Comparing the metabolic pathways of different species is useful for understanding metabolic functions and can help in studying diseases and engineering drugs. Several comparison techniques for metabolic pathways have been introduced in the literature as a first attempt in this direction. The approaches are based on some simplified representation of metabolic pathways and on a related definition of a similarity score (or distance measure) between two pathways. More recent comparative research focuses on alignment techniques that can identify similar parts between pathways.We propose a methodology for the pairwise comparison and alignment of metabolic pathways that aims at providing the largest conserved substructure of the pathways under consideration. The proposed methodology has been implemented in a tool called MP-Align, which has been used to perform several validation tests. The results showed that our similarity score makes it possible to discriminate between different domains and to reconstruct a meaningful phylogeny from metabolic data. The results further demonstrate that our alignment algorithm correctly identifies subpathways sharing a common biological function.The results of the validation tests performed with MP-Align are encouraging. A comparison with another proposal in the literature showed that our alignment algorithm is particularly well-suited to finding the largest conserved subpathway of the pathways under examination. Metabolism is the chemical system that generates the essential components for life. All living (micro)organisms possess an intricate network of metabolic routes for the biosynthesis of amino acids, nucleic acids, lipids and carbohydrates and for the catabolism of different compounds driving cellular processes. Subsystems of metabolism dealing with specific functions are called metabolic pathways. Over the last ten years these pathways have been the subject of a great deal of research, conducted primarily through two kinds of studies: one focusing on the analysis of single pathways, the other on the comparative analysis of a set of pathways.The studies that compare metabolic pathways of different species can provide interesting information on their evolution and may help in understanding metabolic functions, which are important in studying diseases and identifying pharmacological targets. In the literature many techniques have been proposed for comparing metabolic pathways of different organisms. Each approach chooses a representation of metabolic pathways that models the information of interest, proposes a similarity or a distance measure and possibly supplies a tool for performing the comparison. The automatization of the whole process is enabled by the knowledge stored in metabolic databases such as KEGG , BioModeMore recent comparative research has proceeded by focusing on alignment techniques that can identify similar parts between pathways, providing further insight for drug target identification ,5, meaniMP-Align, which has been used to perform several validation tests reported herein.We propose a methodology for the pairwise comparison and alignment of metabolic pathways that aims at providing the largest conserved substructure of the pathways under consideration. The methodology relies on a hypergraph representation of metabolic pathways and defines a reaction similarity score that takes into account the chemical similarity and homology between pairs of reactions. The alignment technique uses the reaction similarity score and the pathway topology to identify the largest conserved subpathway between the two given pathways. The proposed methodology has been implemented in a tool called MP-Align.This section describes the methodology proposed for the pairwise comparison and alignment of metabolic pathways. We represent metabolic pathways as directed hypergraphs and define a reaction similarity score based on both compound and enzyme similarities. On the basis of these choices we define the alignment algorithm, which has been implemented in directed hypergraph is a mathematical structure H= where V is a finite set of nodes and E is a set of directed hyperedges. A directed hyperedge is an ordered pair of subsets of nodes E=; X is the set of input nodes of E while Y is its set of output nodes.A Metabolic pathways can be easily represented as directed hypergraphs: metabolites, enzymes and compounds can be modeled as nodes and reactions as hyperedges. Despite the simplicity of this representation, we made the modeling choices described below. H2O, phosphate, ADP and ATP as hypergraph nodes. The same is true for enzymes, which are represented as reaction attributes and used to compute the reaction similarity.− We do not represent ubiquitous substances, such as − Most of the reactions in metabolic pathways are reversible. A reversible reaction can occur in two directions, from the reactants to the products (forward reaction) or vice versa (backward reaction). The direction depends on the kind of reaction, on the concentration of the metabolites, and on conditions such as temperature and pressure. We model reversible reactions by two corresponding hyperedges, one for the forward reaction and the other for the backward reaction.source) or output only (sinks) nodes.− In a metabolic pathway one can distinguish between internal and external metabolites. The former are entirely produced and consumed in the network; the latter represent sources or sinks, that is, connection points produced or consumed by other pathways. We represent external metabolites as input only  denote a hyperedge representing a reaction, where Ii is the set of its input nodes (substrates), Ei the enzyme that catalyzes the reaction and Oi the set of its output nodes (products). The similarity score for every pair of reactions Ri= and Rj= is given by the following formula , i≠j, Ri and Rj are different reactions;− for each i∈ , some of the output nodes of Ri are input nodes of Ri+1;− for each k of the path p is maximal.− the length H. It is obtained through an in-depth search algorithm iterating over the source nodes of H.We denote by H1 and H2, respectively.This step results in the sets H1 and H2 in terms of their sets of reaction paths R in the path p and a reaction R′ in the path p′ is given by SimReact. The score of the obtained sequence alignment is denoted by scorePath.The second step establishes a first correspondence between lgorithm , where tH1 and H2 by defining a matching p and p′ is the score scorePath of their sequence alignment obtained in the previous step. The matching σ is the result of the maximum weighted bipartite matching algorithm applied to the complete bipartite graph.The third step refines the correspondence between M on a bipartite graph is a subset of edges such that no two edges in M share an endpoint. The cost of M is the sum of the cost of its edges. A matching is called a maximum weight matching if its cost is at least as great as the cost of any other matching.Recall that a matching Arginine and proline metabolism for the organisms Homo Sapiens (hsa00330) and Methanocaldococcus Jannaschii (mja00330). Once they have been represented as hypergraphs, the matching between their reaction paths and the corresponding score can be computed, as shown in Table Consider, for example, the KEGG pathway σ into a well-defined matching between reactions in H1 and reactions in H2. This is done by analyzing the alignments of all pairs of reaction paths ∈σ and by building a corresponding match-frequency matrixM whose rows and columns represent the reactions (hyperedges) of H1 and H2, respectively. Each entry mi,j of the matrix M counts the number of times that the reaction Ri in H1 is aligned to the reaction Rj in H2 in all pairs of reaction paths ∈σ.The fourth step translates the reaction path matching Ri appears in k reaction paths in Ri is aligned to Rjk′ times (with k′≤k) in the corresponding paths of σ). In this case, the match-frequency matrix records the value mi,j=k′.Suppose, for example, that reaction M has been determined, the best match between reactions is sought, taking care to associate each reaction in H1 with exactly one reaction in H2. This is done by employing, once again, the maximum weighted bipartite matching algorithm: given the frequency matrix M as input, it produces a matching ρ⊆E1×E2 as output, which provides the final reaction matching between H1 and H2.Once the matrix ρ) and sums the score of the ‘most similar’ paths they belong to (determined by σ), thus taking into account the topology of the two given pathways. Formally, the similarity score of H1 and H2 is defined as follows: The fifth and last step of the algorithm determines the similarity score and the alignment of the two given hypergraphs. Intuitively, the similarity score considers all pairs of their ‘most similar’ reactions (determined by  where H1 and H2 is defined in terms of their largest conserved substructure (sub-hypegraph). More precisely, the alignment of H1 and H2 is determined by using the reaction matching ρ to build a relational graph G as follows: The final alignment of H1− the nodes of G are the reactions in Ri,Rj), with Ri, Rj reactions in H1, is introduced in G if and only if − an edge  are input nodes of ρ(Rj), i.e. their images through ρ are also connected hyperedges in H2.− some output nodes of ρ. The largest connected subgraph in the relational graph G corresponds to the largest conserved substructure (subpathway) between H1 and H2 through ρ and defines the final alignment of the two hypergraphs.Intuitively, the relational graph G expresses the connections between the reactions matched by Arginine and proline metabolism for H. Sapiens (hsa00330) and M. Jannaschii (mja00330). The final alignment obtained by MP-Align is shown in Table Urea Cycle (highlighted in boldface).Let’s consider once again the hypergraphs corresponding to the KEGG pathway MP-Align algorithm is exponential in the size of the two input hypergraphs. This is already true in its first step, the Reaction path computation. Nevertheless, in our experience, MP-Align works fine on the hypergraphs representing metabolic pathways. To give an idea of the MP-Align efficiency, we report its execution times for the phylogeny recovery test illustrated in the next Section. It is a complex test that compares all the common pathways of eight selected organisms: there are 40 common pathways and there are 1440 pairwise comparisons and alignments to be performed; that is, MP-Align is executed 1440 times. We used a server with 16 processors at 2500 MHz and 24 GB of RAM memory. Since MP-Align is sequentially implemented, each pairwise comparison was performed by one processor. For this test, 30% of the pairwise comparisons and alignments were executed in 0.6 seconds at most; 60% were executed in 1.23 seconds at most; 90% were executed in 5.61 seconds at most and the 100% were executed in 4570.88 seconds at most. More precisely, only four pairwise comparisons and alignments were performed in more than one hour.The complexity of the MP-Align to validate our similarity score and alignment algorithm. The statistical analysis was done using the R[This section reports the tests performed with ing the R basic paThe first group of experiments employed cluster analysis methods to assess whether our similarity score and alignment algorithm could use metabolic information to provide organism classifications that are correct from the evolutionary point of view. The second group of experiments sought to validate the recognition and alignment of pairs of pathways that are known to contain functionally similar subunits but have different reaction sets and topologies.The first test of a similarity score between objects is typically cluster analysis, in which biological data objects are partitioned into groups such that the objects in each group share common traits.Glycolysis pathway of all organisms in the KEGG database, which currently contains 1758 organisms: 52 Animals, 118 Archaea, 1491 Bacteria, 53 Fungi, 18 Plants and 51 Protists. We used MP-Align to compute the similarity score of all pairs of organisms and then converted the similarity score into the following distance measure: The first test considered the Glycolysis universe of the two-dimensional MDS. This could be due to the fact that there are considerably more Bacteria than other organisms, and a higher dimensional representation is required to discriminate between them and the other domains.The results were visualized and