding the reductive pathway, not many efforts have been implemented regarding the optimization of this route, mainly due to cofactor imbalances. However, reported strategies involve the deletion of fermentative competing pathways, as observed in some of our solutions .In this work, we present a comparison of two relevant constraint-based strain design approaches for ME applications through in silico analysis of the resulting knockouts strategies. Two alternative formulations for MCS enumeration were tested, yielding mutants with varying growth and production demands, demonstrating the flexibility of the intervention problem framework coupled with the MCSEnumerator algorithm. The results shown in this work reinforce the importance of MCSs as a rational approach for ME applications.When comparing EA and MCS-derived strategies, there is a clear trade-off between robustness and maximum cell growth. Growth-coupling strength increases as maximum biomass decreases, leading to strongly-coupled cMCSs with high product flux and low growth and weakly-coupled EA strategies with opposing features. Phenotypes with very high yields have been found using cMCSs, largely surpassing those from SPEA2, but cell viability is still unknown as the low cell growth rate may indicate that these strategies incur in lethal modifications in vivo.MCSw formulation firstly described in this work. Strategies obtained using this method have lower predicted productivity, but overall higher growth rates. While possibly unsuitable for direct in vivo application, these strategies may serve as starting points for other strain design algorithms, upon which more knockouts can be added to achieve higher productivity.Nevertheless, it is still possible to obtain growth-coupled phenotypes with low production robustness, using the EAm formulation, SPEA2 can also be used to obtain strongly-coupled strategies, with similar robustness to those found using MCSEnumerator. However, the latter can enumerate smaller strategies, which is advantageous for in vivo implementation.With the MCSf formulation presented and tested in this work allowed us to find strategies with high product flux values and robustness but lower cell growth rates overall. This helped us reveal the importance of including a coupling component (such as the maintenance ATP assumption on the MCSe formulation) when searching for MCS-based strategies. Indeed, when the maintenance ATP assumption is replaced with a fixed substrate constraint, other cofactor wasting mechanisms arise.The EAw strategies confer high BPCY at a relatively low number of knockouts to achieve that goal. However, product synthesis can only be guaranteed with growth rates close to the wild-type, which casts uncertainty on whether these strategies will have a noticeable effect in vivo.For practical purposes, it is clear that The strong growth-coupling knockouts suggested by MCS enumeration have practical implications concerning their size, which exceeds six knockouts in the best possible case, aside from the relatively low growth rate and BPCY. However, they are promising candidates for adaptive laboratory evolution, since the target organism may acquire mutations that lead to increased growth rates while maintaining the growth-coupling with product synthesis.Moreover, using multiple strain design approaches with varying productivity and robustness demands further helped us to find knockouts providing a beneficial compromise between maximum cell growth, production flux and yields.Saccharomyces cerevisiae case study featured in this work show that strongly growth-coupled strategies point towards increased usage of the GABA degradation pathway as production demands grows, at the cost of lower growth rates. This design strategy requires further experimental validation, despite the existence of studies acknowledging it as a potential ME target for succinate production.The findings from the This work only includes CSOMs for finding sets of reaction deletions; however, it is possible for both methods to also suggest strategies involving modification of the expression levels of metabolic genes , which cOverall, we conclude that EMA-based CSOMs are valuable tools for strain design that yield design strategies with the highest production fluxes and robustness and uncover the biochemical mechanisms that lead to these phenomena. A thorough analysis of these algorithms’ outputs serves as valuable tool for guiding rational approaches to strain optimization by highlighting relevant deletions to achieve the ME goal. Aiming for both weak and strong coupling phenotypes using these algorithms is essential towards finding a compromise between viability and productivity and identifying common targets whose modification is essential to produce a given compound. We show that it is possible to fine-tune the compromise between robustness and growth rates by choosing different algorithms or setups among those tested in this work.Saccharomyces cerevisiae using the iMM904 reconstruction presented by Mo and colleagues [In this work, we attempt to find design strategies for lleagues  with addlleagues  