An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results. Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored.Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes.In this study, we measured growth phenotypes of 465 Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources. Escherichia coli [Recent advances in both high-throughput experimental approaches and computational analysis techniques have provided opportunities to explore biological function at the system level. An area in which this research has flourished is the study of genome-scale metabolic networks. Genome-scale metabolic network stoichiometries, encompassing all known metabolic reactions for a given organism, have been published for a diverse set of organisms, ranging from hia coli  to humanhia coli . These nhia coli ,4. In pahia coli ,6.Although flux balance predicted distributions represent rough approximations of the complex reality of cellular metabolism, numerous studies have demonstrated the ability of flux balance models to reproduce various types of experimental results ,8. A typThe high predictive capacity of genome-scale models, as inferred from these assessments, has also stimulated their use in studies that address questions currently at the edge of experimental feasibility. These studies have typically taken advantage of the speed of flux balance model computations to make system-level observations that are experimentally challenging or unfeasible. They include the exploration of global patterns of epistasis -16, esseAs models become increasingly reliable and useful as discovery tools parallel to experimental methods, new paradigms for the integration of experimental and computational analyses may be explored. It is particularly important to understand how such integration can be used to gain novel biological insight beyond that attainable from independent experimental and modeling studies. Recent integrated analyses have employed iterations of experiments and modeling to drive biological discovery -24.Here, we use model predictions and high-throughput experimental data in a bidirectional and synergistic manner. Specifically, we compare yeast flux balance model predictions with a new compendium of single gene deletion phenotypes under 16 different conditions. Contrary to the usual direction of refinement , we start by using computational predictions to identify potential weaknesses in the experimental results. This model-based refinement leads to the identification of several mutant defects, increasing our confidence that discordances are the result of model deficiencies. Based on this refined data, we evaluate the predictive capacity of different modeling frameworks, using an array of statistical metrics, and describe the global features of a yeast metabolism growth phenotype map. In addition, by combining the growth phenotype maps with automated visualization of detailed flux predictions, we present a case study (glycerol utilization) that provides additional insight on the power and limitations of stoichiometric models. Finally, we show how an integrated data analysis approach allows us to discriminate between different hypotheses on the mechanism of raffinose utilization in yeast.Saccharomyces cerevisiae. We experimentally determined growth phenotypes for mutants with single gene deletions of metabolic enzymes under a diverse set of metabolically relevant conditions. Specifically, we focused on 465 of the 892 genes present in one of the stoichiometric models , which low, and   under elow, and   and its minimization of metabolic adjustment (MOMA) variant. In these approaches, mass conservation laws translate into linear constraints on reaction rates (fluxes). The additional constraints imposed by gene knockouts are implemented by setting the values of the corresponding fluxes to zero . Within the space of flux distributions compatible with such constraints one can identify biologically meaningful states  by computing the optima with respect to an objective function hypothesized to mimic the result of evolutionary or physiological adaptation. FBA predictions of gene deletion effects are often obtained by maximizing biomass production ('growth')  whereas FBA and MOMA calculations were applied to three of the most recent publicly available genome-scale yeast models  disagreed under at least one condition, including 10 mutants that disagreed under all conditions tested  products of meiosis following mating and sporulation [Several classes of errors have been observed before in the yeast deletion set, including strain-to-well tracking errors, chromosomal aneuploidy , and therulation . The avaWhile our experimental validation of discordant mutants identified several faulty strains, it is likely that additional experimental error still went undetected. Yet, this refinement process did have a significant impact on the quality of the data set. In order to quantify the impact of the experimental refinement, we compared the concordance of the original  and refined  phenotypic measurements with model predictions. Figure S. cerevisiae. Next, two-dimensional hierarchical clustering was performed in order to group together conditions that require similar sets of enzymes. The clustered heatmap representation of the discretized data shown in Figure Using our refined set of experimental growth phenotypes, we next examined the patterns of essentiality under different conditions. Given the premise that each genetic unit should provide some fitness benefit under some habitually encountered condition ,38, the After utilizing the model results to refine the experimental phenotypes, we next took advantage of this refined data set to build a benchmark for assessing model performance across different conditions through multiple statistical metrics. In addition to assessing model predictions using the current compendium of deletion mutant data, we also included mutants that have no growth under YPD, so that we could gain a more complete picture of model performance. Specifically, genes required for growth under YPD were assumed to be required under all conditions. While this assumption is not universally valid, it is likely to be largely correct due to the fact that most nutrients provided under other conditions are also provided under YPD.A common metric for quantifying the ability of metabolic network models to predict the consequences of single gene deletions is the overall fraction of correctly predicted growth phenotypes, i.e. the number of correct predictions divided by the total number of predictions. A previously reported issue with this metric ,39 is thThe tendency for the different models to vary in their relative performance when considering different metrics can largely be explained by considering their previously mentioned differences. For example, the observation that the iLL672 model predicts more essential genes than the other models is predominantly due to its altered biomass definition. Specifically, the fact that the biomass definition for the iLL672 model contains 12 additional metabolites dictates that genes in pathways leading to the production of those metabolites will be required for growth. Therefore, in the absence of an exogenous supply of a given biomass metabolite, the corresponding biosynthetic genes will be predicted as being essential. It should be noted that because the definition of biomass for a given model is independent of the condition, changes in the biomass definition will not improve the ability of the model to differentiate between the metabolic requirements under different conditions. For instance, ubiquinol, a cofactor required for respiratory function, is one of the 12 metabolites added to the biomass definition for the iLL672 model. As a consequence of this imposed requirement for ubiquinol, ubiquinol biosynthetic genes are correctly predicted to be essential in the presence of non-fermentable carbon sources, where respiratory function is required. On the other hand, in the presence of fermentable carbon sources these genes are incorrectly called essential, as respiratory function is no longer essential to growth .A more focused approach for assessing the ability of the models to accurately capture diverse cellular behaviors is to consider only the propensity of the models to identify condition-specific essential genes. Therefore, for the current analysis we did not include genes required for growth under YPD. Figure In addition to tabulating the number of condition-specific essential genes identified using the assumption of maximal mutant growth, we determined how many additional genes could be identified by implementing the alternative optimization criterion of MOMA . Rather Focusing on our condition specific essentiality predictions, we found that utilization of MOMA led to the correct identification of an additional six (average among three models) condition-specific essential genes, beyond the set identified using the assumption of optimality Figure . Using aAssessment of the concordance between computationally and experimentally determined mutant growth phenotypes provides a coarse evaluation of a model's propensity to correctly reproduce metabolic function. To more rigorously establish that a metabolic model accurately depicts metabolic behavior under a particular condition, one must judge the accuracy of the predicted flux distribution underlying the predicted growth rate ,30. UnfoE. coli grows suboptimally in glycerol, and that the FBA-predicted optimum is achieved only upon several generations of in vitro evolution [We used our visualization framework to explore the underlying basis of some of the model predictions. Specifically, we examined in detail the fluxes predicted by the iFF708 model for mutants in the respiratory chain under glycerol conditions. As described previously, these mutants were incorrectly predicted to be able to grow under this condition using the FBA assumption of optimality, and correctly predicted as non-growers using MOMA. Examination of these mutants was especially interesting as it had the potential to provide insight into why yeast does not utilize the predicted optimal metabolic route when confronted with such gene deletions. Previous studies have indeed found that volution . The mutvolution ,52.The flux distribution predicted by the iFF708 model in glycerol with respiratory function intact Figure  