Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. For decades the regulation of metabolic networks at genome scale and its mechanisms has been studied to further our understanding of this process, especially after the massive increase of sequencing data during the post-genomic era. Cell regulation can be accomplished through two complementary strategies. Genetic regulation (genetic circuits) occurs at genome level, controlling the expression of certain genes. This regulation affects the presence or absence of enzymes in the metabolic network. On the other hand, post-transcriptional regulation operates in two forms: RNA mediated regulation and the dynamic control of enzyme activities. The latter is achieved by the activation or inhibition of certain enzymes by means of controlling metabolites, as is the case with allosteric regulation.The idea that the metabolic pathways and regulation strategies that take place in a cell are the result of an evolutionary optimization process is widely accepted Unfortunately, finding a regulation design of a metabolic system as a result of an optimization process is an NP-hard problem in the majority of cases In their work in 1995 In this paper we take up again the challenge of finding a universal pattern of post-transcriptional dynamical regulation for this kind of model, set out by Gilman and Ross. We accomplish this goal through the study with different global optimization techniques and within the context of multi-criteria optimization. The latter has been carried out by calculating the Pareto-optimal The aim of this kind of optimization is to find a potentially universal mechanism of regulation of a specific metabolic network, by simulating the natural evolutionary optimization process.A and B) are interconverted by a pair of enzymes (α and β). These enzymes are regulated by two external “reservoirs” of metabolic species, and their concentrations are specified externally at any time (these variations of concentrations in a certain period of time are named “courses” from now on).In this work we have used the model examined by Gilman and Ross α catalyzes the conversion of A into B with rate αv and β catalyzes the conversion of B into A with rate βv the kinetic equations describing the temporal variation of these metabolic intermediates are described by the following differential equations:Since α and β, in the presence of effectors, is of the form:MK is the Michaelis-Menten constant and maxV the maximum velocity of the corresponding enzyme. The factors modifying the intrinsic Michaelis-Menten rate expression are:The enzyme-catalyzed reaction for α,FK and α,TK are the dissociation constants for the complex of enzyme α, and α,Fr and α,Tr are the ratios of the catalytic rate constants for the enzyme for the effectors T and F respectively. Similar notation is used for the enzyme β. Depending on whether the resulting expression of R, Eq. 3, is greater or less than 1 the corresponding enzyme, α or β, is activated or inhibited. A regulation diagram can be drawn from these statements. For example, if α,FR is greater than 1 the enzyme α will be activated by the effector F and the connection between F and α in the diagram will have a ‘+’ symbol. However, if α,FR is less than 1 the enzyme α will be inhibited by F and the connection between F and α in the diagram will have a ‘−’ symbol, while if α,FR is 1 the connection will not be shown since F has no effect on α. The same reasoning is applied to enzyme β. An example of a regulation diagram can be seen in α and β, is determined by the values of the set of these eight parameters . In order to optimize the flux response of the system the proper values of these parameters need to be selected. The main criterion for such optimization is the proper direction of the flux according to the system’s need. The response of the system should be able to provide an appropriate flux of both F and T, in response to a given external condition. For example, the system metabolizes blood glucose for energy as long as the concentration in blood is adequate but synthesizes glucose for export if the glucose concentration in blood is too low. In order to evaluate the system response Gilman and Ross, Eq. 4 in 2B−k−2T)(k and −1A−k1F), and the flux should flow from B to A in order to produce F. However, if the concentration of F is above the target concentration a negative need state will be induced (F = ξ−1) and the flux should flow in the opposite direction (from A to B). The same applies to T.ξThe regulation of the system, through the activation or inhibition of the enzymes f is considered to be a good response.If the algebraic sign of both the net flux and the need state into a reservoir is the same, the flux will be directed in the proper direction, so in this equation a positive value of F and T the integral of f over a period of time has been calculated. Eq. 5 gives some indication of the fraction of the period of time during which the flux was directed properly.In order to know how the network would behave in the different time-courses of τ was calculated by Gilman and Ross α, defined as:The energy “cost” for performing this operation during the period of time The nonlinearity and frequent multimodality of this kind of model make the optimization of its parameters a difficult task for traditional optimization methods, which are very sensitive to the initial values. Such problem models can contain several local optima, hence if the initial values are far from the global optimum it is difficult to assure a convergence towards it In OF is obtained not only when the network responds properly to changes of external concentrations but also when it does so at a low biological cost. Therefore a set of parameters must be found that maximizes 1f and minimizes 2f, resulting in an optimal solution which would be a trade-off between a proper performance of the network (1f) and the cost (2f), merging these two concepts into one equation. As asserted in OF was found making it difficult to assure the convergence towards an optimum. In this paper we have performed a Mono-objective study of the system using three different stochastic global optimization methods: a variation of the GA used by Gilman; the enhanced scatter search SSm method described in F and T, which are pictured in a-d  are mathematically equivalent. If one is interested in achieving only one final solution, there is a need for a decision-making process that allows one of the solutions in the set to be selected, using additional information. The choice of a particular solution is often subjective or difficult to express in mathematical terms, and it is therefore difficult to obtain systematically. However, the Pareto front of some multi-objective optimization problems shows a solution that can be considered to be the best compromise, i.e. the optimum of the front. These solutions are called “knee” points f1) would come at the cost of a much worse value of the other objectives (in this case f2).The simultaneous optimization of multiple objectives differs from traditional mono-objective optimization in that if the objectives are in conflict with each other, the solution to the optimization problem will not be unique; instead, there will be a family of solutions known as a Pareto-optimal set One of the advantages of the Pareto front perspective is that it allows the representation of the solutions within a diagram. Since the present model has two objectives, the solutions can be displayed in a 2D diagram dividing the graph in different regions. For instance, a set of solutions which control the flux properly but at a high cost will be situated together on one side of the diagram while solutions which do not control the flux so well but minimize the cost optimally will be placed on the opposite side, leaving the solutions which represent a trade-off between the two objectives in the middle. Since the solutions are laid out in regions along the Pareto front, with this approach we were able to organize them in a graphical and more visual way, thus obtaining a wider perspective in the study of optimization applied to biochemical systems.Optimizations were carried out with the NBIWT weighted Tchebycheff method presented in An optimal (Pareto) set was computed for each of the eight courses for the cost, Eq. 6, and flux response, Eq. 5, simultaneously. The NSGA-II −3 has been used for the objective function OF, Eq. 7. Ten optimization runs were repeated using the three methods on each of the three courses. To allow a fair comparison between the three methods, we ran them with equivalent setting parameters: in the case of GA a population of 100 individuals and 100 generations was used which represents a total of 10,000 evaluations. The same number of evaluations was set for SSm and Globalm. The optimum was achieved in less than 1000 evaluations, a relatively small number. The remarkable result was that the same OF optimal value was obtained by each method for each course. No significant differences were found between the three of them in terms of computation time or convergence towards the optimum. As stated above, stochastic global optimization methods do not generally guarantee convergence to the global optimum. However, the fact that the three different methods reached the same objective function value in several runs strongly suggests that in this case the global optimum has been achieved.Following R, Eq. 3, depends on the values of F and T which vary during time, we compute their averaged values F and T,  these values are also shown in In terms of regulation, due to the nature of the system, certain degeneracy in the solutions could be expected since different schemes of regulation could achieve the global optimum. However it is worth mentioning that after running any of the algorithms ten times in a particular course the regulation scheme corresponding to the optimal solution obtained was in most of the cases the same. Nevertheless, among the different courses the resulting regulation scheme can be different, as shown in f1) at a low cost (f2). After applying the NBIWT weighted Tchebycheff method on each of the eight different time courses we obtained eight fronts of solutions. Strikingly, each of them exhibited the same kind of Pareto front characterised by containing a clear knee point, which represents the ideal trade-off between the two objectives: in this case that solution combines a high flux response (K’s and four r’s). The regulation diagrams corresponding to the different solutions of the front are shown in the small diagrams of c (d (Each of the solutions (points) of the Pareto fronts corresponds to a set of parameters  is directly related with αv, and if this enzyme is inhibited the value of 2f will be low. In contrast to this, on the top left-hand side the solutions of the fronts presented a high value of 2f and the most frequently found scheme was the one in which α was activated by the two effectors.There are some remarkable similarities among certain regions of the Pareto fronts within different courses that can be observed in a also yielded optimal values of 1f and 2f for the other seven courses, and that happened with every