Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the  The in silico design and the novel cell factory become described with quantitative measures in contrast to qualitative descriptions. Modelling of metabolism is involved in both design and evaluation of the novel cell factories. Modelling of metabolism, or its component metabolites and reactions, provides a simplified representation of the reality. In computational applications, a model is a quantitative, mathematical, representation of the biological systems or components on a suitable level of simplification. Different mathematical representations of metabolism, metabolic pathways, or metabolic components, enzymes and metabolites, are useful in different phases of the development of a cell factory by synthetic biology. In this minireview, the role of metabolic modelling in the emergence of synthetic biology will be discussed with a review of the current status of compatible methods and models for the design and evaluation of a cell factory. The workflow of the development of novel cell factories with synthetic biology is ideally linear with subsequent steps traversed in contrast to an iterative cycle of conventional strain improvement. The ideal, linear, workflow can be attained with the quantitative engineering approach using high-quality predictive metabolic models, and libraries of well-characterized parts. The workflow of the development of a novel cell factory can be divided in the design phase, the phase of the evaluation of a novel strain, and a strain construction and experimentation steps in between the former, in silico design of the chassis, and the modelling of pathway components is required for the simulation of novel, optimally orthogonal, production pathways. Further, modelling of the metabolic entity consisting of the designed pathway within a chassis enables the simulation of the performance of the alternative designs in the cell  who simulated the optimal flux distribution for L-lysine production in Corynebacterium glutamicum and compared it to the in vivo flux distribution determined by 13C flux analysis to determine the differences for targets of strain improvement . De. De43]. compound . They alcompound . Other seactions . Strengteactions . Alreadyeactions . This me finding . Atom tr finding , 50. Whi finding . Howevermappings . The novin silico pathway design tend to result in huge numbers of routes in contrast to a single design provided by the optimization based methods. The benefit is that the alternative designs could include solutions having superior properties not taken into account in the optimization. Yet it is valuable to embed some selection for feasibility into the enumeration phase, and post-ranking is required to choose the pathways to be introduced into the chassis. Ranking criteria is somewhat context-dependent but the feasibility in terms of pathway thermodynamics is generally essential for successful function in the chassis. Henry et al. (2010) applied thermodynamic metabolic flux analysis (TMFA) [et al. (2012) includes the consideration of the pseudoisomers of the molecule's structural groups, which gains accuracy in the estimation [et al. (2012) the yields of target molecules are evaluated at maximum growth rate [et al. (2011) pointed out the additional importance of the characteristics of pathway intermediates such as toxicity, reactivity, and permeability [Utilization of enumerative methods in the s (TMFA)  to novels (TMFA) . TMFA uts (TMFA)  in the etimation . Feasibiwth rate . Thus, twth rate  as an obwth rate . Furthereability . In manyeability . Alternaeability . Even ifLack of compatible regulatory mechanisms in the host may further hamper the production of target molecules with pathways encoded by multiple heterologous genes . Design et al. (2011), Ghosh et al. (2011), and Jouhten et al. (2012) [in silico design phase of the workflow in in silico design phase). Different dynamic set ups from batches to fed-batches with variable feed programs can be simulated and optimized for high productivity or titre [S. cerevisiae, which was recently modelled by Moisset et al. (2012) [S. cerevisiae. A glucose batch culture with a diauxic shift from glucose utilization to ethanol oxidation was successfully simulated with the model. Although the signalling and transcriptional regulation in S. cerevisiae in response to glucose is well known, the regulation of the metabolic fluxes including all the post-transcriptional and metabolic regulation has not been completely solved. Metabolic regulation also by a flux sensor which mediates the regulation of the distribution of flux to fermentative and respirative pathways in response to the flux in the upper glycolytic pathway has been suggested by Kotte et al. (2010) [Since microorganisms tend to strive for steady state growth, dynamic flux balance analysis (dFBA) is powerful in the simulation of the behaviour of microbial cell culture under dynamic cultivation conditions , 65. DFB. (2012) –68. Theror titre . If, dur. (2012) . They in. (2010) .in silico evaluation of different designs in complete process simulations. DFBA has been applied to a consolidated bioprocess by Salimi et al. (2010) who simulated the consolidated process of a co-culture of Clostridia species with native cellulolytic activity [et al. (2011) [E. coli strains to secrete cellulase, xylanase, beta-glucosidase, and xylobiosidase enzymes for the breakdown of raw material outside the cells and to produce biofuel components and precursors via heterologous metabolic pathways within the cells. In a similar case the optimal compatibility of the strain modifications and the process conditions could be computationally designed by dFBA.DFBA offers also means to model and simulate a consolidated bioprocess where the breakdown of polymeric resource into metabolizable molecules and the fermentation occur in a single process. The breakdown of raw material, the uptake of the metabolizable carbon source, the biosynthesis of the product by the novel strain, and the process set up can be included into a single model which enables activity . An exam. (2011) . They enin vivo performance of the strain need to be quantitatively evaluated to validate if the in silico designed and simulated performance has been reached (in vivo pathway fluxes (in vivo flux estimation via isotopic tracer experiments (www.metabolome.jp) [et al. (2011) have provided a novel computational solution [E. coli a fully atom mapped  genome-scale metabolic model for flux analysis by the global iterative fitting approach was published by Ravikirthi et al. (2011) [13C labelling is commonly applied for a quantitative analysis of in vivo fluxes. 13C labelled substrate is feed to the cells after which the cells are harvested for the detection of 13C labelling patterns in either the metabolic intermediates or in the biomass components. The resolution of the quantitative information on the in vivo fluxes is strongly dependent on the choice of the 13C labelling strategy. The substrate molecules may be fractionally uniformly labelled, labelled in particular carbon positions of the molecule, or the labelling strategy may combine both of the previous approaches. An optimal labelling strategy for resolving a particular flux or fluxes is dependent on the structure of the metabolic network and can be addressed computationally [et al. (2012) applied Monte-Carlo sampling of feasible flux spaces in constraint-based models [13C labelling patterns.After the novel strain has been constructed by molecular biology the eriments . Atom malome.jp) ; KEGG lilome.jp) ), and alsolution . For E. . (2011) . Stable tionally . In the d models , and Crod models , 79 to s13C labelling patterns are detected either by nuclear magnetic resonance (NMR) spectroscopy or by mass spectrometry (MS). Even though the recent hardware development of NMR spectroscopy in terms of the minimisation of thermal noise in the electronics has substantially increased the sensitivity, MS is intrinsically more sensitive method than NMR spectroscopy. The detection method options differ also in NMR spectroscopy being capable of providing direct positional information on the 13C label whereas MS can directly detect mass differences between molecules or molecule fragments. Fragmentation in the MS measurement enables generation of partially positional information. Identification of fragments in MS spectra is alleviated not only by database information but also by computational methods suitable for novel fragments lacking the database information  approached the extension of the metabolic flux analysis into genome-scale by constraining the fluxes in a genome scale stoichiometric metabolic model with the flux solution from a smaller, central carbon metabolism scale problem [et al. (2010) were able to decrease the computational burden of 13C constrained flux analysis with the implementation of the elementary metabolite unit (EMU) formulation and flux coupling into a global iterative fitting approach [ methods , 81). Frpathways  or via gpathways . The relsolvable , 85. Bec13C data , 87. In an cells –90. Theran cells ; 13C-FLUOpenFLUX ) which ad unique . However problem . More reapproach .in vivo regulation  [Metabolic modelling plays an essential role in the development of cell factory by the means of emerging synthetic biology. Modelling and simulation provide quantitative data on the performance of alternative designs. Especially large-scale modelling of metabolism is central when optimal designs of cell factory systems are sought. In particular the constraint-based modelling scales well in genome-scale whereas dynamic models in large-scale are only emerging. Thus, the constraint-based models have successfully been applied in the identification of engineering targets. On the other hand, the shortcomings are also being confessed. In constraint-based models the detailed dynamic features and the regulatory dependencies on the metabolome are not implemented. However, dynamic models require a lot of experimental data for parameterisation, which is both laborious and expensive or even impossible to generate. Therefore, the modelling approaches are developing to cope with the scarcity of s (2011)  and simis (2011) . Simultas (2011) . Both ths (2011) –68, is rMany previously developed path finding methods are applicable to the design of novel, heterologous, production pathways and specific methods for synthetic biology are also already available. BNICE, which is capable of finding truly novel pathways, constrains the space of possible biochemical conversions only with general enzymatic reaction rules, the particular enzyme need not to be known or characterized . CatalysE. coli. A true establishment of synthetic biology will require supporting efforts to develop the models and parts. The iterative cycle of information generation and model improvement, which systems biology focuses on, could roll faster to support the emerging synthetic biology. Knowledge on the regulation of the metabolism should be integrated into the metabolic models to reach a good level of predictability under various conditions the cells may be exposed to. Urgency to rationalize the engineering of the regulatory systems of cell factories was recently stressed also by Yadav et al. (2012) [The need to accumulate knowledge and to improve the models by systems biology studies still remains. The ideal synthetic biology workflow, . (2012) . Modelli


