Clostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering.iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles  between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production.We constructed a genome-scale metabolic model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.Our developed genome-scale model The online version of this article (doi:10.1186/s13068-016-0607-x) contains supplementary material, which is available to authorized users. For a sustainable energy economy, the necessity of producing fuels and chemicals from renewable feedstocks is well acknowledged, and the use of bio-based resources is a promising route for significantly lowering the carbon footprint of liquid transportation fuels . To prodClostridium thermocellum, which exhibits a high growth rate on cellulose ..−1)  of C. thermocellum provides key precursors for cell synthesis, and consists of alternative means of generating energy and shuttling electrons, especially when coupled to the complex redox metabolism [In particular, the cascade of carbon from PEP to pyruvate to acetyl-CoA in tabolism . ExaminiC. thermocellum. Four key, interrelated motifs can help to shed light on this mechanism: Motif 1—energy modulation via acetate production, Motif 2—redox metabolism, Motif 3—regulation of PEP to pyruvate conversion, and Motif 4—PFL-dependent redox relief valve.Taken altogether, we can use the simulation results presented along with literature reports to propose a mechanism which explains the metabolic differences between cellobiose and cellulose cultures of C. thermocellum. Acetate production during growth on cellobiose likely drops because less PTA-ACK activity would be necessary to generate the required ATP for cellulosome synthesis. Indeed, it has been estimated previously that the amount of ATP required for cellulosome synthesis and the amount of ATP produced by PTA-ACK are roughly equivalent [C. thermocellum, and it has been proposed that electron perturbations are more influential than PTA-ACK perturbations on ethanol production [Energy modulation via acetate production is one of the critical motifs regulating bioenergetics of uivalent . While nuivalent  and tranuivalent  Fig. 8)C. thermouivalent  which suoduction .Fig. 8PrC. thermocellum is very robust and a critical motif in controlling cellular bioenergetics. To account for high E:A ratios for growth on cellobiose, the cell must have an ample supply of NADH. This is facilitated by an increase in RNF and decrease in BIF activities on cellobiose as observed in the simulations  responsible for ethanol production are also seen to be highly expressed under multiple conditions, although ethanol production varies , 64. TheC. thermocellum in its native environment, i.e., degrading complex biomass in a co-culture in soil [C. thermocellum prefers to make acetate and hydrogen if these products can be consumed by its cohabitant [The simulation results and literature summaries presented above offer some interesting suggestions. To properly understand why cellobiose cultures produce so much more ethanol, it is useful to think of  in soil , 70, whehabitant . Isolatehabitant . This reWithout the ATP burden of cellulosome synthesis, less flux through PTA-ACK is needed, and this triggers a dramatic restructuring of carbon and electron fluxes to maintain the rates of glycolysis examined here. As a result, there is an increase in RNF flux, which leads to more PPDK flux. Both these reactions can enhance the supply of NADH and lead to the observed overflow of ethanol production on cellobiose. As RNF reaches its maximum capacity, PFL flux increases to balance the need for acetyl-CoA and the redox state of the cell.C. thermocellum. However, we believe these questions can be addressed as more OMICs and fermentation datasets become available and are integrated.Generally, these results indicate that for growth on cellulose, a high glycolytic flux and sufficient conversion of reduced ferredoxin to NADH  will be critical for high ethanol production. A significant level of control is necessary to accomplish this goal, although it is still unclear at this point how exactly the proposed motifs are controlled with respect to each other, or what additional regulatory elements might be active with, or instead of, the proposed mechanism above. There are still many questions regarding the bioenergetic control mechanisms which balance carbon and electron fluxes in C. thermocellum, with its dynamic cellulosome component, displays a significant increase in function and predictive capability to the previous model. We built and extensively refined this model using the KEGG database and our previously reported core model [Our genome-scale model for re model . From thre model . As an aC. thermocellum metabolism which arose after compiling multiple sets of training data. Namely, what is the mechanism behind the increase in ethanol production when growing on cellobiose versus cellulose? Using our GEM, we sampled 100,000 flux distributions for simulations on cellobiose and cellulose which were constrained to experimental data. By examining the difference between the carbon sources at different growth rates, we show how ethanol production in C. thermocellum is part of the overflow metabolism when growing on cellobiose.We then used the model to address a few fundamental questions about iAT601, C. thermocellum can be designed as a manufacturing CBP platform to produce a large space of potential biofuels and chemicals beyond ethanol, isobutanol, and hydrogen using the modular cell design concept [We envision that with the tuned GEM  concept . AltogetgetKEGGModelForOrganism of the RAVEN toolbox [ctx) with the complete set of coding sequences from the genome assembly (GenBank: CP002416.1) so that each reaction is linked to a specific protein encoded by the genome [A workflow of the model construction process is given in Fig.  toolbox . This fue genome . This drClostridial GEMs [hmmscan [hmmscan and 1e−50 for RBH.Transport reactions were linked to genes by compiling a list of putative transporters and then curating reactions via manual inspection. The list was compiled by three methods. The first method used InterProScan 5 , 75 to fial GEMs , 76, 77 [hmmscan  to deter[hmmscan  to extraC. thermocellum cofactor specificity in glycolysis is atypical [Further, as it is known that atypical , we inveatypical . For enzatypical . This soi.The central metabolic network recently reported  was manuii.We adapted the dry cell weight composition presented for strain ATCC 27405  to refleiii.d-fructose 6-phosphate → d-fructose 6-phosphate) within the network to efficiently close gaps between discrepancies.We added in several artificial reactions to convert identical yet alternately described metabolites .The refined model is included in the Supplementary Materials and has been deposited in the DOE KBase  and elementary mode analysis (EMA)-based techniques were employed to analyze the GEM i can be calculated byj in which the metabolite participates.FBA is a commonly used computational tool using stoichiometric and thermodynamic constraints to optimize a cellular objective, such as maximum cell growth . In our EMA seeks to find all solutions to Eqs. p (mmol/g DCW/h) is the specific rate (or flux) of metabolite P, μ (h−1) is the specific growth rate, YP/X is the yield of metabolite P per unit DCW X, and CP (mmol/L) and CX (g DCW/L) are concentrations of P and X, respectively.Experimental data were obtained from multiple sources see “”, and asD is the dilution rate (h−1).For chemostat cultures, the fluxes were calculated as follows:C. thermocellum. The previous GEM of C. thermocellum, iSR432 [The cellulosome is the cellulose-degrading protein complex covalently bound to the surface of certain cellulolytic bacteria, such as , iSR432 , 27, rou, iSR432 , and the, iSR432 , 95, 96.The fractional composition for dry cell weight was 0.5285 g protein + 0.026 g DNA + 0.0655 g RNA + 0.076 g lipid + 0.2242 g cell wall + 0.00494 g solute pool + 0.0304 g total_LTA → g dry cell weight (DCW). A second reaction was implemented to combine the DCW term and the cellulosome term, and this whole cell term was adjusted depending on carbon source, specifically: 1 g DCW + 0.02 g cellulosome term → biomass for cellobiose cultures and 1 g DCW + 0.2 g cellulosome term → biomass for cellulose cultures [A was created by counting the amino acids required to synthesize cellulosomal proteins. The entry Ai,j corresponds to the number of amino acid i encoded by the sequence of protein j. Second, the abundances of each protein were condensed into a condition-specific vector c normalized to CipA as presented by Raman et al. [A·c. Finally, the condition-specific amino acid count is converted to mmol/g cellulosome similar to the calculation of protein or DNA terms  to cellohexaose (G6). Glucose and cellobiose transport were included in the automatic reconstruction. It has been shown that ligomers . Furtherligomers  and, conligomers . Upon enligomers  and so rligomers .rN)G was set at 3.27 mmol ATP/g DCW/h  and no gTo tune the ATP cost of cellulosome synthesis, the coefficient for ATP in the cellulosome synthesis reaction was varied from 40 to 100 mmol ATP/g cellulosome/h while optimizing for maximal growth and maintaining experimental constraints, similar to above. Tuning these additional ATP requirements allowed finding the best fit to experimentally observed growth rate. All subsequent simulations used these parameters.Investigation of the difference in ethanol production between culture conditions was performed by constraining ethanol and acetate production as a function of cell growth. To implement these constraints, we used a series of flux ratios. First, to constrain the sum of ethanol and acetate yields, we calculated the following flux ratio:−1) as well as 100 values for the glucose uptake rate between 5.5 and 7.5 mmol glucose equivalent/g DCW/h, the range seen across multiple datasets. For each growth rate and glucose uptake rate, the sum of ethanol and acetate yields and E:A ratios was calculated. All other fermentation products were unconstrained. The uniform sampling was performed using optGpSampler [Given the variability in reported E:A ratios, we chose to sample the phenotypic space of cellobiose and cellulose cultures given the constraints above to minimize bias between flux distributions. To perform the sampling, we first randomly generated 100 normally distributed values of μ between 0 and 0.3 (hpSampler  with a s


Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted. Escherichia coli http://ww(MIRIAM) , PDB , only one variant, or often both variants associated with both genes in these instances, which makes it challenging to assign the correct reaction to the respective isoform. In addition, several enzymes are able to catalyze various reactions, and the catalyzed reactions depend on the availability of a specific cofactor. This leads to the incorporation of all potentially catalyzed reactions that vary only by their cofactor requirements [Another issue commonly observed when reconstructing metabolic networks is the difficulty of selecting the right cofactors for reactions, specifically the right redox pairs. The assignment of cofactors to reactions is complicated by the fact that the cofactor requirement is organism specific and cell specific, explaining at least partially that the cofactors requirements vary between databases . Furthertor used . The reacofactor . Unfortuirements , which iirements . Howeverirements  and are There have been attempts to create collections of metabolic networks, e.g. Model SEED  or BiGG Model SEED is aimed at providing a platform for model reconstruction based on automated genome annotation using RAST . While tet al. [In contrast to this approach, MetRxn and MetaNetX aim at identifying common reactions by combining multiple pieces of information. Bernard et al.  give a get al. . Both MeS. cerevisiae. Several models of yeast had been published [The first example of a successful community effort for organism-specific reconstruction is the creation of the consensus model of ublished  until, iublished . This eaublished , which iet al. [et al. [Key PointsThe increasing amount of metabolic reconstructions necessitates a more unified way of representation to make models comparable.Available unification sources could provide a basis for this process.Associations to genetic information in metabolic reconstructions need a clearer and more structured association.Transcript-specific association rules would improve the specificity of network activities.Cofactor specificity needs to be addressed more carefully during reconstruction.Another example of community effort to merge models is the HMR Recon 2 . The firet al. , and the [et al. . These c [et al.  and furt [et al.  in an ef


Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. Metabolism is the basic process in every biological system providing energy, building blocks for cell’s development and adaptation, as well as regulators of a variety of biological processes. It is becoming generally understood and accepted that metabolomics delivers the closest insight into physiological processes, providing valuable data for disease diagnostics, toxicological studies and treatment follow-up or optimization in a variety of applications from clinical to environmental or agricultural sciences. Recent analysis has shown that in the domain of precision medicine metabolomics provides highly complementary information to next generation sequencing, allowing a very good method for distinguishing between genes that are actually causing disease or are benign mutations, therefore helping in disease risk assessment and customization of drug therapy . In addiMachine learning has been defined in many ways, from “a field of study that gives computers the ability to learn without being explicitly programmed”  to the mBox 1Unsupervised learning algorithms identify patterns from data without any user input providing for example data-driven sample separation or feature grouping based only on the data;Supervised learning algorithms use previously labeled data (training set) to learn how to classify new, testing data. This approach determines major features or combinations of features that can provide the highest sample label accuracy in the training set and uses this information for future input labelling.Classification is a process of mapping input data to a discrete output, e.g., sample label. The quality of classification results is determined using accuracy, i.e., sensitivity and specificity;Regression is a process of mapping input data to a continuous output variable, e.g., quantity or the size. The quality of regression prediction is determined using root mean squared error.Analyses of points or bins from the metabolomics measurements such as NMR spectra or MS spectrograms (examples of use of PCA and PLS methods are too many to reference). A recent example of an application of deep learning, as well as SVM, RF and several other machine learning algorithms was presented by Alakwaa et al. ;Assignment of peaks in spectrograms or spectra for metabolite identification ,21;Quantification of metabolite concentrations from high throughput data ,23;Selection of a sets of the most informative attributes (“feature selection”) for given sample groups ,24;The combination of features or points using certain rules  aimed at reducing the number of features for analysis or selection of major variances .Machine learning methods have been used for analyses in all steps of the metabolomics process  includinExamples include unsupervised and supervised  type anaSeveral different applications of machine learning in modeling of cell metabolism will be presented in an attempt to show the variety of possibilities that can result from combining metabolism modelling, omics data and machine learning rather than trying to show all examples of machine learning utilization in metabolism modelling and metabolomics.in silico models provide an avenue for a controlled analysis of targets, biomarkers or cell growth under different conditions. In this way, modeling can help unravel complex diseases while at the same time allowing the determination of possible treatment targets or in silico testing of drug or toxin effects. Integrated analysis of the heterogeneous, high-throughput omics data is necessary in order to determine true factors leading to observed or possible physiological states, to obtain the most sensitive and specific markers or fully understand the effects of treatments, pathogens or toxins. Mathematical and computational models provide platforms for the integration of disperse knowledge and for testing and prediction of behaviours under different environments or following different manipulations. In other words, Any biological process, including metabolic processes, can be represented as a pathway with interconnected nodes, and thus, mathematical models such as graph theory, ordinary differential equations, Petri nets, etc., have an obvious role in the simulation of biological systems. For a structural view, a metabolic network can be perceived as a bipartite graph that consists of two sets of nodes representing metabolites and biochemical processes. Graph visualization of the metabolic network provides a way to gather and combine information from the literature, expert knowledge, public databases, as well as omics results, but does not allow simulation of the processes in the network. In a simulation, the dynamics of chemical processes in the metabolic network can be described at various levels of detail. Models can, on one side, include comprehensive kinetic representation of each included pathway thus allowing accurate prediction of the dynamic behavior of a subset of pathways or, at the other extreme, the genome scale models of the whole network can be subjected to stoichiometric analysis with constrained parameter ranges and the assumption of the steady state while allowing modeling of a complete cell network showing the steady state behavior of cells or even organisms . BetweenDetailed kinetic models have been built from ex vivo enzymatic measurements and Michaelis–Menten or related kinetic laws with the possibility for optimization of a few factors using experimental data and optimization methods. An increasing availability of genomics data and a variety of highly innovative bioinformatics techniques led to an ever-increasing number of biological networks constructed and presented . Genome-Machine learning algorithms have been used to build or optimize kinetic and genome-scale models from example data in order to make data-driven predictions or conclusions ,57,58. FRegardless of the application, the development of predictive models using a machine learning approach is accomplished in several steps: (a) selection of learning attributes; (b) construction of training and test sets; (c) selection of learning algorithms; (d) design of the machine learning approach; (e) evaluation of the predictive performance of models. All of these steps have to be considered and optimized in machine learning development or the application of metabolism models, either kinetic, genome scale or mixed. In the following, we will present examples of machine learning application in kinetic, genome-scale and mixed model development and their applications.Machine learning methods can enhance the development and application of metabolism models with applications broadly divided into model parameter determination, metabolic network analysis and model application. A schematic, highly simplified example of the model parameter determination is outlined in Parameters used in metabolic models, kinetics or flux ranges