Saccharomyces cerevisiae has become a suitable cell factory for industrial terpenoid biosynthesis due to developments in omics studies , and mathematical modeling. Besides that, fermentation development has a significant importance on achieving high titer, yield, and productivity (TYP) of these compounds. Up to now, there have been many studies and reviews reporting metabolic strategies for terpene biosynthesis. However, fermentation strategies have not been yet comprehensively discussed in the literature. This review summarizes recent studies of recombinant production of pharmaceutically important terpenoids by engineered yeast, S. cerevisiae, with special focus on fermentation strategies to increase TYP in order to meet industrial demands to feed the pharmaceutical market. Factors affecting recombinant terpenoids production are reviewed (strain design and fermentation parameters) and types of fermentation process  are discussed. Terpenoids, also known as isoprenoids, are a broad and diverse class of plant natural products with significant industrial and pharmaceutical importance. Many of these natural products have antitumor, anti-inflammatory, antibacterial, antiviral, and antimalarial effects, support transdermal absorption, prevent and treat cardiovascular diseases, and have hypoglycemic activities. Production of these compounds are generally carried out through extraction from their natural sources or chemical synthesis. However, these processes are generally unsustainable, produce low yield, and result in wasting of substantial resources, most of them limited. Microbial production of terpenoids provides a sustainable and environment-friendly alternative. In recent years, the yeast  Althoch 2021) . Their ech 2021) .2=C(CH3)–CH=CH2) structural units (or building blocks) synthesized from mevalonate (MVA) or 2-C-methyl-d-erythritol 4-phosphate (MEP)  pathway [Terpenoids are formed from several isoprene   generally fails in terms of quality and supply management due to seasonal and geographical alterations [From early ancient times up to now, terpenoids have been playing important roles in many medical treatments as pharmaceuticals with diverse biomedical activities, such as antimalarial, anticancer, anti-inflammatory, antibacterial, antiviral, hypoglycemic, preventing and treating cardiovascular diseases, and promoting the transdermal absorption. Besides that, they can have insecticidal, immunomodulatory, antioxidant, antiaging, and neuroprotective effects ,3,8,9. Terations . In addierations . Due to Saccharomyces cerevisiae strain developed by Amyris [Escherichia coli by optimizing the P450 expression of taxanes and other related enzymes [Among terpenoids, the sesquieterpenoid artemisinin have been often used as an antimalarial drug and the diterpenoid taxol (paclitaxel) have been developed to be an important anticancer chemotherapy drug for many years . Semi-syy Amyris , which iy Amyris . So far, enzymes . S. cerevisiae, has been mainly used in the industrial production of alcoholic beverages , bakery products, and bioethanol. However, with the latest developments in synthetic biology, it became one of the most widely industrially used cell factory in the microbial production of a wide variety of products, such as alcohols, organic acids, amino acids, enzymes, therapeutic proteins, chemicals, and metabolites [S. cerevisiae strains for many years. Many pharmaceutical companies have chosen this yeast as the most suited host organism to produce a large variety of recombinant products due to its well-known genetics, physiology, biochemistry, and genetic engineering background, the availability of genetic tools, and the suitability of dense and large scale fermentation [For centuries, the baker’s yeast, abolites . Among tentation ,17,18. S. cerevisiae has emerged as a model organism for the production of terpenoids since it has many additional advantages other than mentioned above, such as generally regarded as safe (GRAS) status, high genetic tractability, ease of manipulation, possessing universal endogenous MVA pathway, ability to express eukaryotic cytochrome P450 enzymes, robustness, relatively absence of secondary metabolites, high sugar catabolic, fast growth rate, and high tolerance against harsh industrial conditions [In the same line, nditions ,20,21,22S. cerevisiae, other microorganisms have been explored for terpenoids production. Among them, E. coli has the most restrict chassis, since its produces natively limited amounts of terpenoids  and, therefore, the improvement of MEP pathway by engineering enzymes for IPP and DMAPP synthesis, or the introduction of heterologous MVA pathway, is required [S. cerevisiae has an endogenous MVA pathway, producing high amounts of ergosterol and native cytochrome P450 enzymes for the modification of terpenoids skeleton. Nonconventional yeast Yarrowia lipolytica has been also considered as a suitable yeast to synthesize terpenoids due to its capacity to produce large amount of acetyl-CoA, the initial substrate of the MVA pathway [Rhodosporidium toruloides can naturally accumulate several carotenoids (C40 terpenoids), indicating that it might have high carbon flux through MVA pathway, ensuring pools of intermediates for producing diverse types of terpenes [S. cerevisiae [2 instead of sugar feedstocks. However, terpenoids titer and productivity obtained are still below industrial levels and further studies to overcome the barriers for efficient conversion of CO2 to terpenoids are needed [Besides required . In cont pathway . In additerpenes . This yerevisiae . Cyanobae needed . S. cerevisiae has as main advantage over E. coli and cyanobacteria hosts its intrinsic MVA pathway, and the disadvantage over Rhodosporidium toruloides host the incapacity of using directly lignocellulosic hydrolysates as feedstock. Nevertheless, S. cerevisiae is quite superior to the other microorganisms in respect to higher process robustness, fermentation capacity, plenty of available genetic tools in pathway engineering and genome editing, and proven capacity to attain industrial levels of relevant terpenoids [Overall, rpenoids . S. cerevisiae through optimized fed-batch fermentation [S. cerevisiae and focuses attention on fermentation strategies to improve their production scale. Different fermentation factors and processes are discussed. To date, there has been a strong effort for terpenoid biosynthesis through metabolic engineering of microbes, however, production levels are at the mg/L scale in scientific literature, which are generally too low and commercially insufficient. Economically meaningful metrics of titer (g product per L broth), yield (g product per g substrate), and productivity (g product per L broth per hour) should be provided for industrial production . Fermententation ,27,28. FA vast number of terpenoids have been widely used in medicine and medical sciences to prevent and treat many diseases due to their pharmaceutically bioactive properties. Their wide pharmaceutic effects and medical functions have already been extensively revised in the literature , is the most effective traditional antimalarial drug with the property of high efficiency and low toxicity. It shows to be effective against a broader range of life cycle stages of the apicomplexan parasite than conventional antimalarials such as quinine. Antimalarial property of artemisinin arises from its extraordinary endoperoxide ring, which has an impact on overcoming Plasmodium spp. infections  and has effective anticancer activities on several type of tumors, such as ovarian and breast cancers. Its unique mechanism of action as a microtubule stabilizer and inhibitor of mitosis on ovarian and breast cancers was approved by the US Food and Drug Administration (FDA) in 1992 and 1994, respectively. Since then, Paclitaxel has been also widely used in the treatment of colorectal, head and neck, small-cell and non-small-cell lung cancers (NSCLCs), and acquired immune deficiency syndrome (AIDS). It contributes the conjunction of tubulin into microtubules and hinders the breakage of microtubules, which inhibits cell cycle progression, stops mitosis, and prevents the generation of tumor cells [Paclitaxel, also known as its commercial brand name “Taxol”, is a tricyclic diterpenoid compound  used as or cells ,8,14,33.Taxus brevifolia were applied to meet world demand of Paclitaxel [Paclitaxel is very expensive due to its small concentration found in original plant source (yew tree). For instance, 10,000 kg bark of yew trees are required to produce 1 kg paclitaxel. This amount can treat 500 patients but requires the harvest of 300 yew trees ,46. Prodclitaxel ,14. MoreCannabis spp. (Cannabaceae), mainly in the plant Cannabis sativa. Apart from plant derivatives, Cannabinoids also involve Endocannabinoids, Phytocannabinoids, and Synthetic cannabinoids, which are able to bind to the human cannabinoid receptors [C. sativa and C. indica have been used for different purposes for more than 5000 years. The fiber-type of Cannabis (hemp), which contains the major cannabinoid, cannabidiol (CBD)  , has beeol (CBD) , found iCannabis spp. [Cannabinoids also called as terpenophenolic in the class of meroterpene, are produced as secondary metabolite of Cannabis through concatenation of isoprenoid precursor with a second fatty acid derivative precursor by a prenyltransferase enzyme. This enzyme synthesizes cannabigerolic acid (CBGA), through the prenylation of olivetolic acid and geranyl diphosphate (GPP). CBGA is then converted into different cyclized compounds, such as tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA), and cannabichromenic acid (CBCA). Finally, these compounds undergo decarboxylation to produce biologically active compounds THC, CBD, and CBC, which are the main cannabinoids placed in bis spp. ,8,34,51.Cannabis spp. has been legally used for the medical treatments in many countries, such as USA, Canada, Israel, and several European countries including the Netherlands, Germany, and the Czech Republic. However, Cannabis spp. cannot be cultivated in several countries due to the legal issues. Agricultural production of cannabinoids via Cannabis cultivation has several disadvantages, such as low yield due to climate changes and plant diseases, low concentrations of cannabinoids in plant, necessity of extraction processes, and sociopolitical factors for the use of Cannabis, which has been mostly seen as a source of narcotics. On the other hand, chemical synthesis of cannabinoids does not offer an alternative production way because of synthesis complexity, which generally causes low yields and high production cost. For these reasons, microbial production of these compounds by engineered yeast would serve a sustainable, reliable, eco-friendly, and cost-effective alternative approach [In recent years, approach ,34. Bacillus subtilis, Staphylococcus