Zymomonas mobilis (Z. mobilis), a bacterium known for its ethanol production capabilities, can also generate electricity by transitioning from ethanol production to electron generation. The purpose of this study is to investigate the ability of Z. mobilis to produce bioelectricity when utilized as a biocatalyst in a single-chamber microbial fuel cell (MFC). Given the bacterium's strong inclination towards ethanol production, a metabolic engineering strategy was devised to identify key reactions responsible for redirecting electrons from ethanol towards electricity generation. To evaluate the electroactivity of cultured Z. mobilis and its ethanol production in the presence of regulators, the reduction of soluble Fe(III) was utilized. Among the regulators tested, CuCl2 demonstrated superior effectiveness. Consequently, the MFC was employed to analyze the electrochemical properties of Z. mobilis using both a minimal and modified medium. By modifying the bacterial medium, the maximum current and power density of the MFC fed with Z. mobilis increased by more than 5.8- and sixfold, respectively, compared to the minimal medium. These findings highlight the significant impact of metabolic redirection in enhancing the performance of MFCs. Furthermore, they establish Z. mobilis as an active electrogenesis microorganism capable of power generation in MFCs. The application of these microorganisms in bioenergy production  and developing a new approach to treat organic wastewater are significant potentials that have turned this issue into one of the hot academic topics7. The microbial fuel cell (MFC) is an innovative platform to recruit electrochemical active microorganisms for bioelectricity production9. Although the MFCs’ applications in power generation and waste treatment13 were demonstrated, the chief barrier to accelerating the commercialization of MFCs is their low performance15. It is obvious that the activity of electrogenesis microorganisms plays the most critical role in this issue17, and manipulating metabolic reactions could be a unique and influential strategy to improve the efficiency of MFCs20.The capability of electrochemically active microbes to extract the electrons in organic waste and transfer them extracellularly exceedingly attract academic interest22, the microbial oxidative metabolism23, and the extracellular electron transfer (EET) mechanism24 affect the rate of electron production in MFCs. Considering the metabolic reactions of electrochemically active microorganisms, an increase in the concentration of Nicotinamide adenine dinucleotide (NAD) + hydrogen (H)  improves the efficiency of EET25. Li et al. proved that metabolic engineering methods could be employed to increase the intracellular ratio of NADH/ NAD+, and consequently electron generation26. The cofactors NADH and NAD+ are essential for microorganism metabolism and their balance as a prerequisite for higher electricity output is crucial.The redox potentials of the involved speciesZymomonas mobilis (Z. mobilis) is an ethanologenic bacterium that keeps redox balance during ethanol fermentation in the glycolysis pathway28. This bacterium metabolizes sugar via the Entner–Doudoroff (ED) pathway in conjunction with the enzymes pyruvate decarboxylase and alcohol dehydrogenase to produce ethanol. ED is an alternative glycolysis pathway, and in this pathway, glucose is converted directly to pyruvate without the production of adenosine triphosphate (ATP) or indirectly changed to pyruvate based on ATP production29. The fermentation pathway of Z. mobilis for ethanol production has a relatively simple respiratory system and it is the main pathway for electron transport. It chiefly consists of a type II NADH dehydrogenase (NDH), coenzyme Q10, and cytochrome bd terminal oxidase, and cytochrome C peroxidase as the major electron carriers30. One of the main consumptions of NADH in Z. mobilis catabolism is ethanol fermentation31. NADH is used as an electron carrier by respiratory chain NADH dehydrogenase, and NADH is oxidized by alcohol dehydrogenase of the pyruvate-to-ethanol pathway in ethanologenic Z. mobilis. The TCA cycle in bacteria is incomplete and does not function as catabolic pathways. Therefore, NADH can only be synthesized from GAP and glucose-6-phosphate dehydrogenase (ZWF) in the ED pathway33. The EET pathway transports intracellular electrons from NADH to extracellular electron acceptors25. Regarding the ability of Z. mobilis to transfer electrons extracellularly20, Yu-Geng et al. illustrated that Z. mobilis was able to generate considerable bioelectricity (2.0 mW/m2). In this research, to improve the microbial fuel cells several strategies were employed which most noticeable of them were the removal of biofilm and using Tween 80. The removal of the biofilm and c-type cytochrome contributed to the bioelectricity production of the cell, and also by using Tween 80 a higher voltage output was achieved. This revealed that the redox balance is an important requirement for higher electricity output34. But so far, the performance of microbial fuel cells inoculated with Z. mobilis bacteria has not been studied and the technique of metabolic engineering to improve its efficiency has not been investigated.Z. mobilis, some studies used metabolic engineering strategies to produce byproducts by reducing the efficiency of ethanol production. Oliver et al.35 investigated the active respiratory chain of Z. mobilis to regenerate NAD+ and produce acetaldehyde by developing the stoichiometric model of the central metabolism. In their work, Z. mobilis' electron transport chain was demonstrated to be capable of balancing the redox state. By increasing flux through the electron transport chain and reducing the activity of the alcohol dehydrogenase reaction instead of ethanol, the production of acetaldehyde increased and the yield of acetaldehyde would reach 70% of the theoretical maximum value. Also, in another research, Yang et al.27 improved the yield of bioproducts such as lactate and isobutanol by the redirection of central carbon metabolism in Z. mobilis to redox-balanced products. Zymomonas mobilis pdc under inducible diverts pyruvate accumulation to other pathways as ethanol production is inhibited in PDC depletion conditions. Anaerobically, Lactate and isobutanol were produced at high yields from glucose using the PDC inducible strain. Lactate yield reached 70% and isobutanol yield reached 65% of theoretical maximums, respectively. So, these reports demonstrate that Z. mobilis has the potential of redirecting electron from ethanol production to electricity generation which has not been studied.Regarding the performance of Z. mobilis aims to balance NADH/NAD+ on the one hand and electron balance in the cell on the other hand27, Z. mobilis has a high potential for electron production. In this study, a metabolic engineering strategy was utilized to design a regulatory-defined medium and influence the metabolic flux in Z. mobilis in order to shift NADH from ethanol production to electron production. This causes an increase in the regeneration of intracellular NADH which increases the NADH/NAD+ ratio and consequently can increase electron generation. To achieve this, a framework for identifying critical reactions that enhance electricity production in Z. mobilis was comprehensively investigated by useful technique flux balance analysis (FBA) as a constrained-based modeling approach to find multiple optimal solutions18. The model was capable to identify up-regulated and down-regulated reactions by comparing the fluxes of each reaction under NADH and ethanol maximization in order to redirect electron to produce more instead of ethanol production. In the second step, the enzyme regulators found in BRENDA36 were added to the culture medium to regulate target reactions. Finally, this modified medium was used to inoculate an MFC and the performance of the MFC was evaluated by comparing polarization and power density curves under various conditions.Regarding the issue that ethanol production in Zymomonas mobilis ZM1 was performed. This model includes 439 genes, 692 metabolic reactions, and 658 metabolites37. The distribution of metabolic flux was modeled using flux balance analysis (FBA), a constraint-based modeling approach. In this analysis, it is assumed that an organism will reach a steady state under any given environmental condition38. A pseudo reaction for reduction of NAD to NADH was added to the metabolic model and considered as a objective function to calculate the maximum possible rate of electrons production. All reversible reactions were set to 1000 and − 1000 mmolgDCW−1 h−1, except the ATPS4rpp reaction, whose bound was set to − 4 mmolgDCW−1 h−137. The intercellular irreversible reaction fluxes ranged between 0 and 1000 mmolgDCW−1 h−1 while the range for reversible reactions was between − 1000 and 1000 mmolgDCW−1 h−1. In order to simulate the growth of Z. mobilis in a glucose minimal medium, the lower bound of all exchange reactions except glucose, NH4+, H2O, SO4−2, O2, H+, PO4−2, and inorganic ions were set to zero. In addition, under anaerobic conditions, the lower bound for the oxygen exchange reaction was set to 0 mmolgDCW−1 h−137. All in-silico and batch-culture simulations were run with the glucose uptake rate setting at − 10 mmolgDCW−1 h−1. The simulation was done with MATLAB (R2017b) software and COBRA Toolbox and GLPK (GNU Linear Programming Kit) were used to solve linear programming problems39.To find the desirable metabolic reactions and the related compounds, in-silico analysis using iEM439, the GEM for 18 algorithm was applied to identify flux variability of each reaction and compare the metabolic reactions causing NADH and ethanol synthesis. By setting an objective function (generation of NADH or ethanol), FBA determined the maximum value of the objective function as the optimal viable solution by linear programming (LP)41. For all in-silico experiments, initially, the cell growth of the strain was simulated in the glucose minimal media, and the maximum biomass generating reaction was defined as an objective function. The optimal growth rate was set as a constraint in the following steps of simulation. Then, by limiting 100% of the optimal growth rate and setting the NADH or ethanol synthesis reaction in the metabolic network as the second objective function, the maximum NADH or ethanol production rate was obtained, and hence, the optima; value was bound in the metabolic model. Ultimately, for both ethanol and NADH objective functions, the model computes 10,000 optimal solutions. Using a threshold of 10−8, active reactions were identified, and then, for each of these conditions of maximum NADH and ethanol production, the flux variability for each reaction was calculated.The LAMOSZ. mobilis in electron generation by metabolic engineering, these regulators were added to the medium culture, and the effects of up- or down-regulation of predicted reactions were investigated.In order to identify the candidate reactions for up or down regulations, two criteria were considered. The first criterion, as demonstrated in Fig. Z. mobilis  was taken from the Persian Type Culture Collection. The strain was grown in 50 ml of a complex medium containing 10 g L−1 peptone from meat, 10 g L−1 yeast extract, and 20 g L−1 glucose and cultured in a shaker incubator at 30 °C, 150 rpm for 14 h. Then, the culture approached medium-logarithm-phase conditions and it inoculated in the minimal medium. The composition of the minimal medium was glucose 20 g L−1, KH2PO4 1 g L−1, K2HPO4 1 g L−1, (NH4)2SO4 1 g L−1, NaCl 0.5 g L−1, MgSO4·7H2O 0.2 g L−1, CaCl2·2H2O 0.2 g L−1, Na2MoO4·2H2O 0.025 g L−1, FeSO4·7H2O 0.025 g L−137. After 18 h, the strain cultured in the minimal medium can be used to inoculate the MFC.By using the BRENDA database and