The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.In this contribution we present the A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology. About oApplication of systems biology holds tremendous promise because it aims to understand and quantitatively describe biological phenomena within the framework of the hierarchical levels of metabolic pathways and regulatory networks at the different scales of cells, tissue, organs and ultimately whole organisms ,6. HowevCurrent activities can be grouped into(1) quantitative structure-activity-relationship (QSAR) methods, computational models based on compound structure and focused on potential interactions of small molecules with major classes of proteins such as drug metabolizing enzymes -15, tran(2) in vitro kinetics for prediction of in vivo drug clearance using kinetic data from recombinant cytochromes P450 (CYPs), microsomes and hepatocytes (IVIVE: in vitro-in vivo extrapolations) .(3) physiologically based PK (PBPK) modeling -28 whichIn addition to these simulations based on mathematical models various computational and bioinformatics approaches are applied to extract information from high throughput data of drug response experiments at cellular, tissue, organ and whole organism level.A critical assessment of the aforementioned tools, essentially to outline gaps that must be addressed for more reliable predictive simulation-based toxicology, indicates needs for more rigorous network models focusing at systems dynamics beyond kinetics of individual enzymes, consideration of inter-individual variability and systematic investigations of parameter sensitivity and its impact on model verification, discrimination and reduction, to name a few. The first issue is related to the design of the dynamic models for the drug elimination process in the hepatocyte, which should be based on the integration of membrane transport processes for substrates and products as well as phase I and phase II reactions. These models need to be tightly linked to stimulus (dose)-response experiments.The issue of model parameterization in the context of modeling in toxicology has been already addressed in 1995 by Andersen et al . In addiYet another question of interest concerns the subtle integration of the enormous inter-subject variability in enzymatic phenotypes into the model. This is of outermost importance for predictions in toxicology and also in clinical pharmacology in order to design optimal treatments for individual patients. Consideration of this variability in phenotype should rest on quantitative proteomics and activity data from human liver tissue or hepatocytes representing a statistically significant portion of the population.rmax/KM-values estimated from in vitro studies using human liver microsomes, primary or cryopreserved hepatocytes or recombinant expressed enzymes. This is a promising approach; however, improvement of the strategy is imaginable by separation of the two parameters rmax and KM .The importance of this issue has been extensively addressed in the review of Rostami-Hodjegan and Tucker  and discThis concept, which is a focus of the present article, is driven by the possibility to incorporate separate information from pharmacokinetics and quantitative proteomics as well as future incorporation of the regulatory network responsible for variation of expression level of the enzymes, transporters and receptors. As model drug we chose atorvastatin (AS), one of the most frequently used 3-hydroxymethylglutaryl coenzyme A reductase inhibitors (statins). Statins thereby reduce cholesterol synthesis and they also stimulate the uptake of LDL-cholesterol from the blood. Although they are relatively safe drugs, the lipid-lowering effect of statins is inadequate in some patients, and unpredictable drug-drug interactions can occur, as well as hepatic and extrahepatic adverse effects including hepatotoxicity, myopathy, and rare but severe rhabdomyolysis.Some aspects of dynamic modeling of statins have been tackled in previous studies, including the deterministic modeling of transport kinetics ,34 and tThis study describes a deterministic modeling approach of the dynamic biotransformation and transport processes of AS in human hepatocytes considering both kinetics of transport processes as well as intracellular detoxification processes via Phase I and Phase II enzymes. Attention is given to the detailed enzymatic and chemical reactions, including conversions between the acid and lactone form of AS as well as unspecific binding between metabolites and macromolecules like proteins. The additional integration of quantitative protein data of Phase I and Phase II enzymes enables the dynamic analysis of the underlying inter-individual-variability of expression levels of these enzymes.Williams Medium E (WME) without phenol-red and without L-glutamine and stabilized L-glutamine were obtained from Pan-Biotech-GmbH . Penicillin/Streptomycin and ITS-X were obtained from Invitrogen . Bovine serum albumin (BSA) and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich Chemicals . Dexa Inject was obtained from Jenapharm . AS, its metabolites and deuterated standards were purchased from Toronto Research Chemicals Inc. . PBS was obtained from Invitrogen  and Complete Mini, EDTA-free from Roche Diagnostics . NaPP, sucrose and LiChrosolv were purchased from Merck ; acetonitrile from Roth , formic acid from Fluka, (Germany), UGT1A3 monoclonal mouse antibody from Abcam . Trypsin was purchased from Promega . Synthetic peptides were purchased from Sigma-Genosys .6 cells/well. Cells were allowed to attach to the collagen layer. After transport, culture media was disposed and attached cells were cultured 24 h at 37°C in a humidified chamber with 95%/5% air/CO2 in serum-free medium WME, supplemented with albumin (0.1% (v/v)), penicillin/streptomycin (100 U/ml), stabilized L-glutamine (2 mM), dexamethasone dihydrogenphosphate (0.025% (v/v)) and ITS-X , further named SFM.Tissue samples from human liver resections were obtained from patients undergoing partial hepatectomy. Experimental procedures were performed according to the guidelines of the charitable state-controlled foundation HTCR (Human Tissue and Cell Research) Regensburg, Germany, and the institutional guidelines for liver resections of tumor patients with primary or secondary liver tumors, Technical University Munich, MRI, Munich, Germany. The use of human hepatocytes for research purposes was approved by the local ethics committees of the Ludwig-Maximilians-University of Munich  and the 2 in 2 ml SFM, supplemented with 10 μM AS, 0.1% (v/v) BSA and 0.1% DMSO. At specified time-points, three wells were further treated for the preparation of samples for the measurement of extracellular and intracellular metabolites, respectively. SFM media was collected and 50 μL formic acid and deuterated internal standard was added for the further measurement of extracellular metabolites. Cells were harvested in pre-cooled albumin-free SFM, disrupted by freeze/thaw and ultra-sonification and centrifuged. The supernatant was used for the determination of intracellular metabolites.Incubation with AS was started by disposing the culture media and cultivation of the attached cells at 37°C in a humidified chamber with 95%/5% air/COFor the preparation of samples for the protein measurements, culture medium was disposed and cells were harvested in pre-cooled PBS, supplemented with Complete Mini EDTA-free (1 Tablet/10 ml Buffer). Cell suspensions were centrifuged 5 min (500 g) at 4°C and cell pellets were resuspended in 150 μL NaPP-buffer , containing 250 mM sucrose and Complete Mini EDTA-free (1 Tablet/10 ml Buffer). Cells were disrupted by ultra-sonification and lyophilized for the analysis of total protein concentration, CYP3A4 and UGT1A3 content.For cell number determination, cells from two wells were fixed with methanol-acetic acid fixative solution (10 min at 37°C and 4°C) and afterwards nuclei were stained for 15 min with Meyers Hämalaun , rinsed with water and air-dried. Stained nuclei were counted in digital images (10 per well) at 40-fold Magnification .m/z.AS and ASL, and their para-  and ortho-hydroxy-metabolites , were determined by LC-MS-MS analysis using the respective deuterium labeled analogues as internal standards, essentially as described . HPLC se2H5]ATV , ATV-L (541.2 and 448.2), [2H5]ATV-L (546.2 and 453.2), p-OH-ATV , [2H5]p-OH-ATV , p-OH-ATV-L (557 and 448.4), [2H5]p-OH-ATV-L (562 and 448.4), o-OH-ATV (575 and 466.4), [2H5]o-OH-ATV (580 and 471.2), o-OH-ATV-L (557 and 448.4), [2H5]o-OH-ATV-L (562 and 448.4). Sample quantification was possible in a range from 0.5 to 500 pmol.Precursor and product ions (m/z) of analytes and internal standards, respectively, were ATV , [Protein quantification of CYP3A4 and UGT1A3 in human liver microsomes and relative protein quantification of UGT1A3 in lyophilized samples of primary hepatocytes was performed by immunoblotting as described previously ,39.