To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92–94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model  that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R Antibiotic efficacy is traditionally assessed with a single value, the minimal inhibitory concentration, despite its limited predictive power. Dose-response curves that assess efficacy over a range of antibiotic concentrations are promising, but extremely work intensive. Using the well-characterized antibiotics ciprofloxacin and ampicillin as proof-of-concept, we provide a mathematical framework that allows predicting optimal dosing of antibiotics based on drug-target binding parameters. These could be measured early in drug development and can also change when bacteria become resistant. Thus, we can predict the probability that resistant strains are selected at a given dosing regimen without isolating and testing a multitude of resistant strains. Predicting optimal dosing with early accessible experimental data can reduce the prevalent trial-and-error approaches and thereby the number of antibiotic candidates that fail late in drug development. The rise of antibiotic resistance represents an urgent public health threat. In order to effectively combat the spread of antibiotic resistance, we must optimize the use of existing drugs and develop new drugs that are effective against drug-resistant strains. Accordingly, methods to improve antibiotic dose levels to i) maximize efficacy against susceptible strains and ii) minimize resistance evolution play a key role in our defense against antibiotic resistant pathogens.It is noteworthy that dosing strategies for treatment of susceptible strains  [The next challenge to successfully designing antibiotic treatment arises when the experimental information is integrated into mathematical pharmacodynamic models that then predict efficacy under realistic, fluctuating concentrations in patients. Pharmacodynamic models from 1910 (E-models)  are stil-models) –21. HoweIn this report, we describe a mechanistic computational modeling framework  that allows us to predict full pharmacodynamic profiles based solely on accessible biochemical parameters describing drug-target interaction. These parameters can be determined early in drug development. We use this framework to investigate how changes in drug target binding, either due to improvements in existing antibiotics or due to resistance mutations in bacteria, affect antibiotic efficacy. We first show that COMBAT accurately predicts bacterial susceptibility as a function of drug-target binding and, conversely, allows inference of these biochemical parameters on the basis of observed patterns of bacterial growth suppression or killing. We then use COMBAT to predict the susceptibility of newly arising resistant variants based on the molecular mechanism of resistance and determine the resistance selection window.KD ~10−4–10−7 M) but are of similar molecular sizes and have a similar mode of action [To investigate how biochemical changes in antibiotic action modifies bacterial susceptibility, we explored how the affinity of antibiotics to their target affects the MIC. We compared the MICs of quinolones, an antibiotic class in which individual antibiotics have a wide range of affinities to one of their targets, gyrase  drug-target binding occurs much more quickly than bacterial replication, ii) the antibiotic concentration remains constant and iii) that during the 18 hours of an MIC assay, the concentration gradient of the drug inside and outside the cell has equilibrated. Under these assumptions, the MIC can be expressed as the MIC . Accordip < 0.018) linear correlation between MIC and KD in all species, confirming the qualitative model prediction.While it was encouraging that our model can qualitatively predict MIC changes, our aim was to quantitatively predict antibiotic treatment performance. The simplified model assumes that the binding kinetics are much faster than bacterial replication, which may not be true in all cases. To expand the generalizability of the model, we extended the modeling framework to allow that bacterial replication may occur in a similar time frame as drug-target binding events.θ target molecules per cell with a system of θ + 1  ordinary differential equations (ODEs). This system increases in complexity with the number of target molecules and makes fitting the model to data computationally too demanding for most settings. To simplify this prior approach, we developed new mathematical models based on partial differential equations (PDEs), where a single equation describes all bacteria simultaneously. The sum of bacteria within all target occupancy states over time can be described by a time kill curve  describes the binding and unbinding of antibiotics to their targets and predicts how such binding dynamics affects bacterial replication and death . In prevll curve , during ll curve . This cull curve .kf) and unbinding (kr) to bacterial target molecules , 31, and2 = 0.93, r(x) and death as a function of target occupancy δ(x) based on the fit obtained in E. coli .E. coli. We then quantified these biochemical changes by fitting COMBAT to corresponding time-kill curves and compared them to the experimental results. Ciprofloxacin acts on gyrase A2B2 tetramers [E. coli strain for which both gyrase A and gyrase B are under the control of a single inducible promoter (PlacZ), such that the amount of gyrase A2B2 tetramer can be experimentally manipulated [Having shown that COMBAT can quantitatively fit experimental data on antibiotic action within biologically plausible parameters, we continued to test the predictive ability of the model. Given our hypothesis that modifications in antibiotic-target interactions lead to predictable changes in bacterial susceptibility, we experimentally induced changes in the antibiotic-target interaction of ciprofloxacin in etramers . We usedipulated . We measE. coli more susceptible to ciprofloxacin [2B2, to vary. We assumed that the only change between the different conditions was the amount of target. We further assumed that the relationship between bound target and bacterial replication or death did not differ between the control strain containing a mock plasmid (no IPTG) and the experiments with overexpression   . Fig 5C Staphylococcus aureus (84–99%)[Having established that COMBAT can also adequately capture the pharmacodynamics of ampicillin, we next tested whether we can estimate experimentally determined target occupancy at the MIC. Our estimated mean occupancy considering both living and dead bacteria is 89% , a value (84–99%).KD, have more profound effects than changes in target molecule content, bacterial reaction to increasingly bound target (i.e. δ(x) and r(x)), or changes in target molecule content. We also predict that the individual binding rates kr and kf, and not just the ratio of these terms, the KD, are important factors in efficiency. The faster a drug binds, the more efficient we predicted it will be. One intuitive explanation for the observation that kf drives efficacy is that a slow binding fails to rapidly interfere with bacterial replication, which may allow for the production of additional target molecules and thereby reduce the ratio of free antibiotic to target molecules.It is possible to vary all parameters in COMBAT and explore their effect. We used this to test how hypothetical chemical changes to ampicillin or ciprofloxacin would affect antibiotic efficacy –S11 Fig.Resistant; 15 min, MICResistant; 45 min, MICResistant; 18 h). In all cases, the bacterial population first increases and then decreases slowly. This may have consequences for the selection of resistant strains. Finally, we illustrate how COMBAT can be used to explore how the molecular mechanisms of resistance mutations affect antibiotic concentrations at which resistance can emerge, i.e., the resistance selection window. We compared predicted net growth rates as a function of ciprofloxacin concentrations for a wild-type strain and an archetypal resistant strain. For this analysis, we assumed that the resistant strain has a 100x slower drug-target binding rate  and thaThus, we estimate that sustained levels of 1.27-7mg/L would safeguard against resistance. While ciprofloxacin plasma concentrations typically reach concentrations of 2mg/L after oral uptake and 6mg/L after intravenous administration , levels Optimizing dosing levels of antibiotics is important for maximizing drug efficacy against wild-type strains as well as for minimizing the rise of resistant mutants. Antibiotic efficacy is traditionally described by a single value, the minimal inhibitory concentration (MIC), which has limited predictive power , 8. In ma priori. Recent studies have reported methods to predict MICs from whole genome sequencing data [a priori how chemical changes to an antibiotic structure or novel resistance mutations affect bacterial growth at a given antibiotic concentration.Because of the experimental effort, pharmacodynamic profiles for either novel drug candidates or novel resistant strains are often not obtained. Thus, we need a transferrable framework that allows quantitative predictions based on parameters that can be determined ing data , 47. HowHere, we accurately predict antibiotic action on the basis of accessible biochemical parameters of drug-target interaction. Our computational model, COMBAT provides a framework to predict the efficacy of compounds based on drug-target affinity, target number, and target occupancy. These parameters may change both when improving antibiotic lead structures as well as when bacteria evolve resistance. Importantly, they can be measured early in drug development and may even be a by-product of target-based drug discovery . When thIn previous work, for example on antipsychotics , antivirIn drug development, our mechanistic modeling approach provides insight into which chemical characteristics of drugs may be useful targets for modification. For example, our sensitivity analyses indicate that antibiotics with a similar affinity but faster binding inactivate bacteria more quickly and therefore prevent replication and production of more target molecules, which would change the ratio of antibiotic to target. Furthermore, because e.g. antibiotic binding and unbinding rates can be determined early in the drug development process, such insight can help the transition to preclinical and clinical dosing trials. This may contribute to reducing bottlenecks between these phases of drug development and thereby save money and time.Avoiding antibiotic concentrations that select for resistance is challenging for two reasons. First, the differences in the pharmacodynamic curves of wild-type and resistant strains are not trivial. Resistance can affect not only the MIC, but also the maximal kill rate at high drug concentrations and the steepness of the dose-response curve . TherefoCOMBAT offers insight into determinants of the resistance selection window and builds transferrable knowledge that allows estimating useful dose ranges. In concordance with a recent meta-analysis of experimental data , our senGood quantitative estimates on the dose-response relationship of new drugs would also help defining the therapeutic window, i.e. the range of drug concentrations at which the drug is effective but not yet toxic. For example in ciprofloxacin, the doses found