MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions . The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.This is the first investigation using Near Infrared Spectroscopy (NIRS) The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time. By 2007 the sales of all biopharmaceuticals totalled £60 billion representing 16% of the overall pharmaceutical industry and it is still growing, including the commercialisation of monoclonal antibodies (mABs) .13C-metabolic flux analysis using 13C-labeled substrates [off-line analytical methods  or isotope tracing methods, which makes it complex, costly and time consuming. It is difficult to envisage such an approach lending itself to rapid on-line analysis [off-line measurements (27) were still necessary using various analytical methods, with results obtained post-process, highlighting the need for combining MFA with real time metabolite information [Metabolic flux analysis (MFA) is considered a central pillar in modern systems biology for investigating metabolic networks . The clabstrates . The appbstrates -9 and cebstrates  by identbstrates . Howeveranalysis . By contanalysis  investigormation .in situ, techniques could be integrated into metabolite balancing to achieve this capability? Vibrational spectroscopy, driven by the Food and Drug Administration (FDA) initiative Process Analytical Technology (PAT) framework [at-line (rapid off-line) or on-line, usually in situ[in situ probes and the potential to predict chemical and physical parameters from a single spectrum [The obvious question which arises now is: Which real time, preferably ramework , has alry in situ,16. Despy in situ. Howeverspectrum .in situ NIRS probe as a monitoring technique to predict near real-time intracellular metabolic fluxes in chemostat cultures of a Pichia pastoris strain. We chose Pichia pastoris, a methylotrophic yeast, as it is currently one of the most effective and versatile expression systems used in the biopharmaceutical industry for recombinant product production [i.e. that does not synthesize recombinant protein) to test the validity of our approach in a simplified model system. The findings of this study represent a significant step forward towards real-time metabolic control of cell factories and establishes a viable platform for using on-line vibrational spectroscopic measurements as inputs to MFA models in bioprocessing, replacing the use of off-line measurements (wet chemistry methods).In the present study, we investigate the feasibility of establishing a robust platform technology for MFA by using a single probe, an oduction ,20. ChemP. pastoris cultures was analysed using the methods described in (Methods). The use of a parental strain of P. pastoris (not producing recombinant protein) simplified the system to allow the establishment of a framework for determination of intracellular metabolic fluxes by using NIR as an input-generating tool for MFA. This experimental system allowed the unambiguous examination of the link between changes in cell growth rate (determined by dilution rate) and the culture response, which would have been difficult in batch or fed-batch cultures. If the current study successfully proves that it is feasible to use such an approach combining real time NIRS measurements with metabolic flux modelling (MFA) in a simplified model organism, it will then be possible to take this research to the next level by examining the industrial relevance of this approach in fed-batch cultures of a protein secreting Pichia strain.Central carbon metabolism in a chemostat culture for three glycerol-limited steady state -1 in order to obtain three different states: low, medium and high dilution rates, designated as A, B and C (Table off-line measurements (reference) and on-line NIR predictions (section NIR modelling).Dilution rates were varied between 0.05 to 0.15 h-1. An increase of carbon evolution rate (CER) and oxygen uptake rate (OUR) with dilution rate (from state A to C) were observed  concentration of glycerol (<1 g/L) and only slight changes in the concentration of biomass during the continuous phase, models developed based solely in this phase failed validation and therefore, the correspondent predictions could not be used for the metabolic flux analysis (data not shown). Because PLS-NIRS models are based on the amount of variance captured during the calibration procedure: the more changes in the process it includes, the more robust the models will be. These changes can be: inherent variations of analyte levels, accumulation profiles, spectral matrix differences and interfering absorptions . If the R2-values above 0.90 or above for both calibration and external validation, indicating a good fit between the measured and predicted data. The RMSEP/RMSECV ratios were very close to one, indicating no significant differences between the performance of the calibration and validation models and so robustness in the face of process and analyte variation. This result is a good indicator of how in practical terms the models would behave when exposed to process-to-process variability seen in fermentation processes. Figure From the calibration statistics Table , both glet al. (2006) [off-line methods used. The present authors have intensively studied the application of NIRS to fermentation and cell culture systems to predict a number of different analytes simultaneously  [R2, RMSECV and RMSEP values close to other published studies using on-line NIR probes [According to the SEL values shown in Table . (2006)  consider. (2006) ,26, and roteins) ,27,28 anR probes ,30. ThisR probes . Thus, ton-line and in-situ measurements either due to specific aseptic conditions requirements, low or single number of measured analytes, drift and other rather low physiological relevance [eg. stirring, aeration, broth conductivity, pH and viscosity), or the in-situ probes are still in development, or are dependent upon the cell type and size, and not always are able to measure multiple fermentation parameters [There are other sensor systems used in bioprocessing, but their applicability is still very limited with respect to elevance . Raman aelevance ,33. Fluorameters ,34.on-line sensors, which is often a limiting step in a fermenter set-up. NIRS technology is applicable to most fermentation expression systems, to batch and fed-batch systems, being able to monitor not only biomass and glycerol (as in this study) but also other metabolites such as glutamate, glutamine, ammonium, alcohols, proteins, organic acids, etc. [NIR probes offer a high-quality signal from a bioprocess, they are physically robust instruments compared to the aforementioned sensors, suited for industrial manufacturing processes, being able to monitor multiple analytes simultaneously, avoiding the need to use multiple ds, etc.  with theds, etc. ,35.on-line in situ technology to form the basis for near real-time measurements of multiple inputs to metabolic flux modelling of cell factories in bioprocessing. The findings presented here constitute the first step part of a structured multidisciplinary research programme, to be applied for modelling cell factories (such as protein expression systems) using microbial and cell line approaches as well as dynamic (fed-batch) fermentation processes. Transferring this technology from the simplified system used here to an industrial process will involve addressing significant new challenges. These include: first, high cell density and associated very short process time constants; second, increased metabolic complexity. Regarding the first point, it has been demonstrated that NIR can deal with measurements in high cell density systems [Pichia, especially where the medium is soluble (as here). In situ spectroscopy is also very fast . As to increased metabolic complexity, there are existing published metabolic models of Pichia that can be built upon [Thus, the NIRS constitutes one of the most favourable  systems -3, incluilt upon ,5. Due tP. pastoris (adapted from [m-n); the matrix and the list of reactions is given in the Additional file off-line and on-line (NIRS) measurements (section Extracellular flux rates); and the stoichiometric model described above. The stoichiometric model was constrained by the extracellular metabolites, i.e. uptake/production rates for O2, CO2, glycerol and biomass and the production rates for ethanol, citrate and pyruvate, which based on HPLC analysis were not detected and therefore considered null (results not shown). The following major assumptions were used in the model: (i) the biomass synthesis reaction (r44) was considered constant under the different process conditions and it was based on the macromolecular composition of P. pastoris from [A simplified biochemical reaction network was adopted here for ted from ), and isris from ; (ii) thris from .off-line measurements and on-line NIR predictions were used for the calculation of the specific extracellular rates shown in Figure 2 and biomass specific production rates were observed . However, the changes relative to the oxygen consumption rate were not significantly different between each state. Using a t-test statistical analysis between off-line and on-line results for each dilution rate and each of the extracellular rates determined, it was clear that there were no significant differences between the on-line and off-line extracellular rates (p<0.001). The extracellular rates were then used to compute intracellular metabolic fluxes for state A to C using Equation 5. The predictions from NIR extracellular rates were very close to the rates derived from the off-line assays, as expected from the input rates. For that reason, only the intracellular fluxes predicted by the on-line NIR measurements for the P. pastoris chemostat cultures at different dilution rates are shown in Figure off-line measurements can be found in Additional file The concentration of both glycerol and biomass calculated from both the es p<0.00. The exth for each steady state  were lower than the corresponding χ2 (chi-square) values for a 95% confidence interval (3.84) and the one redundant equation (i.e. consistency test degrees of freedom), as seen in Table off-line and on-line data are both consistent with the pseudo-steady assumption and also the assumed biochemistry, and that there are no systematic or gross measurement errors. The results from statistical analysis on the extracellular rates and the consistency test show that NIR spectrometry can be used as an on-line tool in fermentation systems to determine real-time intracellular metabolite fluxes.The values obtained for the consistency index erval 3.8 and the on-line NIR intracellular carbon fluxes observed in the chemostat culture of P. pastoris under different dilution rates. In some instances, the term ‘relative flux’ will be used to describe fluxes normalized to the specific glycerol uptake rate  and to allow a direct comparison among the different states investigated (state A to C), this type of terminology is widely used in the context of MFA [Figure t of MFA .P. pastoris metabolic network. At all dilution rates the majority of glycerol is funnelled into the glycolytic pathway (r6), and much less towards gluconeogenesis (r4) , while an increase is seen from gluconeogenesis towards the PPP . The increase of the relative flux through the PPP (r21/r40) coincided with an increase in biomass yield (from 0.63 gDCWg-1Glycerol at low dilution rate to 0.67 gDCWg-1Glycerol at 0.15 h-1). Furthermore, the carbon flux through the PPP (r21) that re-entered glycolysis at the level of fructose-6-phosphate (F6P) and glyceraldeheyde-3-phosphate (GAP) (r26) was at least 50% less at higher dilution rates (states B and C) than at lower dilution rate (state A). This indicates that the PPP is being used for NADPH generation to support the production of biomass. It is known that the oxidative part of the PPP (r21) is considered to be the major NADPH regenerating reaction in yeast and is driven by the demand for biomass synthesis, and in fact here it showed the highest flux of all the PPP reactions  [Glyceraldehyde-3-phosphate (GAP) links the glycolysis/gluconeogenesis pathways with the methanol and glycerol uptake pathways in the genesis r  ”. The hi10 to r13) were zero  was approximately the same at all dilution rates. This had consequences in terms of the TCA cycle: from alpha-ketoglutarate (r18) onwards the TCA cycle activity significantly decreased at the highest dilution rates (state B and C).In all steady states (from A to C) the cells exhibited a respiratory metabolism, as the fluxes from respiro-fermentative metabolism  are not involved in the synthesis of biomass. It is possible that the TCA cycle is operating at its maximum capacity when the specific growth rate is high as it has been proposed in other studies [S. cerevisiae it has been reported that a decrease in the cell protein content at higher dilution rate may be related to a significant decrease in the TCA cycle fluxes [P. pastoris cells grown at high dilution rates might need nutrient supplementation to maintain high rates of protein synthesis, while at lower dilution rates the cultures would be more self-sufficient. This effect would be more pronounced if a heterologous protein were being expressed at high levels, as seen in [Alpha-ketoglutarate (aKG) represents a key metabolite linking the entry and exit of carbon sources from the TCA cycle to pathways involved in amino acid metabolism for biomass synthesis ,42. At h studies ,45, and e fluxes . This su seen in ,41. In tP. pastoris is reported herein. A single in-situ NIR probe was used to predict biomass and glycerol consumption rates in near real time. Focusing only on central carbon metabolism of these cultures enabled us to assess the viability of this novel approach in light of previously published objections which indicated that this approach was not appropriate.The applicability of NIRS with advanced process modelling to define real-time metabolic fluxes in the industrially relevant cell factory The results of the MFA showed that the NIR predictions were equivalent to the off-line reference methods, thus showing the potential of NIRS to be used in real-time MFA. The implementation of NIRS-MFA has potential utility in mainstream biotechnology industries, in particular, it expands our ability to monitor and control the metabolism of a key cell factory in real time using a low cost, robust multivariate monitoring technology as opposed to the current costly, complex and time consuming methods. Advances in metabolic flux modelling are essential to complement other rapidly expanding applications in biotechnological techniques, such as transcriptomics and proteomics, in the development of novel biopharmaceuticals. Further work using more complex systems e.g. protein producing strains where the metabolic network will be expanded to include protein synthesis pathways, is on-going. This substantially extends the challenges encountered in this study and permits a systematic evaluation of this potential platform technology’s industrial relevance and utility.Pichia pastoris strain CBS7434 MutS was supplied by Ingenza Ltd (UK). The yeast strain was kept as frozen stock cultures in Yeast Nitrogen Base (YNB) medium (34 gL-1 YNB) at −80°C. Inocula were prepared from 1 ml of the frozen culture sample which had grown on a 2-L shake flask in 600 ml of standard BMGY medium, containing 1% Yeast Extract, 2% Mycological Peptone, 1% Glycerol, 1.34% YNB, 100 mM Potassium phosphate (pH 6.0) and 4×10-5% Biotin; This was grown at 30°C, 250 rpm, for 20–24 hours until an OD600 of approximately 2.5 was reached. Finally the contents of the flask were transferred to the bioreactor to create an inoculum concentration of 10% (v/v). The chemostat culture contained per liter: 40 g of glycerol; 26.7 mL H3PO4 (85%); 14.9 g MgSO4⋅7H2O; 0.93 g CaSO4; 18.2 g K2SO4; 4.13 g KOH; 4.35 mL of PTM1 salt solution; and 0.1 mL antifoam.A 4OH). The dissolved oxygen concentration (DO) was maintained above 30% of saturation by a cascade controlling system (maintained by an agitation rate of 300–1200 rpm and aeration rate 1–3 vvm). Exit gases (O2 and CO2) were measured as % using a digital gas analyzer TANDEM Pro . Real time values of pH, DO, agitation speed, temperature, air-flow rate, O2 and CO2 were recorded automatically by the bioreactor software MFCS DA . After 24 h, the feed and waste pumps were started to initiate chemostat cultivation. Following three to four residence times, time invariance of the following variables was assumed to indicate a steady state: optical density, glycerol concentration, oxygen uptake and carbon evolution rate, OUR and CER respectively. Three steady states were achieved with the following dilution rates: A=0.05 h-1, B=0.10 h-1 and C=0.15 h-1. Knowing that the maximum specific growth rate, μmax, of P. pastoris on excess glycerol is 0.17 h-1[The reactor used was a 15-l  stainless steel bioreactor . The cultivation was carried out at a temperature of 30.0±0.1°C and a pH of 5.5±0.1 : 5-ml of culture fluid was filtered onto a pre-dried, pre-weighed 0.2 μm filter . Cells were washed with 2×5 ml of sterile water and the filter dried to a constant weight in an oven (105°C for 24 h). Glycerol determination was carried out using a Boehringer Mannheim Glycerol enzymatic kit  at 340 nm.Spectra were acquired with a dual beam NIR process spectrometer  using a transflectance probe submerged in the bioreactor with a gap of 0.5 mm resulting in an effective path length of 1 mm was sufficient to acquire reasonable spectra. Spectral measurements were referenced against a NIST traceable reference material . Due to the nature of the reference and the design of the probe the referencing procedure was carried out with a reflectance probe . A correction factor was then applied to compensate for the differences in the acquired spectra from the reflectance probe, used in the instrument calibration procedure, and the transflectance probe that was utilised for spectroscopic measurements. The probe was mounted in one of the side sampling ports of the bioreactor. As these ports also housed the pH and dissolved oxygen probes, any sample measurements made should have been representative of the whole reactor contents and would not have had a significant impact on the mixing efficiency of the reactor. Spectral measurements (X data) were an average of 32 scans, taken every 30 minutes over the NIR range of 800–2200 nm.lv) number, and the respective correlation coefficient, R2, between the predicted and measured values for both calibration and validation sets [off-line measurements were interpolated using the interp1 command (linear interpolation) in Matlab and matched to the NIR spectral data. A random number table was used to divide the data into calibration (85 samples) and validation sets (30 samples), so that the model could be externally validated in the absence of more samples. To aid in gaining an analytical basis for the model development, NIR spectra of aqueous solutions of glycerol (0–50 g/l) and aqueous suspensions of biomass (0–30 g/l), all adjusted to the bioprocess pH, were also prepared and scanned  and calculations were carried out using Matlab version 7.12 (2011a)  and the PLS Toolbox version 6.5.1 . Multivariate calibration models were developed with the PLS algorithm. All the spectra were mean-centered, and second-order Savitzky-Golay (SG) derivatives were applied  before development of the calibration models. The performance of developed models was assessed by global analysis of the root mean square errors of cross-validation (RMSECV) and prediction (RMSEP), latent variable (ion sets . BiomassOff-line measurements). Accuracy can be determined by agreement between the RMSEP and SEL. As a rule [R2 and the RMSEP indicate the precision achieved in the NIR calibration, R2 values higher than 0.90 indicate excellent precision, as well as RMSEP values lower than 1.5 × SEL. R2 values between 0.70 and 0.90 mean good precision, as do the RMSECV values between 2–3 × SEL. While, models with R2 values lower than 0.70 are only suitable for qualitative purposes, allowing distinction between low, medium and high values for the measured parameter being analysed.Reference method qualification was accessed using the statistic standard error of laboratory (SEL). SEL is defined as the standard error of variance between replicates analyzed by the reference method and was calculated using Equation 1 . 