can be determined using either the in vitro enzymatic assay in the bottom-up approach or can be indirectly inferred from metabolomics time series data in the top-down method. The bottom-up approach for the development of kinetic models requires experimental measurements of the kinetics of each enzyme providing information that is then integrated in the model. This approach is highly experimentally demanding, and although information has been made available for many enzymes leading to a number of kinetic models of some major pathways, the bottom-up approach is unlikely to provide information for all enzymes, particularly when considering possible kinetic changes under different conditions present in vivo . Optimization of kinetic parameters can be done from metabolomics time series data. The majority of methods use optimization algorithms that try to find the best model parameters from available experimental data. Examples of the utilized optimization methods are: genetic algorithms, evolutionary programming, simulated annealing, Newton–Raphson and Levenberg–Marquardt methods (reviewed in ). HoweveSaa and Nielsen ,60 have Decision trees have been used to search for discriminating patterns in the model parameter space in structural kinetic modeling ,61,62, bMycobacterium tuberculosis, and this type of analysis was used to determine targets for inhibitors, as well as suggesting several possible modulators of the production of mycolic acid by this bacterium [Both kinetic and structural kinetic models provide very detailed simulation avenues for the subset of pathways. Metabolic network models, on the other hand, try to describe all reactions that are available to an organism with every node in the network representing an intermediate in a chain of chemical, metabolic reactions. These large networks can be modeled in the constraint-based approach where models have an associated solutions space in which all feasible phenotypic states exist under imposed constraints. Typically in these models, metabolite flow is constrained by network topology, the steady state assumption and by upper and lower bounds for each individual reaction flux. The challenge then in this type of modelling is to determine and impose major constraints in order to define and investigate the solution space in a way that determines physiologically-relevant fluxes or phenotypes ,66,67,68acterium   satisfies the given property. In the presented case, the requirement was that f <0 without knowing the exact functional form of f. With an adequate training dataset, machine learning, in this case, the decision tree learning algorithm, was able to determine allowed ranges for kinetic parameters of metabolic reactions that were consistent with observed physiology. The ORACLE approach combined with machine learning iSCHRUNK analysis of allowed ranges for kinetic parameter provides a very interesting approach for the creation of genome-scale kinetic models that will be increasingly feasible and increasingly accurate as more data become available.Genome-scale stoichiometric models lack information about enzyme kinetics and cannot accurately account for the response of metabolism to changes in enzyme expression or enzyme inhibition. Development of large, genome-scale kinetic models is therefore of great interest; however, it is still hampered by uncertainty in metabolite concentration levels and thermodynamic displacement, as well as uncertainty in the kinetic properties of enzymes ,74. TowaEscherichia coli to create reaction-gene combinations and label the essentiality of each combination based on experimental data. The authors also combined the data from Flux Coupling Analysis (FCA) in order to account for the inherent limitations of metabolic network flux distribution analysis on environmental dependences. FCA was included as one of 64 features describing each reaction-gene combination. SVM-RFE was trained using experimentally-determined gene essentiality information and 64 features determined for each reaction-gene combination providing information about major features and essential genes.Metabolic pathways have to be sufficiently robust to be able to tolerate fluctuations in protein expression levels, as well as changes in environmental conditions. Analysis of metabolic models can thus help in identifying genes that might be essential for cell growth, survival or production. Machine learning models can be trained to predict and classify genes of an organism as essential or non-essential based on a training set of known, labelled essential or non-essential genes. A number of different machine learning methods has been used to try and determine gene essentiality from metabolic network information, including: SVM, ensemble-based learning, probabilistic Bayesian methods, logistic regression and decision tree-based methods (reviewed in ). PlaimaA method, SELDOM (enSemble of Dynamic lOgic-based Models), was recently presented by Henriques et al. , attemptMetabolomics or genomics data can be used to discover unknown metabolic pathways and their regulation, as well as to determine preferred metabolic routes in a particular system with a known metabolic network. In this application, the goal is the construction of correlation and causation networks between metabolites. The correlation network determines the probable relation between metabolites and enzymes using usually statistical analysis of measurements from different biological conditions. The correlation network provides static information about the most likely network. The causation network is usually obtained from time series metabolomics data and provides cause-effect information about the metabolites in the network. Some examples of methods used for causation network determination are dynamic Bayesian models, Granger causality approaches, as well as the BST-loglem approach, which combines mathematical and statistical methods .Machine learning has been used to predict inhibitory effects of substances on the metabolic network. In the examples presented by Tamaddoni-Nezhad et al. ,80, backE. coli metabolism; the second layer represents the abundance of resulting essential proteins; third layer represents the phenotypes of E. coli; the fourth and fifth layers were reconstructed protein and gene layers, respectively. Layers in the presented work were not fully connected. Instead, the authors used biological knowledge to define rational connections, thereby reducing the risk of over-parametrization. Connections between the first, second, as well as fourth and fifth layers were built from gene-protein associations from the genome-scale metabolic model of E. coli; connections between the second and third and the third and fourth layers was determined from COBRA analysis of the genome-scale model of E. coli used to identify proteins that were essential for given phenotypes. A schematic presentation of the deep network created is shown in In order to try and take advantage of the large amount of sequencing data and detailed genome scale models for more accurate phenotype prediction, Guo et al.  have devMachine learning methods have also been utilized for pathway prediction, showing good performance when compared with standard methods presented in the hard-coded pathway prediction tool PathoLogic, while at the same time allowing easier extensibility, tenability and explanation of the results . MachineExperimental analyses measure values, i.e., concentrations, of variables in a model, e.g., metabolite concentrations or gene or protein expressions. In order to define the model, it is necessary to obtain information about the parameters that control the changes in these variables. Solving the system identification problem, i.e., determining parameters from variables, is often very strongly underdetermined with many parameters that need to be resolved from few variables in addition to many possible combinations of parameters, leading to the obtained values for variables. Mechanistic models use expert knowledge to derive information and develop models from targeted experiments. However, the large amount of data makes the application of data-driven, machine learning methods a highly attractive option. In addition, metabolic models can provide a large amount of data, and deriving actionable information from these model results can also greatly benefit from machine learning approaches. Thus, recent developments in machine learning and metabolism modeling compounded by the huge increase in computer power accessible for this task have made it possible to link data-driven and mechanistic models. Machine learning algorithms are able to learn optimal solutions for the analysis of new data from previously determined information. Without a doubt, with the availability of more advanced algorithms and the mounting availability of data, machine learning will be increasingly relevant for the analysis of metabolism. One of the major applications of metabolism models is in metabolic engineering where models can test genetic and regulatory changes towards increased productivity and reduced cost of production. Metabolic network models have an important role in helping determine targets, the side effect and toxicities of either drugs or toxins, through their direct effects or side-effects, as well as test possible toxic effects of chemicals in the environment. Machine learning can be implemented to integrate metabolic networks with available data. A recent example presented by  shows anMetabolism modeling can also be utilized for integrated analysis of different types of omics data, and some examples of the use have been shown in several recent reviews ,88,89, oIn 2006, Kell  said: “B


Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models. Genome-scale models of metabolism enable the exploration of the phenotypic landscape of an organism. Unlike probabilistic approaches such as genome-wide association studies, these models describe the mechanistic link between genotype and phenotype, predicting the response to genetic and environmental perturbations. However, this connection is hampered by the complexity of gene-protein-reaction associations. In this work, we implement a model transformation method that untangles this complexity by allowing gene-wise phenotype predictions using genome-scale models. The transformed model explicitly accounts for the individual flux carried by the enzyme or subunit encoded by each gene. Previously published simulation methods are automatically leveraged by this transformation, enabling new features such as the formulation of objectives and constraints at the gene/protein level. We demonstrate the application of different kinds of analysis and simulation methods, showing in each case how the gene-wise formulation can result in higher prediction accuracy in comparison to experimental data and improve the biological insight that can be obtained from available models. At the rbations , 3. Studrbations , drug tarbations  and pathrbations , and therbations  and syntrbations .Understanding the complex relation between the genotype and phenotype of an organism is a fundamental part of systems biology research. Unlike statistical approaches such as genome-wide association studies (GWAS) , genome-ad-hoc fashion. This is the case in rational strain design, where optimization procedures are used to find optimal interventions to maximize the production of a given compound [a posteriori, without guarantee that the optimality of the predicted phenotype is preserved. Undesired side-effects may arise if any of the target reactions involve promiscuous enzymes.Since most constraint-based methods do not explicitly account for GPR associations, they can only provide analysis at the reaction level. For instance, simulating a steady-state flux distribution predicts the rates of all metabolic reactions for a given phenotypic state, but fails to elucidate the contribution of individual genes/enzymes to that phenotype. GPR associations, typically implemented as Boolean rules, can be used to interpret the results of constraint-based analysis in an compound , 10. Witcompound –13, suchIn this work, we present a model transformation that generates a stoichiometric representation of GPR associations that can be directly integrated into the stoichiometric matrix. We show that the results obtained with the transformed model are consistent with those obtained from reaction-level models, and highlight the advantages of performing different kinds of analysis at the gene level. We also propose new variants of existing methods that take advantage of this representation to formulate gene-wise objective functions and test their predictive ability using experimental datasets.enzyme usage reactions (u), are added to the model. For each gene, this variable accounts for the total amount of flux carried by the respective enzyme (or enzyme subunit). This model transformation can be represented by an extended stoichiometric matrix  stand out as outliers with regard to promiscuity due to their participation in nearly 250 transport reactions.The iAF1260 genome-scale model for se study . A statiThe proposed transformation was applied to the model after pre-processing to remove blocked reactions . The simplified model contains 914 genes, 1532 reactions, and 1032 metabolites . After transformation, the extended stoichiometric matrix contains 3853 (pseudo-)reactions and 1946 (pseudo-)metabolites. The increase in the size of the stoichiometric matrix is caused by introducing the genes as species in the model, decomposing reversible and isozyme-catalyzed reactions, and introducing the artificial “enzyme usage” reactions see . In the enzyme usage variables, which determine the amount of flux carried by each individual enzyme.Simulating steady-state flux distributions using flux balance analysis (FBA) is the most common application of constraint-based models. FBA requires the definition of an objective function based on evolutionary assumptions . The maxin vivo, if such a considerable deviation of flux would occur, it would have been frequently reported. Hence the pFBA prediction seems less plausible .FBA results are usually affected by solution degeneracy, as different flux distributions can have the same objective value. Parsimonious FBA (pFBA) is a two-step variant of FBA that determines the flux distribution that satisfies the optimal objective while also minimizing total absolute flux through all reactions. It is based on the assumption that the cell tries to achieve its goal with the most efficient allocation of resources . AlthougE. coli ME-model [We compared our simulation results with those obtained using the recent ME-model  under thMultiple alternative methods to standard FBA simulations have been proposed for prediction of phenotypic effects of gene deletions in the constraint-based framework –21. SuchE. coli mutants [In order to evaluate the simulation accuracy of the proposed methods we performed a systematic evaluation using a fluxomics dataset for 24 single gene  mutants  , which is only the case under particular circumstances . Also, ilpd gene  and only afterwards affect the rate of 2-oxoglutarate-dehydrogenase (AKGDH). Using reaction-based constraints to simulate the over/under-expression of any of these reactions would predict a linear effect between the regulation level and the respective flux, disregarding the potential redistribution of flux between promiscuous enzymes and isozymes. Considering the large fraction of promiscuous enzymes  see . For eacnditions . In thisThis analysis is more informative then traditional determination of essentiality (binary test) as it reveals the minimal  flux that can be carried by each enzyme. With this approach one can also determine “blocked” genes (i.e. genes encoding enzymes that cannot be used under any conditions), which can be used to guide the model reconstruction process. Furthermore, one can use the shadow price and reduced costs information to analyse the sensitivity of the results with respect to internal  and external  constraints. For instance, the non-zero shadow prices for an essential gene represent the set of precursor metabolites that cause the essentiality, whereas the reduced costs of exchange reactions represent the effect of changing the medium composition with regard to essentiality.Random sampling of the flux solution space is a suitable strategy to analyse all possible physiological states described by a model . Like FVE. coli [Lpd relative to other enzyme subunits  given its simultaneous participation in different enzyme complexes.One limitation of flux sampling is that it does not account for the flux load distribution between isozymes or the overall flux carried by promiscuous enzymes. With the extended representation, it is now possible to analyse flux sampling results at the gene/enzyme level. An illustrative example for a model of core metabolism of E. coli  is presesdh* and overexpression of frd*, ppc, and aceA) are commonly proposed interventions to increase succinate production. It is also possible to observe some extent of agreement between our sampling results and gene expression measurements of succinate producing mutants [aceE, aceF, icd, pykA and pykF.Flux sampling at the gene level can be used to guide rational strain design, since non-overlapping sampling distributions for a given gene between wild-type and the desired mutant indicate that the flux carried by the respective enzyme must necessarily change. We compared these results with those obtained by strain design methods that account for modulation of gene expression –28. Some mutants , most noomics data has fostered the development of constraint-based methods that make use of these data to improve predictions. In a recent work, we evaluated several methods for integration of transcriptomics (and proteomics) data into constraint-based simulations, and observed that none of the methods resulted in consistent improvement of flux predictions compared to simple FBA simulation under the assumption of optimal growth and parsimonious enzyme usage [The continuous improvement of high-throughput techniques to measure different kinds of me usage . This lime usage , 39.enzyme usage variable  . Elementenzyme usage reactions become part of the support vector of EFMs, being automatically computed by any EFM computation algorithm. This extended support vector contains a gene-wise representation of each EFM, denoting the genes that participate in the given pathway.Since EFM computation does not account for GPR associations, they do not entirely reflect the topology of a metabolic network, disregarding that a promiscuous enzyme is a common link between different pathways and that isozymes provide alternative routes within the same pathway. Our stoichiometric representation of GPRs solves this problem by explicitly accounting for this complexity in the computation of EFMs. This concept is illustrated in E. coli . The deletion of this gene would cause the least impact in the network. We also compared the frequency of each reaction in the original model and the transformed model , that are guaranteed to couple product formation to growth . This meGiven that any gene encoding a subunit of an enzyme complex can be deleted to disable the respective function, the number of potential designs significantly increases when converting reaction to gene deletions . This maIn order to test the feasibility of each design, accounting for possible side-effects, we calculated the actual set of reactions effectively disabled by the gene deletions required to implement a given cMCS. We then evaluated each phenotype and observed that only a small fraction of the original set of solutions (∼7%) are valid with respect to the