Recently, some in vitro engineering approaches were proposed as alternative strategies to solve this problem. In brief, by reconstituting a biosynthetic pathway in a cell-free environment with the supplement of cofactors and substrates, the performance of each biosynthetic pathway could be evaluated and optimized systematically. Several value-added products, including chemicals, nutraceuticals, and drug precursors, have been biosynthesized as proof-of-concept demonstrations of in vitro metabolic engineering. This mini-review summarizes the recent progresses on the emerging topic of in vitro metabolic engineering and comments on the potential application of cell-free technology to speed up the “design-build-test” cycles of biomanufacturing.With the breakthroughs in biomolecular engineering and synthetic biology, many valuable biologically active compound and commodity chemicals have been successfully manufactured using cell-based approaches in the past decade. However, because of the high complexity of cell metabolism, the identification and optimization of rate-limiting metabolic pathways for improving the product yield is often difficult, which represents a significant and unavoidable barrier of traditional  This has become an attractive, sustainable way to produce molecules e.g., H2 production from glucose and water) and prohibit them from achieving the theoretical yield of commodity chemicals.As high-valued products, biologically active compound is one kind of the most attractive engineering targets nowadays because many of them demonstrate important pharmacological activities or biotechnological significance Escherichia coli and Saccharomyces cerevisiae, which are well studied and equipped with a broad spectrum of biomolecular tools to allow metabolic engineering easily, the effects of heterologous expression of pathways are often unpredictable to guarantee a high productivity, as witnessed in metabolic engineering of S. cerevisiae to produce n-butanol E. coliThe unsatisfactory results of large-scale biomanufacturing of high-value products and commodity chemicals are largely due to two challenges: complex cell-wide regulation of metabolic pathways, and difficulty in balancing biosynthesis of target products and innate cell physiology. First, a lot of organisms are difficult to be engineered because of unknown regulation patterns and the lack of engineering tools for non-model organisms in vivo metabolic engineering, especially in its potentials of speeding up the DBT cycles in vitro metabolic engineering approaches: cell-free synthetic enzyme engineering and cell-free protein synthesis (CFPS)-based metabolic engineering  enzymes and the acyl carrier protein (ACP) in the natural E. coli host, and purifying the enzymes to homogeneity. Upon supplementing the ten protein species with acetyl-CoA, malonyl-CoA and NADPH, C14-C18 fatty acids were observed in the system, evidenced by 14C-isotope incorporation experiments and subsequently via UV-spectrophotometry in vitro. Basically, in order to develop a route to synthesize the jet fuel farnesene, Zhu and colleagues reconstituted the mevalonate (MVA) pathway in a cell-free synthetic enzyme system in vitro by expressing and purifying eight enzymes of the MVA pathway as well as the α-farnesene synthase from an E. coli host As a powerful method to investigate natural enzymes and metabolisms, some remarkable achievements have been reported. One remarkable example is the study of the bacterial fatty acid synthases. Although being investigated extensively at the genetic and enzymatic level, it is still not easy to manipulate enhanced production of specific fatty acids because of the complex cell-wide regulation of fatty acid synthesis. In 2010, Liu et al. revealed the strong dependence of fatty acid synthesis on malonyl-CoA availability and several important phenomena in fatty acid synthesis by a quantitative investigation of the fatty acid biosynthesis and regulation in a cell-free synthetic enzyme system in vitro. The biosynthetic pathways of dhurrin, which plays an important role in plant defense against pathogens in vitro using enzymes from the natural host organism Sorghum bicolor lysates. It was found that the microsomal environment could allow functional expression of catalytically active CYP79 and P450ox, and thus dhurrin synthesis was observed by radioactive TLC analysis when combining the three enzymes with 14C-tyrosine, UDP-glucose, and NADPH. In another study, camalexin pathway was constructed in vitro by purifying three enzymes: CYP79B2, which catalyzes decarboxylation and N-hydroxylation of tryptophan to indole-3-acetaldoxamine (IAOx); a second P450 enzyme, which was previously unknown and is believed to catalyze an oxidative coupling of cysteine to IAOx; and CYP71A15, which decarboxylates and cyclizes the resulting cysteine-indole-3-acetonitrile (Cys-IAN) compound to form the thiazole ring structure within camalexin. By using a combination of gene expression data and protein sequence analysis, Klein and coworkers were able to identify a P450 enzyme capable of performing the C–S coupling reaction and to reconstitute the entire camalexin pathway in vitro for the first time In addition to bacterial pathways, some eukaryotic pathways were also reconstituted 2.2in vivo systems 2 was 11.2 mol per mole of anhydroglucose unit of cellobiose, corresponding to 93.1% of the theoretical yields. This yield was more than 2 times higher than the yield from microbial fermentations which is limited to 4 H2 per mole of glucose in vitro non-natural, ATP balanced pathway for n-butanol production from glucose n-butanol production from two acetyl-CoAs. As a result, one molecule of glucose was able to produce one molecule of n-butanol, two molecules of CO2 and one molecule of water. Recently, Opgenorth et al. described a robust, efficient synthetic glucose breakdown pathway and implemented it to produce bioplastic PHB 2. Because the PBG pathway generated more reducing equivalents than are needed to produce PHB (4 NADPH per glucose produced but only 1 NADPH needed), the authors designed a NAD(P)H purge valve regulatory nodes which composed of a mixture of dehydrogenases to prevent the buildup of NADPH. Reactions were initiated with 60.7 glucose and continuously monitored in 10-h cycles by absorbance at 600 nm. It was observed that by the end of the third cycle, the