The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering. Whether biological systems can be understood as a mechanistic composition of physical parts is a fundamental philosophical and scientific problem, epitomized by the understanding of the brain and the emergence of consciousness.2 Furthermore, engineering of biological systems has emerged as one of the most exciting recent technologies. Applications for human health include tumor-killing bacteria, in vivo diagnostics, engineered viruses and immune cells that target specific disease cells, synthetic drug delivery vectors, and even completely synthetic tissues.3–5 Environmental applications include microbes that sense, report, and degrade toxic chemicals,7 while synthetic biology has the capability to produce a variety of chemical products ranging from therapeutics to plastics and biofuels.8–10Although the synthesis of urea by Wohler in 182811 In 2004, a US government report energized the metabolic engineering community by providing a list of targets that would have a transformative impact on biomanufacturing,12 if they could be made using microbial platforms from sustainable carbon sources. Efforts in the last decade, aided by systems and synthetic biology, have succeeded in producing a variety of these and other products, typically at titers of μg/l to mg/l. However, production of only a few compounds, such as 1,3-propanediol,13 1–4-butanediol,14 and artemisinin15 has reached commercial scale, attesting to the difficulty of this process.16 Improving the yield, titer, and productivity of microbial processes to enable commercialization requires detailed manipulation of microbial physiology, stress response, and metabolism, with particular emphasis on carbon and energy balance. In the case of bulk commodities, where commercial viability requires capturing every available carbon atom and eliminating every unneeded ATP sink, metabolic engineering requires a systems biology17 approach where the interaction of the exogenous pathway with host metabolism can be explicitly considered and understood.This last application of microbial bioengineering to produce biofuels and other commodity products has attracted significant attention due to its potential to mitigate climate change, reduce society’s reliance on fossil fuels, and improve energy security. Renewable chemical production is critical to achieving these goals, particularly given the needs of the growing populations of emerging economies.18 but requires an influx of talent from a variety of engineering and physical sciences,19 this review presents the challenges that we think future metabolic engineers and synthetic biologists may want to address for fastest development of the field.In this review, we present the difficulties of taking a microbial production process from conception to commercialization along with the tools that can be used to address some of the challenges and gaps in our knowledge and engineering capabilities . At a tide novo, selecting promising future targets for biological production will require extensive collaboration between chemists, engineers and biologists, and the development of tools able to predict the bulk properties of materials composed of novel molecules.The power of using microbial processes for chemical production is twofold: first, renewable carbon sources can serve as substrates, and second, the range and specificity of molecules that can be made biologically surpasses that of synthetic chemistry. The impressive selectivity of biological systems allows precise control over the chemical features of the final product, such as chirality and positioning of functional groups. This large range of molecules means that, while current efforts focus largely on well-characterized molecules, the most exciting applications will come when new molecules with unique chemical properties give rise to new materials, polymers, and fuels. Given the challenge in predicting chemical and material properties 20 As interest has developed in high-volume, low-cost markets such as biofuels, economic considerations have become paramount, and development of a new project must begin with an analysis of the potential of process commercialization. This multi-disciplinary effort unites very disparate sets of knowledge. On one hand, the commercial potential of the molecule must be assessed. This potential depends not only on chemical properties and known performance but also on highly volatile market data: from the feedstock price  to the current molecule price, both of which are tightly coupled to other market and political processes such as fluctuating oil prices, economic cycles, and uncertainties in government regulatory policies. On the other hand, the feasibility of producing the desired molecule biologically must be assessed, something which is still extremely difficult in the current state of bioengineering. Adding to these difficulties is the complexity of predicting the extra economic costs derived from scaling up production (described below) and downstream processing . All these factors make target molecule selection the least systematic part of the metabolic engineering process.Because of the difficulties of entering new chemical markets, most biological production has focused on molecules with large existing markets. Initial efforts focused on high-price, low-volume markets such as pharmaceuticals, where biological production could make an immediate and obvious impact.21–24 and rely on their own personal expertize to judge which of them can be biologically tractable. Currently, this approach works satisfactorily because troubleshooting the bioengineering effort takes significantly more time than choosing the target. However, as it becomes possible to biologically produce new types of molecules more efficiently,18 it would be desirable to create tools that combine technoeconomic analysis with market needs based on chemical properties, as well as systematic biological feasibility estimates and scale-up considerations.Due to the difficulty of assessing economic viability, metabolic engineers typically resort to a variety of available reports indicating high-value targets or intermediates as indicated by experts in the field,25 and biomanufacturing provides a path toward a decarbonized, more sustainable economy. As countries across the world work to integrate the full social and environmental cost of fossil-derived fuels and products, bio-derived products will become increasingly attractive. The biomass-derived biofuel farnesene,26 e.g., is used to power part of the bus fleet in Sao Paulo, one of Brazil’s largest cities, even though the prize is higher than standard diesel: the city has pledged to reduce fossil diesel fuel use by 10% every year and are willing to subsidize higher prices to achieve this goal. Similarly, in the United States the establishment by congress of a renewable fuel standard27 that requires the use of 36 billion gallons of renewable fuel by 2022, provides the necessary incentives to pay for the offset in greenhouse gas emissions that renewable fuels provide as their main competitive advantage.Even in the cases where biomanufacturing has not achieved economic cost-competitiveness, it can help improve the sustainability of energy sources and other chemical commodity products. Despite the recent drop in oil and gas prices, 195 nations committed to reducing greenhouse gas emissions at the 2015 negotiations in Paris,29 or select pathways based on homology-predicted enzyme function retro-synthesis of products.30–33 Yet, identification of gene function through homology comparison is quite poor for genes with unique functions, and functional screens are needed to identify specific steps in a desired pathway. Furthermore, experimental validation of predictions from these bioinformatic tools remains limited, so the success rate is unknown. Even when there is high confidence in the gene function, most retro-synthesis tools do not have the data needed to identify the organism containing the gene version that would yield optimal protein production and activity, so multiple gene variants must be tested to find the optimal enzyme for a given reaction step.Once a target is selected, the best production pathway needs to be identified. Historical methods for pathway discovery were limited to insights from experts in the field and manual gene selection that was informed by little functional data. More recently, computational methods based on biochemical reaction databases and genome mining have automated the process and implemented scoring methods to prioritize pathway selection. Developments in next-generation sequencing technology along with bioinformatic analyses have revolutionized gene discovery methods for metabolic engineering by providing a vast resource of genomic data to query for target genes. Many tools are available to use this data to identify biosynthetic gene clustersde novo DNA synthesis coupled with standardized expression vectors and genomic integration methods. Although traditional pathway construction is iterative and specific to the desired construct, these methods open the door to combinatorial pathway construction of vast libraries consisting of host genetic contexts, open reading frames, and/or protein expression variants. By producing a deep library of different pathway constructs a researcher can select a subset of strains either from pre-determined parameters or from design-of-experiment methods for subsequent testing and analysis. For systems with a well-defined high-throughput screen,34 selection, or biosensor system, large (107–1010) combinatorial libraries can be constructed, analyzed, and the identity of the best-performing strains can be determined by sequencing. This data driven workflow for strain construction and selection redistributes resources toward experimental design and data analysis efforts to reach production goals. Although these capabilities are ready to be deployed with traditional model systems such as Escherichia coli, they can also be used for many other microbes where synthetic biology and genetics are rapidly maturing, such as pseudomonads,35 autotrophs such as cyanobacteria,36 and Ralstonia eutropha.37 The challenges and opportunities to improve pathway construction, especially the automation of DNA design and construction, are described in several recent reviews38–40 and research efforts,42 and are not reviewed here.Pathway construction methods have been revolutionized by the advent of next-generation sequencing and affordable 43 or TMT44) for accurate protein quantification.Merely introducing the genes into a host provides no guarantee that the synthetic pathway will function effectively. Often, the first troubleshooting steps involve ensuring expression of the introduced genes. Although reverse transcription–PCR or RNA sequencing can verify transcription, defects in translation or protein stability are more common, necessitating protein-level analysis. Consequently, development of rapid and accurate high-throughput assays to monitor protein expression can dramatically accelerate engineering efforts. Protein analysis is frequently achieved via immunoblot assays because they are selective and easily analyzed in parallel. However, assaying many different pathway proteins in the same strain can be challenging and quantification is often inaccurate and difficult to reproduce. Mass spectrometry-based proteomic methods have risen in popularity, as they can identify and quantify thousands of proteins. Relative quantification of proteins between different engineered strains is typically based on targeted proteomic methods via selected-reaction monitoring mass spectrometry, and takes advantage of isotopically labeled substrates or chemical tags  and phosphomevalonate kinase (PMK), were particularly low. To overcome these bottlenecks the MK and PMK genes were codon-optimized for translation in E. coli and also expressed from a stronger promoter. These changes led to significant improvements in the protein levels and amorpha-4,11-diene production.The first reported targeted proteomics study of engineered 46 A number of recent studies have characterized cellular responses to potential biofuel products47–51 and identified metabolic sinks that impact carbon utilization.52 Such analysis will become even more critical as heterologous pathways increase in efficiency, demanding more cellular resources and imposing