A metabolic system consists of a number of reactions transforming molecules of one kind into another to provide the energy that living cells need. Based on the biochemical reaction principles, dynamic metabolic systems can be modeled by a group of coupled differential equations which consists of parameters, states (concentration of molecules involved), and reaction rates. Reaction rates are typically either polynomials or rational functions in states and constant parameters. As a result, dynamic metabolic systems are a group of differential equations nonlinear and coupled in both parameters and states. Therefore, it is challenging to estimate parameters in complex dynamic metabolic systems. In this paper, we propose a method to analyze the complexity of dynamic metabolic systems for parameter estimation. As a result, the estimation of parameters in dynamic metabolic systems is reduced to the estimation of parameters in a group of decoupled rational functions plus polynomials  or in polynomials. Furthermore, by taking its special structure of improper rational functions, we develop an efficient algorithm to estimate parameters in improper rational functions. The proposed method is applied to the estimation of parameters in a dynamic metabolic system. The simulation results show the superior performance of the proposed method. Living cells require energy and material for maintaining their essential biological processes through metabolism, which is a highly organized process. Metabolic systems are defined by the enzymes dynamically converting molecules of one type into molecules of another type in a reversible or irreversible manner. Modeling and parameter estimation in dynamic metabolic systems provide new approaches towards the analysis of experimental data and properties of the systems, ultimately leading to a great understanding of the language of living cells and organisms. Moreover, these approaches can also provide systematic strategies for key issues in medicine, pharmaceutical, and biotechnological industries . The forIn general, all algorithms for nonlinear parameter estimation can be used to estimate parameters in metabolic systems, for example, Gauss-Newton iteration method, and its variants such as Box-Kanemasu interpolation method, Levenberg damped least squares methods and Marquardt's method , 9. Howe In this paper, we propose a systematic method for estimating parameters in dynamic metabolic systems. Typically mathematical model of dynamic metabolic systems consists of a group of nonlinear differential equations, some of which contains several rational functions in which parameters are nonlinear. In k substances (molecules), and m reactions can be described by a system of differential equations as follows:xi represents the concentrations of molecule i, rj represents the reaction rate j, and cij represents the stoichiometric coefficient of molecule i in reaction j. The mass action law in biochemical kinetics k×m. Let X = T and r = T, and let β = T represent the vector consisting of all independent proportion constants, and then  w w2) we hIn the following, we describe our proposed method to analyze the complexity of model  through Cr and collect other columns  to construct a submatrix Cp. Therefore, we haveCollect the columns in the stoichiometric matrix corresponding to the rational reaction rates in model  to constrr is the subvector of r and consists of all rational reaction rates while rp is another subvector of r and consists of all polynomial reaction rates. In this step, we should make sure that the rank of matrix Cr equals the number of rational reaction rates. If the rank of matrix Cr does not equal the number of rational reaction rates, it means that some rational reaction rates are not independent. Then we combine dependent rational reaction rates together to create a new reaction rate such that all resulted rational reaction rates should be linearly independent  and rp = . The rank of matrix Cr equals 3, which is the number of rational reaction rates.and Cr. That is, calculate Cr− such thatCalculate the left inverse matrix of Cr has the column full rank, matrix Cr− satisfying  + rank(Cr−) = the  number  of  rows  in  Cr. Note that Cr⊥ can be easily found by solving  ≥ the  number  of  columns, then solving (Let  solving  yields(D) < the  number  of  columns, it means that some polynomial reaction rates in (If rank(rates in  are lineand then solving  yields(D) < the  number  of  columns. As the first four columns are linearly dependent, we can have a new reaction rates −2r2 − 2r5 + r6 − r7. Therefore, we haved/dt)(ATP + ADP + AMP) = 0, from , those in the denominator of the rational function βDj  , and those in the polynomial βPk  , where we have that pD + pN + pP = p. Ni(X)  , Dj(X)  , and Pk(X)   are the known functions nonlinear in the state variable X and do not contain any unknown parameters. Either N0(X) or D0(X) must be nonzero, and otherwise from sensitivity analysis T can give the nonlinear least squares estimation of parameters βP, βN, and βD. We rewrite the objective function  and vector b by (Iteratively construct matrix tor b by  and 29)A(βDs) βs = T is the estimation of parameters β at step s.which gives the solutionβ1, β2,… is converged to β*, the objective function  = 0 mM, ATP(0) = 2.1 mM, ADP(0) = 1.4 mM, and AMP (0) = 0.1 mM. The trajectory of this system is depicted in To investigate the method developed in previous sections, this study generates artificial data from the dynamic metabolic system in the running example with the biochemically plausible parameter values  listed ix, we adopt the five-point central finite difference formula as follows:Although no noise is added to the artificial data in the simulation, noises are introduced in numerically calculating the derivatives by finite difference formulas. In general, the higher the sampling frequency and more data points are used, the more accurate the numerical derivatives are. On the other hand, we may not obtain data with the high frequency because of experimental limitations in practice. In this study, the sampling frequency is 100 data points per minute. In numerically calculating the concentration change rate at each time point from concentration The estimation accuracy of the proposed method is investigated in terms of relative estimation error which is defined asAs all parameters to be estimated are nonnegative, initial values are chosen as 0 or 1 in this study. The experimental results are listed in columns 3 and 4 in In this study, we have first described a method to analyze the complexity of metabolic systems for parameter estimation, based on the stoichiometric matrix of the metabolic systems. As a result, the estimation of parameters in the metabolic systems has been reduced to the estimation of parameters in the improper rational functions or polynomial functions. Then we have developed an iterative linear least squares method for estimating parameters in the improper rational models. The results from its application to a metabolism system have shown that the proposed method can accurately estimate the parameters in metabolic systems.We do not consider the noises in the data except those introduced by numerical derivatives in this study. One direction of future work is to investigate the influence of noises in the data to the estimation accuracy. In addition, low sampling frequency is expected, particularly for molecular biological systems as in practice measurements from them may be very expensive or it is impossible to sample measurements with high frequencies. Another direction of future work is to improve the estimation accuracy of the proposed method with low sampling frequencies.


Agrobacterium tumefaciens. The model includes kinetic regulation at the enzyme level and transcription regulation at the enzyme synthesis level. The objective of this work was to study the key factors regulating the metabolic response of the denitrification pathway to transition from oxic to anoxic respiration and to find parameter values for the biological processes that were modeled. The metabolic model was used to test hypotheses that were formulated based on the experimental results and offers a structured look on the processes that occur in the cell during transition in respiration. The main phenomena that were modeled are the inhibition of the cytochrome c oxidase by nitric oxide (NO) and the (indirect) inhibition of oxygen on the denitrification enzymes. The activation of transcription of nitrite reductase and NO reductase by their respective substrates were hypothesized. The general assumption that nitrite and NO reduction are controlled interdependently to prevent NO accumulation does not hold for A. tumefaciens. The metabolic network model was demonstrated to be a useful tool for unraveling the different factors involved in the complex response of A. tumefaciens to highly dynamic environmental conditions.A metabolic network model for facultative denitrification was developed based on experimental data obtained with  Each reduction step is catalyzed by a specific reductase enzyme.Denitrification is an important process in the global nitrogen cycle, which is investigated at many levels ranging from gene expression to global nitrogen fluxes. Denitrification is mostly performed by facultative denitrifiers, which reduce oxygen when available and switch to nitrate or nitrite after oxygen depletion. During denitrification nitrogen oxides are used as electron acceptor by an electron transport chain similar to that used in aerobic respiration Zumft, . A compl2O , nitrite reductase (nirK), and NO reductase (norB), but it lacks the genes encoding N2O reductase  and interference of presence of minimal nitrogen in the trace element solution. Inocula of fully dispersed aerobically grown cells were injected into the flasks. The headspace NO, N2O, O2, and CO2 concentrations were monitored during oxygen depletion and subsequent anoxic respiration. Measured NO concentrations in the nitrate experiments were corrected for calibration errors in the data that were originally presented by Bergaust et al.  or could be calculated using mass balances (nitrite). In the nitrate experiments, nitrite concentrations and nap expression were unknown, complicating the interpretation of the experimental data. For the reference experiment gene expression of NorB and NirK was analysed using RT-PCR. Gene expression data of the experiment conducted at 1 mM nitrate and 1% initial oxygen concentration were used to identify NirK expression kinetics in relation to nitrite concentrations.The experiment with 1% oxygen in the headspace (corresponding to ~10 μM oxygen in the liquid) and 1 mM nitrite as initial concentrations was used as reference experiment. The nitrite experiments were found more suitable for modeling because the concentrations of all nitrogen species involved were measured  with oxygen (aerobic respiration) or with nitrate, nitrite, and NO (denitrification steps). In the experiments denitrification rates are changing over time due to availability of electron acceptors  and varying enzyme concentrations. Consequently, denitrification is described in the metabolic model by two levels of cellular organization: (1) enzyme expression (in which DNA transcription and translation are lumped) and (2) enzyme activity leading to substrate conversion and microbial growth. The lumping of transcription and translation means that the model assumes that the rate of enzyme production is proportional to the mRNA level and that the response time for enzyme translation is insignificant. It is further assumed that the translation rate is equal for nap-, nirK-, and norB-transcripts. This simplification led to