identifying the effective reactions through simulation and the LAMOS algorithm, the substances restricting the reactions of ethanol production were identified. In order to increase extracellular electron transfer, the additive must first decrease ethanol production and then increase the production of an electron. So, to choose the most effective substance, gas chromatography GC-2550TG ) was used to measure the ethanol concentration and ferrozine iron kits  as a method to examine the amount of the electron generated, were applied. The amount of biomass was measured using a spectrophotometer  at the same time interval and an optical density of 600 nm. The remaining glucose in the culture medium was quantified using a colorimetric glucose oxidase kit . It was an enzymatic method based on glucose oxidation by glucose oxidase enzyme.Z. mobilis was investigated using a soluble Fe (III) reduction method. It is commonly used for detecting microbial EET activity and can be measured using the ferrozine complex, a colorimetric dye42. This complex is a bacteria-produced Fe (II) and the intensity of the light absorbed at 560–600 nm was used to measure Fe (II) concentration. The minimal medium with inhibitor concentrations was inoculated with a 10% inoculum of the middle logarithmic phase. After growing the strain for 12 h in any new culture, a ferrozine reagent was used to determine Fe (II) concentrations in each sample. In addition, based on Fe (III) reduction assays, for the minimal medium without inhibitors, Fe (II) concentrations were measured for 12 h, and the results were analyzed to determine appropriate materials for modification of the minimal medium.The electroactivity of 3, an electrode surface area of 9 cm2, and a height of 2 cm was designed and fabricated. This device is composed of three two cubic (Poly methyl methacrylate) (PMMA) chambers , and the center of these chambers is a square with the size of 3 × 3 cm was cut using a laser beam to place anode and cathode electrodes.The single chamber air cathode (MFC) with a total volume of 18 cm45. Anode characteristics affect biofilm formation, electron transfer, and substrate oxidation, making its material a crucial component in the performance of MFCs46. Nickel electrodes were found to provide a higher power density than carbon cloth47. Due to the hydrophilic nature of nickel, it is capable of rapidly absorbing anolyte and attaching biocatalysts instantly, which drastically increased the bioelectricity generation. Therefore, nickel was selected for the anode electrode. The anodic compartment was covered with a nickel plate . Regarding the importance of the surface area to expedite the cathodic reactions, graphite-coated stainless-steel mesh (mesh 400) was used for the cathode of the MFC. It was treated to achieve 0.5 mg cm−2 platinum (Pt) loading according to the previously published study48. The presence of Pt plays a critical role in the reduction of the activation over potential49. Bolts and nuts were inserted into holes at four corners of the top and bottom chambers. A schematic of the fabricated MFC is shown in Fig. The selected materials for MFCs’ electrodes should be biocompatible, corrosion-resistant, with high surface area, low electrical resistance, and chemically stable50, the start-up of the MFC was done during the measurement of the open circuit potential (OCP). The MFC were fed glucose as a substrate, and the anolyte`s volume was refilled with a fresh medium whenever a huge reduction in the volume of the anolyte due to the substrate evaporation and bacterial activity was observed. The OCP of the MFC was monitored in two cases, it was first done for the MFC inoculated by the minimal medium and without addition of any regulatory substance and then it was fulfilled for the MFC inoculated with the minimal medium enriched by an appropriate regulatory substance. The selection process of an appropriate regulatory substance was explained in "Considering the effective growth of biofilm under the open circuit conditionsV (A m3) and coulombic efficiency (CE) of the MECs were calculated using the following equation in order to assess their performance49:S indicates the molecular weight of the substrate (g/mole), tb is time (s), F represents Farada’s constant (C/mole−), ν indicates the volume of anolyte (L), ∆C is the concentration difference during a defined period (g/L), bes denotes the number of electrons exchanged per mole of oxygen (mole−/mole), and finally, I indicates the produced current (A).The volumetric current density IZ. mobilis. Ethanol is naturally produced along with the reduction of acetaldehyde in the presence of alcohol dehydrogenase which its production results in ATP production. ATP is produced by NAD+ in the glycolysis cycle and it is necessary to generate NAD+ and maintain energy production. To produce NAD+, 3-phosphate glycerol (g3p) must be oxidized by an electron receptor during glycolysis reaction (GAPD-f reaction). Thus, the electron is transferred from NADH to acetaldehyde, and under the influence of the enzyme alcohol dehydrogenase, acetaldehyde is converted to ethanol (ALCD2x-f reaction)51. In this manner, by targeting the enzyme alcohol dehydrogenase and inactivating it, the ethanol production pathway is blocked, therefore, NADH consumption for ethanol is prevented and it is reserved for electron generation.Based on the results of the simulation by the LAMOS algorithm, the effective reactions to increase NADH production were detected. The details of these reactions are presented in supplementary materials, Tables −1) to the amount of consumed glucose (g L−1)) is shown in Fig. Z. mobilis cultured in the minimal medium and other bars indicate the influence of the selected regulatory compounds on the yield of ethanol production by adding them to the Z. mobilis medium. The ethanol production of Z. mobilis in the medium without regulatory compounds was presented in the Supplementary file The effect of different regulatory compounds on the yield of ethanol production  namely ferrozine assays were conducted. Regarding the reduction reaction of Fe (III) to Fe (II) (Fe (III) + e− → Fe (II)), the higher concentration of Fe (II) in the mixture indicated a higher rate of electron production of inoculated bacteria. The amount of Fe (II) concentration is illustrated in Fig. As mentioned in "Z. mobilis in the minimal medium was higher in the presence of BaCl2, AgNO3, and CuCl2 compared to other additives. In other words, these additives decrease ethanol production. The highest concentration of Fe (II) in the modified medium containing CuCl2 reveals that this substance is the most effective regulator to revolve the EET activity of Z. mobilis. To speculate the evolution of electron production of Z. mobilis, the reduction of Fe (III) in the cultured Z. mobilis without a regulatory compound as a control sample was also achieved and shown in the Supplementary file 52. Therefore, the present work mainly focused on the implementation of the modified culture medium to motivate Z. mobilis towards electron production and evaluate its performance by the power generation platform.As can be seen in Fig. Z. mobilis in the minimal medium and (ii) the cultured Z. mobilis in the modified medium .Microbial enrichment was done during open circuit conditions. Two types of anolytes were used for this stage: (i) the cultured Z. mobilis, and reduce the electrical loss, the growth of an effective biofilm is essential. The critical role of electroactive biofilm in MFC performance has been repeatedly reported54. It was demonstrated that the biofilm formation at the highest external resistance is much more uniform compared to lower external resistance which facilitated the nutrient diffusion into it and accelerated release of electrons. In addition, the possibility of biofilm detachment in the uniform biofilm is less than non-uniform one. These were the main reasons that microbial enrichment was implemented under open circuit conditions. The evolution of OCP measuring during microbial enrichment is shown in Fig. To maximize the flux of extracted electrons from 55. The maximum OCPs of 1070 and 1012 mV were obtained for the MFCs cultured Z. mobilis in minimal and modified medium, respectively.The dynamic response of the system as the variation of system potential to the refreshment of the consumed substrate was visible as long as microbial inoculation continued during OCP evolution. A potential cycle during the evolution of MFC potential can be divided into three phases: ascending phase, stationary phase, and descending phase. An increase in the stationary phase  of OCP evolution indicated the organic saturation of MFC’s anolyte. In other words, MFC’s bacteria have sufficient food for growth and achievement of redox reaction. The observation of the stable OCP after three stationary phases indicated that the biocatalyst had stabilized and microbial enrichment was successful56. Figure 57. In other words, applying an external resistance catalyzes the electron generation of the new formed biofilm and accelerates its growth. This plays a critical role during the inoculation of the MFC58. Mclean. et al. demonstrated the anode potential correlated with biofilm development. When an external resistance of 1 MΩ was applied, the growth of shewanella oneidensis MR-1 increased and a thick biofilm on the graphite anode was obtained, whereas only a thin biofilm formed on the anode with an external resistance of 100 Ω. As a result, the anode potential was enhanced by the development of biofilm59.It was observed that changing to the closed circuit by applying an external resistance and then returning to open circuit conditions by removing the external resistance could enhance biofilm growth and increase OCP2PO4, K2HPO4, (NH4)2SO4, NaCl, etc.) establish a hybrid system and the MFC can also operate as a Nickel-carbon battery. This was proved in the previously published study in which the utilization of different metal-based electrodes increased the OCP of the MFC even higher than theoretical values60.Another interesting point in Fig. Z. mobilis and produce much more electrons instead of ethanol, the effect of applying different external resistance should be investigated. For all electrogenesis microorganisms, the electron is extracted due to the conversion of NADH to NAD+49. Concerning the EET of Z. mobilis, as mentioned previously, the strategy of metabolic engineering should affect alcohol dehydrogenase of the fermentation pathway and prevent consumption of electron by converting NADH to NAD+ and producing ethanol and hence, the accumulated electron can be redirected toward generation of electricity. To show this variation in the electron production of electrogens, the MFCs were polarized by applying different external resistances. Figure Z. mobilis with minimal and modified mediums. The gradual depletion of organic content in MFCs decreased the rate of electron production. As soon as microorganisms were fed with fresh nutrients the rate of electron production increased. For both MFCs, an injection of fresh medium, increased current and power densities abruptly. This increase was shown in Fig. Z. mobilis cultured in the minimal medium at 15 kΩ resistance were 4.6 mA m−2 and 0.29 mW m−2, respectively. Meanwhile, for the modified medium (namely Z. mobilis + medium + CuCl2), these values at the same external resistance were 40.00 mA m−2 and 21.60 mW m−2, respectively. This shows that the modification in the bacteria medium increased the power density by more than 77-folds.Regarding the main objective of this study to demonstrate that the suggested procedure of metabolic engineering could improve the bioelectricity of 61. The polarization and power density curves for the MFCs are shown in Fig. Regarding the gradual reduction in the organic content of the MFCs, the