Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for  Leishmania protozoan parasites, spread through infected female sandfly bites. There are around 1 million cases reported for cutaneous leishmaniasis in the last 5 years and between 200,000 to 400,000 infected cases reported annually for the deadlier clinical manifestation, visceral leishmaniasis1. The long-standing association with hosts has equipped the parasite with unique metabolic features that support their existence within the hosts2. Mechanisms of metabolic adaptation of the Leishmania parasite to a variety of external metabolite sources and their stage-specific utilization has been a recurring question4. Recent13C-isotope-labelled tracing studies in L. mexicana indicate that glucose and amino acids, like aspartate, alanine, proline and glutamate, are catabolized through common metabolic routes employed by both the promastigote and amastigote stages of the parasite and propose stage-specific flux changes of pathways that are hard-wired to the differentiation signals within the parasite6. The common metabolic routes specify the use of a core set of enzymes within L. mexicana metabolism that can metabolize different sources in both stages. The knowledge of these core metabolic routes and enzymes is still incomplete in other Leishmania species. Furthermore, the causal reasons for the choice of specific metabolites and their metabolic routes to satisfy the cellular demand of the parasite are still debated upon. One of the speculated reasons is that the differentiation of promastigote to amastigote brings about a lower uptake of non-essential amino acids, higher fatty acid uptake and hypoxic environment in amastigote metabolism as compared to the promastigote stages, that constrains the choice of metabolic routes6; although the intrinsic adaptation of the metabolic network structure to the environment is less discussed. Also, the metabolic enzyme repertoire across developmental stages in Leishmania (DNA microarrays in L. major and mass-spectrometry based quantitative proteomics in L. infantum) has been speculated to be constitutively expressed8, raising the question of how the flux changes between metabolic states for the two stages might be achieved. This is also supported by the observations from whole genome comparisons of codon usage among different species of Leishmania, which outline very few differences within metabolic genes between the species9. All the above observations motivated us to investigate the role of the underlying metabolic network structure in metabolic adaptation of L. infantum to the host environment.Leishmaniasis is a complex, widespread neglected tropical disease in humans caused by 13. No genome-scale metabolic reconstruction is available for Leishmania infantum till date. For the first time, we propose a new manually reconstructed genome-scale metabolic network model (iAS556) for the species Leishmania infantum JPCM5 (a well characterized L. infantum strain), consisting of 1260 reactions and 1160 metabolites . Results from this study were applied to represent the total metabolite pool within the parasite for the given time span of growth. The signal intensity per cell protein of each metabolite averaged for 3–6 days was normalized to total metabolite signal intensity per cell protein (eq. 1). The normalized intensities thus, represents the probable fraction of each metabolite available in the total metabolite pool  that can be used for generating unit biomass in the given growth phase.A 12. As the metabolic demand reaction of L. infantum could not be completely established based on available literature, and L. donovani being evolutionary related to L. infantum9, as a proof of concept, the formulated metabolic demand reaction can be applied to L. infantum. Similar usage of metabolomics data for formulation of objective function was also considered in the L. infantum energy metabolic network15, which gave comparable results with experiments. Similarly, the results in this study indicate that the formulated metabolic demand reaction can capture the essential, realistic distribution of fluxes within the iAS556 metabolic network. Furthermore, a recent study16 demonstrates that, between the L. infantum and L. donovani species, the ratio of membrane phospholipids known to be produced by the parasite (considered within the metabolic demand reaction) [Phosphatidylcholine (PC): Phosphatidylethanolamine (PE): Phosphatidylinositol (PI)] remains similar across the two species in both stages; supporting the use of the metabolic demand reaction curated from L. donovani data in simulating the phenotypes of L. infantum.This available fraction represents the coefficient of that metabolite in the metabolic demand reaction  constraints  and compared  and mannan synthesis (7.36%) when compared with the sufficient glucose situation. The remaining glucose is utilized towards alanine formation (45%) and phospholipid formation (38.27%), whereas asparagine, tyrosine and isoleucine are utilized for formation of glutamate, glutamine and proline.Previous studies characterize a low uptake of non-essential amino acids, fatty acid uptake to compensate for the scarcity of glucose, reduced secretion of overflow metabolites, a hypoxic and acidic environment as factors governing the metabolic state within the infective amastigote stage of the parasitered Fig. . The pre2 fixation within glycosome or secreted as overflow in both abundant and constrained scenarios. Similarly, environmental aspartate in the promastigote is consumed and transaminated into glutamate (96.83%) and oxaloacetate in cytoplasm6. Oxaloacetate forms malate, which enters mitochondria as fumarate, ultimately forming alanine (56.69%). In the amastigote, aspartate uptake reduces despite being catabolized via a similar route, the only difference being the sole formation of alanine (47.83%). Sufficient environmental glutamate is used for synthesis of glutamine (14.72%) via glycosomal GMP synthase, synthesis of proline (44.74%), aspartate by reverse de-amination of glutamate (14.72%), and glutamylcysteine to form trypanothione. In the amastigote, reduced glutamate uptake coerces its utilization into cellular metabolic demand and not as a precursor. Proline uptake , as biomass formation is sub-optimal, their utilization into biomass decreases.As opposed to glucose, majority of alanine was utilized for satisfying the cellular demand; some of it (6.05%) was converted to pyruvate, which was either utilized for COlutamine 4.72% vialutamine 4.72% via18, thereby eventually forming glutamate. All the aforementioned predictions have also been experimentally observed from metabolomics studies5. In addition to the above carbon sources, the utilization of amino sugars (N-acetyl glucosamine) and mannose was also investigated. N-acetyl glucosamine is exclusively utilized under intracellular glucose-deficient, hypoxic conditions. N-acetyl glucosamine and mannose, both are utilized for provision of fructose-6-phosphate, which enters into mitochondria via succinate formation to produce alanine . The uptake rates of both these sugars were predicted to decrease under hypoxic conditions of the amastigote; although, they do compensate the scarcity of glucose required for production of non-essential amino acids and mannan19. In addition, mannose also forms mannan (4.89%) and compensates the reduction in glucose uptake within the amastigote. To further support the accuracy of model predictions, experimentally observed knockout phenotypes were compared with model-predicted phenotypes. 81% known phenotypes in promastigote and 84% known phenotypes in amastigote situations were accurately predicted from the model  and primarily provide acetyl-coA to pyruvate dehydrogenase complex13C isotope enrichment of metabolites, for the glucose-only situation is given in Fig. 13C metabolomics study available for L. infantum JPCM5 and because the metabolic microenvironment experienced by different Leishmania species is rather similar4, the predicted reaction fluxes were compared with the 13C isotope enrichment of metabolites reported in the metabolomics study available for L. mexicana. Normalization was performed for percentage values of fluxes and experimentally available 13C isotope enrichment in a 0 to 1 scale, where 1 represents the highest normalized percentage flux value/isotope enrichment in mole percent and 0 the lowest. From Fig. L. infantum model, as compared to L. mexicana. The upregulation of mannan synthesis in amastigote was also observed in previous studies4. A slight upregulation of the pentose-phosphate shunt was observed in the L. infantum amastigote indicating its role in satisfying the requirement of AMP under a reduced glucose uptake. It can also be observed that similar metabolites are produced from glucose in both L. infantum and L. mexicana but with significant quantitative differences in both stages. The quantitative differences observed between the predicted reaction steady state fluxes in L. infantum JPCM5 and the13C isotope enrichment of metabolites available for L. mexicana might be due to – a) species-specific genes unique to L. mexicana or L. infantum. For example, metabolites of the incomplete urea cycle, are largely independent of glucose utilization and are optimally produced from different carbon sources like arginine for utilization into the metabolic demand within the L. infantum network which might not be the case in L. mexicana, due to differences in underlying network structure, b) multiple subcellular locations of different enzymes that might lead to alternative paths for distribution of intermediate metabolites produced from glucose within the pathways of glycolysis, pentose-phosphate-pathway and non-essential amino acid synthesis unique to the L. infantum JPCM5 metabolic network, and/or, c) metabolic demand being different between L. infantum and L. mexicana, leads to an increased drain of intermediate metabolites like non-essential amino acids into the L. infantum metabolic demand as compared to L. mexicana. The aforementioned reasons are only suggestive of the possibilities underlying the observed differences. Further inferences based on comparison of flux profiles in the two species can only be performed from an evolutionary perspective, given the degree of evolutionary divergence between L. infantum and L. mexicana, and the relatedness of L. mexicana with the Sauroleishmania20, which can be addressed in a future work.The stage-specific comparison between promastigote and amastigote flux-profiles as predicted by the model andThe observation of glucose being primarily diverted towards alanine under glucose-deficient conditions specifies that glucose can be substituted by a variety of other carbon sources to fulfill metabolite requirements other than alanine. The foremost question here is how this substitution can be achieved? To answer this, we