original production constraints . This drThe shortcomings of reaction-based design can be avoided by directly searching for gene-based designs. We applied MCSEnumerator to the transformed model and computed all minimal gene-based cut sets up to 8 deletions . It can E. coli and the same production goals as before, thousands of designs were found with as few as 3 gene manipulations  and methods for integration of transcriptomics data , where the objective function and model constraints are reformulated in a gene-based representation. Systematic evaluation of these methods by comparison with experimental flux data revealed that the new methods have higher average prediction accuracy than their original versions. This shows that not only it is possible to reformulate the evolutionary assumptions that support these methods in a more biologically meaningful way, but doing so can actually improve flux predictions.enzyme usage variables. This representation is more accurate than applying these constraints at reaction level. For instance, deleting a single isozyme has no effect in a reaction-based representation, whereas the gene-based representation accounts for the redistribution of flux among other isozymes of the affected reaction. We also demonstrated the non-trivial consequences of performing over/under-expression of promiscuous enzymes. Forcing an increased or decreased activity in a promiscuous enzyme results in an uneven redistribution of flux among the respective reactions in order to reach a new optimal phenotype. This effect would not be predictable by current methods.Predicting the effect of gene-level modifications with our extended model representation can be directly performed by constraining the respective The integration of GPR associations reveals a distinct topology of the metabolic network. It accounts for the fact that promiscuous enzymes provide a common link between different pathways, and that isozymes provide alternative routes within a pathway. We demonstrated how this affects the characterization of the network in terms of its elementary flux modes. The EFMs computed for the transformed model have an extended support vector that accounts for the genes that must be active to support the respective pathways. This enables different types of elementary mode analysis  to be performed at the gene level. One disadvantage of the extended representation is the combinatorial explosion of EFMs caused by the decomposition of isozyme-catalyzed reactions. Nonetheless, gene-wise EFM computation is still amenable to the application of EFM-based methods that do not require complete enumeration of the full EFM set –51.post hoc analysis of proposed designs. We used MCSEnumerator to determine all minimal reaction cut sets (up to size 7) for ethanol production in E. coli. We tested the feasibility of these cut sets when GPR associations are considered, and observed that only less than 10% of the solutions are actually feasible. Since many of these reaction-based solutions would also be determined by other strain design methods, this problem affects such methods as well. Another advantage of computing gene-based designs is to effectively minimize the number of genetic interventions, which is the desired scenario for in vivo construction of the mutant strain. We observed that determining minimal reaction deletions can result in a large number of gene deletions. On the other hand, gene-based design effectively minimizes the number of gene deletions regardless of the number of affected reactions. In terms of computation costs, one would expect the increased model size to be detrimental for efficiency. However, the complexity of MILP problems (used in MCSEnumerator and many other strain design methods) is related to the number of decision variables (deletion targets). Since the number of genes is lower than the number of reactions, we were able to compute gene-based cMCS up to size 8 with the same computational resources used to compute reaction-based cMCSs up to size 7.Rational strain design is likely the application where our proposed transformation will be most useful. The large number of strain design methods published so far (∼50) can be automatically used to search for gene-based manipulations without any modification to the underlying method by first applying our transformation to the model. This provides several significant advantages compared with reaction-based design. First, reaction-based design ignores the side-effects of enzyme promiscuity and requires careful It is important to note that other strategies to integrate GPR associations in constraint-based simulations have been proposed. All methods that integrate transcriptional regulatory networks and metabolic networks need to account for GPRs, as they provide the connection layer between the two types of network. In SR-FBA, genes are encoded as integer variables and the GPR associations are encoded as linear inequalities, transforming the model into an MILP formulation . This apE. coli [etc), GPR associations can only rely on the quality of gene annotations. With our approach, incorrect assignment of GPR associations will be reflected in the extended stoichiometric matrix and may generate misleading results. On the positive side, our proposed approach may also facilitate the development of new methods for curation of GPRs (e.g.: gene-wise gap-filling). Another potential disadvantage of performing gene-level analysis is the increased computational cost of working with an extended stoichiometric matrix. Although the effect is barely noticeable for single simulations (∼ 1.5-fold increase in simulation time) it may be undesirable for more computationally intensive types of analysis such as random flux sampling or flux variability analysis.Finally, it is important to note the potential pitfalls of using a gene-based analysis. In this work, we used a highly curated genome-scale model of E. coli . NowadayE. coli –58. Whilunderground metabolism of E. coli revealed an even larger number of metabolic reactions available in the cell as a result of enzymatic side activities, playing an important role in the fitness landscape of the organism [The integration of GPRs directly into the stoichiometric matrix enables bidirectional analysis between the gene and reaction levels. In one direction it is possible to observe the impact of gene perturbations on reaction fluxes. In the other direction one can perturb the environmental conditions and observe the required adaptations at the gene level. The complexity of GPR associations and their evolution has been recently analysed considering the role of environmental adaptation in driving enzyme specificity . A recenorganism .Our framework provides a mechanistic link between genotype and phenotype and should facilitate the development of new methods to integrate multi-omics datasets into genome-scale models, as well as methods to integrate metabolism with other biological processes. In this work, we explored the reformulation of previously published simulation methods with gene-wise constraints. It would be interesting to explore other suitable applications, such as the formulation of simulation methods that account for enzyme production costs , 62.A new generation of genome-scale models and simulation methods is on the rise . This inhttps://github.com/cdanielmachado/GPRTransform.All the source code, models, and generated data are publicly available in the following repository: E. coli [Unless otherwise stated, all simulations were performed using the iAF1260 genome-scale metabolic model for E. coli  and the E. coli  was usedE. coli .S be the original stoichiometric matrix, v the steady-state flux vector , and ub the respective vector of flux bounds, such that S ⋅ v = 0 and 0 ≤ v ≤ ub. The extended stoichiometric matrix S′, flux vector v′, and flux bounds ub′ are defined as:Sgpr is the stoichiometric representation of GPRs , Ik is the identity matrix for k genes, and u is the enzyme usage vector. This transformed model can be readily used by any kind of constraint-based method with the general form:f is a given objective function.The model transformation to explicitly include GPRs in the stoichiometric matrix .The methods to predict mutant phenotypes  method of the COBRA toolbox . The wilEFMs for the simplified glycolysis model were computed using CellNetAnalyzer version 2015.1 .enzyme usage reactions. Computations were performed with CPLEX 12.6.3 using a 6-core Intel i7 processor with 64 GB of RAM.MCSEnumerator  was testE. coli core model [cRegMCS  was testre model  was usedS1 Figenzyme usage variables, calculated for a minimum biomass production of 10% of the maximum theoretical yield on glucose minimal media.Gene essentiality determined by the flux variability analysis of the (TIFF)Click here for additional data file.S2 FigE. coli. For each gene, the curves represent the probability distribution of the flux carried by the respective enzyme. Two scenarios are considered: wild-type phenotype near optimal growth (blue curves) and succinate overproduction near optimal yield (green curves). Genes where the blue and green distributions do not overlap are targets for modulation of gene expression.Flux sampling results for the core metabolism of (TIFF)Click here for additional data file.S3 FigE. coli under anaerobic conditions : a) size of the solution pool for each cut size; b) total number of interventions of each type in the solution pool.Gene-based strain design using cRegMCS for growth-coupled ethanol production in (TIFF)Click here for additional data file.S4 FigComparison between the protein translation rates predicted by the ME-model and the respective enzyme usage predicted with gene-pFBA for a wild-type strain growing under aerobic conditions on glucose minimal medium with a glucose uptake rate of 10 mmol/gDW/h.(TIFF)Click here for additional data file.


