Saccharomyces cerevisiae enabling it to ferment xylose and Lactobacillus reuteri fermenting glucose via the phosphoketolase pathway, are here used to introduce a quantification parameter to capture what limits the growth rate of these microorganisms under anaerobic conditions. This dimensionless parameter, the cofactor formation flux ratio (RJ), is the ratio between the redox formation flux (JNADH+NADPH), and the energy carrier formation flux (JATP), which are mainly connected to the central carbon pathways. Data from metabolic flux analyses performed in previous and present studies were used to estimate the RJ-values. Even though both microorganisms possess different central pathways, a similar relationship between RJ and the specific growth rate (μ) was found. Furthermore, for both microorganisms external electron acceptors moderately reduced the RJ-value, thereby raising the μ accordingly. Based on the emerging profile of this relationship an interpretation is presented suggesting that this quantitative analysis can be applied beyond the two microbial species experimentally investigated in the current study to provide data for future targeted strain development strategies.Most fermentative microorganisms grow well-under anaerobic conditions managing a balanced redox and appropriate energy metabolism, but a few species do exist in which cells have to cope with inadequate energy recovery or capture and/or redox balancing. Two cases of these species, i.e., the metabolically engineered  In intracellular metabolism redox reactions stand central. Many of these reactions are mediated by the cofactors NADH, NADPH, and ATP. There are connections between these co-factors through a complex metabolic interplay and the cell seeks to balance their levels to reach homeostasis , it was found that the central carbon metabolism consists of simultaneous operation of the Phosphoketolase pathway (PKP) and the Embden-Meyerhof-Parnas pathway  that are similar to the pfkB gene of Escherichia coli. In this case, growth on glucose was not limited by the ATP-formation flux, because in the presence of an external electron acceptor, as NADH sink, the ATP-formation flux did not improve, even though the growth rate had increased nearly 2-fold H/ATP formation flux ratio, and its graphical relationship with the rate of anaerobic growth of S. cerevisiae strains were obtained elsewhere  gene and overexpresses genes encoding the enzymes of the non-oxidative pentose phosphate pathway   and XYL1 (N272D), respectively : NaAc 0.5, MnCl2·2H2O 0.013, L-glutamine 0.20, and glucose 25. Subsequently, an outgrown culture was used as inoculum (5% v/v) to start 1 L batch-mode cultures in a 2-L pH-controlled Biostat A bioreactor  kept thermostatically at 37°C. The pH was kept at 5.5 through automatic titration with 3 M KOH. The stirring speed was set to 100 rpm. Anaerobic conditions were maintained by the production of carbon dioxide in the culture. Sample treatment and analysis was similar as described previously  glucose and sucrose in L. reuteri were estimated based on stoichiometric models as described in Sonderegger et al.  were estimated during the exponential phase of batch cultures (mmol metabolite· g dry weight−1). The fluxes (r) . For S. cerevisiae the ATP formation fluxes were calculated from the fluxes through glyceraldehyde-3-phosphate (GAPDH) to pyruvate pathway and the acetate pathway, and the NAD(P)H formation fluxes were calculated from the fluxes through the xylose pathway, oxidative pentose phosphate pathway, GAPDH, and the acetate pathway. For L. reuteri the ATP formation fluxes were calculated from the fluxes to acetate and lactate, whereas the NAD(P)H formation fluxes were calculated from the PKP flux and the flux through GAPDH.For both cases, the metabolic flux analyses were done for batch cultures during the exponential growth phase. The metabolic fluxes through the central carbon pathways operating during anaerobic growth on (i) glucose and xylose in r et al.  and Årskr et al. , respectum Table , includir) Table  were calS. cerevisiae was simulated using the COBRA toolbox in MATLAB R2012b . The genome-scale metabolic model iMM904 . The parameters in the rate equations H]. With the energy carrier formation flux (JATP) we propose to express their interrelationship as a dimensionless quantification parameter, i.e., the cofactor formation flux ratio:As the previous studies imply, anaerobic growth of both −1·h−1. The relationship of this parameter with the growth rate will now be analyzed for the two cases presented.with both fluxes having the unit mmol·g dry weightS. cerevisiae provides a series of strains with successive improvements of anaerobic growth on xylose  is based on absence of a redox imbalance . In contrast, at high RJ-values cellular metabolism lacks capacity for growth. Interestingly, plotting the discrete values of RJ against the corresponding maximum specific growth rate revealed a sigmoidal profile , at which the growth rate was maximal, to 1.85  was logically built on the conclusion of a previous study that a combination of redox imbalance and a limited ATP formation flux forces metabolically active cells of S. cerevisiae on xylose to meander close to the edge of propagation H represents also a proton formation flux (JH+). However, the influence of the redox can be questionable. On the one hand, both the NADPH/NADP ratio and the NADH/NAD ratio declined somewhat during the shift from glucose to xylose metabolism in S. cerevisiae TMB 3057  is dependent on the level of the H+-ATPase activity . It earns very low biomass yields due to a net yield of 1 mol ATP·mol glucose−1 (Fuhrer et al., Z. mobilis is able to do so because of having a metabolic highway from glucose to ethanol comprising 50% of the total cell protein and other physiological adjustments, allowing for exceptionally high metabolic fluxes to ethanol and only minor fluxes to biosynthesis (Sprenger, E. coli that is only able to grow anaerobically on xylose in the presence of pyruvate formate lyase (PFL; Hasona et al., RJ of 1.11; a Δpfl mutant failed to grow, but then the value for RJ would have been a fatally high 2.5.One notable exception is RJ, based on kinetics. The current analysis of the available data reveals a relationship between this new dimensionless parameter and the specific growth rate. In what mechanistic way, the cofactor formation flux ratio is connected to acidification of the cytosol merits specific investigation. The value of this parameter implies how far the metabolism is from the critical situation beyond which unrestricted growth is allowed. Therefore, RJ can be used as a novel analytical tool for targeted design of new strains, and is easily implemented in metabolic flux model simulations.A step toward answering the long-standing question why certain types of metabolic pathways do not guarantee growth under anaerobic conditions is proposed here via introduction of a quantification parameter, Ev conceived and designed part of the experiments and drafted the manuscript with BH. BB performed the model simulations. All authors critically read, contributed to, approved the final version of the manuscript, and agreed to be accountable for all parts of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models. E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for  Stoichiometric models have been used to study the physiology of organisms since 1980s , and witin vivo. However, both FBA, TFA and other steady-state approaches cannot capture the dynamic response of metabolic networks, which requires integration of detailed enzyme kinetics and regulations [With increasing popularity of GEMs, different techniques to analyse these networks have been proposed ,11. Fluxulations . Hatzimaulations –31. Receulations .ad hoc, with different criteria and aims, which have not been consistently and explicitly justified [As the quality and the size of the models increase with better annotation, the complexity of the mathematical representations of the models also increases. Hatzimanikatis and colleagues  observedustified –41. An austified  has beenustified  to compuIn this study, we have developed redGEM, a systematic model reduction framework for constructing core metabolic models from GEMs. Herewith, we propose an approach that focuses on selected metabolic subsystems and yet retains the linkages and knowledge captured in genome-scale reconstructions. redGEM follows a bottom-up approach that allows us to handle the complexity and to yield comprehensive insights in connecting the metabolic model to actual cellular physiology. redGEM can be tailored to generate minimal models with conserved functions. However, our approach is not strictly focused only on the reduction of the stoichiometry for the generation of highly condensed network, but aims also to preserve the constitutive characteristics of metabolic networks.E. coli iJO1366 [E. coli iJO1366 models.In redGEM, we use as inputs: (i) a GEM, (ii) metabolic subsystems that