Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems.Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes.We reduced the iJO1366 We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.The online version contains supplementary material available at 10.1186/s12859-021-04066-y. Kinetic models of cellular metabolism can provide comprehensive understanding on the dynamics of the cell and its response to environmental changes and perturbations. In depth understanding of cellular metabolism can allow metabolic engineers to tailor cells according to sought specifications and objectives. This could enable the design of cell factories where flux is directed towards the production of biofuels, pharmaceuticals or other specialty chemicals. To be useful though, a kinetic model should represent the dynamics of the cell accurately enough to provide the required study-specific knowledge . To dateWith larger and better quality kinetic models, the mathematical representations become increasingly complex. Furthermore, the parameter sensitivities of systems biology models are in general “sloppy” . We haveIt is important to preserve certain level of detail in a metabolic model in order for its predictions to be realistic. The model needs to include important carbon fluxes from the central carbon up to the biomass building blocks, which also includes the synthetic pathway of interest that we would like to engineer. The fact that energy and redox are used by the synthetic pathway of interest makes it important that we account for “all” the reactions and subsystems that carry significant energy and redox fluxes, such as a detailed tricarboxylic acid (TCA) cycle and electron transport chains (ETCs). Keeping such level of detail is essential in order to avoid making false conclusions from kinetic models.However, nowadays algorithms for reducing GEMs in a more systematic and complete manner are starting to emerge , 12–15. E. coli iJO1366 GEM to three different models, namely D1, D2 and D3, encompassing 271, 307 and 327 enzymatic reactions and 160, 188 and 197 metabolites, respectively. The thermodynamic formulation of the stoichiometric models allowed integration of fluxomics and metabolomics data for aerobically grown E. coli  from the iJO1366 GEM [. [E. coli. Reduced stoichiometric models D1, D2 and D3 inter-connect the pairs of subsystems with up to one, two and three reactions, respectively  around 6 GEM [. . D corre6 GEM [.  and captd-fructose (HEX7) and d-fructose 6-phosphate phosphatase (F6PP), that connect d-glucose with d-fructose 6-phosphate via d-fructose. D2 also includes the maltodextrin system which connects the d-glucose to d-glucose 1-phophate via the maltodextrin phosphorylase and maltodextrin glucosidase reactions. In D2, dihydroxyacetone phosphate can react to methyglyoxal, which in turn can react to d-Lactate, providing increased connectivity between glycolysis and the pyruvate node. Additionally, pyruvate can react to 2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate, which can react to form 2-oxoglutarate, thus connecting the TCA cycle with the pyruvate metabolism. D2 also includes three different ways to connect with two reactions from fumarate to l-aspartate, which—via argininosuccinate, adenylsuccinate and adenylosuccinate—further link the TCA cycle with the ETC. The adenylate kinase (ADK3), nucleoside-diphosphate kinase (NDPK1) and nucleoside-triphosphatase (NTP3) enzymes provide D2 model with additional flexibility in the system’s energy metabolism.The additional reactions in D2 include xylose isomerase (XYLI2), hexokinase d-lactate and l-lactate. Methylglyoxal is a hub metabolite that provides connectivity between upper glycolysis to the pyruvate node. The pyruvate and phosphoenolpyruvate nodes are connected to the TCA cycle via chorismate. Fruthermore, the glutamine and glutamate synthases provide additional flexibility in allowing conversion between l-glutamate and l-glutamine. In D3 the presence of AMP nucleosidase (AMPN) provides an additional connection between the PPP and the ETC. However, the expansion from D1 to D2 resulted in more central carbon metabolites that change in connectivity . To preserve equivalency across the kinetic models, it was desirable that the flux profile and the concentration vector steady-states in D2 and D3 resemble the ones selected in D1. The nested nature of the core models generated with redGEM eased the transferability of steady-states across models, allowing us to preserve similar values for fluxes and concentrations for the overlapping reactions of the three models.. [We connected the core models to the biomass building blocks (BBBs), as defined by Neidhart et al. , via lum. . A lumpeWe studied the lumped reactions in D1–3 and observed that 103 were common between the models Fig. D. D1, D2d-Alanine (DALAtex) that deviates by 8%  formulation that imposes similarity between the representative steady-states of the models (Methods). The D2 fluxes of central carbon reactions are within below one percent deviation from the reference flux of D1  of μ were ranked for D1–3 based on their absolute means across stable models. The models were compared pairwise in increasing order of size  to assess the impact of systematic network expansion on MSCs , phosphofructokinase (PFK) and ATP maintenance (ATPM) are the most consistent in terms of sign and magnitude when comparing D1 with D2 Fig. A. PyruvaWe then compared top cellular growth FCCs in D2 and D3, which are in great sign and magnitude agreement  were mostly from the central carbon metabolism Fig. . The reans Figs. . ACS is ns Figs. .Fig. 4E.l-Lactate dehydrogenase (l-LACD2), as cofactors ubiquinone-8 and ubiquinol-8 partake in both reactions. Interestingly, periplasmic glucose dehydrogen (GLCDpp), where ubiquinone-8 and ubiquinol-8 also participate, has a high DI as well. GLCDpp possibly causes its neighbouring reactions gluconokinase (GNK) and d-gluconate transport (GLCNt2rpp) to have high DIs too, due to stoichiometric coupling. These observations suggest that alterations in flux split ratios around important branching points—caused by network expansion—could result into higher DIs in reactions at their vicinities.We repeated the above analysis for D2 and D3, where we analogously compute the DIs for the 307 common enzymatic reactions (Methods). Similar observations were made for the reactions having low DIs (0–25 percentile) as most were located in central carbon metabolism, within the subsystems around which reduction was performed  when comparing D2 and D3  cover the essential biomass precursor metabolites according to Neidhart as we focused primarily on cellular growth control and, (2) that we capture the ETC essential to account for redox potentials.d-fructose 6-phosphate when expanding from D1 to D2 appeared to significantly affect growth control patterns  that gave a numerical indication on the consistency of the FCCs with respect to a reaction. Most of the reactions around central carbon metabolism, carrying a higher carbon flux, appeared to have lower DIs. Flux control for reactions with larger fluxes were more robust, particularly if the number of connecting reactions did not change between models for the metabolites participating in the reaction. The larger DIs were noted in the ETC and peripheral reactions. Nevertheless, the consistency in the control patterns of network regions that carry larger carbon flux was consistent across the reduced models when the DI was low, suggesting that their MSC-based predictions are independent of the network complexity. Thus, the DI can be used to study the structural robustness of a kinetic model.We could argue that the larger the kinetic model is, the more confident and robust the metabolic engineering decisions derived from the model are. However, using our methodology, a modeler can use the DI as an indicator of structural robustness of the system to assess the confidence and quality of the model’s metabolic engineering predictions. In this context we suggest that the experimental design should first target the steps that are consistently better targets across different sized models. Next, we should focus on identifying to what reactions the model predictions are more sensitive to in the larger models, allowing the identification of changes that are responsible for the redistribution of control across the system. Such investigation will serve as a focused analysis for modeling and experiments – understanding which new reactions and pathways, when added during size increase, impact the control distribution in the reference pathways can provide a great insight on how structural changes in a biological network change its function, beyond single enzyme over/down-regulation.Failing to preserve certain level of model complexity when constructing kinetic models can result in erroneous conclusions. Model reduction – whether ad hoc or systematic – is a necessary step when constructing kinetic models and could result in false predictions if not done appropriately. To our knowledge, we propose for the first time a workflow for systematically constructing large-scale kinetic models and tailor their size to match the requirements of the studied biological system. We suggested the deviation index as a metric that highlights differences across model MSCs and serves as an indicator in testing kinetic model size adequacy. The nested nature of the reduced models enabled the maintenance of maximum equivalence between the steady-states and kinetic parameters of the populations of kinetic models. This allowed us to assess the impact of model size on the MSC-based conclusions. We showed that despite consistent model reduction and preserving model equivalency, control coefficients can be significantly affected by network size. However, our method can be used to study and assess the adequacy of models based on control coefficients. As systematic model reduction algorithms gain momentum in the field, we hope to pave a path towards building more robust and transferable kinetic models for the community. Classical statistical model assessment tools could be additional avenues to explore when studying structural robustness of models.E. coli iJO1366 genome-scale model. The lumpGEM algorithm was used to generate lumped reactions for the biosynthesis of biomass building blocks (BBBs) for these models. We used thermodynamic-based variability analysis (TVA) [We developed a workflow for building consistently reduced kinetic models from a genome-scale metabolic model Fig. . We usedis (TVA)  to studyE. coli iJO1366 genome-scale model was reduced, with aerobic minimal media, glucose as the sole carbon source, and the selected starting subsystems corresponding to central carbon metabolism . We incorporated all the reactions that use metabolites of the quinone/quinol pools  as the electron transport chain subsystem in order to account for the energy metabolism of the system. redGEM allows the user to define a degree of connection, D, to define the level of connectivity