Trypanosoma cruzi is a Kinetoplastid parasite of humans and is the cause of Chagas disease, a potentially lethal condition affecting the cardiovascular, gastrointestinal, and nervous systems of the human host. Constraint-based modeling has emerged in the last decade as a useful approach to integrating genomic and other high-throughput data sets with more traditional, experimental data acquired through decades of research and published in the literature.Trypanosoma cruzi strain CL Brener. The model includes four compartments , 51 transport reactions, and 93 metabolic reactions covering carbohydrate, amino acid, and energy metabolism. In addition, we make use of several replicate high-throughput proteomic data sets to specifically examine metabolism of the morphological form of T. cruzi in the insect gut (epimastigote stage).We present a validated, constraint-based model of the core metabolism of T. cruzi metabolism under a wide range of conditions and perturbations, and should eventually aid in the identification of urgently needed novel chemotherapeutic targets.This work demonstrates the utility of constraint-based models for integrating various sources of data  to generate testable hypotheses. This model represents an approach for the systematic study of  The increasing availability of complete genome sequences has spurred efforts to model biological systems on a comprehensive scale ,2. ConstTrypanosoma cruzi is a protozoan parasite of the order Kinetoplastida that infects humans and a wide variety of other mammals. Like other members of its order, T. cruzi is characterized by a single mitochondrion containing a complex network of DNA fibrils known as the kinetoplast [T. cruzi displays many unusual biological features: specialized intracellular compartments, such as the glycosome  [T. cruzi is the causative agent of Chagas disease, a potentially lethal condition affecting the cardiovascular, gastrointestinal, and nervous systems of the human host. The impact of Chagas disease is significant; approximately 10 million persons are affected, primarily in Latin America [etoplast . T. cruzs occur) ,17, acids occur) , and ress occur) ; widesprs occur) ; polyciss occur) ; and tras occur) , to names occur) . T. cruz America , and lif America . Despite America . However America ,27 and b America .T. cruzi strain CL Brener core metabolism. The model, hereafter referred to as iSR215 (see Methods for model naming convention), includes four compartments, 51 transport reactions, and 93 metabolic reactions covering carbohydrate, amino acid, and energy metabolism. In addition, we make use of several replicate high-throughput proteomic data sets to specifically examine metabolism of the morphological form of the parasite in the insect gut, or epimastigote stage of T. cruzi (see Methods for details on T. cruzi life-cycle). In doing so, we demonstrate the utility of constraint-based models for integrating various sources of information  to generate testable hypotheses. Previous work has used sequence analysis of the T. cruzi genome to produce improved annotations and thus extend our understanding of T. cruzi metabolism [T. cruzi of which we are aware. It represents an approach to the systematic study of T. cruzi metabolism under a wide range of conditions and perturbations for the identification of novel chemotherapeutic targets.To permit a systems-level understanding of this parasite and to provide a basis for detailed modeling of host-parasite interactions, we present a validated, constraint-based model of tabolism ,29. HoweiSR215 network reconstruction accounts for the function of 215 genes and includes 162 reactions, of which 144 are metabolic reactions and 18 are exchange reactions . Of the reactions in iSR215, 76 are supported by both genomic and direct biochemical evidence  in T. cruzi, and 100 reactions are supported by genomic or literature-derived evidence in T. cruzi or related organisms. Most reactions not associated with literature-based evidence are intracellular transports, reflecting the fact that little is known about such processes in T. cruzi.The iSR215 as grouped by pathway and compartment. As illustrated in panel A, several central pathways of metabolism are fully or partially included: glycolysis, tricarboxylic acid (TCA) cycle, the pentose phosphate pathway, oxidative phosphorylation, and metabolism of various amino acids. Most reactions are part of carbohydrate metabolism . Further, as depicted in panel B, reactions are distributed across three intracellular compartments – cytosol, glycosome, and mitochondrion – and the extracellular compartment. The membrane-spanning group consists of reactions that involve the transfer of metabolites between subcellular compartments as well as between the cytosol and extracellular space. Because iSR215 contains four compartments, there are many membrane-spanning reactions included . The glycosome of T. cruzi is thought to be relatively impermeable to metabolites, especially to adenine nucleotides and NAD(H) [iSR215 contains no reactions transporting ATP/ADP/ATP or NAD(H) between the glycosome and cytosol, i.e., in any feasible solution, there is no net change in ATP/ADP ratio or in NAD+/NADH ratio within the glycosome. Furthermore, the model also accounts for substrate level and proton motive force coupled ATP synthesis, as well as the synthesis of various intermediate metabolites critical to the formation of biomass .Figure d NAD(H) ; thus, tT. cruzi epimastigotes to generate several large replicate proteomic data sets (see Methods). A total of 1047 distinct proteins were identified across 8 epimastigote samples. Many of the identified proteins were not associated with any well-defined function . Of the identified proteins that were linked to functions, the most commonly occurring functional categories were metabolic processes (133) and translation (110). Some of the more commonly occurring functional subcategories of metabolic processes were: nucleobase, nucleoside, nucleotide and nucleic acid metabolic processes (68); amino acid and derivative metabolic processes (61); and carbohydrate metabolism (44). The complete proteomics results can be found in additional file We used cultured iSR215  to an epimastigote stage-specific model . If there was no evidence for the expression of a given protein in the epimastigote stage, the upper and lower bounds of the corresponding reaction  were constrained to zero, thereby restricting the flux of the reaction to zero. In the case of a reaction catalyzed by an enzyme complex, we allowed the reaction to occur  if any protein component of the complex was detected by the proteomics experiment.The proteomics data were used to constrain A graphical depiction of a section of the compartmentalized reconstruction is shown in figure Graphical representations of flux distributions calculated using the genome-based full model and the epimastigote, stage-specific model are depicted as additional files . As shown in table All reactions in We also simulated all possible double reaction deletions, i.e., simultaneous elimination of two reactions. In total, there were 10440 double deletions. In the full model, 1968 of these cases proved to be lethal. Most of these (1872) were "trivial" in that the double deletion involved at least one reaction that was lethal in a single deletion. There were 96 non-trivial double deletions, i.e., involving reactions that, while not lethal individually, were lethal when deleted together. In the epimastigote model, there were 3063 lethal double deletions, including 2880 trivial and 183 non-trivial cases. For a complete listing of all single and non-trivial double reaction deletions, see additional file iSR215 network reconstruction, we used the model to determine byproducts of metabolism under aerobic and anaerobic in silico culture conditions  is comparable to that reported for other organisms, e.g., 83% for Saccharomyces cerevesiae [Leishmania major [Escherischia coli [Our attempts to validate the model by comparing predicted and experimentally observed reaction lethality should all be interpreted in light of three caveats. First, as noted in Table  similar , but the similar . Second,revesiae , 70% foria major , 91% forhia coli .T. cruzi epimastigotes, as opposed to other morphological stages or related trypanosomatid species.We found three instances of reactions incorrectly predicted as nonessential in the full model that were corrected by the imposition of the epimastigote constraints when compared with published literature  is incorrect because 2) is incorrect. The reason that 2) is incorrect is that without pyruvate dehyrogenase, there is no way to produce acetyl-CoA in the core metabolic model (and no other route for consuming CoA produced in the mitochondrion). Adding an additional source of acetyl-CoA and sink for mitochondrial CoA results in a positive-growth solution by FBA. Threonine dehydrogenase, which might otherwise produce acetyl-CoA is inactivated in the epimastigote model. Glycosomal fumarate reductase is also incorrectly predicted to be essential in the epimastigote case. The reason for this incorrect prediction is that without glycosomal fumarate reductase, there is no existing route to stoichiometrically convert NADH back to NAD inside the glycosome. Adding such a process back to the model results in a positive-growth solution by FBA. Glycerol-3-phosphate dehydrogenase and glycerol kinase could fill this role, but they are inactivated in the epimastigote model. Thus, these incorrect predictions may be either due to the incomplete nature of the model  or perhaps because of possible changes in protein expression that are not explicitly represented in the model .Three activities were wrongly predicted to be essential in the case of the epimastigote model when compared to published data  malic enzyme, 2) pyruvate kinase, and 3) succinate – CoA ligase. Here again, these discrepancies are likely due to the fact that the core model remains incomplete. Specifically, we have not attempted to fully account for proton gradients in rylation . This exT. cruzi, the protozoan parasite responsible for Chagas disease. This model accounts for central metabolic processes such as ATP generation and production of key intermediate metabolites. Moreover, the model includes three major intracellular compartments: the glycosome (a characteristic feature of T. cruzi and other trypanosomes), the mitochondrion, and the cytosol. Thus, many key features of trypanosomatid metabolism are represented. Most reactions in the model are supported by both direct biochemical evidence from the primary literature and by the genome annotation of T. cruzi. Many aspects of the cellular physiology and data concerning gene/reaction essentiality are accurately captured by iSR215. Future work on iSR215 will initially be focused on expanding the scope