polarization and power density should be irrespective of substrate concentration. As a result, by applying each external resistance, the refreshment of the medium was done and after 180 h, the maximum value of the cell potential was recorded. The range of the external resistances to polarize the cell was 10 kΩ to 5.5 MΩ. To assess the MFC’s over-potentials or the galvanic losses of MFCs, the polarization and power density curves are useful tools. Considering the overpotentials of an MFC including activation, ohmic and concentration overpotentials, the variations in each of them can be observed in separate sections of the polarization curves. The activation overpotential is the first section of the polarization curve describing the loss due to the metabolic reactions in extracting electrons and the required activation energy to run the redox reactions in both cathode and anode electrodes. The ohmic overpotential as a linear potential drop (remarked in the second section) represents the loss of electron transfer in the anolyte and the electrical connection. Finally, the concentration overpotential describes the limitation of the mass transfer of species during the redox reactions and through the proton exchange membrane as wellZ. mobilis by more than 5.8-folds and six-folds compared to the minimal medium, respectively  decreased significantly. Therefore, the produced electrons in the anolyte of the modified medium had the least loss to reach the electron acceptor. The first speculation might attribute this matter to the enhancement of anolyte conductivity due to the CuCl2 presence but the polarization curve of the MFCs fed with the minimal and modified mediums without the bacteria presence revealed that the presence of this compound produced a maximum of 3.79 mA m−2 and 1.29 mW m−2 which were a small proportion of total current and power densities of the cell, respectively , which was quite low. However, by enhancing the electron production pathways in Z. mobilis through metabolic engineering, the power generation increased to 21.6 (mW/m2). This accomplishment highlights the importance of metabolic engineering in strengthening electrogenesis pathways, a strategy with the potential for broader application to various microorganisms. It can significantly accelerate the commercialization of Microbial Fuel Cells (MFCs), particularly when the limited performance of introduced microorganisms poses a significant hurdle. Consequently, future research endeavors should consider adopting this approach or investigating alternative electrogenic species.One of the primary motivations behind the current study is to demonstrate the importance of metabolic engineering in enhancing the performance of MFCs enriched with bacteria that do not possess a remarkable tendency for electron generation. Z. mobilis with the minimal medium  were analyzed and the morphology of these MFCs anodes is shown in Fig. Z. mobilis to electron production and the higher aggregation of them on the anode surface of MFC with the modified medium is crystal-clear evidence for this claim , thereby facilitating the efficient transfer of generated electrons.To being with, a compreheansive analysis of the metabolic pathways involved in bioelectricity generation is conducted. This analysis helps identify key bottleneck genes that effect the efficiency of these pathways. Bottleneck genes are those that have a significant impact on the overall metabolic flux and can potentially be targeted for improvement. LAMOS and BRENDA are compoutational tools used for the initial screening of existing compounds to identify potential candidates for switching metabolism and enhancing NADH production by analyzing the structure activity of compounds, these tools can predict their potential to modulate metabolic pathways and increase power density generationFig. .. The meZ. mobilis biofilm on the anode surface further supports the role of CuCl2 as a regulatory enzyme in enhancing MFC performance.The medium modification has also proven beneficial in preventing the occurrence of the overshoot phenomenon. By deactivating alcohol dehydrogenase and ensuring sufficient NADH availability, the modified medium enables the extraction of electrons even under conditions of sudden electron depletion at low external resistances. Furthermore, the accumulation of Z. mobilis and the efficacy of CuCl2 as a regulator, leading to improvements in bioelectricity production. The modification of the growth medium has shown promising results in reducing overpotentials, preventing overshoot, and promoting biofilm formation, ultimately enhancing the overall performance of MFCs.In summary, this study demonstrates the successful metabolic engineering of The authors wish to acknowledge Tarbiat Modares University for its financial support of this research.Supplementary Information.


Saccharomyces cerevisiae and Pichia pastoris are excellent manufacturing systems for the production of NPs. This Perspective discusses a comprehensive platform for sustainable production of NPs in the two yeasts through system-associated optimization at four levels: genetics, temporal controllers, productivity screening, and scalability. Additionally, it is pointed out critical metabolic building blocks in NP bioengineering can be identified through connecting multilevel data of the optimized system using deep learning.Secondary natural products (NPs) are a rich source for drug discovery. However, the low abundance of NPs makes their extraction from nature inefficient, while chemical synthesis is challenging and unsustainable.  Saccharomyces cerevisiae and Pichia pastoris, are promising chassis for the production of nature products (NPs). Here, the author discusses establishing a comprehensive platform for sustainable production of NPs via system-associated optimization at genetics, temporal controllers, productivity screening, and scalability levels.Yeasts,  Importantly, NPs have been sources of new drugs over the past four decades2, and are used as prototypes to discover new drugs because of their massive scaffold diversity and structural complexity3. Plants are excellent producers of NPs with diverse biological activities5. Currently, the principal source of most NP-derived pharmaceuticals remains direct extraction from plants3. However, the lack of continuous availability of sufficient amounts of natural sources and variations in yields have limited NP extraction5. Considering the pressures of food security and population ageing6, alternative approaches are urgently needed to enable environmentally friendly production of NP drugs.More than 50% of small-molecule therapeutic agents in current clinical usage are natural products (NPs)7. However, only a few natural compounds produced by microbes have reached commercial scale9. Opportunely, the increasing sizes of multi-omics datasets and the acceleration of technologies supporting structural analysis and enzyme characterization, the time to discovery of enzymatic steps for synthesis of complex NPs in plants has decreased10. To capitalize on this opportunity, it’s urgent to establish a standardized chemical engineering approach. Here, I discuss yeast as a microbial cell factory for production of complex plant NPs with pharmaceutical importance, such as alkaloids11 and flavonoids12.Thanks to the rapid progress of synthetic biology, the microbial synthesis of more complex NPs has been possible. A prime example is the successful construction of a yeast microbial cell factory for strictosidine and its downstream vinblastine, positioning yeast as a scalable system to produce more than 3000 reported NPsSaccharomyces cerevisiae and Pichia pastoris (syn. Komagataella phaffii), are the best characterized and the most widely used microorganisms in the fermentation industry13. They can potentially resolve issues associated with low heterologous NP product yield observed in other expression hosts, such as E. coli and mammalian systems14. Yeast is able to synthesize the required precursors of diverse plant NPs including alkaloids, an imperative group of NPs containing at least one nitrogen atom15. Yeast expresses endoplasmic reticulum (ER)–localized cytochrome P450 monooxygenases , membrane-anchored cytochrome P450 reductases (CPRs), scaffold Pictet-Spenglerases, and berberine-bridge enzymes, which are essential enzymes for producing NPs17. Yeast, like higher eukaryotic organisms, is equipped with subcellular organelles, such as mitochondria, that have been employed to shield the heterologous enzymes18. Compared to mitochondria, peroxisomes are more appropriate organelles for the compartmentalization of engineered NP pathways. Peroxisomes are detoxifying organelles with greater ability than other organelles to tolerate and sequester toxic molecules such as NPs19. Moreover, they are near the ER, thus facilitating efficient cytochrome P450-mediated oxidation of the intermediates required to produce NPs such as monoterpene alkaloids20.Multi-purpose yeasts, including S. cerevisiae is typically used as a host for manufacturing complex NPs21. Recently, the non-conventional methylotrophic yeast P. pastoris has emerged as an attractive host for NP production22. P. pastoris has the advantages of methanol utilization, compatibility with higher density cell growth and large-scale fermentation. Furthermore, it shows a higher expression level of heterologous protein than S. cerevisiae. For example, recombinant proteins expressed under the control of the yeast alcohol oxidase 1 (AOX1) promoter can contribute more than 30% of the total proteins in P. pastoris23. A recent study has shown that a cytochrome P450 enzyme, which catalyzes the conversion of scoulerine (a product downstream of reticuline) to cheilanthifoline, exhibits a higher conversion rate in P. pastoris cells (70%) than S. cerevisiae cells (20%)24. Interestingly, when P. pastoris is grown on methanol, its peroxisomes substantially multiply25, which can be deployed to increase the production of heterologous enzymes.The 26. To enable yeast cells to produce a chemical-of-interest  and promoters29, terminators30, and genome editing tools31 have been developed to facilitate regulation of heterologous protein expression in S. cerevisiae. Fortunately, these tools have been found to be functional in tuning heterologous protein expression levels in P. pastoris, for which a limited number of tools and methods are available for metabolic engineering. Considering the dual application of engineering tools and methods in both chassis’ organisms, P. pastoris has attracted attention as an ideal platform for metabolic engineering of plant NPs.The genes coding for NPs are typically randomly distributed through the genome in the native hostest Fig. , each in1) resources, e.g., carbon dioxide (CO2); and (ii) an optogenetic inducer  one-carbon  to achieve net zero emissions of natural gases will be necessary34. In the next three subsections, I discuss strategies for carbon-negative manufacturing through enabling yeast to metabolize CO2 or pairing yeast with autotrophic microorganisms, as well as approaches to using light to activate NP production.Yeast tends to offer modest emissions savings over conventional agricultural processes in the production of pharmaceutical chemicals, including less consumption of water, nutrients, energy, and land, lower costs, faster and more stable production cycles, and higher quality products35. However, applying acetogenic bacteria or other natural C1-utilizers for industrial-scale production is hindered by the limited availability of genetic manipulation tools, slow growth, and/or a narrow product spectrum36. Hence, chemical engineering strategies enabling S. cerevisiae and P. pastoris to utilize CO2, as a carbon source, rather than sugar, are increasingly being considered.To switch to carbon-negative