Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types . These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim. In recent years, there has been increasing interest in the possibilities offered by regenerative medicine , a fieldOne important area of regenerative medicine is the use of stem cells to enhance available therapeutic applications and to further the development of new ones . In partFor the past decade, MSCs metabolism has received growing interest due to mounting evidence suggesting that the manipulation of metabolism allows enhanced therapeutic uses of these cells  in cell-based medicine and tissue engineering . Cellulain silico models  metabolism can be used to answer the fundamental question: “How can GEMs be used to optimize MSC therapeutics?” First, we describe the biology of MSCs, their differentiation and immunomodulation properties and their applications and limitations in regenerative medicine. Next, we detail how metabolism affects or can be used to manipulate these functions. We then discuss how mathematical modeling of hMSC metabolism can aid in developing pre-clinical and clinical experiments. Finally, we give our vision for the future of using metabolic modeling to study hMSCs and how the resulting insights could prove transformative for the field of regenerative medicine.Mesenchymal stromal cells comprise non-hematopoietic cells originating from the mesodermal germ layer and are capable of both self-renewal and multilineage differentiation into various tissues of mesodermal origin . These m•Adhere to plastic and develop as fibroblast colony-forming units and differentiate into cells of mesodermal origin . See •in vitro culture expansionExpress the surface markers CD73, CD90, and CD105 during •in vitro culture expansionLack expression of CD11b, CD14, CD34, CD45, CD19, and HLA-DR surface markers during It is likely that this definition will continue to evolve to account for new findings.One of the identifying characteristics of MSCs is their ability to differentiate into cells of mesodermal origin . In addiDifferentiation of MSCs is primarily induced through media supplementation , with different supplements being required for the various differentiations. Adipogenesis, for example, is induced through supplementation with dexamethasone, indomethacin, insulin, and isobutyl methyl xanthine. Osteogenic differentiation is induced by dexamethasone, ascorbic acid, β-glycerophosphate, and sometimes bone morphogenic protein 2 (BMP-2) . The comDifferentiation is controlled by an interlinked set of regulatory molecules forming complex signaling pathways. These pathways are somewhat distinct between differentiation lineages, although there are important areas of overlap. This phenomenon is demonstrated by the inverse relationship that exists between pathways relating to adipogenic and osteogenic differentiation . Most diBeyond their potential for differentiation, hMSCs have remarkable immunomodulatory properties; they possess the ability to inhibit or promote the immune response of the host’s body though mediated immunosuppression. These mechanisms include direct inhibitory effects and other indirect regulatory effects. This regulatory response involves inhibition of B and T cell proliferation, cytokine production inhibition, decreased natural killer (NK) cell activation, and dendritic cell maturation .The immunomodulatory response of hMSCs is activated by inflammatory cytokines  that are secreted by T cells and other antigen-presenting cells . In respIndoleamine 2,3-dioxygenase (IDO) is one of the well-known paracrine factors released by hMSCs and has been shown to promote kidney allograft tolerance . It suppAnother reported soluble factor with immunoregulatory effects is the unstable oxidative molecule nitric oxide (NO), which is generated by NO synthase. Inducible NO synthase (iNOS) is responsible for the immunomodulatory effect of NO. Increased secretion of NO results in modulation of both the proliferation and function of T cells. At very high concentrations, it can lead to the apoptosis of immune cells . A list Mesenchymal stromal cells secrete paracrine factors that promote tissue repair. In response to physical tissue damage, MSCs secrete factors that allow them to navigate to the site of injury, referred to as homing . An examin vitro expansion, and cultural heterogeneity of the hMSCs. Cells derived from different sources seem to express different profiles of the homing molecules  on a host‘s immune system, hMSCs must be mainly in a glycolytic state. Further, it is not well understood to what extent energy metabolism is mixed at different stages of differentiation (early versus late) or whether it is dependant upon the type of differentiation the cells are undergoing. An understanding of how cell source and age affects differentiation and if different states affect the survival and function and, therefore, usability of cells The proliferation, differentiation, and immunomodulatory functions of hMSCs are linked to cellular metabolism . EmerginThere is strong evidence suggesting that, in their undifferentiated state (while undergoing proliferation), hMSCs rely primarily on glycolysis for energy production. This phenotype has been demonstrated in BM-hMSCs, which show a preference for glycolysis during proliferation  but shifThe ability of BM-hMSCs to differentiate has also been shown to be affected by mitochondrial functions . ConsistThe mitochondria of hMSCs seem to be primarily inactive while cells remain in their proliferation stage, during which metabolic pathways related to glycolysis and its associated signaling pathways required for adenosine triphosphate (ATP) generation and general anabolitic activity are most active see . GlycolyThe effect of interferon regulation on hMSC metabolism can be varied. IFN-γ has also been used to inhibit proliferation and alter AD, OD, and neural differentiation (ND) by activating IDO . The kynIFN-β has been shown to enhance immunomodulatory functions of hMSCs in other reports. Overall reports discussing hMSC metabolism seem to, for the most part, agree that during proliferation the cells primarily generate ATP through glycolysis. However, upon initiation of differentiation, cells seem to turn toward mitochondrial metabolism, with reported increases in metabolism and biogenesis indicating the importance of mitochondrial activity when it comes to hMSC functionality .de novo lipogenesis-related genes, and thus de novo lipogenesis produced fatty acid elongation and desaturation) before returning to normal . Their conclusion was that de novo lipogenesis and desaturation comprised the major fatty acid flux during adipogenic differentiation of bovine Ad-MSCs.There is not much known about whether amino acid metabolism is affected during functional progression of hMSCs or what effects they induce if modified through metabolic changes. For example, Ornithine decarboxylase (ODC) and polyamine biosynthesis are important in the proliferation of stem cells . The rolAs suggested in section “MSC Function Is Linked to Metabolism,” active mitochondria are necessary for successful differentiation. Accumulating evidence indicates that mitochondrial enzymes and regulatory pathways are of great importance for MSCs in proliferative and differentiating states . MitochoOther enzymatic activity has been confirmed that further supports the importance of mitochondrial activation for MSC functionality. Creatine kinase (CK) activity, which is involved in buffering and recovery of ATP, has been reported in Ad-hMSCs. It stimulates glycogenolysis by increasing cytoplasmic concentration of inorganic phosphate. Activity of CK was found in both differentiated and proliferating Ad-hMSCs, with more mitochondrial CK activity in AD cells. This further supports the theory of a shift toward oxidative metabolism/mitochondrial metabolism during differentiation of MSCs .Through the reversible mitochondrial nicotinamide adenine dinucleotide phosphate (NADP)-dependent reaction of isocitrate dehydrogenase (NADP-IDH), an anaplerotic pathway exists that forms isocitrate from glutamine through a process called glutaminolysis. Through this pathway, glutamine can compensate for the lack of glucose for both ATP production and anabolic precursor supply . This paReactive oxygen species (ROS) are known to serve as signaling molecules capable of regulating biochemical pathways that are a part of normal cell function. They are particularly important in metabolism and inflammatory signaling . RegulatSince functionality and survival of hMSCs is affected by changes in their metabolism, there is the potential to enhance the efficacy of hMSC therapies through manipulation of metabolism. hMSCs can effectively reconfigure metabolism to respond to the biochemical demands of tissue repair, be it secretion of immunomodulatory factors or integration and differentiation toward tissue specific cell types . CurrentOne such approach is to alter the functional capacity of MSCs through oxygen manipulation, due to the importance of mitochondria and mROS discussed above. 