With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks.A symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast.We were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition. The explosion of tools available to simulate the systems level properties of biological systems is indicative of the wide scale uptake of integrative biology. The Systems Biology Markup Language (SBML) Web site  now listAt the core of many of these analytical tools is the strict requirement of conservation of mass for each biological transformation. Because models of metabolic systems are typically under-determined, a common task when analyzing them is to find all possible steady state regimes when the concentrations of each metabolite do not change appreciably with time.With the advent of genomic technology, the size of networks that are subject to conservation analysis is growing. This is true also of the amount of data that constrains biological function, forcing the analysis procedure to become more involved. This is especially true when faced with the realities of compartmentation in large biological systems.The analysis of systems of chemical reactions can be traced back to 1921 when Jouguet established the notion of independence of reactions and the invariants of a system of reactions . These wIt may come as a surprise to many biologists that the mathematically simple operation of finding a set of parameters that describe the steady state solution of large chemical systems continues to challenge the limits of widely used numerical libraries used to perform this task, and the development of robust computational routines for this purpose continues to be an active research area . Sauro aIt is notable that existing software packages do not take into account the inherit sparsity of large metabolic networks . This isTo our knowledge, no software package is available that both makes use of rational arithmetic and accounts for the inherit sparsity of large metabolic networks. To use sparse representations of metabolic networks, SVD based algorithms need to be replaced with alternative algorithms that would preserve the sparsity property in their results. To achieve the same numerical robustness of these algorithms as SVD provides, rational arithmetics can be used. The decrease of performance due to the use of rational arithmetics ought to be balanced by the sparsity of matrices as the number of numerical operations is reduced considerably. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large chemical systems.Specifically, we have developed a routine that solves for the kernel of large stoichiometric matrices using a symbolic Gauss-Jordan Elimination (GJE) routine. For a given metabolic network the routine computes steady state solutions in a form of steady state flux relations that define how certain fluxes termed as dependent fluxes vary when the rest of fluxes termed as independent fluxes are changed. The list of dependent and independent flux variables can be either computed by the routine or specified by the researcher. The performance of this method is compared with Singular Value Decomposition (SVD) implemented in a widely used numerical routine. In addition, we demonstrate that the usefulness of solving for the stoichiometric matrix kernel symbolically goes beyond the avoidance of numerical errors. Specifically, the kernel arrived at using GJE consists of flux vectors that align with actual metabolic processes which is useful for applying constraints on steady state metabolism.A symbolic GJE routine was developed within SympyCore  during tFive large metabolic networks of increasing complexity were selected to test the performance of symbolic GJE to that of numerical SVD. These metabolic networks were formulated in a closed form as described by Famili and Palsson . To obtaO(mn2), that is, increasing the network size by a factor of 10, the complexity should increase by 1000 times. The actual complexity increase (about 400 for SVD and 640 for GJE) is smaller because of using threaded libraries for the SVD routine and because of computing with high sparsity for the GJE routine. The numerical errors introduced when using SVD were found to be insignificant for the purpose of biological flux calculations and confirm the fact of numerical robustness of the SVD routine. This assessment was made by calculating the maximum relative flux error εSVD using Equation (11). Note that this loss of accuracy is in agreement with the condition number calculated for Vindep in Equation (9); the number of inaccurate digits is approximately equal to the order of magnitude of εSVD.The kernel of five stoichiometric matrices were solved for using both numerical SVD and the symbolic GJE routine with the results given in Table With our test computer system both numerical SVD and symbolic GJE routines can easily cope with 4000+ reaction networks. To test the limits of these routines, we repeatedly doubled the sizes of considered networks by repeating given stoichiometric matrix diagonally within a doubled stoichiometric matrix and then randomly shuffling the columns. The doubled stoichiometric matrix would then correspond to two independent but identical metabolic networks. The shuffling is needed for modeling the structure of actual metabolic network models where the order of columns is arbitrary. The process of increasing the sizes of networks was repeated with doubled stoichiometric matrices until applying our routines were close to exceeding the resources of our computer system. Figure Often one needs to constrain the flux values that are physiologically meaningful, that is, either they have been experimentally measured or they must be non-negative due to the irreversibility of some reactions. We demonstrate the application of constraints by calculating a flux distribution for an example yeast network. The metabolic network is given as an SBML file in additional file The symbolic GJE of the stoichiometric matrix for the open system provides 91 relations for the dependent fluxes expressed in terms of 39 independent fluxes. A full list of reactions, metabolites, and steady state flux relations is given in additional file The independent fluxes can be selected prior to performing GJE. We compose the set of independent fluxes from biomass production rates that have been experimentally measured. In total, 26 measured biomass fluxes taken from Cortassa et al  were useBAT1 and BAT2, the split between LEU4 and LEU9, ALT1 and ALT2, and AAT1 and AAT2. Two determine the interconversion and transport of glutamine, glutamate, and oxoglutarate via the split between GLT and GDH. The remaining six determine: (1) urea cycle flux, (2) relative production of glycine from either serine or threonine, (3) the flux of D-Glucose 6-phosphate directed towards D-Ribulose 5-phosphate, (4) production of pyruvate by the malic enzyme MAE1, (5) the production of phosphoenolpyruvate by PCK1, and (6) the relative production of acetaldehyde to acetyl-CoA from pyruvate. Figure After substituting the biomass production values into the steady state flux relations, 27 dependent fluxes become fully specified with 64 relations described by the 13 internal variables. Inspection of this system of equations  and hence the constraints on the coefficients have a straightforward interpretation.Within EFMA, it is mathematically more convenient to use convex polytope to represent the restricted part of the flux subspace because the conditions of reaction irreversibilities directly define the representation. This approach has given rise to the now widely used notation of elementary flux modes  and extrA symbolic GJE routine was developed within SympyCore  to efficConstraints can be applied directly to each independent flux. The independent flux variables can be specified in the symbolic GJE routine to match the measured data available. In addition, it was demonstrated that knowledge regarding dependent flux variables can be used to find limits on the possible ranges of independent flux variables.We found that independent fluxes influence only specific portions of the metabolism and sub-networks can be identified from the GJE kernel matrix. This property has potentially far reaching implications for the physical interpretation of steady metabolism in genome-scale metabolic networks.Note that usage of the symbolic GJE routine does not introduce numerical errors while numerical SVD routines do. We estimated the relative flux error introduced by the numerical SVD routine and concluded that the numerical errors are insignificant for biological applications and confirm the numerical robustness of the SVD routine. Both numerical SVD and symbolic GJE routines are equivalent with respect to computation time, however, the memory consumed by numerical SVD routine increases two times faster than that of the symbolic GJE routine using sparse data structures.The main arguments for using symbolic GJE routine for analyzing large metabolic networks are memory efficiency, numerical robustness, freedom of choosing different sets of independent fluxes, and the ability to define sub-networks.Our results show that symbolic implementation of relevant algorithms are competitive with highly efficient numerical algorithms when taking into account the inherit sparsity of genome-scale metabolic networks.In this section we present two alternative procedures to obtain steady state solutions of possibly large under-determined metabolic networks. The first approach uses a symbolic GJE algorithm that guarantees exact solutions and the second approach uses SVD implemented in a numerical algorithm that ought to give better performance. In addition, we describe a method for applying constraints to the steady state solution.Every chemical reaction and thus reaction system has the strict requirement of conservation of mass. A system of mass balances around each species has the form:N is the m × n stoichiometric matrix that links metabolites to their reactions via stoichiometry, and ν is a length n vector that describes the flux through each reaction. For a system at steady state with n reactions and m species, the system of chemical reactions becomes:where N. Let us denote the vectors of dependent and independent flux variables as νdep and νindep of length r and f, respectively. Then with a n × n permutation matrix P that reorders the columns of N such that columns corresponding to dependent flux variables appear earliest, the steady state Equation (2) readsThe number of flux variables that need to be specified to calculate a viable steady state is f = n - r where r is the rank of N = [N1 N2] PT. Clearly, when the m × r matrix N1 is regular (m = r