The immense chemical diversity of plant-derived secondary metabolites coupled with their vast array of biological functions has seen this group of compounds attract considerable research interest across a range of research disciplines. Medicinal and aromatic plants, in particular, have been exploited for this biogenic pool of phytochemicals for products such as pharmaceuticals, fragrances, dyes, and insecticides, among others. With consumers showing increasing interests in these products, innovative biotechnological techniques are being developed and employed to alter plant secondary metabolism in efforts to improve on the quality and quantity of specific metabolites of interest. This review provides an overview of the biosynthesis for phytochemical compounds with medicinal and other related properties and their associated biological activities. It also provides an insight into how their biosynthesis/biosynthetic pathways have been modified/altered to enhance production. Being sessile organisms, plants constantly interact with a multitude of variable and potentially damaging factors in their habitats that range from an abiotic to biotic nature. The survival of floral diversity within ecosystems thus requires elaborate mechanisms of defence. Among these, chemical defences represent the main trait of an innate immune system to cope with the hostile environment. Their metabolic plasticity evolves and exploits a range of inherent systems to create a rich repertoire of complex metabolites that hold adaptive significance for survival in diverse ecological niches.These phytochemical derivatives of secondary metabolism confer a multitude of adaptive and evolutionary advantages to the producing plants . As a stet al. [A significant number of these biologically active phytochemicals have historically been exploited for a number of purposes and most prominently in the search for pharmaceutical agents, an aspect that gives credence because of their diverse chemical attributes. Their production is tightly regulated at the level of expression of the biosynthetic genes by developmental and tissue-specific factors as dictated by external signals . The higet al.  pointed et al. ,9,10.Despite this immense structural diversity, secondary metabolites derive their synthesis from limited products of primary metabolism. The ongoing research efforts have elucidated the basic biochemistry and molecular biology of some biosynthetic pathways of secondary metabolism, with most of the findings supporting that the diversification of secondary metabolism originates from the elaboration of a few central intermediates ,7,10,11.The heritable and adaptable nature of this metabolism and subsequent chemical variation presents a unique biogenic resource that man continues to rationally engineer using the unique perspectives of evolution, genomics and structural biology to create novel compounds. In this review, we discuss the biosynthesis and biological activity of some selected classes of secondary metabolites. We also provide insight into the approaches used to alter metabolism for human-directed production compound. Arabidopsis thaliana is not restricted to the metabolic space surrounding its biosynthesis but rather tightly linked to more distal metabolic networks of primary metabolism [A. thaliana overexpress two clades of genes, ATR1-like and MYB28-like genes that regulate the aliphatic and indole glucosinolate biosynthetic pathways. Transgenic A. thaliana concomitantly induced genes are involved in the sulphur assimilation pathways as well as in the formation of precursor molecules for the biosynthesis of both glucosinolates. In this same system, all genes responsible for the enzymatic reaction steps of the TCA cycle from oxaloacetate to methionine were induced concurrently with genes encoding enzymes that regulate the committed steps in aliphatic glucosinolate biosynthesis in plants overexpressing the TFs. These changes were accompanied with changes in the levels of the affected central metabolites. The relatively broad view of transcripts and metabolites altered in transgenic plants overexpressing the different factors (ATR1 and MYB28) [The interdisciplinary concerted research efforts and successes in identifying genes and enzymes involved in plant secondary metabolism has brought about our current understanding of metabolic pathways leading to the biosynthesis of secondary metabolites. Secondary metabolism comprises a coordinate series of coupled enzymatic conversions that utilises limited products of primary/central metabolism as substrates/intermediates. Secondary metabolism uses highly organized systematic mechanisms that integrate into developmental, morphological and biochemical regulatory patterns of the entire plant metabolic network. The inevitable link between metabolic fluxes of central metabolism and the biosynthesis of secondary metabolites  further tabolism . Transged MYB28) ,17 underd MYB28) ,17,18,19A. thaliana across environmental stresses, Li et al. [et al. [Catharanthus roseus, overexpression of ORCA3, a jasmonate-responsive AP2/ERF-domain family TF, led to an induction of genes encoding two enzymes (anthranilate synthase and D-1-deoxyxylulose 5-phosphate synthase) involved in central metabolism [ORCA3 overexpression resulted in enhanced expression of several metabolite biosynthetic genes and, consequently, in increased accumulation of terpenoid indole alkaloids [To address the potential breadth of transcriptional regulation that alters accumulation of defensive glucosinolate metabolites in i et al.  further i et al. ,21,22. Mi et al. ,24. Henk [et al.  identifi [et al. . Similartabolism ,26. ORCAlkaloids .At the helm of metabolic engineering for improved secondary metabolite biosynthesis, is the consideration of the entire metabolic network to redirect central metabolites into secondary metabolites without compromising plant fitness. The fact that secondary metabolites are derived from several different precursors and that the intersection of their metabolism with other metabolic pathways produces key metabolic intermediate compounds suggests complex regulation of their production. Such regulatory networks should modulate levels of each metabolite either coordinately or separately to optimise fitness, as dictated by developmental and/or environmental cues . AgainstThere is no fixed, commonly agreed upon system for classifying secondary metabolites. However, based on their biosynthetic origins, they can be broadly divided into three main groups: nitrogen or sulphur containing compounds such as alkaloids and glucosinolates phenolic compounds, and terpenoids/isoprenoids, respectively. A number of these phytochemicals have established ecophysiological and defence adaptive roles in plants. A reservoir of more than 200,000 known secondary metabolites, with many more that continue to be discovered, provides mankind with a biogenic resource to exploit for pharmaceuticals. We now describe briefly the biosynthetic pathways of each of the main groups of secondary metabolites and then provide some mechanisms through which these can be manipulated to produce compounds beneficiary to humans.Alkaloids encompass an enormous class of approximately 12,000 low-molecular weight natural products . The priN-methyltransferase genes involved in caffeine biosynthesis were overexpressed in tobacco, the resulting increase in caffeine production improved the tolerance of the plants to certain pests [Although the exact roles of many alkaloids are not well understood, the compounds are believed to play an important ecological role, enabling the producing organism to interact defensively with its environment. They confer a survival benefit through their ability to bind to cellular targets in antagonistic organisms ,33. Due in pests ,35. DespErythmxylon coca). Ornithine and/or arginine serve as the first precursor molecules from which putrescine is derived through decarboxylation. The early steps of tropane alkaloid and nicotine biosynthesis are also common to polyamine metabolism. The first committed step for the two alkaloid pathways begins with the methylation of putrescine to N-methylputrescine by putrescine N-methyltransferase which is subsequently deaminated by a diamine oxidase to 4-aminobutanol. The 4-aminobutanol then undergoes spontaneous cyclisation to form the reactive N-methyl-Δ1-pyrrolinium cation, a central intermediate for the biosynthesis of tropane alkaloids and nicotine [The tropane class of alkaloids are an important class of plant-derived anticholinergic compounds, such as hyoscyamine and scopolamine and the narcotic tropical anesthetic cocaine, that occur mainly in Solanaceae. The central nervous system stimulant cocaine is a tropane which is found outside Solanaceae , cytotoxicity, antibacterial, antiviral, anti-inflammatory, antiparasitic, antihistaminic, antiproliferative, anticancer and adrenergic activity, antineoplastic against murine P-388 lymphocytic leukemia, and treatment of mental disorders and age-related dementia ,52,53,54Narcissus pseudonarcissus. With galanthamine and pancratistatin established as leads, and owing to the wide chemical diversity and the limitless ability of the Amaryllidaceae members to produce these chemical compounds, the Amaryllidaceae alkaloids thus provide a rich and accessible platform for the discovery of novel and innovative drugs. The lycorane derivatives pancratistatin and narciclasine, hold promising chemotherapeutic potential due to their potent, selective anticancer properties, although the specificity of the compound to cancer cells and its mechanism of action remain unknown ,56. In tet al. [Camptotheca acuminata during stress and development. These results suggest that the enzyme plays a dual role in primary and secondary defence function.These alkaloids are distributed mainly in the Apocynaceae, Rubiaceae, Loganiaceae, and Nyssaceae families . Exampleet al.  identifiThe first committed step in secologanin biosynthesis is the hydroxylation of geraniol to 10-hydroxygeraniol, with the conversion of loganin to secologanin representing the last step in the pathway . SecologCamptotheca acuminate (Nyssaceae), possesses anti-tumour activity which is due to its ability to inhibit DNA topoisomerase [Leishmania [Catharanthus roseus, are examples of the terpenoid indole group of alkaloids. Quinine from C. officinalis is an antimalarial drug and strychnine is a rat poison and homeopathic drug from Strychnos nuxvomica. Topotecan and irinotecan are to date some of the US Food and Drug Administration (FDA) approved agents for the treatment of ovarian and colon cancer [Many of the terpenoid indole alkaloids are physiologically active in mammals. Camptothecin, isolated from somerase . Throughsomerase , the comsomerase ,62 and hishmania . The thrn cancer . Some exN-methylations via 