Shewanella oneidensis MR-1 is one of the model microorganisms used for extracellular electron transfer. In this study, to elucidate the capability and the relevant metabolic processes of S. oneidensis MR-1 involved in an electron transferring environment, we employed genome-scale modelling to model the necessary metabolic states and flux adjustments for electricity generation in the cytochrome c-based direct electron transfer (DET) mode, the NADH-linked mediated electron transfer (MET) mode and a presumable mixed mode comprising DET and flavin secretion. These are difficult to develop experimentally.The results showed that the microbe had the potential to achieve current outputs of up to 2.610 A/gDW in the DET mode, 2.189 A/gDW in the MET mode and 2.197 A/gDW in the mixed mode. Compared with the DET mode, which relied on cytochrome c oxidase (EC: 1.1.1.2) to mediate the electron transfer, the MET mode was mainly dependent on two routes, catalysed by isocitrate dehydrogenase (NAD) (EC: 1.1.1.4) and NAD transhydrogenase, for the computed high current density value. In the mixed mode, whereas the cytochrome c-based DET accounted for most of the computed maximum current output value, the two flavins combined, riboflavin and FMN, played a much less important role in the probed current value.Shewanella oneidensis MR-1 has the potential to sustain a high extracellular electron transfer rate similarly to Geobacter sulfurreducens, but relies on different intracellular mechanisms. Various levels of electron transfer rates are achieved by different combinations of metabolic pathways. Flavins can significantly degenerate the maximum electricity generation capability of the cell and the biomass formation, and thus should be avoided in order to achieve a high coulombic efficiency.The online version of this article (doi:10.1186/s13068-014-0118-6) contains supplementary material, which is available to authorized users. Shewanella oneidensis MR-1 is a facultative aerobic, Gram-negative bacterium capable of using various reducing exogenous extracellular acceptors such as Fe (III) to preserve energy for growth [S. oneidensis MR-1 has been used in microbial fuel cells (MFCs) for wastewater treatment and bioelectricity generation [, S. oneidensis can potentially produce electricity through three modes: 1) the direct electron transfer (DET) mode based on c-type cytochromes on the outer membrane of the cell and cell appendages called conductive pili , 2) self-secretion of flavins to convey electrons toward the electrode and 3) the mediated electron transfer (MET) mode, which relies on exogenous mediators .Geobacter: the first claims that the cytochromes mediate the electron transfer via the pilus with metallic-like conductivity, and the second suggests that the electrons are hopping between heme groups in cytochromes aligned with the pilus to reach remote electron acceptors [Geobacter sulfurreducens [S. oneidensis [G. sulfurreducens and S. oneidensis have been studied extensively and proposed as classic models in MFC research [Geobacter, Shewanella can use oxygen and thus can have wider applications.The DET route depends on the c-type cytochrome (MtrC and OmcA) in the cytoplasmic membrane interacting with the electrode and supplying electrons to the electric circuit . The DETcceptors . These preducens –12, and eidensis . Both dieidensis . The metresearch ,15. UnliS. oneidensis can still reduce a remote electron acceptor by producing flavins into the surrounding medium to serve as extracellular electron shuttles [Without a direct contact with external electron acceptors via pili, shuttles ,17. The shuttles ,19. The shuttles . The flashuttles ,21. This+ cycle could be considered as a way to probe the maximum metabolic potential for ideal electricity generation.For the MET mode, the reduced form of nicotinamide adenine dinucleotide (NADH) has been suggested as the optimal intracellular electron-shuttling compound to be targeted in electricity generation , becauseShewanella cells in a microbial biofilm [In conventional MFC research, a few pathways  can  biofilm –28, whicShewanella with a certain level of oxygen was able to achieve higher maximum current outputs [S. oneidensis MR-1 under aerobic conditions, in order to identify its maximum potential for electricity generation and the pertaining metabolic mechanisms. To achieve this goal, we followed a five-step procedure. 1) Seven assumptions were made based on previous experimental findings for the modelling  We then demonstrated theoretical trade-offs among the amperage yields, the biomass production (growth) rates, the flavin secretion rates and the cytochrome c regeneration rate. 4) The fundamental metabolic mechanisms behind the current outputs in the three modes were elucidated by flux variability analysis with target flux minimization (FATMIN). 5) Finally, a sensitivity analysis was conducted to investigate the effect of varying lactate uptake rate on the growth rate and the amperage output in the three modes, and the computed current outputs were compared and discussed.Previous findings showed that an MFC using a pure culture of  outputs –31. Ther see the  section.-1 , whereas in the MET mode, the electron flux always increased with the decrease in the growth rate. The slope of the DET and MET outputs versus biomass growth curve as shown in Figure Figure -1 did not improve the DET rate, forming a steady plateau at the DET output of 81.68 mmol gDW-1 h-1. Likewise, the MET production curve can also be divided into two stages: Stage 1, an increase in the MET rate was accompanied with a decrease in the biomass production rate, but the conversion efficiency of the metabolic resource into the MET flux decreased with the decline in the MET output; Stage 2, at the left-side tail of the net NADH production curve, the rise in the MET rate was linearly proportional to the drop in biomass production rate, indicating a constant efficiency. On the other hand, as a result of increasing DET or MET outputs, the relationships between the varying biomass production rate and the NADH consumption for maintenance  curve, Stage 1 of the MET production curve describes the compromise between the two goals, maximizations of biomass growth and NADH production. The higher growth rates correspond to more active metabolic pathways available to cope with the NADH drain, and thus the metabolism tends to choose those pathways with higher efficiency in converting the metabolic resource originally for the biomass production to the current output. As the current output augments, which means that the conversion efficiency becomes less important than the flux capability, the metabolism has to abandon the pathways with high conversion efficiency but lower maximum capability and reallocate the metabolic resources to those with higher flux capability.The Stage 2 behaviour sets in at extremely low growth rate values (less than about 10% of the maximum growth rates) in each of the two modes. In this stage, the reactions with lower catalytic efficiency in NADH regeneration were inactivated to leave the metabolic resources to a limited set of reactions with higher efficiency, in order to produce the extremely high current output. From an optimization point of view, the Stage 2 behaviour corresponds to the fact that the solution space was greatly reduced by the high MET rate and the remainder solutions are rigid in the selection of reactions. As a result, the decrease in the biomass production rate was linearly proportional to the increase in the MET flux.The right part of Stage 1 of the NADH production behaviour coincides with Type 1 of the NADH consumption behaviour, which means that the increase in the MET is accompanied by a drop in the NADH consumption for cellular maintenance. This indicates that, at those production rates of biomass and NADH (corresponding to Type 1), the increase in the flux of NADH_mfc  originates from a combination of two mechanisms: increasing the NADH production and decreasing the NADH needed for biomass production and maintenance.+, a building block for NADH production. Thus, Type 2 consumption of NADH is a metabolic mechanism enabling Shewanella to achieve higher current outputs. In addition, the increased consumption of cytosolic NADH can be considered as a metabolic strategy to reallocate the metabolic resources to those reactions that can regenerate NADH more efficiently.The left parts of the Stage 1 and Stage 2 productions are correlated with Type 2 consumption, at lower biomass production rates. Type 2 behaviour may be contrary to intuitive expectations, since a reduction in biomass growth and an increase in NADH demand would be anticipated to reduce the NADH consumption rate. However, the increased consumption of NADH is also a way to increase the availability of NADShewanella did not increase the “ceiling” regeneration rate of the reduced cytochrome c when current was generated in the DET mode. A flux value of 81.68 mmol/gDW/h for the reduced cytochrome c production was necessary for the optimal growth rate of 0.2864 h-1, and a metabolic perturbation of a drain flux of cytochrome c entailed a compromise on the growth rate. This suggests that the capability of the Shewanella cell for regeneration of the reduced cytochrome c is inherently fixed. For the MET mode, compared with the base state optimized for growth, the metabolism of Shewanella had a potential to increase the NADH regeneration rates by about 130% (2.297-fold) under the highly NADH-perturbed metabolic states.Overall, compared with the base state optimized for growth, the metabolism of B value, to quantitatively compare the suitabilities of different combinations of multi-objectives, so as to identify the metabolic state that can most efficiently coordinate pathways for achieving the objectives. The higher the calculated B value achieved, the more efficiently the metabolic state can produce the metabolite of interest. A detailed explanation for the calculation of a B value can be found in the Methods section. Figure Although the discussion above indicates that the reducing equivalent production competes with the biomass growth for metabolic resources, not all the gains in reducing equivalent production are derived from the decrease in the growth rate, and some of them can result from reconfiguration of metabolism. We used a fractional benefit measure, the B values increased from 50% at wild-type growth rates (right-hand side of the figure), to reach an apex of near 81% at a growth rate of 0.192 h-1, and then dropped to about 51% when the growth rate approached zero. The initial increase in the B values indicates that there is a rise in the metabolic output of the combined objectives - a small redox disturbance causes an adverse effect on the growth, but a further increase in the degree of the redox perturbation can improve the metabolic output of the combined two goals. The highest B value, about 81%, suggests that the corresponding metabolic state with a growth rate of 0.192 h-1 possesses the best output of the multi-objective. The subsequently declining B values indicate that the further, forced, increase in the DET is detrimental to biomass production. Similar to the B value curve of the DET mode, the B values calculated for the MET mode generally increased from a base value (50%) for the wild-type growth rates to reach an apex (57%), the best metabolic state for the