manufacturing, a recent study has suggested a promising metabolic engineering approach by harnessing autotrophic acetogens that uses the Wood-Ljungdahl pathwayP. pastoris can obtain all the carbon and energy needed for growth and metabolism from reduced C1-molecule methanol37. However, it leads to release of CO2 that cannot be used by P. pastoris37. By adding eight heterologous genes and deleting three native genes, Gassler et al. have engineered the peroxisomal methanol-assimilation pathway of P. pastoris into a CO2-fixation pathway resembling the Calvin–Benson–Bassham cycle 38. The Calvin cycle is then targeted to either the cytosol or peroxisome. Next, the team have replaced the formaldehyde-assimilating dihydroxyacetone synthase enzyme of P. pastoris with the bacterial ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme. After introducing phosphoribulose kinase from spinach, the generated yeast strain directly assimilated CO2 instead of methanol, and converted it to organic acids39. Portions of the Calvin cycle have been introduced into the yeast S. cerevisiae40. To enhance CO2-fixation efficiency via the Calvin cycle in yeast, several optimization routes, for example, expressing RuBisCo derived from various origins have been evaluated41. Li et al. have increased the production of adenosine triphosphate (ATP), and relieved the nicotinamide adenine dinucleotide phosphate (NADPH) imbalance involved in the CO2-fixation Calvin cycle42. A highly active RuBisCo from the endosymbiont of the deep-sea tubeworm Riftia pachyptila43 may offer a unique application avenue for creating an efficient artificial photosynthetic pathway. Ultimately, yeast requires conversion of the Calvin cycle’s final product, glycerate 3-phosphate (G3P), to precursors for NP manufacturing /ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) has been established for the in vitro fixation of CO245. The CETCH cycle, based on crotonyl-CoA carboxylases, allows converting CO2 into glyoxylate at a higher carbon fixation rate than the Calvin cycle. CETCH has been successfully coupled to (i) encapsulated photosynthetic membranes for light-powered CO2 fixation to glyoxylate46, and (ii) the β-hydroxyaspartate cycle, a glyoxylate assimilation cycle from marine bacteria, to convert CO2 to acetyl-CoA in vitro47. Although light-responsive CETCH cycle has not yet been implemented in vivo, it may offer a way to couple CO2 assimilation pathways with NP-producing pathways in yeast , which may lead to oxidative damage to cells, and the toxic formaldehyde48. To minimize oxidative damage, balanced expression of the peroxisomal H2O2-generating oxidases and catalases through combinatorial optimization is an interesting subject for investigation. To decrease the production level of formaldehyde, Gassler et al. have deleted the AOX1 in P. pastoris 38. In fact, a small fraction of the formaldehyde is required to generate energy 38. Therefore, the balanced expression of formaldehyde synthesizing and consuming enzymes may enhance energy production in P. pastoris using methanol. To improve the performance of P. pastoris rewired for CO2 assimilation and methanol-based energy system, Gassler et al. have enhanced growth of the autotrophic P. pastoris by reverse engineering and adaptive laboratory evolution49. However, to deploy C1’s potential for producing complex NPs in yeast, it should be stressed that a long way has to be taken concerning an efficient energy-producing route establishment in yeast. One promising path to circumvent the problem is the electromicrobial conversion of C1 sources into NPs using “green hydrogen” energy carriers50. Theoretically, in a custom-designed bioreactor, yeast cells engineered to express hydrogen-oxidizing enzymes can consume hydrogen produced by solar electricity to drive CO2 fixation. Cheng et al. have established a bacterial mixotrophical CO2 assimilation system utilizing hydrogen, generated by hydrolysis of sodium borohydride, as an energy source51. This is an important step toward utilizing green hydrogen to support carbon-negative manufacturing in yeast. However, low solubility, poor mass transfer rate, and process safety issues of hydrogen in microbial cultures are the conceivable obstacles. Hopefully, these can possibly be addressed via a systematic design of chemical-biological interface52.CETCH and other routes relying on C1-assimilation require an energy source to convert thermodynamically stable CO1-fixation pathway to produce NPs in yeast, we must consider that yeast needs to be engineered to express many heterologous enzymes (in addition to C1-fixation enzymes), that may cause a metabolic burden on the yeast cell. Division of labor between cells can alleviate the metabolic burden. However, a few artificial cell-cell communication systems in yeast have been developed53. Alternatively, the use of autotrophic microorganisms and yeast in synthetic microbial consortia (SMC) divides labor between cells. Recent progress in SMC designing strategies allows the production of value-added chemicals in an environmentally friendly manner. In an established SMC through co-culture of bacteria Clostridium acetobutylicum and C. ljungdahlii, CO2 has been fixed and converted to C2-C4 alcohols54. In another example, a consortium for a cooperative relationship between alginate-utilizing Vibrio sp. dhg and 3-hydroxypropionic acid (3-HP)-producing Escherichia coli strains has been established, allowing the direct conversion of alginate to 3-HP55. For carbon-negative processing, pairwise SMC can be defined between C1-consuming autotrophic bacteria and yeast56. However, establishing such artificial communities fails in most cases, as one organism dominates in nutrient competition in co-cultivation condition57. Molitor et al. have established a two-stage bioprocessing system between acetogenic bacteria C. ljungdahlii and S. cerevisiae58. First, in a bioreactor with a pure culture of C. ljungdahlii, CO2 with oxygen and nitrogen supplies (sourced by wastewater) has been converted to acetate. Next, in the other bioreactor with a pure culture of yeast, the acetate has been converted to animal feeding protein58. In another two-stage system, CO2 (mixed with reducing agents carbon monoxide or hydrogen) has been converted into acetic acid using acetogen Moorella thermoacetica in the first-stage anaerobic bioreactor59. Subsequently, acetic acid has been utilized for lipid production by engineered oleaginous yeast Yarrowia lipolytica in the second-stage aerobic bioreactor59. Although establishing a two-stage bioprocessing system can overcome the drawbacks of co-culture schemes, the start-up of two connected bioreactors still generates high capital costs.In engineering the C60. Recently a methacrylate HEMA-EDMA porous microplate living material has been designed to maximize neighboring well interactions61. This culturing system enables exchange of small molecules, but not cells, between wells61. With this porous microplate, a spatially segregated co-culture of autotrophic bacteria and yeast can potentially be established. However, extensive follow-up work must be performed on the co-culture of autotrophic bacteria and yeast61. Another technique called microbial swarmbot consortia (MSBC) has been created by encapsulating subpopulations in polymeric microcapsules62. The cross-linked structure of microcapsules spatially segregates the microbial cells but permits the small-size molecules and proteins to pass. In an MSBC for sustainable production of small NP drugs, photosynthetic Synechococcuselongatus can capture and convert inorganic CO2 to the carbon-based organic compound G3P in the presence of blue light 64 can be engineered into S. elongatus (the autotrophic partner of SMC). Alternatively, through artificial endosymbiosis ght Fig. . In factsis Fig. , various66. To date, exogenous chemical inducers  have been used to control gene expression67. However, the application of chemical inducers is limited owing to hypersensitivity and toxic effects68. Therefore, the development of cost-effective inducers that enable modulation of protein expression levels in a fast-acting and robust manner would support stable biomanufacturing of pharmaceutical drugs69. In the context of the proposed platform, an optogenetic system is required to regulate the expression of synTFs. A customizable synthetic DNA-binding domain (DBD), such as a transcription activator-like effector70, to target the upstream promoter of synTF and a yeast activation domain (AD) to activate transcription are, respectively, linked with the light-using domain and its interacting partner domain  to access their substrates18. Increasing the ER size to cope with P450 protein folding and accumulation has yielded a concentration of 31.01 mg/L for parthenolide, a precursor for the anti-glioblastoma drug ACT00176. In another study, to maximize the peroxisome membrane storage capacity of S. cerevisiae, the number and size of peroxisomes have been increased through the expression of peroxisome biogenesis-associated peroxins77. The peroxisomal compartmentalization of acetyl-CoA, coupled with increased expression of sesquiterpenoid synthase and redirected precursor supply from the mitochondria to the cytosol, has increased production of terpene (+)-valencene 549-fold (1.2 g/L)74. Dusseaux et al. have introduced the mevalonate pathway into the peroxisomes of S. cerevisiae32, which results in 125-fold greater plant terpene limonene production compared to that achieved in cytosolic production. Srinivasan and Smolke have reported an engineered yeast for increased production of hyoscyamine and scopolamine through chemical engineering of cytosol, mitochondrion, peroxisome, ER membrane, and vacuole21. The expression of four plant transporters, engineering cofactor regeneration mechanisms, and optimized growth conditions have resulted in an increase in de novo tropane hyoscyamine production of more than 100-fold (480 μg/L)78. Overall, depending on the complexity of the NPs80, different modifications should apply to the yeast strain to enhance precursor level, provide energy supply, and deliver an adequate level of NAD(P)H.A considerable hindrance in microbial bioprocess scale-up for NP production is that the accumulation of NPs within intracellular membranes decreases cell growth (and consequently the yield of the desired NP)81. To achieve the optimal level of NP production in yeast, besides rewiring the native global regulatory networks, the optimal expression level of heterologous enzymes must be evaluated. Therefore, many genetic diversities must be created and tested, in so-called synthetic evolution inspired by nature, which is a time-consuming task82. Here, I focus on combinatorial optimization methods to simultaneously assemble millions of variants, followed by high-throughput screening of optimal variants, to enable rapid synthetic evolution.The balance of metabolism in yeast cells has been optimized by natural evolution, wherein natural selection for desired traits creates iterative rounds of genetic diversity-selection cycles over timeS. cerevisiae, and P. pastoris, which may create a metabolic burden on the cell (thus negatively affecting growth)66. The absent or very low transcription levels of the main biosynthetic genes for complex metabolites plays an essential role in the failure to detect compound production in native or heterologous hosts15. These issues are more severe and challenging for the long biosynthetic pathways of NPs, particularly alkaloids, which are highly toxic metabolites for the host cell19. Fast-growing collections of synTFs for S. cerevisiae67 and P. pastoris28 provide a sophisticated solution to this problem, because they enable orthogonal, tunable, and inducible expression of the target gene(s) at desired processing time. The synTFs are well suited to the