The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by  In recent years, the field of microbial metabolomics has expanded dramatically. This has been mainly due to improvements in analytical platforms, such as mass spectrometry (MS) and nuclear magnetic resonance (NMR), and a more widespread accessibility to these technologies in the research community. While firstly developed for the analysis of plants , metabolUnlike plant tissue, microbial metabolomics faces two major problems. Firstly, the cells often need to be separated from a complex culture broth prior to analysis. This is a major challenge since absolute metabolite amounts inside the cells are often multiple orders of magnitude smaller than the respective amounts outside the cells. This means that the smallest carryover from the separation process may bias the measured intracellular concentrations. The second problem is the rapid growth of microbes and their optimization for fast turnover of substrates. This means that metabolism needs to be efficiently stopped (quenched) in a matter of seconds ,4. It haThe most common procedure for quenching involves rapid cooling to temperatures below −20 °C which, for many different bacteria, leads to leakage of the cytoplasm into the extracellular solution ,6. This Overcoming the hurdles of sampling mentioned above enables further advancement of metabolomics to a more quantitative approach. Given the original definition of metabolomics as a non-targeted analysis of all metabolites , the terMicrobial systems biotechnology is systems biology with an applied focus . Rather 13C fluxomics is performed, labelling enrichment data for metabolites is acquired using MS or NMR. This review will describe recent developments for the quantitative analysis of metabolites in microbial experiments and will then explain how a thermodynamic approach can be implemented and used to constrain the solution space for flux analysis. Finally, metabolic flux analysis principles, especially 13C fluxomics, will be introduced.While thermodynamics relies on accurate measurements of intra- and extracellular metabolite concentrations, fluxomics relies on the quantitative measurements of only a few substrates being taken up and products being secreted by the cells, as well as growth and biomass composition. When Several analytical platforms are available for the quantitative analysis of metabolites from microbial origins. Here, some of the most commonly used platforms will be presented.HPLC remains a workhorse technology for the quantification of metabolites, with a broad range of available columns and detectors enabling the targeted analysis of many compound classes. Its advantage over other platforms is the robustness of the methods and the availability of many detectors which are compatible with buffers or salt gradients which are unsuitable for mass spectrometry-based applications. However, sensitivity and selectivity can be an issue with HPLC. These depend on the chromatography applied, the detection method and the sample matrix. Derivatized amino acids, for example, can be detected at very low fluorescence levels (picomoles injected) with good selectivity. Another problem is that separate instrument runs are usually required for each compound class and, since identification relies on the retention times of external standards rather than mass fragmentation patterns or chemical shifts, this bears the risk of false positives. Some examples of commonly used methods are amino acid analysis , organicGC-MS in metabolomics is mainly applied as a non-targeted semi-quantitative profiling tool, but it is a powerful tool for the quantification of volatile compounds. Quantification of non-volatile, polar compounds (after derivatization) is more complicated, since the derivatization chemistry is very matrix-dependent and the concentration of different compounds influences the derivatization efficiency of others. Each compound usually has its own reaction kinetics with the derivatization reagent , making 13C fluxomics, however, GC-MS is currently the instrument of choice, delivering highly reproducible mass isotopomer distributions with the relative error of technical repeat injections often under 0.5%. This is possible because derivatization of the target compounds  or multiple (MRM) reaction monitoring mode on a triple quadrupole instrument. These modes select a target parent ion in quadrupole 1, fragment it in the collision cell (quadrupole 2), and then measure the abundance of the daughter ion selected in quadrupole 3. The idea is that the parent-daughter relationship is very specific, enabling the filtering of a small peak among a vast pool of compounds and achieving high sensitivity when quantifying in the presence of a very small background. For example, LC-ESI-MS/MS in combination with ion-pairing chromatography was used to quantify metabolites of the central carbon metabolism .The major problem with LC-ESI-MS/MS is ion suppression or enhancement during ionization . This meThe second problem with LC-ESI-MS/MS is its inherently low dynamic range. In order to get quantitative data for a microbial extract it is not uncommon to run each sample in a whole range of dilutions, especially when trying to analyze a broad range of metabolites that usually cover multiple orders of magnitude in concentration.Another problem, that affects all analytical platforms, can be the availability of metabolite standards. For many intracellular metabolites, calibration standards may be not available off the shelf and need to be custom synthesized, adding significantly to the analysis cost, while other metabolites might not be available at all.13C labelled internal standards to overcome the ion suppression/enhancement effects caused by the sample matrix k the concentration of reactant I at the power of its stoichiometric coefficient k. ΔrG determines the directionality of a reaction, i.e., the net flux of the reaction occurs in the direction where ΔrG is negative.The criterion of spontaneity is difficult to grasp because the entropy of the universe cannot be measured. For biological systems, where constant temperature and pressure apply, spontaneity is defined by the Gibbs free energy (ΔG ≤ 0). For a generic reaction (Equation (1)), the Gibbs free energy of the reaction  and the standard Gibbs energy of reaction (ΔrG0); which finally depends on the standard Gibbs energy of formation of the metabolites (ΔfG0) and the stoichiometric coefficients  (see Equation (3)). Thus, the directionality of reactions depends on the metabolites’ concentrations and their thermodynamic properties. The ‘'’ notation indicates physiological conditions are assumed rather than the standard biological conditions  , differing from the experimentally-observed value of −915.9 [kJ/mol] by 2%  instead of standard conditions (ΔrG0) ), and are likely to be genetically or allosterically regulated  glucose and 99% [U-13C] glucose was used as the labelled substrate together with naturally-distributed glutamate. Cell hydrolysis and labelling analysis are described elsewhere . 13C fluxomics independently confirmed that the net flux of this reaction was in the direction the metabolite data and the thermodynamics predicted (d-ribose-5-phosphate ketol-isomerase and triose-P-isomerase. In these cases the ΔrG was between −1.3 and −0.2 (kJ/mol) and 0.22–28.32 [kJ/mol], respectively (13C fluxomics. This highlights the fact that a ΔrG range close to equilibrium (ΔrG = 0) meaning that the reaction is likely to be reversible. In fact, the 13C fluxomics also showed a high reversibility for these reactions. There was no measurement of di-hydroxy acetone phosphate (DHAP) available, which may help to fix the directionality of the triose-P-isomerase reaction, underlining the importance of complete metabolite datasets.Subsequently, metabolite data of the  pathway , namely redicted . Two othectively . The pre13C fluxomics to describe the metabolic phenotype. Moreover, when opposing information on reactions directionality was found between the methods, the reactions are close to equilibrium and should be described as reversible. If it is not possible to estimate metabolic fluxes with 13C fluxomics due to the impossibility to reach metabolic pseudo-steady state or the feeding with labeling substrate is too expensive, thermodynamic-based network analysis is presented as a powerful tool to determine a reactions’ directionality, information