dual objectives, and then decreased to the base value (50%), since the further improvement in reducing equivalent generation rate was at a high cost of biomass production rate. However, since the maximum B value could not exceed 57%, it is indicated that MET could not liberate the maximum metabolic capability of the cell for growth-coupled current production.The B value achieved for the DET mode (81%) was much higher than that for the MET mode (about 57%). This indicates that the metabolism of S. oneidensis MR-1 is much more suitable for the cytochrome c-based DET mode than the NADH-linked MET mode. In other words, the metabolism favoured the DET mode more than the MET mode for higher growth rates.Notably, the highest Shewanella strain needs to reduce the biomass production rate to a very low level and increase the reduced cytochrome c production rate to about 30 mmol/gDW/h. It was found that the highest B value of about 54.6% was achievable when the growth rate was lowered to 0.003 h-1 and the cytochrome c production rate stayed at about 29.36 mmol/gDW/h. A further increase in cytochrome c can compromise the capability of the cell to secrete flavins.Turning now to flavin-based current production, Figure The three-dimensional graph Figure  shows th-1), an increase in the flavin production rate did not always necessitate a decrease in cytochrome c production rate. When the flavin production rate was below about 0.45 mmol/gDW/h and the cytochrome c production rate was above about 78 mmol/gDW, the decrease in biomass growth did not further improve the production rates of cytochrome c.The vertical plane of the graph indicates that, at lower biomass production rates  cytochrome c and growth rate, 2) flavins and growth rate and 3) flavins and cytochrome c are further discussed and analysed in the following three sub-sections. The steepnesses of the curves are used in these discussions as indicators of the relative production efficiency. This is based on the interpretation that, for example, a steep downward curve means that a small increase in the quantity on the horizontal axis implies a large decrease of the complementary quantity on the vertical axis. As these are trade-offs consuming the same amount of metabolic resource, this implies that the vertical quantity is produced more efficiently than the horizontal one.-1 and Phase three, a efficiency plateau which means any further decrease in the biomass growth does not improve the cytochrome c production rate. Note that as the production rate of the flavins increased (indicated by the green dashed line with an arrowhead), the curve for the cytochrome c production versus biomass growth only comprised the Phase one and two relationships, and the Phase three relationship diminished, which indicates that the flavin synthesis rate increased, with a rise in the average slope for the curves of cytochrome c versus growth rates.Figure The zig-zag part of the curves corresponds to flux distribution solutions switching from one metabolic state to another. These adjacent metabolic states in the zig-zag areas are randomly obtained from independent calculations, indicating that a very tiny change in the numerical value of one parameter can produce a similar but distinct metabolic state. This is thought to correspond to the biological phenomenon that cells even maintained in the same environmental condition can express individual behaviours.-1; it then plummeted as the growth rate approached zero. The decrease in the cytochrome c production rate not only augmented the maximum capability of the cell for flavin secretion, but it also raised the efficiency of the flavin synthesis, as is evident from the elevated slopes of the curves (indicated by the green dashed line with an arrowhead).Figure Figure -1, corresponding to the bottom curve of the graph), there is generally a simple linear relationship between the flavin production and cytochrome c production. This indicates that, to maintain an optimal or high growth rate, all efficient metabolic pathways need to be exploited to convert metabolic resources into the biomass components, leaving no freedom for the metabolism to adjust the priority of producing flavins versus cytochrome c.At very high growth rates , based on the efficiency of conversion of riboflavin versus cytochrome c, the curves can be divided into three regions.High efficiency of producing flavins compared to cytochrome c. In the metabolic states of this region (bottom right of the curve), the synthesis of cytochrome c is relatively less metabolically costly than the synthesis of flavins, and FMN did not influence the maximum capability of the cell to produce reduced cytochrome c.Medium efficiency. In the metabolic states of this region (the part of the curve for which the cytochrome c production rate ranges from about 80 to 30 mmol/gDW/h), the further reduction in production rate of cytochrome c increased the flavin production with a decreased conversion efficiency (a steep slope) of metabolic resource into flavins. Compared to the previous high efficiency region, a downward curvature can be seen with decreasing slopes of the lines between the data points.Low efficiency. At the left-side tail of the curve (top left of the curve), a significant drop in the cytochrome c production rate is required for a rise in the secretion rate of the flavins, which is reflected by the gentle slope of the line.At lower growth rates  for cytochrome c production than for the flavins, the cytochrome c production is much more important in the metabolism. This can also be seen by the fact that the flavins change by a factor of about 5, that is, 1.44 (flavins)/0.286 (growth rate), to obtain a zero growth rate, whereas the cytochrome c changes by a much larger factor of around 286, that is, 81.68 (cytochrome c)/0.286, to reach a zero growth rate. The present modelling results indicate that a high and flexible production rate of cytochrome c is allowed by the stoichiometric constraints of the MR-1’s metabolism, which is in keeping with the experimental genome evidence of more cytochrome c-type proteins being found in the MR-1 than in other prokaryotes .Our results also show that the production of riboflavins and FMN can heavily suppress the growth rate and the availability of cytochrome c. This implies that regulation of expressions of particular enzymes can cause a significant change to the metabolic flux distribution, and the metabolism of MR-1 is not as naturally optimized for cytochrome c production as for synthesis of the flavins.The reactions (enzymes) that were responsible for the enhanced diversion of the reducing equivalent flux towards current production are summarized in Tables During MFC operation of the DET mode, the electrons are continuously extracted from the membrane by the anode, imposing a metabolic pressure on cellular activities. The surplus flux of the reduced cytochrome c for the DET mode was derived from the reaction CYOO2 EC: 1.1.1.2)  to regenerate reduced cytochrome c, S. oneidensis MR-1 did not use CYOR1m and chose CYOO2  to produce the high flux of the reduced cytochrome c.Unlike + at a high rate had to be activated to restore the redox balance for survival (growth). It is suggested that two mechanisms will be triggered to avoid NADH imbalance: 1) the growth rate decreases, and 2) a metabolic reconfiguration mechanism is employed by the cell to restore the balance.As a response to draining electrons away from NADH, a metabolic mechanism that regenerates NADH from NADS. oneidensis MR-1 was much less than the ten reactions for the MET mode of G. sulfurreducens [In total, there were 63 reactions associated with NADH production or consumption. Among them, only two reactions, ICDHxi (EC: 1.1.1.4) and THD5, were responsible for the identified maximum net NADH flux (48.53 mmol/gDW/h) Table . Each ofreducens .+ oxidoreductase. A high NADH flux (47.03 mmol/gDW/h) yielded from NADPH by THD5 was also observed for modelling an optimal anaerobic growth of S. oneidensis MR-1 in a previous study [Whereas ICDHxi is associated with the tricarboxylic acid (TCA) cycle, THD5 is catalysed by NADPH:NADus study . On the i min/vi max > 0.99) under high current output. The low flux values and rigid variability reflect the fact that NADH is involved in many reactions and that increasing NADH fluxes in a few reactions can limit the variability of metabolic fluxes through many other reactions.The excess fluxes (the flux portion higher than 48.53 mmol/gDW/h) were mainly consumed by GAPD (EC: 1.2.12), which can consume up to 8.745 mol/gDW/h of the NADH flux, accounting for 18.02% of the maximum net NADH flux. All other NADH-involved reactions usually have extremely low flux values  with no variability  were concurrently up-regulated to sustain the high reducing equivalent fluxes. As a result of maximization of the production rates of both the cytochrome c and flavins, two reactions in the metabolism, CYOO2 EC: 1.9.3.1) and SO3De (EC: 1.8.2.1), were mainly associated with the maximum cytochrome c output flux (75.12 mmol/gDW/h) (Table .1 and SOThe maximum net flux (0.5008 mmol/gDW/h) of the flavins was achieved by a combination of fluxes of three reactions, RBFK (EC: 2.7.1.26), RBFSb (EC: 2.5.1.9) and FMNRx (1.5.1.29). Half of the flux of RBFSb was responsible for the surplus riboflavin flux of 0.2504 mmol/gDW/h, and the other half was consumed to produce FMN. The riboflavin flux value was the same as the FMN flux value, indicating that the metabolic costs for synthesis of the two metabolites are the same. The three reactions underlying the flavin-linked current production had rigid flux ranges, implying that the synthesis of the flavins is limited by the diversion of metabolic resources toward biomass growth.To elucidate how the metabolism is reconfigured to cope with different levels of energy perturbation, we have examined the key solution reactions, of which the fluxes significantly contribute to a series of selected growth rates and current outputs. The results are shown in Tables As the redox perturbation increases from the lowest level (DET flux is zero) to the highest level (DET flux is maximized using a λ of 0.999926), the metabolism decreases the cytochrome c flux diverted to the two reactions, CYOR7 and SO3D3, and increases the cytochrome c production of the reaction CYOO2, to achieve a high net cytochrome c flux for the DET mode current production Table .A low redox perturbation of NADH_mfc drain (λ below 0.9934) did not have an impact on the choice of metabolic pathways involving NADH regeneration, when compared with the control metabolic state Table . As the Table The maximum flux of the flavin secretion was relatively low (about 1.441 mmol/gDW), and to achieve such a flavin secretion rate, the cell needs to heavily decrease the reduced cytochrome c production and the growth rates Table . Since tThe simulation results obtained by varying λ for the cytochrome c output while fixing a λ of 0.023399 for the flavin secretion show that, under the mixed mode, the metabolism had rigid solutions in choosing pathways to synthesize flavins, but had flexible solutions comprising a number of different combinations of the cytochrome c-producing and consuming reactions.Based on the conversion efficiency of lactate to biomass, the relationship between biomass production rates and the lactate uptake rates can be categorized into two stages