and det N1 ≠ 0), the relation between νdep and νindep vectors can be computed directly:where N may contain linearly dependent rows (r < m). In addition, νindep or P are not known in advance.However, for many metabolic networks the stoichiometric matrix In the following we consider two methods based on GJE and SVD procedures that solve Equation (2) for the relation between dependent and independent flux variables: The general solution is written as:R as a kernel of the steady state solution where the columns are flux basis vectors and νindep are flux coordinates.We identify N to a row-echelon form NGJE where all columns corresponding to dependent flux variables would have exactly one nonzero element and Equation (5) can be easily composed (P is constructed during the GJE process while applying the leading row and column selection rules (pivot element selection). One of the advantage of using GJE is that it allows one to influence the pivot element selection rules so that a preferred flux basis for the system will be obtained. If the selected flux variables cannot form a basis, the routine will move one or more of the preselected independent variables to become dependent.Solving the steady state problem via GJE is based on transforming the stoichiometric matrix Note that in numerical GJE algorithms the typical leading row and column selection rule consists of choosing a pivot element with largest absolute value for maximal numerical stability. Symbolic GJE algorithms that calculate in fractions avoid numerical rounding errors and can implement more optimal selection rules that take into account the sparsity of the system. In SympyCore  the leadN into a dot product of three matrices:Solving the steady state problem via SVD is based on decomposing the stoichiometric matrix u, v = [Vim Vker] are orthogonal matrices and σ is a diagonal matrix with nonzero values on the diagonal. The solution to the steady state Equation (2) iswhere α is a f vector of arbitrary parameters. Note that the SVD approach does not provide a numerically reliable and efficient way to determine the vectors of dependent and independent flux variables and in the following we use these in the form of the permutation matrix P found from the GJE approach:where which gives Equation (5b):Vindep is a regular f × f matrix.where SBML models of metabolic networks were obtained from the BiGG database . During Each metabolic network was transformed into open form using the following rule: if a species participated in exactly one reaction, a reaction transporting this species across the system boundary was added. As an alternative rule used in the example yeast network, if all transport reactions out of the system are known, then transformation to open form is accomplished by removing rows for the species that are external to the system.Both of these approaches result in equivalent steady state solutions because adding extra reactions extends linear pathways that each contain a species that exits the system. Both approaches were applied to the example yeast network: external species were removed to calculate the flux distribution in Figure The example yeast network given in Figure The example yeast network makes use of fictitious metabolites that link the stoichiometry of coupled reactions. The three pentose phosphate pathway reactions are broken into two parts each linked with a fictitious metabolite that represents the carbon skeleton that is broken off of one metabolite in the first step and transferred to the next. In the additional file The GJE routine provides a flux based coordinate system to describe the steady state flux space while SVD provides an orthogonal coordinate system. When specifying a flux value that is part of a flux coordinate system, one dimension from the steady state flux space is removed.Many different sets of independent flux variables can form a coordinate system for the steady state flux space. The GJE routine allows the researcher to specify which flux variables forms a flux coordinate system and thus can match the choice of coordinate system with the experimental data available. The basis vectors formed from a flux coordinate system are often sparse and tend to span connected portions of the metabolism.i ∈ . The problem being solved is how the constraints on νdep constrain the independent flux variables ν indep. The goal is to determine how the steady state flux space is bounded. This is useful for many techniques used to analyze the properties of metabolic networks, for example in optimization procedures that must scan the steady state flux space while avoiding regions that are not feasible [Let us assume a relation between dependent and independent flux variables as given in Equation (5). In addition to that, let us assume some constraining knowledge about the dependent variables, for example, the flux positivity for irreversible reactions: feasible .ν indep, we set up the following system:To find the constraints for G and g vector b define g measured data constraints for νindep; q × r matrix Q that defines q positivity constraints for νdep. The system in Equation (10) defines a convex polytope and due to the constraining parts it is redundant. The redundancy can be removed by using the following geometric computation algorithm: solve the vertex enumeration problem for the convex polytope defined by Equation (10) and then using the obtained vertexes and rays solve the facet enumeration problem. The solution to the facet enumeration problem is a set of inequalities that has no redundancies and defines the same convex polytope as Equation (10). Note that the intermediate result of the vertex enumeration problem (polytope vertexes and rays) provides convenient information to volume scanning applications.where g × f matrix get_gauss_jordan_elimination_operations that allows one to specify the list of preferred leading columns  for the GJE algorithm and after applying the GJE process the method returns a matrix object that is in row-echelon form. In addition to that, the method returns also a list of all applied row operations that can be later efficiently applied to other matrix objects. This feature is especially useful for adding extra columns to a stoichiometric matrix and then applying GJE process without the need to recompute the row-echelon form of the original matrix. One could use this to add transport reactions to a metabolic network during the constraint process.The GJE results of this paper are obtained using a Python package SympyCore  that impThe SVD results of this paper were obtained using a Python package NumPy  that proSince the results obtained with the symbolic GJE routine are correct and the results of the numerical SVD routine contain numerical rounding errors then in the error analysis we are using maximal relative flux errorSVD characterizes relative errors in dependent flux variables introduced by the numerical SVD routine.where For solving vertex and facet enumeration problems we use a Python package pycddlib , a wrappThe Python scripts used for computing the results are available in SympyCore . The perDS and PP developed the GJE technique with DS providing chemical insight and PP providing mathematical insight. DS composed the example yeast network and analyzed constraints. All authors contributed to the text and approved the content of the final manuscript.SBML model of the example yeast network. This file is marked up in SBML and contains all of the reactions of the example yeast network.Click here for fileSBML model details. This is a PDF file that summarizes the details of the model given in additional file Click here for file


The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman’s least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed.The open-source software “Least Square Mass Isotopomer Analyzer” (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations. Metabolism is central for all cellular processes including adaptation of organisms to their respective life style and conditions. Triggered by the presence and activity of metabolic enzymes and the metabolite fluxes through pathways, cellular reactions constitute a highly dynamic network that can be rapidly and efficiently modulated in response to environmental changes. A number of theoretical techniques has been established to predict metabolic fluxes -4. Implein vivo experiments using stable isotope labelled precursors, such as 13C-glucose or 13CO2. The transfer of label to the metabolic network and the specific isotope distribution in metabolic products can then be taken as evidence of metabolic pathways and fluxes during the experimental period. However, robust technology is required to quantitatively determine the isotopomer abundances in multiple metabolites. Specifically, experimental intensities of mass signals  have to be converted into relative and molar isotopomer abundances.In contrast, only few methods allow direct determination of metabolic fluxes, one of which is based on Isotopologues are species of a compound that differ only in their isotopic composition glucose [13C6]glucose is efficiently incorporated into most amino acids of Salmonella enterica via intermediates of glycolysis. Under these conditions, alanine is mainly composed of the unlabelled isotopomer (derived from unlabelled glucose in the medium) and the fully 13C-labelled isotopomer due to de novo synthesis of alanine from [U-13C3]pyruvate made from [U-13C6]glucose via glycolysis.The software was now tested with different data sets. ]glucose . We haveo, M-1, Mmax (the range of mass in which to pick the correct intensities), the predicted relative natural abundances, and the relative abundances of isotopomers in the labelled sample with its absolute 13C enrichmentAs an example for the involved numerical steps, Table 12C only), the estimated natural abundance is 96.7%, whereas the same isotopomer accounts for 70.0% in the labelled compound. The isotopomer group with one 13C-atom has 3.26% or 0% abundance in the unlabelled or labelled compound, respectively. The relative abundances for the isotopomer group comprising two 13C-atoms are 0.037% or 0% in the unlabelled or labelled alanine sample, respectively, whereas the abundances for the fully labelled isotopomer are 0.00014% or 30.0% in the unlabelled or labelled sample, respectively. On this basis, the observed absolute enrichment value of 13C in the labelled sample from S. enterica results in 30.0%. This is in line with our expectations and calculations also using other software tools for isotopomer analysis.As shown in Figure 13C isotopomer peak in this example is at 263.1 in the correct range of Mo, M-1, Mmax so the values for 260.1, 263.1 and 264.1 are shown in Figure The resulting spectrum is shown in Figure For the pathway analyses of Salmonellae mentioned above and in similar studies on other bacteria, we tested the processing of different data sets; input data file preparation and management, experimentation and data analysis. This allowed also an analysis of error rates. Accuracy of the calculation