16 samps a rule ,24, consyij is the ith replicate of the jth sample, th sample, r is the number of replicates and N is the total number of samples.where The specific growth rate was determined from the mass balances in the fermenter, resulting in-1), F the flow rate (L/h), V the fermenter volume (L), X the biomass concentration (mol/L), and t is time (hours). Accordingly, specific uptake or production rates for glycerol, O2 (OUR) and CO2 (CER) were calculated based on:where μ is the specific growth rate (hq is the specific uptake or production rate (mmol/(gDCWh)), while Cin and Cout are the reactor inlet and outlet concentrations (mol/L) of the nutrients or metabolites. The extracellular rates were determined from Equations 2 and 3 using six separate data points from the end of each dilution state  and averaged to obtain a single value for each state. The biomass concentration was determined either by the off-line method described in 4.5 or by the on-line NIR predictions.where Assuming a pseudo-steady state approximation (PSS), the material balance equation for the intracellular metabolites  can be wA is the matrix of stoichiometric coefficients consisting of m rows corresponding to the intracellular metabolites and n columns corresponding to the number of metabolic reactions ) and vector c denotes the concentrations of the intracellular rates. A and r were split into the unknown (Anand rn) and known (Aband rb) matrices and vectors of stoichiometric coefficients and rates, respectively. The known and unknown rates correspond to the extracellular and intracellular metabolic fluxes, respectively. The intracellular metabolite fluxes can be determined from Equation 5 using simple matrix inversion if A is square (m = n). If A is not a square matrix, the system becomes redundant and it is valuable to include the surplus information to check the consistency of the data and the assumed biochemistry. In this case m >n and the fluxes can be determined via the method of weighted least squares [The intracellular fluxes were calculated based on the material balance model expressed in matrix notation where  squares :h given by:where J, the variance-covariance matrix of the vector of residuals (Equation 9), pinv(J) the pseudo-inverse matrix of J and e the vector representing the deviation from zero of the intracellular metabolites concentrations calculated as followsWith R is the redundancy matrix expressed by:J):And the variance-covariance matrix of the vector of residuals (A. This is done by comparison of the number of degrees of freedom of the matrix A (DF=8) and the number of known measurements used in the MFA model (nine). Comparison of h with the χ2 test function determines whether the residuals of Equation 5 deviate beyond their expected distribution around 0 for a specified significance (confidence level). If a given r fails the consistency check , then there is a (confidence level)% chance that either r contains gross measurement errors or the assumed biochemistry is incorrect. For this, one additional redundant measurement was used as the degrees of freedom for statistical hypothesis testing. The confidence interval of the known and the unknown fluxes was analysed by calculating the estimates of the respective variance-covariance matrices (Ψb and Ψn) given in Equation 10. The standard deviation vectors of the fluxes were then obtained by the squared root of the respective variance covariance matrix diagonals [The consistency index method is based upon statistical hypothesis testing to determine whether redundancies are satisfied within the experimental error ,51. The iagonals .(10)Ψ′b=All the computational tasks required to perform metabolic flux analysis were implemented in Matlab  .off-line and on-line measurements) and the results compared. A diagram of the approach used is shown in Figure To obtain the estimates for the intracellular metabolic fluxes from the MFA model, extracellular rates were calculated using Equations 2 and 3 from two datasets (AcCoA: Acetyl CoA; ACD: Acetaldehyde; ACE: Acetate; aKG: Alpha-ketoglutarate; BMGY: Buffered Glycerol-complex medium; CER: Carbon evolution rate; CO2: Carbon dioxide; Cyt: Cytosol; DCW: Dry cell weight; DHA: Dihydroxyacetone; DHAP: Dihydroxyacetone phosphate; DO: Dissolved oxygen; E4P: Erytrose-4-phosphate; ETH: Ethanol; F6P: Fructose-6-phosphate; FBP: Fructose-1,6-biphosphate; FDA: Food and Drug Administration; G6P: Glucose-6-phosphate; GAP: Glyceraldehyde-3-phosphate; GLC: Glucose; GOL: Glycerol; H: Consistency index; HCHO: Formaldehyde; ICIT: Citrate; LV: Latent variable; mABs: Monoclonal antibody; Mal: Malate; MFA: Metabolic flux analysis; Mit: Mitochondria; NIRS: Near Infrared Spectroscopy; OA: Oxaloacetate; OUR: Oxygen uptake rate; PAT: Process analytical technology; PEP: Phosphoenolpyruvate; PG3: 3-phosphoglycerate; PLS: Partial least squares; PPP: Pentose phosphate pathway; PYR: Pyruvate; R2: Correlation coefficient; R5P: Ribose-5-phosphate; RMSEC: Root-mean square error of calibration; RMSECV: Root mean square error of cross-validation; RMSEP: Root mean square error of prediction; RU5P: Ribulose-5-phosphate; S7P: Sedoheptulose-7-phosphate; SEL: Standard error of laboratory; SG: Savitzky-Golay; Suc: Succinate; TCA: tricarboxylic acid cycle; XU5P: xylulose-5-phosphate; YNB: Yeast Nitrogen Base.The authors declare that they have no competing interests.MLF carried out fermentations and all the experimental procedures. MLF and AN carried out NIR modelling. JMLD and MLF carried out metabolic flux analysis. B M, LMH and DL were the supervisors and REE the co-supervisor and the coordinators of the research. All authors read and approved the final manuscript.Metabolic network for P. pastoris. List of reactions, metabolites and stoichiometric matrix and predictions of intracellular fluxes.Click here for file


Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done.To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context.The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. Metabolism has been implicated in most major human diseases including obesity, diabetes, cancer, and heart disease . Thus, mGenome-scale metabolic network reconstructions have been shown to provide an appropriate context for analyzing biological content . MetabolRecon 1 was developed to provide a global genome-scale description of human metabolic capabilities, without consideration of tissue-specific information. As particular cells in the human body do not use all the metabolic capabilities encoded on the genome, procedures have been developed to tailor Recon 1 to tissue and cell specific functions -8. ModelTo study the metabolic interdependence within the human body, a novel multi-tissue modeling approach was developed to combine three cell type specific metabolic reconstructions  are detailed.• The process of building the integrated multi-tissue type network is described and the integrated network is used to compute and compare three established metabolic states .• Expression-profiling data was utilized to build two context-specific multi-tissue type models detailing metabolism in i) obese vs ii) diabetic obese individuals to ascertain their differences.Three human cell-specific metabolic networks were reconstructed for three major tissue types: liver (hepatocyte), fat (adipocyte), and skeletal muscle (myocyte). The overall metabolic network reconstruction workflow used to establish the three networks is depicted in Figure • The models were tested for internal pathway cycles that could generate excess energy when all exchanges were closed. Appropriate changes were made to the model to remove all futile cycles.• All extraneous dead end and gap-containing pathways were removed to build fully functional networks.• All reactions were elementally and charge balanced and none of the metabolic pathways were lumped into single reactions.• Published data for the different tissues was used to define biomass objective functions .The Alanine and Cori cycles are physiologically relevant metabolic cycles, but they do not occur exclusively physiologically. In order to amplify and study each one separately, constraints were set on the MM's ability to produce either alanine or lactate and hence affect HM substrate utilization. An unconstrained assessment of MM production and HM use of alanine and lactate to produce glucose and urea in the liver is presented in the Supplementary Material  and amino acids. The liver absorbs a fraction of the blood glucose and the rest is taken up by peripheral tissues in the body to generate ATP for energy maintenance requirements. In addition to energy generation, the absorbed glucose is stored as triacylglycerol in the adipose tissue and as glycogen and proteins in the muscle tissue. The excess glucose in the liver is similarly stored as glycogen and triacylglycerol, however unlike the adipocyte, only a small amount of the synthesized triacylglycerol is stored in the liver and the rest is transported to adipose tissue.In vivo amino acid levels in the blood are elevated between two- to seven-fold during the absorptive state [An absorptive multi-tissue simulation was performed and consisted of the three cell types, HM, MM, AM, and blood compartment connecting them. Nutrients were absorbed from the extra-system entering the blood compartment. Absolute values for glucose uptake rates were taken from literature . In vivove state . AssuminDue to the many metabolic objectives of the absorptive state, for simulation, a Pareto optimality approach was used . BrieflyWhen comparing the multi-tissue absorptive flux span with the individual models in similar conditions, there were some differences as compared to the Cori and Alanine cycles Figure . InitialThree physiologically relevant cycles were simulated using the multi-tissue approach. The flux span computations yielded two main results: 1) the intracellular dependence between different cell types in a multi-cellular organism and 2) the transfer of fatty acids to the AM from the HM. Multi-tissue models can provide a platform for mapping isotopomer flux measurements to further increase quantitative accuracy of physiological cycles and holistically understand human metabolism.A major application of genome-scale reconstructions is providing a systems context for integrating high-throughput data, also known as "context for content" . TranscrThe metabolic differences between i) obese and ii) Type II diabetes obese individuals were analyzed using the multi-tissue type model developed above. Transcriptomic data was obtained from adipose, liver, and skeletal muscle tissue samples of gastric bypass surgery patients in a fasting state (see Methods). In order to properly simulate the flux conditions, a baseline metabolic state was established. The absorptive state was adapted to simulate a prolonged starvation state. To perform this analysis, the objectives of the absorptive state (AM triaclyglycerol production and MM protein production) were converted into the inputs of the system and the objective was set as the HM glucose production. The changes in the multi-tissue simulation provide a functional backdrop to analyze the obese and Type II diabetes obese states.Context-specific multi-tissue networks were built using the GIMME algorithm  that mapThere is a considerable difference in reaction activity between the two context-specific states Figure . 