2)] involved in OxPhos. The lower citrate levels indicate a more pronounced metabolic uncoupling of glycolysis and TCA cycle for cells in OD compared to undifferentiated cells  OD of BM-hMSCs may be enhanced and adipogenesis decreased  . HO-1 isMetabolism in MSCs is a complex and dynamic system. We have outlined several gaps in the collective knowledge of MSC metabolism that are actively being addressed by the community. As we gain insights into questions regarding the primary energy-generating pathway(s) utilized during differentiation, we will move closer to manipulating these systems. However, we will need a holistic perspective that integrates knowledge at the various biological levels of MSC differentiation.A bottom-up systems biology approach allows for a mechanistic understanding of a system . Such maThe first step in modeling metabolism at the genome-scale is to reverse engineer the network structure . This reThis ability to integrate information from multiple types of -omic data with previously acquired detailed biochemical data makes metabolic modeling a powerful technique to answer biological questions regarding how phenotypes occur due to genetic mutation or environmental perturbations . As furtVarious community-driven efforts have led to several reconstructions of the global human metabolic network . CompiliRecon3D is the latest update of the Recon family of human metabolic network reconstructions . The keyA base GEM may be utilized to produce general predictions about whole human metabolism, providing useful insights into both human health and predictions of currently unknown human metabolic functions.The various human models have been used to predict biomarkers for inborn errors of metabolism (the accuracy of previous predictions feeds into improving these models) . Inborn By analyzing reactions in Recon1 that were defined as being present due to genome annotation or literature data, but that were not predicted to be active, and then adding surrounding reactions to activate them, predictions have been made about previously unknown human metabolic functions such as that of iduronic acid following glycan degradation, N-acetylglutamate in amino acid metabolism, and the human activity of gluconokinase . Such inOnce a base GEM has been selected, there are a variety of methods available to make the model specific to a particular cell type and circumstance by integrating transcriptomic, proteomic, and metabolomics data. These context-specific models are able to provide more detailed insights into human metabolism in a particular cell type, and comparison of models is particularly useful. Previous context-specific human models have produced useful insights into healthy and diseased metabolism.A cell’s metabolic capabilities are defined by which enzymes it expresses. Transcriptomic and proteomic data provide information about the enzymes expressed under certain circumstances. Both of these data types correlate to enzyme activity, although not perfectly . SeveralAnother way to make models more context specific is to use metabolomic data collected by mass spectrometry or NMR to constrain what the model takes up or secretes to realistic values. By either measuring changes in the concentration of various metabolites in the medium over time or by comparing the relative values of metabolites at different times, the rate of uptake or secretion of a range of metabolites is determined. These rates can be applied to the model as additional constraints that will restrict the model predictions to those consistent with the metabolic dataset . These aModels can also be used to analyze isotope labeling data and this data can, in turn, contribute better constraints to improve the model. Cells may be fed on medium containing glucose or glutamine labeled with heavy isotopes of carbon or nitrogen. The proportions of various metabolites labeled with these heavy isotopes in cells that are sampled and analyzed at different time points after this treatment allows inferences to be made about the production of the labeled metabolites. Sholmi et al. used this technique to elucidate the differences in the TCA cycle during the cell cycle. Further information may be obtained if the cells are fractionated into different organelles before analysis . For exain silico gene deletion screens) that resulted in identification of 52 metabolic drug targets; 8 of these currently correspond to cancer therapeutics. In addition, a set of genes were identified in the healthy cell model network that were downregulated in the cancer model. By inhibiting the genes more highly expressed in cancer cells, targeting could be achieved  , which gachieved .More specific cancer models have also been produced. For example, a model has been created for hepatocellular carcinoma by Many constraint-based metabolic analyses have historically relied upon an objective function, which is defined as a metabolic objective of a cell; flux through this reaction is either maximized or minimized to compute the flux state  across the entire network. For metabolic states that do not have as well-defined objective functions as cancer does , algorithms that are able to create tissue- or cell-specific models without a specific objective function are needed. An algorithm often used for this purpose is the metabolic transformation algorithm (MTA) , an algoObesity has been addressed through the use of the human metabolic reconstruction by identifying pathways implicated in the disease process. As with many diseases, pinpointing a specific genetic or environmental marker as a cause for obesity, making the determination of progression, and deciding on a “treatment” a difficult task. Using the HMR and transcriptomic data from both healthy and obese individuals, a GEM with the objective function defined as acetyl-CoA production and formation of lipid droplets was produced. Through this analysis, two possible drug targets were identified by considering reactions with significantly changed flux values, and a potential biomarker for obesity was identified through reporter metabolites, which is an algorithm allowing the analysis of transcriptomic data in the light of the metabolic network structure to predict highly affected metabolites .Drug toxicity levels and side effects over both short and long periods of usage can also be identified in an easier and more cost-efficient manner using GEMs. It is possible to make predictions on system-wide perturbations using previously determined information on how protein structural analysis can be used to determine off-target binding of drugs, in combination with metabolic networks, as was done by Biological systems often change dynamically over time. Analyzing how these changes occur can be challenging but is being addressed through the integration of time-course experimental data. One approach, dynamic flux balance analysis (dFBA) {ref 10.1016/j.celrep.2017.07.04}, integrates time-course measurements of the major inputs and outputs of the system to provide more accurate flux predictions. dFBA provides a continuous prediction based on these changing inputs and outputs . This method has been applied to murine embryonic stem cells and revealed changes to mitochondrial metabolism and one carbon metabolism during priming . more reA GEM of MSCs, iMSC1255, was recently created to improve understanding of the function of MSC metabolism. This model is based upon publicly available transcriptomic data sets from proliferating, early passage bone marrow MSCs. The data was used with the mCADRE algorithm to generate a tissue-specific version of the global human model Recon1, which was then manually curated by comparison to proteomic data and the literature to ensure that all desirable reactions were included to account for known MSC metabolic functions . FurtheriMSC1255 has subsequently been used to computationally predict metabolic interventions to optimize proliferation and chondrogenic differentiation of MSCs. By analyzing the maximum growth rate predicted FBA with and without allowing the uptake of a range of nutrients, it was proposed that supplementing the MSC medium with the phospholipids phosphoethanolamine and phosphoserine would improve proliferation. This was confirmed experimentally . This pain silico metabolic engineering (gene knock-outs and knock-ins); (2) identifying biomarkers of diseases; (3) predicting drug targets and therapeutic windows; and (4) optimization of cellular functions without the cost of wet lab experimentation. Some success has already been reported, as mentioned