necessary to prevent resistance after marketing were found to be toxic , and an de novo resistance evolution.Our quantitative work can help to identify optimal dosing strategies at constant antibiotic concentrations for homogeneous bacterial populations. These measures are commonly used to assess antibiotic efficacy. In addition, previous work has demonstrated that drug-target binding models outperform traditional pharmacodynamic models for the fluctuating concentrations that actually occur in patients , 51. To COMBAT incorporates the binding and unbinding of antibiotics to their targets and describes how target binding affects bacterial replication and death. This work extends the model developed in . COMBAT A, assuming that the time needed for drug molecules to enter bacterial cells is negligible. We model ciprofloxacin  and ampθ. The binding process is defined by the formula A + T ⇌ x, where the intracellular antibiotic molecules A react with target molecules T at a rate kf and form an antibiotic-target molecule complex x, where values for x range between 0 and θ. If the reaction is reversible, the complex dissociates with a rate kr.We describe the action of antibiotics as a binding and unbinding process to bacterial target molecules . For simkf is the association rate, Vtot is the volume in which the experiment is performed, nA is Avogadro’s number, kr is the dissociation rate, Bi is the number of bacteria with i bound targets, and θ is the total number of targets.In , the assx ∈ ℛ. Under this continuity assumption, we consider the bacterial cells as a function of x and the time t, thereby reducing the total number of equations to two.This approach requires the use of a large number of ordinary differential equations, (0 + 1) for the bacterial population and one for the antibiotic concentration. To generalize this approach, we assume that the variable of bound targets is a real number x ∈ ℛ), we can rewrite the binding kinetics in the formvr = krx can be considered as two velocities, i.e., the derivative of the bound targets with respect to the time Under the continuity approximation (r(x), is dependent on the number of bound target molecules x. The function r(x) is a monotonically decreasing function of x, such that fewer bacteria replicate as more target is bound. r(0) is the maximum replication rate, corresponding to the replication rate of bacteria in absence of antibiotics. Thus, r(x) describes the bacteriostatic action of the antibiotics, i.e., the effect of the antibiotic on bacterial replication.We assume that the replication rate of bacteria, K. At that point, the replication term of the equation isK, and 0 ≤ Flim ≤ 1.Replication ceases as the total bacterial population approaches the carrying capacity x bound target molecules of the mother cell to its progeny is described by a hypergeometric sampling of n molecules from x bound and 2θ−x unbound molecules. Under the continuity assumption, we generalize the concept of hypergeometric distribution. Because the hypergeometric distribution is a function of combinations and because a combination is defined as function of factorials, we can use Γ functions in place of factorials and redefine a continuous hypergeometric distribution as a function of Γ functions. A Γ function isζ is a complex number. In this way, the distribution can be expressed as a probability density function of continuous variables. The amount of newborn bacteria is given by the term r(x)BFlim(t). We assume that bound target molecules are distributed randomly between mother and daughter cells, with each of them inheriting 50% upon division on average. This means that twice the amount of newborn cells must be redistributed along x to account for the random distribution process. For example, if a mother cell with 4 bound targets divides, we have two daughter cells, each with a number of bound targets between 0 and 4 (their sum has to be 4), following the generalized hypergeometric distribution. For simplicity, we define S to be a function related to the replication rate that depends on the number of bacteria with a number of bound target molecules ranging between x and θ, their specific replication rate r(x), and the fraction of their daughter cells expected to inherit x antibiotic-target complexes h:We assume that the total number of target molecules doubles at replication, such that each daughter cell has the same number as the mother cell. We also assume that the total number of drug-target complexes is preserved in the replication and that the distribution of δ(x) depends on the number of bound target molecules. The function δ(x) is assumed to be a monotonically increasing function of x, where δ(θ) is the maximum death rate, when all targets in the bacteria have been bound by antibiotics. The shape of this function describes the bactericidal action of the antibiotic.The death rate function r(x) and/or the death rate δ(x) can be obtained by fitting COMBAT to time-kill curves of bacterial populations after antibiotic exposure. The sigmoidal shape of r(x) and δ(x) can be written as:xrth is the replication rate threshold, xdth is the death rate threshold, and both represent the point where the sigmoidal function reaches ½ of its maximum. γr and γd represent the shape parameters of the replication and death rate functions, respectively. These factors determine the steepness around the inflection point. When they are extreme, the relationship approaches a linear or a step function.We consider several potential functional forms of the relationship between the percentage of bound targets and replication and death rates, because the exact mechanisms how target occupancy affects bacteria is unknown . We use B is the number of bacteria.Putting these components together, the full equation describing a bacterial population is:The free antibiotic concentration results from mass conservation, i.e., all antibiotic molecules associating with their target are subtracted and all dissociating antibiotic molecules are added. kr = 0. To reflect the recovery of target molecules, we substituted the dissociation rate kr in the equation describing the bacterial population with the deacetylation rate ka, as described in [Beta-lactams acetylate their target molecules (PBPs) and thereby inhibit cell wall synthesis. The acetylation of PBPs consumes beta-lactams. However, PBPs can recover through deacetylation. We modified the term of drug-target dissociation in the equation describing antibiotic concentrations , and setribed in .t = 0, we assume that all bacteria have zero bound targets (x = 0), and the initial concentration of bacteria is B = 0, x > 0, and B = B0.At x = 0, x = θ), we specify that the outgoing velocities are zero. For x = 0, i.e. no bound target molecules, the unbinding velocity vr = 0, and in x = θ, i.e. all targets are bound, the binding velocity vf =0. When the replication term at x = 0 and the death term at x = θ are known, we can solve the partial differential equation with two ordinary differential equations at the boundaries. They are similar to the equations at x = 0 and at x = θ described by Abel zur Wiesch et al. [At the boundaries of the partial differential equation  and the spatial approximation vr < 0). For the time approximation of both the PDEs and the ODEs, we used the forward approximation To solve our system of differential equations, we used a first-order upwind scheme. Specifically, we used the spatial approximation Bn+1-BnΔt. We also2 and B2, respectively, which in turn bind to each other to form the tetramer TR [We assumed that gyrases A and B first homo-dimerize to Aramer TR . The fol2B2 tetramers (~100) per wild type bacterial cell [4 sets of six parameters in k−3, … k3) in a Latin hypercube approach from a biologically plausible range where the association rates are between 107–109 M-1 s-1 and the dissociation rates between 10−3–10−1 s-1 [4 estimates for each of the six experimental replicates quantifying gyrase A and B  and 10 μM (mild induction) or 100 μM (strong induction) of isopropyl β-D-1-thiogalactopyranoside (IPTG)  when necessary. BW25113   were used to determine the minimal and maximal kill rate, respectively. Samples were taken immediately prior to addition of the antibiotic and in ~20 min intervals or after 45 min, respectively. Samples were washed once in phosphate buffered saline (PBS) before colony forming units (CFUs) were determined for each sample by plating a 1:10 dilution series in PBS on LB agar plates.Overnight cultures of BW25113 or SoA3329 and SoA3330 were diluted 1:1000 in pre-warmed LB or LB-Cm and LB-Cm-IPTG, respectively, and grown with shaking to OD600 = 1 were harvested by centrifugation. Pelleted cells were lysed at room temperature for 20 min using B-PER bacterial protein extraction reagent  supplemented with 100 μg/mL lysozyme, 5 units/mL DNaseI  and 100 μM/mL PMSF . Samples were stored at -80°C until further use.To quantify the relative amount of GyrAB, samples of SoA3329 and SoA3330 were collected after 45 min of drug treatment as described above. An equal number of cells corresponding to 1 mL culture at ODSamples were heated to 70°C for 10 min after addition of 1x Bolt sample reducing agent  and 1x fluorescent compatible sample buffer . Proteins in whole-cell lysates were separated on 4–15% Mini-Protean TGX Precast gels  and transferred to 0.2 μm Nitrocellulose membranes .E. coli CRP, 664304, Nordic Biosite antibodies) were diluted 1:250, 1:250, and 1:2,000 in Odyssey blocking buffer-TBS, respectively. The blocked membranes were incubated with the appropriate primary antibodies overnight at 4°C, washed 4x for 15 min each in TBS-T solution , and incubated for 2 h at room temperature with fluorescent labelled secondary antibodies  in Odyssey blocking buffer-TBS. Finally, the membranes were washed 4x for 15 min each in TBS-T solution and imaged at 700 nm and 800 nm using a Li-Cor Odyssey Sa scanning system.Membranes were blocked in Odyssey blocking buffer-TBS  for at least one hour at room temperature. Primary antibodies raised against GyrA , GyrB , and CRP , and death, δ(x), on target occupancy for antibiotics with bacteriostatic, bactericidal or mixed action. Solid lines indicate a sigmoidal relationship, dotted lines indicate a step function, dashed lines indicate a linear relationship, and dash-dotted lines indicate independence, i.e. a constant replication or death rate. The left panels, show the replication rates (blue), the right panels show the death rates (red). The top panels, show rates for a bacteriostatic drug, the middle panels, show rates for a bactericidal drug, and the bottom panels, show rates for a drug with mixed effects. The sum of the replication and death rates at certain target occupancies gives the net growth or decline rate of the bacterial population.These graphs show which functions were used to fit the dependence of bacterial replication, (TIF)Click here for additional data file.S2 Figa, b, E. coli expressing gyrA and gyrB under control of the same IPTG-inducible promoter (SoA3329) grown in the presence of 10 μM IPTG (mild overexpression) and 100 μM IPTG (strong overexpression). A control strain containing a mock plasmid (SoA3330), representing wild-type GyrAB levels, was grown in the absence of inducer. Whole cell lysates were separated on a SDS-PAGE gel, blotted, and detected with specific fluorescent antibodies against GyrA (green), GyrB (green) and CRP (red). CRP (cAMP receptor protein) was used as loading control.