® Tips  according to manufacturer's suggested protocol and separated on a nanoliter-flow Ultimate HPLC system . After injection (15 μl), peptides were trapped and desalted on a precolumn  at a flow rate of 30 μl/min in 0.1% TFA for 6 min. Peptides were transferred to the separation column  and separated in a linear gradient of mobile phase  from 5% B to 35% B over a period of 35 min with a flow of 290 nl/min. The column effluent was continuously directed into the NanoSpray II source of a 4000QTrap mass spectrometer . The MS was set up to run a multiple reaction monitoring experiment essentially, as described previously [Cell lysates for absolute quantification analysis of CYP3A4 were prepared from lyophilized human primary hepatocytes by sonification in the presence of glass beads in buffer containing Complete Mini-Protease Inhibitor Cocktail, and following homogenization. In the CYP3A4 quantification assay, three synthetic isotopically labeled peptides (13C/15N amino acid) were used as internal standard for calibration. These isotope labeled standard peptides represented sequence analogues to proteotypic peptides of CYP3A4, which arise from tryptic digestion. After acetone precipitation and resolving of the proteins in 8 M Urea, a definite amount of internal standard peptides was added. The sample mixture was reduced with 5 mM DTT and alkylated with 15 mM iodacetamide in 50 mM ammonium bicarbonate. Subsequently samples were digested with trypsin at 42°C for 4 h (enzyme/substrate ratio of 1:10). Efficiency of tryptic digestion was checked by SDS-PAGE followed by silver staining. The resulting peptides were purified using C18 OMIXNumerous metabolic and physicochemical aspects about AS had to be considered in the initial model building. AS exists in two forms, a very lipophilic lactone (ASL) and a comparably hydrophilic hydroxyl-acid (AS). AS is converted enzymatically via an instable intermediate product into ASL, mediated by different UGT isoenzymes ,42. ReceBoth AS and ASL are hydroxylated in human hepatocytes leading to para- and ortho-hydroxy-metabolites, ASpOH, ASoOH, ASLpOH and ASLoOH ,50, mainFurthermore, AS is transported into the cell via organic anion transport polypeptides (OATP), and recent studies on recombinant systems showed, that OATP1B1 and OATP2B1 contribute to the AS import ,54, whicOATP1B3 is supposed to be also a main contributor to drug transport , since iOATP transporters have been reported to be bidirectional facilitated diffusion transporters, independent from ATP and Na+, K+ and H+, but with a possible involvement of reduced glutathione ,59. HoweAs shown with the recombinantly expressed transporters mentioned above , transpoAS and its hydroxylated metabolites, ASpOH and ASoOH, are actively exported out of the hepatocytes into the bile by the ATP-dependent MDR1 transporter ,62. In aPassive diffusion might play also an important role in the transfer of AS, ASL and the corresponding metabolites, ASpOH and ASoOH, ASLpOH and ASLoOH, respectively. Since the acidic forms of AS are rather hydrophilic and the lactone forms of AS are rather lipophilic, it can be assumed that passive diffusion plays a more important role for the lactone forms, and the transporter mediated active transport plays a more important role for the acidic forms, as reported earlier for statins .Finally, lipophilic drugs have a high affinity to bind non-specifically to proteins, and previous studies concentrated on the modeling of drug binding in the intracellular and the extracellular space as well as on the surface of the cells ,68,69.The mathematical model can be described by the system of non-linear ordinary differential equationscj in the extra - or intracellular compartment with Vcomp is effected by the conversion or production of contributing chemical or enzymatic reactions or by transport steps rij, respectively. The extracellular volume, where the change in extracellular, intracellular or unspecific bound metabolite concentration of 30 μm .rij are modeled for the CYP3A4 hydroxylation, the UGT1A3 lactonization, the chemical and enzymatic lactone hydrolysis and intracellular unspecific binding to macromolecules.Appropriate reaction kinetics Previous studies determined substrate inhibition kinetics of the CYP3A4 mediated hydroxylation of AS on human microsomes . Howeverc) or outside (m) the cell, or enzymatically by the contribution of PON enzymes inside the cell. Both reactions are described as first order kineticsThe lactone metabolites are either hydrolyzed chemically to the respective acid metabolites  inside (Unspecific binding of intracellular metabolites to macromolecules is formulated askdis and the intracellular fraction unbound [with the dissociation coefficient  unbound (6)eq) whereas the active transport kinetics of the other metabolites are assumed to be of first-order .(8)Besides the active transport, metabolites undergo passive diffusion through the double-layer lipid-membrane. Passive diffusion, described asd, through all cells with the total surface area Acells, and controlled by the diffusion coefficient Dj and is comprised as the permeability coefficient Pj.is driven by the concentration difference between outside and inside the cell, The optimization procedure is based on evolutionary strategies which are implemented with JavaEva  and a MVA (main vector adaptation) mutation operator . The optsj, squared and summed over all metabolites J and all time points N, has to reach a minimum. Additional optimization constraints are fuAS > fuASL, PASL > PAS, PAS > PASOH and PASL > PASLOH, because the lactones have a higher lipophilicity than the acids and the metabolites are supposed to be more hydrophilic than the corresponding parent lactone or acid drug.where the deviation of calculated and measured concentrations divided by the measurement standard deviation The integration of the differential equations (1) using the reaction kinetics in equations (2) to (9) was performed by the differential algebraic equation solver LIMEX  .For pharmacokinetic predictions taking inter-individual variability of CYP3A4 and UGT1A3 expression levels into account, maximal rate parameters are predicted via a relative abundance approach, which is based on the assumption that the maximal rate of the reaction is proportional to the enzyme concentration:e, here CYP3A4 or UGT1A3, in the conversion to the product Pj in the liver of interest li is estimated from the respective maximal rate li.The maximal velocities The mathematical model of AS metabolism was coded in FORTRAN language and linked to the numerical integrator LIMEX, also written in FORTRAN. After compilation to the executable program, optimization was started by the call of JavaEva. The mathematical model is supplemented as SBML-file for review purpose.The model structure of AS biotransformation in primary human hepatocytes is schematized in Figure Since the calibration and quality samples were prepared in the same media as the experimental metabolite samples and because it can be assumed that the intracellular protein concentration is much higher compared to the outside of the cells, the unspecific protein binding was only considered for the intracellular space.Regarding the unspecific protein binding, it can be assumed that the intracellular protein concentration is much higher compared to the outside of the cells. This assumption is based on the estimation of the ratio of intracellular protein to extracellular media protein concentration. From the total cell protein measurement and the determination of total cell volume a total intracellular protein concentration of about 30 g/l can be estimated. The media includes as sole protein component 0.1% (v/v) albumin, corresponding 1 g/l. Therefore, this assumption is justified.Because our hepatocyte culture model does not allow to distinguish experimentally import and export, single irreversible, actively mediated transport steps for both directions, as well as passive diffusion steps were considered for each compound, respectively, similar to mechanistic transport models of other studies ,34. OnlyModel verification was performed by quantitative measurements of AS and all metabolites in the extra- and intracellular space during time-series experiments, performed first on a single batch of primary hepatocytes from a single individual 1. Special emphasis was given to the issue of parameter sensitivity and correlation between parameters to improve the quality of the model.From the metabolite concentrations in primary human hepatocytes of individual 1  and using the nominal parameter values of the reactions, transport and diffusion steps in equations 2) to (9). The optimization criterion in equation (10) was used, which demands to minimize the difference between experimental concentration data and model simulation. In the optimization procedure certain parameters were fixed to values (Table  to (9).  enzymes ,53,54. Tcrosomes . The ratrvations  and assuchanisms .AUC0-600 min of the acidic form to that of the lactone form equals 20.1 in case of the para-hydroxy-metabolites and 23.6 in the case of the ortho-hydroxy-metabolites.The model predicted concentration-time-profiles are illustrated together with the measured concentrations ) using the rmax-parameters of individual 1 ) differ from the measured mean value , which is in the range of physiological plasma concentrations [Simulations were performed with an initial extracellular AS concentration of 50  of the concentration-time-profiles of either AS alone or the sum of concentration of AS, ASpOH and ASoOH were calculated for each liver sample over a time period of 1200 min and the distributions over all liver samples were evaluated, respectively. Finally, appropriate probability density functions are fitted to the distributions  have lower values in case of AS alone compared to the sum of all acidic metabolites. The population mean of AUC is 75051 (pmol ml-1 min) in the case of AS and 203617 (pmol ml-1 min) in the case of the sum of AS, ASpOH and ASoOH. Also, cmax is lower in the case of AS alone, 201 (pmol ml-1), compared to the sum of the acidic metabolites, 366 (pmol ml-1). Further, the maximal concentration appears at a shorter time point, 48 min, in the case of AS alone, compared to the time point, 100 min, of the sum of AS, ASpOH and ASoOH. The results are quite explainable, since ASpOH and ASoOH are the hydroxylated products of AS and therefore their maximal concentrations event at a delayed time-point compared to AS.The most important question that arises from this dynamic analysis was, how the metabolic profiles of the intracellular metabolites AS, ASpOH and ASoOH are influenced by this variability, since they are considered to be the active drugs, which inhibit HMGCoA-reductase . Therefos Figure . The promax and t(cmax) are lower than 50%, except for the probability density function of AUC of intracellular AS, which equals 123%.Further, except for the AUC of AS, the probability density functions show a very narrow shape, regarding the relative standard deviation, when comparing to the sample standard deviations of the underlying distribution of CYP3A4 and UGT1A3, respectively. The probability density functions fitted to CYP3A4 and UGT1A3 distributions have a relative standard deviation of 259% and 137%, respectively, whereas the relative standard deviations of the probability density functions of AUC, cin-vitro investigation on isolated primary human hepatocytes was performed over rather short time intervals (hours), whereas analyses in humans usually cover longer periods (days). A second explanation could be the relatively high initial concentration of AS of about 10 μM used in this study, compared to plasma concentrations observed to be lower than 200 (pmol ml-1). Furthermore, we found that the recovery of metabolites decreases over the time. This confirms on the one hand the contribution of unspecific binding to macromolecules, most severely in the intracellular space, as observed previously [The deterministic modeling of drug metabolism has a major advantage compared to traditional