knee point. This result suggests the existence of an underlying universal regulation pattern. In order to find such pattern, several runs of the method NBIWT were carried out for each course and the regulation scheme of the knee point of each run was studied. It was observed that between different runs of a course the regulation scheme corresponding to the knee was slightly different, however it was noticeable that certain regulation pattern was more frequent than others, we consider this a consensus regulation scheme for this system, see α,F, Kα,T, Kβ,F, Kβ,T, rα,F, rα,T, rβ,F, rβ,T,K).Interestingly, it was found that if the different knee points were interchanged within the different time courses the resulting behavior was also optimal in each of them. For example, the parameters set corresponding to the knee point of course 1f) and cost (2f) for course f, evaluated with the different knees of all the courses  and also with the consensus set of parameters. Similar results were obtained for the other seven courses. Remarkably, these values are very similar to each other, indeed the deviation of the different values of flux response for a single course evaluated with its optimal set of parameters, the other knee solutions, and the consensus set, is always less than 0.006 (that corresponds to a maximum deviation of 0.7%). In the case of the cost the deviation is always less than 0.019 (maximum deviation of 1.38%). α is activated by both effectors F and T, favoring the production of B, and enzyme β is inhibited by F. These schemes, where the enzymes are regulated by products and substrates of the reaction, are frequent in metabolism The regulation scheme corresponding to the consensus set of parameters is depicted in The starting point of this work was the hypothesis that the regulation mechanisms of metabolic networks are the result of an evolutionary process of optimization. The idea that nature carries out optimizations in terms of metabolic regulation led us to search for existing universal regulatory patterns. In this paper we have investigated the existence of a global optimal solution in a substrate-cycle previously presented in A first objective of the research reported here was to investigate this aspect further by, on the one hand, reproducing the original results of 1f and 2f) we applied a multi-objective strategy  as presented in As a second objective, we wanted to find a unique regulatory scheme by means of multi-criteria optimization. Here we have been able to find a generalist solution by switching from mono- to multi-criteria optimization. Instead of optimizing with respect to an objective function consisting of a fixed combination of the performance and cost terms  and can therefore be tailored to arbitrary multi-criteria optimization problems. Besides, the implications of the work presented in this paper go beyond the analysis of regulation based on optimality principles. For example, we can use a similar multi-criteria optimization scheme for the optimal design of biological circuits, as considered in synthetic biology. Optimization methods have recently been used for such designs, as discussed in e.g. the review by Marchisio and Stelling


Biopharma process development accumulates growing collections of physicochemical product information and fermentation data, partly in response to initiatives like Process Analytical Technology (PAT) and Quality by Design (QbD) backed by regulatory authorities. The key goal of these initiatives is to ensure robust processes and consistent product quality based on a scientific and mechanistic understanding of the product compound itself and of the manufacturing process. Biopharma companies gather much high-quality data during fermentations including online measurements and omics data such as transcript and metabolite measurements. Typically, these data will be stored for documentation purposes only whereas data evaluation and interpretation lags behind and often is sporadic at best.Cellular network models can tap this underused resource for predicting fermentation outcomes and for analyzing why certain fermentations failed or succeeded based on a mechanistic representation of cell physiology. In particular, network models of cell metabolism upgrade metabolomics data by enabling predictions of cell behavior from concentration time series of extracellular and – if available – intracellular metabolites. Model simulations can be used for rapid hypothesis testing, e.g. to evaluate the impact of changes in feeding on intracellular metabolism, growth, or product formation. Identifying suitable metabolic target genes for cell line engineering represents another application area of such models. Here, we illustrate this approach using the prediction of optimal media compositions for a Chinese hamster ovary (CHO) cell line employing a genome-based CHO network model as example.in vivo reaction reversibilities can be obtained from labeling experiments combined with transient 13C-Metabolic Flux Analysis [The CHO stoichiometric metabolic network was reconstructed using information from public databases as well as from primary literature and accounts for the specific amino acid composition and glycoform structure of the product molecule. In a first step, we applied the network model to a comprehensive metabolic characterization of the existing fermentation process. Rates of cellular nutrient uptake, growth, and product formation in physiologically distinct process phases were determined from concentration time series of extracellular metabolites during a fermentation run. These cell-specific rates served to compute intracellular flux distributions using the CHO network model. Comparing flux distributions for different process phases provided insight as to when and where in intracellular metabolism significant changes occur during the fermentation. This is often not obvious from inspection of concentration time series alone. Especially for fed-batch processes, multiple feed streams and volume changes due to pH control and sampling impede interpretation of raw data. If desired, further information about the usage of alternative intracellular pathways and Analysis , which iIntracellular flux distributions also provide an ideal starting point for process optimization. Distinct optimal media compositions were computed for different fermentation phases based on the observed nutrient demand of the clone inferred from flux distributions. The chosen optimization approach combines stationary and dynamic model simulations on high-performance computing clusters. For dynamic simulations, the stoichiometric CHO network representation was transformed into a kinetic model. Model parameters were determined using evolutionary strategies and cluster computing based on the observed metabolite time series and considering thermodynamic constraints on reaction directionality. Integration of intracellular metabolite data into this workflow is easy and can further increase the predictive capabilities of the resulting model. The dynamic model also comprised a description of the fermenter including feeds and sampling. In this way, it can be predicted how changes in medium composition and feed flows impact rates of cell growth, productivity, and byproduct formation as well as intracellular metabolite profiles. Finally, media were optimized to maximize final product titer and specific productivity by varying the concentrations of glucose and individual amino acids in two continuous feed streams using evolutionary strategies on high-performance computing clusters.The resulting optimized media were tested experimentally in a fed-batch process. The improved feeding resulted in a 50% increase of final product titer and in an increased integral of viable cells already in the first iteration Figure . SimultaThe combination of metabolomics data and network models not only improves our quantitative understanding of cell physiology, but can also support and accelerate multiple steps of rational process development strategies:• Tailor media compositions to specific clones and curtail the time and experimental effort required for medium optimization compared to standard DoE techniques• Identify highly productive and robust clones for scale-up through comprehensive metabolic characterization during selection at small scales• Devise cell line engineering strategies for overcoming metabolic bottlenecks in cell growth and product formation by incorporating intracellular metabolite measurements and other omics data • Employ metabolic network models for controlling feed additions at the production scale.The above methodology enables biologics manufacturers to add value to data collected in PAT and QbD initiatives and to harness metabolomic data for quantitatively predicting and optimizing fermentation outcomes. This approach is not restricted to CHO cells, but is readily transferable to other cell lines and also to microbial production strains.


Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli’s metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors.Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for  Escherichia coli alliance was formed with the aim to generate data and tools necessary to formulate a whole cell computer representation of this bacterium Cell-scale modeling is one of the great goals of computational biology. In fact, in 2002 an international E. coli has been updated, refined, and extended over the last two decades iAF1260, which accounts for function of 1260 metabolic genes and represents almost 30% of the open reading frames (ORF) in E. coli’s genome E. coliE. coli genes. To-date, only few examples of integrated networks of cellular functions have been published, including i) a metabolic-regulatory network using metabolic reconstruction and transcriptional regulatory network in form of Boolean expressions, for E. coliThe metabolic reconstruction of The degeneracy of the genetic code implies that one or more cognate tRNA species can recognize the same codon (a triplet of nucleotides using a four letter code) on a messenger RNA (mRNA), while a tRNA species can also read two or more synonymous codons . There iSo far, key questions of molecular evolution of genome sequences could not be investigated with COBRA networks as they do not explicitly account for genes and proteins in a sequence-specific manner. In this study, we developed a novel framework that permits the analysis of sequence-related questions and potential phenotypic consequences.E. coli, deemed ME-matrix for metabolism and expression. We first reconstructed and validated this comprehensive, sequence-specific ME-matrix. Then, we determined how conserved the ME-matrix genes are in other bacteria. Lastly, we modified the codon usage of the wildtype model towards more biased and more equilibrated codon usage model strains. Subsequently, we tested the growth phenotypes of the model strains in different environmental conditions to assess the impact of the genotypes (being codon usage pattern) on the growth phenotypes.In this study, we present a comprehensive, mechanistically detailed, integrated network of metabolism and macromolecular synthesis machinery for