Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: download . The biological revolution unleashed by the use of high–throughput technologies provides a detailed picture of the protein interaction map of diverse organisms. The investigation of complex protein interaction maps requires the development of methodologies to understand the complex associations, localization and binding partners among thousands of proteins For more than a century, the traditional experimental practice to investigate reaction mechanisms and estimate reaction kinetic parameters has entailed measuring the reactants and products as a function of time Method to Infer Kinetics And Network Architecture) is a computational method to infer reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. It identifies the sequence of elementary reaction steps of a biochemical pathway using a global nonlinear modeling technique. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. MIKANA is described in Srividhya et al. MIKANA  for MIKANA. Users can employ the GUI to input, validate, process and interpret the results of the reconstruction of a mechanism. We also added a new feature to MIKANA, which permits the exclusion of physically or biochemically unrealistic reactions from the inferred mechanism. This addition improves the performance of the method. We illustrate the GUI with three practical examples, described in the results section. Importantly, this article provides a detailed description of the methods and source code of MIKANA, so that any researcher can reproduce the experiments and/or continue the development of the software.In MIKANA, the task of identifying biochemical pathways from time course data consists of, firstly, identifying the connectivity of the pathway (the reaction diagram relating reactants and products) and, secondly, determining and parameterizing the reaction mechanisms involved in each step of the pathway. This requires a good deal of chemical knowledge about plausible interconversions for the species in the pathway. We managed to incorporate this chemical knowledge by reconstructing reaction mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The identification is made by selecting reactions from a dictionary of functions, which is built by assuming mass action kinetics in all chemically plausible steps between the chemical species measured in the whole reaction system. For a description of MIKANA, we invite the user to read The MIKANA's GUI is composed of three separate panels: (i) the INPUT panel (at the top); (ii) the OUTPUT panel (at the bottom left); and (iii) the MAIN panel (bottom right) . The InpThe input time course data file should contain a header or first line with the chemical species names or symbols. The interface accepts as many symbols as the number of chemical species. The following lines should only contain numeric data or an invalid insertion will be reported back to the user. In the numeric data, each line should contain one time column plus n chemical species columns. The time column should contain the time at which the following chemical species values associate, as in After successfully reading a time course data file, an option to plot and visualize the data over time is made available through the button Plot Data. The other four Input subpanels are made available as options to select an information criterion , a reaction order (First order or Second order), interaction molecularity (uni– or bi–molecular) and a set of reactions to exclude from the final reaction set solution. These latter reactions are read from a file and follow the grammar described previously. We generate six different types of error messages based on different types of errors that may arise upon insertion of a reactions set file:Reactions file not found or permission deniedNumber of molecules not allowed in reactionSpecies identifier greater than number of species availableReaction symbol Character not allowedEmpty fileTo process the data and infer the metabolic pathway, the user will need to click the button Process (the first button in the Main panel). The Output panel (bottom left) of the GUI is composed of three subpanels. At the left, the Used reactions subpanel shows all elementary reactions considered in the model selection process. The Predicted reactions subpanel shows the final selection of reactions that constitute the inferred metabolic pathway. The Differential equations subpanel shows a set of differential equations that correspond to the final mechanism. We provide three different views of the results in each of these subpanels. The first view displays all chemical species that participate in the reactions. The second view excludes from the reactions species with zero stoichiometry. The third and last view display the chemical species by their name or symbol, as introduced in the input time course data file.The Main panel (bottom right) of the GUI is composed of eight buttons . The firLactococcus lactis. This pathway was also analyzed using MIKANA in a previous paper To illustrate the GUI for MIKANA, we reconstructed the reaction mechanisms of three biochemical pathways. The first case deals with one of the simplest pathways in biochemistry: the single-enzyme, single-substrate catalyzed reaction following a Michaelis-Menten mechanism. In this example, we used ‘in silico’ time course data, which was analyzed using MIKANA in a previous paper As a practical example, we considered the single-enzyme, single-substrate reaction, known as the Michaelis–Menten (MM) mechanism of enzyme action and solved it numerically in MATLAB with the stiff solver ‘ode23s.m’.Bacillus cereusWe selected the kinetic parameters from reported literature in the hydrolysis of Dnp-ADCA  deacetoxycephalosporanic acid) by For the MM system, the header line contains 4 symbols and the numeric data consists of 30 lines and five columns. Along with the MIKANA package, we provide an example input file for the MM system. To read it, the user should click the button Read File in the Time Course Data subpanel (upper left side of the main screen). Upon selection, the data will be validated by the GUI, which will promptly notify the user of any problem. If the data in the file is properly structured, the contents will be displayed in the Time Course Data subpanel text window. After reading the file, a plot of the temporal evolution of the species is made available by clicking Plot Data in the Time Course Data subpanel.a priori knowledge about the MM system, we set the input options to allow second order reactions and a maximum of one molecule for each species in every reaction (uni–molecular reactions). This is done using the Reaction Order and Molecularity subpanels of the Input panel, respectively. For illustrative purposes, we also chose to reconstruct the mechanism using two information criteria: Empirical and Akaike. The user can select one of three information criteria in the Information Criteria subpanel. Additionally, the user can choose a set of reactions to be excluded from the solution, using the Non-Possible Reactions subpanel of the Input panel. While we chose not to remove any reactions in this example, this feature will be used in the next example. We also fully describe the exclusion of unrealistic biochemical reactions in the Design and Implementation subsection (Methods).Using some By clicking the button Process in the Main panel, the GUI invokes MIKANA to predict a set of biochemical reactions for the designated input. If the computation is successful, the results are written in the text windows of the Output panel. To satisfy different users, the GUI provides different views of the same results. These views are accessed through the buttons Change View in the Used Reactions, Predicted Reactions, and Differential Equations subpanels of the Ouput panel.When the Empirical information is selected, our method predicts 2 out of 44 elementary reactions. Together with the estimated kinetic rate constants, the two predicted reactions are governed by the following differential equations:Here, On the other hand, MIKANA can reconstruct correctly the MM reaction mechanism and estimate the kinetic parameters with the Akaike information criterion. The predicted differential equations are:Here, MIKANA can produce output time course data by solving the differential equations of the reconstructed mechanism. In this example, we considered second order reactions and a maximum of one molecule for each species in every reaction (uni–molecular interactions). However, the user can choose a different set of options for the mechanism reconstruction.Phosphocreatine (PCr) has been found to play an important role in the contraction of skeletal muscle cells In an experiment by Scopes  Given that the experimental data does not contain a detailed measurement of all interacting species (enzymes and intermediates), we chose to proceed with the mechanism reconstruction assuming that the reactions follow a pseudo-first order kinetics. This will permit us to reconstruct the interaction map between the three chemical species. We chose to use a maximum of two molecules for each species in every possible reaction and an Empirical information criterion. We further tell MIKANA to exclude reactions having either PCr as a reactant and lactate as a product, or lactate as a reactant and PCr as a product. For the purpose, we use the following rules: The reconstruction leads to 7 elementary reactions. These reactions, together with the estimated kinetic rate constants, are described by the following differential equations:Even in the absence of a large number of unknown intermediates, MIKANA is capable of reconstructing a mechanism that agrees with the known relations between Pi, PCr and lactate. MIKANA predicts that Pi (X1) decreases independently of the concentrations of PCr (X2) and lactate (X3). Furthermore, MIKANA predicts that Pcr (X2) and Lactate (X3) growth inversely correlates with Pi (X1). When the concentration of Pi (X1) is high, Pi (X1) diminishes quickly while PCr (X2) and Lactate (X3) increase quickly. As Pi (X1) is depleted, both PCr (X2) and Lactate (X3) evolve to a steady state. Notice that PCr (X2) does not depend on lactate (X3) and vice-versa. They both only relate to the phosphate Pi (X1).Lactococcus lactis glycolytic pathway has been explored experimentally in great detail The  In this example, we used data from 13C (Nuclear Magnetic Resonance) NMR experiments Lactococcus lactis glycolytic pathway. There are some small discrepancies. For example, MIKANA predicts that G6P (X2) is produced independently by glucose (X1) and pyruvate (X6). However, in reality, glucose (X1) and PEP (X5) are involved in the production of G6P (X2). MIKANA also predicts that FBP (X3) is produced by glucose (X1) and G6P (X2). Experimentally, glucose (X1) is not known to participate in FBP production. These discrepancies can be resolved by reconstructing the mechanisms with experimental replicates or under different initial conditions as we have shown before MIKANA is capable of reconstructing a mechanism in close agreement with the MIKANA is a computational method to infer reaction mechanisms and estimate kinetic parameters of biochemical pathways from time course data. This paper presents a Graphical User Interface (GUI) for MIKANA. MIKANA's input consists of chemical species time course data and a set of reactions to be excluded from the inference. The user can select between three different types of information criteria: (i) Empirical; (ii) Akaike; and (iii) Bayesian, along with a choice for first– or second–order reactions (Reaction Order), and interaction molecularity . The output includes a set of reactions used in the model selection process and a set of differential equations generated from the inferred reactions and kinetic parameters. Furthermore, the user can visualize and compare the input and the output time course data produced by the method.Lactococcus lactis. As evidenced by our examples, a number of different options is available for the reconstruction of a reaction mechanism. The choice between these options should be made according to the background knowledge in each case. For example, if no a priori knowledge is available for the order and/or molecularity of the biochemical reactions, the user should consider all possible reaction steps for the known reactants. If the user knows the order and/or molecularity, the user can use the GUI to introduce this knowledge and exclude some of the possible reaction steps.We illustrate the capacity of the GUI using three different examples. In the first example, we reconstruct the irreversible single-enzyme, single-substrate Michaelis–Menten reaction mechanism. In the second example, we reconstruct the interaction map of chemical species of the muscle cell pathway. In the third example, we reconstruct the metabolic pathway of the glycolytic pathway of The GUI provides easy access to experimentalists or researchers alike to infer relations among chemical species. Hopefully, these predictions should aid in generating hypothesis that can be experimentally tested. In addition to the GUI, this paper presents a description of MIKANA's code and methods. The description we provide is in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. By making MIKANA's GUI and code available to the scientific community, we hope to increase current knowledge on chemical species interactions, possibly foster working collaborations and expand the knowledge in the field.A significant feature of MIKANA is that it uses a nonlinear modeling technique based on the law of mass action: the rate of any given elementary reaction is proportional to the product of the concentration reactant species. This technique produces plausible