The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry  and selects for (non-)native reactions  that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.Existing computational tools for  Microbial metabolism describes the full range of enzymatic conversions of carbon substrates to cellular biomass precursors, energy equivalents and biochemical molecules. Metabolic engineering harnesses this metabolic machinery for converting feedstock substrates to a growing range of products1256791114181921de novo pathway design rely on either optimization techniques or graph-search approaches. Linear Programming (LP) and Mixed Integer Linear Programming (MILP) approaches for pathway design, in general, extract a minimal stoichiometry-balanced sub-network that converts a source metabolite to a target chemical with high yield24252728313233Existing computational procedures for the de novo reaction steps between any two metabolites often absent in existing databases  by exploring exhaustively co-reactant/co-product combinations (Step 1). The coefficients in the overall stoichiometry  are critical as they ultimately define the carbon and energy efficiency of the conversion while meeting thermodynamic feasibility. Following the selection of the overall reaction  to a single product (B). However, metabolic conversions do not generally involve linear paths from substrate to productet al.2 to stoichiometry-feasible C2+ products. Finally, the third study identifies suitable co-reactant and co-product pairs to drive forward the thermodynamically unfavorable methane to acetate conversion. The two-stage procedure recapitulates existing pathway designs454647We demonstrate the two-step procedure for three separate case studies of increasing complexity. The first one exhaustively identifies networks that convert glucose to acetate while conserving all carbon atoms with no provision for any additional co-reactants or co-products in the spirit of the recent study by Bogorad The first step (optStoic) identifies the stoichiometric coefficients of the reactant and product metabolites in the overall conversion while maintaining thermodynamic feasibility considerations . At thisThe second step searches through a database of elementally and charge balanced reactions (accessed from MetRxnThe two-stage stoichiometry and intervening reaction design procedure (optStoic + minRxn/minFlux) was implemented for three case studies that highlight the potential of the approach in identifying novel stoichiometries and network designs.2. Recently, a synthetic, non-oxidative glycolysis (NOG) cycle was designed and assembled demonstrating the feasibility of this conceptE. coli heterologous phosphoketolase enzymes xylulose-5-phosphate phosphoketolase (XPK) and fructose-6-phosphate phosphoketolase (FPK) from B. adolescentis Bogorad et al.et al.Conversion of glucose through central carbon metabolism to acetyl-CoA (through EMP glycolysis or Entner-Doudoroff pathway) is required by most microorganisms for the production of biomass precursors and cofactorsA → 3B is fixed. Only networks that conserve all carbon flux from glucose to acetate satisfy the imposed overall stoichiometry. Results from both minRxn and minFlux (see Methods) converged to the same designs that achieve the overall conversion albeit with substantial differences in computational time. The smallest network  whereby the thermodynamically unfavorable conversion of ribose-5-phosphate (r5p) to ribulose-5-phosphate (ru5p) is avoided by routing Pentose Phosphate Flux through C5 metabolism of phosphoribosyl pyrophosphate (prpp) and ribulose (rub) in E. coli  here is redundant as the overall stoichiometry in the form of  network  recapitucoli see  where glcoli see  removes C. ljungdahliiMoorella thermoacetica2 lost in the conversion of pyruvate (pyr) to acetyl-CoA in pyr ferredoxin oxidoreductase enzyme (PFOR)2 released in pyruvate decarboxylation (PFOR) and glycine cleavage (GLYCL) may make these pathways difficult to engineer.We next explored whether it is possible to construct the entire pathway in a single organism (see Methods) unlike the engineered NOG pathway that requires at least one heterologous enzyme . iAF1260 E. coli modelThe trade-off in flux allocation between acetate production and cellular growth was assessed by including biomass formation . Methanol is an important commodity chemical and petrochemical by-product2+ molecules. A recent study has addressed this limitation by proposing and constructing a biological route for the direct condensation of methanol to ethanol2 towards C2+ product metabolites with zero loss of carbon flux . Note that product metabolite B is treated as unknown and could potentially be any of the 3,330 C2+ metabolites in set I (list of metabolites). The negative free energy change for methanol condensation  allows for the endergonic uptake of some CO2 to become thermodynamically feasible. As a result, designed pathways have the potential to achieve higher carbon efficiency than existing methanol condensation routes2+ product metabolites.In the second case study, we focus on the design of the overall conversion when not all participating metabolites are known 2 in the overall stoichiometry as the design objective in optStoic while keeping the methanol coefficient at a value of one. This objective function maximizes the carbon yield of the desired product per mole of the limiting reactant (methanol). As many as 496 target metabolites from set I were identified as thermodynamically feasible products for the conversion (see 2 uptake. CO2 uptake varies in concert with the oxygen to hydrogen ratio (O:H) in the product metabolite. For example, malonate (C3H4O4), which has an O:H ratio of one, has the largest stoichiometric coefficient for CO2 , while 2-methyl butanal (O:H = 0.1) has the lowest . As hydrogen availability in the reactants is fixed (stoichiometry of methanol is fixed to one), a low O:H ratio in the product reduces water production to satisfy elemental hydrogen balance. Because the oxygen atoms in CO2 are routed to water for products with low O:H ratio, reduced water production limits caused by the fixed availability of H, reduces the co-utilization of CO2 and thus overall carbon yield. It is important to note that the O:H ratio is also critical in deciding whether a desired metabolite is a feasible target product. For example, several platform chemicals such as ethylene glycol (O:H = 0.33), 1-butanol (O:H = 0.1) and 1,3-propanediol (O:H = 0.25) are not feasible solutions as mass conservation of hydrogen and oxygen cannot be simultaneously satisfied in the overall stoichiometry. Furthermore, metabolites with a very high O:H ratio, such as oxalate (O:H = 2) and glyoxylate (O:H = 1.5) are also not feasible due to limitations in oxygen atoms availability from CO2. Therefore, constraints of the overall design stoichiometry determine the feasibility of the target products as well as their maximum yields. It appears that in all designs thermodynamic feasibility is not the limiting constraint, instead elemental balances for H and O limit overall reaction stoichiometries.We chose maximization of the coefficient of CO1 substrates to an intermediate product , which was subsequently routed towards the final product. As a result, the first example (acetate production) spans alternate routes of fixing CO2 with methanol to acetyl-CoA (and acetate). Each one of the identified networks is subsequently coupled with additional reactions to synthesize the three other target products . The optimal network for converting methanol and CO2 to acetate is divided into two modules  required for powering this conversion is supplied by methanol oxidation in methanol dehydrogenase (MEDH) in the second module. This module assembles an alternative methanol condensationFrom 41 modules . The fir2 for acetate production. A third design using predominantly E. coli reactions The design shown in eactions  was alsoeactions . Here, h2 to a C4, a C5 and a C6 product, respectively. Pathways for 3-hydroxybutyrate (3hbut) recapitulate existing engineered routes through acetoacetyl-CoA69β-oxidation reversal) routes  routes . In addientified . In the entified  in red. entified  in greenThis case study highlights several of the advantages of the proposed procedure over existing pathway prospecting tools. Performance metrics can be directly imposed without the need for post-processing steps unlike existing procedures314136412+ liquid biofuel and biochemical targets1 co-substrate  is thermodynamically unfavorable . Recent studies have suggested that such a conversion can be achieved when coupled with a suitable electron acceptor772 product) and rank them according to their carbon yield to acetate. Subsequently, we select five solutions from the list of feasible electron acceptors to design pathways that convert methane and a C1 co-substrate (CO2 or CO) to acetate along with a C2+ platform chemical1In the previous case study, only co-product (B) was unknown in the overall conversion. In the third case study, both co-reactant (C) and co-product (D) are design variables. With increased production of methane from natural gas deposits through advanced extraction technologiesI. Non-carbon containing metabolites including metal ions, anions, protons