7-methylxanthosine, 7-methylxanthine, and theobromine [Coffea arabica and Coffea canephora), tea (Camellia sinensis), chocolate, and soft drinks. The mechanism of bioactions are thought to be mediated via a blockade of the adenosine A1 and A2A receptors. Adenosine is a modulator of CNS neurotransmission and its modulation of dopamine transmission through A2A receptors is implicated as the mechanism of psychomotic effects of caffeine [Purine alkaloids are widely distributed in the plant kingdom and examples include caffeine, theobromine, and theacrine . The alkobromine ,65 with obromine ,67. Caffcaffeine ,43,44. Tcaffeine . Xanthincaffeine .The successful evolutionary adaptation of plants to land can largely be attributed to the massive formation of “plant phenolic” compounds. Although most of these substances assumed cell wall structural roles, a number of adaptive, defensive, and certain distinguishing features exists in some plant species. Phenolic compounds or polyphenols are characterised by at least one aromatic ring bearing one or more hydroxyl substituents, and other functional derivatives such as esters, methyl esters, and glycosides . They arp-coumaryl-CoA to generate a tetrahydroxychalcone. In certain plant species, the coordinated action of this enzyme and an NADPH-dependent reductase generates isoliquiritigenin. Both chalcones can then be converted into aurones, a subclass of flavonoids found in certain plant species. Beyond this point, the next shared step by most of the flavonoid biosynthesis pathways is facilitated by chalcone isomerase, which catalyses a stereospecific ring closure isomerisation step to form naringenin and, less commonly, liquiritigenin. The two flavanones represent the most important branching point in flavonoid metabolism. The generic flavone structure is a illustrated in Built upon a flavone skeleton, flavonoids are the most widespread and diverse class of low molecular weight phenolic compounds . BiosyntStructural and chemical diversity of flavonoids is related to their diverse properties and roles in plants. In the producing plants, flavonoids provide protection against ultraviolet radiation, invading pathogens, and herbivores . One of Tannins are phenolic compounds that exhibit complex and highly variable chemical structures. They are broadly categorised into hydrolysable and condensed tannins, based on whether acids or enzymes can hydrolyse the components or whether they condense the components to polymers . Both cld-glucose to form gallotannins. Derivatives of hexa-hydroxydiphenic acid (ellagitannins) are derived from oxidative coupling of adjacent galloyl ester groups in a polygalloyl d-glucose ester [Hydrolysable tannins are based upon the fundamental structural unit of gallic acid  and are almost invariably found as multiple esters with se ester ,87. The Condensed tannins, commonly referred to as proanthocyanidins, are oligomers of 3-flavanols (catechins) and 3,4-flavan-diols (leucoanthocyanidins) linked together by single interflavan carbon to carbon bonds . The flaet al. [in vitro [Due to their antibiotic, antifeedant, or biostatic effects on a variety of organisms that consume them, the chemical properties of both condensed and hydrolysable tannins have been exploited in the discovery of versatile medicinal agents. Tannins are, thus, reported to possess numerous pharmacological properties such as antibacterial, antifungal, antiviral, anti-diarrhoeal, free radical scavenging, immunomodulatory, anti-inflammatory, anti-tumour, and antidote activities ,91,92,93et al.  evaluateet al. . This prin vitro ,96.Polyphenol and protein complexation is essentially a surface phenomenon, maximised at or near the isoelectric point of the protein . InteracTerpenes, one of the largest and perhaps most structurally diverse groups of secondary metabolites, are all synthesised from two precursors, dimethylallyl pyrophosphate (DMAPP) and isopentenyl pyrophosphate (IPP). Two independent pathways contribute to the formation of isopenteny1 diphosphate and dimethylallyl diphosphate, the two central building blocks of isoprenoids in higher plants. Plants invariably utilise the mevalonate pathway in the cytosolic compartment and the non-mevalonate pathway in plastids, an aspect that signifies subcellular compartmentalisation of the pathways. In general, the cytosolic mevalonate pathway provides the precursors for sesquiterpenes and sterols, whereas the plastidial methylerythritol pathway (MEP) furnishes the monoterpene, diterpene, and carotenoids ,98. ReceArtemisia annua, and taxol, a high-value diterpenoid-derived anti-cancer drug from the bark of Taxus brevifolia, are some of the examples of pharmaceuticals derived from a family of terpenes. Artemisinin, in the form of combination therapies, is today the only effective treatment for multi-drug-resistant strains of the malaria parasite Plasmodium falciparum. Digitoxin, the glycone digitoxigenin extracted from foxglove , is used widely in carefully prescribed doses for treatment of congestive heart disease. Azadirachtin A is a powerful insect antifeedant terpenoid compound isolated from Azadirachta indica. A few examples of these are presented in The chemical diversity of plant terpenoids is probably a reflection of their multiple biological activities in nature. In the producing plants, isoprenoids serve numerous biochemical functions that include: electron transport chains, as components of membranes (sterols), in subcellular targeting and regulation (prenylation of proteins), as photosynthetic pigments , as hormones , and as plant defence compounds as well as attractants for pollinators ,102. ArtAfter decades of elucidating secondary metabolic pathways, gene regulations, enzymes involved, and factors affecting various important metabolites, accumulated evidence has enabled, to a greater extent, the ability to model these systems and engineer plant metabolic pathways for enhanced metabolite production. A multitude of factors, the complex integrated regulatory mechanisms and coordinated networks of metabolic routes leading to the synthesis of specific metabolites, as well as the general plasticity and adaptability of the various biosynthetic pathways, shape the profiles and fluxes of plant secondary metabolite. Exploitation of the plant biosynthetic capacity presents numerous exciting opportunities but with equally complex challenges. Much of this rich chemical diversity arises from a limited pool of chemical scaffolds which are subsequently modified though specific chemical substitutions as catalysed by substrate and/or regio-specific enzymes. The enzyme-driven reactivity and regio- and stereo-chemistry during the multi-step conversion of substrates into precise products in the biocatalytic landscape of secondary metabolism is one of the lucrative key points of exploitation. The biomimetic exploitation of enzymes, particularly those that exhibit strict stereospecificity, is an interesting aspect in the production of improved pharmaceutical compounds. Equally intriguing is the biosynthesis of novel metabolites via protein engineering aimed at altering the substrate specificity of biosynthetic enzymes . Accordithe improvement of cellular activities by manipulation of enzymatic, transport, and regulatory functions of the cell with the use of recombinant DNA technology” [Rational metabolic engineering, as defined by Bailey , involvehnology” . Broad ahnology”  argues thnology” ,107,108.hnology” . Other aTaken collectively, genomic and biochemical approaches and an appreciation of molecular evolution and environmental influence, as well as structural enzymology, holds great promise for altering the complex plant secondary metabolic pathways towards synthesis and accumulation of desired bioactive compounds. Notwithstanding the notable success of these efforts, complex cellular responses to metabolic perturbations often complicate predictive output and hence significant failures have equally been reported, perhaps more than the successes themselves. However, the regulatory architectures of these pathways, and the ways in which they are integrated into broader metabolic networks, are less well understood, often making it hard to predict the results of overexpressing a single gene or multiple genes within a particular pathway .Several attempts to dissect secondary metabolism for the purpose of improving bioactive metabolites using classical genetics have yielded, to some extent and in some species, positive results. Owing to the multitude of approaches employed in this regard, it would be impossible for this review to exhaustively discuss all the possible manipulation techniques applied to enhance production of fine chemicals. In this review we give an insight into some of these aspects and highlight a few examples in each case.cis-regulatory elements in an organised hierarchical gene regulatory networks TF [Understanding the basic network of metabolic intermediates and enzymes forms the fundamental basis in unravelling these attributes. Beyond this level, knowledge of the spatial and temporal regulatory architectures of secondary metabolic pathways, and the ways in which they are integrated into broader metabolic networks is key. It forms the focal point in the exploitation of TFs for predictive plant metabolic engineering. Transcription factors, a diverse group of proteins that recognise specific DNA sequences in the promoters of the genes, negotiate the regulation of gene expression at the level of transcription. TFs mediate the assembly of the basal transcription machinery resulting in the activation of RNA polymerase II and mRNA synthesis. The control of specific sets of genes within the metabolic network is accomplished by the combinatorial interaction among TFs, between TFs and non-DNA-binding proteins, and between TFs and works TF ,111. OwiEscherichia coli to make the plant flavanones pinocembrin and naringenin from phenylalanine and tyrosine, respectively [et al. [Saccharomyces cerevisiae in Escherichia coli led to concentrations of amorphadiene, the sesquiterpene olefin precursor to artemisinin, reached 24 μg caryophyllene equivalent/mL. Because isopentenyl and dimethylallyl pyrophosphates are the universal precursors to all isoprenoids, the strains developed in this study can serve as platform hosts for the production of any terpenoid compound for which a terpene synthase gene is available.The traditional approach to engineering plant metabolic pathways has often been to target single slow or regulated steps that may limit pathway flux. Although this approach can be successful, identifying rate-limiting steps is often difficult. Beyond simple genetic metabolic manipulations that lend success to the absence of non-obvious limitations, well-articulated metabolic studies would enable us to devise new, non-obvious metabolic