Figure : 1) a hiIn the DET mode, it was found that, when the lactate uptake rate was below 8 mmol/gDW, the biomass production rates or the electron transfer rates were linearly proportional to the lactate uptake rate in the MET and the mixed modes  was higher than the mixed mode (slope = 7.5622) and the MET mode (slope = 4.8532)  can lead to a plateau of the flavin production, we artificially increased the literature value of lactate uptake rate, 10 mmol/gDW/h, to 15 mmol/gDW/h, and found that any lactate uptake rates higher than 11.05 mmol/gDW/h could not further increase the total redox flux production rate; the highest total redox flux production rate stayed at 82.22 mmol/gDW/h. A lactate uptake flux of 15 mmol/gDW/h may not be practical, but using such a high value in the simulation can predict the trend of the curve  for the flavin production rate versus lactate uptake rate if the innate burden on the lactate uptake capability were to be removed.The mixed mode (slope = 7.5622) achieved a higher efficiency of converting the substrate into electricity in MFCs compared to the MET mode (slope = 4.8532), but a lower efficiency than the DET mode. This suggests that the stoichiometry of the metabolism is naturally optimized for synthesis of the reducing equivalent in the following order: cytochrome c > flavins > NADH.Finally, the three operation modes are compared for their theoretically maximum current output  supplying an electron uptake flux of 144 mmol/gDW/h, which is higher than the lactate uptake rate of 10 mmol/gDW/h comprising an electron uptake flux of 120 mmol/gDW/h used in the simulation of S. oneidensis MR-1.neration . The comG. sulfurreducens, are generally in agreement with previous experimental opinions that Geobacter can generate higher current densities in MFCs than all other pure cultures [G. sulfurreducens PCA produced ten times higher current levels in lactate-fed microbial electrolysis cells than S. oneidensis MR-1 [S. oneidensis MR-1 to secrete flavins can preclude the need for direct contact with the electrode, establishing a long-distance electron transfer without involvement of exogenous mediators and can be exploited in the development of some particular applications.Our calculations showing that the MR-1 produced a slightly lower maximum current output than its counterpart, cultures .This consis MR-1 . DespiteShewanella containing MFCs [Compared with the sole DET mode, the present calculation did not show a further increase in the total current output for the assumed DET mode with flavin secretion, and any flavin synthesis rate higher than 0.2587 mmol/gDW/h can significantly reduce the overall capability of the cell for current generation. This is partially contradictory to a previous conclusion that the two electron-mediated mechanisms - the nanowires-mediated electron transport and secretion of flavins - together determine the efficiency of the current generation in ing MFCs .S. oneidensis [Shewanella [Shewanella, and focuses on elucidation of the overall capability of a single cell for extracellular electron transfer. This is different from previous MFC studies that used to report the current output for the whole cell culture in MFCs, which resulted from a combination of contributions from different electron transfer mechanisms of cellular phenotypes, namely, secreted flavins of cells distant from the electrode and cytochrome c of cells near the electrode. Therefore, the observed importance of flavin secretion for cell culture-based extracellular electron transfer should not decrease the priority to develop cytochrome c-based DET mode for high current outputs in future studies.Previously, the flavins (riboflavin and FMN) were found to contribute as high as 73% of the total current generated in an MFC using eidensis . Therefoewanella . By contG. sulfurreducens (2.771 A/gDW) achieved a slightly higher maximum current output than that of S. oneidensis MR-1 (2.610 A/gDW), but with a lower CE of 71.79%. The use of exogenous electron shuttles in the MET mode of an MFC may be toxic to the normal metabolism of the MR-1, but can improve the current output when the self-synthesized flavins are not sufficient to convey all the available electrons inside the metabolism towards the electrode. This was confirmed by the observation that the addition of exogenous soluble flavins into the media can improve the electron transfer rates of an MFC [The MET mode of f an MFC .2) or current generated per unit volume of the cell (A/m3). Since different MFC studies use different configurations involving different types of reactors, surface area and types of electrodes , it is difficult to directly compare current output in these units (A/m2 or A/m3) with the unit (A/gDW) in the metabolic flux model. The advantage of the flux unit (A/gDW) is that it allows the calculated current density values to be applied to any MFC configuration by taking into account the cells that are actually contributing to the current generation in that situation.Experimentally, it is very difficult to measure the number of cells that actually contribute to the observed electric current output. Consequently, the MFC experimental studies often report the current density as current generated per unit area of the anode surface area /cm2 [A recent study has reported a maximum current output of 11.09 ± 0.28 A/mght)/cm2 . With thS. oneidensis MR-1 has an unusually high growth rate-dependent ATP requirement (GAR) [Shewanella model contains a high stoichiometric value for the ATP, the free electron fluxes divertible for electricity generation are suppressed. Although the high GAR is required to account for the fact that energy-dissipating futile cycles can be activated in S. oneidensis MR-1 under aerobic conditions [2 concentrations or O2-rich conditions, which may lead to a lower GAR, leaving more free electrons for diversion towards the electrode.Based on the previous study, nt (GAR) . Such a nditions , in the Furthermore, because the current metabolic network cannot distinguish the difference in the metabolic cost and pathways underlying the production of the two flavin molecules, riboflavin and FMN, the priorities for these two flavin molecules are set to be the same in the modelling. However, the phenomenon that the two flavins are exchangeable based on the stoichiometric constraints of the model may be attributed to the incomplete set of constraints in the model . To further investigate the relationships between syntheses of riboflavin and FMN, integration of the thermodynamic constraints into the metabolic model is required, to quantitatively differentiate the metabolic costs of the syntheses of the two redox molecules. In addition, identification of the kinetic parameters through isotopic tracers such as 13C-metabolic flux analysis and measurement of enzymatic concentrations can also elucidate the exact relationships between riboflavin and FMN for a particular metabolic state of the cell.S. oneidensis MR-1 for MFC current production and examine the properties of alternative genotypes arising from the energy perturbation. The results show that S. oneidensis MR-1 has the potential to produce an electric current at up to 2.610 A/gDW in the DET mode, 2.189 A/gDW in the MET mode and 2.197 A/gDW in a presumed mixed mode based on cytochrome c and flavins.In this study, we integrated electron transfer mechanisms with a genome-scale metabolic mode to determine the capability of To maximize the current output, the DET mode relied on only one reaction, mediated by cytochrome c oxidase (EC: 1.9.3.1), and the NADH-linked MET mode was mainly contributed by two reactions, catalysed by isocitrate dehydrogenase (NAD) (EC: 1.1.1.41) and NAD transhydrogenase. The maximum secretion rate of flavins was quite low, namely, 1.414 mmol/gDW/h, and the flavin production can lead to significantly repressed growth and c-type cytochrome production rates. Therefore, although the flavin secretion can improve the long-distance electron transfer in the absence of exogenous mediators, it can significantly curtail the objective to maximize the overall availability of the electrons for the electrode.G. sulfurreducens and S. oneidensis MR-1 shows that the two microbes are likely to employ different mechanisms to sustain the high current outputs. This suggests that the differences in the fundamental metabolic mechanisms, in addition to the engineering aspects of the configuration, should be taken into account in the optimization of the performance of MFCs based on different organisms.Furthermore, the comparison of S. oneidensis MR-1 culture is used for electricity generation. ii) The MFC reactor is fed continuously, providing all the necessary nutrients to meet the requirements for optimal microbial growth. iii) Sufficient oxygen was present to ensure the aerobic metabolism of S. oneidensis MR-1. Oxygen intrusion into MFCs used to be considered detrimental since it can intercept the electrons from anodes, resulting in a lower coulombic efficiency. However, recent studies have suggested that addition of oxygen to the anodic chamber does not necessarily lower the performance of MFCs based on the pure culture S. oneidensis MR-1, and instead can prominently increase their maximum current outputs [The modelling conducted was based on seven assumed conditions: i) Only a pure  outputs –31. The  outputs . iv) In  outputs ) can be The interactions with an MFC anode were captured by adding additional transport reactions into the model. These reactions are subject to the mass balance law in the FBA modelling. The process of conveying electrons towards the electrode is schematically shown in Figure FB and the reducing equivalent diversion flux FN. There are two objective formulations; one (Equation The objective equation was formulated as described in previous studies . BrieflyEquation  is for mEquation  is for tFB and FN are measured respectively in units of mmol/gDW/h and g/gDW/h or (h−1). For the MET mode, gN is set as 0.66343 g/mmol, which is the molar mass of NADH, and for the DET mode, gN is set as 0.8869 g/mmol, which is the molar mass of cytochrome c. These g values ensure that the λ is a dimensionless fraction that can be directly interpreted as the relative contribution of the reducing equivalent fluxes to the combined objective.Here a and λb. The one (λa) inside the square brackets adjusts the priority between biomass and cytochrome c, and then the other (λb) adjusts the priorities between the flavins and the combination of the biomass and cytochrome c.Here mentclass2pt{minimB, is computed by adding up the fractions of the maximal values of the objectives that they can ever achieve in a particular metabolic state. Equation B values for a dual objective optimization , whereas Equation A fractional benefit analysis, as described previously ,52, was B value of 100% would be attained if a metabolic state reaches the “utopian” ideal of simultaneously producing the maximal growth rate and the maximal reducing equivalent production. On the other hand, based on Equation B value of 50%. B values above 50% signify that the gain in achieving the target objective  outweighs the losses in the others, and vice versa for values below 50%.Here B value of 50% (25% for biomass formation and 25% for cytochrome c production), whereas the two flavins (riboflavin and FMN) combined give a base B value of 50% . A value of 25% indicates that either a wild phenotype of maximal growth rate or a maixmum cytochrome c production rate is achieved. In the theoretically ideal situation, a maximum B value of 100% would be reached, implying that the flavin secretion is not metabolically competitive with the biomass and cytochrome c productions. In reality, the productions of the various metabolites often involve different combinations of pathways and thus compete with one another for the limited pools of cellular resources, resulting in B values lower than 100%. Similar to the dual objective formulation , and vice versa for values below 50%.Based on Equation Equation , a B valB measure was used in Figure This 18 electrons, and 1 mol = 6.02 × 1023 electrons . Therefore, one flux unit (mmol/g/h) can be converted into A/g as follows:Current (in amperes) was integrated over time and converted to electrons recovered by using the following conversions: 1 C = 1 A × 1 s, 1 C = 6.24 × 10Coulombic efficiency (CE) is commonly used to quantify the performance of MFCs and is defined as the ratio of electrons transferred to the anode to that in the starting substrate. We use the full oxidation of lactate with oxygen as the oxidant as the reference reaction to characterize the energy efficiency of the respiratory metabolism:3H6O3 + 3 H2O → 3 CO2 + 12 H+ + 12 e-.Lactate oxidation reaction: C2, such as in the aerobic conditions [Each molecule of lactate oxidation can donate up to a maximum of 12 electrons per molecule if all of the carbon is oxidized to COnditions ,53.