for imprS as a m-by-n matrix encoding the network stoichiometry, v as the flux vector, and l,u as the vectors encoding the lower and upper bounds, respectively.The constraint-based approach to modelling represents stoichiometry restricted by steady-state , throughTypical approaches such as Flux Balance Analysis (FBA)  attempt The most common approach is to maximize cell growth, although other functions have also been proposed, mainly to address phenotype prediction in mutant cells , 4. The These two methods are later used to analyse the performance of our design strategies. pFBA extends the FBA problem by constraining the optimum to that which minimizes overall flux usage   and FVA An alternative approach to analyse fluxes using constraint-based models is to employ convex analysis, usually with EMA methods, since the linear system of equations is a convex polyhedral cone when all reactions are irreversible , which ie that solves the system in Eq. e is a feasible solution, thus satisfying steady-state and thermodynamic constraints: (3) if an active reaction of e is removed, e is no longer feasible. These properties assure EMs are the smallest sets of reactions allowing valid metabolic conversions, and any steady-state flux distribution complying with thermodynamic constraints can be defined as a linear combination of the full set of EMs [EMA is based on the concept of elementary modes (EM), defined as any flux distribution t of EMs .Minimal cut sets (MCSs) are an extension of this concept, defining sets of reactions that, when removed or blocked, disable certain selected EMs . With apI  requires two inputs, namely:T), containing undesired EMs to be blocked.Target flux space (D), comprising desirable EMs, which cannot be blocked.Desired flux space  as representative of SB CSOMs. This approach allows for the definition of multiple objective functions , providiGenerate an initial random set (population) of candidate strategies  containing reaction knockoutsDecode each individual and convert reaction deletions into flux constraints to be applied to the CB modelDetermine the fitness value for each individual using a suitable objective function. This function can be linear or nonlinear.Select the best individuals according to their fitness values. The assumption here is that fitter individuals will give rise to better offspring strategies.Replace the population for a new one by applying mutation and crossover operators to the previous set of strategies, thus yielding new strategies that combine reaction knockouts from the fittest individuals.Obtain the design strategies from the population if the stopping criteria are met. Otherwise, return to step 2.The overall algorithm employed in this setup includes the following steps:To provide a better comparison with EMA-based CSOMs, two sets of fitness functions are used. The first is a typical approach in ME problems, maximizing both product and biomass fluxes, while the second represents a search for more robust strategies, enforcing growth-coupled compound synthesis at lower growth rates to reach growth-coupling phenotypes similar to those found with EMA-based CSOMs. Both are represented on Table A multi-objective approach using maximum cell growth and production flux at maximum cell growth as two separate objective functions. This represents growth coupling as the strategies are evolved towards increased product and biomass production values but does not guarantee strong coupling as it inherently assumes biomass maximization.This approach includes the maximum cell growth rate and minimum product flux at maximum growth as objectives. The latter is determined through FVA  by setti5 objective function evaluations. Due to the heuristic nature of the algorithm, it was executed 10 times for each case.For this work, candidate strategies were subject to a maximum of 20 deletions. Although that amount of deletions is not acceptable for in vivo implementation, we have decided not to restrict the enumeration with a smaller strategy size to avoid discarding potentially useful phenotypes for our analysis. Since this is an evolutionary algorithm, a pool of solutions is evolved until a stopping criterion is reached, in this case being a cap of 10m-by-n stoichiometric matrix S with reversible and irreversible reactions identified by sets of indices r and i, respectively.The MCSEnumerator approach is based on the findings of Ballerstein et al.  reportinv and h) and consumed by the reactions added as part of the target space T. EFMs enumerated in this network in which fluxes in w are active are MCSs of the original network. The original mapping is obtained by checking which fluxes in the vectors v and h are active, and thus, satisfying the demand caused by the fluxes in w.The dual network/system is essentially a transposed and extended network based on the original, where reactions become metabolites and vice-versa and is represented in Eq. v and h which are set to 1 if the respective flux is active or 0 otherwise. Additional constraints ensure that the split reaction pairs cannot be simultaneously active. Finally, the objective