itself is quite high (error less then 1 part per billion). This estimate is based on several hundred test runs of the software and meticulous testing for bugs and unexpected behavior. Regarding errors from the non-trivial coefficients of matrix A involving experimental errors from atoms of the derivatization agent present and overlap of fragment spectra as well as the mode of ionization (positive or negative), we expect error rates of less than 1%.Another inherent source of error is the analysis of network fluxes for complex biological system that typically result in notably higher deviations when looking at the results from replicates. As these are indirectly inferred from the changes of measured isotopomer concentrations, these data already carry the error due to the mathematical procedures. In addition, experimental errors regarding growth conditions must be taken into account . Indeed, the resulting overall error for flux analyses considering all these effects can be as high as 10% based on our experience. Some limitations arise from the fact that not in all pathways metabolized label is present, but this is an inherent prediction limitation for the approach.13C Labelling of metabolites has proven to be a powerful method in quantifying metabolic routes and fluxes, especially, if there are alternative pathways between two metabolites. Isotopomer balancing provides the basis for deducing metabolic pathways and fluxes.13C-incorporation studies coupled to software-based isotopomer calculation allowed us to identify the pathways of amino acid biosynthesis [Listeria monocytogenes and its modulation by the transcription factor PrfA [Salmonella in the Salmonella containing vacuole [Salmonella depends on isotopologue data with the use of suitable processing software.In own studies, we have shown that ynthesis  under retor PrfA ). Anothe vacuole . Again, In general, without transforming the mathematics into applied software doing the calculations, none of these and other insights ,20,21 reDespite this potential power of the methodological approach, it is still difficult to perform metabolic flux analyses due to the lack of user-friendly and open-source software tools. This limitation also demands the development of mathematical modelling of metabolism for each substrate to obtain more detailed and accurate results. Before the implementation of LS-MIDA, we relied on the usage of a lab-specific Excel/Solver-based software doing the required calculations. However, this approach did not provide user-friendly output formats nor included a database allowing extensive comparative studies. As an alternative to this lab-specific solution, commercial packages may also be used ,13,15. HIn order to establish tools that can be widely distributed, we have established the LS-MIDA software. Briefly, Brauman´s least square algorithm is used and developed in the form of a versatile software application iteratively analyzing the estimated abundance resonances  after biFurthermore, LS-MIDA provides a file-based data management system for fast and accurate MS-based isotopomer analyses.In comparison with other existing approaches -26, the LS-MIDA was compared with in-house software demonstrating its robustness. This showed that LS-MIDA can be used as an open-source platform for many (even non-expert) users in consortia in research programs such as the ongoing priority DFG program dealing with “host adapted metabolism of pathogens” in our example as well as other interested academic groups (German and foreign) for which we did provide and also will provide in future the software free of charge. Training courses and service are offered to support the use of the software within the program and for other users. In consequence, the software is freely available for the world-wide academic community.LS-MIDA is a versatile, open-source, and user friendly software with rapid calculation, integrated overview on all isotopomers, least square correction and database management system, with good application potential for biology and biotechnology such as studying the core metabolism of organisms, the pathways and fluxes leading to desired products in biotechnology, and complementing methods from genomics, proteomics or metabolomics.LS-MIDA is free available software for all academic users with open license; a commercial license can be obtained on request.LS-MIDA is developed using the Microsoft C# (sharp) programming language and Microsoft Dot Net framework 2008. It is compatible  for all Microsoft Windows operating systems. Moreover, LS-MIDA automatically adopts the language of the installed operating system and presents numerical values accordingly e.g. in case of English language decimal values are ‘.’ (dot) separated and in case of German language decimal values are ‘,’ (comma) separated. Numerical values are separated by ‘#’ (hash) symbol for all languages.Further details are available in the Additional file The authors declare no competing interest .ZA: software designing, programming and testing. SZ assisted ZA. DS, RM, TD provided software expertise. CH, WE, MH provided experimental expertise. TD and WE lead and guided the study. All authors  participated in data analysis and evaluation of the software as well as in writing of the manuscript. All authors read and approved the final manuscript.LS-MIDA software application. An executable software file (setup) is included.Click here for fileExample data for different amino acids. Example data for different amino acids analyzed by LS-MIDA.Click here for fileTutorial, installation, evaluation, further data, glossary.Click here for file


Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius.Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association.The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of  Buchnera aphidicolaBlochmanniaCarsonella rudiiGenome reduction is a common evolutionary process observed in bacterial lineages that have established associations, both symbiotic and pathogenic, with eukaryotic hosts. In advanced stages of the process one finds highly streamlined genomes and minimal metabolic systems unable to function outside their hosts S. glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few available complete genomes of a bacterium at the initial stages of symbiosis Wigglesworthia glossinidia. The latter has an ancient association with the tsetse host, having a highly streamlined genome (698 kb) that mostly retains cofactor biosynthetic pathways responsible for supplying the host with vitamins lacking in vertebrate blood, its sole nutrient source S. glossinidius represents a much more recent symbiotic association. This is revealed by the fact S. glossinidius can be cultured under laboratory conditions, as can other recent bacterial endosymbionts, like secondary endosymbiont of hippoboscid louse flies Escherichia coli, without any compositional bias S. glossinidius an ideal model system to study the complete genome reduction process covering both the initial transition from free-living to host-dependent lifestyle and the reductive evolution towards minimal metabolic systems associated to long-term symbiotic associations. This is because its current gene content, with the whole set of genes and pseudogenes, represent a direct hallmark of the ancestral gene content of the bacteria before the ecological transition to host-dependent lifestyle, after which the changes in selective pressures and population dynamics of the bacteria generates the massive accumulation of pseudogenes observed in the actual genome. Furthermore, predictions can be made as to how the current system could evolve further within the context of a reductive evolutionary process. This can be analyzed through the reconstruction of S. glossinidius metabolic networks at different stages of the genome reduction process and through functional analysis by Flux Balance Analysis (FBA). FBA enables quantitative assessment of the function of metabolic systems by finding the optimal distributions of metabolic fluxes across reactions of the network that optimize a particular objective function, normally defined through a biomass equation. Biomass production maximization is equivalent to experimentally determined cellular growth phenotypes, as demonstrated in model organisms like E. coli K12 B. aphidicola from the pea aphid Acyrthosiphon pisum was published, revealing a highly streamlined functional profile and high fragility E. coli K12 metabolic network, combined with comparative genomics, have revealed the predominant role of horizontal gene transfer over gene duplication in the recent evolution of E. coli K12, with horizontally transferred genes playing an essential role under specific external conditions B. aphidicola and W. glossinidia, can be accurately predicted by successive gene deletions in the E. coli K12 metabolic network. This is possible because external conditions mimic the nutrient availability within the insect host corresponding to each bacterium B. aphidicola lineage Available genome-scale metabolic networks range from important model organisms to pathogens and bacterial species of biotechnological interest S. glossinidius, can quantitatively assess the gene loss events in a system for which the ancestral genome can be reliably inferred from the whole set of genes and pseudogenes.Gene loss dynamics can be studied by comparative genomics in many of these ancient symbiotic lineages from the point of divergence from their hypothetical free-living ancestor S. glossinidius at different stages of the genome reduction process, in order to study its reductive evolution from a systems biology perspective. Metabolic phenotypes of ancestral and extant metabolic networks were quantitatively assessed by FBA, to determine the evolutionary events that could account for the transition from a free-living to a host-dependent lifestyle. Finally, the robustness of these metabolic systems to gene deletion events was compared to analyze how the transition to a host-dependent lifestyle correlates with the ability of a metabolic system to adapt to deletion changes.The present work reports the reconstruction of genome-scale metabolic networks of S. glossinidius comprised 668 gene products, 741 internal reactions, and 690 metabolites, of which 547 metabolites were cytoplasmic and 143 were extracellular  in the genome, which was in the range of other genome-scale network reconstructions  was analyzed by FBA and compared with the functional profile of a free-living relative, like E. coli K12. As objective function, the biomass equation defined for the E. coli K12 iJR904 network S. glossinidius. This biomass equation reflects the basic requirements in terms of essential metabolites  ensuring cell survival. The recent transition of S. glossinidius to a host-dependent lifestyle can be tested by evaluating the functionality of its ancestral and functional metabolic networks in a minimal aerobic environment with glucose as sole external carbon source for biomass production. This was done by fixing the lower bounds of