demonstrS. cerevisiae the Ura1 enzyme is cytosolic, and uses fumurate as an electron acceptor [To elucidate the route by which FBA circumvents the apparent redox imbalance that should occur in the absence of respiratory function, we visualized the flux distribution predicted by FBA when complex III of the ETC was knocked out. As can be seen in Figure acceptor ,55. Theracceptor .S. cerevisiae does not exhibit optimal responses to gene deletions in the respiratory chain under glycerol conditions, possibly because glycerol is a non-preferred carbon source. This case illustrates the fact that a correct interpretation of model results may require a detailed examination of the flux predictions underlying the phenotypic predictions. While it is unreasonable to examine all flux predictions in such detail, our results suggest that there is value in validating major conclusions at this level.The finding that a subset of the predictions discordant between FBA and MOMA were in this case due to an inaccurate model reaction, and not a biologically meaningful difference between MOMA and FBA, illustrates the value of verifying model-based conclusions at the level of fluxes. Analysis of predicted fluxes revealed that the discordant predictions were attributable to the propensity of FBA, and not MOMA, to drastically reroute fluxes so as to utilize the incorrect model reaction. While the growth maximization objective exploits the misrepresented flux, the minimal adjustment objective, lacking the foresight of how to rearrange fluxes optimally, identifies a solution that uses this flux minimally, and fails to produce some biomass components. Had we simply taken the differing phenotypic predictions between FBA and MOMA at face value, we might have reached the conclusion that their predictions differed due to the fact that YBR184W and YIL162W (SUC2). The reactions dependant on Ybr184w and Suc2 cleave the α-galactosidic and β-fructosidic bonds in raffinose, respectively, resulting in the release of all three saccharide units. The second pathway evaluated is illustrated in Figure α-galactosidase, Ybr184w. In the absence of the α-galactosidic bond cleavage reaction, raffinose is converted to fructose and melibiose. While S. cerevisiae readily metabolizes fructose, the strain of S. cerevisiae used in this study is unable to metabolize melibiose [In addition to their utility in identifying potential errors, patterns of concordance and discordance between experimental data and models can be exploited as a means of generating biological insight. For example, an approach for elucidating the structure underlying biological systems is to build a set of models differing in their representation of the system of interest, and identify the model that can best reproduce experimental data. While this approach has been applied with other modeling platforms and data ,57, to oelibiose . The finα-galactosidase enzyme, our analysis focused on determining whether available experimental data and models support the presence of this enzymatic activity. In order to clearly determine the contribution of model predictions to this analysis, we started by consulting genome-scale experimental data alone, independent of the models. We hence first asked whether the phenotypic data support the participation of the putative α-galactosidase, Ybr184w, in raffinose utilization. To this end we identified those genes that were experimentally determined to be essential only under the raffinose condition. This analysis revealed that the only gene uniquely essential under this condition is the β-fructosidase, Suc2. The fact that only SUC2 was identified as essential, and not YBR184W, is a first indication that Ybr184w does not participate in raffinose utilization in yeast.As the central difference between the two hypothetical pathways is the presence of an SUC2 and YBR184W in raffinose metabolism, we looked at the expression of these two genes in the presence of a variety of carbon sources, utilizing a publicly available set of mRNA micorarray experiments [ybr184w deletion mutant in the presence of raffinose suggests that YBR184W does not participate in raffinose utilization and is likely misannotated as an α-galactosidase.To gain further insight into the role of eriments . ExaminaYBR184W does not function in raffinose utilization, these analyses do not preclude the presence of an as yet unannotated α-galactosidase. While additional hypothesis-driven experiments could be performed to test for α-galactosidase activity, we next asked whether utilization of the model predictions, in conjunction with the phenotype data, could eliminate the need for additional experimentation. In order to assess whether or not yeast utilizes an α-galactosidase during raffinose metabolism, we used the iLL672 model as a framework in which alternative hypotheses for the metabolic fate of raffinose could be tested. To determine which route of raffinose metabolism is more consistent with experimental observation, we compared the concordance of experimentally determined single gene deletion phenotypes in the presence of raffinose, with the predictions made by the two versions of the iLL672 model, differing only by the presence or absence of an α-galactosidase reaction. We found that the iLL672 build lacking an α-galactosidase correctly predicted the phenotypes for six genes that were predicted incorrectly by the model in which an α-galactosidase was present. The six genes whose deletion phenotype were correctly predicted by the model lacking the α-galactosidase included four genes correctly predicted as not required for growth in raffinose  and two genes correctly predicted as required for growth in raffinose . Descriptions of these genes and the source of the differential prediction between the two model builds are detailed in Table While the analyses performed with phenotype and expression data strongly suggest that α-galactosidase, in agreement with previous reports [Evaluation of the two model builds based on their relative concordance with the phenotype data provided strong support for the build lacking an  reports ,62. WhilWhile the cases examined above illustrate specific examples of how the combination of experimental and computational data can help gain biological insight, we expect that most discrepancies between the refined experimental data and model predictions could yield additional useful information and novel testable hypotheses. Guidelines on how to interpret the results of the comparison between experimental phenotype data and FBA and MOMA model predictions, along with potential strategies for validation, are presented in Table S7 in Additional data file 2. By applying these guidelines systematically to our data, we generated an easily accessible list of all inconsistencies unresolved between model and experiment, together with possible biological interpretations . Paired with the online interactive flux visualization tool described above, this list might be used 'on demand' to help understand specific pathways, or as the starting point for further systematic analyses, such as integration with other data sets.By generating a yeast compendium of experimentally determined phenotypes for single gene deletion mutants of metabolic genes and predictions from stoichiometric models, we explored ways in which genome scale experimentation and modeling can be utilized synergistically. We found that utilizing the models to explore the experimental data proved useful in both experimental quality control and hypothesis testing. These analyses demonstrated that genome-scale models are not only useful in addressing questions beyond experimental tractability, but can function in parallel with experimentation to drive biological discovery. At the same time, our analysis of predicted intracellular fluxes was essential for drawing accurate conclusions in cases otherwise wrongly predicted based on phenotype comparisons, indicating that all outcomes of flux balance models should be critically analyzed for biological interpretation.Genome-scale studies often aim to systematically extract biological information from model-data comparisons. As such systematic comparisons are increasingly common in different organisms, it is important to understand how experimental data imperfection, stoichiometric model choice and output interpretation can affect biological conclusions. We hope that our assessment of models and data, based on a new publicly available data set will provide a platform for more informed future systematic understanding. Moreover, increased awareness of the advantages and potential drawbacks of these approaches should help promote the fact that large-scale systems biology methods are not limited to providing high-level insights, but can also be used to expand our knowledge of specific biological pathways.a/α his3Δ1/his3Δ1, leu2Δ0/leu2Δ0, ura3Δ0/ura3Δ0, met15Δ0/MET15, lys2Δ0/LYS2) [We measured the growth phenotypes of the 465 homozygous diploid mutants  present in both the yeast FBA model  and the Δ0/LYS2) . The groΔ0/LYS2) . Six ricΔ0/LYS2) . YPD andΔ0/LYS2)  were useThe 465 strains of interest were selected from the 4,710 strain homozygous diploid yeast deletion set  and re-arrayed into a 96-well format. Strains in 96-well format were grown to saturation in liquid YPD, transferred to agar plates containing each of the 16 media conditions by replica pinning, and grown at 30°C until the wild-type controls had reached a sufficient growth level. All conditions were performed in duplicate. Mutant growth under each condition was quantified using an automated system that uses image analysis software to quantify strain growth . Plates The variability and sensitivity of this method for determining mutant growth phenotypes have been evaluated in a prior study . To evalMutants were selected for experimental refinement on the basis of disagreement of the experimental growth phenotype with the corresponding prediction made by the iFF708 model, under at least one of the 16 conditions. A prediction was deemed discordant if the difference between mean growth rate of the two duplicate growth measurements differed from either the MOMA or FBA predicted growth rate by more than a selected threshold (see below). A total of 87 mutants were flagged with this approach, of which 77 were retested with PCR and linkage analysis (see next section).his3Δ1, leu2Δ0, ura3Δ0, met15Δ0) haploid