are of interest for a physiology under study; (iii) information about utilized substrates and medium components; and (iv) available physiological data . After a iJO1366  under boE. coli, iJO1366 to generate a reduced model consistent with its parent GEM model. Firstly, we selected 6 central carbon metabolism subsystems , as they are defined in original E. coli GEM. In addition, we have included all the reactions that use quinone/quinol pool metabolites  in oxidative phosphorylation subsystem to capture the coupling between the core carbon metabolism and energy/redox metabolism. Some of those reactions had different subsystem definition in original GEM. These subsystems include a total of 185 reactions and 126 metabolites. We next redefined the content of each starting subsystem by performing an intra-expansion analysis to identify the RT , i, j), which overall define the core network CND with respect to selected degree of connection parameter D  but include only MS, E. coli iJO1366 and we generated all core networks with degree of connection up to D = 6.After the intra-expansion, the network expansion by directed graph search finds metabolites and reactions between subsystems in a pairwise manner for non-common metabolites  with respect to the degree of connection D. D is the distance between a subsystem pair and can be either equal to the inherent minimum distance between each pair, or imposed by the user for all subsystem pairs. Depending on the network topology, the inherent minimum distance can be equal to the input D imposed by the user . redGEM also performs pairwise connections between the metabolites of the same subsystem. The algorithm calculates ameter D . The addad hoc built models, such as malic enzymes 1–2 between glycolysis and TCA cycle that connect L-malate to pyruvate, phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase that connect oxaloacetate and phosphoenolpyruvate. Moreover, it captures many other reactions, such as 2 types of L-aspartate oxidases, which are using quinone/quinol cofactor pairs and labeled as electron transport chains reactions. There are two more L-aspartate oxidase reactions that are added to the D = 1 core network by redGEM  reaction is explicitly included in the reduced model, and its corresponding minimum requirement of the GEM is preserved for further analysis in this study.At D = 1, redGEM captured many connecting reactions that are part of many y redGEM . One useWhen we analyze the pairwise connections between subsystems with respect to different connection parameter D, we observe that there is no D = 1 connection between certain pairs, such as pentose phosphate pathway (PPP) and glyoxylate metabolism (GLX) . HoweverFollowing the analysis for reactions, we identified the metabolites that connect the subsystems in a pairwise manner. There are no such intermediate metabolites between subsystems connected by D = 1, since this degree of connection only captures reactions between the unshared metabolites of a subsystem pair . When thThere are metabolites, such as pyruvate and succinate, that already participate in D = 0 reactions , and they appear later to connect at least one subsystem pair with D = 3 connection. This indicates that there is no path in GEM with length less than 3 that can connect any starting subsystem pair using these intermediates, excluding the reactions that already belong to this subsystem pair.Methylglyoxal is known to be a hub metabolite, since it can connect dihydroxyacetone phosphate to lactate in 2 reactions. Lactate is a metabolite that belongs to different starting D = 0 subsystems such as oxidative phosphorylation and pyruvate metabolism. Moreover, it can be converted to pyruvate by lactate dehydrogenase, and pyruvate is already known as a hub metabolite that can connect different subsystems. As another example, L and D tartrate connect pentose phosphate pathway and citrate cycle in 3 steps through the following path: With an antiporter, cytosolic succinate transports L and D forms of tartrate to cytosol. Then, L and D-tartrate dehydratase enzymes convert these two forms of tartrate to oxaloacetate and water. Following this biotransformation, oxaloacetate can be converted to pyruvate by many enzymes. As we observed in methylglyoxal case, pyruvate is part of many different starting D = 0 subsystems , and L and D tartrate appear as intermediates that connect 7 pairs of subsystems in D = 3.Another layer of information that we can extract through this analysis is the subsystems that connect the selected starting subsystems, thus demonstrating the proximity of these subsystems to the defined starting core carbon ones. By starting from 7 subsystems (including extracellular metabolites as extracellular subsystem), the network expansion procedure results in capturing reactions as core from 32 different subsystems for D = 6 . In GEM,i) they do not make use nor obey mass conservation; hence the pathways they generate are not guaranteed to be able to carry flux in metabolic network or to be elementally balanced, ii) and they cannot manage pathways that are not linear, such as branched pathways. To overcome these limitations, we used lumpGEM [non-core reactions In GEM, lumpGEM generated a 17 reactions subnetwork to synthesize histidine from core carbon metabolites -5-amino-4-imidazolecarboxamide and L-Glutamine cannot be balanced in a network that is composed of core reactions and the linear pathway from ribose-5-phophate to histidine. These metabolites are balanced in the network by other non-core reactions. Hence, the generated sets of reactions are not linear routes from precursor metabolites to biomass building blocks, but branched, balanced subnetworks . The size and the complexity of the composition makes it necessary to develop techniques to keep the information stored in GEM for the biosynthesis, but yet reduce the size of the network significantly. Methods, such as graph-search algorithms can be used for identification of biosynthetic routes between two metabolites in metabolic networks ,46. Howe lumpGEM , an in-babolites . HistidiE. coli is well-known to be robust against deletions by having many duplicate genes and alternate pathways[Using lumpGEM, we replicated all the biosynthetic pathways in databases such as EcoCyc , either  pathways. Some of pathways extreme  pathways,52 and e pathways,54, as wSmin  subnetworks generated under aerobic conditions are using molecular oxygen, thus cannot carry flux under anaerobic conditions. This necessitates the generation of lumped reactions without any oxygen in the media. For Smin, lumpGEM generated only 4 new lumped reactions for anaerobic case, for 3 metabolites, namely, heme O, lipoate (protein bound) and protoheme. All the other lumped reactions generated for anaerobic case are a subset of the 250 lumped reactions  it drops to 0.27/hr. rGEM grows with 0.27/hr specific growth rate both with FBA and TFA. When we analyzed the discrepancy between the FBA and TFA growth rates for GEM, we saw that the difference is emerging from reactions that use molecular oxygen in GEM. These oxygen-using reactions do not belong to oxidative phosphorylation or ETC reactions, and are not a part of rGEM network. Moreover, the standard Gibbs free energy of those reactions range from 19kcal/mol to 294 kcal/mol in the oxygen producing direction[One of the most important criteria for the reduced GEM (rGEM) validation is the maximum biomass production. We performed biomass maximization with FBA and TFA. With all 254 lumped reactions, maximum specific growth rate of the rGEM is the same as GEM’s μdirection and are To incorporate experimental fluxomics data to validate the model, we included 13C-MFA data from Haverkorn et al. . In thisThe core networks generated by redGEM are the same for different possible carbon sources, since they are incorporated in the core network as extracellular subsystem. Thus, the only difference that can emerge for the reduced models growing on different substrates will be the generated lumped reactions. Before rerunning the lumpGEM algorithm under different environmental conditions, we tested specific growth rate of the reduced model generated under glucose for different carbon sources . The theE. coli iJO1366 growing under glycerol anaerobically. The growth rates of rGEM and GEM under these conditions with TFA are the same, 0.113/hr. The number of generated Smin subnetworks under glycerol is 910, compared to 1212 generated under aerobically grown E. coli with glucose as sole carbon source. There are 237 unique lumped reactions for growth under glycerol, whereas there are 250 unique lumped reactions for glucose case. Among these 237 lumped reactions for glycerol, there are only 8 different lumped reactions compared to glucose case for heme O, protoheme and lipoate (protein bound).To compare the subnetworks and lumped reactions under different carbon sources and environmental conditions, we generated a reduced model of in silico gene deletion (knockout) experiments to i) identify essential and nonessential genes, ii) study the gene deletion impact on the organism physiology, iii) develop strategies for metabolic engineering [irJO1366, generated with D = 1, shares 307 genes with GEM, and among these 307 genes, 25 are essential. 