introduce the notion of a “non-essential amino acid motif”, which re-routes different metabolic inputs towards specific outputs under absence of preferred carbon sources, via implicit non-essential amino acid inter-conversions. The motif comprises of glutamate dehydrogenase, aspartate aminotransferase, tyrosine catabolic pathways, proline biosynthesis, glutamine biosynthesis, incomplete urea cycle, glycolytic and succinate fermentation pathways. To investigate their functionality, a sensitivity analysis was performed to understand the effect of glucose and uptakes of all amino acids, while optimizing for the formation of glutamate, alanine, aspartate, glutamine, proline, glycine, myoinositol and mannan, the formation of each considered as a separate objective function (see Methods). Apparent optimal production of each amino acid due to self-uptake was not included in analysis. Further, the metabolic demand reaction was constrained to zero flux in each case.22. Asparagine and aspartate, each of them when singly present in the environment can sub-optimally produce alanine , although the drain of glucose into succinate fermentation for formation of alanine is largely compromised. This is compensated by the NAD redox coupling between succinate fermentation and glycosomal fatty acid β-oxidation, governed by the presence of glycosome. The specificity of fatty acids as preferred carbon sources only in deficient glucose conditions is thus regulated by the glycosome. Removal of glycosome ; although under glucose-sufficient conditions, the generated profiles seem to be similar ; although under glucose-sufficient conditions, the generated profiles seem to be similar . This probably suggests that biomass-forming reactions are not blocked any more than other non-terminal reactions and that any effect is due to a handful of biomass reactions which demonstrate a low flux-coupling, rather than being a general effect specific to all biomass reactions. Also, 37 of the 40 biomass metabolites are formed by more than one reaction groups (connected components) within the flux-coupled graph, probably for unhindered production of the important biomass metabolites. The connected components and the corresponding pathways, in which each metabolite is involved, are given in Supplementary Table We further wanted to investigate the biological significance of these specific couplings towards parasite metabolism and its effect on the biomass metabolites. To answer this, the fraction of biomass metabolites whose synthesis would be totally blocked after each sequential deletion, was calculated. The synthesis of a biomass metabolite was considered to be completely obstructed, if all the reactions forming that metabolite were deemed to be blocked after a sequential deletion (see Methods). The median percentage of metabolites expected to be blocked, also gradually increase with increasing number of reaction deletions; although only around 7% of biomass metabolites can be expected to be completely blocked after 20 sequential deletions Fig. . Also, tLeishmania infantum JPCM5 parasite by considering extensive available information about metabolic reactions catalyzed by enzymes that span the entire genome is reconstructed newly. Flux analysis of parasite metabolism established that the parasite possesses a unique network of metabolic reactions whose functioning can be explained with respect to the stoichiometric and reversibility constraints imposed by the underlying metabolic network structure alone; the network structure being largely governed by the species-specific occurrence of enzymes in multiple subcellular locations , updated as of 16/12/2011. See Sections The up-to-date iAS556 genome-scale metabolic network of 34 was performed on the iAS556 network to predict metabolic fates of given input metabolite exchanges, while optimizing for a Leishmania-specific metabolic demand reaction. Details of the constraints applied on the exchange & intracellular reactions are given in Section Representing the reconstructed network as a constraint-based model with appropriate intracellular and exchange constraints, flux balance analysis (FBA)35 was performed, while considering the presence of all input exchanges. Flux coupling analysis is a constraint-based procedure that utilizes the stoichiometric and reversibility information of reactions within the metabolic network to identify flux dependencies between a pair of reactions, given specific environmental (exchange) constraints. If a non-zero flux in one reaction implies a non-zero flux in the other reaction then the two reactions are coupled with each other. To obtain an actual sense of the coupled fluxes and to avoid artificial coupling of large set of reaction fluxes with the metabolic demand, the metabolic demand reaction was removed from the network while including independent reversible drains for the demand metabolites in the network. See Section To identify physiological flux coupling relationships (flux-coupled subnetwork) within the iAS556 network, flux coupling analysis (FCA)13C-isotope labeling profiles of the above metabolites generated in different developmental stages of L. mexicana5. The 13C isotope labeling profiles in this experimental study were obtained by growing L. mexicana promastigotes and amastigotes within a chemically defined growth medium (CDM) containing specific 13C -tagged carbon sources and analyzing the isotope enrichment of 13C labeled intracellular metabolite pools using gas chromatography-mass spectrometry (GC-MS).13C isotope enrichment measures the amount of incorporation of 13C tagged environmental metabolite (in mol percent) into its intracellular catabolic products, thereby indicating the preference of utilization of a particular metabolite through specific pathways.The fate of carbohydrates, like glucose (Glc) and mannose (Man), amino sugars like N-acetylglucosamine (Acgam), non-essential amino acids, like alanine (Ala), aspartate (Asp), glutamate (Glu) and hexadecanoate fatty acids (Hdca), was predicted for both the stages by computing the steady state fluxes for each reaction under uptake of each carbon source one at a time, while optimizing for the metabolic demand reaction. Exchanges of proline (Pro), glycine (Gly), essential amino acids, like serine (Ser), threonine (Thr), leucine (Leu), isoleucine (Ile), valine , lysine (Lys), phenylalanine (Phe), were all provided simultaneously for all the other amino acids (AA) situation. The fate of the mentioned metabolites was specifically studied in order to compare the model predictions with observations from experimental in-silico stoichiometric-based utilization of each metabolite produced from previous reaction into subsequent reaction/s within the iAS556 network was traced by the contribution of previous reaction flux into its subsequent reaction flux. Statistical comparison of model predictions with experimental data was performed for the glucose-only situation as – i) only glucose is catabolized by a large number of steps (sufficient sample size for comparison) each of which is labeled from 13C isotope-resolved metabolomics and, ii) because the normalized 13C isotope enrichment values were reported for only labeled glucose. Spearman rank correlation was computed to identify the strength of associativity between predicted reaction steady state fluxes  and 13C isotope enrichment values of metabolites  grown on labeled 13C glucose5. In all the mentioned conditions, uptake of glucose, essential amino acids, cofactors and ions were always kept unconstrained irrespective of change in environment, as they are absolutely essential for growth. Also, drains for overflow metabolites were provided in the model to release by-products.The Leishmania-specific knockout phenotypes. See Section Single reaction knockout phenotypes that correspond to a zero flux through the metabolic demand were predicted by constraining the upper and lower bounds of each target reaction to zero flux and performing subsequent optimization. The model-predicted knockout phenotypes were predicted with experimentally determined 36.To understand the role of non-essential amino acids, a sensitivity analysis was performed to understand the effect of glucose and uptakes of all the amino acids, while optimizing for the formation of glutamate, alanine, aspartate, glutamine, proline, glycine, myoinositol and mannogen, each formation considered as a separate objective function. The uptake of glucose and choice of myoinositol and mannogen synthesis as objectives is to understand and compare the coherent role of glucose with amino acids. Due to the role as intermediate by-products of reactions that are consumed for optimizing metabolic demand, for each simulation, drain (secretion/release) for alanine, glutamate, glutamine and proline was considered. Each simulation was performed separately, considering one input at a time while constraining all other exchanges to zero. All remaining exchanges in the model were kept default (lower bound = −1000 and upper bound = 1000)The considered input uptakes can be given by x, where x i) only one input source (x) constrained to a non-zero flux value (lower bound = −1000) at a time and all other inputs (A\{x}) are constrained to zero;ii) two inputs given at a time – glucose which is kept fixed and a variable second input (x) with non-zero flux given one at a time and all other inputs are constrained to zero;iii) two inputs given at a time – one being isoleucine which is kept fixed and a variable second input (x) constrained to a non-zero flux value given one at a time and all other inputs are constrained to zero;iv) three inputs given simultaneously – glucose, tyrosine which are fixed and a variable third input (x) with non-zero flux given one at a time and all other inputs are constrained to zero.For exploring the role of subcellular compartments in reinforcing coupling relationships, reactions occurring in specific subcellular compartments like glycosome or mitochondrion, were shifted to the cytoplasm, while deleting the corresponding compartment from the iAS556 model. Also, it was ensured that, reactions occurring in dual or multiple locations  were considered as a single reaction in cytoplasm to avoid redundancy in the re-created model.Scenarios for perturbation of flux-coupled relationships due to random reaction malfunction were created by deleting random single or multiple reactions while re-performing FCA on the network for each case. 1000 random simulations were performed for each case. Each deletion represents the complete loss of function of a given gene or sets of genes absolutely eliminating the role of their corresponding reactions.Supplementary InformationSupplementary Data S1Supplementary Data S2Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5