deletion set  and gridded in 96-well format. These strains were crossed to YAD641  by replica plating the grid to a YAD641 lawn and allowing cells to mate overnight on YPD at 30°C. Diploids were selected by growth on SD +histidine +leucine +G418 [To confirm the presence of the correct gene deletion in the original strain screened, the homozygous diploid mutant was examined by PCR . To test whether the observed phenotypes were linked to the deleted gene, we developed a high-throughput genetic linkage strategy. Strains of interest were selected from the BY4741  in a metabolic network, and has been described in detail elsewhere . FBA relv is the vector of reaction fluxes. In addition to the steady state assumption, inequality constraints can be imposed to set upper and lower bounds on individual fluxes (αi ≤ vi ≤ βi). As done before, we use these constraints to impose maintenance requirements and irreversibility of specific reactions, and to set limits on nutrient uptake rates. See Table S4 in Additional data file 2 for bounds used to implement each of the modeled conditions.where vgrowth), corresponding to a requirement of optimal utilization of resources towards maximal growth. The linear programming problem solved is therefore:In the second step of FBA, linear programming is used to identify, among the flux vectors that satisfy the above constraints, one that optimizes a given objective function. In the current FBA calculations we used as our objective function the biomass flux  to carry out all linear optimizations, as well as quadratic optimizations (see below).Previous work has shown that when working with genome scale metabolic models, the above linear program can have multiple solutions ,56. In og, additional constraints are added, which set the flux through all reactions requiring the protein product of gene g to zero. Before comparing model predictions of mutant growth to the discretized experimental growth rates (see above), model growth rates were first normalized to be between 0 and 2. This allowed for a direct comparison of experimental growth rates and model predictions. If the difference between the mean experimental growth rate for two duplicates and the normalized model growth rate was less than or equal to one, then the model prediction was deemed correct. For comparison between FBA and MOMA, this threshold was lowered to 0.9, in order to identify more differences between the two optimization approaches.In order to implement the deletion of a gene In addition to determining mutant phenotypes using FBA, we also utilized the minimization of metabolic adjustment (MOMA) approach . Rather Yeast models were retrieved either from authors' websites or supplementary materials ,13,25,68VisANT is a Java-based, platform-independent tool for the visualization and analysis of biological networks and is freely available for download over the web at . VisANT In order to standardize the layouts of the network graphs, such that they more closely resembled traditional textbook representations of the corresponding biochemical pathways, we extracted the node coordinates from a template network. This was done by parsing the VisML file associated with the template network. VisML is an XML formatted representation that contains the necessary information to reconstruct a VisANT network. Once the node coordinates were extracted, different flux distributions could be overlaid on the same network map. As a supplement to this paper we have provided an online interface through which the flux predictions made by the iLL672 and iFF708 models, for all single deletion mutants, can be visualized in VisANT .ETC: electron transport chain; FAD: flavin adenine dinucleotide; FBA: flux balance analysis; MOMA: minimization of metabolic adjustment; SD: standard deviation.AMD, GMC and DS conceived of the study. AMD designed the experiments. AMD, DJ and KW performed the experiments. ESS and DS designed and performed the computational analyses. ESS, AMD and DS wrote the manuscript.The following additional data files are available with the online version of this paper. Additional data file Figure S1a-c are heatmaps representing the concordance between the experimental gene deletion phenotypes and the corresponding predictions made by each of the three yeast models. Figure S2a, b are comparisons of the experimental data generated in the current study with similar data generated in previous studies using different experimental approaches.Click here for fileTable S1 contains the original experimental phenotype measurements for 465 gene deletion strains under the 16 different conditions. Table S2 contains the changes made to the experimental data based on the re-checking of mutants whose original phenotype disagreed with iFF708 model predictions. Table S3 contains the refined experimental data. Table S4 contains the upper bounds used for the three yeast models in order to model the 16 experimental conditions. Table S5 contains a list of changes made to the three models used in the current study. Table S6 contains a list of mutants that were predicted to be discordant by the iFF708 model, and therefore submitted to experimental verification. Table S7 contains guidelines for interpreting patterns of discordance between experimentally generated mutant phenotypes and corresponding model predictions. Table S8 contains a list of all discordances that remained after the experimental refinement, and our interpretation of them, based on the guidelines laid out in Table S7.Click here for file


Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression.Escherichia coli, we have implemented the OptORF algorithm  and compared its metabolic engineering strategies for ethanol production to those found using OptKnock (which considers reaction deletions). Our results found that the reaction-based strategies often require more gene deletions to remove the identified reactions (2 more genes than reactions), and result in lethal growth phenotypes when transcriptional regulation is considered (162 out of 200 cases). Finally, we present metabolic engineering strategies for producing ethanol and higher alcohols (e.g. isobutanol) in E. coli using our OptORF approach. We have found common genetic modifications such as deletion of pgi and overexpression of edd, as well as chemical specific strategies for producing different alcohols.Here we propose an effective method to systematically integrate transcriptional regulatory networks and metabolic networks. This allows for the formulation of linear optimization problems that search for metabolic and/or regulatory perturbations that couple biomass and biochemical production, thus proposing adaptive evolutionary strain designs. Using genome-scale models of E. coli is evaluated here, the developed OptORF approach is general and can be applied to optimize the production of different compounds in other biological systems.By taking regulatory effects into account, OptORF can propose changes such as the overexpression of metabolic genes or deletion of transcriptional factors, in addition to the deletion of metabolic genes, that may lead to faster evolutionary trajectories. While biofuel production in  Metabolic engineering has emerged as an important field aimed to improve cellular production of valuable biochemicals and biofuels. Conventional approaches in metabolic engineering for identifying targets for manipulation focus on metabolic branch points, where undesired reactions are eliminated from competing branches to enhance flux through desired reactions using genetic modifications. However, these metabolic network modifications will not only affect fluxes through local metabolic pathways, but also have system-level effects on metabolic behavior due to changes in carbon, energy, and electron flow. Correspondingly, such conventional approaches may fail to identify modifications in distant pathways that can potentially improve cellular production.Escherichia coli, genome-scale models of metabolic networks have been used to identify metabolic engineering strategies such as gene deletions or additions to maximize production of primary or secondary metabolites [Computational models of metabolism have been successful in predicting the consequences of gene deletions at a systems level -4. In Esabolites -7. Some abolites , identifabolites ,10. A nuabolites ,11,12.Computational strain design methods evaluate the effects of gene or reaction deletions to search for the mutants with improved production capabilities. A gene deletion is simulated by removing the reactions associated with the target gene from the metabolic networks; however, most current methods are often based on reaction deletions, and not gene deletions. However, genes and reactions do not always have a one-to-one relationship due to the presence of multi-functional enzymes, enzyme subunits, orphan reactions, and isozymes. Thus, knockout mutants based on reaction deletions can sometimes be genetically impossible or difficult to construct. Also, existing methods do not take into consideration the transcriptional regulatory networks that control metabolism. As a result, predicted strains with high production capabilities may not be able to grow initially or evolve to the desired final state due to regulatory restrictions.In this study, we present a new optimization approach, OptORF, to identify metabolic engineering strategies based on a minimal number of metabolic and transcription factor gene deletions and metabolic gene overexpression, which couple biomass and biochemical production. Here, gene to protein to reaction (GPR) associations are modeled directly using a Boolean approach and reactions are removed when the associated genes are deleted. Interactions between the regulatory and metabolic networks are also modeled using Boolean approaches by turning on or off metabolic gene expression in response to transcriptional factor (TF) status. These Boolean relationships can be effectively formulated as linear constraints using binary variables and matrices, which are more systematic and/or computationally efficient than previously suggested formulations for modeling GPR associations and integrating metabolic and regulatory models -17.E. coli [E. coli is evaluated here, the OptORF approach can be easily applied to other biochemicals and microorganisms.The integrated model of metabolism and regulation can predict the