and water were included as candidates in the overall stoichiometry. optStoic identified many distinct designs with alternative electron-acceptor combinations. Ten such designs are shown in 762S) accepts the maximum number of electrons (eighteen) thus converting 4.5 moles of methane to 2.25 moles of acetate. In contrast, one mole of iron(III)/iron(II) reduction Goverall of the reaction  was below the conservatively chosen ΔGmin for this case study.Using optStoic and keeping the stoichiometry of acetate fixed at one (as a basis), we maximized the stoichiometric coefficient of methane while allowing the production of up to two additional metabolites from set I (similar to Step 1). The co-substrates were restricted to CO2 and CO while the co-products were chosen from all C2+ compounds . Goverall = −5 kcal). It is also interesting to note that For the second step we selected five pairs of electron acceptors from 2 (or CO) towards acetate  recruited for converting CO to 23 bdo in a recent study performed on three separate acetogenic clostridial speciesE. coli4789β-oxidation pathway4591E. coli and K. pneumonie  can systematically be imposed to rank-order synthetic pathways according to the design objective. The case studies highlight the potential of this procedure for designing a thermodynamically feasible overall conversion stoichiometry, selecting the optimum combination of co-metabolites and co-products, and constructing non-intuitive synthetic biological routes for the overall conversion. We compared the optimal pathways identified here with two existing pathway design tools  for designing E. coli, GEM-Pathde novo reactions. By combining minRxn/minFlux with compilations of hypothetical reactions and metabolites  maximum yield for primary product B (i.e. max b while a = fixed), (ii) maximum co-utilization of free reactant C  for a given product yield, (iii) minimum formation of by-product D (min d while b = fixed) or combinations thereof. The overall optimization formulation for Step 1 is as follows (optStoic):Using this representation either all the metabolites participating in the reaction are known a, b, c, d can be declared as integer or real depending on the adopted scaling of the overall conversion. It is straightforward to extend the optStoic formulation to account for more than two reactants and two products. In fact, a comprehensive list of putative co-reactants and co-products could be designed in the overall stoichiometry and then rely on the optStoic optimization problem to identify the optimal combination of co-reactants and co-products. Additional binary variables, associated with each metabolite from set I, are included in the formulation to decide whether the respective metabolite should be a part of the overall stoichiometry or not. A negative value for a stoichiometric coefficient declares a metabolite as a reactant while a positive value defines it as a product. Here, both the participating metabolites and their respective coefficients are decision variables, and the modified optStoic formulation is a mixed integer linear programming (MILP) problem or an integer programming problem (IP) depending on whether the stoichiometric coefficients are declared as real or integer variables respectively.In formulation (optStoic), The impact of cellular growth requirements on the flux allocation towards the target metabolite can be directly assessed by updating the overall stoichiometry to include biomass, growth (and non-growth) associated maintenance ATP as additional product metabolites:δ is the cellular growth coefficient, and biomass is represented as a weighted (by coeffk) sum of all the biomass precursors Mk . Constraints for elemental and charge balances, and thermodynamic feasibility limitations are updated accordingly in optStoic:Here positive variable δ at different levels between zero and maximum growth and subsequently maximizing the target product coefficient.The trade-off between product yield and biomass formation can be assessed by fixing profitmin.Profit margin considerations  from the MetRxn databaseI  of the products raised to their stoichiometric coefficient to that of the reactants. The reaction quotient Q for the overall conversion aA + cC → bB + dD is:where G for each reaction in J are obtained by varying the concentration of the metabolites participating in the reaction by 1,000 fold of their transformed standard concentration . Reaction bounds LBj and UBj are determined according to the following rule:where the expressions within brackets are the molar concentrations of each participating metabolite. The minimum tion see . The widM is a large positive value  in relation the exchange flux of the metabolites of the overall stoichiometry (from Step 1). Additional information on directionality of reactions obtained from literature can be used, whenever available, to further constrain LBjand UBj. The directionality for 4,447 reactions  was assigned by this procedure while the remaining 2,424 that lacked the free energy of formation for one or more metabolite were treated as reversible. Out of the 4,447 analyzed reactions, 2,205 were found to be irreversible whereas 2,242 were reversible. The identification of the minimal set of reactions that can match the desired overall stoichiometry (found after Step 1) is formulated as the following MILP (minRxn) problem:where yj defined asBinary variables j in the overall network. The objective function involves the minimization of the sum of yj’s (Constraint 12) to extract the minimum number of reactions that can support the desired overall stoichiometry (optStoic) of Step 1. Constraint (13) ensures that all metabolites, including cofactors, are used in a stoichiometrically balanced manner. Constraint (14) sets the flux of the exchange reactions  equal to their respective stoichiometric coefficients thus matching the overall desired stoichiometry (from Step 1) and providing a flux scaling. Constraint (15) ensures that reaction j is removed from the designed network when yj is equal to zero. Networks for different overall stoichiometries (from Step 1) can be obtained and contrasted for their overall performance metrics. The use of integer cut constraints to (i) disallow previously identified solutions and (ii) search for alternate optimal as well as sub-optimal solutions can be used to exhaustively explore all possible ways of linking reactants to products (Constraint 16).control the addition (or not) of reaction k = 1, …, K is the set previously of explored solutions.where J. A related LP formulation (minFlux) that is significantly  less computationally taxing can be solved as a surrogate of minRxn. In contrast to minRxn that directly minimizes the total number of reactions in the designed network, minFlux minimizes the total metabolic flux of the chosen reactions. This modified objective function could be viewed as an approximation of the total enzyme-load imposed on the organismThe resulting (minRxn) MILP formulation can be computationally challenging to solve as it involves as many as 6,871 binary variables associated with all the reactions in set xj is minimized (Constraint 17). Constraints (13) and (14) are the same as in (minRxn) formulation whereas Constraint (18) imposes bounds on fluxes without multiplication with a binary variable. Constraints (19) and (20) ensure that xj is greater than both vj and −vj. The minimization operator in the objective function ensures that for every j either Constraint (19) or (20) becomes active.In minFlux, the sum of the absolute values of the reaction fluxes denoted by k from reappearing in subsequent flux combinations  to positive variable sl (where L is the set of variables for problem in standard form). Subsequently, a set of integer cut constraints similar to the ones described for formulation (minRxn) can be constructed. Binary variables ul denote the presence of nonzero basic variable l in a previously identified solution k. The following constraints prevents the reappearance of a previously found basic feasible solution:Alternate optimal  basic flux combinations can also be explored by preventing all nonzero basic variables from an earlier solution sl is nonzero when ul = 1. It is important to stress that the addition of integer cuts converts the LP (minFlux) formulation to an MILP problem. Nevertheless, it is can be solved significantly faster (about two orders of magnitude) than minRxn as it involves much fewer binary variables.where pt):Additional constraints can be added in either minRxn or minFlux optimization formulations to maintain the total number of reactions with a positive standard change in free energy at a minimum  ensures that a reaction j cannot carry any flux unless it is present in at least one organism o  best suited to construct the synthetic network. In addition, the definition of set O can be altered accordingly to minimize diversity in chosen genus instead of individual organisms. Alternatively, the production host could be pre-specified and then restrict the total number of heterologous reactions.Here, parameter How to cite this article: Chowdhury, A. and Maranas, C. D. Designing overall stoichiometric conversions and intervening metabolic reactions. Sci. Rep.5, 16009; 10.1038/srep16009 (2015).