chemical reaction steps of a pathway MIKANA's performance is dependent on several quantitative and qualitative properties of the time course data. This information is important to experimentalists as it could allow them to setup their experiments in ways that optimize the network reconstruction. As described in a previous paper, we performed an extensive test using several combinations of parameter values and determined the properties that provide the best results. Among the data tested, the best results are obtained using the Empirical information criteria. We also tested different parameters such as initial chemical concentrations, experiments (time courses), number of data points and noise. For enzyme catalyzed reactions, we found optimal results when substrate and enzyme have roughly the same initial concentrations, when we used more than 3 time courses and more than 30 data points. We also found optimal results when noise is lower than Lactococcus lactis, but the difference between this and MIKANA's approach makes it hard to compare results. In addition to the seven variables , Voit also decided to include as constants in his model other metabolites, such as ATP and NADTo the extent of our knowledge, there are not similar approaches available in the literature from which we can compare results. One possible source of comparison comes from the work of Voit http://sitemaker.umich.edu/schnell.lab/products. This website provides a quick guide for installing and using MIKANA. We also made available for download the three input time course data used as examples in this article. In this way, we have provided all the information necessary for anyone to replicate our results. In MIKANA, we traverse the search space of possible models by removing one reaction at a time from the initial set of all possible elementary reactions that compose the model. While possibly leading to better results, an exhaustive search would be impractical. To overcome this problem and potentially find better solutions, we are currently exploring alternatives to the one–reaction–at–a–time approach. One such alternative is to consider an heuristic method which would speed up the process of solution finding. At the moment, MIKANA also infers reaction mechanisms with the same or less number of chemical species than the ones used as input. Based on the current employed methods, we are analyzing the possibility of identifying unknown chemical species in the pathway. These and other limitations may be addressed by us or anyone else interested in our method. This would definitely result in possible extensions of MIKANA and the GUI.MIKANA is implemented in MATLAB (MATrix LABoratory). It is open-source and freely available for non-commercial and commercial use. The source code is available in The input, main process components and output of MIKANA are shown in The core process of MIKANA involves the generation of a complete dictionary of possible chemical interactions (which we will refer to as elementary reactions). A second process excludes specified reactions from the model design matrix. A final process applies a model selection technique to deduce the reaction mechanism. The latter process is aided by a cost function, an information criterion, that penalizes the use of an excessive number of reactions to reconstruct the pathway.A key feature of our method lies in the construction of a model design matrix The above general framework is used to construct a set of chemically feasible elementary reactions if we restrict the reactions to a maximum molecularity. For example, for two species, the general elementary reaction can produce 18 chemically realistic schemes up to and including bi–molecular reactions:Here, For a species k, an element of the model design matrix is then defined as Only for those reactions in which species k takes part will n be nonzero. The overall matrix for the biochemical pathway is a concatenation of such matrices for each of the M species, resulting in a matrix of dimensions In MATLAB, besides the model design matrix, we create two other auxiliary matrices, labeled index and nindex. Both matrices are useful throughout the program. Their size is The construction of the index and the nindex matrices is essential to the process of exclusion of reactions in the final mechanism, as it is described below.The number of possible elementary chemical reactions increases with the number of chemical species affecting the performance of MIKANA. We build a simple language that allows the user to introduce chemical species interaction information of biochemically unrealistic reactions. The language can also be used to identify known chemical interactions in the reaction. The syntax of our language makes the use of the following symbols:Examples of uni–molecular and bi–molecular interactions are, respectively: (i) We exclude reactions from the model design matrix , and fro[matchstr splitstr]  =  regexp and (ii) could be used to define a closed system. Reaction (iii) would make the conversion of x1 into any other species without interacting with another chemical component impossible. Reversibly, reaction (iv) makes it impossible the conversion of any species into x1 without interacting with another chemical species. The last four reactions could be useful for introducing restrictions on chemical species interactions, such as to an enzyme or to a substrate, in an enzyme catalyzed reaction. Our next step is to identify which reactions in the matrices index and nindex match with the reactions in the matrices removal-index and removal-nindex. We exclude from the model design matrix and the matrices index, nindex all those reactions for which a match occurs. Those reactions are not considered in the model selection process , which wThe iterative scheme proposed by Judd and Mees Errors. To obtain the error for each reaction column position i We then multiply mdm by the solution vector a and calculate the squared difference to the derivative vector y (step 2 in the box). The result is an error vector. Here, each point corresponds to an estimated error of using the current elementary reactions (columns of mdm) to explain a specific chemical species value on a time point.Cost. To calculate the cost of dropping a reaction, we use an information criterion. The information criterion is used to penalize the use of an excessive number of reactions to reconstruct the pathway. We make three different types of information criteria available:Here, Error is the error vector, N is the length of the Error vector and k is the number of reactions in the current model design matrix.Information S1MIKANA Source Code and Examples.(ZIP)Click here for additional data file.


The application of post-genomic techniques in plant respiration studies has greatly improved our ability to assign functions to gene products. In addition it has also revealed previously unappreciated interactions between distal elements of metabolism. Such results have reinforced the need to consider plant respiratory metabolism as part of a complex network and making sense of such interactions will ultimately require the construction of predictive and mechanistic models. Transcriptomics, proteomics, metabolomics, and the quantification of metabolic flux will be of great value in creating such models both by facilitating the annotation of complex gene function, determining their structure and by furnishing the quantitative data required to test them. In this review, we highlight how these experimental approaches have contributed to our current understanding of plant respiratory metabolism and its interplay with associated process . We also discuss how data from these techniques may be integrated, with the ultimate aim of identifying mechanisms that control and regulate plant respiration and discovering novel gene functions with potential biotechnological implications. Metabolism represents one of the most important and most likely the best characterized network within biological systems. From pioneering studies defining key metabolic pathways, subsequent decades of enzymology characterizing the catalytic and regulatory properties of enzymes, through to more recent genetic studies of metabolism, there is an unprecedented density of both mechanistic and descriptive data relating to metabolic behavior. Advances in the understanding of metabolic regulation and control, however, still suffer from insufficient research concerning the mode of operation of metabolic networks, and plant respiratory metabolism provides an illustrative example of this problem. Due to the crucial roles of mitochondria in meeting the energy demands of the cell whilst simultaneously being involved in amino acid metabolism and photorespiration, precise control of mitochondrial metabolism and function is crucial for cellular homeostasis, and unsurprisingly mitochondrial dysfunction leads to diverse metabolic and phenotypic consequences . HoweverGiven the complex patterns of regulation and the interactions between respiratory metabolism and other processes uncovered by such studies, it is likely that only through the use of model-based approaches we will be able to understand how the individual components of metabolism  work together to produce a functioning system, and in this way truly understand the function of individual components of that system. In this context systems biology offers the promise of a better understanding of respiratory metabolism and its role within the metabolic network.Systems biology aims to understand how populations of molecules, cells, and organisms interact to give rise to complex biological processes, including cell division, growth, development, metabolism, and behavioral and ecological patterns . AlthougIn the case of plant respiratory metabolism, whilst the core components, including enzymes of the TCA cycle and components of the mitochondrial electron transport chain, have been identified and characterized in numerous species , how theTable 1; Figure 1) has increased our ability to annotate gene functions within plant respiratory metabolism, obtain quantitative data and uncover unanticipated relationships between respiration and other cellular processes such as photosynthesis, photorespiration, redox regulation, and signaling  expression in Arabidopsis, has also been investigated using transcriptomics. Such plants, when grown in moderate light under drought conditions, displayed 10-fold increases in leaf anthocyanin levels, together with alterations in photosynthetic efficiency, increased superoxide radical production, and reduced root growth  has renewed the use of 2-D gels for quantitative comparisons of the mitochondrial proteome. Studies using DIGE have investigated knockout mutants for carbonic anhydrase-like proteins , complexbidopsis  and ricebidopsis . These sArabidopsis in particular has been extensively analyzed by both gel-based and gel-free strategies , introducing side activities to plant complex I, while all the remaining nine subunits are rather small (7–12 kDa) and of so far unknown function  complex in tomato led to alterations in whole plant development that were linked to reductions in total amino acid and nitrate pools despite unaltered photosynthesis . These rAntisense inhibition of the iron–sulfur subunit of the SDH in tomato plants, on the other hand, resulted in increased photosynthesis and whole plant biomass via an organic acid-mediated effect on stomatal aperture. These results contrast with those obtained for antisense inhibition of fumarase, where decreased photosynthesis and biomass were observed . FurtherD-2-hydroxyglutarate dehydrogenase provide electrons to the plant ubiquinol pool via the electron transfer flavoprotein (ETF)-ETF:ubiquinone oxidoreductase (ETF/ETFQO) complex  spectroscopy it is possible to determine both enrichment and location of incorporation of label in individual metabolites without laborious chemical cleavage. This method was used to investigate the fate of BA shunt . Additiocle flux , most li2 released by respiration  are expected.Whilst investigation of the transcriptome, proteome, and metabolome alone may be highly informative, cellular physiology and the regulation of plant respiratory metabolism is the result of complex interactions between transcripts, proteins, and metabolites. Bearing that in mind straightforward relationships between these components of the metabolic network are therefore not to be expected. Thus, despite the apparent easy understanding of simple relationships between changes in transcripts, proteins, metabolite, and downstream biological functions, quite often there is a major discordance. This apparent discrepancy can often be explained by quantitative analysis of the data sets and information about translational regulation and protein turnover. These incoherencies are not only due to technical issues but also related to the complexity and structure of metabolic networks. Accordingly, some priorities in metabolic research to reduce these problems have recently been proposed . For thiArabidopsis  will additionally allow a fuller description of the metabolic status of a plant. When this is achieved, global analyses of RNA, protein, and metabolites will allow us to obtain a full picture of the complexity of the system under study. Although the application of these techniques requires substantial financial investment, it is likely to bring returns in the form of an improved ability to carry out rational engineering of the plant metabolic network.Overall, whilst post-genomic technologies have already aided our understanding of respiratory metabolism at a systems level through additions to the “parts list” of the metabolic network , they arThe 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.


Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol.A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production.To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures. An essential component of the quest for energy independence is to develop renewable sources of energy via the conversion of plant biomass to liquid transportation fuels. Lignocellulosic biomass is a heterogeneous collection of polymers that compose plant cell walls, namely celluloses, hemicelluloses, pectins, lignins and proteoglycans . The proThe fermentation of sugar mixtures that result from biomass hydrolysis is a significant bottleneck in the overall process. Few fermentative microbes that efficiently convert both hexose and pentose sugars to liquid fuels such as ethanol have been identified ,5. The mSaccharomyces cerevisiae is a robust, budding yeast that has been widely used for fermentation of refined corn starch to fuel ethanol [S. cerevisiae is unable to utilize pentose sugars, such as xylose and arabinose, that result from the hydrolysis of hemicellulose. The engineering of pentose metabolism into the S. cerevisiae genome has been achieved, but problems with co-factor imbalances and gene expression have hindered the efficiency of these mutants [ ethanol . Because ethanol . However mutants .Scheffersomyces stipitis (formerly known as Pichia stipitis), can natively ferment xylose to ethanol but it retains a preference for glucose as the carbon source. The growth and metabolite profile of this yeast is highly sensitive to the oxygenation level. Unlike S. cerevisiae, S. stipitis is a Crabtree-negative yeast that only produces ethanol under oxygen-limited conditions [S. stipitis is among the most efficient native fermenters of xylose when grown under microaerobic culture conditions. In certain aeration regimes, however, this yeast can reassimilate ethanol while often simultaneously consuming other growth substrates [S. stipitis is unable to grow anaerobically. Insertion of the URA1 gene from S. cerevisiae has been shown to enable S. stipitis to grow anaerobically on glucose [S. stipitis anaerobic growth on xylose [S. stipitis.Another species of yeast, nditions . S. stipbstrates . Unlike  glucose . Howevern xylose ,21. ThusS. cerevisiae and S. stipitis have been co-cultured for the production of ethanol from glucose and xylose mixtures [S. cerevisiae was used so the dissolved oxygen concentration could be more easily controlled at a level that was favorable for ethanol production by S. stipitis. Because it lacks the respiratory capability of a wild-type strain, respiratory-deficient S. cerevisiae could not utilize non-fermentable carbon sources such as ethanol once glucose had been exhausted [mixtures -24. In txhausted . The inaxhausted .Constraint-based analysis using genome-scale metabolic reconstructions is a widely used computational tool for predicting how fluxes through microbial metabolic pathways will respond to changes in the culture environment or gene deletions/insertions ,27. WithS. cerevisiae and wild-type S. stipitis co-culture growing microaerobically on a mixture of glucose and xylose. A dynamic model of S. stipitis metabolism was developed from a recently published genome-scale reconstruction [S. cerevisiae mutant had non-specific genetic alterations, we considered several plausible modifications of the wild-type metabolic network [S. cerevisiae dynamic model consistent with batch pure culture data. Co-culture experiments demonstrated that S. cerevisiae competed less successfully for glucose than expected from combining the pure culture dynamic models under the community objective of total biomass maximization. A revised co-culture model with reduced S. cerevisiae glucose uptake was shown to provide accurate predictions of batch co-culture data over a range of microaerobic growth conditions. The experimentally validated model was used to optimize the batch ethanol productivity by adjusting the inoculum concentrations and the aeration level. Finally, the model was used to examine what modifications to the S. stipitis xylose transport system would yield the largest improvements in co-culture ethanol production.In this study, we used dynamic flux balance analysis (DFBA) to drive the experimental optimization of ethanol production from a respiratory-deficient truction  by estim network  to develS. stipitis metabolism did not include a balance on dissolved oxygen in the culture media. We were unable to satisfactorily fit this model to measured biomass and ethanol concentration profiles through adjustment of the sugar uptake rate parameters (see below). Because the dissolved oxygen (DO) concentration was above the assumed microaerobic value during the initial portion of the batch, we added the DO balance (8) to account for the apparent gas–liquid mass transfer limitations. The mass transfer coefficient (kLa) was determined from the gas sparge rate using a linear correlation . Starting with literatures values when available [kLa = 5.5 h-1) and 50 cc/min (kLa = 10.1 h-1). The resulting uptake parameters are compiled in Table Our initial dynamic model of vailable , the uptS. stipitis dynamic models with and without the DO balance (8) are compared in Figure kLa value of 10.1 h-1 was used in the DO balance, while the model without the balance used a constant DO value of 0.0072 mM, the measured level at the end of the batch fermentation. The constant DO model could not reproduce the lag in ethanol production that resulted from the high level of dissolved oxygen present during the early stages of the fermentation. Additionally, the constant DO model produced lower biomass yields and higher ethanol yields than observed in experiment. Once the DO balance was added, the model could more accurately predict the time at which S. stipitis began microaerobic substrate consumption and ethanol production started to outpace biomass growth. The other S. stipitis pure culture experiment used for uptake parameter fitting of the variable DO model was performed with a kLa of 5.5 h-1 and initial conditions of 0.25 g/L biomass, 16.8 g/L glucose and 8.4 g/L xylose . We found that these discrepancies could be partially rectified by reducing the S. cerevisiae maximum glucose uptake rate from the pure culture value of 21.5 mmol/gdw/h to 18.5 mmol/gdw/h. Otherwise, the substrate uptake parameters remained fixed at the pure culture values listed in Table A preliminary dynamic co-culture model was developed by direct combination of the two individual yeast models assuming no species interactions other than competition for glucose . This approach resulted in faster glucose consumption and a higher final concentration of kLa of 5.5 h-1 with an equal inoculum of 0.15 g/L of each yeast species grown on 19.1 g/L glucose and 9.7 g/L xylose. Figure kLa of 10.1 h-1, 0.10 g/L of each yeast species, 16.5 g/L glucose and 7.9 g/L xylose. With the implemented change in the S. cerevisiae maximum glucose uptake rate, the dynamic co-culture model proved to be as accurate as the individual species models. The glucose consumption rate was slightly overpredicted during the initial portion of the batch at both aeration levels. Small discrepancies in the predicted biomass and ethanol concentrations were observed during the glucose consumption phase for kLa = 5.5 h-1, while small but longer lasting deviations were apparent in the biomass and xylose concentrations at kLa = 10.1 h-1.Figure kLa of 7.6 h-1 with 0.145 g/L of each yeast as the inoculum and initial sugar concentrations of 16.5 g/L glucose and 8.5 g/L xylose. The model and data comparisons shown in Figure kLa < ~10 h-1.To examine prediction accuracy of dynamic co-culture model at aeration levels not used for parameter adjustment, we performed three additional co-culture fermentations at a kLa value and the initial yeast concentrations, and the case that yielded the largest productivity was deemed the optimal solution. All simulations were performed with a mixture of 16.0 g/L glucose and 8.0 g/L xylose, while the total inoculum concentration was fixed at 1.0 g/L to allow direct comparison of results for different inocula.Following experimental validation, we utilized the dynamic co-culture model to computationally determine optimal bioreactor operating conditions for maximization of ethanol productivity. The productivity was defined as the final ethanol concentration divided by the batch time, which was chosen as the time at which the xylose concentration dropped below 0.5 g/L. Our simulations indicated that the aeration level had little effect on the ethanol titer but strongly affected the batch time due to the xylose consumption rate . Our simkLa value was constrained to be below 10.1 h-1 since larger values had the potential to result in undesirable ethanol reassimilation by S. stipitis that was not captured by the co-culture model. Regardless of the inoculum, we found that increasing kLa improved productivity due to a decrease in batch time that resulted from enhanced xylose consumption. Consequently, the optimal solution was achieved at kLa = 10.1 h-1, and the optimization problem was reduced to determining the inoculum concentrations. This result demonstrated the importance of developing improved metabolic reconstructions that more accurately predict the relationship between aeration level and the onset of ethanol reassimilation. Figure S. cerevisiae 311 concentration on the ethanol titer, batch time and ethanol productivity. The initial S. stipitis concentration is not shown since the total inoculum concentration was constrained to be 1.0 g/L. The ethanol titer increased with increasing S. cerevisiae concentration since this yeast converts glucose to ethanol at higher yields than S. stipitis. Conversely, the batch time decreased with increasing S. cerevisiae concentration because S. stipitis could better compete for glucose during the initial growth phase and the larger S. stipitis biomass concentration that resulted allowed for more rapid xylose consumption. These two competing effects produced a clear optimum in the ethanol productivity at 0.1 g/L S. cerevisiae and 0.9 g/L S. stipitis.The in silico. Figure in silico analysis at kLa = 10.1 h-1, the optimized co-culture would outperform both a pure S. stipitis culture (9.43 g/L ethanol at a productivity of 0.505 g/L/h) and a co-culture with equal inoculum (9.07 g/L ethanol at a productivity of 0.469 g/L/h).To validate the model predictions, we performed three batch fermentations at the optimal conditions identified S. stipitis xylose metabolism was the rate limiting process that limited co-culture conversion efficiency. Moreover, xylose transport has been identified as the main bottleneck in pentose sugar metabolism with pure S. stipitis cultures [S. stipitis xylose transport parameters on ethanol productivity. To model engineering of the associated transport proteins, the nominal values listed in Table vz,max), the xylose uptake