steady-state metabolic flux distributions and regulatory states simultaneously. Consequently, the OptORF framework allows for the identification of optimal metabolic gene knockouts as well as transcription factor knockouts. In addition, overexpression of genes that are unexpressed under a given condition can be found in order to improve the production of a target biochemical. Using genome-scale metabolic and regulatory models of E. coli ,19, we hE. coli . While eOptORF is a bi-level optimization problem which identifies the optimal metabolic and regulatory gene deletions as well as gene overexpressions that maximize biochemical production at the maximum cellular growth under transcriptional regulatory constraints Table . The innAn example of an integrated metabolic and transcriptional regulatory network is shown in Figure Since the cellular objective is maximizing biomass production (B) from substrate (S), pathways involving R5 (producing P2) would be normally preferred to ones involving R2 (producing P1). Given an engineering objective of producting P1, close inspection of the reaction network indicates that removal of reactions R3 and R4 or reaction R5 would couple maximum biomass production to production of P1 instead of P2. OptORF will identify genetic modification strategies involving gene deletions that are associated with these reactions . However, G1A expression is inhibited by TF1, and TF1 is active in the presence of S, and thus reaction R1 cannot happen. Therefore, OptORF will also identify the overexpression of G1A along with the gene deletions mentioned above. An alternate strategy to the overexpression of G1A would be the deletion of TF1 which inhibits expression of G1A. In fact, when TF1 is deleted, the genes activated by TF1 (G3 and G5) would be no longer expressed, which reduces the number of genes that are needed to be deleted. Therefore, OptORF will first identify double knockout strategies including the TF1 deletion, and then find the alternate strategies with the G1A overexpression ) and binary variables for reaction (dj), enzyme (bn), and gene (yg) status, where j, n, and g specify a reaction, enzyme, and gene, respectively. Each reaction with a known GPR association (j ∈ JGPR) can be carried out by the associated enzyme complex(es) (n ∈ N(j)), and each enzyme complex is associated to gene products (g ∈ G(n)), where JGPR, N(j), G(n) are defined as the following:Constraints for GPR associations are systematically formulated using a three dimensional array (j are present (any bn(j) = 1), the reaction can have a non-zero flux (dj = 1) where dj indicates whether a reaction can or cannot occur. If all the enzymes are not present  = 0), then the reaction cannot occur (dj = 0). This reaction-enzyme logical relationship can be formulated as the following:If any of the enzymes for reaction n are expressed  = 1), then the enzyme is present (bn = 1). If any of the subunits are not expressed (any yg(n) = 0), then the enzyme is not present (bn = 0). This enzyme-gene logical relationship can be formulated as the following:If all of the associated genes for enzyme Reactions without known GPR associations are not constrained by these GPR rules.TR) and binary variables for gene expression/transcription factor (TF) activity (yg), condition (am), and effector status (xr), where m specifies a condition for gene expression/TF activity and r affects these conditions (m). Effectors (r) can be TFs, flux values, and environmental conditions. The sets that are used in the formulation are defined as follows:Transcriptional regulation of metabolic genes are also formulated as linear constraints using a three dimensional array , are satisfied. If any of the conditions for gene g expression is satisfied (any am(g) = 1), the corresponding gene is expressed (yg = 1). If none of the conditions for expression are satisfied  = 0), then the gene is not expressed (yg = 0). Similarly, each transcription factor, g ∈ GTF, is active (yg = 1) if any of the conditions for its activity is satisfied. If none of the conditions for activity are satisfied, then the transcription factor is inactive (yg = 0). Then, the binary variables for TF activity (yg) are used to constrain the binary variables for TF effectors (xg) which determine the conditions for metabolic gene expressions or other TF activities.Each metabolic gene, m, has its associated effectors, r(m). If all the associated activators are active . If any of the activators are inactive (any am = 0).Each condition for gene expression or transcription factor activity, r, can be a transcription factor (TF), positive metabolic flux (PF), negative metabolic flux (NF), or other environmental stimuli (ES). Intracellular and extracellular stimuli are reflected by the positive or negative metabolic flux indicators by constraints, where intracellular stimuli are dependent on the flux values (vj) of internal reactions and extracellular stimuli are dependent on the flux values of exchange reactions (secretion or uptake). A threshold value (ε = 10-3) is used to determine whether the flux is positive (vPF ≥ ε) or negative (vNF ≤ -ε) as the following:Each effector, The constraints including 'if' indicators were implemented directly using the GAMS/CPLEX indicator constraint facility instead of the Big M method. Other environmental stimuli (ES) such as oxidative stress or high osmolarity were assumed to be absent in this study.zg), gene overexpression indicators (wg) and surrogate gene expression indicators (A gene deletion or overexpression is implemented by introducing gene knockout indicators (yg = 1) to be unexpressed (yg = 0) to be expressed , the value of its surrogate gene expression indicator is equal to zero (yg = wg = 0), then the surrogate gene expression indicator assumes a value of zero (zg = 0). If a repressed gene is overexpressed , its surrogate gene expression indicator takes a value of 1 , and the surrogate gene expression indicator assumes a value of 1 (yg = 1) to be deleted (zg = 1). However, the variables wg are not introduced for all TFs to prevent an inactive TF from being active. The total numbers of gene deletions and overexpressions are limited to desired values, K1 and K2, respectively.The gene knockout indicator allows an expressed gene -(23) which replace equations (3),(4), and (7).The bi-level optimization problem can be formulated as an MILP using the strong duality theorem in the similar way as described in OptKnock . Here, aj ∈ J\JLB) are only constrained by the mass balance equation, and associated with these constraints are unconstrained dual variables (ui). The uptake, secretion, or irreversible reactions are additionally constrained by the lower bounds (λj). The reactions removed by gene knockouts are constrained to zero by using binary variables (vj = 0 if dj = 0), and associated with these constraints are unconstrained dual variables (hj). The dual LP (D) is formulated as follows.The reversible reactions (dj) are fixed to 0 or 1. The inner problem can be written as:At optimality, the values of the objective functions in (P) and (D) are equal, and primal and dual variables satisfy the constraints of (P) and (D), respectively. The following optimality conditions for the inner problem are always satisfied as the values of all binary variables (pj = 1 for j = biomass formation). The constraints including 'if' indicators are implemented directly using the GAMS/CPLEX indicator constraint facility. We also constrained the dual variables for reaction removal (hj) to be within a small range (-1 to 1) in order to reduce the solution time .In this study, we used the biomass formation as the objective function of inner problem (α) to an additional gene deletion, and the third applies a penalty (β) to an additional overexpression. In other words, the biochemical production rate should increase at least by α or β if an additional gene is deleted or overexpressed, respectively. These penalty terms can be very useful for eliminating strains needing more genetic modifications if the improvement in production is small. When α or β is a very small value (≈ 10-6), it effectively eliminates unnecessary modifications from the solution without affecting the optimal biochemical production. For example, if deleting gene A results in the same product yield as deleting gene A and B (i.e. deletion of gene B does not improve the yield), then the gene B deletion would not appear in the optimal solution.The objective function in the outer problem of OptORF formulation is a linear combination of fluxes with penalty terms for the total number of gene deletions or overexpressions (δ), which is the number of differences in genes among identified strategies. A previously identified solution (k) is comprised of a set of gene deletions and overpressions that are stored in parameters δ is set to 1, integer cuts prevent OptORF from finding the same solution as the previous ones. One may set δ to a higher value in order to obtain a more diverse set of metabolic engineering strategies. In this study, we used α = 10-6, β = 10-6, and δ = 1. The complete OptORF algorithm is defined by the following equations:If multiple solutions are desired, integer cuts constrain successive optimal solutions with a parameter  in glucose minimal media. Maximum glucose uptake rate (GUR) and oxygen uptake rate (OUR) are specified in order to simulate anaerobic growth conditions  [-1 for all simulations. The optimization problems were solved using CPLEX 11.2 accessed via the General Algebraic Modeling System (GAMS).In this study, we have implemented an integrated model of metabolism and regulation for C1010 v2 , which c/gDW/hr) . A minimE. coli using the OptORF formulation with an integrated model of metabolism and regulation, and compared the resulting strategies to ones using a previous approach based on reaction deletions (OptKnock). First, a set of reaction deletion strategies was obtained using OptKnock, and then a corresponding set of gene deletions needed to remove the reactions in each OptKnock strategy was identified. These OptKnock gene deletion designs were then compared to the gene deletion strategies found by OptORF without considering transcriptional regulation to examine the differences between the reaction-based strategies and gene-based strategies. To investigate how transcriptional regulation affects adaptive evolution of microbial strains, we analyzed