\docuAn upper limit for the cell voltage is calculated in this work based on formal potentials of the biological and electrochemical redox processes, as given by:Here, S. oneidensis MR-1 was chosen as the analysis backbone for all the simulations [S. oneidensis uses lactate as the sole source of carbon and energy. When lactate is sufficient, the Shewanella strain produces pyruvate and acetate, and the carbon utilization is in the following order: lactate ➔ pyruvate ➔ acetate [Shewanella strain. The maximum oxygen consumption rate was constrained to -20.42 mmol/gDW/h [2, Fe2+ Fe3+ Na+, NH4+, PO43-, NO2-, NO3- and SO42- were allowed to freely enter and leave the network, since they are required for optimal growth of the cell, as described previously [The iSO783 metabolic construction of ulations . This meulations . S. onei acetate . In the  acetate . The max acetate . These vol/gDW/h . The oxyol/gDW/h . CO2, Feeviously . The comSetting a lower limit on growth rate is necessary to ensure that the cell maintains a viable metabolic state at which the redox molecules  can be derived from the metabolism. To obtain a realistic upper value for the current that can be extracted from a microbe, 5% of the maximum theoretical biomass production rate was chosen as the viability threshold for computational identification of the lowest growth rate.The flux balance analysis (FBA) and flux variability analysis with target flux minimization (FATMIN) were implemented in MATLAB , using the ORCA toolbox . The max


Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type  Cellular metabolism involves a myriad of regulatory processes and metabolic components functioning together through a complex set of interactions and reactions. Although “omics” technologies provide an increasingly large body of information on each individual component involved in metabolism, our knowledge of how these components as a system give rise to multiple phenotypes under different conditions is far from complete. A powerful approach to investigate metabolism and metabolic processes is to analyze the flow of material and energy through a metabolic network. In particular, the analysis of metabolite fluxes in a metabolic network serves as an essential tool in many biotechnology and biomedical applications, for example, to enhance the production of food and biofuels 13C) 13C-MFA traces isotope-labeled metabolites using mass spectrometry and determines individual reaction fluxes by fitting 13C data to a network model with the help of additional measurements on exchange fluxes, such as nutrient uptake and product excretion rates. Due to experimental difficulties in obtaining quantitative and precise measurements that cover a large-size network with diverse pathways and many metabolites, the use of 13C-MFA is typically limited to the determination of fluxes related to the central carbon metabolism A standard experimental technique to quantify the distribution of fluxes in a network is to perform a metabolic flux analysis (MFA), which is based on isotope labeling techniques  S. cerevisiae and Escherichia coli metabolism by a comparison with experimentally determined flux data and other model predictions discussed above. The analysis showed that, whereas other algorithms performed well for one condition/organism, our algorithm showed consistently good predictions of flux distribution for both organisms under different conditions. Thus, we believe that E-Fmin will provide a robust capability for analyzing complex metabolic systems.We evaluated the developed E-Fmin algorithm through case studies of E-Fmin employed a similar structure to that of GIMME, with the following optimization problem that minimizes a weighted sum of flux magnitudes, i.e., ir is the ith flux, its weight iw is a function of gene expression level, m×n) stoichiometric matrix, r is the vector of rL and rU are vectors of upper and lower limits of r, respectively, RMF stands for required metabolic functionalities, and Eq. 2 denotes stoichiometric balances of fluxes under steady-state conditions. Flux bounds in Eq. 3 are determined by accounting for their directionality as constrained by thermodynamics, i.e., upper and lower bounds are ±∞ if a reaction is reversible, and if one of the reactions is irreversible, the opposite reaction bound was set to zero. By the inequality constraint in Eq. 4, one or more reactions classified as RMF are forced to carry fluxes above iw as ith reaction and Eq. 4 with the constraint Br is the specific rate of biomass production and Eq. 5 allows the specific rate of biomass production to vary from a negligible value to its theoretical maximal value, and thus should be valid even under conditions of significantly suppressed growth. In this regard, we view Eq. 5 as a context-independent constraint that can be applied across a wide range of conditions. It should be noted that iw is represented as all reactions (except those fully expressed) in inverse proportion to their associated gene expression levels. For more details on the E-Fmin algorithm, see materialsandmethods. To develop a tool that enables quantitative flux predictions, we made modifications to GIMME. First, we replaced P1 to P4) for converting substrate into product and biomass. Path P1 leads to product formation through reactions r1 and r2, whereas paths P2, P3, and P4 lead to biomass formation through reactions , , and , respectively. This metabolic network is underdetermined, as it has nine unknown fluxes, r1 to r9, but only five available equations given by the stoichiometric matrix S in iw = 1 . E-Fmin then selects P2, the shortest among the biomass-producing pathways, as the solution. Note that P1, while shorter than P2, is not the solution of E-Fmin due to the constraint that forces biomass production to carry flux.In the absence of gene expression data, E-Fmin treats all weights as equal, i.e., cases 1 and 2). The weights are given as a function of expression level, 1 – ig, where ig is the normalized gene expression level for the ith reaction, ranging from 0 to 1. We set the weights of the reactions for which no gene expression levels are available to 1. In case 1, E-Fmin discards P2 as the associated genes are not highly expressed. For paths P3 and P4, which are similar in their gene expression levels, E-Fmin selects the former because of the smaller sum of flux magnitudes. Case 2 shows that even the longest pathway, P4, can be selected as the solution of E-Fmin if the associated relative gene expression levels are sufficiently high.Incorporation of gene transcript expression levels into E-Fmin allows us to use biological and condition-specific information to estimate the flux distribution in the system. case 1, it is probable that both P3 and P4 could carry nonzero throughput fluxes. To account for these situations, we would implement flux variability analysis (FVA) after the E-Fmin analysis to account for the range of flux variation through each reaction.E-Fmin, like other LP-based algorithms, yields only a single pathway among alternative solutions, despite the likelihood that multiple routes can simultaneously be activated at a certain ratio according to their associated gene expression levels. For example, in S. cerevisiae. We used experimental data collected by Lee et al., which included RNA-seq transcriptomic data and exchange flux measurements of S. cerevisiae aerobically growing in chemostat cultures. This study provides data under two different growth conditions, i.e., glucose uptake fluxes of 16.5 and 11.0 mmol/(gDW⋅h), which correspond to 75% and 85% of the maximal attainable biomass levels, respectively. We used a genome-scale metabolic network of S. cerevisiae, Yeast 5 We applied our algorithm to the aerobic growth of materialsandmethods for details on their implementation.2 are the predominant products, whereas the production of glycerol, acetate, trehalose, lactose, and biomass is insignificant. This trend is well captured by E-Fmin, E-Flux, and Lee et al., but not by GIMME, FBA, and iMAT. aterialsand Methods, we obtained a P value of 0.04 and rejected the null hypothesis that the ranking of average correlation coefficients in a priori. We provided the flux data and model predictions in The experimental data showed that ethanol and COE. coli in chemostat cultures. We obtained microarray gene expression profiles  and 13C-MFA-based flux data of E. coli K-12 strains from Ishii et al. E. coli to environmental and genetic perturbations and provided multiple high-throughput omics data for both wild-type and mutant strains. To study the effect of environmental perturbations, they cultured wild-type cells at varied dilution rates, while the effect of genetic perturbations was examined by disrupting 24 single genes contained in glycolysis and in the pentose phosphate pathway. As a result, they observed that gene disruptions lead to only subtle changes in mRNA levels, suggesting that E. coli is able to adequately compensate for the loss of a single gene or enzyme in the central metabolism by using excess or complementary capacity of other available enzymes. Conversely, they reported that wild-type E. coli cells appreciably change mRNA levels in response to the variation of the dilution rates but exhibit low-variations (robustness) in metabolite concentration levels. To test our algorithm across organisms, we additionally applied it to the aerobic growth of E. coli network model into the E-Fmin framework as detailed in materialsandmethods. Multiple genome scale metabolic reconstructions of E. coli have been built in the last decade E. coli K-12 strain, iAF1260 E. coli's genome For computational predictions, we incorporated the gene expression data of Ishii et al. and an E. coli strains obtained from E-Fmin and 13C-MFA at respective dilution rates. Predicted fluxes show good matches with 13C-MFA data in all cases. P values. Low P values indicate that the correlations obtained from E-Fmin are statistically significant. 