reaction stopped by the depletion of glucose with a production of 57 ± 6 mM PHB , corresponding to a 94% yield. When reactions were initiated with 109.2 mM glucose, the system maintained > 50% of the maximum activity over the entire 55 h run at room temperature and generated 93.8 ± 6.1 mM PHB, corresponding to an 86% yield. The high yield emphasized the importance of cofactor recycling for SyPaB system. Compared to the microbial production of PHB using Cupriavidus necatorPerhaps a more advanced and systematic application of cell-free synthetic enzyme engineering, especially for reconstituting long biosynthetic pathways that involves a large number of enzymes for chemical production purposes in vivo cell metabolism, the relative simplicity of in vitro biological systems makes them far easier to simulate processes and predict optimal enzyme ratios for maximizing product yield and accelerating volumetric productivity. This simplicity could be concluded into five aspects: 1) it is free of complex transcriptional or translational regulations; 2) lower background noises in the defined system; 3) accurate measurements of metabolic components; 4) better defined model parameters, and 5) smaller modeling scales compared to in vivo systems. With the development of high-speed computers and the accumulation of huge biological data, numerous computational tools have been developed to simulate the in vivo cell metabolism and to facilitate the design of in vivo metabolic engineering e.g., metabolites, mRNA, and peptides) and are solved by a set of differential equations with defined kinetic parameters of biological reactions or processes in vivo metabolic engineering with tremendous success for rational design of the host cell in vitro synthetic systems, even with the fact that in vitro synthetic systems could be easier and more precisely described via kinetic models compared to in vivo systems 2 and CO2, by estimating the kinetic parameters with the best fitting of experimental data e.g., E. coli) based on the advantages of kinetic modeling in cell-free synthetic enzyme systems i.e., product-induced feedback regulation) and the complex allosteric machinery . Recently, a forward design method has been reported to establish an in vitro glycolysis biological process, which constituted of 10 enzymes In order to further understand and predict the performance of biological systems, computational modeling has been commonly applied in vivo metabolic engineering in speed, simplicity, and easiness of manipulation. However, there are still several drawbacks associated with cell-free synthetic enzyme engineering such as SyPaB. For example, in order to get the purified enzymes, researchers still need to spend numerous time and effort in plasmids construction, expression optimization and protein purification. Also, SyPaB was assembled in a complete artificial manner, which could lead to the instability of certain purified enzymes and coenzymes in vivo metabolic engineering. The cell-free synthetic enzyme system itself, on the other hand, is arguably difficult in being scaled up for biomanufacturing In summary, cell-free synthetic enzyme engineering is advantageous to 3in vivo protein expression and purification steps could be bypassed in the latter, which further speed up the DBT cycles. After decades of improving, current CFPS is well established, which could yield 200–2300 mg/mL protein in the batch mode reaction in vitro with the use of cell-free lysates and mixing multiple crude lysates to initiate the DBT cycle. A panel of cell-free lysates are selectively enriched and prepared in parallel, in each of which a target enzyme is overexpressed by using CFPS technology. Their cell-free lysates were next mixed in a combinatorial manner to construct a mevalonate biosynthetic pathway involved in isoprenoid synthesis n-butanol biosynthesis E. coli lysates could support a highly active 17-step CoA-dependent n-butanol pathway derived from Clostridia metabolism involving CoA intermediates in vitron-butanol synthesis, another E. coli enzyme (AtoB) converts acetyl-CoA to acetoacetyl-CoA, and heterologous enzymes  convert acetoacetyl-CoA to n-butanol. It was found that by adding both NAD and CoA with glucose to initiate n-butanol synthesis, the cell-free system could produce 1.2 g/L n-butanol. In order to improve pathway performance, the researchers replaced some of initial Ter and AdhE enzymes with a variety of homologs. In less than a day, they studied 4 Ter and 3 AdhE homologs by using CFPS-based metabolic engineering framework. Also they demonstrated the possibility of using linear DNA templates  instead of plasmids for pathway prototyping , which would further expedite the process as the laborious cloning steps could be avoided. Finally, the n-butanol production was improved by 200% of the initial starting conditions (up to 1.5 g/L) by optimizing the performance of different enzymes' sets and adjusting the physicochemical environment.A key difference between the cell-free synthetic enzyme engineering and the CFPS-based metabolic engineering is that the laborious i.e., cell free protein synthesis and the SyPaB. In this case, it is possible to combine a CFPS model with the SyPaB models that are described in previous section to simulate and predict the performance of CFPS-based metabolic engineering. In spite of the unknown kinetic parameters of CFPS systems and the unclear composition of cell lysates in vitro protein synthesis and enabled the investigation of the polysome dynamics under the non-steady-state and non-continuum conditions e.g., peptide chain elongation Currently, no computational modeling approach has been reported to model the CFPS-based metabolic engineering framework e.g., transcription factors) by using the kinetic parameters with constant values e.g., CFPS experimental designs and properties of target proteins) and outputs , the trained algorithm can predict the outputs from system inputs with high accuracy. Although CFPS is already simplified from the in vivo protein synthesis, it still has highly non-linear regulations and large-dimensional impact factors for the protein production E. coli CFPS kits with fixed basic reaction system. Next, the authors chose 11 variable components in CFPS system and specified a vector of values for each component to build up a space of possible experiments. By using a robotic