of the core. D is an input parameter of the redGEM and lumpGEM. D corresponds to the number of reaction required to connect the pairs of metabolites between starting subsystems, as defined in [The stoichiometry of the core networks was defined with the redGEM algorithm, which reduces systematically genome-scale model reconstructions of metabolism . The E. fined in . For thefined in  was usedE. coli iJO1366 and in our reduced model. However, these metabolites, unless they are highly polar or very large, are subject to passive diffusive transport through the cell membrane. Therefore, we explicitly added transport reactions for these metabolites that operate at least at basal level (10−6 mmol/(gDW*h)).For some of intracellular metabolites, a corresponding transport reaction has not been biochemically characterized and does not appear in the https://github.com/EPFL-LCSB/redgem.redGEM and lumpGEM algorithms , 12 haveE. coli physiology as in our previous study [FBA in mid-lower glycolysis operates towards catabolism .Magnesium and phosphate transporters are both set towards uptake , 25.Acetate kinase (ACKr) and phospho-transacetylase (PTAr) operate towards acetate production, as acetate is a by-products .Succinyl-CoA synthetase (SUCOAS) operates towards producing succinate .Polyphosphate kinases  are set towards the polyphosphate polymerization .As we model the same aerobically grown us study , we makeThese directionality assumptions were made in agreement with the modeled physiology. Nevertheless, our workflow could be applied to other physiologies and under other directionality assumptions.E. coli under aerobic conditions and minimal media was integrated in our models. The MILP formulation of the thermodynamics-based flux analysis was used to implement these data into D1, D2 and D3. Since the models were used to build kinetic models, it was undesirable for reactions to be at thermodynamic equilibrium, which would result in them having equal backward and forward fluxes. We imposed MILP constraints to ensure that the thermodynamic displacement, Γ [ ≈ 1. This constraint ensures that we do not have reactions that have net fluxes equal to zero in our system.The available fluxomics and metabolomics data for the optimal growth of ement, Γ , 31, 36,https://github.com/EPFL-LCSB/matTFA and https://github.com/EPFL-LCSB/pytfa.Software used for performing thermodynamic-based flux analysis and TVA has been published  and is aWe sampled the flux space of D1 in order to characterize the solution space without violating physiological, thermodynamic and directionality constraints. The convexity of the solution space enabled us to efficiently sample using the Artificial-Centering Hit-and-Run sampler in the COBRA Toolbox , 38. We ubrxn,i and Flbrxn,i respectively, into D2. Consequently, we use an MILP formulation to minimize the number of violations of flux boundaries that we are trying to impose. For each intracellular reaction that is shared between the two models we create a binary variable zrxn, i so that when it is equal to 1, the constraints that we impose become inactive. We add for each of these reactions the following constraints:UB and LB are the upper and lower bounds of the net fluxes NF of the reactions. We minimize the sum of the binaries, zrxn, i, in order to have minimal violation of the flux constraints:In order to make the comparison of the models equitable, we wanted to maintain most similar steady-states between the models. For instance, for D2 we would like the flux vector to be the equal possible to the one from D1. Topological differences in the models make it impossible to have numerically exactly the same flux distribution in larger model for the same reactions. Hence, we take the representative flux from D1 and apply it with percentage relaxation with upper and lower bounds, FMinimize:Subject to:zrxn, i). After applying the constraints that are not violating model boundaries of D2, we proceed to sampling the solution space. We selected a sample based on mean PCA as with the representative flux of D1. We then implied in a similar manner the concentration profile from D1 into D2 with a 1% relaxation and sampled the concentration space for this flux profile. We repeat this procedure when scaling up the flux and concentration steady-states from D2 into D3.We implied a 1% relaxation to apply and test how many flux constraints we can impose without violation  derived from the summation theorem to quantify the discrepancies in control patterns of a flux between two different models. We define DI as:m enzymes of our system. However, if we perform this computation of DI for all the enzymes m, except the enzymatic reactions added to the system by a model expansion, we can obtain a deviation from zero. This happens if these enzymatic reactions that were added to the system by the expansion exhibit control over the flux v being studied. Hence, if the DI value is not zero for a model expansion, this could suggest that some of the added reactions are important in terms of control to the system.The value of DI will be zero due the summation theory of MCA if we sum over all Additional file 1. MATLAB models of D1, D2 and D3. Models used for obtaining thermodynamically feasible steady-states for constructing populations of kinetic models. Software for reading models is available on GitHub: https://github.com/EPFL-LCSB/matTFA.Additional file 2. Fluxomics and metabolomics experimental data incorporated in the model for optimally grown E. coli. Table with the flux boundaries in mmol/gDW/h and the concentration boundaries in log(mM) that were applied as constraints to the D1-3 TFA models based on the experimental data reported by McCloskey et al. Additional file 3. Thermodynamics-based variability analysis of models. Spreadsheet with the list of metabolites and reactions inside the models with variability analysis of flux and metabolite concentrations. The lumped reactions generated with the redGEM and lumpGEM algorithms begin with LMPD. Reactions added to the E. coli iJO1366 for passive diffusive transport start with TransFlux. The rest of the reactions come from E. coli iJO1366.Additional file 4. Document with additional figures, details about methods and further analysis carried out in this study. Thermodynamic-based variability analysis was performed to study the feasible ranges of metabolic flux across central carbon reactions for D1-3 models. Flux control coefficients for other reactions were analyzed to see how they are affected by model complexity. Mathematical formulations of the kinetic mechanisms describing the reactions in the kinetic models are also provided.Additional file 5. Flux and concentration steady states. Spreadsheet providing metabolic flux and concentration reference steady-states across the models with a comparative study of these steady states between pairs of models. Additional file 6. Analysis of absolute deviations in means of flux control coefficients for the entire systems. The mean flux control coefficient v and the horizontal to the enzymes p. A large deviation indicates a discrepancy in the prediction of the model for that given control coefficient Additional file 7. Kinetic mechanism allocation. Reaction mechanisms assigned for the reactions of D1, D2 and D3 models. The assigned reaction mechanisms in this file refer to the kinetic mechanisms for which a mathematical description is given in Additional file 4.


Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run.Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users’ production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead .https://github.com/chang88ye/NIHBA.Source code implemented in the MATALAB Cobratoolbox is freely available at math4neu@gmail.com or natalio.krasnogor@ncl.ac.ukBioinformatics online. With the advance of genome-scale metabolic modelling (GSMM), the past decades have witnessed a significant number of computational tools for microbial metabolic engineering  so as to be solved by exact MILP solvers. It can take up to a week to solve a MILP resulting from a medium-sized GSMM  introducin silico infeasible. Model reformulation may alleviate numeric issues and potentially reduce computational costs. However, a proper model reformulation is often time-consuming and laborious as extra care has to be taken to prevent other numeric difficulties while fixing one.In company with intensive computations, the resulting MILP often has weak LP relaxations due to disjunctive big-M constraints , anotherThe third limitation is that only a single solution is obtained in each execution of optimization by modern solvers. Multiple runs are required to generate more solutions, which inevitably increases computational burden. Despite that some commercial software platforms, such as Gurobi and CPLEX, provide options to preserve multiple solutions in a single run, many alternative solutions exist for the optimal production rate in underdetermined metabolic systems, and they are likely to have similar production envelopes  identification have also been developed to remove all possible design strategies that do not meet specific requirements of a desired production strain. MCS-basEvolutionary game theory for metabolic modelling has achieved great success, especially in situations not governed by simple optimization , for theMore often than not, wild-type strains maintain homoeostasis and thus avoid overproducing a product of interest while maximizing biomass . MetabolWe proposed a NI model for identifying gene-associated reaction knockouts, but up-/down-regulation of genes can be considered in this model as well. The NI model is a special case of bilevel optimization. It was recast into a standard MILP problem  using a Our case studies investigate the production of both native biochemicals (i.e. succinate and ethanol) closely linked to energy metabolism and a non-native secondary metabolism product (i.e. lycopene).cn) of carbons are not considered as knockout candidates since they are assumed unlikely to carry high flux. We tested different values of cn = 100 indicates no reaction removed due to carbons), resulting in different sizes of candidate set (see sn in NIHBA was tested on iML1515 (l-lactate dehydrogenase (LDH_L) under anaerobic condition (where the oxygen uptake rate is zero) predicts high succinate production by our method  by knocking down only reactions of low carbon number. Therefore, excluding as many reactions of high carbon number is beneficial for computational efficiency while achieving a high-production design strategy. The number of growth-coupled solutions for ethanol is, however, much fewer than that for succinate.Next, we analyse the performance of NIHBA on a varying number of knockouts. We observe that, for both succinate and ethanol, the production rate increases sharply when more knockouts are allowed but levels out from five knockouts . The numd-lactate dehydrogenase (LDH_D) appear frequently as knockout targets in design solutions for both products, and high succinate-producing