analyzed using a classical multidimensional scaling (MDS) method. We represented the considered pathways as points in a two-dimensional space: the more distant the points in space, the less similar the corresponding pathways with respect to the considered distance. The results are shown on the left side of Figure Glycolysis pathways are identical for different organisms, we selected one representative from each group of organisms with an identical pathway. Table Glycolysis pathways. Note that the various groups are homogeneous w.r.t. the classification into Bacteria, Archaea, Protists, Fungi, Plants and Animals, up to one group comprising Arthropods and Plants. We ended up with 160 different Glycolysis pathways. The results of this test are shown on the right side of Figure The test was repeated with all the previous domains except the Bacteria. Moreover, after noting that some of the KEGG This test combined hierarchical clustering and pathway alignment. The idea was first to compare a set of pathways using our similarity score and produce a hierarchical clustering, and then to use our alignment algorithm to look for the largest conserved motifs in each cluster. The latter was done by computing the pairwise alignments of the pathways in each cluster (in a predetermined order) and by considering their common set of aligned reactions, that is, the intersection of their largest common motif. The overall goal was to explore whether the alignment technique could help in validating, or detecting the flaws of, the clustering results. Consider, for instance, two organisms having an identical pathway that forms a connected hypergraph. Now suppose that a reaction is removed from one of the pathways disconnecting its hypergraph. In this case, the similarity score considers the two organisms very close together, while their largest common motif reveals their structural difference. In fact, the comparison of two given pathways is based on their underlying sets of reactions and ignores their structure. A subsequent alignment phase includes structural information as well.Glycolysis pathway of Animals. In KEGG there are currently 53 distinct Animals having 25 distinct Glycolysis pathways. Table Glycolysis pathway in each row. Here as well, we took just one representative from each group of Animals. We performed the hierarchical clustering using Ward’s method [We focused on the s method  as well Glycolysis pathway. The label of each leaf reports the classification of the organism, the number of represented organisms (within parenthesis), the organism name (in KEGG nomenclature), the cluster number, and the size of the common motif in the cluster (in terms of the number of reactions). For singleton clusters, the latter is just the number of reactions in the largest connected component of the organism itself.The dendrogram can be cut at different heights to obtain different partitions of the 25 pathways. We considered the cuts producing a total number of clusters ranging from 3 to 20, resulting in 18 different partitions. This allowed us to observe how the clusters evolve by incrementing their total number. For each partition, we looked for the conserved motifs in each cluster using the procedure described above, and we observed how the common motifs evolve as the number of clusters increases. In Figure Glycolysis pathway of these organisms reveals that the Aedes Aegypty (aag) Glycolysis pathway is disconnected, so it can hardly share a common motif with the other organisms. When considering the 19 final clusters in the bottom right dendrogram, Aedes Aegypty forms a singleton cluster, and the other organisms are divided into two clusters, both having quite significative conserved motifs. The structural difference of the Aedes Aegypty Glycolysis pathway, invisible to the similarity score, could be revealed by the alignment phase.One can note how the clusters evolve by incrementing their total number, and how the common motifs for each cluster become more and more significative. In particular, Vertebrates are separated from all other Animals from the very start, and their alignment confirms that they form a very cohesive cluster. In fact, in the top left and right dendrograms, the Vertebrates cluster has a common motif composed of 31 reactions. In the bottom left and right dendrograms this cluster is refined into two different clusters, with a common motif of size 48 and 50, respectively. In the top left dendrogram none of the other clusters share a common motif. This means that there are structural differences among the pathways in each cluster that could not be captured by the similarity score. In the top right dendrogram only cluster number 2 lacks a common motif. This remains true for cluster number 3 (composed of the same organisms) in the bottom left dendrogram. A closer look at the Glycolysis pathway is disconnected are Tribolium Castaneum (tca), Apis Mellifera (ame) and Trichinella Spiralis (tsp). Notice in the dendrograms that, as soon as these organisms are isolated (by increasing the number of clusters), the conserved motifs in the newly formed clusters can evolve.Other organisms whose Glycolysis pathway. Figure For the sake of completeness we repeated the same test without including the organisms with a disconnected A. Fulgidus (afu), C. Perfringens (cpe), H. Influenzae (hin), L. Innocua (lin), M. Jannaschii (mja), M. Musculus (mmu), N. Meningitidis (nme) and R. Norvegicus (rno). They belong to Bacteria , Archaea  and Animals .One of the questions that arises when comparing metabolic pathways is whether it is possible to reconstruct robust phylogenetic trees from non-genomic data such as metabolic pathways. In  the authWe repeated the same experiment using our similarity score. We performed the pairwise comparison of all organisms for each common pathway and combined the obtained scores as follows.k common pathways, we used the average score For any pair of organisms with and the following distance measure MP-Align. The only cluster that is not present in the phylogenetic tree is {cpe, lin}.The average score is suitable in this case because it makes it possible to capture comprehensive information from the comparison among all common pathways of the given organisms. Once all the distance measures between organisms were obtained, we made a hierarchical clustering using the single, average and complete linkage methods. The three methods produced exactly the same clustering, thereby confirming the robustness of the average score employed. The result is reported on the right of Figure cpe,lin} and {hin, nme}, as in the NCBI taxonomy.We repeated the test considering just one pathway, the Glycolysis pathway, and also considering randomly chosen subsets of 10, 20 and 30 pathways. The resulting phylogenetic trees are shown in Figure MP-Align can reconstruct robust phylogenies that are meaningful and very close to the NCBI reference taxonomy.Therefore, we can conclude that the similarity score provided by patchwork evolution model is the Urea Cycle, which, in terrestrial animals, clearly evolved by adding a new enzyme, Arginase, to a set of four enzymes previously involved in the biosynthesis of Arginine[Arginine and proline metabolism for Homo Sapiens (hsa), Anolis Carolinensis (acs), and M. Jannaschii (mja) and performed their alignment using MP-Align. Since M. Jannaschii belongs to the Archaea domain, the Arginase enzyme is not present in its pathway and urea is not synthesized. Instead, the reptile A. Carolinensis and the mammal H. Sapiens share part of the Urea Cycle. As a result, we learned that MP-Align can recognize the identical parts of the Urea Cycle when comparing H. Sapiens and A. Carolinensis and finds a mismatch when comparing H. Sapiens and M. Jannaschii. Table MP-Align when considering the Arginine and proline metabolism for H. Sapiens and M. Jannaschii. Note that the highest score is about 0.836, which corresponds to the reaction path alignment starting at N-Acetyl-L-citrulline for M. Jannaschii and at N-Acetylornithine for H. Sapiens and both ending at Spermine, where the β-Alanine metabolism or the Glutathione metabolism is reached. Thus, in its first step, MP-Align is able to recognize and align the longest path that both organisms share. Moreover, Table MP-Align when reconsidering the Arginine and proline metabolism for H. Sapiens, A. Carolinensis and M. Jannaschii. The reactions that appear in the Urea Cycle are shown in boldface. The reaction catalyzed by the Arginase enzyme, R00551 in KEGG nomenclature, only appears when considering H. Sapiens and A. Carolinensis  are conserved, and the whole cycle is correctly aligned. Table H. Sapiens and M. Jannaschii: note that reaction R00551 is not aligned and, consequently, the Urea Cycle is not a common conserved subpathway.Several tests were performed to evaluate our alignment tool, some of which were taken from . As expl Arginine. TherefoMP-Align, an attempt was made to compare it to the SubMAP alignment tool [a. This comparison was limited by the fact that the SubMAP utility required to translate KEGG pathways into the SubMAP input formalism is no longer available. Our analysis had to rely on previously translated pathways, namely the Arginine and proline metabolism pathway for H. Sapiens, S. Cerevisiae and C. Elegans.To complete the validation of ent tool a. This cUrea Cycle of the selected organisms, we observed that H. Sapiens and S. Cerevisiae share the same Urea Cycle, while urea is not synthesized in C. Elegans. We performed the alignment between H. Sapiens and S. Cerevisiae and H. Sapiens and C. Elegans using both MP-Align and SubMAP. As shown in Table Urea Cycle (highlighted in boldface) in H. Sapiens and S. Cerevisiae. However, when considering the complete reaction matching done by both tools, it is clear that they perform quite differently: the MP-Align reaction matching appears to be more thorough.Focusing once again on the H. Sapiens and C. Elegans the two tools performs differently. As evident in Table R00565  with reaction R00554 , although the two reactions belong to different parts of the pathways. The wrong match is highlighted in boldface. In the matching provided by MP-Align, however, reaction R00565 is not matched, so it is not included in the final alignment of the two pathways.Concerning the alignment between MP-Align takes the entire network topology into account in the final alignment, thereby identifying the largest connected subpathway.This test revealed a difference between the two tools, which became evident when reporting their matchings back to the corresponding KEGG maps. The matchings computed by SubMAP allow the alignment of individual reactions, or small groups of reactions, without considering the topology of the whole pathway. This paper presents a new methodology and tool for the pairwise comparison and alignment of metabolic pathways. The methodology is based on a hypergraph representation of metabolic pathways and defines a reaction similarity score based on enzyme and compound similarities. The proposed alignment technique uses the adopted reaction similarity score as well as the pathway topology to identify the largest conserved subpathway between the two given pathways.MP-Align to perform several tests to validate the proposed similarity score and alignment algorithm. The first was a comparative analysis test that showed that our approach allows for discriminating between different domains. The second was a phylogenetic reconstruction test that showed that, by considering all the common pathways of eight specific organisms, our approach makes it possible to recover a robust phylogeny that is very close to the NCBI reference taxonomy of those organisms. The last was an alignment test that showed that our alignment algorithm correctly identifies subpathways sharing a common biological function.We used our tool MP-Align and the SubMAP alignment tool [MP-Align identifies the largest conserved subpathway.Finally, we performed a comparison between ent tool . The twoa SubMAP allows the matching between reactions to be either one-to-one (one reaction is matched to exactly one reaction) or one-to-many (one reaction can be matched to many – maximum five – reactions). We performed our tests using the one-to-one alternative.The authors declare that they have no competing interests.MT and DS implemented the algorithms. RA designed and analyze the tests. ML and MS conceived the methods and wrote the paper. All authors read and approved the final manuscript.