(TIF)Click here for additional data file.S3 Figkf . a, Net growth rate —log10)/18 h) as function of drug concentration for different values of the binding rate kf (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the corresponding MICs. b, Sensitivity of the MIC to kf obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data to explore the sensitivity of our results to changes in (TIF)Click here for additional data file.S4 Figkr . a, Net growth rate —log10)/18 h) as function of drug concentration for different values of the binding rate kr (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. b, Sensitivity of the MIC to kr obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data to explore the sensitivity of our results to changes in (TIF)Click here for additional data file.S5 Figkr and kf  while keeping the ratio between kf and kr, the affinity KD, constant. a, shows the net growth rate —log10)/18 h) as function of drug concentration for different values of the turnover rate. The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. b, Sensitivity of the MIC to turnover rate obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data to explore the sensitivity of our results to changes in the turnover rate of the drug-target complex. We changed (TIF)Click here for additional data file.S6 Figr(x). We change the value of bound target at which we obtain a half-maximal replication rate, x1/2. a, Functions connecting bacterial replication rates r(x) to percentage of bound target molecules with different half-maximal replication rates. b, Net growth rate —log10)/18 h) as function of drug concentration for different values of x1/2 (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. c, Sensitivity of the MIC to r(x) obtained from simulations in (b). The color code indicates the MIC corresponding to the simulation with the same color in (a&b).We use the model fitted to experimental data to explore the sensitivity of our results to changes in the replication rate with increasingly bound target (TIF)Click here for additional data file.S7 Figδ(x). We change the value of bound target at which we obtain a half-maximal death rate, x1/2. a, Functions connecting bacterial death rates δ(x) to percentage of bound target molecules with different half-maximal death rates. b, Net growth rate —log10)/18 h) as function of drug concentration for different values of x1/2 (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. c, Sensitivity of the MIC to δ(x) obtained from simulations in (b). The color code indicates the MIC corresponding to the simulation with the same color in (a&b).We use the model fitted to experimental data to explore the sensitivity of our results to changes in the death rate with increasingly bound target (TIF)Click here for additional data file.S8 Figkf . a, Net growth rate —log10)/18 h) as function of drug concentration for different values of the binding rate kf (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. b, Sensitivity of the MIC to kf obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data Fi  to explo(TIF)Click here for additional data file.S9 Figka . a, Net growth rate —log10)/18 h) as function of drug concentration for different values of the binding rate ka (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. b, Sensitivity of the MIC to ka obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data  to explo(TIF)Click here for additional data file.S10 Figka and kf  while keeping the ration of ka/kf constant. a, Net growth rate —log10)/18 h) as function of drug concentration for different values of the turnover rate (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. b, Sensitivity of the MIC to turnover rate obtained from simulations in (a). The color code indicates the MIC corresponding to the simulation with the same color in (a).We use the model fitted to experimental data  to explo(TIF)Click here for additional data file.S11 Figδ(x). We change the value of bound target at which we obtain a half-maximal death rate, x1/2 (see legend). a, Functions connecting bacterial death rates δ(x) to percentage of bound target molecules with different half-maximal replication rates x1/2 (see legend). b, Net growth rate —log10)/18 h) as function of drug concentration for different values of δ(x) (see legend). The dotted horizontal line indicates zero net growth. The intersections of the simulated dose-response curves with this line indicate the respective MICs. c, Sensitivity of the MIC to δ(x) obtained from simulations in (b). The color code indicates the MIC corresponding to the simulation with the same color in (a&b).We use the model fitted to experimental data  to explo(TIF)Click here for additional data file.S12 Figkf) 100-fold and imparts a 15% fitness cost. The dotted horizontal line represents no net growth. The first vertical dotted line indicates where the resistant strain becomes fitter than the wild-type (the start of the competitive resistance selection window), the solid vertical line indicates the MIC of the wild-type , and the dashed vertical line indicates the MIC of the resistant strain, above which selection for resistance should be minimal because both growth of the wild-type and the resistant strain is inhibited.The drug concentration of ampicillin is shown on the x-axes, and the average bacterial net growth rate over 18 h is given on the y-axes. The blue line represents the wild-type strain based on the fits shown in (TIF)Click here for additional data file.S13 FigV1. From there, it can enter the peripheral “tissue” compartment, characterized by the apparent volume V2, with a rate k12. Conversely, the drug can also re-enter the plasma compartment with a rate k21. From the plasma compartment, the drug is eliminated with a rate k10.We simulate plasma and tissue concentrations of ampicillin with a two-compartment pharmacokinetic model. This model described intravenous drug input into the “plasma” compartment, which has an apparent volume of (TIF)Click here for additional data file.S14 Figa, Simulated drug concentrations in the tissue  compartment of a two compartment pharmacokinetic model over two days. The dotted black line indicates the MIC of the pathogen (2.6 mg/L). b, Pathogen load in the tissue compartment in response to fluctuating drug concentrations predicted by COMBAT over the same timeframe.Two modes of drug administrations, 2 g of drug per day given as single, 5 min i.v. infusions (blue line) and 2 g of drug per day given as continuous i.v. infusion (red line), are simulated. (TIF)Click here for additional data file.S1 Tablekr and association rate kf, KD, diverge by more than an order of magnitude in the literature, we chose to fit the association rate kf as a free parameter in our model while constraining KD to remain within the published range .To our knowledge, the association rate for ciprofloxacin has not been determined directly. Because the values of the ratio of dissociation rate (DOCX)Click here for additional data file.S2 Tabler(x) and the death rate δ(x) as a function of the number of bound targets.(DOCX)Click here for additional data file.S3 Table2B2 show the estimated tetramer levels resulting from each measurement. For the GyrA2B2 tetramer estimation, we sampled 104 sets association and dissociation rates from a uniform distribution within their reported limits (Latin hypercube approach). We report the standard deviation for each estimate. We give summary estimates in the last row of the table.(m) indicates mild overexpression, (s) indicates strong overexpression. Columns headed GyrA and GyrB show the experimentally determined overexpression as fold expression compared to the wild type. The columns headed GyrA(DOCX)Click here for additional data file.S4 Tableδ(x) as an exponential function of the number of bound targets δ(x) = a3 eb3x + c3.(DOCX)Click here for additional data file.S5 TableThe parameters were obtained from .(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


A considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and fuels.Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG assimilation.In this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Overall, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic parity with petrochemical feedstocks while sequestering carbon dioxide. However, it is possible to select for a strain that does, and to our knowledge, only one study has ever reported EG utilization by E. coli. [E. coli by overexpressing two genes: fucO (encoding propanediol oxidoreductase) and aldA . This synthetic pathway is similar to the second natural EG utilization pathway previously introduced: EG is sequentially oxidized to glyoxylate thereby providing a gluconeogenic carbon substrate for growth. More specifically, the promiscuous activity of propanediol oxidoreductase converts EG to glycolaldehyde, which is subsequently converted to glycolate by glycolaldehyde dehydrogenase. The native glycolate oxidase then transforms glycolate to glyoxylate to support cell growth and maintenance.Wildtype E. coli. . That stE. coli. , 25. In E. coli. –21. Henc2 reduction (eCO2R), and it has already been successfully employed within biological systems [Motivated by the prospect of utilizing EG as a renewable and alternative feedstock, we sought to compare EG with more conventional feedstocks for the production of select chemicals of industrial significance. In particular, formate, glucose, and xylose were selected as the comparative feedstocks, while succinate, ethanol, glycolate and 2,3-butanediol, were selected as the products of interest. Formate was selected as it is another well-studied, non-sugar feedstock that can be produced via electrochemical CO systems –30. Mean systems . The fou systems , 32–34.E. coli as a biocatalyst capable of growth and glycolate production, using EG. This case study attempts to validate our orthogonal approach for chemical production, relating the network topology and two-stage fermentation.In this study, we began by comparatively evaluating EG as a feedstock by measuring the orthogonality of each substrate-product combination, using select bioconversion pathways. Consistent with our previous evaluation , EG demoE. coli have focused on using glucose and/or xylose as a substrate, and typically implement genetic strategies that couple production to growth [E. coli [E. coli and is predicted to reach molar yields of 2.5 and 3, on xylose and glucose, respectively [E. coli, it has not yet reached the model-predicted yields in vivo. In the identified studies, the highest glycolate titer in E. coli is reported to be 65.5 g/L with a corresponding yield of 0.765 g/g, using glucose as the substrate [E. coli, the highest reported glycolate titer is 110.5 g/L with a corresponding yield of 94.4%, using EG as the substrate. This feat was achieved by Hua and colleagues in Gluconobacter oxydans using an integrated production, separation and purification technology [E. coli [Glycolate is an alpha-hydroxy acid used in the synthesis of a variety of different plastics and polymers, cosmetics and industrial detergents , 35, 36.o growth , 37–41. o growth , 40, 41,[E. coli , 37, 38.ectively . However[E. coli –45.E. coli growing on EG , for which mutants have reportedly shown promiscuous activity with EG [aldA). From here, glycolate can be accumulated as the desired