22 of those genes are also essential in GEM. 2 out of 3 conflicting genes do not have an effect on the maximum theoretical yield of E. coli under aerobic, minimal glucose medium in the GEM. The first case is the gene transcribing thioredoxin reductase enzyme, which interconverts NADPH to NADP by using oxidized thioredoxin and reduced thioredoxin as cofactor pairs. This reaction is not essential in GEM, however it is essential in rGEM, since the cofactor pair oxidized thioredoxin and reduced thioredoxin participate in lumped reactions, and due to flux coupling, the reaction that thioredoxin reductase catalyzes becomes indispensible. We searched for alternative lumped reactions so that this gene will not be essential in rGEM. However, lumped reactions constructed from Smin do not make this gene non-essential. The second discrepancy of the responses to gene deletion between rGEM and GEM is the gene transcribing Glutamate dehydrogenease, which shows a different behaviour compared to thioredoxin reductase enzyme. The reaction it catalyzes is the only reaction that synthesizes glutamate in the rGEM, and knocking out this enzyme automatically results in no specific growth rate. Deleting this enzyme in GEM results in a growth rate drop of 3.3%, and alternative synthesis pathways for glutamate in GEM abolishes the essentiality. The deletion of adenylate kinase (adk) is the third discrepancy between rGEM and GEM. Knocking out this gene does not result in any drop in growth rate for GEM, however it prevents the cellular growth in rGEM. The reason for this discrepancy is similar to the case of thioredoxin reductase, i.e. the loss of alternative reactions/pathways that can complement this deletion. Although these reactions/pathways can be a part of the subnetworks, the corresponding lumped reactions cannot add such flexibility in the rGEM network. Interestingly adk is reported as essential in literature[adk gene in GEM are either not active or not catalytically efficient/favorable.One of the most common analyses for genome-scale models is ineering . Consistiterature, thus shTo further validate the model, we compared the physiologically allowable flux ranges with flux variability analysis (FVA), allowable concentration ranges for metabolites and Gibbs free energy of reactions by performing Thermodynamics-based Variability Analysis (TVA) for the reactions and metabolites that are common between rGEM and GEM. Comparisons for the allowable flux ranges revealed that most of the common intracellular reactions between rGEM and GEM have consistent flux ranges, however, there are some reactions in the rGEM with reduced flux variability as compared to GEM counterparts . The varWe next performed a Concentration Variability Analysis (CVA) (within TVA) on common metabolites between rGEM and GEM . Almost Reduced models have been used to understand and investigate cellular physiology for many years. Before the emergence of genome scale models (GEMs), different groups with different aims built reduced models for their studies with a top-down approach. Conversely, GEMs provide the platform to understand all the metabolic capabilities of organisms, since GEMs encapsulate all the known biochemisty that occurs in cells. However the complexity of GEMs make their use impractical for different applications, such as kinetic modeling or elementary flux modes (EFMs). The need to focus on certain parts of these networks without sacrificing detailed stoichiometric information stored in GEMs makes it crucial to develop representative reduced models that can mimic the GEM characteristics. Within this scope, we developed redGEM, an algorithm that uses as inputs genome-scale metabolic model and defined metabolic subsystems, and it derives a set of reduced core metabolic models. These family of core models include all the fluxes across the subsystems of interest that are identified through network expansion, thus capturing the detailed stocihiometric information stored in their bottom-up built parent GEM model. Following the identification of the core, redGEM uses lumpGEM, an algortihm that captures the minimal sized subnetworks that are capable of producing target compounds from a set of defined core metabolites. lumpGEM expands these core networks to the biomass building blocks through elementally balanced lumped reactions. Moreover, redGEM employs lumpGEM to include alternative lumped reactions for the synthesis of biomass building blocks, thus accounting for alternative sytnhesis routes that can be active under different physiological conditions.P. putida, S. cerevisiae, Chinese Hamster Ovary cell (CHO) and human metabolism. For instance, redGEM algorithm has generated core networks of sizes between 168 metabolites/164 reactions to 360 metabolites/414 reactions for iMM904 [S. cerevsiae with degree of connection parameter D varied from 1 to 6. The generated reduced model irMM904 with D = 1 has the same biomass yield with the parent model GEM as 0.29/hr under 10 mmol/gDWhr glucose uptake. Similar to E. coli case, flux and concentration variability, and gene essentiality characteristics of the rGEM are in agreement with the GEM counterparts . Moreover, reduced models are promising platforms for the comparison of central carbon (or any other) metabolism of different species. This approach can help us to better investigate the metabolic capabilities and limitations of organisms and to identify the sources of physiological differences across different species.redGEM builds reduced models rGEMs that are consistent with their parent GEM model in terms of flux and concentration variability and essential genes/reactions. These reduced models can be used in many different areas, such as kinetic modeling, MFA studies, Elementary Flux Modes (EFM) and FBA/TFA. redGEM algorithm is applicable on any compartmentalized or non-compartmentalized genome scale model, since its procedure does not depend on any specific organism. As a demonstration, we have applied the redGEM algorithm on different organisms, namely r iMM904  GEM recoE. coli iJO1366 [We applied redGEM algorithm on the latest genome scale model of  iJO1366 , which iIn redGEM, we introduce and use the following definitions:Si: Core subsystem i that is selected/defined by the user.Si.Si.Degree of connection D: The path length between two subsystems. It corresponds to the number of reactions that link subsystems Si and Sj.D between the subsystems Si and Sj; these reactions do not belong to either D between the subsystems Si and Sj; these metabolites do not belong to either Sm with m ≠ i and m ≠ j.Postulate 1: Reactions that belong to unique reactions for the degree of connection D.Postulate 2: Some of the reactions in Si and Sj, however it cannot capture the intra-connections between the metabolites of the same subsystem or the metabolites that are shared between Si and Sj.Postulate 3: D that intra-connects the metabolites of the subsystem Si.D that intra-connects the metabolites of the subsystem Si.RT: Reactions where only RT is composed of reactions that only cofactor pairs, small metabolites and inorganics participate. All the other reactions that include other core metabolites  will be a part of Postulate 4: CND: The core network for redGEM that is composed of metabolites RT.Core Network, rGEM: Consistently reduced model from its parent GEM.D is the minimum distance between Si and Sj.We can also generate the core network from the chosen subsystems using the minimum distance between the chosen subsystems and report the connecting reactions and metabolites. In this case, the degree of connection Lmin,ij: The length of the shortest path between the subsystems Si and Sj.Si and Sj with a path of length Lmin+n,ij in where n is a user defined parameter.Si or Sj and are intermediates of the path of length Lmin+n,ij in between these two subsystems.Lmin,ij = 1 then Postulate 5: If redGEM uses the following inputs and parameters:A Genome-scale Metabolic model.The starting subsystems or sets of reactions/metabolites defined by the user.Media conditions .Possible carbon sources for the studied physiology.Possible by-products or relevant extracellular metabolites. Together with possible carbon sources, these metabolites form a new subsystem that redGEM names as Extracellular Subsystem, this subsystem is treated as other subsystems defined in Step 1 above.Organism specific cofactor pairs.D defined by the user.Degree of connection The central workflow of redGEM involves 4 steps:Choose subsystems  based on the studied physiology and the part of the metabolism under interest.Derive a new stoichiometric matrix that excludes all cofactor pairs, small metabolites and inorganics.SIdentify R, RT, and MS, ○Perform a graph search on the new stoichiometric matrix.