As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells.We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative.We present an in silico tool for the design of multi-enzyme biosynthetic production pathways starting from a pan-organism network. The method is highly customizable and each module can be adapted to the focus of the project at hand. This method is directly applicable for (i) in vitro enzyme cascades, (ii) cell hydrolysates and (iii) permeabilized cells.The online version of this article (doi:10.1186/s12859-017-1773-y) contains supplementary material, which is available to authorized users. While thousands of enzymes are already known, numerous new enzymes or new enzymatic activities are still discovered every year. Many of these biocatalysts accept multiple substrates and even catalyze different reactions. From a biotechnological point of view, the enzymatic potential of nature can be considered an extremely versatile tool potentially giving access to countless valuable products ranging from bulk chemicals to most complex drug compounds. The methods for such syntheses can range from using single isolated enzymes over multi-enzyme systems or enzyme cascades up to syntheses with cell lysates or permeabilized cells .However, the full exploration of the enzymatic potential is often hampered by the sheer amount and complexity of available reaction data. When manually designing a multi-step synthesis route to a certain metabolic intermediate, the network of alternative synthesis pathways quickly grows highly complex as more reaction steps are introduced. Additionally, assembling all reactions that lead to each reactant is extremely time consuming. The manual determination of the most suitable pathway candidate is challenging as multiple aspects such as thermodynamics, cofactor use, etc. need to be considered. To more easily harness the full potential of the enzymatic toolbox we developed a computational tool for the directed design of biosynthetic production pathways for interesting products in cell extracts and permeabilized cells.The search for pathways in genome-scale metabolic networks is a common task of wide interest and there is a large variety of path-finding and pathway design methods. Most of those methods can be categorized into one of two types, namely stoichiometric methods and graph-based methods. Stoichiometric methods make use of the stoichiometry of a network to analyze the metabolism under the assumption of a steady-state condition. Popular and mathematically well understood methods are for example elementary flux modes  or flux Graph-based methods in general neglect stoichiometry and treat the networks as graphs in a mathematical sense and search for pathways based on connectivity , with thHowever, the majority of these methods tackles the problem of finding pathways between two given metabolites and does not take into account a search starting with an arbitrary metabolite in the network. Another drawback of these methods for our focus of application is that most of them assume a steady-state condition for the major part of the network. This is valid for living cells or cells with intact membranes. In these cases the actual reactions are running in a cellular compartment that keeps all intermediates separated from the bioreactor, whereas in the case of enzyme cocktails and permeabilized cells the reaction compartment is identical to the bioreactor used. Examples of the latter type of reaction systems are becoming increasingly popular –23.We thus propose a tool which encompasses the reconstruction of a genome-scale pan-organism metabolic network, the implementation of a path-finding algorithm and the ranking of pathway candidates for proposing suitable synthesis pathways starting from arbitrary substrates.In the following we will present the individual parts of our method. Figure The first step is the network reconstruction where the network is built with data from KEGG , 25 and We combine data from different KEGG databases and eQuilibrator 2.0 for the reconstruction of a pan-organism network with data from all organisms contained in KEGG release 78.1 from May 1, 2016.The reaction network was reconstructed with COBRA Toolbox  using re1 containing reaction pairs for each reaction. The reaction pairs in KEGG are classified into five categories [From all remaining reactions in the model we built a network of reaction pairs, the so called arcs. A reaction pair is a biologically meaningful substrate-product pair in a reaction. We derived the arcs from the KEGG RPAIR databasetegories  from whiOur network reconstruction comprises a total of 9038 reactions (10160 including reversible reactions), 7405 metabolites and 14803 arcs.ΔrG′° expresses the change of the Gibbs free energy of a reaction at a given pH and ionic strength I in 1 M concentration of the reactants. However, for metabolic reactions in cells it makes more sense to use physiologically meaningful concentrations. For ΔrGm′ the concentration of the reactants is thus set to 1 mM. For all calculations standard parameters are used which are a temperature of 25 °C (298.15 Kelvin), a pH of 7 and a pressure of 1 bar. We set the threshold for the discrimination of reversible and irreversibleto ΔrG = 15 kJ/mol. Reactions without available thermodynamic data are considered irreversible in the direction given in the reaction equation from KEGG.The KEGG REACTION database does not contain any detailed information about reaction directions, so we incorporated thermodynamic data from the biochemical thermodynamics calculator eQuilibrator 2.0. The component contribution method used  providesWe categorize the metabolites in the model into different sets which we treat differently in our path-finding method. All sets are given in the Additional file start metabolitesS we denote all metabolites that can be potential start points of a metabolite path. A metabolite path is a sequence of metabolites through the network connected by arcs. We compiled the list of possible start metabolites with all metabolites in the model contained in arcs with a molecular mass between 0 and 300. A subset of the start metabolites are the so called basis metabolites B. They are an expert-curated set of metabolites that are hubs of the arc network, easily available and inexpensive, such as D-glucose (C000312) or pyruvate (C00022).As cofactors we denote metabolites that are required for the activity of the enzymes catalyzing the reactions in the network but are not directly part of the reaction chain. We exclude arcs containing cofactors from the set of arcs to prevent biologically meaningless shortcuts in the network. The list is expert-curated and contains mono-, di- and triphosphates (e.g. AMP (C00020), ADP (C00008) and ATP (C00002)), electron carriers such as NAD + (C00003) and others. The mono- and diphosphates are usually not considered cofactors, but we chose to incorporate them into the list to avoid unnecessary interconversions between them on the pathway candidates.As excluded metabolites is treated in the same way as the cofactors. It contains metabolites that are considered as freely available, such as water, oxygen or CO2.The set of metabolite poolEm we denote the superset of metabolites we consider as freely available. This set consists of start metabolites, basis metabolites, cofactors and excluded metabolites.As the external metabolites we denote all metabolites that are not contained in the metabolite pool. They have to be produced in a production pathway and cannot be externally supplied. Generic metabolites are metabolites that are marked as ’generic’ in their KEGG entry, such as peptide (C00012) or protein (C00017). In our network we treat them as external metabolites and exclude arcs containing those metabolites from the arc network. The pool of external metabolites also contains metabolites with arcs that are not start metabolites as well as all other metabolites that are not part of any other set.As In the following we introduce our method for finding pathway candidates in the network by means of a MILP.R and the set of metabolites M we build the network of arcs. We also use the |M|-by- |R| stoichiometric matrix of the network, where each row corresponds to a metabolite and each column corresponds to a reaction. An entry in the matrix represents the stoichiometric value of a metabolite in the respective reaction, where negative values indicate a reactant and positive values indicate a product. Reversible reactions appear in the model as two different reactions with opposite directions.Given a metabolic model with the set of reactions K-shortest flux paths between a given source and a target metabolite. Different from this problem statement we do not specify any specific starting metabolite, but our algorithm identifies suitable starting metabolites for finding a pathway to a target metabolite P.The algorithm presented is based on an algorithm proposed by Pey et al. . Howeverpathway consists of two parts. The first part is a sequence of metabolites connected by reactions. It starts with a reaction that has one of the possible start metabolites as substrate and ends with a reaction with the desired target metabolite as a product. This part is called the linear path. The second part is a minimal set of reactions supplying substrates that are needed by the reactions on the path which are not contained in the metabolite pool. These are called supplying reactions.In our definition, a uij which are 1, if an arc from i to j is part of the linear path, and 0 otherwise . The first constraint given by Eq.  e ek, thenken from , but hasConstraint , which wS to a given end node P.Constraints  to 6) e e6) ensuSmr for a metabolite m in reaction r . The variables vr assign each reaction r a non-negative flux. Constraint 10),  then at least one reaction r containing this arc (dijr=1) has to be active. This constraint was adopted from [The binary coefficients ted from ).Constraints  to 12)  12) defiThe objective function of the problem is formulated in Eq. . 