saturation constant (Kz), and the glucose inhibition constant for xylose uptake (Kigz) were perturbed both upward and downward to determine their impact. The ethanol inhibition constant for xylose uptake (Kiez) was excluded from detailed analysis because the ethanol concentrations achieved in silico were too small to cause significant xylose uptake inhibition. For the remaining three parameters, scaled sensitivity coefficients were calculated as:Our simulations demonstrated that cultures . TherefokLa = 10 h-1 and inoculum of 0.1 g/L S. cerevisiae 311 and 0.9 g/L S. stipitis.where vz,max was changed upward and Kz and Kigz were changed downward. Increasing the maximum xylose uptake rate offered the largest improvement in ethanol productivity as indicated by the relatively large sensitivity coefficients, with a productivity enhancement of 14% achieved with a 25% vz,max increase . The simulated parameter changes could potentially be realized through protein engineering of the sugar transporters [S. stipitis transporters uptake both glucose and xylose [Figure e Figure A. As inde Figure B and thee Figure C. Howevesporters ,45. Becad xylose , a compld xylose .Scheffersomyces stipitis to efficiently convert pentose sugars to ethanol, co-cultures of S. stipitis and respiratory-deficient Saccharomyces cerevisiae are promising for renewable ethanol production from glucose/xylose mixtures [S. cerevisiae/S. stipitis co-cultures that was capable of predicting microaerobic metabolism in batch culture. As a first step towards co-culture modeling, dynamic models were developed for the individual yeasts using data from pure culture experiments. The Crabtree-negative yeast S. stipitis was shown to be very sensitive to the aeration level such that the incorporation of a dissolved oxygen balance was necessary to capture non-microaerobic oxygen levels during the initial batch phase and the subsequent switch from respiratory and to fermentative growth. The S. stipitis dynamic model was able to accurately reproduce measured glucose, xylose, biomass and ethanol concentration profiles over a range of microaerobic growth conditions characterized by oxygen-liquid mass transfer coefficient (kLa) values below ~10 h-1. However, the S. stipitis model was unable to capture ethanol reassimilation observed at higher oxygenation levels due to model energetics that required a kLa greater than 34 h-1 just to meet the ATP maintenance demand for growth on ethanol. Therefore, subsequent co-culture experiments and simulations were restricted to microaerobic conditions with kLa values less than ~10 h-1.Due to the ability of mixtures ,47. StarS. cerevisiae 311 introduced unknown genetic alterations that could not be directly implemented in the wild-type metabolic network. We used flux balance analysis to screen putative gene knockouts in the mitochondrial compartment that would reproduce the S. cerevisiae 311 phenotype of lower biomass yields, higher ethanol yields and smaller oxygen demands than the wild-type strain. The best agreement with S. cerevisiae 311 pure culture data was obtained for deletions of two genes that encode the ubiquinol-6 cytochrome c reductase and mitochondrial cytochrome c oxidase enzymes involved in the electron transport chain. With the fluxes through the two reactions catalyzed by these enzymes constrained to zero, the oxygen demand was greatly reduced because oxygen was not needed to serve as a final electron acceptor for ATP synthesis. These results were consistent with more directed genetic engineering efforts to create respiratory-deficient S. cerevisiae mutants through direct removal of these two genes [The procedure used to create the respiratory-deficient strain wo genes .S. cerevisiae biomass formation rates. Much improved predictions were obtained by reducing the S. cerevisiae maximum glucose uptake rate from its pure culture value of 21.5 mmol/gdw/h to 18.5 mmol/gdw/h. This parameter change suggested the presence of unmodeled species interactions that resulted in S. stipitis having an antagonistic effect on S. cerevisiae growth. Crabtree-negative yeasts like S. stipitis have an established advantage when competing against a Crabtree-positive yeast such as S. cerevisiae for the same growth-limiting substrate [S. cerevisiae[S. cerevisiae could be at a disadvantage in competing for nitrogen sources such as ammonium when grown with respiratory competent S. stipitis. Because sugar uptake parameters reflect the action of numerous transport systems, each having distinct mechanisms and affinities, the decrease in this parameter may indicate differences in transporter expression between pure and co-cultures of the two microbes.A preliminary version of the dynamic co-culture model was developed by directly combining the dynamic models of the two yeast species under the assumption that each species attempted to maximize its individual growth rate and the only interspecies interaction was competition for glucose substrate. By comparison of model predictions to batch co-culture data collected at several microaerobic conditions, we found that the preliminary model overpredicted the glucose consumption and ubstrate . This efubstrate . One poserevisiae. Respirain silico analysis suggested that the co-culture should be operated at the largest possible kLa value before the onset of ethanol reassimilation by S. stipitis because this aeration level maximized the rate of xylose conversion to ethanol. Because the S. stipitis metabolic network model did not accurately predict this transition point, the optimization was constrained by experimental data such that the optimal solution was achieved at kLa = 10.1 h-1. Optimization of the initial cell concentrations produced an inoculum with 90% S. stipitis and only 10% S. cerevisiae to form sufficient S. stipitis biomass following glucose exhaustion for efficient conversion of the remaining xylose. Experimental validation of the optimal solution showed that the co-culture model provided excellent agreement with measured concentration profiles. Compared to an unoptimized co-culture with equal initial cell concentrations, the optimized co-culture was shown experimentally to produce a 11% improvement in ethanol productivity. Additional in silico analysis indicated that the co-culture would yield higher ethanol productivities than a S. stipitis pure culture due to the higher glucose to ethanol conversion efficiency of S. cerevisiae.Following experimental validation, the dynamic co-culture model was used to compute the aeration level and initial cell concentrations that maximized batch ethanol productivity. Our S. stipitis is the main bottleneck that limits overall co-culture performance. The dynamic co-culture model was used to explore the potential impact of transporter engineering efforts aimed at enhancing xylose uptake by increasing the maximum xylose uptake rate, reducing xylose uptake saturation and reducing glucose inhibition of xylose uptake. Based on sensitivity analysis for moderate parameter changes, the maximum xylose uptake rate was predicted to yield the largest improvement in ethanol productivity. However, substantially larger improvements were predicted for complete elimination of glucose catabolite repression in S. stipitis because this modification eliminated the diauxic growth pattern and allowed xylose consumption to commence at the beginning of the batch. Taken together, our computational results suggest that an engineered S. stipitis strain which rapidly consumes xylose in the presence of glucose and does not reassimilate ethanol under microaerobic conditions would be an ideal candidate for enhancing co-culture performance. Future work should focus on the creation of such S. stipitis strains as well as the model-based characterization of co-culture performance for growth on actual biomass hydrolysates with inhibitory compounds.The relatively slow conversion of xylose by S. stipitis strain NRRL Y-7124 (ATCC 58376) was used in this study. S. cerevisiae 311 (ATCC 42511), a mutant that was created by treating a wild-type strain with ethidium bromide [S. cerevisiae strain. Stocks of the two yeasts were stored at 4°C on YM agar slants.The wild-type 1 ATCC 421, a muta4 ·7 H2O, 1.10 g/L KCl, 0.15 g CaCl2 · 2 H2O, 1.00 g (NH4)2HPO4, 8.75 g/L (NH4)2SO4, 60.3 mg myo-inositol, 30.0 mg Ca-panthothenate, 6.0 mg thiamine-HCl, 1.5 mg pyridoxine-HCl, 0.03 mg biotin, 10.6 mg MnSO4 · H2O, 9.0 mg ZnSO4 · 7 H2O, 5.0 mg FeSO4 · 7 H2O, and 2.4 mg CuSO4 · 2 H2O. Pre-cultures in media containing 20 g/L glucose and 20 g/L xylose for S. cerevisiae and S. stipitis, respectively, were grown at 30°C for 36 hours on a shake table set at 175 RPM. The inoculum concentration for each experiment was determined by calculating the volume of preculture required to obtain the target initial concentration of each cell type using the measured biomass concentration in the shake flask media.All pure and mixed cultures were performed in a synthetic yeast minimal medium . The comAll fermentations were performed in a HEL BioX array of 4 250 mL vessels situated in a shared block that provided both electric heat and independent magnetic agitation . Electrochemical probes monitored the dissolved oxygen and pH in each vessel, while individual thermocouples recorded the media temperatures. Bioreactor cultivations were performed at a constant temperature of 30°C and pH of 5, the optimal growth conditions for each yeast species . The pH kLa) was determined using the static gassing out method [Aeration of culture media was found to be a crucial operating variable. The agitation speed was held constant at 500 RPM for both pure and mixed culture fermentations. The gas flow rate into each reactor was altered according to the aeration level required for each experiment. A linear relationship between the gas sparge rate and the gas–liquid oxygen mass transfer coefficient  and dry cell weight. Cell counts of S. cerevisiae and S. stipitis in co-culture were performed on a hemacytometer in triplicate and averaged. A typical cell count considered approximately 50 S. cerevisiae cells and 200 S. stipitis cells. Conversion factors between dry cell weight and number of cells were found by drying pure culture samples of each yeast after cell counts had been performed. These factors were found to be 0.006943 gdw/L for S. cerevisiae and 0.001944 gdw/L for S. stipitis. Ethanol, glucose and xylose concentrations were measured by YSI 2700 SELECT biochemistry analyzers  configured with the enzyme-bound membranes specified for each metabolite. Raw readings were interpreted by 2700 Xylose PC Software  to resolve cross-talk between the xylose and glucose specific membranes.Total cell weight was measured using a correlation between OD595 measured on a WPA UV1101 Biotech Photometer  and S. stipitis (vs) were calculated by solving the following linear program based on the assumption that the two species attempted to maximize