pharmacokinetic models. The distinction between metabolism and elimination inside the hepatocytes and the detailed description of the biotransformation network structure allows the observation of the reaction kinetics and the transport mechanisms involved. Our modeling considered the major acid and lactone metabolites in the extracellular and intracellular space as well as appropriate reaction kinetics and transport steps. An experimental limitation in primary hepatocytes concerns the lower limit of quantification of each metabolite. Therefore, a relatively high initial concentration of AS of about 10 μM had to be chosen in order to permit measurement of the described major metabolites. Interestingly, the intracellular concentration of the parent drug AS was higher than the extracellular concentration. This indicates, that either the OATP mediated import does rather follow an active uptake mechanism than the proposed facilitated diffusion mechanism ,59, or seviously , but on eviously , especiain-vivo.The parameter identification was considered satisfactory, as the simulation profiles fitted well to the corresponding measured metabolite concentration data. But, it was questionable if the optimized parameters could be considered to be sensitive and identifiable. Therefore, a local parameter sensitivity and identifiability analysis based on the Fisher-Information-Matrix was performed. The results showed that the full model of AS metabolism is not identifiable. Consequently a model reduction procedure was set-up and could be applied successfully, indicating that the parameter identification difficulties are caused by non-identifiable transporter steps, displayed by low parameter sensitivities and high parameter errors and correlations, which were then reduced in the model. However, this outcome does not necessarily mean that the non-identifiable transport steps are not present or not used in human hepatocytes, but rather implies, that they cannot be distinguished from the remaining transport steps in the model verification. Thus, the remaining transport steps in the model capture the probable superimposed contribution of several transport mechanisms Unfortunately, the local identifiability of the final model version reveals some uncertainties caused by remaining high correlations present in the transport steps, as well as in the intracellular reaction network. An additional problem attributed to the modeling and parameter optimization is related to the fact that several parameters have lumped characteristics, because they are used for more than one compound, like the fraction unbound factor, which is set to be the same for all acidic or lactone AS compounds, respectively. A possible solution would be to set individual parameters for each compound. However, then the parameter identifiability difficulties would be even more severe. This is due to the fact, that the experimental observation on primary human hepatocytes is constrained strongly on several limitations. For example, the range of initial concentrations is strongly limited to the quantification of metabolites of interest. Further, the number of data points is strongly limited to the available cell number of primary hepatocytes, isolated from surgery removal. Finally, also the individual character of human cells due to individual genetic and medical background precludes an exact reproduction of experiments and the reproducibility of experimental results. The remaining parameter uncertainty seems to be crucial for the prediction of the population probability on the first sight. Actually, this is not the case. Since the parameter errors and the correlations indicate which parameter changes in what extent do not influence the computed time courses, the variability in the respective parameter error range would not influence the probability distributions. In general, the question if parameter errors and insensitivities are sufficiently small can only be answered when knowing the requirements of the pharmacological application as drug, which was not given in this study.The simultaneous model verification based on primary human hepatocytes from different individuals illustrates the individual character of drug metabolism in the liver. Further, it indicates the inter-individual variability of rate parameters not only of the phase I and II enzymes, but also of the transport enzymes. In case of patients with type 2 diabetes, AS metabolism is probably influenced strongly by beta-oxidation and further so far unknown effects. Therefore, the individual model verification might also be used to reveal undesired pharmacokinetic effects. However, this analysis tool should be checked carefully on other drug systems in the future, too.The individual nature of the AS metabolism was investigated in this study in the domain of inter-individual variability of CYP3A4 and UGT1A3 protein expression levels in human hepatocytes, by performing dynamic analysis on the verified model, individualized by implementation of a comprehensive set of protein data from a liver bank. The results show, that the inter-individual variability strongly affects the biotransformation behavior and therefore reveals the individual character of AS metabolism. Consequently, this individuality in the pharmacokinetics of AS also points out the individual character of pharmacodynamics at the drug target, namely the HMGCoA-reductase in human hepatocytes.KM. Thus, further efforts should also consider this individual difference, leading to the improvement of the model prediction.However, this subject-variability can also be expected to be present in the transport protein expression, as previously shown for OATP-C . TherefoTo resolve the contributing transport mechanisms in more detail, additional information should be implemented from transport investigations on recombinant systems, as described previously ,83, whicWe believe that the results of our simulations provide strong arguments for rigorous dynamic modeling of drug biotransformation at the cellular level embedded in a systems biology approach. By resolving the detailed metabolic network structure with metabolites and catalyzing enzymes, we investigated the dynamic variation of atorvastatin metabolism affected by the inter-individual variability of expression levels of phase I and phase II enzymes.In contrast to experimental investigations on recombinant systems or tissue fractions of hepatocytes, like microsomes, the investigation on primary human hepatocytes enabled the holistic and most realistic in-vitro observation of drug biotransformation, because it is possible to observe the coupled contribution of metabolism and transport to the entire processes. De novo of this study, we identified intracellular concentration profiles of atorvastatin metabolites in primary human hepatocytes in a time-series approach.Such an approach is essential for integration of further-reaching issues, such as drug-drug interactions, impact of regulation networks linked to nuclear receptors and particularly to quantitatively account for subject-variability. The integration of this variability caused by genetic or environmental variations is crucial for predictive pharmacokinetic modeling.Such a rigorous modeling approach critically depends upon a tight link between experimental observations and model design, simulation and verification. While the results are promising, some limitations in the parameter identification were still encountered. In the long term, these open problems can only be solved by stronger links to other research areas, such as pharmacogenetics, characterization of transporters, etc. On the whole, our contention is that the problem of parameter identifiability is an indispensable ingredient of model verification. Systematic investigations - if possible linked to optimal experimental design - can greatly strengthen the credibility of the models.However, we present a model that goes much further. The domain of application does not remain the system behavior for which it has been elaborated. The model provides the possibility to link further modules such as gene regulation, drug target metabolism and present the important links to be implemented into the PBPK environment. Finally, the model structure used in this study should be considered as a module to be integrated into the framework of multi-scale whole body modeling and simulations necessary to tackle the drug disposition in patient populations.c: index: intracellular (cytosol); calc: index: calculated; CR: index: chemical reaction of hydrolysis of lactones; CYP: cytochrome P450 monooxygenase; Den: Denominator; e: index: enzyme; eq: Index: equilibrium; ex: index: export; FIM: Fisher-Information-Matrix; fu: fraction unbound; i: index: reaction; im: index: import; j: J; index: compound; k: index: parameter; rate constant; KM: Michaelis-Menten constant; l: index: parameter; m: index: extracellular (medium); meas: index: measured; n:N; index: time-point; MDR: multidrug resistance protein; MRP: multidrug resistance-related protein; OATP: organic anion transport protein; PON: paraoxanase; r: reaction rate; rmax: maximal reaction rate; UGT: UDP-glucuronosyl-transferase; 1A3: index: UGT1A3; 1B1: index: OATP1B1; 2B1: index: OATP2B1; 3A4: index: CYP3A4;AS: atorvastatin acid; ASL: atorvastatin lactone; ASOH: hydroxy-atorvastatin acids (para- and ortho-); ASpOH: para-hydroxy-atorvastatin acid; ASoOH:ortho-hydroxy-atorvastatin acid; ASLOH: hydroxy-atorvastatin lactones (para- and ortho-); ASLpOH: para-hydroxy-atorvastatin lactone; ASLoOH: ortho-hydroxy-atorvastatin lactone; B-OX: beta-oxidation of atorvastatin acids; JB carried out the experiments on primary human hepatocytes, set-up the model of atorvastatin metabolism, performed the model verification and dynamic analysis, and drafted the manuscript. SR performed the quantification of metabolites. SR and AS performed the quantification of protein data, and contributed to the experimental design and the manuscript draft. GV guided the culturing of primary human hepatocytes at the Institute of Biochemical Engineering and assisted in the conduction of the experiments. KM contributed to the project design and guided the protein quantification. AKN, TSW and WET provided primary human hepatocytes. UZ and MR contributed to the design of the project and the fund raising, guided the research, and revised the manuscript draft critically. UZ further provided individual liver protein data. MR was the coordinator of the project. All authors read and approved the final