aureus, Streptococcus spp., E. coli, and Candida albicans [Helicobacter pylori [Apart from artemisinin, taxol, and cannabinoids, there are also other medically important terpenoids with several pharmaceutical activities and functions. It was reported that the monoterpenes perillyl alcohol, D-limonene, and geraniol are effective in the prevention and treatment of various types of cancers  3,52,53,5352,53.albicans . In addialbicans . These tr pylori . In addir pylori . They alr pylori ,38. Veryr pylori .S. cerevisiae, starts from acetyl-CoA and ends up with the production of DMAPP. In the second stage, geranyl pyrophosphate (GPP), farnesyl pyrophosphate (FPP), or geranylgeranyl pyrophosphate (GGPP), which are the precursors of monoterpenoids, sesquiterpenoids, or diterpenoids, respectively, are created by condensation reactions of one molecule DMAPP with one, two, or three molecules of IPP under the action of isopentenyl transferase. On the other hand, triterpenoids are similarly generated via polymerization of two molecules of FPP. In the third stage, these linear isopentenyl pyrophosphate precursors are cyclized or rearranged by terpene synthases to form primary terpene carbon skeleton. Finally, this carbon skeleton undergoes several post-modifications and tailoring reactions  under the action of cytochrome P450s (CYPs) to form biologically active terpenoids  and dimethylallyl diphosphate (DMAPP) are synthesized via the mevalonate pathway (MVA pathway) and the methyl-erythritol phosphate pathway (MEP pathway). MVA pathway, which exists in ews see: ,56).S. cerevisiae for terpenoid biosynthesis is herein given. The common metabolic engineering strategies for terpenoids synthesis in yeast, as described in recent reviews [Modifying endogenous pathways for synthesis of desired terpenoids.Finding and introducing new heterologous enzymes and pathways into yeast.Determination of rate limiting steps in selected pathways by application of omics studies.Elimination of rate limiting steps in target pathways via overexpression of genes, and cofactor and transporter engineering.Developing enzyme activity and/or specificity by protein engineering.Improving expression level of target key enzymes.Blocking or down regulating competing pathways.Increasing precursor and cofactor supply.Balancing cell growth and terpenoid synthesis for fermentation process.A brief overview of metabolic engineering applications in  reviews ,56,57,58S. cerevisiae. For this reason, heterologous genes for the functional expression of terpene synthases from plant resources  or synthetic sources (from NCBI gene accession number) should be introduced into S. cerevisiae [S. cerevisiae has only three CYPs, which are Erg5 and Erg11 in the ergosterol pathway and Dit2 . For specific or non-native terpenoid production, heterologous expression of CYPs is required. As a successful example, ref. [A. annua in S. cerevisiae, obtaining 25 g/L of artemisinic acid in optimized fed-batch fermentation.As above mentioned, the direct precursors in sterol pathway, FPP, GPP, GGPP, and squalene epoxide, can be converted into specific terpenes through a series of enzymes designated as terpene synthases, which do not exist in revisiae . On the revisiae . S. cerele, ref.  have expS. cerevisiae. HMG-CoA reduction to MVA, catalyzed by Hmg1p or Hmg2p, products of HMG1 and HMG2, respectively, was reported as the rate limiting step in the sterol pathway of S. cerevisiae. Overexpression of N-terminal truncated HMG1 (tHMG1) and Hmg2p in anaerobic conditions increased squalene titer in this yeast [Another strategy is to identify the rate limiting reactions and overexpress the gene, or genes, responsible for these reactions to promote flux into the MVA pathway of is yeast ,57. Moreis yeast ,60. In ais yeast .S. cerevisiae to improve terpenoid synthesis results in draining the acetyl-CoA and NADPH pools, which may negatively affect the cell growth, causing in parallel the decrease in the terpenoid production. Therefore, the increase of cytosolic acetyl-CoA and NADPH pools is required. The most effective way for achieving this was to establish a synthetic pathway. Ref. [Acetyl-CoA and NADPH are the essential precursor and cofactor for the biosynthesis of several products  as well as terpenoids. Overexpression of structural genes in MVA pathway in ay. Ref.  combineday. Ref. .S. cerevisiae by engineering dedicated synthases upon identification of a single residue switch that converts C10 plant monoterpene synthases to C11 specific enzymes [Interestingly, to expand the diversity of terpenoid structures, C11 terpene scaffolds were produced in  enzymes . More re enzymes .S. cerevisiae include strain engineering, inoculum size, pH value, temperature, oxygen rate, and fermentation medium composition such as carbon and nitrogen sources. In addition, secondary products like ethanol, and some organic acids can affect the productivity of terpenoids. Below, the general factors affecting terpenoid biosynthesis are discussed.The main factors affecting the production of terpenoids in S. cerevisiae strains  have endogenous sterol biosynthesis process, including MVA pathway. Ergosterol is synthesized through this process. By using molecular biology methods, namely gene deletion and high-through screening methods, all enzymes and genes upstream the MVA pathway, and downstream ergosterol synthesis in S. cerevisiae, have been clarified [S. cerevisiae without genetic modifications because it requires the availability of precursors for transfer of intermediates between compartments (the cytoplasm and endoplasmic reticulum) and diverse class of Cytochrome P450 (CYP-CPR) enzymes for the oxygenation reactions to produce structurally diverse terpenes [S. cerevisiae, with the capability to produce different type of terpenoids, including industrial products, such as farnesene, artemisinin, patchouli alcohol, squalene, geraniol, and β-carotene, have been constructed [Wild type larified . Terpenoterpenes . In ordeterpenes ,65. Highstructed . In addistructed . Becausestructed . Some ofS. cerevisiae, such as amorpha-4,11-diene (precursor of artemisinin) [S. cerevisiae. As a response to this situation, ergosterol synthesis is usually stimulated. This metabolic mechanism had improved the availability of the main precursor 2,3-oxidosqualene, resulting in two times higher total triterpenoid productivities and 2.4-fold increase in carbon yield in ethanol pulse fed fermentations, compared to glucose fed fermentations [S. cerevisiae strains are unable to metabolize xylose and, thus, metabolic engineering must be performed, by introducing appropriate heterologous genes of xylose pathway in this yeast, to allow fermentation of this pentose. Significant advances have been made in recent years in regard of xylose use by yeast to obtain value-added bioproducts [The selection of fermentation carbon source depends on the precursor pool designed to be used . Ethanolmisinin) , artemismisinin) , β-amyrimisinin) , limonenmisinin) , geraniomisinin) , protopamisinin) , and patmisinin)  Table 3misinin) . Howeverntations . Howeverntations , zerumbontations , α-humulntations , miltirantations , protopantations , and ginntations  -limntations , sabinenntations , bisabolntations , valerenntations , and polntations  , peptides, and urea. When these compounds are present in the growth medium, they are transported into the cells by permeases and used as building blocks in biochemical pathways or catabolized into glutamate and ammonium for nitrogen metabolism [S. cerevisiae uses preferentially good nitrogen sources, such as ammonia, glutamine, and asparagine, over poor ones, such as proline and urea. Growth rate of this yeast is relatively high in good nitrogen sources rather than in poor nitrogen sources [4)2SO4), ammonium dihydrogen phosphate ((NH4)H2PO4), ammonium chloride (NH4Cl), yeast extract, peptone, tryptone, and sometimes combination of those (4)2SO4 was used as nitrogen source , was 7.5 [In general, as an acidophilic organism, e strain . The pH e strain , it was e strain . In anotrevisiae . It was  was 7.5 . AlkalinS. cerevisiae, respectively  decreases quickly. Simultaneously, ethanol is produced because of the Crabtree effect. When glucose concentration decreases to a relatively low level, growth of yeast cells slows down due to inadequate carbon source in the medium; therefore, the DO increases promptly. If the glucose is added to culture medium, DO will again slowly decrease with the consumption of glucose. Thus, this approach of controlling DO plays an important role on cell growth and product formation, especially in fed-batch fermentations. The optimum glucose consumption rate and the yeast growth rate can be tightly controlled by the DO . In a stependent . Althougependent . Becauseependent .600) of 0.05 to 0.5 is used instead (600 of 0.3 [v/v) with S. cerevisiae strain BY4741 and 8.1 and 2.6% (v/v) for strain EGY48 were favorable for squalene production in semi-anaerobic shake flask fermentations [S. cerevisiae in high cell density fermentation showed that as inoculum size  increased, stress protectants (glycerol and proline) in glycerol biosynthesis and amino acid metabolism improved. However, citric acid cycle (TCA) intermediates are depressed and metabolites (myo-inositol and ethanolamine) associated with membrane structure and function decreased [The size of inoculum used for terpenoid production is generally applied between 2.5 % and 14% of medium volume or initial optical density  are continuously fed and output materials (culture broth) are simultaneously removed from fermentation tank ,103. TheAlthough metabolic engineering strategies, and pathways improvement, are employed to enhance terpenoids expression, high cell density cultivation is needed to obtain high product titers, yield, and productivity for industrial processes. For high cell density cultivation, it is also important to choose an optimum method of reactor operation, since environmental conditions affect the growth and product formation. Among these conditions, nutrient and substrate concentrations are important to be kept in specific ranges to avoid overfeeding or underfeeding . S. cerevisiae strains were performed in batch fermentation, mainly in flask assays. For instance, monoterpenes, such as limonene [S. cerevisiae strains in the range between 0.49 and 17.5 mg/L  was employed for improving production of some terpenes in flask assays. Immobilized cells prepared by agarose entrapment and free cells (FCM) of oduction . It was oduction . Higher oduction . Although batch fermentation has several advantages, as explained above, some limitations also exist. The main disadvantage is the growth inhibition of microorganism due to product toxicity, especially in the production of monoterpenes. This problem can only be overcome by continuous removal of the inhibitor product  from culture during