Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites, and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model oxidative phosphorylation (OXPHOS) functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudometabolites DPH (∆pH) and DPSI (∆ψ). To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used flux balance analysis (FBA) to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect. Genome-scale models of metabolism (GEM) greatly help to understand how metabolism varies in different physiological conditions, in different environments, in case of enzyme deficiencies, and in interaction with other metabolisms. Used in conjunction with methods such as flux balance analysis , GEM are+/Glutamate cotransporter [+ entry is sensitive to the ΔµH+ and can be considered as irreversible in normal physiological conditions). Inside the mitochondria, glutamine-derived AKG replenishes the tricarboxylic acid (TCA) cycle and can be metabolized either through the canonical forward mode or via reductive carboxylation, leading to citrate and acetyl-CoA in the cytosol. For instance, Chen et al. [To illustrate the practical interest of such a reduced model of metabolism in mammalian cells, we studied glutamine’s metabolism and compared it with glucose metabolism. Glutamine is the most abundant amino acid in plasma and has long been recognized to be essential in proliferating cells. Additionally, glutamine has been identified as an alternative to glucose to fuel the Krebs cycle in cancer cells or in hypoxia conditions or mutations ,18,19,20nsporter  , we systematically studied and quantitatively discussed all the possible fates of glutamine in central carbon metabolism and demonstrated that glutamine can sustain cell energy production and be used as a carbon and nitrogen source to synthesize essential metabolites, thus contributing to cell proliferation. Here, we show how C2M2N can be used to explore the results of more complex metabolic models by comparing our results with those of MitoCore, a metabolic model of intermediate size built on the same bases.In a more general approach, we used C2M2N to follow how glucose and glutamine together share energy metabolism and anabolism in different physiological or pathological conditions . We demonstrated the interest of a core stoichiometric model such as C2M2N in quantitatively tracing the origin of the carbon and nitrogen atoms in the different syntheses and in specifying the respective role of glutamine and glucose in energy metabolism. We demonstrated the role of ammonia release or input or recycling and study some urea cycle dysregulation (UCD). Ultimately, we gave a quantitative account of Warburg effect evidencing the main challenge of proliferating cells, i.e., energy production while maintaining nicotinamide adenine dinucleotide (NAD/NADH) balance.+ exchanger), ASS1_ASL_FH  and finally ARGASE (arginase). The ornithine synthesis from glutamate and acetylCoA, NAGS_ACY (N-acetylglutamate synthase + Amino Acylase) is completed by the regeneration of acetyl_CoA from acetate by ACS (AcetylCoA Syntethase) and by the ornithine/H+ transporter ORNT2. The urea cycle is supplemented by NOS (NO Synthase).The reactions involved in the metabolic model C2M2N  are listed in The synthesis of nucleotide bases (NUC) is represented by a simplification of purine and pyrimidine biosynthesis obtained by averaging the stoichiometry of the different metabolites and cofactors in the metabolic pathway of each nucleotide and taking into account their different amounts in human (30% of A and T or U and 20% of G and C). It should be stressed that nucleotide synthesis necessitates glutamine which is converted to glutamate and aspartate, itself converted to fumarate and subsequently to malate.The synthesis of serine from 3-phosphoglycerate involves 3 steps: A dehydrogenase, a transaminase involving the glutamate/α-ketoglutarate pair, and a phosphatase. These three steps are assembled in one reaction: SERSYNT.The malate–aspartate shuttle (MAS) is fully represented in the direction of NADHc consumption and NADHm production. It involves the malate/α-ketoglutarate exchanger T2 (OGC) and the glutamate/aspartate exchanger T4 (AGC), the malate dehydrogenases  and the glutamate-oxaloacetate transaminases . A detailed representation of MAS was mandatory because MAS enzymes are not always operating with the stoichiometry and the direction of MAS for exchange of NADHc for NADHm, i.e., MAS components are not always used to run the MAS as such.The synthesis of fatty acids is a major pathway in proliferating cells. It starts with citrate lyase (CL) and is represented in the case of palmitate by the reaction PL1 with the corresponding stoichiometries.The respiratory chain is represented by three reactions: RC1 which is the respiratory complex I, RC2 , and RC34 which represents complex III + IV. +/H+ exchanger), or physiologically, the action of ion exchangers in the inner mitochondrial membrane. DPH and DPSI are introduced, when necessary, in mitochondrial carriers equations. ATP hydrolysis is symbolized by ATPASE activity.Finally, the remaining steps of oxidative phosphorylation are represented by ASYNT (adenosine triphosphate (ATP) synthase), ANT, the ADP/ATP exchanger, T5 the Pi carrier, and L the protons membrane leak. The proton gradient is represented by two pseudometabolites DPH (∆pH) and DPSI (∆ψ). It allowed us to only take into account the “vectorial” protons across the mitochondrial membrane . We intrOnly two entries are considered here, the entry of glutamine (GLNUP) and the entry of glucose (GLUCUP). The possible outputs in this study are pyruvate (T16), serine (SEROUT), aspartate (T14) nucleotides (T13) and palmitate (T17), arginine (T18), urea (T19), and ammonia (T20). The entry of glutamine in the mitochondria goes through the transporter T8. It will give rise to glutamate inside the mitochondria through glutaminase GLS1.MitoCore  is a manhttp://f-a-m-e.org/) [We used FAME  (-e.org/)  to derivIn order to understand how glutamine and glucose share the increase in biomass of proliferating cells, we maximized a biomass function (BM) defined from : BM = 0.18 SERc + 0.11 Palmitate_c + 0.04 XTPc + 0.19 GLNc + 0.17 GLUTc + 0.7 ASPc + 0.05 ARGc + 0.12 PYRc + 4 ATPc => 1 Biomass + 4 ADPc + 4 Pic.PYRc takes the place of alanine, ARG summarizes arginine and proline in biomass and also polyamines synthesis, and SER is for serine + glycine.Energy production is symbolized by ATPASE activity which is the objective function in this section. About 24 molecules of ATP can be synthesized from 1 molecule of glutamine without any other synthesis . One molThe production of pyruvate  from glutamine can follow the production of glutamate and α-ketoglutarate (AKG) entering the “left”, -oxidative- part of TCA cycle to produce