their corresponding exchange reactions to −6 mmol gr DW−1 hr−1 for glucose and to −20 mmol gr DW−1 hr−1 for molecular oxygen (O2), as described in the E. coli K12 iJR904 metabolic network S. glossinidius showed a biomass production rate of 0.545 gr DW (mmol Glucose)−1, very similar to the biomass yield of the E. coli K12 iJR904 network (0.5391 gr DW (mmol Glucose)−1). By contrast, the S. glossinidius extant network was completely non-functional under these minimal conditions. This was due to two main gene inactivation events affecting biosynthetic pathways and a single pseudogenization event affecting an essential enzyme activity of the central metabolism, occurring during S. glossinidius evolution. First, the pseudogenization of genes glgA and glgB, encoding glucose 1-phosphate adenylyltransferase and glycogen synthase respectively, impeded glycogen biosynthesis from ATP and α-D-glucose 1-phosphate. Glycogen was a component of biomass equation, and consequently these two pseudogenization events render a lethal phenotype in terms of biomass production in the S. glossinidius extant network. The elimination of glycogen from the biomass equation solved this problem and rendered a functional phenotype in terms of biomass production. Second, the pseudogenization of genes argA, argG, argD and argC prevented L-arginine biosynthesis from L-glutamate, as recently described in the re-annotation of the genome speC gene, which encodes an L-ornithine decarboxylase (ps_SGL1267c) catalyzing single-step decarboxylation of L-ornithine to putrescine S. glossinidius extant network by incorporating an external source of L-arginine (0.7509 gr DW (mmol Glucose)−1).Functionality of both metabolic networks of ppc (ps_SGL1383), encoding the enzyme phosphoenolpyruvate carboxylase (PEP carboxylase), had particularly lethal effects on the overall metabolic system. This key enzymatic activity catalyzes the anaplerotic reaction of carboxylation of the glycolytic intermediate phosphoenolphyruvate to the TCA cycle intermediate oxalacetate E. coli K12 iJR904 and S. glossinidius metabolic networks on reactions of the central metabolism and L-arginine metabolism −1. Under these conditions, the supply of exogenous L-arginine was essential for the S. glossinidius functional metabolic system. This was due not only to its role as biomass constituent and precursor of additional biomass constituents, such as putrescine and spermidine, but also because putrescine must be broken down to TCA cycle intermediate succinate to overcome the absence of PEP carboxylase −1 in FBA simulations. This number increased to 204 essential genes in the S. glossinidius extant network, reflecting a decrease in network robustness to gene deletion events that can be put down to the gene inactivation process. Comparison of robustness to double gene deletion events over the three metabolic systems to identify synthetic lethal pairs, whose simultaneous deletion prohibits growth, gave similar results E. coli K12 iJR904  to the S. glossinidius ancestral network  and to the S. glossinidius extant network . To determine whether this decrease in network robustness extended to bacterial endosymbionts at more advanced stages of the genome reduction process, the same single and double knockout simulations were carried out on the metabolic networks of two B. aphidicola strains: BAp (from the pea aphid A. pisum) Cinara cedri). BCc was reconstructed from B. aphidicola BAp by removing genes and reactions absent in the more reduced genome of B. aphidicola BCc and adjusting the biomass equation composition. E. coli K12 (17.6% of network genes), to a bacterial endosymbiont in the initial stages of transition to a host-dependent lifestyle, like S. glossinidius extant network (44.5% of network genes). This fraction increases to 73% and 71.8% of network genes in the metabolic networks of an ancient bacterial endosymbiont with a highly streamlined genome like B. aphidicola BAps and BCc, respectively. Double gene deletion simulations with B. aphidicola metabolic networks rendered 57 lethal double knockouts in BAps and one lethal double knockout in BCc, which contrasts with the results obtained for S. glossinidius and E. coli K12 iJR904 metabolic networks. However, this can be explained by the reduced number of non-essential genes included in the analysis of BAps (57 genes) and BCc (29 genes) compared with E. coli K12 iJR904 (747 genes), S. glossinidius ancestral (462 genes) and S. glossidius extant networks (254 genes). Also accountable, are the differences in external conditions and biomass composition between B. aphidicola metabolic networks and E. coli K12-S. glossinidius metabolic networks. Overall, these results revealed a common trend in the reductive evolutionary process with a progressive decrease in the robustness of metabolic systems to gene deletion events associated with the transition to a host-dependent lifestyle.The genetic robustness of S. glossinidius functional metabolic network, reductive evolution simulations were carried out, as described in To analyze the possible evolutionary outcomes of genome reduction in the E. coli K12 iJR904 and S. glossinidius networks. The removal of any reactions involved in these amino acids' biosynthesis makes AroP an essential gene in a nutrient-rich environment. S. glossinidius in the context of its ecological association with the tsetse host , encoding a diaminopimelate decarboxylase responsible for the final decarboxylation of meso-diaminopimelate to lysine, was included as a conditionally essential gene. The remaining genes and reactions for meso-diaminopimelate biosynthesis from L-aspartate were essential under all conditions. This can be explained by the key role of meso-diaminopimelate as precursor of peptidoglycan, which is a biomass constituent, coinciding with the experimental assessments of gene essentiality in E. coli K12 by transposon mutagenesis S. glossinidiusSerratia symbioticaThe gene and reaction content of minimal networks obtained under both external conditions were also compared. This analysis showed that 138 out of 139 essential genes under nutrient-rich conditions were also essential under nutrient-limited conditions, whereas all 141 essential reactions under nutrient-rich conditions were also essential under nutrient-limited conditions. These results coincided with theoretical expectations, in that a reaction or a gene essential for system viability under a nutrient-rich environment was also expected to be essential in a more restricted minimal environment with glucose and L-arginine as sole external metabolites. The only gene always present in all minimal networks under nutrient-rich conditions, but not under nutrient-limited conditions, was SG0465, coding for an aromatic amino acid permease (AroP), responsible for transporting the biomass constituents histidine, phenylalanine, tyrosine and tryptophan in both host see . In factS. glossinidius at different evolutionary stages. The functionality of ancestral and functional metabolic systems has been evaluated by FBA, using biomass production as objective function. The biomass equation represents a weighted ratio of the components forming the dry weight of a cell, together with the energy demands for cellular growth and maintenance by ATP hydrolysis. Also, specific determination of a particular organism requires specific measurements of metabolites over pure cell cultures S. glossinidius, we adopted the biomass equation defined for E. coli K12 as objective function for FBA simulations on the metabolic networks E. coli K12 iJR904 and S. glossinidius metabolic networks can be compared, thus indicating the relative degree of metabolic independency of S. glossinidius with respect to a free-living bacterium, like E. coli K12.The present work analyzes the genome reduction process from a systems biology perspective. To do so, we have reconstructed and functionally analyzed genome-scale metabolic networks of the secondary endosymbiont of tsetse flies S. glossinidius' very recent transition to a host-dependent lifestyle. This is discerned by the complete functionality of its ancestral metabolic network under these minimal conditions at a biomass production rate equivalent to that of a free-living bacterium, like E. coli K12. This finding represents novel evidence of its recent transition to a host-dependent lifestyle, together with the lack of co-evolution between S. glossinidius and its corresponding Glossina host species in vitroS. glossinidius extant network reveal that a few gene inactivation events, like those in glycogen biosynthesis and, especially, arginine biosynthesis and ppc genes, produce drastic metabolic changes in the bacteria, associated with the evolutionary transition to a host-dependent lifestyle  to S. glossinidius ancestral (92 reactions) and extant (39 reactions) networks, with BAps and BCc networks having two and zero reactions catalyzed by isozymes, respectively. A recent study reports that many of these synthetic lethal pairs in E. coli are due to isozymes catalyzing essential reactions, and increasing deletion size reveals increased numbers of complex k-connected clusters, thereby indentifying key branching points of the central metabolism E. coli K12 to the S. glossinidius extant network may be because many of the additional synthetic lethal pairs in S. glossinidius probably correspond to higher-order synthetic lethals in E. coli . This indicates that under the same external conditions and with the same biomass equation as objective function, genome reduction decreases the robustness of biological systems to higher-order single gene deletions.work See . In factS. glossinidius.A similar trend was observed by Gabaldon and collaborators on studying the robustness of a hypothetical minimal metabolic system S. glossinidius render a set of minimal networks able to produce a functional phenotype in terms of biomass production under different external conditions in FBA simulations. The concept of minimal genomes and minimal gene sets is a common issue in evolutionary genomics, arising as soon as the first complete genomes were available for comparison E. coli K12 iJR904 network, under external conditions simulating the B. aphidicola and W. glossinidia environment within their corresponding insect hosts, produce minimal gene sets that accurately reproduce their respective gene content S. glossinidius. Thus, minimal gene sets or minimal genomes must be defined in the context of the environment associated with the organism under study Reductive evolution simulations on the extant metabolic network of S. glossinidius are available. The use of E. coli K12 as pivotal organism for network reconstruction has the pitfall of the long divergence time between both strains, which can have negative influence in the reconstruction of ancestral metabolic system. This is especially relevant in the identification of gene deletion events, as our parsimony-based approach forcing gene order conservation has the drawback of the rapid loss of gene order in divergent species, enhanced by the