Actinoplanes sp. SE50/110 produces the α-glucosidase inhibitor acarbose, which is used to treat type 2 diabetes mellitus. To obtain a comprehensive understanding of its cellular metabolism, a genome-scale metabolic model of strain SE50/110, iYLW1028, was reconstructed on the bases of the genome annotation, biochemical databases, and extensive literature mining. Model iYLW1028 comprises 1028 genes, 1128 metabolites, and 1219 reactions. One hundred and twenty-two and eighty one genes were essential for cell growth on acarbose synthesis and sucrose media, respectively, and the acarbose biosynthetic pathway in SE50/110 was expounded completely. Based on model predictions, the addition of arginine and histidine to the media increased acarbose production by 78 and 59%, respectively. Additionally, dissolved oxygen has a great effect on acarbose production based on model predictions. Furthermore, genes to be overexpressed for the overproduction of acarbose were identified, and the deletion of treY eliminated the formation of by-product component C. Model iYLW1028 is a useful platform for optimizing and systems metabolic engineering for acarbose production in Actinoplanes sp. SE50/110. Type 2 diabetes mellitus is a chronic disease that affects more than 320 million people worldwide, it is the fourth or fifth leading cause of death in most developed countries, and its incidence is rising gradually . Since then, laborious, conventional mutagenesis and screening experiments were conducted to develop strains with higher acarbose production. Some strains with which acarbose were produced at the bench scale in recent years are listed in Table Actinoplanes sp. SE50 was screened from soil by Bayer AG  Conventional random mutation and screening, followed by optimization of media composition and culture conditions, often requires tremendous effort, and it has reached its limit. (2) Some metabolic mechanisms involved in the production of acarbose in Actinoplanes were unclear; thus, the major bottleneck in acarbose fermentation is still unknown. (3) Very little is known about the overexpression or knockout of gene targets to improve acarbose production at the system level. Therefore, a systematic understanding of physiological features and a global understanding of metabolism are greatly needed. In this regard, genome-scale metabolic model (GSMM) plays an important role in fermentation optimization and metabolic engineering.Starting in 1990, the industrial production of acarbose was performed using improved derivatives of the wild-type producer Actinoplanes sp. SE50/110 is known to be the wild-type producer of the α-glucosidase inhibitor acarbose, and because its complete genome sequence is known . The three thresholds in BLASTp were an amino acid sequence identity ≥35%, an e-value ≤10−6, and a matching length ≥70% of the query sequence. Afterwards, the reaction list from the auto-reconstructed model and BLASTp were combined in Excel with the same format, which formed the draft model. Additionally, some reactions from the literature were supplemented into the draft model, and the biosynthesis pathway of acarbose was included. Public databases, such as KEGG, Metacyc, BiopathExplore, BRENDA, CELLO, TCDB , a nitrogen source (5 g/L), 1.0 g/L K2HPO4.3H2O, 0.5 g/L KCl, 0.5 g/L MgSO4.7H2O, and 0.1 g/L FeSO4.7H2O, and the initial pH was adjusted to 7.0. The medium used for seed cultures was NBS medium, which contained 11 g/L glucose, 4 g/L peptone, 4 g/L yeast extract, 1 g/L MgSO4.7H2O, 2 g/L KH2PO4, and 5.2 g/L K2HPO4.3H2O 2SO4, 6.55 g/L K2HPO4·3H2O, 5 g/L KH2PO4, 5.7 g/L trisodium citrate·2H2O, 1.0 g/L MgCl2·6H2O, and 2.0 g/L CaCl2·2H2O. Solution 2 contained trace elements, including 15.75 mM FeCl2, 25.00 mM MnCl2, 33.75 mMCaCl2, 3.75 mM ZnCl2, 0.50 mM CuCl2, 0.05 mM NiCl2, and 0.02 mM [Co(NH3)6]Cl3. Solution 1 was sterilized by moist heat, while solution 2 was filter sterilized, and afterwards, 0.2 mL of solution 2 was added to solution 1.Sucrose medium contained 30 g/L sucrose, 2.0 g/L peptone, 1.0 g/L casein hydrolysate, 1.0 g/L K2 to 50 mL NBS medium in a 500 mL Erlenmeyer flask, followed by culturing for 50 h at 28°C with shaking at 200 rpm. A 5 mL aliquot of first seed cultures were used to inoculate 50 mL of second seed medium in a 500 mL Erlenmeyer flask , and the seed cultures were formed. Subsequently, the seed cultures were centrifuged for 6 min at 6000 × g, and then washed twice with 0.9% NaCl solution. After another wash and centrifugation step , the supernatant was decanted and the resulting pellet was resuspended in 50 mL NaCl solution, of which 5 mL  was inoculated into 50 mL of acarbose synthesis medium in 500 mL flasks and into 5 L of acarbose synthesis medium in a 7.5 L fermenter (INFORS). The cultures were grown at 28°C, with shaking at 200 rpm for the 500 mL flasks, and with shaking at 400 rpm for the 7.5 L fermenter.To prepare for the fermentation process, the strain was cultured on a sucrose medium for 72 h. Next, seed cultures were prepared by two sequential seed cultures. The first seed cultures were prepared by transferring a colony of about 1 × 1 cmActinoplanes sp. consist of protein, DNA, RNA, lipids, cell wall constituents , and small molecules. The biomass equation is important to obtain a high-quality model  . The column temperature was maintained at 40°C, the UV detector was set at 210 nm, and the mobile phase was composed of acetonitrile and phosphate buffer (0.3 g/L KH2PO4 and 0.35 g/L Na2HPO4) at a volumetric ratio of 70:30, and run at 1.0 mL/min. The residual maltose concentration was determined by the 3,5-dinitrosalicylic acid method. The concentration of ammonium was determined based on the Berthelot reaction.The dry cell weight (DCW) was measured by filtration: 15 mL of culture was filtered via a vacuum pump, followed by washing three times with distilled water and drying at 70°C overnight to a constant weight. The acarbose concentration was measured by HPLC using a Thermo Fisher Scientific  Hypersil APS-2 NH−6, then the gene was defined as an essential gene. For simulation of acarbose production, the biomass was set to a fixed value, and the acarbose exchange reaction was set as the objective function. The identification of gene amplification targets was based on the strategy of flux scanning based on enforced objective flux (FSEOF)  as the constraints to predict the acarbose production rate at each controlled oxygen uptake rate. Each amino acid was constrained to have a maximum consumption rate of 0.001 mmol/g DCW/h when simulating the effects of amino acids on acarbose production and cell growth, and the biomass equation and acarbose exchange reaction were set as the objective function, respectively.The resulting model was analyzed using COBRA Toolbox-2.0 , TCDB (http://www.tcdb.org/), and extensive literature mining to obtain a better understanding of substrate utilization. For example, various sugar transport reactions and metabolic pathways were checked and added to the draft model. The maltose/maltotriose ABC transporters aglEFG, which were not included in draft model, were added by literature mining  and Mycobacterium tuberculosis H37Rv, which all belong to the Actinomycetales, was also conducted , and M. tuberculosis H37Rv) was 12.5, 9.1, and 16.8%, respectively. Additionally, 265 reactions were shared by the three models. The sum of reactions in carbohydrate metabolism, amino acid metabolism, the metabolism of cofactors and vitamins, and nucleotide metabolism accounted for more than 85% of the 265 shared reactions. There were 330 metabolites shared by the three models, and the number of unique metabolites in the three models , and M. tuberculosis H37Rv) were 502, 94, and 95, respectively. Model iYLW1028 had more abundant unique metabolites than the other two models, demonstrating that it was more comprehensive.To understand the characteristics of model Actinoplanes sp. SE50/110, the biomass composition was determined and referenced from the literature of related species. For biomass composition measuring, strain Actinoplanes sp. SE50/110 was collected during the mid-exponential growth phase, washed three times with distilled water, and lyophilized and measured as detailed in the Methods section. The total biomass was composed of 58% protein, 1% DNA, 11% RNA, 5% lipids, 22% cell wall, and 3% small molecules. The biomass equation and their compositions are shown in Supplementary Data Sheet iYLW1028, the biomass compositions were assumed to be independent of the environmental conditions.Because no detailed biomass information was available for iYLW1028 and that model iYLW1028 could predict the catabolic pathways of various carbon sources. Three inconsistent cases were inositol, mannitol, and acarbose. The reasons for the three inconsistent results are: (1) genes in the metabolic pathways of inositol and mannitol were not annotated, so the metabolic pathways were interrupted; (2) Actinoplanes sp. SE50/110 could grow on acarbose minimal medium, and the reason for acarbose was that its transport into the cytoplasm was unclear  for cell growth was qualitatively predicted by flux balance analysis. Each carbon source was used as the sole carbon source in a minimal