function is set towards the minimization of the number of active reactions in v and h.In MCSEnumerator, this dual network is used as input for the k-shortest algorithm which is integrated in the linear problem by adding binary variables for each flux in I, with T and D representing flux spaces to block and keep. The desired space D can be defined as appropriate environmental conditions supplied as constraints. Additionally, a lower bound constraint on the biomass pseudo-reaction discards lethal cMCSs. Both T and D are matrices representing the linear coefficients for inequalities in the form T.v ≤ b, assuming v as the flux vector and b as a vector of size c with one value for each constraint to add.The intervention problem framework described in the previous section is used to formulate cMCS enumeration problems, with all formulations written in the form T were tested with MCSEnumerator. Four main constraint types can be summarized, namely:Product constraint, indicating that the product must stay above a certain threshold. Product/Substrate and Product/Biomass yields, as well as simple product flux constraints were tested.Substrate constraint, either fixed or with an upper bound on the maximum uptake rate. Can be considered as an environmental condition restricting the feasible solution space.Coupling constraint, referring to additional flux bounds introducing assumptions to the problem to further restrict the solution space. In our setup, we have tested a constraint to set the flux of the maintenance ATP pseudo reaction.Biomass constraint, where a biomass lower bound is set to further restrict the target flux space.Different combinations of constraints for the definition of the target flux space From this early analysis, we have determined that biomass constraints together with product yield constraints negatively affect computational speed, and do not result in additional solutions. Furthermore, we have also determined that non-fixed substrate constraints require an additional coupling component, otherwise leading to an infeasible enumeration problem. In such scenarios, it is possible that the origin point of the flux cone is included in the undesired space, for which there are no MCSs that can block it, should the problem only contain homogeneous constraints.MCSe: The undesired space contains low product yield flux vectors with a maximum substrate uptake rate of smax and maintenance ATP rate above m. A similar formulation is employed in Escherichia coli ME case studies with MCSEnumerator [umerator .MCSf: Low product yield phenotypes are blocked, but assuming a fixed substrate uptake rate of smax. The aim is to eliminate the maintenance ATP assumption, while maintaining the origin point of the flux cone out of the undesired space.MCSw: This formulation attempts to find less robust solutions by blocking low product flux phenotypes only when biomass fluxes are above a fraction F of bmax (maximum growth rate). The aim is to try and reach strategies with less strict demands regarding product synthesis and its coupling with growth. While many strategies may not allow growth-coupling at all, some weak-coupled cMCSs can theoretically be found using this formulation.Three formulations were selected to consider different strain design objectives. Table Compliance with environmental conditions: Strategies must be feasible with substrate uptake and maintenance ATP constraints, among others;Growth rate: Maximum mutant growth rates must reach at least 1% of the wild-type strain;Growth-coupling with product: Mutants must carry non-zero product flux with the cell at or above 90% of the maximum mutant growth rate (growth coupling phenotypes).Strategies from the optimization algorithms were filtered according to their expected growth-coupling strength. A filtering pipeline using FVA  was deveMCSe and MCSf formulations would always lead to growth-coupled cMCSs, this is not the case for MCSw nor solutions based on evolutionary algorithms. Thus, this step is required to discard any strategy that does not lead to growth-coupled production.It is worth noting that while the Performance – Predict production titres and robustness;Pathways – Compare overall pathway usage across strategy groups;Phenotype – Investigate individual strategies from various groups to find mechanisms leading to growth-coupling phenotypes;Structure – Assess the impact of individual knockouts in each strategy group relative to their phenotype/pathway pattern.A global assessment of the selected solution sets was performed regarding four key aspects:Production robustness: Minimum feasible production rate given a lower limit for cell growth. Production is considered robust for a certain biomass threshold, if the corresponding minimum rate is not null.Predicted fluxes: Assuming the pFBA flux distribution as the predicted phenotype, maximum cell growth, substrate uptake and production rate fluxes are obtained and considered as predictions of in vivo behaviour.Production yield: Biomass-product coupled yield [ed yield  was usedThe constraints and objective functions used to obtain the productivity metrics are