of the model to include all known T. cruzi metabolic reactions using procedures similar to those described here. Previous experience suggests that as the model is expanded and refined through successive iterations, the accuracy of the essentiality predictions will improve [We have presented the first constraint-based metabolic model and analysis of  improve ,57-59. WT. cruzi epimastigotes specifically, applying the proteomic constraints produced the correct prediction; without these additional constraints, the model prediction was incorrect. Constraints reflecting data on gene expression have previously been shown to improve the accuracy of constraint-based model predictions [iSR215 . Thus, constraint-based models can provide a systematic framework for the interpretation and evaluation of findings from proteomics experiments.Our incorporation of several replicate high-throughput proteomic data sets illustrates the integrative capabilities of constraint-based models. This data was used to further constrain the space of possible solutions and enabled us to examine stage-specific aspects of metabolism. In the available instances where experimental confirmation was available for dictions ,61. Our Our comprehensive gene and reaction deletion testing illustrates the way in which constraint based models can become tools for drug discovery. Each of these predictions represents a testable hypothesis. Assuming that at least some of the reactions predicted to be essential are experimentally verified, these suggest points of vulnerability in the parasite's metabolism, which could potentially be exploited to develop new, urgently needed chemotherapeutics. Additionally, model-based predictions of the effect of combined environmental manipulations and specific reaction inhibitors can be generated using this model.T. cruzi genome , information from biochemistry textbooks and standard databases , and primary biochemical literature on T. cruzi or closely related species . To identify relevant primary biochemical literature, we searched PUBMED for review articles on Trypanosomatid metabolism, with special focus on the pathways of central metabolism . For individual reactions under consideration, we searched using a common variant of the reaction name, plus T. cruzi or the name of a related species . We examined hundreds of articles in detail and screened the abstracts of many more. While we cannot claim to have exhausted the literature on T. cruzi metabolism, we searched to the point of reaching a high degree of confidence for most reactions in the model . In some cases, references sought in connection with one reaction were found to report activity measurements corresponding to other model reactions. The recently published genome-scale constraint based model of L. major was also used as a guide in model construction and analysis [The theoretical basis and procedures underlying construction and analysis of constraint-based models have been extensively reviewed ,8. The p, our inanalysis . These iWe defined core metabolism as those metabolic reactions directly or closely related to energy generation (ATP) or involved in production of critical metabolites . Transports between intracellular compartments were often represented as simple, bidirectional diffusion reactions, or occasionally as co-transports with hydrogen ions, as there currently are no data to suggest more specific mechanisms. This approach has proven useful as a first approximation for representing eukaryotic intracellular transport processes .The glycosome is thought to be relatively impermeable to metabolites, especially to adenine nucleotides and NAD(H) . Therefom × n, where m is the number of metabolites and n is the number of reactions. Each element Sij represents the stoichiometric coefficient of metabolite i in reaction j. If the metabolite in a particular reaction is a reactant, the coefficient is negative (the metabolite is consumed by the reaction); if it is a product, the coefficient is positive (the metabolite is produced by the reaction). The S matrix was then used as the basis of further analysis, i.e., Flux Balance Analysis . Model construction and analysis were carried out using SimPheny .Once the network was reconstructed, the list of metabolic reactions was transformed into a stoichiometric matrix (S), i.e., the mathematical representation of the reconstructed network. The dimensions of the S matrix are i' to denote in silico, followed by the first author's first and last initials ('SR'), followed by the number of genes that are part of the model ('215').We follow the previously established convention of naming constraint-based models . Model nOnce constructed, the constraint-based model was analyzed using FBA. In essence, FBA uses linear programming (LP) based optimization to identify a particular flux distribution  that optimizes a given metabolic objective . The LP optimization problem is formulated as:S is the stoichiometric matrix defined above. Each reaction flux is subject to lower and upper bounds, as indicated . The solution to this problem is an optimal flux distribution, v, a vector that contains flux values for each reaction in the network. The solution is optimal in the sense that it maximizes the flux through the objective Z.Z is the objective function; for this study, Z was defined as the biomass equation (see below). The second two statements are the flux constraints. S is a transformation of the reaction flux vector, v, to a vector of time derivatives for each metabolite concentration:Mathematically, in silico growth conditions can be defined via ai and bi. For example, if an analysis is to be conducted with glucose, but not glycerol, as an available metabolite, the flux for a process corresponding to glucose input would be assigned some nonzero positive maximal value, while the flux for a process corresponding to glycerol input would be constrained to be zero (ai = bi = 0). In all of our FBA simulations, we allowed the following extracellular metabolites to enter the system: glucose, glutamate, proline, aspartate, threonine, phosphate, CO2, water, and NH4; oxygen was allowed to enter in aerobic, but not anaerobic, simulations. The following extracellular metabolites were allowed to leave the system: succinate, alanine, glycine, acetate, glycerol, oxygen, water, CO2, NH4, and hydrogen.Since the time constants for metabolic transients are fast (< tens of seconds), but the time constants for cell growth are long (hours to days), the metabolites can be considered as existing in a quasi-steady state . This lein T. cruzi has been reported as ~46 nmol/min per mg of protein [i) on each reaction to ~0.65 mmol/hr per gm dry weight. In a similar fashion the maximum flux on glutamate uptake was set to ~0.027 [We also incorporated information from the literature on maximal rate of uptake of glucose and amino acids where possible. For example, Vmax for glucose uptake  protein . Given t protein , this coo ~0.027 , on proto ~0.027 , on ATP-o ~0.027 , for aspo ~0.027 , and on Bacillus subtilis [B. subtilis using detailed modeling and isotopic tracer data. In constructing our equation, we made several minor changes to the biomass requirements previously reported, simply to account for the fact that iSR215 does not include certain metabolites and to enable cycling of cofactors. Although the exact requirements of B. subtilis for critical metabolites are no doubt different from those of T. cruzi, previous work has demonstrated that FBA results are relatively insensitive to changes in biomass component stoichiometric coefficients [In FBA, optimization is used to find a particular flux distribution that maximizes a given metabolic objective. Typical metabolic objectives chosen for optimization include maximization of ATP production, byproduct secretion, or biomass production. In our work, we chose to optimize for biomass production . Biomasssubtilis . These rficients .i = bi = 0) and reran FBA in each case. If no solution was found (biomass reaction flux = 0), the reaction was deemed essential; if a solution was found, the reaction was deemed nonessential.To simulate the effect of deleting reactions, we constrained each of the reactions in turn to have zero flux  in T. cruzi is complex [T. cruzi replicates intracellularly, as a nearly spherical amastigote, in many cell types. Rupture of the cell releases non-dividing trypomastigotes, which circulate and infect cells in remote organs and tissues. When ingested by blood sucking insect vectors, trypomastigotes transform and replicate as epimastigotes in the insect digestive tract. Epimastigotes are rapidly lysed by complement and are not infective for mammalian hosts. Conditions in the colon and rectum of the reduviid bug induce metacyclogenesis, or differentiation of non-infectious epimastigotes into infectious metacyclic trypomastigotes. The cycle is completed when the bug deposits contaminated excreta near the bite wound and the metacyclic trypomastigotes are mechanically introduced into the host. The metacyclic trypomastigotes are complement resistant and circulate in the host briefly prior to infecting a host cell.The life cycle of  complex . In the T. cruzi involves experimental results derived from the insect gut epimastigote stage. We performed both 2D gel analysis and 2D nano LC MS/MS to produce proteomic data for T. cruzi epimastigotes. We used these data to further constrain iSR215 to be epimastigote-specific. Epimastigotes were grown exponentially in LIT medium, supplemented with 10% fetal calf serum, and different subcellular fractions were collected using subcellular proteome kit . Each fraction was submitted to 2D nano LC MS/MS, as previously described [T. cruzi database using Bioworks v3.2. Peptide and protein hits were scored and ranked using the new probability-based scoring algorithm incorporated in Bioworks v3.2. Peptides identified as possessing fully tryptic termini with acceptable cross-correlation scores , with delta Cn greater than 0.25, and with a probability score of less than 0.0001 were initially accepted. For increased stringency we used reverse database search to adjust the above scores to obtain less than 1% false discovery rate. Proteins that passed this final criterion were accepted for use in the model.Most biochemical literature relating to escribed . Proteini = bi = 0, see above). For example, the protein responsible for glucokinase activity (reaction ID "GLUKg") was detected in the epimastigote by our proteomics experiments; therefore, no special constraint was applied to this reaction. On the other hand, the proteins responsible for aldose 1-epimerase activity (A1Eg) were not detected by the proteomics experiments; therefore, this reaction was constrained to have zero flux, since the data suggested no enzymes to catalyze the reaction were expressed. For reactions requiring multiple proteins, we allowed the reaction to occur if there was evidence for the expression of any of the corresponding proteins in the proteomics data.The model resulting from the procedures described in "Metabolic Reconstruction" above was designated as the "full model," since all reactions included could operate at non-zero flux values. We used the proteomics results to apply further constraints to the full model, resulting in a stage-specific "epimastigote model." Specifically, if there was no evidence at the protein level supporting the existence of a specific metabolic reaction in epimastigotes, then that metabolic reaction was constrained to a flux value of zero  using full model and epimastigote model.Click here for fileiSR215 in SBML format. iSR215 in SBML format.Click here for fileT. cruzi core metabolic network. Map illustrating the core metabolic network in T. cruzi.Click here for fileFlux distribution for full model. Graphical depiction of flux distribution for full model.Click here for fileFlux distribution for epimastigote model. Graphical depiction of flux distribution for epimastigote model.Click here for file


Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome. The key parameter is the on-rate of RNAP binding to the promoter (k_on), and we compare the total number of transcripts produced from a plasmid vector generated as a function of this rate constant, for two versions of our gene expression model, one incorporating the host cell genome and one excluding it. By sweeping parameters, we identify the k_on range for which the difference between the genome and no-genome models drops below 5%, over a wide range of doubling times, mRNA degradation rates, plasmid copy numbers, and gene lengths.Simulating the major molecular events inside an We assess the effect of the simulating the presence of the genome over a four-dimensional parameter space, considering: 24 min <= bacterial doubling time <= 100 min; 10 <= plasmid copy number <= 1000; 2 min <= mRNA half-life <= 14 min; and 10 bp <= gene length <= 10000 bp. A simple MATLAB user interface generates an interpolated k_on threshold for any point in this range; this rate can be compared to the ones used in other transcription studies to assess the need for including the genome.Exclusion of the genome is shown to yield less than 5% difference in transcript numbers over wide ranges of values, and computational speed is improved by two to 24 times by excluding explicit representation of the genome. The complexity of cellular pathways and networks often makes it difficult or impossible to reliably predict the behavior of a system from knowledge of its components, and thus there is considerable interest in formulation of quantitative, predictive mathematical models of cellular functions. Such efforts, collectively described by such terms as systems biology and  biology -9, aim iThe internal dynamics of cells are driven by the kinetics of a complex set of biochemical reactions: the state of the cell may be viewed as the numbers and binding states of all species of interest, and the time evolution of that state is defined by how those species react with one another. A central challenge in cellular modelling is to formulate correct biochemical reaction schemes to represent a process of interest, and then to populate the reaction system with appropriate rate constants -9. WithiEscherichia coli to deduce the numbers of free RNA polymerases available to transcribe a target gene of interest [We have previously carried out a study aimed at the first of these problems, in which we used bulk expression data from interest ; this ininterest  able to interest -18, or ainterest ,8,19-21,interest ) to creainterest .We turn now to the second of the challenges mentioned above, that of computational time. Having the rest of the genome present in the system, even in our bulk-averaged way, added significantly to the computational demands of the simulations. Further investigation shows, however, that there are regimes in which the target system is not significantly affected by the presence of the remainder of the genome, and may thus well approximated by excluding the genome portion and simulating only the target system. The key quantity is the "on rate" of binding between RNA polymerase and the promoter of the target gene: for certain ranges of this parameter, the perturbation introduced by the presence of other genes (the rest of the genome in the cell) is small enough to be neglected, saving significant amounts of computational time. We explore the details of these ranges, as a function of other system parameters, below. We view this work as complementary to the various ongoing large-scale cellular simulation projects ,19,22-25Escherichia coli as a model organism, we have formulated a picture of the biochemical reactions underlying gene expression from an inserted plasmid carrying a promoter controlling the transcription of our target gene. We implemented a "mean-field" modelling approach, generating genome-wide averages for the mean transcript length, mean elongation time, and so on, adjusting the model parameters so that it generated numbers matching the bulk averages that had previously been reported experimentally [Our technique relies on the existence of experimental results  reportinmentally ,12. A fumentally .v, is used to represent volume: v → 2v, with rate constant k = ln(2)/τ, where τ is the doubling time of the cells. At cell division, all species present are divided between two hypothetical daughter cells, and the simulation follows one of these daughters. We treat the system as ergodic, and average over long times for a single cell to obtain ensemble averages across the cellular population.The cellular volume grows exponentially until a threshold is reached, at which point it is approximately halved  and exponential growth restarts. A counter species, RNA polymerases (Rpoly) are responsible for initiating and catalyzing the transcription of messenger RNA (mRNA) strands. As the model assumes all mRNA transcripts reside in operons, Rpoly binds to promoter sequences in the DNA (operon) and forms a closed complex (Rpoly+operon→closed_Rpoly_prom). This closed complex then must isomerize into an open complex (closed_Rpoly_prom→open_Rpoly_prom) before transcription can begin.RNA polymerases clear the promoters, leaving those sites free to bind additional enzymes while transcription proceeds further down the DNA strand. We model this by regenerating the promoter after clearance occurs, forming an enzyme-template complex plus the original site: open_Rpoly_prom→Rpoly_operon+incom_mRNA+operon. We create a nascent transcript (mRNA_incom) at this step to allow subsequent translation to proceed; this feature will prove very helpful in studying future simulated studies of protein synthesis. Conservation of the number of promoters is maintained: when the enzyme-template complex finishes elongation, only the enzyme and the polymerized product are released.elongation = ρ/λ, where ρ and λ are the polymerization rate and length of template, respectively.To avoid the complexity of accounting for each enzyme at different stages of elongation, a single reaction is used to represent the process of completing the mRNA chain: Rpoly_operon→Rpoly+mRNA. Compliment to this reaction is the disappearance of the nascent transcript made available during transcription: incom_mRNA→, where  is a null placeholder. Both the reactions have the same elongation rate constant that can be summarized as krep = (ν/1.5)/τ, where ν and τ are the average number per cell and cellular doubling time, respectively.Since the kinetics of RNA polymerase assembly are not fully known, the model is simplified by treating enzyme production as a zero-order process in which enzymes appear from outside the model at a constant rate: →Rpoly. The enzymes are partitioned at cell division like all other species. The rate constant for production can be summarized as kDNA replication in bacteria is a complex process involving multiple replication forks. We represent the coding portion of the genomic DNA by the number of operons present (operon), and simplify the replication process as a zero-order process: →operon. Rate constants for this process are chosen to match the number of genomes per cell at different growth rates.E. coli, and we represent the degradation of mRNA by these enzymes with first-order reactions: mRNA→, and incom_mRNA→; the latter is an additional RNase-driven degradation, beyond the above-mentioned rate of disappearance of incomplete mRNA through conversion to complete mRNA strands.RNases act to destroy mRNA in lac operon. The model keeps track of which gene on the mRNA operon Rpoly is currently transcribing and makes available completed transcripts of the nascent operon (this latter point will prove relevant in future protein synthesis models): Rpoly_operon1→Rpoly_operon2+mRNA. In response to the genome-wide average of 6.9 genes per operon [We assume that all genes in the genome are clustered into operons: groups of genes transcribed from a single promoter, as in the r operon ,12 the mIn addition to messenger RNA, other forms of RNA collectively known as stable RNA (sRNA) are produced within the cell. Since sRNA is transcribed but not translated the model does not consider nascent sRNA production.We have constructed two versions of the model, one containing a representation of the host cell genome and the reporter gene, the other neglecting the cellular genome and representing only the reporter gene on the plasmid. The with-genome model incorporates 26 reactions involving 27 species, while the no-genome version has 10 reactions involving 10 species; the two versions are shown schematically in Figures The chemical kinetics of this system were initially simulated using the Gillespie Monte Carlo algorithm -28, and The original experimental measurements in the literature were carried out over a range of cellular growth rates, each of which yielded different average quantities of biomolecules per cell. Stochastic simulations of our system were carried out at each experimentally-examined growth rate  and samk, the Poisson rate of the forward reaction is kab/V, where a and b represent the numbers of molecules of species A and B present, and V is the reaction volume . We use the unit "n" to represent the number of molecules present in the system, rather than concentration units such as molarity. To advance the simulation, the timing of the next reaction event is randomly selected using the exponential distribution of inter-event times for the set of Poisson processes representing the reactions, and the probability of each reaction being the one that occurs at that instant is given by its fraction of the sum of all reaction rates [Deterministic chemical kinetics apply in the regime of large numbers of randomly interacting