engineering strategies. Metabolic flux data rarely reveal a direct engineering target, primarily because fluxes that result from multiple component interactions and genetic manipulations must be made at the component level through, for example, overexpression of a gene . Methodsectively . Yeast hectively . Martin  [et al.  reportedAgrobacterium tumefaciens T-DNA. In Catharanthus roseus cells, for example, T-DNA activation tagging of the octadecanoid-derivative-responsive ORCA3 transcription factors conferred resistance to a toxic derivative of the metabolic intermediate tryptamine in the terpenoid indole alkaloid pathway [Nicotiana attenuata. Virus-induced gene silencing of PMT genes by constructs with inverted repeat expressed from the CaMV 35S promoter led to reduced transcript levels in roots and reduced nicotine contents in leaves, whereas in contrast to wild-type plants, the silenced plants contained anatabine, which is presumably formed because of the accumulation of nicotinic acid, which is normally coupled to a downstream product of PMT to form nicotine [Nicotiana tabacum plants expressing antisense PMT [One of the powerful tools in phenotype-driven genetics that has been employed in plant metabolic engineering is gain-of-function mutagenesis with a strong constitutive promoter that is carried on an insertion element such as  pathway  TIA bios pathway . Overexpnicotine . A similense PMT .Papaver somniferum using a chimeric inverted repeat that targets all seven members of the gene family, a significant reduction in the morphine and codeine levels of the transgenic poppy latex was recorded concurrently with a drastic switch in the alkaloid pattern, with the transgenic latex accumulating rare alkaloids [Hyoscyamus niger to low-scopolamine (an anticholinergic agent) producing Atropa belladonna plants through the introduction of a constitutively expressed H6H transgene resulted in an increase in scopolamine accumulation [Atropa beatica, suggesting that the enzyme is a rate-limiting step in scopolamine biosynthesis. This represents one of the first successful examples of engineering a medicinal plant to produce a valuable end product. In Brassica napus, transformation with the cDNA encoding the C. roseus tryptophan decarboxylase involved in the biosynthesis of monoterpenoid indole alkaloids, has led to the redirection of tryptophan pools away from indole glucosinolate biosynthesis and into tryptamine [When a terminal enzyme in morphine biosynthesis, codeinone reductase, was knocked out by RNAi in lkaloids . These imulation . Similaryptamine , making F3H [CHI, which encodes chalcone isomerase, an early flavonoid pathway enzyme that is expressed at low levels in tomato fruit, resulted in increased flux through the flavonoid pathway in the fruit, although, rather unexpectedly, fruit flavonols rather than anthocyanins increased [et al [Leaf colour (Lc) and C1 in a fruit-specific manner and observed the same accumulations pattern of metabolites. Collectively, these results underline that, in contrast to a pathway gene, a pathway activator can induce the accumulation of metabolites in a tissue in which most of the relevant enzymatic activities are normally insufficient.Transcriptional factors have also been used to drive flux through a pathway and the expression of an enzyme used to divert pathway intermediates to the desired final product. Examples in this application are illustrated by the production of isoflavones in tobacco, Arabidopsis, and maize, which normally do not accumulate these phytochemocals, by the coexpression of the flavonoid regulators with isoflavone synthase . A similF3H ,122. Thencreased . Similard [et al  overexprubiC gene encoding chorismate pyruvate lyase was introduced into Lithospermum erythrorhizon hairy root cultures, which normally produce the naphtoquinone shikonin via the phenylalanine pathway, the chorismate-derived 4HB contributed to 20% of the overall 4HB production in the hairy roots and the levels of menisdaurin, a nitrile glucoside, were increased fivefold [Hyoscyamus muticus hairy root cultures, a 100-fold increase of scopolamine levels was reached compared with controls that produced hyoscyamine as the major alkaloid [Coptis japonica SMT gene in a plant cell culture of Eschscholzia californica, a plant lacking this enzyme, resulted in the production of columbamine, which is normally not found in this species [When the microbial fivefold . This exalkaloid . Not onl species . One fas species  as outliFrom the few empirical examples discussed herein, it is clear that metabolic engineering technology is feasible for secondary metabolism. However, all approaches to metabolic engineering require a thorough knowledge of the biosynthetic pathways involved. This is in fact one of the major limitations, as most pathways are not fully understood, with most of them only known at the level of intermediates. Thorough mapping of biosynthetic pathways thus becomes a prerequisite for any successful metabolic engineering programme. The enormous number of enzyme-catalysed reactions in secondary metabolism provides unprecedented opportunities for the selection of suitable enzymes for use in metabolic engineering. Although the potential of metabolically engineering plant-derived secondary metabolites is high, there have been few successes in modifying pathways to produce important beneficiary compounds. The impeding question, however, is how much of their metabolic and regulatory architecture do we know? Functional genomics approaches are perhaps some of the powerful tools that can be exploited for accelerating comprehensive investigations of theses biological systems. A great variety of plant-derived pharmaceutical compounds stand to benefit from yield improvement produced by genetic engineering if this limiting knowledge gap is filled.Although plant cell and/or organ cultures are often used and economically feasible for production of certain compounds, optimisation of the culture environment for enhanced production is among the challenges to this innovation. Shikonin, taxol, and paclitaxel, for example, are perhaps the few compounds of plant cells so far produced on a commercial scale. Other products that came close are ginseng roots, rosmarinic acid, sanguinarine, and certain polysaccharide mixtures from cell cultures. Secondary metabolism is, by definition, a form of differentiation. The first and logical approach to improved production would thus be the culture of differentiated cells. A major limitation of differentiated organ cultures is their difficulty to cultivate on a large scale. As a result, their application is probably restricted to serve as models for studies of the regulation of the biosynthesis. Overcoming this hurdle could possibly unlock more potential for commercial secondary metabolites but requires further studies on the regulation of the differentiation process. The intracellular localisation of target products could also be another important target for metabolic engineering.Empirical data show that there is a great potential for a broad range of applications, ranging from improving the production of certain secondary metabolites to the introduction of new pathways in plants. For further developing the full potential of metabolic engineering it is thus necessary to increase our knowledge about plant secondary metabolism, at the level of the intermediates, the enzymes and the genes. Understanding the physiology of the pathway is equally essential, as transport, pH, and cellular and subcellular compartmentation also play an important role . Genomic2, etc., in mircoculture and/or related cultivation setups has previously been employed to tilt metabolism towards desired outputs, with varying degrees of success [et al. [C. roseus cell cultures following treatment with a fungal elicitor, and rapid induction of TDC, STR, and SGD gene expression has been reported [TDC and STR gene expression when added exogenously [Manipulation of environmental plant stress factors such as light, mineral nutrition, water, temperature, salt stress, CO success ,131,132. [et al.  reportedreported , a phenogenously .As earlier mentioned herein, this review is not intended to exhaustively discuss all the possible metabolic alterations to improve on secondary metabolite biosynthesis. Against this backdrop, we further emphasise the multiplicity of the metabolic alteration approaches that can be employed in secondary metabolism as paralleled by the overly complex network of metabolic pathways leading to the biosynthesis of these compounds. In addition to the genetic, enzymatic, and transcriptome metabolic alteration techniques, numerous other approaches can be exploited that range from the use of elicitors (abiotic and biotic) in cell cultures to the manipulation of environmental factors including plant growth regulators at cellular, organ, and whole plant level. Plant secondary metabolites represent enormous chemical diversity with largely unexplored pharmacological activities. Our extensive knowledge on the chemistry and pharmacology of some secondary metabolites has led to their use in a range of medical applications. The wide chemical diversity of secondary metabolites throughout the plant kingdom, therefore, represents an extremely rich biogenic resource for the discovery of novel and innovative drugs. The fact that no general conclusions can be drawn about the chance that a certain approach will be successful or the outcome predicted with certainty is what makes plant secondary metabolic alterations for novel and innovative bioactive compounds a lucrative aspect and offers limitless opportunities in the metabolism itself. This, therefore, positions plants, through their plastic, complex and diverse secondary metabolism, as natural chemical factories able to carry out combinatorial chemistry that mankind exploits to his benefit. In several cases, overexpression results in the production of unexpected products, demonstrating the complexity of the metabolic networks and our knowledge gap of these networks and their regulation. Owing to their ability to control both multiple pathway steps and cellular processes that are necessary for metabolite accumulation, transcription factors, among other techniques, offer much promise for the manipulation of metabolic pathways. This is particularly true of complex pathways whose component enzymes are poorly characterised. Notwithstanding the significant contributions of other factors and metabolic alteration approaches to metabolite production, the pleiotropic action of central transcription factors on a wide array of genes involved in metabolic differentiation of plant cells, enable the development of new strategies to engineer complex metabolic pathways and thus holds great promise for increasing the level of pharmaceutically active compounds.