stresses and constraints on host metabolism. As current state-of-the-art methods still require relatively long chromatographic separation times (tens of minutes to hours) and are performed by using nano-flow chromatography, they are susceptible to variations in sample preparation and chromatography. As a result, these methods are not immediately suitable to high-throughput analysis and method development will need to address both throughput and data quality.Aside from identifying pathway bottlenecks, comparative proteomics is also commonly used to quantify native host proteins, which can identify cellular stresses and perturbations to host metabolism. These may be based on targeted methods as above, or untargeted shotgun proteomic analyses via many “label-free” techniques.53–57 However, this approach is hampered by a variety of challenges, such as lack of reliable data for enzyme activity and substrate affinity parameters, in vivo protein quantification, and the effects of activators and inhibitors. In spite of these hurdles, approaches that parameterize the kinetic model based on a subset of data via ensemble modeling59 have been successfully implemented, for instance to improve neurosporene productivity. In other studies, kinetic models have been used to pinpoint rate-limiting steps for hydrogen production.60 In the absence of accurate kinetic models, heuristic statistical approaches have been successfully used to guide product yield increases. Typically, predictions are extrapolated from data obtained from a relatively small  set of pathway designs. In this vein, Ajikumar et al.61 improved taxadiene production by dividing the pathway into different modules and performing a multivariate search, an approach that was expanded by the use of a linear regression model62 for amorphodiene production. A linear regression model was also used in order to relate protein expression levels to the production titers of the four primary products in the violacein pathway.63 Alonso-Gutierrez et al.64 used quantitative proteomics data and principal component analysis to increase limonene and bisabolene production, while George et al.65 used correlations between proteomic and metabolomic data to derive a qualitative model of the mevalonate pathway for isopentenol production. However, it must be recognized that for any of these methods, even if the desired expression level for maximum production is known, it is a non-trivial task to design the right pathway to obtain it.Product yield optimization can be achieved, in part, through fine-tuning of exogenous pathways in order to maximize the flux through the introduced pathway. Nonetheless, it is not straightforward to know in advance which pathway designs will produce the highest production. Ideally, the optimal expression level for each enzyme would be guided by accurate kinetic models of the pathway.67 Overexpression of membrane proteins commonly leads to growth inhibition, likely due to altered membrane physical properties and limited expression and translocation of key proteins such as electron transport chain components, which has significant downstream impact on central carbon metabolism.68 These tradeoffs must be considered when expressing multiple membrane proteins during strain engineering.69 Protein expression burden can be addressed using directed evolution to obtain variants that perform efficiently within the range of expression that the strain accommodates70 or by controlling protein expression more dynamically in response to conditions under which the tolerance phenotype is required.72In addition, optimizing expression levels is crucial if overexpression of key proteins imposes a large cellular burden due to toxicity. Although most observations of toxicity caused by protein expression relate to protein function, certain categories of proteins are more directly toxic to the cell. Examples include transport proteins that are important both for the import of carbon sources or nutrients, as well as exporters that are required to secrete final products or exclude harmful by-products left over from substrate processing.73 As external pathways and enzymatic reactions are introduced into cells, it is not surprising that a number of the pathway intermediates with undesirable negative effects on cell growth and target production may accumulate. Even when the pathway is entirely native, metabolic engineering involves increasing flux by several orders of magnitude, meaning that imbalances in enzyme activity levels may lead to larger fluctuations in metabolite concentrations than would be found naturally. This has been observed in several pathways prominently used in metabolic engineering in the last decade. For example, the mevalonate-based isoprenoid pathway derived from Saccharomyces cerevisiae has been used for the production of anti-malarial drug artemisinin, as well as a number of biofuel molecules. Two intermediates in this pathway are known to cause cellular growth inhibition. One is 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA), which leads to a feedback regulation in the fatty acid biosynthesis pathway due to its similarity to malonyl-CoA.74 The second is downstream of the main monomer generated from this pathway, isopentenyl pyrophosphate. Isopentenyl pyrophosphate itself is toxic to the cell, but the subsequent prenyl diphosphates geranyl pyrophosphate (GPP) and farnesyl pyrophosphate (FPP), are progressively more toxic. The toxicity of these compounds was evaluated using strains that accumulate isopentenyl pyrophosphate, GPP, and FPP, and the accumulation of FPP was found to be highly growth inhibitory in E. coli.75 Accumulation of prenyl pyrophosphates is also inhibitory in other metabolic engineering strain platforms,76–78 and has required optimization.Natural metabolic pathways have evolved intricate mechanisms to avoid the formation of potentially toxic intermediates.78 However, careful static manipulation of the expression of every pathway gene is difficult, and a more systems biology approach is to mimic the circuits built by nature to address this challenge. Circuits in which a metabolite regulates its own production or consumption can afford very tight control over the intracellular concentration. Such an approach was taken in Dahl et. al.,79 where a native E. coli promoter that was negatively regulated in response to FPP stress was used to regulate the upstream atoB gene, while a positively regulated promoter was used to drive the ads gene downstream.Alleviation of intermediate toxicity can be achieved by rebalancing the expression of the pathway enzymes, in particular by increasing the expression of enzymes downstream of the intermediate.80 by using promoters regulated by the cellular metabolic state to control the rate-limiting steps of the pathway. An analogous approach was also used to optimize fatty acid ethyl ester production in E. coli. Here the FabR transcriptional factor that interacts with a modified promoter only in its apo form was used to regulate expression of the pathway genes, resulting in optimal gene expression only in the presence of free fatty acids, again resulting in greater and more stable production levels.81 Since then a variety of regulatory motifs have been developed82 with one of the most advanced being a tiered control that could integrate five separate cues into one final output.83 Such regulatory motifs will be required as we develop strains that can withstand a range of perturbations and dynamically optimize production in the growth modes needed for industrial production.The potential applications of dynamic regulation go beyond the relief of intermediate toxicity. Ideally, the activity of every enzyme would be regulated by the balance of its substrates and products, and overall pathway activity would be regulated by signals corresponding to sufficient carbon and energy availability, ensuring that production does not interfere with cell viability. The potential of this approach for improving production was demonstrated more than a decade agoAlmost all instances of dynamic regulation developed to date rely on transcriptional regulatory circuits, since transcriptional control is highly modular and well characterized. However, transcriptional control circuits have serious limitations, mostly due to two closely related factors: first, metabolic fluctuations occur on time scales much faster than the transcriptional response, and second, transcriptional activation is largely irreversible, as accumulated protein must either slowly be diluted over a long generation time, or be degraded and resynthesized at a great metabolic cost. A significant challenge will be to engineer circuits based on allosteric regulation , or othe84 but this metabolic control is optimized for growth and survival, making host engineering necessary to optimize the cell for production. On the other hand, an overwhelming demand for carbon and energy from the engineered pathway may impose an insurmountable burden for the cell, which has to carry out a variety of other processes,85 necessitating significant systems-level approaches in host engineering that rely on large-scale models of cell metabolism.In parallel to pathway optimization, manipulating host metabolism to direct as much flux as possible into the desired pathway is a critical aspect of metabolic engineering. An engineered pathway must operate in the context of the rest of cellular metabolism, which acts as both a blessing and a curse. On one hand, metabolic engineers can rely on innate cellular pathways for sugar catabolism and generation of not only the building blocks that form the starting point for the desired pathway, but also energy and redox cofactors such as ATP, NADH, or NADPH. Cells have machinery to sense an increased demand for these cofactors and adjust their metabolism accordingly,86 The first and simplest use of stoichiometric models  is finding the maximal theoretical yield of product and the distribution of fluxes in central metabolism that leads to the optimal yield.87 Algorithms such as OptStrain88 can search reaction databases and find additional reactions that could be added to the network to improve theoretical yield. Second, stoichiometric models can be used to predict the effects of gene deletions on the flux distribution of the considered strain. These algorithms rely on some heuristic assumptions, typically that the distribution will be as close as possible to the wild-type  or that some aspect of the wild-type distribution, such as a specific flux ratio, will be conserved (PFF92). Despite the necessary heuristic assumptions, these methods have been quite successful and have found applications in metabolic engineering, such as for dihydroartemisinic acid production.93 When product yield optimization is the main goal, a third use involves identifying the fluxes that are required to change in order to achieve the desired product yield increase . The corresponding genes are attractive targets for overexpression or knockdown, even though there is no guarantee that manipulating their expression will lead to a corresponding change in flux. For the last two use cases, a critical input is the reference (wild-type) flux distribution, which is often obtained in silico by assuming maximal biomass yield . However, since results may significantly depend on this reference flux profile and it is not clear that the growth maximization principle is universally applicable (particularly for bioengineered reference strains), it is desirable to obtain this reference flux distribution more accurately through 13C tracing experiments.98–100 The combination of these labeling experiments with genome-scale models102 provides a seamless route to use this information to improve predictions. A final use for stoichiometric models is designing “growth-coupling” strategies wherein maximization of biomass yield actually forces flux through the desired production pathway,103–105 as explained in detail below.Stoichiometric genome-scale metabolic models are now frequently used for considering the entire metabolic network and understanding how alterations in central pathways propagate to the rest of cellular metabolism.106 or by coupling simulations for all the constituent cellular processes.107 It remains to be seen whether, for metabolic engineering purposes, the extra predictive power compensates for the added complexity in these modeling frameworks.Next-generation