○D between each subsystem pairs Siand Sj, and will not find any reaction or metabolites between two subsystems if Lmin,ij > D.This will find all the links up to degree n as defined by the user.○Si is bigger than 1, then:If the length of shortest path between a metabolite and ○○To connect all Extracellular Subsystem metabolites to other subsystems, find all reactions MS, i, j pairs), i, j pairs), RT .The core carbon network is defined as all the reactions and metabolites in Smin) that are stoichiometrically balanced and capable of synthesizing a biomass building block from defined core metabolites. Moreover, it identifies alternative subnetworks for the synthesis of the same biomass building block. Finally, lumpGEM generates overall lumped reactions, in where the cost of core metabolites, cofactors, small metabolites and inorganics are determined for the biosynthesis. redGEM defined the core network by the algorithm above, and then we generated all minimum sized subnetwork (Smin) for each BBB. Then lumpGEM calculated the unique lumped reactions for all the BBBs, and we used these lumped reactions for further validation and other analysis. lumpGEM takes the following steps to build elementally balanced lumped reactions for the biomass building blocks. In the workflow, lumpGEMWe used the lumpGEM algorithm to generate pathways for all biomass building blocks (BBB) as they are defined in GEM. lumpGEM identifies the smallest subnetwork  and compare the ranges of values of the common reactions between rGEM and GEM.Allowable flux ranges of the common reactions between rGEM and GEM.Perform Thermodynamics-based Variability Analysis (TVA) and compare the ranges of substrate/product concentrations and Gibbs free energy of the common reactions between rGEM and GEM.Allowable metabolite and Gibbs free energy of reaction ranges for common metabolite and reactions between rGEM and GEM using TVA.While these are the basic consistency tests, one could define additional checks, which can be specific to the organism and problem under study. We recommend that in all cases one should perform the checks using FBA and TFA, i.e. with and without thermodynamics constraints.corresponding author.The first release of the redGEM toolbox is available upon request to the S1 FigThe comparison of some common metabolite concentration ranges between rGEM and GEM.(EPS)Click here for additional data file.S1 TableThe core network generated with connection parameter D = 1. This core is the core network used to generate lumped reactions reported in this study.(XLSX)Click here for additional data file.S2 TableE. coli iJO1366 with core network of D = 1. There are multiple lumped reactions for many biomass building blocks.All the lumped reactions generated for the minimal sized subnetworks for (XLSX)Click here for additional data file.S1 FileThis model do not include the reactions that cannot carry flux under glucose minimal media, moreover it has the transport reactions for the core metabolites across periplasm and extracellular media.(XLSX)Click here for additional data file.


Burkholderia multivorans is an important pathogen in people with cystic fibrosis (CF) and its epidemiology suggests that strains are acquired from non-human sources such as the natural environment. This raises the central question of whether the isolation source (CF or environment) or the multilocus sequence type (ST) of B. multivorans better predicts their genomic content and functionality. We identified four pairs of B. multivorans isolates, representing distinct STs and consisting of one CF and one environmental isolate each. All genomes were sequenced using the PacBio SMRT sequencing technology, which resulted in eight high-quality B. multivorans genome assemblies. The present study demonstrated that the genomic structure of the examined B. multivorans STs is highly conserved and that the B. multivorans genomic lineages are defined by their ST. Orthologous protein families were not uniformly distributed among chromosomes, with core orthologs being enriched on the primary chromosome and ST-specific orthologs being enriched on the second and third chromosome. The ST-specific orthologs were enriched in genes involved in defense mechanisms and secondary metabolism, corroborating the strain-specificity of these virulence characteristics. Finally, the same B. multivorans genomic lineages occur in both CF and environmental samples and on different continents, demonstrating their ubiquity and evolutionary persistence.The natural environment serves as a reservoir of opportunistic pathogens. A well-established method for studying the epidemiology of such opportunists is multilocus sequence typing, which in many cases has defined strains predisposed to causing infection.  Burkholderia cepacia complex (Bcc) bacteria are rare but potentially virulent pathogens in cystic fibrosis (CF) patients [B. multivorans is the most prevalent Bcc CF pathogen in many countries [B. multivorans strains in CF patients suggests acquisition from non-human sources, such as the natural environment [patients . Epidemiountries –7. The cironment . Environironment , 10.et al. [B. multivorans STs were globally distributed and that the natural environment (e.g. water and soil) may be an important reservoir for infection with this species [Multilocus sequence typing (MLST) is a well-established method for studying the epidemiology and population structure of Bcc organisms , 12. Theet al.  demonstr species .Burkholderia genomes vary in size from 2.4 Mb (Ca. Burkholderia schumannianae UZHbot8) [Burkholderia terrae BS001) [B. multivorans, we sequenced the genomes of eight isolates representing four distinct STs. For each ST, a CF and an environmental isolate were sequenced using the PacBio Single-Molecule Real-Time (SMRT) sequencing technology. The present study provides the first comprehensive comparative genome analysis of B. multivorans and assesses to which extent isolates with the same ST but from different origin  differ in genetic potential.UZHbot8)  to 11.5 e BS001) , are chae BS001) , 18. To http://pubmlst.org/bcc/) [B. multivorans STs that included both CF and environmental (ENV) isolates and selected eight isolates for whole-genome sequencing  , identifquencing . StrainsB. multivorans strain ATCC 17616 (PRJNA17407) using Mauve [High-quality DNA was prepared using Qiagen Genomic tips 20/G) and genomes were sequenced using the P5-C3 chemistry on the PacBio SMRT II platform of the Department of Genetics and Genomic Sciences of the Icahn School of Medicine at Mount Sinai . One SMRT cell per isolate was sequenced, except for isolates ST189-CF and ST287-ENV for which a second SMRT cell was run to increase the quality of the raw data. PacBio reads were assembled in the sequencing center using the SMRT analysis software (including HGAP3 and Quiver) and contigs were ordered against the complete reference genome of ng Mauve . We furt/G and geng Mauve . Contigsng Mauve  with defng Mauve  to exclung Mauve  and Gap5ng Mauve . ImportaBurkholderia Genome database  [Annotation was performed using Prokka v1.11  with a gch 2015) . The annB. multivorans strain ATCC 17616 (PRJNA17407) was included as a reference in all further analyses. A multiple genome alignment was performed using Mauve [The genome sequence of ng Mauve  to assesB. multivorans genomes of the present study and the ATCC 17616 reference strain using custom perl scripts  as described previously [We mapped for each protein-coding gene (CDS) on which chromosome it was located and to which cluster of orthologous groups (COG)  it beloneviously , 31. In B. multivorans genomes of the present study and the ATCC 17616 reference strain) was calculated based on the sequences of the panorthologs as described previously [The whole-genome phylogeny  [B. cenocepacia CDS was performed as described above for B. multivorans. The number of CDS per COG category for each species (B. multivorans versus B. cenocepacia) was counted and the distributions were compared using Pearson chi-square analysis.Complete genome sequences of ia.com/) . COG mapData visualization and statistical analyses were performed using RStudio with R v3.2.3. Pearson’s chi-square analyses were used to test the association between different sets of categorical variables. When a significant relationship was found between two variables, we further examined the standardized Pearson residuals. Standardized Pearson residuals with high absolute values indicate a lack of fit of the null hypothesis of independence in each cell  and thusB. multivorans isolates. The genomes were 6.2-6.9 Mb in size with a G+C content of ˜67 mol% and the number of predicted CDS ranged from 5,415 to 6,155 CDS per genome , multiple integrases and several hypothetical proteins. The multiple genome alignment also revealed a fairly large translocation  within the ST650-CF isolate from C1 to C2 that was delimited by rRNA operons at both ends. All isolates except ST650-CF contained 3, 1 and 1 