29 reacThe reaction activity changes determined by the context-specific networks can be grouped into three main categories, as detailed below. The first two categories were differences that were not necessarily obvious, but understandable through known macroscopic physiological changes found in diabetic patients. Importantly, the multi-tissue model provides mechanistic explanations as to why the macroscopic physiological changes occurred. These two groups can be viewed as validations of the multi-tissue model and the importance of using a reconstruction, as almost all of the predictions made were not evident in the expression data. The third group consists of one reaction that was completely non-obvious and provides new and interesting metabolic insights into diabetes in obese individuals. A full listing of all differentially active reactions found in the expression data and the final context-specific models are provided in the Supplementary Material for further research  is only active in the normal obese model. CDO has been shown to be an important regulator in cysteine and sulfur metabolism in adipocytes and hepatocytes  but has Fine-grained studies of integrated human metabolic states have proved to be difficult due to complex intracellular and intercellular interactions. The recently available human genome-scale reconstruction, Recon 1 , is a biFirst, using Recon 1 we generated cell-specific reconstructions for three key tissues involved in diabetes and obesity: adipocytes, hepatocytes, and myocytes. The three cell-specific reconstructions were converted into mathematical models and put through stringent testing to validate tissue-specific physiological functions.Second, intercellular metabolic interactions were then described by developing a multi tissue-type modeling paradigm that combines the cell-specific models through a blood compartment. The multi-tissue model is not simply a trivial sum of the cell-specific reconstructions. As an example, a bicarbonate buffering system was required to simulate the mathematical model.Third, using the multi-tissue models, simulations for the Alanine and Cori cycles and the absorptive state are possible. These three physiologically relevant cycles are presented. When comparing the nutrient limited cycles to the individual models (e.g. Alanine and Cori cycles), the multi-tissue simulation approach showed a constrained solution space. In a nutrient rich environment (e.g. absorptive state), the HM and MM were constrained while the AM was not. The AM is not as metabolically independent as the HM and MM and thus gains potential phenotypes when integrated with the other metabolic reconstructions.Fourth, utilizing the multi-tissue approach, the metabolic differences in obese and diabetic obese individuals were studied by incorporating gene expression data as a constraint on the metabolic networks. The approach was validated and provided potential mechanisms for known macroscopic physiological changes seen in diabetic patients such as increased blood metabolite concentrations and oxidative damage of tissues. In addition, cysteine dioxygenase was found to be differentially active and could be a potential factor in oxidative damage to tissues and lower concentrations of taurine in adipose and liver tissues. The differences in the two context-specific models were not obvious and required the multi-tissue modeling approach, as the differential activity of reactions could not be ascertained from the transcription data alone.H. influenzae, representing the first sequenced prokaryote [M. tuberculosis [H. pylori [S. aureus [E. coli with many successive expansions [S. cerevisiae metabolic network [The first genome-scale metabolic network reconstructed was okaryote . Other prculosis ,38, H. p. pylori , S. aure. aureus ). The mopansions -43. The  network . With thUsing the human genome sequence and annotation, biochemical, and physiological data available through online databases and published literature, three metabolic networks were reconstructed for the human hepatocyte, myocyte, and adipocyte. A workflow for this procedure is shown in Figure The draft models were finalized through manual curation. Metabolic pathways in each cell type were included based on the existing knowledge of cell physiology and cell-specific biomass requirements. For each pathway, the presence of each reaction was supported by one or more of the following information obtained from online databases and/or published literature: biochemical data, genetic data, localization data, sequence data, physiological data, and modeling data. Reaction properties were verified through online databases such as KEGG  for sto.Biomass maintenance functions were constructed for the three cell-specific metabolic models. The process involved gathering primary literature data on the metabolic make up of the human tissue or related mammals if human-specific data was scarce. The overall process of formulating biomass objective functions for eukaryotes has been described in previous publications . The comThe overall compartment schematic of the integrated multi-tissue model is presented in Figure Other metabolic changes and degradation in the blood were assumed to be negligible due to the large time-scale differences between small metabolite stability in whole blood versus the amount of time a metabolite spends in the blood for transport. In particular, Liu et al. have shown through time course metabolomic profiling of plasma drawn from incubated whole blood that most small metabolites accounted for in the multi-tissue model's blood compartment are stable (25 of 33 metabolites showed no significant change in the 4 hr study) . The resIn the human body, the three tissues have varying masses. Initially, the simulations were set up using the units: mmol/h/g cell DW; which assumes that all three tissues have the same weight. To properly simulate intercellular fluxes, the biomass objective functions were scaled to the units: mmol/h/body. Thus, the three tissue model fluxes were scaled to represent the entire tissue mass in the average human body. The units were scaled by the mass of the tissue -56, the S). The stoichiometric matrix is very sparse and has dimensions of m × n. The metabolites (m) are represented in the rows as nodes in the network, while the reactions (n) are represented in the columns as links. Ordinary differential equations for the time derivatives of the metabolic concentrations are set up for the reconstruction using the stoichiometric matrix and reaction fluxes (v):After reconstruction in the SimPheny platform, the cell-specific metabolic models were converted into a mathematical format for analysis. The reactions and metabolites were represented with the stoichiometric matrix (S·v = 0) and thermodynamic constraints  (Equation 2). For those unfamiliar with linear programming and FBA, a primer is now available [Due to the lack of kinetic parameters available for intracellular biochemical transformations at the genome-scale, Equation 1 is set to a steady state and flux balance analysis (FBA) is used to characterize the system. FBA uses linear programming techniques to calculate the flow of metabolites throughout the network under mass balance . FVA involves iteratively and independently determining the minimum and maximum fluxes through the metabolic network. The flux span for a reaction is defined as the difference between the maximum and minimum flux. When analyzing the flux span, we did not consider reactions that were perceived to be part of thermodynamically infeasible internal loops.Context-specific metabolic networks for obese and diabetic obese individuals were built using the prolonged starvation multi-tissue model and the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm . The GIMAB updated and integrated the networks, performed the analyses, and drafted the manuscript. RU, JW, and IF constructed the initial networks. AMF, BOP, IF conceived the study and revised the manuscript. All authors approved the content of the final manuscript.Biomass Formulation. Supplementary information on sources and methods used to constract tissue-specific biomass functions.Click here for fileTissue specific models and multi-tissue model XLS (in compressed zip format). Metabolic models for adipocyte, hepatocyte, mycocyte, and multi-tissue network in XLS formatClick here for fileTissue specific models and multi-tissue model SBML (in compressed zip format). Metabolic models for adipocyte, hepatocyte, myocyte, and multi-tissue network in SBML format. The SBML format can be imported into Matlab and COBRA Toolbox for simulationsClick here for fileQC/QA Tests and Futile Cycle Tests. Quality control tests to validate universal and tissue specific functionality and model consistencyClick here for fileUnconstrained simulations of Cori and Alanine Cycles. Additional information on simulating the metabolic interaction between the liver and muscle based on sensitivity analysis of objectives (hepatic glucose and urea production)Click here for fileReaction Activity Changes in Obese and Diabetic Obese Individuals. Differential metabolic reactions in simulations of obese and diabetic obese individuals determined by flux variability analysisClick here for fileLiver metabolic network reconstruction comparison. Comparison of HM, Recon 1, and two existing liver metabolic networks in terms of metabolites and reactions.Click here for fileExpression Data Sensitivity Analysis. Sensitivity analysis involving removing random permutations of patient samples from each group to determine robustness of expression data.Click here for file


One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).Here we employed a systematic and iterative process, designated as Reconciling S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data.This study presents validation and refinement of a newly reconstructed metabolic model of the yeast  Homo sapiens[, include Mus musculus[, Leishmania major[, Aspergillus nidulans[, Aspergillus niger[Saccharomyces cerevisiae[, and Pichia pastoris[Genome-scale metabolic models have proven themselves in a wide range of applications in the field of biotechnology, such as system-wide drug targeting, metabolic engineering of microbial systems for production of various chemicals and materials, and system-wide understanding of cellular metabolism -5. Althoo sapiens,9, inclu musculus, Leishmania major, Aspergi nidulans, Aspergilus niger, Saccharerevisiae,14, and  pastoris.in silico predictions, and experimental results are used to direct revisions to the genome-scale metabolic model to improve its predictive capabilities [In silico/in vivo mutaNt Growth (RING) with the single-gene knockout mutant library and apply RING on the newly reconstructed genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe to validate and improve the metabolic model.However, eukaryotic genome-scale metabolic models are far from being complete due to the complexity of eukaryotic systems, such as the presence of intracellular organelles, requiring compartmentalization of the metabolism, and a more complex regulation and gene expression network than bacterial systems. To ensure that the metabolic model can accurately represent the biological system of interest, the predictive capabilities of the metabolic model is compared