manuscript.Derivation of atorvastatin kinetics at CYP3A4. This file contains the derivation of the atorvastatin kinetics at CYP3A4, which describes the competition of alternative substrate degradation to alternative products (supplemented as .pdf-file).Click here for fileDerivation of kinetics of unspecific binding to macromolecules. This file contains the derivation of kinetics of the unspecific binding of atorvastatin metabolites to macromolecules, for example proteins (supplemented as .pdf-file).Click here for fileAtorvastatin metabolite concentrations from the time-series experiment on primary human hepatocytes of individual 1. Extracellular concentrations (upper part) and intracellular concentrations (lower part) of atorvastatin acid and lactone (AS and ASL) and corresponding para- and ortho-hydroxy-metabolites  at the defined time-points with mean and standard deviation (n = 3) from triplicate measurements per LC-MS/MS . The recovery is calculated from material balance equations and is defined as the sum of intracellular and extracellular metabolite amounts at the respective time-point divided by initial AS amount (supplemented as .pdf-file).Click here for fileAnalysis of parameter sensitivity and following model reduction procedure. This file contains the analysis of parameter sensitivity and the exemplary model reduction procedure, which is necessary for achieving a high quality predictive model of atorvastatin metabolism (supplemented as .pdf-file).Click here for fileAtorvastatin metabolite concentrations from the time-series experiment on primary human hepatocytes of individual 2. Extracellular concentrations (upper part) and intracellular concentrations (lower part) of atorvastatin acid and lactone (AS and ASL) and corresponding para- and ortho-hydroxy-metabolites  at the defined time-points with mean and standard deviation (n = 3) from triplicate measurements per LC-MS/MS (n.d.: not determinable) (supplemented as .pdf-file).Click here for fileAtorvastatin metabolite concentrations from the time-series experiment on primary human hepatocytes of individual 3. Extracellular concentrations (upper part) and intracellular concentrations (lower part) of atorvastatin acid and lactone (AS and ASL) and corresponding para- and ortho-hydroxy-metabolites  at the defined time-points with mean and standard deviation (n = 3) from triplicate measurements per LC-MS/MS (n.d.: not determinable) (supplemented as .pdf-file).Click here for fileModel of atorvastatin metabolism in primary human hepatocytes of individual 1. The sbml-file contains the model of Atorvastatin metabolism in human hepatocytes with the model parameters identified on primary human hepatocytes of individual 1 (supplemented as .xml-file).Click here for file


Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery. Drug target is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or the survival of a microbial pathogen ,2. IdentMycobacterium tuberculosis and used FBA to do in silico systematic gene deletions which identify proteins essential for this pathway and lead to identification of anti-tubercular drug targets  mmol/L. If his empty blood sugar concentration is larger than 7.0 mmol/L, then he may be diagnosed to be a diabetes patient. To cure diabetes, we need to reduce their empty blood sugar concentration to a healthy range. Sridhar et al.’s qualitative drug target identification model cannot handle this case. In m×n and T = n×m are the stoichiometric coefficient matrices of reactions. The kth column of matrix S denotes the coefficients of reactants in reaction kR, while the kth row of matrix T denotes the coefficients of metabolites produced by reaction kR. We can obtain the kth column of matrix S and the kth row of matrix T from the chemical equation of reaction k .R Conversely, the chemical equation of reaction kR can be deduced from the kth column of matrix S and the kth row of matrix T. For example, the chemical equation of reaction kR isSuppose that there are m metabolites {C1 + 3C25→C+ 2C62sl,k = 2, s2,k = 3, is,k = 0,i ≠ 1, 2 and kt,5 = 1, kt,6 = 2, kt,j = 0, j ≠ 5,6.Then After formulating a metabolic network into a bipartite digraph as described above, our method for drug target identification, called as two-stage FBA method, is formulated into two linear programming models. The general scheme is shown in Figure S · v = 0, where v = T denotes the flux vector of the reactions in the stoichiometric matrix S = -S + T.T In metabolic systems, the feasible region in the steady-state flux space may be too large to be meaningful. FBA overcomes this by adding biologically meaningful objectives into the model, such as maximization of growth rate, maximization of ATP production, and minimization of nutrient uptake. These objectives can be represented by a linear combination of reaction fluxes of interest, which results in a linear programming model:Kinetic modeling of large-scale metabolic networks is often impossible due to lack of specific enzyme rate data. A simple and useful alternative for analysis of metabolic capabilities of cellular systems is flux balance analysis (FBA) . FBA igncT ·v (1)max S · v = 0 (2)s.t. Vmin ≤ v ≤ vmax (3)c in the objective function (1) is a vector of weights for the fluxes v, (2) is the set of mass balance constraints, and (3) is the set of enzymatic capacity constraints. FBA has been widely used for in silico phenotype prediction. For more methodological details of FBA, one can refer to , it means that jx should satisfy ja ≤ jx ≤ j .b If jx >jb or jx <ja, some fluxes of biochemical reactions should be adjusted by using drugs so that jx ∈ . At this time, the metabolite jC is the disease-causing compound, other metabolites are non-disease-causing compounds, and the biochemical reactions whose fluxes are to be enzymatically adjusted by using drugs are viewed as drug targets. In this adjustment process, the mass flows of some other non-disease-causing compounds may change to be out of their health ranges, which we define as the side effects of the drugs. A good drug should be potent and have minimal side effects. Aiming to minimize the side effects, we can find the mass flows of metabolites and the fluxes of reactions in the medication state by using the following linear programming model:In the pathologic state, the mass flows of some metabolites are out of healthy ranges which directly result in the disease symptoms. For example, if the healthy range of the jv ≤ jU,j = 1,2, … ,n (14)0 ≤ aj ≤ ix ≤ ib, i ∈ P (15)N is the set of non-disease-causing compounds and P is the set of disease-causing compounds, i biaare respectively the healthy lower and upper bounds of the mass flow of the target compound iC and iC from its healthy range. Constraint (11) is only for intermediate metabolites. Constraint (12) is for intermediate metabolites and those metabolites that are only consumed (not produced) in the system. Constraint (13) is for intermediate metabolites and those metabolites that are only produced (not consumed) in the system.where v0 in the pathologic state and the flux vector of reactions v1 in the medication state, by comparing v0 and v1, we can easily find the reactions whose flux has been changed by medication. We construct a sub-metabolic network by using all these reactions whose fluxes have been changed by medication along with their reactants and products. All the compounds with zero in-degree are then deleted, that is, delete all the compounds which is not a product of any reaction in this subnetwork. These compounds come into the metabolism process from the outside of the system. In the sub-metabolic network consisting of changed reactions and their related compounds, all the reactions that have no reactants are identified. These reactions  and the optimal flux vector of reactions is v0 = , depicted beside the corresponding nodes. Suppose that the healthy ranges of metabolites C1-C8 are very high, the healthy ranges of other metabolites are 10 ≤ xC9,xC10 , xC11 ≤ 15 and 0 ≤ xC12 < 1, and the flux upper bound of each reaction is jU = 10, j = 1,2, … , 8. It is easy to see that the disease-causing metabolites are C11 and C12, whose mass flows are out of their health ranges. We can further find the optimal fluxes of reactions and the mass flows of metabolites in the medication state. The optimal mass flow vector of metabolites is x1 =  and the optimal flux vector of reactions is v1 = , both shown in Figure xC10 ≤ 15.We assume that metabolites C8, C9, C11, C12 are involved in the biomass reaction. In the pathologic state, the upper bounds of all reaction fluxes are taken as 10, and the upper bounds of mass flow of all metabolites are taken as infinity. Solving the pathologic linear programming model, we can obtain the results expressed in Figure v0 and v1, we construct a sub-metabolic network, shown in Figure xC9, xC12 ≤ 15, 5 ≤ xC10 ≤ 15, and 15 ≤ xC11 ≤ 20, then the optimal drug targets are still R2, R3, R4, and the side effect is 0, which means that the disease can be cured by medication treatment on the enzymes catalyzing R2, R3, and R4 without causing side effects.By comparing Hyperuricemia is an enzymopathy which is characterized by the abnormally high level of uric acid in the blood. One of main causes of human hyperuricemia is the increased production of uric acid that results from high levels of purine in the diet and increased purine metabolism. Foods high in the purine, adenine and hypoxanthine are very potent in exacerbating hyperuricemia . From a Homo sapiens purine metabolism in KEGG and the purine metabolic model in . To adjust the abnormal level of the disease-causing metabolite uric acid, we need to use some medication strategy, e.g. adjust the flux of some reaction by manipulating its enzyme activity. In our two-stage FBA method, such medication strategy is found by solving the medication model which minimizes the side effects. The computational result shows that 9 reactions have non-zero fluxes and there exist medication strategies that can adjust the level of uric acid to be normal  this metabolite and incorporate it into the FBA model. Such an extended model allows us to track the change of mass flow of metabolites in the pathologic state and medication state. A similar metabolite-centric approach has been developed for metabolic network analysis and used in prediction of essential genes and discovery of antibacterials -41. SuchOur method finds the fluxes of reactions and the mass flows of metabolites in the medication state with the aim of minimizing side effects caused by