2 production rate in all cases.−10 and rejected the null hypothesis that the ranking of average correlation coefficients in E. coli growing under these specific conditions. Interestingly, despite using the same objective function, the prediction of E-Flux was not comparable to that of FBA. It seems that the E-Flux predictions in this system are more affected by the imposed flux bounds than the choice of objective function. Algorithms based on the direct association with expression levels, such as in Lee et al., showed weak predictive powers, which may be ascribed in part to the use of gene expression data covering only the central carbon metabolism. Similarly to the classical FBA, the flux vector predicted by iMAT also contained outlier fluxes that reached their bounds. After removing them, iMAT showed an improved predictive capability. We provided the intracellular flux data and model predictions at the dilution rate of 0.1 1/h in −10 and rejected the null hypothesis that the ranking of average correlation coefficients in S. cerevisiae and E. coli despite the different metabolic characteristics of these organisms. For E. coli, the assumption of maximal production of biomass underlies the successful application of FBA in many studies of this organism E. coli at balanced growth conditions are close to the theoretical maximum predicted by FBA As demonstrated in the case studies considered above, E-Fmin has strong predictive capabilities for the metabolism of both E. coli, E-Fmin predicted biomass yield of 0.095 gDW/(mmol-glucose) for both wild-type and mutant E. coli strains, which is close to the FBA prediction of 0.096 gDW/(mmol-glucose), but without requiring biomass production to be maximized. In the case of yeast, E-Fmin predicted a biomass yield of 0.028 gDW/(mmol-glucose) for both uptake fluxes of 16.5 and 11.5 mmol/(gDW⋅h), which correspond to ∼25% of the theoretical maximum 0.104 gDW/(mmol-glucose). While Crabtree-negative yeast strains, such as Pichia stipitis, show high biomass yield close to the FBA prediction, the biomass yield of Crabtree-positive yeast S. cerevisiae is known to be much lower due to appreciable production of fermentation products, particularly ethanol S. cerevisiae growing in aerobic cultures has also been observed in other experiments. For instance, Papini et al. S. cerevisiae growing on glucose as 0.031 gDW/(mmol-glucose), which is close to our prediction. Conversely, the algorithm by Lee et al. predicted zero biomass yields for both E. coli and S. cerevisiae. The prediction of iMAT also led to zero biomass yields without additional constraint for biomass production to be larger than a certain level  of the theoretical maximal.The E-Fmin algorithm was designed to account for all of these features. For Eq. 5. The flux distribution obtained as the solution of E-Fmin becomes the same after normalization regardless of the value of ε. The normalized flux vector (obtained from E-Fmin) can be scaled to ‘actual’ values using experimentally measured fluxes, whenever available. Finally, the formulated LP problem leads to fast simulations. This is an important consideration when performing extensive FVA or analyzing systems for a large number of conditions.The main distinguishing features of the E-Fmin algorithm provide multiple advantages when analyzing cellular metabolism based on gene expression data. First, E-Fmin can use a generic network model to predict context-specific flux distribution. This is not the case for other methods, such as EXAMO, where the reconstruction of a condition-specific subnetwork is a prerequisite. Second, E-Fmin uses absolute gene expression data that can be collected directly from a single condition without defining or determining a standard reference conditions. This is advantageous over many of the currently available methods that are based on relative expression data In addition, due to the principle of flux minimization, fluxes predicted by E-Fmin contain no thermodynamically infeasible cyclic paths. The flux minimization also shrinks the space of alternative optimal solutions, leading to very narrow flux variation through individual reactions. We observed no appreciable changes in E-Fmin predictions from the implementation of FVA.In all simulation studies including E-Fmin, we assumed that the cellular composition was known. For cases in which cells grow in conditions that significantly differ from standard cultures, additional measurement of biomass composition is likely to improve model predictions.a priori knowledge of context-specific metabolic functionalities. This feature allows E-Fmin to be applicable across different conditions/organisms without modifying any components of the framework. We validated this capability through studies of S. cerevisiae and E. coli strains exhibiting distinct metabolic characteristics. In comparing the overall ability of the different methods to achieve a ranking above average, i.e., a meta-analysis of all rankings from the separate Saccharomyces cerevisiae data. Thus, compared with other methods, E-Fmin provided more consistently reliable predictions for both organisms. While these studies used gene expression measurements, E-Fmin could incorporate protein expression data as an alternative input to predict flux distribution.The work reported in this article addresses the issue of how one can effectively use gene expression data to study a phenotypic response of metabolism under various conditions by estimating flux distribution. Although a direct link between transcriptomic and fluxomic data is generally weak due to posttranscriptional modifications, the framework we developed was able to extract maximal information of metabolic fluxes by integrating mRNA data with a metabolic network model based on the principle of flux minimization. Importantly, E-Fmin does not require E. coli and low in S. cerevisiae, based on absolute gene expression data. Nevertheless, the inclusion of an expanded set of key reactions may be required to obtain physiologically reliable prediction for a specific condition. A systematic basis for the identification of additional condition-specific constraints is therefore a topic of great general interest. In our future work, we will expand the applications of the E-Fmin framework to examine a broader range of physiological conditions.As a basic constraint, E-Fmin forced nonzero flux through the biomass-producing reaction. This constraint was sufficient for E-Fmin to be able to correctly predict biomass production, high in E. coli and S. cerevisiae reconstructions iAF1260 We used genome-scale metabolic network models taken from the recent 13C-MFA data of E. coli from Ishii et al. S. cerevisiae from Lee et al. 2, glycerol, acetate, trehalose, and lactose. In both experiments, data were collected from glucose-limited chemostat cultures.We obtained preprocessed gene expression profiles and ig to denote the resulting normalized gene expression values for the ith reaction.The initial step in predicting fluxes using E-Fmin is to map gene expression data onto reactions based on gene-protein-reaction associations, which are provided with the iAF1260 and Yeast 5 network models. This mapping is straightforward if a single gene product catalyzes a reaction, i.e., the association of a gene via mRNA and enzyme to a single reaction is unambiguous. In general, a reaction is associated with multiple gene products, and their relations are described using Boolean operators such as “AND” and “OR.” The AND operation represents the involvement of multiple gene products in catalyzing a reaction, whereas the OR operation signifies that only one of the gene products is involved in the reactions. We implemented these operations by taking the minimal and maximal value of the associated gene expression data iw in Eq. 1 as a linearly decreasing function of We designed Eq. 8 ensures that fluxes with lower (higher) gene expression levels are more (less) suppressed.Eqs. 1–3 and 5 into a linear form, as follows: Eqs. 2, 3, and 5. We used 0.01 for ε in Eq. 5.We recast the absolute-sum minimization problem presented in S. cerevisiae and E. coli metabolism, respectively.We solved the above problem using the CPLEX  LP solver. We ran all simulations on a desktop PC with an Intel  Pentium i3 CPU and 4-GM RAM. We provide the MATLAB scripts used for generating the results in Given a table of correlation coefficients of predicted intracellular flux distributions for various conditions and methods, we wanted to test whether the observed rankings could occur by chance. We first normalized the correlation coefficients to scores ranging from 0 to 1 by rank-ordering all of them from small to large and, for each observation, scoring its rank as a percentile. Then, we used the F-test in a one-way analysis of variance i in condition j, i in all conditions, i, The variability values were calculated asRP:i in table t. We exhaustively permuted the three, sorted lists, and computed the P value as the fraction of all permutations that had smaller or equal rank products.Finally, we used rank product analysis Figure S1Wild-type Escherichia coli data collected at different dilution rates : gene expression data , flux data , principal component analysis (PCA) using gene expression data , and flux data . Gene expression data were normalized to range from 0 to 1. Flux data were scaled so that the glucose uptake flux is 100 mmol/(gDW⋅h). For the PCA, both gene expression and flux data were normalized by their maximal value to range from 0 to 1. D denotes dilution rate [1/h].(TIFF)Click here for additional data file.Table S1Sum of squared error (SSE) of E-Fmin and other methods in predicting intracellular flux distributions in Saccharomyces cerevisiae at different glucose uptake rates, wild-type Escherichia coli at varied dilution rates, and mutated E. coli with single gene knockouts.(DOCX)Click here for additional data file.Table S2Comparison of exchange fluxes of Saccharomyces cerevisiae between experimental data and computational predictions.(DOCX)Click here for additional data file.Table S3Comparison of intracellular fluxes of wild-type Escherichia coli at a dilution rate (D) of 0.1 (1/h) between experimental data and computational predictions.(DOCX)Click here for additional data file.Data Set S1Matlab scripts. Provides numeric codes for running E-Fmin and other algorithms to predict flux distributions from given gene expression profiles. At the end of the simulation, the code calculates correlation coefficients (ρ) together with P values and coefficients of determination (R2) as performance measures for comparison.(ZIP)Click here for additional data file.Data Set S2Network model and data files used for simulations of Saccharomyces cerevisiae metabolism. Provides a zipped file containing a genome-scale metabolic network model of S. cerevisiae (Yeast 5) and two sets of gene expression and flux data for two different uptake fluxes of glucose.(ZIP)Click here for additional data file.Data Set S3Network model and data files used for simulations of Escherichia coli metabolism. Provides a zipped file containing a genome-scale metabolic network model of E. coli (iAF1260), five sets of gene expression and flux data for the wild-type strain, and 24 sets of the same for the mutant strains.(ZIP)Click here for additional data file.