workstation for liquid handling, a larger number of CFPS experiments were implemented in a high-throughput manner When using kinetic models to simulate CFPS process, one of the major limitations is the ignorance of the transcriptional and translational regulations  used in the cell-free system, the large-scale biomanufacturing is too expensive even when producing high-value products. Additionally, when using cell-free synthetic enzyme engineering, the stability of the enzymes could cause the reduced productivity during biosynthesis. Nevertheless, novel strategies from synthetic biology and protein engineering are being developed to address both challenges. For example, Caschera et al. have coupled polyphosphate and maltodextrin for bypassing substrate level phosphorylation based on expensive energy sources (phosphoenolpyruvic acid (PEP) and 3-PGA) in vivo expression levels in vitro metabolic engineering remains a challenge to be demonstrated, a milestone of the scale-up of CFPS has been achieved to expression complex high valued proteins in a 100 L reactor in vitro optimized pathway back into the host cells might be a future direction to address this scale-up problem. However, issues of lethality, toxicity of some metabolic intermediates and the compartmentalization of some pathways in the eukaryotic organisms should be aware during this process and might need additional DBT cycles to further improve the productivity. Meanwhile, to simulate and guide the design of in vitro metabolic engineering, data-driven algorithms  represent promising approaches, especially with the fast and high-throughput biological measurements of experimental data in vitro metabolic engineering have been implemented and published, there is no database that curates these studies in a standardized manner, which obstructs the development of data-driven algorithms. The construction of such database requires both time and labors. However, it is still feasible to construct large-scale database including thousands of datasets in three to five years. With sufficient experimental data and appropriate data-driven algorithms, the internal complex interactions in the in vitro biological systems could be captured and explicitly elucidated in near future. It is worth noting that, biased data for training the data-driven algorithm will mislead the data-driven models. Therefore, using the equally distributed data to train the data-driven models is necessary to derive a data-driven algorithm with high prediction accuracy. To conclude, in vitro metabolic engineering, although still being on the infant stage, has great potentials in speeding up the DBT cycles of biomanufacturing and serves as an alternative approach to in vivo metabolic engineering.Compared to traditional


Scheffersomyces stipitis naturally produces ethanol from xylose, however reaching high ethanol yields is strongly dependent on aeration conditions. It has been reported that changes in the availability of NAD(H/+) cofactors can improve fermentation in some microorganisms. In this work genome-scale metabolic modeling and phenotypic phase plane analysis were used to characterize metabolic response on a range of uptake rates. Sensitivity analysis was used to assess the effect of ARC on ethanol production indicating that modifying ARC by inhibiting the respiratory chain ethanol production can be improved. It was shown experimentally in batch culture using Rotenone as an inhibitor of the mitochondrial NADH dehydrogenase complex I (CINADH), increasing ethanol yield by 18%. Furthermore, trajectories for uptakes rates, specific productivity and specific growth rate were determined by modeling the batch culture, to calculate ARC associated to the addition of CINADH inhibitor. Results showed that the increment in ethanol production via respiratory inhibition is due to excess in ARC, which generates an increase in ethanol production. Thus ethanol production improvement could be predicted by a change in ARC.The yeast  Scheffersomyces stipitis [S. stipitis is Crabtree-negative [S. stipitis, the yield of ethanol from xylose decreases with high oxygen supply and increases at low oxygen supply, nonetheless, productivity could drop in the latter case [Xylose fermentation to ethanol is essential to achieve economically viable biofuel production from lignocellulosic biomass –5. Schefstipitis  is a xylstipitis , 8, transtipitis , proteomstipitis  and genostipitis , 12. S. negative , 14 whicnegative –17. In Ster case , 19. HerS. stipitis is able to grow the equivalent of just one doubling time before growth and ethanol production stop [S. stipitis are in a narrow range of oxygen concentrations, so that an effective control of oxygen supply is required [S. stipitis has the proton translocating NADH dehydrogenase complex I (NDH1) and an alternative oxidase (AOX) in the respiratory chain, both absent in the Crabtree-positive yeast Saccharomyces cerevisiae [S. stipitis a higher oxidative capacity for NADH at the respiratory chain than S. cerevisiae [+) cofactors, several reports indicate that cofactor manipulation is a useful tool to improve fermentation [E. coli that an increase in NADH availability induces fermentation by stimulating pathways which are normally inactive under aerobic conditions [et al. [S. cerevisiae. Furthermore, Hou et al. [S. cerevisiae strain disabled for formate anabolism, and modified to overexpress the native NAD dependent formate dehydrogenase, were able to induce fermentation by increasing intracellular NADH concentration via formate supplementation.Also, at complete anaerobic conditions, ion stop , 21. Culrequired –28. S. srevisiae . This ferevisiae . Fermententation . In thisnditions . Besides [et al.  were ablu et al.  using a S. stipitis may prevent NADH excess via its high NADH oxidative capacity at the respiratory chain and by converting NADH to NADPH through a bypass at the tricarboxylic acids cycle (TCA) [S. stipitis may be able to use the arabinose assimilation pathway backwards, oxidizing NADH and producing polyols, in fact, polyol accumulation in S. stipitis is several times greater than in S. cerevisiae; 317 times for arabitol and 46 times for ribitol [S. stipitis by modifying enzymes of the respiratory chain increases yield even under aerobic conditions [+) redox pair affect the dependency between ethanol production and oxygen supply. The different phenotypic