Traditional biochemical research has resulted in a good understanding of many aspects of metabolism. However, this reductionist approach is time consuming and requires substantial resources, thus raising the question whether modern metabolomics and genomics should take over and replace the targeted experiments of old. We proffer that such a replacement is neither feasible not desirable and propose instead the tight integration of modern, system-wide omics with traditional experimental bench science and dedicated computational approaches. This integration is an important prerequisite toward the optimal acquisition of knowledge regarding metabolism and physiology in health and disease. The commentary describes advantages and drawbacks of current approaches to assessing metabolism and highlights the challenges to be overcome as we strive to achieve a deeper level of metabolic understanding in the future. While unquestionably successful, these efforts required considerable research hours and vast resources. Further, they tended to advance the field incrementally over a span of decades. In the current era of rapid technological advances, some consider this pace unacceptably slow, and a cost-benefit analysis raises the question whether modern technologies, and in particular various omics methods, can accelerate the rate of progress in metabolic and physiological understanding without compromising the high-quality standards of traditional methods.The disciplines of genomics and metabolomics continue to evolve and have proven to be game changers in biological data acquisition, but they are not without shortcomings. While these technologies can generate datasets of unprecedented volume and oftentimes technically excellent quality, the demonstration of biological relevance and reliability has not always been maintained. Genomic approaches come with the caveat that gene sequences are several steps removed from the actual function of metabolic and physiological processes, which implies uncertainty regarding genome-based metabolic insights. Indeed, targeted studies have suggested that transcriptomic and proteomic changes often do not correlate well 2.A well-balanced and robust network of biochemical processes is a fundamental feature of any living system. Metabolism, directly or indirectly, affects all cellular functions and is thereby at the very heart of our understanding of life. While genes often receive credit or blame in health and disease, perturbations in metabolism, resulting in the accumulation of a toxic metabolite, or the lack of a needed metabolite, are associated more directly with pathology in vivo and in vitro, defined biochemical reactions, their regulation, and associations with other cellular processes. This collective scientific effort generated advances that were then painstakingly pieced together into an enormous fundus of scientific articles and textbooks that describe our cumulative metabolic knowledge. More recently, these data have been morphed into comprehensive websites like KEGG The current body of metabolic knowledge is the culmination of decades of research hours, in which genetic and biochemical experiments, both An important contribution of experimental approaches is the definition of paradigms, patterns that correlate with the presence of certain enzymes, gene regulation, or other cellular properties. Paradigms might include structural features of a protein that predict the involvement of a specific cofactor or catalytic activity, DNA binding sites that suggest inclusion in a regulon, or phenotypes that predict the presence of a pathway or function. Significantly, a paradigm has hallmark features that can be used to predict (or at least postulate) the presence of a regulon and/or metabolic pathway. For instance, if addition of exogenous cAMP reverses a nutritional phenotype, one immediately invokes the paradigm of catabolite repression 3.Extrapolations from paradigms relieve some experimental redundancy. However, further accumulation of new knowledge is critical for defining additional paradigms that move our understanding of metabolism forward. This new knowledge is highly dependent on experimental rigor and data validation, but the required data acquisition is likely to be costly and time-consuming. The advent of omics technologies brings hope that transcriptomic and metabolomic data will offer new metabolic insights in a more cost- and resource-efficient manner, as long as it is ascertained that these data are of sufficient rigor and pass biological vetting, as described below.The increasing ability to generate system-wide snapshots of the inner workings of a cell has raised our expectations of what is possible and led some bold scientists to conclude that a full understanding of the living cell is just around the corner. The truth might not be quite as rosy. On the positive side, vast datasets can be generated with comparatively little effort. They shed light on correlations between data points in a way that was not possible with the piecewise approach that dominated experimental biology prior to the advancement of global technologies. Computational methods of machine learning and big data analysis can quantify these often complicated, nonlinear correlations and allow the investigator to make predictions and generate hypotheses in the context of the whole cell. The value of such a systems-level approach is that it can uncover trends that would not be seen if one was looking at the read-out of one or a few metabolites or enzymes. No doubt, pursuing hypotheses that were extracted from global datasets has collectively resulted in the generation of a substantial body of new biological knowledge.On the negative side, the vast increase in data generated per unit of effort sometimes leads to a relaxed standard for proof of biological significance, as if somehow the sheer mass of data would obviate the need for deeper biological queries that are more difficult and time consuming to pursue. This risk of a decreased demand for rigor can negatively affect our mechanistic understanding by overlooking salient data, or worse, drawing invalid conclusions that may take hold and become propagated in the literature. Moreover, the dissemination of data from high-throughput technologies has resulted in a subliminal trend where one is tempted to accept the results of large-scale experiments without appropriate critique or analyses of potential caveats. This ready acceptance raises a number of questions: Are we willing to accept conclusions based on cursory analysis, just because so many data points appear to support them? Due to the fact that many conclusions are now drawn by machine learning experts, is there a noticeable risk that the data are biologically inconsistent with our existing body of knowledge? Are we getting intellectually lazy?If conclusions based on the global results of an omics analysis appear to conflict with our intuition or knowledge base, what should our response be? Are we skeptical of the new technology and experimental design, which may include presumably error-free robotic execution of the mechanics of the experiment? Or do we question past results obtained by traditional experimentation? If we choose the latter, does it represent an inappropriate infatuation with newness and technological advancement? Clearly, it is necessary to be aware of the pitfalls and caveats of all methods, but it seems that they are particularly insidious in the realm of omics. As a remedy, we suggest here a strategy of combining traditional and omics approaches in efforts to understand metabolism in the most efficient and reliable manner. Specifically, the significant potential of omics approaches should be tempered by rigorous, intrinsic quality checking and by extrinsic vetting against the traditional body of biological knowledge.4.Improvements in sequencing, and correlations between sequence and function, allow the rapid annotation of genomes, which in turn can be used to predict the metabolic processes that occur in poorly characterized, or even non-culturable microorganisms. This inference strategy, which has become very popular, is based on the reasonable assumption that metabolic systems in related species are more similar than different, an assumption generally supported by genomic and functional analyses. Indeed, this strategy has resulted in efficient predictions regarding the metabolic capacity (phenotype) of an organism based on the enzymes encoded by its genome (genotype). For instance, based on our understanding of the TCA cycle and the enzymes required for its function, we can classify organisms as competent or incompetent to utilize succinate based on the genomic presence of the required enzymes. Moreover, this conclusion is made without an experiment, a pure culture or even a complete genome. Of course, these annotation-dependent, functional predictions and metabolic models are not without caveats. But even if they were 100% accurate, our understanding of the relationship between genotype and phenotype would remain critically incomplete, as we discuss next.At issue are two core shortcomings of the genome-homology-based strategy of metabolic reconstruction and its ability to provide physiological insights. First, the existence of a particular gene, coding for an enzyme of interest, is a necessary but not sufficient condition for the enzyme to be active, and the litany of possible regulatory interventions, including induction, repression, post-translational modification, and others, in the chain of processes from DNA to active protein is long. Also, it is essentially impossible to detect single amino acid exchanges that could not only influence enzyme activity, but even substrate specificity. In addition, annotation and metabolic modelling are often strongly impaired in understudied bacterial and archaeal phyla due to high numbers of hypothetical proteins. As a consequence, the inference of metabolic network architecture from gene sequence, transcription profiles or proteomics is quite indirect. Second, assuming the isolated enzymes do have the function ascribed by their genomic annotation, using this activity to define metabolic potential incorrectly assumes that, when the parts are the same, the metabolic network structure and its function will be the same. It has by now become clear that this is not always a valid assumption, reducing the confidence with which metabolism can be reconstructed simply from genome information So, what is lacking? The inference from a genome yields the prediction of a metabolic network. This network contains nodes (metabolites) and connecting edges . The connectivity is of obvious importance, but it is insufficient to understand the true capacity of the system, because critical contributors to the metabolic system structure cannot yet be extracted from genome sequence. Critically, the amount and type of material that normally flows, or can potentially flow, through a particular enzymatic reaction is not discoverable from the genome sequence. Secondly, genome annotation depends on known pathways and paradigms and is therefore not able to predict new or recruited pathway structures. Finally, one might add that it is difficult to retrieve the regulatory structure of a pathway solely from genome sequence.Two specific examples will illustrate these challenges. First, a system that feeds back on itself e.g.,  may exhiThe second example illustrates that predictions of pathway integration, and thus network behavior, are not reliable if any of the relevant metabolites have roles outside the primary pathway. Consider the diagram in Thus, although the genomes of the organisms represented in The two examples illustrate the importance of an additional layer of information flow that, on top of the stoichiometric network of connections, determines metabolic behavior. The subtle intricacies of this layer of control and regulation are responsible for the plasticity and adaptability that is characteristic of metabolic systems and critical for their responsiveness to perturbations due to external or internal signals 5.Broadly speaking, two complementary strategies are being pursued in metabolomics today. The first seeks to detect as many compounds in a biological sample as possible, despite the fact that many, if not most, cannot be identified as known metabolites. Nevertheless, this strategy can provide evidence of metabolic divergence, for instance by comparing the metabolomes of a healthy cell and a cancer cell At least in principle, metabolomics can determine whether a specific metabolite is present, and at what concentration. Alas, what we need to understand metabolism is not only the concentration of each metabolite, but also details of the enzyme kinetics of each step. Whereas