fermentation, for example, by a two-phase system or by headspace removal . AlternaFed-batch mode is the mostly used operation in the industrial production of diverse type of products as it combines the advantages of both batch and continuous fermentations ,118. It S. cerevisiae strains, have achieved the titer of 1.69 g/L and 0.918 g/L, respectively, with glucose/ethanol mixture or pure ethanol feeding in fed-batch fermentations. These values were quite low (3 to 6-fold lower) when batch fermentations were employed in the same studies. In these works, two strategies were applied to improve productivity of monoterpenes during fed-batch fermentation. Firstly, a two-phase fermentation system was employed using the nontoxic extractive solvents dodecane and isopropyl myristate to alleviate the toxicity of geraniol and limonene. Then, the dynamic control of ERG20 was attempted by replacing ERG20 promoter with the glucose sensing HXT1 promoter, which is stronger in the presence of glucose and weaker in the absence of glucose. Thus, replacement of HXT1 promoter provided the diauxic fermentation process, where glucose was consumed during cell growth with ethanol production, which was then used for monoterpenes biosynthesis. This approach improved the productivity of geraniol and limonene when glucose/ethanol (1/7), or pure ethanol, were fed instead of glucose as sole carbon source during fed-batch fermentations [Many pharmaceutical terpenoids have been produced by fed-batch fermentation . The monntations , indicatS. cerevisiae. In these studies, the exponential feeding rate for glucose/ethanol mixture substrate feeding and pulse feeding for ethanol substrate (10 g/L) were applied and feeding algorithms were automatically triggered through responses of stir rate, DO percentage as well as evaluation of CO2 rate [S. cerevisiae strains with titers of 5.2 g/L, 5.5 g/L, 0.467 g/L, 0.040 g/L and 1.7 g/L, respectively, in fed-batch fermentations. Constant feeding rate triggered by pH rises was used in bisabolene production of glucose fed-batch fermentation [S. cerevisiae [Apart from monoterpenes, great improvement in the production of sesquiterpenes has been achieved using fed-batch processes. For instance, titers of artesiminin precursors amorpha-4,11-diene  and arteCO2 rate ,60. As eCO2 rate ,60. Ref.CO2 rate  used isoCO2 rate , nerolidCO2 rate , patchouCO2 rate , zerumboCO2 rate , and α-hentation  while puentation . Moreoveentation , whereasentation . In thesentation . Moreoverevisiae . S. cerevisiae strains (co-culture with E. coli in oxygenated taxanes accumulation) in fed-batch fermentations. In these studies, after depletion or decrease of substrate in the medium, feed rates of 5.6 g/h of glucose/ethanol mixture for geranylgeraniol, 5 mL/h of glucose and other ingredients for miltiradiene, and feed pulses of 20 g/L glucose and 50 g/L xylose for oxygenated taxanes productions were applied. The pharmaceutical diterpenes, geranylgeraniol , miltiraIn addition, several triterpenes and their precursors, such as protopanaxadiol (1.189 g/L) and dammarenediol-II (1.548 g/L) , β-amyriIt can be deduced from these works that different feeding strategies in fed-batch fermentation depend on the response of engineering strains used and their genetic design, such as the choice of promoters. This is because each strain can have different kind of promoters regulating heterologous gene expression in terpenoids pathway, which may influence the substrate type and the concentrations to be employed in the culture medium. Namely, modifications in inducible gal-promoters and constitutive promoters such as PGPD , PTEF1 , PADH1 , PPGK1 (phosphoglycerate kinase), and uptake control mutations, were reported to affect the feeding strategies in fed-batch terpenoids production .−1. In another study, squalene accumulation of 30 mg/g (product/dry cell weight) was obtained at low dilution rates between 0.05 h−1 and 0.20 h−1 in glucose limited chemostat cultivation [Continuous processes (or chemostat cultivations) have been employed in industrial bioprocesses  and have several advantages over batch and fed-batch, such as decreased costs of bioreactor constructions, minimized plant maintenance and operation costs, convenient bioprocess control, requirement of less downtime for tank cleaning and maintenance, and higher productivities ,103. Onetivation . DespiteS. cerevisiae has been proven to be an efficient cell factory for large-scale terpenoid production. Industrial production of artemisinic acid and β-farnesene are examples of success. Pharmaceutical terpenoid production has attracted increasing attention in recent years because these compounds play significant roles in the prevention and treatment of different types of diseases. As the world’s mortality rate rises, due to the incidence of severe diseases, such as cancer and malaria, the demand for these products increases, and, thus, fast development of alternative and sustainable sources rather than natural and chemical ones is required. Microbial fermentation of these special compounds promises a sustainable, cost effective, and high yield method in contrast to traditional methods. S. cerevisiae, as this yeast cannot produce target terpenoids naturally. Commercial terpenoid application is only possible with successful combination of these two fields, metabolic engineering and fermentation process development. Process optimization can increase productivity and reduce the cost of production by improving medium composition, physicochemical conditions, and applying efficient downstream processing. Most of the terpenoid titers obtained are in mg/L levels, mainly in batch fermentations, which do not have commercial meaning. On the other hand, bioreactor fermentation with fed-batch operation mode can provide titers at g/L scale, and high productivities, while alleviating the toxicity of final product, which constitutes the most promising approach for industrial production of pharmaceutical terpenoids. Among factors affecting terpenoids fermentation, the pH and DO are relevant factors for certain production cases. Although pH range between 4 and 6 is optimal for yeast growth, and achieve high cell density, some terpenes can be insoluble under this condition, which might complicate the downstream processing. However, this can be solved, for instance, by harvesting tanks at high pH by addition of base solution. The use of extraction agents during cultivation is an option to reduce the toxic effects of terpenes, but it brings extra costs to the process. This problem can be overcome by optimizing the feed strategy together with product removal in fed-batch fermentation. The DO is often used as a trigger in fed-batch process for the control of yeast growth and product formation. Optimum oxygen concentrations are required to balance yeast health and productivity, since low levels can minimize yeast capacity, while high levels can lead to oxidative stress, which can be toxic to cells. Similarly, high temperatures can decrease cell viability, while at low temperatures, high cell density is achieved later. Optimization of media ingredients is also of huge importance as they can account for a significant part of the process costs. One way to decrease feedstock cost could be to find and adapt cheap carbon sources, like agricultural by-products, in S. cerevisiae metabolism, as extensively investigated in case of other biomolecules production, which also contributes to process sustainability. Indeed, the trend is for the increase of pharmaceutical terpenoids obtained through optimized fermentation. The power of synthetic biology tools and advances in metabolic engineering strategies for the introducing novel terpenoid biosynthesis pathways in S. cerevisiae will bring new opportunities to produce complex terpenes such as the current trend product “cannabinoids”. The microbial fermentation of these highly promising and rare compounds at an affordable cost and high purity, independently from Cannabis plant cultivation, could absolutely support the pharmaceutical market, when the growing industrial demand for these products is foreseen.Process optimization by screening different fermentation strategies is essential to improve TYP of pharmaceutical terpenoids, along with fine-tuning metabolic engineering in


Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [13C]bicarbonate and [13C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.Nitrite-oxidizing bacteria belonging to the genus IMPORTANCENitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria. Nitrospira constitutes the most diverse and abundant NOB based on marker gene (16S rRNA and nitrite oxidoreductase [NXR]) and metagenomic surveys. The genus Nitrospira consists of at least six lineages that mediate nitrite oxidation across various habitats, including soil, freshwater, marine, terrestrial, and engineered ecosystems  . The genosystems . NitrospNitrospira’s carbon and energy metabolism  and lacks the two key genes (for ribulose bisphosphate carboxylase and phosphoribulokinase) needed to operate the Calvin-Benson-Bassham cycle (CBB) used by some NOB  experiments have also suggested that Nitrospira populations present within activated sludge microbial communities can assimilate pyruvate  using a technique called flux balance analysis (FBA) . This prbolomics .N. moscoviensis representing the ubiquitous Nitrospira lineage II. We examine the metabolism of N. moscoviensis growing under chemolithoautotrophic conditions and also during growth with formate as a substrate. Taking advantage of recent advances to cultivate Nitrospira in continuous flow membrane bioreactors , iNmo686, will serve as a knowledge base for understanding and predicting the function of Nitrospira in both natural and engineered ecosystems.Here, we provide the first constraint-based metabolic reconstruction and analysis of reactors , we subs(This article was submitted to an online preprint archive .)N. moscoviensis (iNmo686) was reconstructed from the most recent N. moscoviensis genome annotation (NCBI accession number NZ_CP011801), aided by reaction annotations in the MetaCyc  in Nitrospira performs this function  during chemolithoautotrophic steady-state growth on nitrite .To obtain qualitative and quantitative outputs from a genome-scale model via FBA, an objective function is required. This is typically accomplished based on a biomass objective function, which assumes that maximization of biomass growth rate is the cellular objective . TherefoN. moscoviensis is presented in N. moscoviensis’ biomass objective function  by the model determined ATP yield (1 mmol ATP per 1 mmol NO2−). Growth-associated maintenance (GAM) was determined by matching the slope of the curve to that determined by the model, based on adjusting the value for GAM and setting the biomass growth rate to different values while minimizing the nitrite uptake