strategies additionally disfavour ATP synthase (ATPS4rpp) (We show in TPS4rpp) . SimilarThe computational cost is low for NIHBA, as shown in A heterologous lycopene biosynthesis pathway, as reported in The minimization of metabolic adjustment (MOMA)  between host cells and metabolic engineers. This framework assumes that host cells have a few objectives. These objectives are not necessarily to optimality individually but reach a trade-off between them. In this sense, NIHBA is different from traditional FBA approaches which often require a single biologically rigorous optimality objective, such as optimal growth or maximum energy generation  and v is subject to thermodynamic constraints:jlb and jub are the lower and upper flux bounds of reaction j, respectively. c is a weight vector specifying the degree of importance to the biological objective.A metabolic network of Pc is a coefficient vector for the target biochemical. That is, Pc is a vector of zeros except for the P-th element  which is set to one. K is the maximum allowable number of knockouts), and jy indicates the reaction j is inactive (jv = 0) if jy = 1 and active otherwise. J, containing candidate knockout reactions.NI for strain design considers metabolic engineers as interdictors or adversaries who attempt to maximally disrupt host cells’ activity that biochemicals of interest are not overproduced due to homoeostasis. The strain design task can, therefore, be formulated as a max–min problem:jy, i.e. jM is a large positive Lagrange multiplier and jM is sufficiently large for all jv = 0 when jy = 1. The value of jM used in this work is around 100 .Observing that in the follower problem The reformulated follower function (3a) can be linearized by adding auxiliary variables U is a vector of maximum absolute flux for each reaction, i.e. The bi-level problem (4) is reformulated to a standard MILP by applying LP duality to the follower problem (4b–4c). For simplicity, the resulting MILP is written in the following compact formThe single-level reformulation (5) can be solved, like OptKnock, by modern MILP solvers. However, the big-M terms in (5) lead to a week LP relaxation , therefoO and F are sets that correspond to the extreme points oπ and extreme rays fπ of the dual of SP, respectively. In each iteration, the Benders decomposition algorithm derives the dual vector π from the SP (8) for oπ is added to the MP; if it is unbounded, it means the SP is infeasible, then a feasibility cut fπ is added to the MP to avoid unboundedness of the dual of SP in future iterations.Benders decomposition avoids these drawbacks as it can deal with complicating binary variables and easy continuous variables separately. Like Benders decomposition , our HBAThe classic Benders decomposition is not able to generate effective Benders cuts rapidly for our strain design problem, and therefore, requires a huge of iterations (consequently long computation time) before it converges. Here, we introduce an HBA with two strategies to speed up the convergence process.https://github.com/chang88ye/NIHBA.oπ be the dual vector of π corresponding to (8), a standard Benders optimality cut is:Let oπ may not be unique, it is important to select an effective cut μ is a sufficiently positive value and Y. In this paper, μ is not calculated as in Since Y, the idea behind local branching is to divide the feasible region of (7) into two subregions by the Hamming distance between y and Another technique we used for accelerating the convergence of Benders decomposition is local branching, which is particularly effective when problems have binary variables . This leads to two scenarios: there is either a feasible or infeasible y in the subregion r may be too small, and r. r is then increased by one at a time until In every iteration of Benders decomposition, the MP (7) is solved in the subregion HBA involves solving the MP (7) and SP (8) in a repeated manner. For efficiency, the following two strategies are used:iter is the iteration counter) is reached.Terminating the MP program prior to optimality. Suboptimal solutions to the MP are sufficient to generate valid Benders cuts. Therefore, the MP is terminated when a MIP Gap of Reversing local branching whenever the a priori knowledge that, for example, they are vital for cell growth or their knockout is not likely to improve target production. We followed the model reduction procedure by E.coli GEM iML1515 . Likewise, many reactions can be excluded from consideration with ® CoreTM i5 Quad Core processor.First of all, all the NI models were transformed into MILPs using duality theory  with botThe data and software used and the tool developed are all available online:GEM model: iML1515 from BIGG database (bigg.ucsd.edu).https://opencobra.github.io/).Simulation software: Cobra toolbox 3.0  for funding project ‘Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)’. N.K. was funded by a Royal Academy of Engineering Chair in Emerging Technology award.Conflict of Interest: none declared.btaa163_Supplementary_DataClick here for additional data file.


Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5–200 μmol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry.Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the composition of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of  Diatoms are unicellular photosynthetic eukaryotes derived from a secondary endosymbiotic event when a heterotrophic eukaryote engulfed a red algal cell and acquired a plastid . They ap2 reduction by the Calvin-Benson-Bassham cycle -[KRH]-[LM]>” and “S-S-L>” [et al. [The protein localization pipeline developed by Levering, et al.  was upda TargetP  was repl TargetP , a plastnalP 4.1 , TargetPnalP 4.1 , HECTAR nalP 4.1 , MitopronalP 4.1 , ASAFindnalP 4.1 , predictnalP 4.1 , and scanalP 4.1 . Predict“S-S-L>”  and the “S-S-L>” ,48 and t [et al.  because Synechococcus elongatus model iJB785 [T. pseudonana. The pigment weight and composition [a/c binding proteins (FCPs) to chlorophyll a in the reaction centers. Photon absorption was constrained for each wavelength using the absorption spectrum of T. pseudonana cells acclimated to different light levels [et al. [iJB785. Photodamage of the D1 subunit was included as a component of the PSII reaction [Using the l iJB785  as a guiposition ,51 of ceposition ,53 to cat levels –56 and t [et al. . We alsoreaction ,57, and reaction  and as Adx) over time (dt) is equal to the stoichiometric matrix (S) describing a reaction network times a vector of fluxes (v), given thatFlux balance analysis (FBA) simulates the flow of metabolites through a network of reactions using the mass balance equation,v to be solved while maximizing an objective function within lower and upper bounds .Intracellular metabolites are assumed to be at steady state, which allows We used parsimonious Flux Balance Analysis (pFBA) to generate single solutions for each simulation. pFBA optimizes the model objective and then minimizes the total sum of flux to obtain a single solution. The second objective imitates a possible cellular objective of minimizing protein biosynthesis. The default GLPK solver in COBRApy  was usediTps1432 in three chemostats maintained at three different light levels  using photosynthetic production measurements from Fisher & Halsey [3, 0.050 mM PO4, 0.106 mM Si(OH)4, 28.8 mM SO4), the chemostat cell concentration, the gram dry weight per cell calculated from biomass composition, and the chemostat dilution rate. Cultures were continuously bubbled to avoid carbon limitation; we assumed the ambient CO2 levels to be 390 ppm pCO2 and calculated the resulting concentrations of CO2 and HCO3 in the media [T. pseudonana for CO2 and HCO3 [Ps and α are parameters of the hyperbolic tangent)GPP) curve generated by Fisher and Halsey , and 2 gDW-1) [T. pseudonana [Photon absorption flux (PFA) was constrained for each 20 nm in the PAR spectral range using the method published in Broddrick, et al. ,PFA=LBλ2 gDW-1) –56. The eudonana  was usedeudonana  were use18O2 signals in the light and the dark and includes all light-driven respiration reactions (the reduction of oxygen to water). We constrained the sum of all respiration reactions with a symbolic expression in which the lower bound is equal to the measured light dependent respiration values. Mitochondrial maintenance respiration is defined as the difference between gross carbon production and net oxygen production (converted to C units) and includes all organic carbon driven respiration reactions. We constrained the sum of all CO2 producing dehydrogenase reactions in the mitochondrial TCA cycle with a symbolic expression in which the lower and upper bounds are equal to the 95% confidence intervals of calculated mitochondrial maintenance respiration values. A Jupyter notebook of this analysis can be viewed at https://github.com/hmvantol/diatom-FBA-notebook.For these simulations, sink reactions for chrysolaminarin and glyceraldehyde-3-phosphate were added to the model to simulate the respiration of a transient carbon pool. Utilization of these pools was calculated as the difference between gross and net carbon production . MeasureT. pseudonana from mid-exponential under NO3-limitation to mid-stationary phase under N-starvation using biomass composition and PSII photochemistry measurements from Liefer et al. [-2 s-1 over a 12h: 12h light: dark cycle at 18°C in filtered seawater amended with half the concentration of all f/2 nutrients except for NO3, which was reduced by ~15 fold to 60 μM. At mid-exponential phase, the cells were diluted into fresh media with no added NO3 [et al. [3, 0 μM Si(OH)4, and 1.86 μM PO4. This assumption fit well with most nutrient measurements made 1 day after transfer to the new media and with estimates of nutrient concentrations in surface seawater at Cape Tormentine (Canada) from the World Ocean Atlas 4, ~2 μM PO4).A dynamic FBA simulation was set up with the solver tolerance set to 1e-8 to simulate the progression in a batch culture of r et al. ,26. Priodded NO3 ,26 and m [et al. . NutrienTo simulate the ten-day experiment, the growth period was divided into eighty 3-hour intervals. Steady-state was assumed for each time interval and pFBA was used to solve for the growth rate and reaction flux distributions every 3 hours. Units were converted from mmol to μmol prior to the pFBA step to avoid numerical precision issues associated with small fluxes. Each time point of the simulation involved four major steps: (1) calculate the biomass objective function, (2) calculate the uptake rates for each metabolite or nutrient and set photosynthetic constraints, (3) solve the pFBA problem, (4) update biomass composition and the environment.bof) used for each time interval was determined by calculating the relative difference between the simulated biomass composition at the current time point (t1) and the next biomass composition measurement (tm) provided by J. Liefer.i is translated from pg/cell (bioi(tm)′) into g/L (bioi(tm)) using cell abundance measurements (c(t2)), and typically f = 1. During the dark period, pigment biosynthesis was not included in the biomass objective function since T. pseudonana lacks a light-independent protochlorophyllide oxidoreductase [f to the fold-change values measured by Jallet, et al. in P. tricornutum grown across a 12: 12-h light: dark cycle [bioi components are < 0, the objective function was switched to the ATP maintenance reaction (ATPM). At each time point, the upper bound of ATPM was re-calculated for changes in mg chl a/gDW using the rNGAM equation [a, and total carotenoid) were also measured over the course of the experiment [The biomass objective function  biomass (bio(t)) per time period Δt, whichever is closer to zero.Using the method described by Chiu et al.  and publ [et al. , we deteT. pseudonana for CO2, HCO3 [3 [4 [4 [Vmax was set to 0.2 and Km was set to 0.05 for all other uptake reactions, with the exception of H2O and H+ which were assumed to diffuse freely and Vmax was set to 1000. The biomass-dependent lower bound assumes that iTps1432 exists in a well-mixed environment where it can sense nutrient-availability and adjust its uptake rate accordingly [Nutrient uptake rates were calculated using Michaelis-Menten parameters from the literature on O2, HCO3 , NO3 [70O3 [3 [4 , and PO4[3 [4 [4  (S3 Dataordingly .a) measured on days 0, 1, 3, 7, and 10 of the experiment  by balancing the equation for the oxidation of new biomass to NO3 or NH4 with the chempy package [2, HCO3, and CO2 was re-equilibrated with the atmosphere so that concentrations remained constant (well-mixed). We included a constraint for photorespiration where the oxygenase flux of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) ranges from 0.001–0.025 the carboxylase flux.PSII flux was calculated from fast repetition rate fluorometry (FRRf) parameters and culture data  ratios. Photon absorption flux was constrained for each time point as before, assuming a light absorption spectrum for cells acclimated to medium light [For each time point, constraints were converted from mmol mg chl um light  and the a, chitin, polyphosphate, chrysolaminarin, protein amino acids, free amino acids, RNA, and triacyglycerides by including sink reactions for each of these components. The availability of each component was calculated as follows if Δbioj is < 0.We allowed for the mobilization of the biomass components chlorophyll a is not characterized in diatoms, but chlorophyll a degradation was required to approximate the mg chl a gDW-1 factor. A demand reaction for chlorophyll a was included in the simulation and its lower bound was set to -1*Δbioj.The degradation pathway for chlorophyll et al. [bio(t2) for the next time interval Δt using the exponential growth equation,vDM_biomass_c. Next we calculated the fraction of new biomass attributed to each component using the biomass objective function. To calculate the concentration of each metabolite xj in the next time interval Δt,vj is the flux of each metabolite, and bio(t1) is the biomass density or sum of all components at the current time point. Scripts for this simulation can be obtained from https://github.com/hmvantol/diatom-dFBA.We adapted the method described by Chiu et al.  to calcuT. pseudonana CCMP 1335 (iTps1432) was generated using as a framework the Phaeodactylum tricornutum (CCAP 1055/1) models iLB1027_lipid [iLB1034 [T. pseudonana (iThaps987) designed to explore production of industrially useful compounds [iLB1027_lipid and iLB1034 and their relevance to iTps1432 are listed in iLB1027_lipid has more reactions than iLB1034 due to the inclusion of detailed lipid metabolism. Additional elements incorporated into the T. pseudonana iTps1432 model include changes in the subcellular localization of reactions based on protein targeting sequences, improvements in modeled light absorption, and the inclusion of known metabolic differences between the diatoms. Several blocked reactions were resolved in iTps1432, and the number of dead-end metabolites in the network was reduced when compared to the P. tricornutum models. The T. pseudonana model contains 6,073 reactions that represent a network of 2,789 metabolites and 1,432 genes, approximately 10–12% of the T. pseudonana genome  were accurately localized in the final model, 9 of which were manually curated  [iTps1432 to reflect known differences in lipid composition [We also extended the e (DHPS) ,81. Lipiposition ,67. We iposition . If geneposition . Followiposition , all newiTps1432 was inclusion of a mechanistic model of photon absorption and electron transfer by fucoxanthin chlorophyll a/c binding proteins (FCPs). We included reactions that describe energy transfer efficiency from excited pigments to chlorophyll a in the photosystems, and pigment de-excitation reactions to dissipate excess energy as heat or fluorescence , triacylglycerides, chitin, chrysolaminarin, osmolytes, a silica frustule, polyphosphate, and a soluble pool of vitamins and cofactors that together define the total biomass of T. pseudonana under a particular environmental condition. Biomass reactions were developed for T. pseudonana cells acclimated to three different light levels . Bulk biomass composition  and chlorophyll a concentration was measured in cells grown in chemostats maintained at 18°C under 5, 60 and 200 μmol photons m-2 s-1 [-2 s-1 [-2 s-1 [T. pseudonana [-2 s-1) or a power law relationship under N-limiting conditions (200 μmol photons m-2 s-1) [A critical step in creating genome-scale metabolic models is development of accurate biomass production reactions in which precursor metabolites are converted into the cellular components that comprise the millimolar contribution to 1 g dry weight (gDW) of cell mass under specific growth conditions . In iTps m-2 s-1 . Pigment [-2 s-1  and from [-2 s-1 . The remeudonana  and was m-2 s-1) . Calcula-2 s-1 [The biomass composition also impacts the modeled photon absorption rate. Photon absorption integrated over the Photosynthetically Active Radiation  spectrum in 20 nm units was calculated from whole cell absorption spectra for cells acclimated to 25, 40–60, and 250 μmol photons m-2 s-1 –56, as w-2 s-1 ,53 and pT. pseudonana under different conditions and thus potential growth and acclimation strategies. The resulting total cell dry weights . Similarly, the silica frustule was the greatest component of biomass at the lowest light intensity  were most abundant at 200 μmol photons m-2 s-1 ..T. pseudm-2 s-1  using our biomass objective functions and production measurements  from chemostats maintained at the same light levels [2 = 0.94) between ATP utilization and growth rate allowed us to estimate the non-growth associated maintenance (rNGAM = -27 ± 52 SE) costs as the y-intercept and the growth associated maintenance (rGAM = 3809 ± 1221 SE) costs as the slope  was calculated for each chemostat using the compensation light level (Ic), which is the light intensity at which photosynthesis is equal to respiration and the growth rate is zero [achl a is the chlorophyll a specific absorption coefficient (m2 mg Chl a-1), r is the ratio of chlorophyll a to gram dry weight per cell (mg Chl a-1 gDW-1), ϕm is the quantum efficiency of photosynthesis (mol O2 mol photon-1), and e is the amount of ATP generated per oxygen. The parameter Ik was estimated by fitting Chalker equation 1 [NPP) curve generated by Fisher and Halsey  for each chemostat .The theoretical upper bound of non-growth associated maintenance . The maximal total electron flow through a cell acclimated to medium light was estimated by setting the level of light exposure to ~2000 μmol photons m-2 s-1 (saturating). PSII, oxygen exchange, and bicarbonate exchange reactions were constrained by the 95% confidence intervals of gross and net photosynthesis and carbon uptake, respectively, measured for cells acclimated to 60 μmol photons m-2 s-1 and exposed to 1952 μmol photons m-2 s-1 [2 molecule evolved [ϕm) of 0.056 mol O2 per photon [Cyclic electron flow (CEF) and respiration both consume the reduced ferredoxin and downstream equivalents generated via linear electron flow (LEF), driving ATP generation and increased flux through PSII  and thusl et al.  proposedl et al.  light re m-2 s-1 . The rat evolved , using ar photon  (S3 Datab6/f: CBFC2). The pFBA simulation calculated 0.38 mmol gDW-1 h-1 as an upper bound of CEF and we used this value in subsequent simulations. CEF is relatively insensitive to short-term changes in light intensity and appears similar in a variety of diatom species [-2 s-1), CEF is an insignificant fractional component of total electron flow (TEF) in T. pseudonana; at the lower light intensity of ~100 μmol photon m-2 s-1, CEF corresponds to about half of TEF  is the sum of electron flux through PSI and PSII  minus the linear flow from PSII through to PSI (represented by cytochrome  species . At the iTps1432 at three different light levels [c oxidase); nitrogen assimilation ; sulfate assimilation ; carbon assimilation and biosynthesis of reduced metabolites  [T. pseudonana cell division is not synchronized [The allocation of photosynthetic energy to biomass production and respiration was quantified using pFBA with t levels , constraabolites . Some pate (G3P) . Additiohronized ). The sihronized .-2 s-1, nitrate reduction is a proportionally more important component of biomass production than at other light levels because of the high protein content of cells acclimated to low light .First, we noticed that photorespiration was not a part of the original pFBA prediction and that it could be an important sink for reductants in photosynthetic organisms. Photorespiratory flux, or the specificity of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) for COpression vcvo=VcKsfloggii  because t 100 μM . The con measure . We therm of 65 μM [2 concentrations are likely higher than estimated because diatoms are not carbon-limited, and O2 concentrations are thought to be lower  Thus, these simulations may overestimate the significance of photorespiration.Photorespiration was constrained with the following symbolic expression, oxidase . For a Kof 65 μM , pyrenoiIn diatoms, a major component of redox balance is the flow of reducing equivalents from the plastid to the mitochondria , based oT. pseudonana and how it may change with acclimation to different light intensities, so the value of this constraint (NADHOR_m/PSI_u = 0.0015) was chosen for exploratory purposes. Flow of reductants from the plastid activates the mitochondrial alternative oxidase reaction in iTps1432  is the closest condition examined in the previous simulation. Biomass composition and macro-nutrient concentrations were determined experimentally at 0, 1, 3, 7, and 10 days after the start of the experiment. Measurements of biomass C, N, and P were made more frequently at 0, 1, 2, 3, 5, 7, and 10 days after the start of the experiment. Liefer, et al. [The previous simulations highlighted the importance of nitrate reduction as an electron sink under steady-state conditions. We next explored