available data for adaptively evolved E. coli mutant strains using the integrated metabolic and regulatory model. OptKnock strategies were then re-analyzed using an integrated metabolic and regulatory model and compared to the OptORF strategies identified when transcriptional regulatory constraints were considered. Finally, we present metabolic engineering strategies for overproducing ethanol or higher alcohols in E. coli that include both metabolic gene deletions and overexpressions, as well as transcription factor deletions, using our developed approach.We identified metabolic engineering strategies for ethanol production in E. coli each of which catalyzes two reactions (TKT1 and TKT2) in the pentose phosphate pathway. By removing TKT1 reaction along with phosphotransacetylase reaction (PTAr), ethanol production can be coupled to cellular growth at an improved production rate. However, deletion of the two genes (tktA and tktB) needed to remove the TKT1 reaction results in a lethal growth phenotype [In this section, we compare reaction-based deletions to gene-based deletions and describe how the OptKnock and OptORF approaches differ in the strategies they identify. In most cases, the relationship between genes, proteins, and reactions is not one-to-one. A metabolic reaction can be carried out by one or more enzymes, each of which can be comprised of multiple gene products. An enzyme can catalyze multiple reactions that utilize different substrates, and different enzymes may catalyze the same reaction. Consequently, removal of a reaction may require deletion of multiple genes and may accompany the removal of additional reactions, which can result in a different metabolic solution space from the one predicted when reactions can be removed individually. Figure henotype , which iE. coli known to function as serine deaminases. In order to completely remove this particular metabolic reaction from the system, one would have to knockout all four genes. If removal of an alternative reaction would serve the same purpose, but require fewer gene deletions, then OptORF would identify the simpler genetic strategy while reaction-based frameworks would not be able to distinguish between them.The number of genetic manipulations needed is an important factor when evaluating metabolic engineering strategies. When isozymes are present, a strategy with the minimum number of reaction deletions does not necessarily correspond to a strategy with the minimum number of gene deletions. For example, there are four gene products in We have analyzed the top 50 ethanol producing strains found by OptKnock for each double, triple, quadruple, and quintuple reaction deletion  logical relationships, which enables us to predict the effects of transcription factor deletions as well as metabolic gene deletions on transcription regulation and metabolism, simultaneously.E. coli, and compared the data to predictions using the integrated model of metabolism and regulation.Metabolic engineering strategies described in this work are based on the assumption that microbial cells would evolve to have higher growth rates, and that biochemical production would increase along with cellular growth rate, the latter being the selective pressure during adaptive evolutionary experiments. An important question that one might ask is how malleable the transcriptional regulatory network is during adaptive evolution. If cells can easily rewire their transcriptional networks to gain higher fitness, it is possible that knockout strains could lose the coupling of biochemical production and growth, if expressing unexpressed genes leads to a higher growth rate without a higher biochemical production. To address this issue, we have analyzed available data for adaptively evolved strains of E. coli strains designed for lactate production [ptaΔadhE and ΔptaΔpfk strains predicted by the metabolic model (blue) and integrated metabolic and regulatory model (green). The experimental observations for lactate yields and biomass yields during 60 days of adaptive evolution are also shown. All the deletions were simulated based on gene deletions and not reaction deletions, and yields were plotted to normalize the fluxes to changes in glucose uptake rate which occured during adaptive evolution. The ΔptaΔadhE strain is only predicted to produce more lactate with increased growth by the integrated metabolic and regulatory model, while no coupling between lactate production and growth is predicted by either model for the ΔptaΔpfk strain. Experimentally observed trajectories for ΔptaΔadhE strain move towards the optimal point predicted only by the integrated metabolic and regulatory model  of the total 13,750 cases , while a metabolic model alone predicts 8,968 (65.2%) cases correctly [cysB and metR) as well as metabolic genes in E. coli [Another advantage of using an integrated model of metabolism and regulation emerges when it comes to predicting essential genes. An integrated model is better at predicting essential genes under a given condition, and hence more likely prevents gene deletions which are lethal from being included in the strategies. It was previously shown that an integrated model of orrectly . An inte E. coli ,24,25. AThe ethanol producing strains identified in the first result section were re-analyzed using the integrated metabolic and regulatory model to demonstrate the differences from using the metabolic model. When we re-calculated the production rates and growth rates for the 200 previously identified OptKnock strains after imposing regulatory constraints, we found that ethanol production was significantly lower for most strains Figure . Also, tfnr and gntR) are identified only by OptORF when transcriptional regulation is accounted for. Deletion of pgi or tpiA was evenly distributed across all methods, indicating that one of them is typically necessary to couple growth to ethanol production (see next section for further discussion).Finally, the strain designs identified by OptKnock (200), OptORF without regulatory constraints (200), and OptORF with regulatory constraints (200) were compared to identify common genetic strategies. Figure pta and eutD appear together since the deletion of both is required to eliminate the phosphate acetyltransferase activity, while either fnr or arcA appears since the deletion of either transcription factor results in a similar phenotype. The pattern of correlation becomes clearer (strategies have less variation) as the structure of the model gets simpler from a gene-based deletion with transcriptional regulation . We have identified a set of strategies consisting of only gene deletions  can be advantageous for ethanol production. Aerobic genes in central metabolism that are repressed by these anaerobic regulators include aceAB, aceEF, lpd, mdh, sucAB, and sdhABCD. The de-repression of malate dehydrogenase (mdh) was predicted to be especially important based on comparisons between flux distributions with and without mdh. If necessary, such repressed genes may be overexpressed, as an alternative to deleting fnr or arcA to ensure that metabolic activity is high enough to achieve the desired level of ethanol production.Deletion of nuo), the predominant NDH under anaerobic conditions, to block electron transfer from NADH to fumarate. As a result, the model predicts a decrease in FRD flux and reduced succinate production in NDH-1 deficient strains, while flux through fumarase and malic enzyme is increased.Genes involved in the electron transfer chain were also identified as needing to be deleted to limit the amount of NADH oxidized by this pathway. NADH:ubiquinone oxidoreductase (NDH) I and II catalyze the transfer of electrons from NADH to the quinone pool, and the electrons are passed to fumarate by fumarate reductase (FRD), an essential enzyme for anaerobic growth. OptORF identified the deletion of NDH-1  pathway or Entner-Doudoroff (ED) pathway. This increases generation of NADPH whose electrons are passed to NAD via NADH transhydrogenase, and the additional NADH is used to reduce acetyl-CoA to ethanol by alcohol dehydrogenase (AdhE). While increasing the amount of NADH available to produce ethanol, the pgi deletion also lowers the net ATP production and thus decreases growth rate as compared to the wild-type strain. The ED pathway consists of two enzymes, Edd and Eda, and the expression of edd is repressed by the transcription factor GntR. Deletion of gntR would de-repress the expression of edd, which allows for the conversion of glucose to pyruvate and glyceraldehyde-3-phosphate. Equivalently, overexpression of edd was identified as an alternative to deletion of gntR. The activation of the ED pathway in a pgi mutant also leads to a significant increase in growth rate, which would be favorable for industrial-scale ethanol production.Deletion of pflD requires either ArcA or Fnr as activators, and a previous study showed that PFL activity was still detected in pflA or pflB mutant [fnr deletion alone is sufficient to decrease PFL activity down to the level of ΔfnrΔarcA strain, while an arcA deletion alone did not decrease PFL activity [fnr, pflB, and tdcE would abolish PFL activity and require cells to use pyruvate dehydrogenase (PDH) [fnr would lower PFL activity and attenuate the repression of PDH, the result being the production of NADH instead of formate when pyruvate is converted to acetyl-CoA. In the absence of oxygen, some of the acetyl-CoA would be reduced to ethanol consuming two NADH molecules to maintain a redox balance.There are three enzymes, PflAB, PflCD and TdcE, which possibly function as pyruvate formate-lyase (PFL). The regulatory model indicates that expression of B mutant . Anotheractivity . Thus, dse (PDH) ,30, whospta and eutD  would reduce acetate production, and hence increase formation of other by-products such as ethanol, lactate, or succinate. However, multiple studies have shown that the mutations in the ack-pta pathway cause accumulation of pyruvate [ΔptaΔeutD mutant. Pyruvate can be either secreted or reduced to form other fermentation by-products, but there is not enough NADH available to ferment all the pyruvate generated by glycolysis. In order to convert pyruvate to ethanol, arcA and gntR deletions are needed to derepress PDH and the ED pathway, along with a pgi or tpiA deletion to re-direct flux from glycolysis to the ED pathway. A ΔtpiA mutant alone could cause methylglyoxal accumulation and inhibit the anaerobic growth [Deletion of pyruvate -33, and c growth , but re-edd replaced the gntR deletion in most strains to activate the ED pathway. In addition, overexpression of fructose-1,6-bisphosphatase (fbp) was predicted to increase the amount of fructose-6-phosphate, and reverse the direction of the non-oxidative branch of the PP pathway in the strains utilizing the ED pathway. The reversed PP pathway results in a decreased flux in the TCA cycle and an increased flux in the ED pathway and PDH, leading to improved ethanol production. The model predicts that ptsH deletion (in addition to other modifications) increases flux through the lower half of glycolysis and decreases succinate production. Switching glucose transport from the phosphoenolpyruvate:sugar phosphotransferase system (PTS) to proton symport has been shown to improve overall performance and production yield for ethanol as well as other compounds [In the strategies that include both gene deletion and gene overexpression, we found that overexpression of ompounds .