E. coli MG1655’s metabolic (M-matrix) We assembled an integrated stoichiometric reconstruction of The conversion of a reconstruction into a mathematical model normally consists of the definition of systems boundaries, the addition of exchange and demand reactions, and the application of condition-specific constraints on exchange and/or intracellular reactions lation), . Couplinlation), . By applE. coli’s genome. Subsequently, the biomass reaction, which accounts for precursors to the macromolecular building blocks, needed to be adjusted for the fraction of amino acids (AA) and nucleotide triphosphates (NTP) used for synthesis of ME-matrix gene products. We carried out a sensitivity analysis to identify the best parameters, such that the model achieved experimentally observed growth rates . Furthermore, the wildtype cells have been evolved on minimal medium supplemented with glycerol or lactate and after 60 days of evolution, with optimal growth as the selection pressure, the substrate and oxygen uptake rates have been measured iAF1260  A. We foiAF1260  that thein silico growth phenotypes under 170 different growth conditions with phenotyping data from Biolog (http://www.biolog.com/). The ME-matrix correctly predicted 75% (128/170) of growth phenotypes (iAF1260 correctly predicted 76% (129/170) growth phenotypes. The ME-matrix agreed in 85% (144/170) of the predictions with iAF1260. It could grow in 16 conditions where iAF1260 could not, whereas in 12 conditions, iAF1260 could grow but the ME-matrix could not. For instance, the ME-matrix, but not iAF1260, could grow on 6 carbon sources -propane-1,2-diol, 4-aminobutanoate, and glycerol 3-phosphate). In contrast to iAF1260, the ME-matrix was unable to grow when formate, 5-dehydro-D-gluconate, glucose-1-phosphate, deoxyadenosine, fructose-6-phosphate, or glucose-6-phosphate were supplemented to the base medium. Also, the ME-matrix improved the predictions for seven nitrogen sources, but did not correctly predict the growth phenotype for eight other nitrogen sources.We aimed to determine how well the ME-matrix could qualitatively predict growth phenotypes. We compared enotypes , whereasin vivo and in iAF1260 in silico. It is likely that the maintenance cost of the macromolecular machinery in the ME-matrix is too high in formate minimal medium condition to support in silico growth. Taken together, our results show that the growth phenotype of the ME-matrix was comparable with the metabolic reconstruction of E. coli. As the metabolic reconstruction served as foundation for the ME-matrix, comparable growth phenotypes were expected.In general, false negative growth predictions indicate missing reactions in a network. No reactions were removed from the metabolic part when creating the ME-matrix. Therefore, the inability of the ME-matrix to grow, under conditions where iAF1260 could grow, was caused by stoichiometric synthesis constraints and/or constraints that couple synthesis and utilization. For instance, one of the carbon sources that did not support growth of the ME-matrix was formate, which showed weak growth in silico growth phenotypes for single gene knockout strains in glycerol minimal medium. Of the 1823 protein coding genes contained in the ME-matrix, 17% (314/1823) were predicted to be essential. We compared the computed gene essentiality with in vivo essential genes based on the Keio collection by Baba et al. In order to assess the predictive potential of the ME-matrix for genome-scale gene deletion studies, we determined the E. coli’s fitness under certain environmental conditions, but its absence is not crucial for growth. These gene products include, for example, the trigger factor , which assists in folding the nascent peptide chain exiting the ribosome, as well as many ribosomal subunits and proteins involved in ribosomal protein, rRNA, and tRNA modification. The in silico essentiality can be explained by the requirement of these proteins for the corresponding reactions. For instance, we did not represent the translation termination with and without the presence of the trigger factor, as for obvious energetic reasons the model would choose to not synthesize the protein and thus the model would represent less accurately E. coli’s biology.Many of the false positive predicted essential genes involve genes, whose gene products may improve iAF1260), our predictions agreed in 89% of the cases  problems, which nonzero for each network reaction  . These rE. coli genes in 65 enterobacter genomes and 40 non-enterobacter bacteria by using KEGG automatic annotation system (KAAS) E. coli K12 MG1665 could be assigned with 2,418 unique KO terms. Overall, the percentage of genes per organism homologous to E. coli ranged from 93% in Buchnera aphidicola 5A Acrysiphonpisum to 15.1% in organism Staphylothermus marinus F1 NC 009033 (E. coli genes and the 105 different organisms by noting the number of orthologous genes found in a particular species (E. coli gene products were part of the metabolic reconstruction (iAF1260), core machinery genes (E) if the gene products were part of E. coli’s macromolecular synthesis machinery reconstruction, and O for all other gene products  belonged to the COG category “information storage and processing”, of which more than 80% were highly persistent in enterobacters and more than two thirds were highly persistent within all species. Given the fundamental functions of these genes within the cell  this high degree of persistency is not surprising. Overall, this group has the largest number of persistent genes in enterobacters and in all species. Only few genes within the COG category “metabolism” and “poorly characterized” appear to be species specific.We were interested to see whether there were particular cellular functions within each group  that had more persistent KO groups than others. We determined the COG classification for each O groups . In genein silico, changes in CUB can alter a strain’s ability to grow in certain environments and affect the growth rate. How is this possible given that perturbation to codon usage only affects stoichiometric coefficients and not the sparsity pattern of the perturbed ME-matrices as no reactions were added or removed?A key interest of systems biology is to develop a mechanistic basis for the genotype-phenotype relationship. The ME-matrix explicitly captured the nucleotide sequence for almost 2000 genes and stoichiometrically represented their cellular functions, so we addressed the question if and how the codon usage bias (CUB) may evolve to maximize growth rate in different growth environments. We generated a range of perturbed ME-matrices differing only in their codon usage from the wildtype ME-matrix; ten ME-matrices with more biased codon usage  and five ME-matrices with less biased codon usage  . As expeStatistical genomic studies have identified GC content as the single most informative determinant of CUB in silico sequence reflect how biased or unbiased the codon usage of the 1,823 genes in the strains is compared to a random distribution  upper bounds on each transcription (initiation) reaction, i.e., RNA polymerase elongation rate times the gene dosage, ii) upper bounds on the sum of tRNA synthesis rates, iii) an upper bound on uptake rates of carbon source and/or oxygen, and iv) a lower bound on non-growth associated ATP maintenance. When analyzing the reduced cost vector associated with each optimal solution, we found that the growth of biased strains in glycerol and glucose (anaerobic) growth conditions was limited by upper bounds on ribosomal RNA (rRNA) operon transcription reactions, as was the case for the wildtype (see above). In contrast, for all seven biased strains in lactate and glucose (aerobic) conditions, the bounds on leucyl-tRNA transcription reactions were limiting growth . Leucineodon CTT . In all odon CTT . Five biE. coli. Using different computational tools, we assessed the predictive potential of the ME-matrix by comparing model prediction with published experimental data. In general, we found similar or improved predictive potential when compared to the metabolic network alone. We then employed the ME-matrix to assess the impact of CUB on the growth phenotype under different environmental conditions and found that the tRNA availability was growth limiting in most growth conditions for the biased strains but not for the wildtype or equilibrated strains.In this study, we created the first sequence-specific, integrated model of metabolism and macromolecular synthesis for the model organism iAF1260 , from http://systemsbiology.ucsd.edu) and imported into MATLAB  using the COBRA Toolbox iAF1260 accounts for 1,260 E. coli genes and 2,077 reactions, including 1,339 unique metabolic reactions, 690 transport reactions, and 304 exchange reactions iAF1260 accounts for 1,039 unique metabolites. A total of 1,148 unique, functional proteins are accounted for including 167 multigene complexes and 346 isozymes iAF1260 with the ‘E-matrix’, all gene associations connected to the artificial gene ‘s0001’ were removed. iAF1260 contains tRNA charging reactions that were also removed from the model before integration.The metabolic reconstruction of E. coli K12 MG1655. The bounds on exchange and transport reactions for metabolites, which were present in the E-matrix and in iAF1260, were set to be zero (lower and upper bound) in the E-matrix prior to integration.The macromolecular machinery reconstruction, deemed expression or ‘E-matrix’, was downloaded and imported into MATLAB iAF1260 requires that all metabolic enzymes (1260 gene products) are synthesized by the network. Therefore, we created template reactions for transcription, translation, mRNA degradation, etc. as well as the gene information :– Gene loci = b0822, gene = ybi, and protein = YbiVSecond, the reaction was converted into the following one (notice the name change):– Third, new reactions were added to the reaction list.