high number of rearrangements in bacterial species in initial stages of transition to host-dependent lifestyle S. glossinidius evolution are probably missed. In addition, with the current data is not possible to estimate precisely the timing of gene loss and gene inactivation along the evolutionary lineage of S. glossinidius, as has been possible in other bacterial lineages where similar parsimony-based approaches has been applied to reconstruct ancestral gene content and timing gene loss events S. glossinidius (i.e. different S. glossinidius genomes from different Glossina species) would allow to identify more precisely the gene loss and pseudogenization events that has took place strictly after the transition to host-dependent lifestyle. Finally, experimental data on S. glossinidius cell cultures would also allow to confirm predictions based on the metabolic models and to improve its predictive capability, as has been done with other biological systems Open questions for future research are a more accurate profiling of the ancestral network reconstruction and timing of gene loss events once the genomes of closer relatives of S. glossinidius metabolic network at different stages of the genome reduction process reveal how this evolutionary process affects the overall functionality of this bacterium in the context of its association with the tsetse host. The functionality of the ancestral metabolic network under minimal conditions confirms the recent transition of S. glossinidius to a host-dependent lifestyle, which can be explained by the drastic changes in the whole metabolic system. These occur due to the inactivation of a few key enzymatic activities, especially the inactivation of the key anaplerotic enzyme phosphoenolpyruvate carboxylase. The comparison with other metabolic networks corresponding to free-living bacteria (E. coli K12) and ancient bacterial endosymbionts (B. aphidicola) reveals there has been a gradual decrease in network robustness to gene deletion events and to changes in particular enzymatic activities in these bacterial endosymbionts since their initial evolutionary transition to a host-dependent lifestyle. In this scenario, S. glossinidius represents the initial stages of this association while B. aphidicola represents the more advanced stages, with a deeper integration in the host lifecycle. In addition, reductive evolution simulations on the S. glossinidius extant network reveal that the gene and reaction content of minimal networks are strongly dependent on the external conditions under which the system is evolving. Moreover, under nutrient-rich conditions, simulating the tsetse environment, the metabolic system may evolve towards outcomes rather similar to a real minimal genome, like that of W. glossinidia. This reflects the potential of network analysis to predict gene content evolution.The reconstruction and functional analysis of the E. coli K12 and S. glossinidius, which include genes of E. coli K12 iJR904 metabolic network, in order to obtain an initial backbone of S. glossinidius metabolic networks. Second, a manual curation step to add S. glossinidius genes and pseudogenes related with metabolic functions and well defined reaction stoichiometry based on the functional re-annotation of the genome E. coli K12. Third, the identification of gene deletion events among the E. coli K12 iJR904 network genes in the S. glossinidius lineage by comparative genomics. A schematic representation of this process is depicted in S. glossinidius ancestral and extant metabolic networks in SBML format are provided as Additional Files iEB668.sbml and iEB458.sbml.The network reconstruction process can be broadly divided into three main steps. First, the identification of orthologous clusters between S. glossinidius ancestral and extant metabolic networks were carried out with the COBRA toolbox http://www.mathworks.com/) using the linear optimization algorithm provided by the LP_solve toolkit (http://sourceforge.net/projects/lpsolve/). The biomass equation defined for E. coli K12 in their iJR904 reconstruction S. glossinidius. Maximum oxygen uptake rate was fixed to 20 mmol grDryWeight−1 hr−1 whereas unconstrained uptake for ammonia, water, phosphate, sulphate, potassium, sodium, iron (II), carbon dioxide and protons was set in concordance with the E. coli K12 iJR904 metabolic network. All other external metabolites, except the carbon source, were only allowed to leave the system by fixing the lower bound of their corresponding exchange reactions to zero.Flux Balance Analyses on E. coli K12 iJR904 and S. glossinidius ancestral and extant metabolic networks was evaluated in two different ways. First, the effect of gene deletions over biomass production rates was compared in the metabolic networks of S. glossinidius and E. coli K12 with the function singleGeneDeletion of the COBRA toolbox, limiting the flux over the reactions catalyzed by the deleted gene to zero and calculating biomass production rates in the resulting knockout network. Second, double gene deletion simulations were carried out on non-essential gene pairs, defined in the single knockout simulations with the function doubleGeneDeletion of the COBRA toolbox in order to identify synthetic lethal pairs. Non-essential genes are defined as genes with less than 1% of biomass production of the maximum theoretical biomass yield. For comparative purposes, similar single knockout simulations were carried out with two genome-scale metabolic networks of B. aphidicola; BAps, corresponding to B. aphidicola from the pea aphid A. pisum (BAps) B. aphidicola from the cedar aphid Cinara cedri, obtained from BAps by removing reactions catalyzed by absent genes in BCc and adjusting the composition of the corresponding biomass equation.Network robustness of S. glossinidius in the context of the reductive evolutionary process, simulations of reductive evolution were carried out on the S. glossinidius extant network to define minimal reactions and gene sets able to support cellular growth in terms of biomass production under different external conditions. The simulations were carried out as follows. Starting with the extant metabolic network of S. glossinidius, a randomly chosen reaction was removed from the network by setting the flux through this reaction to zero, and its impact on the metabolic system was evaluated by FBA. If biomass production in the deleted network was above a given cutoff, the reaction was considered as non-essential and was removed permanently, together with its corresponding genes. In contrast, if the biomass production rate over the deleted network was below a given cutoff, the reaction and its corresponding genes were considered essential and were retained in the reduced network. This procedure was repeated progressively until all network reactions were evaluated. Three different cutoffs were considered in terms of the biomass production rate for defining a reaction as essential or non-essential , and for each cutoff, 500 simulations were carried out. In order to have independent minimal networks in each simulation, the vector of network reactions was randomly permuted in each reductive evolution simulation.To study the possible future evolution of S. glossinidius extant network has a functional transport system  genes present in all minimal networks in each condition, which can be considered as essential genes for the functionality of the metabolic system; and ii) genes absent in all minimal networks, which can be considered as non-essential or disposable genes that can be always removed without affecting system functionality.Table S1Reactions of S. glossinidius ancestral network that includes 27 pseudogenes (highlighted in red) without sequence similarity with E. coli K12. Pseudogene names follow the notation defined in a recent re-annotation of the genome (XLS)Click here for additional data file.Table S2Reactions of S. glossinidius extant metabolic network that include genes without sequence similarity with E. coli K12 (highlighted in red).(XLS)Click here for additional data file.Table S3Common essential genes and reactions in S. glossinidius minimal metabolic networks under nutrient-limited and nutrient-rich conditions.(XLS)Click here for additional data file.Table S4Conditionally essential genes and reactions presents in all minimal networks under nutrient-limited conditions but not under nutrient-rich conditions.(XLS)Click here for additional data file.Table S5Genes and reactions absent in all minimal networks under nutrient-limited and nutrient-rich conditions.(XLS)Click here for additional data file.Table S6Metabolites available for uptake in reductive evolution simulations under nutrient-rich conditions.(XLS)Click here for additional data file.File S1S. glossinidius ancestral metabolic network in SBML format.(SBML)Click here for additional data file.File S2S. glossinidius extant metabolic network in SBML format.(SBML)Click here for additional data file.Methods S1etwork reconstruction protocol.N(DOC)Click here for additional data file.


Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris (bean plant). Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1) extended the metabolic reconstruction reported for R. etli; 2) simulated the metabolic activity during symbiotic nitrogen fixation; and 3) evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced different effects in nitrogen fixation, all of these in qualitative agreement with observations made in R. etli and other Rhizobiaceas.In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in In this work we present a genome scale study of the metabolic activity in bacterial nitrogen fixation. This approach leads us to construct a computational model that serves as a guide for 1) integrating high-throughput data, 2) describing and predicting metabolic activity, and 3) designing experiments to explore the genotype-phenotype relationship in bacterial nitrogen fixation. Rhizobiaceas represents nearly 70 percent of the entire nitrogen transformation required for maintaining life in our biosphere. Simultaneously, nitrogen fixation driven by these bacteria constitutes an appealing and natural strategy for developing sustainable agricultural programs due to its cost-effectiveness in crop improvement and its more environmentally friendly effects in comparison to those produced by chemical fertilizers , lys[c], phb[c], ala[e], asp[e] and nh4[e], respectively. All these metabolites are required to support an effective symbiotic nitrogen fixation  reductase (NADH) protein (FabI2) and S-malonyltransferase protein (FadD); and by transcriptome fadB2 was induced 3.09-fold. As these findings suggest, fatty acid metabolism could play an important role in bacteroid metabolism given that it can supply a variety of precursors such as components of the rhizobial membrane, lipopolysaccharides and coenzymes required in signal transduction. As opposed to the process in other Rhizobiaceas where fatty acids can be supplied by the host plant [R. etli synthesize and metabolize their fatty acids. The assessment of this hypothesis and the biological implications on bacterial nitrogen fixation constitute an avenue to experimentally verify in the future.According to high-throughput data, metabolism of fatty acid can play a significant role in bacterial nitrogen fixation, this being in contrast to the drastic reduction of lipid biosynthesis observed in aponicum . Thus, ast plant , we suppR. etli in symbiosis with P. vulgaris, at present the most complete study made in Rhizobiaceas. Collectively, high-throughput data suggest the following significant clues: 1) R. etli bacteroids are capable of synthesizing several amino acids through integrated carbon and nitrogen metabolisms. In addition, we observe the participation of some minor metabolic pathways such as myo-inositol catabolic pathway, degradation and synthesis of poly-b-hydroxybutyrate and glycogen. 