Chlamydomonas reinhardtii.Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in http://networks.systemsbiology.net/chlamy-portal.We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.The online version of this article (doi:10.1186/s13068-015-0391-z) contains supplementary material, which is available to authorized users. Green algae hold great promise for the manufacture of renewable biofuels . The relC. reinhardtii has emerged as the de facto model organism for algal biofuels research  , and thg., DET2 ). Transc2 [CDC48  and Cre02 [CDC48 ). OveralThe mid-stage response was comprised of 1203 differentially regulated transcripts within 90 co-regulated modules. These modules were organized into 15 waves that changed between 24 and 60 min after N starvation , which exhibited a dramatic drop in abundance from a maximum level of ~1500 to 0 FPKM (fragments per kilobase of transcript per million mapped reads). The overall repression of C fixation is an indication of transition into a lowered metabolic state as a consequence of the cessation of growth. In addition, alternative sources of carbon such as recycled C-backbones from degraded protein and acetate influx might also compensate for the downregulation of C fixation.Transcripts encoding proteins involved in many carbon sequestration processes were downregulated during the mid-stage response including those implicated in carbon concentrating mechanisms and those encoding several key enzymes of the Calvin cycle  required for lipid biosynthesis [GLD2 [DGAT1 and DGTT1 were upregulated. Importantly, DGAT1, which catalyzes the rate-limiting step in the de novo TAG biosynthesis, increased up to fourfold, 45 min after N starvation. Membrane remodeling [de novo fatty acid (FA) biosynthesis, such as the three β-ketoacyl-(acyl-carrier-protein) synthases , which are responsible for the first enzymatic step catalyzed by the multi-enzyme complex fatty acid synthase (FAS) were all downregulated. Importantly, the downregulation of lipid biosynthesis transcripts continued through the late-stage of the N starvation response.While lipids start to accumulate at 8 h after N starvation , within ynthesis  and syntis [GLD2 ). Similais [GLD2 , transcrmodeling  was enhamodeling . NotablyKAS1-3), continued into the late-stage response with the transcriptional downregulation of the 3 enzymes of the FAS complex involved in the subsequent steps of FA biosynthesis: 3-ketoacyl-ACP reductase (Cre03.g172000), β-hydroxyacyl-ACP dehydratase (Cre03.g208050) and enoyl-ACP-reductase (Cre06.g294950). The FAs 16:0 and 18:1 are the most abundant of the TAG fractions in N starved cells [de novo FA synthesis for TAG accumulation, which accounts for 79 % of the TAG content in N starved cells [C. reinhardtii [KAS3 to 572 FPKM for ACP2, presumably to maintain flux towards FA biosynthesis , which incorporate double bonds into FAs at the cost of reducing equivalents, were downregulated, potentially to conserve energy while producing lower amounts of unsaturated FAs.The observed commencement of significant lipid accumulation 8 h after N starvation was set up by transcriptional changes occurring in the late-stage response, which began after 1 h from N starvation. Comprised of 665 monotonic  and 234 transient  transcripts organized into 72 co-regulated modules in 10 waves, the late-stage response was characterized by lipid metabolism remodeling, downregulation of photosynthesis, oxidative stress and protein folding stress responses, and an upregulation of sugar catabolism. While an increased metabolic flux into FA biosynthesis would be expected to support TAG production, the late-stage response involved the downregulation of many transcripts associated with FA biosynthesis such as the chloroplastic pyruvate dehydrogenase complex (PDC), acetyl-CoA carboxylase (ACC), and malonyl-CoA:ACP transacylase, all involved in the biosynthesis of FA precursors , which serves as a major link between carbohydrate metabolism and lipid metabolism, is upregulated as part of the late-stage response, in agreement with previous studies of the C. reinhardtii transcriptome during N starvation [Phaeodactylum tricornutum cells overexpressing GPD had a 60 % increase in neutral lipid content, reaching 39.7 % of dry cell weight in stationary phase [GPD has also been reported to increase lipid content in plants [GPD1 (Cre12.g511150) was downregulated as part of a late-stage response, highlighting the importance of cellular compartmentalization to achieve modularity in metabolism. Additionally, consistent with the previously described role for lipases in lipid accumulation [TGL9 and Cre09.g391838 upregulated, while TGL15 was downregulated up to 18-fold. Undoubtedly, TAG lipases are key modulators of TAG accumulation in C. reinhardtii as previous works have demonstrated [C. reinhardtii.The transcript encoding the mitochondrial glycerol-3-phosphate dehydrogenase  that coordinate the N starvation response, we computed the distances between temporally ordered, smoothed expression profiles of the transcriptional regulators (TRs) and the transcriptionally co-regulated modules. We selected the first fifth percentile of all distances within the physiologically relevant window of 15–90 min time lag between a change in the TR and its putative target transcriptional module. This time window was selected based on the time it takes for a differentially regulated TR to exert an observable effect on transcription ; see “Me.Table 3LC. reinhardtii and plants, RWP11 [BLZ8 and Cre17.g746547, both bZIP TFs, and Cre13.g573000, a SET domain methyltransferase , RWP4 and GSM1. While GSM1 is known to function as a regulator of the minus mating type gametogenesis response, the function of these other two TFs is less well understood. Additionally, other TRs downregulated at this point include a FHA domain nuclear inhibitor of protein phosphatase-1 (Cre12.g534450) and several acetyltransferases (NAT1 and NAT11). The late transcriptional wave was putatively orchestrated by the transient upregulation of the subunit D of NF-Y TF (Cre07.g341800) [NAT31 acetyltransferase and the downregulation of NAB1 (Cre06.g268600), which plays an important role in controlling the expression of the light-harvesting antenna of photosystem II in C. reinhardtii [RWP8 during the late stage. While incomplete, the TRN model organized the complex set of transcriptional changes during the N starvation response into a modular framework of co-regulated transcripts that were temporally coordinated by as few as 34 TRs, allowing for model-based systematic analysis of metabolic consequences.Based on this TRN model, the transcriptional cascade started within 12 min after N starvation with the twofold upregulation of a previously described N response regulator in s, RWP11 . By 18 me et al.  which reomain TF , showed g341800) , and thenhardtii . FinallyC. reinhardtii [Using the available genome-scale metabolic network of nhardtii , we perf6f complex (responsible for creating the proton gradient that drives the synthesis of ATP in chloroplasts) and plastocyanin (Cre03.g182551), which is responsible for the flow of electrons between cytochrome b6f and photosystem I. Furthermore, we identified several targets from central metabolism like triose phosphate isomerase (TPI). TPI catalyzes the reversible conversion of dihydroxyacetone phosphate (DHAP) to glyceraldehyde-3-phosphate (GA3P) and coordinates many pathways including glycolysis, Calvin cycle, and glycerol metabolism. Notably, recent studies in Arabidopsis have uncovered the crucial role of TPI in general lipid metabolism [Arabidopsis with a fivefold reduction in transcript levels accumulated DHAP and glycerol, both byproducts of TAG mobilization and precursors for glycerolipid biosynthesis. Another key target identified from this analysis was Cre02.g080200, which encodes for the transketolase TRK1 which is an essential enzyme of both the Calvin cycle and the non-oxidative pentose phosphate pathway (PPP). TRK1 has also been found to play a key role in the regulation of C allocation through calcium-dependent phosphorylation regulation [C. reinhardtii during N starvation. These observations may reflect both network redundancy on TAG production and how different categories of lipids affect biomass, as defined in the metabolic model biomass function.Metabolic targets changing earliest fall into the ATP homeostasis category, particularly control of ATP/NADPH ratios, like the nucleoside-diphosphate kinase (Cre07.g325734), the cytochrome btabolism . Indeed,gulation . Our metFinally, we used the model to identify the metabolic genes predicted to be involved in the biosynthesis of acetyl-CoA, malonyl-CoA and glycerol 3-phosphate, key precursors for lipid biosynthesis. Out of the 100 genes predicted to be involved in this process, 26 are downregulated and only four are transcriptionally upregulated , 2394 reactions and 1133 metabolites, enabling very accurate predictions of gene deletion growth phenotypes, with an area under the receiver operating characteristic curve of 0.92 [de novo detected GREs and predicted TF to transcriptional module interactions would produce a more accurate picture of the functional interactions recapitulated by our TRN.There are several possible limitations to our study. First, our TRN is based on expression data and transcriptional changes do not fully account for changes at the protein level, much less for functional activity of post-transcriptionally regulated proteins , 89, whi2 levels, etc.), and different mutant strains. A second direction would be to refine the accuracy and specificity of the transcriptional regulatory influences with the integration of quantitative data that includes additional influencers, like miRNAs [C. reinhardtii [We foresee two main future directions of this study: first, the expansion of the TRN model with expression data from other phenotypic transitions, e.g., starvation on other nutrients, different growing conditions  Raw RNA-Seq expression data obtained from a N starvation time course experiment on  CC-3269  was alig CC-3269  to the c CC-3269 ). Next,  CC-3269 , we comparvation  or (2) \cis-acting gene regulatory elements (GREs). We obtained the TRs annotation from Schmollinger et al. [C. reinhardtii annotation. Protein–protein functional interaction network was retrieved from STRING [We used cMonkey  version r et al. , which cm STRING , versionTranscript expression trajectories were first smoothed with the csaps routine from MATLAB. Smoothed data was then divided by its maximum absolute value to account for non-linear relationships between transcriptional regulators and transcriptional modules. A matrix of distances between TRs and transcriptional modules was calculated allowing for a defined best time lag between the expression of the TR and the corresponding transcriptional module. For eukaryotic genes, it can take from several minutes up to several hours since the expression of a TF changes, to transcriptional changes on its target genes . We usediRC1080, the hitherto most comprehensive metabolic model of C. reinhardtii [We used the genome-scale metabolic reconstruction nhardtii . We simunhardtii . N is avnhardtii  runs to  ρ > 1 the model perturbation affects the biomass production more severely and causes, thus, more TAG content per biomass. Consequently, a value smaller than one relates to a perturbation that causes less TAG per biomass content. Note that Eq. The interpretation of Eq.