phases of S. stipitis metabolism had been studied in an experimentally validated core metabolic model [Since xylose metabolism yields a positive NADH balance, cofactor usage has been shown to be important on ethanol production when xylose is used , 36. S. le (TCA) , 11. Mor ribitol . On the nditions , 38, 39,et al, [S. stipitis. The metabolic models were obtained from genome-scale metabolic reconstructions [In-silico analyses made on S. stipitis were also performed on S. cerevisiae, which it is well-known for its ethanol production capacity. Also, deletions found in-silico by Acevedo et al, [S. stipitis to improve ethanol production were analyzed using the above mentioned tools.An useful way to study metabolic behaviour is by analyzing the flux distribution in the stoichiometric network, the method most extensively used to this purpose is the Flux Balance Analysis (FBA) , 41. Thret al,  proposedructions , Flux Baructions , 41 was ructions  was usedo et al,  in S. stIn-silico results indicated that inhibiting components of the respiratory chain modifies ARC and it allows to associate ethanol production to growth. Subsequently, S. stipitis batch cultures were performed adding respiratory inhibitors in order to improve yield. Batch culture essays showed that yield increases by 18% when inhibiting the mitochondrial NADH dehydrogenase complex I. Next, by using data from batch cultures and a kinetic model of the culture, uptakes rates were determined and were used as constraints in the FBA of S. stipitis in order to determine the ethanol sensitivities associated to batch cultures. Results showed that the observed increase in ethanol production via respiratory inhibition is related to an excess in ARC for biomass production and to a positive response of ethanol production to this excess.S. cerevisiae the IMM904 model [S. stipitis the IBB814 model [The following genome scale metabolic reconstructions were used: for 04 model  which ha14 model  which ha14 model , 48–53.et al., [To determine metabolic states FBA was used , 41. Looet al.,  method set al., , 51–53.The flux-carrying set (FCS) is defined as the all non-zero fluxes obtained from a FBA. The changes in the FCS are characterized by the fluxes which are increased, decreased, turned on or turned off due to a imposed modification in any given flux.Constraint-Based Reconstruction and Analysis) Toolbox version 2.0 [http://opencobra.sourceforge.net/.Computational methods were performed using Matlab2012a as programming environment. FBA routines were carried out using COBRA (sion 2.0 . TomLab/Phenotypic phase planes (PhPPs) analysis  was perfZ to a perturbation on the availability of a metabolite i. Each sensitivity is defined as:bi corresponds to the mass balance for metabolite i and super index r denotes a relaxation in the steady state. The sensitivity corresponds to the Lagrange multipliers associated to the dual optimization problem. These values were obtained from the solution vector of the dual problem (vector m × 1 where m is the number of metabolites) in the COBRA function optimizeCbModel. From de Z to an increment in Z decreases Z does not change Z  is define as:As described by , the senThe sensitivity of ethanol production to ARC changes S. stipitis batch culture was modeled using differential equations in order to represent biomass, xylose and ethanol mass balances. Thereby the culture model was defined as:x is biomass concentration (g⋅L−1). S: is xylose concentration (g⋅L−1) and P is ethanol concentration (g⋅L−1). Monod equation was used to describe specific growth as follows:μ is the specific growth rate (h−1), S is xylose concentration (g⋅L−1), Ks is the substrate saturation constant (g⋅L−1) and μmax is the maximum specific growth rate. The specific uptake rate of xylose was determined based on substrate usage mass balance as follows:S is the specific uptake rate of xylose (g⋅gcel−1 h−1), Yo is the theoretical yield of biomass from xylose, YP is the theoretical yield of ethanol from xylose and qP is the specific productivity of ethanol (g⋅gcel−1 h−1). The ethanol specific productivity was represented by the Luedeking and Piret equation:α (g⋅gcel−1) and β (g⋅gcel−1 h−1) correspond to the Luedeking and Piret coefficients. It was considered representing in the culture model the lag phase of growth and the delay of ethanol production. Thus two delay factors were added to the model as follows [Where  follows :fr1=q1qfr1 is the delay factor associated to growth, q1 is the delay parameter associated to growth, v1 is the exponent parameter associated to growth, fr2 is the delay factor associated to ethanol production, q2 is the delay parameter associated to ethanol production and v2 is the delay parameter associated to ethanol production.Where, CL is the oxygen concentration in the liquid (g⋅L−1), C* is the oxygen concentration at saturation in water at 30°C (g⋅L−1), kLa is the volumetric oxygen transfer coefficient (h−1), qO2 is the specific oxygen transfer rate (g⋅gcel−1 h−1), x is the biomass concentration (g⋅L−1). Assuming that under oxygen limiting conditions the magnitude of specific oxygen uptake rate is determined by the oxygen transfer rate since it is assumed that all oxygen transferred from gas to liquid phase is consumed, qO2 may be calculated as follows:Therefore, Eqs  and 8)  8) can bμmax, Ks, Yo, m, α, β, q1, q2, v1 and v2 were determined by least squares fitting of the equations to experimental data. Least squares method was implemented in Matlab2012a.The culture model parameters Yeast extract Peptone xylose) medium composed of 7 g⋅L−1 of yeast extract, 14 g⋅L−1 of peptone and 14 g⋅L−1 of xylose. The ethanol production medium consisted of 6.0 g⋅L−1 of yeast extract, 0.20 g⋅L−1 of (NH4)2SO4, 5.0 g⋅L−1 of KH2PO4, 0.40 g⋅L−1 of MgSO4⋅ 7H2O, 0.038 g⋅L−1 de CaCl2 and 15 g⋅L−1 of xylose. By adding HCl 2N and NaOH 2N, pH was adjusted to 5.0. All mediums and the fermenter were autoclaved at 121°C for 15 min.Inoculum propagation was conducted using YPX  was used for all batch cultures.The yeast Inoculum propagation was performed from cells previously stored in 1.5 mL cryovials at −80°C YPX medium with 50% V/V glycerol. The propagation was conducted in YPX medium Erlenmeyer flasks with 500 mL culture volume of 150 mL on a rotary shaker incubator  with temperature controlled to 30°C and agitation of 250 rpm.BioBundle Cultivation Systems w/ ez-control fermenter was used. Agitation was applied using an Rushton turbine impeller, the inlet air flow was controlled by a mass flow controller (MFC), pH was controlled by supplying NaOH 2 N with a peristaltic pumps integrated to the control system and temperature was controlled with a heating jacket. Relative oxygen concentration was measured using a polarographic oxygen sensor. Cultures were carried out at 30°C, 1 L of culture volume and pH 5.An Applikon-Biotechnology model Sampling was performed using a sample taking connected to a syringe of 5 mL. The biomass concentration was estimated by turbidimetry measuring absorbance at 620 nm in a spectrophotometer .