– – – If a reaction was reversible (which G6PP is), the reactions for the reverse direction were as follows:– – – If the equation occurred in the periplasm [p] or extracellular space [e], a corresponding transport reaction was also included. The ME-matrix does not capture protein secretion mechanisms. The reaction G6PP is located in the cytoplasm, not requiring transport reactions, however, hypothetical transport reaction(s) would be as follows:If in the periplasm:– If in the extracellular space:– – The sample reaction represented a one gene to one protein relationship. There are three further possible gene-protein-relationships.The “OR” case. Two or more different genes could code for proteins, which could catalyze the same reaction(s) (isozymes). In this case, each gene was assigned to the reaction as shown above. Therefore, if the G6PP reaction could be catalyzed by The “AND” case. Multiple genes could code multiple proteins that must form a complex to catalyze the reaction. In this case, an additional reaction was created representing the complex formation reaction. Suppose – This new complex would then be used in the reactions above replacing The third instance was the combination of both the “OR” and “AND” case. The rules laid out above were used to combine the two.The integration of reformulated metabolic reconstruction and the extended E-matrix was done computationally by creating a non-redundant reaction list containing the union of the two reconstructions.iAF1260, which describes the relationship between gene products and metabolic reactions in terms of Boolean logic Information about protein complex formation was obtained from E. coli enzymes and primary literature. The information was manually assembled, while the network reactions were formulated based on the template reactions  of the There were three dominant sets of constraints applied to the ME-matrix.Constraints on the exchange reactions to simulate different environmental conditions.Constraints on the maximal transcription rate for stable and messenger RNA - these two set of constraints are on Coupling constraints on reactions, in the form ofwhere between mRNA synthesis and translation (via _mRNA_degr1 and _CONV2):– The parameters between protein synthesis and protein utilizing reactionsCoupling constraints were added:– for E-matrix proteins: via DM_ and _RECYCL* The parameters – for M-matrix proteins (metabolic enzymes): via DM_ and _DREC* The rational behind the parameters * In general, we observed that the between tRNA charging and utilization , which allowed each mRNA species to cycle within the network for a limited time before getting turned over. Hence, the mRNA synthesis corresponds to the maintenance of the internal pool for each transcript within the cell. In fact, Eq. 3 controled the pool size for each transcript within the network. There is a direct conversion possible from _CONV2 and _mRNA_degr1 allowing to back-calculate half-life time and mRNA concentration for each transcript given a flux vector iAF1260 _core_59p81M) were unbounded. The base medium allowed the free uptake of the following compounds by setting their corresponding lower bound to :Experimental measurements of substrate and oxygen uptake rates were applied on the exchange reactions . The uniEX_h2s(e), EX_ca2(e), EX_cl(e),EX_co2(e), EX_cobalt2(e), EX_cu2(e), EX_fe2(e), EX_fe3 (e), EX_h2o(e), EX_h(e), EX_k(e), EX_mg2(e), EX_mn2(e), EX_mobd(e), EX_na1(e), EX _tungs(e), EX_zn2(e), EX_cbl1(e).E. coli K12 MG1655 were downloaded from the company's website (http://biolog.com). A total of 170 tested compounds were in the reconstruction. The oxygen consumption rate was set to iAF1260 were obtained from Biolog data for in silico knockout strain. The same procedure was repeated for all 1,823 ME-matrix genes. We compared the in silico growth phenotype of the single gene deficient strains for the 1,260 metabolic genes in aerobic glycerol minimal medium and with the published experimental study in silico single knockouts of iAF1260  proteins are explicit part of the metabolic reactions and (ii) transcription may occur with other genes (if co-expressed in a transcription unit), and thus coupling constraints would cause all genes in the transcription unit to not be expressed. Therefore, all translation initiation reactions for the gene were identified  and the corresponding lower and upper bounds were set to zero. Then, all coupling constraints were identified and removed. We then maximized for the biomass reaction in the E. coli and each of the bacterial species used for this study was downloaded from the NCBI database .A total of 105 bacteria were considered in this study  The KAAS service provides cross-species gene annotation based on KEGG pathways and BRITE hierarchies E. coli. Then, the homologs with a bi-directional hit rate of higher than 0.95 were selected and divided to KEGG Ortholog (KO) groups E. coli was obtained by uploading the protein sequence files for E. coli to KAAS. This list provided a mapping from one or more E. coli gene “b” numbers (Blattner numbers) to a KO term, which allowed us to divide the 2,418 KO terms into three major subgroups: M-metabolic subgroup based on genes included in the metabolic reconstruction of E. coliE. coliE. coli orthologous gene groups (KO) found in species of the Enterobacteriaceae family. The table was extended to include an additional 40 non-enterobacters and clustering was repeated on this table.The protein sequences of the study organisms were uploaded to the KEGG Automatic Annotation Server  using the k-means clustering function and Hamming distance in MATLAB . The corresponding average silhouette widths were for E = 0.6623, M = 0.3879, O = 0.2686. Instead of delineating between persistent genes and non-persistent genes, we systematically defined persistence as three levels classified as high, mild, or not persistent.As a next step, we transformed the orthologous gene table into a binary table where each 1 represents the presence of an orthologous group, KO, and 0 represents no KO for that species and With the addition of non-enterobacter species, genes in the KO group list were sorted based on the following definitions: universally persistent , enterobacter persistent , species persistent , and non-persistent.in silico CUB mutant strains were generated consisting of ten biased strains and five equilibrated strains. The genetic code as well as the modeled tRNAs used for the formulation of the synthesis reactions in the ME-matrix are listed in A total of 15 The biased strains were generated using the following algorithm:Input: model, sequence for each gene in model, number of iterations mOutput: model_biasedAlgorithm:Choose randomly a codon, Identify possible synonymous codons: Choose randomly one codon from Replace all instances of Update ME-matrix for all genes based on new gene sequence:Transcription reactions.mRNA degradation reactions.Translation reactions (tRNA molecule will be updated based on codon recognition).Repeat 1 through 5 m times, m = 100.The equilibrated strains were produced as follows:Input: model, sequence for each gene in model, number of iterations mOutput: model_eqAlgorithm:Initialize vector codon = zeros, which will count the occurrences of different codons in the genomeDefine a random order of genes to start step 3For each gene For each codon Identify possible synonymous codons: Choose codon iv. Replace Update codonUpdate ME-matrix for all genes based on new gene sequence:Transcription reactionsmRNA degradation reactionsTranslation reactions (tRNA molecule will be updated based on codon recognition)Repeat 1 and through 4 m times, m = 100Note that each strain had its own ME-matrix, which contained the alterations in the w codons . Note thin silico strains.The GC content of the individual strains was calculated by counting the instances of guanine and cytosine residues in the 1,823 protein coding genes included in the ME-matrix. The genome sequence used for the wildtype was version m56, In order to quantify the degree of synonymous codon bias in a sequence, we computed the synonymous codon entropy ctively, .Calculating with the ME-matrix was time-consuming and numerically challenging due to the multiscale nature of the FBA problem . TherefoAll simulations were carried out in MATLAB  using Tomlab  as numerical analysis interface for linear programming.http://notendur.hi.is/ithiele/downloads.html.The ME-matrix reconstruction used in this study is described in the Supplemental Tables and is available in MATLAB format under Figure S1Degeneracy of the genetic code. A. Genetic code employed in this study. Number of cognate tRNAs per amino acid is given in parenthesis. B. Schematic illustration of the degeneracy of genetic code.(EPS)Click here for additional data file.Figure S2Sensitivity analysis. We tested the sensitivity of the predicted growth rate as a function of the remaining amino acid (AA) requirement in the biomass function and as a function of the remaining growth associated maintenance (GAM) that is left in the biomass function. The experimentally observed growth rate is shown with the dotted line. Since the ME-matrix covers about 1,900 of 4,400 E. coli genes, we decided to allocate 50 of the AA requirements and the 50 of the GAM for the ME-matrix genes and gene products. This plot also highlights that finetuning of these two parameters will be important to obtain accurate predictions in growth rate.(EPS)Click here for additional data file.Figure S3Codon usage. Comparison of codon usage in ME-matrix associated genes and across the genome.(EPS)Click here for additional data file.Figure S4Summary statistics of clustering results. O - Unclassified, M -lifestyle coding, E - core machinery coding. * of occurrences in related species.  - minimal and maximal number of species within a gene group  and conservation group.(EPS)Click here for additional data file.Figure S5Shannon entropy of the genome versus the maximal possible growth rate. The 16 in silico strains are shown with their predicted growth rates in glucose minimal medium/aerobic condition (GlcAer), glucose minimal medium/anaerobic condition (GlcAnaer), glycerol minimal medium/aerobic condition (Glyc) and lactate minimal medium, aerobic conditions (Lac). Eq. strains = equilibrated strains. WT = wildtype.(EPS)Click here for additional data file.Figure S6Reduced cost results are shown for the biased strains in the four defined growth conditions. Increasing the flux rate through the transcription reaction of the tRNA transcription units Click here for additional data file.Table S1Summary of genomes included in the gene conservation analysis. Open Reading Frames (ORFs).(PDF)Click here for additional data file.Table S2textbfCodons recognition by tRNA in the ME-matrix.(PDF)Click here for additional data file.