their individual growth rates:The e matrix . Flux dii represents the species, Ai is the matrix of stoichiometric coefficients, vi is the vector of reaction fluxes including exchange fluxes, vi,min and vi,max are vectors of lower and upper flux bounds, μi is the growth rate, and wc and ws are vectors of experimentally determined weights that represent the contribution of each flux to biomass formation in S. cerevisiae[S. stipitis[μ was assumed to be the sum of the individual species growth rates, and the inclusion of multi-level objective functions [S. cerevisiae and S. stipitis by constraining all fluxes of the unmodeled organism to zero.where the subscript erevisiae and S. s stipitis, respectunctions  was deemThe steady-state flux balance model (2) was extended to a dynamic model through the addition of the following extracellular mass balance equations:Xc and Xs are the biomass concentrations of S. cerevisiae and S. stipitis, respectively, G, Z, and E are the concentrations of glucose, xylose, and ethanol, respectively, ve,c and ve,s are ethanol exchange fluxes, vg,c is the glucose uptake rate for S. cerevisiae, and vg,s and vz,s are the glucose and xylose uptake rates, respectively, for S. stipitis. An equation for the dissolved oxygen concentration (O) was necessary to accurately describe microaerobic growth of S. stipitis (see results). In this equation (8), vo,c and vo,s are oxygen exchange fluxes, kLa is the volumetric mass transfer coefficient of oxygen from sparged gas to the culture medium, and O* is the saturation concentration of oxygen. For all simulations, O* was taken to be 0.24 mM, the saturation concentration for water at 30°C and 1 atm.where The following substrate uptake expressions were used to calculate upper bounds on the actual sugar and oxygen uptake rates:vg,max, vz,max and vo,max are the maximum uptake rates of each substrate, Kg, Kz and Ko are corresponding saturation constants, Kieg and Kiez are ethanol inhibition constants, and Kigz is a glucose inhibition constant. The glucose (9) and xylose (10) uptake rates were assumed to follow Michaelis-Menten kinetics with an additional inhibitory term that reflects growth rate suppression at high ethanol concentrations [S. stipitis favors glucose over xylose as the carbon source. The oxygen uptake rate was calculated from a Michaelis-Menten expression based on the dissolved oxygen content of the medium [where trations . The glue medium .S. cerevisiae could not meet the non-growth associated ATP maintenance demand during the xylose-only consumption phase, the maintenance flux was constrained to zero after glucose depletion to prevent the LP solver from returning zero fluxes for the S. stipitis network. Due to time-scale differences between the sugar and oxygen consumption rates, the differential equation system (3)--(8) exhibited a high degree of stiffness. To reduce the time required to generate large numbers of DFBA simulations for parameter fitting and in silico culture optimization, Matlab stiff ODE solvers ode15s and ode23tb were used to obtain approximate solutions. An ODE solver with greater accuracy, ode23, was used to generate model predictions once parameters had been estimated or an optimum had been determined. A typical co-culture batch simulation that was solved in two minutes with ode15s required five hours with ode23.Pure and mixed culture dynamic flux balance models were solved using the Mosek optimization toolbox  to resolve the linear program for intracellular metabolism within Matlab  . Because-1); Ko: Oxygen uptake saturation constant (mM); Kz: Xylose uptake saturation constant (g/L); O: Dissolved oxygen concentration (mM); O*: Dissolved oxygen saturation concentration (mM); vc: S. cerevisiae Flux distribution (mmol/gdw/h); ve,c: S. cerevisiae ethanol flux (mmol/gdw/h); ve,s: S. stipitis ethanol flux (mmol/gdw/h); vg,c: S. cerevisiae glucose uptake rate (mmol/gdw/h); vg,max: Maximum glucose uptake rate (mmol/gdw/h); vg,s: S. stipitis glucose uptake flux (mmol/gdw/h); vi,max: Vector of upper bound flux constraints (mmol/gdw/h); vi,min: Vector of lower bound flux constraints (mmol/gdw/h); vo,c: S. cerevisiae oxygen uptake rate (mmol/gdw/h); vo,max: Maximum oxygen uptake rate (mmol/gdw/h); vo,s: S. stipitis oxygen uptake rate (mmol/gdw/h); vs: S. stipitis Flux distribution (mmol/gdw/h); vz,max: Maximum xylose uptake rate (mmol/gdw/h); vz,s: S. stipitis xylose uptake flux (mmol/gdw/h); wc: S. cerevisiae biomass reaction; ws: S. stipitis biomass reaction; Xc: S. cerevisiae concentration (g/L); Xs: S. stipitis concentration (g/L); Z: Xylose concentration (g/L).μi: Growth rate (1/h); Ai: Stoichiometric Matrix; E: Ethanol concentration (g/L); G: Glucose concentration (g/L); Kg: Glucose uptake saturation constant (g/L); Kie: Ethanol inhibition constant (g/L); Kigz: Glucose inhibition constant (g/L); kLa: Oxygen mass transfer coefficient (hThe authors declare that they have no competing interests.TH carried out the fermentation and modeling work and drafted the manuscript. MH conceived of the study, and participated in its design and coordination and helped to draft the manuscript. Both authors read and approved the final manuscript.


Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12M. gallisepticum of 0.244±0.03M. gallisepticum.Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium  Mycoplasma gallisepticum as our model organism. Using the genome annotation as the basis, the preliminary genome-scale metabolic model consisted of 446 metabolites involved in 380 reactions. Carrying out our analysis, we found over 80 incorrect reactions and 16 missing reactions. Based upon the guidance of the algorithm, we were able to curate and resolve all discrepancies. The model predicted an average bacterial growth rate of 0.358±0.12 h−1 compared to the experimentally observed 0.244±0.03 h−1. Thus, our approach facilitated the curation of a genome-scale metabolic network and generated a high quality metabolic model.Flux balance analysis (FBA) is a powerful approach for genome-scale metabolic modeling. It provides metabolic engineers with a tool for manipulating, predicting, and optimizing metabolism for biotechnological and biomedical purposes. However, we posit that it can also be used as tool for fundamental research in understanding and curating metabolic networks. Specifically, by using a genetic algorithm integrated with FBA, we developed a curation approach to identify missing reactions, incomplete reactions, and erroneous reactions. Additionally, it was possible to take advantage of the ensemble information from the genetic algorithm to identify the most critical reactions for curation. We tested our strategy using  The ability to determine a relationship between an organism's genome and its molecular physiology quantitatively, based upon current understanding of metabolism and molecular biology, continues to improve at a dramatic pace. Establishment of a quantitative model allows for the compact and integrated analysis of complex metabolic networks and their associated and often non-intuitive behavior. Genome-scale metabolic modeling in particular has been used in a number of venues in the biotechnological and biomedical arenas et al.Genome-scale metabolic modeling facilitates a deep understanding of metabolism of microorganisms. Modeling is also a tool to elucidate missed information and recognize errors in new and existing models of metabolic networks. As was pointed out by Thiele in silico results relative to the experimental information is assessed. The iterative process stops when the results from the model are nearly the same as the experimental results. In the future, as more information becomes available, the model may be updated in order to fill all the possible gaps and achieve better results Metabolic network modeling is an iterative process that starts with the generation of a genome-scale reconstruction of metabolism utilizing all current annotations and literature information. Next, curation of the draft reconstruction is carried out manually by an expert, and the computational model is generated. Finally, a comparison of the The model curation process improves the draft reconstruction by identifying and filling the gaps present in the network, removing reactions that are not likely present in the organism, and enforcing overall consistency across the network. Some computational strategies for identifying missing reactions and filling the gaps have been developed previously, such as GapFind Here a new algorithm for facilitating curation of models is presented. The approach integrates a genetic algorithm (GA) with flux balance analysis. The novelty and strength of this GA/Flux Balance Analysis (GAFBA) strategy lies in its ability to both aid in fundamental studies of metabolism and to facilitate curation of genome-scale metabolic networks, rather than functioning solely as a predictive tool. Furthermore, the strategy is independent of a reference database, allowing the researcher to investigate other avenues of curation. However, this approach does not preclude the use of existing databases as one of those sources of information. Rather it provides increased flexibility in evaluating the system of interest. As the quality of curation increases, the evolved model can be used as a predictive tool, but that is a secondary contribution of this approach.The premise of the GAFBA method is based upon the observation that the optimization of an initial genome-scale metabolic model often results in no feasible solution when experimental information is incorporated. This result usually indicates that some of the constraints cannot be satisfied. The GAFBA method identifies the mass balanced constraints that may be relaxed to solve the FBA optimization problem. If relaxing a selected constraint allows the problem to be solved, it is likely that the associated metabolite was participating in a metabolic reaction that, due to lack of information during the reconstruction process, was not taken into account in the model. As a result, the metabolite and the potential reactions it may participate in should be reviewed. The approach developed here employs a genetic algorithm to identify the minimum number of constraints that could be relaxed in order to achieve a feasible solution to the FBA optimization problem.Mycoplasma gallisepticum was chosen as the model organism. The choice of M. gallisepticum for the validation of the GAFBA methodology was driven by our expertise with the organism and the organism's importance to the poultry industry. M. gallisepticum causes chronic respiratory disease in chickens and infectious sinusitis in turkeys M. gallisepticum has been a concern to the poultry industry because of the resultant increase in chick and poult mortality, reduced egg production, and increased costs related to medication, prevention and control programs. For example, in 1994, the layer industry alone lost