highlighted on Table Using the pFBA flux distributions for all the mutant strains, it is possible to compare pathway usage across various sets of solutions. With the wild-type distribution as reference, the purpose is to find how different groups of strategies influence pathway usage.n-by-m binary matrix p with n pathways and m reactions. Each element pnm has a value of 1 if reaction m is part of a pathway n, and 0 otherwise. For each strategy with a flux distribution v, a “pathway distribution” is given by d , a vector with n elements, one for each pathway, containing the number of active reactions for each pathway.Consider a sgn(x) as the sign function of x. Assuming this is performed for s solutions, a s × n matrix P can be assembled, containing all of the pathway distributions for the strategy set. The global difference from the wild-type can be found through determination of the mean value for each pathway in P and subtracting it to the corresponding value in the wild-type.For each strategy in a given group, a pathway distribution is generated according to the formula in Eq. mcslibrary [Java library implementing the methods described by Von Kamp and associates used for MCSEnumerator [https://github.com/MEWorkbench/mcslibrary. A graphical user interface implementing some functionalities of this library is available as a plugin (optflux-mcs) for the OptFlux ME software platform [Enumeration of cMCSs was performed using slibrary , an openumerator . The tooplatform . Currentmewcore library containing core methods used within OptFlux for simulation, analysis and strain optimization. Source-code is available on the Git repository at https://github.com/MEWorkbench/mewcore. The evolutionary algorithm is part of JECoLi (Java Evolutionary Computation Library), a library implementing generic evolutionary algorithms with source code available at https://github.com/jecoli/jecoli.Our SPEA2 workflow is currently available as part of the MEWorkbench’s http://www.bio.di.uminho.pt/csomcomparison/.Data analysis and plots shown in this work were processed using the R programming language. Scripts to perform this analysis are available at Project name: mcslibrary.Project home page:https://github.com/MEWorkbench/mcslibraryOperating systems(s): Platform independent.Programming language: Java.Other requirements: IBM® ILOG® CPLEX® Optimizer version 12 or higher, Java® 1.7 or higher.License: GNU Lesser General Public License 2.1.Additional file 1:Escherichia coli using minimal cut set based strain design. (DOCX 296 kb)Compound overproduction in


Globally, cassava is an important source of starch, which is synthesized through carbon assimilation in cellular metabolism whereby harvested atmospheric carbon is assimilated into macromolecules. Although the carbon assimilation pathway is highly conserved across species, metabolic phenotypes could differ in composition, type, and quantity. To unravel the metabolic complexity and advantage of cassava over other starch crops, in terms of starch production, we investigated the carbon assimilation mechanisms in cassava through genome-based pathway reconstruction and comparative network analysis. First, MeRecon — the carbon assimilation pathway of cassava was reconstructed based upon six plant templates: Arabidopsis, rice, maize, castor bean, potato, and turnip. MeRecon, available at http://bml.sbi.kmutt.ac.th/MeRecon, comprises 259 reactions (199 EC numbers), 1,052 proteins (870 genes) and 259 metabolites in eight sub-metabolisms. Analysis of MeRecon and the carbon assimilation pathways of the plant templates revealed the overall topology is highly conserved, but variations at sub metabolism level were found in relation to complexity underlying each biochemical reaction, such as numbers of responsible enzymatic proteins and their evolved functions, which likely explain the distinct metabolic phenotype. Thus, this study provides insights into the network characteristics and mechanisms that regulate the synthesis of metabolic phenotypes of cassava. Primary metabolic pathways for cellular survival, such as those involved in carbon assimilation, are typically conserved across taxa2. The conservation of these pathways is high among organisms within a kingdom, e.g. Archaebacteria, Animalia, and Plantae3 as well as across ancestral kingdoms. Preservation of metabolic pathways is evidenced by heredity in organisms that share evolutionary lineage2. With these highly conserved metabolic pathways, similarities in the metabolic processes as well as the overall context of metabolism  are expected. Despite the conserved metabolic pathways, some evolutionarily related organisms have different spectrum of metabolic compounds, especially regarding their accumulated metabolites. For example, phyto-products within crops of the Euphobiaceae family include: starch from cassava (Manihot esculenta Crantz), latex rubber from rubber tree (Hevea brasiliensis), and triacylglycerols (TAGs) from physic nut (Jatropha curcas L.)3.Metabolism is the foundation of cellular regulation in living organisms. It is where biological regulatory factors express their modulating