medication. Since the side effects are closely related to the given healthy ranges, the single objective may make the LP model have multiple optimal solutions. A possible extension worth exploring is to use multiple objectives, such as minimizing side effects and maximizing biomass flux. In addition, Shlomi et al. developed a method for predicting metabolic disease biomarkers , which dIn this study, in addition to a simulated network, we also test our method on a true human purine metabolic pathway, which has been widely studied in the context of hyperuricemia. Enriched information is available about drugs and potential targets for this disease in the pathway. But complete quantitative information, such as the healthy ranges of metabolite flows and reaction fluxes in the pathologic state, still lacks. Matching the assumptions of our model with a true target network with complete quantitative information will be helpful for better validation as well as estimation of sensitivity and accuracy. However, so far there is no a database depositing such networks with detailed clinical data, though it is highly possible to have such data in hospitals from a clinical perspective. We believe our model and methods are practical once such data can be accessed publicly.Efficiently identifying drug targets with minimal side effects is one of major challenges in new drug development. High-throughput omics data provide unprecedent opportunities for drug target identification. Previous models for identifying drug targets either are not quantitative or do not consider side effects. In this paper, we develop a quantitative method based on flux balance analysis (FBA) to identify drug targets in metabolic networks. The method involves two linear programming (LP) models to find the steady optimal fluxes of reactions and the mass flows of metabolites in both pathologic state and medication state, meanwhile taking the side effects of drug action into account. The computational results on an illustrative simulated metabolic network and a hyperuricemia-related purine metabolic pathway show that the drug target identification problem can be solved effectively by the proposed two-stage FBA method.ZL conceived the research and designed the models. ZL and RSW designed and performed the experiments. ZL, RSW and XSZ analyzed the data. All authors wrote, read and approved the paper.The authors declare that they have no competing interests.Reactions in the hyperuricemia metabolic pathway This file contains the list of reactions in the hyperuricemia metabolic pathway with reaction IDs and Enzyme IDs.Click here for fileCompounds in the hyperuricemia metabolic pathway This file contains the list of compounds in the hyperuricemia metabolic pathway.Click here for fileEnzymes in the hyperuricemia metabolic pathway This file contains the list of enzymes in the hyperuricemia metabolic pathway.Click here for file


In this work, we have studied the distinct metabolism of CHO cell clones harboring an efficient inducible expression system, based on the cumate gene switch, and displaying different expression levels, high and low productivities, compared to that of the parental cells from which they were derived. A kinetic model for CHO cell metabolism was further developed to include metabolic regulation. Model calibration was performed using intracellular and extracellular metabolite profiles obtained from shake flask batch cultures. Model simulations of intracellular fluxes and ratios known as biomarkers revealed significant changes correlated with clonal variation but not to the recombinant protein expression level. Metabolic flux distribution mostly differs in the reactions involving pyruvate metabolism, with an increased net flux of pyruvate into the tricarboxylic acid (TCA) cycle in the high-producer clone, either being induced or non-induced with cumate. More specifically, CHO cell metabolism in this clone was characterized by an efficient utilization of glucose and a high pyruvate dehydrogenase flux. Moreover, the high-producer clone shows a high rate of anaplerosis from pyruvate to oxaloacetate, through pyruvate carboxylase and from glutamate to α-ketoglutarate, through glutamate dehydrogenase, and a reduced rate of cataplerosis from malate to pyruvate, through malic enzyme. Indeed, the increase of flux through pyruvate carboxylase was not driven by an increased anabolic demand. It is in fact linked to an increase of the TCA cycle global flux, which allows better regulation of higher redox and more efficient metabolic states. To the best of our knowledge, this is the first time a dynamic  In this work, we have further developed a kinetic-metabolic model for CHO cells. The model, which is based on cell energetic and redox states in silico platform to the characterization of clonal variation comparing a parental CHO cell line to its high- and low-producer derived clones. High- and low-producer clones, engineered with the inducible cumate gene-switch expression system Monoclonal antibodies (mAbs) are among the largest segment of today's therapeutic proteins market, with a 21% annual increase rate in launching into clinical trial All cell culture protocols were approved by the ethics committee of the Ecole Polytechnique.5 cells mL−1 in 300 mL of a protein-free medium in 1-L shake flasks, and cultured on a shaker (150 rpm) in a humidified incubator at 37°C and 5% CO2. The medium used was a customized chemically-defined SFM4CHO medium  supplemented with 4 mM glutamine , 30 mM glucose , and 0.05 mg mL−1 dextran sulphate . For the comparative study, the parental clone, together with high- and low-producer clones, were cultured in duplicate. High- and low-producer clones were cultured both in the presence and in absence of cumate, the latter serving as non-induced control. In case of induction, 1 μg mL−1 of cumate was added after 48 hours of incubation, to trigger the recombinant protein expression. It should be mentioned that no visible effects on morphology or growth rates were reported for mammalian cells cultured at a cumate concentration below 200 μg mL−1g for 5 min to remove cells, and supernatant samples were stored at −20°C for further analysis. Cultures were monitored for a total of 6 days.CHO clones that stably produce a recombinant monoclonal human anti-CD20 at different specific productivities (high- and low-producer) were provided by Viropro International Inc. . These cells were derived from CHO-Cum2 cells and stably express the reverse cumate transactivator, as described in details by Mullick et al. 2006 −1 in 50 mM sodium carbonate , and incubated at 4°C overnight. Then, the blocking of non-specific sites was carried out by adding PBS solution containing 1% casein. After incubation for 1 h at 37°C, either samples or standards diluted in PBS-casein were added in triplicate to each well and incubated for 1 h at 37°C. After the plates were incubated 1 h at 37°C, peroxidise-conjugated affinipure fragment Goat anti-human IgG   was added to each well, and the plates were incubated for 1 h at 37°C. After each of the previous steps, the wells were washed three times (PBS with 1% w/v Tween 20). Finally, the reaction was revealed by 3,3,5,5′-Tetramethylbenzidine (TMB)  and stopped after 15–20 min by adding 1 N hydrochloric acid, and the plates were red by an automatic plate reader at 450 nm using a Victor3 V microplate reader . The analysis of amino acid concentrations was performed on an Agilent 1290 UPLC system  coupled to an Agilent 6460 triple quadruple mass spectrometer , following methods previously described Cell density was determined by cell counting using a hemocytometer, and cell viability was estimated using the trypan blue  exclusion method. The concentration of glucose, lactate, glutamine and glutamate in the culture supernatant were determined using a dual-channel immobilized oxidase enzyme biochemistry analyzer , using calibration buffers provided by the manufacturer. Ammonia concentration in supernatants was assayed by an enzymatic kit with respect to manufacturer technical instructions: Ammonia Assay Kit . NAD(P) and NAD(P)H were also extracted and assayed by an enzymatic kit with respect to manufacturer technical instructions: NAD(P)/NAD(P)H Quantitation Kit . mAbs concentration was quantified using an enzyme-linked immunosorbent assay (ELISA). First, 96-well plates (Costar)  were coated with a goat anti-human IgG1 (H+L) solution  diluted to 2.5 μg mL6 cells were inoculated in a 10-mL borosilicate glass syringe , in which the plunger was substituted by an Ingold pO2 probe . At low cell densities, a volume of cell suspension containing 5×106 cells was collected and centrifuged, and the pellet was re-suspended in a total of 3 mL of spent media. The respirometer was kept at 37°C and magnetically agitated (60 RPM) to ensure the homogeneity of cell suspension. Dissolved oxygen was recorded by an acquisition system .Respirometry assays were performed as described by Lamboursain et al. 6 cells were obtained daily, washed twice with cold PBS and extracted with 400 μL of 80% cold methanol in the presence of 0.2 g of sand . After 10 min on dry ice, the mixture was vortexed and then sonicated in ice and water for 5 min. Suspensions were then centrifuged at 4°C for 7 min at 21,000 g. The supernatants were then transferred to a clean tube as extracts. Pellets were re-extracted as mentioned above with 200 μL of 50% cold methanol and 200 μL of cold water. At each extraction, supernatants were combined with the first extract and stored in −80°C prior to analysis.For intracellular metabolomic analysis, 5×104OAc at pH 7.0. Mobile phase flow rate was set at 0.3 mL min−1 with the following gradient: 0–10 min at 10% B, 10–20 min at linear gradient from 10 to 30% B, 20–21 min at linear gradient from 30 to 60% B, 21–26 min at 60% B, 26–27 min at linear gradient from 60 to 10% B and 27–35 min at 10% B. External standard curve was used for quantification. The Agilent 6460 triple quadruple mass spectrometer , equipped with a Jet stream source , was used for the analysis of nucleotides in positive ion mode. The mass spectrometer parameter were 100 ms scan time; 350°C gas temperature; 7 L min−1 gas flow rate; 30 PSI nebulizer pressure; 350°C sheath gas temperature; 12 L min−1 heath gas flow rate and 3500 V capillary voltage. The data was recorded in MRM mode with the mass spectrometer conditions listed in Extracts were filtered through 0.2 μm filters  before analysis. Nucleotides in CHO cells extracts were analyzed using a 1290 UPLC system coupled