metabolomics appears to allow for the former need to be achievable in the future, the latter goal is currently impossible on a global scale, largely because each enzyme has distinct features and catalyzes different reactions. The hope is that the global snapshots of metabolites provided by metabolomics, combined with targeted traditional experiments, will one day provide the data needed to develop computational models of metabolism, and provide new insights into enzyme kinetics and pathway dynamics.6.In the past, paucity of data was usually the bottleneck of metabolic modeling; clearly, this situation has changed The first computational requirement for addressing a dataset is statistical analysis. The omics revolution has mandated expansions of biostatistics in entirely new directions. One deals with the fact that, in contrast to traditional experiments that investigated one or a few features with many replicates, metabolomic and genomic experiments target hundreds or thousands of features, often with comparably few replicates Hypotheses regarding the mechanisms governing a phenomenon may be straightforward, complicated, or possibly even counterintuitive. In the latter two cases, a casual mental analysis, or even a lab experiment, may not be sufficient to assess the veracity of the hypothesis. A potentially effective alternative of addressing this situation is the creation of an explanatory mathematical model, perhaps a dynamic model that is formulated as a set of nonlinear differential equations.Because multiple models may be formulated for the same system, arguably the most important inputs into the model design process are available data and the specific questions to be answered by the model, which will mandate structural features in the model that constrain the range of possibilities to some degree. If we follow the pipeline from raw data via statistics and machine learning toward testable hypotheses, both data and questions are given; however, data and questions can just as well come from traditional experiments.In addition to these considerations, a model structure must be chosen and implemented. The model structure includes variables that represent metabolites, enzymes, and modulators, as well as possibly other factors, and the equations that capture which of the variables contribute to changes in any of the variables over time. Typically, each equation contains terms contributing to increases (production) and decreases (utilization) of a variable. The existence or absence of these terms in each equation is dictated by the connectivity of the metabolic system. By contrast, the actual mathematical format of each term is a matter of debate, and while some defaults are being used time and again, it is in truth almost always impossible to choose objectively from among a variety of formulations Once a dynamic model has been diagnosed and fine-tuned, it is ready for analysis, which raises the question: what are reasonable expectations from a metabolic model? A few typical expectations are the following: the model should permit an adequate account of various structural and regulatory features and be capable of faithfully capturing the movement of metabolites throughout the pathway system. It should fit the data reasonably well, and explain responses of the system to perturbations. Finally, the model should be able to make reliable qualitative or quantitative predictions regarding untested situations, explain why organisms with the same genetic or metabolic components can still act differently, and be useable to compute a state of the pathway system that is optimal with respect to a given objective.Of course, today's models often fall short of these idealized features. These shortcomings are partially due to overly limited, incomplete, and noisy data. Additionally, we often do not realize the importance of factors outside, or even inside, the pathway that have an effect on pathway components. On the mathematical side, it is not clear what functions best represent metabolic processes. Mass-action, Michaelis-Menten, and power-law models are often used as defaults, but there is no objective guarantee that any of these are generally optimal or correct; in fact, many other representations have been proposed In spite of these unsurprising limitations, metabolic modeling has become a valuable complement to experimentation and will continue to improve the characterization of regulated pathway systems of increasing size. One should also mention that a quickly expanding repertoire of metabolic modeling methods permits the use of different types of data, including metabolic responses to gene knock-downs Some of the models of the recent past have been confirmatory, by integrating available information into a model that fitted data reasonably well, and was explanatory or predictive. In particular, many models have demonstrated that important metabolic components were missing or misrepresented, which led to necessary experimental investigation and adjusted explanations. In other cases, unknown modes of regulation were predicted by the model analysis, and specific single and double knockdowns were proposed, purely based on computational results, to achieve desirable phenotypes.In a different vein, the field of flux balance analysis, with hundreds of publications, has been targeting the optimization of metabolic yields or fluxes, typically based on static, whole-organism models derived by inference from genome information 7.Bacterial systems provide several advantages over other organisms for increasing our understanding of the relationship between genotype and phenotype. In particular, the ability to define the genotype of the organism quickly, coupled with ease of phenotypic analysis, provides a rich system to obtain corollary data and test hypotheses rapidly. Significantly, these studies have broad applicability, since the resulting paradigms will be relevant to other organisms due to the conservation of key aspects of central metabolism across the domains of life.Salmonella enterica was recently described as a model system in a proof-of-principle study to integrate quantitative metabolite measurements and biochemical-genetic experimental approaches with mathematical modeling The purine/histidine/thiamine (PHT) node of metabolism in First, generating a symbolic model of the PHT node without parameter values was straightforward. However, when critical data were collected from the literature to facilitate the design of an initial, fully parameterized computational model, lack of information regarding key enzymes and the diversity and unequal quality of the data available in the literature stalled these efforts. We turned to metabolomics data with the assumption that they would provide a single source of high-quality metabolite concentrations with which to refine the model. This assumption was not borne out, and initial work to query the metabolomes of relevant strains under appropriate conditions highlighted weaknesses in this approach Secondly, while several positive correlations between metabolomics data and past knowledge were extracted from these datasets, they relied on general, qualitative trends across metabolomics data from several biological replicates and experiments. Further, the differences we found were seldom supported by a measure of statistical quality, so that it was difficult to assign significance to metabolic differences that, according to past work, were assumed to be true. Ultimately, the limitations in the experimental results were found to be due to significant, unexplained variation among replicates 8.At present, it is difficult to obtain reliable, quantitative metabolomics data across samples and experiments using LCMS platforms. It is worth noting that transcriptomics as a technology went through growing pains, and standards and statistical criteria eventually arose in the field as the technology gained traction. Since those early days, the quality and reproducibility of the data have increased and made transcriptomics a critical and reliable tool in biomedical research. There is every reason to believe the same will be true for metabolomics as the field grows and experimental and computational biologists continue to refine laboratory approaches and statistical analyses.in vivo methods will be critical to quantify cellular metabolism accurately. Such approaches will eventually replace the best current efforts to replicate the pertinent features of the intracellular milieu, which we do not fully understand.A challenge with current LCMS metabolomics methods is the necessary disruption of the intact cell which generates unknowable effects on the metabolites in total. The future development of non-invasive in vivo Nuclear Magnetic Resonance (NMR) spectroscopy provides an exciting possibility for non-invasive assessments of metabolite concentrations (e.g., in vivo NMR approaches with traditional experimental approaches and sophisticated computational modeling has the potential to bring new insights into metabolism, as some case studies demonstrate In this vein, ns e.g., . AlthougIn summary, to understand the relationship between genotype and phenotype we must account for the connectivity between the metabolic components encoded in the genome as well as the cellular context that influences these connections and gives rise to phenotypic plasticity. Given the steep trajectory of technical innovation in the recent past, it is likely that new approaches will support future efforts to understand the subtleties of metabolism. Beyond technological innovations, it is mandatory that a workforce is created and nurtured that is well-versed in both experimental and computational work, trained to bridge the two fields, and able to communicate results effectively. Traditional biological science education programs are often devoid of a computational component, but a change in today's curricular standards will pay dividends as we strive to understand the complex system of metabolism that is central to all living cells.9.A vision on the distant horizon, considered the Holy Grail in biology, could take the form of an animation of the inner workings of the cell, based on a comprehensive understanding of the complex systems of metabolism and physiology, and illuminating the processes in the Central Dogma as they proceed in pseudo-3-dimensional space. While we are far from realizing this vision, continuing efforts toward a deeper understanding of metabolism will require multiple, complementary approaches to advance the field toward this goal. The growing number of available data will demand not only methods of statistical data analysis and machine learning, but also the conversion of simple correlations among data into mechanistic, computational models that offer explanations and suggest novel hypotheses that are testable with combined experimental and mathematical methods.