a good description of the reference experiment but could be responsible for the poor extrapolation of the model to other experiments. A scheme of the conversions and regulation factors of the denitrification pathway in A. tumefaciens as used in the metabolic model is shown in Figure The model developed considers that ization:  enzyme e2, NO, N2O, and CO2. The model complexity was minimized to avoid the introduction of a large number of non-identifiable parameters.Several biological and chemical reactions occur in the liquid according to the stoichiometry, rate expressions and parameters described in the next section. Because the model was developed gradually from simple to more complex, new factors affecting the process were only included if this resulted in an unequivocal improvement of the description of the experimental data. The goodness of fit was analyzed by calculating the sum of squared error (SSE) between the experimental data and the modeled values for the gaseous concentrations of OC2O), nitrate (C3NO), nitrite (C2No), nitric oxide (CNO), nitrous oxide (C2ON), carbon dioxide (C2CO) and bicarbonate (C3HCO). All these concentrations are expressed in molar units. Time-dependent enzyme  and the Agrobacterium biomass concentrations (CX in C-mol/L) are also considered. The model assumes a maximum enzyme concentration in the cell so that the relative dimensionless enzyme saturation, Esat, ranges from 0 (absence of enzyme) to 1. Succinate is present in excess and it is assumed not a rate limiting reactant, therefore it was not included in the kinetic model. It was also considered that the liquid is sufficiently buffered so that pH changes can be neglected and therefore H+ concentration HC can be considered constant in time (pH 7.5). In the gas phase there are four changing concentrations: oxygen (c2O), nitric oxide (cNO), nitrous oxide (c2ON), and carbon dioxide (c2CO).The model is based on batch (time-dependent) mass balances in the liquid and gas phase, for a number of chemical species, enzymes and microbial biomass. In the liquid phase the chemical species with concentrations changing in time are: oxygen , or with nitrate, nitrite and NO (denitrification steps) as electron acceptors.+/ATP ratio = 4, ATP yield = 9.1 g dry weight biomass/mol ATP, proton translocation on oxygen 10 H+/NADH, 8 H+/FADH2, and during denitrification 6 H+/NADH and 4 H+/FADH2 .The stoichiometric equations were derived from redox balances and theoretical knowledge on growth yields, by using the procedures described in Stouthamer  and HeijXC and the specific enzyme levels Esat. The electron acceptor consumption rates were assumed to be limited by the maximum succinate oxidation rate and therewith independent of the electron acceptor  with linear dependency on the biomass concentration 2O, the reaction is second order with respect to NO  of the four considered microbial pathways are:Aerobic conversion of succinate:Nitrate reduction with succinate:Nitrite reduction with succinate:NO reduction with succinate:With these assumptions, the molar stoichiometry and the rates (mol succinate LEsat) was introduced to limit the enzyme concentration in the cells  to maximum enzyme concentration in the cell (Esat = 1). The experimentally observed rates of expression were different for the individual denitrification enzymes, i.e., nor expression is quicker than nir expression. The NAP, NirK, and NorB transcription were activated by their respective substrates nitrate, nitrite, and NO. Additionally the nap transcription was inhibited by oxygen. Because oxygen respiration is constitutively expressed, enzyme synthesis is not considered for this process. The model does not take enzyme decay into account because this was not necessary to describe the experimental data. Additionally, experimental data with repetitive oxygen addition to batch flasks indicated that enzyme decay for oxygen respiration is negligible for the time-scale of these experiments.For the enzyme synthesis Michaelis–Menten kinetics was assumed as a function of the enzyme inducer concentrations. A further saturation factor (1-−1) are:nap:Synthesis of nitrate reductase nir:Synthesis of nitrite reductase nor:Synthesis of NO reductase With these assumptions, the enzyme synthesis rates . Here it can be seen that nirK transcription increases upon the increase in nitrite concentration.Based on the experimental data of Bergaust et al.  it was he Figure . The subnirK transcription in A. tumefaciens does not correlate with the observations of Baek and Shapleigh (nir expression as well as nor expression (see next paragraph). However, it is known that the two genes are differentially regulated  the following mass balances are solved to find the concentrations of chemical species and biomass:i = O2, CO2, NO, N2O):For the gas volume (batch operation) there is only mass exchange with the liquid phase, for each gaseous species , including all reactions and mass transfer terms with the kinetic, physical and operational parameters presented in Table The model solution and parameter estimation procedure were implemented numerically in Matlab . The system of ordinary differential equations is solved from the initial conditions . The experimental parameters were based on the actual experiments. The model code can be found in Appendix I.2, NO, N2O, and CO2. The gaseous concentrations of O2, CO2, and N2O are described very well by the metabolic model (R2 > 0.93), while the best fit for description of the NO concentration in the head-space was an R2 of 0.8 . Also the modeled nitrite concentration and NorB and nirK transcription fit well with the measured data  inhibition of oxygen on nitrite and NO reduction were well identifiable. The substrate inhibition constant for NO reductase was poorly identifiable from this experiment  because the NO concentrations do not reach the inhibitive concentration.2O and CO2 production showed a relatively good correlation with the experimental data but the modeled NO concentrations were much lower than the measured NO concentrations . However, this would only generate limited additional insight. Especially the parameters involved in the enzyme synthesis could not be accurately defined .The model was also applied to experiments at 0.2 and 2 mM initial nitrite and nitrate and 1 and 7% of initial oxygen gas phase. The complete comparison between model results and experimental data on this additional data-set is shown in Appendix II. The model could describe oxygen consumption in these experiments relatively well, but the predicted NO and NThese observations do indicate that some additional phenomena should be included in the model, to have a broader application range of the model. To identify additional phenomena additional experiments are needed, as described in the model limitation section (“Model Limitations and Outlook”).The use of the metabolic model and rate-based analysis of the experimental data enabled quantitative insight of the processes and increased understanding of the interdependence of the processes occurring during transition. For example, the NO accumulation was very tightly dependent on the combination of respiration and denitrification rates, as it also plays an inhibitory role on respiration. The model was used to test hypotheses that were based on the experimental observations .Generally it is assumed that nitrite and NO reduction are controlled interdependently, both at the expression and the enzyme activity level. Interdependent regulation of both enzymes is thought to secure minimal NO accumulation Zumft, . In P. dnap) is in many organisms regulated by nitrate and oxygen limitation . Nevertheless, denitrification activity mainly occurs when oxygen concentrations are low. This suggests that oxygen apparently inhibits the denitrification reactions. However, literature information indicates that oxygen does not directly inhibit the enzymes in the denitrification pathways, except for N2O reductase . These experiments are characterized by very rapid oxygen depletion, because of the high biomass accumulation as a result of the long oxic growth phase. Apparently, the cell metabolism cannot respond adequately to such a rapid oxygen depletion leading to imbalanced expression of the denitrification pathway. It seems that C), it appears that norB transcription even stops when oxygen is depleted, even though NO and nitrite are still present.In the 1 mM nitrate experiment with 1% initial oxygen in the gas phase Figure , panel Cnor expression is included.Experiments with repeated oxygen addition indicated that oxygen addition was beneficial for recovery of the denitrification metabolism after cells had suffered from rapid oxygen depletion (see Appendix III). Oxygen addition led to an increase of the NO reduction rates. This should be further investigated in experiments where quantification of A. tumefaciens was also observed by Baek and Shapleigh . This means that based on the present experimental data-set, some important phenomena could not be identified and additional experiments are needed to improve the model. All experiments described here were batch experiments, characterized by dramatic changes in environmental conditions. Specifically at higher oxygen concentrations the transition from aerobic to anoxic conditions is very fast due to increased biomass concentrations. To which extent these rapid changes affect the overall metabolic state of the cell and the capacity to adjust the metabolism to different conditions remains unclear. To investigate the response of the system to the rates of transition, continuous supply of substrates or products to the experimental system could be applied. Understanding of the regulation network can be further increased by additional measurements of mRNA for known nitrogen oxide sensors and subsequent extension of the metabolic model. Improved parameter estimation could furthermore be established by experiments with external supply of NO, the key intermediate in the denitrification pathway. This may enable improved estimation of inhibition and affinity constants and allow to distinguish better between the effects of nitrite, NO and oxygen limitation.A. tumefaciens clearly show that transition from oxic to anoxic conditions can lead to emission of NO. Two circumstances are responsible for increased emission during transition:Quick depletion of oxygen; this was found to give rise to incomplete expression of the denitrification pathway and consequently to the increased emission of intermediates,Presence of nitrite ; this resulted in expression of denitrification enzymes and conversion in presence of oxygen.The experimental data of nirK and norB transcription can further increase emission since the expression levels of the individual enzymes can easily be unbalanced.The characterized independent regulation of 2O in practice (Burgess et al., 2O emissions from oxygen-limiting, nitrite-containing environments have also been observed (Schulthess et al., Dynamic systems that are characterized by rapid transitions from aerobic to anoxic conditions were shown to give rise to increased emissions of NO and NAgrobacterium cannot be directly translated towards all facultative denitrifiers. Nevertheless, most phenomena described here have also been identified in other organisms [like P. denitrificans, R. sphaeriodes (Baker et al., Pseudomonas (Frunzke and Zumft, Escherichia coli and Azotobacter vinelandii (Borisov et al., Denitrifying organisms are diverse in their regulation of the denitrification pathway (Rodionov et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes.Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation.We present a genome-scale metabolic model for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.The genome-scale metabolic model developed for  Scheffersomyces stipitis (S. stipitis), formerly known as Pichia stipitis [S. stipitis produce around 47 g/l of ethanol with yields of 0.36 g/g xylose at 30°C [S. stipitis has the capability to ferment sugars from hydrolysates with yields equivalent to 80% of theoretical yield [S. stipitis [loxP/Cre recombination system have been developed for functional genomics and metabolic engineering of this yeast [S. stipitis an attractive microorganism for bioconversion of lignocellulose to fuels and chemicals. S. stipitis has already been successfully engineered to produce lactic acid and xylitol [S. stipitis suffers from some drawbacks like lower fermentation rates, lower tolerance to ethanol and absence of anaerobic growth [stipitis , is a hestipitis ,3. It hastipitis ,5. Fed bstipitis ,10. Geneis yeast . The ava xylitol ,13. Howec growth ,15.S. stipitis has been used to engineer xylose metabolism in Saccharomyces cerevisiae. Successive cycles of metabolic engineering have improved xylose utilization in recombinant S. cerevisiae [S. stipitis could provide useful insights to identify shortcomings in S. stipitis and S. cerevisiae metabolic networks.As a parallel approach, xylose utilization pathway from revisiae ,17. Howerevisiae ; cofactorevisiae ,20; low revisiae ,22; and revisiae ,24. The S. stipitis has been sequenced [The complete genome of equenced . The funequenced -30. Due S. stipitis [S. stipitis based on the proposed framework and a recently published protocol [S. stipitis was standardized and used to obtain the biomass composition. Growth and non-growth associated maintenance energy requirements were also estimated from experimental data. The model was refined and validated based on the ability of S. stipitis to grow on different carbon, nitrogen, sulphur and phosphorus sources. In silico analysis of the model was used to identify biosynthetic requirements for anaerobic growth of S. stipitis in glucose and to analyze xylose utilization capability in S. stipitis. Model simulations were carried out to obtain insights on the recycling of nucleotide cofactors and mechanisms involved in mitochondrial respiration and oxidative phosphorylation.We have reported a framework for reconstruction of genome scale metabolic model of stipitis . In thisprotocol . ExperimS. stipitis was developed based on the protocol outlined in the methods section . No growth was predicted by the model simulations under these conditions. Growth under anaerobic conditions is a complex process. Several requirements need to be met for growth including biosynthetic requirements for oxygen, energy requirements, redox balance requirements and regulatory requirements.One drawback for using 2O2 (either directly or through glutathione or pyridoxine) and the remaining 6 reactions were selected for further analysis. Four of these reactions involve phospholipids and the stoichiometry of these reactions in the KEGG database is different from the way lipid metabolism reactions are represented in the metabolic model. The remaining two reactions convert dihydroorotate to orotate. The URA1 gene from S. cerevisiae, which converts dihydroortate to orotate with fumarate added to the medium as electron acceptor, was reported to result in enhanced anaerobic growth in S. stipitis [S. stipitis.While performing the model simulations, even though sterol and unsaturated fatty acids were added there could be other metabolites or biomass constituents in yeasts that requires oxygen for their biosynthesis. To identify the biosynthetic requirements for anaerobic growth, reaction insertion analysis was performed on the model using the reference metabolic database KEGG . An initstipitis . This seS. stipitis strain with URA1 gene was not able to grow on xylose under anaerobic conditions [Model simulations were also carried out with xylose as the carbon source. The gene candidate identified for anaerobic growth on glucose resulted in a very low biomass flux. This flux was observed when xylose reductase activity was solely dependent on NADH and there was no flux when xylose reductase enzyme used NAPDH or a ratio of NADH and NADPH. nditions . In addiS. stipitis xylose reductase accepts both NADPH and NADH as cofactors with higher preference for NADPH [S. stipitis [S. stipitis. Model simulations were carried out for various ratios of NADPH and NADH dependent xylose reductase and for various uptake rates of xylose and oxygen. No xylitol accumulation was observed in these simulations. The capability of S. stipitis to efficiently interconvert NADPH and NADH might be a reason for lower xylitol accumulation. However when the xylose reductase activity was more dependent on NADPH and under lower oxygen uptake rates, it was observed that the xylose uptake rates were limited by oxygen uptake rate  was carried out for growth on glucose and xylose using the COBRA toolbox. Reactions known to result in loops within the major metabolic pathways were manually removed from the model before performing the flux variability analysis. The normal FVA calculates the minimum and maximum fluxes across various reactions in major metabolic pathways when maximizing the objective function (Biomass Flux). A variant of FVA called the sub-optimal FVA has been found to be more informative , whereinrevisiae . This daS. stipitis metabolic network grown in glucose minimal media revealed a few key reactions which could carry zero flux. One such reaction is phosphoglucose isomerase reaction catalyzed by the pgi1 gene (PICST_84923). Phosphoglucose isomerase pgi1-deletion mutants of S. cerevisiae cannot grow on glucose as the sole carbon source. The inability of S. cerevisiae to efficiently recycle the NADPH generated by the oxidative pentose phosphate pathway has been cited as the major reason for this growth defect [S. stipitis in silico growth rates were not significantly reduced. In silico metabolic flux analysis was performed for the pgi1-mutant S. stipitis to obtain insights on various pathways employed by S. stipitis to recycle NADPH generated from oxidative pentose phosphate pathway. Several pathways that could recycle cytosolic NADPH were identified in the S. stipitis network. The most promising pathways are listed belowAnalysis of the suboptimal flux variability values for major metabolic pathways in h defect ,49. Howe1. NAD-dependent glutamate dehydrogenase and NADP-dependent glutamate dehydrogenase which causes a substrate shuffling between 2-oxoglutarate and glutamate which restores NADP from NADPH through the coupled conversion of NAD to NADH.2. NADPH dehydrogenase which couples the oxidation of cytoplasmic NADPH to mitochondrial respiratory chain.3. NAD-dependent alcohol dehydrogenase and NADP-dependent alcohol dehydrogenase which causes a substrate shuffling between ethanol and acetaldehyde which restores NADP from NADPH through the coupled conversion of NAD to NADH.S. cerevisiae, Escherichia coli and Kluyveromyces lactis has indicated that the above mentioned pathways are either present in these organisms or when introduced has enhanced the growth of these organisms. K. lactis is reported to possess the mitochondrial NADPH dehydrogenase and a transdehydrogenase cycle involving the alcohol dehydrogenase [E. coli and S. cerevisiae the introduction of a soluble transdehydrogenase gene was found to enhance the growth of pgi1-mutants on glucose [S. cerevisiae, over expression of the NAD-dependent glutamate dehydrogenase restored growth in these mutants [S. stipitis can grow on glutamate as the sole carbon source. The first step in glutamate utilization is NAD-dependent glutamate dehydrogenase and thus this pathway might be responsible for the growth of pgi1-mutant S. stipitis. However, all these pathways and the pgi1-mutant strains have to be evaluated experimentally to confirm their roles. Nevertheless, the analysis highlights the usefulness of the metabolic model developed in the designing microbial strains with desired properties.Comparison of the literature on the phosphoglucose isomerase mutant (pgi1-mutant) rogenase ,51. In E glucose ,52. Furt mutants . From thS. stipitis metabolic network possesses numerous ways for NADPH consumption, the generation of NADPH and NAD to efficiently utilize xylose under lower oxygen uptake rates was limited. One particular pathway was observed to be induced under oxygen limited conditions when the cells are grown on xylose and this pathway was considered to be effective in tackling the cofactor imbalance caused by the first two steps in xylose utilization [Even though lization ,53. ThisThe role of the model in enhancing the understanding of cellular phenotypes and identifying requirements for metabolism were explained in the previous sections. In addition, the model can also be used to probe mechanisms in metabolism of a microorganism. In this section, the mechanisms involved in mitochondrial respiration and oxidative phosphorylation are analyzed using the model developed.S. stipitis is known to possess a branched respiratory chain which composes of the basic respiratory chain complexes I-IV, an alternative oxidase and alternative NADH dehydrogenases [S. stipitis has been investigated [S. stipitis [S. stipitis, simulations were carried out using the model developed. Simulations were able to predict increase in the ethanol yield observed in a cytochrome-C mutant [ogenases . The effstigated . Experimstipitis -58. HoweC mutant . HoweverS. stipitis on xylose [One of the well established ways by which microorganisms attain different phenotypic characteristics is by gene regulation. However, in the case of mitochondrial respiration, channeling the electron flow by the formation of super-complexes has been reported as a common mechanism -62. Analn xylose . This agS. stipitis. The procedure begins with the reconstruction of genome scale metabolic network which is then converted to a fully functional in silico model by incorporating the experimentally determined macromolecular composition, maintenance coefficients and minimal medium requirements. Even though assumption of macromolecular composition based on other related microorganisms or compiling fragmented data from different sources is a common practice [S. stipitis, higher content of chitin is observed as compared to other yeasts like S. cerevisiae  and was incorporated into the model. The in silico model was validated for consistency against newly generated high-throughput substrate phenotyping data. The model was subjected to iterative refinements based on identified inconsistencies, leading to additional reactions incorporated into the network and other modifications to the model content. The net result is a biochemically and genetically detailed in silico model that consists of 1371 reactions that are catalyzed by 814 genes. To our knowledge this is the first genome scale metabolic model developed for S. stipitis and it qualitatively predicts the phenotypic behaviors for substrate utilization with 74% agreement (252 out of 339 cases).An iterative procedure has been designed and used to develop the genome scale metabolic model of practice ,63-65, ile model ). SimilaS. stipitis. Generation of new annotation for metabolic genes based on network-based gap analysis and high-throughput phenotyping is one such example. Another example is the insight obtained on the mechanism involved in mitochondrial respiratory chain of S. stipitis. The results of the inhibition experiments carried out for the various complexes in the mitochondrial respiration chain where explained based on a hypothetical complex formation using in silico metabolic flux analysis. The hypothetical complex formation has to be verified