platform discussed herein, especially for controlling heterologous gene expression with a light inducer83. Using combinatorial optimization strategies, a library of synTFs can be assembled upstream of the individual genes of a biosynthetic pathway to create synthetic transcriptional units69. Combinatorially assembled units in artificial operons are integrated into the multilocus of the yeast genome for stable metabolite production. For example, nine synTFs have been assembled with four genes (to convert farnesyl pyrophosphate (FPP) to aromatic β-ionone) in only seven combinatorial cloning reactions 27. The resulting 36 synthetic transcriptional unit modules have been used to generate a genomically engineered yeast library, in which various cells produce different levels of β-ionone.Strong promoters of yeast genes are repetitively implemented for metabolic engineering purposes in 84. Modifying substrate recognition sites of a P450 responsible for the first putative oxygenation step of the forskolin pathway has been found to increase its catalytic activity up to 6-fold in plants85. Davies et al. have reported optimized expression ratio between P450s G8H and its redox partner increases the production of the insect-repellent nepetalactone86. It has been shown that combined synthetic biology and protein engineering allows to identify a suitable cytochrome P450 to decorate the terpene scaffolds leading to produce 28 different noncanonical terpenes87. Concerning several ambiguities about obtaining catalytically active heterologous enzymes , yeast must be engineered with multiple gene isoforms to identify the optimal enzyme variant. Including different isoforms of various enzymes in the context of combinatorial optimization strategies enables automated optimization at the levels of transcription and translation, thus opening new routes to enhance NP production in yeast cell factories. As a starting point, Zhang et al. have optimized the production of nepetalactol in S. cerevisiae using screening a combinatorial library of four isoforms for iridoid synthases and cyclases7.In alkaloid biosynthetic pathways, master builder P450s play key roles in generating the structural variety underlying the functional diversity of produced NPs69. The problem is more pronounced when therapeutic NPs, the end product of long-biosynthetic pathways, are participants in combinatorial optimization techniques88. Moreover, stable scale-up of manufacturing processes faces a bottleneck due to reliance on low-throughput analytical methods for assessing strain performance89. The identification of genetically encoded transcription factor (TF) biosensors may provide alternatives to natural selection through fast screening of top-performing cells. Thus genetically encoded biosensors may potentially become part of a general platform for sustainable production of NPs. d’Oelsnitz et al. have developed a generalized approach to identify TF biosensors to detect plant NPs that are based on the TetR-family of multidrug-resistant TFs from Salmonella typhimurium, RamR, and its derivatives89. RamR derivative biosensor libraries containing more than 105 members have been filtered into several high-performing variants in less than one week, and used to detect several benzylisoquinoline alkaloids (BIAs)89. TF-derived biosensors were subsequently used to translate the BIAs production level into a fluorescence signal. In addition, the TetR-family regulator CamR from Pseudomonas putida has been established to detect the monoterpene NPs90. These data altogether highlight the generalized abilities of the approach to detect various alkaloid-based NPs. This strategy can also be used to screen the intermediate metabolites produced by various isoforms of, for example, P450s and their partners within a combinatorial library to identify the most efficient enzyme isoforms. Another general method to generate biosensors for user-defined molecules has been developed by Beltran et al.91. They have used the plant abscisic acid receptor PYR1, with a malleable ligand-binding pocket, to evolve 21 sensors for a range of small molecules, including structurally diverse natural and synthetic cannabinoids91. Using the systematic evolution of ligands by exponential enrichment (SELEX) strategy, we are potentially able to screen a library of synthetic riboswitches for one with high binding affinity to a NP of interest69. Jang et al. have reported RNA aptamers for naringenin using SELEX92. In this study, the high-affinity naringenin-responsive riboswitches have been identified through applying in vivo selection route to the library by supplementing naringenin at different concentrations during enrichments. With the established NP biosensors, yeast cells producing high levels of NP within the combinatorial library can be detected and sorted with a flow cytometer for further optimization at bioreactor scale.Detecting enzymes that can improve yield is a crucial challenge in the microbial production of chemicals93. Cybergenetics involves advanced bioprocess engineering approaches that combine engineering and biology, and support the control of engineered metabolic pathways through computer interfaces and feedback controlling biological processes in real time. In this context, Chi.Bio94 and eVOLVER95 are two optogenetic-ready, open-source bioreactors that have been developed and made commercially available. The possibility of continuous cell culturing in automated bioreactors enables optimizing growth conditions for the top NP producers of the combinatorial library. ReacSight, a complementary strategy to enhance bioreactor arrays for automated measurements, provides a flexible instrument control architecture96. The combined platform of ReacSight and Chi.Bio has been used to demonstrate the real-time optogenetic control of gene expression and to achieve dynamic control of the composition of a two-strain consortium96. Similar strategies can be applied to evaluate the target metabolite production in yeast using the light-inducible synTFs and to establish optimal microbial consortia between yeasts and bacteria for carbon-negative manufacturing. In addition, the NP production output of the system can be quantified by real-time measuring the biosensor-derived fluorescent output using flow cytometry linked to an automated bioreactor. Finally, the protocol extracted from the in situ optimization of NP-producing yeast cells in bulk culture would enable unprecedented strategies for bioreactor operation.Bioprocess engineering aims to address the poor productivity of biomanufacturing systems arising from the manual handling of samples, dynamically changing growth environments, external sources of noise, and a lack of control over the conditions experienced by cells before and during growth97. The multilayer optimization platform discussed herein may potentially generate a large dataset for developing DL algorithms with direct applications in biomanufacturing. Next-generation sequencing (NGS) approaches have revolutionized the identification of DNA diversity98. NGS can unlock new ways to tackle the DNA diversity in not only top producers but also subpopulations of low- to non-producer cells within the  library. Nevertheless, it alone is not appropriate for identifying the combination of DNA sequence of synTFs and genes encoding enzymatic isoforms within a combinatorial library of yeast population. The high similarity of DNA sequences encoding various synTFs  makes it challenging to infer the relative combination of DNAs of synTF and enzymes during assembly analysis of a (sub) population69. The droplet-based single-cell RNA sequencing allows flag-tagging of individual cells within the population99. The detailed description of droplet-based single-cell RNA sequencing to evaluate a yeast NP-producing library is illustrated in Box 100. Nanopore sequencing produces reads as long as 10 kb, thus resolving the sequencing issue for the repetitive region of synTFs used to modulate the production of NPs. Analytic tools that consider the characteristics of long-read data are mandatory101, but the development of such novel tools might take time. Large-scale single-cell genomics and transcriptomic datasets can be potentially generated using RNA sequencing for both undesirable and desirable populations. Together with induction, production, and bioprocessing optimization data, these data can feed DL algorithms to fit mathematical models, including model predictive control algorithms103. Consequently, upon tight control of the experimental conditions and properly processing the generated data, the behavior of the biological system  can be quantitatively described, and its functionality can be enhanced. Ultimately, the mechanisms that trigger loading problems are pinpointed and emergence of non-producer mutants is prevented in scaled-up fermentation.Previous sections have discussed a platform to simplify the complex tasks of maximizing the synthesis of different NPs in microbial cells. To develop rational engineering strategies for producing rare NPs in the microbial cell, understanding the sophisticated feedback regulations controlled by the intermediate compounds is essential104, thus allowing the design of more robust synTFs for bioengineering heterologous pathways. Second, an array of machine learning methods and amino acid property descriptors are applicable to protein redesign problems, essential tasks in NP biomanufacturing105. Third, the PRISM 4 platform can predict chemical structure106. Through combinatorial optimization of NP pathway engineering in yeast, new derivatives of NPs might be produced for new drug discovery. However, the isolation of novel NP molecules is hindered by the challenge of connecting sequence information to the chemical structures of the molecules. PRISM 4 enables the development of machine-learning methods to predict the likely biological activity of new encoded molecules. In addition to new DL algorithms, powerful computational hardware would be essential to improve the quality and reproducibility of small molecule studies. An important subject that must be addressed is the lack of accessibility of such data and their maintenance. More than 120 natural product databases and collections have been published since 2000107. However, many of them are not automated facilities and do not provide open access to researchers. Generating the dataset based on the FAIR data principle 108, followed by uploading the dataset to third-party facilities such as CloudLabs (https://cloudlabs.us/) or biofoundries , can accelerate the design-build-test-learn cycle of NP metabolic engineering in microbial cell factories109.How DL algorithms may specifically contribute to extracting maximum information from an extensive combinatorial library of NP-producing variants warrants an in-depth review. To call synthetic biologists and bioinformaticians to action in this regard, promising DL programs that are applicable to substantially improving NP production are suggested here. First, Enformer can learn to predict promoter-enhancer interactions directly from the DNA sequenceBox 1 Schematic of gDNA capture during RNA-seq of a combinatorial librarya, “Barcode 1” is added to the terminator region of the synTF-encoding sequence, whereas a fragment containing artificially added “CAPdT” and “Barcode 2” is inserted upstream of the synTF binding site. The 10x Genomics gel beads consist of a pool of primer sequences that are used to separately index each cell’s transcriptome. The presence of CAPdT in all primer sequences enables RT of the cellular mRNAs within a library. All primers on a single gel bead contain identical CBC sequences to identify all transcriptomes of a single cell. Each primer has a unique UMI to tag each individual cDNA fragment. b, The Chromium Controller from 10x Genomics enables generation of tens of thousands of labeled single-cell partitions in emulsion droplets within minutes. Each droplet contains an identifying barcoded gel bead. c, In each droplet, the gel beads and cells are dissolved. All fragments from the same cell share a standard 10x barcode. In the given example, “Barcode 1” and “Barcode 2” enable identifying the combination of synTF 1 and CDS 1; “Barcode 3” and “Barcode 4” enable identifying the combination of synTF2 and CDS 2. UMIs 1, 2, 3, and 4 enable molecular counting of the captured mRNA molecules synTF 1, CDS 1, synTF 2, and CDS 2, respectively. After reverse transcription, the oil is removed, and all tagged cDNA reads are pooled to be sequenced  with NGS. The turnkey bioinformatics tools map sequencing reads back by using the identifying barcodes. Abbreviations: CAPdT, capture poly dT; CBC, cell barcode capture; cDNA, coding DNA; CDS, coding DNA sequences; gDNA, genomic DNA; NGS, next-generation sequencing; RT, reverse transcription; synTF, synthetic transcription factor; UMI, unique molecular identifier.Within the combinatorial libraries, each synthetic DNA cassette encoding synTF, and pathway genes in a single cell can conceivably be labeled with unique barcodes. 