in section “Current State-of-the-Art Applications.”Since 2007, when the very first global GEM for humans was reconstructed , researcin silico, the number of experiments needed to optimize growth conditions may be reduced. This may be an interesting avenue to explore for each cell type and cell state, since different responses may be observed. Such testing is more feasible to explore in silico than in vitro. Even though time and money will have to be spent on reconstruction of the GEMs themselves, the savings in experimental time and material costs afforded by the use of validated reconstructions will likely outweigh these costs. Promising in silico outcomes can then be taken further, being validated or explored in vitro and later in vivo. Positive results would further validate the models and perhaps further that particular avenue of cell-based medicine or that particular use of the cells in regenerative medicine.Building on the various uses of existing cell- or tissue-specific human GEMs and the most up-to-date version of the human reconstruction , the potOne potential use for GEMs would be to explore the different metabolic capacities of hMSCs from different sources. There have been some reports, albeit limited, explaining possible differences in the proliferative, differentiation, and immunomodulatory abilities of hMSCs isolated from various tissues . As hMSCThe creation and study of GEMs of MSCs undergoing each of the three classical differentiation  could be approached in various ways. uFBA could be used as a framework to examine MSC metabolomics data collected at different timepoints during each of the differentiation lineages . The stuin silico  also contributed to the writing and editing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity. The liver plays a central role in a wide range of physiological functions, including metabolism, distribution of nutrients, and detoxification of toxic metabolites and ingested substances. The liver is a metabolically active organ that utilizes carbohydrates for synthesis of cholesterol as well as fatty acids for the production of triglycerides for export and storage. It also plays a critical role in maintaining blood glucose levels during fasting by synthesis of glucose from lactate and amino acids, and it converts excess fatty acids into ketone bodies for energy under prolonged starvation. Furthermore, the liver exports fatty acids as triglycerides to adipose tissue for storage and to muscle and other tissues for energy metabolism. Thus, the liver plays a major role in overall human metabolism, and any impairments in the liver metabolic processes due to external factors can lead to liver disorders ranging from simple cellular necrosis to complicated diseases, such as non-alcoholic fatty liver disease, fibrosis, cirrhosis, and liver cancer, which are serious threats to public health ,2,3,4.Several internal and external factors, such as genetic background, gut microbiota, and dietary factors, act simultaneously in the initiation and progression of liver diseases ,6. In adEven though it is evident that toxicant-induced disturbances in hepatocyte metabolism, such as lipid accumulation, mitochondrial disruptions, and oxidative stress, are hallmarks of liver disease processes ,11,12, t4) based on their well-known hepatotoxicity and measured changes in liver metabolism using RNA-sequencing, global plasma metabolic profiling, and 2H/13C metabolic flux analysis (MFA). To capture system-level metabolic changes, we subjected male Sprague Dawley rats to a single acute dose of one of these three model toxicants and included the appropriate control groups for the acquisition of liver gene expression profiles and plasma metabolic profiles 5 and 10 h after toxicant exposure under identical conditions. Furthermore, under similar short-term fasting conditions, we also measured changes in absolute hepatic fluxes in central carbon metabolism to decipher similarities in liver response to these different toxicants. These combined experiments allowed us to determine early changes in the liver metabolism before substantial cellular damage occurs and provided an opportunity to identify common signatures of liver toxicity upon exposure to different toxicants.In this study, we explored the combination of transcriptomics and metabolomics integrated with genome-scale models to acquire a comprehensive perspective on the similarities in rat liver metabolism exposed to three model hepatotoxicants. We chose rats, which share similarities with humans in terms of metabolism , and kno4  water (99.9%) containing 0.9% sodium chloride to enrich total body water to 4.5%. After they had recovered from anesthesia, we placed the rats in bedded containers without food and water, and then connected them to sampling and infusion lines. At 1 p.m. , we delivered glucose (80 mg/kg prime + 0.8 mg/kg/min infusion) as a prime-constant infusion into the systemic circulation through the jugular vein catheter for the duration of the study. Starting 120 min after the [2H2] water bolus, we delivered sodium [13C3] propionate (99%) as a prime-constant infusion (110 mg/kg + 5.5 mg/kg/min infusion). We prepared all infusates in a 4.5% [2H2] water-saline solution unless otherwise specified. Stable isotopes were obtained from Cambridge Isotope Laboratories . At each of the three time points during the last 20 min of the tracer-infusion period , we collected a 300 μL arterial blood sample in an EDTA-coated tube. We centrifuged the collected blood samples immediately to isolate plasma samples, which we stored at −80 °C for further analyses. We performed glucose derivatization and gas chromatography-mass spectrometry (GC–MS) analysis as described previously  glucose infusion rate and rat weights, and then averaged the flux estimates for the steady-state samples to obtain a representative set of values for each rat.To determine the absolute hepatic fluxes in the glucose production pathway, we employed the in vivo MFA methodology described previously ,26,52. BR. norvegicus genome-scale metabolic network model [2 fold changes of liver gene expression into reaction weights. It then calculates the global network demand required for producing a metabolite in the blood. The objective function minimizes the weighted sum of fluxes across all reactions for each condition and metabolite, to satisfy the associated mass balance and the optimal fraction of the maximum network capability to produce a metabolite. We then imposed the measured MFA values as constraints on the model as upper and lower bounds for the respective reactions in the glucose production pathway. Based on values reported in the literature, we used appropriate uptake and secretion rates for the exchange reactions of the liver and kidney under short-term fasting conditions [We used the rk model  and a prrk model  to predinditions . Thus, u2 fold changes of significantly altered (FDR < 0.1) plasma metabolites from the global metabolic profiling data and then compared the corresponding TIMBR production scores from the rat genome-scale model at 5 or 10 h post-toxicant treatment. Here, we considered the metabolite levels as having increased or decreased based on TIMBR production score cut-off values of greater than 0.1 and smaller than –0.1, respectively. We considered metabolites with scores between –0.1 and 0.1 as unchanged. To test the robustness of the results from the multi-tissue model, we randomized the original gene-expression data by randomly sorting the gene names and using the resulting data as the input.We used the experimental logTo understand the biological significance of changes in gene expression levels, we calculated the significantly activated KEGG pathways by using the online tool Database for Annotation, Visualization, and Integrated Discovery (DAVID)  and a lip-value, defined as the probability that the score for randomly selected FC values  is greater than the score from the actual gene set. A small p-value implies that the gene set value is significant. The z-score is the number of standard deviations by which the actual gene set value differs from the mean of the randomly selected FC values . In this study, we used only significantly (FDR < 0.1) changed genes for each toxicant to determine the module activation scores.We used the aggregated absolute FC (AAFC) method to calculate the activation score of liver injury (histopathology) modules that are significantly changed ,56. Brie