product, or further converted to support cell growth. In the latter case, glycolate oxidase converts glycolate to glyoxylate to support cell growth via native metabolic pathways. This pathway fits the ideal network architecture of a branched pathway, for which a metabolic valve can be designed and implemented for dynamic control of production and biomass fluxes. More specifically, in the EG-glycolate pathway shown, the glyoxylate oxidase reaction naturally provides a suitable metabolic valve controllable by oxygen levels. Under fully aerobic conditions, glycolate is converted to glyoxylate and channeled to the central metabolism for growth via glycerate metabolism. Alternatively, under oxygen limiting conditions, this reaction can be limited, and the cell can accumulate glycolate.As previously reported in Table  with EG . This obE. coli MG1655. In this case study, a combination of computational and experimental evaluations were performed to characterize this production pathway, and to explore its implementation within a two-stage fermentation system.Ultimately, as evidenced by the orthogonality scores and the pathway topologies for glycolate production, EG is more orthogonal than the traditional substrates investigated, and hence suitable for validating the concept of pathway design based on orthogonality. Thus, we performed a case study for the production of glycolate from EG in E. coli core model was modified with the addition of the EG assimilation pathway previously shown in Fig. To gain insight into the expected metabolic behaviour, the intracellular fluxes of the production system were first investigated using flux balance analysis (FBA) and flux variability analysis (FVA). For this analysis, the −1 (mass yield of 0.49 gDW/gEG), no glycolate production and no by-product production other than CO2 . Approximately 91% of the glyoxylate flux is channeled towards 2-phosphoglycerate (2PG) and enters lower glycolysis. The remaining glyoxylate is used to generate malate via malate synthase. Of the total carbon (EG) entering the cell, 22% is channeled towards acetyl-CoA and 3.3% enters the TCA cycle. Conversely, about 18% of the total carbon is channeled by gluconeogenic pathways towards upper glycolysis and the pentose phosphate pathways, when accounting for stoichiometry.Under aerobic conditions Fig. a, FBA pr−1 and a glycolate export flux of 2.2 mmol/gDW·h. These values correspond to a biomass mass yield of 0.29 and a glycolate mass yield of 0.56. The only by-product observed under micro-aerobic conditions is CO2. The FVA values reported for both aerobic and micro-aerobic conditions provide a range of flux values that may be possible. In the case of the tartronate semialdehyde reductase (TRSARr) reaction, the large lower bound flux predicted by FVA indicates that alternate optima exist for this system, for the set of constraints used. Overall, the FBA results suggest that micro-aerobic conditions are required for the secretion of glycolate and that oxygen level could be used as an effective control mechanism for switching from growth to production. In the sections that follow, we attempt to experimentally validate the predicted link between oxygen levels and glycolate production and to use modeling to better understand this relationship. Applying these insights, we then develop and test two strategies to optimize glycolate production in E. coli, using EG as the feed material.Under micro-aerobic conditions, modeling suggests that glycolate production will occur Fig. b. More sE. coli through the expression of a suitable 1,2-propanediol oxidoreductase mutant (fucO), and glycolaldehyde dehydrogenase . Previous studies have shown that Fe2+-dependent propanediol oxidoreductases (encoded by fucO) can be inactivated by metal-catalyzed oxidation (MCO) and are therefore sensitive to oxygen [fucO mutants reported as being more oxygen-stable. Variant 1 (strain LMSE11) contained fucO with mutations I7L and L8V, as reported by earlier mutagenesis studies [fucO with a single L8M mutation, as it was also suggested to play a role in alleviating metal catalyzed oxidation (MCO) toxicity in propanediol assimilation by E. coli [. Both variants had the same ribosome binding site and trc promoter upstream of the start codon. As shown in Fig. E. coli iAF1260 model (previously described). Thus, the experimental results suggest that the two genes introduced  are sufficient for supporting the conversion of EG to biomass, when combined with E. coli’s natural biosynthetic pathways. However, the actual yield is less than 60% of the model-predicted yield, thereby suggesting that the pathway may not be operating optimally (i.e. oxygen sensitivity) or that the modeling may need additional constraints. For example, the substrate uptake rate in shake-flasks was determined to be 5 mmol/gDW·h, compared to the 10 mmol/gDW·h assumed previously for modeling. When the modeling is performed instead for an EG uptake flux of 5 mmol/gDW·h, the predicted biomass yield decreases to 0.43 gDW/gEG. Similarly, EG transport was modelled as free diffusion, however if in reality proton symport is required, it may further limit the achievable yield. Oxygen-limiting conditions could also explain a lower than optimal yield, however this is unlikely since analysis of the fermentation media by high-performance liquid chromatography (HPLC) showed the absence of intermediate metabolites  and fermentation products, such as acetate or lactate. The experimental growth rate was calculated to be 0.18 h−1, corresponding to a 3.85 h doubling time. Since LMSE11 showed higher utilization rates, this variant was used in subsequent experiments.As previously described and predicted computationally, EG assimilation and conversion to glycolate can be introduced in o oxygen . Thus, w studies . Variant E. coli . Both vaE. coli, we next explored the use of EG as an orthogonal substrate for the production of glycolate within a two-stage fermentation system. As previously described, the branched topology of the EG-glycolate pathway lends itself to the design of a metabolic valve for the separation of cell growth and chemical production. In particular, glycolate oxidase was identified as a potential metabolic valve, for which oxygen level was predicted to be a control mechanism. A higher oxygen level is expected to support biomass growth, while lower oxygen levels are expected to trigger glycolate accumulation. Thus, two reductions in oxygen (air flow rate) were tested to evaluate the effect of oxygen on glycolate production.Having established EG utilization by an engineered strain of 2 production. These values were then converted to the mass of EG required and compared to the actual mass of EG consumed, leading to the values reported above. Further refinements to the FBA model for E. coli growth on EG may close the carbon balance with improved accuracy.For this evaluation, LMSE11 (variant 1) was grown in bioreactors with minimal media, supplemented with 2 g/L of yeast extract. The bioreactors were inoculated at an initial OD of ~ 0.4 (approximately 0.23 gDW/L), with inoculum prepared as described in the methods. Expression of the EG utilization genes was induced with 1 mM IPTG. During the growth stage, the impeller agitation was set at 1000 rpm, and the reactors were sparged with air to maintain an aeration rate of 300 mL/min (1 v/vm). At 20 h, the aeration was reduced to 150 mL/min (0.5 v/vm) or 50 mL/min (0.16 v/vm), to simulate high and low aeration rates, and the impeller agitation was dropped to 500 rpm  being oxygen-sensitive, it was observed that the higher secondary aeration rate (150 mL/min) led to higher glycolate titers  and the oxygen-dependent regeneration of NAD+ required by the pathway. Based on these results, we turned to metabolic modeling to computationally evaluate the effect of oxygen and thus determine a strategy for improved glycolate production in vivo.Counter-intuitively, despite propanediol oxidoreductase , biomass production (biomass flux), the cell’s respiratory quotient , and the substrate specific productivity (SSP) were modelled as a function of the oxygen uptake rate (OUR). The formation of by-products was also followed over the same OUR range. The normalized results are shown in Fig. PS·μ). Based on Fig. Since the specific oxygen uptake rate is a function of air intake, this analysis allowed us to implicitly correlate the glycolate yield to the flowrate of air into the reactor. Furthermore, as the RQ and glycolate yield can be correlated via their relationship to the OUR, it should also be possible to optimize glycolate production in real-time by measuring and controlling the RQ throughout a fermentation experiment. Controlling the RQ was thus identified as a possible strategy to improve glycolate production. The selected RQ must balance the glycolate yield, substrate specific productivity and cell growth, to optimize glycolate production. The substrate specific productivity (SSP) is a measure of the moles of product obtained per mole of substrate per time (molP/molS h), and is calculated as the product of the product yield and cell growth rate . For both strategies, bioreactor experiments were performed as previously described, but with modified aeration rates and mixing speeds. The first strategy was tested with the goal of reducing the biomass production phase and increasing the glycolate production phase. To achieve this, the aeration rate during the growth phase was increased to 600 mL/min (2 v/vm) to prevent glycolate accumulation and divert flux towards cell growth. In the second phase, the aeration rate was dropped to 100 mL/min. Results for this strategy are shown in Fig. Cell growth continued for approximately 70 h, after which the reactor appeared to reach stationary phase. Production of glycolate began at approximately 70 h and continued until the fermentation was terminated at 140 h (70 h of production). Final glycolate titers reached 6.8 g/L, with an initial production phase biomass concentration of approximately 4 gDW/L, corresponding to an average productivity 0.1 g/L h or approximately 0.32 mmol/gDW·h. The initial yield of glycolate was 0.92 g/g after the first sample was taken, however, the cumulative yield decreased during the course of the production stage with the final overall production yield being 0.75 g/g, or 61% of theoretical (see Table fucO). Even using the more oxygen-stable mutant (strain LMSE11), the increased dissolved oxygen levels and faster oxygen mass transfer rates likely caused oxygen toxicity in the cells due to the inactivation of propanediol oxidoreductase by MCO. This likely placed a high metabolic burden on the cell stemming from high protein demand without a sufficient means to utilize EG as a carbon source. Considering all of the bioreactor experiments up to this point, it seems that higher aeration rates lead to higher glycolate titers and yield, yet also retard cell growth when too high.In implementing this first strategy, we produced significantly more product at a higher yield; however the cells took much longer to reach an appropriate concentration for the production phase. At 600 mL/min (2 v/vm) during the growth phase, it took almost 70 h to reach a concentration of 4 gDW/L, while comparatively the previously tested 300 mL/min (1 v/vm) produced the same cell concentration within 30 h. We hypothesize that it took longer to reach a higher OD due to the