medium, and the results were compared to experimental data, as shown in Table in vivo and in silico was obtained. Of the 23 nitrogen sources, Actinoplanes sp. SE50/110 could grow on 21 sources. Two inconsistent cases were tryptophan and phenylalanine. (2) Phenylalanine could not sustain cell growth in model iYLW1028 because genes in the phenylalanine metabolic pathway were not annotated. (3) Tryptophan as the sole nitrogen source could not sustain growth because the genes of the intermediate metabolite (2-hydroxyphenylacetate) metabolic pathway were not annotated.The capability of utilizing different nitrogen sources for cell growth was also predicted by flux balance analysis. It contained 23 different nitrogen sources . The results were compared to experimental data, as shown in Table iYLW1028 quantitatively. The fermentation data were used as constraints to simulate cell growth, including the specific growth rate (μ), maltose uptake rate (MUR), ammonium uptake rate (AUR), and acarbose production rate (APR). As shown in Table in silico agreed with the fermentation data. In addition, cell growth on 9 carbon source  and 2 nitrogen source (urea and glutamate) under the minimal medium was also used for quantitative validation of model iYLW1028. The in silico growth rates were very close to the in vivo values, and they were lower than 10% of the experimental growth rates. The in silico and in vivo specific growth rates and all the cellular flux on the 9 carbon sources and 2 nitrogen sources were listed in Supplementary Data Sheet in silico and in vivo growth rates indicates that model iYLW1028 could successfully reflect the cellular metabolism of Actinoplanes sp. SE50/110.Batch fermentation with different maltose uptake rates  using acarbose synthesis medium was conducted to evaluate model iYLW1028. However, 2 of 11 unmatched genes were found to be essential in S. coelicolor A3(2). A protein encoded by gene ACPL_1709 and SCO2103 ) catalyzed the formation of 5-methyltetrahydrofolate from 5,10-methylenetetrahydrofolate. Proteins encoded by genes ACPL_6471 and SCO1481 ) also had the same function, as they catalyzed the transfer of orotidylic acid to uridine monophosphate (UMP).The analysis of genes essential for cell growth was performed by single-gene deletion through MATLAB in two media (acarbose synthesis medium and sucrose medium). As described in Figures The analysis of essential reactions was performed with the same method in acarbose synthesis medium. There were 213 reactions that were predicted to be essential for cell growth  from starch, dextrin, and so on, and then transported into the cell, followed by phosphorylation by acarbose-7-kinase. The intracellular acarbose-derived metabolites were deglycosylated by an amylomaltase-equivalent enzyme (encoded by ACPL_3674), which could form a cycle. Acarbose inhibits the α-glycosidases of competitors via the carbophor.Model s Figure  and the acb cluster genes. All these 15 acb cluster genes were essential for acarbose production, and the deletion of any gene prevented the production of acarbose. The 7 acb clusters genes that were non-essential for acarbose production in model iYLW1028 were ACPL_3663 (acbZ), ACPL_3683 (acbE), ACPL_3674 (acbQ), ACPL_3675 (acbK), ACPL_3684 (acbD), ACPL_3671 (acbP), and ACPL_3681 (acbB). All of these genes contribute to acarbose production, except for ACPL_3681, and they are essential genes. The reasons are as described below. (1) Genes ACPL_3675, ACPL_3674, and ACPL_3684 participated in the reaction of the “carbophor.” However, model iYLW1028 failed to simulate the function of the “carbophor,” so these three genes were silent in model iYLW1028. (2) ACPL_3681 and ACPL_3096 had moderate similarities of 52%  The enzymes encoded by ACPL_3683 and ACPL_3663 hydrolyze starch to glucose; thus, in model iYLW1028, they were predicted to be non-essential genes for acarbose production without the addition of starch. (4) The function of ACPL_3671 is still unknown.The essential genes for acarbose production were studied in acarbose synthesis medium. One hundred and thirty three genes were essential for acarbose synthesis, and compared with the essential genes for cell growth, there were 15 unique genes, which all belong to acb cluster genes would contribute to acarbose production.There are four notable aspects in the acarbose synthesis pathway. (1) The starting precursor of the acarbose biosynthetic pathway is 2-epi-5-epi-valiolone, which is derived from sedoheptulose 7-phosphate. Sedoheptulose 7-phosphate is an intermediate metabolite of the pentose phosphate pathway. (2) Glutamate is the primary source of the nitrogen in acarbose. Glutamate and dTDP-4-keto-6-deoxy-D-glucose formed dTDP-4-amino-4,6-dideoxy-D-glucose following catalysis by AcbV, thus introducing the nitrogen into acarbose. (3) Because the starting precursor of acarbose derived from the pentose phosphate pathway, and NADPH was formed along with the pentose phosphate pathway, the cofactor (NADPH) regulation was predicted to affect acarbose production. (4) The expression of the acarbose cluster genes was different in different cultivation media  were also produced, of which component C is the most difficult impurity to separate during downstream processing. Component C was produced from acarbose by TreY  on acarbose production in batch fermentation experiments, a series of experiments under different aeration rates  were conducted. The agitation speed was set at 400 rpm during the whole period of fermentation. The results of the batch fermentation are shown in Figure Actinoplanes sp. SE50/110 is known to be the wild-type producer of the α-glucosidase inhibitor acarbose  , the riboflavin titer of E. coli RF01S/p15Trc-zgp  (Shimaoka et al., E. coli resulted in low cell growth. It is known that the overall demand for NADPH for biosynthesis is >80% of total cytosolic NADPH production (Fan et al., rpe might reduce the flux through the pentose phosphate pathway. However, due to the imperfect genetic manipulation of Actinoplanes sp. SE50/110, including the lack of suitable expression vector and transformation method with a high frequency, the wet experiments of gene overexpression are not conducted, which is a weakness of verification on model iYLW1028. In addition, since the effect of the four simulated genes on acarbose production was not verified by wet experiment, they are not appropriate to apply to acarbose producing strains at the present stage. Therefore, Future work for improving acarbose production should focus on the overexpression of these four genes.Recent strategies to improve acarbose production focused on fermentation compositions (Choi and Shin, Actinoplanes sp. (Li et al., The result of the robustness analysis indicated that a relatively lower oxygen uptake rate promoted acarbose production. Additionally, it is known that the DO level plays an important role in acarbose fermentation by Actinoplanes was successfully used to illustrate the factors thar limit acarbose biosynthetic during fermentation, to simulate gene targets for improved acarbose production, to elucidate the mechanism of the effect of the oxygen uptake rate on acarbose production, and to interpret the effects of amino acids on acarbose. Despite guiding metabolic engineering and fermentation optimization applications, model iYLW1028 has many future perspectives. First, while there are large amounts of batch fermentation data, little is known about its dynamic fermentation data. Based on dynamic fermentation data, the predictions of microbial behaviors in a dynamic model would enable the comprehensive understanding of acarbose fermentation. Second, one can integrate high-throughput data with the GSMM. Gene expression data in three different growth media were analyzed via RNA-sequencing (Schwientek et al., iYLW1028 would be more accurate, which would enable us to interpret the different mechanisms of acarbose production in different growth media.In this work, the first GSMM for Actinoplanes. Model iYLW1028 consists of 1219 reactions, 1128 metabolites, and 1028 genes based on genome information, databases, and literature mining. The prediction of metabolism phenotypes and essential genes based on model iYLW1028 agreed well with the experimental data and the database of essential genes. Using model iYLW1028, in silico metabolic fluxes were analyzed to investigate the effect of additional amino acids and DO on acarbose production. The results showed that additional amino acids had a beneficial effect on acarbose production, especially L-arginine and L-histidine, and that a relatively low DO level was also beneficial for acarbose production. The FSEOF algorithm was used to identify targets whose overexpression could increase the level of acarbose precursors, thereby increasing acarbose production directly. We also examined the effect of the deletion of treY on growth and trehalose synthesis to eliminate the by-product component C. Hence, it is expected that model iYLW1028 will help us understand cellular physiology on a systems level, as well as to perform systems metabolic engineering.This study presents the first GSMM for 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.


Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO2 exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of  Flux balance analysis (FBA) has provided the basic framework for predicting growth and biomass yield as well as investigating reaction fluxes in a metabolic network  for organisms from each of the three domains of life: archaea, bacteria, and eukarya , as mounting evidence shows that cells adapt their metabolism to the prevailing circumstance . In different cellular contexts, only a subset of reactions is typically active, which may lead to differences in biomass composition  include almost all components of the photorespiratory metabolism  metabolism, and redox signal transduction [for recent reviews, see Foyer et al. , termed oxygenation , termed carboxylation. Upon carboxylation, one molecule of RuBP is transformed into two molecules of 3-phosphoglycerate. The oxygenation of RuBP yields one molecule of 3-phosphoglycerate and one molecule of phosphoglycolate. To mitigate the inhibition of the photosynthetic pathway by phosphoglycolate  (effectively refixed by the glutamine synthetase and the glutamate synthase).The plant photorespiration pathway involves 12 reactions which are partitioned among three compartments, namely chloroplast, peroxisome, and mitochondrion, with bypasses in the cytosol Figure . Ribulos Calvin, , or carbA. thaliana, while the bypass of Carvalho et al. , transforming 2-hydroxy-3-oxopropanoate  into glycerate   into malate (and CoA), and catalase (EC 1.11.1.16) , tartronate semialdehyde carboxylase (EC 4.1.1.47), taking glyoxylate and producing CO.1.99.14,) Figure . Malate A. thaliana models, and used them to assess the effect of model size and model quality on the accuracy of predictions. In addition, we tested different scenarios with respect to the increase predicted upon enforcing additional biochemical constraints on the ratio between the fluxes of the RuBisCO carboxylation and oxygenation reactions. To avoid introduction of bias about the directionality of the introduced bypass reactions due to inability to assess the subcellular concentration of the participating metabolites, we systematically investigated the effect of all combinations of reaction reversibilities. As our main contribution, we demonstrate that the predictions for the increase in biomass upon insertion of the photorespiratory bypasses are in accord with experimental evidence across the studied models only upon the consideration of additional biochemical constraints. Therefore, our findings indicate the need for inclusion of additional constraints when using large-scale plant models for the design of viable metabolic engineering strategies.The aim of our study is to test the extent to which large-scale metabolic models reproduce the experimental observations on engineering the two photorespiratory bypasses. For this purpose, we evaluated three state-of-the-art A. thaliana metabolism: (1) a bottom-up  reconstruction, where the operability of the incorporated reactions and metabolites is ensured by starting from well-documented and necessary biochemical pathways . The same approach was used for 2-hydroxy-3-oxopropanoate in the model by Mintz-Oron et al., which contains this metabolite only in the cytoplasm. Moreover, AraGEM and the model of Arnold and Nikoloski do not contain 2-hydroxy-3-oxopropanoate in any compartment. We, therefore, added the metabolite to the chloroplasts to allow introducing the Kebeish bypass. Note that this leads to a full coupling  between the tartronate semialdehyde carboxylase and 2-hydroxy-3-oxopropionate reductase reactions in these models, since 2-hydroxy-3-oxopropanoate can only be produced and consumed by these reactions.n biochemical reactions in a metabolic network that is assumed to operate in a (quasi) steady-state, i.e., the concentrations m metabolites in the network are constant . The steady-state condition and the flux boundaries determine the solution space that usually contains infinitely many flux distributions, since the system of linear equations Sv = 0 is, in practice, underdetermined. To probe the functionality of the network, FBA  over the three growth conditions . Reversibility of the other two reactions  does not affect the predicted biomass yield. This finding implies that the insertion of the Maier bypass in the model of Arnold and Nikoloski had negligible effect on biomass increase. The Kebeish bypass with any configuration of reversibilities of three individual reactions did not result in increase of biomass, similar to the Maier bypass scenario above .The AraGEM model with all irreversible reactions for the Maier bypass did not result in an increase in biomass. However, the insertion of all reversible reactions led to an increase of 0.6% for the single biomass reaction that the model includes. The FVA indicates that the malate synthase carries a negative (net) flux . The insertion of the Kebeish bypass resulted in an increase of 0.3%, see Table S6 in Supplementary Material. However, both results pertain to the unrealistic scenario in which the RuBisCO oxygenase practically does not carry flux, which was revealed by the FVA.We next investigated the larger model of Mintz-Oron et al., which also includes the glycolate oxidase, but only in the cytoplasm, mitochondria, and peroxisome. Upon introducing the Maier bypass, the predicted increase in the optimal biomass yield was 1.0% with every combination of reaction reversibilities. By inspecting the results from FVA, we found that the increase in biomass yield was associated, as one would anticipate, to larger flux ranges. The only reaction that had a fixed value at the optimum biomass with and without inclusion of the bypass was glutamine synthetase; its flux was more than fivefold reduced, lending support for the hypothesis of reduced photorespiration by Peterhansel et al. . These fInserting the Kebeish bypass into the model of Mintz-Oron et al. resulted in 0.4% increase of biomass with all reactions considered irreversible. Allowing for reversibility of tartronate semialdehyde carboxylase and the other two reactions irreversible resulted in a maximum increase of 0.8%. Glutamine synthetase was, again, the only reaction that had a fixed value at the optimum biomass with and without inclusion of the bypass; its flux was reduced by more than 1.2-fold .In this section, we repeated the analysis under the additional assumption that the flux ratio of RuBisCO carboxylase and oxygenase reactions was fixed to 3:2. The Maier bypass in the model of Arnold and Nikoloski did not predict an increase in biomass in any of the three environments. On the other hand, the Kebeish bypass resulted in a predicted biomass increase of 6.2% for optimal, 6.2% for carbon-limited, and 6.3% for nitrogen-limited growth conditions. We also inspected the change in flux variability ranges in the model upon insertion of the Kebeish bypass. As in Scenario A, the fluxes of the reactions in the photorespiratory pathway at the optimal biomass yield with the additional ratio constraint vary in a small range. Upon insertion of the bypass, the upper bounds for the photorespiratory flux were lowered by ~10% .Scenario B with AraGEM resulted in biomass increase of 2.7% for the Maier bypass with all reversible reactions. Here too, we found that the increase in biomass yield was associated with a negative net flux for the malate synthase reaction . The increase for the Kebeish bypass was 1.1% with the three reversibilities of reactions in Table Scenario B with the model of Mintz-Oron et al. resulted in the same increase in biomass yield as in Scenario A, above, for the Maier bypass. Here too, we found that the increase in biomass yield was associated with larger flux ranges. The reaction catalyzed by glutamine synthetase was the only which showed a fixed value at the optimum biomass with and without inclusion of the bypass; its net flux shifted in the direction of synthesizing glutamine (more than fivefold decrease). As in Scenario A, these findings were invariant over all combinations of reaction reversibilites . The results for the Kebeish bypass were largely invariant in comparison to that of Scenario A .In this section, we repeated the analysis under the assumption that the flux ratio of RuBisCO carboxylase and oxygenase reactions was fixed to 4:1. The model of Arnold and Nikoloski predicted an increase in biomass of 0.1% for all three reversibilities for the Maier bypass under nitrogen limiting growth condition. The Kebeish bypass resulted in an increase of 3.0% for optimal, 3.0% for carbon-limiting, and 3.9% for nitrogen-limiting growth conditions, accompanied by increase in flux variability bounds . AraGEM resulted in an increase of 0.1% with the Kebeish pathway and 1.6% for the Maier bypass, while the model of Mintz-Oron et al. resulted in the same predictions as in Scenario B for both bypasses . As in Scenario B, above, glutamine synthase for the model of Arnold and Nikoloski shows a constant value at the optimum biomass upon the insertion of the Kebeish pathway, which is at least 20% smaller than the values at the optimum biomass for the wild type.2 uptake upon varying the carboxylation to oxygenation ratio as well as (3) the comparison of co-factor flux-sums (as proxies for turn-over) between the wild type and transformants .Our earlier analysis demonstrated that consistent predictions can only be made for the Kebeish pathway with the three models. We also showed that this was the case for the model of Arnold and Nikoloski with biomass functions for three different scenarios. Since this model was reconstructed entirely from experimental data, lacks any blocked reactions, and gives the best quantitative match between the predictions and observations from the bypasses, the remaining analyses will consider only this model with and without the Kebeish pathway. Specifically, in the following we focus on (1) biomass increase and (2) variability of CO2 concentration, resulting from the bypass insertion and manifesting itself in increases of carboxylation rate , we expanded the range of investigated ratios to values between 1 and 100. This modeling scenario would account for differences in the internal CO2 concentration in the chloroplast could explain the growth increase observed by introduction of the Kebeish bypass  and 6 to 9% for Scenario C (carboxylation/oxygenation ratio of 4:1) with all reversibilities examined above . In fact, the flux-sum of the chloroplastic ATP increased by at least 7% in Scenario B and 3.5% in Scenario C (% is determined from the maximum flux-sum in the wild type and the minimum flux-sum in the transformant). For the mitochondrial ATP, the increase was 85% in Scenario B and 143% for the carboxylation to oxygenation ratio of 4:1. Moreover, we observed that in the transformants the flux-sum of the peroxisomal NADH is fixed to 0 at the optimal biomass. The latter implies that NADH is not consumed nor produced by any of the modeled reactions in the peroxisome. For the remaining metabolites, while we observe changes in the ranges of the flux-sum, they overlap between the transformant and the wild type, and hence no conclusive statement can be made  between the predictions and experimental evidence can be obtained only by further constraining the optimal states using experimental observations . Therefore, our findings indicate that predictions of metabolic engineering strategies are tightly bound to the reference state used, and bring to question approaches which attempt pathway engineering without considering reaction rates in the rest of the network.A. thaliana metabolic models with different characteristics allows us to test the extent to which the metabolic engineering predictions may differ due to the intrinsic differences between the models. For instance, in the model of Arnold and Nikoloski, all reactions involving metabolic conversions  are annotated and there is experimental evidence for their occurrence in A. thaliana. By contrast, such evidence is missing for 21 and 37% of the annotated reactions in AraGEM and the model of Mintz-Oron et al. as a result of their (semi-)automated reconstruction using reaction databases. Moreover, and most importantly, the model of Arnold and Nikoloski alongside AraGEM does not include blocked reactions . The percentage of blocked reactions in the model of Mintz-Oron et al. is 59%. Due to the inclusion of fewer reactions, the model of Arnold and Nikoloski is, however, less flexible  than AraGEM and the model of Mintz-Oron et al. In addition, the model of Arnold and Nikoloski predicts more efficient conversion of CO2 into biomass than AraGEM, at the cost of assimilating more photons. These increased levels of realism, discussed in detail in Arnold and Nikoloski  (Nikoloski et al., GB and ZN designed the research. AK and GB performed the analyses. All authors interpreted 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.


Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles.http://coremodels.mcs.anl.gov) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis.To overcome this challenge, we developed methods and tools  contains supplementary material, which is available to authorized users. One of the most important elements of an organism’s biochemistry is its ability to produce energy in the form of ATP from nutrients in the environment under a wide variety of environmental conditions. Energy production pathways are of fundamental importance because these pathways define much of the behavior of a microbe and have the greatest impact on the quantitative prediction of biomass and metabolite production yields . CellulaMetabolic models provide a valuable means for simulating and understanding energy metabolism based on annotated genome sequences . Recentlhttp://coremodels.mcs.anl.gov). The CMMs produced by our pipeline had minimal need for gapfilling, demonstrating a key value in CMMs as functional models that are as close as possible to raw annotation output, minimizing model-driven conjectures. In tests of the ATP yield on our models, the results show nearly complete agreement with known values for model organisms. Most importantly, comparative analysis of our core models revealed substantial variation in energy biosynthesis strategies and pathway representation, including variations even at short phylogenetic distances. We observe only a small fraction of theoretically possible combinations of these pathways, with both positive and negative correlations in energy biosynthesis pathways, suggesting a limited number of optimal pathway configurations.Here we present a set of tools and analyses aimed at a focused understanding of energy biosynthesis across the prokaryotic tree of life. Building on important early work in metabolic modeling and engineering , we defiWe applied our new core model reconstruction pipeline Fig.  to generAlthough the pathways included in CMMs are fundamental to energy generation, not all pathways are present in every genome. Individual pathways were annotated as present in as few as 106 (e.g. acetone fermentation) and as many as 6,694 (e.g. lactate fermentation) genomes. We examined pairwise relationships among all pathways present in CMMs in order to understand variation in core metabolism across this diverse set of microorganisms. In this analysis, we filtered out 4062 CMMs from our dataset because their associated genomes were overly close phylogenetically to other CMMs (many mapped to different versions of genomes with the same taxon ID). We found an overall pattern of positive co-occurrence  as one OF. Using this OF we performed FBA on seven minimal media conditions  with a range of electron acceptors to determine the ATP yield under various environmental conditions  was lower than ATP production from glucose (per-mol basis), as expected given that glycerol is a more oxidized carbon source  have neither aerobic nor anaerobic respiration; hence such organisms use fermentation as the sole means of ATP production [Bacillus subtilis or Streptomyces coelicolor, which are classified as obligate aerobes in the public domain, in fact do have the ability to respire anaerobically in the presence of nitrate. These predictions are in agreement with previous studies done on these organisms [As metabolic models generally require an objective function (OF) that is optimized during flux balance analysis (FBA) to predict flux profiles, we explored two OFs see . In ordeacceptor , 12. Thurce Fig. . We alsooduction . Their yoduction , 19. Sperganisms , 21. AnaFermentation is an essential process for obligate anaerobes to produce ATP and for many facultative anaerobes, which use it to produce ATP when suitable exogenous electron acceptors are not present. It is also important for metabolic engineering applications, as it is a primary means of producing many biofuel molecules. Analysis of CMMs for the ability to produce fermentation products showed that obligate anaerobes and facultative anaerobes are able to use a variety of fermentation pathways when oxidizing sugars under anaerobic conditions. For example, FBA simulations of a taxonomically diverse set of organisms including facultative anaerobes and anaerobes grown in glucose solely by fermentation, show ATP yields around 2 mmol ATP/mmol of glucose for wide range of bacteria , 17  of the CMMs have some type of aerobic electron transport chain (ETC), whereas about 5,100 (62 %) have an anaerobic ETC; and 1,279 (15 %) of CMMs do not have any ETC. Furthermore, we see that 5,291 models (65 %) were able to produce ATP in glucose minimal media aerobically, while up to 61 % of the CMMs were able to produce ATP with each of the alternate electron acceptors (AEA) examined here. 4,440 (54 %) CMMs were able to grow solely by fermentation when no electron acceptors were present. If AEAs were present in the medium, then CMMs with ETCs tended to use anaerobic ETCs via reduction of the terminal AEAs. This study demonstrates that using CMMs in standard FBA while maximizing energy production can produce accurate predictions for a wide range of organisms.We also identified 2,495 (30 %) CMMs those were unable to produce ATP under any of the tested conditions ” that is required to supplement oxaloacetate during glucose oxidation in Gram positive bacteria . Among hcoplasma , 23 ” instead of “ubiquinol cytochrome oxidase”. We then integrated these new more-specific functions into our template. We also noticed that the genus Acinetobacter does not appear to have functional glycolysis or ED pathways for the degradation of glucose, yet previous studies have suggested that many Acinetobacter species are able to utilize the ED pathway for oxidization of glucose [Acinetobacter except for the enzyme “Gluconolactonase 3.1.1.17”. This apparent inconsistency may in fact be biologically accurate, because the reaction catalysed by this enzyme has been shown to occur spontaneously, and thus the enzyme may not be necessary in every organism containing the broader pathway [In our analysis we identified multiple missing or incorrect annotations including transporter genes, key genes in central metabolism and fermentation pathways. For instance, through our analysis we noticed an aerobic ETC present in obligate anaerobes belonging to the genus  glucose , 25. We  pathway .Streptococcus produce formate during fermentation [Streptococcus genus. We also identified key missing gene-protein reaction associations within the CMM template, such as Polyphosphate glucokinase (EC 2.7.1.63), a gene that is abundant in many Actinobacteria [It has been known that some members of entation , yet no bacteria . Annotatbacteria . Often, Here we present CMMs and comparative analysis for over 8,000 completely sequenced genomes in diverse phylogenetic groups that are derived from a manually curated core model template. Unlike the complexity of genome scale models, CMMs are simpler, offering a quick and accurate way of determining: (i) respiration type(s)  services, which was built, in part, from RAST and Model SEED. The core metabolic model construction pipeline with supporting commentary can be accessed through KBase Narrative interface at https://narrative.kbase.us/narrative/ws.15253.obj.1. The reconstruction of all models was completed in less than 24 h. It is important to note the flexibility of the model construction process. In this study we designed a CMT supported by RAST annotations, yet additional templates may be constructed based on other annotation databases with their own annotation ontologies. These templates can then be used to construct metabolic models specifically tailored to address unique research goals.In the initial step of the pipeline, genome annotations generated by RAST are applied in combination with our CMT to generate a set of gene protein-reaction (GPR) associations used to reconstruct each CMM  resulting in inaccurate prediction of ATP production. These issues persist because of difficulties integrating ETCs into models. In designing ETCs for the CMMs, we incorporated well-studied variations of ETCs in model organisms that were supported by consistent RAST annotations, and we integrated these variations into our CMT . To control for the increased likelihood of positive co-occurrences with larger CMM size, all analysis were performed on three size classes: small , medium , and large , as well as the entire non-redundant dataset. Boolean vectors containing presence/absence information for each of the 12 key energy biosynthesis pathways was used to examine the diversity of pathway combinations present within the CMMs. Pathway presence and absence data is included for all CMMs in Additional file Pairwise comparisons of the presence or absence of individual pathways were conducted using the Hmisc  and corr98.5) and resulted in a representative tree of 1,864 genomes.We constructed a phylogenetic tree for all CMM genomes using the SEED server tools . SpecifiWe then mapped presence/absence information of subsets of the key pathways to each genome as a Boolean vector  and drew circular trees using the iTOL tree visualization tool . The trehttp://coremodels.mcs.anl.gov). In this tool, a list of models and links to associated genomes, media, and FBA results can be found under “Models”. Once on a model page, comprehensive tables of model reactions, compounds, gapfilled reactions, and ETC diagrams are made available. Tables of genome, media, and FBA data are organized similarly. For comparative analysis, a subset of FBA results can be selected from the models page and compared side-by-side on a heat-map (Additional file We developed a web resource for exploring, comparing, and analysing our CMMs, called the Core Model Viewer (AEA, alternate electron acceptors; BDOH, 2,3 butanediol; CMM, core metabolic models; CMT, core model template; ED pathway, Entner-Doudoroff pathway; ETC, electron transport chain; FBA, flux balance analysis; GPR, gene protein-reaction; OF, objective function; OTU, operational taxonomic units