13\docuij used but additionally we also minimize the number of active reactions on the whole pathway candidate. In contrast to [As proposed by  we also trast to  we are iuij and the set of active reactions given by the values of zr. By minimizing the objective function we ensure that the linear path is connected and cycle-free and the number of active reactions and thus of supplying reactions is minimal. From the active reactions we determine those corresponding to the active arcs, denoted as Z′. One solution represents one pathway candidate.A solution to the MILP described by Eqs.  to 13)  13) is aZ′. Note that a valid new solution can have exactly the same set of active arcs as a previous solution if Z′ is different, since an arc can be derived from more than one reaction. Let uij for the k-th unique solution with respect to the metabolite path. To indicate that a solution is exactly the same as solution k regarding the metabolite path, we introduce a binary variable sk. When a solution is different from solution k regarding the metabolite path, sk has to be 0 and 1 otherwise. Whenever we find a metabolite path To find further solutions we have to exclude solutions with the same active arcs and the same reactions straints , 15), 15) estabnstraint  ensures k′ by We denote the first metabolite in the path in solution Constraint  ensures k (Uk). Let i is active in solution l and corresponds to an active arc in Uk. The number of ones in Zl′ is denoted by ml. This constraint prevents to find a second solution that is exactly the same as a previously found solution with regard to both linear path and reactions. Constraint  is alwayP illustrating a possible solution of the presented MILP.Figure M1 is the starting metabolite of the linear path, whereas the dark orange square P is the target metabolite. The light blue squares are metabolites from the metabolite pool. The linear path highlighted in yellow is defined through constraints ..M1 is thWe rank the pathway candidates generated by the MILP by different criteria in order to highlight the most meaningful candidates for the synthesis of the desired product. As a global optimization method, the MILP cannot take into account if the first reaction of a pathway candidate is feasible only with metabolites in the metabolite pool. We thus have to perform a filtering step before the ranking to eliminate those pathway candidates that do not comply with this requirement. The ranking criteria are listed in Table The first criterion is the number of active reactions in the pathway candidate. Shorter pathways favor a fast product formation, a reduced substrate demand and are generally easier to realize than a pathway with more reactions. The second ranking criterion prefers pathway candidates starting with basic metabolites only.ΔrG are often poorly described. Another ranking criterion is the sum of the ΔrG’s and the absolute value of those ΔrG’s ΔrG. Therefore, pathway candidates with positive ΔrG of intermediate reactions are ranked down, as they would lead to kinetic traps. Furthermore, the pathway candidates are ranked by the overall thermodynamics of the linear path of the pathway candidate. Pathways with a negative overall ΔrG are preferred over those with a positive overall ΔrG.A further ranking criterion favors pathways for which there is thermodynamic information available. This is based on the notion that reactions without known or assessable The ranking also takes into account the number of enzymes that are native in a specified host organism. Pathways with less heterologous enzymes are preferred as they potentially require less genetic engineering work in the practical implementation.The last ranking criterion counts the number of different cofactor species that are required by a pathway candidate. Cofactors are often expensive and require regeneration which can be difficult to implement. Thus, pathway candidates with less cofactors are preferred.In addition to the output of the reactions of each pathway candidate and an overall balance of each reactant in a pathway, further information useful for their assessment is given. The thermodynamic profile allows for a quick visual assessment of each pathway.An SBML  file conA list of possible side reactions for each pathway candidate in a given host organism can help to find pathways with a small number of side reactions or even identify those side reactions that can be deleted.Ⓒ R2015a (8.5.0) (MathWorks). As a MILP solver we used the IBM CPLEX Optimizer 12.5. All data from KEGG is obtained using the KEGG REST API. The eQuilibrator 2.0 source code was cloned from their GitHub repository [Our path-finding tool is implemented in MATLABpository .All computations were carried out on a 64 bit, 3.4 Ghz Intel Core i7-2600 PC with 8 GB RAM.ΔrG. Our tool proposes 11 potential side reactions for the mevalonate pathway and 24 for the non-mevalonate pathway. They are provided in more detail in the Additional file ΔrG in the last two reactions. This final drop has the potential to lead to high conversion. Additionally, all substrates for the synthesis are readily available. However, the mevalonate pathway is not natively present in our chosen host E. coli. The second pathway candidate based on the non-mevalonate pathway displays an alternative method for the production of GPP, which is fully present in E. coli.We use geranyl pyrophosphate (GPP) as a first example to illustrate features of our method. Geranyl pyrophosphate is part of the metabolism of most organisms and plays a key role in the terpenoid biosynthesis. Its precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) can be synthesized via two different pathways. The mevalonate pathway starting with acetyl-CoA is present in fungi, archaea and some bacteria. The non-mevalonate pathway (MEP/DOXP pathway) with pyruvate as a precursor exists in plants, eubacteria and protozoa . From thα-D-glucose 6-phosphate. The generated pathways contain two interesting candidates with both four consecutive active reactions to amygdalin. The first candidate starts with sucrose and the second with α-D-glucose 6-phosphate. Both candidates require a uridyl moiety as substrate. Nevertheless, in the search carried out, UTP, UDP and UMP were considered cofactors to avoid unnecessary interconversion of nucleotides that would add numerous but not meaningful pathway candidates. And in both candidates, two of the reactions are catalyzed by heterologous enzymes. For the first pathway, four potential side reactions are proposed and five for the second. These pathway candidates highlight the impact of the list of potential starting metabolites on the results. While both pathways look promising, the first one starts with the cheap starting substrate sucrose and has a better thermodynamic profile. In an industrial environment it would be advisable to create a customized list of starting metabolites considering more criteria, e.g. of cost and availability.We chose amygdalin as a further example. In this case, we added sucrose as a potential starting and basis metabolite. Sucrose is excluded from the original set of starting metabolites because of its higher molecular mass but is much cheaper than E. coli, this pathway does not exist, but it is native in methanogenic archaea. The pathway requires ATP and NAD + /NADH as cofactors. It has nine potential side reactions.Another example is pyrrolysine. The selected pathway candidate has four active reactions and starts with L-Lysine as substrate. Thermodynamic data for this pathway is not available in eQuilibrator. In + /NADPH and AxP as cofactors. The thermodynamic profile is not ideal with regard to the first and last reaction steps that both have a slightly positive ΔrG. Potentially, the last step could be promoted by an efficient FADH2 regeneration or oxygen supply pushing the equilibrium to the product side. However, it remains questionable if FADH2 can be regenerated in permeabilized cells. Details to all examples shown are given in the respective sections of the Additional file As a last example, we chose (S)-2-phenyloxirane. The selected pathway candidate for (S)-2-phenyloxirane has four consecutive active reactions. It uses cinnamaldehyde as substrate and requires CoA, NADP We presented a method for searching potential synthesis pathways for target metabolites without the specification of a fixed starting point. Due to the nature of the search algorithm, the resulting pathway candidates are unbiased by the user’s knowledge and expectation of the most suitable pathway. Our method leads to a large number of results in a broad solution space which may make it challenging to find the most appropriate candidate. Handling this amount of data requires a sophisticated tool of filtering, ranking and expert assessment together with additional features such as the quick evaluation of potential side reactions and thermodynamics. Altogether, our tool is highly customizable and offers flexible filtering and ranking options. All metabolite lists, especially the metabolite pool can be easily adapted to meet the needs of a specific project. This is especially useful in cases where the metabolite pool should be composed of chemicals of the laboratories’ inventory or of inexpensive chemicals. Analogously, all ranking or filtering criteria can be tailored to the focus of the study, such as reagent costs or a specific host organism.Expert knowledge to assess the pathway candidates is still needed. However, the same applies to any pathway design method available to date. The resulting pathway candidates depend fully on the data used to set up the network. The sheer mass of reactions in KEGG makes errors hard to identify manually, and we did not carry out any data cleaning except the measures discussed in section ΔrG in eQuilibrator. Note, that all ΔrG are estimated using the component contribution method. They can however be replaced by experimental values, if available.Thermodynamics of a pathway is complex. Most substances involved in a pathway are not present at the beginning but are rather formed as the synthesis proceeds. This is not taken into consideration. We fix the initial starting concentrations of all metabolites to 1 mM. However, these can be easily modified by adapting the respective values for the calculation of the Km values. While this would be a relevant addition, to our knowledge this information is not readily available on the scale needed for large networks. It could however be integrated for smaller networks, e. g. [We do not consider enzyme concentrations or any kind of kinetic parameters such as enzyme turnover numbers or s, e. g. , particuThe presented method provides a helpful computational tool for the directed design of biosynthetic production pathways and the planning of syntheses. The tool provides a very useful basis for the eventual selection of pathways to be implemented in the wet lab. Building on this, expert knowledge is required to tackle possible practical problems with the implementation of the most promising candidates. All features presented are autonomous. The generated thermodynamic profiles of pathways are invaluable for selecting the most promising pathway alternatives. Similarly, computing potential side reactions leads to important insights for all kinds of pathways.In different use cases different ranking criteria may be considered important. The user of the tool can easily select or define own criteria for ranking results. For the synthesis with cell lysates or permeabilized cells, the consideration of heterologous enzymes and the choice of the most suitable host as well as potential side reactions are certainly very important.1 Discontinued since KEGG release 80.0, October 1, 20162 KEGG compound IDAdditional file 1E. coli, and all metabolite lists as defined in section Lists of reactions and metabolites in the presented genome-scale metabolic network. The spreadsheets in the file provide all relevant data of our presented genome-scale metabolic network including all reactions, the subset of reactions present in Additional file 2Pathway candidates. The file presents the details of the presented pathway candidates, i.e. a list of all reactions involved, overall balances thermodynamic profiles and potential side reactions. (PDF 145 kb)Additional file 3Computation time of pathway candidates. The file presents details on the computation time of the different examples we presented. (PDF 338 kb)


Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete  High-throughput experimental data  provide distinct glimpses into cellular function. When considering cellular metabolism, metabolomics is unique in being a direct outcome measure of integrated biochemical function, and can provide the starting point information for efficient pathway engineering. However, the dynamics of metabolomic data is not only defined by genetic and environmental factors, but also the life stages of the bacteria. As a broad classification, the difference between the primary and secondary metabolites is the growth phase when it is produced in the bacteria. The early log phase shows a higher concentration of primary metabolites, which is utilized for growth, and reproduction of the cells. This is followed by the production of secondary metabolites as the byproducts of the metabolic process. Many of these secondary metabolites are known for their medical and industrial relevance.Streptomyces). As actinomycetes continue to be studied, additional important compounds, such as siderophores [Actinomycetes have a long-cited history for producing medically-relevant chemicals, beginning with antibiotics . Cellobiose has been studied in past as the inducer of cellulases [mic data . Specifillulases ,11,12,13Metabolomic data was obtained for growth on cellobiose or Avicel with separate samples being analyzed for detection of polar and non-polar metabolites for each growth condition.T. fusca strain on cellobiose, 618 polar and 64 non-polar compounds were detected. For growth on Avicel, 402 polar and 82 non-polar compounds were detected. Of the detected compounds, 15 (cellobiose) and 23 (Avicel) were detected in both polar and non-polar runs. A summary of the number of detected compounds is shown in All the samples were run with technical replicates, and the metabolites detected in both the replicates were used for further processing and analysis. For growth of the wild type The MBRole  softwareT. fusca. For the detected compounds with annotations, the following were classified into the following categories: sugars (10 metabolites), lipids or fatty acids associated molecules (36), nucleotides (48), peptides or amino acids molecules (40), hormone or neurotransmitter like molecules (2), and vitamin molecules (9), which were detected in cellobiose growth condition. Eighty-one metabolites from secondary metabolism  as named by KEGG were also identified. The other interactions associated with transporters (47), reporters (61), and ion channel-associated molecules were also identified. The transporter molecules included ABC type, and amino acid transporters . Among the receptor molecules, 24 glutamate receptors, including metabotropic and ionotropic subclass, were identified. The ionotropic receptors are the ligand gated non-selective ion channels and metabotropic activates biochemical cascades [T. fusca. This might also be useful to explain the strong secretome as studied in past [Out of 667 metabolites detected in the cellobiose growth condition, no annotation could be detected for 474 compounds in cascades . As comp in past .Compounds associated with cytochrome P450 were another important family of compounds that was observed in the analysis. There were 21 metabolites associated with the CYP1 and CPY2 families. The primary function of this superfamily of proteins is to catalyze the oxidation of lipids, steroids and xenobiotic compounds, such as toxic chemicals and drugs .Apart from learning the biological role of the molecules, these detected compounds were also mapped to the metabolic capabilities as pre-compiled by KEGG, using the open access online database interface MBRole . Among tT. fusca on Avicel, 461 compounds were detected, but 330 metabolites could not be associated with a functional category in T. fusca as defined by KEGG. The macromolecular distribution of annotated metabolites for growth on Avicel was: 7 sugar molecules, 21 lipids, 17 nucleotides, 43 peptides (and amino acids), and 6 vitamins. Most of the numbers were lesser as compared to cellobiose condition analysis, except for the number of identified peptides. For the transporter and receptor-associated compounds, the numbers were 43 and 41, respectively. There were 17 cytochrome P450 subfamily proteins and 19 glutamate receptor molecules (ionotropic and metabotropic) identified. One unique entity detected in the Avicel condition was 2-aminoethylphosphonate transporters, which are an ABC-type transporter that helps transport 2-aminoethylphosphonate across the membrane for utilization in bacteria. Bacteria has the capability to use these organophosphates as a source of carbon, energy, and phosphorous for growth. These organophosphates are often found in insecticides and herbicides [For growth of wild type rbicides .Apart from the macromolecular classification, 131 identified compounds were studied for connections to metabolic pathways. Seventy-one metabolites were found to be associated with primary metabolic process, and 31 with secondary metabolism pathway. These networks are shown in Overall, 827 unique metabolites were detected when combining results for growth on cellobiose and on Avicel. Three hundred and one metabolites were common to both growth conditions, 366 were unique to cellobiose, and 160 were unique to the Avicel growth condition. The pathway association of the compounds in these two conditions has been summarized in When MBRole maps a metabolite to a metabolic pathway, it assigns a score of significance based on the background compounds known for each pathway versus the mapped list. Some of the interesting compounds, besides the above list, that were unique to cellobiose media conditions, were dihydrostreptomycin 6-phosphate (C01221) from the streptomycin biosynthesis pathway, hexadecanoic acid (C00249) of fatty acid metabolism, lipoyl-AMP (C16238) of lipoic acid and D-alanine (C00041).Among the 301 compounds whose peaks were detected in both cellobiose and Avicel condition, 88 were observed in differential quantities. Nineteen (out of 88) were detected at higher concentration in cellobiose, and 36 (out of 88) were higher in Avicel. These compounds when mapped with biological functions that belonged to fatty acid synthesis (C05763); lysine biosynthesis ; arginine and proline biosynthesis , and glycerophospholipid metabolism (C00513) for the cellobiose condition. In the Avicel growth conditions, ubiquinone and other terpenoid-quinone biosynthesis (C00811); lysine degradation (C03955); phenylalanine metabolism (C00811); gamma-hexachlorocyclohexane degradation (C12836) and selenoamino acid metabolism (C05699) were the pathways for which the respective compounds were detected in higher concentrations. A summarized list of compounds is shown in T. fusca using MBRole. As per the KEGG’s compilation of tfu01110 Biosynthesis of Secondary Metabolites, there were a total of 1038 compounds in the list. This compilation includes intermediate metabolites and the final products. The metabolomics data for growth on cellobiose and growth on Avicel was compared to the list of 1038 secondary metabolite compounds, and 63 metabolites associated with secondary metabolism were identified. To find of the missing nodes of the pre-compiled tfu01110 list, the metabolomics data was mapped to the list of all the natural products . This gave an interesting list of 88 secondary metabolites. This breaks down into a total of alkaloids (41), terpenoids (25), phenylpropanoids (8), amino acid-related compounds (8), polyketides (4), flavonoids (4), and fatty acids related compounds (2). The details of these subcategories can be seen in The global metabolite detection was done for the general metabolites and mapped to the metabolic pathway of Bacillus sp., Micrococcus, Rhodococcus, E. coli, Pseudomonas, and Agrobacterium was conducted, and concluded that Bacillus ranks highest among all in the production of isoprene. The study illustrated the capability of Gram-positive bacteria to produce this industrially significant product. In our metabolomics analysis for T. fusca, we propose that this actinomycete also possesses similar characteristics and the ability to produce isoprene. The biological role of this compound as defined by Sharkey et al. in 2007 [T. fusca, a naturally thermophilic organism.One of the interesting findings from focusing on secondary metabolism was identification of the hemiterpenoid, isoprene. Past studies have shown that both Gram-positive and Gram-negative bacteria possess the capability to produce isoprene . A compa in 2007  as proteT. fusca [The predictive and analytical capabilities of constraint-based metabolic models can be significantly improved by integrating experimental data as model constraints. To gain a better in-context understanding of the metabolomics data, and to further improve our previously published metabolic model of T. fusca , we implT. fusca model was also based on growth on cellobiose. Starting with 946 reactions in the proteomics-based metabolic model of T. fusca, flux balance analysis predicted 56 reactions with low flux and 173 reactions with high flux, i.e., a total of 229 active flux reactions. Metabolomic data from this study was integrated into the metabolic model using the metabolites with high-confidence annotations, and the pathways with active fluxes was reduced to 224 (57 low flux reactions and 167 high flux reactions).For this study, we have focused only on data associated with growth on cellobiose, since the published The reaction distribution in FBA model (without metabolomics data) and FBA with metabolomics data from cellobiose media is discussed in the following three categories: (1) reactions that remain unchanged in both scenarios (with and without metabolomics data integration), (2) reactions that were active in both scenarios, but there was a change in the flux value, and (3) reactions that were inactive in one scenario, but were active in the other.Unchanged reactions: There were 773 out of 946 reactions that remain unchanged. Accounting for reactions with the same active flux in both scenarios, we found 60 reactions had same active flux. This involves 20 amino acid pathway reactions, 12 carbohydrate metabolism pathways, and 9 nucleotide metabolism pathways. These core, active pathways may constitute core metabolic pathways that remain constant, irrespective of