to a 6460 triple quadruple mass spectrometer . Nucleotides were separated by a Symmetry C18 column   equipped with a Security C18 guard-column  by the ion-pair method, as previously described −1 using the following gradient: 0–5 min at 10% A, 5–10 min at linear gradient from 10% to 20% A, 10–20 min at linear gradient from 20% to 100% A, 20–30 min at 100% A, 30–32 min at linear gradient from 100% to 10% A and 32–40 min at 10% A. The Agilent 6460 triple quadruple mass spectrometer , equipped with a Jet stream source , was used for the analysis of sugar phosphates and low molecular organic acids in negative ion mode. The mass spectrometer parameter were 100 ms scan time; 300°C gas temperature; 7 L min−1 gas flow rate; 35 PSI nebulizer pressure; 400°C sheath gas temperature; 12 L min−1 heath gas flow rate and 3500 V capillary voltage. Data were recorded in MRM mode with the mass spectrometer conditions listed in Extracts were filtered through 0.2 μm filters  before UPLC–MS/MS  analysis equipped with a Hypercarb column  and a Hypercarb pre-column  , as previously described The global structure of the mathematical model developed and presented here is based on a previous model describing CHO cells central metabolism The stoichiometric coefficients of the respective biosynthetic equations were taken from literature brenda-enzymes.infoMathematical formulations of metabolic flux kinetics have been determined based on a previous work brenda-enzymes.info and references therein brenda-enzymes.info and references therein meaX) and simulated values (simX) for each state variable m at time k, where the weight is the inverse of the variance of the experimental data for each state variable, mvar−1:The final fully dynamic model includes 35 reactions and 46 variables. The kinetic formulations for the flux regulation are presented in Least-squares minimization function in MATLAB's Optimization Toolbox  for non-linear regression. Finally, 95% confidence intervals for both model sensitive parameters and model predictions were calculated using built-in MATLAB functions “nlparci.m” and “nlpredci.m”, respectively.The sensitivity analysis procedure allowed to rank the parameters by their decreasing influence, and to remove parameters that were not contributing to model sensitivity from further optimization cycle, keeping them at their initial value. Optimal parameter values (for the sensitive ones) were then obtained by minimizing the normalized sum-squared errors using a The model was first applied to parental cell line culture data obtained in shake flasks. Model performance assessment with cumate-induced and non-induced cultures of low-producer and high-producer clones is presented thereafter, and the details on model parameters calibration are shown as , S2, S3.4+, glutamate), amino acids , sugar phosphates of glycolysis (G6P and PEP) as well as glucose, cell density and energetic nucleotides, which are all well simulated by the model implemented with regulation terms I and II. Similar observations can be drawn for scenarios of intracellular organic acids such as PYR and SUC. In addition, model simulations corresponded more closely to experimental data for organic acids such as AKG and MAL in induced low- and high-producer cultures, but to a lesser extent to no clear effect for other amino acids and AMP (2), for which simulations were closer to experimental data comparing to the case with no regulation  were calculated as previously described. Resulting WSSRES values were then further normalized to that obtained for original optimal parameter values (i.e. 0% change). Parameters showing a deviation of ±15% and higher were considered sensitive; a colormap . The model reveals to be primarily sensitive to parameters of glycolysis, TCA cycle and energetic reactions, amino acids catabolism pathways, partially to glutaminolysis, and to a lesser extent to the pentose phosphate pathway. The specific glucose uptake rate (νmaxHK) and other parameters of glycolysis  show to strongly affect simulation error. Moreover, maximum reaction rates for three enzymes in TCA cycle , and for the reaction connecting glycolysis to TCA cycle , also reveal to be significant. The model is also highly sensitive to three reactions related to glutaminolysis , and to a lesse extent to two parameters related to the pentose phosphate pathway oxidative branch . The model shows a high sensitivity to energetic reactions, represented here by parameters related to non-specifically described ATP (maxATPaseν) and NADH consuming reactions (maxrespν). Furthermore, the maximum specific growth rate (maxgrowthν) also strongly influences the simulation error. Finally, parameters related to amino acids catabolism  also demonstrate to be influential. There are therefore a high number of non-influential parameters with 65 out of 95. This lack of sensitivity may partially come from the experimental space used to calibrate and to challenge the model. Although these non-sensitive parameters are biologically relevant, describing existing active pathways and enzymatic reactions, they may require expanded experimental culture conditions to be solicited, as we proposed in a recent work A sensitivity analysis was performed on the resulting model, aiming to identify the most critical parameters. Values of model parameters were changed from −85 to +300%, one at a time, from their optimal value, and the normalized sum-squared differences , amino acids pool to glutamate and nucleotide ratios showed a high sensitivity level compared to pyruvate and succinate . Not sur6 cells mL−1, while parental cell line reached slightly higher maximum viable cell density (∼4.5×106 cells mL−1). The model also simulates extracellular metabolites profile with time, although significant differences in behavior are observed between the parental, low- and especially high- producer clones  are well simulated for all cultures , and ATP consumption reactions, which are lumped as ATPase proton pumps requirements (maxATPaseν). However, in the case of the induced high-producer clone, maxr LDHν and maxATPaseν are both significantly changed in addition to one parameter related to glutaminolysis (maxfASTAν) and one related to the reactions connecting glycolysis to TCA cycle (maxPDHν). The high-producer clone thus resulted in a significantly different in silico behavior compared to the parental cell line and, to a lesser extent, to the low-producer clone  were calculated . In low-er clone  regardinThe dynamics of mAb production, modeled as multiplicative Michaelis-Menten kinetics of amino acids, resulted in the simulation of mAb titers  both in 13C-labeling in silico platform, made of a kinetic-metabolic model, confers a unique capacity to explore mAb CHO producing cells beyond experimental observations onto which the model has been anchored a priori. Therefore, the remaining discussion will be based on the results derived from the developed in silico platform.Induction of recombinant proteins in microbial cell platforms has been shown to cause an increased energetic demand in support to a metabolic burden de novo metabolic load associated to the recombinant proteins to be low compared to the intrinsic one related to endogeneous protein synthesis. This estimate is calculated taking the carbon mass in 1 mol of mAb to the augmentation of the total mass of carbon from cellular growth, considering a specific productivity of ∼1×10−6 mmol 10−6 cells h−1 and a specific growth rate of ∼0.04 h−1, and assuming a dry cell weight of 350 pg cell−1, a cellular molecular weight of 150 g mol−1 and the reported elemental formulas for both biomass and mAb HKV, PGKV, GLNSV, TCA flux and ATP turnover rate. In the case of the high-producer clone, the contribution of glutamine to TCA cycle is lower in the induced culture. Indeed, the higher deviation between induced and non-induced cells is observed comparing the mAb specific production rates with time, with production rates that are 13 to 8 times higher in the low-producer clone and 5 to 1 times higher in the high-producer clone. This higher deviation level in the low-producer clone looks surprising but it can be attributed to a higher leakage level of the cumate gene switch in the high-producer clone in the absence of cumate induction , glutamine metabolism (GLNSV) and ATP turnover rate for the whole culture duration. However, interestingly, although hexokinase and phosphoglucose isomerase activities are both not affected by cumate induction, lactate dehydrogenase activity shows the lowest values for the high-producer clone, and to a lesser extent for the low-producer clone   , Table 2(50–60%) , Table 2(50–60%) , Table 2(50–60%) ; a behav4+. A reversed flux has been reported in literature from a medium concentration threshold of 10 mM NH4+4+ is staying at concentrations within the order of magnitude of the Michaelis affinity constant of glutamate dehydrogenase for NH4+ (KmNH4) (0.5 to 3.2 mM) may partially explain the low level for the direct contribution of glutamine to TCA cycle via glutamate dehydrogenase. Interestingly, alanine, which is constantly produced during cell growth, is then consumed after glutamine depletion, and at higher rates for the high-producer, and to a lower extent for the low-producer. The alanine aminotransferase flux even shows to turn negative  Greater pyruvate dehydrogenase and pyruvate carboxylase fluxes provide higher TCA cycle activity in the high-producer compared to the low-producer clone, and to the parental. Concurrently, the high-producer clone exhibits high values of NADH-to-NAD ratio . In agrein silico platform presented here enabled to better describe and quantify the metabolic differences resulting from CHO cells clonal variability. Such platform represents a valuable tool for cell line selection, as well as bioprocess development, but it may have also interesting applications in biomedical and medical applications. In fine, although this study is based on a large amount of experimental data for both culture media and cell content, including thermodynamic considerations on.This work on the characterization of different CHO mAb cell clones and their parental cell line, brings a wide set of experimental data for extra- and intracellular metabolites concentrations that were used to develop a descriptive and predictive kinetic-metabolic model. The Figure S1Sensitivity analysis on model parameters for parental cell line culture. The colormap represents the normalized sum of squared difference between model simulations and experimental data, when the parameter (row) is changed from −85% to +300% (column) from the optimal value. The values for sum of squared difference are normalized by the value corresponding to optimal values for parameters.