actions in pursuit of normal cellular activities, resulting in the diverse survival behaviors observed in various environments4, with very low protein and lipid contents compared to cereal grains7. Although the metabolic pathway of starch biosynthesis is highly conserved amongst starch crops8, starch yield varies. For example, cassava yields more starch in comparison with rice, maize, and potato. To gain insight into the complexity of metabolism and unravel the metabolic advantage of cassava over other starch crops in terms of starch production, direct comparison of the constituent metabolic reactions is insufficient. In addition, there is need to understand the enzymatic proteins involved as well as their biochemical properties and functions.Cassava is an important source of calories, especially for poor households in the tropics. Its starch is synthesized and stored in underground roots making up 70–85 percent of the total dry weight of cassava roots9. The cooperative role of these genes is represented by the metabolic network, the series of biochemical reactions describing the conversion of substrates to products of metabolism11. The large number of genes governing the metabolic processes introduces complexity into the metabolic network, of which metabolic flexibility12 and plasticity13 due to redundancy in reactions and metabolic paths are indicative14. Thus, system-level analysis is crucial to comprehensively study metabolic processes in living organisms; it might be the only way to access the hidden factors behind the distinct metabolic characteristics of organisms13.Comprehensive understanding of interspecies differences in metabolism extends beyond the knowledge about individual genes and gene functions. The metabolic processes underlying metabolism involve about 15–20 percent of protein-coding genes in genomes, related to thousands of enzymes in the metabolic pathways15, and the availability of complete genome sequences for a number of organisms have provided a plethora of resources for comparative genomics studies16, and have made finding results more promising11. These resources have facilitated the large-scale reconstruction of metabolic pathways for a wide range of species, especially for non-model organisms17. The biochemical reactions in cellular metabolism are inferred from information in publicly available databases: species-specific databases19 and metabolic pathway omnibus databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes)20, PMN (Plant Metabolic Network)21, MetaCyc22, and BioCyc22. Genome-scale metabolic pathway reconstruction enhances our understanding of metabolic characteristics and capabilities.Advances in high-throughput sequencing technology have enabled studies on the global metabolic capabilities that help define metabolism. The exponential growth of genome sequencing projects in the last decades24. For example, the comparative analysis of four halophilic archaea species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and Natronomonas pharaonis demonstrated similarities in nucleotides and prenyl-based lipids synthesis and differences in glycerol degradation, pentose metabolism, and folate synthesis25. Furthermore, metabolic routes for acetyl-CoA biosynthesis related to lipid production are present in oleaginous fungi , but are absent in non-oleaginous strains  as determined by comparative analysis26. In addition to these metabolic paths, the characteristics of metabolism may also be explained by the number of enzymatic protein isoforms (multiple proteins that perform the same function), heteromeric enzyme complexes (more than one subunit required for carrying out the enzymatic function), and enzymes that catalyze several reactions which determine the flexibility of metabolic conversion of substrates to products27. While there is no absolute way for investigating metabolism, detailed study including extensive features would help to decode the metabolic characteristics underlying the distinct behaviors.The comparison of reconstructed metabolic pathways could reveal conserved as well as diverged metabolic pathways linked to distinct metabolic phenotypes in closely related species. The findings would highlight the crucial metabolic paths which are conserved in various species throughout the phylogenetic lineage as well as underline the special paths required to retain the specific metabolic activity of specieset al.8 with the inclusion of turnip — a starchy root crop as cassava, to enhance the protein annotation. In order to reflect the complexity of carbon metabolism in cassava, the pathways of amino acid, cell wall, fatty acid, and nucleotide biosynthesis and respiration were incorporated into MeRecon in addition to those of starch and sucrose biosynthesis and the Calvin cycle as contained in Saithong et al.8. Following the manual curation and validation with available transcriptome data and literature, the starch biosynthesis, sucrose biosynthesis, and Calvin cycle pathways in MeRecon were enhanced their quality in comparison to Saithong et al.8, and the overall network completeness is better in MeRecon than