by designing appropriate experiments. Further, the formation of mitochondrial super complexes has already been reported in several yeasts like S. cerevisiae and Yarrowia lipolytica [The model developed has also generated several experimentally verifiable hypotheses that could provide insight into the metabolism of polytica ,62.S. stipitis. The gene insertion analysis performed on the metabolic model was able to identify the particular conversion essential for anaerobic growth on glucose and this was validated by comparing with literature. However, the identified reaction did not enable in silico growth on xylose and this was also observed in experiments [S. stipitis [The predictive capability of the genome scale metabolic network was demonstrated by identifying the biosynthetic requirements for anaerobic growth of eriments . As menteriments . Since astipitis , other pS. stipitis metabolic network may not possess sufficient NADPH and/or NAD generation capability under lower oxygen uptake rates. Even though S. stipitis is considered to be an efficient xylose utilizing yeast, the metabolic model developed emphasizes the need for further characterization of this yeast to understand and improve its fermentation performance. High throughput techniques routinely used in the characterization of industrial yeasts like S. cerevisiae should also be developed for S. stipitis. Furthermore, analysis of pgi1-mutant shows that S. stipitis metabolic network possess many pathways to consume NADPH and recycle NADP. When grown on glucose the xylose utilization pathway itself can be considered as one such pathway. Thus by performing a detailed analysis on the various NADP recycling pathways and carefully designing suitable mutants the S. stipitis metabolic network can be optimized for simultaneous consumption of glucose and xylose. This trait has been a subject of active investigation and is of industrial significance [Xylose uptake analysis carried out using the model indicated that ificance .S. stipitis by combining information from genome sequence annotation, pathway databases, literature and experimental data. The model was refined using high-throughput phenotyping data. The model predictions were in good agreement with experimental observations, thus allowing us to systematically investigate the physiological characteristics and metabolic capability of this yeast. In silico model analysis shows that S. stipitis possesses several pathways to recycle nucleotide cofactors and thus efficient xylose utilization. However, the flux through of these pathways needs experimental investigation. Analysis of mitochondrial respiration and identification of mitochondrial super-complexes demonstrate the novel applications of the model developed. Incorporation of thermodynamic constraints, enzyme kinetics information and high-throughput omics data can further improve the predictability of these modelsIn this study, we have reconstructed a genome scale metabolic model for S. stipitis. The entire procedure is based on the recently published protocol for generation of genome scale metabolic models [Scheffersomyces stipitis [http://www.ncbi.nlm.nih.gov/. The biochemical reactions corresponding to the ORFs were mainly compiled from KEGG Database [Figure c models . The recstipitis  and ORF Database ,69,70 anDatabase , resultiThe next step is the refinement of the draft. For each reaction, gene-protein-reaction association, localization, cofactor specificity, and directionality were identified and assigned. The gene-protein-reaction associations include definitions for isoenzymes and enzyme complexes. The metabolic reactions in the model were organized into three compartments  based on the localization of associated enzymes, which was obtained using the protein localization predictors ,73  was purchased from American Type Culture Collection. It was routinely cultured in yeast extract, peptone, dextrose (YPD) medium and samples were collected at the exponential growth phase for the estimation of biomass macromolecular composition. Experimental data for biomass composition estimation were collected in duplicates and average values obtained are reported. For growth rate and substrate uptake rate measurements, experiments were carried out in minimal media containing 0.17% yeast nitrogen base without amino acids, 0.5% ammonium sulphate and 2% glucose. Experiments were carried out in 1 liter Erlenmeyer flasks at 30°C and 200 rpm. The OD600 was measured periodically for several hours to calculate the growth rates and the supernatant was collected and analyzed using HPLC for calculating the substrate uptake rates. The macromolecular composition of S. stipitis biomass was estimated by measuring carbohydrate, protein, lipid and nucleic acid content and the components making up these macromolecules.The cell wall polysaccharides  and intracellular carbohydrates  together contribute to the total carbohydrate content. Cell wall isolation and quantification of polysaccharides in the cell wall was carried out as described by Francois, 2007 . Cells wThe sugars glucose and mannose was measured by HPLC using the Biorad Aminex HPX-87H column. 5 mM Sulphuric acid was used as mobile phase and sugars were detected using RID detector operating at 50°C.S. stipitis biomass is determined using nitric acid method [S. stipitis biomass was measured using standard protocols as explained in AOAC Official Method 994.12 and AOAC Official Method 985.28 [Total protein content of d method . The hard 985.28 .3.6H2O in concentrated HCl) [The DNA and RNA content was determined using Orcinol reagent  . Freeze-S. stipitis was obtained using standard protocols as explained in AOAC Official Method 996.06 [The total lipid content was determined as explained by Matyash et al., 2008 . Harvestd 996.06 .The composition of the various biomass macromolecules estimated were converted to a biomass synthesis equation to be incorporated into to model developed. The growth and substrates uptake data estimated in the present study, along with reported data from continuous cultivation experiments  were useS. stipitis network were calculated by using flux balance analysis and linear optimization. For growth simulations, biomass synthesis was selected as the objective to be maximized and the optimization was solved using the COBRA Toolbox. The external metabolites were allowed to freely cross the system boundary by having unconstrained exchange reaction. Units of all flux values are in mmol/gDCW/h. For the simulation of aerobic growth on minimal media, the following external metabolites were allowed to freely enter and leave the network: NH4, O2, H+, SO4, PO4, CO2 and H2O (exchange fluxs -1000-1000 mmol/gDCW/h). All other external metabolites, except the substrates tested were only allowed to leave the system (exchange fluxes 0-1000 mmol/gDCW/h). Growth on different substrates was simulated by allowing the corresponding external metabolite to enter the system with the definite exchange rate (10 mmol/gDCW/h). For the simulation of anaerobic growth, oxygen uptake was reduced to zero and unconstrained uptake of sterols and unsaturated fatty acids was allowed.The metabolic network was loaded into Matlab using functions available in the COBRA toolbox ,90. The Scheffersomyces stipitis. It permits assays of 190 carbon (PM1- and PM2A- microplates), 95 nitrogen (PM3BMicroplate), 59 phosphorus and 35 sulphur-source (PM4A-Microplate) utilizations at once. A defined medium containing 100 mM glucose, 5.0 mM NH4Cl, 2.0 mM NaH2PO4, 0.25 mM Na2SO4, 100 mM NaCl, 30 mM triethanolamine HCl (pH 7.1), 0.05 mM MgCl2, 1.0 mM KCl, 1.0 mM FeCl3, and 0.01% tetrazolium violet was used for the PM tests. The PM plates contained various carbon-, nitrogen-, phosphorus-, or sulphur-sources which are omitted from the defined medium. The microplates were incubated at 30°C and the dye reduction data were collected in 15-min intervals for 48 h. In addition to time profile dye reduction data, the absorbance was measured at 590 nm and 750 nm after 24 h and 48 h incubations. The colorimetric assay was considered positive when the absorbance corresponding to reduced dye was 1.2 times higher than the negative control. Similar threshold was applied for the absorbance at 750 nm. The threshold was set at 1.3 for sulphur source data as the negative control was observed to have a higher background. The confidence with which growth can be predicted based on these measurements was determined based on the number of positive reactions out of 4 absorbance measurements. High confidence growth (+) if all measurements were above threshold and high confidence no-growth (-) if below. The remaining cases were classified as low-confidence data.Biolog's Phenotype MicroArray™ technology  was usedThe authors declare that they have no competing interests.BB and SJ designed the model development framework. BB executed the model development work. LT and BB designed the experiments and collected the data. BB and SJ involved in the analysis of results. RS supervised the study and assisted in implementation. BB drafted the manuscript. All the authors read and approved the final manuscript.Estimation of growth and non-growth associated maintenance requirements.Click here for fileScheffersomyces stipitisDetails of the reactions and metabolites in the genome scale metabolic model of .Click here for fileList of essential reactions in the genome scale model.Click here for fileComparison of model predictions and Biolog substrate utilization data. The details of substrates tested, abbreviations used for these substrates in the model, additional experimental evidence and model refinements are described in this file.Click here for fileScheffersomyces stipitis metabolic model Sub-Optimal Flux variability analysis of .Click here for file


Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability.The robustness of S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis ; 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds ; 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds ; 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds ; 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.Based on the production data of about 40 chemicals produced from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.We generated simple mathematical models for first-order approximation of chemical production yield from  S. cerevisiae continues to emerge as a preferred production platform [S. cerevisiae is typically known for its robustness in fermenting sugars into alcohol. In the recent past, it has also gained importance as a heterologous platform to synthesize many precursors of commodity chemicals and pharmaceuticals [a priori estimation of chemical production yield from engineered S. cerevisiae when given a set of parameters, namely the number of steps in the biosynthetic pathway of interest, genetic modifications, cultivation conditions, and nutrient and oxygen availability. The coefficients of these parameters were obtained from the regression of the yields and production conditions reported by recent literatures. Such model predicted the empirical yields that were lower than the theoretical productivities under "ideal" conditions. The model results could give metabolic engineers guidelines for increasing desired products and for reducing futile attempts.Producing small-molecule chemicals from microbial biocatalysts offers several advantages. Unlike conventional chemical synthesis which are heavily dependent on petroleum-derived substrates, microbes are able to use renewable materials to synthesize many commodity chemicals and fuels  Figure . Due to platform . S. cereeuticals . In geneC1 (without "pushing" pathway flux), OVEC2 (enhancing 1~2 enzyme activities), and OVEC3 (improving a number of key enzyme functions). KNO was also categorized by KNOC1 and KNOC2 . Table The model defined several important parameters that influenced the efficiency of chemical production from microbial hosts. The first group of parameters accounted for the number of enzymatic steps in the biosynthetic pathway of interest since it had been shown that this parameter was often inversely correlated with microbial product yield . To enumThe yield of metabolite production is also a function of cultivation conditions and nutrient availability. For instance, production of metabolites from a bioreactor is often higher than a shaking flask, due to the increased efficiency of mass transfer of oxygen, substrates, and nutrients. Moreover, culture acidification that often generates cytotoxicity and maintenance burden to the microbial hosts can be mitigated in a bioreactor by automated pH control. Based on these basic properties, we introduced the variable CUL to represent the general property of a cultivation condition. We also introduced the variable OXY and NUT to capture the effects of oxygen availability and nutrient supplementation, respectively -10. MoreSeveral assumptions were made to simplify our model development. A) Yield calculation was based on the conversion of major carbon substrate to final product if multiple nutrient sources were supplemented . B) We calculated the yields based on two factors: initially added carbon substrate in the culture and final measured product. We neglected the unused carbon substrate that remained in the end of the production. C) To calculate enzymatic steps from the carbon source, the model only considered the key route from the major substrate (mostly glucose) to the final products (enzyme steps for co-factors or ATPs synthesis were neglected). D) For product synthesis promoted by the addition of an intermediate, we had no means of differentiating the carbons derived from added precursor or from the carbon substrate . To account for the contribution from both carbon sources, the yield calculation was assumed to be an arithmetic mean of the two yields . Meanwhile, the number of primary steps or secondary steps were also assumed as an arithmetic mean of two data sets .i) can be described by a general power law expression of the type:Biochemical systems theory  states tj represents the system variables and the parameters αi, gij are the constants. Equation (1) yields a linear form in logarithmic coordinates. Based on similar assumptions, our model for yield prediction used system variables  to describe the relative carbon flux to the final products.Where X10 Y was the dependent variable which represented production yield (mol C in product/mol C in primary substrate), given each independent variables βi [0 as the intercept in Equation 2, which represented the combined contribution of Category 1 of all ordinal variables. β0 was defined as:In Equation 2, logables βi . We defiS. cerevisiae under various experimental conditions. Table The ordinal variables (using a binary system) were assigned a value of one if and only if the condition fitted the category in Table S. cerevisiae. These ordinal variables consisted of the number of modified genes or pathways (OVE), the number of gene knockouts in known competitive pathways (KNO), nutrient source (NUT), intermediate (INT), cultivation mode (CUL), and oxygen availability (OXY). We described the yield of chemical production as the summation of these independent variables in Equation 2. We fitted Equation 2 and determined the coefficients of the variables using linear regression analysis of ~40 compounds. Although multiple data of production yields were often reported in each literature, the model only considered the best yield under a denoted experimental condition. Then, all experimental conditions were categorized by numerical and ordinal variables. The linear regression coefficients obtained for Equation 2 were given in Equation 4, such that:We constructed simple models which linked several numerical and ordinal variables that affected the yield of chemical production from 2 and the P-value. Here, we used a P-value of 0.1 as the limit below which the result was considered significant [2 value of 0.55, which reflected the moderate discrepancy between reported yields and the model-predicted yields. Figure The accuracy of obtained coefficients in Equation 4 was evaluated based on Rnificant . Out of Interestingly, the number of enzymes in the primary pathway (PRI) did not significantly affect production yield  Table . This suoduction . With th-1.53- (0.01 × 10) + (-0.19 × 14) + 0.52+0.73 = 0.0009 . In most cases, our model-predicted yields were within the range of one order of magnitude compared to the experimental values.Yield = 10Since the number of steps in central metabolism (PRI) did not significantly affect production yield, we computed another set of regression coefficients for Equation 2 without the variable PRI, to yield a simplified form Equation 5.2 value. This result indicated that the number of enzymatic steps in primary metabolism did not significantly affect product yield. Presumably, fluxes in central metabolic pathways were typically high and robust [As shown in Table d robust , when cod robust .C2 and OVEC3 had positive values  indicated that statistically, the overexpression of a small number of genes (1-2) was uncertain to improve production yield. However, the coefficient of OVEC3  indicated the effectiveness of multiple gene modification to resolve the bottleneck steps. This observation is consistent to the fact that metabolic fluxes generally do not sensitively respond to changes of single enzyme activity, but are controlled by all key enzymes along the biosynthesis pathway. On the other hand, the regression coefficients of KNOC2 had positive value , and thus the removal of competitive pathways could be effective to increase production yield.Metabolic engineering typically involves pathway modification -22 to shS. cerevisiae produced fermentative products (ethanol and glycerol) (Crabtree effect) [S. cerevisiae could operate similarly to metabolism under oxygen-limited condition. The coefficient for the variable INT was 0.77, which represented that the supplementation of a precursor metabolite translated to an approximately six fold increase of the product yield . Similarly, the addition of nutrients (such as yeast extract) also significantly increased production yield (the coefficient of NUTC2 was 0.73). The contributions of INT and NUT to product formation indicated that intermediates/nutrients provided building blocks or energy sources that reduced the rate-limiting steps in biosynthetic pathways.It is a general knowledge that bioprocess conditions affect cellular viability and product yield. Our model suggested fermentation using a well-controlled bioreactor improved production yield by 3.2 times  effect) ,19 underWe used Equation 2 to compute the production yield of chemicals according to the specifications listed in Table 2 value from 0.55 to 0.58, demonstrating that ethanol could be better assumed as a central metabolite for S. cerevisiae. Using Equation 6, we predicted ethanol production based on a recent reference [Regression of the data using Equation 6 improved the Reference  by specieference .S. cerevisiae. The model were validated by "unseen data"  [Pichia pastoris, a common species for protein expression, shows high S-adenosyl-L-methionine productivity if a large amount of the intermediate methionine was repeatedly added in the medium [Pichia stipitis also has high yields of L-lactic acid and ethanol from glucose and xylose [S. cerevisiae), therefore, these yeast species may be alternative hosts for certain biotechnology applications.Furthermore, the model can reveal the metabolic features of s Figure . For exadiction) . Pichia e medium . Besidesd xylose . Figure SEC from model regression could not account for the big variances of biosynthesis efficiency or potentially feedback inhibitions in secondary pathways. For example, butanol synthesis is significantly improved via non-fermentative amino acid pathways compared to traditional acetyl-CoA routes [2 fixation  [The accuracy of the model predictions for some products could be poor due to several limitations during model development. First, the category was a rough estimation of experimental conditions especially for variables related to gene modifications (OVE and KNO), and the yields could be very different even in the same category. Second, some products, despite large synthesis rates, were either not very stable or difficult to accumulate in a large quantity due to consumptions by downstream pathways or product degradations . Their A routes , becauseacetate)  or the nE. coli model using same modeling approach. Compared to the E. coli model, S. cerevisiae shows several differences: 1. Oxygen conditions made a more significant impact on biosynthesis yield in E. coli than that in S. cerevisiae; 2. The genetic modification in E. coli had higher uncertainty for metabolic outcomes; 3. For metabolic pathways from precursors to final products, loss of yield per biosynthesis step (~30%) in S. cerevisiae is higher than that in E. coli (10~20%). Interestingly, E. coli model states that primary metabolism influences product yield  which indicates the balance of precursor production from central metabolism is also an important consideration for metabolic engineering of E. coli. For example, it has been demonstrated that lycopene production with E. coli was enhanced by redirecting the carbon flux from pyruvate to G3P [S. cerevisiae model indicates that it is less likely that the number of steps in central metabolism play a bottleneck role in the production of metabolites derived from it, while the bottlenecks are more likely in the secondary pathways . Therefore, the metabolic strategies should focus on the secondary pathways to have a better chance for increasing final yield. Although modification of central metabolism may affect microbial physiologies, a few studies indicate the robustness of the central metabolism in S. cerevisiae because of its importance to cell vitality. For example, S. cerevisiae may maintain central metabolic fluxes via gene duplication and alternative pathways under different environmental and physiological conditions [S. cerevisiae is likely to render metabolic engineering strategies ineffective when targeting enzymes in central metabolism. In general, the unique metabolic features of yeast and bacteria can be of important consideration when choosing a production host.Recently, we have constructed the e to G3P , but feenditions ,35. TherS. cerevisiae has been widely used as a robust industrial organism for metabolic engineering applications, many metabolic features of this organism for biosynthesis under various conditions remain unknown. In this study, the statistic model for yeast biosynthesis permits a priori calculation of the final product yield achievable by current biotechnology. Unlike other in silico models based on mass balance or thermodynamics (such as FBA model) [S. cerevisiae, metabolic bottlenecks may be more likely to be in the secondary metabolic pathways rather than primary pathways, and thus it can narrow down the genetic targets and avoid futile work. 3. This model may be used to qualitatively benchmark yields of different engineered production platforms.Although A model) ,37, our The authors declare that they have no competing interests.Conceived and designed the models: YJT, AMV, and EL. Data collection and analysis: AMV and YX. Wrote the paper: AMV, YJT and EL. All authors have read and approved the final manuscript.