OAA. From OAA, mitochondrial PEPCK2 produces PEP, which exits the mitochondria through citrate and malate cycling . The maximal yield from one glutamine is one aspartate and 7.6 ATP.Aspartate is an amino acid which participates in many reactions, particularly in nucleotides synthesis and appears at a high level in the biomass. Due to its low concentration in blood, aspartate synthesis is crucial for cell survival ,29,30,31Aspartate synthesis from glucose also requires the activity of GOTs  but is more complicated . In the +, i.e., the ETC activity. We can confirm this result in our model as represented in In recent papers ,28,29, sOther reactions also appear essential such as the pyruvate carboxylase (PYC) and the pentose phosphate pathway.4+ (0.04) and is accompanied by the production of 0.98 ATP.The synthesis of nucleotides (XTP) requires glutamine, aspartate, and R5P. In the absence of ASP and R5P, glutamine has to be used to ensure these syntheses. In the solution of 4+) by glutamine synthase (GS1) and aspartate is recycled from fumarate then malate and oxaloacetate. There is the need to reoxidize glycolytic NADHc and the NADHc produced by nucleotide synthesis inducing a 0.74 flux through the malate–aspartate shuttle (see 4+ and no ATP production despite a higher activity of respiratory chain.The synthesis of nucleotides from glucose requires R5P synthesis and the recycling of glutamine and aspartate. R5P is produced by the pentose phosphate pathway. Glutamine is recycled from glutamate (+NHttle see . The yieFatty acids (FA) synthesis is an important pathway in proliferating cells, especially for phospholipids syntheses. In the case of palmitate synthesis, 0.17 palmitate molecule can be synthesized from one molecule of glutamine and because this is rather energy consuming, no ATP molecule is synthesized a. The syThe involvement of reductive use of glutamine and participation of IDH1 in fatty acids synthesis is well recognized ,15,33 inWith glucose as carbon substrate b, oxalacSerine is the major source of one-carbon units for methylation reactions via tetrahydrofolate and homocysteine. It is the precursor of glycine  [4+ feeding, but also in part for carbon supply and around 80% of ATP to sustain biomass formation as noticed in [3 for the biomass as already noted [4+ released by glutaminase and incorporated in part of the AKGm, mainly by the reversion of glutamate dehydrogenase, as shown experimentally by Spinelli et al.  necessitate the input of NHgy (ATP) . In the ticed in   we obtain the reactions allowing biomass synthesis, i.e., CL, NUC, PL1, SERSYNT, ASS1_ASL_FH, and T12 which represent more reactions due to the fact that most of these reactions stand for several consecutive reactions. All of which are therapeutically targeted in cancer . Curious4+ is incorporated from the microenvironment instead of glutamine by the reversion of Glud1  necessary for the biomass increase because the additional glucose carbons were lost with lactate excretion.The Warburg effect or aerobic glycolysis is a salient feature of most cancer cells. It is characterized by an increase in glucose uptake and lactate release in the presence of oxygen. To simulate an increase in cell proliferation, we started from the situation of We can take this problem the opposite way b, still As noticed in ,55, the + in the form of pseudosubstrates DPH and DPSI as was already done in MitoCore [We developed a core metabolic model of central carbon and nitrogen metabolism, C2M2N, with a limited number of reactions (77) and of metabolites  to explore the potential of glutamine to supplant glucose in metabolic syntheses, energy and biomass production. A salient feature of this model is that it takes into account the actual stoichiometries of the reactions particularly the stoichiometries of cofactors, even for concatenated reactions. A second characteristic of this model is to consider mitochondria as an isolated compartment with the relevant transporters. This led us to the third characteristic, to develop a relevant model of oxidative phosphorylation taking into account the mitochondrial ΔµHMitoCore  and in [MitoCore  in the sThe advantage of C2M2N is that, due to the low number of reactions and metabolites, the interpretation of the results is rather straightforward and can be easily represented and understood on a metabolic scheme. All the solutions maximizing an objective function (described by FVA) can be more easily explored, as done in the 2+ activates the glutamate/aspartate carrier (T4) enhancing pyruvate formation and mitochondrial respiration [With C2M2N, we demonstrated that glutamine is a precursor as good as glucose for the syntheses of the main metabolites necessary for cell proliferation and energy production and we were able to give the quantitative yield in these productions . The sampiration . This efC2M2N takes into account the constraints introduced by the nitrogen metabolism which appear in the simultaneous presence of glucose and glutamine for biomass synthesis . We quanIn line with , we usedC2M2N allowed us to look for the steps whose inhibition could decrease biomass production and, thus, cell proliferation. In our model, the inhibition of any pathway leading to a biomass component will inhibit cell proliferation. This is a good indication of the main targets for anticancer drugs which has to be nevertheless considered with caution because several other minor pathways may exist which are not considered in C2M2N but can be overexpressed in pathological cells. However, it appears in our model that the inhibition of the first steps of glutaminolysis, GLS1 and Glud1 see , are notA drawback of genome-scale models, and of large models in general , is that flux balance analysis is the only possible approach. The number of elementary flux modes (EFMs) is usually out of calculation, and taking into account the rate equations in differential equations, reflecting the change in the concentration of metabolites, faces a number of difficulties . Similarly, metabolic control analysis (MCA) which can give an idea of pertinent therapeutic targets is unrealizable. These tasks are possible and easy with smaller metabolic models restricted to well-known reactions. The interest of having different approaches to explore a metabolic network is to be able to choose the most appropriate method for each particular biological question and, more generally, to confront the solutions given by the different theoretical approaches.We think that C2M2N is a good compromise between a comprehensive compilation of all the metabolic steps and a much lower number of the most important reactions of central carbon and nitrogen metabolism that can give a realistic and quantitative representation of metabolism in many physiological or pathological conditions.