the impact of nitrogen limitation and starvation on reductant flow using dynamic Flux Balance Analysis (dFBA) to simulate growth in batch culture where cells are not in steady-state. Throughout the simulation, we tracked simulated nutrient and excreted metabolite concentrations in the media as well as molecular and elemental biomass composition. The simulations relied on experimental data ,26 derivT. pseudonana uses up protein and accumulates carbohydrates and lipids; there is also accumulation of DNA as cells stop actively dividing, a decrease in RNA and pigments per cell, and accumulation of residual P likely corresponding to polyphosphate storage  . As nitrstorage , Fig 4a)T. pseudoposition . Small dposition . Neverth3 (and NO2) in the experimental media were depleted sometime between 1 and 3 days after the initiation of the experiment; low levels of background NH4 in the media were depleted after 1 day. Simulated NO3 was completely depleted from the media shortly after 1 day of growth whereas simulated NH4 was released into the media concurrent with the NO3 depletion and was subsequently taken up and re-excreted into the media between the different target days. The simulated PO4 concentration in the media matched the observed gradual decrease over time 4 concentrations were simultaneously drawn down with the NO3 during the first day and then plateaued at ~10 μM. The simulated drawdown of Si(OH)4 instead plateaued at ~25 μM shortly after 1 day in culture. This difference likely reflects errors in the weight of the cell frustule used in the biomass objective function. This value was not experimentally measured and so the value used for the biomass objective functions was extrapolated from the difference between dry weight measurements and the sum of other biomass components  to see how this assumption impacts metabolite secretion . Conversely, formate production is highest when respiration is high . Similar3 depletion . APS reduction to SO3 consumes a reduced thioredoxin, SO3 reductase is plastid localized and consumes 6 reduced ferredoxin to produce H2S. SO4 reduction to sulfide becomes more prevalent immediately after NO3 is depleted and results in DMSP excretion  and use of chemostat production measurements to constrain flux for different light intensities ; so we may be underestimating its importance at these light levels due to the poorly constrained values of growth-associated ATP maintenance, and given the contribution of CEF to energy generation.Photosynthetic organisms have evolved a variety of mechanisms to deal with the primary challenge of photosynthesis: how to capture light energy while evading potential damage. Over longer time periods, diatoms adjust their pigment composition to deal with incoming light energy, but short-term fluctuations in light intensity with inadequate sinks for electrons could theoretically cause a plastid to become over-reduced and damaged. Energy dissipation as heat or fluorescence  is one potential mechanism, summarized in periment . Our FBAperiment . The absperiment . We used [et al. , CEF makc oxidase is activated by the respiration of transient organic carbon pools by the mitochondrial TCA cycle. Some of the transient organic carbon that was calculated to be available (as the difference between gross and net carbon production measurements) is directly utilized, as evidenced by the decreased use of the Calvin cycle at 200 μmol photon m-2 s-1  may impiTps1432 reduces ATP demand by limiting flux through the TCA cycle and instead secretes ethanol and formate  or dimethyl sulfide (DMS) [Most of the metabolites secreted by n source , and bacn source . Many ban source . Alphaprde (DMS) . Differede (DMS) .Thalassiosira pseudonana CCMP 1335 was isolated from Moriches Bay, New York, in 1958, and the whole genome was sequenced in 2004 [T. pseudonana has been studied from a systems-wide perspective using transcriptomics, proteomics, and metabolomics –125Thalaing work , incorpoT. pseudonana under steady-state light limited conditions and found that cyclic electron flow plays an important role in generating energy and balancing redox under low-light conditions, while the role of energetic coupling between plastids and mitochondria is minimal. We also found that nitrate reduction plays a critical role in dissipating reductants in the plastid under nutrient-replete conditions, while sulfate reduction replaces nitrate assimilation during non-steady-state simulations of nitrate-limitation and N-starvation. The character and quantity of metabolites secreted during our simulations depends on the conditions of redox balance, the relationship between photosynthesis and respiration, and biomass re-modeling.We simulated the metabolism of iTps1432 incorporates a large body of experimental and genetic knowledge. We caution, however, that our predictions also rely on a few network reactions for which there is little or no experimental or genetic evidence. Many enzymes are promiscuous, catalyzing a wider variety of substrates than represented here, and errors in annotation are possible. iTps1432 should be considered a preliminary version of a metabolic network that will improve with further curation and expansion as research on this diatom continues. iTps1432 could be extended in a variety of directions in the future. Reactions describing complex formation for metal- and cofactor-requiring proteins could be added to the model to better describe vitamin and trace metal utilization, as trace metal limitation is an important nutrient condition in the ocean [iTps1432, could allow us to integrate molecular data with models of ocean biogeochemistry in the future.he ocean  and vitahe ocean ,127. AddS1 Fig(PDF)Click here for additional data file.S2 Fig(a) Fermentative metabolism, (b) Chitin synthesis and hydrolysis.(SVG)Click here for additional data file.S3 Figx error bars represent the 95% confidence interval of measured dilution rates, and the y error bars follow from the 95% confidence intervals of bulk biomass measurements .The (PDF)Click here for additional data file.S4 Figa /gDW, and (P) comparison of simulated elemental composition versus measured composition over the course of the experiment .The best and worst  estimates of biomass composition were marked for each biomass component. (A) Bulk biomass components, (B) RNA nucleotides, (C) Protein amino acids, (D) Free amino acids, (E) EPS sugars, (F) Pigment molecules, (G) sulfolipids, (H) phosphtidylcholines, (I) phosphatidylethanolamines, (J) phosphatidylglycerols, (K) diacylglycerides, (L) monogalactosyldiacylglycerides, (M) digalactosyldiacylglycerides, (N) triacylglycerides, (O) mg chl (ZIP)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Dataset(XLS)Click here for additional data file.S2 Dataset(XLSX)Click here for additional data file.S3 Dataset(XLSX)Click here for additional data file.S4 Dataset(XLSX)Click here for additional data file.S1 Note(DOCX)Click here for additional data file.S1 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S2 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S3 FileLevel 3, Version 1, fbc ver. 2.(XML)Click here for additional data file.S4 File(DOCX)Click here for additional data file.


Using a metabolic flux analysis with a 1-compartment mathematical model of brain metabolism, we report that compared to hippocampus, hypothalamus shows higher levels of aerobic glycolysis associated with a marked gamma-aminobutyric acid-ergic (GABAergic) and astrocytic metabolic dependence. In addition, our analysis suggests a higher rate of ATP production in hypothalamus that is accompanied by an excess of cytosolic nicotinamide adenine dinucleotide (NADH) production that does not fuel mitochondria via the malate-aspartate shuttle (MAS). In conclusion, our results reveal significant metabolic differences, which might be attributable to respective cell populations or functional features of both structures.Glucose is a major energy fuel for the brain, however, less is known about specificities of its metabolism in distinct cerebral areas. Here we examined the regional differences in glucose utilization between the hypothalamus and hippocampus using in vivo indirect  The brain is well known to be a particularly energy-demanding organ , however13C) MRS allows the investigation of metabolism with cell-specific information through dynamic assessment of metabolic fluxes in vivo upon administration of a 13C-labeled substrate [1H-[13C]-MRS) with 13C-labeled substrates such as glucose  [13C6]glucose ([U-13C6]Glc) Glc)  or [U-133C6]Glc) . Because1H-[13C]-MRS upon infusion of Glc. By combining an updated mathematical model of brain metabolism that includes GABAergic contribution and cerebral metabolic rate of glucose (CMRGlc), we have been able to assess simultaneously glycolytic and mitochondrial contributions non-invasively. We report that hypothalamus has higher rates of ATP synthesis and undergoes higher levels of aerobic glycolysis than hippocampus, possibly related to its glucose sensing properties. Better characterizing and understanding region-specific metabolic properties will help further our understanding on how metabolic diseases or stress may affect cerebral substructures differently.In this study, we have investigated for in vivo differences in the energy metabolic function of hippocampus and hypothalamus in mice using 13C-coupled 1H resonances such as GluC4, GlnC4, GlxC3, GlxC2, GABAC2 and GABAC3 were all clearly observed after ~1 h of Glc infusion , Tau  and Asp  were found to be higher, while Lac  was lower in hippocampus as compared to hypothalamus. Antioxidant Asc level was higher  in hippocampus, while GSH was comparable (n.s.). Importantly, neurotransmitter metabolites Glu and Gln were not found to be different (n.s.), while GABA levels were lower  in hippocampus. The membrane-degradation metabolite GPC was lower in hippocampus , while no difference in membrane-precursor PCho was seen (n.s.). Finally, the glia-specific myo-Ins metabolite was lower in hippocampus .The neurochemical profile was quantified from the non-edited spectra, as described in the methods, and revealed important metabolic differences between hypothalamus and hippocampus . Energy-2 = 0.973; hippocampus, R2 = 0.994) and is shown in Glc; hypo: 0.47 ± 0.06 vs. hippo: 0.29 ± 0.02 μmol/g/min; p < 0.0001) was found to be higher in hypothalamus than hippocampus, while tricarboxylic acid (TCA) cycle  and neurotransmission flux were comparable . Accordingly, the relative higher glycolytic activity of hypothalamus led to an apparent increase in lactate efflux  and a decreased influx  from blood lactate. Although total mitochondrial oxidative metabolism  appeared to be lower in hippocampus (−16%), the transmitochondrial flux , reflecting malate-aspartate shuttle (MAS) flux, and glial AcCoA dilution flux , associated with ketogenic substrate uptake and glial dilution, were both increased in this structure as compared to hypothalamus. Nevertheless, this did not appear to be directly related to a difference in glial