α-ketoglutarate to glutamate using NADPH. This pathway is encoded by glutamate dehydrogenase (gdhA), the deletion of which would require cells to use the ATP-dependent pathway that normally operates when the concentration of ammonia is low [Glutamate can be synthesized via multiple pathways depending on the availability of nitrogen sources. When ammonia is abundant, an ATP-independent pathway functions to save energy by converting a is low . This ATΔfnrΔpflBΔtdcEΔpgi+edd strain. These were predicted by the integrated metabolic and regulatory model . The ethanol production rate was predicted to be approximately 86.2% of the maximum theoretical yield and the corresponding growth rate was 0.225 h-1 for the mutant strain (39.3% and 0.467 h-1 for the wild-type). This result is somewhat similar to the previously reported values [E. coli for ethanol production [Predicted flux distributions corresponding to maximum biomass production and gene expression states are shown in Figure d values , but theoduction ,37. The E. coli does not naturally produce these higher alcohols, we have augmented the iMC1010v2 network with non-fermentative reactions and corresponding GPR associations for synthesis of these alcohols based on a recent study [In addition to ethanol, we have also identified metabolic engineering strategies using OptORF for over-production of higher alcohols such as isobutanol and 2-phenylethanol from glucose. Since nt study . In summpntAB) was identified to be beneficial for increasing isobutanol production. This prevents the electron transfer from NADH to NADP, and thus more NADH would be available for the production of isobutanol.Our computational results found that most of the strategies for ethanol production can also be modified for the production of some higher alcohols with the additional deletion of AdhE (which produces ethanol) and addition of KDC and ADH. In particular, we have found that the anaerobic production of isobutanol can be coupled to growth at ~94% of the theoretical maximum yield (Table zwf or gnd) and non-oxidative  branches of the PP pathway. The model predicts that these gene deletions would increase the fluxes in the aromatic amino acid biosynthesis pathways, which leads to the increased availability of phenylpyruvate, the precursor for 2-phenylethanol. Analysis of these higher alcohols illustrates how OptORF can be used to couple biomass and production of metabolites which are not part of central metabolism.In addition to isobutanol, we found that the production of 1-propanol and 2-phenylethanol can also be coupled to growth, but their yields were much lower than the isobutanol yield  for designing microbial strains for metabolite production. We compared our new approach to OptKnock, and found four primary differences between the strains that are identified using the two approaches. First, OptKnock may propose removing reactions that do not have any genes associated with them, making the construction of such strains experimentally impossible. Second, OptORF can find metabolic engineering strategies requiring the smallest number of gene deletions while still achieving high production yields. Since OptKnock strategies are based on reaction deletions they often require more gene deletions than those found using OptORF. Third, OptKnock may suggest reaction deletions that result in a different solution space when the necessary genes are deleted or transcriptional regulatory effects are accounted for. In this case the adaptive evolutionary outcome would be different than what is predicted when only reaction deletions are considered, sometimes resulting in reduced production yields or lethal phenotypes. Lastly, OptORF can propose changes such as the overexpression of metabolic genes or deletion of transcriptional factors that may lead to faster evolutionary trajectories.Based on our analysis of experimental data using integrated metabolic and regulatory model it is unclear to what extent, if any, cells re-wire their transcriptional regulatory network during adaptive evolution. Given that a finite number of mutations are found in adaptively evolved strains , it seemIn its current implementation, OptORF uses Boolean approximations to describe how transcriptional regulation affects metabolic fluxes. Although the use of Boolean variables do not exactly represent the dynamic nature of metabolism and regulation, it has been previously shown that constraint-based models using these approximations successfully predict the cellular behavior in continuous and batch culture ,19,21,24The OptORF approach is currently applied to produce metabolites that can be coupled to biomass production. A recent study has used a genetic algorithm to design strains with un-coupled metabolite and biomass production, where a bi-level problem is used and the inner problem uses an objective function to predict un-evolved cellular phenotypes . OptORF The novelty of the method developed here is that it accounts for transcriptional regulatory networks in addition to metabolism in the design of strains for metabolic engineering. However if desired, the approach can be used with and without transcriptional regulatory constraints to consider the interdependence of reactions through their GPR associations. It should be noted that the integrated model of metabolism and regulation allows for simulating the effects of both gene overexpression (where un-expressed genes are expressed) and gene deletion. The OptORF approach can also suggest transcription factor deletion as an alternative to metabolic gene deletion or overexpression, which provides greater flexibility in metabolic engineering strategies. By further incorporating flux modulation approaches such as those proposed in OptReg , additioThe approach we have developed here is general and can be used to engineer production of a variety of products in different microorganisms, for which constraint-based models exist. The number of microbial transcriptional regulatory network models continues to grow, which has been enabled by high-throughput datasets and computational analysis -52. ReguJK implemented the model and approach, performed the analysis, and drafted the manuscript. JLR conceived of the study, participated in its design and coordination, and helped to analyze the data and draft the manuscript. All authors read and approved the final manuscript.Implementation of OptORF for the example network. The OptORF approach is implemented in the General Algebraic Modeling System (GAMS). A free demo version of GAMS can be downloaded from http://www.gams.com. This file contains the example network described in Figure Click here for fileList of strain designs for ethanol production. This spreadsheet contains the list of strain designs described in Figures Click here for fileList of strain designs for higher alcohol production. This spreadsheet contains the list of strain designs for 1-propanol, isobutanol, 2-methyl-1-butanol, 3-methyl-1-butanol, and 2-phenylethanol.Click here for file


Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species.Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps.We reconstructed a constraint-based metabolic model of The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model. The need to interpret and integrate these datasets at the systems level has triggered the development of model-based computational methods . Among tn fluxes . In addin fluxes . Nearly n fluxes , typicaln fluxes .Systematic evaluation of gene essentiality has proved to be a valuable resource for investigating gene functions; knockout mutant collections have been recently built in this aim for a number of bacteria -8. RigorAcinetobacter baylyi ADP1, a strictly aerobic γ-proteobacterium. Although phylogenetically close to the Acinetobacter baumanii pathogenic strains, responsible for a growing number of nosocomial infections [A. baylyi ADP1 is an innocuous soil bacterium. Because of its metabolic versatility and high competency for natural genetic transformation, it is a model organism of choice for genetic and metabolic investigations [A. baylyi is able to degrade a wide range of molecules, including components of suberin, a protective polymer produced by plants in response to stress. Its harmlessness, nutritional versatility, and high capacity for adaptation have led bacteria of the Acinetobacter genus to be used for a variety of biotechnological applications–including the degradation of pollutants  and the production of valuable biochemical products such as lipases, proteases, bioemulsifiers, cyanophycine and different kinds of biopolymers [Our focus is on fections , A. bayligations -16. As apolymers ,18. Follpolymers , a genomA. baylyi with the help of high-throughput experimental data. Following an initial reconstruction using metabolic information extracted from the genome annotation and the literature, the model was iteratively assessed and improved by comparing its predictions with (1) large-scale growth phenotyping results of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentiality data from the mutant collection, and (3) conditional gene essentiality data derived from growth phenotyping of A. baylyi mutants on eight defined media. We examined each inconsistency between experimental results and model predictions, and corrected the model when