Heterogeneous photocatalysis is a prominent area of research with major applications in solar energy conversion, air pollution mitigation, and removal of contaminants from water. A large number of scientific papers related to the photocatalysis field and its environmental applications are published in different journals specializing in materials and nanomaterials. However, many problems exist in the conception of papers by authors unfamiliar with standard characterization methods of photocatalysts as well as with the procedures needed to determine photocatalytic activities based on the determination of “apparent quantum efficiencies” within a wavelength interval or “apparent quantum yields” in the case of using monochromatic light. In this regard, an astonishing number of recent research articles include claims of highly efficient (photo)catalysts or similar terms about materials with superior or enhanced efficiency for a given reaction without proper experimental support. Consequently, the comparison of the efficiencies of photocatalysts may result as being meaningless, especially when reports are only based on expressions determining (1) a reaction rate per weight of catalyst or its surface area, (2) quantum efficiencies or quantum yields, and (3) turnover frequencies or turnover numbers. Herein, we summarize the standards needed for reporting valuable data in photocatalysis and highlight some common discrepancies found in the literature. This work should inform researchers interested in reporting photocatalysis projects about the correct procedures for collecting experimental data and properly characterizing the materials by providing examples and key supporting literature. Instead, the use of “quantum yield” (Φ(R)λ) refers to the use of monochromatic radiation (or constrained to a very narrow range of wavelengths). The two previous expressions are defined in a report by IUPAC to account for the number of absorbed photons [Because the key driver of the photocatalytic process is the absorption of photons, the quantum yield (not just the rate constant) must be used for reporting and comparing photocatalytic activities . However photons . Also re photons ) to comp photons . This co photons . Thus, t photons , which cI0 (instead of aI) are not reliable and prevent the researcher from comparing their results to those from other groups [A common mistake to avoid is the comparison of reaction rates assuming the absorbed light intensity should be same in different reactions. Light absorption may be altered even by a slight change of an experimental condition . Typically, the measurement of the incident photon flux on the preferably flat optical window of the photoreactor is recorded ,32,33,34r groups .κ in Equation (3) can be substituted by the product of λS  [The determination of AQE can be alternatively performed with diffuse reflectance measurements of the semiconductor for a material that is the only light-absorbing species . In thisonstant) ,37, to oSuch a correction must be applied with the understanding that it is valid only if scattering and reflection remain the same for both DRUVS measurements and photocatalytic experiments. This is the case when the surface properties in the experiments or photoreactors to be compared are the same. Reporting AQE for photocatalyzed reactions by combining measurements of reaction rates and diffuse reflectance spectra based on ISO standards is especially useful for systems studying reactions on the solid/gas interface. Finally, if monochromatic light is used during the photocatalytic process, it is technically much simpler to report the AQY.Two final concepts of general interest regarding photoelectrochemical studies are the internal quantum efficiency and the external quantum efficiency . The intIn addition to reporting the AQE as discussed above, it is important to consider the stability of the photocatalyst during irradiation for a long time. Extended irradiation can cause a decrease in the photocatalytic performance because of the deactivation of the photocatalysts, reoxidization of the reduction products, and poisoning effects . The ads2O/TiO2 nanocomposite. In the previous work, Aguirre et al. repeated four cycles of irradiation of Cu2O/TiO2 lasting 1 hour and concluded that the production of carbon monoxide from the reduction of CO2 in the presence of water vapor as a hole scavenger was reproducible [oducible .2 reduction and water splitting). Therefore, the analysis of reactant decay and/or product growth from both reduction and oxidation reactions must be covered. Often, the yield of products is only monitored from the reduction reactions , disregarding completely the oxidation reactions [2 reduction on the surface of a Cu2O/TiO2 nanocomposite in the presence of water vapor as the hole scavenger [A final consideration for a praiseworthy work with photocatalytic systems is to include a meaningful mechanistic proposal that integrates all observations. Such a proposal must include both common phenomena of reduction and oxidation reactions that occur simultaneously in photocatalysis  and sulfite (SO32−) in aqueous phase are usually employed as SEDs in different photocatalytic systems [1 products, hydrogen, etc. via oxidation reactions, and inorganic ions after protonation can also undergo oxidation reactions to produce hydrogen [Several organic molecules such as alcohols, organic acids, and hydrocarbons and inorganic ions such as sulfide (S systems ,42. Orgahydrogen . The asshydrogen ,44. Therhydrogen .


Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly describes all known ligand-binding events requires no simplifying assumptions while allowing for the computation of enzymatic states. Here, we employ such a modeling approach to examine the “catalytic potential” of an enzyme—an enzyme’s capacity to catalyze a biochemical reaction. The catalytic potential is the fundamental result of multiple ligand-binding events that represents a “tug of war” among the various regulators and substrates within the network. This formalism allows for the assessment of interacting allosteric enzymes and development of a network-level understanding of regulation. We first define the catalytic potential and use it to characterize the response of three key kinases  in human red blood cell glycolysis to perturbations in ATP utilization. Next, we examine the sensitivity of the catalytic potential by using existing personalized models, finding that the catalytic potential allows for the identification of subtle but important differences in how individuals respond to such perturbations. Finally, we explore how the catalytic potential can help to elucidate how enzymes work in tandem to maintain a homeostatic state. Taken together, this work provides an interpretation and visualization of the dynamic interactions and network-level effects of interacting allosteric enzymes. Enzymatic rate laws have historically been used to simulate the dynamics of complex metabolic networks with regulated reactions represented by allosteric rate laws. Here, we use detailed elementary reaction descriptions of regulatory enzymes that allow for the explicit computation of the fraction of the enzymes that are in a catalytically-active state. The fraction of the enzyme that is in the active state represents the time-dependent utilization of the enzyme’s “catalytic potential,” its capacity to catalyze a reaction. We apply this interpretation to red blood cell glycolysis, examining how three key kinases with allosteric regulation modulate their utilization of their catalytic potential based on ligand-binding events throughout the network in order to maintain a homeostatic state. We then examine how an enzyme modulates its utilization of its catalytic potential using personalized data as a case study, visualizing the systems-level properties of a kinetic model. The human red blood cell (RBC) metabolic network has historically been the target of complex kinetic model building due to its relative simplicity and the vast amounts of data and information available on its biochemistry and physiology. RBCs lack cellular compartments  and therefore certain cellular functions, such as transcriptional and translational regulation and the ability to use oxidative phosphorylation to produce energy . As a reMathematical models have been used to study the dynamics of RBC metabolism since the 1970s . ConstraHistorically, the primary way to visualize the output from a kinetic model is to plot the time profiles of individual network components . While these quantities are informative, they fail to provide insight into systemic qualities of the network. Dynamic phase portraits have been explored as an alternative . With thIn this study, we use previously described enzyme modules to model the allosteric regulatory effects of hexokinase (HEX), phosphofructokinase (PFK), and pyruvate kinase (PYK), the three major regulatory points in RBC glycolytic energy generation. We compute and visualize each kinase’s utilization of its catalytic potential as a function of the energy charge, a systemic variable. We analyze the response of each enzyme module to perturbations in ATP utilization, simulating the impact of various physiological stresses on the RBC that affect the energy charge . We then examine the robustness of the catalytic potential as a qualitative systemic measure of the state of an enzyme using randomized models and existing