S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models.Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models ( S. cerevisiae and Chinese Hamster Ovary (CHO) C. griseus are model cell lines for understanding metabolism in eukaryotes [iFF708 [i.e., mitochondria and cytosol). Subsequent efforts improved this model not only by including additional ORFs, metabolites and metabolic pathways, but also by integration of enzyme-localization information for compartmentalization  [Both budding yeast karyotes  as well karyotes ,3 for bikaryotes . The ear [iFF708 ) includeticulum) . The latticulum) . CHO celticulum) . It has ticulum)  worldwidticulum)  of commein silico, limits the biomass flux to lower than 10% of its theoretical maximum. Whereas, SL analysis identifies the list of in silico gene pairs (and higher order) whose removal constrains the biomass flux to lower than the aforesaid essentiality criterion. These analyses serve the dual purpose of model refinement (by comparing with available in vivo knockout information) and prediction for identifying genes (or combination of genes) whose knockouts could potentially be lethal. The latter is particularly useful in strain engineering applications for avoiding synthetic lethal gene deletions. In earlier efforts, these analyses were used extensively to curate metabolic models of well-studied organisms such as E. coli [S.cerevisiae [et al. [et al. [et al. [S. cerevisiae (Yeast 7.11) and thereby update it to iSce926 and subsequently for C. griseus (CHO 1.2) with existing experimental measurements in terms of gene essentiality and synthetic lethality and propose a list of corrections and follow-up assessment of predicted gene deletions.Essentiality and SL analyses ,14 have  E. coli ,16 and Srevisiae . Model i [et al. . Other r [et al.  and Soh  [et al. , all of et al. [i.e., nutrient or biomass precursor deficiency) for lethality. For CHO 1.2, we identified eight instances where model and experiment does not match. Upon supplementing this mismatched set with another 11 cases of model and experiment discrepancies from the mouse model [The proposed model modifications on Yeast 7.11 involve 50 literature-supported changes that improve the sensitivity, specificity of Yeast 7.11 by 2.66% and 20.4% and decrease the false viable rate (FVR) by 8.42% see . They buIn vivo essentiality and synthetic lethality information were mostly obtained from gene deletion studies by Tong et al. [in silico results included (i) the Saccharomyces Genome Database (SGD) [g et al. . Gene lese (SGD) , (ii) sise (SGD) , (iii) vse (SGD) , (iv) dase (SGD) ,26,27 anse (SGD)   in silico but are non-essential (G) in vivo whereas box SL2ES refers to gene pairs that have been found to form a synthetic lethal pair in silico (SL2), however at least one of them is essential (ES) in vivo. Overall, our current model iSce926 improves the sensitivity and specificity of Yeast 7.11 from 0.288 to 0.347 and 0.951 to 0.977 respectively and decreases the false viability rate (FVR) from 0.712 to 0.652  so far in the literature. These unverified results often reveal non-intuitive lethal gene sets to be avoided while designing overproduction strains. Conversely, predicted viable synthetic lethals are prime candidates to be tested experimentally to assess the functionality of all the pathways present in the model. Absence of isozymes and alternate pathways in the model may also lead to discrepancies between in silico results and in vivo data. For example, gene TYR1 (YBR166C) which is essential (ES) for tyrosine biosynthesis [i.e., a ESSL2 discrepancy). There is a single pathway for the formation of tyrosine in Yeast 7.11; however, chitin synthase (CHS1) [BAT2 (YJR148W) encoding branched-chain amino transferases in isoleucine, leucine and valine biosynthetic pathways [BAT2 forms an in vivo synthetic lethal pair with its paralog BAT1 (YHR208W). However, both have been identified as essential in silico. Reconciliation between model and experiment was achieved by adding the pretyrosine pathway [tion see . The ver.652 see . Brief sSL2U see  containsynthesis  accordine (CHS1) , which ipathways . BAT2 fo pathway  and alloiSce926 and Yeast 7.11 see HEM13 whose deletion causes an ESG discrepancy has a non-metabolic function in chromatin assembly and interacts with RNA-polymerase II in transcription. It forms a synthetic lethal with CDC73 [Overall, we reconciled 50 growth discrepancies  that can also transport iron (II) across the plasma membrane rendering the FET3-FET4 gene combination an in vivo synthetic lethal. Thus, adding the FET3 mediated transport reaction for iron (II) reconciles FET4 from ESG to GG and also identifies FET3-FET4 as an SL2SL2 match.A total of six reactions were added to Yeast 7.11 see . They geodel see . Anotherodel see . Yeast 7evidence  revealedin silico double mutant ΔFUR1ΔURA3 lethal in agreement with in vivo data. The fact that the removed pyrimidine-nucleoside phosphorylase reaction lacked a GPR association possibly alludes to the putative nature of its inclusion into the model. The reactions -tetraen-3beta-ol) involved the elimination of a lumped form of a reaction whose elementary steps  are already present in the Yeast 7.11 model.Three reactions see  were remions see  were taget al. [A total of 13 GPR modifications were made to the model that corrected for 15 inconsistencies. For example, α, α-trehalose phosphatase synthase (UDP-forming) and trehalose phosphate phosphatase catalyze the formation of α,α-trehalose phosphate from glucose-6-phosphate, which is converted to trehalose in the presence of UDP-glucose see . The GPRet al. .S. cerevisiae, most of the ATP formation occurs from glycolysis. A Δfba1 strain shows more than five-fold reduction in net ATP production in silico, which proportionately reduces the biomass flux (from 2.44 h−1 to 0.465 h−1). However, this is above the in silico viability threshold rendering the fba knockout non-essential. A possible reason for this contradiction could be the inability to capture the in vivo suppression of mitochondrial respiratory enzymes under accumulation of cytosolic fructose 1,6-bisphosphate due to short-term Crabtree effect [MIR1 mediated phosphate/proton mitochondrial symporter (PIt2m) [fba1 mutant in silico lethal (reducing the net ATP production by ~35-fold) thereby resolving a GES case to an ESES case. The same observation also holds true for a tpi1 deletion in S. cerevisiae.In e effect . In fact (PIt2m)  makes fbiAZ900 [PRS1 and PRS3), (PRS2 and PRS5) or (PRS4 and PRS5) is capable of encoding the sub-units required for catalyzing the reaction. However, it was later shown [PRS1 and PRS5 contain NHR while the rest do not. Therefore, the current GPR (YHL011C and YKL181W) or (YOL061W and YBL068W) or (YOL061W and YER099C) was corrected accordingly to (YKL181W and YER099C) or (YKL181W and YHL011C) or (YKL181W and YBL068W) or (YER099C and YOL061W) or (YBL068W and YOL068W). This correction not only recapitulates all in vivo observations but also predicts one lethal pair and two lethal triplet mutants .Phosphoribosyl diphosphate synthase catalyzes the essential reaction PRPPS. The inconsistency of this GPR was addressed in iAZ900  and was er shown  that anyde novo biosynthesis of pyrimidines both of which undergo feedback inhibition by UTP (uridine tri-phosphate). The existing GPR identifies  and  as SL pairs. In fact, experimental evidence [CPA1, CPA2 and URA2 are essential  or YJL130C)) to (YJR019C and YOR303W and YJL130C) rectifies two SL2ES cases to ESES and three GES cases to ESES. Dolichyl phosphate mannose mannosyl transferase catalyzes the conversion of dolichyl mannosyl phosphate to mannan in the endoplasmic reticulum, which is then transported out to the cytosol and forms a biomass precursor. The existing GPR , (sub-family 2: PMT2 and PMT3) and (sub-family 3: PMT4). Literature evidence [PMT4 in combination with deletions in both sub-family members 1 and 2 is lethal. Therefore, the GPR is modified from ((PMT1 and PMT2) or PMT3 or PMT4 or PMT5) to ((PMT1 and PMT5) or (PMT2 and PMT3) or PMT4). This modification puts forth the following four lethal triplet gene deletions: Δpmt1Δpmt2Δpmt4, Δpmt5Δpmt2Δpmt4, Δpmt1Δpmt3Δpmt4 and Δpmt5Δpmt3Δpmt4.Bifunctional carbamoyl phosphate synthetase catalyzes the first two enzymatic steps in the evidence  and the evidence  reveal ttial see . Similaralbicans . This GP GPR see  suggestsevidence ,70 implievidence  suggestsE. coli [S. cerevisiae contains an E. coli ortholog of ubiC gene that encodes for chorismate-pyruvate lyase enzyme, which enables the addition of the corresponding GPR to the reaction that was already present in the model. Gene deletion studies on ubiC reveal that it is non-essential matching model predictions. In another example, assigning BIO6 and BIO8 genes in the GPR for putative KAPA synthetase as seen in the YJM627 and A364a strains of S. cerevisiae [BIO6 or BIO8 as non-essential.There are 15 instances where GPR associations were assigned to orphan reactions in the model see . This adE. coli . For exaE. coli  that S. revisiae ,54 correi.e.,YDL040C and YMR307W) from the model despite the presence of experimental data on their deletion phenotype. They are integral to nine in vivo SL2 cases (see YMR307W (GAS1) encodes for beta-1,3-glucanosyltransferase [YDL040C (NAT1) encodes for N-acetyltransferase and is primarily involved in cell wall integrity (CWI-MAPK) signaling pathway with a few ancillary functions such as cell-cycle, heat shock resistance, sporulation and telomeric silencing [ that are missing in Yeast 7.11. In the current effort we have successfully incorporated NAT1 and NAT2 and have correctly