μL were centrifuged at 12,000 rpm for 10 min, then supernatant was filtered through a membrane of 0.22 μm pore size and 200 μL were deposited in glass vials.From each sample 300 Infinity Quaternary LC System was used with UV/IR detector and automatic sample injection. A column Aminex BioRad HPX-97H and H2SO 5 mM as mobile phase were used, conditions were 0.6 mL⋅min−1 as flow and temperature of 45°C. Retention times were 9.5 and 21.3 min for xylose and ethanol, respectively.Xylose and ethanol concentrations were measured by high-performance liquid chromatography (HPLC). An HPLC system Agilent Model 1260 et al. [−1; a condition with kLa = 25 h−1 established at 250 rpm and another with kLa = 15 h−1 established at 150 rpm.The absorption method described by García-Ochoa et al.  which coμ) as a function of the oxygen (qO2) and xylose (qxyl) uptake rates. A single phenotypic phase is defined as the region where the derivative of μ with respect to qxyl and qo2 is constant. Each single phenotypic phase is associated to a particular active set of reactions (non-zero fluxes). On the other hand, each single point on a given phenotypic phase corresponds to a metabolic state, which is characterized by the flux values of the active set.Phenotypic Phase Plane (PhPPs) analysis  was usedμ the specific ethanol productivity (qetoh) and the ethanol yield from xylose (Yetoh/xyl) are shown over the PhPP as colored contours, respectively. Three relevant subsets of metabolic states can be distinguished on the PhPP (see i) The line of optimality for biomass production, which is represented by the dashed black line, where the uptake rates for oxygen and xylose are in exact proportion to allow maximum biomass yield. ii) The phenotypic phase to the right hand side of the dashed black line, where growth is limited by xylose uptake rate. iii) The phenotypic phases to the left hand side of the dashed black line, where oxygen uptake rate is below the value necessary to achieve fully aerobic growth and ethanol is produced. The dashed blue line between the contours of Yetoh/xyl in i and ii do not allow ethanol production, corresponding to fully aerobic metabolism. On the other hand, subset iii corresponds to fermentative metabolism since ethanol is produced.As shown in PhPP see : i) The etoh to changes on ARC is referred as i) Increasing ARC generates an increment in qetoh, then ARC limits ethanol production. ii) qetoh decreases when increasing ARC, thus ARC is in excess for ethanol production. iii) Changes in ARC do not affect qetoh. Whether case i, ii or iii occurs is determined as follows (see methods section for details): If The sensitivity of qS. stipitis using⋅glucose and xylose, respectively. The following regions can be observed starting from the line of optimality for biomass toward the left: First, a region where ARC limits ethanol production (g xylose  than wheg xylose  as carboetoh) and the PhPP for the ethanol yield from xylose of S. stipitis using⋅glucose and xylose, respectively. It can be seen that the line of optimality for ethanol production is over the line that divides the regions where On the other hand, RCetoh=0 .S. cerevisiae was also determined in order to compare it with the PhPP of S. stipitis. As S. cerevisiaeThe PhPP of S. stipitis, unlike S. cerevisiae, an increment in ARC does not always increase ethanol production,The sensitivity analysis of the both yeasts indicates that in et al. [in-silico reaction deletions able to associate ethanol production (qetoh) to biomass production (μ) in S. stipitis, all of them related to the respiratory chain. S. stipitis with the cytochrome oxidase (COX) and the alternative oxidase (AOX) deleted. It can be seen that the highest values for μ and qetoh coincide, the same was observed for the other deletions.Previous work by Acevedo et al. , reporteOn the other hand, sensitivity analysis showed that there is an excess of ARC for ethanol production through all the PhPP . Hence, S. stipitis were done. Inhibitors of the respiratory chain were added to batch cultures of S. stipitis using xylose at conditions of limited oxygen supply. Salicylhydroxamic acid (SHAM) was used to inhibit AOX, while potassium cyanide and sodium azide were used to inhibit COX. As shown in in-silico deletions corresponded to the respiratory chain, the inhibition of the NADH dehydrogenase complex I was also considered. As it can be seen in Experimental essays to determine whether the inhibition of the respiratory chain improves ethanol production in kLa = 25 h−1 and kLa = 15 h−1 were performed. kLa = 15 h−1 are shown in kLa = 25 h−1 are shown in etoh/xyl) increases by ∼24% at kLa = 25 h−1 and by ∼18% at kLa = 15 h−1 (etoh) decreased ∼41% and ∼35% at kLa = 25 h−1 and kLa = 15 h−1, respectively.Batch cultures in fermenter with limited oxygen supply considering = 15 h−1 . On the etoh are slightly higher in cultures with rotenone (red lines), this effect is more pronounced at kLa = 15h−1 . Solid lines in  = 15h−1 . Fig 7c ll cases .xyl and qO2 were plotted over the PhPP, this allowed to identify trajectories on the phenotypic phases. As neither xylitol nor acetic acid was detected under the studied conditions, the possible effect of the xylitol production on the balance of redox cofactors can be left aside. Then the effect of the respiratory chain, in this case the Complex I NADH Dehydrogenase, can be considered as the main contributor to changes in redox balance. μ and qetoh, respectively. All trajectories cover the three subsets previously defined over the PhPP, starting from subset ii  are associated to lower values for μ than the trajectories at kLa = 25 h−1 (solid and dashed red lines). According to the colored contours of the PhPP in La = 