Numerous secondary metabolites from plants are important for their medicinal, nutraceutical or sensory properties. Recently, significant progress has been made in the identification of the genes and enzymes of plant secondary metabolic pathways. Hence, there is interest in using synthetic biology to enhance the production of targeted valuable metabolites in plants. In this article, we examine the contribution that metabolic flux analysis will have on informing the rational selection of metabolic engineering targets as well as analysis of carbon and energy efficiency. Compared to microbes, plants have more complex tissue, cellular and subcellular organization, making precise metabolite concentration measurements more challenging. We review different techniques involved in quantifying flux and provide examples illustrating the application of the techniques. For linear and branched pathways that lead to end products with low turnover, flux quantification is straightforward and doesn’t require isotopic labeling. However, for metabolites synthesized via parallel pathways, there is a requirement for isotopic labeling experiments. If the fed isotopically labeled carbons don’t scramble, one needs to apply transient label balancing methods. In the transient case, it is also necessary to measure metabolite concentrations. While flux analysis is not able to directly identify mechanisms of regulation, it is a powerful tool to examine flux distribution at key metabolic nodes in intermediary metabolism, detect flux to wasteful side pathways, and show how parallel pathways handle flux in wild-type and engineered plants under a variety of physiological conditions. •Plant secondary metabolites have high economic value to human health and pleasure.•Plant secondary metabolites are synthesized by pathways in subcellular compartments.•Metabolic flux analysis can guide the selection of metabolic engineering targets. Secondary metabolites are derived from building blocks synthesized in primary and intermediary metabolism, such as aromatic amino acids from the shikimate pathway , or isopAlthough there have been impressive advances in the ability to engineer plant pathways into microbial hosts for secondary metabolite production , due to 1.1in vitro enzyme kinetic characterization. The results of these experiments are used to make estimates of the relative contribution of enzymes competing for the same substrate, and thus the flux split at a given branch point. However, the use of inhibitors or gene knockdown experiments may have off-target effects and perturb the cellular system. In contrast, MFA is an approach to quantify the flux splits under the physiological condition.In plant secondary metabolism, evolution has led to parallel pathways that can synthesize a particular metabolic intermediate. Some examples of this type of network are shown in Another major reason for performing MFA in plants is the quantification around key intermediary precursors of secondary metabolites. For example, quantifying the flux around acetyl-CoA is important for determining the amount going towards fatty acids vs. terpenoids. Another example is solving mass balances around phenylalanine to compare the flux to protein synthesis vs. phenylpropanoid metabolism.Additionally, within secondary metabolism it is imperative to identify and quantify the main competitive branches that lead flux away from the secondary metabolite of interest. In this manner, the ‘wasteful fluxes’ leading to undesired side products can be identified and subjected to metabolic engineering for elimination. Therefore, MFA is a powerful method for investigating plant secondary metabolic networks in a systematic and quantitative manner.1.2Eukaryotes have a more complex spatial distribution of metabolic pathways than bacteria , which p13C]-mevalonolactone or -1-deoxy-D-xylulose, the activity of each pathway and the magnitude of crosstalk between the pathways could vary in different organs and during different developmental stages. This may be one reason of the considerably varied labeling of isoprenoids in different experiments  . Though 2.1Intuitively, analyzing the flux in secondary metabolic pathways with little branching seems straightforward. If the pathways are not cyclic, and assuming the turnover of the final products is small relative to their synthesis, then the flux can be calculated by following the concentrations of all the end products with time . However2.213C-labeled or 2H-labeled substrates to plants and profile the changes to metabolite concentrations and isotopic incorporation. However, few of these studies actually use this data to calculate fluxes. The flux calculations will depend on whether steady-state labeling is achieved and whether the pool sizes remain constant  measures the isotopic abundance of metabolic intermediates or end-products at metabolic and isotopic steady-state to obtain flux maps by the workflow shown in Steady-state isotopic labeling based metabolic flux analysis  is a method that has successfully been employed for analyzing central carbon metabolism in microorganisms . For orgoworkers , they she plants .2.2.32H5-Phe) to Petunia flowers and measured the time course of label, internal pools, and emitted volatiles from the benzenoid network (Similar to INST-MFA, dynamic MFA measures transient isotope abundances of metabolites following addition of an exogenously fed isotopically labeled precursor. In this type of experiment, time course measurements of pool sizes of metabolic intermediates are also performed because of the perturbations resulting from the external feed. To detect significant labeling in downstream metabolites, it is often necessary to feed levels of substrate that affect intracellular pool sizes. This requires the dynamic measurement of both intermediate pool sizes and the respective isotopic label . As a sp network . From th network  that gen2.2.413C-MFA can provide evidence for the existence of metabolite channeling and/or inactive metabolite pools. Metabolite channeling is a process by which intermediates are transferred directly between catalytic sites in multi-enzyme complexes without diffusion into the bulk phase of the cell. Hence, intermediates outside of the channel are modeled as metabolically inactive pools and remain unlabeled, causing the phenomenon of the label abundance of the upstream precursors to be lower than that of the downstream products. Similarly, the existence of an inactive pool in another location can result in the same observation. Because of the subcellular compartmentation in plant cells, there may exist a separate metabolite pool outside the compartment where the reaction is taking place. The intermediates in the separate metabolite pool are not able to incorporate the isotope from the labeled precursor in the time scale of the labeling experiment. However, the metabolites extracted from the plant cells will mix the active and inactive metabolite pools, consequently diluting the 13C-labeling level. By including dilution parameters in the isotopomer reaction models, the contributions of metabolic channeling (The analysis of fluxes by anneling  and inacanneling  were estanneling .3There is much still to be discovered about the function, localization  and reguIn addition, there would be a substantial benefit to quantify fluxes in primary metabolism which provide precursors for secondary metabolism. As stated earlier, this will have to be performed with separate MFA experiments and modeling . This kn4This review described the adaptation of tools that have been developed and applied previously to microbial or mammalian cell cultures. Improvements in precise quantification of pool sizes in specific tissues, cell types, and even subcellular localization will enable the improved estimation of fluxes . We enviThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.  •Fluctuations and perturbations affect dynamics and titers in cell factories•Extended metabolic biosensors expand product sensing and enable pathway regulation•Closed-loop pathway regulation provides robustness to cell factory production•A closed-loop regulated flavonoid production pathway shows robust dynamic response Bioengineering; Metabolic Engineering; Biotechnology Intracein silico screening of the extended metabolic space  requires malonyl-CoA, an essential metabolite that is used in fatty acid production and plays an important role in cell metabolism. Intracellular concentrations of malonyl-CoA are typically low (4–40 μM in E. coli) (The naringenin pathway consists of four enzymatic steps from the E. coli) . MoreoveE. coli) . HoweverDynamic redistribution of cellular resources and optimal control of pathway expression offer alternative strategies for engineering metabolic pathways with high productivity and yield. In Here, we will use a different approach where a naringenin biosensor is used to establish a feedback control system that will regulate the levels of expression of the naringenin chalcone synthase enzyme in order to provide a robust response to the fluctuations in malonyl-CoA availability and maximize the production of the naringenin target. Notably, our strategy, as depicted in −1, which means that, even if that biosensor could be used during the prototyping stage to implement pathway regulation, its use for feedback regulation is not viable at the desired industrial levels. Therefore, it is necessary to search for a transcription factor-based flavonoid biosensor, such as those reported in the literature  of the combined TF-based extended biosensor controller B. This cOnce the amount of enzyme is selected, for instance, the amount shown in dotted black line in In order to analyze the pathway dynamic regulation and eventually design a robust feedback regulation, the dynamic response of the two enzymatic steps in the extended biosensor in −1). The direct feedback controller (see −1) for the same conditions .The open-loop response results by replacing the P20 promoter that responds to σ (and in turn to naringenin via the biosensor) with a constitutive promoter  we found that the plasmid copy number of the biosensor (CσNa) must be 1 copy (250 solutions), 5 copies (230 solutions), or 10 copies (180 solutions). All together these three values represent more than 80% of the best solutions. With respect to the plasmid copy number of the actuator (CNh) the results were 5 copies (407 solutions) and 10 copies (387 solutions). All together these two values represent 100% of the best solutions  consumption of the target chemical through the extended metabolic biosensor in order to feed back the signal to the genetic controller. Tuning this metabolic pathway involves both reducing the affinity of the enzyme F3H to its substrate by a factor of 106 and reducing the catalytic activity by a factor of 60. In fact, these modifications are not an issue, as they entail a reduction in the enzymatic activity .Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Pablo Carbonell (This study did not generate new unique reagents.https://github.com/sb2cl/EMBA).Code for the models and simulations is available through GitHub (https://doi.org/10.17632/hpxhkyvctb.2).All the data obtained from the implemented mathematical model has been published as a Mendeley dataset (All methods can be found in the accompanying


It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells  Quantit testing .Mycoplasma genitalium, which has the smallest genome among freely living organisms , FBA, stochastic simulations and Boolean rules.More recently, hundreds of GSMMs have been reconstructed for different organisms, with an increasing number of represented genes . GSMMs hin silico/in vivo model validation. A complete WCM should ideally integrate multiscale interactions at the cellular level design of network circuits that recapitulate definite biological functions. Knowledge of regulatory mechanisms in biological pathways has been gained by considering living systems as a composition of functional modules, which are investigated through minimal computer models. Examples include controllable oscillators , circadiThe understanding of a living organism at a system’s level may be reached through decomposing it into functional modules or modular circuits . The capCurrently, the majority of multiscale models (not WCMs) lack components able to bridge cellular networks or function . Identification of hubs, i.e., elements with high connectivity in the cellular environment that integrate cellular networks, is a critical feature of WCMs. Transcription factors have recently been identified as hubs that integrate multiscale networks, potentially connecting the cell cycle to metabolism , and canDesigning synthetic gene networks by modeling and integrating them within WCM formalisms as in  could beMinimal genomes can be defined as reduced genomes containing only the genetic material which is essential for a cell to reproduce . StudyinM. genitalium (0.58 mb and 525 genes), there are thousands of possible combinations of gene knockouts to be performed. Of note, this figure is most probably underestimated, accounting for the fact that the order in which gene deletions are performed can alter the resulting phenotypes  algorithm to a GSMM of mization . In cont genomes ; their iGSMM-based genome reduction algorithms such as MinGenome or analogous, adaptable metaheuristic techniques  are curCell-free transcription/translation systems, based on crude cellular extracts, are a valuable platform to address fundamental biological questions in a controllable and reproducible way. In recent years, the decrease of costs associated with this technology and significant improvements in synthesis yield capabilities  have madde novo design of circuits, and to quantitatively compare in vitro cell-free products with their in vivo counterparts.So far, deterministic models  have been proposed to describe specific processes within cell-free platforms such as transcription and translation , resourcin vivo diagnostic applications [e.g., detection of environmental signals in the gut  have been engineered, allowing, for example: arsenic detection , detecti the gut  and diag the gut ; see  toward “green” chemistry approaches, allied to increases in gene synthesis speed and capability and associated cost reductions, are making biosynthesis an increasingly appealing route for the manufacture of high-value chemicals . This inA major challenge, however, remains the development of robust, scalable microbial chassis, whose metabolic processes can be predictably tuned for a desired outcome . CurrentAdditionally, the complexities associated with population heterogeneity during chassis fermentation must be resolved . For ferCritical to the success of a computer-aided whole-cell design approach is the quality of the employed model . Microbiin silico prior to undertaking costly lab experimentation.The capacity to design-in explicit control over cellular behavior is also critical for industrial adoption of model-derived chassis. It can be argued that the ability to regulate cellular processes is as important as defining the processes themselves. Tunable regulatory systems must afford a degree of both intrinsic and extrinsic control. Synthetic biology-based approaches for constructing genetic circuitry are now placing us on a path to broad-reaching cellular regulation, though issues still exist. These systems are often insufficiently orthogonal, with bespoke designs required for different chassis due to variations in core metabolic process . Again, In recent years, advances in genomic measurement technologies for data generation, the establishment of data repositories, and the development of WCM simulation platforms have significantly facilitated the derivation of WCMs [see  for a reM. genitalium WCM, values from related organisms were incorporated due to a lack of organism-specific data , likelihood- and Bayesian-based approaches have been proposed for model selection  and semiin silico design prediction with in vivo tests . Frameworks where intuitive logic is coupled to rule-based models have started to be developed recently .As we produce ever-increasing amounts of experimental data and increasingly sophisticated computational tools to realize detailed and complex representations of actual cells, approaches instead focusing on deliberately abstract and parsimonious simulations of artificial cellular systems provide a valuable change of perspective. Such “toy models” might be a valuable tool to test different algorithms for model derivation and fitting, while offering an opportunity to engage with broader research communities and with the public .in vivo and in silico whole-cell data-sets, for example by applying Bayesian inference, to integrate data from diverse sources and supplement sparse data  extraction, visualization and functional exploration of regulatory interactions among cellular layers through novel multiscale modeling frameworks.In the (re)design of cellular network functionalities, it is therefore important to quantitatively analyze and predict, through dedicated modeling strategies, the dynamics of interactions between various layers of cellular regulation. Thus, WCMs should take into account how different cellular layers are integrated, and how regulatory feedback among these layers occurs in time. These challenges must be tackled through integrative computational and experimental collaborative efforts aimed, respectively, toward: (i) engineering As synthetic biology moves toward the (re)engineering of entire genomes and multicellular systems, interdisciplinary communities need to collaborate for the development of tools that are required to improve the predictive power of WCMs. Although challenges remain, it is clear that the adoption of model-based methods has the potential to transform both basic research and the current bioproduction development process, leading to marked improvements in host performance and product yield on an industrial scale.Ultimately, as the development of human genome-scale kinetic models becomes more feasible , we antiLM, MB, JK, OR, and PR wrote the manuscript. MS prepared the figure. All other authors participated to discussion within the workshop, helped with editing, and/or provided feedback.