against experimental data as a means of validating the metabolic model. This standard in evaluating metabolic models is applied to different conditions for which data are available . Discrepbilities -19. HereS. pombe is widely used as a model system for studying eukaryotic systems in life science research [e.g., cell division through medial fission instead of budding) make it an ideal model in the studying mammalian cell cycle control. A high percentage of the research on S. pombe is dedicated to understanding cell cycle control in S. pombe, as well as other cellular functions, such as DNA repair and cellular maintenance. Little research on the metabolism of S. pombe is found beyond the catabolism of substrates other than glucose, ethanol production and even less on the metabolic engineering of S. pombe. With the introduction of a genome-scale metabolic model of S. pombe validated with RING, research into the metabolism of this yeast will gather momentum.The fission yeast research ,21. Thisresearch . It possresearch . Genome research . FurtherS. pombe, SpoMBEL1693, consists of 1693 metabolic reactions and 1744 metabolites, distributed among 8 different compartments representing the intracellular organelles. Employing the single-gene knockout mutant library of S. pombe, RING was applied to improve and refine SpoMBEL1693 to accurately represent the metabolic network of S. pombe[in silico predictions compared to the single-gene knockout mutant library resulted in a 61.2% of all predictions correctly reflected the observed phenotypes . After analysis and reconciliation of the false predictions, SpoMBEL1693 was updated and the accuracy was improved to 82.6% of all the predictions of the single-gene knockout mutant growth phenotypes matched the observed in vivo phenotype .The genome-scale metabolic model of  S. pombe. Initialin silico predictions and in vivo observations (RING) is applied to validate and upgrade the first reconstruction of the genome-scale metabolic model of S. pombe, SpoMBEL1693. The ability of this newly reconstructed metabolic model to represent the metabolic physiology of this yeast was analyzed by comparing the growth phenotypes obtained by single gene knockout simulations with those experimentally observed for the single-gene knockout mutant library [in silico predictions and in vivo observations were systematically and iteratively resolved. The overall scheme for the process can be seen in Figure Here the strategy for reconciling differences between  library . Using RIn silico growth phenotypes for the deletion of every metabolic reaction were generated and the respective genes associated to each metabolic reaction were identified. These growth phenotypes were then categorized as either positive or negative  with a viability threshold of 10% of the “wild-type” growth rate. Then the in vivo phenotypes for each gene was then retrieved from the single-gene knockout mutant library publically available [in vivo phenotypes were retrieved and compared against the in silico predictions, the growth phenotypes were then further categorized based on whether the predictions matched the in vivo observations . The false predictions were then sorted and analyzed in a step-wise manner outlined in Figure vailable . Once thin silico predictions over the total number of predictions made, of the metabolic model. By reconciling discrepancies between in silico prediction and in vivo data, the genome-scale metabolic model was able to accurately represent the metabolic characteristics of S. pombe. Simulations were performed under YES media conditions and the knockout results were categorized as either positive or negative, where the positive represents viable phenotype for a given knockout and the negative represents a lethal phenotype for that knockout. When compared against the published results obtained with mutant library, the results are categorized as either true/false positives or true/false negatives according to whether the prediction agrees with the in vivo results. True results indicate that the in silico predictions match with the in vivo results and false results indicate a discrepancy between the two. A false positive indicates that SpoMBEL1693 predicts a viable phenotype while the in vivo result shows a lethal phenotype  is deleted. Analysis of the initial positive, or viable, predictions of mutant phenotypes of SpoMBEL1693 resulted in 65.4% of the positive predictions matching the observed in vivo phenotypes   Figure . StrategThe first step in reconciling false positive predictions the identification of all duplicated or redundant metabolic reactions localized in a different compartment of the metabolic network. The presence of these redundant metabolic reactions are the result of localization data placing the respective proteins in these compartment and as a result provides an alternate route through another cellular compartment Figure  Case 1. Metabolic reactions with false positive predictions were then checked for their connectivity to the metabolic network. Analysis of the connectivity of these metabolic reactions showed that false predictions were also correlated to dead end metabolic reactions in pathways which are not connected at the downstream end, but connected at the upstream end (dead end reactions) and non-redundant orphan metabolic reactions. The orphan metabolic reactions Figure  Case 2 a. However, the genes encoding for the metabolic reactions of the heme biosynthesis pathway were found to be essential for growth according to the single-gene mutant library as evidenced by the lethal phenotype displayed in knockouts of genes in heme biosynthesis. Thus, heme was incorporated into the biomass metabolic reaction with a coefficient calculated with a negligible cellular concentration to prevent any drain of cellular resources by heme biosynthesis. By incorporating heme into the biomass metabolic reaction, the biosynthesis of heme becomes linked to cellular growth. A consequence of linking heme to biomass is the inclusion of iron ions into the YES media. Sterol biosynthesis is one instance where linking the metabolic pathways to the rest of the network was sufficient for resolving false positive predictions. Gaps in the metabolic pathway of sterol biosynthesis were filled (SPBC1709.07 and SPBC16E9.05) and confirmed through GeneDB to resolve the false positive predictions. A total of 37 metabolic reactions with false positive predictions were resolved and re-categorized as true negatives.Metabolic reactions in dead end pathways were reconciled by connecting the ends of the pathways to the metabolic network Figure  Case 3. The gene associations to metabolic reactions were then examined to reconcile false positive prediction from the knockout simulation. One instance of this case is the association of multiple metabolic reactions to a single gene Figure  Case 4. . Sixty-two metabolic reactions with false positive predictions showed no flux in the in silico wild-type flux distribution, indicating that these metabolic reactions are not used for growth, despite the fact that the deletion of their corresponding genes gives a lethal phenotype in vivo. The absence of any flux through these 62 metabolic reactions could be attributed to the lack of regulatory information that would direct the flux through that metabolic reaction. Thirty seven metabolic reactions that showed false predictions were not reconciled with high confidence due to the simultaneous assignment of both viable and lethal genes to the metabolic reactions. Eight of the 37 metabolic reactions overlap with the previous category where the metabolic reactions exhibit no flux in SpoMBEL1693. The remaining 29 metabolic reactions are utilized and exhibit fluxes when the growth rate is maximized. However, there is no indication whether the deletion of the reaction results in a lethal phenotype or the lethal gene(s) functions in another capacity that is essential for growth, but not reflected in the metabolic network. Therefore, to resolve these cases with high confidence, detailed characterization of all the genes associated to the metabolic reaction is needed. Overall, the correct prediction rate of viable phenotype was improved to 79.6%   Figure  after RI) Figure .False negative predictions are results where the growth phenotype is predicted to be lethal, but instead is viable experimentally. Initial negative prediction rate was 41.4%  Figure . These fAnalysis of false negative predictions started with the examination of the genes associated to the metabolic reactions with false negative predictions. The large majority of false negative metabolic reactions were found to have multiple genes associated with the metabolic reactions Figure  Case 1. Another instance of Case 1 is where all the genes associated with the metabolic reaction are viable; it is also uncertain if the metabolic reaction is essential to the metabolic network (true negative) or if the negative prediction is indeed a false prediction. If the metabolic reaction is truly essential to the metabolic network, then the knockout of all the genes that are associated with the metabolic reaction would give the lethal phenotype when predicted using SpoMBEL1693. Single-gene knockout mutants for these genes would not be sufficient in suppressing the metabolic reaction as it would be compensated by the presence of alternate genes that can function in place of the deleted gene. Due to the lack of information that would allow for the reconciling of these false predictions, the metabolic reactions were removed from the analysis and noted for future research.i.e. an essential intermediate necessary for the biosynthesis of biomass components), strategies were investigated to supply the essential metabolite from other sources within the metabolic network (e.g. another compartment). For example, in the cytoplasm, acetyl-CoA was produced only through the metabolic reaction represented by the enzyme Acetyl-CoA synthetase, which is a non-essential enzyme for growth based on the single-gene knockout mutant library. However, knockout simulations show that acetyl-CoA in the cytoplasm is essential for growth, a precursor to the synthesis of biomass components. Thus, an alternate pathway that can produce acetyl-CoA is needed in the cytoplasm. Alternate metabolic reactions capable of producing acetyl-CoA were found in the mitochondria. However, localization data of the metabolic enzymes in S. pombe does not support the presence of the corresponding metabolic reactions in the cytoplasm [S. pombe genome was searched for a carnitine-acetyl-CoA shuttle that has been reported in S. cerevisiae . However, a search through the genome annotation and a BLAST search for the carnitine-acetyl-CoA shuttle in S. pombe resulted in no candidates. Due to