between $118 and $150 million in the United States M. gallisepticum biology, including virulence, genomics, and metabolic processes has the potential to allow for the development and improvement of the vaccines and control strategies for this disease.To develop and evaluate this strategy, M. gallisepticum was developed by using the organism's annotated genome sequence, compiling existing enzymatic data, employing genome-scale bioinformatics-driven homology searches, referencing the metabolism of other closely related Mycoplasma strains, generating a comprehensive biomass equation, and finally analyzing how all the metabolites in the system interacted with each using GAFBA algorithm.In this work, the metabolic network of The hybrid Genetic Algorithm/Flux Balance Analysis Algorithm (GAFBA) algorithm embeds the Flux Balance Analysis (FBA) optimization problem within a genetic algorithm to identify problematic metabolic constraints and is schematically depicted in M. gallisepticum genome The initial genome-scale metabolic model was based upon an updated annotation of the The GA started with the random generation of an initial population of data structures referred to as “chromosomes” per the GA parlance A list of the problematic metabolites was generated upon completion of the run. Problematic metabolites were defined as those that were unable to fulfill the mass balance under the given conditions; manual review of each of the metabolites, as well as some of the upstream and/or downstream metabolites in the associated pathway was required. The analysis of the metabolites upstream and/or downstream in the pathway helped to determine if the problem could be corrected by adjusting the mass balance of a closely connected metabolite instead of the problematic metabolite directly. For example, it is possible that one of the downstream reactions responsible for consuming the problematic metabolite is blocked because the reaction is producing a dead end metabolite. Thus, it is necessary to adjust the mass balance of the dead end metabolite and not the mass balance of the so-called problematic metabolite. An example of an upstream issue is when a reaction responsible for producing the problematic metabolite has no flux due to the lack of a reactant. Under such conditions, the mass balance for the reactant has to be adjusted rather than the mass balance of the initially identified “problematic” metabolite.After updating the model, a new simulation was started. If a metabolite was added or removed, a new initial population was generated randomly and evolution was allowed to proceed. If no metabolite was added or removed, GAFBA could either be continued from where it left off using the final population from the previous run as the seed population, or an entirely new population could be generated. The evolutionary process was then re-started. The ultimate termination criterion was when none of the mass balances constraints needed to be relaxed.M. gallisepticum was to analyze the published annotation of Strain R Clone 2 M. gallisepticum were subsequently added. Reactions shown to be inactive were removed. Once the pool of published enzymatic data had been exhausted, the Pathway Tools Hole Filler was used to generate a list of gene candidates to potentially fill gaps in various metabolic pathways.The first step in the reconstruction of a genome-scale metabolic model of Mycoplasma genitalium, Mycoplasma pneumoniae, and other members of the Mycoplasma family was taken into account. Moreover, it considered was whether or not the products of a given pathway were known to be present in the cell biomass, utilized by another pathway, or secreted from the cell. Reactions whose products were not utilized or secreted were considered unlikely to be present. Additionally, candidate reactions experimentally proven to be absent in Mycoplasma gallisepticum metabolism were removed.Any candidate reaction from Pathway Hole Filler with a probability assignment over 0.9 was selected for further evaluation. Selected candidates were evaluated by a curator based on a number of criteria. First, presence or absence of a candidate reaction's associated pathway in evolutionarily related species such as M. gallisepticum. These reactions were subsequently removed and are listed in A total of 23 reactions were added due to supporting experimental data and are listed in M. gallisepticum model resulted in no feasible solution. Thus, the model was analyzed using GAFBA to identify reaction holes, unproduced but necessary biomass components, and metabolites with no membrane transporter or degradation pathway. The biomass equation was meticulously investigated. A large number of citations were accumulated for many metabolites that were known to be biosynthesized by M. gallispeticum but were not accounted for based on the genome annotation. For example, phosphatidylcholine, cardiolipin, and sphingomylin are all phospholipids known to have working biosynthesis pathways in M. gallispeticum because of previously conducted fatty acid radioactive labeling assays M. gallispeticum. Therefore, logical reactions were assigned to complete these biosynthesis pathways, usually by referencing similar pathways in other Mycoplasma genomes. However, it is critical to note that the rationale for including these pathways was to complete the model. It does not constitute proof of existence of that pathway. That being said, the requirement of such pathways for the model does open up interesting experimental questions and new hypotheses.FBA of the initial The GAFBA algorithm also revealed a number of metabolites that were not degraded and could not be transported out of the cell. A total of 16 reactions were added based on the results of the first GAFBA run. A list of all of these added reactions, along with a brief description of the rationale for the addition and all relevant citations are shown in Despite application of the changes shown in M. genitalium model iPS189 Through use of GAFBA, it was found that the mass balance on the charged tRNA's and uncharged tRNA's could not be enforced under the given metabolic description. The discrepancy was due to uncharged tRNA's being converted to charged tRNA's by the tRNA's ligase reactions but participating in no other reactions. This ultimately resulted in the depletion of uncharged tRNAs if the mass balance was enforced without modification. To rectify this imbalance, another reaction was added representing a recycling process to convert the charged tRNA's to uncharged tRNA's and complete the cycle. These reactions were added to the biomass equation. This was similar to the approach taken for M. genitaliumM. laidlawiiBecause the growth medium used experimentally was undefined, many of the necessary exchange fluxes for the model could not be explicitly resolved. The decision to add an exchange flux to the model was based on experimental information and on the composition of defined media for relative species, such as M. gallisepticum. Thus, the GAM of M. pneumoniaM. genitalium iPS189 model Sufficient experimental data was unavailable to directly calculate the value for the growth associated maintenance (GAM) and non-growth associated maintenance (NGAM) for MycoplasmasM. gallisepticum metabolic network.The reaction catalyzed by the enzyme formate dehydrogenase (E.C. 1.2.1.2) was added to the model in order to complete the mass balance on formate. This reaction is present in other M. gallisepticum model required choline for the production of phosphatidyl choline, which was ultimately necessary for biomass. However, although the reaction for consumption of choline was present, no choline was predicted to enter the cell. The transport reaction for choline, represented by flux V21 in MycoplasmaM. gallisepticum proteome for Na-ATPase using BLASTP Aspergillus fumigatus Af293 sodium P-type ATPase (GenBank Accession no. XP_751881.1) as probe, the best match  was identified at locus NP_853020.2, the previously annotated cation transported ATPase MGA_1061.Another problematic metabolite identified by GAFBA that was hindering the successful simulation of the metabolic network was choline. The The mass balance for dCTP was also required to be unconstrained based on GAFBA. It was initially predicted that more dCTP was produced from dCDP than was consumed by the biomass reaction. dCDP was produced from CDP coupled with reduced thioredoxin and oxidized NrdH glutaredoxin-like protein. When the metabolic networks of these metabolites were checked, the thioredoxin reductase reaction (E.C. 1.8.1.9) was found to be reversed The last two metabolites for which the mass balance constraints were relaxed were 2-phospho-4-{cytidine 5′-diphospho}-2-C-methyl-D-erythritol and nicotinamide adenine dinucleotide (NAD+).M. penetransM. gallisepticumM. gallisepticum model ended in the production of (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMB-PP), a dead-end metabolite in the model. No gene was associated to the reaction HMB-PP reductase (E.C. 1.17.1.2) responsible for converting HMB-PP to IPP. Two questions that remain are whether M. gallisepticum has a complete MEP pathway and what is the purpose of the HMB-PP metabolite.2-phospho-4-{cytidine 5′-diphospho}-2-C-methyl-D-erythritol is a metabolite in the methylerythitol phosphate pathway (MEP). It is used for the production of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the two precursors of isoprenoid. For many years, it was assumed that all organisms produced IPP from acetyl-CoA through the mevalonic acid pathway (MVA), and then IPP was isomerized to DMAPP M. gallispeticum had a partial NAD+ salvage pathway. The partial pathway starts with the conversion of nicotinate to nicotinate mononucleotide catalyzed by the enzyme nicotinate phosphoribosyltransferase (E.C. 2.4.2.11). Then nicotinate mononucleotide is converted to nicotinate adenine dinucleotide through the reaction catalyzed by nicotinate-nucleotide adenyltransferase (E.C. 2.7.7.18). Next nicotinate adenine dinucleotide is converted to NAD+ by NAD+ synthethase (E.C. 6.3.5.1), but no gene was found for this enzyme in M. gallisepticum. Interestingly, M. pneumonia uses reaction NAD+ synthetase to synthesize NAD+, but blasting this gene against M. gallisepticum shows no close homology. One more option was that NAD+ was taken from the media, but no literature evidence was found about the requirements of NAD+ M. genitaliumM. laidlawiiM. gallisepticum had two reactions of the partial pathway and the requirement of the starting component in other relatives, it was hypothesized that NAD+ synthetase reaction was present in M. gallisepticum. However, more experimental and computational work is clearly required to validate this theory.The second constraint that required relaxing was that of the NAD+ mass balance. NAD+ is recovered by passing the electrons from NADH to oxygen and lactate but these mechanisms are insufficient to recover NAD+ in the model. Thus a reaction to synthesize NAD+ was required. Interestingly, when the NAD+ synthetase reaction was added to the model, it was possible to enforce all the mass balance constraints in the model. The mass balance of 2-phospho-4-{cytidine 5′-diphospho}-2-C-methyl-D-erythritol was consistent after adding NAD+ synthethase because the MEP pathway no longer had any flux going through it.