15 h−1 reaches a specific growth rate of about 0.035 h−1, while trajectories at kLa = 25 h−1 reaches about 0.06 h−1. These values predicted by the PhPP are close to the values for μ obtained from the batch cultures at kLa = 25 h−1 and at kLa = 15 h−1 shown in La = 15 h−1 reaches an ethanol specific productivity about 0.040 mmol⋅gcel−1⋅h−1 of, while at kLa = 25 h−1 about 0.050 is obtained over the PhPP. These values from the PhPP are close to the values for qetoh calculated from the cultures at kLa = 25 h−1 and at kLa = 15 h−1 shown in iii is shown in etoh is predicted if ARC increases. Therefore, considering that inhibiting complex I NADH dehydrogenase implies an increase in ARC, the latter sensitivity predicts that ethanol production should improve.With the purpose of characterizing the metabolic states associated to the batch cultures, the values of qetoh but it does not characterize subsequent metabolic states with an inhibited complex I. Then it is necessary to analyze the subsequent metabolic states considering the inhibition of complex I, in order to verify whether changes in sensitivities are consistent with states favoring ethanol production. To this purpose, qxyl and qO2 trajectories were used as constraints for the FBA and the inhibition were simulated by applying a constraint on the upper bound of the flux associated to complex I. Thus by using FBA metabolic states were successively calculated and the sensitivities associated to the trajectories were obtained. Along with The results shown in hibition , meaningChanges in the flux-carrying set (FCS) were analyzed in order to explain a change in the ethanol production generated by the inhibition of the CINADH. The FCS of a metabolic state with inhibited the CINADH was compare against the FCS of a metabolic state without inhibition in order to obtain the changes in the FCS caused by the inhibition of the CINADH.According to the analysis made, when CINADH is inhibited, NADH becomes available to other reactions, including ethanol production reaction. This does not mean that ethanol flux would increase significantly because its production is dependent as well on carbon flux. in-silico in S. stipitis and S. cerevisiae. Flux Balance Analysis (FBA) was used to characterize reachable metabolic states in both yeasts at different glucose and oxygen uptake rates. These states are represented as a Phenotypic Phase Plane (PhPP). Metabolic states, ranging from aerobic growth with no ethanol production to metabolic states with low oxygen availability and high ethanol production, using xylose as carbon source were determined (The effect of the Available Reducing Capacity (ARC) on ethanol production rate was studied termined .S. stipitis; one in which ARC is in excess, another where ARC has no effect on ethanol production, and a third where ARC is limiting the ethanol production . Based on the obtained results, three regions were identified for oduction . Whereasoduction . Therefoe et al,  have preterature , 44, altterature –60. Hencet al, [S. stipitis, were analyzed using PhPP and sensitivity analysis. The resulting PhPP and its corresponding sensitivities showed that partial inhibition of the reactions reported by Acevedo et al, [S. stipitis for ethanol production. Consequently, inhibitory essays in batch cultures were done in order to verify the effect of inhibiting the respiratory chain. Values for kLa of 25 h−1 and 15 h−1 were considered, which are equivalent to OTR values of 5.9 and 3.5 mmol O2 L−1⋅h−1, respectively. These microaerobic conditions allowed to reach yields between 0.16 g⋅g−1 and 0.32 g⋅g−1. [2 L−1⋅h−1 to reach a yield of 0.29 g⋅g−1. On the other hand, [2 L−1⋅h−1 as optimal to reach a yield of 0.40 g⋅g−1. [2 L−1⋅h−1 to achieve a yield of 0.39 g⋅g−1. In the present work rotenone addition allowed to increase yield from 0.16 g⋅g−1 to 0.20 g⋅g−1 at an OTR of 5.857 mmol O2 L−1⋅h−1 and from 0.32 g⋅g−1 to 0.38 g⋅g−1 at an OTR of 3.514 mmol O2 L−1⋅h−1. Although yields were not higher than those reported by other authors, increasing ethanol production through respiratory inhibition can be considered as a complementary strategy to aeration control. [S. stipitis generates an increment of 40% in ethanol specific productivity and a decrease of 33% in maximum specific growth rate, final yields were not reported. Also, [S. stipitis, they reported an increment of 10% in final ethanol concentration.Reaction deletions reported by Acevedo et al,  aiming to et al,  generate2 g⋅g−1.  suggesteer hand,  reported0 g⋅g−1.  suggestecontrol.  reportedd. Also,  studied μ and qetoh over the PhPP were close to the ones obtain from the cultures (Figs λ ≤ 0 in Trajectories over the PhPP were obtained using the uptake rates calculated from the kinetic model, it was observed that the values for res Figs  and 9. AS. stipitis, to evaluate the effect of available reducing capacity on ethanol production. The inhibition of NADH dehydrogenase complex I with rotenone leads to an increase in ethanol production in batch cultures and it can be characterized by means of the PhPP and sensitivity analysis of the S. stipitis genome scale metabolic network. The analysis shows that cofactor availability, measured as the sensitivity to available reducing capacity In this work sensitivity analysis of genome-scale metabolic modeling and phenotypic phase plane analysis were used to characterize metabolic response on a range of uptake rates in


Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.Using a systems biology approach, we studied the response of the marine diatom  Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide.Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism. DIC is a combination of CO2, HCO3, and carbonate. The relative amounts of the three DIC species are set by the pH. At modern seawater pHs, the most dominant form of DIC is HCO3. Under bloom conditions, the pH increases, resulting in a rising carbonate content but also a lower total DIC content. Under ocean acidification conditions, there is a higher total DIC level but also the percentage of CO2 increases, essentially resulting in ocean carbonation. Overall, future atmospheric increases can be expected to facilitate higher CO2 diffusive uptake through decreased pH and active HCO3 uptake through increased total DIC.Here, we investigated the metabolic response of sporters . Rising 2 levels. Here, we exploited a genome-scale metabolic network recently developed for the model diatom P. tricornutum to understand its metabolic response to rising CO2 concentrations  inferred from transcriptome sequencing (RNA-Seq) data in order to gain a comprehensive understanding of the interconnection between metabolic and regulatory mechanisms that could drive the adaptation to increased CO2 concentrations.Within this study, the genome-scale metabolic network for P. tricornutum that allows detailed insights into its physiology and provides a framework to analyze and predict genotype-phenotype relationships  and the reaction catalyzed by one of the genes and the corresponding information in the genome-scale model (right) are shown. The TRN consists of coexpressed genes and experimental conditions organized into biclusters. The metabolic network contains information about the metabolic reactions and the gene products (GPRs) by which they are catalyzed. Both networks interact via the metabolic genes. By using the information included in the genome-scale model, subsystems are associated with the genes in the TRN. Download Copyright © 2017 Levering et al.2017Levering et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 or iron levels , as well as whether they are enriched in genes involved in carbon and/or nitrogen metabolism, are listed. Download Copyright © 2017 Levering et al.2017Levering et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00142-16.2TABLE S2 TABLE S2, XLSX file, 0.5 MB.Annotations associated with the regulon for each cluster. For each cluster, the genes and the reaction association and annotation , as given in the genome-scale model, as well as the Ensembl or NCBI database (for genes located on organelle genomes) annotation, are listed. Download Copyright © 2017 Levering et al.2017Levering et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00142-16.3TABLE S3 TABLE S3, XLSX file, 0.02 MB.Abbreviations used for the experimental conditions in Table S1. Download Copyright © 2017 Levering et al.2017Levering et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. tricornutum. To investigate this strong correlation between carbon metabolism and nitrogen metabolism more precisely, we performed a GO enrichment analysis and associated each significant GO term with carbon or nitrogen metabolism. By using the subsystems given in the model, genes associated with reactions involved in amino acid metabolism, nitrogen metabolism, nucleotide metabolism, or the urea cycle were categorized into nitrogen metabolism. Genes associated with reactions involved in aldehyde degradation, ascorbate metabolism, butanoate metabolism, the Calvin-Benson cycle, carbon fixation, galactose metabolism, nucleotide sugar metabolism, oxidative phosphorylation, photosynthesis, pentose interconversions, the pentose phosphate pathway, or pyruvate metabolism were categorized as carbon metabolism. Genes associated with reactions involved in the tricarboxylic acid cycle were classified as both carbon metabolism and nitrogen metabolism. On the basis of this mapping, 48 clusters were enriched or purified in genes involved in carbon metabolism and 40 clusters contain enriched or purified genes taking part in nitrogen metabolism. Twenty-two clusters are enriched in genes involved in both carbon metabolism and nitrogen metabolism. Of the 18 clusters containing nitrogen but not carbon metabolism genes, 9 are not enriched in genes belonging to any other subsystem, 1 is also enriched in genes involved in transport, 1 is also enriched in genes belonging to glycan metabolism, 3 are also enriched for genes involved in cofactor metabolism, and 4 also contain enriched or purified genes involved in lipid metabolism.On the basis of the TRN see , genes t2. However, they exhibit different expression patterns indicated by their clustering into different modules. Not surprisingly, the two clusters share two predicted regulatory proteins, J49099 and J47726, that provide a tangible link between transcriptional regulation and metabolite exchange between different pathways and subcellular compartments. Metabolite exchange between subcellular compartments has been shown to be important to diatom metabolism . Note that we used the biomass objective function (BOF) and the constraints previously determined experimentally , product secretion, and biomass composition were used under low- and high-carbon conditions. The biomass function accounts for all known biomass components, i.e., DNA, RNA, protein, pigments, carbohydrates, membrane lipids, and the storage lipids triacylglycerols (TAGs), as well as energetic requirements and their fractional contributions to the overall cellular biomass. As depicted in 3 levels. Thus, the reaction fluxes are increased to meet the larger demand of biomass components accounting for P. tricornutum’s increased growth at increased HCO3 levels.To simulate the effect of rising HCO2 concentrations. Half of the genes (93 genes) were downregulated at high CO2 concentrations, whereas the other half (94 genes) were upregulated. By using the P. tricornutum genome-scale model, the genes were mapped to 1,323 metabolic reactions belonging to 63 model subsystems, which were further categorized into nine groups   . Note th2 concentrations, whereas this group does not show up in the sampling results. Although N-glycan biosynthesis is included in the genome-scale model, glycans are not accounted for in the biomass reaction. Instead, a demand reaction was included in the model  to account for the missing knowledge of the fraction of glycans present in the biomass. This demand reaction was blocked prior to the solution space sampling and thus cannot be accounted for in the sampling result.Compared to the genome-scale model solution space sampling results, where increased reaction fluxes were identified at higher carbon levels  are identified by the sampling and differential expression analysis, the reactions within these groups are different between the two analyses, as shown in 2 levels, as observed between the metabolic model predictions and the differential expression analysis, might be explained by the different types of data exploited for the two analyses. The differential expression analysis is based on RNA-Seq data, whereas the metabolic model predictions are based on simulated reaction fluxes and thus protein activities. The differential