metabolism as the pyruvate carboxylase flux was low in both regions . As expected from the higher GABAergic neuron population of hypothalamus, the GABA-Glu cycle  was found higher in this structure, but not the GABA dilution flux .The fitting of metabolite enrichment time courses with a 1-compartment model of glucose metabolism  led to c1H-[13C]-MRS upon infusion of Glc. By comparing both regions during a similar experimental protocol and using a simple model of brain metabolism that considers GABAergic contribution with minimal assumptions, we have been able to estimate the cerebral metabolic rate of glucose without addition of 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) measurement. We report that under our experimental conditions, the hypothalamus shows a higher rate of glycolytic activity, as compared to the hippocampus that appears to rely more on oxidative metabolism , that corresponds to the labeling exchange between two distinct GABA pools, provides a more accurate means of fitting the GABA labeling curves -MRS study that compares neuroenergetics of hippocampus and hypothalamus in vivo. The distinct neurochemical concentration characteristics . Although the transmitochondrial flux was comparable ) was found to be slightly higher in hypothalamus compared to hippocampus here, we observed higher mitochondria-related metabolites  and transmitochondrial flux (Vx) in the hippocampus, suggesting an important mitochondrial dependence for energy metabolism. Little is known about region-specificities in mitochondrial function, and only mitochondrial mass has been compared between hippocampus and hypothalamus, suggesting higher content in the latter -MRS protocol does not allow the quantification of ATP synthesis rate, however, by assuming a similar ATP yield for both structures  -MRS at 14.1T upon infusion of Glc enables detection of region-specific differences in mitochondrial and glycolytic metabolism in vivo. Comparison of the hippocampus and hypothalamus metabolism under similar experimental and analytical conditions allowed to identify a higher level of aerobic glycolysis in the hypothalamus, which might be associated to its particular glucose sensing properties and/or specific cellular population densities.Several factors are likely to influence brain metabolic fluxes, such as differences in the protocol  and analysis . However, our approach aimed at minimizing these effects by comparing both structures under similar experimental and analytical conditions. Nevertheless, anesthetics, and in particular isoflurane are known to affect neuronal electrical and metabolic activity, and the resulting metabolic state observed, even though studied in similar conditions, might be influenced by the respective neuronal/glial populations. For instance, we cannot exclude that the high level of glycolytic activity in hypothalamus is merely a consequence of a higher impact of isoflurane on its metabolic/electric activity. Nevertheless, this potential “differential response” to isoflurane between brain structures might by itself provide important metabolic functional information. We conclude that n = 8) or the hypothalamus (n = 6) after a 10h-long fasting period (glycemia at 7 ± 1 mM). First, a femoral vein was cannulated for infusion of a 20% glucose solution of Glc . This solution was first administered as a bolus , and secondly at a constant infusion rate  during the rest of the experiment Glc infusion leads to pyruvate C3 labeling from glycolysis in the brain (CMRGlc) and enters mitochondrial TCA cycle (VTCA), leading to aspartate and glutamate labeling through the transmitochondrial flux (Vx). In parallel, the absence of pyruvate C2 labeling, specific to the use of Glc, dilutes the C3 position of oxaloacetate (OAA), through the action of glia-specific pyruvate carboxylase (VPC). Consequently, anaplerotic balance is maintained with an efflux of extra glutamine from glial cells (Veff). After neurotransmitter release, glutamate is recycled in astrocytes by conversion into glutamine and recycled back into neurons for replenishing glutamate (VNT). In inhibitory neurons, GABA is synthesized from glutamate and recycled through the TCA cycle (VGABA). Dilution of GABA occurs in inhibitory neurons through the exchange between two metabolite pools attributed to the effect of both GAD isoforms (Vexi). Brain lactate is in exchange with plasma lactate through an inward flux (Vdilin) and outward flux (Vdilout), while AcCoA labeling is influenced by plasma acetate and other dilution processes (Vdilg). A schematic description of the model is presented on 13C-labeling curves, i.e., GlcC6, LacC3, GluC4, GlnC4, GlxC3 , GlxC2 , GABAC2 and GABAC3, were fitted in the proposed model with MATLAB  using a standard built-in ordinary differential equation solver with a modified Levenberg-Marquardt nonlinear weighed regression method. GlxC2 labeling curves were corrected to account for their high correlated quantification with GlcC6, as described in Lizarbe et al. [13C metabolite resonance were used for weighing in the regression cost function. Monte-Carlo simulations  were used to evaluate the fluxes precision and providing a probability distribution [A single-compartment model of brain glucose metabolism was adapted from Lizarbe et al.  and modie et al. . Glucoseribution .t-test, with a significance threshold set at p < 0.05 and no correction for multiple comparison. Group comparisons between metabolite fluxes of the two regions was done using a permutation test analysis with 2000 random permutations, followed by individual unpaired two-tailed Student’s t-test, as previously described [Differences in metabolic profile of both regions were assessed using unpaired Student’s escribed . Statist


Saccharomyces cerevisiae, based on the System Dynamics approach, a general process-based model for two prokaryotic species of biotechnological interest, such as Escherichia coli and Bacillus subtilis, is proposed. The model is based on the main assumption that glycolytic intermediates act as central catabolic hub regulating the shift between respiratory and fermentative pathways. Furthermore, the description of a mixed fermentation with secondary by-products, characteristic of bacterial metabolism, is explicitly considered. The model also represents the inhibitory effect on growth and metabolism of self-produced toxic compounds relevant in assessing the late phases of high-cell density culture. Model simulations reproduced data from experiments reported in the literature with different strains of non-recombinant and recombinant E. coli and B. subtilis cultured in both batch and fed-batch reactors. The proposed model, based on simple biological assumptions, is able to describe the main dynamics of two microbial species of relevant biotechnological interest. It demonstrates that a reductionist System Dynamics approach to formulate simplified macro-kinetic models can provide a robust representation of cell growth and accumulation in the medium of fermentation by-products.The metabolic shift between respiration and fermentation at high glucose concentration is a widespread phenomenon in microbial world, and it is relevant for the biotechnological exploitation of microbial cell factories, affecting the achievement of high-cell-densities in bioreactors. Starting from a model already developed for the yeast  Glucose is the main carbon and energy source for microbial metabolism. Glucose uptake supplies the glycolytic process producing different intermediates, with pyruvate representing a central catabolic hub, followed by the respiratory or fermentative pathway, depending on oxygen availability.Respiration is able to maximize ATP production and consequently biomass yield. However, despite the fully aerobic conditions, in several microbial species when glucose concentration is high, the respiratory metabolism is replaced by a fermentative one, which produces partially oxidized products .S. cerevisiae, others are Crabtree-negative (S. cerevisiae GAL4) in Komagataella phaffii results in a switch of the Crabtree phenotype from negative to positive with an increase in specific glucose uptake , glycolysis intermediates from glucose-6-phosphate to pyruvate (P), acetate produced by fermentation (A), cellular components produced by either fermentation or respiration (CM), reserve compounds (R), growth-associated inhibitory by-products (I), and dead cells (D).The resulting model is composed of a set of 7 ordinary differential equations representing glucose in the growth medium (G) is provided according to the feeding conditions of the bioreactor described for the simulated experiment. Glucose is assimilated by microbial cells, and then converted into the different intermediate products of glycolysis, from glucose-6-phosphate to pyruvate (P). These are used for the construction of new cellular material (CM), either through respiration or fermentation. In the case of fermentation, acetate (A) is the main end-product, which can also be used as carbon source for the respiratory pathway when glucose is limiting.Glucose (P) in the regulation of the metabolic shift between respiration/fermentation and, in general, cell metabolism. Therefore, high levels of P are assumed to be responsible for (i) the activation of aerobic fermentation due to overflow metabolism, (ii) the repression of respiration (“glucose effect”), (iii) the accumulation of reserve materials (R), and (iv) the induction of mortality with accumulation of dead cells (D)  .Moreover, considering that acetate is not the only end-product of bacterial fermentations even in aerobic conditions , in the I), as already described in The model also considers growth-associated inhibitory by-products  with a variable order solver (ode15s). The model calibration was performed by minimizing the sum of the squared errors (SSE)1, n2, n3 are the number of samples per observed outputs, CMi, G_i, A_i, are the values of the ith measured outputs and ith outputs predicted by the model. The minimization was performed by using the fminsearch MATLAB routine which implements a Nelder–Mead simplex algorithm  perturbation on the model outputs; Xj(P) represents the simulation values of the state variables Microbial mass, Glucose and Acetate without any parameter perturbation; max(Xj(p))−min(Xj(p)) is the standardization factor referring to the values of the baseline simulation; k is the number of parameters.where, E. coli among the most used in biotechnological applications, namely W3110 . The possible metabolic shift occurred according to the value of the specific growth rate of the population, determined by the specific feeding rate (SFR) applied to the bioreactor during the fed-batch phase .E. coli W3110, performed by MAX) of 0.31 h–1, presents the typical exponential growth behavior of an E. coli population growing on glucose and displaying a fermentative metabolism with acetate production. When glucose is completely depleted in the medium, fermentative metabolism is replaced by a respiratory one, with