−1 compared with the experimental value of 0.244±0.03 h−1. The final M. gallisepticum model accounted for 441 metabolites and 395 reactions. The model had 234 intracellular reactions; 86 transport reactions for the transfer of metabolites between the extracellular space and the cytosolic compartment; and 73 exchange reactions that allowed the uptake or secretion of metabolites either to or from the system boundary. A complete description of the model is provided in an Excel format in supplementary file http://www.gnu.org/software/glpk/).The final model was obtained after 40 runs of GAFBA with each run consisting of 2,000 generations. M.gallisepticum model, it is compared to iPS189 of M.genitaliumTo provide a better context for M. gallisepticum model was larger than M. genitalium, as was expected since genome size of M.genitalium is 0.58 Mb compare to 1.01 Mb of M.gallisepticum. The genome of M. gallisepticum had 817 genes that encoded for 763 proteins M.genitalium had 524 genes that codified for 475 proteins M.gallisepticum is 31.5%, while the G+C content of M. genitalium is 32% The M.genitalium model had 46 fewer reactions than the M.gallisepticum model. However, it turns out that iPS189 model had more exchange fluxes. The additional exchange fluxes in the M. genitalium model were required to account for exchange of amino acids as a combination of dipeptides; M.gallisepticum, only had fluxes for amino acid exchange as monopeptides. The exchange of dipeptides in M. gallisepticum model was represented as a general reaction (trans-rxn7tv-3945) due to lack of information about specificity of this transporter.The M.gallisepticum model had 86 more metabolites than iPS189 model. These metabolites were mainly located in the cytosol space and included lipids. The iPS189 model did not have fully specified lipids in the biomass reaction or the associated lipid reactions. The M. gallisepticum model included cardiolipin, sphingomyelin, phosphatidylcholine, cholesterol, etc; and a set of reaction related with lipid metabolism such as phosphatidate phosphatase (E.C.3.1.3.40); phosphatidylglycerophosphatase (E.C.3.1.3.27); diacylglycerol chlorinephosphotransferase (E.C. 2.7.8.2); sphingomyelin synthase (E.C. 2.78.27); etc.Additionally, the M. gallisepticum model via the web-based resource (http://iris.kbase.us). During this process, Model SEED created a biomass reaction; the media chosen was a defined media already present in Model SEED. After, running the auto-completion tool of Model SEED, a growth rate of 2.02 M. gallisepticum model from SEED was constrained to the experimentally measured glucose uptake rate and lactate production rate, the model predicted no growth. It was not possible to use the model optimization tools of Model SEED as they were not available on the website at that time. As a result, a better fitting model incorporating the experimental growth data could not be created. Instead, GAFBA was used to perform a 2,000 generation run, resulting in a growth rate value of 2.5 h−1. However, the mass balances constraints of acetaldehyde, and dCMP had to be relaxed.The RAST annotation server M.gallisepticum model from Model SEED and adding the biomass reaction and media composition defined in this work without adding any of the lipid metabolites and tRNA charged/uncharged, since the M.gallisepticum model from Model SEED did not have them. The model generated had 354 reactions and 391 metabolites. The glucose uptake rate and lactate production rate were constrained to the experimental values, and again; no growth was predicted. GAFBA was used to evaluate the potential gaps of Model SEED. A 2,000 generation run was performed and resulting in a growth rate of 1.32 h−1, with 12 mass balance constraints being relaxed. These results indicate that GAFBA may be used with Model SEED to identify problematic metabolites.One more trial was done, using the The frequency of metabolites requiring relaxation of their mass balance constraint was determined for the best 20 models of the population. The rationale for such an analysis was that it might facilitate the identification of the most pathologic metabolites with respect to achieving a feasible solution to the optimization problem. The frequency analysis was done at simulation 3, 5, 17, 27, and the results are presented in At simulation number five, 16 metabolites with relaxed mass balance constraints were common in the pool of best 20 chromosomes. By reviewing the list of metabolites, it was observed that nine metabolites were already dropped in simulation number 3, and four of them were charged/uncharged tRNAs. Thus, a recycling reaction for the charged/uncharged metabolites was added to the biomass equation.Nine commons metabolites were found at simulation number 17 among the best 20 chromosomes. Six of them already appeared in either simulation number 3 or 5. One of them was ribose-5-phosphate, and by checking the metabolic pathway around this metabolite, the directionality of two reactions Ribulp3epim-Rxn (E.C. 5.1.3.1) and Rib5pisom-Rxn were changed to reversible (E.C. 5.3.1.6).Finally, at simulation number 27, five metabolites were present in all the best 20 chromosomes, and four of them had previously appeared in the preceding simulations analyzed. At this point, the metabolites' studied were sodium ion and L-phosphatidate. The solution to the mass balance of sodium ion has already been discussed. The other metabolite L-phosphatidate was also part of the choline pathway. By adding the Na+ATPase reaction, the choline pathway was activated and the mass balance constraint for L-phosphatidate was closed.M. gallisepticum. By using GAFBA, it was possible to find gaps and inconsistencies present in the network that went beyond genome annotation. It was generally possible to fill these gaps based on the described heuristics and/or searching through the literature. Even when the process was not automated, time spent at the curation level was minimized because problematic metabolites were identified allowing one to focus only on the significant issues remaining.The goal of this work was to develop an approach which could be used for the curation of metabolic networks and facilitate fundamental understanding and discovery of metabolism. To test this idea, a strategy was applied to the reconstruction of a genome-scale metabolic network for An argument could be made for using optimization strategies other than a genetic algorithm, such as a mixed integer linear programming (MILP) approach, which MetaFlux, Model SEED, and GapFill use. Although the MILP approach is a powerful one, the GA method provides certain advantages such as scaling more effectively for the study of large networks, and generating a population of solutions that, as a whole, provide further insight into the problem domain.A factor in favor of MILP is that MILP would potentially be faster than using GA. This is especially true for smaller models. However, even when the GA running time is greater than MILP, the computational time is not the rate-limiting step in the overall curation process. Rather the manual portion of the curation, such as the literature review and the assembly of the preliminary model, consume the bulk of the time.n is the number of metabolites in the model In general, a GA based method may be seen as unnecessary for a small model such as the one developed in this work. However, it is anticipated that in the future, systems to be analyzed will grow significantly. Such growth may be due to organism complexity or due to dealing with microbial consortia. Under such circumstances, it is expected that GAFBA will be comparable or faster than MILP based methods since the computational complexity of GA falls within a range of O(n∧1.5) to O(n∧2), where Another critical point and significant advantage regarding the GAFBA algorithm is the ability to carry out a population frequency analysis to better determine the most problematic metabolites. By observing the most frequently occurring relaxed constraints in that population, it was possible to identify candidate metabolites that were likely the most problematic. These results are extraordinarily valuable, as they provide the researcher with a starting point regarding which metabolites are most likely causing the model to fail.Additionally, the GAFBA approach provides some important advantages over existing curation strategies. The most significant advantage is the possibility to identify problems without relying on a database for reference. For example, MetaFlux requires MetaCyc, Model SEED requires the SEED reaction database, and GapFill requires a customized multi-organism database. GAFBA provides flexibility by allowing identification and potential resolution of metabolic inconsistencies without requiring access to any type of database. It is important to realize, however, that GAFBA may be used in conjunction with the other approaches described, as was illustrated earlier with Model SEED. Thus the information from these databases is not lost. Rather, GAFBA may be used to complement existing approaches or it may be used on its own.MetaFlux requests that the user set the values for the weights of the objective function in the MILP objective function. This step necessitates the proper understanding of the meaning of each weight. The GAFBA methodology does not need the user to define any weights. Thus, the set of metabolites to be studied will not depend on values defined by the user. In addition, MetaFlux does not have the ability to add reactions between compartments (e.g. transport reaction), resulting in additional level of complexity for curation purposes. GAFBA, however, does permit addition of reactions between compartments. Interestingly, MetaFlux does not give information about the metabolites that may be problematic; instead it provides a set of reactions to add in order to resolve metabolic inconsistencies. Conversely, GAFBA does not provide a list of reactions to add, but rather points out problematic metabolites that may be used to direct further research. These complementary approaches suggest that some synergy may be possible.M. gallisepticum model created by Model SEED was used with the experimentally determined glucose and lactate constraints, the model predicted no growth, suggesting that more curation was needed. By applying GAFBA to the Model SEED generated model incorporating the experimental data, it was possible to identify the two problematic metabolic constraints inhibiting the viability of the model. This result