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plant natural products (PNPs) possess important pharmacological activities and are widely used in cosmetics, health care products, and as food additives. Currently, most PNPs are mainly extracted from cultivated plants, and the yield is limited by the long growth cycle, climate change and complex processing steps, which makes the process unsustainable. However, the complex structure of PNPs significantly reduces the efficiency of chemical synthesis. With the development of metabolic engineering and synthetic biology, heterologous biosynthesis of PNPs in microbial cell factories offers an attractive alternative. Based on the in-depth mining and analysis of genome and transcriptome data, the biosynthetic pathways of a number of natural products have been successfully elucidated, which lays the crucial foundation for heterologous production. However, there are several problems in the microbial synthesis of PNPs, including toxicity of intermediates, low enzyme activity, multiple auxotrophic dependence, and uncontrollable metabolic network. Although various metabolic engineering strategies have been developed to solve these problems, optimizing the location and adaptation of pathways on the whole-genome scale is an important strategy in microorganisms. From this perspective, this review introduces the application of CRISPR/Cas9 in editing PNPs biosynthesis pathways in model microorganisms, the influences of pathway location, and the approaches for optimizing the adaptation between metabolic pathways and chassis hosts for facilitating PNPs biosynthesis. Plant natural products (PNPs) are secondary metabolites that are mainly used for defense and signal transduction in plants . They hain vivo, including zinc-finger nucleases (ZFNs), transcription activation-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR) system. In microorganisms, CRISPR can accurately edit and regulate the metabolic pathways of PNPs in an efficient manner without selection markers  binding protein β promotes homologous complementarity between ssDNA and the lagging strand of the replication fork during DNA replication. Consequently, sequences are integrated into the genome of offspring cells, leading to allelic replacement . Based oE. coli . Among tmutS-deficient E. coli, which could improve recombination efficiency. Strain-independent MAGE was developed to reduce the accumulation of undesired mutations and to broaden the range of applicable hosts , bidirectional loxPsym sites were inserted downstream of each non-essential gene . With CrWhen the synthetic yeast strains containing a Cre recombinase plasmid were cultured, even without adding estradiol, a fraction of strains would switch on SCRaMbLE and their growth was slightly affected , indicatS. cerevisiae, reducing the detection time per sample from 5 min to 84 s  was developed . The N-tDue to insufficient understanding of stress response mechanisms in microorganisms, some genes related to product synthesis were omitted during modification, which may sometimes be remedied via SCRaMbLE. This black-box approach provides a platform for rapid generation of phenotypic and genotypic diversity. Multiple rounds of SCRaMbLE were found to improve the production further . MeanwhiSome enzymes of heterologous pathways are located in different positions intracellularly. For multienzyme reaction systems, the large distance among different enzymes or between enzymes and substrates would restrict product synthesis . The subE. coli (Zinc fingers (ZFs) can bind to specific DNA sequences and form a multienzyme complex with DNA scaffold . Fusing E. coli . Apart fE. coli . The sysE. coli . The plaE. coli .E. coli, the production of mevalonate was enhanced 77-fold by anchoring key enzymes fused with peptide ligands to the corresponding aptamer domain  increased the yield of resveratrol 2.7 times, compared with using the fusion protein in S. cerevisiae  have strong affinity with peptide ligands and are widely used in protein scaffold . In E. cr domain . Based orevisiae . Recentlrevisiae .loxP-KanMX-pBR322ori-loxP as the selection marker, which could be recombined and removed under Cre induction to realize the recyclable utilization of the marker. All candidate genes involved in β-carotene biosynthesis were controlled by GAL1-GAL10 bidirectional promoters. The knockout of GAL80 enabled high-glucose inhibition and low-glucose induction, so that the glucose concentration could regulate the switching time of the integrated pathway. The dynamic regulation of the assembled pathway balanced the metabolic flux between regular cellular activities and product accumulation, shifting more intracellular resources to β-carotene synthesis.Constructing controllable and genetically stable heterologous multi-gene metabolic pathways is an effective strategy to promote product biosynthesis. In order to avoid homologous recombination among multiple repeated sequences at the same location , genes wloxp-kanMX-URA-loxp was designed to substitute pMRI (To simplify marker excision and shorten the integration period, pUMRI with the marker ute pMRI . The marute pMRI  and lycoute pMRI .The location of metabolic pathways and the adaptation of the chassis cells to the heterologous pathways have a significant influence on PNPs biosynthesis. However, there are relatively few studies on the effects of different integration loci on PNPs production, which should be explored in the future. When targeting metabolic pathways to organelles, the possible negative effects of targeting signal tags on enzyme activity need to be considered. Although the genomic diversity generated by whole-genome engineering methods such as MAGE or SCRaMbLE can greatly accelerate strain evolution, high-throughput screening methods are required to identify beneficial mutants in the enormous mutant library, which is a pivotal step during laboratory evolution. There are few high-throughput screening methods. In most cases, these methods are only applicable to the specific reactions. The lack of efficient screening methods is a universal bottleneck in current research. For genome editing, an automated high-throughput screening platform is an important direction for future research. In addition to cytoplasmic protein scaffold, emerging membrane-bound scaffold is expected to improve PNPs biosynthesis. Examples include cohesion-based protein scaffold located on the membranes of lipid droplets , Tat-assAll authors contributed to conception and design of the study. BY participated in searching and analyzing literature for this review and wrote the manuscript. XF and CL edited and corrected the manuscript. All authors approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Methylosinus trichosporium OB3b is an obligate aerobic methane-utilizing alpha-proteobacterium. Since its isolation, M. trichosporium OB3b has been established as a model organism to study methane metabolism in type II methanotrophs. M. trichosporium OB3b utilizes soluble and particulate methane monooxygenase (sMMO and pMMO respectively) for methane oxidation. While the source of electrons is known for sMMO, there is less consensus regarding electron donor to pMMO. To investigate this and other questions regarding methane metabolism, the genome-scale metabolic model for M. trichosporium OB3b (model ID: iMsOB3b) was reconstructed. The model accurately predicted oxygen: methane molar uptake ratios and specific growth rates on nitrate-supplemented medium with methane as carbon and energy source. The redox-arm mechanism which links methane oxidation with complex I of electron transport chain has been found to be the most optimal mode of electron transfer. The model was also qualitatively validated on ammonium-supplemented medium indicating its potential to accurately predict methane metabolism in different environmental conditions. Finally, in silico investigations regarding flux distribution in central carbon metabolism of M. trichosporium OB3b were performed. Overall, iMsOB3b can be used as an organism-specific knowledgebase and a platform for hypothesis-driven theoretical investigations of methane metabolism. Methylosinus trichosporium OB3b belongs to an obligate aerobic methane oxidizing alpha-proteobacterium (type II methanotrophs) [M. trichosporium OB3b has been established as a model organism used to study methanotrophic metabolism [The Gram-negative otrophs) . After itabolism . In parttabolism ,11,12,13The activation of pMMO and sMMO depends on the extracellular copper concentration. sMMO