that can be used with conventional MFA to determine a reduced solution space for the metabolism of the studied organism.In summary, this example shows how the different datasets are complementary to one another and can be used to gain deeper insights into metabolism and higher confidence in the results. Thermodynamics is useful to check the quality of the metabolomics data and 13C-labelled metabolites are required the availability of standards become an issue due to the high price of these labelled metabolites. An approach recently applied to overcome this issue is the use of labelled internal standards generated by culturing microbes with labelled carbon substrates. However, the generated labelled metabolites will depend on the used organism and the reproducibility is highly dependent on the standardization of the culture conditions. Therefore, there are still challenges that need to be overcome in order to increase metabolomics data quality.Despite a great increase in sensitivity, selectivity, and coverage of the platforms to measure metabolomics, it is still not possible to generate a full organism’s quantitative metabolomics data. Furthermore, when The introduction of the second law of thermodynamics into a metabolic network will determine directionality of initially-reversible reactions, constraining the solution space of metabolic networks using experimental data and thermodynamics principles. With the increase on coverage an accuracy of Gibbs energy determination is now possible to include thermodynamics constraints into metabolic flux determinations. The determination of compartment/organelle specific metabolite concentrations is in its infancy, presenting an extra challenge when working with compartmentalized models, such us yeast and humans. Despite NET analysis being able to estimate compartment-specific ranges of metabolites concentrations from average cell metabolites concentrations, the higher coverage of compartment specific metabolomic data will increase the power of network thermodynamics approaches.13C fluxomics and network thermodynamics analysis. It was shown that both approaches complement each other. However, some discrepancies were found which are mainly explained by the need of; (1) more accurate Gibbs energy or known errors of the Gibbs energy values; and (2) higher coverage of endo-metabolomics data. Moreover, other omics data, such as proteomics and transcriptomics, could be used in combination with the previous technologies to provide a deeper biological understanding of the organism metabolic phenotype and regulation.Here an example was presented on how quantitative metabolomics data: endo- and extra-metabolites’ concentrations and enrichment level of isotopically-labelled metabolites are used to determine an organism metabolic phenotype. Two different approaches were employed:


Actinobacillus succinogenes, which naturally produces succinate by a mixed-acid fermentative pathway. To engineer A. succinogenes to improve succinate yields during mixed acid fermentation, it is important to have a detailed understanding of the metabolic flux distribution in A. succinogenes when grown in suitable media. To this end, we have developed a detailed stoichiometric model of the A. succinogenes central metabolism that includes the biosynthetic pathways for the main components of biomass—namely glycogen, amino acids, DNA, RNA, lipids and UDP-N-Acetyl-α-D-glucosamine. We have validated our model by comparing model predictions generated via flux balance analysis with experimental results on mixed acid fermentation. Moreover, we have used the model to predict single and double reaction knockouts to maximize succinate production while maintaining growth viability. According to our model, succinate production can be maximized by knocking out either of the reactions catalyzed by the PTA (phosphate acetyltransferase) and ACK (acetyl kinase) enzymes, whereas the double knockouts of PEPCK (phosphoenolpyruvate carboxykinase) and PTA or PEPCK and ACK enzymes are the most effective in increasing succinate production.Succinate is a precursor of multiple commodity chemicals and bio-based succinate production is an active area of industrial bioengineering research. One of the most important microbial strains for bio-based production of succinate is the capnophilic gram-negative bacterium  The flux through this pseudo-reaction constitutes the biomass objective function that is optimized for most of the constraint-based analyses discussed in the Results and Discussion section.13C labeling experiments to investigate the fermentative metabolism in A. succinogenes grown in a chemically defined minimal medium AM3 with the medium NaHCO3 concentration varying from 5 to 150 mM. However, the fluxes for the cellular uptake of NaHCO3 and L-glutamate and the non-growth associated ATP requirement (NGAM) for A. succinogenes have not been estimated as part of the above-mentioned studies. We assumed a Michaelis-Menten relation between the medium bicarbonate concentration and the bicarbonate uptake flux into the cell. We thereby estimated the values of the maximal rate (Vmax) and the Michaelis-Menten constant (KM) for bicarbonate uptake, and the L-glutamate and non-growth associated ATP requirement for A. succinogenes in the AM3 medium by fitting model predictions to experimentally determined ratios of fermentation product secretion to glucose uptake fluxes. More precisely, this fitting is achieved by solving the following optimization problem: minimizing the objective function F, given byi ∈ . In the above expression, i consistent with a fraction f of optimal growth, ei and eglc represent the experimentally estimated secretion flux of i and glucose uptake respectively. The flux values A. succinogenes and the objective function F is minimized with respect to these model parameters. For the fitting the glucose uptake into the cell was restricted between 5.9 and 6.2 mmol gDCW-1 hr-1, where the limits are obtained by averaging the lower and upper bounds of the experimentally estimated values of the glucose uptake flux. The value of the fraction f of the optimal growth that we have used to determine the flux ranges for the fitting is 96.5% since the flux ranges for fermentation product secretion corresponding to 96.5% of the maximum growth are the narrowest ones that completely contain the experimentally determined values of the fermentation product secretion fluxes. The values of the model parameters for A. succinogenes fermentative metabolism in AM3 medium are tabulated in Table C in McKinlay et al. have undertaken A. succinogenes.The comparison of the experimental results and the model predictions corresponding to the best fit of the model parameters are presented in 3 concentrations in the medium. Increasing the NaHCO3 concentration in the medium, and hence the CO2 uptake rate, causes the reverse flux through the reaction catalyzed by malic enzyme to preserve mass balance. As a result, the net flux through the malic enzyme reaction decreases. On the basis of their experimental results, McKinlay et al. postulated that decreasing malate decarboxylating flux and constant flux through the PEPCK reaction with increasing medium bicarbonate concentration resulted in increased flux through the C4 pathway and hence increased succinate production [Detailed experimental investigation by McKinlay et al.  indicateA. succinogenes fermentation pathway with increasing medium bicarbonate concentration. Since bicarbonate uptake follows Michaelis-Menten kinetics, increasing medium bicarbonate concentration translates to increasing bicarbonate uptake flux. The model predictions for the reactions PEPCK (phosphoenolpyruvate carboxykinase), MDH , ME , FUM (Fumarase), FR (fumarate reductase), THD2 (transhydrogenase) and ALCD , shown schematically in We have used parsimonious flux balance analysis (pFBA)  and fluxThe gradual decrease of the reaction flux in the forward direction of ME with increasing bicarbonate uptake can be interpreted as increasing flux in the reverse direction of the ME see . This chA. succinogenes in minimal medium AM3 and a rich medium known as Medium A [3 concentration in both AM3 and Medium A was taken to be 150 mM. In medium A, the vitamins, minerals, amino acids, NaCl, and NH4Cl in AM3 are substituted by 5g/liter of yeast extract. We found a detailed account of the molecular composition of yeast extract from Smith et