CassavaCyc. Analysis of MeRecon pathway allowed us to characterize the complexity of carbon metabolism in cassava in comparison to the template plants. The results revealed that although the overall topology was highly conserved, differences at sub-metabolism level were found, especially in non-carbohydrate related pathways. In addition, our study showed that some enzymatic proteins of cassava evolved from their ancestral orthologues, as exemplified by phosphoenolpyruvate synthase  and aldehyde dehydrogenase (NAD+) , which could explain the distinct metabolic pathway of cassava and might result in functional adaptation of enzymatic proteins.To examine the metabolic characteristics of cassava that facilitate starch production, we reconstructed the carbon assimilation pathway using a comparative genomic approach based on multiple template plants proposed by Saithong 28  — a starchy root crop, Arabidopsis  — a model plant, maize (Zea mays) — a C4-starchy cereal crop, rice (Oryza sativa) — a C3-starchy cereal crop, castor bean (Ricinus communis) — an oil-seed crop of the Euphorbiaceae family, and potato (Solanum tuberosum) — a starchy tuber crop  and match score (MS)8, were calculated for each annotated protein to represent the confidence level. The preliminarily reconstructed carbon assimilation pathway, pre-MeRecon, was curated with the biochemical reaction obtained from the CassavaCyc database (http://www.plantcyc.org/). Following curation, MeRecon was visualized using SmartDraw, an informative graphical platform, to reveal the constituent metabolites, reactions, protein IDs with the CS and MS scores, and the template plants from which the cassava orthologues were identified.The carbon assimilation pathway of cassava (MeRecon) was reconstructed based upon the plant genomic data from various databasesop Table . The ann29 and gene expression data from six transcriptome datasets on: leaf and stem30, root development32, cold stress33, drought stress34, and fungal infection35.Here, cassava genes annotated to the carbon assimilation pathway (MeRecon) were verified using cassava cloned sequences from GenBank databaseHD) was calculated for each sub-metabolism to measure the distance between cassava and the templates, based on the co-existence of reactions in each sub-metabolism. Additionally, the biochemical properties of the enzymes were determined by calculating the P score as shown below.MeRecon was comparatively analyzed with the existing carbon assimilation pathways of turnip, Arabidopsis, maize, rice, castor bean, and potato based upon their evolutionary and physiological relatedness to cassava, availability of well-defined genome information, and being starch crops. The overall pathway topology, metabolic functions of the sub-metabolisms, biochemical properties of enzymes in each sub-metabolism, and enzymatic function of proteins were examined to identify conserved and diverged enzymatic proteins Fig. . The norj ϵ {Calvin cycle, sucrose biosynthesis, starch biosynthesis, respiration, amino acid biosynthesis, cell wall biosynthesis, fatty acid biosynthesis, nucleotide biosynthesis} pathway, HD estimates the similarity of pathway topology according to the co-existence (ai) of the enzymatic reaction i  in cassava (MeRecon) and the k ϵ {turnip, Arabidopsis, maize, rice, castor bean, potato} plant species. HD was calculated as follows:For the whereai = co - existence of reaction i in pathway j of cassava and species kai = {0, reaction i occurs in both organisms1, otherwisebj = the number of enzymatic reactions in pathway jP score of the j ϵ {Calvin cycle, sucrose biosynthesis, starch biosynthesis, respiration, amino acid biosynthesis, cell wall biosynthesis, fatty acid biosynthesis, nucleotide biosynthesis} pathway estimates the similarity of biochemical properties of enzymatic functions (EC number) l  between cassava (MeRecon) and the k ϵ {turnip, Arabidopsis, maize, rice, castor bean, potato} plant species as follows:CP/E, l = the number of cassava enzymatic proteins annotated to an enzyme (EC number) according to all template plant species.The        cl = the number of cassava enzymatic proteins annotated from the k plant species in sub-metabolism j.       dj = the number of enzymes (EC number) in pathway j.36 with default parameters. The maximum likelihood (ML) tree was constructed based on the Jones-Taylor-Thornton (JTT) amino acid substitution model to determine the evolutionary relatedness of the protein sequences. Bootstrap values were also applied with 100 replicates.The phylogenetic trees were constructed using the ClustalW alignment of the MEGA7 programEscherichia coli (representative of bacteria), and red algae  were retrieved from KEGG database20 and analyzed to identify conserved protein domains using InterPro37 (https://www.ebi.ac.uk/interpro/) with default parameters.The amino acid sequences of cassava annotated to phosphoenolpyruvate synthase (EC 2.7.9.2) and their orthologues in two template plants (maize and rice), and other species including moss (representative of primitive plants), Chinese alligator , 38 (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi). To