is expressed in medium with either low or no copper concentrations, whilst pMMO is expressed in mediums with relatively high copper concentrations ,15. BothMethylocystis, another type II methanotroph genus [Redox-arm mode: In this mode, ubiquinol is the electron donor for methane oxidation while methanol dehydrogenase (MDH) gives electrons via cytochrome c directly to complex IV. This leads up to proton motive force and ATP production. Recently, this mode of electron transfer has been suggested to be the main mode in ph genus ,18.Direct coupling mode: In this mode, methanol dehydrogenase passes electrons directly to pMMO with cytochrome c as an electron donor for methane oxidation.In uphill electron transfer, a reverse electron flow from cytochrome c to ubiquinone via ubiquinol-cytochrome-c reductase occurs. This mode is expected to significantly reduce efficiency of electron transport chain in methanotrophs.Three potential modes of electron transfer have been proposed, none of which cannot be ruled out completely :Redox-arM. trichosporium OB3b have been investigated as well. A few examples are biodegradation of recalcitrant hydrocarbons like trichloroethylene and attempts to produce polyhydroxybutyrate (PHB), an important biodegradable polymer [M. trichosporium OB3b has been shown to produce PHB up to 67% of its dry cell weight, the highest PHB content reported for a methane-utilizing organism [In addition to the physiological studies described above, several attempts of industrial applications of  polymer ,24,25,26organism . M. trichosporium OB3b, makes it a promising platform for industrial applications [Recent concerns regarding global warming and a drop in natural gas price due to hydraulic fracturing has led to a spike of interest in methane utilizing organisms . This, aications .M. trichosporium OB3b (model ID: iMsOB3b), which can be used for hypothesis-driven metabolic investigations and metabolic engineering purposes. Here, we try to investigate type of mode of methane oxidation which is active in M. trichosporium OB3b by comparing in silico predictions with the available experimental data. We have found that redox-arm seems to be the most optimal mode of methane oxidation which agrees with previous findings. We further validate the model, both quantitatively and qualitatively, by comparing in silico growth rate predictions with the available experimental data. Finally, we describe in silico flux distribution predictions in iMsOB3b to check several hypotheses about its central carbon metabolism. A genome scale metabolic model (GEM) is used to study and optimize biological systems. It allows to both compile a knowledgebase of the organism of interest and test hypothesis of interest in silico . Here, wMethylorubrum extorquens AM1, and the genome sequence of M. trichosporium OB3b  were used to generate the draft model [M. extorquens AM1  and M. trichosporium OB3b was run and reactions with gene reaction rules identified as orthologs were kept in the draft model based on accelerated genome-scale reconstruction protocol described previously [The updated version of GEM of the related organism, ft model ,32. In peviously ,34. The M. trichosporium OB3b’s Genbank genome annotations.The resultant draft model was manually checked for gene-reaction associations by paying special attention to central carbon metabolism, nitrogen metabolism and electron transport chain. Literature-specific information and the KEGG database were used to construct and refine selected biochemical pathways in the draft model ,37,38,39To keep reactions balanced and to avoid metabolite production out of nowhere, reactions in the draft model were checked for mass and charge balance;To avoid unfeasible ATP production in the model, only reactions related to electron transport chain and lower glycolysis (phosphoglycerate kinase and pyruvate kinase) were allowed to produce ATP;To avoid proton flux outside the cell, only reactions of electron transport chain or reactions linked to some energy cost were allowed to have flux towards exterior of the cell;To make the draft model user-friendly and comparable with other models, all reaction and metabolite IDs were changed to BiGG Models IDs .Several modifications of the draft model were also performed: M. trichosporium OB3b, flux balance analysis solutions were quantitatively and/or qualitatively compared against experimental values from the literature [To find the relevant mode of methane oxidation in terature ,46,47,48M. trichosporium OB3b, the methane uptake rate was limited to allow mostly 14.9 mmol gCDW−1 h−1, a value adapted from the closely related organism, Methylocystis parvus OBBP [Since there was no specific methane uptake rate available for the highest specific growth rate measurement available for vus OBBP . The uptM. trichosporium OB3b yet, the biomass equation was adapted from M. extorquens AM1 instead. The adaption of template biomass equations from semi-automatic reconstruction workflows  may lack non-universal cofactors and other organism-specific biomass precursors which, in turn, may affect in silico growth rate predictions . Since tFlux maximization via a biomass equation was used as a model objective for parsimonious flux balance analysis (pFBA) with which in silico simulations were performed. pFBA was used for in silico calculations because it assumes that the cell tries to minimize its overall flux whilst still maximizing the growth rate. This decrease in the overall flux leads to the smaller number of active reactions in the model which, in turn, should provide better approximation of flux distribution than the regular FBA . M. extorquens AM1 and there is no measured NGAM value for M. trichosporium OB3b, the value (3.5 mmol gCDW−1 h−1) for non-growth associated maintenance has been adapted from M. parvus OBBP [The non-growth associated maintenance (NGAM) value has been shown to affect in silico growth predictions in some cases . Since tvus OBBP .Since each mode of methane oxidation could be represented by specific reactions in the model , the switch between redox-arm and direct coupling modes of methane oxidation was done by allowing flux through each corresponding reaction. To allow an uphill electron transfer mode, ubiquinol-cytochrome-c reductase (model reaction ID: UQCYOR_2p) was allowed to proceed in the reverse direction whilst keeping PMMOipp active at the same time.https://github.com/ensakz/gem_methylosinus_trichosporium.Cobrapy, a python version of the popular Cobratoolbox software, was used for the described model manipulations. The model was developed and tested in the Cobrapy-compatible json format and systems biology community standard sbml format . The EscBesides copper, nitrogen metabolism has been also shown to affect pMMO activity ,53. To aM. trichosporium OB3b model can utilize nitrogen sources such as nitrate (NO3−) and ammonium (NH4+) in different ways. The model possesses two potential ammonium assimilation reactions: glutamine synthetase (model reaction ID: GLNS) and NAD/NADP-dependent glutamate dehydrogenase (model reaction IDs: GLUDxi and GLUDy). Since ammonium assimilation has been shown to occur exclusively via glutamine synthetase/glutamate synthase (GS/GOGAT) pathway, the directionality of NAD/NADP-dependent glutamate dehydrogenases has been reversed to allow ammonium production only [The ion only . In addiNitrate assimilation can be done by its conversion into nitrite via ubiquinol or NADP-dependent nitrate reductase (model reaction IDs: NO3Ras or NO3R1), followed by the subsequent reduction via NAD-dependent nitrite reductase (model reaction ID: NTRIR2x). M. trichosporium OB3b has been also shown to utilize molecular nitrogen [nitrogen ,54. The M. trichosporium OB3b. Since each mode of methane oxidation is related to the way pMMO receives electrons, the available data of M. trichosporium OB3b growth in high-copper medium with nitrate added were used. The oxygen:methane consumption ratio was chosen to be the key parameter for the determination of electron transfer mode in M. trichosporium OB3b since it is a function of the electron transfer mode to pMMO. The oxygen:methane molar consumption ratios for the nitrate high-copper medium was 1.48 ± 0.02 [The literature values were used to constrain the genome-scale model to determine the type of electron transfer which is active in 8 ± 0.02 .M. trichosporium, the efficiency of each mode of electron transfer was altered . To further investigate the type of electron transfer which is active in  altered .For redox-arm, the number of moles of ATP produced per 1 mole of hydrogen transferred via electron transport system was altered. This did not change the predicted values for the oxygen:methane specific uptake rate suggesting that the system is reducing power limited rather than energy limited. Interestingly, these results are consistent with the similar simulations performed with type I methanotroph models ,55.For direct coupling, the reaction’s ratio constraints were used to force a portion of the flux via regular pMMO while keeping direct coupling mode active. Even when the efficiency of direct coupling mode was significantly reduced, the maximum determined oxygen:methane ratio was still 1.43 which is below the experimental values. Overall, it seems that direct coupling mode is not supported by the data.