(TIF)Click here for additional data file.Figure S2Partial sensitivity analysis on model parameters for parental cell line culture. Each colormap represents the normalized sum of squared difference between the simulated and measured extracellular metabolite concentration over time, when the parameter (row) is changed from −85% to +300% (column) of the optimal value. The values for sum of squared difference are normalized by the value corresponding to optimal values for parameters. The number for each row corresponds to the parameter presented next to the same row in (TIF)Click here for additional data file.Figure S3Parameter estimates with their error bars for sensitive parameters. Glycolysis (A), TCA cycle and Redox state (B), glutaminolysis and pentose phosphate pathway (C), amino acids metabolism (D), energetic (E) and growth (F). Horizontal solid lines are 1.96 standard error bars and represent parameter estimate ±1.96 standard error. Parental cell line: open triangles for parameter estimates, induced low-producer cell line: open squares for parameter estimates, and induced high-producer cell line: open circles for parameter estimates. A parameter is considered highly sensitive if a small variation in its value (±25%) causes more than a 15% increase of in the objective function.(TIF)Click here for additional data file.Figure S4Comparison of model simulations regarding enzymatic regulation for parental culture for extracellular and energetic metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S5Comparison of model simulations regarding enzymatic regulation for parental culture for intracellular metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S6Comparison of model simulations regarding enzymatic regulation for induced low-producing culture for extracellular and energetic metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S7Comparison of model simulations regarding enzymatic regulation for induced low-producing culture for intracellular metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S8Comparison of model simulations regarding enzymatic regulation for induced high-producing culture for extracellular and energetic metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S9Comparison of model simulations regarding enzymatic regulation for induced high-producing culture for intracellular metabolites. Same conditions as in (TIF)Click here for additional data file.Figure S10Simulated and experimental data for parental and induced/non-induced cell line. Parental , induced low-producer , non-induced low producer , induced high-producer , and non-induced high-producer .(TIF)Click here for additional data file.Table S1MRM transition and retention time of each amino acid quantified.(DOCX)Click here for additional data file.Table S2MRM mode with the mass spectrometer conditions for determination of nucleotides.(DOCX)Click here for additional data file.Table S3MRM mode with the mass spectrometer conditions for determination of nucleotides.(DOCX)Click here for additional data file.Table S4Reactions of the metabolic network.(DOCX)Click here for additional data file.Table S5Biokinetic equations of the metabolites fluxes (1-35) of the model.(DOCX)Click here for additional data file.Table S6State variables description and initial conditions.(DOCX)Click here for additional data file.Table S7Affinity (Km), activation (Ka), and inhibition (Ki) constants.(DOCX)Click here for additional data file.Table S8Maximum reaction rates (νmax) and comparison of highly sensitive parameters in parental, low-producing and high-producing clones.(DOCX)Click here for additional data file.


Chromohalobacter salexigens  is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype.C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress.iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network  Extreme environments, generally characterized by abnormal temperature, pH, pressure, salinity, toxicity and radiation levels, are inhabited by various organisms - extremophiles - that are specifically adapted to these particular conditions. Studies on these microorganisms has led to the development of important molecular biology techniques such as polymerase chain reaction (PCR) ,2 and heC. salexigens is a halophilic Gammaproteobacterium of the family Halomonadaceae with a versatile metabolism allowing not only fast growth on a large variety of simple carbon compounds as its sole carbon and energy source but also resistance to saturated and aromatic hydrocarbons and heavy metals [C. salexigens with the ability to grow over a wide range of salinities [0.5-4 M NaCl] has been the most euryhaline of the bacteria [C. salexigens has also many promising biotechnological applications as a source of compatible solutes, salt-tolerant and recombinant enzymes, biosurfactants and exopolysaccharides [Halophilic microorganisms are extremophiles that are able to survive high osmolarity in hypersaline conditions either by maintenance of high salinity in their cytoplasm or by intracellular accumulation of osmoprotectants such as ectoine and betaine . C. saley metals ,6. C. sabacteria  and to unities 0.-4 M NaClbacteria -9. Moreocharides .Archaeaglobus fulgidus [Halobacterium species NRC-1 [Acidithiobacillus ferrooxidans [C. salexigens DSM 3043 [in silico comprehensive metabolic model that enables the integration of canonical experimental data in a coherent fashion.Genome sequence of extremophiles, such as sulphate-reducing archaeon fulgidus , halophies NRC-1  and acidooxidans  have beeDSM 3043  the biolDSM 3043 ,16. On tSaccharomyces cerevisiae [Arabidopsis thaliana [Escherichia coli [Bacillus subtilis [Helicobacter pylori [Lactococcus lactis [Staphylococcus aureus [Clostridium acetobutylicum [Pseudomonas putida [Pseudomonas aeruginosa [Geobacter metallireducens [Corynebacterium glutamicum [Methansoarcina barkeri [Halobacterium salinarum [Metabolic reconstruction is non-automated and iterative decision-making process through which the genes, enzymes, reactions and metabolites that participate in the metabolic activity of a biological system are identified, categorized and interconnected to form a network . The recrevisiae ,27-29, hrevisiae , Arabidothaliana ), prokarhia coli -34, Bacisubtilis , Helicobr pylori ,37, Lacts lactis , Staphyls aureus ,40, Closutylicum , Pseudoms putida , Pseudomruginosa , Geobactreducens , Corynebutamicum ), and ar barkeri , Halobacalinarum  species)alinarum  for an eC. salexigens DSM 3043's metabolism was established based on genomic, biochemical and physiological information. Being the first comprehensive metabolic model of a halophilic bacterium, it was labeled as iOA584 following the naming convention proposed by [in vivo C. salexigens phenotypic features, the transport and use of different substrates but also against experimental observations on the choline - betaine and ectoine synthesis pathways which are important parts of the osmoadaptation mechanism.Here, a genome-scale reconstruction of posed by . The preC. salexigens DSM 3043 has been assembled in 2005 by the Joint Genome Institute [C. salexigens DSM 3043 genome size is 3.696 Mb with 3352 candidate protein-encoding gene models.The complete genome sequence of nstitute  and genenstitute  and Joinnstitute , which rC. salexigens DSM 3043, a non-automated but iterative decision-making process is designed based on the conceptual reviews [C. salexigens [For the reconstruction of a genome-scale metabolic network of the halophilic bacterium  reviews ,19,22 an reviews . In the  reviews ,49 coupl reviews , BiGG [5 reviews , ExPASy  reviews , BioCyc  reviews  and BREN reviews , which p reviews  were con reviews ,57 was p reviews -54 and clexigens ,58,59. Ilexigens ,58,59. Tm × n) where m is the number of metabolites and n is the number of reactions. The corresponding entry in the stoichiometric matrix, Sij, represents the stoichiometric coefficient for the participation of the ith metabolite in the jth reaction. A constraint-based optimization framework, Flux Balance Analysis (FBA) [The interconnectivity of metabolites in a biochemical reaction network can be represented by a set of equations defining the stoichiometric conversion of substrates into products . The recis (FBA) ,62, was is (FBA) . The empis (FBA) -42,46,47C. salexigens. The use of a generic biomass formation reaction in FBA simulations was previously tried and led to successful predictions [C. salexigens metabolic network with other published reconstructed networks [E. coli iAF1260 [No thorough biomass composition has been published for dictions ,39,64. Hdictions , phylogenetworks ,43,46,47 iAF1260 .The flux variabily analysis was performed  to obserC. salexigens DSM 3043.Based on the conceptual reviews ,18,22 anC. salexigens [In the first stage, a draft catalog of gene-enzyme-reaction associations was prepared via coupling genome annotation data ,49 with lexigens ,49 not olexigens  and BiGGlexigens , were sylexigens , BioCyc lexigens  and BRENlexigens  were expSecond stage comprised of semi-automatically refinements of the draft reconstruction through gap analysis. Using the draft catalog of gene-enzyme-reaction associations, the stoichiometric matrix, the reaction and metabolite adjacency matrices were constructed, metabolic maps were drawn and topological analysis was performed ,56 Analyin silico phenotypic characterizations were completely represented by the simulation results.In the last stage, the reconstructed metabolic network was automatically converted into a mathematical model that could be analyzed through constraint-based approaches, and was validated through comparison of model predictions with phenotypic data. The biomass formation and transport reactions, which describe the intra- and extracellular exchange of metabolites, were added to the metabolic network predominantly based on the experimental evidence ,16,58,59The reconstruction process resulted in a metabolic network that consisted of 1387 metabolic reactions including biomass reaction and 1411 metabolites  whereas the rest were multifunctional accepting several different substrates. Therefore, the published genome for their corresponding genes were carefully checked during reconstruction process in order not to lead to false gene-enzyme-reaction associations in the reconstructed genome-scale metabolic model.iOA584 were divided