rRNA copies on C1, C2 and C3, respectively. As a result of the translocation, isolate ST650-CF contained 2, 2 and 1 rRNA copies on C1, C2 and C3, respectively.The multiple genome alignment of the examined rans STs  revealedB. multivorans. The ortholog analysis identified 6,254 homologous protein families  = 1829.6, p<0.001) and ST (X2(3) = 67.3, p<0.001), but not isolation source (p>0.05). The genomes of isolates ST287-CF and ATCC 17616 were significantly enriched with non-orthologous CDS, while those of ST180-CF, ST287-ENV, ST650-ENV and both ST189 isolates were significantly deprived in non-orthologous CDS (The frequency of orthologous versus non-orthologous CDS (i.e. CDS with versus without orthologs in the dataset) varied significantly per isolate  = 213.4, p<0.001)  . C1 was p<0.001) .2(22) = 5101.2, p<0.001)  . The nonp<0.001) .For each ortholog family we examined whether it was present in all eight isolates (i.e. showed core specificity), specific for isolates of one or more STs, specific for isolates of a specific source or randomly present . None ofBased on the chromosome and COG mapping for the individual CDS, we mapped the consensus chromosome location and COG category for each ortholog family . Consist2(8) = 469.8, p<0.001) (2(88) = 649.8, p<0.001) (The specificity of the ortholog families varied significantly among the chromosomes (Xp<0.001)  and COG p<0.001) . C2 and p<0.001) . The ST-p<0.001) .Both ST287 genomes were considerably larger and contained a higher number of CDS , suggestC1 was enriched with orthologs that were present in only one, two or three isolates from the same source (CF-only or ENV-only) . CF-onlyAdditionally, C1 was also enriched with orthologs showing random specificity  and thesB. multivorans and B. cenocepacia have been the most prevalent Bcc pathogens in CF. Historically, B. cenocepacia strains have been responsible for large epidemics within the CF community and are often extremely virulent [B. multivorans outbreak strains were described and B. multivorans is generally considered a less virulent Bcc pathogen as compared to B. cenocepacia [B. multivorans, we compared its average COG profile to that of B. cenocepacia. Generally, B. cenocepacia genomes contained more genes   (2(22) = 102.9, p<0.001) (B. cenocepacia genomes harbor significantly more CDS in the COG categories transcription (K), defense mechanisms (V) and general function prediction only (R) and significantly less in translation, ribosomal structure and biogenesis (J) and replication, recombination and repair (L). Conversely, B. multivorans genomes harbor significantly more CDS in the COG categories replication, recombination and repair (L) and less in transcription (K)   than B.  genome)  and more genome) . We compp<0.001) . B. cenotion (K) .B. multivorans or B. cenocepacia genomes. In total, 124 COGs were exclusively present in one or more B. multivorans genomes, but only 21 COGs were uniquely present in all nine of the B. multivorans genomes  [B. multivorans strains in different niches and on different continents.The clinical isolates of ST189 and ST287 were obtained from samples of Canadian CF patients, but the environmental isolates of these STs were soil isolates from Belgium and the United Kingdom, respectively . Yet, ouof genes . Moreoveof genes . Finallyrg/bcc/)  for addiB. multivorans, we analyzed all protein-coding genes in terms of homology, specificity, chromosome location and predicted function (i.e. COG category). Firstly, we identified orthologous genes because the conservation of genes may hold clues about which genes are essential for the species-specific lifestyle of B. multivorans. Secondly, we mapped the chromosome location of each CDS because the different chromosomes are associated with different gene copy numbers, mutation rates and expression levels and because the chromosomal location of a gene has an influence on its evolutionary course [B. multivorans genomes  and replication (L) and deprived in COGs involved in transcription (K). Since COG category K contains many transcriptional regulators, the deprivation in this category may indicate a lower adaptability of B. multivorans to varying environments. This different distribution in COGs involved in information storage and processing may also reflect the overall difference in genome size between these two Bcc species   . This fiocepacia , 51. Sim [et al.  recentlyompounds . B. multeductase , which cactivity , 54. AltBurkholderia genomes can only be fully resolved using long-read sequencing technologies such as PacBio SMRT sequencing [B. multivorans isolates and the final assemblies produced closed genomes for five of the eight isolates  = 1829.6, p<0.001). Each cell in the contingency represents the observed frequency and standardized residual (in between brackets) and is preceded by + or − if the standardized residual is >1.96 or <-1.96, respectively, and significant at p<0.05.Pearson chi-square analysis testing the independence of gene conservation (orthologous vs. non-orthologous CDS) and isolate (X(PDF)Click here for additional data file.S2 Table2(3) = 67.3, p<0.001). Each cell in the contingency represents the observed frequency and standardized residual (in between brackets) and is preceded by + or − if the standardized residual is >1.96 or <-1.96, respectively, and significant at p<0.05.Pearson chi-square analysis testing the independence of gene conservation (orthologous vs. non-orthologous CDS) and ST (X(PDF)Click here for additional data file.S3 TableCF, cystic fibrosis; ENV, environmental.(PDF)Click here for additional data file.S4 TableCOG categories: J, translation, ribosomal structure and biogenesis; K, transcription; L, replication, recombination and repair; B, chromatin structure and dynamics; D, cell cycle control, cell division, chromosome partitioning; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelope biogenesis; N, cell motility; W, extracellular structures; U, intracellular trafficking, secretion, and vesicular transport; O, posttranslational modification, protein turnover, chaperones; X, mobilome: prophages, transposons; C, energy production and conversion; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport and catabolism; R, general function prediction only; S, function unknown.(PDF)Click here for additional data file.S5 TableOrtholog, orthologous CDS; Unique, non-orthologous CDS. C1, chromosome 1; C2, chromosome 2; C3, chromosome 3. TL4B, translocation from C1 to C2 in the ST650-CF isolate. COG categories: J, translation, ribosomal structure and biogenesis; K, transcription; L, replication, recombination and repair; B, chromatin structure and dynamics; D, cell cycle control, cell division, chromosome partitioning; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelope biogenesis; N, cell motility; W, extracellular structures; U, intracellular trafficking, secretion, and vesicular transport; O, posttranslational modification, protein turnover, chaperones; X, mobilome: prophages, transposons; C, energy production and conversion; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport and catabolism; R, general function prediction only; S, function unknown.(XLSX)Click here for additional data file.S6 TableCore, present in all eight (spec-profile) or nine  isolates; ST, specific for isolates of one or more STs; ENV-only, only occurring in environmental isolates; CF-only, only occurring in CF isolates; Random, randomly present. C1, chromosome 1; C2, chromosome 2; C3, chromosome 3. TL4B, translocation from C1 to C2 in the ST650-CF isolate. COG categories: J, translation, ribosomal structure and biogenesis; K, transcription; L, replication, recombination and repair; B, chromatin structure and dynamics; D, cell cycle control, cell division, chromosome partitioning; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelope biogenesis; N, cell motility; W, extracellular structures; U, intracellular trafficking, secretion, and vesicular transport; O, posttranslational modification, protein turnover, chaperones; X, mobilome: prophages, transposons; C, energy production and conversion; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport and catabolism; R, general function prediction only; S, function unknown.(XLSX)Click here for additional data file.S1 FigScale bar represents number of changes of state required in each character. The tree was rooted on the branch with the largest branch length.(PDF)Click here for additional data file.S2 FigBar plot showing the average number of CDS per genome per COG category. COG categories: J, translation, ribosomal structure and biogenesis; K, transcription; L, replication, recombination and repair; B, chromatin structure and dynamics; D, cell cycle control, cell division, chromosome partitioning; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelope biogenesis; N, cell motility; W, extracellular structures; U, intracellular trafficking, secretion, and vesicular transport; O, posttranslational modification, protein turnover, chaperones; X, mobilome: prophages, transposons; C, energy production and conversion; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport and catabolism; R, general function prediction only; S, function unknown.(PDF)Click here for additional data file.S3 FigB. multivorans isolates from this study were aligned using Mauve [B. multivorans strain ATCC 17616 (PRJNA17407) as a reference.The eight sequenced ng Mauve  against (TIF)Click here for additional data file.


Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism. Lipids are crucial players in a wide range of cellular processes. Their production is required for cell cycle progression and cell division. Furthermore, lipids are important mediators in signaling pathways, components of essential cofactors, building blocks of lipoproteins and glycerolipids and can be used to store and mobilize excess energy. In those contexts lipids execute structural as well as functional roles: By building membranes, they can create isolated reaction environments. The membranes form diffusion barriers and stabilize membrane associated enzymes , a constraint based method that considers the stoichiometric wiring of reactions in a cell. FBA models often aim to cover the entirety of metabolic reactions within a cell. Extended methods can include thermodynamic, fluxomic, proteomic or gene expression data, but all lack the ability to capture dynamic changes in the system. On the other hand, small scale ordinary differential equation (ODE) based models simulate the time evolution of fluxes and metabolite concentrations, while integrating detailed knowledge about enzyme kinetics. They require a much deeper understanding of the examined system alongside a dense data coverage, but also yield in-depth, functional understanding.Both methods are based on the detailed topological description of reactions and metabolites in the modeled network—which, in the case of lipid metabolism, is highly complex with the need to account for thousands of combinations of many enzymes with even more lipid species.Opposed to the above described deterministic methods, several modeling approaches try to capture stochastic aspects of biological systems. Those become important at small molecule numbers that show a stochastic interaction with their environment and such that their time evolution cannot be described accurately by the mean over their population.One common method is agent-based modeling, where the agents are autonomous objects that are able to make decisions. Following defined rules, each agent decides in each time step how to behave, depending on their own state as well as on interactions with other agents  the classical stochastic simulation algorithm requires extensive computation times, as it simulates each reaction step successively. A workaround for such problems can be τ-leaping , drastically reducing the complexity of the implementation.These stochastic methods are dealing with many different types of molecules, which do not require one detailed reaction equation for each type. Rather, the algorithms keep track of each Stochastic simulations are usually not applied to metabolic systems, due to the high concentrations of reactants, which would result in short time steps and long computation times. In the case of lipid metabolism, this might be true for the overall system (the concentration of lipids within the cell is high) but due to the vast diversity of lipid types, single species can easily be represented in a very low number only.iFF708  uses a rather small set of lipid species to build up its membranes. The four main fatty acids found in yeast lipids are palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), and oleic acid (C18:1)  lipase Tgl4 which can also function as a steryl ester hydrolase .The model represents the lipid metabolism in yeast. As the complexity of a metabolic system in a cell is much too high to be reproduced in a new modeling approach, we focused on the main reactions of the lipid producing c.f. Table c.f. Figure 4.We implemented a simulation framework that combines aspects of agent-based modeling and τ-leaping in Python. We defined the participating biomolecules (Nmax per time step (corresponding to the vmax) and a certain probability p that the reaction actually takes place in each execution. Following the analogy, the probability is calculated from the substrate-limited KM term in the MM equation asp is updated before every time step according to the substrate concentrations after the previous time step. For reactions with more than one substrate we used a product of the above simple saturation terms for each substrate, resulting in exactly one KM parameter per substrate and reaction.To describe the production of lipids from metabolic precursors lumped enzymatic reactions were implemented, similarly to the rules in purely agent-based approaches  = 650) and low for lipid synthesis reactions  = 5). The production rate of precursor molecules was determined to not be the limiting factor for lipid synthesis leading to a nearly constant amount of them.For parameter adjustment, the model was executed many times. After each of these test model runs, the parameters 2), the vacuolar membrane (5 μm2), and the outer mitochondrial membrane (5 μm2). The areas of the remaining subcellular membranes were unknown and set to physiologically reasonable estimates (Table 2. We assume that the cell doubles its size within the simulation time of 120 min (average period of one cell cycle) to 100 fL of which we account 60% (62.5 fL) to mother cell, the rest to the bud (37.5 fL). The assumption of two individual round cells leads to a total final plasma membrane surface of 130.4 μm2. Assuming a general conversion constant of 5 × 106 lipids per μm2 lipid bilayer , serine, ethanolamine, choline, and inositol.The model includes several subtypes of phospholipids, neutral lipids, sterols and a sphingolipid species  activates the PA headgroup, making it accessible for chemical changes: transformations to all other phospholipid species are now possible. Directly derived from the CDP-DG (active PA) are PI, cardiolipin (CL) and PS. PS in turn is used as a precursor for PE and which can be further processed to PC ), aromatic elements (sterols: ergosterol (ES) and steryl ester (SE)) or a ceramide backbone (sphingolipids (SL)). Ergosterol and the sphingolipids are synthesized via individual pathways with connections to intermediates of the phospholipid metabolism. The ergosterol synthesis in yeast normally includes more than 15 reactions and almost 30 enzymes, but is lumped to a single reaction in the model: 18 acetyl-CoA molecules build up one ergosterol : the G1 phase, the synthesis phase (S phase), the G2 phase and at last the Mitosis, when cell division takes place. Lipid droplets store neutral lipids (TAG) and steryl esters during G1 phase and release them via a Cdk1/Cdc28-dependent activation of the lipase Tgl4  fatty acids and additional saturation degrees.Second, despite its structural difference from common metabolic modeling approaches, our implementation also allows to simulate many types of experimental perturbations . This can be used to understand the effects of single nodes in the lipid metabolic network in mechanistic detail, even if they should be inaccessible to experimental methods. Thus, comparable analysis opportunities are available as for common ODE based models.4). In strict terms, this reduces the precision, however to a degree of no relevance. It does not seem to affect predictive power of the approach.While the approach allows in principle to keep track of every individual molecule, the molecule numbers in lipid metabolism are too high to allow for useful simulation times. Therefore, we applied an upscaling by multiplication with a factor  the transport of lipids between different membranes, (ii) lipid degradation and recycling, (iii) the influence of energetic or redox limitations in the cell on the model reactions or (iv) the mechanistic details of lipid distribution to the different membranes. The consideration of these aspects would allow for more detailed analyses, given that significant data become available to compare to.In mathematical terms, the combination of agent-based modeling with stochastic simulation meets the general problem that there are no established methods for parameter estimation in stochastic systems. The aim is always a minimal deviation of the simulation results from available experimental data. While mean values are easy to obtain, it remains a challenge to compare higher moments. Here, we estimated two parameters for each reaction, the probability and the maximal velocity. A reaction and substrate specific Michaelis-Menten constant is calculated with this adjusted set of parameters at the beginning of a model run and is used for all executions of this particular reaction. To test the validity of the approach, we compared the model's stochastic description with the deterministic Michaelis-Menten-kinetic. As shown in Supplementary Figure Here, the applicability of the hybrid, object-oriented approach has been demonstrated for the complex lipid metabolism in yeast. It now opens the way to more complicated types of analysis. For example, it will allow to understand the experimentally determined distributions of lipid species, fatty acids and saturation degrees. It also permits to investigate the effects of different types of perturbation and regulation such as gene knockout or overexpression or inhibition of specific enzymes.The interesting challenge remains to extend the approach to the human or mammalian metabolism, which entails more different lipid species, fatty acid chain lengths and saturation degrees. The other way around, more detailed, time and compartment resolved measurements with newly available techniques would greatly advance the efforts in modeling yeast lipid metabolism.Conceived the work: EK, performed simulations: VS, JH, analyzed the data: VK, KT, JH, EK, wrote the paper: VS, KT, JH, EK. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To date, the most significant sources of biofuels are starch- or sugarcane-based ethanol, which have been industrially produced in large quantities in the USA and Brazil, respectively. However, the ultimate goal of biofuel production is to produce fuels from lignocellulosic biomass-derived sugars with optimal fuel properties and compatibility with the existing fuel distribution infrastructure. To achieve this goal, metabolic pathways have been constructed to produce various fuel molecules that are categorized into fermentative alcohols (butanol and isobutanol), non-fermentative alcohols from 2-keto acid pathways, fatty acids-derived fuels and isoprenoid-derived fuels. This review will focus on current metabolic engineering efforts to improve the productivity and the yield of several key biofuel molecules. Strategies used in these metabolic engineering efforts can be summarized as follows: (1) identification of better enzymes; (2) flux control of intermediates and precursors; (3) elimination of competing pathways; (4) redox balance and cofactor regeneration; and (5) bypassing regulatory mechanisms. In addition to metabolic engineering approaches, host strains are optimized by improving sugar uptake and utilization, and increasing tolerance to toxic hydrolysates, metabolic intermediates and/or biofuel products. Ethanol and biodiesels have been industrially produced from biomass by fermentation and chemical trans-esterification of plant oils, respectively. For example, sugarcane-derived sugars (sucrose) have been used for ethanol fermentation in Brazil, and corn-derived starches (glucose) have been the major feedstock in the USA. Since consumption of these feedstocks for biofuel production competes with demands for animal feeds and human consumption , lignoceBeyond native fermentation pathways or natural biodiesel resources such as vegetable oils, advanced biofuel molecules are now synthesized in microbial hosts where heterologous or synthetic metabolic pathways are reconstructed in fermentative hosts. In this review, we will summarize recent achievements and progress in microbial biofuel production with updates on biofuel production from LCB-derived sugars. Various pathways and hosts for ethanol and advanced biofuel production will be discussed, with a particular emphasis on metabolic engineering strategies to improve the microbial conversion bioprocess.2). The ethanol fermentation process has been extensively studied and exploited in Saccharomyces cerevisiae (yeast) and Escherichia coli [Zymommonas mobilis has been suggested as an alternative host to yeast due to its advantage for ethanol yield since it utilizes the Entner-Doudoroff (ED) pathway instead of Embden-Meyerhof-Parnas (EMP) pathway for glycolysis [Z. mobilis [Z. mobilis produces less biomass than EMP pathway-dependent species such as S. cerevisiae and E. coli. Consequently, Z. mobilis has more available carbons for ethanol fermentation with 2.5-fold higher specific ethanol productivity than that of S. cerevisiae, and produces up to 97% theoretical yield [Z. mobilis has been engineered to co-utilize glucose, mannose and xylose, expanding their capability for ethanol fermentation of LCB-derived sugars [S. cerevisiae because Clostridia naturally secrete enzymes that are capable of hydrolyzing complex carbohydrates (oligosaccharides and polysaccharides) into fermentable sugars and utilizing both hexose and pentose [Ethanol is produced from glucose via fermentative consumption of pyruvate . Glycolyhia coli ,3, due tycolysis  Figure . The ethycolysis . Among v mobilis . While td sugars . Clostri pentose . As a re pentose .In this section, we briefly introduce various aspects on selection of microbial hosts for ethanol production: energetics of the glycolysis pathways, available genetic engineering tools, flexibility in sugar utilization and compatibility to effective fermenter operation. The following section will discuss metabolic engineering strategies that have been applied to optimize and improve microbial hosts for ethanol production.S. cerevisiae strain with intracellularly localized sucrose invertase (iSUC1) was evolved in sucrose-limited chemostat, and the evolved strain showed higher sucrose-proton symporter activity and increased ethanol yield by 11% [Candida intermedia and XUT3 from Scheffersomyces stipitis were expressed, and it showed an increased growth rate of yeast on xylose by 70% and a changed pattern of diauxic shifts [Since sugars are both carbon and energy sources for biomass and ethanol production, more efficient uptake and utilization of various sugars are important factors that can improve ethanol productivity. For example, the uptake rate of sucrose was improved in yeast for more efficient utilization of sucrose from sugarcanes . In thisd by 11% . In gened by 11% . In one c shifts . In a foc shifts . In anotc shifts  screenedc shifts .S. cerevisiae have been engineered to maintain lower level of cytosolic NADH by various genetic modifications such as the deletion of NADPH-dependent glutamate dehydrogenase (GDH1) along with the overexpression of glutamate-ammonia ligase (GLN1) and glutamate transporter (GLT1); and substitution of innate glyceraldehyde-3-phosphate dehydrogenase (GAPDH) with heterologous GAPDH from either Bacillus cereus or Streptococcus mutans. The latter approach aimed to decrease cytosolic NADH formation and ATP production by using an alternative non-phosphorylating, NADP+-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPN) of GAPDH [E. coli were redesigned at the systems level by elementary mode analysis for metabolic coupling of biomass and ethanol production. Deletion of 9 genes in central metabolism was suggested by elementary mode analysis, and the engineered E. coli strain produced 90% of theoretical yield after 48 h fermentation [Z. mobilis in Streptomyces lividans TK24 [Another metabolic engineering strategy is to maximize the flux of sugars to ethanol while minimizing the flux to biomass or to other fermentation byproducts such as glycerol. Since formation of highly reduced fermentation products such as glycerol is driven by accumulation of cytosolic NADH, of GAPDH . In addientation . On the ans TK24 .In addition to metabolic engineering efforts to diversify sugar utilization of microbial hosts and to regulate carbon metabolism fluxes, there have been other approaches to improve ethanol production, especially by improving the industrial bioprocess. One example is to improve resistance toward ethanol itself, growth inhibitors and toxic components from LCB-hydrolysates as well as other general stresses in ethanol-producing hosts . RecentlAlthough ethanol is the most widely produced biofuel together with biodiesels, ethanol is not an ideal alternative fuel or blending fuel due to its low energy content (only about 70% of gasoline) and hygroscopic nature . As a reThe general overview of the pathways for advanced biofuel production is summarized in 1-butanol is naturally produced by species of Clostridia, which have innate 1-butanol fermentative pathways. The 1-butanol fermentation pathway and general aspects of Clostridia physiology  were summarized along with metabolic engineering efforts to improve 1-butanol fermentation in the recent review . Other sE. coli by incorporating seven enzymes from three different species [Treponema denticola [fdh1), and as a result, the titer could reach up to 30 g/L in E. coli [fdh1 to minimize redox imbalance by excessive regeneration of NADH, and it subsequently showed increased 1-butanol productivity in E. coli [acrB efflux pump conferred tolerance of E. coli to 1-butanol [Although Clostridia species are natural 1-butanol producers, they grow slowly and their genetic manipulation is still limited. To overcome these limitations, the 1-butanol fermentation pathway was re-constructed in  species . One of enticola . This Te E. coli . Another E. coli . Further-butanol . Not onl-butanol .4) and