The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in  Among malignant brain tumors, the most common one is glioblastoma . It is also one of the most lethal cancer types, with a 5-year survival rate of only 3%, compared to an average of 30% for other types of brain tumors , lipid metabolism, nucleotide metabolism, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. In total, 42 pathways are covered by the model. iMS570 does not have a growth reaction to simulate the proliferation of brain tumors since mammalian brain cells do not grow in non-tumor states. Therefore, an extended literature survey was performed to define a growth reaction for tumor proliferation in brain   algorithm uses binarized gene expression data and a genome scale metabolic network to generate a context-specific reconstruction such that the highest consistency with the available data is ensured  was used as a solver in both tools.In addition to GIMME, we used MADE algorithm to see if there is any difference between the methods that maps transcriptome data on metabolic models to obtain condition-specific models. MADE  algorithm uses the expression levels of significantly changed genes or proteins to generate a functional metabolic model that most accurately recapitulates the expression dynamics  and glutamine release (r580) were constrained as zero due to the fact that glutamine exchange between astrocytes and neurons in healthy brain is perturbed within GBM  and ketone body metabolism , which are used as alternative pathways in case of low activity of the glucose metabolism , which is defined only as uptake reaction in iMS570, were changed to a reversible exchange reaction to allow NH3 release as observed in the GBM . Five GBM metabolic models were created by GIMME, three for the comparison of Mes, PN, and Pro subtypes of GBM, one to compare the effect of different microarray platforms (The GBM metabolic model obtained using a different microarray platform but from the same dataset as GBM subtypes), one to compare the effect of different datasets (The GBM metabolic model obtained using the same microarray platform as GBM subtypes but from a different dataset). In-silico GBM phenotypes obtained via metabolic modeling were compared with the literature based experimental results. Metabolic fluxes calculated for healthy brain in resting state , ProNeural (PN), and Proliferative (Pro) GBM, are classified based on the clustering of gene expression profiling  cycle activity. Flux values of the lactate production (r11+ r56) by GBM metabolic models were around 1.68 mmol/gDW/h for all GBM subtypes , with flux values 0.073, 0.055, and 0.073 mmol/gDW/h for Mes, PN and Pro respectively. GBM subtype metabolic models also exhibit a much lower flux in pyruvate carboxylase reaction (r12) (Table 25+ r69) is active but possesses a very low flux value compared to the healthy brain metabolic model (Table 26−27+ r70−71) and then alpha-ketoglutarate (α-KG) (r30−32+ r73−75), carried much lower flux than the citrate synthase reaction (see Supplementary File 31−32+ r74−75) can be explained by insufficiency in the wild type isocitrate dehydrogenase genes.Unlike initial cancer studies which reports that the glycolytic phenotype in cancer is due to a permanent impairment of mitochondrial oxidative phosphorylation Zheng, , both re2) Table . Acetyl-96+ r97) and then to α-KG by the anaplerotic glutamate dehydrogenase reaction (r89+ r90+ r92+ r93) in order to replenish the TCA cycle intermediates  to complete turnover of the TCA cycle, and converted to pyruvate by malic enzyme reaction (r39+ r82), resulting in NADPH production. All GBM subtype metabolic models exhibit similar flux value (around 0.066 mmol/gDW/h) for malic enzyme reaction. In agreement with this finding, labeled glutamine experiments showed that labeled carbon was observed in lactate derived from glutamine through malic enzyme reaction /(r10 + r55), was around 1/25 for GBM subtypes. Flux values for the conversion of the malate to OAA by malate dehydrogenase reaction (r37 + r38 + r80) were around 0.012 for GBM subtypes. As a result, major sources of the acetyl-CoA and OAA pool used in TCA cycle were respectively the pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, which was found to be consistent with both in-vitro and in-vivo experiments  and oxidative phosphorylation (r45 + r88) were around 2.7 and 0.4 mmol/gDW/h respectively, which shows the phenomena that although the turnover of TCA cycle can be completed, major energy source is aerobic glycolysis in GBM cells. The flux value of ATP production from oxidative phosphorylation for GBM subtypes were calculated to be 3-fold lower than the value calculated by the healthy brain metabolic model  producing R5P were higher for all GBM subtype metabolic models than healthy brain metabolic model (Table 28 + r72), which is the precursor for fatty acid, thereby lipid synthesis (Table 289), which is the first committed step for the fatty acid synthesis (Table 17 + r61), anaplerotic reaction through glutaminolysis (r89 + r92) and malic enzyme reaction (r39 + r82), which are all active for GBM subtype metabolic models  using the formula ln2/td  but from the same dataset GSE13041) and from the same platform (GPL96) but from a different dataset (GSE13276) were additionally used to derive GBM-specific metabolic models and calculate corresponding flux distributions . This is an important issue to be considered to validate our results since platform or laboratory differences may cause serious reproducibility problems in microarray experiments  and ATP production flux via oxidative phosphorylation (r45 + r88) were higher than GBM subtypes  by both GIMME and MADE. Although there are some significantly different flux values for same reactions generated by GIMME and MADE, MADE-based metabolic model obeys the GBM metabolic remodeling depicted in Figure 28 + r72) producing acetyl-CoA as the precursor for fatty acid is active for all GIMME-based metabolic models; whereas flux value of this reaction is zero for MADE. TCA cycle behavior differs quantitatively for GIMME and MADE-based models. After maximizing biomass growth rate as objective function, the minimization of Euclidean norm of internal fluxes was applied in GIMME to narrow flux ranges of reactions due to alternate optima . The comparison of resulting flux distributions allowed to check if there is any difference between the algorithms mapping gene expression data to the brain metabolic network, This study provides GBM-specific genome scale metabolic models, derived from a brain-specific metabolic network. A growth reaction for tumor proliferation is implemented based on the lipid and protein content of white matter, where the GBM arises. By incorporating appropriate constraints from the literature, different GBM datasets were shown to predict similar metabolic flux reroutings, both validating our results and providing a proof of data consistency over transcriptome datasets. Moreover, the effect of different computational methods to incorporate transcriptome data with genome-scale models was investigated, and the two different methods, GIMME and MADE, which differ considerably in terms of dealing with the gene expression data, give similar qualitative results. Although there are several studies to apply genome-scale constraint-based modeling to cancer metabolism, an analysis of brain tumors with this approach is scarce. Only a recent study analyzed GBM with the FBA approach, by using a metabolic model with 147 genes and 12 pathways, without the incorporation of gene expression data , but it is the use of measurement and gene expression constraints together that leads to a better prediction of GBM flux remodeling. This also shows the importance of incorporating gene expression data for flux calculations.When the constraints are directly applied to İAnother commonly used method for constraint-based genome-scale metabolic modeling is the sampling of solution space. The solution space is randomly uniformly sampled for a high number of flux vectors rather than searching the space for an optimum flux vector (Thiele et al., TÇ conceived and designed the study, EO performed all simulations, TÇ, EO analyzed the results and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force). The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase–pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly thermodynamic favorable under conditions analysed. However, the most important challenge identified for improving photo-electro-autotrophic growth is increasing the proton-pumping rate of the rhodopsin photosystems, allowing for higher ATP regeneration. Alternatively, other designs of autotrophy may be considered, therefore the herein presented integrated modeling approach allows synthetic biologists to evaluate and compare complex pathway designs before experimental implementation.The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated  Escherichia coli. Recently, features such as  carbon fixation pathways  = 0.3 mM, [HCO3-] = 7 mM. These concentrations are based on pH 7.5 and equilibrium with a gas feed phase containing 1% (v/v) CO2, which is a feasible CO2 concentration for an industrial set-up. For the following co-factors concentrations and ratios were fixed: [orthophosphate] = 10 mM, [pyrophosphate] = 1 mM, [CoA] = 1 mM, [ATP]/[ADP] = 10, [ADP]/[AMP] = 1, [NADPH]/[NADP+] = 10, [NADH]/[NAD+] = 0.1 as in [red]/[ferredoxinox] we assumed a ratio of 10, contrary to the ratio of 1 assumed before /[ferredoxinox]: 0.5–50, [ATP]/[ADP]: 5–20, [NADPH]/[NADP+]: 0.5–50 and [NADH]/[NAD+]: 0.1–10. Additionally, we computed the MDF for a varying range of [CO2 (aq)]: 0.01–3 mM and [HCO3-]: 0.25–70 mM.Most concentrations and ratios chosen, are based on the characteristics of ytoplasm ,45. HoweWe optimized the thermodynamic profile using Python 2.7 and a module for sequential least squares programming from the package scipy.optimize (fmin_slsqp).To compare the kinetics of the carbon fixation pathways, we used two methods. The first method, Flux-Force Efficacy (FFE), is related to the thermodynamic driving force of the thermodynamic bottleneck reaction in a pathway, which