personalized data. Finally, we explore how the catalytic potential can be used to investigate how various enzymes work in tandem to respond to external perturbations.We are interested in studying the “catalytic potential” of an enzyme—its capacity to catalyze a reaction—from a network-level perspective. An enzyme achieves its catalytic potential when all individual enzyme species are in an active form; an enzyme with allosteric regulation modulates its utilization of its catalytic potential based on ligand-binding events throughout the network in order to maintain a homeostatic state. Here, we propose that an enzyme’s utilization of its catalytic potential can be visualized by computing the fraction of total enzyme that is available to catalyze a reaction as a function of the adenylate energy charge . In thisThe energetic state of a cell can be measured using the adenylate energy charge , which rgulators , 29 whicfA) can be calculated for an enzyme fromn is the number of enzymatic binding sites, Ri is the unbound enzyme in the active state , Ri,A is the enzyme bound to the cofactor, Ri,AS is the enzyme bound to the substrate and cofactor, and Etotal is the total amount of enzyme. The subscript i represents the amount of activators bound to allosteric sites; for tetrameric structures like PFK and PYK, i ranges between 0 and 4 [fA were computed from model simulations.To examine individual enzymatic reactions that are regulated by at least one metabolite in the combined adenosine phosphate pool  from a network-level perspective, we can compute properties of enzymes as a function of the energy charge. A kinetic model that explicitly represents each of the elementary steps for an enzymatic reaction  provides enough detail to compute the fraction of uninhibited enzyme primed to facilitate the conversion of substrate to product for enzymes allosterically regulated through effector molecules. This catalytically active fraction  for HEX, PFK, and PYK . In the following sections, we detail the construction and validation of models with enzyme modules and examine each enzyme’s utilization of its catalytic potential in response to perturbations in ATP utilization.in vivo due to arterial constriction [in vivo, while internal ATP concentrations can drop by as much as 27% or 50% due to aging or the presence of acute disease states such as gastrointestinal tumors [fA [fA versus energy charge plot.We constructed a model of RBC metabolism that comprises glycolysis, the Rapoport-Luebering (RL) Shunt, and the interaction of hemoglobin with 2,3-diphosphoglycerate ; the stotriction —can resul tumors . To modemors [fA ; the quaOur primary test case was PFK, which plays a major role in determining glycolytic flux through the conversion of fructose 6-phosphate (F6P) to fructose 1,6-bisphosphate (FDP). The regulatory mechanism of PFK is complicated , so we hfA and energy charge  into the base model and introduced the same ATP utilization perturbations. We examined the disturbance rejection capabilities of this complete model compared with models with fewer enzyme modules, noting increased regulation generally improved the ability of a model to maintain a homeostatic state Fig A in  as expecfA) for each pairwise combination of enzymes , but the observed behavior of PYK is opposite what would be expected based on the law of mass action. A decrease in energy charge would intuitively result in more catalytically active PYK since that would then result in more ATP. The literature reports this expected behavior for initial velocity of PYK . HoweverSecond, we have shown that examining an enzyme’s catalytic potential can provide additional insight into how metabolic networks maintain a homeostatic state following physiologically-relevant perturbations. A small-scale model that explicitly accounted for the regulatory mechanisms of the three glycolytic kinases allowed us to directly investigate the interplay among these three enzymes . When wefA could be explored in the future. Mass action kinetics were used for the other enzymes in the network and represent an approximation previously examined in the literature [The use of kinetic models to study the dynamics of cellular metabolism presents many well-documented challenges and limitations , 44. Manterature . While tterature , we haveterature , and theThe RBC metabolic network consists of well-studied metabolic pathways and their associated metabolites. New methods for the visualization of regulatory behaviors—such as the catalytic potential plot introduced here—can lead to new insights and discoveries. We have evaluated the utilization of an enzyme’s catalytic potential as a sensor which can be used to visualize the state of that enzyme in the context of the metabolic network. Viewing enzymes as regulators through which we can tune the system response opens the door for us to investigate what the optimal state might be and how that state helps maintain homeostasis.https://github.com/opencobra/MASS-Toolbox). Details for formulating a MASS model are found in Jamshidi et al. [All calculations were performed in Mathematica 11.1 . Simulati et al. . The sysThe base glycolysis network included all 10 glycolytic enzymes and lactate dehydrogenase; the complete stoichiometric matrix is provided in We compared our model against the cell-scale model constructed by Bordbar et al. . Our modS is the stoichiometric matrix, and v is a vector containing reaction fluxes as a function of metabolite concentrations (x) and rate constants (k).Regulation was manually incorporated into the enzyme reactions. Initial conditions from the glycolysis and hemoglobin MASS toolbox example data were used in conjunction with equilibrium constants which were obtained from from various sources . These values were subsequently utilized to solve for new kinetic parameters by setting the following constraint:ki is the kPERC for reaction i and vi is the flux through that reaction [K − eq .The parameters for all enzyme modules were determined using the methods described by Du et al. . In shorreaction ; reactioWe constructed a total of ten different models with varying amounts of regulation, spanning from the base glycolytic model with no enzyme modules (and therefore no regulation) to a model with three enzyme modules and the Rapaport-Luebering Shunt. The remaining models represented each combination of the three kinase modules. Enzyme module incorporation was accompanied by the deletion of the original single-step reaction in order to avoid redundant reactions. Stability for all systems was verified by simulating the network and ensuring that a steady-state point was found for all metabolites.HEX (EC 2.7.1.1) was modeled as a monomer to account for the fact that it contains only one active catalytic site. The previously specified mechanism was chosen to match that used by  because PFK (EC 2.7.1.11) was modeled as a homotetramer to account for its four catalytic and allosteric binding sites . The prePYK (EC 2.7.1.40) was modeled to include allosteric regulation. Additional reactions were also included to account for the equilibration of both enzymes between the relaxed (R) and tense (T) state . AdditiokATP) associated with ATP hydrolysis:i represents inorganic phosphate which was modeled as a variable quantity to allow the system to respond to these perturbations. Increasing this rate decreases the amount of available ATP and ADP. We calculated the sum of squared error (SSE) for each model in order to quantify the total deviation of the output from its setpoint, which is zero. The resulting quantity  is compared between models, with a smaller value indicating better disturbance rejection capabilities.In order to mimic a physiologically-relevant perturbation away from the homeostatic state, we simulated a 50% increase in ATP utilization for 1,000 hours and a 15% decrease in ATP utilization –35. ThesUsing the procedure outlined in Du et al. , we consPersonalized models were constructed by replacing all primary intracellular glycolytic metabolite concentrations and equilibrium constants with values reported by Bordbar et al. . New pseQ test [Q value is greater than 0.598; the Q values for the ATP and F6P binding steps had Q values of 0.84257 and 0.73164, respectively.To identify outliers within the reaction PERCs compared with the other personalized models, we performed a Dixon’s Q test :Q=gapravformation represents the forward rate of the enzyme reaction and possesses units of mmol/L ⋅ hr. Defining the rate pools in this manner neglected effects of reversible reactions contributing to the formation of product. A negative value corresponds to a net-consumption of ATP.Rate pools for enzymes were defined as the rate at which enzyme produced product. This was accomplished by defining a pool from the product’s ODE consisting solely of the terms contributing to product formation. In other words:S1 File(PDF)Click here for additional data file.S1 DataS) matrix for system. (F) Metabolite list and metabolite ID mapping for S matrix. (G) Reaction list and reaction ID mapping for S matrix.(A) Parameterization for non-personalized models. (B) Initial flux values for non-personalized models. (C) Parameterization for personalized models. (D) Initial flux values for personalized models. (E) Stoichiometric ((XLSX)Click here for additional data file.S2 Data(ZIP)Click here for additional data file.S1 Video(MP4)Click here for additional data file.