rate. Download FIG S1, JPG file, 0.06 MB.Maintenance energy determination based on plotting the experimentally determined nitrite uptake rate versus the growth rate. Experimental values were taken from the study by Mundinger et al. . NongrowCopyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. moscoviensis grown on nitrite was obtained to confirm expression of the reconstructed metabolic network. Enzymatic digests using three different digestion enzymes alone and in combinations were tested to ensure a representative membrane proteome was obtained  , the reductive TCA cycle, and nitrogen assimilation pathways (see A high-coverage proteome of  (39.2%) . The resways see .10.1128/mSystems.00173-21.1TEXT S1Text S1, PDF file, 0.07 MB.Proteomic method validation. Download Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00173-21.7TABLE S2N. moscoviensis CDSs with function in key metabolic pathways during growth on nitrite. Download Table S2, XLSX file, 0.04 MB.Protein coverages of five different proteomic approaches and gene expression levels of selected Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. moscoviensis (r = 0.54)  . While siNmo686 genome-scale model was first used to quantify the carbon and electron flux distribution in N. moscoviensis during aerobic growth on mineral medium with nitrite as an electron donor and CO2 as a carbon source. The model was constrained by the nitrite uptake rate measured during chemostat cultivation, and the objective function was maximizing growth. Consistent with experimental data, when the nitrite uptake rate was set to 8.5 mmol NO2− g dry weight−1 h−1, the specific biomass growth rate equaled 0.006 h−1 (∼116-h doubling time)  and 17% is oxidized for nongrowth-associated maintenance  in the form of ATP . Electron transport chain (ETC) reactions involved in nitrite oxidation carried the highest flux and were at an order of magnitude higher than carbon fixation fluxes, where the predicted amount of carbon fixed per mole of nitrite oxidized was 32 mol C/mol N (N. moscoviensis’ large maintenance energy demand and the high abundance of ETC proteins measured in the proteome (The ng time) . Under t ATP see . The rem C/mol N  and 4. Tproteome .10.1128/mSystems.00173-21.3FIG S2N. moscoviensis. Solid lines indicate electron flow, dashed lines indicate redox half-reactions. FBA solutions can be found in FIG S2, PDF file, 0.4 MB.Electron flux distribution in Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. moscoviensis during chemolithoautotrophic growth . These flux values agree with the high protein expression levels of rTCA cycle enzymes measured in the N. moscoviensis proteome (G > 40 kJ/mol), as well as pyruvate:ferredoxin oxidoreductase (ΔG > 14 kJ/mol) (The predicted carbon flux distribution in c growth  showed tproteome  and geneproteome . In partproteome . This is kJ/mol) .N. moscoviensis is predicted to result from reverse electron flow from the cytochrome c pool (−1 h−1) of the oxidized nitrite is used to generate quinol (largely for nitrite assimilation via octaheme nitrite reductase and reduction of fumarate via succinate dehydrogenase), 4% (0.32 mmol g dry weight−1 h−1) is used to generate NADH (via complex I), and 3% (0.27 mmol g dry weight−1 h−1) is used to generate reduced ferredoxin (via the 2M-type complex I) + transhydrogenase. While N. moscoviensis harobrs three NAD(P)+ transhydrogenases, only NITMOv2_1092 was detected in the proteome  . Reductiproteome , indicat13C-labeled bicarbonate incorporation into the metabolome of N. moscoviensis. Cells were grown in a membrane bioreactor under steady-state conditions, and [13C]bicarbonate was rapidly introduced into the bioreactor, which equilibrated with the dissolved inorganic carbon (DIC) pool in the liquid medium to approximately 65% 13C enrichment. Following [13C]bicarbonate addition, multiple metabolite samples were collected from the bioreactor over a 2-h time period by rapid quenching and extraction of N. moscoviensis cells. A time zero sample corresponding to the period immediately before [13C]bicarbonate addition was also collected as a control.To confirm the biosynthetic pathways predicted by genome-scale modeling, isotopic tracers combined with high-resolution metabolomics was used to follow 13C label incorporation was measured in phosphoenolpyruvate (PEP), acetyl coenzyme A (acetyl-CoA), succinate, and aspartic acid   . Other Ttic acid . This sulutarate . We hyporganisms –24.10.1128/mSystems.00173-21.10DATA SET S213C enrichment values from the [13C]bicarbonate and [13C]formate isotope tracer experiments. Download Data Set S2, XLSX file, 0.07 MB.Metabolite mass isotopomer distributions and Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. moscoviensis.In addition to the TCA cycle, fast labeling of 3-phosphoglycerate, fructose 6-phosphate, glucose 6-phosphate, and sedoheptulose 7-phosphate was observed . This isNitrospira species can use formate as an energy source and that their genomes contain a formate transporter (FocA) and a soluble NAD+-reducing formate dehydrogenase (FDH), which catalyzes the oxidation of formate to CO2 with concomitant reduction of NAD+ to NADH  unique and common in different proteome preparations. (A) Comparison of the whole-cell and the membrane proteomes. A protein was counted as identified in the membrane proteome when it was identified with ≥2 unique peptides in at least one of the five membrane proteome preparations. Given in parentheses are the ratios of membrane proteins (containing ≥1 TMH) to soluble proteins. (B) Comparison of five membrane proteome preparations using different enzymatic digests. Given in parentheses are the total numbers of proteins identified in the sample. (C) Relative proteome coverages of the whole-cell proteome and five different membrane proteome preparations given per number of transmembrane helices. The relative coverage is given as a percentage of the total number of nonidentical proteins predicted to contain 0 to 18 transmembrane helices. Given in parentheses are the total numbers of proteins containing 0 to 18 transmembrane helices. Download Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00173-21.5FIG S4−1 h−1; and formate, 6.0 mmol g dry weight−1 h−1. (B) Growth of N. moscoviensis using formate (yellow) or nitrite (red) as electron donor with oxygen as the terminal electron acceptor at different pHs. *, P ≤ 0.1; **, P ≤ 0.05. Download FIG S4, PDF file, 0.3 MB.(A) Biomass growth rates on formate and nitrite calculated from the study by Koch et al.  and the Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. moscoviensis on formate compared to those under chemolithoautotrophic conditions. Comparisons were performed on minimal NOB medium plus ammonia with the following substrates: (i) nitrite and O2, (ii) formate and O2, and (iii) nitrite plus formate and O2. The nitrite uptake rate was derived from chemostat measurements, whereas the formate uptake rate was estimated from data provided by Koch et al.  provides a framework for examining Nitrospira metabolism at a systems level and will serve as a knowledge base that can be continually refined to drive understanding and improve predictions of Nitrospira ecophysiology. Our analysis shows that the model quantitatively predicts N. moscoviensis chemolithoautotrophic growth on nitrite. Flux balance analysis provided estimates for the amount of nitrite used for energy conservation and CO2 fixation into biomass, offering a quantitative model for linking catabolism and anabolism. The model also accurately provides quantitative estimates of carbon and electron flux distribution, reinforcing the metabolic network predicted based on the proteomic measurements from this study and from previous genomic and transcriptomic analyses of Nitrospira , differences in their labeling will result in an aggregated measurement of the mixed pool because they are extracted together. If only one of those pools is actively participating in metabolism, the aggregation of both labeled and unlabeled pools will serve to dilute the overall 13C enrichment. While electron micrograph images suggest that no intracytoplasmic membranes or carboxysomes are present in N. moscoviensis cells  as well as pyruvate synthase (ΔG > 14 kJ/mol)  , substrarganisms , 24.N. moscoviensis. Nitrospira species have been observed to oxidize formate to CO2 for energy conservation under laboratory conditions  by a respiratory chain that is potentially not well adapted for electron flow in the oxidative direction. The metabolic cost for increased ROS defense might result in low growth efficiency. However, the exact metabolic mechanism resulting in poor growth on formate awaits further physiological analysis. In addition, it also remains to be tested whether all Nitrospira species grow slower using formate as an electron donor and whether other environmental conditions can boost formatotrophic growth.Interestingly,  nitrite , even thN. moscoviensis anabolism. While modeling predicted that small growth improvements would be achieved by directly assimilating formate via the reductive glycine pathway, in vivo [13C]formate tracer experiments demonstrated that N. moscoviensis adheres to its autotrophic lifestyle, assimilating CO2 derived from formate oxidation via the rTCA cycle. This may be an evolutionary adaptation to avoid energetic costs associated with remodeling its proteome in environments where formate may only become transiently available, for example, at oxic-anoxic interfaces common to the habitats of Nitrospira  provides quantitative estimates of chemolithoautotrophic growth and pathway fluxes and serves as a valuable tool for hypothesis-driven discovery. Our 13C labeling metabolomic results also provide the first insights on Nitrospira’s in vivo central carbon metabolism, confirming the high activity of the rTCA cycle for CO2 fixation. Future efforts to combine 13C metabolomics with genome-scale modeling should provide a valuable approach for quantitatively understanding the regulation of Nitrospira metabolism under different environmental conditions and microbe-microbe interactions. This will further expand the systems biology framework developed in this study, ultimately leading to the systematic prediction and control of Nitrospira metabolism in natural and engineered ecosystems.In conclusion, our work provides the first genome-scale reconstruction and analysis of N. moscoviensis M-1 was grown in NOB mineral salts medium for lithoautotrophic growth as described in Spieck and Lipski  inoculated with an active batch culture and operated as described by Mundinger et al. (2 (95%:5% [vol/vol]) and air at a rate of 10 ml/min to maintain a dissolved oxygen concentration of ∼30%. pH was controlled at 7.7 using a 1 M KHCO3 buffer. Temperature was maintained at 39°C using a loop-type heat exchanger, and the reactor was continuously stirred at 150 rpm. Reactor and all