al. [In addition to experiments in the AM3 medium, McKinlay also undertook an experimental investigation to compare the growth trends and fermentation balances of h et al. .A. succinogenes grown in the rich Medium A. The predictions are made for a glucose uptake rate of 8.65 mmol gDCW-1 hr-1 and a medium carbonate concentration of 150 mM in agreement with the experimentally derived fluxes for the rich medium A.Assuming that the parameters in Table C in In We also assessed the effect of deletion of each gene and each reaction in the models for both the minimal (AM3) and rich (medium A) media, for a medium bicarbonate concentration of 150 mM. Both FBA and minimization of metabolic adjustment (MOMA) methods were used for these simulations but the results were identical for both the methods as evident from the Both the numbers of lethal gene and reaction deletions were lower in the rich medium compared to the minimal media. The rich medium (Medium A) contains yeast extract, so it already contains most of the amino acids . Hence, A. succinogenes in the AM3 medium [A. succinogenes on various precursors of L-glutamate.McKinlay et al. have experimentally examined whether glutamate precursors support growth of A. succinogenes at different NaHCO3 and H2 concentrations using 13C metabolic flux analysis. For this investigation, they used a variant of the AM3 medium in which the Cys–HCl, Met, and monosodium glutamate concentrations were changed relative to the minimal AM3 medium [3 in this growth medium, 25 mM and 100 mM, both in the presence and absence of H2. For medium NaHCO3 concentrations of 25 and 100 mM, the H2 oxidation rate were measured as 2.8 mmol gDCW-1 hr-1 and 4.7mmol gDCW-1 hr-1 respectively. The experimentally estimated glucose uptake rate varied between 9 and 10 mmol gDCW-1 hr-1 for the above-mentioned medium. In our model, we have assumed that the H2 input fluxes equal the H2 oxidation rates in McKinlay et al. [2 concentration. Since the energy and redox cofactor yield of different fermentation product pathways are similar, the ranges of predicted product fluxes are large in comparison with the experimental error. These ranges therefore represent all stoichiometrically feasible growth modes capable of yielding a similar biomass growth rate.McKinlay et al.  also exp3 medium . McKinlaA. succinogenes in the presence of H2 results in higher succinate excretion and this effect is accentuated at higher bicarbonate uptake fluxes, which agrees qualitatively with the experimentally derived results [2 is available as an electron source, additional reducing equivalents are available so that the flux through the FR reaction increases, resulting in higher succinate formation. It is evident from 2. Not only that, in accordance with the experimental results [2 is more pronounced at the higher bicarbonate concentration than that at the lower bicarbonate concentration. It should be noted that though our model consistently overestimates the ethanol formation fluxes at various medium bicarbonate concentrations and with/without hydrogen uptake, it qualitatively reproduces the trend for ethanol formation as a function of these media conditions. Experimental papers on A. succinogenes metabolism [A. succinogenes in KEGG and MetaCyc seem to indicate a degree of reversibility of the reaction from acetaldehyde to ethanol in A. succinogenes in MetaCyc, ethanol can be degraded to acetate via acetaldehyde. The presence of such a degradation pathway in vivo, which we do not consider in silico, might be the cause behind the overestimation of the ethanol production flux by our model.As shown in  results . McKinla results  have sug results , our modtabolism , 4, 6, 8-1 and the non-growth associated ATP maintenance flux to be at 1.082 mmol gDCW-1 hr-1. A subset of model reactions was selected for knocking out that excluded the ATP synthase, NGAM, and the uptake and excretion reactions.We used the OptKnock algorithm to predict both single and double reaction knockouts for maximizing succinate production in the minimal AM3 medium while restraining the growth to be at least 0.1 gDCW hrin silico double mutants, instead of running the FVA for all possible pairs of reactions from the set selected for knockouts, we created a set of reactions corresponding to the top FVA predictions for single reaction deletions. We then chose all possible reactions pairs from this latter set and ran the FVA after in silico deletion of each possible reaction pair.Depending on whether we predicted single or double reaction knockouts, we compared the OptKnock results with results from flux variability analysis, after knocking out one or a pair of reactions from the above-mentioned subset. However, in the case of -1 hr-1. However, the L-glutamate flux for knockout strains might vary over a range and we chose the range of L-glutamate fluxes from the robustness analysis of A. succinogenes growth with respect to L-glutamate uptake flux. Hence, the extended range of L-glutamate uptake fluxes that we considered based on robustness analyses was 0–0.5 mmol gDCW-1 hr-1. Similarly, the range of bicarbonate fluxes chosen for the reaction knockout study was chosen as 0–5.0 mmol gDCW-1 hr-1. The single knockouts that are most effective in maximizing succinate production involve knocking out the reactions catalyzed by the PTA (Phosphate acetyltransferase) and ACK (Acetyl kinase) enzymes. The double knockouts that are most effective in increasing succinate production are the  and  knockouts.For the minimal AM3 medium, our estimate for the L-glutamate flux was about 0.439 mmol gDCWKnocking out the reaction catalyzed by PTA or ACK reduces the carbon flux from pyruvate to acetyl-CoA since the conversion of acetyl-CoA to acetate is favored over the conversion of acetyl-CoA to ethanol, as the latter route requires more reducing equivalents . For thein silico single reaction knockouts. Additional NADPH required for the increased flux in the reverse direction of the malic enzyme reaction is made available by splitting the flux from β-glucose-6-phosphate to both the oxidative branch of the pentose phosphate pathway and to β-glucose-6-phosphate via the PGI reaction  and , the reaction flux in the PEPCK reaction from phosphoenolpyruvate to oxaloacetate in the wild type and single knockout mutants is diverted to the PK (pyruvate kinase) reaction . There ireaction , with a reaction . Insteadin silico WT strain, whereas for the double knockouts  the predicted growth flux is 74.9% of the predicted growth flux for the in silico WT strain. This observation is indicative of the robustness of our model system.It should be noted that both the single and double reaction knockouts cause increase in succinate flux without stalling the biomass production or growth flux. Thus, for the single ACK or PTA knockouts, the model predicted growth flux is reduced to about 84.5% of the predicted growth flux for the A. succinogenes. This model contains most of the pathways relevant to the biosynthesis of the main components of biomass, namely glycogen, proteins, lipids, DNA and RNA and UDP-GlcNAc. We have validated this model by comparing the model predictions with results and postulations from published reports on experimental investigation of the mixed acid fermentation in A. succinogenes. We then use variants of this model corresponding to different growth media to make predictions about single and double reaction knockouts to optimize succinate production.We have developed a detailed metabolic model for the gram-negative succinogenic bacterium, On the modeling and computational front, future work using this model would involve extending the model to the genome scale and examine if the model extension causes model predictions to vary. In a different direction, it would be worthwhile to work towards experimentally executing the knockouts predicted by the model. In an ideal situation, these two directions would go hand in hand and form different steps of a combined and iterative computational and experimental workflow where the model helps to predict knockouts and the experimental validation or invalidation of the predicted knockouts helps to refine or extend the model.S1 File(DOCX)Click here for additional data file.S1 Model(XML)Click here for additional data file.S2 Model(XML)Click here for additional data file.S1 Data Set(XLSX)Click here for additional data file.S2 Data Set(XLSX)Click here for additional data file.