Out of three ANN algorithms, the neuralnet package with two activation functions, “logistic” and “tanh” were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations  The iehaviour ,7 and usehaviour ,9 etc. Trevisiae ,10–12, Ehia coli –15, othehia coli  and manyhia coli .in-vivo conditions [in-vitro studies using purified enzymes which might not represent the exact properties of in-vivo enzymes [max value measured in vitro may not represent the value of an in vivo system because of the destruction of enzyme complexes, cellular organisation and the absence of an unknown inhibitor or activator [Many different kinds of mathematical models exist to study biological systems ,18. Sevenditions . To builnditions . Determinditions  linked t enzymes . For exactivator ,31. A coctivator . There activator  and metactivator . Flux bactivator ,34 and tctivator . This mectivator ,32,34,35ctivator . Metabolctivator ,37,38. Tctivator  or mass Many of the biomolecules like organic acids ,41, antiGlycolysis is the centre of the metabolic system in all living organisms. It is an anaerobic pathway present in almost all living cells and helps in ATP generation. Glycolysis has been established as the central core for the fermentation. It contributes to the production of different metabolites, like citric acid, succinic acid, amino acids, etc., through pyruvate, the end product of glycolysis .The neural network is an architecture, modelled on the brain, that is organized with neurons and synapses present in a structure of nodes  connected together ,54. Each13C labelling of metabolites in mammalian gluconeogenesis by M.R. Antoniewicz et al. [The ANN has been used earlier in predicting the fluxes from z et al. . Three lz et al. .i) assess the structure of the ANN using three different approaches, ii) evaluate different activation functions and iii) compare the prediction of flux of NAD+ to the fluxes predicted by Fievet & al. (2006).Due to the challenges in estimating the flux using different methods like constraint-based and kinetic-based modelling approaches –66, we dipi (observed node) of the ni values in the input layer. To define the output value, a function called activation function is applied to this value. We note ni for the node i, the weight pi corresponds to the connection between node i, the observed node and the activation function f, associated with the observed node  and the method to estimate the weights.in-vitro reconstructed upper part of glycolysis [The flux measurement data from ycolysis  was usedin vitro  using three different packages: nnet (version 7.3–12) [The artificial neural network built with a single layer of hidden units  using st 7.3–12) , neuraln 7.3–12)  and RSNN 7.3–12) .2) (Equation 3) values between the three methods. The algorithm with the lowest RMSE value and highest R2value during the leave-one-out cross validation was chosen as an algorithm of interest and the effect of the number of hidden units on RMSE and R2 was analysed between 1 to 25 hidden units.The network consists of three layers: a) Input (I), b) Hidden layer (H) and c) Output. These layers are connected by edges or neurons. The weighted sum of neuron inputs is submitted to a function which conditions neuron activation. There is no rule for deciding the number of neuron units in a single hidden layer; to choose the best algorithm out of three , we first chose the number of hidden units according to Equation 1 and compared the RMSE (Equation 2) and coefficient of determination  and tanh (Equation 4 and Equation 5 respectively).The three-neural network algorithms: nnet , neuralnEquation 4: logistic activation function.Equation 5: tanh activation function.2 was studied  and tanh, with RMSE and RIt has not escaped our attention that the artificial neural network model depends on the diversity of the training data, and hence training the model with a maximum of variability in the concentration of enzymes plays a crucial role. The model built in this study might not be enough to extrapolate a model for all other enzyme concentrations. In future, the study will be extended to predict the wide range of flux and the enzyme concentration for a particular flux. Experiments in a wet laboratory will be carried out for an upcoming application paper. Even though further work has to be performed, the artificial neural network approach is a promising method in metabolic pathway modelling and could find its place in metabolic engineering and industrial scale biomolecule synthesis.S1 FileTable A: Input used to build artificial neural network from Fievet et al., 2016. Table B: Comparision of RMSE and R-squared values during the leave-one-out crossvalidation between neuralnet, nnet and RSNNS algorithm. Table C: RMSE and R-squared for values between the activation functions logistics and tanh during leave-one-out cross validation for the neuralnet method. R-Scripts A: R-script used to obtained the (DOCX)Click here for additional data file.


Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data.The green microalga D. salina.The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.The reconstructed metabolic network of  Dunaliella salina is an excellent model organism to investigate changes in metabolism β-carotenβ-carotene flux (Car14). The same growth rates as in scenarios A - D were calculated (0.1287 h-1 and 0.7934 h-1 for nutrient-replete conditions and 0.0007 h-1 for nutrient-depleted conditions). However, the predicted β-carotene flux was different compared to scenarios A - D.The tested scenarios E - H Table  were simβ-carotene accumulation of 0.6962 mmol/(g dw · h) was predicted under low light conditions whereas a β-carotene flux (Car14) of 1.2972 mmol/(g dw · h) was predicted under high light conditions. Under nutrient-depleted conditions, the predicted β-carotene flux (Car14) was 0.7556 mmol/(g dw · h) under low light and 1.5359 mmol/(g dw · h) under high light conditions  is in the range of previously published reduced networks for green microalgae .D. salina de-novo synthezise up to 10% of their dry weight as the isoprenoid molecule β-carotene [D. salina, the downregulation of PDH induced by high NAD(P)H levels under abiotic stress conditions favors β-carotene hyperaccumulation rather than massive TAG accumulation [With respect to the carotenoid synthesis, it was essential that all enzymes of the isoprenoid biosynthesis were identified, because under environmental stress cells of carotene . Furthercarotene  proposedmulation .β-carotene fluxes shown in Table D. salina CCAP19/18 where low light and nutrient depletion led to the lowest β-carotene fraction of 30 mg/g dw followed by high light without nutrient stress with 43 mg/g dw. The highest experimentally observed β-carotene fraction was detected under high light coupled with nutrient stress, namely 80 mg/g dw [β-carotene acting as a metabolic sink under conditions where growth is limited by excess light or nutrient stress [The results of the predicted  mg/g dw . This ist stress . The absD.salina CCAP19/18 based on the recently announced annotated genome [D.salina cultivated under nutrient repletion and depletion in a flat-plate photobioreactor [D.salina metabolism. In conclusion, the metabolic network reconstruction is suitable to gain a better understanding of the flux distribution in the carbon core metabolism during carotenogenesis in D. salina. The ongoing experimental and computational advances will thereby accelerate the engineering of industrially valuable strains and provides the basis for effective biotechnology with photosynthetic microorganisms.This work presents a metabolic network reconstruction of the carbon-core metabolism of d genome . The netoreactor . All modD. salina CCAP19/18 carbon-core metabolism was reconstructed using a traditional (bottom-up) approach, which relied on manual reconstruction. It is based on the assignment of all annotated genes in the nuclear genome of D. salina CCAP19/18 to their proteins and the corresponding reactions supported by biological databases such as KEGG [The stoichiometric model of  as KEGG . The com as KEGG .Some metabolites in our stoichiometric network model may have one or more designations denoting their presence in different cellular compartments. Exchange reactions were added allowing the import and export between the considered cellular compartments.The complete set of reaction equations was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility . Unless Additional file 1 List of reactions, metabolites and biomass composition.Additional file 2 SBML file.Additional file 3 Flux values, constraints, gene-protein reaction associations.