E. coli to several different perturbations can always be derived from a sequence of greedy and myopic resilencings. This simple mechanism provides a detailed explanation for the experimental dynamics both at cellular (proliferation rate) and molecular level (13C-determined fluxes), also in case of appearance of multiple phenotypes. As additional support, we identified an example of a simple network motif that is capable of implementing this myopic greediness in the regulation of the metabolism.A way to decipher the complexity of the cellular metabolism is to study the effect of different external perturbations. Through an analysis over a sufficiently large set of gene knockouts and growing conditions, one aims to find a unifying principle that governs the metabolic regulation. For instance, it is known that the cessation of the microorganism proliferation after a gene deletion is only transient. However, we do not know the guiding principle that determines the partial or complete recovery of the growth rate, the corresponding redistribution of the metabolic fluxes and the possible different phenotypes. In spite of this large variety in the observed metabolic adjustments, we show that responses of  A considerable effort has recently been devoted to studying the effect of different genetic and environmental changes, both experimentally and from a theoretical point of view, e.g. by measuring genes expression, rationale that does not make use of any specific information from the available experiments, we formulated a simple hypothesis about a possible unifying principle; (II) we validated the proposed hypothesis by a stoichiometric reaction model: without any tuning of the parameter to fit the experimental data, we tested whether experimental results can be reproduced by this hypothesis, both as growth rate recovery and as fluxes redistribution; (III) once it has passed this validation, the criterion is used to predict and to derive an explanation for some interesting cases of metabolic responses; (IV) finally, we showed how a possible motif in the regulatory network can implement the proposed principle at the molecular level.In order to find such a basic interpretation, our analysis consists of four steps: (I) from a general biological rationale: as already mentioned, genetic and environmental perturbations cause a transient activation of the non-essential metabolic pathways that are progressively silenced as the microorganism adapts to the new conditionOur starting point is the following E. coli after a wide set of different perturbations  and because of the complete independence of the construction of the method from the specific experimental results it is asked to describe, we believe that the comparison between simulations and experimental data represents a stringent test on the validity of our hypothesis.where E. coli, namely glucose, α-ketoglutarate, lactate, malate and succinate as carbon source and acetate kinase (ack), fumarate reductase (frd), glucose-6-phosphate dehydrogenase (zwf), phosphoenolpyruvate carboxylase (ppc), phophoenolpyruvate carboxykinase (pck) and triose-phosphate isomerase (tpi) as deleted genes . These results suggested then a general validity of the greedy hypothesis regardless of the knockout and growing condition.We considered 17 different combinations of single carbon sources media with gene knockouts on It is worth noting that also in the case of high recovery, simulated growth rate does not reach exactly the maximal value suggested by classical FBA. Part of this small discrepancy might be due to the “calibration” of the nutrients availability see . Howeverm-th moment expressionTo further validate the greedy principle, we verified that the mathematical expression (1) used for greediness quantification was not stringent: as shown in was used instead of the Boltzmann 13C-determined fluxes at the intermediate steps and at the final steps of the adjustment in glucose for five different knockouts, namely zwf, ppc and tpi, plus phosphate transacetylase pta and phosphoglucose isomerase pgi1913C-labelling experimental data. Therefore, for each knockout and regardless to which intermediate state along the trajectory has been used for the validation , the observed flux re-routing can be well described by our hypothesis. A comparison with other FBA methods is reported in So far, we found that this greedy regulation of the metabolism appears to be a good principle for explaining the macroscopic evidence of the growth rate recovery. This led us to the question about the re-routing of the metabolic pathways. Can the redistribution of the reaction fluxes be explained by the greedy hypothesis? For this purpose we looked into the details of the dynamics by identifying which reactions have been resilenced/activated along the adjustment and comparing this result with the pgi knockout. It has been shown that this mutant is characterized by a reduction of the growth rate to approximately 0.3 h−1 and by a re-routing of the fluxes toward the pentose phosphate pathway21−1) has been identified. Measurements by 13C-labelling indicated that the difference between these two phenotypes is due to the use  of glyoxalate shunt and of acetate secretion. Surprisingly, these two possible growth rates were obtained also in our trajectories: 0.31 and 0.60 h−1 with about 70% and 20% probability, respectively. As indicated by 13C-measurements for both phenotypes , the final outcome is a phenotype with low proliferation rate. As seen before, Δ−1  ref. , which i−1) for about 10 days making these cells unable to compete for the common nutrients against the greedy cells that are transiently proliferating faster .The fact that the highly proliferating phenotype has not been observed can then be interpreted as a confirmation of the existence of the greedy strategy (i.e. the low probability for the resilencing of phosphoglucose isomerase): if the cells do not follow the greedy criterion, it would have been easier to find this high proliferation phenotype. Moreover, as seen in the trajectories in Concerning the resilencing time, pgi condition. Indeed, although a mutation that stops this reaction has been found in the adaptation of GLU-pgi−1, instead of 0.3 or 0.6 h−1  . Since M is used for the biomass production, this implies also that the growth rate recovery induced by resilencing reaction A is smaller that the growth rate recovery induced by the resilencing of reaction B  have been described through classical Hill equations with constants HA and HB, respectively. This led to the following set of equations:The sketch of the simple motif is reported in g(t) = h[M]. We considered the case when a previous step in the regulation dynamics has caused an increase of the availability u(t) of the precursor P. Given such a input, we analysed how the two feedbacks may affect the response of the systems to this change. HA and HB. This result shows that, in order to induce an higher increase of the growth rate, it is sufficient that evolution had shaped the inhibitory effects of M by increasing HA and/or decreasing HB such that HA > HB. We would like to point out that, as mentioned above, in such a simple network the induced short-term increase of the growth rate coincides with a global advantage: this is the reason why this system reaches high biomass production.The growth rate is then given by A through its effective kinetic parameter in A gives gA = h[M], this leads to:It is also interesting to estimate the probability of resilencing reaction HA such that hHA = gB, is identical to the second-moment formula (2) that we already proved to describe well the metabolic regulation (see m = 2). Indeed, simulations of the network motif (3) with HA = gB/h and HB = gA/h reproduces the resilencing of reaction B, coherently with our greedy criterion .The analogy with the energy minimization problem over a corrugated landscape is also straightforward. Considering the growth rate as the opposite of a Hamiltonian, a metabolic adjustment can be viewed as the dynamics of a particle that is moving toward the minimal energy .


Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted. Saccharomyces cerevisiae has been extensively utilized for the production of isoprenoids, polyphenols, and recombinant eukaryotic proteins. Central metabolism in yeast is typically quantified using isotope tracer techniques [S. cerevisiae have been used to identify essential genes and lethal gene pairs [E. coli [Metabolism in yeasts is primed for the maintenance of homeostasis while ensuring the ability to adapt to changing environmental conditions ,2. Uniquchniques , whereaschniques ,13,14. Achniques ,16, and chniques  using stchniques . GSM-aidchniques , succinachniques , vanillichniques , and seschniques . In addine pairs ,23, and ne pairs ,25,26. One pairs  employ sne pairs  and fermne pairs  and reve[E. coli , highlig[E. coli  and peri[E. coli .Saccharomyces cerevisiae is the most extensively studied species of yeast using 13C-MFA [ 13C-MFA . This te 13C-MFA  and oxyg 13C-MFA ,31, asse 13C-MFA , quantif 13C-MFA , explore 13C-MFA  and non- 13C-MFA ,38. In a 13C-MFA . 13C-MFA 13C-MFA ,41. The  13C-MFA . Increas 13C-MFA . More re 13C-MFA . Most of 13C-MFA . This is 13C-MFA ,45. Seve 13C-MFA ,47, thus 13C-MFA  is accomS. cerevisiae. Three major requirements in terms of accuracy of model annotation, reaction atom mapping and sufficiency of metabolite labeling data are established. The sources of flux resolution loss arising from compartmentalization of metabolism and alternate routes, and additional data necessary to completely resolve such flux are discussed. Finally, approaches to address the computational challenges arising from increased network complexity and variability of the biomass equation arising from accumulation of macromolecules are proposed.This review is aimed at outlining a workflow, key requirements, challenges, and considerations for successfully performing 13C-MFA at the genome-scale in S. cerevisiae, atom mapping data for every reaction within the GSM model, and intracellular metabolite labeling distribution data to resolve all fluxes within the GSM model. Despite various modifications to the consensus yeast model [S. cerevisiae containing 3494 reactions and 2223 metabolites [S. cerevisiae GSM model from 28% to 35%. In contrast, the E. coli GSM model (iAF1260) has a specificity of 79% [S. cerevisiae model arises from its poorly characterized sterol and soluble pool biosynthetic pathways, and differences in biomass composition of mutants and wild-type [E. coli model [2 and formate [The primary requirements for performing MFA at the genome-scale are the availability of a well curated GSM model for st model ,51,52,53st model . This moild-type  with an li model ,56 confi formate , which aS. cerevisiae is quantified in terms of growth rate, biomass and product (acetate and ethanol) yields on glucose. Respiratory quotient (CO2 evolved per mole oxygen consumed) is also routinely measured to constrain overall oxygen uptake. This restricts NADH re-oxidation and ATP synthesis via oxidative phosphorylation, thereby constraining network-wide redox metabolism. Incorporation of transcriptomic and proteomic data using frameworks such as binary ON/OFF type regulation [The reduction of this model can be accomplished by imposing fermentation data as mass balance constraints and eliminating all blocked reactions using FVA. A fermentation process involving gulation  or R-GPRgulation  may furtgulation . For exagulation ,61,62. HS. cerevisiae