cultivation media and solutions were sterilized by autoclaving or sterile filtration prior to use, and the reactor was operated aseptically to maintain culture purity. Nitrite and nitrate concentrations were check daily to ensure all nitrite was consumed stoichiometrically to nitrate  and that nitrite was always limiting. Cultures were maintained in steady-state growth at a dilution rate of 0.006 h−1 and a substrate feeding rate of 2 to 2.5 mmol NO2− liter−1 day−1.r et al. . The worN. moscoviensis (iNmo686) was reconstructed from the NCBI’s genome sequence for N. moscoviensis (accession number NZ_CP011801.1) using the ModelSEED pipeline (N. moscoviensis (see https://github.com/celawson87/Nitrospira-moscoviensis-GEM). Flux balance analysis was used to simulate in silico growth by solving the linear program:vbiomass is the flux through the biomass objective function, S is the stoichiometric matrix generated from the reconstruction with rows representing metabolites, columns representing reactions, and entries representing metabolite stoichiometric coefficients, v is the vector of steady-state reaction fluxes, and vmin and vmax are the minimum and maximum allowable reaction fluxes, respectively. Flux balance analysis was performed in Python version 3.7.2 using the COBRApy package  to obtain cell pellets, which were subsequently freeze-dried prior to analysis. Total protein concentration was determined using the Pierce bicinchoninic acid (BCA) protein assay kit (Thermo Fisher Scientific), and amino acid composition was determined according to Carnicer et al.  using a i et al. . Total Ri et al. . Total iiAF 1260 .13C-labeled sodium bicarbonate was rapidly introduced (within 1 min) into the bioreactor containing N. moscoviensis cells growing under steady-state conditions to a final concentration of approximately 30 mM. Following 13C-label introduction, samples were rapidly withdrawn from the reactor at time points 0, 1.5, 3, 5, 8, 11, 15, 20, 30, 45, 60, 90, and 120 min. Samples were immediately filtered (Millipore 0.45-μm hydrophilic nylon filter HNWPO4700) using a vacuum pump to remove extracellular medium, and filters were placed face down in 1.5 ml of −80°C extraction solvent (40:40:20 acetonitrile-methanol-water) for cell quenching and metabolite extraction. Samples were then centrifuged , and 1 ml of cell-free supernatant was collected and stored at −80°C for metabolomic analysis. The time zero sample corresponded to the period directly before 13C-label addition. The ratio of 13C to 12C dissolved inorganic carbon remained constant during the course of the 2-h experimental period as determined by gas chromatography-mass spectrometry (GC-MS) analysis as described below.N. moscoviensis cells were harvested from the membrane bioreactor. The biomass was centrifuged at 8,000 × g for 15 min at 25°C and washed by resuspending the cells in fresh mineral NOB medium. This was repeated until no nitrite or nitrate was detectable via nitrite/nitrate test strips  in the culture. Subsequently, the cells were transferred to sterile 120-ml serum bottles  containing 50 ml NOB mineral salts medium with 0.5 mM sodium formate and no nitrite but 0.187 mM NH4Cl as the nitrogen source. Bottles were crimp sealed with a rubber stopper to allow monitoring of the gas headspace and were incubated at 39°C in the dark. Following 24 h of acclimation, 1 mM 13C-labeled sodium formate  was added to all incubations. At each time point , the isotopic composition of the gas headspace was measured using GC-MS (see below), and subsequently, all 3 bottles corresponding to a given time point were sacrificed for metabolomics analysis. Bottle contents were filtered immediately using a vacuum pump, and metabolites were extracted using −80°C extraction solvent (40:40:20 acetonitrile-methanol-water) as described above.N. moscoviensis cells from the membrane bioreactor were harvested and washed until no nitrite or nitrate was detectable via nitrite/nitrate test strips  (see above). The cells were transferred to sterile 120-ml serum bottles with 50 ml NOB mineral medium containing 0.187 mM NH4Cl as the nitrogen source and a pH adjusted to 6.6, 7, or 7.7 with 1 M KHCO3 and were sealed with rubber stoppers. The experiment was performed in duplicates , and 5 mM sodium nitrite or 5 mM sodium formate was added to the incubations as the sole energy source. Growth was monitored for 7 days using optical density measurements at a wavelength of 600 nm.For growth experiments, m/z. LC separation was achieved using an ACQUITY UPLC BEH C18 column . Solvent A was 97/3 water-methanol with 10 mM tributylamine (TBA) adjusted to pH 8.1 to 8.2 with 9 mM acetic acid. Solvent B was 100% methanol. Total run time was 25 min with the following gradient: 0 min, 5% B; 2.5 min, 5% B; 5 min, 20% B; 7.5 min, 20% B; 13 min, 55% B; 15.5 min, 95% B; 18.5 min, 95% B; 19 min, 5% B; and 25 min, 5% B. Flow rate was 200 μl min−1. The autosampler and column temperatures were 4°C and 25°C, respectively. Mass isotopomer distributions were corrected for natural abundance using the method of Su et al. -MS system consisting of a Vanquish UHPLC system (Thermo Scientific) coupled with electrospray ionization  to a hybrid quadrupole high-resolution mass spectrometer  operated in full scan mode for the detection of targeted compounds based on their accurate masses. Properties of full MS-selected ion monitoring (SIM) included a resolution of 140,000, automatic gain control (AGC) target of 1E6, maximum isolation time (IT) of 40 ms, and scan range from 70 to 1,000 u et al. , and 13C2 or Ar gas to void the headspace of background CO2. Samples were equilibrated with the acid in the bottles for at least 1 h at room temperature to drive all dissolved inorganic carbon into the gas phase. Fifty-microliter gas samples were then collected in a gas-tight syringe with a needle (Hamilton) from the bottle’s headspace and the isotopic fractions of 12CO2 and 13CO2 were determined using a gas chromatograph (Agilent 6890 equipped with 6 ft Porapak Q and molecular sieve columns) coupled to a mass spectrometer (GC-MS) .Isotopic fractions of dissolved inorganic carbon in the liquid media were measured based on a modified headspace method . Three m(i) Whole-cell fraction sample preparation and proteolytic digestion. Biomass was harvested by centrifugation, washed once with Tris-EDTA buffer, pH 7.7, and frozen using liquid N2. Cell pellets were stored at −80°C until further processing. For protein extraction, cell pellets (445 mg wet weight) were resuspended in 5 ml 50 mM triethylammonium bicarbonate (TEAB) buffer, 1% sodium deoxycholate, pH 8, and mixed 1:1 with B-PER reagent . Cells were lysed by two rounds of sonification  at setting 6 for 30 s, intermitted by cooling on ice. Cell debris and unopened cells were separated from the sample by centrifugation . Proteins were precipitated by addition of 250 μl 100% (wt/vol) trichloroacetic acid (TCA) to 1 ml of sample and incubation for 20 min on ice. Proteins were collected by centrifugation  and washed twice with ice-cold acetone. Next, the protein pellet was resuspended in 400 μl 50 mM TEAB buffer aided by a heating step at 40°C for 1 h with intermittent vortexing. One hundred microliters of the protein extract was diluted 5 times with 6 M urea, 200 mM ammonium bicarbonate (ABC) buffer followed by vortexing and a heating step for 20 min at 45°C at 1,450 rpm and 25 min of sonification. Acetonitrile (ACN) was added to a final concentration of 50%, followed by another heating step. A 200-μl aliquot of the sample was reduced by the addition of dithiothreitol (DTT) to a final concentration of 2.3 mM followed by incubation at 37°C for 1 h. Next, the sample was alkylated by the addition of iodoacetamide (IAM) to a final concentration of 3.75 mM followed by incubation in the dark at room temperature for 30 min. The sample was diluted with 200 mM ABC buffer to a concentration of urea of <1 M. Lastly, a 150-μl aliquot of the protein sample was digested with 5 μg of sequencing-grade trypsin  overnight at 37°C.(ii) Membrane fraction sample preparation and proteolytic digestion. Biomass was harvested by centrifugation, washed once with Tris-EDTA (TE) buffer, and frozen using liquid N2. Cell pellets were stored at −80°C until further processing. For protein extraction, cell pellets (1.95 g wet weight) were resuspended in 13 ml 50 mM TEAB buffer, 1% sodium deoxycholate, pH 8, and mixed 1:1 with B-PER reagent (Thermo Fisher Scientific). Cells were lysed by three passages through a French press at 138 MPa pressure. Cell debris and unopened cells were pelleted from the sample by centrifugation . The membrane fraction was prepared using ultracentrifugation  and washed once in 200 mM ABC buffer, followed by another ultracentrifugation step to collect the membrane pellet. Finally, the membrane pellet was resuspended in 400 μl 200 mM ABC, 1% N-dodecyl β-d-maltoside (DDM), and membrane proteins were solubilized overnight in an orbital shaker at 4°C. The sample was clarified by centrifugation . Urea was added to the sample to a final concentration of 6 M. The sample was reduced by the addition of DTT to a final concentration of 2.3 mM followed by incubation at 37°C for 1 h. Next, the sample was alkylated by the addition of IAM to a final concentration of 3.75 mM, followed by incubation in the dark at RT for 30 min. The sample was diluted to a concentration of 1 mg/ml with 200 mM ABC, 1% DDM, and 6 M urea. Five aliquots of 100 μg protein each were digested or doubly digested using 2 μg of each selected proteinase; all digestion steps were performed at 37°C. The following different proteinases and combinations were tested: (i) LysC (Pierce), 4 h; (ii) trypsin (Promega), 4 h; (iii) chymotrypsin (Pierce), overnight; (iv) LysC, 4 h followed by trypsin, overnight; and (v) trypsin, 4 h followed by chymotrypsin, overnight. In digests with trypsin and chymotrypsin, ACN was added to a final concentration of 20%, and the concentration of urea in the sample was diluted to ≤1 M by the addition of ABC buffer.(a) Solid phase extraction for membrane protein digest. Proteolytic digests were desalted using an Oasis HLB 96-well plate  according to the manufacturer’s protocol. The purified peptide eluate was further dried using a speed-vacuum concentrator.