Metabolic modeling reveals why mitochondrial respiration and chloroplast NAD(P)H export are required in illuminated leaves. 2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO Within the photorespiratory cycle, stoichiometrically equal amounts of NADH are generated by mitochondrial Gly decarboxylase and consumed by peroxisomal hydroxypyruvate reductase  knockout mutants of the mitochondrial isoforms of malate dehydrogenase show that they have pronounced growth defects under low-CO2 conditions that promote photorespiration (The role of mitochondria in leaves in the light has long been a matter of debate . This iseductase . This haeductase . Consistpiration . HoweverHordeum vulgare) protoplasts decreased the ATP-ADP ratio in both mitochondria and cytosol, whereas the chloroplast ATP-ADP ratio was unchanged  experienced by the leaf in relation to the flux of the Calvin-Benson-Bassham cycle and other ATP/NAD(P)H-consuming metabolism in the chloroplast. If excess energy is available, then the chloroplast can contribute to the peroxisomal demand for NADH and the cytosolic demand for ATP, exporting NADH via a malate-OAA shuttle or other dicarboxylate transporters H pools in the different subcellular compartments, and the net energy balance of the leaf is hence a system-level property of the metabolic network operating at steady state.Computational models are powerful tools for understanding such system-level properties, with two main approaches being used to model metabolism, namely kinetic models and stoichiometric models. Kinetic models capture the response of enzymes, transporters, and electron transport chains to their substrates and effectors, and they provide a powerful predictive tool for analyzing the response of the system to variable conditions . For lea3 leaf metabolism , is readily scaled to incorporate the entire metabolic system of a cell . This aptabolism , but the2 release, and the simplicity of the model meant that the energy demands of the leaf were not fully accounted for (e.g. the costs associated with subcellular transport of metabolites and ions). Moreover, the central question that we are asking, specifically what is the fate of mitochondrial photorespiratory NADH, cannot be predictively explored in the model of An earlier and simplified stoichiometric model of leaf metabolism did consider the balance between mitochondrial respiration and photosynthesis . HoweverSupplemental Figure S1, and this format is fully searchable by metabolite or reaction name. The basic configuration of the model accounting for the day and night phases of leaf metabolism and the constraints applied are shown in The engagement of mitochondrial ATP synthesis in a leaf in the light is likely to depend upon the balance between the available light energy (PPFD) and energy demand. To assess this, we set up a diel FBA model of primary metabolism of an Arabidopsis leaf  to opera2 assimilation rate for Arabidopsis . Similarly, data for the maximal catalytic activities of phosphorylating NAD-GAPDH  with reduced cytosolic pyruvate kinase showed that the main role of this enzyme in leaves is associated with nocturnal respiration (Xanthium strumarium) leaves suggest that cytosolic PEP-pyruvate interconversion in the light is in the wrong direction for chloroplast ATP export (−2 s−1) means that it cannot have a major bearing on the overall exchange of energy and reducing power between compartments in this system. The conclusion from this analysis is that capacity limits in the chloroplast ATP-exporting system lead to the use of the mitochondrial respiratory chain to generate sufficient ATP to meet cytosolic demands in a source leaf. This conclusion also holds for a growing leaf .When these constraints were applied to a source leaf model with an incident PPFD of 200 μmol mpiration . MoreoveP export . As an aP export . HoweverP export . HoweverP export . Note thTo assess the validity of this conclusion, we looked at experimental data in which components of the chloroplast TP-3PGA shuttle had been genetically manipulated. The three components are the triose phosphate translocase (TPT), cytosolic GAPDH, and cytosolic PGK. Of these, manipulations of the TPT are not particularly informative, because the TPT is also the principal route of carbon export from the chloroplast and reduction in TPT leads to a major compensatory alteration in carbon flows between starch and Suc as a result . HoweverConsistent with this, Arabidopsis lines with decreased cytosolic GAPDH are slow growing and have decreased levels of ATP in illuminated leaves . These lUltimately, the most direct test of the model predictions about the extent of mitochondrial ATP synthesis in the light would be to measure the rate of respiration in illuminated leaves. Unfortunately, there is no current method to do this, due to the complication of photosynthetic gas exchange occurring simultaneously with respiratory gas exchange.−2 s−1, this leads to an excess of both ATP and NADPH being produced in the chloroplast in relation to the constrained amount of CO2 being fixed. Some of this excess energy is exported from the chloroplast, as shown in Supplemental Data Sets S1 and S2).Given that the chloroplast has an excess of energy, even at low light levels , the queIn practice, there are also nonphotochemical quenching (NPQ) mechanisms that function to dissipate excess light energy before it reaches the photosystems . NPQ funSupplemental Data Set S3. Note that in this simulation, no constraints were placed on the chloroplast ATP-export shuttles and, hence, apart from light utilization, it is comparable to that of Supplemental Data Set S1.To assess this, we changed the way that we set the light input into the model. Rather than forcing all of the incident light to be used for photosynthesis, we instead constrained the model so that the incident PPFD represented an upper bound on the summed PSII and PSI fluxes, leaving the model to use less light if this better satisfied the optimization objective function. Given that the optimization objective is to minimize the sum of all fluxes to achieve a set metabolic output flux and the utilization of light fluxes will contribute significantly to this flux sum, the model will likely use less light than the maximum PPFD, in order to match photosynthetic ATP and NADPH production rates with the total demand of the system for energy and reducing power. This indirectly mimics the operation of NPQ. The full set of flux predictions for this simulation is provided in 2 assimilation constraint, with 42% of the incident light being used at a PPFD of 200 μmol m−2 s−1, falling to 23% at a PPFD of 1,500 μmol m−2 s−1. These predictions are broadly in line with experimental measurements for Arabidopsis . Interestingly, above 200 μmol m−2 s−1, the model began to use cyclic electron transport in the chloroplast, with the fraction of cyclic electron transport relative to linear electron transport rising from 4% to 11% as light increased . Mitochondrial ATP synthesis was required throughout the light range  exceeded the upper flux limit of 0.75 μmol m−2 s−1 estimated by kinetic modeling . This is unsurprising given the relatively small ATP export flux shown in −2 s−1 PPFD compared with 42% without the NAD(P)H chloroplast export constraint . This was because the model was forced to use a proportion of fixed photoassimilate as a respiratory substrate in the mitochondrion: starch accumulation was increased by 5% to allow high rates of vacuolar citrate storage at night (approximately 5-fold higher), which was then transferred to the mitochondrion during the day, when the action of isocitrate dehydrogenase generated NADH for respiration. The predicted rates of citrate accumulation, one order of magnitude lower than the rate of starch accumulation , are unrealistically high.The fact that the export of chloroplast ATP and reducing equivalents occurred even when light utilization was being minimized by the model suggests that this energy export is necessary to enable the experimentally constrained amount of carbon fixed to be converted to sugars and amino acids and exported to the phloem. This is consistent with the observation that the export of chloroplast ATP and reducing equivalents occurs in all of the various simulations we have described, including that of a growing leaf. Export of chloroplast ATP is dispensable, with the model still able to generate a feasible solution when all chloroplast ATP shuttles were constrained to zero flux . This demonstrates that these different flux modes are equivalent in terms of carbon use efficiency. Hence, it is stoichiometrically possible for mitochondrial respiration in the light to be entirely dispensed with, although this requires higher rates of export of ATP from the chloroplast than is likely possible in the plant, given capacity limits of the chloroplast ATP export shuttles.The flux maps of −2 s−1 there is substantially more energy available than is required for the leaf to generate experimentally observed rates of CO2 assimilation and to utilize the fixed carbon for the synthesis of Suc and amino acids for export to the phloem. Our analysis shows that this is connected to the use of efficient methods of energy metabolism within the model: in simulations in which minimal amounts of light were used, the model used mitochondrial ATP synthesis to meet cytosolic ATP demands  from the night phase. The prevailing view in the literature is that export of chloroplast reducing equivalents during the day mainly occurs in conditions of excess light energy and functions to regulate the NADPH-NADP ratio in the chloroplast stroma to avoid photooxidative stress . The ana+ ratios in the mitochondrial matrix and cytosol. Because of the steady-state constraint, FBA models are blind to these ratios, and such shuttles are free to run in either direction in our model. Constraints could be added on shuttle directionality, but this would require accurate data on subcellular NADH-NAD+ ratios in the different scenarios being measured, and this is hard information to acquire.Although the FBA modeling presented here demonstrates how a stoichiometric model can provide insight into system-level properties of metabolic networks such as energy balancing, this type of modeling does not incorporate regulation at the enzyme/protein or metabolite level. Despite this, FBA can generate remarkably realistic flux predictions, , and thi−2 s−1 in a model free to use less than the incident PPFD and with the constraints of using the metabolic output to attain an experimental rate of net CO2 assimilation while minimizing the sum of fluxes of the system. The complex nature of these constraints makes it difficult to intuitively understand why different energy-rebalancing methods are chosen by the model.Another example of the influence of regulatory mechanisms is cyclic versus linear electron transport in the chloroplast. In many of the solutions presented here, photosynthetic electron transport is exclusively linear , even though our model is capable of cyclic electron transport . Cyclic + in different subcellular compartments. For example, the rate of NADH generation by Gly oxidation in C3 plants can exceed the capacity at which it can be oxidized in the cytochrome branch of the respiratory chain, and this triggers NADH oxidation via the uncoupled alternative respiratory chain , which may generate more precisely defined flows of energy and reductant between subcellular compartments . Additio2 assimilation) and utilization (source leaf versus growing leaf). The analysis revealed that capacity limits of metabolite shuttles for the export of chloroplast ATP mean that a substantial proportion of NADH generated by photorespiratory Gly oxidation must be respired by mitochondria to generate sufficient ATP to meet cytosolic demands. This, in turn, requires that reducing equivalents are exported from the chloroplast in order to meet the peroxisomal demand for NADH to keep the photorespiratory cycle running. This analysis provides a new metabolic perspective for the role of the malate valve and other chloroplast NADPH-exporting shuttles and emphasizes the importance of mitochondrial respiration in illuminated leaves.The diel FBA framework presented here allows leaf metabolic network fluxes to be predicted for different scenarios of energy availability , ∼50 m2 kg−1 dry weight . To convert from per mg of protein to g fresh weight, we used a value of 5.7 mg protein g−1 fresh weight of Arabidopsis rosette leaves (In some simulations, constraints were applied to the upper bounds of reactions that are components of the metabolite shuttles that export ATP and/or reducing equivalents from the chloroplast. Experimental data for the maximal catalytic capacity of these enzymes were available in units of nmol mine leaves .The following supplemental materials are available.Supplemental Figure S1. Cytoscape format version of the model diagram shown in Supplemental Table S1. Comparison of light used by the photosystems at different incident light intensities for two modeling scenarios.Supplemental Table S2. Predicted relative use of linear versus cyclic electron transport in the chloroplast at different incident light intensities.Supplemental Information S1. List of constraints common to all simulations.Supplemental Data Set S1. Predicted fluxes and flux variability analysis in source and growing leaf diel FBA models with a light input of 200 μmol m−2 s−1 and constrained to achieve an experimentally measured CO2 assimilation rate.Supplemental Data Set S2. Predicted fluxes and flux variability analysis in source and growing leaf diel FBA models with a light input of 200 μmol m−2 s−1 and constrained to achieve an experimentally measured CO2 assimilation rate plus additional constraints on the upper bounds of reactions of the chloroplast ATP-exporting TP-3PGA shuttle and PEP-pyruvate shuttle to match experimental measurements of the maximal catalytic capacity of the respective enzymes.Supplemental Data Set S3. Predicted fluxes and flux variability analysis in source and growing leaf diel FBA models with a light input of 200 μmol m−2 s−1 and constrained to achieve an experimentally measured CO2 assimilation rate.Supplemental Data Set S4. Predicted fluxes and flux variability analysis in a source leaf diel FBA model with a light input of 200 μmol m−2 s−1 and constrained to achieve an experimentally measured CO2 assimilation rate.Supplemental Data Set S5. Predicted fluxes and flux variability analysis in a source leaf diel FBA model with a light input of 200 μmol m−2 s−1 and constrained to achieve an experimentally measured CO2 assimilation rate.


Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature.Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus. Methylococcus capsulatus (Bath) is a methane oxidizing, gamma-proteobacterium. Since its initial isolation by M. capsulatus as a participant in the carbon cycle has been elucidated (M. capsulatus (Bath) as the primary microbe used for the production of Single Cell Protein (SCP) as animal feed starting in the 70 s (M. capsulatus (Bath) for this purpose is being explored again  is able to express two distinct types of methane monooxygenases: a soluble form of methane monooxygenase (sMMO) and a particulate, or membrane-bound form (pMMO). The expression of these enzymes is strongly influenced by the extracellular concentration of copper; when M. capsulatus (Bath) is grown in the presence of low levels of copper the sMMO is active, while the pMMO is predominantly active at high levels. Both enzymes require an external electron donor to break a C-H bond in methane. While the electron donor for the sMMO is NADH , formate dehydrogenase (FDH) and any NAD reducing reactions which ultimately contribute electrons through the NADH dehydrogenases . See Figure 1) In the arm mode , the metarm mode  and cytoarm mode  to eithearm mode  and thus In the adirect coupling mode, the MDH is able to directly pass electrons to the pMMO ((2) With the the pMMO . Here, cthe pMMO .uphill electron transfer mode supposes that the electrons from cytochrome c553 can reach the ubiquinol-pool facilitated by the PMF at the level of the ubiquinol-cytochrome-c reductase. This mode was proposed by (3) The M. capsulatus Bath, with the intent of supplying the basis for hypothesis-driven metabolic discovery and clearing the way for future efforts in metabolic engineering (M. capsulatus (Bath) by comparing the model’s predictions against experimental data from Methylomicrobium buryatense 5G(B1)  not only represents a knowledge base that combines the available physiological, genetic and biochemical information of a single organism , but alsineering . Using te 5G(B1)  and explM. capsulatus (Bath) termed iMcBath is based on BMID000000141026, an automatic draft published as part of the Path2Models project (M. capsulatus (Bath)  as reported by In stoichiometric models biological growth is represented as the flux through a special demand reaction. The so-called biomass reaction functions as a drain of metabolites, which are either highly reduced non-polymeric macromolecules such as lipids and steroids or precursors to typical biopolymers such as nucleic acids, proteins or long-chain carbohydrates. The stoichiometry of an individual precursor was calculated from the principal composition of M. capsulatus are yet to be determined experimentally. Therefore, a GAM value of 23.087 mmol ATP gDW-1 h-1 was estimated according to Escherichia coli published by M. buryatense 5G(B1), we assumed the NGAM of M. capsulatus (Bath) to be similar to that of E. coli thus setting it to 8.39 mmol ATP gDW-1 h-1 (The growth-associated maintenance (GAM) and the non-growth associated maintenance (NGAM) requirements for DW-1 h-1 .M. capsulatus (Bath), they were each implemented in the model. The implementation is illustrated in Figure redox-arm we implemented the reaction representing the particulate methane monooxygenase, in the model termed as PMMOipp, utilizing Q8H2 as a cofactor. Accordingly, a variant of the pMMO reaction was added to account for a possible direct coupling to the MDH. In this variant reaction, termed PMMODCipp, the cofactor is cytochrome c555, represented as the metabolite focytcc555_p in the model. To enable an uphill electron transfer, the reaction representing the ubiquinol-cytochrome-c reductase (CYOR_q8ppi in the model), was constrained to be reversible while keeping PMMOipp active.To study which of the three modes of electron transfer is active in Following the path of carbon through metabolism downstream from the MDH, the model includes both the reaction for a ubiquinone-dependent formaldehyde dehydrogenase , termed M. capsulatus (Bath) occurs primarily through the ribulose monophosphate (RuMP)-pathway. As outlined by Formaldehyde assimilation in M. capsulatus (Bath) encodes genes for a complete Calvin Benson Bassham (CBB) cycle (M. capsulatus (Bath) can metabolize glycolate [a product of the oxygenase activity of the ribulose bisphosphate carboxylase (RBPC)] via this pathway. Both Taylor and Ward, further suggested the presence of unique key enzymes to complete the Serine pathway, such as hydroxymethyl transferase, hydroxypyruvate reductase and malate-CoA lyase. However, since the gene annotation did not reflect this and the RuMP pathway is reportedly the main pathway for formaldehyde assimilation (The genome of B) cycle  and a paB) cycle . It was milation , these pin vivo  were adopted from iJO1366  under thMethylococcus capsulatus (Bath) is able to metabolize the nitrogen sources ammonium (NH4) and nitrate (NO3) in a variety of ways. When the extracellular concentration of NH4 is high (>1 mM), alanine dehydrogenase (ADH) is the primary pathway for nitrogen assimilation into biomass, when it is low (<1 mM) assimilation is carried out via the glutamine synthetase/ glutamine synthase (GS/GOGAT) pathway  is able to fix atmospheric nitrogen (N2)  . The nitgen (N2)  and annogen (N2) , and thegen (N2) . A schemA metabolic map of all the reactions in the model was built using Escher  and is ahttps://github.com/ChristianLieven/memote-m-capsulatus. Nitrosomas europaea, which is able grow on fructose as a carbon-source with energy from ammonia oxidation (M. capsulatus (Bath).As a last step we added transport reactions that were not in the draft reconstruction. Inferring membrane transport reactions from the genome sequence is difficult, as usually the precise 3D structure of transport proteins dictates which metabolite classes can be transported . Even ifxidation . Thus, tM. capsulatus (Bath), we constrained the model based on experiments conducted by M. capsulatus (Bath) in high-copper medium, which is the condition in which pMMO is predominantly active. We used the average of carbon and oxygen-limited measurements as a reference. Having constrained the model, we compared the Leak and Dalton’s measurements for the ratio of oxygen consumption to methane consumption (O2/CH4) to the predictions of the model , as it is a function of the mode of electron transfer to the pMMO. The central carbon metabolism was left unconstrained.To determine which combination of the three aforementioned electron transfer modes is active in l Figure . We cons2/CH4 ratios of the model constrained to employ one of the three modes of electron transfer exclusively to the corresponding reference values of M. capsulatus (Bath) grown on either NO3 or NH4. However, neither of the modes adequately represented the measured O2/CH4 ratios of about 1.43 and 1.6, respectively. Although Leak and Dalton had proposed that the reverse or uphill electron transfer is the most probable mode  to investigate growth yields and energy requirements in different conditions. Similar to the results presented herein, they found that the redox-arm mode correlated least with their experimental data for M. buryatense.We altered the efficiency of the three modes to determine whether the fit could be improved. For the direct coupling mode (PMMODCipp) by forcing a portion of the flux through the regular pMMO reaction (PMMOipp) using ratio constraints .To investigate this effect, we introduced a ratio constraint (see Methods) coupling the uptake of NHo Figure . Accordi-1 were measured by M. capsulatus (Bath) on copper-free and copper-rich NO3 mineral salt medium, respectively. Simulations of iMcBath with an active sMMO reaction yielded 0.19 h-1 on NO3 and 0.26 h-1 on NH4 as the nitrogen source, as well as 0.20 h-1 for growth driven by the pMMO on NO3 and 0.30 h-1 on NH4. The predicted growth rates scale together with the uptake rates for methane and the respective nitrogen sources. The uptake rate for methane in iMcBath was constrained to 18.46 mmol gDW-1 h-1, a value adopted from M. capsulatus (Bath). Uptake rates for NO3 and NH4 have been left unconstrained for the same reasons. An adjustment of the scale will be possible as soon as these data become available.Specific growth rates of 0.25 and 0.37 hM. capsulatus (Bath) is capable of using certain amino acids as sources for nitrogen  as observed previously by 2 as the source of energy  to reversibly convert sedoheptulose-1,7-bisphosphate to sedoheptulose-7-phosphate and fructose bisphosphate to fructose-6-phosphate thus providing these intermediates to the RuMP cycle    to illusUnsurprisingly, the automated draft generally performs quite poorly in comparison with the curated models. It’s not possible to produce biomass from methane, even in rich media conditions, which is indicative of gaps in the pathways leading toward individual biomass components. Moreover, ATP can be produced without the consumption of substrate, which in turn means that key energy reactions are not constrained thermodynamically to have the correct reversibility. In the draft, 51% of the reactions are not supported by Gene-Protein-Reaction rules (GPR), while in iMb5G(B1) this percentage is only 32% and in iMcBath the percentage of these reactions is under 20%. GPRs allow modelers to distinguish between isozymes and enzyme complexes by connecting gene IDs either through boolean “OR” or “AND” rules. In iMcBath, 25 complexes in total were curated and formulated as GPR. Neither the draft model nor iMb5G(B1) make this distinction.M. capsulatus (Bath), like M. buryatense 5G(B1), expresses both the soluble and the particulate monooxygenase depending on the availability of copper in the medium. In iMb5G(B1), however, only the particulate monooxygenase is represented by the reaction “pMMO.” The ability of M. capsulatus (Bath) to grow on ammonia, nitrate and nitrogen has been characterized experimentally  is reported to grow on nitrate, ammonia and urea, yet without adding the respective transport reactions iMb5G(B1) only grows on nitrate. While the draft model for M. capsulatus (Bath) contains exchange reactions for all the tested nitrogen sources except for urea, it couldn’t grow at all, which again is likely because of gaps in the pathways leading to biomass precursor metabolites.In the automated draft, the oxidation of methane was only possible through a reaction representing the sMMO (MNXR6057). In iMcBath, this was corrected by also implementing reactions that represent the pMMO, one with ubiquinone as the electron donor (PMMOipp), and another that receives electrons from the MDH (PMMODCipp). mentally . In addimentally , hence iM. capsulatus (Bath) and M. buryatense 5G(B1) becomes apparent when comparing the growth energetics of iMcBath and iMb5G(B1). iMb5G(B1) produces