Escherichia coli. The central ammonium assimilation network in E. coli converts carbon skeleton α-ketoglutarate and ammonium into glutamate and glutamine, which further serve as nitrogen donors for nitrogen metabolism in the cell. This reaction network involves three enzymes: glutamate dehydrogenase (GDH), glutamine synthetase (GS) and glutamate synthase (GOGAT). In minimal media, E. coli tries to maintain an optimal growth rate by regulating the activity of the enzymes to match the availability of the external ammonia. The molecular mechanism and the strategy of the regulation in this network have been the research topics for many investigators. In this paper, we develop a flux balance model for the nitrogen metabolism, taking into account of the cellular composition and biosynthetic requirements for nitrogen. The model agrees well with known experimental results. Specifically, it reproduces all the 15N isotope labeling experiments in the wild type and the two mutant (ΔGDH and ΔGOGAT) strains of E. coli. Furthermore, the predicted catalytic activities of GDH, GS and GOGAT in different ammonium concentrations and growth rates for the wild type, ΔGDH and ΔGOGAT strains agree well with the enzyme concentrations obtained from western blots. Based on this flux balance model, we show that GS is the preferred regulation point among the three enzymes in the nitrogen assimilation network. Our analysis reveals the pattern of regulation in this central and highly regulated network, thus providing insights into the regulation strategy adopted by the bacteria. Our model and methods may also be useful in future investigations in this and other networks.Nitrogen assimilation is a critical biological process for the synthesis of biomolecules in  Escherichia coli, ammonia is the preferred nitrogen source that supports its fastest growth E. coli. One pathway involves the NADP-linked glutamate dehydrogenase , which converts ammonium and α-ketoglutarate (αKG) to glutamate. The other pathway involves the combined activities of the glutamine synthetase , which aminates glutamate to form glutamine, and the glutamate synthase , which transfers the amide group from glutamine to αKG to produce two molecules of glutamate E. coli are derived from glutamate and glutamine, the two primary products of ammonium assimilation For E. coli tend to maintain an optimal growth under a wide range of the external ammonia concentration Experimental observations on bacteria growth suggested that 15N isotope labeling process for the wild type and mutation strains. The results agree well with the isotope labeling experiments max values in different growth conditions, which are also found to be consistent with experimental observations Since Stadtman's pioneer work in the late 1970s E. coli starts from the ammonium (NH4+ + NH3) diffusion across the cellular membrane. However, only the uncharged NH3 can diffuse freely through the membrane with a high permeability a of NH4+ is 9.25, external NH3 concentration (NH3ex) is relatively low: about 55.92 µM at pH 7 when total ammonium (NH3ex + NH4+ex) is 10 mM. Besides the free diffusion of neutral ammonia, E. coli can transport ammonium (NH4+ex) by its transporter protein AmtB 2) and transporting efficiency (10 to 104 ammonium per second per transporter) 3ex diffuses into the cytoplasm, internal NH3 (NH3in) is protonated into NH4+in, which serves as the substrate of GDH and GS 3 can be described byE. coli cells The nitrogen assimilation process of E. coli exponentially increases with the growth rate, and the cellular volume increases with a similar speed as the cell mass As shown in several experiments, the cell mass of E. coli from E. coli, the input of synthesizing flux equals to the output of consuming flux. Therefore we used the measured consuming flux of Gln directly taken from 3, using pH = 7 in the medium and pH = 7.6 inside the cell To obtain the respective contribution of Glu and Gln to aminotransferase reactions and as the carbon skeleton, we used the cellular composition and biosynthetic requirements for nitrogen in in vivo experimental data reported by Yuan in 2006 15N isotope-labeled ammonium, the ammonium in the medium and the nitrogen in the intracellular metabolites, such as amino acids and nucleotides, can be traced. The dynamics process can thus be recorded. With the above obtained data of fluxes and the concentrations of the external ammonia and NH4+in, and taking the concentrations of Glu and Gln as the values measured in We verified the flux distribution using the To further validate our model, we also investigated the labeling process of two mutant strains, GDH knockout (ΔGDH) and GOGAT knockout (ΔGOGAT). Since the growth rate of both knockout strains are almost the same as that of the wild type in 10 mM and 2 mM ammonium concentrations (see the experiment details in m for ammonium of GDH (about 1.1 mM) is much higher than that of GS (0.1 mM) T strain . The flu4+in were estimated using Eq. (1) for the wild type, ΔGDH and ΔGOGAT strains. As shown in Next, based on the flux distribution in The experimental validations gave us confidence in our flux balance model. However, it does not provide any information about the details of the regulation on the enzymes involved in the model. To investigate the regulation details in the nitrogen assimilation network, we employed the kinetic equations built by Bruggeman and coworkers to model the fluxes of the reactions catalyzed by GDH , let us assume that the concentrations of the energy related metabolites  are constants . Eq. (3) gives the fluxes of the enzymatic reactions as functions of the environment (NH4+in), the concentrations of substrates , and Vmax. Using the substrate concentrations measured in max for various experimental conditions. We did this for two cases in which there were experimental data that can be used to estimate Vmax: (1) the samples grown in 10 mM ammonium in the exponential growth phase, and (2) the samples obtained 3 hours after the cells grew on 2mM ammonium plates to nitrogen limitation, for the wild type and the mutant strains of ΔGDH and ΔGOGAT, respectively , GDH and GS reactions were considered as reversible and GOGAT reaction as almost irreversible E. coli was observed to maintain a relatively constant doubling time (about 60 min) in a wide range of the external ammonium concentration When grown in a minimal medium, E. coli would prefer a strategy of using fewer regulations to achieve the same objective, assuming everything else being equal. Even if in reality more enzymes are being regulated in the regime of nitrogen availability we consider, investigating the capability of the regulation on the three major enzymes towards maintaining a constant growth rate would still be illuminating In principle, a global control that involves regulating each and all of the fluxes in ammonium assimilation can result in constant nd GOGAT . It is c4+in) with VmaxGDH, VmaxGS, VmaxGOGAT. For a changing NH4+in concentration, one can find corresponding changes in these Vmax's, which would reflect the regulations on the respective enzymes  to maintain the constant growth rate. However, there are many more variables than equations in this system. Certain assumptions are needed to confine the solution space of the Vmax's. We assume that the energy related metabolites  do not change significantly under the nitrogen limitation conditions we are considering. Among the three substrates Gln, Glu and αKG, Glu was observed to stay at a constant high level to maintain the internal pool of K+, the most prevalent osmolyte inside the cell maxGDH, VmaxGS, VmaxGOGAT, Gln and αKG) that should satisfy the two equations of mass conservation. Since the system is still under-determined, we proceeded with the following two approaches.Using the wild-type values ofmax's to vary in response to the changing ammonia concentration, and kept the other three variables fixed. , and the variation of the Vmax for GS also needs to exceed 10-fold (from 607 to 7070 mM/min) (max of GS was fixed at 649 mM/min. The curve of the Vmax of GDH with changing NH4+ shows that its variation was nearly 550-fold (from 276 to 151258 mM/min)   . For the mM/min) . When NH mM/min) . To sum maxGDH, VmaxGS, VmaxGOGAT, Gln and αKG) vary in response to a changing ammonium concentration. There will be infinitely many solutions. We focus on the “minimal solution” for each ammonium concentration. A minimal solution is the one that minimizes the summed changes of the 5 variables. We searched for the minimal solutions corresponding to different ammonium concentrations that minimized the squared distance Z from their original reference values :max are the maximum reaction rates for GDH, GS and GOGAT, respectively; K, L and M represent the reaction constants involved in the reactions Click here for additional data file.Table S1(DOC)Click here for additional data file.Table S2(DOC)Click here for additional data file.Table S3(DOC)Click here for additional data file.File S1The ordinary differential equations describing the labeling process of the metabolites.(DOC)Click here for additional data file.File S2Predicted key enzyme activities for ΔGDH and ΔGOGAT Strains.(DOC)Click here for additional data file.


Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research.P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed.In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.In conclusion the first GEM of  Pichia stipitis possesses the highest native ability of any yeast to metabolize xylose [Saccharomyces cerevisiae [+, hence the formed NADH cannot be properly recycled at oxygen limited conditions [P. stipitis produces ethanol at yields close to the maximum at low oxygen transfer rates (~2 mM h-1), but xylose uptake rate is only half of the maximum attained under fully respiratory conditions [S. cerevisiae into a xylose fermenter. However, the expression of target genes from P. stipitis into S. cerevisiae has not been quite successful due to the difficulties in regulating redox balances during xylose consumption and ethanol conversion [S. cerevisiae can produce ethanol via aerobic fermentation (the Crabtree effect) or anaerobically, especially in the presence of high concentrations of glucose [P. stipitis does it mainly in response to oxygen limitations (Pasteur effect) [e xylose , and is revisiae -4. Xylosnditions . Attemptnditions ,7. Due tnversion ,4. Furth glucose , P. stip effect) ,8,9.Pichia pastoris belongs to a small group of microorganisms capable of catabolizing methanol and fatty acids (e.g. oleic acid) as the sole carbon and energy source [AOX; and the multifunctional beta-oxidation protein, FOX2), and multiply peroxisomes when it is growing on such carbon sources [P. pastoris has therefore been used extensively for the production of recombinant proteins using the AOX gene promoter, as well as a model for studying peroxisome proliferation [P. pastoris to produce sialylated glycoproteins has increased the expectations for its use to produce pharmaceutically relevant proteins[S. cerevisiae [y source ,11. It c sources . P. pastferation . Consequferation . Cultivaferation -16. The  proteins, as wellrevisiae .P. stipitis and P. pastoris [in silico methods, GEMs can act as scaffolds for the design of optimal metabolic fluxes [With the availability of the complete genome sequences for pastoris ,20, therpastoris . GEMs capastoris , and forpastoris . Moreovec fluxes -26, or tc fluxes ,27,28.P. pastoris have been published [P. pastoris (iLC915), together with the first GEM for P. stipitis (iSS884). Both GEMs correctly represented available experimental phenotypes. The iSS884 model, for example, can predict xylose consumption and distribution into biomass and ethanol using different oxygen up-take rates. The iLC915 model can closely simulate the physiological differences of P. pastoris growing at different carbon sources alone (including oleic acid and methanol), as well as methanol mixed with glycerol or sorbitol. iLC915 also includes reactions for the production of a model recombinant protein, for which the production under different oxygen uptake rates compared well with experimental results. The use of such networks on the design of fermentation technologies is also discussed.Thus far, two GEMs of ublished ,30. HoweP. stipitis (CBS 6054) [P. pastoris (GS 115) [S. cerevisiae was used as reference framework because of its accurate annotation of fatty acid metabolism, and the extensive information about metabolites and genes, which allowed for a convenient comparison with the KEGG database.Reconstruction of the GEMs was comprised by a semi-automatic approach enriched with functional analysis and extensive manual curation based on available literature. Figure BS 6054) , and P. (GS 115) . The iIN-40), and on the score to sequence length ratio according