identify the conserved protein domain superfamilies by comparing the protein query sequence against the Conserved Domain Database (CDD) with the parameters set as follows: E-value of 0.01, composition-corrected scoring applied with the low complexity region turned off, and maximum number of hits of 500.The amino acid sequences of cassava annotated to aldehyde dehydrogenase (NAD+)  and amino acid sequences of the six template plants were analyzed using the Batch CD-Search Tool39 (https://itol.embl.de/) was employed to integrate the phylogenetic tree and protein domains.Finally, the iTOL (interactive tree of life) program8. Protein sequences of enzymes in the carbon assimilation pathway from multiple well-studied plants  were compared against all protein sequences in the cassava genome to identify genes that are conserved among the species, as well as genes that give cassava its unique characteristics. In addition, turnip was also employed as representative template of starchy root crops to exhaustively identify the genes that are related to carbon assimilation in cassava. The preliminarily reconstructed carbon assimilation pathway of cassava, pre-MeRecon, comprised eight sub-metabolisms: Calvin cycle, starch biosynthesis, sucrose biosynthesis, respiration, amino acid biosynthesis, nucleotide biosynthesis, fatty acid biosynthesis, and cell wall biosynthesis and contained 259 reactions—253 enzymatic reactions, two spontaneous reactions, and four gap reactions (three gap reactions in amino acid biosynthesis and one gap reaction in nucleotide biosynthesis), 259 metabolites, and 864 metabolic genes corresponding to 1,046 enzymatic proteins , as shown in Supplementary Table 30, root development32, cold stress33, drought stress34, and fungal infection35 were applied to support the existence of the reactions in MeRecon  in three of the eight sub-metabolisms were supported by cloned sequences of cassava with exact EC numbers  were confirmed by multiple transcriptome datasets  and match score (MS), were assigned to each annotated protein in MeRecon based on the method of Saithong et al.8. The CS-MS plot , and expression data (underlined) were used to indicate the confidence of annotation.The confidence scores, consisting of conservation score  were investigated. The complexity was analyzed based on the level of metabolic redundancy, i.e., the relationship between metabolic reactions and their corresponding enzymatic proteins, as multiple proteins catalyze each reaction in cassava pathway. The conservation was studied by comparing MeRecon with the corresponding pathways in the representative plants. Comparative analysis of the metabolic pathway was performed to demonstrate the conservation of carbon assimilation between cassava and the other six template plants, in terms of pathway structures and metabolic functions.41. The observed complexity could be due to the extensive number of metabolic components  or their complicated association in cellular metabolism. For a metabolic process, biochemical reactions not only link up in a sequential manner, but also are interconnected through common metabolic intermediate compounds. Enzymes are typically required to convert substrates to products in a biochemical reaction, but the enzyme-to-reaction proportion often varies. Some enzymes with different amino acid sequences are often involved in the same biochemical reaction (isozymes) with overlapped function, e.g., acid phosphatase , esterases , malate dehydrogenase , and shikimate dehydrogenase  isozymes in cassava42. In addition, enzymatic proteins often necessitate intricate regulatory pathway to pursue their normal function9, such as the heteromeric enzyme complex (protein complex). The complication, thus, would even increase for enzymes that require multiple amino acid sequences to perform their catalytic activity. These scenarios introduce flexibility and complexity into the carbon metabolic network  and enzymes  in MeRecon included: 1 enzyme/1 reaction, 1 enzyme/many reactions, 1 enzyme (>1 sub-metabolism)/many reactions, and many enzymes/1 reaction Fig. , an indiCP/E score is shown in Fig. 45. We also observed that such complexity was not uniform across all sub-metabolic pathways. For example, the cell wall biosynthesis and respiration pathways contained a larger number of proteins that confer the same enzymatic function compared to others, indicating their superior complexity  and enzymatic proteins involved in the pathway. Later, the conservation of the biochemical properties was determined for each metabolic reaction based on the variety of proteins involved in the reaction. Finally, for each protein, the enzymatic function was determined based on the evolutionarily preserved sequence.Hamming distance (HD), ranging from 0 (closely related) to 1 (distantly related), was calculated for each sub-metabolism to measure the distance between cassava and each plant template with respect to the co-existing biochemical reactions. The results showed that the overall topology of carbon assimilation pathway in cassava was highly conserved in all six model