2) Gene expression in bacteroids suggests the presence of a specialized transport system for sugars, proteins and ions. 3) An antioxidant defense mechanism based on peroxiredoxine, regulated by nifA, prevails during nitrogen fixation, as opposed to in free-living condition, where the mechanism is rooted in catalases [R. etli over-expresses genes and enzymes required in fatty acid and nucleic acid metabolism, contrary to other studies in bacteroids. Finally, 5) this study contributes a computational model that serves as a useful framework for integrating data, designing experiments and predicting the phenotype during bacterial nitrogen fixation, see Figure In this study we present a systemic metabolic description of bacterial nitrogen fixation carried out by atalases . 4) R. ein silico but were not detected experimentally, and conversely, those enzymes that were detected experimentally but not in silico, see Figure in silico predictions.This systemic and integrative approach constitutes a valuable effort toward a systems biology description of the metabolism in bacterial nitrogen fixation; however, to increase our understanding and predictive accuracy some issues should be addressed in the future. Thus, particular attention should be directed toward those enzymes that were predicted metabolically active i cin the objective function--see methods section--we have shown that the in silico model is capable of qualitatively predicting the activity of classic metabolic pathways and successfully describing some phenotype behavior in bacterial nitrogen fixation. Even though this represents a significant advance toward a systems biology description of bacterial nitrogen fixation, some improvements should be addressed in future. For instance, additional metabolites with a biological role in nitrogen fixation should be considered in order to obtain a more proper objective function that contributes to uncovering the role that less known metabolic pathways, such as nucleotides and fatty acid metabolisms, have on this biological process. As described here, these improvements will be guided by high-throughput data and the cyclic crosstalk between model and theory, a needed step in integrating, interpreting and generating biological hypotheses in a more accurate fashion.Notably, even though the simulations have been carried out without a detailed numerical description of the coefficients R. etli (iOR450) will contribute to the rational design of optimal experiments that help us understand biological principles and identify those molecular mechanisms in order to improve this biological process, all this from a systems biology perspective.Overall our study contributes to establishing the bases toward a systems biology platform capable of integrating high-throughput technology and computational simulation of bacterial nitrogen fixation. In particular, we envision that this metabolic reconstruction for R. etli CFN42 wild type [R. etli, and plant experiments were accomplished as previously described in reference [The bacterial strain used was ild type . Cultureeference .Phaseolus vulgaris cv. Negro Jamapa seedlings were inoculated with R. etli CFN42 strains as previously described by Peralta et al. [Three-day-old a et al. . After 1et al [Microarray experiments were carried out using three independently isolated RNA preparations from independent cultures and set of plants. Approximately 3 g of nodules were immersed in liquid nitrogen and macerated. Total RNA was isolated by acid hot-phenol extraction as described previously by de Vries et al . For micet al ,49. Aftehttp://www.bioconductor.org/). Specifically, microarray normalization was carried out by applying the maNormMain function in the marray library. MA-plots before and after normalization are depicted in Additional File z-score of the log-ratio associated to each gene. The set of genes differentially expressed during nitrogen fixation was selected as those genes with a z-score higher than 1.65, see Additional File http://www.ncbi.nlm.nih.gov/geo) with accession numbers: GPL10081 for Rhizobium etli platform and GSE21638 for free-life and symbiosis data.Spot detection, mean signals, mean local background intensities, image segmentation, and signal quantification were determined for the microarray images using the Array-Pro Analyzer 4.0 software . Statistical treatment of microarray data was accomplished with bioconductors software (fabI2-RECH000938f (5'-GTA TTG CCA AGG CCA TTC AT-3'), fabI2-RECH000938r (5'-CCC ACA GTT TTT CGA CGT TT-3') for the fabI2 gene. idhA-RECH003170f (5'-TTT CTT CAT GAC CCG CTA CA-3'), idhA-RECH003170r (5'-TTG ATC AGC TTG CCT TCC TT-3') for the idhA gene. ppK-RECH001491f (5'-TCC TGG CAC TGA ACA CTC TG-3'), ppK-RECH001491r (5'-GAG AAG GAA CTG GAC CAC CA- 3') for the ppK gene. hisD-RECH000581f 5'GAT CTG AAG CAA GCC ATT CC 3', hisD-RECH000581r (5'-ACA TAA TCG CCG ATG ACC TC-3') for the hisD gene. nifH-REPD00202f (5'-CCT CGG GCA GAA GAT CCT GA-3'), nifH-REPD00202r (5'-CAT CGC CGA GCA CGT CAT AG-3') for the nifH gene. fixA-REPD00224f (5'-ACA TCA ATG GGC GCG AGA TT-3'), fixA-REPD00224r (5'-TGT CGA TCT GCT CCG CCT TT-3') for the fixA gene. cpxP2-REPD00252f (5'-TCC GTG CCA TTT CAA AGA CC-3'), cpxP2-REPD00252f (5'-CCG CCA AAT GAG AAG ATT GC-3') for the cpxP2 gene. hisC-RE1SP0000233f (5'- CGA TGG CGA GAC AGC TAA AT-3'), hisC-RE1SP0000233r (5'-ATC ATC GCA ACG CTA TCT CC-3') for the hisCd gene. Each reaction contained 12.5 μl SYBR green PCR mastermix (Applied Biosystems), 3.5 μl H2O, forward and reverse primers in a volume of 5 μl, and template in a volume of 4 μl. PCR reactions were run with the ABI Prism 7700 sequence detection system (Applied Biosystems) using the following steps: 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The dissociation protocol was 95°C for 15 s, 60°C for 20 s, followed by ramp from 60°C to 95°C for 20 min. The transcript of the histidinol phosphate aminotransferase protein (hisCd) was used as an internal (unregulated) reference for relative quantification. This gene was selected as a reference because its expression is constitutive in all tested conditions (free live and symbiosis). Results of RT-PCR in real time were analyzed using the ΔΔCT method [We used real-time quantitative PCR to provide an independent assessment of gene expression for selected genes. The cDNA used for microarrays or freshly prepared cDNA was used as a template for Real-time PCR. Primer sequences used were as follows: http://ProteomeCommons.org Tranche using the following hash:Bacteroids purification, protein extraction and two dimensional gel electrophoresis were done as previously described in . BrieflyBY/eCcVjwTWN1+m+2ArvJ0QVnesGx5Ekgd4wUOASACfm/ueNl7YI3iLf4xz0lnGsepV5LkpMWOQOrZtjYExlNpQkIBcAAAAAAAABjA = =R. etli by including those reactions whose enzyme activity were supported by high-throughput data. Thus, the fatty acids metabolism was included in the metabolic reconstruction, and some metabolic improvements were made along the network. Additional File iOR450). Overall, the updated metabolic reconstruction for R.etli consists of 377 metabolites and 450 genes codifying for enzymes participating in 405 metabolic reactions. The gene-protein reaction association for the entire metabolic reconstruction, lower and upper bounds and reversibility information associated to each reaction are shown in Additional File With the purpose of establishing an integrative description between modeling and experimental data, we extended the metabolic reconstruction for Rhizobium etli was predicted in silico by constraint-based modeling [FixZ, consists of certain key compounds required for sustaining nitrogen fixation and others required for mimicking the physiological conditions prevailing in the boundaries of the nodules. Thus, objective function was mathematically written as a linear combination of these metabolites (iX) and their contribution to nitrogen fixation was weighted by coefficients (ic), which for simplicity's sake were all selected as a unit. With the purpose of obtaining a computational profile of metabolic fluxes, we assumed that the metabolic state of the bacteroid during nitrogen fixation is one that optimizes the objective function, FixZ. This latter issue was solved by taking into account linear programming and considering that the fluxes are constrained by their enzymatic and thermodynamic capacities,Metabolic flux distribution supporting nitrogen fixation in modeling . Brieflyi,j Srepresents the entries of the stoichiometric matrix, j vis the metabolic flux of the j-th reaction and j αand j βaccount for thermodynamic and enzymatic constraints, see Additional File Tomlab optimization package called from COBRA toolbox in Matlab [where n Matlab .2), water (H2O), oxygen (O2), malate  and glutamate (glu-L). In addition, exchange reactions for nitrogen (n2), alanine , aspartate (asp-L), succinate and ammonium (NH4) were included in the reconstruction for representing their possible bidirectional exchange from plant to bacteroids. On the other hand, metabolites in class II include those that contribute to the defining of internal frontiers in the bacteroids. Importantly, these sink reactions were included as a representation of metabolites originating from metabolic processes currently absent in the metabolic reconstruction. Thus, phosphate (pi), myo-inositol (inost), L-histidinol phosphate (hisp), palmitoyl-CoA (pmtcoa), dodecanoyl-CoA (dodecoa), decanoyl-CoA (decoa), octanoyl-CoA (otcoa) and hydrogen (h) fall in this classification.In order to explore the phenotype capacities of the bacteria metabolism, we included in the reconstruction certain exchange and sink reactions for limiting our metabolic modeling and representing the microenvironmental conditions in the plant nodules. In general, these can be classified as one of two categories. Class I includes those metabolites that can be exchanged between the bacteroid membrane and the plant environment. Among them, we included carbon dioxide  as the set of enzymes (genes) that form the j-th metabolic pathways in KEGG database, with j-th ranging from 1 to 22. Similarly, the set of enzymes (genes) that integrates the i-th metabolic pathway in the reconstruction