into 12 main categories based on their functional roles  were associated with sugar transport mechanisms in the annotated genome of C. salexigens [The enzymes included in s Figure . The tralexigens . In addilexigens . DecipheC. salexigens is known for its capability to utilize many amino acids as a carbon and nitrogen source [de novo synthesis pathways for all 20 amino acids are present in C. salexigens' genome [in silico amino acid utilization as a carbon and nitrogen source, FBA simulations were carried out and growth on all of the 20 amino acids were obtained. For instance, at a specific uptake rate (1 mmol/gDW/h) of isoleucine, growth rate was calculated as 0.129 h-1. Additionally, glycan biosynthesis and metabolism; and biosynthesis of secondary metabolites have the lowest number of enzymes (1%).Moreover, n source ,6,59. Th' genome ,48. To viOA584  were positioned primarily, followed by transferases (EC 2), ligases (EC 6), oxidoreductases (EC 1), lyases (EC 4), and isomerases (EC 5). Hence ligases and transferases were less substrate specific than the other enzyme classes in C. salexigens, as in the case of E. coli [S. cerevisiae isomerases and transferases were found to be the least substrate-specific enzyme classes [Throughout the reconstruction process, 584 protein-encoding gene models have been assigned to the metabolic reactions. The distribution of the ratios of number of reactions per number of gene models in each enzyme class ,32 was i4 Figure . In the  classes .iOA584 was also compared with previous metabolic models from different domains [iOA584. As expected, highest number of metabolic reactions were shared by E. coli (iAF1260) with 320 reactions, P. aeruginosa (iMO1056) with 309 reactions and P. putida (iJN746) with 282 reactions. Number of shared metabolic reactions for S. cerevisiae, S. aureus N315, B. subtilis, and H.salinarium were obtained as 274, 265, 260, and 221, respectively; while C. salexigens and M. barkeri association indicated the lowest number with 205 metabolic reactions.Related species of the same domain share a substantial amount of conserved reactions for essential biological processes -68. The  domains ,43,46,47C. salexigens, E. coli and the eukaryote S. cerevisiae  metabolism followed by carbohydrate metabolism (31 reactions). Comparison of the distribution of metabolic reactions for C. salexigens, P. putida and P. aeruginosa  and investigating the associated utilization reaction fluxes and objective function biomass flux to assess a positive growth. For example, 1 mmol/gDW/h uptakes of fructose and sucrose resulted in growth rates of 0.1290 h-1 and 0.0646 h-1, respectively.The resultant metabolic model features ,16,58,59in silico growth fluxes for batch cultivation of C. salexigens at varying glucose uptake rates ( 3.193 - 3.751 mmol/gDW/h) were illustrated in Figure Additionally, the FBA simulations were performed in order to validate experimental growth rate values with glucose as the only carbon source in chemically defined media which were reported . Experimin silico flux distribution in C. salexigens metabolic pathways. Reaction flux data and gene information were provided for Omics Viewer to generate overall diagram colorized with flux data. The details of the connectivity aspects of the reconstructed metabolic network  ,72. C. slinities -8. Osmoaal level ,73,74. Fin vitro metabolic and genetic analyses, in further studies, the presented genome scale model iOA584 could be used to elucidate osmoadaptation mechanisms and to design strategies  for optimum production of compatible solutes such as ectoine, which has industrial applications for cosmetics and dermopharmacy and is widely used in stabilizing enzymes for molecular biology.Via integration of data from C. salexigens iOA584 was used to simulate the experimental observations on osmoadaptation of C. salexigens, in order to demonstrate that the model could be used for further studies in understanding the metabolic pathways behind osmoadaptation and to design or improve the adaptation mechanisms in extromophiles.Here, C. salexigens, the osmoprotectant betaine is synthesized from its precursor choline in two steps  was stimulated by the presence of choline and that the presence of betaine had an inhibitory effect on the intracellular oxidation of choline.In s Figure . In the in silico model simulations of the choline - betaine pathway of the osmoadaptation mechanism were compared with these experimental observations [-1) and in betaine production flux (from 0.45 to 0.89 mmol/gDW/h) were observed; hence stimulation of growth by the presence of choline was predicted, which is in agreement with the reported experimental observations [For validation of the model's predictive potential, rvations ,7-9,59. rvations . It is krvations . TherefoC. salexigens in core osmoadaptation mechanism via ectABC genes (74). Its biosynthesis is a branch of the synthesis pathway for the aspartate family of amino acids  demonstrating the high level ectoine production when glucose was the only carbon source. In addition, Fallet and coworkers (2010) reported batch cultivation data for ectoine production with glucose as the sole carbon source. The performed FBA simulations with 1.5 mmol/gDW/h glucose uptake resulted in an ectoine production rate of 0.75 mmol/gDW/h, which was comparable with the reported experimental result of 0.72 mmol/gDW/h [To simulate metabolic model in the view of ectoine synthesis, the required conditions were implemented and the resulting flux values were investigated. To demonstrate high-level ectoine production when the other external osmoprotectants are not accessible, as stated by Vargas and co-workers (2008); under the absence of exogenous osmoprotectants (i.e. choline and betaine uptake as well as choline oxidation fluxes were constrained to zero), FBA simulations were performed for 3-10 mmol/gDW/h glucose uptake rates. Ectoine production increased from 1.4975 up to 4.9722 mmol/gDW/h with a yield within a range of 49 - 50% mmol ectoine/mmol glucose with concomitant increase in biomass (0.1934 to 0.642 hol/gDW/h .-1), whereas the ectoine production was improved 9.08% (from 0.5497 to 0.5996 mmol/gDW/h).Comprehensive analysis of the ectoine biosynthesis Figure  revealedC. salexigens DSM 3043. The in silico model was able not only to represent the potential of the network in terms of phenotypic characterization but also to predict metabolic fluxes during osmoadaptation, both of which were consistent with the experimental observations. The reconstructed model will accelarate the research on halophilic bacteria towards application of systems biology approaches, design of optimal culture conditions and metabolic engineering strategies for improved production of biological and industrially important products.A non-automated but iterative decision-making process was employed in order to reconstruct the first comprehensive genome-scale metabolic model of a halophilic bacterium, ETO and KYA conceived and directed the study. OA and KYA designed the algorithms and the reconstruction framework. OA performed the reconstruction process, analyzed the data and evaluated the model. OA, ETO and KYA wrote the paper. All authors read and approved the final version.C. salexigens iOA584 metabolic model (Excel file).The metabolite and reaction lists of Click here for fileC. salexigens DSM 3043 (Excel file).The complete list of genes and their annotations related to the transport mechanisms of Click here for fileC. salexigens iOA584 metabolic model (Excel file).Topological analysis of the reconstructed Click here for fileC. salexigens iOA584 metabolic model (Word document).The overall network map and detailed batch images of Click here for file


Blattabacterium cuenoti (Mercier 1906) strains Bge and Pam are the obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively. The genomes of both bacterial endosymbionts have recently been sequenced, making possible a genome-scale constraint-based reconstruction of their metabolic networks. The mathematical expression of a metabolic network and the subsequent quantitative studies of phenotypic features by Flux Balance Analysis (FBA) represent an efficient functional approach to these uncultivable bacteria.Cockroaches are terrestrial insects that strikingly eliminate waste nitrogen as ammonia instead of uric acid. Blattabacterium strains Bge (iCG238) and Pam (iCG230), comprising 296 and 289 biochemical reactions, associated with 238 and 230 genes, and 364 and 358 metabolites, respectively. Both models reflect both the striking similarities and the singularities of these microorganisms. FBA was used to analyze the properties, potential and limits of the models, assuming some environmental constraints such as aerobic conditions and the net production of ammonia from these bacterial systems, as has been experimentally observed. In addition, in silico simulations with the iCG238 model have enabled a set of carbon and nitrogen sources to be defined, which would also support a viable phenotype in terms of biomass production in the strain Pam, which lacks the first three steps of the tricarboxylic acid cycle. FBA reveals a metabolic condition that renders these enzymatic steps dispensable, thus offering a possible evolutionary explanation for their elimination. We also confirm, by computational simulations, the fragility of the metabolic networks and their host dependence.We report the metabolic models of Blattabacterium metabolic networks are surprisingly similar in strains Bge and Pam, after 140 million years of evolution of these endosymbionts in separate cockroach lineages. FBA performed on the reconstructed networks from the two bacteria helps to refine the functional analysis of the genomes enabling us to postulate how slightly different host metabolic contexts drove their parallel evolution.The minimized  Blattabacterium cuenoti (Mercier 1906), Bge and Pam, obligate primary endosymbionts of the cockroaches Blattella germanica and Periplaneta americana, respectively, have been sequenced ))Blattaba is provided. Under these circumstances, the metabolic fluxes are redirected around the TCA cycle Fig.  and, as Blattabacterium is an example of how the essentiality of genes may change when the environmental conditions change. Studies of flux connectivity  , ready to perform simulations with COBRA toolbox [43].Click here for fileMetabolic network model of Pam strain in Systems Biology Markup Language (sbml) format [44], ready to perform simulations with COBRA toolbox [43].Click here for file