genome-scale models expand current metabolic models by taking into account the metabolic investment in expressing proteins,pgi) gene, forcing glucose catabolism through the NADPH-generating pentose phosphate pathway,108 while genome-scale stoichiometric models can identify other less intuitive genetic changes likely to increase NADPH production.110 A recent successful example focused on overexpression of Entner-Douderoff pathway enzymes from Zymomonas mobilis in E. coli.111Highly reduced products such as biofuels often rely on reductive anabolic pathways, which typically require NADPH as an electron donor. As most typical microbes generate NADH during glycolysis, the stoichiometric analysis described above often identifies the imbalance between these cofactors as a major problem. Alternative catabolic pathways may be available—a popular method includes deletion of the phosphoglucoisomerase  without losing performance.18 Average process development typically takes 5–10 years and is significantly more expensive than scaling up an equivalent chemical process.131 Typically, initial attempts lead to reduced yields, undesired side products, and ultimately a diminished batch-to-batch consistency and product quality, with important economic consequences.132A grand challenge in metabolic engineering is scaling production from typical lab conditions (1–100 ml volumes and relatively low substrate and biomass concentrations) to commercial reactors  constant. Probably the most applied scale-up variable is the volumetric oxygen transfer coefficient (kLa), characterizing the oxygen availability.Many possible factors can account for this drop in performance. Although initial strain testing is done in batch cultures, the fed-batch process used at larger scale leads to different physiology and metabolic states, which may no longer provide the necessary flux distribution in central metabolism. A longer growth period may select for strains that avoid the fitness cost of diverting large amounts of carbon and energy to the product. The strategies outlined above, to either couple production to growth, or separate the growth and production stages, are critical to success. For strain improvement beyond the initial pathway characterization, it is critical to consider process parameters, and design small-scale testing procedures that mimic the large-scale process as much as possible.133 When similar inhomogeneities were reproduced in a smaller bioreactor for a S. cerevisiae fermentation, the aerobic/anaerobic shifts increased by-product formation, reduced biomass growth, and increased oxygen demand.135 Similarly, large-scale E. coli studies revealed increased acetate formation and reduced biomass growth as compared with lab-scale fermentations.133 More interestingly, Ying Lin et al.136 showed reduced heterologous protein formation for E. coli in an oscillating glucose supply, which also resulted in increased carbon dioxide production. Furthermore, transcriptional studies in E. coli under shifting aerobic/anaerobic conditions showed a 1.5- to 6-fold increase in transcript levels for mixed acid fermentation genes and several global regulators.137One aspect of the scale-up process that is challenging to mimic at laboratory scale is the direct effect of reactor volume and associated inhomogeneity in the bioreactor. In typical bioreactor setups, inefficient mixing produces gradients of glucose and oxygen, which can create deviations as large as 400-fold and reduce biomass yield by as much as 20%.138 This combination has been carried out by coupling kinetic models of metabolism with fluid dynamics simulations140 . This leads to a widespread opinion142 that more accurate modeling of microbial metabolism based on data gathered in scale-down experiments is required for effectively predicting performance at, e.g., 100 l from data obtained at a 35 ml shaking flask. This integration remains an open problem in the field that, if solved, would facilitate the commercialization of the products of metabolically engineered strains.Understanding and predicting the volume-related performance changes in large-scale fermentation usually consists of combining the information from down-scaling experiments with fluid dynamics simulations of the physical and chemical conditions in the bioreactor.tions140 . However143 that range from 1–4. Most microorganisms, even solventogentic microbes such as clostridium,144 are extremely sensitive to solvents in this range.145–147 A large number of systems biology studies have been used to explore the underlying causes of toxicity, spanning from transcript and proteome analysis in several different microbes 148 to MAGE, recombineering, and genome-wide fitness screens. Comprehensively reviewed elsewhere,149–153 these studies aim to identify candidates that can then be used to improve the tolerance characteristics.155 Screening heterologous gene libraries or fosmid libraries from other microbes with desirable functions is also an effective method to obtain genes that provide such phenotypes and has been used to discover novel efflux pumps from organisms that provided improved tolerance and increased production for monoterpenes in E. coli,156 and to mitigate toxicity from by-products of biomass pretreatments.157Another common failure mode of scale-up is the toxicity associated with the higher titer of the final product. Main categories of commodity compounds being targeted in metabolic engineering include fuels, fuel additives, precursors of polymers, plastics, materials, lubricants and adhesives, surfactants and solvents. C2–C12 compounds in these categories often have characteristics of hydrophobic solvents with octanol:water partition co-efficients158The combination of the unprecedented capabilities afforded by synthetic biology with the comprehensive description of biological entities provided by systems biology presents a unique range of opportunities in designing and understanding biology. The recent technical advances in next-generation sequencing, high sensitivity proteomic and metabolomic methods, and developments in fluxomic techniques make systems biology methods more powerful and accessible to the synthetic biology community. The opportunity to use these methods to inform both synthetic biology tool development and metabolic engineering efforts makes significant scientific discoveries possible. The successes described above form a foundation for systems-level development of a wide variety of synthetic biology approaches. Comprehensive genome engineering for tolerance or target molecule production directly benefits from sequencing methods for strain characterization leading to rapid progress toward the goal of developing robust microbial cell factories. However, despite many successes, exciting new techniques or systems that work beautifully in the laboratory often do not directly scale to industrial fermentation conditions, or are not robust to minor changes in system parameters. Efforts to standardize and report experimental conditions have greatly improved the power of transcriptomic data sets, yet the same efforts have not permeated the proteomic, metabolomic, or fluxomic worlds to a comparable degree. Furthermore, the low-sample throughput of these -omics methods severely limits the usefulness of the data beyond the hypotheses of direct interest. Consequently, both systems and synthetic biology would benefit from research efforts that emphasize reproducibility, method and data sharing, and increased throughput of proteomic, metabolomic, and fluxomic analyses.159 Whereas we can model and predict the physical side of chemical engineering , our capability to predict biological systems lags behind, hindering our capability to assure proper functioning under non-studied conditions.Limitations in reproducibility hamper predictability. A process that is not quantitatively reproducible or exhibits a large unknown variability can hardly be used to parameterize predictive models, and cannot be expected to perform as needed under the required conditions. This inability to predict the behavior of biological systems under conditions not experimentally studied lies at the base of our inability to predict the behavior of large-scale fermentations.161 However, few laboratories perform experimental and computational work simultaneously because of the difficulties of doing so. Hence, the collaboration between experimentalist and computational specialists could become much more fruitful and frequent through a more robust exchange of data. Several initiatives tackle this problem,162–164 but there is more work to be done to enable effective and fast data exchange. These data exchange are particularly important for the further development of synthetic biology and biological computer aided design:158 as has been shown by Davidsohn et al.,165 the rigorous characterization of pathway parts enables the accurate prediction of the behavior of full pathways.Greater reproducibility and predictability is enabled by efficient data and metadata collection and sharing. One cannot fully compare local experiments with similar ones performed in different laboratories without a detailed account of the conditions and reagents. Concurrently, computational biologists, modelers and data scientists worldwide are limited by experimental data with which to validate a growing number of computational approaches designed to predict biological behavior.166 from understanding fundamental scientific questions to unique practical applications. Once biological systems are pushed to their limits , systems biology becomes fundamental to understand and predict them. However, bioengineering takes significantly more time and effort and is much less precise than other types of engineering in more established fields . Changing the status quo will require significant investments in the basic tools that will improve productivity and precision in biological design, just as, e.g., integrated circuits have for the computer sciences. In the information technologies field, implementation has been optimized to the point that a limited amount of money and a small team is sufficient to create enough value to launch a company. The situation is the opposite in biological engineering, where implementation of ideas takes much more effort than creating them. In this review, we have presented what we believe are some challenges that need to be overcome in order to reach that level of maturity.The uses of synthetic biology showcase the extraordinary opportunities in what has been called the century of biology,


Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose–xylose consumption in a recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated “modeling and experiments” systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.We present a design–build–test cycle composed of modeling efforts and experiments with a glucose–xylose co-utilizing recombinant The online version of this article (doi:10.1186/s13068-017-0838-5) contains supplementary material, which is available to authorized users. Saccharomyces cerevisiae, which is widely used in industry for production of a variety of chemicals, including bioethanol from glucose, is a promising candidate for bioethanol production from sugars. However, the native strain of this organism is unable to utilize xylose because of the lack of a mechanism converting xylose into metabolic intermediates . XYL1 and XYL2 genes of S. stipitis were chromosomally integrated into URA3 locus under PGK1 and ADH1 promoters, respectively. The integration cassette was constructed as described earlier [XKS1 of S. cerevisiae was integrated into XKS1 locus as described below.The yeast strain used in the study was VTT C-10880, i.e., [CEN.PK113-1A ( earlier . XKS1 ofXKS1 expression cassette pADH1m-XKS1-tADH1 was released as a BamHI fragment from plasmid pMLV14 (BluescribeM13) and ligated into the bacterial plasmid pMLV24. The plasmid pMLV24 contains loxP-S. cerevisiae MEL5 -loxP marker cassette with 60-bp flanking regions for targeting to the XKS1 locus. The XKS1 flanking regions were from nucleotides −250 to −192 and from nucleotides 1801 to 1861, where numbers are relative to the nucleotide A in the XKS1 ATG start codon. The BamHI cloning site was included in the XKS1 5′ flanking sequence. The resulting plasmid, containing XKS1, was named pMLV46. The expression cassette for XKS1, together with the loxP-MEL5-loxP marker, was released from pMLV46 with PstI and SpeI and introduced into yeast cells by transformation. Blue-colored, MEL5 -expressing yeast colonies were collected from agar-solidified YP containing 2% w/v d-galactose, supplemented with 5-bromo-4-chloro-3-indolyl-α-d-galactopyranoside . To remove the MEL5 marker cassette from the yeast chromosome, the transformant was retransformed with a plasmid pSH47 [ADH1m-XKS1-tADH1 into the XKS1 locus in the genome was verified with PCR. The yeast strain generated was named VTT C-10880.The id pSH47 , expressBamHI + EcoRI digestion. KanMX4 was further ligated into YEplac195 [BamHI + EcoRI resulting in pKK27-1.Plasmid pKK27-1 was constructed by releasing KanMX4 from pFA6a-KanMX4  by BamHIEplac195  digestedHXK2 of VTT C-10880 was deleted by integration of KanMX4 into HXK2 locus. KanMX4 deletion cassette was amplified by PCR from pKK27-1 plasmid using forward 5′ TCTGTTTAGCTTGCCTCGTC and reverse 5′ CACTGGATGGCGGCGTTAGTA primers with the homologous flanking sequences to HXK2 locus (−27 to −33 for the forward primer and +1 to +60 for the reverse primer). The resulting PCR product was integrated into the genome of VTT C-10880 by homologous recombination by following the transformation procedure of Woods and Gietz [HXK2 was verified by PCR using primer pairs 5′GGTTGTAGGAATATAATTCTC3′ and 5′CAACGCTACCTTTGCCATGT3′ and 5′ATCCTGATATGAATAAATTG3′ and 5′CATGTTCACATAAGTAGAAAAAGGGCACC3′.nd Gietz . TransfoHXK2 deletion strain were studied for 170 h in 1.5-l batch cultures in Biostat B bioreactor  at pH 5 and 30 °C using agitation at 200 rpm, under anaerobic conditions with 0.5 l/min nitrogen flushed to reactor headspace. The HXK2 deletion strain was cultivated in duplicate experiments. The inocula were prepared by transferring the cells from an YPD plate to 25 ml mineral medium in 100-ml Erlenmeyer flasks and incubated on a plate shaker  overnight. The inocula were further transferred into 75 ml mineral medium in 250-ml Erlenmeyer flasks and incubated  for 4-6 h. The inocula were centrifuged  and the cells were resuspended into the growth medium lacking the source of carbon. Initial cell dry weight in bioreactor was 0.15 g/l. The minimal mineral medium [4)2SO4/l, 3 g KH2PO4/l, 0.5 g MgSO4·7H2O/l, 15 mg C10H14N2Na2O8·2H2O (EDTA)/l, 4.5 mg ZnSO4·7H2O/l, 0.84 mg MnCl2·2H2O/l, 0.3 mg CoCl2·6H2O/l, 0.3 mg CuSO4·5H2O/l, 0.4 mg Na2MoO4·2H2O/l, 4.5 mg Na2MoO4·2H2O/l, 3.0 mg FeSO4·7H2O/l, 1.0 mg H3BO3/l, 0.1 mg KI/l, 0.05 mg biotin/l, 1.0 mg Ca-Pantothenate/l, 5 mg nicotinic acid/l, 25 mg myo-inositol/l, 1.0 mg thiamine HCl/l, 1.0 mg pyridoxol HCl/l, and 0.2 mg p-aminobenzoic acid/l.The strain VTT C-10880 and the l medium  containe2SO4 (Merck Titrisol) with a flow rate of 0.6 ml/min to separate the analytes at 55 °C. Concentrations of lactic acid, succinic acid, and malic acid were measured using a P/ACE MDQ capillary electrophoresis system  equipped with a PDA detector [2, O2, and ethanol in the exhaust gas of the bioreactor were measured using an Innova gas analyzer .Samples were taken frequently with a sampling robot  into a cold bath where the samples froze at the temperature of −30 °C. The frozen samples were thawed, OD600 and cell dry weight (CDW) measurements were performed, and supernatants were collected for HPLC and CE measurements. Concentrations of xylose, glucose, glycerol, xylitol, ethanol, and acetate were measured with a Waters Alliance 2690 HPLC system with a Waters 2410 RI detector . Aminex HPX-87H column and Aminex fast acid analysis column  were used in series with 5 mM Hdetector . Concent2.A kinetic model was applied to smooth the measurement data to reduce the noise for better estimation of the calculated continuous variables such as specific accumulation rates. Performing these calculations directly from the measured data would provide data too noisy for interpretations. The dynamic system of anaerobic yeast cultivation was described by a set of non-linear ordinary differential equations (ODEs), with the system components comprising concentrations of biomass, the substrates glucose and xylose, the products ethanol, glycerol, xylitol, acetate, lactate, succinate, and malate, and the off-gas COc, the subscript s stands for either of the substrates, glucose and xylose, respectively, and the subscript x for biomass. The maximum specific substrate uptake rate is denoted by The consumption of both substrates, glucose and xylose, was described by Monod kinetics with an initial lag phase. Concentrations are written as Growth of the biomass is determined by the rate of substrate consumption, and thus2, denoted by the subscript p, are released proportionally to substrate consumption with a yield parameter The products, i.e., ethanol, xylitol, glycerol, acetate, and minor acids, as well as the off-gas COe being a linear function of the fermentation broth volume vEthanol is evaporating from the broth during the cultivation in non-negligible amounts. In order to fit the model to the measurement data, ethanol evaporation is included in the model by first-order dynamics. The measured ethanol concentration is then described byThe parameters of the model were fit to the available measurement data by means of a maximum likelihood estimator in two subsequent steps. In a first step, the parameters for the coupled system containing the ODEs for glucose, xylose, and biomass concentrations were identified, and in a following step all the remaining parameters were estimated , acting as an external activator, and adenosine triphosphate (ATP), acting as an inhibitor, bind to the same site. Detailed account about the used kinetic mechanisms is given in the Additional files For each of the reactions within the studied metabolic network, a kinetic mechanism has been assigned based on the available literature data. The kinetic mechanisms used include reversible Michaelis–Menten kinetics, ordered Bi–Bi, Bi–Ter, and Ter–Bi . WheneveFor any metabolic reaction, the displacement from the thermodynamic equilibrium can be expressed as a function of the equilibrium constant, The equilibrium displacement, equation , 63:\docThe optimization and risk analysis of complex living entities (ORACLE) methodology used in this contribution is based on the MCA paradigm. ORACLE is composed of several successive computational procedures, each of them bringing a new level of information about metabolic networks Fig. . ConceptWe first determine the stoichiometry of the metabolic network using biochemical data or genome reconstructions –70. For Accurate and consistent determination of the space of metabolite concentrations plays a significant role in appraising all viable kinetic models sharing the same steady-state flux profile for two reasons: (1) the metabolite concentration levels intrinsically affect the Gibbs free energy difference; consequently, these have to be consistent with the reaction feasibility and directionality of computed flux profiles; (2) the metabolite concentration levels are one of the factors determining local stability of the kinetic models . Uniformequilibrium displacements of thermodynamically feasible reactions, We use the levels of allowable metabolite concentrations along with the standard Gibbs energy of reaction, We integrate the kinetic properties of enzymes at this level. Available kinetic data from literature are incorporated. Whenever kinetic information about enzymes is incomplete or missing, we sample to recover the missing kinetic data. This is performed either through sampling of enzyme states , or throelasticities consistent with thermodynamically feasible metabolite concentrations.At this level, we also perform consistency checks and pruning. In these tests, we evaluate the local stability of the resulting kinetic models and the consistency with the experimental information. As a result of the computational procedures performed at this level, we obtain the populations of populations of control coefficients and store them. Only the control coefficients stemming from stable systems are retained. If at a later time experimental evidence about the control coefficients becomes available, e.g., the response of a metabolic flux to an increase of activity of an enzyme becomes known, we prune the samples of the population with inconsistent control coefficients.Using the information gathered in previous levels, we compute We perform statistical analysis and data mining on the populations of control coefficients in order to quantify the importance of the enzymes in possible metabolic engineering strategies –89. HypoAll the above-mentioned levels include visualization of the obtained knowledge about the metabolic network.n represents the number of producing (or consuming) fluxes q denotes the set of system parameters, such as the enzyme activity and extracellular metabolite concentrations, and The turnover of a metabolite, tMet, can be defined as the sum of fluxes that produce, or alternatively as the sum of fluxes that consume, the metabolite: Additional file 1. Stoichiometry of used reactions and the corresponding mass balances.Additional file 2. Thermodynamic displacement of reactions, Γ. Reactions can operate: (i) strictly far from thermodynamic equilibrium, i.e. 0 < Γ ≤ 0.1; (ii) with the middle displacements, i.e. 0.1 ≤ Γ ≤ 0.9; and (iii) strictly near equilibrium, i.e. 0.9 ≤ Γ < 1. Reactions whose displacements spanned more than one of these ranges were denoted with: (iv) I + II, for 0 < Γ ≤ 0.9; (v) II + III, for 0.1 ≤ Γ < 1; and (vi) I + II + III, for 0 < Γ < 1.Additional file 3. List of reactions, their substrates and products, together with the used kinetic mechanisms  Control coefficients of the xylose uptake (XTR) for a set of most important enzymes; (ii) Correlation of the distributions of the XTR control coefficients for HXK, ATPM and ZWF; (iii) The effects of manipulations of HXK, ATPM and NDR on metabolic fluxes. Sheet “Concentration Control Coeffs”: Control coefficients of the cytosolic ATP, cytosolic NADH and cytosolic NADPH during glucose-xylose co-utilization. Sheet “Turnover Control Coefficients”: (i) Turnover control coefficients of cytosolic ATP, mitochondrial ATP, cytosolic NADH, cytosolic NADPH, cytosolic protons and mitochondrial protons for HXK, ATPM and NDR; (ii) Flux control coefficients of producing and consuming reactions of cytosolic ATP, mitochondrial ATP, cytosolic NADH, cytosolic NADPH, cytosolic protons and mitochondrial protons for HXK, ATPM and NDR.Additional file 5. Control coefficients of the flux through xylulose kinase (XK) during glucose-xylose co-utilisation for the original (purple) and refined (green) kinetic models. The bars represent the mean values of the control coefficients, and the error bars denote the 1st and the 3rd quartile of the control coefficients with respect to their mean value, i.e. 50% of the samples closest to the mean value are within the error bars.Additional file 6. Predicted effects of changes in ATPM on the metabolic fluxes. The increased ATPM would result in reactions with: increased flux (red), decreased flux (green) or negligible changes in flux (grey). Higher intensity of red or green indicates larger changes in fluxes. The numerical values shown above reactions denote the mean control coefficients upon changes in ATPM, i.e. Additional file 7. Predicted effects of NDR manipulations on the metabolic fluxes. The increased activity of NDR would result in reactions with: increased flux (red), decreased flux (green) or negligible changes in flux (grey). Higher intensity of red or green indicates larger changes in fluxes. The numerical values shown above reactions denote the mean control coefficients upon activity changes in NDR, i.e. Additional file 8. Flux Variability Analysis (FVA): (i) obtained ranges of the fluxes of reactions involved in the cytosolic ATP and NADPH metabolism for 4 different values of NDR flux; and (ii) scatter plots of the fluxes of reactions involved in the cytosolic ATP and NADPH metabolism for 4 different values of NDR flux.Additional file 9. Estimated parameter values of the kinetic model used to smooth the measurement data together with the raw data (dots) and fitted model outputs (lines) for the cultivation of the VTT C-10880 and HXK2 deletion strains. Correlation parameters for the measurement data and simulated values are also provided.Additional file 10. Rate expressions for used kinetic mechanisms and the expressions for the corresponding metabolite elasticities.