to David et al. (2008) [S. cerevisiae from the genome sequences of the two Pichia species following the RAVEN Toolbox pipeline. Finally, the metabolic network of S. cerevisiae iIN800 was used to map genes from P. pastoris and P. stipitis having homologs in S. cerevisiae.BLASTp (ncbi-blast software ver. 2.2.24) was used to identify homologous proteins among the three yeast species. Protein homologs were identified based on stringent cutoff values  . KEGG OnP. pastoris or P. stipitis was lacking, data from other closely related yeasts was used instead , which is part of the RAVEN Toolbox. F-LocA incorporates subcellular localization predictors (CELLO and WoLFPSORT) , togetheboidinii ,34). Bot-1), and the appropriate coefficients for every building block present in each macromolecule (mmol g-1 of macromolecule) were calculated based on compositional analysis reports available for both yeast species [Biomass reactions were assembled from the macromolecular components . The contribution of each component to biomass , since there are some experimental reports using it [P. pastoris codon usage reported by De Shutter et al. (2009) [Production of a model heterologous protein was only considered in the GEM of using it ,40. Acco. (2009) .There are two primary ways for a cell to use the generated ATP. The ATP required for biomass synthesis (i.e. precursor biosynthesis and polymerization), and that used for cell maintenance. Both are added to calculate the total amount of energy required for a cell to grow, as it is shown in the following equation:ATP specifies the total amount of ATP being utilized, YxATP corresponds to the energy utilized for biomass synthesis, μ is the specific growth rate, and mATP is the ATP used for maintenance. The value of YxATP was calculated in two steps according to the procedure previously described [μ/rglc = Ysx with experimental data [In equation (1), rescribed ,41. Firsescribed . The caliX). The relation between metabolites' coefficients in the stoichiometric matrix (ijS) and fluxes through reactions (jv) is represented in equation (2).Flux balance analysis was used in the simulations with the reconstructed genome scale metabolic models ,44. ThisAfter each simulation an additional flux variability analysis was performed to ensure that the reported fluxes represent the only optimal solution for the resulting objective function value.-1 h-1 of each carbon source , and maximizing for cell growth. Experimental values for specific consumptions were fixed when it was required to predict experimental data. A minimal media was used for all simulations, i.e. reactions for consuming ammonia, sulfate and phosphate remained open during simulations. In simulations with dual carbon sources (e.g. methanol-glycerol or methanol-sorbitol), the upper boundaries of their respective transport reactions were constrained to the experimental fluxes.A general overview of the physiological states through utilization of different carbon sources was performed by feeding 1 C-mol gDW2, as well as the consumption of glucose, were then quantified and compared against experimental data. Another set of simulations were carried out to evaluate the maximum production of FAB using different carbon sources separately or in combination. In this case, the reaction exporting FAB was used as the objective function and the maximum production was attained.FAB production was evaluated using glucose as carbon source and ammonia as nitrogen source. The objective of these simulations was to assess the effect of oxygen uptake rate and FAB production on the physiological state of cells. This state can depend on the availability of glucose and oxygen, as well as on the metabolic over-load exerted by the transcription, translation and post-translational processing of the recombinant protein. The amount of FAB that cells should synthesize was fixed, since the production of a recombinant protein is subjected to a number of posttranslational processing and regulatory functions, not included in the GEM. The fluxes through the reactions to produce biomass, ethanol and COIn silico evaluation of the effect of oxygen uptake rate on the production of ethanol and biomass using glucose or xylose was evaluated with the GEM of P. stipitis. The oxygen uptake rate (rO2) was used as the limiting parameter in simulated continuous cultures with xylose as sole carbon source . Experimental values of rO2 reported by Skoog et al. (1986) [O2 value. The stream from this reactor was used to feed the second one. Here, the glucose was completely depleted and xylose started to be consumed at a rate calculated from the experimental data. In this simulation the oxygen uptake rate was decreased to favor the production of ethanol. This strategy was also used in the third reactor where xylose was mainly converted to ethanol.. (1986)  were use. (1986) . In the in silico mutant strain. The preceding procedure was repeated for all reactions in the network. Percentages of the mutants growth (Pmw) with respect to the wild type were calculated using fluxes through the biomass reaction (Pmw = νX-mutant/νX-wild 100), and three different labels were assigned. Reactions were classified as essential (E), non-essential (NE), and partially essential (PE). Essential reactions are those where their deletion caused a non-growing phenotype in the in silico mutant, getting a Pmw of zero. Non-essential reactions have no effect on the growth rate upon deletion. Partially-essential reactions had Pmw values between of 0.01-to-0.99, hence a diminished growth rate was seen upon their deletion.Reactions deletion in GEMs can be applied by constraining the flux through a single reaction, or to a set of reactions associated with one gene, to zero. Flux balance analysis is then performed to predict the maximum growth rate of the P. pastoris, named iLC915, and P. stipitis, named iSS884, are provided in Microsoft Excel, SBML and BioOpt formats and are available in the BioMet Toolbox http://www.sysbio.se/BioMet.GEMs for S. cerevisiae [P. pastoris and the iMM904.Table revisiae . The iSSrevisiae  and iPP6revisiae ), iLC915P. stipitis, P. pastoris, and S. cerevisiae) by their GEMs. In total, 1468 genes orthologs are common  neither found in S. cerevisiae [P. pastoris [P. pastoris and P. stipitis were not detected during this analysis. These genes were also not included in the previous models for P. pastoris [P. pastoris and S. cerevisiae were not included in the GEMs since they encode functions of cellular and genetic information processing. A higher number of shared genes orthologs were found among P. stipitis and S. cerevisiae (217). Genes, whose proteins are required to metabolize xylose and arabinose, were annotated in iSS884 and iIN800. There are also some hydrogenases, reductases and phosphatases without gene association in P. pastoris . Additional file Gene orthology analysis using the KEGG orthology (KO) system was performed to improve GEMs reconstruction . In total, 1417, 1328, and 1361 unique reactions were deleted individually from iLC915, iSS884 and iIN800 GEMs, respectively. Out of these deleted reactions, 173 in iLC915, 155 in iSS884 and 193 in iIN800 were found to be essential for growth in minimal media with glucose. Reactions which only caused growth retardation  accounted for 85 in iLC915 and 58 in iSS884 and iIN800.S. cerevisiae accumulated a higher number of lethal phenotypes in reactions located in the lipids network , which is likely due to the fact that the iIN800 comprises more details on lipids composition in the biomass equation [Deleted reactions for the three models were grouped into different metabolic pathways Figure . Reactioequation .In silico evaluation of carbon assimilation using the iLC915 and iSS884 GEMs was carried out in simulated chemostats with minimal media. With iSS884, glycerol was better assimilated  and the efficiency of aerobic respiration measured as P/O ratio were calculated for each carbon source. With glucose, glycerol, sorbitol, or trehalose, the computed values for YxATP using the iLC915 GEM were 70.5, 76.5, 73.1, and 74.2 mmolATP gDW-1, respectively. Even when the biomass yield with alanine was low compared with the other carbon sources, a comparable result of the YxATP was calculated (72.3 mmolATP gDW-1). The highest energy requirements for growth were estimated with methanol and oleic acid (152.5 and 95.8 mmolATP gDW-1). Mixtures of glycerol with methanol at decreasing ratios  showed increasing YxATP values , and reducing biomass yields (Ysx) accordingly . With the iSS884 model of P. stipitis, ATP required for biomass formation was similar for some carbon sources . Xylose, arabinose, and cellobiose assimilation needed 87.5, 87.4 and 86.0 mmolATP gDW-1, respectively.Energy requirements for biomass synthesis strongly depend on the nature of the carbon source . The totP. stipitis to investigate its physiology have been done [in silico predictions of biomass yields (Ysx), dilution rates (μ), ethanol yields (YsEtOH), CO2 specific productivities (rCO2) and ethanol specific productivities (rEtOH) were compared with experimental data. Figure in silico simulations predicted the inability of P. stipitis to grow in anaerobic conditions, as well as the small amount of ethanol that cells produce in-vivo. Xylitol production was not observed neither in the experimental results [in silico evaluations (indicating that the regeneration of NAD+ was not limiting).Only a few studies on continuous culturing of een done ,54. Parteen done . The iSSIn silico physiological predictions with the iLC915 GEM of P. pastoris using glucose, methanol, glycerol and a mixture of methanol with glycerol at various dilution rates were also carried out. Predicted values for specific growth rates (μ), oxygen consumptions (rO2), and CO2 productions (rCO2) are shown in the Figure P. pastoris phenotype on glucose. Such predictions were less accurate when using the iPP668 model, which computed higher μ values. This was also the case when the models were used to simulate glycerol metabolism. Results of methanol utilization in the three GEMs were different. While iPP668 predicted higher values of μ, the PpaMBEL1254 computed lower ones (0.0135 and 0.005 h-1). This was reflected in the amount of CO2 produced by both models (0.91 and 0.392 mmol/gDW-1). Computations with iLC915 provided values of 0.0125 h-1 and 0.48 mmol/gDW-1 for μ and rCO2. Those values were closer to P. pastoris phenotype on this carbon source, as it is shown by comparing with experimental data , which has been experimentally assessed ,40. SimuxFAB and rO2, model predictions are very close to the experimental values. The maximum biomass yield was obtained experimentally and in silico under fully aerobic conditions (case 1). By decreasing the percentage of oxygen in the inlet air to 11 and 8%, Baumann et al. (2010) [Table . (2010)  observed. (2010) , the simP. pastoris and the xylose fermenting P. stipitis were reconstructed and evaluated. Taken together, the results of constraint-based analysis used to evaluate cellular functions of these models were in close agreement with experimental results .The genome-scale metabolic models iLC915 and iSS884 of the methylotrophic yeast observed ,53. SuchP. pastoris as host. One of them included the use of other well-assimilating carbon sources (e.g. glycerol and sorbitol) together with methanol, which is poorly assimilated and very toxic [P. pastoris for the synthesis of amino acids and recombinant proteins using methanol alone or in combination  [ellulose . The metellulose , therefo. (1991) , and shoPichia species with S. cerevisiae, we found that the maximum capacities for the production of the 20 amino acids and the essentiality of reactions for the three yeasts to grow on glucose are similar  used as model recombinant protein.Click here for filePichia stipitis, Pichia pastoris, and Saccharomyces cerevisiaeGene orthology analysis among the three yeast species: .Click here for fileAnalysis of reaction essentiality predicted by simulations.Click here for filePichia stipitis, Pichia pastoris, and Saccharomyces cerevisiaeMaximum capacities for amino acids production among the three yeast species: .Click here for file