Pseudomonasaeruginosa and Pseudomonas putida, respectively.The result is a pair of revised genome-scale reconstructions for these organismsthat can be analyzed at a systems level with confidence that differences areindicative of true biological differences (to the degree that is currentlyknown), rather than artifacts of the reconstruction process. The reconstructionswere re-validated with various experimental data after reconciliation. With thereconciled and validated reconstructions, we performed a genome-wide comparisonof metabolic flexibility between P. aeruginosa and P.putida that generated significant new insight into the underlyingbiology of these important organisms. Through this work, we provide a novelmethodology for reconciling models, present new genome-scale reconstructions ofP. aeruginosa and P. putida that can bedirectly compared at a network level, and perform a network-wide comparison ofthe two species. These reconstructions provide fresh insights into the metabolicsimilarities and differences between these importantPseudomonads, and pave the way towards full comparativeanalysis of genome-scale metabolic reconstructions of multiple species.In the past decade, over 50 genome-scale metabolic reconstructions have beenbuilt for a variety of single- and multi- cellular organisms. Thesereconstructions have enabled a host of computational methods to be leveraged forsystems-analysis of metabolism, leading to greater understanding of observedphenotypes. These methods have been sparsely applied to comparisons betweenmultiple organisms, however, due mainly to the existence of differences betweenreconstructions that are inherited from the respective reconstruction processesof the organisms to be compared. To circumvent this obstacle, we developed anovel process, termed metabolic network reconciliation, whereby non-biologicaldifferences are removed from genome-scale reconstructions while keeping thereconstructions as true as possible to the underlying biological data on whichthey are based. This process was applied to two organisms of great importance todisease and biotechnological applications,  P. aeruginosa and P.putida) were compared such that differences between the models notupheld by biological evidence were eliminated. These reconciled models werere-validated with experimental data, and used to explore differences in thebiology of these organisms and to gain insight into the reconstruction processitself.Over the past decade, the increasing availability of fully sequenced genomes,functional genomics databases, and a broad existing scientific literature onmetabolism have been leveraged towards the generation of genome-scale metabolicreconstructions for a wide variety of organisms. A major hurdle in the field,however, is the lack of standardization in this ‘metabolicreconstruction’ process, which makes it difficult to comparereconstructions of multiple species with each other. Notably, it is difficult todetermine if differences seen between the models are of biological origin, or ifthey reflect noise from the respective reconstruction processes. To address thisproblem, we have developed a novel method, termed metabolic networkreconciliation, in which genome-scale models of two well-studied andphylogenetically related bacteria ( With the development of rapid genome sequencing methodologies and powerful, scalablecomputational tools, the past decade has seen the generation of an increasing numberof genome-scale metabolic reconstructions (metabolic GENREs) Despite the reconstruction of over 50 metabolic GENREs to date, little effort hasbeen put towards comparison of multiple species at a genome level with anetwork-centric approach. Such a comparison is bound to yield interesting insightsinto the relationships between the structure of a metabolic network and theresulting phenotype of an organism, as well as contribute to the explanation ofvarious physiological features such as virulence pathways and unique metaboliccapabilities. Yet in order to draw meaningful conclusions from such a comparison, itis necessary to ensure that the identified differences are representative of truedifferences between the organisms, rather than artifacts from the reconstructionprocesses.The metabolic reconstruction process integrates the genome sequence and annotation ofan organism with a multitude of different sources, including biological databases and primary literature, to construct the metabolic GENREs. Reconciliation is similar to but distinct fromconsensus building efforts such as metabolic reconstruction jamborees Pseudomonas aeruginosa (PAO) andPseudomonas putida (PPU). These bacteria represent an idealpair of organisms for a genome-scale metabolic comparison due to their tremendousscientific and medical importance. P. aeruginosa is anopportunistic pathogen, notorious for its chronic inhabitance of the lungs of cysticfibrosis patient and its role in causing acute and deadly nosocomial infections inimmunocompromised patients P. aeruginosa and P.putida are ubiquitous environmental organisms, capable of living variedlifestyles in many habitats, and are of interest for biotechnological applicationsP. aeruginosa, P. putida is not a humanpathogen. This last feature makes reconciliation of these two important speciesparticularly valuable, as it paves the way for comparative analyses that could lendinsight into metabolic features contributing to pathogenicity.To address these concerns, we have performed the first metabolic GENREreconciliation, a process of eliminating erroneous differences between two existingmetabolic GENREP. aeruginosa andP. putida, labeled iMO1086 and iJP962 respectively followingstandard conventions P.aeruginosa and P. putida, a comparison uniquelyenabled by this reconciliation process, and to provide insight into metabolicfactors that might help in characterizing the lifestyle capabilities among thesebacteria.The metabolic GENRE of each of these species was previously published by our groupsP. aeruginosa and P. putida, and differentcomponents of the process are expanded in the upcoming sections.Network reconciliation is a process in which two previously reconstructed metabolicGENREs are compared with the null hypothesis that given no discriminating biologicalevidence for a given reaction to be included in one reconstruction but not theother, the reactions should be identically included in both. If sufficient evidenceemerges that there is in fact a difference between the two organisms, then thosedifferences are preserved in the final ‘reconciled’ GENREs that emergefrom the process. The process of reconciliation is detailed in in silicovia Flux Balance Analysis (FBA) The result of this process is a pair of metabolic GENREs for which every differencehas been examined and verified with genomic, physiological, or literature-derivedevidence. Finally, the two reconciled GENREs are grown P. putida and P. aeruginosa are closelyrelated genomically and phylogenetically, it was expected that many orthologswould exist with high homology, and thus shared functions. Therefore, as a firststep in the reconciliation process, we identified highly homologous gene pairs,which we termed “reciprocal genes”. These reciprocal gene pairs wereused in the reconciliation process to determine the similarity of geneassociations for reactions in the two reconstructions, so identifying thesepairs was a crucial first step for performing the reconciliation.As each metabolic reconstruction is a reflection of the genetic content of therespective organism, the comparison of two reconstructions requiresidentification of the overlap between genomic content of the compared organisms.As P. putida genome sequence databasewith P. aeruginosa genes as queries and vice versa. Thesearches were performed both with nucleotide and protein sequences. Two geneswere termed reciprocal only if, in every aforementioned BLAST search performedwith one of these genes as the query sequence, the other gene was reported asthe first hit . The metric used for comparing BLAST matches was the score of the highestscoring pair Text S1). This analysis provided a standard for comparinggene functions between P. aeruginosa and P.putida, as the reciprocal genes were assumed to have the samefunction in the absence of opposing evidence.In order to identify reciprocal genes, we performed a homology search using theBLAST algorithm against the swere built and are maintained by different groups and often incorporatedifferent names for many chemical species and reactions, this step was notcompletely straightforward and could not be performed entirely automatically. Ofthe set of 1328 reactions present in both reconstructions, we were unable tofind a match in the other reconstruction for 619 reactions; 277 reactions wereinitially determined to have a functional but not an exact equivalent in theother reconstruction . These numbers areshown in Table 4 in Text S1. Of the Gene-ProteinRelationships  synthesis. Many of the reactions inthe LPS pathway, for example, were reconstructed originally from literaturesources rather than from database information, so reconciling this pathway as agroup made more sense than breaking the reactions up into groups based on thetype of gene associations present.P. putida gene PP1842 was added to the reconciledGENRE, since available evidence indicated that this gene, like its reciprocal inP. aeruginosa, encodes a protein with GMP synthaseactivity.The panel on the bottom right of P.aeruginosa and P. putida reconstructions. Whilethe original reconstructions shared 432 reactions, with 451 and 445 reactionsunique to iMO1056 and iJP815 respectively, the reconciled final reconstructions share 925 reactions, with only 103 and 48reactions unique to the each reconstruction ; (ii) added, describing reactions added to one of thereconstructions during the reconciliation process, such as in cases where asecondary function of an enzyme had been added to one of the pre-reconciliationmodels but not to the other, and the reconciliation resulted in the functionbeing included in both; (iii) removed, describing reactionsremoved from the one of the reconstructions during the reconciliation process;and (iv) minor change, describing reactions whose generalfunctions were preserved despite modifications to the specific stoichiometriesor pathway participation of the reactions involved in the process. This lastcategory includes, for example, reactions in the lipopolysaccharide productionpathway, which was preserved in both GENREs but was modified based on apublication that appeared after the original reconstruction processes had beencompleted removed class is not represented in these charts. Thelargest of these meta-groups is no change, followed byadded, minor change, and thenremoved . A fullerdescription of the different types of changes made to the models is provided inText S2 (see section “Categories of changes madeduring reconciliation”).The reconciliation process resulted in a large convergence between the P. aeruginosa and P.putida, we mapped out the KEGG pathways of all reactions in thepre- and post- reconciliation GENREs. P. putida and P. aeruginosa, as thepathways with the most changes tend to be enriched in reactions thatrequired subjective decisions during the initial reconstruction processesfor iMO1056 and iJP815, and therefore would be subject to more alterationsduring reconciliation than other more well defined pathways. Transportreactions are the most altered on the list, partially because of the largenumber of transport enzymes present in P. aeruginosa andP. putida, but also due to the high degree of ambiguitypresent in current knowledge of these transporters P. aeruginosa and P. putida during thereconciliation, including purine metabolism and fatty acid metabolism, fallinto the high confidence category in the human network. This is likely areflection of differences in the knowledge available for human versusbacterial pathways.To gain a sense of where changes occurred in the reconstructions duringreconciliation as well as