S. cerevisiae as a model organism we study the effect of acute exposure to crude MCHM on metabolism. Yeasts were treated with MCHM 550 ppm in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. A total of 215 and 73 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 26 and 23 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 49 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis, with a potential impact on the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cellular growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM's toxicological data were limited, an issue that has been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. Using  On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents [MCHM is considered a moderate-to-strong dermal irritant, causes fetal malformations in rats when orally exposed to 400 mg/kg/day . The higSaccharomyces cerevisiae is one of the most intensively investigated, well-consolidated and widely used eukaryotic model organism. Its use has allowed the gain of insights in basic cellular mechanisms such as cell cycle progression, DNA replication, vesicular trafficking, protein turnover, longevity and cell death [S. cerevisiae an ideal model for functional toxicological studies, such as: being unicellular, the ease of genetic manipulation, availability of a huge repertoire of dedicated experimental tools, protocols, software and databases, a high degree of functional conservation with more complex eukaryotes, among others [S. cerevisiae by a battery of omics approaches, including transcriptomics, chemogenomics, proteomics and metabolomics .2+ exposure [Focused on the analysis of the whole repertoires of endogenous or exogenous metabolites that are present in a biological system at a given time point metabolomics serves as a link between genotype and phenotype ,15. Metaexposure , toleraexposure , ethanolexposure ,21, amonS. cerevisiae to date [Flux balance analysis (FBA) –25 with  to date . The con to date .The accuracy of FBA predictions can be improved by the integration of experimental data . SeveralIn the present work, we study the effect of MCHM on metabolism using yeast as a model organism, combining metabolomics tools with FBA simulations on genome-scale metabolic network models of yeast constrained by RNA-Seq data. We found that MCHM treatment altered metabolites and gene expression across metabolic pathways. Amino acid levels as well as, many amino acid precursors increased while phospholipids decreased. The increase in amino acid levels could be explained at the transcriptomic level as amino acid biosynthetic genes were upregulated. Ribosome biosynthesis genes were downregulated, which is often seen in response to stress. Several genes involved in mitochondrial function were upregulated. The role of the mitochondria in MCHM was further supported by merging the metabolomics and transcriptomics data in a flux balance analysis. This predicted that the growth inhibition of MCHM would be minimized as the concentration of D-Glucose decreased. FBA simulations suggest Ubiquinol:ferricytochrome c as the limiting reaction, which would be caused by the MCHM induced under-expression of CYT1 gene.his3, ura3, leu2, met15) [Wildtype yeast from the S288c background   were gro2O, following a modified version of a published protocol [™ Kontes™ KIMAX™ Reusable High Strength Centrifuge Tubes from Fisher Scientific. Half of the original protocol volume values were used. For extractions headed to GC-MS analysis, 50 μL of ribitol internal standard (10 mg/mL) were added. 3 mL of the polar and 3 mL of the lipid phase were collected per sample. The polar phase was dried in SpeedVac (ThermoFisher Scientific). The lipid phase was dried overnight in a fume hood. For ESI-MS experiments, but not for GC-MS, the dried polar phases were re-suspended in 500 μL of MeOH and the lipid phases were re-suspended in 500 μL 1:1 chloroform: MeOH. All extracts were stored at -20 oC for analysis within 48 hours.Lipid and polar metabolites were extracted with a 1:2:0.8 mixture of chloroform: MeOH: Hprotocol . HPLC groC, S-lens RF level: 25.0. For lipid compounds in positive mode the following parameters were modified; scan range: 150.0–2,000.0 m/z, sheath gas flow rate: 15, aux gas flow rate: 11, spray voltage: 3.50, capillary temperature: 300°C, S-lens RF level: 25.0. For lipid compounds in negative mode the previous parameters were kept, except for the spray voltage, which was set to 3.20 kV.Samples were analyzed by direct injection of the resuspended extracts in a Thermo Fisher Scientific Q-Exactive, with an ESI (electrospray ion source), using positive and negative modes. For polar compounds in positive mode the injection speed was 10 μL/min, the scan range was 50–750 m/z, no fragmentation, 140,000 resolution, 1 microscan, AGC target 5x, maximum injection time of 100, sheath gas flow rate of 10, aux gas flow rate of 2, no sweep gas flow, spray voltage 3.60 kV, capillary temperature of 320°C, S-lens RF level 30.0. For polar compounds in the negative mode most parameters remain the same, except for spray voltage: 3.20 kV, capillary temperature: 300 mass spec wavelet method from the MassSpecWavelet 1.46.0 R package [Mzclust method, followed by groupChromPeaks. All features were plotted visually inspected. The intensity values of each feature in each sample were obtained with the featureValue method as the integrated signal area for each representative peak per sample. The feature intensity and feature definition tables were saved as CSV files. Feature intensities were normalized by the total sum of the intensities of all the features detected in the corresponding spectrum (being identified or not). The normalization was done spectrum wise, so the normalized feature intensity values were a percentage of the total intensity of the spectrum of origin. Features were identified via MetaboSearch 1.2 [50 scans were obtained per sample and later averaged with Thermo Scientific Xcalibur 2.1 SP1. Averaged spectra in the positive and negative mode were processed for polar and lipid fractions, separately with xcms 3.2.0 . Peaks w package . Peaks warch 1.2 , with thAfter the feature identification, normalized feature intensity tables (keeping only identified features) coming from the same biological replicate (both positive and negative modes from polar and lipid fractions) were merged as a single intensity table.Features ids were confirmed by targeted MS/MS experiments, with selected features m/z values included in a target list. The isolation width in the quadrupole was 1.0 m/z and nitrogen was used as the collision gas. The fragment ions were measured in the Orbitrap with a resolution of 17,500 FWHM at 200 m/z, accumulation target 1E5, maximum fill time 60 ms and normalized collision energy of 29. The resulting fragmentation spectra were queried against Metlin and HMDB, with a mass error of 5 ppm.Six biological replicates per group for MCHM treated and untreated controls were used. The experiment was repeated twice with consistent results. These biological replicates were not the same used in GC-MS experiments.