serves to highlight GAFBA's role as a curation tool and its complementarity to existing model development software available.When comparing Model SEED with GAFBA, Model SEED provides a significant advantage in that it generates a biomass reaction and defined media for an organism. However, when the In general, GAFBA provides an alternative and complementary method curation and modeling strategy to existing approaches. GAFBA could help to improve the quality of the existing metabolic networks and to generate new and more complete networks. Furthermore, because the approach used by GAFBA is complementary to existing approaches, it can be used after a model is created with Model SEED or MetaFlux.M. gallisepticum specifically, the new genome-scale model generated using GAFBA replicated the experimentally observed data well. It is likely that the predicted growth rate was higher than the experimental value observed because the NGAM value and the GAM used in the model were based on the M. genitalium iPS189 model M. pneumoniaeM. gallispeticum.With respect to M. gallisepticum, their presence still needs to be experimentally verified. The elucidation of MEP pathway role in M. gallispecticum metabolism may ultimately help to fundamental metabolic understanding of the organism. It is also important to determine if the MEP pathway is essential for the survival of M. gallisepticum, since it will help to elucidate possible drug targets as was recently illustrated in research for control of Mycobacterium tuberculosisHaemophilus influenzaeThe results of GAFBA have highlighted a number of pursuable experimental avenues. For example, while GAFBA predicts the presence of formate dehydrogenase, NAD+ synthethase and Na-ATPase enzymes for elite selection strategy The genetic algorithm was implemented using an The theory behind the development of FBA has been extensively discussed in the literature M. genitalium iPS189 model To reduce the number of allowable solutions to this system of equations, a set of constraints is defined,M. gallisepticum. The term Finally, the optimal metabolic flux distribution was determined using the linear objective function,M. gallisepticum Rlow, Rlow Clone 2 (RLC2) A previously sequenced clonal isolate of 620 of the culture. To accomplish this, 500 ml of RLC2 was grown in Hayflick's media The CFU/ml concentrations for all experiments were calculated using a previously determined correlation 100 ml aliquots of Hayflick's media with 3.5 g/L of glucose in 250 ml plastic flat-bottom centrifuge tubes were inoculated with mid-log cultures of RLC2 and incubated at 37°C. Sampling of these vessels was initiated approximately 15–19 hours post-inoculation, when the dry cell density in each vessel reached 46.89 mg/L (7.27e7 CFU/ml) and ended once the dry cell density exceeded 125 mg/L. Samples of culture were taken at nearly one-hour intervals over the aforementioned period during late log-growth. This allowed for four to five individual sample points to be taken during this late log-phase growth for each run. From these samples, cell dry weight concentration, glucose concentration, and lactate concentration were determined by the assays described below. Seven runs were completed for RLC2. Three sets of runs were performed on separate days. Each set included approximately three RLC2 runs.6 CFUs of RLC2 (growing in late log-phase) was added to 100 ml of media in a 250 ml plastic flat bottom centrifuge tube. This tube was incubated at 37°C while being shaken at 140 rpm for 15–19 hrs. 1 ml samples from each flat bottom centrifuge tube were then placed into 1.5 ml microfuge tubes. A 700 µL volume of each sample was immediately centrifuged at 20,000×g for 15 minutes at 4°C in an Eppendorf 5417 microfuge using a fixed-angle aerosol-tight rotor (FA-45-30-11) for 1.5–2.0 ml tubes.10620 of the remaining 300 µL of each sample was measured while the other 700 µL was being centrifuged. This OD620 reading was used to determine the biomass concentration using the experimentally determined correlation between cell dry weight and OD620. This sampling process was repeated until the dry cell density in each vessel reached 125 mg/L. At this point, sampling ended and the remaining culture was allowed to grow overnight and was checked 24 hours later for visible signs of contamination. No contamination occurred.The supernatant was transferred into new 1.5 ml microfuge tubes and placed in a −80°C freezer for storage until further analysis. The ODAfter two to five days of storage at −80°C, the 700 µL supernatant samples were thawed and prepared for analysis using a YSI 2700 SELECT single-channel Biochemistry Analyzer. Supernatant samples were filtered with 13 mm GHP 0.2 micron syringe filters (WAT097962) to remove any remaining cellular debris. The supernatant samples were then placed in a −80°C freezer for another 1–7 days.To assay for lactate, the standard YSI 2700 SELECT protocol for measuring L-lactate concentration using a L-lactate membrane (part # 2329) was followed. Standard buffer (part # 2357) and L-lactate calibration standard (part # 2776) were used. 25 µL of sample was taken and analyzed for a 30 second period before being flushed. Two readings were performed for each supernatant sample. Six supernatant samples at a time were thawed, assayed, and then refrozen to −80°C.To assay for glucose, the standard YSI 2700 SELECT protocol for measuring glucose concentration using a glucose membrane (part # 2365) was followed. Standard buffer (part # 2357) and glucose calibration standard (part # 2776) were used. 25 µL of sample was taken and analyzed for a 30 second period before flush. For each supernatant sample, three replicates were performed and averaged. Six supernatant samples at a time were thawed, assayed, and then refrozen to −80°C.The correlation between optical density and dry cell weight per ml is shown in M. gallisepticum and other similar mycoplasmas. The starting equation was taken from a metabolic reconstruction of M. genitalium, one of M. gallisepticum's closest relatives Mycoplasma metabolism that did not possess any cited synthesis or absorption pathways, such as Menaquinol 7, were modified or removed. The relative ratios of the various components of DNA, RNA, and amino acids reported in the M. genitalium reconstruction were kept due to the similar GC content and large number of shared proteins between the two organisms M. gallisepticumThe biomass equation was determined by incorporating all available published information about the makeup of M. gallisepticum and other related Mycoplasmas. First, the ratios of the classes phospholipid, sterol, and triglyceride were determined relative to each other from previously published work M. gallisepticum may possess a capsule, it has not been directly shown that its capsule or M. gallisepticum itself contains glycolipids M. gallisepticum's chemical composition relative to other related glycolipid possessing species, glycolipids were not included in the biomass composition The composition of the lipid fraction was calculated from published data on the lipid fractions of The ratios of the subclasses of sterol and phospholipid groups relative to each other were then analyzed. First, sterols was analyzed. The amount of cholesterol and cholesterol ester was calculated using published ratios of cholesterol to cholesterol ester M. gallisepticum's membrane was estimated from previously reported values M. gallisepticum's membrane along with the reported sphingomyelin membrane fraction et al. The amount of phosphatidic acid was decreased by 25% before incorporation into the biomass equation due to an author statement suggesting the value was higher than it should have been Mycoplasma pneumoniaeM. gallisepticum lacked this phospholipid as well. Tourtellotte et al. reported that phosphatidylglycerol likely made up less than 10% of the phospholipids. Since no definitive quantitative assay of phosphatidyl-glycerol have been performed on M. gallisepticum or any of its close relatives, the phospholipid percent of phosphatidyl-glycerol was estimated from a published value in A. ladwaiiM. gallisepticum has yet to be studied. However, cardiolipin has been shown to be present in Mycoplasma pneumoniae's membrane M. pneumoniae was shown to have the metabolic reactions necessary to biosynthesize cardiolipin from fatty acids and glycerol Mycoplasma such as M. mycoides and M. hyopneumonaieNext, phospholipids were analyzed. The amount of sphingomyelin present in M. gallisepticum's phospholipids, sterols, and triglycerides R groups were by weighted averaging the molecular weights of the constituent fatty acids Click here for additional data file.Data S2A description of the FBA model for M. gallisepticum in SBML format.(TXT)Click here for additional data file.Data S3The source code of the M. gallisepticum model for use with GLPK.(TXT)Click here for additional data file.Figure S1Case 1. Change directionality of reactions. The purple circle showed the dropped metabolite. The blue arrows are the fluxes and the orange arrows are the proposed solutions. The solid blue line is the plasma membrane and the dashed blue line is the system boundary.(TIFF)Click here for additional data file.Figure S2Case 2. Add exchange flux. The purple circle showed the dropped metabolite. The blue arrows are the intracellular fluxes and the orange arrow is the proposed solutions. The solid blue line is the plasma membrane and the dashed blue line is the system boundary. The red circle represents the Biomass pool.(TIFF)Click here for additional data file.Figure S3Case 3. Add reaction. (formate case). The purple circle showed the dropped metabolite. The blue arrows are the fluxes, the blue thick arrows are the exchange fluxes, and the orange arrow is the proposed solutions. The solid blue line is the plasma membrane and the dashed blue line is the system boundary. The red circle represents the Biomass pool.(TIFF)Click here for additional data file.Table S1OD/(g/cfu) correlation. Calculations of the g/cfu conversion.(DOCX)Click here for additional data file.Table S2Lipid fraction estimation. Percent composition of the lipid fraction of M. gallisepticum, the estimated molecular weights, and the relevant references used to generate these percentages.(DOCX)Click here for additional data file.Table S3Average fatty acid molecular weight. The calculation of the average fatty acid molecular weight from the fatty acid composition of Mycoplasma gallisepticum as reported by Tourtelloute et al. (DOCX)Click here for additional data file.Table S4Estimation of molecular weights for major lipid components making up biomass. Using the chemical structure, the molecular weights of each molecule, and the estimated molecular weights of the R group, whose calculations are shown in (DOCX)Click here for additional data file.Text S1Examples of methodologies for resolving infeasible models.(DOCX)Click here for additional data file.