the functional differences present in thereconciled GENREs of P. aeruginosa and P.putida reconstructions, as these GENREs represent our bestunderstanding of metabolic differences between these organisms. Theseresults can be seen in P. aeruginosa of reactions forsynthesizing quorum sensing molecules, phenazines and rhamnolipids, and forperforming denitrification P.aeruginosa to thrive anoxically, whereas P.putida is a strict aerobe. Unique assignments of severalaromatic degradation pathways for P. putida are also asexpected, including those for degradation of naphthalene, anthracene,phenylalanine, and benzoate P. aeruginosa in thereconciled reconstructions. Further, some pathways  show a number of unique reactions inboth P. aeruginosa and in P. putida,indicating that these pathways utilize different mechanisms in the twoorganisms. Overall, more unique reactions appear to belong to P.aeruginosa than P. putida, and these span awide variety of pathways involved in many different cellular processes. Itis important to note that many hypothetical enzymes exist in both of theseorganisms, but that P. putida has been considerably lesswell studied than P. aeruginosa, which might partiallyexplain why more unique reactions are present in the P.aeruginosa reconstruction. However, it is likely that most ofthe uncharacterized enzymes participate in peripheral metabolism, so thefunctioning of central metabolism in the two organisms should still beamenable to comparison.We were interested in examining the differences in reaction participationbetween the final (reconciled) s of P. aeruginosa andP. putida. Both original reconstructions had been validatedby comparison of in silico growth predictions versus BIOLOGsubstrate utilization data and growth yield data collected from literature, andthe P. aeruginosa network had been further validated bygenome-scale gene essentiality data and P. putida against a setof auxotrophic mutations. We compared the reconciled GENREs to the same data todetermine how the reconciliation process affected accuracy of the models inpredicting in vitro phenotypes.After completing the reconciliation process, it was important to re-validate thepost-reconciliation GENREs with the same data we had used to validate theoriginal metabolic GENREP.aeruginosa PAO1 and P. putida KT2440 to assessutilization of various carbon sources on otherwise minimal media . It is important tonote that there are different sets of genes associated with iMO1056 andiMO1086, with 143 genes unique to iMO1086 and 123 genes unique to iMO1056.An examination of the 933 genes common to both iMO1056 and iMO1086 indicatesthat essentiality predictions among this set improved marginally . This indicates thatonly slight improvements were made to the GPRs insofar as gene essentialitypredictions are concerned, although the improvements might turn out to bemore profound if more or different data were available for validation.Additionally, we assessed the accuracy of iMO1086 in predicting essentialityof metabolic genes in TextS1). The most prevalent conversion (24 genes) wasTN→FP, which is in agreement with the overall higher fraction of falsepositive calls in the reconciled reconstruction. The lack of significantchange in accuracy of the predictions of gene essentiality between the pre-and post-reconciliation reconstructions suggests the possibility of anintrinsic limit on accuracy, which would likely differ in differentorganisms based on the completeness of the genome annotation and otheravailable databases, as well as on the amount of validating experimentaldata available.The slight improvement that was seen in the essentiality predictions for the933 genes common to iMO1056 and iMO1086 was the result of an almost exactlybalanced deterioration (true positive (TP) → false negative (FN) andtrue negative (TN) → false positive (FP)) and improvement of calls forapproximately 6% of genes  in these essentialreaction sets are approximately equivalent to the percent seen in the modelas a whole for P. putida . This increase is due to an increasein P:O ratio and, in the case of P. putida, a slight changein the biomass composition from the original model. For a more detailedanalysis of yields in the models, see section “Analysis of changes inyields” in Text S2 and Table 7 inText S1.To assess how the reconciliation affected the yield predictions of the twometabolic GENREs, we performed flux balance analysis (FBA) simulations forgrowth of the original and the reconciled reconstructions. In both models,the yield increased slightly from the original models  of the objectives, the biggest differencein area held within the pareto curve for P. aeruginosaversus P. putida was −4.2%, a negativepercentage indicating that the area held within the iMO1086 pareto curve issmaller than that for iJP962  and pairingsshowing greater flexibility in P. aeruginosa to the right. While the differences between P.aeruginosa and P. putida in flexibility ofvirulence precursor production tend to be small (less than 5%), thereare significantly more flexible pairings in P. putida thanin P. aeruginosa.This pareto analysis surprisingly showed only small differences inflexibility of P. aeruginosa and P.putida is small, P. putida might have slightlylower metabolic costs to producing these factors. It is notable that many ofthe virulence factor precursors are fairly central to metabolism, and can beused for a variety of metabolic ends unrelated to virulence. Therefore, thedifferences in flexibility might relate to selective pressures having to dowith non virulence related processes. Still, it is interesting andsurprising that P. putida shows more flexibility for thisfunctionality.This result suggests that while the difference in ease of production ofvirulence precursors between P. aeruginosa and P. putida are ableto apportion resources between multiple competing pathways.The results of the pareto analysis of virulence precursors give a glimpse ofhow P. aeruginosaand P. putida for all paired combinations of 73 metabolicpathways. To do this, we first constructed pareto optimum curves (asdescribed in the previous section) for 10 randomly chosen pairs of reactionsbelonging to each pathway pair. All simulations were done on insilico glucose minimal medium.To extend this analysis to the genome-scale, we performed a survey ofdifferential flux carrying capacity between P.aeruginosa or P. putida displayed asignificant degree more flexibility for reaction pairings within eachpathway pair , but the more similar flexibility between the two organisms inproduction of virulence factor precursors, as shown in the previous section.Tradeoffs between production of precursors for these same virulence factorsshowed minor and mixed differences in flexibility between P.aeruginosa and P. putida, as shown with thecomparison of differential percentages in P. aeruginosa and P. putida.Increased flexibility in flux through virulence factor demand reactionsrepresents a direct metabolic advantage of P. aeruginosa appears todisplay greater metabolic flexibility than P. putida in avariety of pathways. Greater flexibility was seen in iMO1086 versus iJP962in 267 pathway pairings, as opposed to 98 pathway pairings in which iJP962displayed greater flexibility than iMO1086. The most notable exception tothe observation of greater flexibility in P. aeruginosathan in P. putida is the ascorbate and aldarate metabolismpathway, which contributes 50 of the 98 pathway pairings that aresignificantly more flexible in P. putida than in P.aeruginosa. It is unclear what biological role a greaterflexibility in this pathway would play, particularly as related to virulencephenotypes.Several other processes that display more flexibility in iMO1086 versusiJP962 are more difficult to link to a specific virulence trait, but supportthe general observation that P. aeruginosa andP. putida metabolism, but for the purpose of clarity wehave restricted our analysis to accepted KEGG pathways.It is likely that many more interesting results are embedded within thepathways deemed non-significant through this analysis, as the cause fornon-significance in many cases is the grouping of reactions into a singleKEGG pathway when these reactions do not necessarily act in a biologicallyconcerted manner. An unbiased modularization of metabolic pathways mighttherefore reveal more differences between Comparison of genome-scale metabolic reconstructions (GENREs) and the subsequentcomparison of the metabolic phenotypes available to different organisms is a novelmethod that promises to yield fresh insights into the functioning of metabolism as awhole. Despite the large number of manually curated genome-scale metabolicreconstructions built to date (more than 50), a conspicuous gap exists in theliterature for meaningful comparisons between two or more reconstructions of relatedspecies P. aeruginosa andP. putida. These bacteria are both important forbiotechnological applications, in addition to the notorious role of P.aeruginosa as an opportunistic pathogen. The biotechnologicalusefulness of these organisms is based on their extremely varied and expansivemetabolic capabilities In order to overcome this hurdle and to enable a high-quality constraint-basedcomparative analysis of two organisms at genome scale, we developed a novelprocess termed metabolic network reconciliation. Reconciliation assessesreactions present in two genome-scale reconstructions, and determines whether ornot changes should be made to the reconstructions in order to uphold the nullhypothesis that no difference exists between the reconstructions. Differencesbetween the two reconstructions for a given reaction are only upheld ifsufficient  evidence for the differences is present inliterature, genome annotations, or online databases such as KEGG and Expasy. Inthis way, the reconciliation allows for verification of two reconstructionsagainst each other and leads to removal of non-verifiable differences betweenthe metabolic reconstructions, while still staying within the bounds ofavailable knowledge about the organisms. We performed the reconciliation betweenmanually-curated metabolic GENREs of P.aeruginosa and P. putida reconstructions. While inthe original reconstructions only around 50% of the reactions were incommon, the reconciled GENREs shared around 90% of their reactions.Furthermore, although a significant portion of the reactions in thereconstructions were altered, added, or removed during the reconciliationprocess, it is clear from minorchange meta-class) or an addition of pathways that had initiallyonly been reconstructed in one model despite evidence for inclusion in both (asrepresented by most of the subcategories in the addedmeta-class). Few of the changes altered the fundamental biology of the networks,but these changes were critical in aligning similar functions in thereconstructions so that they could be meaningfully compared. If a comparisonbetween P. aeruginosa and P. putida had beenattempted without first reconciling the metabolic reconstructions, anybiological variance between the bacteria would likely have been obscured bydifferences in the reconstructions derived from the respective reconstructionprocesses rather than from true biological differences between theorganisms.The reconciliation process resulted in a significant alignment of the P.aeruginosa and P. putida most strongly divergephenotypically), the network reconciliation might have played a larger role inremoving non-verifiable functional differences between P.aeruginosa and P. putida than is suggested by thephenotypic stability of the pre- and post- reconciliation reconstructions withregards to validating data.The similar performance of the pre- versus post- reconciliation reconstructionsas compared to validating data suggests that the reconciliation did not alterthe overall biology of the reconstructions. However, as all analyses performedin the revalidation process relied on FBA with a growth objective, thesevalidating analyses inevitably focus on the portions of the metabolic