Improving our understanding of processes at the core of cellular lifestyles can be aided by combining information from genetic analyses, high-throughput experiments and computational predictions.Rhodobacter sphaeroides. We focused our analysis on pathways crucial to the production and recycling of pyridine nucleotides during aerobic respiratory and anaerobic photosynthetic growth in the presence of an organic electron donor. In particular, we assessed the requirement for NADH/NADPH transhydrogenase enzyme, PntAB during respiratory and photosynthetic growth. Using high-throughput phenotype microarrays (PMs), we found that PntAB is essential for photosynthetic growth in the presence of many organic electron donors, particularly those predicted to require its activity to produce NADPH. Utilizing the genome-scale metabolic model iRsp1095, we predicted alternative routes of NADPH synthesis and used gene expression analyses to show that transcripts from a subset of the corresponding genes were conditionally increased in a ΔpntAB mutant. We then used a combination of metabolic flux predictions and mutational analysis to identify flux redistribution patterns utilized in the ΔpntAB mutant to compensate for the loss of this enzyme. Data generated from metabolic and phenotypic analyses of wild type and mutant cells were used to develop iRsp1140, an expanded genome-scale metabolic reconstruction for R. sphaeroides with improved ability to analyze and predict pathways associated with photosynthesis and other metabolic processes.We combined data and predictions derived from phenotypic, physiological, genetic and computational analyses to dissect the metabolic and energetic networks of the facultative photosynthetic bacterium These analyses increased our understanding of key aspects of the photosynthetic lifestyle, highlighting the added importance of NADPH production under these conditions. It also led to a significant improvement in the predictive capabilities of a metabolic model for the different energetic lifestyles of a facultative organism. Information about an organism’s capabilities can be derived from a variety of sources. When genomic information is combined with biochemical, phenotypic or genetic data, functional roles and interrelationships of components within metabolic or regulatory networks become better defined -5. Thus,Rhodobacter sphaeroides represents one of the best studied photosynthetic organisms, and has been used to develop models of photon capture, light-driven energy metabolism and other aspects of its diverse lifestyles [R. sphaeroides has been studied for potential biotechnological applications including the ability to produce H2[2 and N2[R. sphaeroides an excellent system in which to improve our understanding of solar energy capture, metabolic and energetic aspects of photosynthesis and other energetic pathways, and the networks which regulate processes of societal and biotechnological interest. To obtain an integrated understanding of photosynthesis or other aspects of R. sphaeroides’ lifestyles requires the use of high-throughput data to develop better predictive models of its metabolic network.We study purple non-sulfur bacteria, a group of photosynthetic microbes that display great metabolic and energetic diversity . The purfestyles ,12. Thisfestyles ,12. Furtroduce H2-15 and uroduce H2, productroduce H2, remediaroduce H2, and its[2 and N2,19,20. TR. sphaeroides by combining data from genetic, phenotypic and transcriptional analyses with constraint-based modeling. We use high-through phenotypic microarrays to show that wild type R. sphaeroides grows on a diverse array of substrates and that this nutrient utilization profile varies significantly between photosynthetic and non-photosynthetic growth conditions. Using the conserved bioenergetic enzyme pyridine nucleotide transhydrogenase (PntAB) as an example, we identify carbon sources where recycling of pyridine nucleotides by this enzyme is essential for photosynthetic or non-photosynthetic growth. We use a genome-scale metabolic model to predict flux distributions and identify alternative NADPH producing reactions that can compensate for the loss of PntAB and thereby explain the conditional growth of ΔpntAB cells on selected carbon sources. Transcriptional and phenotypic analyses of defined single and double mutants were used to verify the potential use of some of these alternative NADPH producing reactions under defined conditions. The new data derived from analyzing the growth of wild type and mutant cells were used to develop iRsp1140, a significant update to the existing genome-scale reconstruction of the R. sphaeroides metabolic network [In this work, we take a systematic approach to expand our knowledge of the metabolic and energetic networks of R. sphaeroides has been reported to grow on 27 carbon, 3 nitrogen, 1 phosphorus and 4 sulfur sources [R. sphaeroides metabolic network, iRsp1095, predicted an ability to grow on a significantly larger number of substrates  [One important step in acquiring a global understanding of cellular processes in an organism is to develop a broad perspective of its metabolic repertoire. An assessment of the literature reveals that  sources . In contays (PM) ,22 to asR. sphaeroides cells  as the terminal electron acceptor . PntAB is a heterotetrameric membrane-bound enzyme consisting of α and β subunits that catalyzes the reversible exchange of reducing equivalents between pyridine nucleotides based on the magnitude of the proton gradient across the cytoplasmic membrane [To illustrate how knowledge of the substrate utilization profile of WT membrane ,27.Thus PntAB plays a major role in maintaining the balance of cellular pyridine nucleotides (NADH/NADPH). NADPH is a source of reducing equivalents in a large number of crucial anabolic pathways such as the Calvin cycle in autotrophic cells, fatty acid biosynthesis and tetrapyrrole or pigment biosynthesis in photosynthetic organisms .E. coli PntAB expression is induced when there is a demand for NADPH [E. coli PntAB is required for optimal growth on carbon sources whose metabolism does not directly generate NADPH, such as glycerol [E. coli also possesses an energy independent soluble transhydrogenase, UdhA, which is induced when there is an excess of NADPH  and mediates conversion of NADPH to NADP+[R. sphaeroides wild type and ΔpntAB cells  using PR. sphaeroides into 3 groups  transcript levels are increased >5 fold during aerobic respiratory growth on glucose compared to acetate or succinate, in both the WT parental strain and PntA1. Furthermore, transcripts for genes encoding putative malic enzyme (RSP_1217), isocitrate dehydrogenase (RSP_0446) and methylenetetrahydrofolate dehydrogenase (RSP_0661) enzymes were each increased >2 fold in PntA1 during aerobic respiratory growth compared to its WT parent , which require PntAB for photosynthetic growth, growth of PntA1 was indistinguishable from its WT parent under aerobic respiratory conditions . However, growth is partially restored for glucose and succinate when the PntA-Zwf1 mutant is complemented with a plasmid containing the e Figure B,D. Thesnditions  and withR. sphaeroides produces significantly larger amounts of fatty acids, photopigments and other components of the photosynthetic apparatus [R. sphaeroides. Given the ubiquitous nature of PntAB across biology, it is possible that this enzyme plays a major energetic role in other photosynthetic and non-photosynthetic organisms.The conditional ability of alternative pathways to compensate for the loss of PntAB could reflect a higher demand for NADPH during photosynthetic or other energetic conditions. For example, under photosynthetic conditions, pparatus  that areR. sphaeroides that can be utilized to refine biochemically, genetically and genomically structured databases employed in constraint-based analysis [R. sphaeroides, called iRsp1095, using its annotated genome, published organism-specific data and information from continuous cultures of WT cells [The above results provide new information about the metabolic capabilities of analysis . We prevWT cells .R. sphaeroides since release of iRsp1095 (see Methods). An initial refinement process resulted in addition of 280 reactions, 81 metabolites and 27 genes to iRsp1095, while removing 23 reactions and 3 genes . An anamporters )  which can provide reduced pyridine nucleotides is essential for photosynthetic growth on many carbon sources. We used a genome-scale model for R. sphaeroides to make flux predictions, as well as generate and test hypotheses on alterative NADPH-generating pathways that allow growth in the absence of PntAB. The products of various anabolic pathways require NADPH that is derived mainly from PntAB activity, so exploiting these and other alternative NADPH generating pathways we identified, could improve growth and metabolic end products derived from photosynthetic and non-photosynthetic wild type cells or those engineered to produce compounds of biotechnological interest.In this study, we took an integrated approach to gain new insight into the metabolic, energetic and photosynthetic lifestyles of nd generation genome-scale reconstruction for R. sphaeroides, iRsp1140 with significantly improved coverage of metabolic functions and predictions that are in better agreement with experimental data. iRsp1140, provides an improved depiction of the R. sphaeroides metabolic network, so it will be useful in studying photosynthesis, as well as a wider range of metabolic processes in this and related organisms.Previous studies have shown the utility of high-throughput data sets in refining and validating genome-scale metabolic models -44. We uR. sphaeroides strains 2.4.1 and Ga were used as parental strains. All mutants were made in strain 2.4.1 except PntA1 [E. coli DH5α was used as a plasmid host, and E. coli S17-1 was used to conjugate DNA into R. sphaeroides.pt PntA1  and Zwf1R. sphaeroides cultures were incubated at 30°C in Sistrom’s Minimal Medium (SMM) [2, while anaerobic respiratory cultures were incubated in screw capped tubes in the dark with the media supplemented with 0.9% DMSO. Aerobic cultures were shaken in flasks. Optical