plants as indicated by very low HD scores  (EC 1.1.1.2), phosphoenolpyruvate synthase (EC 2.7.9.2) and guanosine ribohydrolase (EC 3.2.2.1), and deoxycytidine kinase (EC 2.7.1.74) were specifically annotated from rice, maize, and potato, respectively  to 1 (high conservation) and represents the number of template plant species underlying each protein annotation. Figure CS score of 1,046 proteins in the carbon assimilation pathway of cassava. The positively skewed curves of fatty acid biosynthesis, cell wall biosynthesis, amino acid biosynthesis, and nucleotide biosynthesis  of each metabolic reaction was used to represent the biochemical property, and its conservation in cassava and representative plant species was examined based upon the P score, which ranges from 0 (dissimilar) to 1  and indicates the tendency that two contrasting metabolic reactions possess similar level of complexity (CP/E). The complexity of reactions in MeRecon differed from those in the representative plant species   were selected to demonstrate the conservation and possible variations in enzymatic functions. A reaction driven by PEPS (R00199) was reconstructed in MeRecon using the PEPS cassava protein, MeRecon: 000266_000266, annotated from maize, with high sequence similarity. The ALDH is a large class of enzyme relevant to numerous proteins.The previous sections highlighted the conservation in the topology, metabolic function, and biochemical properties of reactions in the carbon assimilation network of cassava and revealed variations in enzymatic reactions. Here, we investigated the conservation of enzymatic protein functions based on the conserved amino acid sequences. Utilization of metabolic substrates by the action of enzymatic proteins may be affected by variation in amino acid sequences. Two proteins, namely, phosphoenolpyruvate synthase  and aldehyde dehydrogenase , moss (a representative of primitive plants), Chinese alligator , Escherichia coli (a representative of bacteria), and red algae . The 000266_000266 protein contained the PEP/pyruvate binding domain, a typical domain found in PEPS proteins of all species  by phosphoenolpyruvate carboxylase (PEPC)48. The activity of PEPC is much higher in cassava than in typical C3-plants49. In stress environment, such as high temperature and soil water stress, PEPC of cassava has greater activity and affinity to CO2 than ribulose-1,5-bisphophate carboxylase/oxygenase (Rubisco)50, which might explain the high photosynthetic capacity of cassava over C3-plants50 leading to its unique carbon assimilation, although it lacks the C4-Kranz anatomy51.PEPS catalyzes the conversion of pyruvate to phosphoenolpyruvate. The PEPS protein  in cassava (MeRecon): 003883_003883, 003907_003907, 004132_004132, 004210_004210, 004263_004263, 004458_004458, 004471_004471, 004471_006399, 005092_005092, 005124_005123, 005124_005124, 005124_005133, 005135_005135, 005392_005392, 005744_005744, 005882_005882, 005882_005896, 006024_006024, 006024_006072, 006047_006047, 006263_006252, 006263_006254, 006263_006263, 006263_008624, 006263_008651, 006522_006522, 026719_026719, and 027225_027225) was studied with respect to 65 orthologous proteins in template plant species   is responsible for converting acetaldehyde to acetate in the respiration sub-metabolism. ALDH belongs to a large protein ALDH superfamily that catalyze the detoxification of board aldehydes to their corresponding carboxylic acids through oxidationies Fig. . The ALDCP/E) of MeRecon pathway, the carbohydrate-related sub-metabolisms may require higher enzymatic protein complexity  for their functions compared to non-carbohydrate sub-metabolisms. In addition, the MeRecon pathway was compared to the six model plants with respect to pathway topology, metabolic function of sub-metabolisms, biochemical properties of enzymes in each sub-metabolism, and enzymatic function of proteins. Although the topology and metabolic function were highly conserved across templates, different enzyme properties and enzymatic function of proteins were identified which might affect the synthesis of metabolic compounds and the global metabolic behavior.The carbon assimilation pathway of cassava (MeRecon) was reconstructed and subsequently investigated through comparative network analysis to identify conserved and diverged metabolic characteristics of cassava that might relate to the distinct metabolic phenotype of cassava in terms of starch production. First, MeRecon pathway was reconstructed using comparative genomic approach based upon annotated cassava enzymatic proteins from six plant templates. Reaction gaps were filled using cassava enzymatic proteins obtained from the CassavaCyc database. The MeRecon pathway consists of 199 EC numbers (enzymes), 259 reactions, 259 metabolites, 870 genes, and 1,052 proteins. More than 95 annotated enzymatic proteins were validated using either the six cassava transcriptome datasets or the cloned sequences of cassava that were obtained from GenBank database or both when available. Based on the complexity analysis (Supplementary InformationSupplementary Table S3Supplementary Table S4