the external parameters.Changed active reactions: The reaction flux comparison with respect to FBA simulations (without metabolites data) can be divided into 4 parts—reactions with high flux (1000 to 250), moderate flux (249 to 3.8), low flux (3.8 to 0), and negative flux (0 to −1000).The overview of reaction flux variation can be seen in It was observed that the moderate and low flux reactions showed an overall shift in the value to the lower range, however, the trend of which reactions were used remained largely unchanged. However, among the 39 reactions with high flux, there was a wide range of variability on how the flux changed for when the metabolites data was integrated.This variability is further explained with respect to amino acid a and carIn carbohydrate metabolism, significant changes were observed in glycolysis, TCA cycle, and pentose phosphate metabolism. In glycolysis, the pyruvate kinase mediated conversion of glucose to glucose 6-phosphate is reduced from a high flux reaction to inactive after data integration. In the TCA cycle, there is a positive shift (low flux to moderate flux) in all the reactions from succinyl-CoA to oxaloacetate, whereas there is a slight reduction in flux through intermediate reactions leading to formation of succinyl-CoA from alpha-ketoglutarate. In the pentose phosphate metabolism pathway, there is a significant increase in flux for the conversion of glucose 6-P to 6-phosphgluconolactone. This variability is illustrated in the graphs shown in Inactive to active change: Finally, there was a set of nine reactions where flux balance analysis predicted active fluxes, but integration of metabolomic data indicated the reaction should be inactive , the biochemical production capabilities of a poorly-characterized organism are often speculative, if based upon genome-centric knowledge. In considering An extended use of metabolomic data can be integration into other computational analysis approaches, such as metabolic models to help better understand the coordinated functionality of an organism’s metabolic network. In this study, we employed an algorithmic approach to integrate metabolomics data with a constraint-based metabolic model, focusing on using metabolites that were uniquely identified with high-confidence. Use of metabolomics data provides a completely complimentary type of information to the information that can be obtained from genomic, transcriptomic, or proteomic data. All of these data types can identify potential biochemical functionality, but they all also have the potential pitfall of being multifunctional (annotations for different functions for the same gene/protein). While genomic, transcriptomic, or proteomic data potentially has a wider coverage across a metabolic network, only metabolomics data has the potential to directly distinguish and confirm biochemical functionality. This helped confirm activity of some central metabolic pathways, and helped identify shifts in pathway usage.T. fusca was limited, but the data that was obtained was used to help identify some differences in functional states between growth on cellobiose and Avicel. Most of the observed changes were consistent with what might be intuitively expected. Cellular growth is faster on cellobiose than on Avicel, and this is consistent with some of the changes that show increased fluxes and metabolites detected in amino acid synthesis that are required to support growth. Growth on Avicel is slower and requires secretion of proteins  and uptake of a broader range of substrates, and thus, there was an increase in support subsystems and transporters. Furthermore, integration of metabolomics data with metabolic modeling was able to identify subtle shifts in pathway usage that were not possible using proteomic data.In this study, the breadth of metabolomic data coverage across the metabolic network of T. fusca pre-compiled compounds listed by KEGG. This was partially explained by limited functional annotation for T. fusca on KEGG. There was a significant increase in mapped compounds to this database. Due to these shortcomings, it is almost impossible to obtain an exhaustive coverage like in the case of genomics or transcriptomics level dataset. And if it is possible, the hierarchy of techniques as shown in The extent of utility of the metabolomics data in this study was severely limited by the ability to uniquely and definitively associate detected metabolites with a single chemical species. Raw metabolomic data currently suffers from a limitation of compound identification and accurate prediction due to the data available on the resource banks, such as KEGG, HMDB, etc. This is seen in compounds that have closely related masses and retention times. Some of the very closely related masses with similar retention times that were observed in the dataset are as follows: UDP (C00015), with the mass of 404.002200, and aluminoparaaminosalicylate calcium (C13104), with the mass of 404.011300. These two compounds vary at the second decimal place for the masses, and had similar retention time. Another example was 1-methyl-4-phenylpyridinium  and furfural diethyl acetal . Their mass varies at the third decimal place. Similarly, there were compounds that varied at fourth and fifth decimal points also having similar retention time. In our opinion, this is not the limitation of the detection technique, but more of the inadequacy at the resource bank end or the mapping/identification algorithm end. This leads to problems at both ends of the analysis pipeline, not only overestimation, but also sometimes underestimation or no annotations (in this case). Approximately, seventy percent of the compounds could not be well annotated in the context of the Thus, this method is useful to get an idea of focus targets, and has to be paired with the targeted metabolite search for the specific pathway of interest.Global metabolite profiling experiments attempting to characterize all metabolites of a cellular system involve a huge combinatorial hierarchy of techniques . Figure In the current scope, only untargeted metabolite profiling using HPLC/MS-TOF in positive polarity was conducted. Using a methanol/water/chloroform extraction method, the aqueous and non-aqueous layers are separated for two different runs: polar and non-polar. The peak and mass data acquired on Agilent Mass Hunter Software from LC-MS was sent to the Omics Discovery Pipeline at Bindley Bioscience Center for deconvolution, alignment, normalization, and identification of the compounds . The parT. fusca wild type grown on cellobiose and Avicel. This is the first ever attempt to investigate metabolomics of T. fusca. It opens an arena for target based metabolite profiling if required to specifically look into the pathways/product of interest. Raw metabolomics data is included in This analysis detects a subset of polar and non-polar compounds representing general metabolites and lipids, respectively. Technical replicates of T. fusca YX strain (ATCC BAA-629) cells were grown on Hagerdahl media (ATCC medium: 2382) with cellobiose and Avicel as the carbon source (5.0 g/L) at 55 °C. The two growth conditions resulted in the dry cell weight of 8.4 mg/mL for cellobiose, and 4.5 mg/mL for growth on Avicel. Analysis and preparation of cells for metabolomics analysis was conducted during exponential growth.3 (3 mL) was added to each tube and sealed with foil to avoid contamination. The tubes were incubated for 5 min in a sonicator–water bath for cell disruption. MeOH/H2O (3:2) was added to the cell lysate and kept aside, to allow for phase separation. The final ratio of solvents MeOH/H2O/CHCl3 was 4:2:3. The 6mL of polar and 3 mL of non-polar phase were separated and kept on speed vacuum overnight. These vials were stored at −80 °C after sealing with parafilm for subsequent analysis.Frozen cell pellets were re-suspended in 1mL methanol and transferred to glass tubes. CHClThe processing of small molecules detection along with the instrumentation conditions are described in m/z, intensity, and retention time data is fed to XALIGN to align the data from technical replicates, to reduce the possibility of instrumentation error. Further, t-tests were conducted to see significant difference from the blank runs, and eliminate the peaks due to solvents and spiked standards (mass of 121.050873 and 922.009798 respectively) [The raw data from the Agilent Mass Hunter software was fed to the Omics discovery pipeline developed by Purdue Bindley Biosciences Core Metabolite Care Lab Facility. This pipeline processed the data by performing deconvolution of peaks using XMASS. Preprocessing of ctively) .www.omicsDP.org) provided by Purdue [The polar and non-polar extracts were run as separate experiments, and the peak data was collected in the form of mzData files by the software Mass Hunter provided by Agilent. These mzData files were processed using Omics Discovery Pipeline (y Purdue . An illuT. fusca pathways using the precompiled compound list on KEGG [The identified set of metabolites was then mapped to  on KEGG ,16 as a  on KEGG , an onliFor a metabolic reconstruction, a flux distribution is a set of relative reaction rates that satisfy conservation of mass constraints. To derive a flux distribution that reflects the presence or absence of metabolites observed in metabolomics data, we adopt a simple modification of the standard flux balance analysis model. We find a flux distribution that maximizes the number of metabolites that are present in both the metabolomics data and our flux distribution. Presence of a metabolite in the metabolomics data is defined as being observed beyond a certain threshold in the metabolomics data. Presence of a metabolite in a flux distribution means that the sum of fluxes through reactions producing that metabolite exceeds a certain threshold.Let The objective function maximizes the number of metabolites present in both the metabolomics data and the flux distribution. The first set of constraints enforces conservation of mass. The next set of constraints require that if a metabolite is counted as present in the flux distribution, then the sum of fluxes producing that metabolite must be at least


Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the aﬄuent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and  Oryza sativa L.) is a staple food consumed by more than half of the world’s population. Tremendous progress has been made in rice production during the past half century with a doubling of yield since the Green revolution in 1960s  in all three domains of life . Based olsewhere .Once the network is reconstructed, it can be readily converted into a mathematical model, which is represented in a matrix form where the rows and columns represent the metabolites and reactions, respectively. This “stoichiometric matrix” or simply “S matrix” is the centerpiece of constraint-based model reconstruction and analysis methodology where the stoichiometric coefficients constrain the flow of metabolites from various substrates to products in the metabolic network. In addition, measured extracellular uptake and secretion rates can be used as additional constraints. Together, these constraints confine a region within the multi-dimensional solution space of allowable reaction fluxes where the actual solution exists . SeveralFigure 2). First, the existing rice metabolic networks and models are surveyed with their analyses and applications. Next, the potential and challenges in translating the modeling approaches applied and knowledge gained from other plant models to rice are discussed. Lastly, several future applications of rice genome-scale metabolic models are proposed for the improvement of rice production.In this review, we outline the current status and future prospective of the analyses and applications of rice metabolic networks and models , suggesting that this latest model has the broadest coverage of rice metabolism and supports the simulation of the highest number of metabolic functions in rice cells.The first computation-ready rice GEM was built from RiceCyc, with three subcellular compartments, cytosol, chloroplast, and mitochondrion . Reaction AraCyc  and the n AraCyc . We test in glycine synthesis via glutamate decarboxylase, succinic semialdehyde dehydrogenase and serine hydroxymethyltransferase, proposing the possible mechanism for observed anaerobic GABA accumulation  pathways during photosynthesis and cell growth and identified that all three electron flow pathways are possibly operational at all times . The most direct approach is to understand the metabolism during seed development. Thus, several studies using constraint-based modeling were carried out to model metabolism in barley seeds  and oilsrbations . Recentlrbations . Using frbations . Fifty-srbations , though rbations . The modrbations . Glycolyrbations . ClearlyArabidopsis. The metabolic fluxes in Arabidopsis heterotrophic cell culture under increased temperature and hyperosmotic conditions were accurately predicted using Arabidopsis GEMs  it can be contextualized in metabolic networks and models, for example transcriptomic data was mapped to rice networks and models to determine the regions of the metabolic system which are significantly perturbed  or underith GEMs . Using sbidopsis , 2014, rbidopsis  and maizbidopsis  have effbidopsis  and genebidopsis .3 plants are generic single-cell models for simulating the metabolism of a single cell or tissue. The published rice GEMs were used to model growing photosynthetic leaves based on tissue-specific biomass compositions  of the cell or tissue. However, a plant cell or tissue does not operate in isolation but is dependent on other cells, tissues and organs. Hence, there is a need to extend the existing genome-scale metabolic modeling framework to a whole-plant scale. To this front, a multi-scale metabolic modeling approach integrating a dynamic whole-plant functional model with a multi-organ flux-balance metabolic model was developed to study the source-sink interaction during seed development in barley plants . Using dArabidopsis GEM to simulate the metabolism of mature (non-growing) C3 and CAM leaves by defining the metabolic function of mature leaves with model constraints of sucrose and amino-acids export into the phloem  must be extended beyond cell or tissue growth. One example is the use of an e phloem . Fruit me phloem .One of the major challenges in GEMs is the incorporation of regulatory aspects into the modeling framework. To do so, Boolean logic formalism was incorporated in one of the studies to control the reaction activity of rice central model, accounting the regulation of seed germination under aerobic and anaerobic conditions . Here, iAnother problem that stemmed from the mentality of simulating growth is the bias in manual curation of GEMs for reactions involved in synthesis of core biomass components . Most plant metabolic models were tested for biosynthesis of biomass components but relatively little effort was made in their catabolism, which is important for modeling senescing plant tissues. While carbohydrate and lipid degradation were included in most plant metabolic models, this is generally not the case for catabolism of other biomass components such as amino-acids, nucleic acids and chlorophyll. With respect to secondary metabolism, although the biosynthesis of a limited set of secondary metabolites was curated for a number of plant GEMs, the vast majority of complex plant secondary metabolites could not be produced from the models. Furthermore, the biosynthesis of most secondary metabolites was neglected in model simulations, primarily due to the lack of experimental data on the production of secondary metabolites. The exclusion of secondary metabolites from the model constraints or biomass equations could have an impact on the results from various analyses such as gene essentiality and energetic costs calculations.Plant metabolic models were applied to simulate metabolism under multiple abiotic conditions. While a number of abiotic factors directly relate to metabolism and can be captured directly by constraints in flux-balanced models, such as anoxia and light intensity, some require indirect proxies to translate the abiotic condition into model constraints. For example, Rubisco carboxylation to oxygenation ratio was used as a proxy for stomatal closure to simulate drought stress . In addi4 photosynthesis, at both biochemical and anatomical levels, into rice  and a rhizobium (Sinorhizobium meliloti), the metabolic interactions underlying the symbiotic relationship between the two organisms were modeled, suggesting that oxygen availability limits symbiotic nitrogen fixation and influences the form of nitrogen supplied to the plant (Xanthomonas oryzae pv. oryzae and Magnaporthe oryzae can be developed and integrated with rice GEMs to investigate rice–pathogen metabolic interactions, similar to a study on modeling human–pathogen metabolic interactions (Up until now, plant flux-balance models have mostly been applied to investigate metabolism under different abiotic conditions, with relatively little work done on modeling biotic conditions. A well-known example of plant–microbe interaction is legume–rhizobia symbiosis. Using GEMs of a legume (he plant . Regardihe plant . To underactions . AnalysiWhile GEM on its own is already a powerful tool for investigating the behavior of metabolic systems, a lot more can be done by making use of the gene-protein-reaction associations in the models, illustrated by a number of plant modeling studies  . As diffArabidopsis and other plant models will undoubtedly be applied to rice models to better understand the metabolic systems of this important crop plant. While there are several limitations and challenges in applying constraint-based techniques in modeling plant metabolic systems, progress are being made in overcoming these limitations and challenges, which will extend the scope of future applications of GEMs of rice and other plants. With the ever increasing availability of multiple -omics data and experimental measurements, we envision that rice GSMN/GEMs will play a key role in contextualizing and analyzing these complex data to give novel insights into rice metabolism, which will lead to improvements in the yield and quality of rice production.In summary, the development and application of rice GSMN/GEMs in recent years have increased our understanding of the metabolic and transcriptional regulations and the metabolic responses of rice to multiple environmental conditions and stresses through the integration of -omics data and/or constraint-based modeling. As the field of plant metabolic modeling progresses, modeling approaches developed for ML, CC, and D-YL have contributed in the design and write up of the manuscript. ML, BM, and D-YL have edited, reviewed, 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.


The lack of knowledge about the regulation of dynamic intracellular processes under mild hypothermia restricts bioprocess optimization. To address this issue, a mathematical model that quantitatively describes Chinese hamster ovary (CHO) cell behavior and metabolism, mAb synthesis and mAb N‐linked glycosylation profile before and after the induction of mild hypothermia is constructed. Results from this study show that the model is capable of representing experimental results well in all of the aspects mentioned above, including the N‐linked glycosylation profile of mAb produced under mild hypothermia. Most importantly, comparison between model simulation results for different culture temperatures suggests the reduced rates of nucleotide sugar donor production and galactosyltransferase  expression to be critical contributing factors that determine the variation in Fc‐glycan profiles between physiological and mild hypothermic conditions in stable CHO transfectants. This is then confirmed using experimental measurements of GalT expression levels, thereby closing the loop between the experimental and the computational system. The identification of bottlenecks within CHO cell metabolism under mild hypothermic conditions will aid bioprocess optimization, for example, by tailoring feeding strategies to improve NSD production, or manipulating the expression of specific glycosyltransferases through cell line engineering. Biotechnol. Bioeng. 2017;114: 1570–1582. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals Inc.Despite the positive effects of mild hypothermic conditions on monoclonal antibody (mAb) productivity ( The material balances for the viable and total cell population areXv and Xt are the viable and total cell density in the bioreactor (cell L−1), respectively. μ, μd are the specific CHO cell growth and death rates (h−1), respectively. Klysis describes the specific lysis rate of dead CHO cells (h−1). Glucose and certain amino acids are regarded as growth‐limiting factors, while ammonia and lactate are considered to be growth‐inhibiting factors. However, lactate is also consumed in the second phase of our cultures. It is therefore included in both the growth inhibition and limitation expressions. Monod type kinetics are used to determine the specific growth rate (μ) using the following equations:μmax is the maximum specific growth rate (h−1) and flim,finh are the nutrient limitation and product inhibition, respectively. flim, finh are defined as follows:Kglc, Klac, Kasn, Kasp, Kglu, Karg, Klys, Kpro are the Monod constants for growth with respect to glucose, lactate, asparagine, aspartate, glutamate, arginine, lysine, and proline (mM), respectively, while KIamm and KIlac are the inhibition constants for ammonia and lactate (mM), respectively. [Glc], [Lac], [Asn], [Glu], and [Amm] are the extracellular concentrations of glucose, lactate, asparagine, glutamate, and ammonia , respectively.The volume of the cell culture is dependent on the inlet and outlet flow rates:μd) isμd,max is the maximum specific death rate (h−1), and Kd,amm is the constant for cell death by ammonia (mM).Ammonia is assumed to be the main contributor to cell death and the specific death rate , kT,[Lac] is the Michaelis–Menten constant of lactate transport into the cells from culture medium (h−1). mglc is the maintenance coefficient of glucose (mmol cell−1 h−1), which conceptually represents the consumption of glucose for metabolic activities that are not cell growth related.In the case of glucose, its material balance contains an additional feeding term ([Lac][Lac]+Klac) functions as a modulator for lactate production/consumption during late exponential growth phase. When [Lac] ≫ Klac, then ([Lac][Lac]+Klac) is close to 1, indicating lactate consumption. Inversely, when [Lac] ≪ Klac, the value of ([Lac][Lac]+Klac) is small, thus limiting lactate consumption.Despite lactate being a metabolic by‐product, it is consumed by cells when they experience a metabolic shift, serving as an additional carbon source. The term qamm and Yxv,amm represent the specific rate of ammonia production (mmol cell−1 h−1) and the yield of viable cell production from ammonia to make essential components for biomass and recombinant products, respectively.Amino acids also play a significant role in cell maintenance, metabolism, and protein production. Indicative consumption terms for amino acids involved in the reaction scheme of Figure 2‐LC‐LC assembly pathway for mAb synthesis . Vcell is the average volume of the CHO cell (dm3). Nglc,nucleotide and Ngln,nucleotide indicate the flux of intracellular glucose or glutamine required for the synthesis of each named nucleotide (mmol mmol−1). Similarly Nglc,NSD and Ngln,NSD represent the consumption rates of intracellular glucose and glutamine in the respective NSD production reactions (mmol/mmol).In addition to the material balances for nucleotide synthesis described above, the intracellular concentrations of glucose and glutamine are defined in Equations rNSD equations from Jimenez del Val  and KNSD,y is the saturation constant of species NSD with respect to species y (mM), where y denotes glucose, glutamine or nucleotides. rCMPNeu5Ac and rGDPFuc also account for competitive inhibition constant Ki,CMPNeu5Ac and non‐competitive inhibition constant Ki,GDPFuc, respectively (mM).It is now possible to relate NSD production with both nutrients and nucleotide dynamics within the cell throughout the culture period. We modified the  del Val  to incluThe last change in the model involves the incorporation of NSD consumption for host cell protein glycosylation in material balances that define the rate of NSD transport into the Golgi apparatus. The following equations define the transport rates for UDP‐GlcNAc, UDP‐Glc, UDPGal, GDP‐Fuc, and GDP‐Man:KTP,NSD (mM), as proposed by Jimenez del Val  (mmol cell−1) at 36.5°C was 1.38 × 10−12 mmol cell−1, based on the calculations from Harrison et al.  (mmolNSD mmolglycan−1) was taken from Raman et al.  was calculated from the experimentally determined Fc‐glycan compositions , is 2 assuming that all IgG molecules produced are glycosylated  is included in the material balance for the rate of CMP‐Neu5Ac transport, which is defined in Supplementary Table SII App. Equation  (48ii). By considering the consumption rates of NSDs on host cell protein glycans, the updated NSD model provides a better estimation of resources available for mAb Fc glycosylation.The rate of transport for UDP‐GalNAc and CMP‐Neu5Ac only accounts for host cell protein glycosylation (Supplementary Table SII App. Equation  (48i and ii)). In addition, owing to the competitive inhibition of the CMP‐Neu5Ac Golgi transporter protein in the presence of UDP‐GlcNAc  and assumes successful assembly of mAb and addition of the glycan precursor within the endoplasmic reticulum. To adapt this glycosylation model to the CHO cell system at hand, the minimal concentrations of glycosyltransferases required for 100% processing were re‐estimated. Since our adapted model includes cytosolic concentration of NSD species, re‐estimation of saturation constants of each glycosyltransferase was also required. The parameter estimates obtained in this way can be used for comparison between the two experimental conditions but are not intended to be taken as exact quantification of enzyme concentrations. In all kinetic rate expressions, enzyme concentrations are multiplied by the turnover number for the specific enzyme, which makes these two parameters structurally unidentifiable. It is therefore not possible to arrive at unique values for these two parameters without targeted in vitro enzymatic assays, unless the enzyme concentrations are quantified by other means, for example, quantitative proteomics.Model simulation and parameter estimation was carried out in gPROMS v.4.1.0  for two experimental conditions: fed‐batch culture at 36.5°C for the entire duration or with a temperature shift to 32°C induced on day 6 . Unknown parameter values were estimated using the maximum likelihood formulation, which maximizes the probability that the model will match the variable values obtained experimentally, and provides statistical analysis that indicates the level of uncertainty in the estimated parameter values. By using the same model structure for both conditions, model simulation results from two parameter sets can be compared across the two process conditions.Model parameters were estimated using the experimental profiles for nutrients, metabolites, total and viable CHO cell concentrations and mAb synthesis including product transcripts and polypeptides. Figure 2 and H2L assembly intermediates are described reasonably well by the model, which, however, overestimates their concentrations under mild hypothermic conditions  and translation , rates of transport of mAb from the ER to the Golgi (KER) and from the Golgi to the extracellular matrix (KG), are all estimated to be higher by the model (Table (KA) to be a potential bottleneck for further improvement in qmAb in our case, where its reduced value at 32°C could indicate reduced rate of collision between the mAb polypeptide molecules at lower temperature. As a result, the mAb assembly pathway can be a potential focus to further improve mAb production during mild hypothermia. In addition, the model provides an early indication of mAb glycosylation efficiency during mild hypothermia, where the Golgi glycosylation efficiency factor (ϵ2) is lower at 32°C. The full list of model parameter values for both conditions can be found in the Supplementary Table SV.By comparing the different sets of parameter values for the two culture temperatures examined Table , it becon et al. . The modVmax) in reactions that utilize ATP and ADP at 32°C (Supplementary Table SII App. Equations  (38) and (39), (42)–(44)), which leads to the accumulation of ATP/ADP species and decreased production of AMP. Reduced consumption of ATP is in good correlation with lower rate of cell growth at 32°C. In the case of GTP synthesis, its accumulation observed at 32°C is suggested by the model to be encouraged by a much higher production rate (Supplementary Table SII App. Equation  (37)). UTP synthesis is comparable between the two temperatures with only a slight increase of its concentration at 32°C, which is well reflected by the similar Vmax values of enzymes involved in its synthesis, and a lower UTP to CTP conversion rate (Supplementary Table SII App. Equations  (43) and (44)).Next, we sought to understand nucleotide and NSD metabolism through the second module of the model. Firstly, nucleotide concentrations are determined by CHO cell growth, glucose and amino acid metabolism. Based on the metabolic rates calculated for each culture temperature using the CHO cell dynamics model, NSD temporal profiles can be simulated. Model simulation results compare well with experimental data. Specifically, Figure In the case of NSD metabolism, the model successfully captures the reduction of intracellular concentrations of UDP‐Glc, UDP‐Gal, UDP‐GlcNAc, and UDP‐GalNAc at 32°C, which are in good agreement with the experimental data. Despite this, discrepancies are observed for GDP‐Fuc and GDP‐Man, where the current model fails to simulate their peak concentrations. Since the production of GDP‐Fuc is dependent on the concentration of GDP‐Man, the lumped pathway that is used in the NSD model might not be adequate to represent species generated from this series of reactions. Additionally, the material balance for GDP‐Man does not account for the mannose salvage pathway . These results are in good agreement with literature studies that have shown a positive correlation between kcat and temperature (Thomas and Scopes, qmab (Jimenez del Val et al., In order to estimate the antibody Fc‐glycosylation profiles through this Golgi model, it is necessary to relate model outputs from both cell culture dynamics and NSD synthesis modules to the Golgi N‐linked glycosylation model adapted from Jimenez del Val et al. . It is aFigure Table In an attempt to test the capability of the current model to describe the biological system, we compared model outputs with the protein levels of GalT experimentally using Western blotting. As shown in Figure This study describes the development of a modular model that can successfully describe CHO cell culture in terms of cell growth, metabolism, recombinant mAb production, and glycosylation under physiological culture temperature and mild hypothermia. The model enables a thorough comparison of intracellular processes and their rates under the two culture conditions. Despite changes in cell growth and metabolism observed upon the induction of mild hypothermia, our modeling study suggests that UDP‐Gal synthesis, galactosyltransferase level and activity are the critical factors determining the variation in Fc‐glycan profiles between the two culture temperatures. As a result, manipulation of glycosyltransferase expression can be a way to streamline Fc‐glycan distribution at 32°C to that obtained at physiological temperature.We thankfully acknowledge the Biotechnology and Biological Sciences Research Council for supporting this research through the studentship awarded to SNS by the Bioprocessing Research Industry Club and Imperial College's Impact Acceleration Account. The financial contribution of MedImmune plc is also gratefully acknowledged. The authors thank Kalpana Nayyar, Andrew Smith, and Neil Birkett for their assistance in glycan, mAb titre analyses, and mAb purification, respectively. KMP and CK thank Research Councils U.K. for their fellowships. CK thanks Lonza Biologics for their financial support.Additional supporting information may be found in the online version of this article at the publisher's web‐site.Supporting Data S1.Click here for additional data file.