110, leading to demand for increasing cropland that competes with the need for land to produce plant-made pharmaceuticals. In a step toward sustainable manufacturing, a desirable strategy is to switch to low-carbon or carbon-neutral manufacturing. Some progresses have been made in converting C1 to value-added chemicals at the industrial scale by acetogens. For example, a recent study has suggested a promising approach for carbon-negative manufacturing of isopropanol chemicals at a large scale by harnessing autotrophic acetogens through metabolic engineering111. However, the conversion of C1 gases to complex NPs of plant, like MIAs, BIAs, by engineering acetogens has still been hindered mainly by the limited availability of genetic manipulation tools, compared to a variety of advanced tools available for yeast36. The slow growth and low productivity under autotrophic conditions are other barriers preventing the commercialization of acetogens on the industrial scale112.We will face the consequences of the largest population explosion in human history in this century113. Encouraging chemical engineering laboratories to apply similar principles in designing their toolboxes and methods, for example, according to the BioBrick design rules, and delivering the parts to the BioBrick repository may be a large part of the solution114. To rapidly iterate and improve the NP production protocol, the discussed platform (Fig. 115 may offer promising avenues toward scale-up production of the NPs using CO2 and optogenetic inducer. The flexible arrangement of different types of bioreactors of UNLOCK platform allows for high-throughput cultivation of mixed cultures in MSBC and further optimization of the upstream and downstream bioprocessing protocols. Notably, safely storing the multilayer metadata produced according to the FAIR guiding principles108 is of particular relevance for simultaneously ingesting, exploring, and analyzing using DL approaches.To face possible difficulties to achieve sustainable NP production, a generalized microbial biomanufacturing platform using a wide range of state-of-the-art tools and techniques, as discussed herein, is necessary. To practically use the tools and methods in various laboratories, we must consider the requirement for standardization effortsorm Fig.  was equi1 feedstocks by producing high-value NPs116. Recent progresses, like establishing anaerobic biofoundries, leads to the fast growth of this path117. Nevertheless, analytic studies on the quality, stability, and safety of C1-derived NPs, and the potential effect of by-products on the efficiency of processing during fermentation118 should be seriously considered. Furthermore, to develop cost-effective, large-scale biomanufacturing, deliberating on the process cost and technical and economic performances of the entire supply chain is necessary. For example, 30–50% of the manufacturing costs of supplies are typically allocated to downstream processing118. In addition, to successfully establish the discussed platform, communication issues between the disciplines must be addressed. Therefore, organizing annual interdisciplinary conferences focusing on sustainable NP production and workshops teaching various aspects of the involved disciplines are of critical importance. Providing more funding opportunities to support interdisciplinary projects can be an impetus for collaboration between different disciplines. For example, the willingness of biopharmaceutical companies to potentially commercialize C1-derived products opens new doors to defray some of the research costs to continue the investigation on improving the potential of the C1 technology. As a breakthrough step toward facilitating the funding of interdisciplinary fields, ‘Executive Order 14081’ on advancing biotechnology and biomanufacturing was recently approved in the USA, which aims to encourage federal investment in biotechnology and biomanufacturing. Hopefully, more countries will also take into consideration to further support research on developing the interdisciplinary field of sustainable biomanufacturing of small-molecule drugs.Before transitioning NP production technology to the commercial production level, an essential consideration is developing effective regulatory compliance, besides establishing the scale-up protocol. One of the highest commercial potentials is to focus on carbon-negative biomanufacturing strategies, as they may offer promising global warming solutions while concurrently providing further economic benefits from cheap CSeveral tools and approaches to establish the discussed platform, e.g., synTFs, combinatorial optimization techniques, light inducers, bioprocessing optimization strategies, and single-cell sequencing technologies, can be implemented individually to optimize production of NP drugs in yeast. They can also be adapted to other bioengineering projects to facilitate the production of high nutritional value proteins, enzymes, and vitamins. Overall, the proposed biomanufacturing platform’s roadmap provides a unique niche for academia-industry collaboration.


Vibrio natriegens is a Gram‐negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism‐specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome‐scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry‐based proteomics data confirmed the translation of at least 76% of the enzyme‐encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP‐generating system and the discovery of a role for a sodium‐dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens. Vibrio natriegens. This new model enabled the study of V. natriegens metabolism, halophilic adaptions, and resource analysis.iLC858 is the first constructed and validated genome‐scale metabolic model for the fast‐growing bacterium  Vibrio natriegens is a Gram‐negative bacterium that in recent years has been gaining attention as an academically and industrially promising host for synthetic biology and biotechnology applications due to its exceptionally fast growth rate. The V. natriegens genome consists of two circular chromosomes, like the majority of members of the genus Vibrio  or Escherichia coli (7 rRNA operons and 99 tRNA genes). The annotation also suggested that V. natriegens has a versatile metabolism equipping it with the ability to grow on a vast range of carbon substrates, to reduce nitrate, and to fix atmospheric nitrogen under anaerobic, nitrogen‐limited conditions   is a mathematical reconstruction containing all the known metabolites, enzymatic metabolic reactions, and the corresponding genes for a certain organism  and subsequently used to generate a draft model using the SEED server  of reactions in our model, not counting passive transmembrane transport reactions or metabolite sink reactions, are associated with at least one known coding sequence in the Vibrio vulnificus model VvuMBEL943 , the model will maximally utilize any provided sources of carbon to generate biomass. Hence, constraints on substrate uptake rate and secretion of metabolites must be imposed to generate realistic simulations.We first simulated aerobic growth on minimal medium by providing glucose as the sole carbon source in the V. natriegens secretes part of its substrate as acetate, indicating a mixed respiratory‐fermentative growth phenotype. This behavior is similar to the Crabtree effect observed in yeast under aerobic conditions, which is hypothesized to be a reflection of the fitness trade‐off between the efficiency gained by full respiration and the cost of the expression of the respiratory pathways .Even under aerobic conditions, rt et al  and Longl  pathway, and the glyoxylate shunt (GS). For this simulation, the same glucose uptake rate and acetate secretion rate as in the aerobic growth simulations were used. The predicted fluxes were represented as percentages of the glucose uptake rate, in order to enable comparison to Long's data. Comparing the fluxes predicted by the model's optimal solution and experimental fluxes yielded a high Pearson's correlation coefficient of 99.2%, suggesting high consistency between our model and V. natriegens' central metabolism.Pathways such as glycolysis, pentose phosphate pathway (PPP), and the tricarboxylic acid cycle (TCA) form the central motor driving metabolism in bacteria. Given the central role these pathways play in metabolism during growth, we investigated how well the predicted corresponding fluxes compared to experimentally determined central metabolic fluxes. We simulated aerobic growth on glucose minimal medium using iLC858, and compared the fluxes to data reported by Long et al , who useet al , producing boundaries on these fluxes allowing solutions yielding at least 98% of the optimal solution in terms of produced biomass. The results of this comparison, together with a schematic overview of experimentally determined fluxes through central metabolism, are shown in Fig et al .et al  carbon sources on which V. natriegens cannot grow, leading to an overall predictive accuracy of 83.8%.We experimentally assessed pply Fig . An overin silico predictions and experimental data, we suggest a few explanations for the discrepancies between prediction and experimentation in addition to what is discussed above. Firstly, it is important to notice that the experimental data quantify respiration and not growth, and some of these substrates, while potentially used for cellular respiration, may not be enough to allow biomass synthesis. Secondly, homology‐based functional annotation of substrate‐specific transport proteins is a notoriously complicated task , being significantly higher than the biomass yield reported for glucose  had associated genes of which the corresponding coded enzymes were entirely found in at least one of the proteomics samples. Moreover, 331 reactions (84.9%) had associated genes of which all enzymatic subunits except one were found in at least one of the proteomics samples.Flux sampling was used to determine reactions in the GSMM which are predicted to be able to produce fluxes of at least 10no et al . Across E. coli. Mapping reactions from one GSMM to another allows to identify reactions that are shared between or unique to one of the metabolic networks. Hence, we performed a reaction mapping of all cytoplasmatic reactions in ILC858 to the E. coli GSMM iML1515 and found 772 shared reactions out of 1,187 V. natriegens cytoplasmatic reactions , in addition to pumping out protons using terminal oxidases. The resulting proton motive force (PMF) and sodium motive force (SMF) are interchangeable through sodium‐proton antiporters and drive processes such