expression analysis takes into account regulation by TFs but not posttranscriptional and -translational modifications, as well as allosteric regulation on the enzyme level. In contrast, the metabolic model does not include any regulatory effects and the predicted flux distributions are based on the mature enzyme activities, i.e., after modifications. However, random sampling of the metabolic model’s solution space identifies all possible flux distributions and, combined with further physiological data such as transcriptomics, more complete insights into the organism’s metabolism can be gained.The discrepancy in the metabolic response to rising COP. tricornutum  supply, NO3 becomes limiting and is not stored internally with increasing HCO3 concentrations, as shown in 3 is used for biomass production to enable maximal carbon fixation into biomass. Consequently, biomass production stagnates because of limiting NO3 availability. Since HCO3 further increases and the cell is forced to take it up, 3-(dimethylsulfonio)propanoate (DMSP) is secreted, which is the only reaction allowing the release of excess carbon.According to the predictions of scenario 1 with stepwise increasing HCO3, is indeed the limiting factor constraining biomass production using scenario 2 with stepwise increasing HCO3 uptake and allowing the NO3 supply to increase by 0.1 mM with each carbon uptake increase when the HCO3 uptake is ≥5.5 mM. The available NO3 is incorporated into biomass for HCO3 levels below 5 mM. The supplied rising nitrogen is sufficient to enable nitrogen storage and fixation of all presented carbon into biomass; thus, no DMSP is secreted.3 uptake is constrained and constant throughout the simulation, nitrogen storage decreases with increasing HCO3 concentrations. At an HCO3 uptake of 5 mM, the biomass production increase is slowed down before it finally stagnates when the HCO3 uptake reaches 5.5 mM. NO3 storage shows a similar pattern and vanishes at HCO3 levels higher than 5.5 mM. With HCO3 uptake higher than 5.5 mM, excess carbon is stored as TAG, which increases with HCO3. In this scenario, excess carbon is not stored as chrysolaminarin.In scenario 1, excess carbon was released in the form of DMSP. This reaction (DM_dmsp_c) was introduced into the model because of a knowledge gap in DMSP metabolism and to prevent its accumulation . In the 3 levels until P. tricornutum encounters nitrogen limitation. A strong connection between carbon metabolism and nitrogen metabolism was also observed in the TRN, where we showed that genes involved in nitrogen and carbon metabolism are often coregulated. It should be noted that temperature, nutrient deprivation, and other factors are also known to influence the growth of P. tricornutum but were not taken into account in this study. Our work describes for the first time which genes are coexpressed and coregulated upon different environmental perturbations and provides insight into the effect the limitation of a nutrient such as carbon or nitrogen has on the phenotype. The integration of the regulatory network developed here and the published metabolic model offers new insights into the model diatom P. tricornutum’s metabolic and regulatory interactions and provides a comprehensive framework of responses to increasing atmospheric carbon levels.Using the genome-scale metabolic network, we demonstrated that biomass production increases with elevating HCOhttps://www.r-project.org), scaled by using DESeq scaling factors (2 transformed (a constant +1 was added prior to log2 transformation), and adjusted for batch effects introduced by the utilization of different sampling platforms with the ComBat software  by using an in-house mapping table. The expression profiles of 196 known TFs in P. tricornutum  and high  COge limma . The Ben2 algorithm discovers coregulated modules in transcriptome profiles by integrating additional information, such as the genome sequence, protein-protein interactions, and the de novo detection of cis-regulatory DNA sequence motifs, and aggregates it into a combined score to improve clustering  was obtained from Ensembl Protist (http://protists.ensembl.org/Phaeodactylum_tricornutum/Info/Index). Chloroplastic and mitochondrial genomic information was obtained from NCBI   , 21. Thecornutum . Proteincornutum  and repl2 H, protons, oxygen, inorganic phosphate, pyrophosphate, and CO2, were removed from the cluster-substrate-product list.On the basis of the clustering result and the le model , a clustHighly connected clusters were identified on the basis of substrates or products. The connectivity of two clusters was defined as the number of common substrates (or products) divided by the total number of substrates (or products) in both clusters. Here, two clusters are strongly connected if their connectivity is >0.9.Modeling simulations were performed with the Gurobi Optimizer version 5.6.3  solver in MatLab  with the COBRA Toolbox . NutrienP. tricornutum metabolic network iLB1027_lipid (3 uptake was set to 1.57 mM and doubled to 3.14 mM for the simulations of high-carbon conditions. All other constraints were identical between the two sampled conditions (2 conditions. Corresponding reactions and their gene-reaction associations were extracted from the genome-scale model. However, this analysis did not result in any reaction knockout. The reduced metabolic model contains 3,904 reactions and 1,734 metabolites. Both networks, representing low- and high-carbon conditions, were sampled by using 50,000 sample points with a step count of 7,808, which is double the number of reactions in the model.To uniformly sample the solution space of the 27_lipid , optGpSa27_lipid  for MatLnditions . Before 3 levels are significantly different, the minimal distance between the histograms was determined for each reaction in the metabolic model by randomly permuting the flux vectors and subtracting them from each other. To get a representative distance, this procedure was repeated 100 times, yielding 100 distance vectors for each histogram comparison. The mean of the minimum number of positive and negative entries in each distance vector was used to calculate the P value for the two-sided test. To correct for multiple-hypothesis testing, a false-discovery rate correction was applied, setting the significance threshold at 0.05.To determine if the flux distributions at low and high HCO