the lack of any possible candidates as a transport protein for acetyl-CoA across the mitochondrial membrane and the improbability of a direct transport of acetyl-CoA, the inconsistency of acetyl-CoA synthetase remained unresolved. The remaining 16 metabolic reactions were unable to be reconciled due to insufficient information. After RING analysis of false negative predictions, the reconciliation between in silico and in vivo phenotypes resulted in the improvement of the correct prediction rate from 41.4% to 92.5% of the negative predictions matching the observed in vivo phenotypes   Figure .S. pombe genome-scale metabolic model was compared to the predictive capability of another yeast metabolic model that has been reconstructed, S. cerevisiae iMM904 [iMM904 was employed for similar studies in predicting the in silico growth phenotypes and was used as a basis for eukaryotic metabolic model’s prediction capability of mutant growth phenotypes [S. pombe and S. cerevisiae is the lack of metabolic reactions localized in the peroxisome, due to the scarcity of knowledge on peroxisome in the fission yeast, highlighting the need for additional studies into peroxisomal metabolism in S. pombe[S. pombe.The predictive capability of the e iMM904 ,18. iMM9 S. pombe. The cenS. cerevisiae metabolic model iMM904 [iMM904, the statistical classification function, specificity and sensitivity, were employed in the analysis of the essentiality simulation to represent the proportion of negative and positive  phenotypes correctly predicted as negative and positive, respectively  was lower than that (46.4%) obtained with iMM904 , was determined for each yeast using their respective genome-scale metabolic models (SpoMBEL1693 and iMM904). Results show a difference in maximum in silico yield for the metabolites acetate and lactate and no difference in the yields for ethanol and succinate (Table S. pombe has a higher yield in producing lactate than S. cerevisiae (approximately 15% less than S. pombe) suggesting that S. pombe would be a more ideal host for producing lactate from glucose. With acetate, S. pombe shows a slightly lower yield than in S. cerevisiae, which is an advantage for S. pombe as acetate is commonly found as a metabolic by-product. Furthermore, the lower acetate yield may also be a reflection of the absence of acetate during the aerobic ethanol fermentation in S. pombe, whereas acetate was observed in S. cerevisiae[.With the te Table . Simulaterevisiae.S. pombe, SpoMBEL1693, presented here for the first time. The experimental results imported from the publically available single-gene knockout mutant library were utilized to improve the accuracy of SpoMBEL1693 in predicting mutant growth phenotypes. The strategy designated as RING, was employed to identify and reconcile discrepancies between the in silico prediction results and the experimental results. Iterative application of RING resulted in a step-wise improvement in the accuracy of the genome-scale metabolic model of this less studied yeast. The first iteration of RING resulted in improving the overall accuracy by 9% (61.2% to 70.5%). The second iteration was then performed and further increased the accuracy by another 12.2% (70.5% to 82.7%).Here we reported the validation and improvement of the newly reconstructed genome-scale metabolic model of a fission yeast in silico prediction and in vivo observations of mutant phenotypes [iMM904, GrowMatch was employed to resolve discrepancies between the in silico predictions and in vivo observation [S. pombe not fully characterized for most of the metabolic reactions, it was decided that a direct metabolic reaction knockout would be more suitable in simulating the mutant metabolic phenotype as opposed to the knock out the metabolic reaction through uncertain GPR relationships for metabolic reactions. Furthermore, without the constraint of a preconceived GPR relationship, information into the actual GPR relation of the genes, proteins and reactions can be illuminated.Previous studies have been done in reconciling differences between enotypes ,18,31. Iervation . With thS. pombe by SpoMBEL1693 or its upgraded future version.Analysis of the false predictions has identified a number of areas for which insufficient information is available to improve the accuracy of the metabolic model in predicting growth phenotypes of single-gene knockout mutants. For instance, the case where multiple genes are associated with a single metabolic reaction was discussed. In some instances, both viable and lethal genes are associated whereas in other instances multiple viable genes are associated to a reaction that is predicted to be essential in the metabolic network. Further experimental data on these metabolic reactions and their corresponding genes will provide hints on how to resolve these issues and further improve the representation of the yeast in silico prediction results and experimental results showed that a majority of the reconciled metabolic reactions are those that were predicted to be false positives. The reconciliation of these false positive predictions was achieved through the linking of the metabolic pathways to cellular growth. This contributed to the improved accuracy in negative predictions by increasing the number of true negatives. Literature evidence supporting these modifications is lacking, and therefore are potential points of interest for further studies and characterization into the metabolism of S. pombe. Of the remaining false positives, many of the metabolic reactions displayed no flux in the metabolic model. This indicates the lack of specific characterization on the role of the metabolic reaction in the metabolism of the yeast. As many of these metabolic reactions are found in nucleotide metabolism or secondary metabolic pathways, it is likely that the annotation of the genes for these enzymes is incomplete. Included in the false positive predictions were the results for which no experimental data or literature evidence, were available, and so whether they are truly false positive prediction or true positive predictions was undeterminable. Thus, they were included as the false predictions to highlight the need for experimental data for these genes.Reconciliation of discrepancies between the S. pombe prevents the addition of metabolic reactions into compartments which the respective enzymes have not been localized in. Thus, these alternate pathways that would allow cellular growth were made accessible through exchange reactions between the different compartments. This is shown in the case with Acetyl-CoA synthetase, where alternate pathways that synthesize acetyl-CoA in the mitochondria were made available to the cytoplasm through the exchange reaction for acetyl-CoA between the two compartments. However, due to the absence of any known acetyl-CoA shuttle system, the discrepancy could not be resolve with a high level of confidence. While this discrepancy remained unresolved, it did manage to deduce the presence of an uncharacterized transporter for acetyl-CoA across the mitochondrial membrane. Furthermore, in comparison with the S. cerevisiae metabolic network, it was found that Acetyl-CoA synthetase is also found to be essential, but S. cerevisiae has two genes associate to the reaction: one essential and one non-essential gene. As S. cerevisiae possesses the carnitine-acetyl-CoA shuttle (absent in the metabolic model of S. cerevisiae), this suggests a more in-depth study into the essential gene associated to Acetyl-CoA synthetase in S. cerevisiae. Other false negative predictions require additional information beyond what the single-gene knockout mutant library can provide. For instance, an essential metabolic reaction can be associated to two genes where one gene may compensate for the knockout of the other and would require a double-gene knockout to determine the validity of the predicted in silico phenotype.Reconciliation of false negative predictions required a different approach from reconciling false positive predictions. Because the pathways were important for the synthesis of components used in the generation of biomass for cellular growth, alternate pathways would be required to bypass the deleted metabolic reaction and allow cellular growth. However, localization data for the enzymes in S. pombe with the study of reconciling single-gene mutants of S. cerevisiae using GrowMatch, demonstrates the advantages of the flexibility RING brings to the process. It should be noted that while both approaches examined the problem of reconciling in silico predictions with in vivo observations at both the gene and reaction levels, the simulations done at the reaction level (RING) and simulations done at the gene level (GrowMatch), may not be directly comparable. Yet, using the RING strategy, we were able to resolve a higher proportion of false positive predictions as demonstrated by the higher specificity . Also the proportion of viable phenotypes accurately predicted to be viable (sensitivity) in S. pombe is comparable, though slightly lower than the study with S. cerevisiae. Considering that the volume of knowledge on S. cerevisiae is far greater than that on S. pombe, the results attain with RING is notable.Comparison of the RING analysis results on reconciling single-gene mutant phenotypes in S. pombe SpoMBEL1693 and the strategy for refining the model’s ability to predict the growth phenotypes of the single-gene knockout mutants. An iterative process called RING was employed in reconciling false in silico predictions with experimentally observed phenotypes to improve the accuracy of the metabolic model by 21.5%. Despite the huge increase in accuracy of the metabolic model in predicting single-gene mutant phenotypes, unresolved inconsistencies between in silico predictions and in vivo observations highlight the gaps in our knowledge regarding the metabolism of S. pombe. The lack of literature evidence supporting the reconciled changes to the metabolic model based on our analysis highlights the gaps in our knowledge. Detailed characterization of GPR relationships specific to S. pombe would increase the confidence level in resolving the inconsistencies. Furthermore double-gene mutant phenotypes would also aid in revolving many of the inconsistencies where gene duplicates exist. The SpoMBEL1693 metabolic model reconstructed and validated here is a first step towards enhancing our understanding of eukaryotic metabolism.In this paper, we reported the reconstruction of the genome-scale metabolic model of the fission yeast S. pombe gene database on GeneDB [The initial reconstruction of the metabolic model was performed using the set of biochemical reactions annotated from the genome and presented in the Kyoto Encyclopedia of Genes and Genomes , NCBI, an