(b) Large-scale shotgun proteomics. All dried peptide fractions were resuspended in H2O containing 3% acetonitrile and 0.1% formic acid using mild vortexing. An aliquot of every sample corresponding to approximately 100 to 200 ng protein digest was analyzed in duplicates using a one-dimensional shotgun proteomics approach , and a QE plus Orbitrap mass spectrometer (Thermo). The flow rate was maintained at 300 nl/min over a linear gradient using H2O containing 0.1% formic acid as solvent A, and 80% acetonitrile in H2O and 0.1% formic acid as solvent B. The soluble protein extract fractions were analyzed using a gradient from 5% to 30% solvent B over 90 min, and finally to 75% B over 25 min. The soluble membrane protein fractions were analyzed using a shorter gradient from 4% to 30% B over 32.5 min followed by a second step to 65% B over 12.5 min, and data were acquired in total over 50 min. In either case, the Orbitrap was operated in data-dependent acquisition mode acquiring peptide signals from 350 to 1,400 m/z at 70,000 resolution, where the top 10 signals were isolated from a window of 2.0 m/z and fragmented using a normalized collision energy (NCE) of 30. The AGC target was set to 1E5, at a maximum IT of 54 ms and 17,500 resolution.approach . Briefly(c) Database search and data processing. Raw shotgun proteomics data from membrane proteins and soluble protein extract fractions were analyzed and combined using PEAKS Studio 8.5 . Database search was performed allowing 20-ppm parent ion and 0.02-Da fragment mass error tolerance. Search conditions further considered 3 missed cleavages for the respective enzymes used, carbamidomethylation as fixed and methionine oxidation and N/Q deamidation as variable modifications. Peptide spectra were matched against a N. moscoviensis sequence database , which was modified by filtering out duplicated entries. The database search included the GPM cRAP contaminant database (https://www.thegpm.org/crap/) and a decoy fusion for determining false-discovery rates. Peptide spectrum matches were filtered against a 1% false-discovery rate (FDR), and protein identifications with 2 or more unique peptides were considered significant hits. For the prefractionated whole-cell proteome, proteins were counted if they were significantly identified in at least one of the subfractions.http://phobius.sbc.su.se [The combined transmembrane topology and signal peptide prediction tool Phobius (bc.su.se ) was empN. moscoviensis under stable cultivation conditions grown on nitrite (GSE123406) using the N. moscoviensis genome annotation version CP011801.1 as a reference, from which the UniProt protein sequences were derived (N. moscoviensis genome (NCBI accession number CP011801.1) using the mapping tool BBMap v35.92 (https://sourceforge.net/projects/bbmap/) and counted using featureCounts  correspond to only 4,733 UniProt entries for nonidentical proteins. To directly compare transcription to proteome data, the transcriptome reads of those identical genes were thus summed up.2 fold to median was calculated for genome-wide visualizations of the gene expression levels in a Circos plot (Expression levels of CDSs were compared by ranking them from high to low based on their reads per kilobase per million reads (RPKM) values. Additionally, the logcos plot .GSE123406.The transcriptomic data set reanalyzed here can be found under the NCBI Gene Expression Omnibus data set accession number http://proteomecentral.proteomexchange.org) via the PRIDE partner repository  are given followed by the relative coverage percentage in parentheses. Download Copyright © 2021 Lawson et al.2021Lawson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the  Changes in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments.In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell.Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition. The use of BOF as a cellular objective is a reasonable assumption for many microbes, as an organism’s ability to quickly replicate is a property that often provides a fitness benefit  arginine in the group protein in −1 h−1 based on the compounds’ molecular weight in the model.Also shown in A. latus , or , this tallies to  genes in the respective groups. For the experimental minimal medium we find . For a semi-quantitative gene knockout phenotype analysis, we calculated the angle θ between the two vectors π indicates reversed vectors. This analysis was repeated for all pair-wise combinations of experimental and computational knockout predictions and is reported in Fig C in We compared the computational single-gene knockout phenotype predictions for the different biomass compositions with experimental data from Rousset et al.  for E. cS1 TextThis supporting information text contains the numbers for single-gene knockout results (Table A in S1 Text), presents non-relative heatmaps (cf. (PDF)Click here for additional data file.S1 TableNote, that these BOFs are artificial and not based on measurements. Additionally we list the groups used in the manuscript for scaling shown in (XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Due to its role in maintaining the proper functioning of the cell, the pentose phosphate pathway (PPP) is one of the most important metabolic pathways. It is responsible for regulating the concentration of simple sugars and provides precursors for the synthesis of amino acids and nucleotides. In addition, it plays a critical role in maintaining an adequate level of NADPH, which is necessary for the cell to fight oxidative stress. These reasons prompted the authors to develop a computational model, based on queueing theory, capable of simulating changes in PPP metabolites’ concentrations. The model has been validated with empirical data from tumor cells. The obtained results prove the stability and accuracy of the model. By applying queueing theory, this model can be further expanded to include successive metabolic pathways. The use of the model may accelerate research on new drugs, reduce drug costs, and reduce the reliance on laboratory animals necessary for this type of research on which new methods are tested. The preparation of an accurate model simulating the course of PPP could potentially reduce the time needed for drug testing and reduce the number of laboratory animals on which new drugs are tested3.In recent years, there has been significant progress in metabolomics. New and improved test methods allow for the measurement of many important biochemical parameters. The acquired data can be used to create simulation models of biochemical reactions and entire metabolic pathways. Queueing theory can successfully model metabolic processes, as demonstrated by the example of the glycolysis pathway5. Products of the PPP are essential for the formation of new cells. However, under stress, cell growth is slows down and the PPP is responsible for maintaining cellular levels of NADPH. In fact, such conditions increase the reliance of the PPP in the cell over glycolysis to maintain the needed ratio between NADP6. In most living organisms, this pathway takes place in the cell cytosol.The PPP is a metabolic pathway whose main substrate is glucose-6-phosphate (G6P). Throughout the reactions that make up this pathway, numerous molecules are formed, such as: nicotinamide adenine dinucleotide phosphate (NADPH), which is used in the biosynthesis of fatty acids, ribose 5-phosphate (R5P), which is a precursor in the synthesis of nucleotides, and erythrose 4-phosphate (E4P), which is used in the synthesis of aromatic amino acids7. In the non-oxidative phase, various simple sugars are synthesized. 5-carbon sugars derived from the digestion of nucleic acids can be utilized in the PPP, where their carbon backbones are metabolized into intermediates for glycolysis or gluconeogenesis. In the non-oxidative phase, one of the enzymes- transketolase—is responsible for catalyzing two different reactions, with two sets of substrates. Therefore, these substrates act as inhibitors to each other, since they are competing for the same active site of the enzyme.There are two phases in the PPP: the oxidative phase and the non-oxidative phase. During the oxidative phase, NADPH is produced8. The PPP is most active in the liver, adrenal cortex, and mammary glands. The activity for this pathway is high in red blood cells, making it extremely important in erythrocytes9. NADPH formed by the PPP is used in the cell to prevent oxidative stress and the formation of dangerous free radicals that could harm the cell10. Reactive oxygen species (ROS) can damage cellular lipids, proteins, and nucleic acids, and eventually cause cell death11. It is worth noting that ROS are associated with many diseases14. Since erythrocytes do not have mitochondria, they have no other source of reducing oxidative stress other than the PPP. For example, large amounts of NADPH generated in erythrocytes are used to reduce glutathione (GSH). This reduced form of GSH is essential for maintaining the proper state of the cell. If GSH level is lowered in erythrocytes, hemolysis may occur15.It is estimated that as much as 60% of NADPH comes from the PPP18. The authors in19 presented a PPP model based on ODEs. This approach was beneficial because it did not require complicated operations that strained the capabilities of computers in the past, resulting in lower computing power. However, the simplifications and assumptions made when using ODEs in metabolic simulations do not reflect the stochastic nature of cell biochemistry20. The Chemical Master Equation (CME) was another approach used to model the stochasticity of biological reactions21. However, due to the complexity and computing requirements, networks based on these models cannot be too extensive. A relatively new approach to computational metabolic modeling is the use of queueing theory. Queueing theory has wide applications in telecommunications, but also in biological and medical science topics, such as modeling drug pharmacokinetics22 or HIV infectivity23. Using this method, it was possible to accurately model a simple metabolic network and mimic the interactions between metabolites24, as well as the Krebs cycle2. A genetic algorithm was used to optimize the kinetic coefficients. A variety of AI methods can be used for this purpose, but genetic algorithm was chosen because it was used with success when modeling the Krebs cycle.The most common approach used to model metabolic changes in a cell is to use Ordinary Differential Equations (ODE). For metabolic reactions, ODEs provide quantitative information on interactions that occur between metabolites in specific reactions taking place in the cell. Previously, ODEs have been successfully used in simulation studies of biochemical kinetics and biochemical connections2. A genetic algorithm was used to find values corresponding to those in the literature.The aim of this work was to prepare a PPP model capable of tracking concentration changes of specific metabolites occurring in this pathway over time. Additionally, the usefulness of the genetic algorithm for finding values of the kinetic constants used in the model was confirmed2. This feature allows for proper simulation of metabolite flow depending on the current needs of the cell , averaged over 50 simulated cells. This number has been selected experimentally. However, the model is designed to vary this number depending on the needs of the researcher. Figure 25. Any existing PGL is almost immediately converted