oxygen sensitivity of propanediol oxidoreductase  [fucO) in the presence of excess aeration and could be addressed by using O2-tolerant Zn2+-dependent variants.Our results demonstrate the applicability of 100 g/L) , this co100 g/L) . FurtherfucO mutants predicted to have improved O2-tolerance were tested. Ultimately, the I7L/L8V mutant (LMSE11) showed the greatest EG assimilation and growth rate/yield, and was thus used for subsequent experiments. It should be noted, however, that the growth yield with this strain was still lower than the value predicted by modeling: 0.28 g DW/g EG compared to 0.43 g DW/g EG predicted under aerobic conditions at the measured substrate uptake rate. This difference suggests that the enzyme is still not operating as efficiently as possible and that additional improvements may be achievable through enzyme engineering. In particular, directed evolution or rational design approaches could be used to improve the enzyme activity and oxygen tolerance of fucO [Based on previous reports from literature, two  of fucO , 57. Suc+-dependent, we believe that the reduced oxygen results in increased NADH pools, leading to a decrease in the rates of reaction catalyzed by fucO and aldA (which each require NAD+ as a cofactor). This change in the rates had a net effect of lowering the flux of EG into the cell. This finding necessitates a further study of cellular physiology under EG utilization in order to understand the trade-off in yield and productivity as a function of the dissolved oxygen feeding in bioreactors. For example, in the first set of bioreactor experiments we found that using a higher aeration rate led to a higher overall glycolate titer (4.1 g/L at 150 mL/min compared to 2.5 g/L at 50 mL/min). Comparatively, by maintaining a target respiratory quotient during the second set of bioreactor experiments, an even higher product titer was achieved at the lower aeration rate (10.4 g/L at 50 mL/min). Hence, optimization of aeration in the bioreactor would substantially improve economic performance, not only in terms of product formation but also in terms of the absolute cost of aeration.An important observation made was a reduction in the substrate uptake rate during oxygen limiting conditions. When oxygen is limiting, reduced metabolites and electron carriers can accumulate . Since tIn this study, computational modeling allowed us to predict and better understand the metabolic behaviour for EG utilization. Through flux balance analysis (FBA) it was demonstrated that EG is assimilated into native metabolism through glyoxylate oxidase, and that this enzyme could serve as an effective metabolic valve for the control of cell growth and production. FBA predicted that glycolate was unlikely to be produced under aerobic conditions, however under oxygen-limiting conditions glycolate was likely to accumulate. This agreed well with bioreactor studies, which showed that glycolate accumulation could be induced through a reduction in the aeration. Modeling also predicted a reduction in EG uptake rate, under oxygen-limiting conditions, which was equally seen experimentally.During the experiments, we also observed small amounts of acetate and trace amounts of ethanol in the fermentation media during the micro-aerobic glycolate production phase. The presence of acetate and ethanol in the fermentation medium, typical products of anaerobic growth, suggests that EG may be a suitable feedstock for the production of other anaerobic products. Comparatively, ethanol production, depending on the ratio of the EG uptake flux and the OUR Fig. b. These Finally, by extending the observations from FBA, we were able to correlate the glycolate production with the respiratory quotient. Thus, by measuring the respiratory quotient in real-time, using data from the process mass spectrometer, we were able to use this correlation to improve and control glycolate production during the course of the fermentation. This computational-based strategy, which led to the highest glycolate titers and production rate at a relatively low aeration rate, could be employed in other production systems to achieve similar improvements in production. Ultimately, we believe that our results successfully demonstrate the design and optimization of bioproduction pathways using computational tools and their metrics , particularly for the design of pathways for unconventional, non-sugar feedstocks.E. coli using EG as a substrate. We describe, for the first time, the successful production of glycolate from EG using the substrate as a feedstock for growth and for production. We also used metabolic modeling to identify the oxygenation conditions that optimize the production of glycolate and validated the strategy experimentally, thereby illustrating the value of metabolic models in bioprocess optimization. Further, we also showed that ethylene glycol utilization pathways are highly orthogonal to cellular metabolism making it an important feedstock for accelerated metabolic engineering using dynamic control. We also illustrated the value of orthogonal pathways for dynamic control of metabolism using oxygen as a control valve to switch to and optimize glycolate production. We believe this can have important implications in the future for integrating biorefineries into industries where carbon dioxide can be captured and converted from point sources.The results described in this study establish a framework for future production of chemicals in fucO mutant, informed based on previously published studies (discussed further in text). The best growing strain was then selected for further testing in bioreactors  as per manufacturer’s instructions  for all strain construction and fermentation pre-cultures. Pre-cultures were grown in LB media in 10 mL test tube cultures overnight and transferred to fresh 250 mL shake-flaks containing 50 mL LB, 1 mM IPTG and 10 g/L EG. After 24 h, these cells were harvested by centrifugation, re-suspended in 2 mL of residual supernatant and used as inoculum for bioreactor or minimal media shake-flasks for characterization at 37 °C.4Cl, 3.0 g/L KH2PO4, 6.8 g/L Na2HPO4, 0.50 g/L NaCl. Supplements of yeast extract at 2 g/L were added to minimal media. EG was used as the carbon source at a concentration of ~ 10 g/L. IPTG was used at a concentration of 1 mM to induce expression of the EG assimilation pathway. A trace metal solution was prepared according to the following composition prepared in 0.1 M HCl per litre and added to the media at a concentration of 1/1000: 1.6 g FeCl3, 0.2 g CoCl2⋅6 H2O, 0.1 g CuCl2, 0.2 g ZnCl2⋅4H2O, 0.2 g NaMoO4, 0.05 g H3BO3. 1 M MgSO4 and 1 M CaCl2 was also added to the media at a concentration of 1/500 and 1/10,000, respectively. For all cultures, carbenicillin was added as appropriate at 100 µg/mL. All characterization experiments were conducted with 50 mL media in 250 mL shake flasks, continuously agitated at 230 rpm and at 37 °C. Culturing techniques employed in the bioreactors are described below.When characterizing strains see Fig. c, cells E. coli strain LMSE11 in minimal media, supplemented with yeast extract at 2 g/L. The bioreactors contained 1 mM IPTG to maintain induced expression of the EG pathway genes. All bioreactor cultivations were carried out in fed-batch. Systematic changes in aeration rate and impeller speed were applied between cultivations, as detailed below. Flowrate was controlled using a Books Instruments mass flow controller (GF Series) and gas was analyzed using Thermo Scientific™ Sentinel dB mass spectrometer for online gas measurement.Applikon MiniBio500 500 mL fermentation vessels with a 300 mL working volume were used for cultivating strains in bioreactors. The fermentation vessels were equipped with condensers to prevent changes in volume due to aeration. Dissolved oxygen and pH probes were used in accordance with the manufacturers operating guidelines. pH was maintained at 7 with the addition of 3 N KOH. Growth conditions were maintained at 37 °C. Bioreactors were inoculated with pre-culture (previously described) at OD ~ 0.4 (approx. 0.23 gDW/L). In total, four bioreactor cultivations were conducted using Cultivations 1 and 2: Cells were grown at an impeller speed of 1000 rpm and sparged with air to maintain oxygen at 300 mL/min (1 v/vm). At 20 h, the aeration was reduced to 150 mL/min (0.5 v/vm) or 50 mL/min (0.16 v/vm) to simulate high and low secondary aeration rates, and the impeller speed was dropped to 500 rpm. See Fig. Cultivation 3: Cells were grown at an impeller speed of 1000 rpm and sparged with air to maintain oxygen at 600 mL/min (2 v/vm) during the growth phase. In the production phase, the impeller speed was reduced to 500 rpm while the aeration rate was dropped to 100 mL/min (0.33 v/vm). See Fig. Cultivation 4: During the growth phase, cells were sparged with air to maintain oxygen at 50 mL/min 0.16 v/vm) throughout the cultivation and the impeller speed was controlled to maintain a minimum oxygen level of 20%, up to a maximum impeller speed of 1000 rpm. In the production phase, the impeller speed was reduced as necessary to achieve a respiratory quotient (RQ) of ~ 0.4. See Fig. 6 v/vm th2SO4 as the eluent and a flowrate of 0.4 mL/min at 50 °C. Organic acids were detected at 210 nm. Cell densities of the cultures were determined by measuring optical density at 600 nm (GENESYS 20 Visible Spectrophotometer). Cell density samples were diluted as necessary, to fall within the linear range. A differential refractive index detector  was used for analyte detection and quantification. Yields were calculated between two time points, whereas the cumulative yield was calculated between the initial and final measurements.Analysis of fermentation production was measured via high performance liquid chromatography (HPLC). We used a Bio-rad HPX-87H organic acids column with 5 mM HfucO and aldA were cloned from E. coli MG1655 genomic DNA and assembled using Gibson Assembly [aldA. AAGTTAAGAGGCAAGA was the RBS sequence used in front of fucO. The trc promoter was used to drive expression. Wild-type strains of E. coli MG1655 were obtained from the Coli Genetic Stock Centre .Genes Assembly  into a pE. coli core model, as indicated below. The ATP maintenance reaction was left unchanged for both models, corresponding to a value of 8.39 mmol/gDW·h. Both the core and genome scale models were modified by the addition of missing reactions in the EG assimilation pathway, including the transport and exchange of EG by free diffusion (no proton translocation). The default glycolate transport and exchange reactions were used for the genome scale model and equivalent reactions were added for the core model. Formate exchange was knocked out for all analyses in which oxygen was present.Flux balance analysis (FBA) was performed using both MATLAB R2015a installed with COBRA 2.0 toolbox and the COBRApy toolbox with Python 3.7.3, using the GLPK linear solver (GNU Project). All modeling was performed using either the genome scale model iAF1260 or the The orthogonality evaluation was performed using the core model, as previously described . All otht), while all those contributing to biomass production compose the biomass subspace (Sx). To determine the orthogonality of the given chemical product and metabolic network, the elementary flux modes (EFMs) must first be determined and assigned to the product and biomass subspaces. All EFMs with non-zero chemical flux and zero biomass flux are assigned to the product subspace, while those with non-zero biomass flux and zero chemical flux are assigned to the biomass subspace. The degree of similarity between these two subspaces is defined as the average similarity (AS) coefficient, and is determined by the number of shared reactions, normalized by the size of the supporting biomass mode  conditions on glycolate production, as predicted by modeling at different substrate uptake rates (SUR) and non-growth associated ATP maintenance (NGA ATPM) values. (A) SUR of 5 mmol/gDW·h and default NGA ATPM value (8.39 mmol/gDW·h.). (B) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 4.8 mmol/gDW·h. (C) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 3.2 mmol/gDW·h. (D) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 1.6 mmol/gDW·h. In each case, flux balance analysis (FBA) was used to predict the glycolate yield (mol glycolate/mol EG), cell growth rate (gDW/gDW·h), respiratory quotient  and the substrate specific productivity  over a range of oxygen uptake rates . All values other than the SSP are shown on the primary vertical axis. An RQ value of 0.4 (shown by the dotted black line) was selected for the RQ-based strategy employed in the final bioreactor experiment. The molar yield (mol glycolate/mol EG) predicted at this RQ value is indicated for each case.


Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications. Zymomonas mobilis, as an ethanologenic Gram-negative bacteria, possesses desirable characteristics because of its unique usage of the Entner-Doudoroff (ED) pathway. Compared to other ethanologenic microorganisms, Z. mobilis shows a higher glucose uptake rate and also higher yields of ethanol production. These characteristics are considered as the prerequisites for an industrially effective ethanol production process2.Bioethanol production has received increasing attention as a sustainable replacement for fossil fuels. Z. mobilis’ metabolism consists of endogenous metabolic pathways that produce other industrially notable products including levan, sorbitol, glycerol, gluconic, succinic, lactic and acetic acids3. Levan is an important biopolymer has versatile applications in pharmaceutical, food and cosmetic industries3. Levan is a homo-exopolysaccharide which is composed of d-fructose monomers linked by β (2 → 6) bonds. Levansucrase (EC 2.4.1.10) is involved in the polymerization of fructose units, using sucrose as substrate4. All these characteristics suggest that Z. mobilis has significant potential for the production of an extended range of valuable biochemical compounds for industrial biotechnology applications4In addition to ethanol, Z. mobilis have been reported via systems biology and computational modeling, evolutionary engineering, genome editing, omics technology  and synthetic biology5. In addition, diverse metabolic engineering approaches have been used to redesign the new metabolic pathways in Z. mobilis such as deletion of competing pathways, insertion of new genes to increase substrate ranges, improve tolerance to extreme process conditions, ethanol toxicity and lignocellulosic hydrolysate inhibitors6.Recently, manipulation and improvement of Z. mobilis ZM4 was published and its annotation upgraded in 20098. Recently, complete chromosome along with four native plasmids were sequenced in ZM4. These plasmids were in the size range of 32 to 39 kb and containing about 150 predicted ORFs9. In recent decades, in silico genome-scale metabolic models (GEMs) have introduced novel avenues for systems-level redirection of metabolic fluxes, and consequently, rational strain improvement11. Z. mobilis is generally considered as a suitable platform for genetic engineering because of its GRAS , genome size, and efficient fermentation performance during a low biomass production rate2. Thus, purposeful metabolic engineering modifications can potentially be done in this organism.In 2005, the primary genome sequence of Z. mobilis ZM4. Several studies have previously reported core metabolic model12 or GEMs14 for Z. mobilis ZM4. Furthermore, a GEM for a related strain, Z. mobilis ZM1 was reported15. However, due to the existing limitations and inconsistencies of those models, we decided to reconstruct an updated, reconciled and comprehensive Z. mobilis ZM4 GEM. None of the previously-published Z. mobilis GEMs were made in formats that are ready for simulation testing. We tried to check and find the required reactions to make a working model because none of these GEMs were able to produce biomass. The process of biomass correction, which was done for both GEMs (ZmoMBEL60 and iZM363), was in fact non-negligible. The previous GEMs use different metabolite names and different metabolic subnetworks. In addition, in some cases, multiple names of metabolites and reaction abbreviations were included. Both of the previously-published GEMs were constructed in the same year. Interestingly, their metabolic coverage, biochemical evidence and GPRs are not consistent and differ in some cases. The core metabolic network was improved mainly based on literature evidence, but new enzyme-related together with new reactions and GPRs were not necessarily included.In the present study, our goal is to develop an updated GEM for Z. mobilis ZM4, it is necessary to update the GEM of this organism by retrieving the relevant data from literature and publicly available databases. We did this update with additional manual curation  involved in the previous Z. mobilis models were collected. As a key bottleneck in metabolic models reconstruction, incompatibilities in the content of metabolic models such as multiple metabolites names, reaction abbreviations and stoichiometric errors were fixed. The redundancies were then verified and components with multiple/inconsistent IDs were detected and removed. Consequently, in our model, a consistent and unique ID (compatible with the KEGG database) was assigned to each protein, reaction and metabolite. obtained and manually checked for association with enzyme and/or reactions in KEGG.The three previous 16 and BRENDA17 databases. Furthermore, the list of all genes present in the Z. mobilis ZM4 genome was obtained and manually checked for association with enzyme and/or reactions in KEGG. Next, new genes that were not present in previous GEMs were identified and added to the model in order to improve gene-protein-reaction association correctness. In some cases, for confirming the functions of genes, sequence similarity with Escherichia coli was used. Non-gene-associated reactions, including spontaneous reactions and literature-based metabolic activities were also manually curated and added to the model. All instances of mass imbalance and stoichiometric errors were then found and fixed. The reversibility types of reactions were assumed to be the same as those of the template models. In cases of discrepancies among the templates, the reversibility types of reactions were determined based on BioCyc18 and KEGG. Exchange reactions were retrieved from the relevant literature and TransportDB19.In the next step, for each enzyme, all associated genes as well as all possible reactions were determined using KEGG20. We identified such metabolites in the draft model. Then, in an iterative procedure, we tried to find a minimal set of biologically relevant reactions that were able to fix the gaps using fastGapFill21. Additionally, GapFind22 algorithm was used to find further dead-end metabolites and blocked reactions. Some reactions were proposed by these reaction-addition algorithms. We tried to identify those that are biologically reasonable by using genomic and biological evidences to avoid overfitting model. We also evaluated the accuracy of identifying filled gaps manually when possible, by finding missing reactions when compared to the GEMs in BiGG23 database. In some cases, direction of reactions in model was changed based on biological knowledge. Some spontaneous reactions that were previously confirmed experimentally in E. coli were also added to our network. Finally, as there are several orphan reactions in each GEM, we analyzed blocked reactions of the E. coli model24. So, more biological discoveries should be done to fill these gaps.No-production and/or no-consumption metabolites often represent knowledge gaps in metabolic networks10. Unless stated otherwise, in the present work, maximization of biomass production rate is considered as the objective function. All analyses were performed using COBRA Toolbox v2.025. GLPK was applied as the linear programming solver to perform FBA.In a metabolic network model, there are prevalent interdependencies among reaction fluxes due to stoichiometry, reversibility and capacity constraints of metabolism. Flux balance analysis (FBA) is used for optimizing a pre-defined objective function in the specified metabolic constraints. Fundamentals of FBA have been explained previouslyin silico simulations based on carbon source usage, the lower bound of the carbon source uptake rate was considered as −10 mmol gDCW−1 h−1. Unless stated otherwise, for all reactions, the upper bound was assumed to be +1000 mmol gDCW-1. The lower bounds of the irreversible and reversible exchange reactions were set 0 and −1000 mmol gDCW−1 h−1, respectively.For all −1 h−1 was considered for oxygen exchange reaction, respectively. ATP hydrolysis and synthesis flux values were set to −10 and 50 mmol gDCW−1 h−1 respectively, based on Motamedian et al. results15. The lower bound of amino acid exchange reactions was set to 0.1 mmol gDCW−1 h−1 when the medium was consisting of yeast extract. Non-growth-associated (NGAM) and growth-associated maintenance (GAM) were same as Widiastuti et al. results14.For simulation of the aerobic and anaerobic conditions, −1000 and 0 mmol gDCWZ. mobilis ZM4 grown on different culture media in aerobic and anaerobic conditions was done to examine whether our model correctly predicts the growth rate patterns. Moreover, a comprehensive literature search was performed to find studies of genetic engineering and gene knockout on Z. mobilis. Furthermore, gene essentiality analysis was done to check the accuracy of the model prediction. In order to further evaluate the accuracy of our model, the profile of differentially expressed genes in aerobic vs. anaerobic conditions was investigated. Then, the transcriptionally-changed enzymes were used to qualitatively validate the oxygen-related changes in the predicted fluxes26. Additionally, Biolog GN2 MicroPlate™ was used in order to profile growth phenotypes of Z. mobilis ZM4 on diverse carbon sources. According to the standard protocol, a colony of the bacterium was inoculated on 5% Sheep Blood that is supplemented to a Biolog Universal Growth medium for an overnight at 37 °C. The bacteria were then removed using a sterile swab and suspended in GN/GP Inoculating Fluid to the definite turbidity. Then, 150 µl aliquot of cell suspension was inoculated into each of the 96 carbon source-containing wells. In positive cases, i.e., where Z. mobilis can grow, by making a redox potential, a purple color will appear because of reduced tetrazolium dyes. Additionally, a previously reported list of biochemical results was used to validate the predictions27.We validated our model through four major approaches. Batch fermentation of Z. mobilis ZM4 ATCC 31821 was cultured in RM