-1 and held for 5 min. For lipid-soluble metabolite analysis, after an initial temperature hold at 150°C for 1 min, the oven temperature was increased to 320°C at 12°C min-1 and held for 7 min. Injector and detector temperatures were set at 250°C and 250°C, respectively. An aliquot of 1 μL was injected with the split ratio of 70:1. The helium carrier gas was kept at a constant flow rate of 1.2 mL min-1. The mass spectrometer was operated in positive electron impact mode (EI) at 70.0 eV ionization energy at m/z 40–500 scan range.50 μL of Methyl heptadecanoate 2 mg/mL was added as the internal standard to each lipid sample before derivatization. Lipid and polar fractions were derivatized with BSTFA  and MSTF™ Chromeleon™ . Features were identified against a locally characterized set of central metabolites (targeted metabolomics), when possible. Other features were identified querying NIST database (untargeted metabolomics). Feature intensity tables were saved as CSV files, keeping only the identified features.Peak identification and grouping, and feature intensities calculation were performed with Thermo ScientificFeatures intensities from lipid and polar fractions were normalized against its corresponding internal standards (methyl heptadecanoate for lipid and ribitol for polar fractions) and then the ones coming from the same biological replicate (both lipid and polar fractions) were merged as a single intensity table.Six biological replicates per group for MCHM treatment and untreated controls were used. The experiment was repeated three times with consistent results. These biological replicates were not the same used in ESI-MS experiments.Feature intensity tables from ESI-MS and GC-MS were processed with MetaboAnalyst 4.0  and R 3.PLS-DA and OPLS-DA models were validated by permutations as implemented in MetaboAnalyst 4.0 . BrieflyThe performance of the sPLS-DA models was evaluated using leave-one-out cross-validations.The heatmaps of the relevant compounds were done with the R package pheatmap 1.0.12.Saccharomyces cerevisiae pathway library was used, as well as the hypergeometric test for the over-representation analysis and relative-betweenness centrality for the pathway topology analysis.The Pathway Analysis was performed with MetaboAnalyst 4.0 using the name of the relevant compounds from the ESI-MS and GC-MS combined. The Some pathways were represented as Escher maps  with thelys5) cells in YPD treated or not with MCHM [www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108873, containing count data generated via Rsubread and the differential expression data generated via DESeq2. MA plot and KEGG Pathway Enrichment Analysis were done with R packages ggpubr and clusterProfiler [A fraction of previously reported data was used, including only the samples with wildtype S288c . Two models came out as the result of this procedure, one for MCHM treated yeast and one for the untreated control.optimize method from COBRApy and with the default yeastGEM media. Phenotype phase plane of Growth vs D-Glucose exchange was calculated with the production_envelope method and the corresponding graphics generated with ggpubr.Default solutions were determined for each model using the Upper bounds of selected reactions were manually modified to test for the importance of such reactions in growth.mmol/(gDW*hour).All fluxes are in To assess how MCHM treatment affects metabolism, 215 and 73 metabolites were identified by the ESI-MS and GC-MS procedures, respectively . The comFeatures from the MS spectra were detected, grouped, identified and their intensities calculated as described in Materials and Methods. Intensities were normalized to facilitate multivariate analysis see  top and Relevant compounds were selected by a majority voting model, which takes into account the result of the t-test and eight multivariate analysis  and GC-MS (right) , 49 uniqThese relevant metabolites were used as input for pathway analysis , which chttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108873 [We used a data set generated previously by our laboratory and available from SE108873 . For thiFrom gene expression measurements for 3946 genes, 87 were upregulated and 30 downregulated due to MCHM treatment  potentiaSDA1 and RRP1, involved in 60S ribosome biogenesis [ESF1, its depletion causes severely decreased 18S rRNA levels [BFR2, involved in pre-18S rRNA processing and component of SSU processome [MRD1, required for the production of 18S rRNA and small ribosomal subunit [NOP4, constituent of 66S pre-ribosomal particles and critical for large ribosomal subunit biogenesis and processing and maturation of 27S pre-rRNA [NOP7, component of several pre-ribosomal particles [SDA1 function causes cells to arrest in G1 before Start and to remain uniformly as unbudded cells that do not increase significantly in size [Seven downregulated genes were involved in ribosome biogenesis: ogenesis ,49. ESF1A levels . BFR2, iocessome . MRD1, r subunit . NOP4, cpre-rRNA . NOP7, carticles . Loss of in size ,56.CWP1 and TIR1) and three involved in iron and zinc transport and homeostasis . MCHM affects the intracellular levels of iron and zinc [Among the rest of downregulated genes, there are two that encodes for cell wall mannoproteins  : ARG1, Aynthesis .AHA1, GRE2, PDR3, PDR16, ICT1, TPO1, ENB1, SNQ2, and QDR3.Nine stress response-related genes are up-regulated due to MCHM treatment: MAE1, BAT1, ILV6, IDP1, GCV2, and LYS12) and three mitochondrial transporters  were upregulated. From these, MAE1 codes for the mitochondrial malic enzyme which catalyzes the decarboxylation of malate to pyruvate ; BAT1 and ILV6 products are involved in branched-chain amino acid biosynthesis and ODC2 codes the 2-oxodicarboxylate transporter, which exports 2-oxoglutarate and 2-oxoadipate from the mitochondrial matrix to the cytosol for use in glutamate biosynthesis and in lysine metabolism.It is of note that genes encoding for six mitochondrial enzymes , and using as the objective function the maximization of growth. A summary of the result of FBA simulations with these models is shown in Tables Using the expression data and the gene rules from the yeastGEM model (version 8.3.0) upper bounds were calculated for 2504 reactions of the model. Two new metabolic models were created from the original yeastGEM model, named Our FBA simulations predict that the effect of MCHM on growth was diminished when the concentration of D-Glucose in the medium was decreased . There wWe then focused on the seven significant pathways from the pathway analysis , to analtreated model. Two reactions operated at max flux in the model of the treatment (The reaction or reactions limiting the growth (limiting reactions) must be operating at the maximum allowed flux  in the ubiquinol:ferricytochrome c reductase was the limiting reaction we modified its upper bound in the control model to the one it has in the treated  or (Q0105 and YBL045C and YDR529C and YEL024W and YFR033C and YGR183C and YHR001W-A and YJL166W and YJR048W and YOR065W and YPR191W)COB (Q0105), COR1 (YBL045C), QCR7 (YDR529C), RIP1 (YEL024W), CYC7 (YEL039C), QCR6 (YFR033C), QCR9 (YGR183C), QCR10 (YHR001W-A), QCR8 (YJL166W), CYT1 (YOR065W) and QCR2 (YPR191W). The genes encoding for the second were COB (Q0105), COR1 (YBL045C), QCR7 (YDR529C), RIP1 (YEL024W), QCR6 (YFR033C), QCR9 (YGR183C), QCR10 (YHR001W-A), QCR8 (YJL166W), CYC1 (YJR048W), CYT1 (YOR065W) and QCR2 (YPR191W). The maximum flux of a multisubunit complex will depend on the gene with the lowest average expression, which will be the limiting factor of the complex assembling. For both possible complex configurations, in both control and treatment conditions, CYT1 (YOR065W) had the lowest average expression , leading to amino acid accumulation. The downregulation of ribosome biogenesis is the first step in stress response such as starvation, heat, or chemical. To respond to stress, energy-intensive functions are down-regulated and inhibition of rRNA occurs in less than ten minutes of dextrose depletion .2+ increased the levels of L-glutamate, L-phenylalanine, and L-leucine and decreased the level of L-aspartate in S. cerevisiae [There is evidence of other cellular stressors which also variate the levels of some amino acids. Curevisiae . From thMCHM treatment provokes the upregulation of nine genes related to the stress