as the rotation of a flagellum and ATP synthesis through oxidative phosphorylation. The central role of Na+‐NQR and the mechanisms related to proton and sodium gradients in V. cholerae have been previously described in detail   strain for this gene, and a reverted strain harboring the re‐inserted OAD gene. We found that only under high‐salinity conditions, the OAD knockout grew slightly slower and to a lower OD than the WT and OAD reversion strain, while at lower salinity, we observed no difference in growth  transporters for glycine‐betaine, another osmoprotectant used by E. coli is involved in the regulatory response to the accumulation of misfolded or unfolded proteins in the extra cytoplasmatic space .V. natriegens RBA model to simulate aerobic growth on glucose. The model was calibrated by estimating apparent catalytic rates for all the enzymes detected in our proteomics dataset using RBApy scripts . When comparing the networks for their theoretical capacities to produce amino acids, we found that V. natriegens' Alanine dehydrogenase and L‐alanine:glycoxylate aminotransferase, two genes not found on the E. coli genome, provide it with a metabolic advantage over E. coli for the production of the amino acid glycine. We did not detect high levels of expression of these genes in our proteomics samples of growth on minimal medium but found evidence of high transcription of these genes on MOPS medium in a ribosomal profiling dataset , a unique enzyme whose central role has been studied in V. cholerae and which has no equivalent on the E. coli genome  was highlighted as another functional membrane pump interacting with V. natriegens' sodium gradient. The role of this pump was so far unknown. Simulations with the GSMM suggested that this pump might work as a gradient‐driven pyruvate carboxylation and, therefore, an alternative anaplerotic carboxylation. We detected expression of this pump in our proteomics data, in high levels in the same order of magnitude as the Na+‐NQR. Further experimental work with a knockout strain for this pump supported the functionality of the Na+‐OAD pump during aerobic growth. To our knowledge, this is the first demonstration of the functionality of this enzyme during aerobic growth in bacteria. These observations suggest that this enzyme plays a role during V. natriegens growth that has no equivalent in E. coli. Moreover, they may provide insights into the lifestyle of the important human pathogen V. cholerae, as it also possesses a homologous Na+‐OAD that is decoupled from the citrate fermentation operon  was annotated using RAST  for Phenotype MicroArrays (PMs) for Microbial Cells™ analysis on the OmniLog System®. Cells were grown on BioLog Universal Growth media with sheep's blood at 30°C overnight. They were then added into the wells of the plates, which were run according to a proprietary protocol for running Gram‐negative bacteria. The base test media for the metabolic test plates is proprietary, but it is a minimal media composed mostly of salt and buffers. For more information, see the BioLog website  and reference papers Bochner et al  with 10 g l−1 of carbon substrate, at 700 rpm. Fully grown cultures were centrifuged and dried to measure dry weight. Yield was then calculated as the final dry weight divided by the initial carbon substrate weight.For the determination of biomass yields, et al, in silico knockout strains, using biomass formation as an objective. Correspondingly, genes where knockouts lead to infeasible FBA, or a growth rate reduced below 90% of nonimpaired growth were classified as “essential.”Gene knockouts were simulated by applying the COBRApy built‐in knockout function to genes (Ebrahim V. natriegens OAD knockout strain (OAD KO) was generated using the MuGENT protocol  with the pMMB67EH‐‐tfox(Vc) AmpR plasmid was used as the base/wild‐type strain for genome modification.The et al, First, fusion PCR was used to generate a 5.2‐kb linear fragment in which the Cmp cassette was fused with 2‐kb flanking regions of the OAD gene. The Phusion Flash Master Mix (ThermoFisher) was used. As per manufacturer's instructions, the three fragments to be fused were first separately amplified. The cassette was amplified using the primers Cm4OAD‐F (aacatcctgcacctgtcaGAGCGATTGTGTAGGCTG) and Cm4OAD‐R (gcgagtatgttacccacaTGGGAATTAGCCATGGTCC), the upstream fragment was amplified using primers OADup‐F (CGTTGCTTCAGGTAACTACTAA) and OADup‐R (cagcctacacaatcgctcTGACAGGTGCAGGATGTT), and the downstream fragment was amplified using OADdn‐F (ggaccatggctaattcccaTGTGGGTAACATACTCGC) and OADdn‐R (GGATATAACGCTTCTGACATG). These were run as 20‐μl reactions, gel‐verified, and purified using a Zymo DNA Clean and Concentrate Kit as per manufacturer's instructions. To perform the fusion reaction, 15 μl of Master Mix, 2 μl of the Cmp cassette, and 1 μl of each of the flanking cassettes were added to a 30‐μl reaction . The following program was run: 98°C for 1 min, then 11 cycles of: 98°C for 1 s, 75°C for 1 s, Ta ‐ ramped down to 72?C at −0.1°C/s from 75°C, hold for 30 s at 72°C for 10 min. For the purification reaction, 5 μl of the above reaction was taken and added to 50 μl of Master Mix, 0.5 μM each of nested primers  in a 100 μl reaction. A 2 step PCR was run, as per the manufacturer's protocol. The purified band was gel verified and purified using the Zymo DNA Clean and Concentrate Kit, and then used in the MuGENT protocol  and OADdn‐R (GGATATAACGCTTCTGACATG) to amplify the OAD gene and up and downstream regions out of the V. natriegens genome. A second cassette was created by replacing the DNS gene in V. natriegens with the ampicillin resistance cassette. The cassette was created as following: primers for region upstream of DNS: Dns‐3 kb‐F (ctaacatggctaagcacctg) and DNS4AmpR‐R (tatttcctattgagacttaattgaactgagg); Primers for ampR cassette: AmpR‐F(tcaataggaaatattagacgtcaggtggcact) and AmpR‐R (aaagattagcgattgagtaaacttggtctgacagttacc); Primers for region downstream of DNS: DNS4AmpR‐F (ccaagtttactcaatcgctaatctttctgtttgagg) and DNS‐3 kb‐R (actggtaagccataacgacc). MUGENT with the addition of 100 ng of the DNS:Amp cassette and 250 ng of the OAD complement cassette was preformed, and cells were selected on ampicillin. The colonies that grew were then patched on Cmp, and those which did not grow on Cmp were confirmed for OAD in situ complementation and Amp insertion at DNS by colony PCR and sequencing. MUGENT was also performed on the V. natriegens RifR and RifR OAD KO strains with the DNS:AmpR cassette only to create appropriate controls.The confirmed OAD KO strain was the complemented V. natriegens strains were inoculated and grown overnight from glycerol stocks at 30°C in modified minimal medium , 6.78 g Na2HPO4, 3 g KH2PO4, 2 mM MgSO4, 0.1 mM CaCl2, 25 μM FeCl3, indicated amount of NaCl, and 0.4% glucose, pH 7.4). The following morning, cells were diluted 1:200 in the same media and dispensed in triplicate into the wells of a BioScreen C Analyzer 100‐well honeycomb plate , 150 μl per well. Growth was measured at OD600, with measurements taken every 15 min. for 24 h. All chemicals, including carbon sources, were from Sigma‐Aldrich  or Fisher Scientific .The indicated Vibrio natriegens (ATCC 14048) was grown in a modified M9 minimal medium  supplemented with glucose and NaCl as indicated below. Three different colonies of the expected morphology were used to inoculate each test tube containing modified M9 minimal medium with 0.4% (w/v) glucose and 0 mM NaCl and grown for 16 h at 36.5°C and 250 rpm in an orbital shaker. Based on the density of the overnight cultures, using a conversion factor of 1.75 × 108 CFU ml−1 OD600−1, the appropriate volumes containing ∼ 105 cells were transferred to flasks containing prewarmed modified M9 minimal medium with 1% (w/v) glucose and either 0, 300, 540, or 650 mM NaCl. The flasks were incubated at 36.5°C with continuous shaking and OD600 measurements were monitored, and samples were collected during early log, mid‐log, and stationery phases of growth. Aliquots for proteomic analysis (2 ml) were taken directly from the flasks at the specified growth points and pelleted at 5,000 g for 1 min. at 20°C.et al, Cell pellets were taken from −80°C storage and placed on ice. Lysis buffer (10% n‐propanol in 50 mM ammonium bicarbonate) was added and volumes adjusted based on optical density measurements . The LC–MS/MS system consisted of an Ultimate U3000 LC coupled with an Orbitrap Fusion Lumos mass spectrometer  via a Nanospray Flex Ion Source. The U3000 system was configured for nano‐flow separations using an autosampler, loading pump, and analytical pump. The autosampler loaded samples onto a trap column  via loading pump . The analytical pump (flow rate 300 nl/min) was used to elute peptides from the trap onto an analytical column  and into the mass spectrometer. Mass spectra were acquired on a Fusion Lumos Orbitrap mass spectrometer in data‐dependent mode with 3 s cycle times. A survey scan range of 400–1,600 Da was acquired on the Orbitrap detector (resolution 120 K). The maximum injection time was 50 ms and automatic gain control (AGC) target was set to standard. Ion intensities > 5.03 with predicted charges of 2+ to 7+ were fragmented using higher energy collisional dissociation (HCD). Dynamic exclusion was enabled for a duration of 30 s for subsequent MS/MS submissions. The MS/MS detector was set to Ion Trap rapid scan with maximum injection time 35 ms and AGC target set to standard.et al, V. natriegens ATCC 14048 predicted proteome and 190 sequences representing common contaminants, proteolytic enzymes, and mass standards . Variable modifications for the search included oxidation of methionine and deamidation of glutamine and asparagine. Proteolytic cleavage search parameters used trypsin with > 3 missed cleavages. The precursor ion tolerance was set to +50 ppm and fragment ion to +0.6 Da. Scaffold  was used to validate MS/MS based peptide and protein identifications. Following Scaffold delta‐mass correction, identifications were retained at >80.0% probability and > 95.0% probability at the peptide  via in‐house scripting . Rates for these processes were taken from Jahn et al  and 0.03 g g−1 ectoine production targets were taken from literature on Vibrio (Chien et al, et al, Macrocomponent production targets were set to match the macrocomponent production targets of the et al (et al (Finally, the model was calibrated estimating apparent catalytic rates for all the enzymes detected in our proteomics dataset. For this, we followed the procedure proposed by Jahn et al , which il (et al  was used−1, as reported by Hoffart et al (For RBA growth simulations, an acetate production rate was enforced at a value of 0.42 g gDWrt et al .The Python package scipy was used to perform one‐way ANOVAs between the 0 mM and 300 mM NaCl proteomics samples.Lucas Coppens: Data curation; formal analysis; investigation; visualization; methodology; writing – original draft. Tanya Tschirhart: Data curation; formal analysis; writing – review and editing. Dagmar H Leary: Data curation; formal analysis. Sophie M Colston: Data curation; formal analysis. Jaimee R Compton: Data curation; formal analysis. William Judson Hervey: Data curation; formal analysis. Karl L Dana: Data curation; formal analysis. Gary J Vora: Resources; methodology; writing – review and editing. Sergio Bordel: Data curation; formal analysis; investigation; methodology. Rodrigo Ledesma‐Amaro: Conceptualization; resources; data curation; formal analysis; supervision; funding acquisition; investigation; writing – original draft; project administration; writing – review and editing.The authors declare that they have no conflict of interest.Appendix S1Click here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Dataset EV9Click here for additional data file.Dataset EV10Click here for additional data file.


Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second leading infectious killer worldwide after COVID-19; and Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseases.Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, M. leprae and My. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets for each pathogen are significant for precision medicine initiatives. The models and associated datasets described in this paper are available in GigaDB, Biomodels, and PatMeDB repositories.In this study, metabolic models have been developed for two bacterial pathogens,  Mycobacterium leprae (NCBI:txid1769) and Mycobacterium tuberculosis (NCBI:txid1773) are two related pathogenic mycobacteria responsible for leprosy and tuberculosis in humans. Another related mycobacterium, Mycobacteroides abscessus (NCBI:txid36809), causes opportunistic infections in healthcare-related settings. Previous analyses of the metabolic models of M. tuberculosis have supported studies demonstrating the evolutionary drivers of antibiotic resistance and the identification of novel drug targets against mycobacteria , 2. Here, we demonstrate newly built genome-scale metabolic models of M. leprae and My. abscessus, including curation, simulation, and model-optimisation strategies –5. To ensure the development of standardised metabolic models for the global systems biology community, we have implemented the recently released community standards and used Metabolic Model Testing MEMOTE quality control software to evaluate our models , 7.M. leprae and My. abscessus were downloaded from BioModels and evaluated against other organism-specific databases, such as BioCyc, (RRID:SCR_002298) and Kyoto Encyclopedia of Genes and Genomes  , 9. COBRApy (RRID:SCR_012096), a Python toolbox for the construction, manipulation, and analysis of constraint-based models , and GNU Linear Programming Kit (GLPK: RRID:SCR_012764), a software package that solves efficiently large-scale linear programming problems , were used to manipulate and simulate the models.Automated draft reconstructions of My. abscessus and M. leprae, additional reactions, gene-to-reaction associations, and pathways (that were not in the automated model) were integrated from KEGG and BioCyc , 9. The annotations of genes and metabolites were improved by comparing and transferring annotations from the related M. tuberculosis models in the iEKVIII model and BioCyc database , 8. Further improvements to the models were made by comparing them with the compound formula and charge from the MetaNetX database, and by mapping the genes and reactions of the GEMs to the BiGG (RRID:SCR_005809), ChEBI (RRID:SCR_002088), KEGG (RRID:SCR_012773), and MetaCyc databases (RRID:SCR_007778) –13. Figure iMab22 and iMlep22) of the pathogens My. abscessus and M. leprae. The revised model reconstructions of M. leprae (iMlep22) and My. abscessus (iMab22) can be instantiated without error on the COBRA software (version 0.16.0) .To create new genome-scale metabolic models (GEMs) of My. abscessus and M. leprae using the methodology in the pathway tools software  and the corresponding BioCyc database . The software tool findCPcli, which implements the computational method for identifying bottleneck reactions as drug targets, is available in GitHub .We generated biomass reactions for My. abscessus and M. leprae organisms and comparisons with the BioCyc, KEGG, MetaNetX 4.2, BiGG, and ChEBI databases , 11–13.The dead-end metabolite reactions that were previously present in the automated model were eliminated to enhance the models’ quality. The models were then iteratively evaluated, considering model-specific reactions for the . In the process, Systems Biology Ontology (SBO) annotations and gene annotations from the KEGG database and 728 new formulae from the MetaNetX database were added . As a result, the MEMOTE score increased from 49% to 63% on the M. leprae model and from 48% to 66% on the My. abscessus model.To improve the quality of the models, the reactions with dead-end metabolites previously found in the automated models were removed. MEMOTE, a standardised genome-scale metabolic model testing programme, was used to undertake quality control checks during the models’ iterations and optimisation M. abscessus and M. leprae are encoded in the Systems Biology Markup Language (SBML)  and designated as follows:The new GEMs for iMlep22 for the M. leprae model: i for in silico, Mlep for M. leprae, and published in 2022. iMlep22 consists of 5,625 reactions, 4,016 metabolites, and 871 genes.iMab22 for the My. abscessus model: i for in silico, Mab for My. abscessus, and published in 2022. iMab22 consists of 8,580 reactions, 6,273 metabolites, and 1,837 genes.et al. and Lieven et al. , 7, to produce gold-standard metabolic network reconstructions of My. abscessus and M. leprae. The development of My. abscessus and M. leprae (iMab22 and iMlep22) metabolic models is illustrated in Figure Standardisation and curation have been done according to the community standards for the development of metabolic models, as described in Carey My. abscessus, an opportunistic pathogen, has a large genome size of 5.1 MB , with more biochemical and bottleneck reactions in the iMab22 model compared with the obligate pathogens M. tuberculosis (with a genome size of 4.4 MB) and M. leprae  . The fewer metabolic and bottleneck reactions in the iMlep22 model can be attributed to the reductive evolution that occurred in M. leprae and the subsequent loss of genes. An illustration of the distribution of unique enzymes and bottleneck reactions in each of the models (iMab22 and iMlep22) in comparison with each other and with M. tuberculosis is demonstrated in Figure , 5, 20.M. leprae (iMlep22) and My. abscessus (iMab22) have been developed using the systems biology community standards for quality control and evaluation of models , 7, and are available for reuse by the global scientific community.The standardised genomic scale metabolic models for  Reviewer name and names of any other individual's who aided in reviewer Grace MugumbateDo you understand and agree to our policy of having open and named reviews, and having your review included with the published papers. YesIs the language of sufficient quality?YesPlease add additional comments on language quality to clarify if needed
First person reporting has been used with the word "We' used extensively. Are all data available and do they match the descriptions in the paper? YesAdditional CommentsAre the data and metadata consistent with relevant minimum information or reporting standards? See GigaDB checklists for examples http://gigadb.org/site/guideNoAdditional CommentsThere is need to specify the type, size, standardisation and curation of the data that was used, especially when additional data was obtained from different databases.Is the data acquisition clear, complete and methodologically sound?YesAdditional CommentsSources of data are indicated in the paper, however the size of the data sets and type of data is not clear. Is there sufficient detail in the methods and data-processing steps to allow reproduction?NoAdditional CommentsThere is need to give more detail in the methods for reproducibility.Is there sufficient data validation and statistical analyses of data quality? NoAdditional CommentsValidation was performed, however no statistical analyses was mentioned. Is the validation suitable for this type of data?YesAdditional CommentsIs there sufficient information for others to reuse this dataset or integrate it with other data?NoAdditional CommentsMore detail is needed on data retrieval to allow reuse of the dataset.Any Additional Overall Comments to the AuthorThe Authors presented their work entitled 'Mycobacterial Metabolic Model Development for Drug Target Identification'. This is very innovative work that led to generation of M. laprae and M. abscessus models, important tools for drug target identification. Target identification for a number of infectious diseases provides information for structure-based molecular modification of new and alternative diseases. The target specific compounds will help reduce side effects among other things. Generation of the models by the authors is commendable.

 There are a few corrections:
 1) Under Abstract: Line 4: Please note that Mycobacterium tuberculosis is not a disease but the bacterium that causes the diseases tuberculosis.
 2) Mehtods, GEM reconstruction, curation and simulation (i) Line two: Name the "other organism specific databases"
 (ii) Give a brief description of the COBRApy and the GLPK even if the source had been given.
 3) The Method section need to be more informative to allow for reproducibility.RecommendationMinor Revision Reviewer name and names of any other individual's who aided in reviewer Nagasuma ChandraDo you understand and agree to our policy of having open and named reviews, and having your review included with the published papers. YesIs the language of sufficient quality?YesPlease add additional comments on language quality to clarify if needed
Are all data available and do they match the descriptions in the paper? YesAdditional CommentsAre the data and metadata consistent with relevant minimum information or reporting standards? See GigaDB checklists for examples http://gigadb.org/site/guideYesAdditional CommentsIs the data acquisition clear, complete and methodologically sound?YesAdditional CommentsIs there sufficient detail in the methods and data-processing steps to allow reproduction?YesAdditional CommentsIt would be useful if the authors could comment on how the models vary between the two species and with respect to M. tuberculosis. Specifically, a note on how the authors deal with alternate enzymes and whether they included enzymes specific to each species, would be helpful. Is there sufficient data validation and statistical analyses of data quality? YesAdditional CommentsIs the validation suitable for this type of data?YesAdditional CommentsA figure depicting the overall capability of the models would be usefulIs there sufficient information for others to reuse this dataset or integrate it with other data?YesAdditional CommentsAny Additional Overall Comments to the AuthorGenome-scale metabolic models are useful to the community as they can be used to address a variety of questions. It would be useful if the authors could include a section on the comparative performance of the models and link it to the known metabolic capability of these microbes.RecommendationMinor Revision Comments to the AuthorPlease check the proofs