a short period of acetate consumption by respiration observed during the batch phase. Then, an exponential increasing feeding regime is activated (after 13 h from the beginning of the experiment) at a Specific Feeding Rate (SFR) value of 0.22 h–1, equal to the population specific growth rate, μ. This value is high enough (75% μMAX) to switch back the population to a fermentative metabolism with accumulation of acetate up to 0.3 g l–1. Three hours later (16 h from the beginning of the experiment), a constant feeding is applied to the bioreactor beginning at a SFR value of 0.11h–1, so that the bacterial cells are allowed to display a respiratory metabolism with no accumulation of acetate. During this phase, however, three glucose pulses are performed inducing temporary acetate production , starting at 0.11 h–1. In the first phase of exponential feeding, the simulated microbial population follows the observed growth corresponding to the feeding regime, while in the second phase (constant feeding) the model properly describes the declining growth rate due to self-produced inhibitory compounds. In turn, the reduced growth rate compared to the glucose feeding induces the metabolic switch reactivating acetate production. In the second simulated experiment  are below the threshold value for acetate production reported for the strain  at the end of the simulated experiment, and it is characterized by a continuous, although very low, production of acetate. vG and Gη) affected all strains (E. coli W3110 appeared to be sensitive to the fermentation process (vF and FAη); E. coli TG1 showed higher responsiveness to the secretion rate of inhibitory compounds (ρ) and the respiration parameters (vR and RPη); B. subtilis showed to specifically respond to the metabolic shift between respiration and fermentation (b1) and the efficiency of biomass production by fermentation (FPη).The last simulation presented in  strains , reflectEscherichia coli strains, as well as of Bacillus subtilis growing both in batch and fed-batch cultures on glucose as carbon as energy source. The highly significant agreement between experimental data and simulations obtained for different microbial species and strains demonstrates how the process-based System Dynamics approach, already followed in the case of the yeast Saccharomyces cerevisiae . The obtainment of the desired products  by the recombinant strains was achieved by shifting the temperature to 42°C or by addition of 0.5 mM IPTG after 22 h fed-batch phase. In both cases, induction of the products did not affect the dynamics of growth, in terms of glucose consumption and acetate formation, as shown by the simulations.This essential assumption characterizing the presented model proved indispensable to predict the occurrence of the metabolic shift between respiration and fermentation in two species of prevalent biotechnological interest such as tal data . TherefoB. subtilis (–1), a limited production of acetate was observed during all the culture run, showing a less clear-cut metabolic shift for this microbial species. This is also reflected by the high sensitivity of this strain to the parameter related to the metabolic switch (b1). A further assumption of the proposed model is related to glucose transporters which have been considered as constant within each microbial strain. Clearly, this is a strong simplification since it is known that transporters can be modulated according to glucose availability in the media and future specific studies could address this point more in depth.Noteworthy, in the case of subtilis , even thS. cerevisiae model presented by Moreover, in the previous S. cerevisiae in aerobic fed-batch cultures and later extended to describe the fermentation dynamics of E. coli by E. coli. Our proposed model has a similar level of simplification compared to the abovementioned works, although it was designed to be more general in order to reproduce different microbial species and strains rather than being applied to a single one. Moreover, our model is the only one considering the inhibitory effect on growth and metabolism of self-produced toxic compounds different from fermentation products which is relevant to reproduce the late phases of high cell-density cultures when this phenomenon becomes significant in limiting the growth rate. The phenomenon of self-toxicity regulating cell proliferation is evident only in prolonged fed-batch cultures, and it was clearly pointed out in the case of both wild-type and auxotrophic yeast strains cultured in fed-batch reactors   . For thiE. coli, show a detailed description of the metabolic pathways . On this point, Following a completely different approach, metabolic flux analysis models specifically focused on the central carbon metabolism of ys e.g., . RecentlInstead, our modeling procedure directly aims at the identification of a minimal number of processes sufficient to simulate the emergent properties of a complex system (top-down approach) and it is based on logical reasoning on existing knowledge of the system. Then, in this work only those variables relevant for the growth of microbial populations on glucose and their metabolic shift were mathematically described, whereas the many secondary pathways, also directly or indirectly affecting the selected variables, were not taken into account. In summary, our approach is based on the principle of parsimony and has the advantage of keeping the mathematical formulation simple and robust with a reduced number of parameters.S. cerevisiae 90032-q, https://doi.org/10.1016/0168-1656(94)00143-z, https://doi.org/10.1016/0168-1656(94)90215-1, https://doi.org/10.1007/s00449-004-0391-z, and https://doi.org/10.1021/bp034348k.Publicly available datasets were analyzed in this study. This data can be found here: SM, FC, CV, and FG coordinated the model design and its implementation in System Dynamics language. FC, FG, AO, CV, and EP performed the numerical simulations and model calibration. The manuscript was written by FC, EA, and SM. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cyanobacteria, given their ability to produce various secondary metabolites utilizing solar energy and carbon dioxide, are a potential platform for sustainable production of biochemicals. Until now, conventional metabolic engineering approaches have been applied to various cyanobacterial species for enhanced production of industrially valued compounds, including secondary metabolites and non-natural biochemicals. However, the shortage of understanding of cyanobacterial metabolic and regulatory networks for atmospheric carbon fixation to biochemical production and the lack of available engineering tools limit the potential of cyanobacteria for industrial applications. Recently, to overcome the limitations, synthetic biology tools and systems biology approaches such as genome-scale modeling based on diverse omics data have been applied to cyanobacteria. This review covers the synthetic and systems biology approaches for advanced metabolic engineering of cyanobacteria. A. AAnabaetivities . In addiadiation .PKS and NRPS are representatives of enzymes responsible for the biosynthesis of secondary metabolites in various organisms. Enzymes of these classes consists of at least three essential modular domains that facilitate chain elongation and modification . First, M. producens, and lyngbyabellin from M. bouillonii  enzymes [M. viridis, is a serine protease inhibitor, and patellamide A produced by Prochloron didemni has moderate cytotoxicity [RiPP is a class of secondary metabolites that includes, as its name depicts, ribosomally synthesized and post-translationally modified peptides. Post-translational modifications include leader peptide hydrolysis, cyclization, and disulfide bond formation. RiPP BGC generally consists of a short precursor peptide with an  enzymes ,88. The  enzymes . For exatoxicity .M. producens and Lyngbya semiplena, respectively  pathway to biosynthesize diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) from CO2. DMAPP is converted to farnesyl diphosphate (FPP), a substrate for squalene biosynthesis, by FPP synthase (ispA). Heterogenous genes, including 1-deoxy-d-xylulose-5-phosphate synthase (dxs), isopentenyl diphosphate isomerase (idi), and ispA were introduced into the S. elongatus PCC 7942 genome by homologous recombination to increase the intracellular concentration of FPP. Next, a squalene synthase (SQS) was also introduced by homologous recombination, resulting in a maximum of 5.0 mg/L/OD730nm squalene production [730nm by constructing a fusion protein of SQS with cpcB1, which encodes the β-subunit of phycocyanin and is highly expressed under the strong endogenous cpcB1 promoter [S. elongatus PCC 7942 strain to repress two essential genes, acnB and cpcB2 encoding aconitase and phycocyanin β-subunit, respectively, resulting in an improved squalene production [Genetic manipulation tools explained above have been applied in cyanobacteria to increase the production of secondary metabolites . In receutumnale ,102. HowPCC 7942 ,104,105.oduction . The titPhormidium autumnale [S. elongatus PCC 7942 [S. elongatus PCC 7942 has the methylerythritol phosphate (MEP) pathway to biosynthesize diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) from CO2. DMAPP is converted to farnesyl diphosphate (FPP), a substrate for squalene biosynthesis, by FPP synthase (ispA). Heterogenous genes, including 1-deoxy-d-xylulose-5-phosphate synthase (dxs), isopentenyl diphosphate isomerase (idi), and ispA were introduced into the S. elongatus PCC 7942 genome by homologous recombination to increase the intracellular concentration of FPP. Next, a squalene synthase (SQS) was also introduced by homologous recombination, resulting in a maximum of 5.0 mg/L/OD730nm squalene production [730nm by constructing a fusion protein of SQS with cpcB1, which encodes the β-subunit of phycocyanin and is highly expressed under the strong endogenous cpcB1 promoter [S. elongatus PCC 7942 strain to repress two essential genes, acnB and cpcB2 encoding aconitase and phycocyanin β-subunit, respectively, resulting in an improved squalene production [Genetic manipulation tools explained above have been applied in cyanobacteria to increase the production of secondary metabolites . In receutumnale ,102. HowPCC 7942 ,104,105.oduction . The titSynechocystis sp. PCC 6803 [crtW) and β-carotene hydroxylase (crtZ) were integrated for astaxanthin production. The promoter combinations with diverse strength were tested for expressions of crtW and crtZ, because their relative expression level is known to be critical to produce astaxanthin in E. coli [crtZ expression and a pea promoter PsbA for crtW expression, which showed two-fold higher activity than Pcpc560, resulting in improved production of astaxanthin. Further, based on liquid chromatography-mass spectrometry (LC-MS) metabolomics data, fructose-1,6-/sedoheptulose 1,7-bisphosphate (FBP/SBPase), which is involved in the Calvin–Benson–Bassham cycle, was found as an additional