GeneDB . Compartn GeneDB ,27. Trann GeneDB   and was cultured in yeast nitrogen base media without amino acids to create a stock of the yeast stored at −80°C until thawed for fermentation and wet experiments performed for the validation of the model. S. pombe was cultured at 30°C.Cultures of the fission yeast were performed to obtain data utilized in the reconstruction of the genome-scale metabolic model. Batch cultures were carried out as follows. Seed cultures were prepared by transferring 500 μL of 10 mL overnight cultures prepared in yeast nitrogen based media without amino acids plus 10 g/L of glucose into 250 mL Erlenmeyer flask containing 100 mL of the same medium and incubated in a shaker at 30°C. Cultured cells used to inoculate the fermenter containing 2 L of yeast nitrogen based media without amino acids medium containing 20 g/L glucose at 30°C. Batch culture was carried out in a 6.6 L Bioflo 3000 fermenter . The agitation speed was initially set at 200 rpm and was increased accordingly using automatic controlling to maintain a dissolved oxygen concentration (DOC) at 40% of air saturation or greater. The pH was adjusted at 6.00 ± 0.1 using 28% (v/v) ammonia solution. Foaming was controlled by the addition of Antifoam 289 . Aeration was done at a flow rate of 0.25 vvm during the whole period of fermentation.Samples for the measurement of amino acid composition, to be used for cellular growth, was taken from the batch cultures during the exponential phase. Nine milliliters of the culture was centrifuged and the supernatant was removed, leaving the cell pellet, which was used to analyze the amino acid composition .600) with an Ultrospec3000 spectrophotometer . Cell concentration defined as gram dry cell weight (gDCW) per liter was determined by using the correlation found in literature relating the OD600 to dry weight (1 OD600 = 0.62 gDCW/L). The concentrations of glucose and by-products in the media were determined by high-performance liquid chromatography  equipped with UV/VIS  and RI  detectors. A MetaCarb 87 H column  was isocratically eluted with 0.01 N H2SO4 at 60°C and a flow rate of 0.6 mL/min.Cell growth was monitored by measuring the absorbance at 600 nm  which consists of two 510 HPLC pumps, a gradient controller, 717 automatic sampler, 996 photodiode array detector, and a Millennium 32 chromatography manager together with Waters pico-tag column (3.9 x 300 mm). Absorbance at 254 nm was measured. Other components were adopted from the literature or assumed , subject to the constraints pertaining to mass conservation, reaction thermodynamics, and capacity, was carried out to determine the fluxes [j,min ≤ vj ≤ vj,max) for each reaction j, and used together with an objective function Z, usually the growth rate [For the analysis of the genome-scale metabolic model, dy state . This ree fluxes . These cwth rate ,36.in silico simulations to mimic the growth conditions which the single-gene mutant library was conducted in and where nutrients found in yeast extract and adenine, histidine, leucine, uracil, and lysine [Gene/reaction essentiality and mutant growth phenotype simulations were performed in GAMS: Integrate Development Environment using the CPLEX solver. Reaction knockout was simulated by constraining each flux to zero, while the objective function was set to maximize cellular growth. If the resulting cellular growth, or biomass formation, was less than 10% of the “wild-type” value while the flux of the metabolic reaction was constrained to zero, then the deletion of the corresponding gene was considered to be lethal and the metabolic reaction to be essential. If no change to the cellular growth was observed or biomass formation was greater than 10% of the “wild-type” when the metabolic reaction was constrained to zero, then the resulting growth phenotype was considered to be viable and the metabolic reaction to be non-essential. The media YES was used in the d lysine  were uncSeung Bum Sohn and Tae Yong Kim contributed equally to this workThe authors declare that they have no competing interests.SBS, TYK, JHL, and SYL participated in the design of this study and editing of the manuscript. SBS, TYK, JHL, and SYL performed the simulations, analysis and drafting of the manuscript. All authors read and approved the final manuscriptList of metabolic reactions and metabolite abbreviations used in SpoMBEL1693.Click here for fileSpoMBEL1693 Characteristics and biomass composition.Click here for fileAdditional validation studies for SpoMBEL1693 - Carbon source utilization and Flux Variability Analysis of ethanol production capacity.Click here for fileList of False positive and False negative predictions from single knockout simulation using SpoMBEL1693.Click here for fileSpoMBEL1693 in SBML format.Click here for file


The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users.The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers ( Escherichia coli outbreak The growth in the number of sequenced genomes has exceeded the most ambitious past estimations http://theseed.org/wiki/Annotating_1000_genomes); today, over 50,000 viral and prokaryotic genomes have been annotated by the subsystems technology. More recently, the SEED Project has been expanded to include the capacity to automatically generate genome-scale metabolic models based on SEED annotations of genome sequences, and the Model SEED system developed from this pipeline has now been applied to construct metabolic models for thousands of genomes With the vast speed and minimal costs of modern sequencing technology, new demands are being placed on the bioinformatics pipelines used to analyze sequence data http://pubseed.theseed.org), this interface is inadequate to make full effective use of the massive quantities of data now embodied within the SEED. To truly expose the SEED data for efficient large-scale use by the global scientific community, we initially developed a Simple Object Access Protocol (SOAP) server to allow the needed programmatic access to the SEED data While a web interface is available for visualizing the data within the SEED . The ERDB model is defined by a set of XML metadata describing the entities, relationships, and attributes in a form that can be used to generate queries as well as the documentation and an up-to-date database diagram may be viewed at http://servers.nmpdr.org/figdisk/FIG/ErdbDocWidget.cgi?database=Sapling. Thus, the public description of the database always remains synchronized with the internal data structures–an important benefit in a database designed for public use.The Sapling Server is architected such that new features can be added quickly. New data tables may be added as updates to the XML metadata, which is processed by a special load program to build the initial database tables. The list of services offered is maintained on the server, so that client software does not need to be updated in order for users to access new features. A web application that converts general database queries to Perl code helps speed implementation of new functions.The Sapling Server allows direct queries against the database; however, a set of common data requests is implemented as direct server functions. Sapling Server functions typically accept multiple input values within a single call, allowing a client to minimize the number of requests that must be made to the server. Additional input parameters allow a client to modify the query, for example, to request that the output be in FASTA format or to ask for protein rather than DNA sequences.A database query is specified by naming the entities and relationships along a path through the ERDB diagram  along wiids_to_sequences request . The server also houses newly developed high-performance methods to assign function to protein sequences or regions of genomic DNA sequences, based on FIGfams The Sequences can be submitted to the server in three ways:Programs can directly access the services needed to call genes and assign functions to the proteins encoded within the genome.http://servers.theseed.org/sapling/server.cgi?pod=svr_assign_using_figfams.pl.If the protein-encoding genes have already been identified, the program can assign functions to these sequences. An example program is provided in the download library and is described at http://servers.theseed.org/sapling/server.cgi?pod=svr_assign_to_dna_using_figfams.pl).A program can take as input fragments of DNA  and use the services to detect pieces of protein-encoding genes. Again, an example program is provided in the download library and is described at Annotation Support Server also provides the ability to take as input a set of functional roles (in the controlled vocabulary established by the subsystem collection) and to produce a detailed estimate of which subsystems are represented by those functional roles. That is, one can also use the server to develop a metabolic reconstruction based on the functional roles that have been assigned to the protein-encoding genes.The RAST Server, retrieval of job status, and retrieval of the final set of annotations. These scripts and the underlying application programming interface (API) enable users to submit genomes to the RAST Server, test the status of submitted jobs, and retrieve the output .The RAST (Rapid Annotations using Subsystems Technology) Three types of input are supported:A FASTA file of contigs that make up the genome to be annotatedA file of GenBank-formatted entries An Entrez ID or a Genome Project ID.RAST Server will perform de novo annotation, i.e., will start by calling RNA- and protein-coding genes, and then will proceed to functional annotation, subsystems assignment, and subsequent metabolic reconstruction. In the second case (GenBank-formatted files), gene calling is optional, since GenBank-formatted files already include the coordinates for open-reading frames and RNA genes. In the last case (Entrez ID or Genome Project ID), the tools provided within the RAST Submission/Retrieval Server will query NCBI using the query ID(s) and will retrieve the sequence (whether that sequence consists of one or a set of contigs that make up the genome project); that sequence then becomes the input to the RAST Server.In the case of the first type of input (FASTA file), the Metabolic Modeling and Flux Balance Analysis (FBA) Server provides programmatic remote access to the Model SEED biochemistry and genome-scale metabolic model database, as well as some model analysis algorithms. The Model SEED biochemistry database integrates into a single, nonredundant set all the reactions and compounds found in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database The Metabolic Modeling and FBA Server capabilities are documented in detail at http://servers.theseed.org/.Currently this database consists of 16,279 