molecules. Inside cells, molecule numbers are often small enough to produce significant fluctuations ,20,28-44on rates -28.in vivo consistent with normal diffusion [Bacterial cells have often been approximated as well-stirred reactors: based on their small size, it is assumed that diffusion is sufficiently fast to yield a well-mixed system. Early experimental results showed protein mobility iffusion , and thoiffusion ,46-49 haiffusion  have repiffusion ,52, and iffusion ,46,49,53The gene expression model was initially implemented using BioNetS  , which pTo derive the on-rate constant between RNA polymerase and the reporter promoter where there is 5% difference in transcript average between models, we employ bulk cellular averages obtained by Bremer and Dennis for several different cellular growth rates . We implThe model has been constructed to be as detailed as possible, using all available information about the biochemical processes underlying gene expression. This leads to a large number of species and reactions, the full details of which are provided in the Appendix. For a derivation of average genome parameters, please see Iafolla and McMillen .The first step in deriving the on-rate constant that determines a 5% difference in transcript averages between models is to obtain steady-state values of all species in the simulations. Figure R2 ≥ 0.90 were accepted for interpolation; the range was narrowed until this level of linearity was achieved.Parameter sweeping begins by using on-rates that vary by a factor of 10 Figure . When th2 ≥ 0.90; this is achieved by using a minimum of 1000 cell divisions, although some simulations use more cell divisions to obtain the desired linearity. Since the doubling time of the cells is varied, the total duration in real time varies among the simulations; the number of cell divisions explored appears to be the key factor in obtaining well-converged statistics, rather than the absolute duration.The duration of the stochastic simulations was varied to obtain linearity with R-7 n-1s-1 to 1 n-1s-1); the range of on-rate constants that bound the percent difference in the above statistical parameters by 5% was investigated for duration analysis (this range was from 10-5 n-1s-1 to 10-2 n-1s-1). Ultimately, longer-duration runs produced averages that were not statistically different from those obtained after 1000 divisions , implying that longer durations only increase computational expense.The minimum 1000 cell division duration was deduced by qualitative analysis of multiple simulations with the same seed but different durations; we examined the effect of duration on the mean values obtained from the reporter mRNA histograms. The on-rate constants used in the duration analysis was determined by comparing the histograms between models over a range of on-rate constants  were derived using standard chemical kinetics and solved numerically using the solvers provided by MATLAB. To take cell growth and division into account, the ODEs were solved one cell cycle at a time, with the numbers of molecules at the end of the cycle cut in half to simulate division, then used as the initial state for the next cell cycle. Within each set of parameter values, each ODE was run for ten cell cycles to allow the system to reach a steady state, then for more ten more cell cycles, during which state values were averaged to obtain the average mRNA numbers for the reporter gene. As shown in Figure Similar to the parameter sweeping carried out for the stochastic simulations, we used the deterministic simulation results for each parameter set to calculate the RNA polymerase-promoter binding on rate, k_on, at which there will be a five percent difference between the models with and without a representation of the host cell genome; for the deterministic results, the 5% threshold was determined using the fzero function in MATLAB, which searches for a zero-crossing between two given points.The on-rate (k_on) threshold above which a 5% deviation between the genome and no-genome models occurred has been calculated explicitly only at the set of parameter values listed above . To allow the k_on threshold to be calculated at values other than those explicitly simulated, we created a MATLAB script to carry out the necessary interpolation using a local minimization method. In local linear fitting, to find the unknown point at a desired parameter value, one draws a straight line connecting the known points on either side of the desired value, and takes the point on that straight line as the interpolated result at the desired parameter value. Note that this process minimizes the total distance between the interpolated point and the two known points, and we use this idea to perform our interpolation in our 5-dimensional space . For any single given 4-dimensional parameter set, the nearest available set of parameter values is determined by finding the two nearest parameter values in each direction on this 4-dimensional mesh; combining all four dimensions yields the 16 nearest points on the mesh. Since these 16 data points do not generally fit well to a linear function, we obtain the interpolated on-rate value for a given parameter set by searching for the k_on value that minimizes the total distance in 5-dimensional space to those nearest 16 points, using the MATLAB fminsearch function to carry out the minimization operation.The above interpolation has been implemented in MATLAB script that presents a simple user interface allowing the user to enter the desired parameter values (within the ranges spanned by the simulations), after which the script will carry out the interpolation for the given point and return the k_on value above which a 5% difference arises between the genome and no-genome models: any promoter on-rate constant larger than this predicted value can exclude the computationally expensive genome from the simulations without creating more than a five-percent error, while any constant smaller than this should include the genome. The user interface is shown in Figure -10n-1s-1 to 10-7 n-1s-1) prevents the RNA polymerase from binding to the promoter, thereby producing an insignificant number of transcripts, usually less than one per cell division, as shown in Figure As shown in Figure 2 to 104, a magnitude that is much more amenable to experimental access and thus potentially more significant for use in other studies.Figure To test the accuracy of the interpolated on-rates, the on-rates were entered back into both versions of the model and run for 30 different simulations seeds for a duration of 30 cell divisions, after creating steady state values for all species within the model. The percent differences were assembled and statistically compared to the population mean of 5% using a level of significance α = 0.95. This process was repeated for all 240 different kinetic situations generated using the stochastic simulations. There was no statistical difference between the population mean and the sample mean obtained from the simulations (data not shown), thereby ensuring that the interpolated values are the correct ones for producing a percent difference of 5%.Excluding the genome from simulation studies does reduce CPU simulation time in the computationally intensive fully stochastic simulations. To illustrate this, the verification runs were used for comparison between models; these simulations employ the same kinetic parameters and duration, and offer a large population size (since each run was repeated multiple times with varying random seeds).Dividing the average run time of the genome by those models excluding it produces a direct measure of the benefit of excluding the host cell genome in the simulations. As Figure The simulation spends most of its time on the RNA polymerase binding/binding reactions: the reactions operon_ns+Rpoly, operon_s+Rpoly, and plas+Rpoly in the with-genome model, and simply plas+Rpoly in the no-genome model. Figures Figures Simulating the translation of mRNA to protein, downstream of the transcriptional events discussed here, requires a significantly more elaborate model  with corThe ability of RNA polymerase to produce an approximately equal amount of transcripts at large enough binding constants for both models raises an important question: are there enough RNA polymerases left when a large rate law exists for the reporter promoter to transcribe the necessary genomic genes for cell division? The presence of a large rate for the reporter transcript will produce metabolic strain on the cell -56, possEscherichia coli genomic networks using chemical equations results in large reaction schemes, with reactions potentially proceeding at a wide range of rates. The large computational time required to simulate these reactions is a persistent problem for large-scale cellular simulation. To help address one aspect of this problem, we have investigated the necessity of simulating the presence of the E. coli genome when studying a target gene inserted on a plasmid. The presence of the genome, introduced using our "mean-field" approach, is felt by the target gene through the competition for free RNA polymerases available to bind to the target gene's promoter and generate transcripts. However, there are ranges of the parameter space in which the presence of the genome yields a negligible difference in the number of reporter transcripts produced from the target gene, and in these cases is it possible to exclude any explicit representation of the genome and save the computations required to simulate the associated additional reactions. Stochastic simulations show speed increases of from two to 24 times, when the genome is excluded from our models. We have generated a set of fully stochastic simulations and found the promoter on-rate values for which the genome and no-genome models differ by less than 5%, and augmented these stochastic simulations with cross-validated deterministic runs to increase the number of sampled points in parameter space. Within the ranges of our four independent parameters , we have produced a MATLAB user interface that will allow the user to input any set of parameters and obtain the promoter on-rate value (k_on) above which the effect of the genome will fall below our 5%-difference threshold. Given the increasing computational demands of cellular simulations, we hope that this approach will aid in the efficiency of other studies, and suggest other methods in which portions of the full cellular system may be excluded without significantly affecting the final results.Efforts to create accurate, quantitative models of MAJI conceived of the study, designed molecular simulations, implemented stochastic simulations, compiled data, analyzed results and drafted the manuscript. GQD carried out deterministic simulations, compiled data, analyzed results, implemented networked runs of stochastic