is quantified by the MDF, as discussed in the section above . The FFER is the gas constant (8.314 J/mol/K), T is the temperature (310.15 K) and -ΔrG’ is the MDF of the pathway.The second method for estimating kinetics is the Pathway Specific Activity (PSA) . PSA is m, wi and vi as the number of enzymes in the pathway, their stoichiometric coefficients in the pathway and the specific activities of those enzymes, respectively. Most of the specific activities of the enzymes were taken from [in vitro conditions, as done in [2 and HCO3- concentrations to be saturated, similarly to all other concentrations. This assumption allowed to have the most fair comparison between pathways, since the Km values for the carbon species for the ferredoxin-oxidoreductase enzymes are not available. Furthermore, in a potential industrial application a gas feed with high CO2 concentrations close to saturation is feasible. As the pathway end product for PSA we chose pyruvate, since this is a central metabolite, and an important precursor for biosynthesis of many products of commercial interest, further it is an important product of several analyzed carbon fixation pathways.With ken from . The speken from . Howeverken from . So, we  done in . All enz done in , we assu2 for the growth rate predicted by FBA. It is defined as:FCO2 (mmol/gCDW/h) is the CO2 uptake flux from the FBA for the simulated growth rates, PSA is the Pathway Specific Activity (μmol/mg protein/h) and n is the number of CO2/HCO3- molecules needed to produce one molecule of pathway end product. In our analysis this pathway end product is pyruvate, (n = 3).We propose the Pathway Protein Burden (PPB) (g protein/gCDW) as a method to estimate the fraction of cellular dry weight that needs to be dedicated to carbon fixation enzymes to produce pyruvate from CO2 uptake flux to produce pyruvate.Since PSA is an estimate for the fastest kinetics of a carbon fixation pathway, the PPB represents a minimal estimate for the protein burden of the carbon fixation pathway to support the predicted COFor electron uptake mechanisms and PPRs, which are mostly single proteins or protein complexes, similarly an estimate for the Protein Burden (PB) (g protein/gCDW) of these systems can be made as follows:F (mmol/gCDW/h) is the electron uptake flux or proton-pumping flux for the simulated growth conditions, SA (μmol/mg protein/h) is the specific activity of the related protein, i.e. the electron uptake enzyme or PPR. n is a stochiometric constant for example for converting the number of electrons per hydrogen or formate molecule (n = 2).Where E. coli and the added pyruvate synthase. The cycle taken, turned out to be an alternative natural cycle, the DC-4HB cycle, found in anaerobic archaea [We used FBA to predict photo-electro-autotrophic growth rates for different carbon fixation cycles  and to i archaea , which w2. This led to additional flux for electron uptake, other than for biomass production. In an industrial setting, where photo-electro-autotrophic E. coli strains would possibly produce a specific product, separating undesired by-products complicates downstream purification of desired products, reducing the economic advantage of the whole process. Hence, we excluded certain reactions to block the generation of all by-products  to 0.071 h-1 (rTCA cycle). This PPR flux threshold is caused by the fact that the non-growth associated ATP requirement for maintenance (NGAM) needs to be overcome before ATP is available for growth. E. coli normally has a NGAM flux of 8.39 mmol ATP/gCDW/h. This NGAM value is based on standard heterotrophic growth conditions and includes ATP consumption for other processes than anabolic reactions, e.g. membrane leakage [We found by FBA that PPR proton-pumping flux and, consequently, ATP regeneration severely limited autotrophic growth rates. We varied the PPR flux between 0 and 50 mmol H+/gCDW/h . At 50 m leakage .E. coli, a reliable estimate for the maintenance term is not available and it may turn out to be higher or lower [E. coli value are feasible, as for several organisms lower NGAM values have been determined. For example, for the strictly anearobic Shewanella oneidensis and Geobacter metallireducens, which both can transfer external electrons directly, NGAM values were determined to be 1.03 and 0.81 mmol ATP/gCDW/h, respectively [Rhodobacter sphaeroides has a NGAM of 8.4 ATP/gCDW/h during photoheterotrophic growth at high light intensities, but during light-limited photoheterotrophic growth this can decrease down to 0.5 ATP/gCDW/h [The NGAM value is normally determined in chemostat experiments. However, for a completely new growth phenotype of an engineered, and potentially laboratory evolved, autotrophic or lower . The buror lower , of for ectively ,50. The P/gCDW/h .+/gCDW/h  to 0.209 h-1 (rTCA cycle). However, when we considered a somewhat more realistic proton flux for the PPRs of 20.0 mmol H+/gCDW/h, for the ATP-efficient synthetic PyrS-PyrC-Glx bicycle, a growth rate of 0.057 h-1 seemed feasible. Such a growth rate would be a high growth rate for autotrophs, considering that most natural autotrophic organisms have growth rates below 0.1 h-1. For instance, the photosynthetic model cyanobacterium Synechocystis PCC6803 has a maximum photoautotrophic growth rate of 0.085 h-1 [E. coli may only achieve growth rates in this range if several criteria are met: a low NGAM, a high PPR flux and an ATP-efficient CO2 fixation cycle.Based on this naturally feasible lower range of NGAM values, we repeated our FBA simulations with a lower NGAM of 1.00 mmol ATP/gCWD/h. With the low NGAM, the PPR flux threshold above which growth is feasible decreased to 4.0 mmol H+/gCDW/h . Consequscenario . These m.085 h-1 ,53. HoweHydrogenobacter it is reported to be NADH [E. coli and consequently in our analyses, a native ubiquinol-dependent fumarate reductase could be employed for the rTCA cycle, DC-HB cycle and the synthetic bicycles. The reduction of resulting ubiquinone via an NADH dehydrogenase created an additional export of protons. The majority of available proton motive force resulting from proton efflux, was imported by the ATP synthase to regenerate ATP. The influx through the ATP synthase was even larger than the total efflux of PPRs and ubiquinol-dependent reductases. This gap could be explained by additional proton uptake from the external medium, which we could relate to the formation of water in the carbon fixation pathways  had an additional efflux of protons from ubiquinol recovery . For mos be NADH . Alternapathways .2 uptake flux for all scenarios. The maximum electron uptake varied from 14.4 mmol e-/gCDW/h for the cycles with the slowest predicted growth  to 35.7 mmol e-/gCDW/h for the fastest predicted growth (rTCA cycle) ) to 8.7 mmol CO2/gCDW/h (rTCA cycle)  . AccordiA cycle) . The feaE. coli. In addition, we found considerably varying growth rates for the different carbon fixation pathways. Therefore we analyzed in more detail the ATP consumption per mole biomass formed for every carbon fixation pathway, given the reactions toward biomass precursors that carry flux according to FBA. The FVA showed that there is no variability in the reactions toward biomass precursors that carry flux, which justifies using the results from FBA as a basis for this ATP calculation.By FBA we found that the PPR flux, and consequently the ATP production, was the limiting factor for the photo-electro-autotrophic growth of E. coli this GAM is 59.8 mmol ATP/gCDW, independent of the carbon fixation pathway utilized, whereas the ATP requirement for the biomass precursors is different for each carbon fixation pathway. Therefore, the total ATP requirement varied from 124 mmol ATP/gCDW (rTCA cycle) to 238 mmol ATP/gCDW  (The ATP consumption per mole biomass consists of the ATP needed for the biosynthesis of biomass precursors and the Growth Associated Maintenance (GAM). For n cycle) , S9 Fig.n cycle) . Roughlyn cycle)  which nen cycle) .We determined MDF values for all carbon fixation pathways to determine their thermodynamical favorability. A negative MDF means the pathway is not feasible, and a higher MDF value indicates that a pathway is thermodynamically more favorable (Section 2.2.3 and ). Our anE. coli (pH ~7.5). So thermodynamically these pathways seemed all quite robust for pH deviations around the E. coli optimum.To check how sensitive the thermodynamics were if the conditions differed from the defined standard conditions we varied the pH, as well as important co-factor concentrations and ratios. From this, we observed that for a pH range between 5.0 and 9.0 all pathways were thermodynamically feasible . The DC-red/ferredoxinox ratio got very low, i.e. 0.5  when the ferredoxini.e. 0.5 , in part+ ratios had only a limited effect on thermodynamic favorability, however again the rTCA had the strongest decrease in MDF values when this cofactor ratio dropped , substituting this enzyme by a heterologous variant of using NADPH as electron donor would result in a higher MDF value of around 6 kJ/mol , the PyrS-PEPC-Glx bicycle (1.6%) and the PyrS-PyrC-Glx bicycle (1.9%). For these cycles the extra pathway protein requirement to sustain their higher CO2 fixation flux was compensated by their high PSA values. The other nature cycles had a higher burden up to 2.8% for the DC-4HB cycle.To determine the protein burden of each carbon fixation pathway, we calculated the Pathway Protein Burden (PP)  used in FBA. Assuming this specific activity and 3*10−13 gCDW/cell, one cell would require to have ~500,000 PPRs/cell, while so far only levels up to 100,000/cell have been measured [The expression of proton-pumping rhodopsin systems (PPRs) imposes a significant additional protein burden to the cell. For the PPRs based on literature, a proton-pumping activity of 300 mmol Hfeasible ,36. Assufeasible , and henmeasured . A protemeasured ,so this For estimating the protein burdens of electron uptake mechanisms specific activities of the relevant enzymes are needed. With formate as an electron donor, formate dehydrogenase is needed, for this enzyme some specific activity values are available . For the+-dependent, ferredoxin-dependent