identified them as GG cases.This refers to missing (M) genes (ases see . YMR307Wnsferase  (belonginsferase ) a eukarilencing . The CWIilencing ,75) invoS. cerevisiae genome with 3,912 metabolic genes would require approximately 7.5 million double-knockout experiments to verify the viability of all double deletion mutants. Computational tools, such as the SL finder can identify in silico synthetic lethal combinations thus narrowing down the combinations to be tested in vivo. in silico lethal but have not been tested yet in vivo. While the lethal effect of some of these deletions is straightforward, a number of cases reveal non-intuitive lethal deletion combinations from distal parts of metabolism that either hinder cofactor synthesis or transport to another compartment. Four such cases identified using the corrected Yeast 7.11 model are described next in more detail.Experimental gene deletion compilations such as the Keio collection  and SaccYDR300C (PRO1) is a gamma-gluatmyl kinase that initiates proline biosynthesis by catalyzing the conversion of cytosolic glutamate to L-gamma glutamyl-5-phosphate. In silico removal of PRO1 redirects the flux through the L-ornithine transaminase reaction to produce proline via L-glutamate 5-semialdehyde (LG5S)  cannot catalyze the conversion of ornithine to LG5S. Therefore, the double deletion Δpro1Δcar1 strain cannot form LG5S and hence it is proline auxotrophic and thereby lethal.G5S) see . In silileu4Δleu9 double mutant is devoid of α-isopropylmalate synthase (IPMS) activity leading to leucine auxotrophy. YNL104C (LEU4) encodes IPMS that catalyzes 2- isopropylmalate formation from 3-methyl 2-oxobutanoate essential in leucine biosynthesis. YOR108W (LEU9) is alpha-isopropylmalate synthase II and can carry out a residual α-IPMS activity in a ΔLEU4 strain. Existing in vivo studies [LEU4 or LEU9 are non-essential which makes the double mutant a candidate for a synthetic lethal pair as suggested by the metabolic model.Δ studies  suggest YBR291C (CTP1) encodes the citrate-Pep antiporter from peroxisome and mitochondria to cytosol. YKL029C (MAE1) codes for the mitochondrial malic enzyme, which catalyzes oxidative decarboxylation of cytosolic S-malate to pyruvate  double deletion strain cannot uptake myo-inositol thereby showing decrease in vegetative growth in corroboration with in vivo studies [YJL153C (Δino1) cannot convert cytosolic glucose-6-phosphate to myo-inositol-1-phosphate conversion according to the model  in all calculations explaining why Δino1 is found to be non-essential in silico.Lipid (a biomass precursor) is an intermediate metabolite formed by 15 precursor molecules in appropriate biological ratios in Yeast 7.11. The  studies . ΔYJL153odel see . Therefo studies ,79 with al media  without The essentiality and synthetic analysis for the well-curated Yeast 7.11 model revealed a number of opportunities for model improvement and predictions for non-intuitive synthetic lethals to be tested experimentally for providing additional backing for the model and/or ways to remedy shortcomings. Subsequently, we switch our attention to a CHO cell model with significantly fewer mutant growth phenotypes tested.in silico revealed 90 essential reactions (see The absence of a comprehensive single-gene knockout database for Chinese Hamster Ovary (CHO) cells (unlike yeast) makes the assessment CHO 1.2 genome-scale model  more difions see . Utiliziions see  for growions see . A compaions see .in vivo gene deletion experiments [in silico as it blocks the pathway for synthesizing biomass precursor glycogen. The existing GPR (gys1 and gys2) suggests that deletion of either of the genes encoding the protein would be lethal in silico at the gene level. However, experiments on CHO K1 cell lines show that single deletion of these genes are not lethal in vivo. Furthermore, in vivo deletion experiments in related organisms with a conserved glycogen synthase activity  to (gys1 or gys2) to reconcile two ESG and two ESSL2 inconsistencies to GG and SL2SL2 respectively  reaction required to synthesize biomass precursor glycogen  that reconciles not only GES for pgm2 to ESES but also SL2ES for pgm1-pgm2 to ESES.eriments  in CHO-Keriments . For exarevisiae ) show thpgm2 case deletion of pcyT1a results in a GES inconsistency. Either of pcyT1a or pcyT1b can encode for the in silico essential choline phosphate cytidylyltransferase (CPCT) reaction. Removal of CPCT blocks the production of phosphatidylcholine and sphingomyelin, which are biomass precursors, thus making the two genes a synthetic lethal pair. However, in vivo single gene deletion studies in embryonic stem cells [pcyT1a is lethal, ΔpcyT1b mutant strains are viable. This observation suggests that pcyT1a is sufficient to encode for CPCT activity, while pcyT1b is a minor isoform. The GPR for CPCT was modified accordingly to pcyT1a or (pcyT1a and pcyT1b) to resolve pcyT1a GES to ESES and pcyT1a-pcyT1b SL2ES to ESES  activity in NAD biosynthesis pathway, blocks the synthesis of biomass precursors NAD+, NADH, NADP+, and NADPH. Removal of QPRT prevents any fresh supply of nicotinate D-ribonucleotide (NDRT) to the nicotinamide regeneration cycle (see +) to be diverted towards other pathways such as NADPH and biomass formation. The cycle functions as a futile cycle dissipating ATP. Production of all nicotinatmide-related cofactors  are impaired resulting in zero biomass formation. Note that deletion of any of the genes (or gene pairs) encoding for the reactions in the nicotinamide regeration cycle  are in silico essential  for directly impairing activity of the cycle.Due to lack of sufficient experimental gene deletion data either in CHO or mouse embryonic stem cells, we have limited resources of confirming most of our airs see  for CHO airs see  of gene in silico removal of slc14a1 and slc14a2 genes. These genes encode for transporter proteins for urea whose removal prevents the export of urea from cytosol out of the cell. As there are no other pathways in the current CHO model to consume urea, deletion of urea export reactions prevents the activity of the urea cycle where it is synthesized. As a result, the model cannot synthesize intermediate metabolite ornithine required for the production of biomass precursor putrescine  activity  (see ebp-encoded (emopamil binding protein- EBP) reaction forming N-cholesta 7, 2, 4-dien 3-betaol (NC724D3B)  see c, which in silico synthetic lethal suggestions for non-intuitive examples, we performed a node centrality analysis synthetic lethal landscape for CHO 1.2  activity in phosphatidyl choline (PTC) pathway, is associated with a maximum number of eight synthetic lethal gene pairs  reaction. In silico simultaneous gene deletion of pc3 and pc4 prevents synthesis of biomass precursor PTC synthesis .In addition to manual inspection of  1.2 see . Node ce 1.2 see  and rankairs see . For exaesis see . Similar SL2 See . One sucysis see  can be uWe have further cataloged higher order lethal gene lists that show 20 lethal gene triplets and three lethal gene quadruplets. This is supplemented with information about the biomass precursor(s) each lethal mutant fails to synthesize see . We herein silico SL3 combinations  and a dihydroorotate dehydrogenase (dhoDh). In silico removal of cox(N), sdhD and dhoDh genes results in inability to regenerate cytosolic FAD (see The complex GPR relationship for the cytochrome-C oxidase (CCO) reaction contributes to 18 in silico quadruple deletion of npl, nanS, st8Sia1 and st8Sia5 genes inhibit anapyruvate lyase (APL) and sialyltransferase (SIL) activities. CHO 1.2 contains four different pathways of synthesizing N–acetyl neuraminate. Loss of all APL and SIL activities shut off all four pathways that form biomass precursor N–acetyl neuraminate  for the CHO 1.2 [k, one can identify lethal reaction combinations of higher-order. For example, setting k equal to one identifies all essential reactions. Accordingly, k = 2, 3… refer to synthetic lethal pairs, triplets, etc. Upon identification of lethal reaction combinations, we elucidated the lethal deletion combinations at the gene level by making use of GPR associations. For example, in yeast, the essential holoenzyme acetyl-CoA carboxylase involves two functional subunits encoded by genes ACC1 and BPL1. Deleting either of these genes prevents conversion of acetyl-CoA to malonyl-CoA therefore both genes are essential. In contrast, methylenetetrahydrofolate reductase is an essential reaction encoded by isozymes MET13 and MET12. Both these genes need to be knocked-out to prevent the formation of 5-methyltetrahydrofolate from 5, 10-methylenetetrahydrofolate. Thus, in this case the reaction is essential but the associated genes form a synthetic lethal pair.The Synthetic Lethality Finder protocol  was appl CHO 1.2  model un CHO 1.2  to ident CHO 1.2  identifi. Glucose and oxygen uptake rates were set at 100 and 20 mmol gDW−1h−1, respectively , nitrogen (as ammonia), sulfur (as sulfate) and phosphorus (as inorganic phosphate). Glucose was used as the sole carbon source. Trace nutrients such as 4-aminobenzoate, biotin, myo-inositol, nicotinate, pantothenic acid and thiamin were included . Glucoseal study  which sho et al. . Maximumo et al.   with gls et al. . We usedDOT graphing language. Graphs were displayed using Omnigraffle 6.0TM operating on the python scripts and post-processed. The updated S. cerevisiae model was generated using COBRA Toolbox v2.0 [The effect of gene deletions on biomass component production was also systematically assessed using a Precursor Identifier algorithm see . Once a box v2.0  in Excelin silico-in vivo inconsistencies (ESG cases in Yeast) arising due to non-metabolic interactions. Incorporation of detailed information for gene transcription and translation in genome-scale models, as described in the ME-model formalisms , offer