compounds and 13,272 reactions. For compounds, the database also includes database IDs from KEGG compounds and SEED models, names/synonyms, molecular masses, molecular formulas, molecular charge, and estimated Gibbs free energy of formation Metabolic Modeling and FBA Server also provides the user with an API to remotely obtain a list of the models in the SEED and to download data on the compounds and reactions in each SEED model. The server returns the following data for each reaction in a specified model: (i) all data from the SEED biochemistry database, (ii) a list of the genes associated with each reaction in the model in a format that captures how the protein products encoded by the genes function to catalyze the reaction (as either independent enzymes or multienzyme complexes), and (iii) a list of compartments in the model where the reaction takes place and the directionality/reversibility of the reaction in each compartment. For the model compounds, the server returns the data from the SEED biochemistry database. As with the biochemistry data, all the model data in the server are accessible either via Perl programs or the API.The SEED database also contains a large number of genome-scale metabolic models, including 14 published models Metabolic Modeling and FBA Server also enables users to run various FBA studies on any of the genome-scale metabolic models stored in the SEED database. These studies can be performed while simulating any of 525 distinct media conditions currently encoded in the SEED database  flux variability analysis (FVA) single gene knockout analysis to predict the genes essential for growth in the selected media.The The simple growth simulation returns the maximum predicted growth rate of the model given the input parameters, the predicted flux through the model reactions during maximum growth, and the predicted uptake and production of nutrients from and to the environment during maximum growth.forward essential or reverse essential if they are required for growth to occur, with forward and reverse referring to the direction in which the reactions must proceed. Reactions that are not essential for growth, but still active, are classified as forward variable, reverse variable, and variable, with forward and reverse indicating when reactions proceed only in a single direction. Reactions are classified as blocked if they cannot carry flux under the conditions specified by the user. Metabolites in the model are classified as essential nutrients or essential products if their uptake or secretion is required for growth in the input conditions, and they are classified as transported if they can be taken up or secreted but are not essential for growth. In addition to classifying the reactions and compounds in the model, the FVA simulation returns the maximum and minimum values for the flux through each reactions and the uptake/secretion of each metabolite.The FVA simulation returns the predicted class of every reaction and compound in the model during growth given the input parameters. Reactions in the model are classified as The single gene knockout analysis rapidly simulates the individual knockout of every gene represented in the model during growth in the input conditions. Based on these simulations, the analysis produces a list of the predicted essential genes and the predicted nonessential genes in the model. Both the Perl program and API allow the user to run any of the three simulation types from the command line.http://servers.theseed.org/for detailed documentation on all Metabolic Modeling and FBA Server functions.All three simulation types accept the same user input: the name of the model to be run, the name of the media formulation that growth should be simulated in, a list of genes in the model that should be knocked out during the simulation, and a list of the reactions in the model that should be knocked out during the simulation. See The SEED servers can have limitless applications. Below, we demonstrate various applications of SEED servers through a small set of potential applications and coding examples.sequence equivalent if the protein sequences are identical and (ii) two IDs that represent either exactly the same protein-encoding gene or the precise protein encoded by the gene  are said to be precisely equivalent. Unfortunately, in the presence of multiple versions of thousands of genomes, perfect maintenance of the “precisely equivalent” correspondence is virtually impossible.Dealing with IDs of genes or the proteins they encode is often nontrivial. In the SEED database, we use IDs that specify protein-encoding genes in a rapidly growing set of genomes, and we support correspondences between these IDs and those used by other annotation efforts. The SEED has two notions of equivalence: (i) two IDs that represent either protein-encoding genes or protein sequences are said to be http://servers.theseed.org/sapling/server.cgi?code=server_paper_example1.pl ; for example, see A great deal has been learned from studying genes that tend to occur close to one another in diverse genomes http://servers.theseed.org/sapling/server.cgi?code=server_paper_example4.pl illustrates the potential for constructing custom tools by going through all of the protein-encoding genes in all of the complete prokaryotic genomes maintained within the SEED looking for “hypothetical proteins” that tend to co-occur with genes encoding functions that can be connected to subsystems. The program constructs a table showing the following:The following program at GeneFunction of the geneGenome id containing the geneBiological name of the genomeNon-hypothetical gene in a subsystem that appears to have the strongest measure of co-occurrenceMeasure of gene co-occurrenceFunction assigned to the co-occurring gene contained in a subsystem.This table can therefore be used to suggest functions for hypothetical proteins that could be tested experimentally. A copy of the code is provided .http://servers.theseed.org/sapling/server.cgi?code=server_paper_example6.pl  and an estimate of phylogenetic neighborhood–a representative genome that is “phylogenetically close” to the genome containing the protein, if an estimate can reasonably be given.http://edwards.sdsu.edu/rtmg) can be performed on computers or cellular phones A similar approach has been adopted for rapid, real-time analysis of metagenomics samples, which might elsewhere take days or months for BLAST-based analysis. This real-time metagenomics analysis  using the “get_compound_id_list” and “get_reaction_id_list” functions, respectively. The program then uses these lists to obtain detailed data on all the E. coli compounds and reactions . These data are stored in two tables: one for compounds and one for reactions. Next the “classify_model_entities” function is used to run a FVA on the SEED E. coli model. In this particular FVA, the reactions and compounds in the E. coli model are classified while simulating growth in LB media . At this point, the data returned by the “classify_model_entities” function is added onto the compound and reaction tables prepared previously. In the next step, the code uses the “simulate_model_growth” function to run a standard FBA on the SEED E. coli model, maximizing the model growth rate in simulated glucose minimal media . The data returned by this function are also added to the reaction and compound tables. In the final call to the server, the program uses the “simulate_all_single_gene_knockout” function to simulate the single knockout of all E. coli genes, and the results of this study are stored in a gene table. The remainder of the program handles the printing of the compound, reaction, and gene tables to the files CompoundTbl.txt, ReactionTbl.txt, and GeneTbl.txt, respectively.The program starts by obtaining a list of all compounds and reactions in the SEED http://bit.ly/server_paper_example8 ), uses two SEED servers-based programs to update SEED to GO comparisons through the use of UniProt The Gene Ontology (GO) project aims to unify biology by providing a controlled vocabulary of terms for all genes and gene products The workflow consists of the following steps:http://www.theseed.org/servers/#mozTocId509100Download and install the SEED servers from ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gzDownload the latest version of UniProt: http://edwards-sdsu.cvs.sourceforge.net/viewvc/edwards-sdsu/bioinformatics/bin/parse_uniprot.pl?view=log : Mac OS, LinuxProgramming language: Perl, JAVALicense: SEED Toolkit Public LicenseAny restrictions to use by non-academics: no limitationshttp://servers.theseed.org.The SEED servers project is documented and can be downloaded from the servers’ web site, The Perl distribution contains the following:Client Packages.The Sapling server - SAPserver.pmThe MODEL server - MODELserver.pmThe Annotation Support Server - ANNOserver.pmThe RAST server - RASTserver.pmProgramming using the servers. The SEED servers provide all necessary network operations in a client package that can be used to access the server functions. One uses these like any other Perl package. For instance, to find all genomes in the SEED, one does the following:#!/usr/bin/perl wuse strict;use SAPserver;my $sapObject  =  SAPserver->new;my $genomes  =  $sapObject->all_genomes;foreach my $g (sort { $genomes->{$a} cmp $genomes->{$b} }keys(%$genomes)) {print "$g\t$genomes->{$g}\n";}The function call $sapObject->all_genomes marshals the correct server-side function call and arguments into a network package, transmits that package to the server, waits for and retrieves the answer, processes any returned error codes, decodes the return package into a Perl data structure, and returns the result. All function calls in all the client packages perform these basic services.The org.theseed.servers.serverConnections package handles connecting to the server, transmitting and receiving the data, and converting data structures from the server into Java data structures. The classes in org.theseed.servers.servers packages handle connecting to each of the servers and making the appropriate calls.http://www.eclipse.org/), netbeans (http://www.netbeans.org/), or a similar graphical integrated development environment (IDE). These are used like any other class. For instance, to find all genomes in the SEED, one does the followingWe recommend that the code be accessed in eclipse  System.out.println(id + "\t"+ genomes.get(id));}}Future directions include expanding the applications and releasing packages for use by other programming languages such as Python.Supporting Information S1Examples of programming using the SEED servers (coded in Perl). Eight application examples are provided, seven of which (1–4 and 6–8) are described in detail in the text.(PDF)Click here for additional data file.