medium  at 30 °C26. For ethanol production, fermentation was carried out in either of two different media, namely, standard RM and modified RM 2SO4 1 g/L)14. For levan production, the culture medium (SRM) contained sucrose 100 g/L, Yeast Extract 10 g/L, and KH2PO4 2.0 g/L. Cells were grown for preculture preparation in 50 mL medium in a 250 mL Erlenmeyer flask. Shaking and incubation were done at 150 rpm and at 30 °C, respectively. Afterward, anaerobic condition for ethanol production was achieved by culturing in sealed bottles filled with 50 ml of culture medium. For aerobic ethanol or levan production, 20 mL medium was used in a 100 mL Erlenmeyer flask. Samples were withdrawn throughout fermentation at different intervals for further analysis. All experiments were repeated at least three times with three replicates under each of the conditions.28. Gas chromatography was applied to determine the ethanol content of each distilled medium. Levan extraction was performed by absolute ethanol and dialysis (MWCO 14000 Da). Quantitative analysis of produced levan was done by phenol-sulfuric acid method and estimated as fructose units29.During fermentation, growth was checked spectrophotometrically by measuring optical density at a wavelength of 600 nm (Rayleigh UV-1601). Glucose content of each sample was quantified by using an enzymatic kit that is based on glucose oxidase activityZ. mobilis ZM4 GEMs were unified and reconciled. Furthermore, in order to construct our comprehensive GEM, several new genes, enzymes, reactions and metabolites were added to the previous models based on database searches and literature evidence.Figure Z. mobilis ZM4 for analyzing sequence similarity by using BLAST and NCBI, (iii) previously-published Z. mobilis GEMs for gathering all reactions and genes, and other literature-based evidence that were reported earlier. Two models of Z. mobilis were genome-scale, while the other one is limited to central metabolism, in which the focus was mostly on biochemical and enzymatic evidence. Consequently, we gathered all these pieces of information for gaining a comprehensive draft model from previous researches. Furthermore, in some cases that dead-end metabolites or blocked reactions were found, E. coli24 and B. subtilis30 GEMs were used, as high quality bacterial templates, to ensure the presence of spontaneous reactions. Then we checked the presence of these reactions in Z. mobilis, as well.Different data sources were used for model compilation, including (i) BiGG, BioCyc, KEGG, BRENDA, and TransportDB databases, (ii) genomic sequence of i.e., non-gene-associated) reactions to check their reliability, followed by reaction gap filling. Altogether, 188 reactions were added from the KEGG database to refine our model. In this procedure, a considerable number of gaps were filled, and several blocked reactions were unblocked.Since the two GEMs had different identifiers for metabolites, we searched for equivalent reactions and removed redundancies. Furthermore, whenever necessary, we fixed the discrepancies in stoichiometries, reversibility type of reactions and gene-protein-reaction associations. We then analyzed the orphan 9. However most of these genes have non-metabolic functions. Their function was characterized in the field of phage structure proteins, toxin-antitoxin proteins, membrane-associated transporters and transcriptional regulators, as well as several hypothetical proteins. It should be noted that certain plasmid-related genes with metabolic functions had the same enzymatic activity as those genes which were present in our GEM. Finally, we updated our GEM and added some reactions based on these plasmid sequence revisions. The final GEM, which will be referred to as iHN446, includes 446 genes, 859 reactions, and 894 metabolites. The iHN446 model, in XLS format, which includes the complete list of reactions and metabolites, can be found in File S1. We also provide the model in SBML format (File S2).A recent report has presented comprehensive data on the sequences and annotations of plasmid-related genes in iHN446. For genetic engineering purposes, the gene-to-protein associations are of central importance. Our results suggest that the reactions included in iHN446 are reasonably reliable, as most of the reactions in the model are associated with their corresponding metabolic genes , the gene coverage of iEM439 should not be compared with the gene coverage of other GEMs.Total gene coverage of model is 22.3% in iHN446, the gene-associated reactions account for 80% of all non-transport reactions. In comparison, ZmoMBEL601, iZM363 and iEM439, 87%, 68% and 84% of the non-exchange reactions are gene-associated, respectively.In iZM363 and ZmoMBEL60, respectively. In addition, iZM363 has 63 more unique enzymes and 30 more unique genes in comparison to ZmoMBEL60.As expected, our reconciled model includes a considerable number of unique metabolites, genes and reactions . The number of unique  genes, enzymes and reactions are depicted in Fig. iHN446 is shared by the previous models, we excluded genes, enzymes or reactions from our model for which we did not find adequate evidence confirming their existence. Additionally, in certain cases, changes in the biochemical data or genomic annotations might have resulted in the inconsistencies . Consequently, simulation results are not performed in the same condition as experimental ones.VI.+, AMP and NMN are considered to be present in the model. Such a dependency to the uptake of NMN (and other metabolites) does not occur in reality.When we tried to build a biomass-producing model, ZmoMBEL60 was dependent on NMN entrance to the cell for biomass production in simulation tests. Besides, some exchange reactions for some metabolites like, NADVII.iZM363 model, phospholipid and cardiolipin were not able to be produced, and consequently, no biomass production was obtained. Therefore, we decided to allow these two biomass constituents to enter the network by adding their exchange reactions and checking the biomass production. In the culture condition proposed in Widiastuti et al. article, biomass production was simulated. However, neither in the original condition nor in this new biomass formulation, a non-zero growth rate was seen. We tried in an iterative process to find essential metabolite(s) for enabling biomass production. In this case we found that isoleucine, glycine and S-adenosyl-l-methionine supplementation is vital for biomass production.In VIII.et al. included a number of biochemical reactions with experimental evidence, mainly on NAD(P)H balance, which is necessary for stoichiometric balance. Also, a number of important changes were made in respiratory chain and catabolic genes in the central carbon metabolism, which were not incorporated in previous reconstructions. Although this model was improved mainly based on literature evidence, new enzyme-related changes, together with new reactions and GPRs were not necessarily included. Basically, this model cannot be considered as a GEM with comprehensive list of genes, reactions and metabolites. The improvements suggested by Pentjuss et al. are included in our GEM.In the core metabolic network, Pentjuss Taken together, we propose that our model is upgraded in various parts in comparison to previous models of Z. mobilis. The Biolog analysis was used to qualitatively study the utilization of several different carbon sources. Additionally, consumption of glucose, fructose and sucrose by Z. mobilis are further analyzed quantitatively. In each case, growth rate, together with ethanol and/or levan production were measured in aerobic and anaerobic conditions.In case of carbon sources, we used both qualitative and quantitative studies to determine the metabolic capabilities of Z. mobilis ZM4. Among the carbon sources available on the Biolog MicroPlate™, we observed carbon source utilization only in two cases, namely fructose and glucose. Although sucrose utilization has been reported in Z. mobilis, our Biolog phenotypic result was negative, which is consistent with the previously reported Z. mobilis growth results27. Since Z. mobilis is known to be able to utilize sucrose for growth and levan production31, we carefully studied sucrose-dependent growth. We used broth culture and plates supplied by sucrose as carbon source. In both of the solid and liquid cultures, the growth of Z. mobilis was observed.A high-throughput 96-well Biolog phenotypic test was performed to profile carbon usage of Z. mobilis GEMs, we used FBA. As it is presented in Fig. Z. mobilis. In iZM363 model, sorbitol, gluconic acid, glycerol, inosine, uridine, thymidine and phenylalanine were incorrectly predicted to be consumed by Z. mobilis. Also, iHN446 mistakenly predicts the gluconic acid utilization as the carbon source. Therefore, a comparison of modeling results with experimental data confirms the precision of our model.In order to computationally simulate the carbon source utilization capabilities and comparing the results obtained with previous et al. for model validation27. Simulations suggest that iHN446 is able to successfully predict l-asparagine, l-aspartic acid, l-glutamine and ammonia consumption as the nitrogen source. ZmoMBEL60 model was able to predict the consumption of ammonia as the nitrogen source. Although, since yeast extract (including amino acids) is not included in the in silico growth medium, simulation on different amino acids as nitrogen source was not possible. In this case, we included transport reactions of nitrogen sources to model. Simulation proposed that ZmoMBEL60 is able to predict l-asparagine and l-aspartic acid, correctly. In contrast, l-glutamine was mistakenly consumed by Z. mobilis. In addition, in iZM363 model, isoleucine and glycine should be added to the growth medium to enable biomass synthesis. Therefore, these amino acids are essential and are consumed, and hence, they can provide the required nitrogen for Z. mobilis. Consequently, it is not possible to simulate the necessity of any nitrogen sources.In case of nitrogen and sulfur sources, we used the previously-published data by Bochner l-cysteine and l-methionine utilization were correctly predicted to be used by iHN446 as the sulfur source. ZmoMBEL60 model, in contrast, could only predict the sulfate consumption. In iZM363 model, S-adenosyl-l-methionine supplementation was necessary for growth, and therefore, one could not simulate the usage of sulfur sources. Taken together, simulation results of the iHN446 model showed an improved consistency with the experimental results, compared to the two previously-published GEMs  were determined experimentally. Then, these data were compared with in silico simulation results obtained by FBA using the biomass production rate as the objective function. In RM and modified RM media, we found the model predictions to be in agreement with the experimental data in all conditions. By performing FBA in the conditions of culture media, the growth rate was determined as 0.136 h−1 and 0.5 h−1, respectively, which is comparable to the experimentally measured growth rate (0.154 ± 0.03 in RM and 0.39 ± 0.04 in modified RM). The ethanol content was 6.28 ± 0.8 g/L and 7.38 ± 0.51 in RM medium in aerobic and anaerobic conditions, respectively. Additionally, in the modified RM medium, ethanol quantities were 7.23 ± 0.36 g/L in aerobic and 8.6 ± 0.48 g/L in anaerobic