simulations, and produced the MATLAB parameter interpolation routines and user interface. DRM participated in design of the study, and helped draft and finalize the manuscript and revisions. All authors participated in the writing and approved of the final form of the manuscript.Below is a detailed explanation of the gene expression model, expanding on the information presented in the Methods section. A full list of kinetic parameters for each reaction is provided in Iafolla and McMillen .The following is a complete list of species names used in the model:The cellular processes represented in the model are discussed individually, below:To reflect the exponential growth of bacterial cells in a nutrient-rich liquid culture, we include cell growth and division, incorporated as a process that grows to a threshold volume and is then halved. At division, all species have their numbers cut approximately in half: for large numbers, a binomial distribution is used to calculate the new number, while small numbers (less than 100) have each molecule explicitly checked and randomly assigned to a daughter cell with equal probability . The modv, whose exponential growth is governed by the following reaction, with rate constants adjusted to produce various doubling times to match the experimental conditions being examined:Cell volume is represented by a "counter" species, v = V0, and cellular division occurs when v reaches 2V0. Our model treats all processes as stochastic, but the resulting degree of variability depends strongly on the number of molecules participating in the reaction. The range of cell division times can thus be tuned by the choice of V0; here we set V0 = 1000, which yields a very slight degree of variability in the cell division times. This variability arises from two sources: the stochastic rate of reaction R1, and the random assortment of the counter v between daughter cells at division: v is cut only approximately in half at cell division, like all other species, and thus the initial volume after cell division lies in a small range around V0.For a doubling time τ, the rate constant is set to k = ln(2)/τ. The reaction is initialized at Since the only enzymes used in this model are RNA polymerases only binding to promoters need consideration. The bimolecular reactions for RNA polymerase (Rpoly) binding to a promoter on a gene (plas or operon) are shown below:v/V0.RNA polymerase initially forms a closed-complex with the promoter region, which then undergoes isomerization (R3) into an open-complex. The rate constants for R2 and R3 are adjacent to the reactions; that for R2 is scaled to mimic dilution of cell cycle progression: as the cell grows, the increase in volume decreases the probability of the two species coming into contact and reacting, effecting reducing the rate constant ; this efFollowing binding, the enzyme clears the promoter at a particular rate. The elementary reactions for this process are shown below:We create a nascent transcript (mRNA_incom) at this step to allow subsequent translation to proceed; this feature will prove very helpful in studying future simulated studies of protein synthesis. Reaction R4 also shows an important assumption: the regeneration of a binding site after clearance allows another enzyme to bind to the same gene, creating the multiple simultaneous elongation processes observed in actual bacterial cells.To avoid the computational complexity of accounting for all elongating intermediates (growing mRNA and peptides of every possible length), the following approximation has been employed: a single intermediate is converted to the final product at a rate corresponding to the average time taken by the complete polymerization process. Using average elongation rates for specific cell growth rates as specified by Bremer and Dennis , the eloelongation = ρ/λ, where ρ and λ are the polymerization rate and length of template, respectively.Compliment to this reaction is the disappearance of the nascent transcript made available during transcription: incom_mRNA→, where  is a null placeholder. The elongation rate constant can be summarized as kE. coli, the average number of RNA polymerases and genome equivalents per cell are known at several cellular growth rates [Many processes involved in molecular biology are either too complex to model or not characterized at present. In our model, we use simplified zeroth-order production rates for complicated species involved: although the assembly details of some species are not fully available, there is considerable information on population size of these species. In th rates , and therep = (ν/1.5)/τ, where ν and τ are the average number per cell and cellular doubling time, respectively.The operon species in R8 is representative of the genome, since our model employs RNA polymerase binding directly to the promoter sequence of the average operon. The rate constant for production can be summarized as kE. coli implies that mRNA possess a finite life-span. The following reactions are used to represent mRNA degradation:The presence of RNases in h, the rate constant for R9 and R10 is set to k = ln(2)/h.For a half-life We assume that RNases can degrade nascent transcripts. To account for degrading a transcript while it is being created we propose the following elementary reaction and rate constant:The reaction indicates that an RNA polymerase currently producing a transcript becomes an unscathed RNA polymerase and a degraded mRNA. Although this reaction implies that all RNA polymerases producing a transcript are subject to degradation, the proportionality to incomplete transcripts is specified in the rate constant. The Rpoly_mRNA species present in the denominator of the rate constant makes the reaction rate independent of the number of elongating RNA polymerases.lac operon [We assume that all genes in our relevant genome are clustered into operons. Our model creates a single transcript for the entire operon, mimicking the c operon . To makeThe numeric suffix on the Rpoly_operon species represents the gene number adjacent to the promoter. Notice that the rate constants for the above reactions are all equivalent. The release of the mRNA while the RNA polymerase is still elongating the operon allows ribosomes to bind and perform translation without requiring additional species; the act of transcription is conserved since RNA polymerase only binds once to the promoter. Evidently, the total time to transcribe all three genes is equivalent to the time for transcribing the whole operon.Contrast to mRNA production, stable RNA is easily produced. Since this RNA is not translated there is no need to include ribosomes translating complete transcripts before the operon is finished elongation. Hence, the length of stable RNA in the model is equivalent to the average stable RNA operon length.). The ZIP file extracts to a directory containing files corresponding to the with-genome and no-genome versions of the model.Bionets files for the models. Files used to generate the stochastic simulations, using the Bionets stochastic simulation tool , and get back the k_on rate above which excluding the genome will make less than a five percent difference in the simulated transcription levels of the plasmid-borne gene of interest. The ZIP file extracts to a directory containing three files that should be placed in the directory where the user interface will be used; the interface may be executed by opening MATLAB and running the script kon_gui.m.Click here for file


The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at:  The Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY An example of the intricacies involved in the selection of the proper data source, when attempting to integrate KEGG metabolic pathways, is the following:• already converted SBML models f• the use of KEGG API for this scope, requires extra programming,• handling the flat files is unwieldy,• the use of KEGG2SBML tool for their conversion to the SBML format has its own drawbacks, since it is operating in Unix-like platforms only. At the same time, downloading the relevant to the conversion flat files, requires the knowledge of the exact url address and path, to the respective directories of the KEGG ftp server.The tool KEGG2SBML was developed in order to create SBML models, by parsing KEGG data found in the KEGG LIGAND database, under the ERATO-SORST Kitano Symbiotic Systems Project . This toKEGG Markup Language (KGML) files provide graph information that can be used to computationally reproduce and manipulate KEGG pathway maps . NeverthSecondly the orientation of the KGML models, residing in the KEGG pathway database, mainly for visualization purposes, incurs serious pitfalls, with respect to the biochemical consistency of the described models for simulation tasks. In order to attain a nicer graphical layout, in terms of clarity of the delineation of the graph, many compounds and reactions are included more than once in the same pathway. This poses a problem regarding the biochemical consistency of the model, given the fact that all reactions take place in the same cell compartment, unless modelling a multi-compartmental model. Consequently this results in erroneous stoichiometries for the respective reaction network. Also the existence of ambiguities regarding the appellations of the compounds included in a model, which represent the result of unresolved inconsistencies in the nomenclatures used, results in problematic definition of the quantitative characteristics of the participating metabolic pools  in the model. This incurs additional flaws and increases the complexity of the model. Unfortunately, all these problems remain unresolved in the KEGG2SBML approach, where the thus transformed models are still simple mappings of the KEGG database to static SBML models, with no addition of kinetic properties and not any correction on their descriptive problems.Furthermore, when scrutinizing the KGML and SBML versions of KEGG residing metabolic pathways, transformed through KEGG2SBML, differences are found between them, with respect to the composition and the stoichiometry of the resulting reaction networks. Indicatively, the SBML version includes fewer reactions than the KGML model and for each reaction it incorporates species such as: H2O, H+, NADH, NAD+, ATP, ADP, CO2, CoA. For simulation purposes though, elimination from the network of such compounds, which participate in numerous reactions simultaneously and which pools are considered abundant, is an acceptable model simplification practice . TherefoIn this work, we implemented the KEGGconverter tool, which executes and integrates -for the first time- the following procedures: pathway merging, conversion to SBML format, proper renaming of the species and addition of default kinetic equations, using as sole input the set of selected KGML KEGG pathway