density of photosynthetic cells in screw capped tubes was measured using a Klett-Summerson photometer and is expressed in Klett units . Other optical density measurements were made by measuring optical density at 600 or 650 nm in a spectrophotometer. When required, the media was supplemented with 100 μM IPTG, 25 μg/mL kanamycin or 25 μg/mL spectinomycin. E. coli cells were grown in Luria Bertani medium at 37°C, supplemented with 25 μg/mL kanamycin where needed.um (SMM)  lacking R. sphaeroides, phenotype microarrays (PMs) were used with a few modifications. To assay aerobic respiratory growth on different carbon sources (Biolog PM1 and PM2A), cells were grown on SMM agar plates aerobically for 3 days. Cells were swabbed from the agar plates and suspended in 4 mL inoculation fluid (Biolog inc.) to an OD600 of 0.38. Two mL of this mixture was placed in 10 mL of inoculation fluid (IF) containing 24 μL of tetrazolium-based dye A (Biolog inc.), resulting in a final OD600 of ~0.07. Then 1.2 μL of vitamin solution  was added and 100 μL was dispensed into each well of a 96 well plate. Cultures were incubated at 30˚C for 72 to 96 hrs in an OmniLog plate reader (Biolog inc).To determine the substrate utilization profiles of 3, 0.4 mM sodium thioglycolate and 1 μM methylene green were added to the IF and this was kept in an anaerobic chamber for ~7 days with periodic shaking to facilitate it becoming anaerobic (the methylene green in the IF turns colorless once oxygen is removed). R. sphaeroides cells were grown photosynthetically on SMM agar plates and PM plates set up as described above, except that the tetrazolium dye was omitted, as thioglycolate reduces the tetrazolium-based dye turning it purple independent of cellular respiration. The PM plates were put in an anaerobic chamber, inoculated, placed in heat sealed anaerobic bags (Biolog inc.) [2) at 30˚C for 72 to 96 hrs, after which OD650 readings were taken. Anaerobic indicator strips and ageless oxygen absorbers  were placed in the sealed bags to report on and maintain an anaerobic environment throughout the experiment.To assay photosynthetic growth, 10 mM NaHCOog inc.)  and incu650 of 0.05 (after background correction) was used as a baseline for scoring photosynthetic growth, as this was the highest value obtained from any well in control experiments where cells were kept in the dark. A threshold of 5 Omnilog units (after background correction) was used as a baseline for aerobic respiratory growth. Only carbon sources that resulted in reproducible growth above the baseline across all replicates were considered to be growth substrates.Any growth in the negative control well (A1) was subtracted from the measured optical density for both aerobic and photosynthetic PM. A threshold ODR. sphaeroides cells were grown aerobically for 5 days on a modified R2A agar [2HPO4, 0.025 g of MgSO4.7H2O, 0.5 g of sodium pyruvate and 15 g of agar per liter of water). Plates were set up as described for photosynthetic growth with the addition of 20 mM sodium succinate and 2 μM ferric citrate. Cell cultures were grown under constant illumination (10 W/m2) at 30˚C for 48 hrs, after which OD650 readings were taken. Aerobic growth on the different nitrogen, phosphorus and sulfur sources is not reported due to significant background growth in the negative control wells, an issue that has been observed previously [To assay photosynthetic growth on different nitrogen, phosphorus or sulfur sources (Biolog PM3B and PM4A), R2A agar ,47  were set up to analyze 41 of the carbon sources identified as R. sphaeroides growth substrates from PM , suspended in anaerobic SMM no carbon (which had been kept in an anaerobic chamber for at least 4 days) to an OD600 of ~0.1 and DMSO was added to a final concentration of 0.9%. Then, 300 μL of a cell suspension was dispensed into wells containing a different carbon source in an anaerobic chamber. Plates were incubated at 30˚C for 10 days with continuous shaking in a Tecan M200 plate reader located within the anaerobic chamber, with OD650 readings taken every 6 minutes. Alternatively, plates were sealed in anaerobic bags, wrapped in foil and incubated at 30˚C for 10 days with periodic shaking.Due to comparatively slow growth rates of R. sphaeroides mutants we constructed contained in-frame markerless deletions. Briefly, regions spanning ~1500 bp upstream and downstream of the target gene were amplified using sequence specific primers containing restriction sites for EcoRI, XbaI or HindIII. These fragments were digested with the appropriate restriction enzymes and ligated into pK18mobsacB plasmid [E. coli S17-1 into R. sphaeroides strains. Cells in which the plasmid had successfully integrated into the genome via homologous recombination were identified by selection on SMM plates supplemented with kanamycin. These cells were then grown overnight in SMM without kanamycin. Cells that had lost the deletion plasmid (and thus the sacB gene) via a second recombination event were identified by growth on SMM plates supplemented with 10% sucrose. Individual gene deletions were confirmed by PCR and sequencing with specific primers.All  plasmid , digesteE. coli S17-1 into the relevant R. sphaeroides mutant. Cells which harbor the desired plasmid were identified by selection on SMM plates supplemented with kanamycin.Ectopic expression plasmids were made by amplifying the target genes from the genome using sequence specific primers containing restriction sites for NdeI and BglII, HindIII or BamHI. These DNA fragments were digested with the appropriate enzymes and cloned into pIND5 digested with the same enzymes. These plasmids were conjugated from R. sphaeroides strains that were grown either photosynthetically in 16 mL screw cap tubes or aerobically in 500 mL conical flasks. RNA isolation and subsequent cDNA synthesis were performed as previously described [ΔΔCT method with efficiency correction [R. sphaeroides rpoZ was used as a housekeeping gene for normalization. Primers used in this analysis are provided in Additional file RNA was isolated from exponential phase cultures of escribed . qRT-PCRm x n, were generated from the reconstructions  with the rows (m) representing metabolites, the columns (n) representing reactions and entries in the matrices representing stoichiometric coefficients for metabolites involved in each reaction. Flux balance analysis (FBA) [in silico growth by solving the linear programming problem:Separate stoichiometric matrices, Sis (FBA)  was usedBiomass is the flux through biomass objective function; v is the vector of steady state reaction fluxes; and vmin and vmax are the minimum and maximum allowable fluxes. The values in vmin and vmax were set to −100 and 100 mmol/g DW h for reversible reactions and 0 and 100 mmol/g DW h for forward only reactions. During simulation, all exchange reactions were assigned as being forward only , except the exchange reactions for media components required for cell growth, which were set to measured values for limiting substrates such as ammonia, or allowed to be freely exchanged with the extracellular space, i.e., -100 ≤ v ≤ 100. The non-growth associated ATP maintenance limit was set to 8.39 mmol/gDW h [where vol/gDW h . Flux vaol/gDW h  was alsoInitial simulations with iRsp1095 in which the transhydrogenase reaction was deleted resulted in the prediction of optimal growth, suggesting alternative NADPH generating reactions existed in the metabolic network. Analysis of iRsp1095 revealed that it includes at total of 61 NADPH requiring reactions, of which only 29 were independently non-essential and capable of functioning in the direction of NADPH synthesis. To identify all reactions within iRsp1095 capable of producing NADPH to support growth, all 29 non-essential NADPH-requiring reactions within iRsp1095, capable carrying flux the direction of NADPH synthesis, were turned off. This resulted in a predicted growth rate of 0. Optimal growth was restored solely by turning on the transhydrogenase reaction, consistent with transhydrogenase being sufficient for generating NADPH required for growth. All other reactions capable of independently restoring growth, while the other NADPH-requiring reactions were still off, were considered as a candidate NADPH producing reaction Table .To assess the predicted NADPH demand during aerobic or photosynthetic growth across growth substrates , all predicted NADPH generating reactions Table  set to hvmax and vmin respectively) through the reactions in the network based on genome-wide gene expression measurements. To achieve this publicly available microarray data obtained from cells grown on succinate and glucose (GEO platform GPL162), as well as from cells grown acetate (this study), were normalized and used to constrain the fluxes through each reaction in the network as previously described [vmax of 100 mmol/g DW h and a vmin of 0 or −100 mmol/g DW h, if the reactions were forward only or reversible respectively. For reactions catalyzed by isozymes, the expression value of gene for the isozyme with the highest expression was used to constrain the reaction, while for multi-subunit enzymes the gene for the subunit with the lowest expression was used to constrain the reaction. After setting the upper and lower bounds, subsequent simulations were conducted with FBA as described above.To predict fluxes through central metabolism, we used an extension of FBA called E-flux . E-flux escribed . For reaR. sphaeroides iRsp1095 [R. sphaeroides[No Growth-Growth (NGG – no growth predicted by model but growth observed in PM) inconsistencies were manually rectified by addition of appropriate transport and/or enzymatic reactions from the multi-organism databases KEGG [R. sphaeroides genome encoding the proteins potentially capable of catalyzing the new reaction(s) to be added; and (ii) if no putative enzymes were identified, the metabolic route that required the addition of the fewest reactions to iRsp1095 was selected. Growth-No Growth (GNG – growth predicted by model but no growth observed in PM) inconsistencies in iRsp1095 were resolved by removal of transport reactions for the substrate in question, when this did not introduce and new inconsistencies with the PM data.The previously published genome-scale model for iRsp1095  was usedhaeroides. Succinases KEGG  and BRENses KEGG ,58. The 20 was selected as a threshold for significance. Enzymatic functions not previously included in iRsp1095 and which were encoded by genes without any previously defined specific function were considered as newly annotated genes, while those with previously defined putative functions were considered as having additional functionality .Click here for fileAdditional figures. File containing additional figures S1-S6.Click here for fileDetails of iRsp1140. Excel file containing tables with the details of the metabolic model iRsp1140. It contains eight tables (Tables S1-8).Click here for fileStrains and Plasmids. An excel file with strains and plasmids used in this paper. It contains 2 tables (Tables S1 and S2).Click here for fileiRsp1140 in SBML format. SBML format of iRsp1140 for distribution and use in other modeling environments.Click here for file