−1 h−1 to at least 1.20 mmol gDCW−1 h−1) so that the model behavior started to resemble redox-arm mode. For uphill electron transfer, the maximum reverse electron flow via ubiquinol-cytochrome-c reductase reaction was controlled by iteratively constraining the reaction’s lower boundary. To achieve values which meet the minimum range for experimental data (1.46), a significant drop in the flux of uphill electron flow was required  [M. trichosporium OB3b. Nevertheless, despite these inconsistencies in the literature data, the model, qualitatively, should predict a higher growth rate under ammonium than the nitrogen source both in copper-added and copper-free mediums.  mediums ,48. Nevectively) ,61. In tM. trichosporium OB3b should possess either one of the following biochemical pathways:M. trichosporium OB3b genome, this reaction (model reaction ID: HAORipp) was added due to multiple reports of nitric oxide and nitrous oxide production by M. trichosporium OB3b [In the first pathway, hydroxylamine is oxidized to nitrite via hydroxylamine oxidoreductase which is then reduced to nitric oxide and nitrous oxide. Despite the fact that no gene for hydroxylamine oxidoreductase was found in ium OB3b ,64.M. trichosporium OB3b possesses a gene for hydroxylamine reductase (CQW49_14985) and the corresponding reaction (model reaction ID: HAMR) was added to the model.In the second pathway, the formed hydroxylamine is reduced back to ammonium. The genome of Since the nitrogen oxidation state in nitrate is higher than those in amino acids, nitrate should be reduced to ammonium before it can be incorporated into amino acids. This, theoretically, should lead to higher and faster growth under ammonium-supplied medium than the nitrate-supplied medium. Nevertheless, ammonium has been shown to competitively inhibit pMMO via ammonium oxidation to hydroxylamine, which is toxic for bacterial growth ,63. Ther−1 and 0.131 h−1 respectively  and do not consider M. trichosporium OB3b’s energetic requirements. The maximum reported specific growth rate of .023 h−1 . NeverthM. trichosporium OB3b’s central carbon metabolism. This is consistent with some other published genome-scale models of methanotrophs which have been verified under one particular environmental condition [Since the copper-supplemented nitrate medium was the only environmental condition in which model was validated against the available experimental data, this condition was used for the subsequent simulations of ondition ,65.Following the path of methane monooxygenase and methanol dehydrogenase, the central carbon metabolism includes three different pathways for formaldehyde oxidation into formate with the tetrahydromethanopterin (H4MTP)-linked pathway being the main pathway . FormateMethylene tetrahydrofolate serves as an entry point for the serine cycle. In the serine cycle, methylene tetrahydrofolate combines with the glycine generated from glyoxylate to form C3 compounds such as 2-phosphoglycerate which are later carboxylated to form C4 compounds such as oxaloacetate. The model contains ethylmalonyl-CoA (EMC) pathway variant of the serine cycle as the number of multi-omics studies have shown it as the primary pathway for glyoxylate regeneration ,37. Both serine cycle and the EMC pathway share reactions with tricarboxylic acid cycle (TCA) cycle, thus, forming a series of tightly connected metabolic cycles a. The seThe C2  and C4 intermediates  seem to play a critical role in the network organization as they are crucial in maintaining the carbon flux between the three major pathways described above. To further study the interplay between these intermediates and flux distributions in central carbon metabolisms in general, several additional constraints have been applied on the model based on the available transcriptomics and metabolomics data ,37. 13C labelling studies have shown that at least 1/3 of acetyl-CoA comes from pyruvate, most likely via pyruvate dehydrogenase (model reaction ID: PDH). The unconstrained network selected malyl-CoA produced via malyl-CoA lyase as the main source of acetyl-CoA, indicating that the serine cycle can replenish the acetyl-CoA pool completely (−1 h−1 vs. 1.2 mmol gDCW−1 h−1). This seems to agree with 13C labelling data that has shown that fumarate is the most likely precursor of malate [mpletely b. After mpletely c. This if malate . M. trichosporium OB3b may possess partially reversible TCA. 13C labelling data have shown that 2-oxoglutarate may be produced from succinyl-CoA via putative 2-oxoacid ferredoxin reductase based on the most recent genome annotation available at the time of study [M. trichosporium OB3b has been updated and 2-oxoacid ferredoxin reductase has been removed from genome annotations [M. trichosporium OB3b contains 2-oxoglutarate dehydrogenase that may produce 2-oxoglutarate from succinyl-CoA. To check the feasibility of reversible TCA, three different enzyme combinations have been tried: (1) 2-oxoglutarate dehydrogenase (model ID reaction: AKGDH) has been allowed to proceed in the direction of 2-oxoglutarate production  2-oduction e; (3) booduction f. 13C labelling data, which strongly suggested flux via lower TCA [−1 vs. 0.123 h−1 model’s initial specific growth rate) and the flux via all 3 major pathways increased significantly. Despite this increase in flux distribution, there was still no flux via lower TCA. It seems like the third enzyme combination is unlikely to occur as the predicted specific growth rate does not match experimental values. Overall, these simulation results suggest that lower TCA plays a crucial role in replenishing oxoglutarate pool in the model while activating oxoglutarate production via another route removes the need of lower TCA to keep the flux via a biomass equation. For the first and the second condition, the model confirmed predictions that succinyl-CoA can be the precursor for oxoglutarate as there was flux through the corresponding reactions. Nevertheless, the lower TCA  stops carrying flux at all, leading to an incomplete TCA cycle. This unexpected flux distribution seems to be at odds with the ower TCA . The thiM. trichosporium OB3b [2 supplementation was implemented by forcing a flux via carbon dioxide demand reaction. Nevertheless, this additional CO2 has not been assimilated in the biomass leaving the system via a carbon dioxide exchange reaction (model reaction ID: EX_co2_e) with the extracellular environment . The number of reactions that are catalyzed by enzyme complexes is 125. The number of reactions without assigned genes is 227. The extended model statistics is provided in the memote report, a state-of-the-art-software used to assess the quality of genome-scale models (M. trichosporium OB3b. Nevertheless, both the model validation and its predictive behavior can be improved in many ways. iMsOB3b represents the first, manually curated GEM for To improve model validation, continuous culture cultivation under different combinations of nitrogen and copper-supplied mediums will provide more reliable experimental data such as flux balance analysis and other in silico approaches used to validate models assume steady-state bacterial growth. The improvement in the predictive behavior of the model also requires additional experimental data. The availability of the specific biomass composition would lead to the inclusion of new metabolites in the biomass equation, for which novel biosynthetic pathways will be included in the model. The improvement in the genome and proteome annotations would decrease both the number of reactions without assigned genes and reactions which were assigned based on erroneous gene annotations. The availability of global non-targeted metabolomics data may be used to improve flux distribution in the model by comparing differences in experimental metabolite concentrations in different environmental conditions with the predicted flux distributions . FinallyM. trichosporium OB3b in particular, play a significant role in carbon cycle, the model can be used along with other models for ecosystem modelling [Future applications of the model include use for metabolic engineering purposes for the increased production of metabolites of interest, as has been previously demonstrated using genome-scale models for type I methanotrophs ,71. In aodelling .