and the set of enzymes detected by high-throughput data are denoted by jRecE (jRecG ) and jHTE (jHTG ), respectively. Finally, the sets of enzymes and genes obtained from constraint-based modeling were denoted by jiModel E and jiModelG . More specifically, jiModel E and jiModel G sets were defined as those enzymes and genes participating in the active fluxes obtained from flux balance analysis. In order to evaluate and create a proper framework for comparison between in silico predictions and high-throughput data, we defined the consistency coefficient as the fraction of enzymes (genes) that were actively predicted in silico and were identified by high-throughput technology. This can be summed up in the following equations:To assess the agreement between Both ratios range from zero to one and constitute our central parameter to assess and quantify the degree of coherence between constraint-based modeling and experimental data.Computational gene deletion analysis was used to quantify the effects that gene silencing has in supporting bacterial nitrogen fixation. Thus, once the gene to be switched off was selected, we identified its gene-protein reaction association and selected as zero its upper and lower bound in flux activity. Having made this adjustment, we applied flux balance analysis and obtained the new resulting objective function. In order to quantify the participation of this metabolic reaction in bacterial nitrogen fixation, we calculated the percentage of reduced activity of the mutated strain in comparison to the wild type, see Figure R. etli and designed the computational analysis to simulate nitrogen fixation. ES realized microarray experiments, and OR-A contributed to their statistical analysis. ME and GM-B prepared the samples and accomplished the identification and processing in spectrometry analysis of all proteome data. YM guided the experimental cultivation for nodule preparation in R etli. SE conceived the study, design and analyzed the experimental data obtained from proteome and microarray technologies. All authors read and approved the final manuscript.OR-A conceived the metabolic reconstruction for Microarray Data Analysis. This table shows those genes that were over expressed during bacteroid activity in nitrogen fixation.Click here for fileProteome Data. By using mass spectrometry, we identified a set of proteins during bacterial nitrogen fixation for R. etli. In each row, we named the protein and presented some of the parameter utilized for concluding the protein identify.Click here for fileIntersect between proteome and transcriptome. Genes that were simultaneously identified by proteome and transcriptome technologies.Click here for fileThis file contains an extended descriptive analysis deduced from the genes identified by proteome and transcriptome data.Click here for file(A) MA plot and representation of Metabolic activity. In this figure we show the MA-plot obtained from microarray data and a selected representation of the metabolic activity predicted by FBA in some metabolic pathways: (B) TCA cycle, (C) purine and pyrimidine metabolism.Click here for fileAbbreviations. This file enlists the main abbreviations used along the paper.Click here for fileRhizobium etliMetabolic Reconstruction for . This table depicts the gene-protein-reaction association for Rhizobium etli metabolic reconstruction, iOR450. Overall the reconstruction contains 450 genes codifying for a set of enzymes participating in 405 metabolic reactions and 377 metabolites.Click here for file


Saccharomyces cerevisiae S288c. Yeast 6 comprises 1458 metabolites participating in 1888 reactions, which are annotated with 900 yeast genes encoding the catalyzing enzymes. Compared with Yeast 5, Yeast 6 demonstrates improved sensitivity, specificity and positive and negative predictive values for predicting gene essentiality in glucose-limited aerobic conditions when analyzed with flux balance analysis. Additionally, Yeast 6 improves the accuracy of predicting the likelihood that a mutation will cause auxotrophy. The network reconstruction is available as a Systems Biology Markup Language (SBML) file enriched with Minimium Information Requested in the Annotation of Biochemical Models (MIRIAM)-compliant annotations. Small- and macromolecules in the network are referenced to authoritative databases such as Uniprot or ChEBI. Molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Yeast 6 is freely available at http://yeast.sf.net/ as three separate SBML files: a model using the SBML level 3 Flux Balance Constraint package, a model compatible with the MATLAB® COBRA Toolbox for backward compatibility and a reconstruction containing only reactions for which there is experimental evidence .Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Database URL:http://yeast.sf.net/ This update maintains an emphasis on standards compliance, unambiguous metabolite naming and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB® scripts to demonstrate our approach for comparing Yeast 5 and Yeast 6 using flux balance analysis (FBA) methods, leveraging on the COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox  files: a GEM using the SBML level 3 Flux Balance Constraint package, a GEM compatible with the MATLAB® COBRA toolbox   and the  toolbox  for backYeast 6 resulted from an effort to improve the predictive accuracy of Yeast 5 through manual curation, with particular focus on removing information that is not well supported by published literature and by adding metabolic pathway information that has been recently discovered. As a result of this effort, Yeast 6 contains fewer metabolites and reactions than Yeast 5 , but is supplementary data, as is the code used to generate these comparisons. The supplementary data also include specific rationale for reaction deletions and additions, as well as detailed descriptions of the function of each gene removed or added. In summary, Yeast 5 has 203 metabolites that are not in Yeast 6, whereas Yeast 6 introduces 6 new metabolites. The smaller number of metabolites in Yeast 6 arises from the removal of metabolites involved in those dead-end reactions annotated as a modeling reaction . No dead-end reactions that are annotated with a gene were removed. Yeast 5 has 242 reactions that are not in Yeast 6, whereas Yeast 6 has 20 reactions that are not in Yeast 5. As detailed in supplementary data, reactions removed in Yeast 6 include those with incorrect cofactor specificity, those involving protein modification, those not supported upon literature review and dead-end modeling reactions. Yeast 5 has 31 genes that are not in Yeast 6, and Yeast 6 has 13 genes that are not in Yeast 5. Of the 31 genes removed, 21 encode proteins involved in protein modification or Glycosylphosphatidylinositol (GPI)-anchor assembly (processes that are not strictly metabolic), 4 are annotated with ‘putative’ function, 1 was removed because of unclear cofactor specificity, 2 annotated poorly characterized reactions involved with ergosterol biosynthesis, 1 encodes a flippase, which would require a finer level of membrane compartmentalization than currently exists in the model, and the metabolic function of the remaining 2 are not well established.Metabolites, reactions and genes differ between Yeast 5 and Yeast 6. Detailed lists of common and distinct metabolites, reactions, genes, constraints, auxotrophs and knockout predictions are included as supplementary datatestYeast.m file), Yeast 6 has 13 new true positive predictions of gene essentiality , 27 new true negative predictions , 1 new gene predicted to induce auxotrophy following mutation and 7 newly correct predictions of auxotrophy phenotypes. Yeast 6 also introduces new predictions that differ from laboratory observations, two new false-positives , one new auxotrophic mutant predicted incorrectly to be incapable of growth in supplemented media and two new mutants incorrectly predicted to be viable in minimal media despite reported auxtrophy. We note that such differences between model prediction and observation may arise from regulatory constraints that are outside the scope of a metabolic reconstruction, or may be informative of opportunities for continued network curation.These changes to the reconstructed metabolic network, combined with the incorporation of suggested changes to the biomass definition , give riSaccharomyces cerevisiae requires supplementation with unsaturated fatty acids and sterols , there remains substantial opportunity to improve the reconstruction of lipid metabolism. This point is most evident from the fact that unsaturated fatty acids are not currently required for simulating anaerobic growth, but also arises through the continued use of generic lipid species .Additional limitations arise from the appropriately limited scope of the metabolic network reconstruction. Condition-dependent constraints that arise from various regulatory mechanisms are not included in Yeast 6. Thus, pathways that are affected through transcriptional regulatory events such as glucose repression may be incorrectly predicted to carry fluxes under FBA . Integration of regulatory and metabolic networks remains an area of active research , 22.Like other metabolic network models, not all reactions in Yeast 6 can carry flux in FBA simulation. Yeast 6 has 738 blocked reactions (39%), a similar portion of blocked reactions as Yeast 5 (38%). Blocked reactions indicate knowledge limitations . Like FBA predictions that differ from observation, the number of blocked reactions is also affected by condition-specific constraints, particularly constraints on reaction reversibility. We have observed that relaxing the reversibility of reactions involving nucleotide cofactors reduces the number of blocked reactions.Unlike Yeast 5, the prediction of anaerobic ethanol production in FBA simulation requires manual restriction of the reaction catalyzed by ATP synthase. The requirement for this condition-dependent constraint may arise from the lack of regulatory constraints as described above, or from a need for physicochemical capacity constraints on allowable flux. Additionally, it may reflect other, presently uncharacterized limitations. We note that as with integrating regulatory constraints, detailed reconstruction of cofactor and proton balancing also remains an area of active research in the constraint-based modeling community ; redox cnetwork.reconstruction@manchester.ac.uk. Metabolites and enzymes should be unambiguously identified, using existing model or database (ChEBI or UniProt) identifiers. New reactions should be supplied with primary evidence for their mechanism and catalysis, via PubMed identifiers. Reactions without evidence should have clear reasons for their proposed addition.Computational reconstruction and modeling of yeast metabolism is an ongoing project, and we invite additional community participation in this effort. Suggestions for improving the yeast consensus reconstruction or derived models should be submitted to Supplementary data are available at Database Online.