isopentanols (C5) are produced from keto acids intermediates of valine and leucine biosynthesis pathways by increasing availability of specific keto acids and by expression of promiscuous keto acid decarboxylase (KivD) from Lactococcus lactis with alcohol dehydrogenase 2 (Adh2) from S. cerevisiae [E. coli with gas stripping [5) to 1-octanol (C8) were also produced from threonine over-producing E. coli [E. coli (EcLeuABCD). The production titer was about 1.4 g/L [Short-chain alcohols can be produced via non-fermentative pathways such as the 2-keto acid pathways, and the latest advances in production titer and engineering approaches can be found in recent review papers ,39. In trevisiae . Subsequtripping . Linear  E. coli . In this 1.4 g/L .Corynebacterium glutamicum and more isobutanol-tolerant B. subtilis have been also used, and mitochondrial targeting and expression of cytoplasmic Ehrlich pathway enzymes improved isobutanol production by 2.6-fold in yeast [E. coli by assembling different pathways modularly [Alternative hosts such as amino acid overproducing in yeast . Recentlodularly .Short-chain alcohols have been produced via various metabolic pathways and to relatively higher titers than other advanced biofuel molecules. Even though they are considered to have better fuel properties than ethanol, there has still been an increasing demand for biofuel molecules with better properties such as higher energy content and lower freezing points. To address this issue, microbial hosts have been engineered to produce biofuel molecules with longer hydrocarbon chains and more branching methyl groups via various metabolic pathways such as fatty acid metabolic pathways . and isoAcinetobacter calcoaceticus ADP1 [E. coli strain where pyruvate decarboxylase (Pdc) and alcohol dehydrogenase (AdhB) from Z. mobilis were co-expressed to provide ethanol [pdc, adhB, tesA’ (membrane targeting sequence truncated E. coli native thioesterase) and fadD (native acyl-CoA synthetase) in E. coli with fadE gene deletion (ΔfadE), where fatty acid metabolism was forced to produce more fatty acyl-CoA [E. coli by balancing substrate supply levels [The energy-rich hydrocarbon chains of fatty acids make them potential precursors for the production of diesel alternatives. Fatty acids are synthesized by fatty acid synthase (FAS) system, which condenses malonyl-CoAs into various lengths of fatty acyl esters with acyl-carrier protein (ACP) . Currentcus ADP1 , and 1.2 ethanol . Conversacyl-CoA , an impoy levels , which rex novo lipids biosynthesis, and it has been shown that lipids could be accumulated via tightly regulated de novo synthesis pathway when acetyl-CoA carboxylase (ACC1) and diacylglycerol acyltransferase (DGA1) were overexpressed [Fatty acid-derived fuels were also produced in yeast by overexpressing all three fatty acid biosynthesis genes  in combination with the expression of downstream enzymes  . Oleaginxpressed .E. coli to produce alkanes and alkenes [Synechococcus sp. PCC7002 [JE) from Jeotgalicoccus species [Marinobacter aquaeolei VT8 exhibited reducing activity for both acyl-CoAs and subsequently produced aldehydes [E. coli strain with fadA gene deletion by overexpression of β-ketoacyl-ACP synthase II (FadB), an acyl-CoA oxidase from Micrococcus luteus, and a thioesterase (FadM) to produce β-keto fatty acids followed by hydrolysis and decarboxylation to ketones [Other fatty-acid-derived biofuel molecules such as fatty aldehydes, methyl ketones, alkanes and alkenes are now produced in microbial hosts , and pro alkenes . Further PCC7002  and fatt species , which aldehydes , which sldehydes . Lastly, ketones ,56.6 to C18 in E. coli by introducing alternative carboxylic acids reductase from Mycobacterium marinum [E. coli [Since fatty acids are the primary precursors, many engineering approaches have focused on increasing available fatty acids . Those a marinum . Further[E. coli . Composi[E. coli .10, GPP), farnesyl diphosphate  and geranylgeranyl diphosphate  [10), sesquiterpenes (C15) and diterpenes (C20). Therefore, engineering efforts to produce isoprenoids-derived biofuels have been focusing on increasing production of prenyl diphosphate intermediates, particularly IPP and DMAPP, and identification and engineering of terpene synthases for desired enzymatic activity.Isoprenoids are a group of diverse chemical compounds which includes over 50,000 compounds. Fuel molecules derived from isoprenoid pathways have branched hydrocarbon chains, which lower their freezing temperature, as well as various ring structures, which makes them potential alternatives to diesel and jet fuels . These b0, GGPP) . These c0, GGPP) . Finally0, GGPP) . Regardl5 precursors, the MVA pathway has been more extensively exploited for production of potential diesel and jet fuel precursors such as farnesene [Although two different biosynthetic pathways  pathway and methylerythritol phosphate (MEP) pathway) have been known to produce two universal Carnesene , bisabolarnesene ,67, pinearnesene ,68 and larnesene ,69  approach [Among sesquiterpene-derived fuels, biological production of farnesene and bisabolene has been reported. Farnesene is converted from FPP by farnesene synthase. The previously developed FPP-overproducing entation . After centation  and commentation . Initialoduction ,75. Abouapproach . In yeasapproach .Mentha spicata and limonene synthase from Abies grandis [E. coli  has bee protein . In addi protein .Production of isoprenoid-derived alcohols were achieved by co-expression of phosphatases: isopentenol ,80,81,82E. coli as a microbial factory for consolidated bioprocesses (CBP) by expressing enzymes required for both biomass degradation  and biofuel synthesis  [E. coli strain, 71 mg/L of FAEE, 28 mg/L butanol, and 1.7 mg/L pinene were produced from 5.5%, 3.3% and 3.9% w/v IL-treated switchgrass, respectively. Although these titers need to be further improved for industrial application, it should be noted that 71 mg/L of FAEE was 80% of the estimated yield from 5.5% switchgrass that could release only 0.14% glucose and 0.14% xylose by the cellulose and the xylanase expressed from the engineered E. coli strain [E. coli was engineered to produce isobutanol from xylose, by integrating the isobutanol synthetic pathway and xylose utilization genes into the genome and using xylose as an inducer for the expression of these genes [Corynebacterium crenatum via keto acid pathways using acid-pretreated hydrolysates of duckweed [S. cerevisiae were expressed in C. creanatum, and 982 mg/L of 2-methyl-1-butanol, ~1.1 g/L isobutanol and ~685 mg/L of 3-methyl-1-butanol were produced from acid-pretreated hydrolysates of duckweed containing 60 g/L glucose without compromising productivity [ascB encoding 6-phospho-β-glucosidase [Biofuel production from LCB has been primarily limited by pretreatment and saccharification of LCB, which determines sugar yields, and by inefficient co-utilization of hexose and pentose of host strains. While ethanol production has been more frequently chosen as a representative pathway to demonstrate biofuel production from LCB-derived sugars, a few studies on advanced biofuels production from LCB-derived sugars have been pursued recently . One of  pinene) . Using ti strain . A receni strain . The IL-se genes . In thisse genes . In addiduckweed . In thisuctivity . Althougcosidase . Even thetc.) as alternatives to ethanol with better fuel properties has been demonstrated by engineering fermentative pathways, non-fermentative keto-acid pathways, and isoprenoid pathways. In addition to higher alcohols, fatty-acid-derived biofuels and isoprenoids-derived biofuels have also been proposed as good diesel alternatives. Various microbial hosts and metabolic pathways were explored extensively to improve yield, titer, and productivity using various strategies. It would be hard to establish a common strategy that works for all kinds of biofuels derived from various metabolic pathways. However, more systemic and more collective efforts would be required in the future to overcome several bottlenecks mentioned in this review, such as extended sugar utilization capability, robustness of microbial hosts against general stresses and toxic products, and scale-up and actual commercialization of advanced biofuels. These bottlenecks are related to the general physiology of microbial hosts rather than to any specific metabolic pathways. Metabolic pathway engineering in addition to improving general physiology of candidate biofuel producers would allow more economically viable biofuel production, which will reduce the heavy dependence on petroleum-based fuel and contribute to slowing down global warming by providing carbon-neutral energy for the transportation sector.Ethanol has been produced from various carbon sources (from corn- and sugarcane-based glucose to lignocellulosic biomass) by engineering or by exploiting native fermentation pathways of various microbial hosts. Production of higher alcohols (1-butanol, isobutanol, isopentanol,