and/or bifurcating. For those no reliable specific activities with physiological electron acceptors were measured yet. Therefore, the protein burden for hydrogen uptake systems, including flavin-based bifurcating enzymes, could not be estimated. Also for direct electron uptake mechanisms also very limited kinetic data are known, however before it was assumed that an electron uptake flux of 30.0 mmol e-/gCDW/h is feasible in engineered E. coli [-/gCDW/h that is required for our highest growth scenario (low NGAM with rTCA cycle).For using hydrogen as an electron donor, hydrogenases are required that are NADP E. coli , this isin vitro assays, which not necessarily correspond directly to in vivo conditions, but it was recently demonstrated that in vitro activities generally concur with in vivo activities [2 fixation cycle (e.g. ~4% for synthetic bicycles and rTCA). This still excludes the burden for electron uptake, which may be high as well, but is hard to estimate given limited data. To reduce the added burden of this electron uptake, kinetically fast uptake enzymes have to be found or engineered. In comparison, in the photosynthetic cyanobacterium Synechocystis sp. PCC6803, 26% of the proteome mass is devoted to energy metabolism [The estimates here for protein burdens are all based on kinetic data from tivities . Based otabolism ,60. The E. coli has a limited feasibility. In general, ATP limited the growth rate and the available models cannot predict what the effective ATP maintenance will be for an engineered photo-electro-autotrophic E. coli. Better in silico prediction of NGAM and strategies for minimizing the maintenance burden in engineered cells requires a lot of attention.The analysis of the presented designs suggests that photo-electro-autotrophic growth of in silico) to heterologously express other, more complex (anaerobic) photosystems [Chlorobaculum tepidum[Rhodobacter sphaeroides [For experimentally implementing anaerobic photo-electro-autotrophy the limitation of growth by ATP, related to the PPR proton export flux is a key challenge. It is therefore crucial to find ways to increase the kinetic rates of the proton-pumping rhodopsin photosystems and/or to reach higher numbers of PPRs per cell without surpassing heterologous expression limits in the membrane. In a previous bioenergetic analysis of PPRs in natural photoheterotrophs it was also shown that PPRs generate a relatively low amount of metabolic energy at relatively high protein costs , also coosystems . One couosystems . Alternam tepidum and purpaeroides ,69.Apart from applying more efficient photosystems for ATP regeneration, electron acceptors could be used to allow for oxidative phosphorylation, such as oxygen or nitrate. However, oxygen may inhibit certain electron uptake pathway enzymes and carbon fixation pathway enzymes, but there are indications that for some of the oxygen-sensitive enzymes there are natural, oxygen-tolerant alternatives . This co2 fixation pathways. These results may guide choices for engineering CO2 fixations cycles in autotrophs in general. Given their relatively low ATP costs, especially the rTCA cycle and synthetic PyrS-PyrC-Glx seem to be attractive, resulting in the highest predicted growth for anaerobic photo-electro-autotrophy. In addition to their ATP efficiency, these pathways were found to have a high kinetic activity, resulting in a limited protein burden. The synthetic PyrS-PyrC-glx cycle, may despite its slightly higher ATP consumption and lower growth rates, still be advantageous compared to the rTCA if the less favorable thermodynamics of the rTCA cycle are taken into account.This work also has shown an extensive integrated analysis of several COE. coli requires functional expression of the pathway enzymes. Therefore, we reviewed native presence of all carbon fixation pathway enzymes in E. coli, and if absent, we checked literature for reports of successful heterologous expression in E. coli autotrophy, as E. coli for example may appear to have high ATP maintenance costs. A more detailed analysis of the potential of (photo-electro-)autotrophic designs in other hosts, as performed in this work, can be performed if a core or genome-scale metabolic model is available for that host. Promising alternative chassis organisms could be acetogens or methanogens, some of which are genetically accessible [in silico using the approach described in this paper.Also other hosts than cessible ,71. Acetin silico analysis is an in-depth exploration of the potential of photo-electro-autotrophy to indicate bottlenecks and steer strategic choices before experimental implementation. We envision that such an integration of tools will prove to be very useful in developing and analyzing designs for synthetic autotrophy and other challenging pathway designs in synthetic biology.In summary, the herein described integrated S1 FigReactions are: 1) Triose-phosphate isomerase, 2) Fructose-bisphosphate aldolase, 3) Fructose-bisphosphatase, 4) Transketolase, 5) Sedoheptulose-1,7-bisphosphate/fructose-1,7-bisphosphate aldolase, 6) Sedoheptulose-bisphosphatase, 7) Transketolase, 8) Ribose-5-phosphate isomerase, 9) Ribulose-phosphate 3-epimerase, 10) Phosphoribulokinase, 11) Ribulose-bisphosphate carboxylase, 12) Phosphoglycerate kinase and 13) Glyceraldehyde-3-phosphate dehydrogenase (phosphorylating).(EPS)Click here for additional data file.S2 FigReactions are: 1) Malate dehydrogenase, 2) Fumarate hydratase, 3) fumarate reductase, 4) Succinate-CoA ligase, 5) 2-oxoglutarate:ferredoxin-oxidoreductase synthase, 6) Isocitrate dehydrogenase, 7) Aconitate hydratase and 8) ATP citrate lyase.(EPS)Click here for additional data file.S3 FigReactions are: 1) Acetyl-CoA carboxylase, 2) Malonyl-CoA reductase , 3) Propionyl-CoA synthase , 4) Propionyl-CoA carboxylase, 5) Methylmalonyl-CoA epimerase, 6) Methylmalonyl-CoA mutase, 7) Succinyl-CoA reductase, 8) Succinate-semialdehyde reductase, 9) 4-hydroxybutyryl-CoA synthetase, 10) 4-hydroxybutyryl-CoA dehydratase, 11) 3-hydroxyacyl-CoA dehydratase (3-hydroxybutanoyl-CoA), 12) 3-hydroxyacyl-CoA dehydrogenase (acetoacetyl-CoA) and 13) Acetyl-CoA C-acetyltransferase.(EPS)Click here for additional data file.S4 FigReactions are: 1) Pyruvate synthase, 2) Pyruvate water dikinase, 3) Phosphoenolpyruvate carboxylase, 4) Malate dehydrogenase, 5) Fumarate hydratase, 6) fumarate reductase, 7) Succinate-CoA ligase, 8) Succinyl-CoA reductase, 9) Succinate-semialdehyde reductase, 10) 4-hydroxybutyryl-CoA synthetase, 11) 4-hydroxybutyryl-CoA dehydratase, 12) 3-hydroxyacyl-CoA dehydratase (3-hydroxybutanoyl-CoA), 13) 3-hydroxyacyl-CoA dehydrogenase (acetoacetyl-CoA) and 14) Acetyl-CoA C-acetyltransferase.(EPS)Click here for additional data file.S5 Fig+/gCDW/h.Predicted by FBA for different carbon fixation pathways with a NGAM of 8.39 mmol ATP/gCDW/h. PPR proton flux was varied between 0 and 50 mmol H(TIF)Click here for additional data file.S6 Fig+/gCDW/h.Predicted by FBA for different carbon fixation pathways with a NGAM of 1.00 mmol ATP/gCDW/h. PPR proton flux was varied between 0 and 50 mmol H(TIF)Click here for additional data file.S7 Fig+/gCDW/h and a NGAM of 1.00 mmol ATP/gCDW/hMajor proton fluxes in and out of the cell during at a proton-pumping flux of 50 mmol H(TIF)Click here for additional data file.S8 Fig+/gCDW/h.Proton pumping was varied between 0 and 50 mmol H(TIF)Click here for additional data file.S9 Figα-D-ribose-5-phosphate (r5p), glyceraldehyde-3-phosphate (g3p), 3-phospho-D-glycerate (3pg), phosphoenolpyruvate (pep), pyruvate (pyr), acetyl-CoA (accoa), oxaloacetate (oaa), L-glutamate (glu_L) and L-glutamine (gln_L).Biomass precursors are: D-glucose-6-phosphate (g6p), D-fructose-6-phosphate (f6p), D-erythrose-4-phosphate (e4p), (TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S11 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: glyceraldehyde-3-phosphate dehydrogenase (GAPD), isocitrate dehydrogenase (ICDHyr), 2-oxoglutarate:ferredoxin-oxidoreductase synthase (OGOR), succinyl-CoA synthetase (SUCOAS), pyruvate synthase (PYRS), (S)-malyl-CoA lyase (MCL), 3-hydroxyacyl-CoA dehydrogenase (HACD1), 3-hydroxyacyl-CoA dehydratase (ECOAH1).(TIF)Click here for additional data file.S12 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: glyceraldehyde-3-phosphate dehydrogenase (GAPD), isocitrate dehydrogenase (ICDHyr), succinyl-CoA synthetase (SUCOAS), pyruvate synthase (PYRS), 3-hydroxyacyl-CoA dehydrogenase (HACD1), acetyl-CoA C-acetyltransferase (ACACT1r).(TIF)Click here for additional data file.S13 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: glyceraldehyde-3-phosphate dehydrogenase (GAPD), ribose-5-phosphate isomerase (RPI), transketolase (TKT2), isocitrate dehydrogenase (ICDHyr), pyruvate synthase (PYRS), pyruvate carboxylase (PYRC), ATP citrate lyase (ACL), 3-hydroxyacyl-CoA dehydrogenase (HACD1), 3-hydroxyacyl-CoA dehydratase (ECOAH1).(TIF)Click here for additional data file.S14 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: ribose-5-phosphate isomerase (RPI), glyceraldehyde-3-phosphate dehydrogenase (GAPD), succinyl-CoA synthetase (SUCOAS), isocitrate dehydrogenase (ICDHyr), malate dehydrogenase (MDH), pyruvate synthase (PYRS), malyl-CoA synthetase (MTK), 3-hydroxyacyl-CoA dehydrogenase (HACD1), 4-hydroxybutyryl-CoA dehydratase (HB4COADH).(TIF)Click here for additional data file.S15 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: glyceraldehyde-3-phosphate dehydrogenase (GAPD), ribulose-5-phosphate 3-epimerase (RPE), isocitrate dehydrogenase (ICDHyr), pyruvate synthase (PYRS), malate dehydrogenase (MDH), citrate hydrolyase (CITHL), 3-hydroxyacyl-CoA dehydrogenase (HACD1), 3-hydroxyacyl-CoA dehydratase (ECOAH1).(TIF)Click here for additional data file.S16 FigBar labels indicate which reaction results in this MDF values, so which reaction forms the thermodynamic bottleneck. Bottleneck reactions are: glyceraldehyde-3-phosphate dehydrogenase (GAPD), isocitrate dehydrogenase (ICDHyr), malate dehydrogenase (MDH), (S)-malyl-CoA lyase (MCL), pyruvate synthase (PYRS), 3-hydroxyacyl-CoA dehydratase (ECOAH1), 3-hydroxyacyl-CoA dehydrogenase (HACD1).(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.


Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications. Lactobacillus casei are Gram positive, non-sporulating, nutritionally fastidious and strictly fermentative bacteria that produce lactic acid as their principal metabolic end product L. casei strains are used in the manufacture of fermented foods, as probiotics, and has significant potential for the production of biofuels and co-products with a broad range of applications such as food ingredients L. casei in all of these applications is strain dependent, due to significant variation in gene content that is present in this species. Comparative genome analysis of seventeen L. casei strains has revealed this species has an average of 2,800 orthologous genes (±151), of which 1,715 are shared by all the strains, and 4,220 non-conserved orthologous genes with an average of 119 unique genes found in each strain Genome-scale metabolic models can serve as tools for evaluating the metabolic potential of strains based on genomic data. These models are becoming a common tool for linking genomic data to the biochemical reaction networks that govern cellular processes in silico models are available for four species of lactic acid bacteria (LAB): Lactococcus lactisLactobacillus plantarumStreptococcus thermophilusLactobacillus reuteriin silico minimal medium and to improve diacetyl production Lactococcus lactis MG1363 was developed; this model couples the carbon and nitrogen metabolism of the strain with catabolic pathways that lead to flavor formation in cheese L. plantarum was successfully used to evaluate nutrient requirements of the WCFS1 strain S. thermophilusS. thermophilus and other LAB. The most recent LAB genome-scale metabolic models have been developed for three strains of Lactobacillus reuteri, the model strain JCM1112, and two human-derived strains, 6475 and 55730; these models were utilized to identify probiotic features of these strains L. casei strains would enhance current understanding of how strain specific characteristics influence the utility of these species in various industrial applications.To date, L. casei: L. casei ATCC 334, a cheese isolate, and 12A a corn silage isolate. A draft genome sequence for 12A was available (GenBank accession number AFYJ00000000) and this genome was closed for the purposes of this study. These models were then utilized to conduct a detailed comparison of these two strains.In this study, genome-scale metabolic models were constructed for two fully sequenced strains of L. casei 12A was prepared using the DNA Isolation Bacterial CTAB protocol as requested by the Department of Energy (DOE) Joint Genome Institute (JGI). This protocol can be found at http://my.jgi.doe.gov/general/protocols.html. Genomic DNA quality was confirmed by agarose gel electrophoresis and was submitted to the Department of Energy Joint Genome Institute for genome sequencing, where Ilumina HiSeq 2000 technology was used. JGI delivered a draft genome containing 397 DNA Scaffolds. This draft genome was assembled in collaboration with DuPont Inc., utilizing a previously available 12A draft genome as a scaffold L. casei ATCC 334 as a scaffold Genomic DNA of L. casei ATCC 334 (GenBank accession number NC_008526 and NC_008502) http://www.ncbi.nlm.nih.gov) and at the RAST Server (Rapid Annotations using Subsystems Technology) (http://rast.nmpdr.org/). The genome sequence for L. casei 12A was imported into the RAST server (http://rast.nmpdr.org/) for gene calling and annotation with subsequent manual inspection and curation. This information was used in the metabolic reconstruction and validation processes, as described below.The complete genome sequence and annotation of L. casei metabolic networks. Genome-scale metabolic draft models for both L. casei strains were generated in 2011 using the RAST server L. casei metabolic models. FBA simulations were performed by maximizing biomass, with an in-silico growth medium containing nutrients in the chemically defined medium (CDM) L. casei 12A simulations. We first maximized growth using just the essential amino acids (without a carbohydrate present). We then introduced each carbohydrate to the in silico media. An increase in maximum growth rate in the presence of a particular carbohydrate indicated the ability of the model to utilize the carbohydrate as a carbon source.Flux balance analysis (FBA) identifies a steady-state metabolic flux distribution that maximizes a stated objective, such as cellular growth rate or product formation Working cultures were prepared from frozen stocks by two sequential transfers in de Man, Rogosa and Sharpe (MRS) medium and CDM broth prepared as described by Christensen and Steele 1- µ2, subject to common gene deletions).We used CONGA to identify functional differences between the two metabolic networks L. casei 12A genome consists of a circular chromosome of 2,907,945 bp. The general features of the 12A genome are presented in L. casei ATCC 334, the 12A genome is 12,681 bp larger. The number of pseudogenes in 12A is 152, a relatively high number compared to the 82 in L. casei ATCC 334, which could indicate that L. casei 12A has undergone more extensive gene decay. Comparative genome analysis between the strains indicates that they share 2,222 orthologous genes; L. casei 12A has 507 unique genes compared to L. casei ATCC334, whereas L. casei ATCC334 has 423 unique genes compared to L. casei 12A. The complete genome sequence of 12A was deposited at GenBank with the accession number CP006690.The closed L. casei ATCC 334 and 12A each contain an extracellular and cytoplasm compartment, and exchange and transport reactions allow metabolites to be imported into or secreted from these compartments. The model for L. casei ATCC 334 (iLca334_548) consists of 548 genes, 905 metabolic reactions and 959 metabolites. The model for L. casei 12A (iLca12A_640) consists of 640 genes, 932 metabolic reactions and 979 metabolites. The properties of the models are summarized in The genome-scale metabolic models of L. casei strains showed the strains have similar amino acids requirements, where the branched-chain amino acids , the aromatic amino acids , and arginine are essential for both strains. However, glutamate is essential for L. casei 12A growth, while in ATCC 334 the absence of glutamate only caused a growth defect.Single amino acid omission experiments in both in vivo results; the draft model incorrectly predicted tryptophan to be non-essential  while cysteine and methionine were incorrectly predicted to be essential . The draft 12A model predictions agreed with the in vivo data for 16 of the 20 simulations. The inconsistencies for tryptophan, methionine and cysteine observed with the ATCC 334 model were also observed with the 12A model. In addition, the 12A model predicted that glutamate was non-essential, while the in vivo data indicated it was essential. SMILEY L. casei ATCC 334 model to fix the model's requirement for methionine. While correcting the methionine requirement in the L. casei 12A model involved adding methionine synthase (E.C. 2.1.1.13) and O-succinylhomoserine sulfhydrylase (E.C. 4.2.99.9). To correct the discrepancy between the model predictions and the in vivo data requirements for tryptophan, some reactions that were initially gap filled by Model SEED  annotated as glucosamine-fructose-6-phosphate aminotransferase (E.C. 2.6.1.16), which converts glutamine into glutamate. After making these corrections the L. casei ATCC 334 model was able to correctly predict the amino acid requirements and the L. casei 12A model was able to correctly predict 19 out of the 20 amino acid requirements  relationships differ in the models; orthology differences, in which genes encoding enzymes with identical functions were not assigned as orthologs; and metabolic differences, where one organism has additional reactions that grant it unique capabilities CONGA identifies gene deletion sets, sets of one or more genes whose deletion are predicted to be lethal by only one of two models. Gene deletion sets are classified based on the reason for differences in growth predictions: iLca334_548  and five gene deletion sets that were lethal only in iLca12A_640. For the second condition , two gene deletion sets were lethal only to iLca334_548; and four gene deletion sets were lethal only to iLca12A_640. These deletion sets were a subset of those found in the first condition . CONGA results are summarized in The CONGA results were insensitive to carbon source . Therefore, the results are summarized based on amino acid availability. For the first condition , CONGA found five