networksrelated to production of biomass and utilization of particular substrates. Thefact that the changes in network validation were minor suggests that fewfunctional changes occurred in the central portions of the reconstructions, butmore functional changes might have occurred in more peripheral portions ofmetabolism. This hypothesis is consistent with our analysis ofreconciliation-derived changes in the essential portions of the reconciledGENREs, as shown in Pseudomonasmetabolism, the reconciliation process offers a unique opportunity to critiqueand examine the metabolic reconstruction process itself. Some of the changes inthe metabolic GENREs through the reconciliation process rectifyinevitable mistakes in the original reconstructions, yet the reconciledreconstructions for P. aeruginosa and P.putida were built with essentially the same data as the originalreconstructions, with the additional constraint that decisions on inclusion ofgenes must be consistent between the two networks. Therefore, in many cases, thechanges are more reflective of ambiguity as to what evidence should beconsidered sufficient for genes to be included in a reconstruction, or whatexact form the reactions they catalyze should take.In addition to contributing to our understanding of When a reconstruction is built, the weights given to different data sources andthe determination of cutoffs for inclusion of genes in the GENRE inevitablyinvolve a degree of subjectivity, since a large amount of available geneannotation data is too vague to enable definitive determinations of genefunction. The lack of changes in the validation outcome indicates that theoriginal and reconciled reconstructions reflect phenotypes of the respectiveorganism equally well. Therefore, there seems to be a degree of ambiguity in thegenetic makeup of a reconstruction that is not fully represented in thephenotypes. This ambiguity can be described in the terms of a space ofcomparably accurate reconstructions of a particular organism, where the pre- andpost- reconciliation reconstructions represent points in the space. The size ofthis ‘reconstruction space’ for a given organism is related to thedegree of vagueness in functional assignments of genes, a metric for uncertaintyin the current state of knowledge of the organism.The plots in P. putidareconstruction with the other independently performed GENREs of this organismIt would be also interesting to reconcile the SaccharomycescerevisiaeHomo Sapiens, and Salmonella, for each ofwhich there are multiple independent reconstructions available (with significantvariance in size and metabolic capability), and these meetings have resulted inprogress in the standardization and improvement of metabolic reconstructionswww.theseed.org) www.microme.eu), a large project that aims to extend the scopeof microbial genome annotation from functional assignment at gene level to thesystematic generation of pathways assemblies and genome-scale metabolicreconstructions, with an initial focus on bacteria. Even with the efforts fromjamborees, SEED, and Microme, ambiguity in the evidence used to build metabolicGENREs is unavoidable. Therefore, some type of reconciliationwill likely be necessary prior to any multi-reconstruction comparison.An issue that contributes greatly to the difficulty of building and comparingmetabolic reconstructions is the lack of standardization of the methods used forthe reconstruction process. This difficulty, which is the basis for the workpresented here, has also catalyzed several efforts towards developing commonstandards for metabolic reconstructions. One approach is based on reconstruction‘jamborees’, in which communities of biologists gather over severaldays for an intensive session of effort to improve and standardize a givenmetabolic GENRE. Jamborees have been held thus far for Even in an ideal case where the knowledge about an organism is complete, therestill remains some ambiguous decisions in the reconstruction process resultingfrom the core approximations of constraint-based modeling, which confine thefundamentally analog nature of biology to digital categorizations . With regards to thischaracteristic, metabolic reconstructions are akin to other types of biologicalmodels.The reconciliation process reveals challenges that might arise if a high qualitycomparison is to be performed between more than two species. Reconciling morethan two models might prove difficult, since the addition of more models beyondtwo adds more degrees of freedom to the task. The issues faced in these caseswill be similar to those faced in standardization efforts such as SEED, in whichsome organism specificity is forfeited in favor of model standardization. Ourreconciliation process can inform these efforts, and the reconciled models canserve as a gold standard against which these automated reconstruction platformscan be compared.P. aeruginosa andP. putida manually, much of the process could be automatedfor future efforts. The workflow developed for performing model reconciliation will help make much ofthe process automatable in the future.Although we performed the reconciliation of The reconciliation we performed was for two species that are closely related.However, the process we developed should be equally applicable to more distantspecies if a comparison is to be attempted. Model reconciliation enables truedifferences in two metabolic networks to be identified above the noise. Thisidentification enables a more confident comparison of differences, which iscrucial whether or not the species are closely related. In comparing speciesthat are more distantly related, more pathways might fall into the‘organism specific’ categories, and thus need to be reconciled ascontiguous pathways as opposed to as collections of reactions ,while P. aeruginosa thrives at this temperature P. putida also lacks many of the factors necessary forestablishing a human infection, including many of the virulence factorspossessed by P. aeruginosa. Metabolic factors might play a rolein forming some of these differential phenotypes.The results of the comparative analysis lent significant insight into differencesbetween P. aeruginosa versus P.putida is the increased flexibility of P.aeruginosa in sulfur related pathways  forms a major source ofnutrition for P. aeruginosa. Several findings in literaturesupport the importance of sulfated mucin in defining the invivo environment in the CF lung, including the enhancement of mucinsulfation in CF patients versus in the normal lung, the presence of multiplehighly regulated sulfur uptake mechanisms in P. aeruginosa thatrespond differently to the presence of inorganic sulfate versus the presence ofmore complex organosulfur compounds, and most notably, the demonstrated abilityof P. aeruginosa CF isolates to utilize mucin as a sole sulfursource P. aeruginosa reconstruction, thisset of pathways is possibly of great import for P. aeruginosavirulence, and is a possible important area of study for the future.One unexpected difference identified through our metabolic comparison that mightcontribute to the virulence of P. aeruginosa is highly multifactorial P. aeruginosa than inP. putida as shown through the pairwise pathway flexibilitystudy, but this result does not extend to virulence precursors. More puzzling isthe observation that P. aeruginosa displays higher pathwayflexibility in production of certain virulence factors than P.putida, even though the flexibility of production of the virulenceprecursors remains similar between the two . Nucleotide sequences for all P.aeruginosa and P. putida genes were downloadedfrom the Pseudomonas Genome Database V2 (http://v2.pseudomonas.com), and the identification was performedas described in the results section.The identification of pairs of reciprocal genes from insilico rich medium composition on the outcome essentiality analysiswas evaluated. The original in silico rich medium did notcontain L-cysteine but rather it contained L-cystine, a dimeric amino acidcomposed of two cysteine residues linked by a disulfide bond. Since it ispossible that this compound can be broken down into its L-cysteine form andutilized, the influence of the inclusion of L-cysteine into the medium wasevaluated. Furthermore, the original in silico medium did notcontain any sources of purines or pyrimidines. The decision to exclude thesecompounds from the medium was based on the listed chemical composition of LBmedium, as listed in the supplementary materials from insilico growth was also assessed in LB medium containingpurines/pyrimidines that could be taken up by the reconciled reconstructions.The gene essentiality validation followed the same procedure as in the originalvalidation insilico minimal media, with various single carbon sources allowedinto the models. If biomass could be produced with a nonzero flux on a givencarbon source , thenthat carbon source was considered to enable growth insilico.The revalidation was performed as in the original validation in silico carbonsource, maximizing biomass production. This simulation was done with each of theset of genes causing acetate-auxotrophy knocked out in silicoto determine biomass yield. If no biomass was produced, the mutation wasconsidered lethal on acetate medium. These in silico resultswere compared against in vitro results collected previously byour group The revalidation was performed as in the original publication DW−1·h−1)in order to obtain yields (in units of gDW·(mmol glucose)−1). For more details, see section “Analysis ofchanges in yields” in Text S2.The yields were computed by performing FBA Pareto optimum curves denote the outline of the solution space of a metabolicnetwork along the plane defined by two reaction fluxes. To construct a paretooptimum curve for two reactions, the flux through one reaction was fixed at aseries of different levels, and the flux through the other reaction was bothmaximized and minimized at each level. These upper and lower boundscorresponding to each flux value of the first reaction gave shape to two paretocurves, which together outline the edges of the solution space along the planedefined by the two reactions. The area held within a pareto curve was calculatedas a metric of metabolic flexibility in trading off resources between the twocellular objectives represented by the two reactions plotted.An analysis was performed to examine the differences in flexibility for allunique pairings of pathways present in both iJP962 and iMO1086. First, reactionsin both models were assigned to pathways based on KEGG assignments with somemanual assistance . ‘Demand’ pathwaysencompass reactions enabling drainage of certain interesting cellularmetabolites from the metabolic network, and these reactions were only enabledfor the purpose of the specific simulations they participated in . Reactions were sampled from the setof reactions common to both models and able to carry flux in at least one of themodels (there were 656 such reactions). The numbers of reactions meeting thesecriteria for each pathway are listed after the pathway names in P.aeruginosa and (the same) 10 computed for P.putida) were randomly assigned to two groups in 1000 permutations,and cases where the true groupings of pareto areas into P.aeruginosa and P. putida groups showed a meandifference of at least 3 standard deviations from the average of randompermutations of groupings of the 40 reactions were considered‘significant.’To generate the plot shown in http://www.gnu.org/software/octave/). Linear optimizations wereperformed utilizing either the free GLPK (http://www.gnu.org/software/glpk/) or CPLEX  linear programming solvers, except for the pathway flexibility study, whichwas performed using Gurobi (http://www.gurobi.com/). Thereconstructions were stored and maintained in Excel and MATLAB and the ToBiNplatform Simulations were done on a PC using the Cobra Toolbox Text S1Supplementary tables.(10.03 MB XLS)Click here for additional data file.Text S2Supplementary information file.(0.24 MB DOC)Click here for additional data file.