Transient physical association between enzymes appears to be a cardinal feature of metabolic systems, yet the purpose of this metabolic organisation remains enigmatic. It is generally assumed that substrate channelling occurs in these complexes. However, there is a lack of information concerning the mechanisms and extent of substrate channelling and confusion regarding the consequences of substrate channelling. In this review, we outline recent advances in the structural characterisation of enzyme assemblies and integrate this with new insights from reaction–diffusion modelling and synthetic biology to clarify the mechanistic and functional significance of the phenomenon. Temporary association of metabolic enzymes is generally assumed to facilitate substrate channelling within the complex. In this review, Lee Sweetlove and Alisdair Fernie outline the nature and functional consequence of organising enzymes into assemblies, and discuss applications within the natural world and synthetic biology. Metabolism is central to cellular function, growth and survival. Hundreds of chemical reactions occur simultaneously to process energy sources and to provide precursor molecules for cell construction and chemical defence. The majority of these reactions are accelerated and regulated by enzymes that not only allow metabolism to occur on an appropriate timescale for life but also allow the outputs of the metabolic system to be specified and controlled. Moreover, enzymes allow feedback loops and other regulatory mechanisms to operate so that the system can maintain homeostasis under variable conditions. Enzymes are highly effective catalysts and biochemical reactions are massively accelerated. Nevertheless, few enzymes have attained catalytic perfection and catalysis remains orders of magnitude slower than the diffusion of the substrates and products. Hence, the long-standing view of metabolic systems is that they are ‘well mixed’ with homogenously distributed enzymes and freely diffusing metabolites.1 and indeed holds true for purified enzymes in the test tube. However, over the past 40 years, evidence has steadily accumulated to suggest that this view is not an entirely accurate description of metabolism in vivo. Repeated observations have been made which suggest that enzymes are not always homogenously distributed in the cell but rather can come together in time and space, due to protein–protein interactions between them. The enzymes in such assemblies usually constitute recognised metabolic pathways and their formation and disassembly is dynamically responsive to biochemical conditions and stimuli. Moreover, metabolites may be channelled between sequential enzymes: i.e. the product of one enzyme is transferred to the next enzyme in the pathway without equilibrating with the bulk aqueous phase of the cell. In this case, the assemblies are known as metabolons, a term coined by Paul Srere some 40 years ago2 who provided the first detailed study of the phenomenon while working on the mitochondrial tricarboxylic acid (TCA) cycle. Since these pioneering studies, assemblies of consecutive enzymes have been observed in a wide variety of pathways, including glycolysis, oxidative phosphorylation, fatty acid, amino acid, polyketide, polyamine and polypeptide biosynthesis4. Enzyme assemblies have also been reported in photosynthesis and natural product metabolons in plants6  those built from specific pairwise interactions between sequential enzymes with complementary structural features; (ii) larger clusters formed from multiple copies of all enzymes of a pathway. These two types of assembly have different implications for substrate channelling, pathway flux and regulation of flux at metabolic network branch points and these will be considered in detail. We will illustrate each type of assembly by considering naturally occurring examples  as well as examples from synthetic biology. Finally, we will consider the relevance of enzyme assemblies for metabolic engineering as well as discussing the crucial experimental evidence that will be required to fully understand the biochemical importance of this enigmatic phenomenon. Owing to space constraints, we cannot cover every reported dynamic superstructure nor can we discuss every feature of such assemblies but the interested reader is referred to several excellent recent reviews that cover these aspects in more detail6. It also has obvious implications for regulation of flux at branch points in the metabolic network—a channelled metabolite is not available for use by a competing enzyme at a branch point. This feature of substrate channelling will be discussed in more detail later.The most common explanation for the dynamic formation of enzymes into physical assemblies is that it promotes substrate channelling—that is, the facilitated transfer of the metabolite product from one enzyme to the next enzyme in the pathway without that metabolite equilibrating with the bulk aqueous solvent. This can be particularly beneficial if the channelled metabolite is labile or toxic, which is quite commonly the case in plant and fungal secondary metabolism14. Instinctively, this feels correct. Surely enzymes must work faster if substrates are passed between them without diffusing away into the bulk aqueous phase of the cell? However, for this to be true, diffusion would have to be a limiting factor in non-channelled reactions, and in most cases, it is not. Diffusion of metabolites is extremely fast in relation to the rate of catalysis by enzymes, even when the crowded and viscous environment of the cell is taken into account. Various calculations have been made that demonstrate this. For example, the calculated diffusion rate for collisions between enzymes and their metabolites is estimated to be 1000–10,000 times faster than the typical Kcat/KM value for enzymes of central metabolism12. Alternatively, it has been estimated the time taken for metabolites to diffuse between enzymes is one-to-three orders of magnitude faster than the average reaction turnover time for enzymes of central metabolism15. Similarly, a diffusion model was used to demonstrate that the concentration of product diffusing from an enzyme active site remains constant at distances of a few micrometres away10. Given that an enzyme concentration of 1 nM corresponds to an inter-enzyme distance of approximately 1 μm between enzymes, it is evident that diffusion is amply fast enough to evenly distribute metabolites between enzymes and does not limit their catalytic rate. In other words, it is reasonable to conclude that, diffusion is not the limiting factor for the rate of a reaction and hence metabolite channelling will not increase the rate of the reaction at steady state.But first we wish to address a major, and widely held, misconception about substrate channelling: which is that substrate channelling makes the metabolic pathway more efficient or kinetically superiorKcat/KM ratio is sufficiently high that the reaction does become limited by diffusion of the reactants16. A notable example in central metabolism is the glycolytic enzyme, triose phosphate isomerase. The second exception is if enzyme concentration is very low, meaning that the average distances between enzymes can increase to the point that the reaction is partially diffusion limited. This is considered to be a more relevant scenario for specialised metabolism of plants and fungi12.There are two exceptions in which metabolite channelling may accelerate the steady-state reaction rate. First, there are a small number of enzymes that are deemed to be catalytically perfect and whose 18. These experiments are often predicated on the notion that enzyme proximity will lead to substrate channelling and that this will accelerate reaction rate. However, as we have already discussed, substrate channelling will not lead to a faster steady-state reaction rate than achieved by the freely diffusing enzymes unless the freely diffusing enzymes are at very low concentrations or are catalytically perfect enzymes. Given that these conditions do not apply to most of the published enzyme-scaffolding studies, then how to we reconcile the fact that almost all of these studies report several-fold enhancements of reaction rate for the scaffolded enzymes? Rate enhancements have been observed for scaffolds made from protein, lipid or DNA/RNA20 and for substrates with widely different physicochemical properties21.Over the past few years, there have been a growing number of experiments in which two or more enzymes of a metabolic pathway have been co-localised onto synthetic scaffold biomolecules22 and experimental approaches24. This has provided a new perspective on the behaviour of co-localised enzymes that is relevant to our understanding of the function of in vivo enzyme assemblies. First, in enzyme-scaffolding experiments, reaction rate is typically monitored after initiating the reaction. Although the reaction is usually followed for tens of minutes thereafter, many reactions take hours to reach steady state in vitro. For example, a kinetic model of the commonly used glucose oxidase–horseradish peroxidase pair shows that, for experimentally relevant enzyme concentrations, the reaction takes >5 h to reach steady state22. In other words, much of the published data for scaffolded enzymes may be following the reaction during its ‘lag-phase’ prior to steady state. Therefore, the observed rate enhancement could be due to substrate channelling due to its effect of shortening the lag time . There is a wealth of evidence from biochemical experiments for interactions between TCA cycle enzymes28 and between TCA cycle enzymes and inner mitochondrial membrane proteins32 or members of the mitochondrial carrier family34. In conjunction, there is a convincing body of evidence from enzyme kinetic and isotope labelling studies that strongly suggests metabolites are channelled between the enzymes of the cycle39. These classical studies have been bolstered by more recent work using modern techniques in cell biology and proteomics. For instance, binary interactions have been systematically assessed between Bacillus subtilis TCA cycle enzymes using a combination of bacterial two-hybrid analysis and affinity purification–mass spectrometry. Interactions were identified between six consecutive enzymes of the TCA cycle (linking fumarate to succinyl CoA), as well as interactions of these enzymes with phosphoenolpyruvate carboxykinase and glutamate synthase40. In plants, a comprehensive range of techniques including affinity purification–mass spectrometry, split-luciferase and yeast two-hybrid assays have recently been utilised to demonstrate protein–protein interactions in the TCA cycle41.Substrate channelling has benefits other than increasing reaction flux and there is abundant evidence that it does operate in naturally occurring enzyme assemblies. Perhaps the best-studied example of specific physical interactions between sequential enzymes associated with substrate channelling is the TCA cycle. Collectively, the