file. The resulted output is an SBML model, which can be tested in a simulation environment, and can be used as a reference point for further improvement and accurate tuning of the model.KEGGconverter is a de novo developed software application. It implements a novel algorithmic workflow Figure , which a• KGML merging,• conversion to SBML,• and the addition of kinetics.In order to enable the fusion of many interrelated KEGG pathways, for the scope of building system-level models, an algorithm was implemented, to merge selected KGML models. This entails the concatenation of all entries, relations and reaction elements from the selected pathways, into a single file. During this concatenation, the 'id' attribute values from the <entry> elements and the 'entry1-2' attribute values form the <relation> elements are modified, so as to incorporate an indication of the file number, they have originated from. Additionally, during the concatenation process, <relation> elements with target pathways that are to be merged, are removed. Thus, discrepancies in the connecting arrows of the resulting integrated pathway are avoided. These arrows will no longer point (map links) to the already incorporated pathways, in the model.Subsequently the conversion of the single file, derived from the previous step to a SBML file, is handled. This is a two stage process, which entails a combination of XSL transformations and then DOM processing through Java programming. This procedure is depicted at Figure Firstly, the elements of <entry> group in the KGML file are forming the <species> group in the SBML format. This group includes compounds, genes or enzymes and neighbouring pathways. Accordingly, the <reaction> elements in SBML are created, using information residing in both <reaction> and <relation> elements of the KGML input file. There, it is specified how the compounds of the network interconnect with each other. Specifically, the <reaction> elements in KGML define the reactants and products, while modifiers are defined in <relation> elements, which link to certain enzymes in the <entry> group, as well as to anchor points of the neighbouring pathways.A core idea in this stage, is the conversion of the <relation> KGML elements, which point to other pathways, into additional SBML reactions. In this way, the map link information of the KGML pathway files is retained during this process. If the map link points at an included pathway, the redundant SBML 'pathway' <species> entry will be eliminated, at the second stage of the conversion. Otherwise, these entries will be retained at the SBML file. They will be considered as conventional reactions in the final simulation, indicating group reaction flows towards neighboring pathways. In this way, more accurate constraining is attained, with respect to the putative biochemical cross-talks of the integrated pathway model with neighboring pathways, which have not been incorporated in the final model. By having distinct 'id' and 'name' values from the rest of KEGG reactions, these reactions are fully identifiable and can thus be easily modified, erased or included fully or partially to the model of the resulting SBML file. Optionally, an XSL file has been created, which removes completely these reactions, if the user wishes so.After the initial XSL transformation stage, the resulting file is already complying with the SBML format, and can be read and edited by every tool supporting this format.However, there are still two main lingering problems, with respect to the thus created SBML models, namely the nomenclature adopted for the species, and the redundancy of both species and reactions. By utilizing the source information from KEGG database, all model species are named according to their enzyme commission numbers (EC), i.e. 'ec:2.3.1.180' and 'cpd:C00229', instead of obtaining informative biochemical names. This clearly renders them illegible and incomprehensive. Another important issue is that of species and relation entries redundancy. Target is the neat and at the same time biochemically consistent, graphical representation of the KEGG pathways. Due to the previous merging step though, multiple pathway xml files are integrated in one file. Consequently, their particular species and relation entries are added in the final file, without been checked, whether they refer to the same species  or relations (reactions), which take place within the same cellular compartment. As a result, multiple graphical instances (nodes for species and arrows for relations) of the same elements are encountered, within the derived xml file. In order to eliminate these problems, the model is further processed through the use of Java programmed procedures, which utilize the Xerces XML parser and the DOM. The curation of the models entails two distinct steps:At this stage, all species are checked based on their KEGG name. If the same name is found in more than one species, then all these species obtain as 'id' value the first value found. An example can be seen at Figure The same method is applied also for duplicate <reaction> elements, where all attributes - such as name, reactants, products and modifiers - are the same. This usually happens, when fusing more than one neighbouring pathway models, which have overlapping sections.Performing this substitution however, deprives some species completely of their reference to any reaction. These "orphan" nodes are eliminated, together with other already "orphan" nodes from the initial model . However, if for any reason, the user wants to preserve these orphan nodes, this step can be overridden either by executing the appropriate commands in the command line tool, or by ticking the respective box in the web application. For a description of the commands of the command line version, one can either check the relevant supplementary material , the solution adopted here was instead, that of parsing selected flat files, from the KEGG database and creating four tab delimited files with all necessary information . In reference pathways, reactions are catalyzed by enzymes, while in organism specific pathways, genes are shown as reaction modifiers. Using the Enzyme-Organism.tab, we can correlate gene ortholog for the specific organism with the related encoded enzyme and both are assigned as a name to the entity. In many cases though, an enzyme is encoded by more than one gene, but only the first of them found in the database, is utilized in this renaming procedure, so as to avoid extremely long name tags.After matching each id with the respective nomenclature found in the KEGG tab delimited files, a tag name is created and imported in the model. This label represents a comprehensible name for the user, when the model is opened from any SBML editing tool. In order to be information rich, final names also include the original KEGG Ids. Specifically in the case of compounds, the biological name is assigned together with the original ID in brackets . In the case of enzyme encoding genes , the resulting name includes both the name of the relevant enzyme and EC number, as well as those of the related protein encoding gene .The final phase to the derivation of a fully capable pathway model for dynamic simulation purposes, is the addition of the layer of kinetic equations. For this purpose, we used a proprietary kinetic library, fruit of our previous development effort , as a pl• If a reaction is of 1-1-1 type  then the hyperbolic modifier rate-law is being added. If the reaction is reversible, the reversible hyperbolic modifier equation is being added.• For all remaining types of reactions, the mass action rate law is being added (appropriately modified for the reversible or irreversible cases). If there are modifiers in the reaction, they are retained in the SBML derived file, although the mass action mechanism does not foresee modifiers, so that no loss of information occurs. In this case, it is left upon the user to decide later, how to tackle each specific case.The implemented tool is freely available to the academic community, both as a web application as well as a java .jar command line tool. The user can select the KEGG pathways he/she wishes to process and may apply several of the following options:• merging to a single KGML model• conversion to SBML format• conversion to SBML plus the addition of default kinetic equations.Regarding the Web based version of KEGGconverter, users can access it through the homepage of the project. A user-friendly shell is wrapping the inner algorithms of the application. This ensures easy access and manipulations to its content, as the whole process is transparently and seamlessly performed. On the introductory page, the user can set all the necessary properties for performing the conversion Figure .In the web version of KEGGconverter, the inner logic of the tool is implemented through the use of listboxes and buttons, which enact appropriate PHP scripts and execute specific scenarios. The selection of input KGML files is performed through the use of three list boxes. The first one, concerns the organisms, which pathways are to be studied. All organisms are listed following the official KEGG taxonomy. Next, through the use of two list boxes, the user can create a list of pathways to be fused and converted, by selecting those from the first box and importing them to the second. After the selection step, the parameters for the type of conversion that will be performed need to be set. By using a set of radio buttons, the conversion to either pure-SBML or Celldesigner-SBML annotated model is defined. The latter encloses more detailed information , appropriate for a "vertical" biochemical description, which spans the distance from the genotype to the cellular phenotype. Pathway fusion and/or kinetic laws incorporation to the final model is implemented by ticking two specific checkboxes. Having set all aforementioned parameters, by clicking the Convert button, the requested operation is performed. When the conversion is completed, the user is navigated to the results screen. In this screen, a text box with the label "Executed Command(s):" lists the exact series of commands executed, in order to perform this conversion. A second textbox labelled "Conversion Log:" lists the output of the tool during runtime. This is useful for debugging purposes, as it provides users with the list of commands that can be used as scripts, for the command line version of the tool. Finally, a link for downloading a zip file with all converted models, can be found at the bottom of the page.Through this convenient web shell, the fusion, correction, kinetic properties incorporation and conversion tasks are performed at a snap. The user needs not download any software or dispose a computer with upgraded capacities in terms of computational power, performance of memory or storage. This tool