to 6PG, therefore the variance is very high. The relative difference of PGL is high because it depends on the measurement time. In the future, we intend to combine the PPP with the already developed Krebs cycle and glycolysis models, so the results of the PPP model are likely to be closer to the experimental results.PGL is rapidly hydrolyzed, so the practical equilibrium between G6P and 6PG is directed towards the formation of 6PGDue to the high demand of glucose and its metabolites by cancer cells, many drugs are aimed at blocking metabolic pathways that supply cancer cells with substances necessary for proliferation. The PPP is one of the pathways with significantly increased activity in neoplastic cells. Compared to healthy cells, the activity of the PPP in cancer cells can be increased up to 8 times. The oxidative part of the pathway provides cells with a large amount of NADPH, helping the cell can effectively fight excess oxidative stress. Effects that reduce the effectiveness of the production of NADPH in the cell, in combination with factors that induce this stress, such as radiotherapy or chemotherapy, can kill cancer cells.26 serving as the benchmark for our model described the effect of a third PPP enzyme, PGD, in lung cancer cells. Inhibition of this enzyme’s activity does not significantly affect the level of NADPH, but inhibits tumor growth. The gene encoding PGD is characterized by increased expression in neoplastic cells. ShRNA molecules were used to reduce PGD expression. This approach resulted in inhibition of tumor growth , indicating an important role for PGD in cancer cell metabolism. Concentrations of several PPP metabolites were measured, however, not all of them had significant changes. Metabolites of the oxidative phase of the PPP, such as 6-phosphogluconolactone (PGL) and 6-phosphogluconate (6PG) had concentrations 7.9 and 11 times higher than their regular concentrations, respectively  expression. This form of knockdown is not expected to achieve 100% silencing. Indeed, 80–99% knockdown of expression is normal and expected. The calculated results are comparable to those obtained experimentally and are consistent with current biological knowledge. Another point to consider is that the glucose metabolism of neoplastic cells remains unknown in some aspects and these cells may possibly bypass a blocked reaction in the metabolic pathway. Simulations using 100% inhibition were also performed, but this led to a significant reduction in the concentration of metabolites downstream of the bottleneck of the pathway. However, it can be observed that due to the bidirectional character of reactions of the second phase of the PPP and the flux of metabolites from other pathways, e.g., F6P generated in glycolysis, we do not observe a complete ‘zeroing’ of metabolite concentration.Several measurements were performed to evaluate the level of inhibition of the GPD catalyzed reaction. The obtained results show that the GPD knockdown caused inhibition at the level of 95–98%. These assumptions are based on the results presented in Table The results generated in our model Table  follows As mentioned in the introduction, most previous models simulating metabolic pathways, not only PPP, have been based on the use of ODEs. However, due to the advantages offered by queueing theory, it seems reasonable to use this method in modeling. The preparation of a quantitative model of a biological pathway such as the PPP requires the necessary information on starting concentrations and kinetic data of the enzymes that catalyze the pathway reactions. The presented model can be viewed as a ’virtual laboratory’. This model tracks the relationships between individual metabolites formed at different stages of the pathway. It is possible to observe changes caused by fluctuations in metabolite concentrations and their impact on the entire pathway.28. However, clinical data indicate that this therapy is not very effective without additional exposure to oxidative stress30. For this reason, the results of studies on the knockdown of the gene encoding PGD in this paper were used26. Even though the knockdown of the G6PD gene does not affect the amount of NADPH, which is important for tumor development, the knockdown of this gene alone results in inhibition of tumor growth. Perhaps the metabolites that accumulate in the cell prior to the blocked reaction are responsible for this situation. Their concentration in cells reaches values significantly greater than their natural concentrations. The exact mechanism of tumor growth inhibition is unknown, however, the effect achieved is important.It can also be used to test the effectiveness of new drugs if their influence on the kinetics of the reaction they affect is known. In this way, one can also theoretically get answers to questions such as which reactions are worth blocking to obtain the best possible therapeutic result. Most studies aimed at blocking the PPP pathway in cancer patients have focused on blocking the first reaction of the pathway catalyzed by G6PDThe proposed model obtained stability based on the data from the above-mentioned paper. We believe that this type of model can be used to predict the impact of therapy, which in turn will lead to an increase in its effectiveness.Thanks to the use of experimental data together with a computational process based on the queueing theory, a model was obtained that can track the metabolic pathway that takes place in the cells of living organisms. In this paper, we present a separate PPP model without detailed analysis of the relationship between PPP and glycolysis. The metabolites common to both pathways have been identified and several principles have been adopted to create a functional PPP model. In the future, our plan is to connect the existing glycolysis, Krebs cycle, and PPP models together. We believe that such a procedure may also positively affect the consistency of simulation and experimental results.The presented results indicate that the model can be used to predict changes in metabolite concentrations. For this purpose, it is sufficient to enter the concentration value of one of the metabolites. In this way, the entire study can prove to be more cost-effective—no need to determine each metabolite separately, which also saves time.As demonstrated by the knockdown of one of the genes encoding the enzyme catalyzing the PPP reaction, this model is adapted to follow the trend of metabolite changes. Moreover, it can determine the specific effect of the inhibition of particular reactions on the concentration of metabolites with relatively high accuracy. Further research providing data on how inhibition of a particular pathway step may affect kinetic constants could contribute to an increase in the accuracy of the presented model.31. Several kinetic constants, and enzymatic properties, like maximum velocity . The next reactions involve changing the length of the carbon chain in the carbohydrates. These two five-carbon sugars then undergo a transketolase-catalyzed reaction. The result is production of glyceraldehyde 3-phosphate (G3P) and sedoheptulose 7-phosphate (S7P). Then G3P and S7P undergo a transaldolase-catalyzed reaction, which produces E4P and fructose 6-phosphate (F6P) Fig. ; Table 332. For this reason, the use of queueing theory in metabolic pathway modeling seems to be the right approach.The complicated nature of metabolic pathways, in which there are huge amounts of biochemical substances constituting the substrates and reaction products, makes modeling metabolism extremely challenging. Methods commonly used to model metabolic pathways require supervision and the use of appropriate constraints, like forcing ODEs not to reach negative values. Such treatments may cause small calculation errors which could accumulate in long-term modeling and result in incorrect calculations. Biological systems are organized to pass the products of individual metabolic reactions further down the pathway, so that they become substrates for downstream reactions or are used by the cell to support necessary life processes21. The advantage of using queueing theory to model metabolic pathways is that they do not require enhanced computing power. Therefore, the results can be obtained close to real time. Networks based on queueing theory can be applied with ease to a significantly greater number of molecules, grouped into the queues representing different molecular species. Due to the nature of this approach, it is capable of combining individual pathways into larger, more complex groups of metabolic pathways.Queueing networks can be thought of as hidden Markov chains, similar to Gillespie’s modelling technique2.Averaging the results from several simulation runs provides information on the average changes in the concentrations of the individual pathway metabolites. The proposed model is based on calculations of the kinetics of Michaelis–Menten enzymatic reactions, which focus on the relationship between the concentrations of the substrate and the product, and the velocity of the reaction. According to this theory, the macroscopic concept of enzymatic reaction speed is the sum of many microscopic reactions that can exchange specific amounts of substances per time unit. The description of the PPP as the probability of decreasing and increasing the concentration of each of the substances present in the pathway and the correlation of their reduction with the accumulation of other substrates results in a self-regulating, stochastic process that imitates the actual course of the PPP. The Michaelis–Menten kinetic equation was used to calculate the probability of the reaction. A detailed description of the methodology used is described in the work describing the Krebs cycle modelk arrivals in the interval The queue processing time of metabolite increment is described by the exponential distribution of the random variable T in the terms of the rate parameter 33.The probability of the reaction can be converted to an average amount of arrivals when measured for a significant amount of time. Therefore, the kinetic equations can be used to calculate the adaptive parameter mentclass2pt{minim1, to determine the likelihood that in this time step the reaction occurs. Since the reaction rates depend on the current concentration of molecules that change from step to step, the resulting inhomogeneous Poisson process implements the feedback loop, which results in a system with memory.Probability of substrate’s increment departure from each queue depends on the current concentration of the substrates and the kinetic constants of the reaction causing that departure. Every queue uses its individual Michaelis–Menten kinetic equation with kinetic constants normalized according to the method based on the formula described inX—vector of substrates describing stable state of simulation.Values of