sufficient justifying evidence could be collected. Combining the three refinement steps, 1262 out of 1412 predictions were initially consistent with experimental results. Among the inconsistent cases, 65 led to improvements, increasing the completeness and accuracy of the model. The final version of the model, called iAbaylyiv4, predicted accurately (1) 91% of the wild-type growth phenotypes, (2) 94% of the genome-wide gene essentialities, and (3) 94% of the phenotypic profiles of A. baylyi mutants on the tested media.We report below on the reconstruction and refinement of a genome-scale metabolic model for We developed a web interface which provides easy access to both model and experimental data. The interface allows browsing of the metabolic network, online computation of phenotype predictions, and comparison of predictions with experimental results .A. baylyi was iteratively reconstructed following a process depicted in Figure v1 using information from the genome annotation, metabolic pathways databases, and the literature. Although facilitated by the automated network reconstruction software PathoLogic [A. baylyi. Physiological information on A. baylyi was especially helpful to build the set of transport processes, as substrate specificities of transporters are difficult to deduce from genome annotation only. For each metabolite shown to be consumed by A. baylyi we added a corresponding transport reaction to the model. Out of 133 transporters, 23 were initially included in the model using this type of evidence only. The dependency between genes and reactions was modeled using Boolean rules, known as GPR (Gene-Protein-Reaction associations) [E. coli enzyme complexes [E. coli model (see Methods)[A. baylyi growth yields could be used to validate these parameters, however. While such validation would be required to get more accurate predictions of growth yields, the current parameters already provide good approximate values  . These romplexes ,25. Ener Methods). No accuronments . The Metv1 gathering 859 reactions grouped in 7 metabolic categories and 697 distinct metabolites, 109 of which could be transported from the environment. As depicted in Figure A. baylyi metabolism, including biosynthetic routes, energy metabolism, and catabolic pathways. Genomic islands of catabolic diversity endow A. baylyi with the ability to degrade a wide variety of soil compounds [A. baylyi is provided in Table This initial reconstruction process led to the model iAbaylyiompounds . The metv1 involves 787 genes out of the 1518 confirmed or putative enzymatic and transport genes of A. baylyi. A large majority  of the enzymatic reactions (excluding transporters) were associated with at least one gene, while the lower proportion  of transport reactions linked to genes is explained by the extensive use of physiological data to include them. The association of nearly all reactions with a gene confers a high reliability to the model. The few reactions that were introduced with no associated gene are most often supported by indirect evidence and introduced in order to fill gaps . Using the model, we predicted the growth phenotypes of the wild-type strain on the corresponding A. baylyi. This relatively small fraction of carbon sources can be explained by the fact that Biolog microplates are only partially adapted to A. baylyi's biotope: they feature sugars, nucleosides or amino acids but relatively few chemicals originating from plant compounds. iAbaylyiv1 model predicted 24 of them and missed 21 , oxidation to cis-aconitate (tcuA and tcuB), and for a regulatory protein required for tcuABC expression (tcuR) were identified in Salmonella enterica [A. baylyi: ACIAD1536 , ACIAD1537 , ACIAD1541 , ACIAD1539 , and ACIAD1543 . Following these clues, we expanded the model by implementing the corresponding transporter and degradation reaction, and annotated the corresponding genes. In four cases, dedicated growth experiments contradicted the Biolog result, weakening the case for further study  transport system for fructose  coupled with a 1-phosphofructokinase  leading to fructose-1,6-bisphosphate  knockout mutant  pathway in the glycolytic direction, as it has been observed for the dissimilation of glucose [Interestingly,  glucose .E. coli, L-ornithine and L-arginine are degraded by A. baylyi using the arginine succinyltransferase (AST) pathway. This pathway allows E. coli to use them as nitrogen sources, but not as carbon sources. Putative explanations include unsuitable regulation and inadequate transport [A. baylyi's inability to use L-ornithine and L-arginine as carbon sources.As is the case in ransport . SimilarA. baylyi's genome annotation includes genes for D-serine transport  and D-serine deaminase activity (ACIAD1048 dsdA), which should allow it to use D-serine as a carbon and nitrogen source. The interpretation of this inconsistency is also unclear; a similar unexplained inconsistency was pointed out in a study involving a metabolic model of B. subtilis [y ACIAD108 dsdA, wv2, raising predictive accuracy on Biolog-measured phenotypes from 86% to 91% of the growth phenotypes  or false dispensable  predictions. Since these inconsistencies are as many clues that the understanding of A. baylyi's metabolism represented in the model is erroneous or incomplete, we examined them carefully in order to find interpretations and, when needed, refine the model.In steps 2 and 3 of the model refinement process, we assessed and improved the model by comparing its predictions to experimentally determined gene essentialities, derived from the ADP1 mutant collection  . Although purely metabolic, the model thus already captured a large part of the bacterium's essential processes. The thoroughly curated but also purely metabolic E. coli model iAF1260 includes a similar proportion of E. coli essential genes on glucose-supplemented minimal medium  [v2 already showed good agreement with the observed gene essentialities as 88% of the predictions were identical to the experimental results  of reactions including at least one medium-confidence gene in their GPR are associated with an inconsistent gene, a similar proportion to that of reactions containing only high confidence genes . We examined the 91 inconsistent predictions of this step and refined the model for 47 of them  while keeping the predictive accuracy for the previous datasets unchanged.Phenotyping experiments pointed out 2 to 10 conditionally essential genes (from the set of model genes) on each medium Table . While aCombining both refinement steps, 56 out of 124 inconsistencies led to model corrections. In the following sections, we will discuss these gene essentiality inconsistencies in more details irrespective to the dataset that triggered them  were identified in the initial reconstruction as isozymes for the catalysis of the ribose-phosphate diphosphokinase activity, which is required for the biosynthesis of 5-phosphoribosylpyrophosphate (PRPP) . Conversely, ACIAD2907 function was supported by a stronger homology with E. coli gene prsA (68% identity) whose ribose-phosphate diphosphokinase has been experimentally confirmed [ilvD) and ACIAD3636. While the annotation of ACIAD1266 is supported by a strong homology with E. coli gene ilvD (74% identity) whose activity has been experimentally shown [E. coli gene ilvD). Gene knock-outs revealed that both genes were dispensable while the essentiality of other genes in the pathway strongly suggested that the dihydroxy-acid dehydratase activity was required. This result strongly suggests that both genes could back up each other and therefore indirectly corroborates the functional assignment to ACIAD3636.A majority of the model improvements 34/56) were applied to the GPR component, with a clear bias towards false dispensable inconsistencies: 26 GPR corrections pertained to experimentally essential genes against only 8 to experimentally dispensable genes  and ACIAD1257 (hisZ) were initially assigned as isozymes of ATP phosphoribosyltransferase reaction in the pathway of histidine biosynthesis  was observed to be dispensable, even though it is the only catalyst of two reactions essential for the biosynthesis of isoprenoids, which are the precursors of vital cofactors  and ispU (undecaprenyl diphosphate synthase) could perform these activities [A. baylyi's homologues to these genes – ACIAD2940 (ispB) and ACIAD1374 (ispU) – were therefore added as isozymes of ACIAD2968 for both reactions , adding new complex subunits  or assigning spontaneous activity (ACIAD2819: encodes for gluconolactonase activity which has been shown to occur spontaneously ). See AdgatABC), annotated as aspartyl/glutamyl-tRNA amidotransferase, catalyzed in iAbaylyiv2 the synthesis of charged glutamine-tRNA and charged asparagine-tRNA through the transamidation of misacylated glutamate-tRNA(Gln) and aspartate-tRNA(Asn) , however. The observed essentiality of ACIAD1920 is inconsistent with the redundancy of these two pathways, suggesting that the transamidation of glutamate-tRNA(Gln) does not occur in vivo. Furthermore, aspartate-tRNA(asn) transamidation is actually the only way of producing asparagine, as A. baylyi is lacking both asparagine synthetase and asparaginyl-tRNA synthetase enzymes. This result strongly suggests that, in A. baylyi, ACIAD0822-0824 genes are predominantly employed for asparagine-tRNA synthesis. To account for ACIAD1920 essentiality, we thus removed the glutamate-tRNA(Gln) transamidation pathway from the metabolic network.Twelve gene essentiality inconsistencies from datasets 2 and 3 led us to improve the NETWORK component of the model , involved in its synthesis, was observed essential, although predicted dispensable  and one B12-dependent encoded by ACIAD1045 (metH). Since coenzyme-B12 is neither synthesized by A. baylyi nor provided in the experimental media, the ΔACIAD3523 mutant was unable to use the MetH enzyme to synthesize methionine. The model could not account for this B12 auxotrophy of the ΔACIAD3523 mutant. In order to properly account for the dependency between MetH activity and the presence of a cofactor, the replenishing flux method can be employed [Six discrepancies were confidently interpreted yet did not lead to model modifications  and ACIAD3349(gltD), and glutamate dehydrogenase, encoded by ACIAD1110 (gdhA). In E. coli, these pathways were shown to be regulated in response to nitrogen limitations [E. coli