fermentation. Furthermore, we ran FVA and analyzed the maximum and minimum flux rates of reactions in which ammonium is involved. As expected, in modified RM with ammonium supplementation, these reaction fluxes changed to zero or decreased significantly. It should be noticed that there are 20 reactions in the iHN446 model that produce or consume ammonium. So, our model could reveal the effect of ammonium addition. Based on FVA results under two different culture media, evaluation of fluxes of amino acid production metabolism revealed that in modified RM amino acid metabolism is considerably less active. These observations confirmed that the addition of ammonium to medium reduces the dependence of Z. mobilis to amino acids because ammonium could go through different pathways. In conclusion, the addition of ammonium and flux changes agrees with the reported experimental results32.The growth patterns of in silico and experimental growth rate were 0.136 h−1 and 0.168 ± 0.03 h−1, respectively. Measurement of levan production showed 0.6 ± 0.07 g/L of product formation under the experimental condition. Furthermore, we used FVA to assess the range of levansucrase flux. FVA results confirmed that this pathway was functional, as the maximum and the minimum fluxes of the reaction were 5 mmol gDW−1.h−1. Moreover, pathways of sucrose and fructose metabolisms were active when sucrose was added as a carbon source to the medium. Minimum and maximum flux of d-sorbitol dehydrogenase were −15.25 and 20 mmol gDW−1.h−1, respectively as fructose is needed for sorbitol production. Also, glucose-fructose oxidoreductase, which is responsible for sorbitol production, showed the minimum and the maximum fluxes of 0 and 20 mmol gDW−1.h−1, respectively. According to experimental evidence, sorbitol and levan are the products of sucrose utilization33. Therefore, our model could show that these pathways are practical in the presence of sucrose and fructose as carbon sources.In the simulation with sucrose as the carbon source, the predicted results confirmed our experimental outcomes. The calculated Z. mobilis lacks a complete pentose utilization pathway. Therefore, many efforts have been taken to introduce the necessary genes to the metabolism of this microorganism1. As shown in Table 34. We simulated the growth of Z. mobilis on minimal medium, and additionally, permitted xylose to go into the system by setting the lower bound of its exchange reaction to −10 mmol gDW−1 h−1. Investigation of the modeling predictions for growth rate in this case by running FBA showed 0.136 mmol gDW−1.h−1. The in silico ethanol production simulations were done using FBA. In the case of glucose and xylose consumption, 29.42 and 21.92 mmol gDW−1.h−1 ethanol was produced, respectively. In the mixed culture of glucose and xylose, 49.42 mmol gDW−1.h−1 ethanol production was obtained. The results showed that ethanol production would be improved in mixed culture as more carbon sources are available for the microorganism. Based on growth and ethanol production, the modeling outcome is compatible with experimental results.Glucose and xylose are the main derivatives of lignocellulosic biomass digestion. Therefore, the conversion of these carbon sources to ethanol is economically important. Z. mobilis, arabinose utilization genes were inserted into this bacterium. Similar to the case of xylose, arabinose is another five-carbon sugar that is found in lignocellulosic biomass. Arabinose isomerase, ribulose-5-phosphate-4-epimerase, ribulokinase and transaldolase were added to the model35. The lower bound was set to −10 mmol gDW−1 h−1 in arabinose exchange reaction. By performing FBA, the model predicted 0.136 mmol gDW−1 h−1 for the growth rate and 24.42 mmol gDW−1 h−1 ethanol production. Therefore, the simulations reflected the experimental observations adequately.Along with the previous attempts for expanding substrate usage ability in Z. mobilis36. By engineering this pathway, flux from ethanol production is redirected to alanine production. In order to analyze the splitting of carbon sources to these two end products, we used FBA. Our modeling observation is compatible with experimental results and showed less ethanol, 28.45 mmol gDW−1 h−1 (compared to 29.42) when alanine is produced.Alanine dehydrogenase gene has been previously employed to demonstrate successive improvements in broadening the product formation in Z. mobilis37. As presented in Table −1 h−1 and −1000 mmol gDCW−1 h−1, respectively. The β-carotene production reaction flux was predicted to be 1.016 mmol gDW-1.h-1. Therefore, our results of FBA demonstrated that the aforementioned pathway is active when β-carotene synthesizing genes were provided.β-carotene is popular mainly because of its vast applications in the production of pharmaceuticals and as a food additive. Biosynthesis of β-carotene is achieved by heterologous expression of β-carotene synthesizing genes in an appropriate host like Z. mobilis38. Here we added butanediol dehydrogenase gene to Z. mobilis metabolic network to model the engineered strain. In order to study the modeling predictions in this condition, we kept the glucose and/or xylose uptake rate at −10 mmol gDW−1 h−1 and allowed butanediol dehydrogenase activity by adding the constraint −1000 mmol gDCW−1 h−1 and 1000 mmol gDCW−1 h−1 to see how it would affect the butanediol production. By using FBA, in the case of glucose or xylose consumption, 14.71 and 10.96 mmol gDCW−1 h−1 was produced, respectively. As butanediol production was 24.71 mmol gDCW−1 h−1, the model could reflect the experimental observations of butanediol production in the mixed culture, successfully,Microbial production of 2,3-butanediol, which is often produced petrochemically, has gained scientists’ attention in recent years. Production of this important chemical may be achieved by introducing related genes to gfo gene. In a previous study, a gfo knockout in Z. mobilis was constructed and the metabolic features of this bacterium were studied33. Here, we report the consequences of the experimental and in silico simulation of these evaluates. In experimental results, the inactivation of gfo had different results on the growth of Z. mobilis at high or low concentration of glucose, fructose or sucrose. However, as shown in the simulation results, the model could not predict the experimental observations correctly. These observations proposed that these effects may be related to regulation mechanisms in Z. mobilis. So, more research is needed in differences in transcription profile in these conditions.The glucose-fructose oxidoreductase is encoded by pdc and ldh are genes that participate in lactate and ethanol production. By omitting these two genes, fluxes are redirected to succinic acid production and resulted in improved concentrations of succinic acid39. By running FVA, when ethanol and lactate were produced, minimum and maximum fluxes of succinic acid production were 0 and 0.33 mmol gDCW−1 h−1, respectively. Here we knocked out the above two genes in silico, so maximum succinic acid production changed to 5.33 mmol gDCW−1 h−1. Consequently, more succinic acid production in knock-out condition was confirmed.Succinic acid can be used in numerous applications like surfactants, detergents and solvents or food and pharmaceutical products. Z. mobilis ZM4 survival. In this case, a gene cluster of cellulose synthase was found to be vital for microorganism40. It should be noticed that, cells within flocs are linked together by a cellulosic extracellular matrix. So, we added a new cellulose-containing biomass reaction to be used for modeling in minimal medium. By simulating this minimal medium in aerobic condition, our model could reflect the essentiality of cellulose synthase, successfully.A recently-published article reports that, floc formation in a defined minimal medium under the aerobic condition is essential for Z. mobilis ZM4, the maximum specific growth rates are comparable under aerobic and anaerobic conditions, while genes related to ethanol (and other byproducts) formation may be differentially regulated, which results in differences in the transcriptomic profiles26. For validating gene activity of different pathways under aerobic vs. anaerobic conditions, in the first step, the “reporter metabolite algorithm” was used41. In this method, by integrating transcriptome data with a GEM, key metabolites that are linked to the most significant transcriptional changes are identified. Glucose 6-phosphate, glycerate, glucose, 2-Phosphoglycerate, l-Phenylalanine, l-Threonine and lactate were found different in the metabolomic profile of Z. mobilis during aerobic and anaerobic fermentation based on reporter metabolite algorithm results  reaction fluxes were compared with the list of up- (or down-) regulated genes based on the transcriptomic data. Comparison of transcriptomes with flux data showed, while some of these changes were consistent with previous transcriptome studies, most of them are not metabolic genes. These observations are the evidence of the role of transcriptionally controlled mechanisms and showed new insights into the physiology of anaerobically growing Z. mobilis ZM4.We compared flux changes between aerobic and anaerobic conditions by using a random sampling of the metabolic flux spaceOur results demonstrated that pathways most significantly impacted by aeration shift were glycolysis, amino acid, purine and pyrimidine metabolism. Between reactions pyruvate decarboxylase and alcohol dehydrogenase had significantly lower flux in aerobic condition. This finding is in accordance with experimental evidence. We also evaluate our results with up or down-regulated genes that were obtained from transcriptomic data. Additionally, we assessed the accordance between results achieved from reporter metabolite algorithm with sampling results. In the case of up-regulated genes, 36% of the sampling positive results were confirmed by the transcriptomic data, while the down-regulated genes were 73% consistent with sampling results. While our results showed an acceptable level of correctness , but there is still room for improvement. (Table Z. mobilis ZM4, iHN446, is presented. The model is more comprehensive in scope than previous models and updated based on the latest gene annotations, databases and literature. The model validation was achieved by comparing model predictions to experimentally gained results or from the literature. iHN446 showed desirable simulated performance when predicting specific growth rate on the carbon sources and the effects of gene deletion or gene insertion on cell growth and/or product formation. We also investigated the predictive power of the model based on transcription data in aerobic and anaerobic conditions by comparing changes in the flux of each reaction vs. transcription data. This advanced and comprehensive genome-scale metabolic model, make a potential platform for a better understanding of Z. mobilis metabolic features at a systems-level and is a valuable tool to develop engineered Z. mobilis strains for several biotechnological applications. Finally, this model can be a framework for the integration of transcriptomic data to provide more accurate metabolic engineering predictions.In this work, an upgraded high-quality metabolic model of Supplementary Dataset 1.Supplementary Dataset 2.Supplementary Table S3.Supplementary Table S4.