body of work on the TCA cycle metabolon by Srere, Sumegi and their co-workers has been absolutely fundamental in setting our contemporary views concerning enzyme interactions and substrate channelling15. However, there is no evidence of such structures forming due to dynamic pairwise interactions of TCA cycle enzymes.Until recently, however, the mechanistic basis by which these enzyme–enzyme interactions facilitates substrate channelling has not been clear. Substrate channelling requires the metabolite product of one enzyme to be passed as substrate to the next enzyme in the pathway while being prevented from diffusing into the bulk solvent. In stable multi-enzyme complexes, the reaction intermediates are entirely enclosed in internal tunnels or chambers within the protein complex, allowing directed diffusion between active sites or the use of swing arms to physically mobilise intermediates or cofactors42. In this study, in vivo crosslinking and mass spectrometry were used to establish a low-resolution structure of the complex. Crosslinking suggested that all eight enzymes of the cycle physically interact, but only the interactions between malate dehydrogenase and citrate synthase were sufficiently strong to allow their recovery in non-crosslinked samples. Using distance constraints derived from the crosslinking process, models of the malate dehydrogenase–citrate synthase–aconitase complex were proposed. The average distances between active sites were 35 Angstroms between malate dehydrogenase and citrate synthase and 50 Angstroms between aconitase and citrate synthase. Analysis of the modelled superstructure revealed that polar residues are considerably over-represented on its surface while less-polar residues tend to be even more buried than is usually the case. Moreover, these positively charge residues are arrayed within a groove that connects the malate dehydrogenase and citrate synthase active sites. Hence, complementary structural and charge features of the interacting enzymes provide a mechanism to promote efficient transfer of the negatively charged oxaloacetate intermediate between adjacent active sites. Electrostatic retention of the intermediate on the enzyme surface would not only reduce equilibration of the intermediate with the bulk, but theoretical models suggest that it would promote efficient transfer between the two enzymes43, provided that the electrostatic transfer groove is not too long44.A recent structural analysis of the malate dehydrogenase–citrate synthase–aconitase complex has provided insight into the potential mechanism by which substrate channelling could occur Fig. 42. In th45. Mutation of one of these residues in the citrate synthase polypeptide to an uncharged reside (R65A) did not affect catalytic activity of the enzyme but reduced the probability of channelling from 0.99 to 0.023. Another intriguing feature which became apparent in these studies was that recombinant versions of malate dehydrogenase and citrate synthase self-assemble in vitro. From this work, it can be concluded that these enzymes have co-evolved complementary structural features that allow them to interact in such a way as to align surface features that may promote substrate channelling.In a follow-up study, site-directed mutagenesis was performed on the highly conserved arginine residues located in the positively charged groove41. It is conceivable that many of these interactions, which occur largely between regulatory subunits and catalytic subunits of different proteins, act as nucleation points that aid in the formation of the specific interactions between the sequential enzymes41. However, it is also possible that these additional interactions give rise to a different type of structure to the classical pairwise interaction: an enzyme cluster or enzyme microdomain. This is a larger assembly of multiple copies of each enzyme of a pathway as illustrated in Fig. One surprising feature of the more recent systematic studies of protein–protein interactions in the TCA cycle was the extensive interactions between non-sequential pathway enzymes in addition to the expected interactions between sequential enzymes46. Recombinant enzymes were created with the relevant peptide tag at their C-terminus. However, because each of the holoenzymes is a homodimer with the two subunits in anti-parallel orientation, the scaffolded enzymes can interact with a second scaffold. Given the flexible nature of the scaffolds used, each enzyme does not necessarily interact with the same two scaffold molecules and it is therefore possible that a large cluster of physically connected enzyme-scaffold complexes can form47  has room for 9200 individual proteins. Taking these calculations into account and what is known about the protein–protein interactions between the purine biosynthesis enzymes . Substrate channelling of CMP between the pyrimidine pathway enzymes CMP synthase (CMPS) and aspartate–CMP–transferase (ACMPT) would direct flux towards pyrimidine synthesis and prevent the arginine pathway enzyme ornithine–CMP–transferase competing for CMP. This flux change can be experimentally detected in bacteria as arginine pseudo-auxotrophy (i.e. growth is stimulated by the addition of arginine to the medium) and an insensitivity to the addition of uracil, a pyrimidine precursor. To test this, a genetic fusion of CMPS and ACMPT was expressed in a CMPS/ACMPT knockout strain of Escherichia coli. This did not result in arginine pseudo-auxotrophy, suggesting that the two enzymes lack structural compatibility/surface features to create channelling directly. However, when the fusion protein was more strongly overexpressed, protein clusters were visible under phase-contrast microscopy, assumed to be μm-scale assemblies of the CMPS–ACMPT fusion. In this situation, arginine pseudo-auxotrophy was observed, with the degree of growth stimulation by exogenous arginine correlated with the extent of protein cluster formation. This mechanism of flux control at branch points is now beginning to be incorporated into metabolic engineering experiments. For example, it was shown that flux from pyruvate to various fermentation end products in E. coli could be controlled by creating complexes of pyruvate kinase and the pyruvate-consuming enzymes of different fermentation branches58.Recently, the concept of substrate channelling as a branch-point regulatory mechanism has been directly experimentally tested by examining the effect of clustering of enzymes at a branch point between pyrimidine and arginine synthesis59. And the TCA cycle intermediate, oxaloacetate, is also used for aspartate biosynthesis60. Hence, in scenarios such as purine depletion where it is necessary to rapidly increase the flux of purine biosynthesis, the formation of the purinosome achieves this by two mechanisms: by increasing local enzyme concentration and by preventing competition for intermediates by branch-point pathways.In the context of the purinosome and TCA cycle, there are known branch points in the local metabolic network that potentially could be controlled by this mechanism. In purine biosynthesis, the intermediate S-aminoimidazole ribonucleotide is a substrate for a reaction in the pathway of thiamine biosynthesis61. Intriguingly, high concentrations of compatible solutes not only promote enzyme folding and stability but also potentially alter the effective enzyme concentration that may affect oligomerisation and kinetics63, which may extend to associations between enzymes. The recent demonstration of a substrate driven chemostatic assembly of the enzyme cascade linking the first four activities of glycolysis (ref. 61), as well as the malate dehydrogenase–citrate synthase–aconitase complex assembly of the TCA cycle45, highlight the influence of small molecule metabolites on setting gradients aiding in dynamic enzyme assemblies. Although in a well-mixed cellular environment, it is unclear how relevant this gradient-driven assembly will be.Enzyme assemblies have been characterised at the functional level, focussing mainly on substrate channelling, and at the structural level. It is clear that, if we are to better understand how these enzyme assemblies work, then the two facets need to be brought together. In the case of the TCA cycle, structural studies are bringing new insights into the potential mechanism of substrate channelling, but as of yet, the effect of mutation of structural features on substrate channelling have not been directly tested and so proof of the proposed mechanism is missing. In the case of the purinosome, there have been no direct tests of the effects of the complex on enzyme kinetics or substrate channelling. And more work is required to define the exact structure of the assembly and in particular whether it contains multiple copies of enzymes as we have suggested here. Another highly interesting aspect of the formation of dynamic enzyme assemblies that is receiving increasing attention is the influence of ionic and organic solute environments on their formation49. None of the proposed functional benefits of enzyme assemblies have been directly tested. To do this, one would ideally exploit the molecular mechanism that controls assembly and disassembly of the complex. Manipulation/mutation of this mechanism would allow the consequences of prevention of the enzyme assembly formation to be investigated and the biochemical role of the assembly to be established.Going beyond the structural–functional level, the most important unanswered question of all is the role of enzyme assembly in the context of cellular biochemistry. We have proposed control of flux at metabolic branch points as a key role, but other advantages of such enzyme assemblies such as protection of labile reactants are also conceivable. Indeed, the latter has been suggested in the context of the purinosome where the product of the first enzyme of the pathway has a short solution half-lifeτ) can be used as an indirect method of demonstrating substrate channelling. Following initiation of a two-step reaction, there is a lag before the reaction reaches a steady state in which the intermediate metabolite concentration reaches a constant value dictated by the kinetics of the producing and consuming enzyme and the size of the reaction container. If channelling occurs, the reaction container volume is effectively reduced and the reaction therefore reaches steady state much more rapidly. In a perfectly channelled system, the lag time approaches zero . In MFA, a 13C-labelled metabolic precursor that is readily taken up is supplied and the pattern of redistributed label at isotopic steady state is analysed. Labelling patterns are then simulated in a metabolic network model and fluxes in the model iteratively varied until a good fit between the predicted and measured labelling patterns is achieved. Because substrate channelling changes the structure of the metabolic network where it bypasses branch points, the approach can be used to determine whether substrate channelling is required to account