more mmol gDW-1 h-1 ATP than iMcBath. Because of this the hypothetical growth-rates predicted by iMb5G(B1) are higher than those of iMcBath regardless of the respective nitrogen source. This difference is likely a direct consequence of the mode of electron transfer to the monooxygenase and thus the efficiency of the methane oxidation reaction in total. When comparing the ratio of the uptake rate of oxygen and the uptake rate of methane for the two models, we can see that the resulting values in iMb5G(B1) are lower than in iMcBath. It was recently reported, that instead of the reverse-electron transfer and redox-arm mode active in M. capsulatus (Bath), a mixture of direct coupling from pMMO to MDH and reverse electron transfer seems to be the most likely mode in M. buryatense 5G(B1) (The difference between e 5G(B1) .M. capsulatus (Bath). With iMcBath, we combine biochemical knowledge of half a century of research on M. capsulatus (Bath) into a single powerful resource, providing the basis for targeted metabolic engineering, process design and optimization, omic-data contextualization and comparative analyses. We applied the metabolic model to study the complex electron transfer chain of M. capsulatus, by analyzing the three modes of electron transfer that had been proposed previously  grown in silico on NO3 as the source of nitrogen. Further experimental validation is required as neither mode can be ruled out exclusively. The experimentally observed effect of NH4 oxidation to NO2 could be replicated by considering the energy burden alone.iMcBath is the first, manually curated, GEM for eviously . We did M. capsulatus (Bath)  , and exps (Bath) . ImproveM. capsulatus (Bath).Starting reconstruction on the basis of an automated draft required additional effort to be able to use it for calculations. The automated draft used two sets of ID namespaces, BiGG  and MetaAfter mapping the reaction and metabolite identifiers from the MetaNetX namespace to the BiGG namespace, we proceeded with the curation efforts as follows: First, we chose a subsystem of interest, then we picked a pathway and using information from either the genome sequence, published articles, the metacyc or uniprot databases, and lastly, we enriched each enzymatic step in said pathway with as much information as possible. Information that was added included for instance: GPR, reaction localization, EC numbers, a confidence score, possible cofactors and inhibitors and cross references to other databases such as KEGG, BIGG and MetaNetX. For each metabolite involved in these reactions, we defined the stoichiometry, charge and elemental formula, either based on the corresponding entries in the BiGG database or on clues from literature.If reactions from a pathway were present in the draft, we checked their constraints and directionality, consulting the corresponding reactions in MetaCyc . This waThe energy metabolism and methane oxidation were curated first. Except for the reaction representing the sMMO, all reactions were newly constructed, as they were absent in the draft. Then, in order to achieve sensible FBA solutions for growth on methane, the central carbon metabolism, amino acid and nucleotide biosynthesis pathways were manually curated. Simultaneous to the manual curation a metabolic pathway map was created in Escher, which helped us to maintain a visual checklist of curated pathways.M. capsulatus (Bath), further pathway curation was necessary to achieve growth in silico. This included the biosynthesis pathways of fatty acids , phospholipids, coenzyme A, Ubiquinol, Lanosterol, FAD, FMN, Riboflavin, NAD and NADP, Heme, Glutathione, Cobalamin, Thiamine, Myo-Inositol, and Lipopolysaccharides. The corresponding biosynthetic pathways were gap-filled and manually curated. To identify the appropriate pathways, MetaCyc pathway maps from the M. capsulatus (Bath) specific database were used to compare the reactions that were present in the draft reconstruction2.The automated draft contained a rudimentary, generic biomass reaction, which only accounted for the production of proteins, DNA and RNA, but not for the biosynthesis of a cell wall and cell membrane, the maintenance of a cofactor pool, the turnover of trace elements or the energetic costs associated with growth. After calculating a more specific biomass composition for M. capsulatus (Bath) when grown in the presence of excess ammonia versus the growth on either atmospheric nitrogen or nitrate, we curated the nitrogen metabolism including the oxidation of ammonia to nitrite via hydroxylamine, the reduction of nitrate and nitrite, the glutamine synthetase/ glutamate synthase reactions and the ADH. Reversible degradation reactions producing ammonia that would potentially bypass the characterized ammonia assimilation pathways were constrained to be irreversible accordingly.To account for the reported differences in ammonia assimilation of After we had enriched the annotations already in the draft with annotations from the metabolic models iJO1366 , iRC1080Throughout the reconstruction process, we iteratively tested and validated the reconstruction. For instance, we checked the mass and charge balance of each reaction, attempting to manually balance those that weren’t balanced. In the majority of cases metabolites were missing formula or charge definitions. In order to remove energy generating cycles, problematic reactions were manually constrained to be irreversible. Validation was carried out against growth data , which wFor the principal components of biomass, measurements were made available through the website of our collaborators On the homepage of Unibio, we were also able to find g/kg measurements of all amino acids except for glutamine and asparagine, trace elements and vitamins, which could directly be converted into mmol/g DW. However, we omitted some of data: The stoichiometries for Selenium, Cadmium, Arsenic, Lead, and Mercury were not included in the biomass reaction as their values were negligibly small. Beta-Carotene (Vitamin A) and Gama-Tocopherol (Vitamin E) were omitted because no genes were found supporting their biosynthesis, in addition to both being reportedly below the detection limit .M. buryatense and M. capsulatus (Bath) are more similar than M. capsulatus (Bath) and E. coli, the stoichiometries of glutamine and asparagine, intracellular metabolites such as ribulose-5-phosphate, organic cofactors such as coenzyme A, and cell wall components such as LPS were introduced from For the lack of better measurements, and assuming that Using the GC content calculated from the genome sequence  and the M. capsulatus (Bath)  . Thus, ws (Bath) , from whFigure 13 of DW-1 h-1 is the sum of the amount of ATP required to synthesize the proportional content of proteins, DNA and RNA per one gram of cell dry weight (DW). For each component i we obtained the synthesis energy coefficient C [mmol ATP/ mmol component] from i per gram DW [mmol component/ gDW], the following formula determines the GAM requirement:The growth associated maintenance (GAM) energy requirements were calculated in accordance with protocol procedures outline in P = 4.324, CD = 1.365, CR = 0.406 and xP = 5.312, xD = 0.047, xR = 0.135.Specifically, the coefficients used herein are CM. capsulatus (Bath).The identification of transport reactions involved the two databases for transport proteins, the TCDB  and the M. capsulatus (Bath) specific entries from the TransportDB, which allowed us to use the PSORT-scores as an additional measure of confidence. At this point, 242 putative transport proteins were identified. From this list we then selected all proteins which were predicted to transport metabolites and were already included in the model, which reduced the number to 133. Since for many of the entries, the exact mechanism of transport is unknown, we combined the previously selected transporters with the results from running BLAST against the TCDB. The e-value and bitscore from BLAST provided yet another measure to confidently assess the correctness of the automatic TransportDB predictions, and the Transporter Classification-Numbers (TC-numbers) allowed us to gather information on the mechanism of transport. This led to a list of 97 transport proteins with high confidence, which was filtered once more as follows.Using the protein sequences in AE017282.2_protein.faa , we deteWe checked the general description for a given specific TC-number, and then considered the BLAST result to read about a given transporter in more detail, especially with regards to finding the specific substrates. When we were able to identify the corresponding transport reaction in the BiGG database, we incorporated only the simplest, smallest set of reactions. In cases of conflicts between our own BLAST results and the automatic TransportDB transporter annotation, we preferentially trusted the BLAST results. Thus we ultimately added 75 transporter-encoding genes connected via GPR to 56 distinct transport reactions.M. capsulatus (Bath), we fit the solutions of flux balance analysis using iMcBath to measured values (2/CH4 ratio and the growth yield of M. capsulatus (Bath) in several conditions. They varied the nitrogen source using KNO3, NH4CL, and both simultaneously; the concentration of copper in the medium, which directly affects the activity of either sMMO or pMMO; and whether the culture was oxygen or carbon limited.To identify which mode of electron transfer is active in d values . The aut4, O2, SO4, PO4, either NH4 or NO3, and an array of mineral salts which are required by the biomass equation. The methane uptake rate was limited to allow at most 18.46 mmol gDW-1 h-1 (adopted from 4 in the medium by blocking the GS/GOGAT (model reaction ID: GLNS). In the presence of NO3 or N2 we allowed flux through both reactions , we were able to investigate each simply by allowing flux through the corresponding reaction.Since each electron transfer mode, respectively, is represented by the flux through one specific reaction in the model (model reaction IDs: 4 is a co-metabolic substrate to the methane monooxygenases in M. capsulatus (Bath) leading to the production of hydroxylamine first and nitrite later , discussed improvements to the model and revised the manuscript. KG, MH, and NS conceived and supervised the study and revised the manuscript critically. All authors read and approved the manuscript.CL collected and analyzed the data, reconstructed the metabolic model, drafted and revised the manuscript. LP and SJ provided feedback on the metabolic behavior of Unibio is a collaborator in the “Environmentally Friendly Protein Production (EFPro2)” project. CL, MH, and NS are publicly funded through the Novo Nordisk Foundation and the Innovation Fund Denmark. The remaining 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.