gene deletion sets that were lethal only in iLca334_548. This highlights the presence of more isozymes in iLca12A_640, suggesting it is more robust to gene deletions than iLca334_548.Four of the ten unique gene deletions sets identified by CONGA were attributed to genetic differences, which arise due to differences in the number of isozymes between the two models for essential reactions. Three of these four gene deletion sets were lethal in 2-THF) dehydrogenase (E.C. 1.5.1.5) is lethal only in the iLca334_548 model. This means that the iLca12A_640 model has a unique mechanism (shown in 2-THF is a precursor to 10-formyltetrahydrofolate (10-CHO-THF) a cofactor involved in purine biosynthesis, an essential activity for cellular growth. The deletion of 5,10-CH2-THF dehydrogenase prevents the biosynthesis of THF in L. casei ATCC 334. Our model for L. casei 12A predicts that this deletion can be rescued by the actions of 5,10-CH2-THF:3-methyl-2-oxobutanoate (E.C. 2.1.2.11) and formate: THF ligase (E.C. 6.3.4.3), in which 5,10-CH2-THF is converted directly to THF, producing 2-dehydropantoate as a by-product. The reactions pantoate 2-oxidoreductase (E.C. 1.1.1.169) and pantothenate amidohydrolase (E.C. 3.5.1.22) convert 2-dehydropantoate to pantothenate, which can be secreted by a transporter unique to the iLca12A_640 model, or some pantothenate can also be used for CoA biosynthesis, important for biomass formation.Using CONGA we also identified one metabolic difference, in which deletion of the enzyme 5, 10-methylenetetrahydrofolate . On the other hand, our strains have considerably more requirements than Lc. lactis and S. thermophilus  biosynthesis pathways in L. casei strains. In the biosynthesis pathway of tryptophan, the enzyme anthranilate synthase (E.C. 4.1.3.27) was not found in the genome annotation of L. casei ATCC 334 strain; presumably this reaction was added to the draft metabolic models as a mean to complete the pathway. Experimental data indicating tryptophan must be supplied support the idea that this pathway is incomplete. In L. casei 12A the reaction catalyzed by tryptophan synthase (E.C. 4.2.1.20) was found to confer the model, the ability to synthesize tryptophan, therefore it was removed base on experimental data. Since tryptophan was required for growth by both strains, a reaction was deleted from both metabolic models. The requirement for phenylalanine and tyrosine for growth was expected, as an enzyme needed for their biosynthesis, prephenate dehydratase, was not found in the genome annotation of either strain.Aromatic amino acids  had incomplete biosynthesis pathways in the two L. casei strains are reported to require arginine for growth L. casei ATCC 334 and no growth for L. casei 12A. This has been well documented in other LAB, due to an incomplete citric acid cycle and limited supply of α-ketoglutarate iLca12A_640, glutamate is predicted to be an essential amino acid, however, this deletion would also make glutamine a predicted essential amino acid, but glutamine is not required experimentally, therefore the reaction was not removed. Experimental studies suggest that the interconversion of glutamine to glutamate results in low yields of glutamate The omission of glutamate in the medium caused a severe defect in growth for L. casei models highlights the presence of additional enzymes that gives the L. casei 12A model the ability to utilize D-rafinnose, panose and pullulan. For instance, the 12A model possesses an ABC transporter predicted to function for raffinose uptake. This model also includes a pullulanase and a neopullulanase, important in pullulan and panose degradation into maltotriose and maltose and glucose, respectively. As discussed by Broadbent et al. (2012), the ability of strains to utilize different carbohydrates predicts the adaptation of the strains to diverse ecological niches; D-rafinnose, panose and pullulan are components commonly found in plant material, the environment from which L. casei 12A was isolated. On the other hand, as observed in growth experiments and confirmed by the metabolic models, L. casei ATCC 334, can utilize fewer of the carbohydrates tested (see Comparison of the two sted see . This comyo-inositol (MI), a component of phytic acid, which can serve as a phosphate storage molecule in plant seeds et al. (2007) reported the identification of a gene cluster which enables L. casei BL23 to utilize myo-inositol under aerobic conditions. The catabolic pathway of MI is absent in L. casei ATCC 334, and experimental studies and prediction by the metabolic models confirmed no growth on MI. Surprisingly, the genome-scale metabolic model for L. casei 12A predicted growth using MI as a carbon source, implying the catabolic pathway for MI is complete in this strain. Further analysis in the genome annotation of L. casei 12A reveals the presence of all the enzymes  needed for converting MI to glyceraldehyde-3P. The environment from which the 12A strain was isolated could have played a role in the retention of the pathway. The reason why L. casei 12A was unable to utilize MI is speculated to be due to regulatory effects that genome-scale metabolic models do not capture.One of the most abundant stereoisomers of inositol is iLca12A_640 for pantothenate was found to confer the model with the ability to overcome the deletion of 5,10-CH2-THF dehydrogenase. This transporter prevents the accumulation of pantothenate inside the cell, therefore allowing the reactions, catalyzed by the enzymes E.C. 2.1.2.11 and 6.3.4.3 to be active, yielding to the synthesis of 10-CHO-THF.A unique transporter (pantothenate sodium symporter) in iLca12A_640 (added by Model SEED) lacks a gene association, however further analysis of L. casei 12A genome annotation reveals the presence of a pantothenate energy-coupling factor (ECF) transporter (GI 410523916). An ortholog of this gene was found in L. casei ATCC 334, which is annotated as a predicted membrane protein which may be the reason it was missed during the automated reconstruction of the model. This provides an example of how CONGA can be used to aide in further model development.The pantothenate transporter in CONGA simulations performed under the glucose and galactose conditions, both six carbon carbohydrates, identified the same differences between the models. Simulations with ribose where also conducted (data not shown), but no new gene deletions sets were found. Even though we were able to identify genetic, orthology and metabolic differences between the models, it appears that pathways in catabolism of these three sugars and central metabolism are shared by the strains.L. casei strains provide a comprehensive overview of the metabolism of the strains. These models are valuable tools for the development of strains with enhanced utility in a variety of industrial applications (e.g. food ingredients), as well as in the design of new metabolic engineering approaches in the production of commodity chemicals such as biofuels.The genome-scale metabolic models of two Table S1Comparison of essential amino acids (listed as E) determined from experiments among lactic acid bacteria, for which metabolic models have been built.(DOCX)Click here for additional data file.Table S2Compounds allowed to be taken up during flux balance analysis in both L. casei models. Flux represents uptake rate, units are mmol/gDW/h.(DOCX)Click here for additional data file.Table S3Type of gene deletion sets, with glucose or galactose with essential amino acids condition, found in iLca12A_640 and iLca334_548.(DOCX)Click here for additional data file.Table S4Type of gene deletion sets, with glucose or galactose and all amino acids, found in iLca12A_640 and iLca334_548.(DOCX)Click here for additional data file.Table S5Complete list of reactions and compounds present in GapfillMatrix.(XLSX)Click here for additional data file.Table S6Complete list of reactions and compounds present in iLca12A_640.(XLSX)Click here for additional data file.Table S7Complete list of reactions and compounds present in iLca334_548.(XLSX)Click here for additional data file.Table S8Enzymes and transporters added to L. casei ATCC 334 and 12A models to fix false negative predictions. Addition of an enzyme/transporter is indicated with filled circles; open circles indicate no addition of enzyme/transporter.(DOCX)Click here for additional data file.Table S9GapfillMatrix in SBML format.(XML)Click here for additional data file.Table S10iLca12A_640 in SBML format.(XML)Click here for additional data file.Table S11iLca334_548 in SBML format.(XML)Click here for additional data file.