for the labelling patterns and, if so, can provide a quantitative estimate of the substrate channelled flux in vivo. This method was applied to heterotrophic Arabidopsis cell cultures and revealed substrate channelling of fumarate to malate but no channelled flux from 2OG or succinate to citrate67 in the plant TCA cycle.It should be obvious from these discussions that new methods are needed that would allow substrate channelling to be assessed in vivo, at a wider scale and with greater throughput. A new approach that has potential to achieve this has recently been described and is based upon the well-established methodologies of 68. Depending on the operation of the channelled pathways, 19 different configurations of the lignin biosynthesis network could be formulated. These were each tested in a large set of Generalised Mass Action models that accounted for saturatable enzyme kinetics and aspects of regulatory crosstalk between the two channelled branches. The models were benchmarked against a data set of lignin content and monomer composition (guaiacyl (G), and syringyl (S) monomers) in transgenic alfalfa lines with reduced expression of different enzymes in the lignin pathway. The modelling suggested that channelling is necessary but not sufficient to explain the results of the transgenic plants and that specific crosstalk between the two channelled pathways is also required. Additionally, the modelling revealed that operation of the G-lignin channel was more important for an accurate description of the data than the S-lignin channel.A related modelling-based approach for quantitative assessment of channelling at the metabolic network scale is to construct an enzyme kinetic model and to assess whether the introduction of specific channelled pathways improves the ability of the model to predict observed data sets. This approach was used to assess channelling between two branch points in lignin biosynthesis in plantsThe study of dynamically formed enzyme assemblies has a long history but it is only in recent years that structural features of these assemblies have begun to be elucidated. Coupled with a growing body of experimental work exploring the properties of synthetic enzyme assemblies, we are now able to provide a more integrated picture of the function of these transient enzyme complexes. Using the purinosome and the TCA cycle as examples, we have described two types of enzyme assembly, one formed from large clusters of multiple copies of enzymes and the other formed from single complexes of paired enzymes with complementary surface features that serve to direct diffusion of the channelled intermediate. We have argued that the role of these enzyme assemblies is regulatory. In the case of large enzyme clusters, the increase in local enzyme concentration is expected to increase pathway flux. And in both types of complex, effective channelling of intermediates  bypasses branch points in the local network, allowing flux through the channelled branch to be increased at the expense of the competing branch.A number of other pertinent questions also remain to be addressed including the precise roles of sub-metabolons and the role of scaffolding proteins, described to be present in both the purinosome and TCA cycle assemblies, remains unclear. As does the role of membranes that are associated with both of these assemblies and many of the others listed in Table E. coli from utilising C5 sugars as fermentation substrates12, a major target for the biofuel industry allowing the use of plant lignocellulosic feedstocks. The argument is that the degradation products of C5 sugars are prevented from entering metabolism due to the channelling of glycolysis. Hence, it may be necessary to disrupt this endogenous channel in order to engineer bacteria that can efficiently ferment C5 sugars.A more complete understanding of the role of enzyme assemblies will also be crucial for engineering of metabolic networks and for the successful introduction of synthetic metabolic pathways into host organisms. Indeed it is possible that proteins within synthetic scaffolds are turned over at a decreased rate than the native enzymes. Failure to account for the presence of enzyme assemblies and substrate channelling will lead to flawed designs for engineered pathways and problematic interactions between synthetic pathways and host metabolism. For example, it has been suggested that substrate channelling in glycolysis prevents bacteria such as 69. A recent breakthrough study of transcriptional regulation of metabolism in E. coli revealed that most of the metabolite feedback to the genetic regulatory system occurred via just three metabolites—cyclic AMP, fructose 1-phosphate and fructose 1,6-bisophosphate70. Given that substrate channelling effectively renders channelled metabolites invisible to metabolite signalling systems, this remarkable discovery may point towards a greater extent of substrate channelling in central metabolism than has hitherto been suspected, with the channelling effectively directing metabolic fluxes between a small number of non-channelled metabolite nodes upon which regulation is focussed —of which two strikingly different types have evolved in eukaryotes: the metazoan and fungal FAS72. The metazoan FAS is a 540-KDa homodimer that shares a common architecture with bacterial polyketide synthases73, while the fungal FAS forms a 2.6-MDa assembly comprising 48 functional domains and, alongside the homologous CMN-FAS recently described in Corynebacterium, Mycobacterium and Nocardia74, constitute some of the most complex biochemical protein machineries known75. A recent study identified bacterial non-canonical fatty acid synthases and trans-acting polyketide enoyl reductases as potential ancestors of the scaffolding regions with striking conservation of insertions to scaffolding elements despite minimal sequence identity72. In contrast, plants and many bacteria harbour only monofunctional FASs76. Similarly, the penta-functional Arom complex of shikimate biosynthesis appears likely to have evolved via gene fusion with the enzymes of most other species being monofunctional9. A final multi-enzyme complex that merits discussion is that of tryptophan synthase77. In their study, Leopoldseder et al. suggest that it is plausible to assume that sophisticated multi-enzyme complexes evolve via stepwise association of protein subunits  to what extent do protein interactions act as constraints during the evolution of the protein sequence; (ii) what role do transient or obligate interactions play in these constraints; (iii) are mutations in the binding site of an interacting protein correlated in those in the binding site of its partner. Intriguingly, a study aimed at addressing these questions observed that residues at the interfaces of obligate complexes evolved at a relatively slower rate allowing them to co-evolve with their interacting partners. In contrast, where the protein–protein interactions are transient, the rate of residue substitution is much higher81. These observations are thus in line with our suggestion that multi-enzyme complexes and metabolons evolve separately and result in distinctive repurposing of the ancestral enzymes, from one another. We propose that the key functional discriminator is the requirement for access of competing enzymes to the channelled metabolite so that metabolic branch points can operate. This would require disassembly of the enzyme complex that supports substrate channelling and would provide a selective pressure against the evolution of a stable multi-enzyme complex for that set of reactions. In other words, stable muti-enzyme complexes can only evolve where there are no other cellular reactions that requires access to the channelled metabolite.The presence of protein aggregates has been argued to be under negative selection. However, protein aggregation may also be unavoidable  and therefore functional organisation around such aggregates may represent an evolutionary compromiseSupplementary Information


Chlamydomonas reinhardtii to analyse how acetate supply affects the distribution of fluxes that sustain cellular activity. We examined the role of metabolic cycles which explain the down regulation of photosynthesis that is observed when cells are grown in the presence of acetate. Our results suggest that acetate modulates changes in global metabolism, with the pentose phosphate pathway, the Calvin-Benson cycle and mitochondrial respiration activity being affected whilst reducing photosynthesis. These results show how the decomposition of metabolic flux into cyclic and acyclic components helps to understand the impact of metabolic cycling on organismal behaviour at the genome scale.The regulation of metabolic networks has been shown to be distributed and shared through the action of metabolic cycles. Biochemical cycles play important roles in maintaining flux and substrate availability for multiple pathways to supply cellular energy and contribute to dynamic stability. By understanding the cyclic and acyclic flows of matter through a network, we are closer to understanding how complex dynamic systems distribute flux along interconnected pathways. In this work, we have applied a cycle decomposition algorithm to a genome-scale metabolic model of  For exaThere are several ways to define “cycles” in networks or graphs, depending on the representation used. In metabolic network analysis, “substrate cycles” are generally defined as combinations of catabolic and anabolic reactions that interconvert one or more substrates into each other, resulting in no net production of substrate but potentially in the conversion of cofactors . They caChlamydomonas reinhardtii is a unicellular green algae which has been a model organism for the study of photosynthesis and cellular function for the past 50 years  of the organism, iRC1080  for each species involved in the reaction. The main reason for using mass fluxes rather than molar fluxes is that the cycle decomposition algorithm requires quantities that are conserved at nodes, whereas molar fluxes are not conserved. For example, when fructose 1,6-biphosphate is broken down into glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, 1 mol is transformed into 2 mol, thus the number of moles is not conserved; nevertheless the sum of masses of glyceraldehyde 3-phosphate and dihydroxyacetone phosphate is equal to the mass of fructose 1,6-biphosphate.In order to assign a mass flux to every arc in the metabolic network, certain reactions had to be modified in the representation. Generic compounds such as glycogen and starch are composed of a core structure associated with a varying number of branched repeats, hence it is impossible to attribute a definite mass value for such generic compounds – they can have distinct masses in distinct occurrences. In addition to generic compounds, proteins and protein complexes described in biochemical reactions can create apparent mass imbalances, despite not being part of the metabolic network. Similarly, transfer (tRNA)-amino-acid complexes, present in GEMs, can introduce apparent mass inconsistencies. In addition, smaller imbalances can appear when the chemical description of substrates and products are associated with different pH levels, resulting in an inconsistency in the number of hydrogen atoms.Mass imbalances originating from the apparent imbalances described above were solved locally, i.e. by correcting the mass of the species to eliminate the apparent mass imbalance, as described in Parsimonious Flux Balance Analysis (pFBA) was used to calculate the molar flux in each reaction, which was then converted into a mass flux. pFBA finds a flux distribution that optimises the objective function and at the same time minimises the total sum of fluxes . This imTarjan’s algorithm  was usedThe final phase was the cyclic decomposition of the flux. The complete algorithm was described in 33.1iRC1080 representing the model organism C. reinhardtii was used for this work , a protein present in the reactions of photosynthesis, was without a formula and without a mass (while the mass of ferredoxin was properly accounted for). We have attributed to plastocyanin the mass necessary to balance the corresponding reaction.Consider R_3HAD60: this reaction is 3-hydroxyacyl-[acyl-carrier-protein] dehydratase, located in the chloroplast catalysing the following fatty acid biosynthesis reaction.Example 3Correction due to missing protons.Both formulae for the reactant and species contain an ‘R’ indicating a side chain that corresponds to an unknown number of repeating units associated to each residue molecule, therefore the mass of this generic species can not be accurately specified. We have attributed to generic compounds the specific mass values needed to balance the reactions in which they occur.Consider R_CCP2m, a cytochrome c peroxidase mitochondrial reaction.3.2We first applied parsimonious Flux Balance Analysis to the iRC1080 model in order to obtain the molar fluxes (mmol/gDW/h) under mixotrophic and phototrophic conditions. Then, we calculated mass fluxes (mg/gDW/h) and applied the decomposition algorithm to the non-zero flux sub-networks under these growth conditions. We have considered in our calculations only mass fluxes ≥ 0.0001 to filter out numerical noise. The first step of cycle decomposition corresponds to cycle enumeration, and we performed this analysis only to four sub-models of interest because of their importance in housing reactions belonging to central carbon metabolism: the thylakoid lumen, chloroplast, mitochondria, and a sub-model composed by reactions and substrates in all three. The restriction to sub-models is operationally important because the number of cycles become too large and not manageable for the complete network. The numbers of cycles obtained in these sub-models under the two growth conditions of interest are shown in 3.3The lists of cycles and their associated flux after decomposition following the algorithm of In mixotrophic conditions the unique cycle carrying the greatest mass flux involves the mitochondrial respiratory electron transport chain (mass_flux 1244). This cycle involves the continued recycling of ubiquinone (M_q8) and ubiquinol (M_q8h2), the two key components involved in facilitating the electron transport between protein complexes, to generate ATP . AssociaInspections of the residual acyclic graphs for mixotrophic growth suggest that acetate is directly transported into the mitochondrion, and ultimately branched towards the reactions of gluconeogenesis replacing the need of pyruvate. In this scenario, pyruvate was channelled into other pathways such as valine, formate and fatty acid metabolism .The unveiled cycles can feed-forward to enhance other cycles downstream in metabolism. Phototrophic cycles have shown that increased flux through pyruvate metabolism and its entry into the TCA cycle allows for the production of TCA cycle intermediates in excess, which was otherwise redundant in the mixotrophic case. Instead, cycling components of the mitochondrial electron transport chain, which are active under mixotrophic growth, are otherwise redundant in phototrophic metabolism and it is only with cyclic decomposition that these perturbations can be analysed.3.4Our next analysis considered the cycles within the thylakoid lumen. This sub-compartment of the chloroplast houses the light driven electron transport reactions of photosynthesis. We found no unique cycle associated with the mixotrophic condition but 16 cycles were unique to phototrophic growth and 4 cycles were common to both conditions. The highest scoring unique phototrophic cycle (mass_flux 0.019) involved ascorbate metabolism, consisting of the cycling of ascorbate (M_ascb) by the violaxanthin:ascorbate reductase reaction (R_VIOXANOR), resulting in the re-oxidation of ascorbate (M_dhdascb) a. This cboth photosystems to support both phototrophic and mixotrophic biomass. Furthermore, the finding that flux through part of the xanthophyll cycle was observed with phototrophic simulations is a proof of principle that cyclic decomposition can be used to unveil important biological cycles otherwise ignored by stoichiometric analysis alone.The 4 cycles common to both conditions all involved cycling of matter through the reactions of photosynthetic electron transport involving both photosystems I and II. This suggests the importance of 3.5C. reinhardtii, resulted in a vast increase in the number of unique cycles for both phototrophic and mixotrophic conditions (5099 and 1621 cycles respectively). Only 52 cycles were found to be common to both growth regimes.To gain a further understanding of cycles across compartments, we expanded the analysis to include the mitochondria, thylakoid lumen and chloroplast compartments together. The addition of the chloroplast, the largest organelle in For both conditions, the cycle carrying the greatest mass flux included the Calvin-Benson cycle a associaThe occurrence of the Calvin-Benson cycle enzyme RuBisCo (ribulose bisphosphate carboxylase) was used as an indicator of the importance of the Calvin-Benson cycle for each growth regime. Out of the 5099 unique phototrophic cycles, 5079 cycles (99.6%) contained the classic Calvin-Benson cycle enzyme RuBisCo. The cycle to carry the largest mass flux (322) was also associated with RuBisCo combined with reactions of gluconeogenesis a.Fig. 5UThere were 1062 unique mixotrophic cycles that contained RuBisCo, or 20.8% of the unique mixotrophic cycles, suggesting a reduced importance of a fully operational Calvin-Benson cycle under mixotrophic metabolism. It is apparent that cells undergoing mixotrophic growth on acetate and CO2 do exhibit a preference for acetate metabolism over CO2 sequestration, but do utilise both carbon sources nevertheless. The fact that we were able to observe ∼20% cycles involving RuBisCo is of much interest, since FBA predicted inactivity of this enzyme under mixotrophic conditions , even thThe cycle to carry the greatest mass flux for mixotrophic metabolism, when the mitochondria, thylakoid lumen and chloroplast compartment were considered, consisted of the cycling of oxaloacetate, with the involvement with a membrane transporter that transports oxaloacetate between the cytosol and mitochondria, in exchange for malate (mass flux 354). Mixotrophic analysis suggested a prominent occurrence of the oxidative pentose phosphate (OPPP) cycle in carbon metabolism. The OPPP results in the synthesis of carbon skeletons for nucleotide synthesis and provides a source of reductant in the form of NAPDH for fatty-acid synthesis. Sedoheptulose 7-phosphate (S7P) M_s7p, b is the d-glyceroyl in the chloroplast, as an indicator of glycolytic activity. Occurrence of this key glycolytic node shows a 43.1% increase associated with phototrophic metabolism  synchronised with the import of oxaloacetate, representing a 433% and 15% increase respectively. These results collectively suggest that both phototrophic and mixotrophic growth are able to channel photosynthate towards the OPPP and to metabolise pyruvate into alternative pathways as and when needed.4C. reinhardtii. The decomposition of metabolic flux into cyclic and acyclic components, as introduced by The main objective of this research was to investigate metabolic cycles occurring as a result of altering the growth conditions of the model organism 2 into triose phosphate. Triose phosphate is incorporated into metabolism by the reactions of gluconeogenesis to generate hexose sugars, which can be stored as starch reserves. In times of need, these reserves can be degraded by glycolytic pathways to release ATP. Glycolysis is compartmentalised in C. reinhardtii with the upper half occurring in the chloroplast and the lower half in the cytosol . Our analysis revealed a positive feedback loop commencing with the fixation of CO2 and culminating with an increased pyruvate cycling . NAPDH is an electron donor that provides a source of electrons in the mitochondrion to sustain electron transfer during respiration . The conC. reinhardtii (iCre1355) was recently published  and TAG accumulation are considered. Nevertheless the study we present follows on from previous work in which we are investigating why acetate represses photosynthesis, a very distinct question to one against which iCre1355 was validated. Upon closer comparison of iCR1080 and iCre1355, it appears the photosynthetic reactions of iCR1080 and iCre1355 are identical and both include the CEF reaction. This piece of research follows on from published work made with iCR1080, and as such we noted and corrected multiple erroneous reactions associated with CEF and the movement of photons from the stroma to the lumen that occurs as a result of CEF (iCr1080, as we had already made these necessary adjustments with the reactions of photosynthesis and a true representation of these reactions is critical to our study on why acetate represses photosynthesis.An updated metabolic model of ublished  which sht of CEF . Since wThere are other possible representations of metabolic networks for the enumeration of feasible paths or cycles. C. reinhardtii as an example.We have unveiled cycles that play fundamental roles in adapting to changes in environmental conditions and explain known algal physiology. We were able to show a feedback loop existing in the presence of acetate, resulting in a decrease of glycolysis and an increase in or activation of the OPPP. These changes in carbon metabolism pathways all have implications on levels of ATP and NAPDH in the cell, which ultimately feed deeper into the respiratory pathways to ultimately reduce photosynthesis. Using cyclic decomposition analysis, we have unveiled novel theoretical cycles that could not be otherwise detected. This methodology provides a stronger and more comprehensive method to analyse the metabolism of any GEM, as we have shown using The project work was conducted by SC and MTS. The project was designed and supervised by MVK, JMS and GNJ. All authors have read and approved the final manuscript.