contains yeast-specific pathways such as the α-aminoadipate pathway for lysine biosynthesis for which atom mapping has been established [Atom mapping information for central metabolism remains conserved across all species and is largely readily available. Atom mapping for peripheral metabolism can be obtained from online databases such as MetaCyc , KEGG 6 and MetRablished . Charactablished  for whicablished . In partablished . Complexablished  or graphablished , which iablished  and MetRablished .2 [in vivo NMR spectroscopy can be utilized to obtain compartment-specific pool sizes and labeling distributions directly for some metabolites based on differences in the microenvironment of various compartments [Labeling distribution data for MFA is typically obtained using GC-MS , LC-MS , or NMR 2  have als2 ,76. Thisartments . This teartments ,79, but artments ,81 and tartments  and nuclartments  can be eartments  and evenartments  to betteartments  can be uartments  can be aS. cerevisiae occurs due to lack of probing techniques capable of obtaining compartment-specific labeling distribution of metabolites. Instead, existing procedures generate a pool-size-weighted average labeling distribution, which must be analyzed using corrections shown in 2 from aeration and pre-existing metabolite pools. Furthermore, catabolism of storage compounds also adds unlabeled carbons, which may alter the estimated flux distributions if not properly accounted for. The longer doubling time of yeast results in slower biomass labeling, which in turn delays the attainment of isotopic steady-state. From the experimental perspective, the above described factors contribute to resolution loss, thereby affecting MFA flux inference. However, recent efforts have confirmed that loss of resolution can also arise from structural identifiability issues existing within the metabolic network [S. cerevisiae, loss of resolution can result from the existence of the methylglyoxal pathway and the γ-aminobutyrate pathway as alternate routes to lower glycolysis and TCA cycle, respectively, resulting in loss of resolution between these two pathways. A previous study has already attributed local flux range expansion to the presence of such alternate pathways in E. coli. In addition to this, intracellular compartmentalization of metabolism introduces metabolic cycles such as the malate shuttle [S. cerevisiae.Inability to completely resolve all fluxes included within the GSM model for  network . In S. c shuttle , which a7 cells). It must be noted that the reliability of the flux estimation procedure depends strongly on the attainment of isotopic steady-state and therefore, at least six doublings [Another potential source of error is cell-cycle dependence of metabolic fluxes. It has already been demonstrated using synchronous cells that the glycolysis/PPP split ratio changes over a 24-h period , causingoublings  must be oublings , althougoublings .S. cerevisiae contain 3494 reactions and 2223 balanced metabolites [E. coli model in which case the number of active reactions was reduced to 697 from 2383 using a flux variability analysis based on growth conditions [S. cerevisiae and E. coli contain 32 [E. coli, this transformation reduces the number of free fluxes from 250 to 99, thereby ensuring statistical significance of the obtained flux distribution [MFA at the genome-scale is performed by first decomposing the genome-scale atom mapping model into subnetworks using an appropriate decomposition algorithm such as the EMU algorithm and then estimating fluxes so as to minimize the deviation of predicted metabolite labeling patterns from experimental data. The fluxes are estimated by solving a non-linear least squares problem, which is then subjected to a goodness-of-fit analysis followed by confidence interval determination ,86. The nditions . In contntain 32  and 75 [ntain 32  reactionntain 32  are repontain 32 . Presentntain 32  have beentain 32 ,92,94, ontain 32  and lackribution . The dirribution , howeverribution . Reductiribution .S. cerevisiae. While the biomass composition of a prokaryote such as E. coli remains relatively constant [S. cerevisiae [Growth-coupling as a means to speed up confidence interval estimation must be used with caution in an organism such as constant , much varevisiae ,101. In revisiae , thus, mrevisiae  using avrevisiae . While sSaccharomyces cerevisiae has a much lower prediction specificity compared to E. coli [In this review, we highlighted the various requirements, challenges, and considerations for achieving genome-scale flux resolution using 13C-MFA at isotopic and metabolic steady-state. While skeletal central metabolic models continue to be the norm, genome-scale MFA holds the potential to validate the various hypotheses proposed by previous analyses ,35,37,38 E. coli ,49, whic E. coli ,69. Spec E. coli ,31,104.  E. coli  for bett


Lignocellulosic biomass is a promising renewable feedstock for biofuel production. Acetate is one of the major inhibitors liberated from hemicelluloses during hydrolysis. An understanding of the toxic effects of acetate on the fermentation microorganism and the efficient utilization of mixed sugars of glucose and xylose in the presence of hydrolysate inhibitors is crucial for economic biofuel production.Zymomonas mobilis 8b when using single carbon sources of glucose or xylose, or mixed sugars of both glucose and xylose. With the supplementation of exogenous acetate, 8b can utilize all the glucose with a similar ethanol yield, although the growth, final biomass, and ethanol production rate were reduced. However, xylose utilization was inhibited in both media containing xylose or a mixed sugar of glucose and xylose, although the performance of 8b was better in mixed sugar than xylose-only media. The presence of acetate caused genes related to biosynthesis, the flagellar system, and glycolysis to be downregulated, and genes related to stress responses and energy metabolism to be upregulated. Unexpectedly, xylose seems to pose more stress on 8b, recruiting more genes for xylose utilization, than does acetate. Several gene candidates based on transcriptome results were selected for genetic manipulation, and a TonB-dependent receptor knockout mutant was confirmed to have a slight advantage regarding acetate tolerance.A new microarray was designed including both coding sequences and intergenic regions to investigate the acetate stress responses of Z. mobilis utilized a different mechanism for xylose utilization, with an even more severe impact on Z. mobilis than that caused by acetate treatment. Our study also suggests redox imbalance caused by stressful conditions may trigger a metabolic reaction leading to the accumulation of toxic intermediates such as xylitol, but Z. mobilis manages its carbon and energy metabolism through the control of individual reactions to mitigate the stressful conditions. We have thus provided extensive transcriptomic datasets and gained insights into the molecular responses of Z. mobilis to the inhibitor acetate when grown in different sugar sources, which will facilitate future metabolic modeling studies and strain improvement efforts for better xylose utilization and acetate tolerance.Our results indicate The online version of this article (doi:10.1186/s13068-014-0140-8) contains supplementary material, which is available to authorized users. Lignocellulosic biomass is considered to be a renewable and sustainable resource to address global challenges on environmental protection, energy security, and economic development, and cellulosic ethanol production has made significant progress at the pilot and demonstration scales. However, the toxic compounds generated during the deconstruction processes of pretreatment and enzymatic saccharification to release fermentable sugars such as glucose and xylose inhibit the microbial catalyst performance during fermentation to ethanol. These inhibitors include weak acids (such as acetic acid), aldehydes , and lignin degradation products (such as vanillin) . Acetic E. coli and Zymomonas mobilis have also received significant attention. Z. mobilis, a Gram-negative facultative anaerobic ethanologenic bacterium, has excellent industrial characteristics such as unique anaerobic use of the Entner-Doudoroff (ED) pathway that results in a high ethanol yield per mole of glucose consumed, high specific productivity, high ethanol titers, and notable ethanol tolerance [Z. mobilis. However, wild-type Z. mobilis can only utilize glucose, fructose, and sucrose as carbon sources, and cannot utilize pentoses like xylose, which is the second most abundant sugar in pretreated and saccharified biomass slurries. An engineered Z. mobilis strain 8b was constructed expressing heterologous genes of talB, tktA, and xylAB for xylose utilization as well as truncating the endogenous lactate dehydrogenase gene ldh for improved flux to ethanol [mobilis 8b is more sensitive to acetate when grown in xylose. The IC50 value  of acetate when Z. mobilis 8b is grown in xylose is 50 mM, compared to the value of 210 mM when glucose is used as the carbon source [Although yeast remains a major microbial biocatalyst for ethanol production, other microorganisms such as olerance -9. In adolerance -14, operolerance , metabololerance -19, and olerance -25 accel ethanol . Z. mobiZ. mobilis for pentose utilization [. Furthermore, despite recent systems biology studies undertaken to unravel the inhibitor tolerance mechanism of Z. mobilis for end-product ethanol [Z. mobilis with high pentose utilization efficiency for economic biofuel (both ethanol and advanced hydrocarbon) production.Despite advances in engineering strains of lization ,26-28, clization -33. Furt ethanol ,34, the  ethanol ,36 or fu ethanol , as well ethanol , no tranZ. mobilis ZM4 [Z. mobilis fermentations. This is especially true with xylose as the carbon source  without exogenous acetate supplementation was lower than that in media with acetate supplementation, delaying the time to reach the stationary phase and maximal ethanol titers  in cultures grown in the absence of acetate (RMG or RMX) were at least 50% higher than the growth rates in acetate-supplemented cultures (RMGAc or RMXAc): 0.43 ± 0.01 and 0.093 ± 0.003 in RMG and RMX compared to 0.28 ± 0.021 and 0.056 ± 0.002 in RMGAc or RMXAc, respectively. The sugar source had a dramatic impact on cellular growth; the growth rate of Z. mobilis 8b dropped sharply from 0.43 when using glucose to 0.093 when xylose was used as the sole carbon source. The supplementation of glucose restored the cellular growth, and the growth rates of Z. mobilis 8b in mixed sugars of glucose and xylose (RMGX and RMGXAc) were same as those in RMG and RMGAc  consumption in all conditions studied in this work; the ethanol was produced gradually as the sugar was consumed and reached its maximum when the glucose or xylose was used up  engineered into Z. mobilis for xylose and arabinose metabolism [To reflect the improved genome annotation , we desitabolism ,27.Samples were taken at exponential phase for the single sugar (RMG or RMX) fermentation experiment, and at different post-inoculation time points of exponential, transition, and stationary phases for mixed sugar (RMGX) fermentation Figure . The timThe quality of data was checked with good correlation , which can help visualize the genome regions with different expression patterns and give an overview of gene expression profiles under different conditions  analyses using JMP Genomics  were carried out using sugar and acetate treatment as variables, and the results indicated that acetate had a more dramatic impact on the transcriptome of 8b in RMG than that of cells grown in RMX, and that the difference between carbon sources is more dramatic than the effect caused by acetate treatment   encoding NADH-flavin oxidoreductase, is upregulated by acetate in RMX and RMGX. In contrast, ten genes are upregulated by acetate in both RMG and RMGX. Three of these are involved in sulfur metabolism: cysteine synthase CysD, I, J  and individual genes are involved in stress response: Fe-S oxidoreductase (ZMO0022), aldo/keto reductase , and catalase (ZMO0918). Other upregulated genes encode the TonB-dependent receptor (ZMO0031), glucan biosynthesis protein D (ZMO0905), L-sorbose reductase (ZMO1449), and a potential alanine/aspartate transporter (ZMO1681) , ZMO1020 (encoding a decarboxylase 2), and ZMO0412 , the other five genes  encode hypothetical proteins.The two genes downregulated by acetate in RMG, RMX, and RMGX are ZMO0128 and ZMO1522, which are annotated as TonB-dependent receptors  and ZMO1395, respectively, and these homologous gene products may compensate for the effect of the deletion. Similarly, the existence of multiple homologues of ZMO0128 in Z. mobilis . However, we were successful in constructing three knockout mutants . These mutant strains grew similarly as their parental strain 8b in RMG without exogenous acetate supplementation (data not shown). 