wraps seamlessly the java implementation tool described earlier. Also, the task of software maintenance or upgrade, in order to keep up with recent developments, with respect to file formats compatibility is averted. The four tab delimited files depicted in Figure In order to highlight the application of KEGGconverter, a case study is presented concerning the integration of 6 KEGG pathways, related to the central metabolism of the Homo sapiens Organism (human), to a single model:• Glycolysis/Gluconeogenesis - map00010• Citrate cycle (TCA cycle) - map00020• Pentose phosphate pathway - map00030• Fatty acid biosynthesis - map00061• Fatty acid metabolism - map00071• Urea cycle and metabolism of amino groups - map00220.Next, we implement a comparative analysis of the performance of KEGGconverter, with two specific workflows, exploiting other state of the art tools, for the implementation of analogous tasks.Regarding KEGGconverter, the necessary data acquisition procedure that must be followed, is the selection of the 6 pathways and their transfer to the input directory of KEGGconverter [See additional file In comparison to this automated procedure, we enlist two attempts to conclude to a similar outcome, either by using KGML-ED which is capable of importing KGML files, or by a combination of other tools starting with KEGG2SBML for the production of SBML converted models.Using KGML-ED which caHere it should be mentioned, that tools such as VisANT and VANTIt seems that -apart of the KEGGconverter- the use of KEGG2SBML tool is so far, the only other functional solution for the SBML conversion task. However, this solution requires a UNIX-based operational environment, together with Perl5 and KEGG database files, as previously mentioned. The execution of the conversion is performed through the assignment from the command line of a command like: "kegg2sbml -l 2 -v 1 -c 0 ./PATHWAY/hsa/hsa00010_cpd.coord", which produces a corresponding SBML/hsa/hsa00010.xml file. The switches shown in the specific command are for the production of Level 2 Version 1 SBML files without CellDesigner annotations.At the next step the converted files were inserted in the semanticSBML tool   to a lower version (L2V1), since L2V3 SBML is not yet supported in CellDesigner. Scrutinizing the derived model, problems were revealed, introduced by the semanticSBML tool, during the SBML conversion of KEGG pathways: the model contained 6 different compartments, one for each of our case pathways Figure . Even afAn alternative approach for the merging of the six SBML models was made possible by running the simple bash script "mergeSBML.sh" mentioned in . This scNevertheless, a comparison to the number of relations included in the two integrated models, shows that the model which was constructed with the alternative procedure (KEGG2SBML → SemanticSBML/mergeSBML) contained 70 distinct reactions, while the model from KEGGconverter incorporated 154 reactions, not counting those that refer to the boundary conditions to neighbouring pathways. This radical difference can be attributed to the significantly less information that KEGG2SBML manages to extract from the KEGG flat files, comparing to the KGML files. This difference, of course, seriously deteriorates, as the number of the merged pathways increases.The last step before the simulation phase for our test case is the insertion of kinetic information about the pertaining reactions of the model, through the tool SBMLsqueezer. This toCurrently, KEGGconverter does not add SBML annotations concerning the involved species and reactions, whereas SemanticSBML provide for a MIRIAM -compliedKEGGconverter is capable of producing integrated metabolic pathway models ready for simulation purposes taking as input KEGG KGML files. It handles automatically transparently and seamlessly the fusion, conversion to SBML, proper renaming of the species, stoichiometrical correction of the integrated pathway and insertion of reaction specific default kinetic laws, in a straightforward manner compared to other solutions mentioned in the case study.The final models thus derived, do not enclose trivial metabolites as they do not reproduce inconsistencies of the KGML visualization-oriented, simplified information pattern. At the same time they can be considered as information rich for dynamic in-silico simulations as they contain all the available information regarding the number the stoichiometry and the putative kinetic properties of the included reactions in each pathway. Furthermore, additional reactions to neighbouring pathways are constructed which indicate the direction of the metabolic flows in the network and thus providing better stability in the boundary conditions of the models.The current version of KEGGconverter is contained within this article  is necessary for kinetics implementation.License: Creative Commons Attribution-Noncommercial-Share Alike 3.0 Greece License The authors declare that they have no competing interests.KM co-drafted the manuscript, co-programmed the application and carried out the case study. IK co-programmed the application and co-drafted the manuscript. AC conceived and designed the application, tested and evaluated the application, co-drafted the manuscript and supervised the implementation of the computational part of this work. IM provided significant corrections to the implementation of the code. FNK revised the manuscript and supervised the work. All authors read and approved the final manuscript.Command line options. File containing the description of the commands of the command line version of KEGGConverter.Click here for fileCase study files. Input: 6 KGML Pathway files: Glycolysis/Gluconeogenesis - hsa00010.xml, Citrate cycle (TCA cycle) - hsa00020.xml, Pentose phosphate pathway - hsa00030.xml, Fatty acid biosynthesis - hsa00061.xml, Fatty acid metabolism - hsa00071.xml, Urea cycle and metabolism of amino groups - hsa00220.xml. Output from KEGGconverter: the initial merged KGML file: mergedKEGG.xml, SBML converted model: sbml_mergedKEGGconverter.xml, SBML converted model with default kinetics: sbml_mergedKEGGconverterKinetics.xml, a circular layout diagram of the resulted model from CellDesigner: CaseStudyFinalDiagram.pdf.Click here for fileCase study files with alternative procedures. • FeaturesComparisonTable.doc: a comparison of features table regarding the applications mentioned in the case study. • hsa_paths.zip: The 6 case study pathways produced by KEGG2SBML. • RamanAllMerged.xml: The intergrated SBML model using [el using  alternatClick here for fileKEGGconverter tool. This is the version (1.0) of the KEGGconverter tool which runs as a command line utility. It searches in the '/in' subdirectory for KGML models and performs the conversion depending on the specific command arguments.Click here for file


To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed.Oryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks.Using capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (Our results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical functions. The application of discrimination of unidentified metabolites and the identification of bottleneck enzymatic steps even to non-targeted comprehensive analysis promise to facilitate an understanding of large-scale interactions among components in biological systems. In the post-genome era, comprehensive data from "omics" technologies  have been extensively analyzed to elucidate the underlying biochemical networks that elaborately regulate cellular mechanisms. Recent contributions from metabolomics are particularly noteworthy; they offer insights into metabolism that complement information obtained from proteomics and transcriptomics . CorrelaHowever, with the current technology, unbiased reconstruction from comprehensive and high-throughput data is challenging; statistical tools are immature and inherent measurement errors and biological noise continue to present problems . MoreoveOryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Our target and experimental conditions were strategically determined: 1) we focused on primary metabolic pathways consisting of carbon fixation/respiration- and nitrogen assimilation/dissimilation pathways, and comprehensively quantified related metabolites, 2) the photocycle was the sole environmental factor, and 3) measurements were made at 1-hr intervals to allow the observation of dynamic profiles.Time-resolved target analysis is an effective way to observe biochemical dynamics. We systematically measured the level of 56 basic metabolites in rice leaves  technology we developed earlier -14, and extracts  and monili cells . Each emli cells . By usinWe applied four information-based methods to analyze the diurnal fluctuation of metabolites: 1) metabolic pathways were classified with SOM to monitor the metabolic dynamics in each time-step, 2) a phenotypic linkage map was constructed from the classified pathways by Sammon's 2D-network layout , 3) unid3  and CO2 were non-target compounds, they are shown in green to demonstrate in and out of carbon and nitrogen.We extracted target metabolites existing in the primary metabolism such as the glycolytic pathway, the reductive- and oxidative pentose phosphate pathway, and the photorespiratory pathway, the tricarboxylic acid (TCA) cycle, and the amino acid biosynthetic pathway. Figure We selected eight enzymatic proteins that have not been annotated at this stage to determine whether they function in the rice plant. These enzymes and the judgment criteria are shown in Table Sedoheptulose 1,7-bisphosphate (S17P) in the pentose phosphate pathway was not identified because the standard reagent was unavailable. Xylulose 5-phosphate (X5P) is a stereoisomer of Ribulose 5-phosphate (Ru5P) and their peak overlap in CE-MS analysis makes the identification even more difficult. Glyceraldehyde 3-phosphate (G3P) and oxaloacetate (OAA) were not accurately determined too, because they were readily reacted or decomposed.The seventy selected target metabolites were classified into four groups according to their chemical structure-based physiochemical characteristics Table . Group AIS) of S7P to PIPES . The other 13 metabolites were under the detection limit  < 3); their names were colored gray in Figure Plant seedlings were grown under a 13-hr light – 11-hr dark photocycle for 20 to 21 days. The level of the 56 metabolites was successfully quantified at hourly intervals over the course of 24 hr. We could identify the peak and determine the peak area for S7P but could not quantify its level, since the reagent was not available at the time of our CE-MS measurement; we later qualitatively identified its peak with the migration time ratio  and Sammon's 2D-network layout (Sammon map). The time-dependent levels of each metabolite were represented as a 24-dimensional vector. On the SOM, the 