strains lacking glutamate synthase show severe growth deficiency at low ammonium concentrations [A. baylyi on the succinate-supplemented minimal medium. These phenotypes contradicted model predictions, which considered the alternate pathway for glutamate synthesis. Further investigation would be required to fully understand the regulatory processes at work in this pathway for A. baylyi and extension of the modeling framework should be conducted to account for regulatory processes within the model.For 62 inconsistencies, we could not reach a validated explanation within the scope of this global analysis , a web interface combining a simulation layer for the model with AcinetoCyc, A. baylyi Pathway-Genome Database [In order to facilitate the exploration of moStudio   and Metabolite Producibility methods to predict growth phenotypes (see Methods). When performed on sets of environmental conditions and sets of gene deletions, prediction results are displayed in a table format in parallel to the actual experimental results. Predictions can thus be readily compared with the experimental observations. Furthermore, predicted and experimental phenotypes are both displayed on AcinetoCyc metabolic maps, and conversely gene deletions can be directly set from these metabolic maps . TheA. baylyi metabolism, providing a valuable complement to the annotation and the literature. The refinement process was particularly useful in validating or contradicting functional annotations that stood in the "grey zone", i.e. for which the annotation process provided only medium-level evidence.Several rounds of comparisons of model predictions to large-scale experimental results led to significant model improvements. First, growth phenotypes of the wild-type strain on 190 distinct environments resulted in the addition of 9 transporters and 2 pathways to the model. After improvement, the model accounted correctly for the growth phenotypes on 173 of the 190 environments. Secondly, we assessed the model against gene essentiality results on 9 defined environments. In contrast with wild-type growth phenotypes, these data can bring indirect information on the gene functions or on the existence of alternate pathways. Investigation on the causes of inconsistencies led us to modify the model in 56 cases out of 124 inconsistent predictions. All model components were modified, the GPR component gathering most of the improvements. The model accuracy in predicting mutant growth phenotypes increased from 88% to 94% on succinate-supplemented minimal medium and from 93% to 94% for the combined conditional gene essentiality results on 8 media. High-throughput phenotype clearly improved the quality of the model and expanded our understanding of Conversely, the model allowed systematic evaluation of the results of these high-throughput experiments by comparing them to its predictions. Inconsistencies directly targeted informative experimental results for which further investigation are required. As shown in this work, not all inconsistencies led to model improvements. Some of them could be interpreted in terms of biological processes lying outside the scope of the modeling framework, probably regulation in most cases. In addition, a significant number of discrepancies reported in this work remained unexplained or led to hypotheses in need of confirmation through further study.The process described here was driven by expert curation: each inconsistency was manually examined in order to search for an interpretation and a possible model correction, a labor-intensive proposition. The systematic use of such experimental data for model refinements would be greatly facilitated by the development of computational methods assisting the curator with his task, however. A number of methods have been developed to search for variants of model which match better with additional experimental data, mainly by seeking additions or removals of reactions in the metabolic network ,49. ThesA. baylyi metabolism with a valuable resource for investigating its phenotypic and physiological properties.Deriving the full benefits from a metabolic model entail both accessing its components and using its predictive capabilities. We realized the former by providing access to a detailed metabolic pathways database, the latter through a software tool that performs online predictions, both being coupled at the level of genes and reactions and accessible through a single, highly-interactive interface. This interface allows end-users to carry systems level predictions, and compare them with corresponding experimental observations, putting the consequences of modeling in the context of the detailed biological information that went into the model. This tool should therefore provide researchers interested in The initial reconstruction of the metabolic network was carried out using data provided by (i) the genome expert annotation , (ii) thE. coli [Escherichia coli and Acinetobacter baylyi to infer enzyme complexes and find AND Boolean associations between genes. Information from the literature was used to close gaps in the metabolic pathways, include pathways specific to A. baylyi that were unknown to the metabolic databases, and check the predicted pathways, for instance for the specificity of the cofactors. Physiological information derived from the literature [A. baylyi. A fixed biomass composition was chosen according to data found in the literature for strains growing on standard media  associations ). For eaThe NETWORK component models the metabolic network using the constraint-based modeling framework . This frS is the stoichiometric matrix of the metabolic network and ν the vector of reaction fluxes. The stoichiometric matrix is a matrix of size (m × n) where m is the number of metabolites and n the number of reactions. Each element Si,j of the matrix represents the relative stoichiometric coefficient of metabolite i in reaction j. Additional constraints on the fluxes, such as irreversibility and capacity constraints, are imposed by inequalities in the form:where νlb,i and νub,i are respectively the lower and upper bounds of the flux of reaction i.where νi ≤ ∞ for metabolites absent from the medium and -∞ ≤ νi ≤ ∞ for metabolites present in the medium, except for limiting nutrients for which a maximum uptake rate is chosen (-νuptake ≤ νi ≤ ∞). When simulating the metabolic network of a knockout mutant, the activity of each reaction is determined by evaluating its GPR association according to the set of removed genes. Fluxes of the inactivated reactions are constrained to be equal to zero.Environmental conditions are applied to the model by constraining the exchange fluxes of extracellular metabolites. Exchange fluxes are sink reactions allowing to control the input or output of metabolites in the model. They are constrained to 0 ≤ The BIOMASS component models the essential metabolic requirements for growth. It consists of a list of metabolites that are considered to be essential biomass precursors. Growth phenotype is therefore determined by checking their producibility . To do sSinternal is the stoichiometric matrix without the biomass precursors, Sbiomass precursors the stoichiometric matrix restricted to the biomass precursors and ε a vector of small reals, taken as 10-3. Linear programming tools are used to query for a flux distribution fulfilling this set of constraints. If a flux distribution could be found, the model predicted growth, otherwise it predicted no growth.where E. coli model [In order to assess quantitative growth defects, Flux Balance Analyses (FBA) were performed . A biomali model . We chos®  using the YALMIP optimization toolbox [Model simulations were performed within FluxAnalyzer  and MATL toolbox  and MOSEThe metabolic model is available both as Excel and SBML files  following experimental procedures described in [A. baylyi was monitored in PM1 and PM2 microplates containing a defined minimal medium supplemented with 190 distinct carbon sources. The Biolog quantitative growth measures were discretized to yield growth/no-growth qualitative phenotypes by choosing thresholds based on the negative growth control measures and previously known growth phenotypes for A. baylyi. Growth phenotypes that were inconsistent with model predictions were checked by examining results from previous work [Growth phenotyping experiments of ribed in . Basicalous work , or reteDetailed experimental protocol for the growth phenotyping of the mutant strains is described in . BasicalMD reconstructed the initial model, performed model predictions, interpreted inconsistent phenotypes, applied model corrections, and wrote the manuscript. FLF reconstructed the initial model and developed the NemoStudio software tool. VDB participated in the experimental phenotyping and the interpretation of inconsistent phenotypes. AK and DV participated in the initial reconstruction and the interpretation of inconsistent phenotypes. CC and SS developed the NemoStudio software tool. MS participated in the experimental phenotyping and the interpretation of inconsistent phenotypes. JW participated in the design and the coordination of the study. VS conceived of the study, participated in its design and coordination, and contributed to writing the manuscript. All authors read and approved the final manuscript.Sensitivity on GAM and NGAM parameters of growth rate predictions. This file contains two plots showing the effect of changing growth associated (GAM) and non growth associated (NGAM) maintenance parameters on quantitative growth rate predictions with iAbaylyiv4.Click here for fileGenome-scale metabolic models. This file contains the description of all model versions as well as information on reactions, species, biomass precursors, modeled environments and literature references used for the model reconstruction.Click here for fileExperimental data and model refinements. This file gathers the experimental results used for model refinements, the model predictions, and the corrections/interpretations associated to the inconsistent predictions.Click here for fileA. baylyiDetermination of biomass composition of . This file gathers all information used to reconstruct the biomass assembly reactions in the metabolic model.Click here for fileGenome-scale metabolic model in SBML format. This file contains the latest model iAbaylyiv4 in SBML format .Click here for file