engineering target and overexpressed with episomal expression vector. Then, heterologous dxs and ispA gene was introduced into Synechocystis sp. PCC 6803 genome, and the engineered strain was finally able to produce astaxanthin of 29.6 mg/g dry cell weight, the highest level in the currently known engineered strain.Besides terpenes, a xanthophyll carotenoid called astaxanthin has been gaining significant attention in the healthcare field owing to the anti-inflammatory and antioxidant properties elucidated in human cells . AstaxanPCC 6803 . First,  E. coli . HoweverS. elongatus PCC 7942 as a host for producing 2,3-butanediol, a biochemical building block for plasticizers, liquid fuel additives, and industrial solvents [galP), glucose-6-phosphate dehydrogenase (zwf), and 6-phosphogluconate dehydrogenase (gnd) into neutral sites, and phosphoribulokinase (prk) and RuBisCO subunits (rbcLXS) into the cp12 site, resulting in a maximum production of 12.6 g/L of 2,3-butanediol. In addition, in Synechocystis sp. PCC 6803, metabolic engineering was performed to enhance the biofuel production [pdc)-inserted ethanol-producing Synechocystis sp. PCC 6803 strain, pyruvate dehydrogenase complex subunit (odhB) was repressed by CRISPRi. Repression of citrate synthase (gltA) in the phosphoketolase (xfpk) and acetoacetyl-CoA synthase (nphT7)-inserted n-butanol-producing strain increased N-butanol production.Along with the naturally produced cyanobacterial secondary metabolites, cyanobacteria also serve as an attractive platform for diverse heterologous biochemical production. For instance, cyanobacterial genome engineering was performed to use solvents . In thisoduction . To impr2. On introducing the isoprene synthase (ispS) obtained from various plants into the Synechocystis sp. PCC 6803 genome by homologous recombination, a maximum of 4.3 mg/L/h isoprene production rate was achieved with the aid of dxs and idi overexpression [ispS, and as a result, expression of Eucalyptus globulus ispS with Ptrc promoter showed the highest isoprene production. As demonstrated in various studies, cyanobacteria can produce industrially valuable biomaterials by utilizing light and CO2. Therefore, cyanobacteria have the potential to be developed into an eco-friendly and economical photoautotrophic biofactory.Isoprene is a plant-derived building block, mainly used in the manufacturing of synthetic rubber. While most of the current synthetic rubber production depends on the petrochemical source, there have been efforts made to increase the cyanobacterial production of isoprene directly from COThe value-added biochemicals that we have discussed are all products of photosynthesis. Thus, enhancement of the photosynthetic efficiency is crucial for supplying sufficient energy and reducing power for productivity increment. Some of the strategies for improving photosynthesis include expansion of the absorption spectra to capture more light energy, downsizing of the antenna to increase high illumination tolerance, and optimization of the electron transport chain. Furthermore, efficient use of photosystem-generated energy is another strategy that can be achieved by enhancing carbon fixation or reducing carbon loss .E. coli or Saccharomyces cerevisiae. In the case of E. coli, which is most widely used engineering host with a lot of information about genetic features and metabolic network, it is easy to apply knowledge-based engineering approaches such as enzyme structure modification, feedback inhibition removal, and precursor pool or cofactor level increasement [E. coli. On the other hand, since cyanobacteria is a photoautotroph, more complex energy generation and distribution, and redox state should be considered when manipulating the metabolic network, and thus a more systematic insight is required. Additionally, when engineering multiple targets in the metabolic pathway, it is difficult to fine-tune the relative expression levels of the genes due to the lack of available bioparts such as neutral site, promoter, and RBS. In order to overcome these limitations, it is essential to systematically understand the complex metabolic network within the cell and to develop various genetic tools by genome-scale screening of native promoters and RBSs or constructing synthetic bioparts. In recent studies, a systematic approach through genome-scale modeling (GEM) has been successfully applied to engineering cyanobacteria [Until now, we have presented studies showing the potential of cyanobacteria in producing various metabolites and that continuous engineering efforts can enhance the native and non-native metabolite production from cyanobacteria. However, despite the proposed and demonstrated potential as a production host, the production levels in cyanobacteria are not compatible with those in model organisms such as easement . In addibacteria ,125. ForSynthetic biology involves development of genetic parts, combination design to fulfill the desired function, and application of the combined tool into an organism. Quantification and standardization of the genetic parts represented by promoters, RBS, untranslated region (UTR) sequences, and terminator sequences are critical for proper employment of synthetic biology. Systems biology deals with the living system as an interactive network more than just a collection of reductive components. Therefore, understanding of the organism as a system is required for precise designing of the synthetic biology tools, and the introduction of synthetic biology tools into an organism affects the system, making the two biological approaches inseparable. The general synthetic and systems biology research flow is represented as the design–build–test–learn cycle . In the E. coli), genetic parts such as promoters and RBS with varying strengths are available, there has been a significant lack of information and diversity concerning the cyanobacterial genetic parts. Currently used genetic parts are cyanobacterial endogenous promoters (PpsbA and Pcpc) and E. coli origin promoters  [T7 derivatives, PpsbA* derivatives, Ptrc-based hybrid, and synthetic promoters have been developed and tested in Synechocystis sp. PCC 6803 [psbA and Pcpc of S. elongatus PCC 7942, were generated to achieve promoters of varying strength [S. elongatus strains, expanding the cyanobacterial synthetic biology toolbox. The RBSs for cyanobacterial gene expression are mostly wild-type RBS associated with native promoter, or RBS of highly expressed genes such as psbA2 and rbcL [Synechocystis sp. PCC 6803 genome sequence (RBS*) are also used in several studies [Synechocystis sp. PCC 6803 were additionally identified, and 13 RBSs were rationally designed based on the known strong RBS sequences [Development of the genetic parts is critical for applying synthetic biology to metabolic engineering of cyanobacteria. While in other model species  . Promoted PnrsB) . HoweverPCC 6803 ,142. Recstrength . A colleand rbcL . In addi studies . Recentlequences .S. elongatus PCC 7942, the operation of the synthetic theophylline riboswitch confirmed that it could control translation initiation [Leptolyngbya sp. BL 0902, Nostoc sp. 7120, and Synechocystis sp. WHSyn [Bacillus subtilis phuE (adenine riboswitch). The chimeric riboswitch was validated in Nostoc sp. 7120 [Riboswitches are another class of bioparts operated based on their RNA structures. A riboswitch comprises of an aptamer and an expression part. While the aptamer directly binds to a corresponding small molecule, the expression part regulates gene expression post-transcriptionally by causing structural changes in accordance with the small molecule binding. In itiation . Recentlitiation . The thep. WHSyn . The thesp. 7120 . OverallSynechocystis sp. PCC 6803 and S. elongatus PCC 7942, are completely assembled. In particular, the complete genome sequence of Synechocystis sp. PCC 6803 was reported as early as E. coli genome sequence, thus settling as a model organism among cyanobacteria [Synechocystis sp. PCC 6803, S. elongatus PCC 7942, and Nostoc sp. 7120 [Euhalothece living in a hypersaline habitat, various salt resistance-related genes, such as Na+ transporting multiple resistance and pH adaptation systems, and glycine betaine biosynthesis enzymes were highly upregulated [S. elongatus UTEX 2973 and two types of Fischerella strains, which were used to elucidate the differences in transcriptional regulation resulting in different phenotypes [Synechocystis sp. PCC 6803 under carbon starvation condition was reported in 2018 [13C isotopically nonstationary metabolic flux analysis have been implemented [Approximately 1500 cyanobacterial genome sequences have been registered in the NCBI genome database, and 196 of them, including the genome sequences of bacteria . Since tegulated . In addienotypes ,154. Add in 2018 . In ordelemented . The masSynechocystis sp. PCC 6803 has been studied most extensively, starting with the central carbon metabolic reconstruction under heterotrophic, mixotrophic, or autotrophic conditions [Synechocystis sp. PCC 6803 GEM was recently updated with more detailed photosynthesis and electron transport chain data [Arthrospira platensis, Cyanothece sp., Nostoc sp., S. elongatus UTEX 2973, S. elongatus PCC 7942, and Synechococcus sp., are being established and further enhanced [GEM is an in silico tool that is useful for explaining the entire metabolism of an organism based on its genomic information. Given the ability to predict cellular metabolic behavior under given conditions or constraints, GEMs are mainly used for designing metabolic engineering strategies. GEM is an iteratively evolving system mended by new input information starting from the prior draft. Thus, more precise GEMs can be constructed by gathering greater types and amounts of genome information. GEM has been witnessing significant advancements with continuous accumulation of massive omics data supplied by various NGS techniques. Among cyanobacteria, nditions ,158. Theain data ,160. GEMenhanced . ThroughCyanobacteria have significant industrial value owing to their ability to generate energy from photosynthesis and to produce various secondary metabolites. However, several improvements are required for cyanobacteria to meet the industry-level expectations and to establish themselves as a potential bioproduction platform. First, when using cyanobacterial native promoter or RBS, unexpected interaction may occur within the cell, which may reduce engineering efficiency. Therefore, development and application of the variety of orthogonal tools for engineering cyanobacteria is crucial. In addition, it is essential to obtain the precise metabolic network information to design strategies for the concise use of the synthetic biology tools. For example, the optimal production conditions can be discovered through promoter and RBS combination randomization, and the kind of neutral sites that can be used for chromosome integration can be expanded based on essential gene information found with transposon mutagenesis ,211,212.