enzyme kinetic constants were found with the use of a genetic algorithm starting from literature data. Every ‘gene’ in the ‘chromosome’ is a vector of kinetic constants describing each Michaelis–Menten kinetic equation. The new values of kinetic constants are found by randomly selecting from which ‘parent’ ‘offspring’ inherits ‘gene’ (set of kinetic constants for a particular reaction). However, mutation occurs on each kinetic value regardless to which parent it belongs. The loss function optimized by the algorithm is the sum of the squared distances between PPP state described by the literature and the current optimization step of the simulation using kinetic constants that makes an individual ‘chromosome’. The formula of loss function is as follows :3\documeThe loss function described above has a trivial solution. If all kinetic constants that are used in Michaelis–Menten reactions as multipliers (instead of dividers) are zeroed, then the results of these equations are equal to zero. As a result, no reactions occur, so the simulation’s stable point is equal to the original literature vector. To prevent such a solution, the genetic algorithm sets a constraint on newly generated ‘chromosomes’. Each reaction parametrized by values of the ‘chromosome’ for a literature vector of substrates must have a probability of occurrence between 0.00005 and 0.05.The first set of ‘chromosomes’ are made of Michaelis–Menten kinetic constants defined in the literature with added gaussian noise. Given the selected starting point, the genetic algorithm is set on finding the optimal value in the proximity of the already established values. This reduces the risk of the algorithm generating an output that minimizes the loss function, but produces kinetic constants significantly different from the literature values.Kinetic constants are grouped into a table of vectors of constant values. There are 14 vectors in the table corresponding to eight different reactions and six balancing flows. Each of the reactions has a unique vector of dimension equal to the number of kinetic constants used in the reaction rate computation and every balancing flow contains a one-dimensional vector.Concentration increment exchanged during the reactions is denoted ‘delta’ and is unique for each reaction. It ranges from the concentrations of G6P and NADP in the cycle vary with 10% Gaussian noise around the constant values of 0.001 mM and 0.0026 mM, respectively. This signal-to-noise ratio aims to reflect metabolic conditions inside the cell.The search for optimal kinetic constants was performed using a PC with AMD Ryzen 7 3800X 8-Core Processor, 3900 MHz, RAM 32 GB. Code was written in The pseudocode describing the computational processes can be found in the 34. Therefore, it is the enzyme that controls the starting velocity of the pathway. This enzyme is strongly inhibited by NADPH35. Drugs aimed at reducing the intensity of the reaction mainly focus on reducing the activity of this enzyme, which leads to a reduction in the velocity of the entire pathway27. However, clinical results indicate that inhibiting this enzyme is not an effective therapeutic approach26. For this reason, data obtained from the study of knockdown expression of the 6-phosphogluconate dehydrogenase (PGD) enzyme were selected for model validation26.G6P dehydrogenase is the enzyme that catalyzes the first reaction of the pathwaySupplementary Information.


Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms.pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.MEWpy can be installed from PyPi ( Constraint-Based Modelling (CBM) provides tools for the integrative analysis of molecular systems and quantitative prediction of physicochemical and biochemical phenotypic states. Recently, several modelling approaches have arisen putting forward a growing integration of the transcriptional and translational layers and respective omics data  with Genome-Scale Metabolic Models (GSMMs), to improve the characterization of cell physiology, while contributing to a better understanding of the organisms’ metabolism. Some illustrative approaches are the GECKO toolbox  and OptRCSO consists on identifying the set of genetic modifications, to be introduced in an organism, that optimize a desired engineering goal. Typically, the goal is to maximize the production of a compound of interest, while assuring that the organism remains viable. Deterministic approaches to CSO problems, such as OptKnock , identifIn this context, and given the lack of integrative tools for the increasing number of modelling approaches, we propose MEWpy, an integrated Metabolic Engineering Workbench written in Python, that offers methods to explore different classes of constraint-based models, including metabolic, enzymatic or regulatory constraints. MEWpy enables using different modelling approaches, such as the GECKO toolbox and OptRAM algorithm, to run different phenotype prediction algorithms, and allowing them to be used to support strain optimization.MEWpy aims to provide a Python implementation of CSO algorithms, which can run over GSMMs defining GPR associations, but also over the previously discussed enhanced modelling approaches. The conceptual architecture of MEWpy, which is highlighted in Problem definition layer. The definition of the CSO problem, includes the selected modelling framework, the definition of the modification targets , the modification strategy , together with the target product and the environmental conditions;Phenotype simulation layer. This layer aggregates the methods used to evaluate the mutant strains generated by the CSO algorithm, specifically, Flux Balance Analysis (FBA), parsimonious FBA (pFBA), Regulatory On/Off Minimization of metabolic flux (ROOM), Minimization of Metabolic Adjustment (MOMA) and its linear version (lMOMA) as well as Flux Variability Analysis (FVA). MEWpy also includes implementations of the regulatory FBA (rFBA) and of the Steady-state rFBA (srFBA) to incorporate transcriptional regulation.Optimization layer. This layer encompasses the optimization heuristics used for strain optimization and respective objective functions. At each iteration of the optimization algorithms, the fitness of candidate solutions is asserted by running the phenotype simulations required by the objective functions.Regarding the CSO algorithms, MEWpy resorts to Evolutionary Algorithms (EAs), given their flexibility in the definition of objective functions. EAs are stochastic algorithms inspired by nature. They maintain a population of solutions, encoded metabolic modifications, whose interactions drive the optimization process. At each generation, mating and mutation operators produce a new solution set, from which the fittest are selected to integrate the next population. Such a meta-heuristic mimics the Darwinian evolutionary principles to find sets of modifications whose phenotype best address the optimization problem.EAs have been applied in metabolic engineering frequently considering a single optimization objective or a weighted aggregated sum of distinct objective functions. Such approaches add increased difficulties: the trade-offs between optimization objectives need to be adequately chosen beforehand; each objective value needs to be normalized; single objective EAs, and in particular Genetic Algorithms (GAs), are more prone to get stuck in a local optimum, evidencing premature convergence, as they manifest more difficulty in preserving high diversity within the populations .Multi-objective EAs (MOEAs), on the other hand, enable the formulation of strain design problems that account for the simultaneous optimization of more than one objective . MOEAs deliver in a single run a set of solutions with different trade-offs between the objectives, providing a broader set of possible perturbations for analysis.Currently, the EAs are implemented by the Inspyred  and JMetMEWpy offers means for the exploitation of constraint-based models that account for the following types of constraints:min, max) functions (this may be overridden according to user preferences) and gene identifiers by expression values. Modifications on reactions fluxes are achieved by altering their bounds.Metabolic Constraints: MEWpy enables to evaluate phenotypes resulting from genes’ or reactions’ over- or under-expression, as well as deletion. Modifications of gene expression are reflected into the catalysed reactions by converting gene-protein-reaction (GPR) rules into flux constraints. GPR rules are converted to algebraic expressions replacing the  Boolean operators by  which affect cells’ metabolism. The incorporation of such elements as additional constraints leads to better and more accurate phenotype prediction. As such, MEWpy offers means to realize strain optimization by imposing enzymatic constraints using GECKO models (GSMM with enzymatic constraints using kinetic and omics data) (cs data)  models.Regulatory constraints: the complex cross talking mechanisms between gene regulation and metabolism are not captured by GSMMs alone. Ergo, MEWpy also contemplates CSO strategies towards designs that impose regulatory constraints, notably, the OptORF  load a model, (ii) choose the optimization objectives, (iii) instantiate the problem and (iv) run the optimization. Next, a minimal example is presented, without the necessary imports, that aims to optimize the yield of a target product by modifying gene expression. The objective functions, to be maximized, are the biomass-product coupled yield (BPCY) and the weighed sum of the minimum and maximum product fluxes (WYIELD). f1 = BPCYf2 = WYIELDproblem = GOUProblemea = EA(problem)ea.runhttps://mewpy.readthedocs.io, covers the extensive list of available configurations. Additionally, some illustrative examples are included in the project github repository in the form of Jupyter Notebooks.In addition, configurations may be added in steps (ii)–(iv) reflecting, for example, the chosen growth medium, a maximum number of allowed modifications, the selected EA, the number of iterations or the number of parallel threads. The MEWpy documentation, which can be found at MEWpy offers a practical interface to several strain optimization heuristics, allowing to model and optimize microbial production on GSMMs defining gene–protein-reaction associations, but also on models enhanced with transcriptional and translational layers. Metaheuristics such as EAs and SA, including multi-objective methods, drive the optimization towards the best set of enzymes, genes or reactions, to under/over-express or delete to maximize the production of a target compound.New methods are presently being added to enable CSO resorting to Metabolism Expression and Thermodynamics Flux (ETFL) models  and MetaThis project received funding from the European Union’s Horizon 2020 research and innovation programme [814408].Conflict of Interest: none declared.