8b-KO1181 and 8b-KO1682 were indistinguishable from the control strain, while 8b-KO0128 had a slight advantage in acetate resistance for cultures grown in RMG plus acetate H for anabolism, and the enzymes involved in glycolysis are among the most abundant proteins in Z. mobilis . The strmobilis ,39. In a[E. coli ,43.Z. mobilis 8b strain was reconstructed, and transcriptomic data were mapped into the pathway for xylose and acetate responsive genes under different conditions  and AdhB (ZMO1596) allowed fast ethanol production with minimal xylitol and acetate produced as byproducts  gene encoding aldehyde dehydrogenase, which catalyzes the conversion of acetaldehyde to acetate, was upregulated eight-fold, and the adhA (ZMO1236) gene, encoding an iron-containing group III alcohol dehydrogenase proposed to oxidize ethanol into acetaldehyde [To investigate the impact of acetate supplementation and xylose utilization on glycolysis and fermentation, the metabolic pathway including xylose metabolism, glycolysis, and fermentation in the s Figure . When gl Pdc ZMO160 and Adaldehyde ,45, was  Pdc ZMO160 and Ad2 = 0.99) between the xylose consumed and the xylitol accumulated . ZMO0293 was downregulated when using xylose (RMX) compared to that in RMG in the absence or presence of exogenous acetate. In addition, when xylose was the sole carbon source, ZMO0293 was downregulated further in the presence of acetate .Glf ZMO0366) has been proposed to serve as a xylose transporter in addition to its primary role as a glucose transporter . A major66 has beZ. mobilis. At the same time, the Pfl gene (ZMO1570) was upregulated, which could drive more pyruvate into the acetate production, causing NADH buildup and more xylitol accumulation. For example, when about half of the xylose (38 g) was consumed, the xylitol concentration was 2.38 g/L in the absence of acetate compared to 3.37 g/L with the supplementation of acetate , chaperone proteins (GroEL), and nucleases, which will help repair the damaged proteins and DNA to ensure accurate cellular function. Furthermore, metabolism of these protein and DNA degradation products may also help resolve any energy imbalances.Previous studies have indicated that, besides energy conservation, microorganisms might bypass the central glycolysis pathway and utilize alternative pathways (such as the nitrogen metabolism in TCC27405 ) for carolerance ,24. In tZ. mobilis may bypass the inhibition on central metabolism by external stressful conditions and utilize alternative carbon and energy metabolism pathways to provide energy and metabolic precursors to keep redox balanced and the cell growing under stressful conditions  encoding N-formylglutamate amidohydrolase, which catalyzes the hydrolysis reaction to release amino acids such as alanine from bacterial cell-wall glycopeptides as a substrate for alternative carbon metabolism. All of these results suggest that s Figure . AdditioLactobacillus sp. [L. acidophilus [Z. mobilis 8b in response to acetate was independent of sugar source of glucose, xylose, or the mixed sugar of RMGX, indicating its critical role in resistance to the weak acid of acetate. ZMO1682 encodes an aspartate aminotransferase and forms an operon with ZMO1681, which encodes a putative aspartate-alanine antiporter. This operon is coexpressed under ethanol and salt conditions [Z. mobilis generate PMF and form ATP for acetate tolerance. During acetate stress conditions, genes encoding proteases and chaperone proteins were upregulated  generation and ATP formation in llus sp. ,50. In adophilus . The connditions  as well glf was only downregulated from the log phase to transition and stationary phases in RMGX in the presence of exogenous acetate. Its expression was similar in RMG or RMX with or without acetate supplementation at log phase  was regarded as the major sugar transporter and reported to be responsive for the transport of non-native sugars such as xylose . In thisE. coli XylE symporter, which is repressed by glucose but induced by xylose. The expression of ZMO0293 was also downregulated by acetate in RMX medium  with a close relationship , oligosaccharides, and polypeptides against the concentration gradient with two other components of a periplasma-facing TonB protein and a plasma membrane localized machinery (ExbBD). In ic space . In thisZ. mobilis strain 8b to acetate stress using glucose or xylose individually or in mixed sugar fermentations were investigated. The non-native sugar xylose poses a stress for 8b, and the supplementation of acetate impedes the cell growth of 8b, resulting in slow substrate utilization and ethanol production. The transcriptomic profiles indicate that the molecular responses of 8b to xylose and acetate are dynamic and complicated. The utilization of the non-native sugar, xylose, elicits a more dramatic effect on transcriptional regulation than the inhibitor, acetate. Our study also suggests that stressful conditions such as growth in xylose in the presence and absence of acetate cause a redox imbalance. As the cells adjust metabolism to address redox imbalance, carbon flux may shift towards the accumulation of a toxic intermediate such as xylitol. In addition, Z. mobilis appears to manage its energy balance through the reduction of energy-consuming reactions such as expression of genes encoding efflux pumps and substrate transporters as well as energy regeneration through alternative carbon and energy pathways such as amino acid decarboxylation. Several target gene candidates based on the transcriptomic results were selected for genetic manipulation, and a TonB-dependent receptor knockout mutant was shown to convey acetate tolerance. Our study has provided insights into the molecular responses of the model ethanologenic bacterium Z. mobilis to the inhibitor acetate in different sugar sources, which will help further strain metabolic engineering efforts for better xylose utilization and acetate tolerance .The physiological responses and transcriptomic profiles of an engineered xylose-utilizing Z. mobilis 8b was revived from frozen glycerol stocks for about 6 to 8 h in 10 mL RM  + 2% glucose at 33°C prior to inoculating the seed cultures overnight in RM + 5% glucose (RMG5) using shake flasks filled to 80% capacity at 33°C at 120 rpm. When the glucose concentration reached approximately 20 to 40 g/L, the cells were spun down at 3,840 × g for 10 min at room temperature (RT) and resuspended in either RMG (8% glucose), RMX (8% xylose), or RMGX  at a ten-fold concentration and used as inocula for fermentation studies.Z. mobilis 8b. RMG, RMX, or RMGX media were used for fermentation. The fermentors were inoculated at an OD600nm unit of 0.1 approximately. The fermentation was conducted at 30°C with an agitation rate of 150 rpm. The pH control set point was maintained at 6.0 by automatic titration with 2 N KOH. Samples were harvested during fermentation at different time points for transcriptomic and HPLC analyses as described previously [Fermentations were performed in BioStat-Q Plus fermentors  at 300-mL working volumes using eviously . The timZ. mobilis cells were revived from overnight culture with OD600nm adjusted to 1.5, and 10 μL seed culture was added into 290 μL media with an initial OD600nm of about 0.05. Growth was then monitored using a Bioscreen C instrument  with three technical replicates. The experiments were repeated at least two times.The high-throughput Bioscreen C assay was carried out as reported previously ,52. BrieHPLC analysis was used for the measurements of the extracellular metabolite concentration of glucose, xylose, acetate, ethanol, xylitol, glycerol, and lactate in 0.2 μm-filtered samples taken at different time points during fermentation. Soluble fermentation products were identified by comparison with retention times and peak areas of corresponding standards as described previously ,35,39,53Total RNA was isolated from the cell pellet resuspended in TRIzol reagent  as described previously . Each to2 transformed and imported into JMP Genomics for analysis. A distribution analysis and data correlation analysis were conducted as a quality control step. The overlaid kernel density estimates derived from the distribution analysis allowed the visualization of sources of variation based on strain and treatment, as well as variation attributed to technical factors, such as array. The data were subsequently normalized using the LOESS Normalization algorithm within JMP Genomics. An analysis of variance (ANOVA) was performed to determine differential expression levels between conditions and time points using the false discovery rate (FDR) testing method (P < 0.05). The significantly differentially expressed gene lists for different comparisons were generated and outputted as Excel sheets. Microarray studies have been submitted to NCBI GEO database with the accession number GSE57553. The interactions among differentially expressed genes were investigated using the STRING 9.05 precomputed database [http://string.embl.de/.Ds-cDNA was labeled, hybridized, washed, and scanned following the NimbleGen protocols. Hybridizations were conducted using a 12-bay hybridization station , the arrays were dried using a Maui wash system (BioMicro Systems Inc.) and scanned with a SureScan high-resolution DNA microarray scanner , and the images were quantified using NimbleScan software . Statistical analyses were done with JMP Genomics 5.1 software  as described previously ,39. Briedatabase  availablaadA in the middle. The assembled product was used as a template for PCR amplification. The purified PCR product was directly electroporated into the Z. mobilis. Transformants appearing on RMG plates with the supplementation of spectinomycin were screened using colony PCR. Colonies with correct PCR product sizes were selected as deletion candidates after sequencing confirmation using the Sanger sequencing service from GenScript .The Gibson Assembly kit  was used to assemble upstream and downstream fragments (about 1.2 kb each) of the target deletion gene with the spectinomycin gene