57 metabolites were classified into a 24 × 24 lattice on the basis of vector similarity. The map was roughly divided into two major groups  of S17P was 0.941 (Table IS = 0.909 (m/z = 369) was identified within ± 5.0% of the predicted values.The estimate for S17P was performed using linear approximation with Ru15P and F16P. The estimated migration time ratio  and the peak was not detected in the dark period. In the SOM analysis, the peak area of such undetected metabolite was calculated as zero. Nevertheless, the proposed estimation method seems to be effective in identifying unknown metabolites.eq (constant), then their relationship is linear and shows a high correlation. Although real metabolic pathways are dynamic and constantly regulated by their influx and/or efflux, the pathway components that are blocked by rate-limiting enzymes should exhibit approximate linearity. For example, 3PG, 2PG, and PEP in the glycolytic pathway are positioned between two rate-limiting enzymes, phosphoglycerate kinase (EC 2.7.2.3) and pyruvate kinase , both of which are regulated by the ATP/ADP ratio  were applied to construct a metabolic correlation network ,10,28. AWe used a hierarchical clustering algorithm, Ward's method , to clasThe same cluster analysis was also applied to the TCA cycle intermediates Figure , and theInspection of the time-course of metabolic modules allowed us to better understand the carbon and nitrogen (C/N) assimilation/dissimilation process and their underlying function during a 24-hr period Figure .In the first half of the light period, some accumulation emerged for carbon-fixed products: Pyr, 2OG, and photorespiratory pathway intermediates (metabolic module M2). This coincides with carbon fixation by activation of several light-dependent enzymes including rubisco (EC 4.1.1.39) at the start of light exposure , as showOn the other hand, the glycolytic pathway and the reductive pentose phosphate pathway intermediates reached their highest levels  at midday, and sugars peaked at the end of the light period (M7).We can hypothesize that carbon fixed in the first half of the light period moves down the glycolytic pathway and the TCA cycle, and amino acid biosynthesis progresses using generated Glu, Pyr, and 2OG. In the latter half of the light period, the flow of fixed carbon leads to the accumulation of the intermediates in the pentose phosphate pathway and to sucrose synthesis by inhibiting the production of ammonia, Pyr, and 2OG.From the end of the light period through the first half of the dark period, we noted an increase in sugar phosphates from the first half of the glycolytic pathway (metabolic module M9). Around midnight, the accumulation of a few organic acids in the first half of the TCA cycle (metabolic module M10) was observed, suggesting the activation of the TCA cycle.In the latter half of the dark period, the level of minor amino acids was increased (metabolic module M11), although they are synthesized from diverse biochemical pathways. The good correlation among these minor amino acids, also reported in potato and wheat , is attrATP and ADP were placed in the dark-activated group in PLM (metabolic module M12); they were accumulated at the end of the dark period, and decreased by illumination Figure . On the We extrapolate that the lower ATP ratio during the light period was caused by an excess demand of ATP by intra- and extra cellular processes for carbon fixation and nitrogen assimilation against ATP supply from photosynthesis. In theory, the amount of ATP consumption in the reductive pentose phosphate pathway and the photorespiratory pathway is more than ATP production in the photophosphorylation . Beside In our analysis, NADPH and NADH behaved similarly (metabolic module M6), whereas NADP and NAD did not. As NADPH and NADH were respectively generated by their unique reaction of reducing NADP and NAD, dependence on the intracellular oxidation-reduction state shifted the formation of oxidation and reduction. In PLM, however, NADP was placed in the light-activated- and NAD in the dark-activated group. This suggests that highly concentrated NAD in the dark is converted to NADPH via NADP in the light period. It was reported that the NADPH/NAD ratio is the inverse of the ATP/ADP ratio in guard cell protoplast, which indicates that ATP phosphorylates NAD in the light period by NAD kinase (EC 2.7.1.23) and the generated NADP is reduced to NADPH in the course of photosynthesis .The ratios of NADH to NAD and NADPH to NADP were 0.16–0.29 and 6.2–6.6. The observed difference in the tendency of oxidized- or reduced form indicates their different cellular roles. NADH is used for oxidative phosphorylation, and a low NADH/NAD ratio constrains this process. On the other hand, NADPH is used for the reductive biosynthesis of metabolites, and the high ratio of NADPH/NADP favors the reduction of metabolites.It is remarkable that Glt  and Spe exhibited similar fluctuation patterns (metabolic module M8). Both peaked at the end of the light period and again just after midnight, suggesting the existence of common regulatory factors. GSH plays a central role in the antioxidant defense by eliminating harmful peroxide during photosynthesis and oxidative phosphorylation . PolyamiWe intended to analyze the rice plant metabolism and to reconstruct its phenotypic networks in an effort to explain underlying biological functions. Our CE-MS technology provided a comprehensive high-throughput system with easy sample preparation and facilitated the generation of high-resolution metabolic time-courses. Data mining with statistical techniques and SOM analysis revealed synchronous dynamics in metabolic modules downstream of C and N assimilation and dissimilation processes and stress responses. Our system was able to discriminate unidentified metabolites and identify bottleneck enzymatic steps. In a comprehensive approach such heuristics become increasingly important because with current technology, the determination of all network components is virtually impossible. For a more precise investigation of biochemical networks, expansion of target metabolites and determination of metabolite levels in each cellular compartment may be suggested. There are technical hurdles, however, in separating organelles without disturbing a wide range of metabolites inside them. Without much technical advancement, therefore, it seems difficult to repeat our time-course measurement for any single cellular compartment although there are reports for such a challenge . FinallyOryza sativa L. ssp. japonica Haenuki, at the third-leaf stage were cultured as follows. Rice seeds were germinated on filter paper soaked with Milli-Q water and kept at 30°C in a dark room for 2 days. After germination, the plantlets were placed on rock fiber , and grown in a growth chamber  for 18 days. The temperature and light conditions were 25°C and 365 μE·m-2s-1 for 9 hr (light), 20°C and 0 μE·m-2s-1 for 11 hr (dark), and 150 μE·m-2s-1 for 2 hr between light and dark. The plants were watered with Kasugai water culture solution (18.9 mg/L (NH4)2SO4, 10.1 mg/L Na2HPO4·12H2O, 4.7 mg/L KCl, 0.79 mg/L CaCl2, 3.0 mg/L MgCl2, 0.17 mg/L·FeCl3·6H2O, and HCl to adjust the pH to 5.0 – 5.5)  methanol-water) was delivered at 10 μL/min. ESI-MS was conducted in positive ion mode; the capillary voltage was set at 4000 V. A flow rate of heated dry nitrogen gas (heater temperature 300°C) was maintained at 10 L/min .b) Anionic metabolites (organic acids and sugar phosphates) were analyzed with a cationic polymer-coated SMILE(+) capillary . The electrolyte for CE separation was a 50 mM ammonium acetate solution (pH 8.5). The sample was injected at an injection pressure of 5.0 kPa for 30 sec (approximately 30 nL). The applied voltage was set at -30 kV, and the capillary temperature was set to 30°C. ESI-MS was conducted in negative ion mode; the capillary voltage was set at 3500 V. Other conditions were as in the cationic metabolite analysis .c) Nucleotides and coenzymes were analyzed with an uncharged polymer-coated gas chromatograph capillary, polydimethylsiloxane (DB-1) (Agilent Technologies). The electrolyte for CE separation was 50 mM ammonium acetate solution (pH 7.5). The applied voltage was set at -30 kV and a pressure of 5.0 kPa was added to the inlet capillary during the run. Other conditions were as in the anion analysis .d) Sugars were analyzed with a fused-silica capillary . Basic anion buffer for CE (Agilent Technologies) was the electrolyte. The sample was injected at a pressure of 5.0 kPa for 10 sec (approximately 10 nL). The applied voltage was set at -25 kV; the capillary temperature, regulated with a thermostat, was 25°C. Sugars were detected by indirect UV detection using a diode-array detector. The signal wavelength was set at 350 nm with a reference at 230 nm .n-dimensional data space (input layer) to a two-dimensional array of nodes (output layer). The vectors in the output layer are the parametric reference vector mi, which has n elements. An input data vector, x, is compared with mi, and the best-match vector, which is the smallest Euclidean distance |x - mi|, is mapped onto this location. During learning, nodes that are topographically close in the array up to a certain distance activate each other to learn from the same input vector, and the reference vectors are corrected so that they become close to the input vector. Thus,A free software package, SOM -PAK , was usehci(t) is the neighborhood kernel, a function defined over the lattice points. The neighborhood size, Nc, around node c is a function of time, and hci is defined aswhere t is an integer, the discrete-time coordinate, and α(t) is a monotonic decreasing function of time (0 <α(t) < 1) called the "learning rate". The learning rate function was defined aswhere α(0) is the initial learning rate and T the running length (number of steps) in training. In this study, 58 metabolic time-courses were formatted and classified in a 24 × 24 hexagonal lattice. The applied SOM parameters were: initial radius of the training area = 12, initial learning rate = 0.025, running length = 65 000.where r were computed depending on the number of metabolite pairs n found throughout the light and dark period, respectively, by calculating t-scores given by t = r (n - 2)0.5/(1 - r)0.5. The critical t-score was set to correspond to the commonly used p-value of 0.05 in two-sided tests.Significance levels for Pearson correlation coefficient Among several algorithms for clustering analysis, we chose Ward's method  in JMP sSS conceived this study, performed the biochemical- and the computational experiments, and wrote the manuscript. MA provided intellectual help for the computational analysis and together wrote the manuscript. TN advised the experimental design. TS and MT supervised the research. All authors read and approved the final manuscript.