response. From these genes, AHA1 encodes a co-chaperone that binds Hsp82 and its expression is regulated by stresses such as heat shock [GRE2 encodes the 3-methylbutanal reductase and its expression is induced by oxidative, ionic, osmotic, heat shock and heavy metals stress [PDR3 is a transcriptional activator of the pleiotropic drug resistance network [PDR16 encodes the phosphatidylinositol transfer protein and it is controlled by the multiple drug resistance regulator Pdr1p. It affects lipid biosynthesis and resistance to multiple drugs [SNQ2 and QDR3 encode multidrug transporters involved in multidrug resistance [ENB1 encodes for an endosomal ferric enterobactin transporter, which is expressed under conditions of iron deprivation [TPO1 codes for a polyamine transporter which exports spermine and spermidine from the cell during oxidative stress, controlling the timing of expression of stress-responsive genes [ICT1 codes the lysophosphatidic acid acyltransferase responsible for enhanced phospholipid synthesis during organic solvent stress [at shock . GRE2 ens stress . PDR3 is network . PDR16 ele drugs . SNQ2 ansistance ,63; ENB1rivation ; TPO1 cove genes ; ICT1 cot stress .In vitro MCHM acts as a hydrotrope, a compound that increases the solubility of proteins by inducing liquid-liquid phase transitions [We did not detect enhanced phospholipid biosynthesis in our metabolomics analysis, by the contrary, the levels of all glycerophospholipids included in the relevant metabolites were decreased due to MCHM, while the levels of the remaining phospholipids did not change. The reduced levels of these molecules of phosphatidylethanolamine, phosphatidylinositol, and phosphatidylserine in MCHM treated cells point toward a significant effect of MCHM in yeast cellular membranes, with potential effects on their biophysical properties, which could impact several cellular processes involving membranes. nsitions . At highnsitions ). The wiCYT1 product was the component limiting the overall reaction flow, and the lower expression of CYT1 due to MCHM can explain the lower growth, at least in the FBA simulations. It is of note that the fold change of the expression levels of CYT1 was not large enough (logFC < 2) for the gene to reach the cutoff as relevant from the RNA-Seq data, but the GSMNM created were very sensitive to its levels. This highlight the extra value of RNA-Seq data integration in FBA simulations, allowing to assess the impact of gene levels in whole-cell functional environment, where apparently irrelevant genes can prove to be the driven force behind observed phenotypes. Transcription of CYT1 is positively controlled by oxygen in the presence of glucose, through the haem signal and mediated by the transcription factor, Hap1 [CYT1 basal transcription is partially affected by Cpf1, transcription factor required for regulation of methionine biosynthetic genes [The FBA simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control yeast were able to reproduce the deleterious effect of MCHM on cell’s growth. These GSMNM integrated the gene expressions from the RNA-Seq data, as explained in Materials and Methods. The flux ratio through several reactions in the six significant pathways from the metabolomics analysis was linked to the simulated growth ratio in MCHM-treated vs untreated control models, but this does not indicate causality. The FBA simulations suggest a critical role to the ubiquinol:ferricytochrome c reductase as the enzyme catalyzing the limiting reaction which determined the reduced growth in MCHM. From this multisubunit complex or, Hap1 . It is aic genes .in vitro measured enzyme specific activities it was determined that fermentation was more catalytically efficient than respiration [The other significant reaction that came from the FBA analysis was the ATP synthase, which maximum allowed flux or upper bound was required to be increased together with the one of ubiquinol:ferricytochrome c reductase to rescue the control growth phenotype in the MCHM treated model. Combining flux balance analysis with piration , producipiration .S. cerevisiae, affecting several amino acid-related metabolic pathways and probably slowing down protein biosynthesis due to the downregulation of genes related to ribosome biogenesis. MCHM affects phospholipid biosynthesis, reducing the levels of different molecules of phosphatidylethanolamine, phosphatidylinositol, and phosphatidylserine, which should affect cellular membranes composition and their biophysical properties. The FBA simulations suggest that the lower flow through ubiquinol:ferricytochrome c reductase reaction, caused by the MCHM-provoked under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.MCHM produced amino acid accumulation in S1 FigScore plots from the Orthogonal-Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) (top) and the Sparse Partial Least Squares—Discriminant Analysis (sPLS-DA) (bottom), for ESI-MS (left) and GC-MS (right) data. The 95% confidence areas are shown as well as the explained variance, shown in brackets in the corresponding axis labels.(TIF)Click here for additional data file.S2 FigPLS-DA models validation by permutation tests based on separation distance for ESI-MS (A) and GC-MS (B). OPLS-DA models validation by permutation tests, showing the observed and cross-validated R2Y and Q2 coefficients, for ESI-MS (C) and GC-MS (E). Plot of the performance of the sPLS-DA models evaluated using leave-one-out cross-validations with increasing numbers of components, for ESI-MS (E) and GC-MS (F).(TIF)Click here for additional data file.S3 FigSignificant features identified by Random Forest for A) ESI-MS and B) GC-MS data. The features are ranked by the mean decrease in classification accuracy when they are permuted.(TIF)Click here for additional data file.S4 FigEmpirical Bayesian Analysis of Microarray (EBAM) for A) ESI-MS and B) GC-MS data. 33 and 23 significant compounds are identified with this method for ESI-MS and GC-MS, respectively.(TIF)Click here for additional data file.S5 FigSignificance Analysis of Microarray (SAM) for A) ESI-MS and B) GC-MS data. The green circles represent features that exceed the specified threshold. 39 and 29 significant features are identified by SAM from ESI-MS and GC-MS respectively.(TIF)Click here for additional data file.S6 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. The ratio value of 0.839 is common among the map.(TIF)Click here for additional data file.S7 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. All the reactions have a ratio value of 0.839.(TIF)Click here for additional data file.S8 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. The ratio value of 0.839 is common among the map.(TIF)Click here for additional data file.S9 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. The glycerol-3-phosphate dehydrogenase reactions has a ratio value of 0.839.(TIF)Click here for additional data file.S10 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. The ratio value of 0.839 is common among the map.(TIF)Click here for additional data file.S11 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values. Only a fraction of KEGG’s reference pathway is present in yeast.(TIF)Click here for additional data file.S12 FigThe flux ratios between treated and control model FBA solutions are represented. Edges' thickness and color are a function of the respective ratio values.(TIF)Click here for additional data file.S1 TableThe experiment of origin is indicated as well as the following values from the comparison of their levels in the MCHM treated vs control samples: p-value (t-test), adjusted p values (“BH”), q-values, log2 of the fold change, and the coefficient of variation of the controls.(XLSX)Click here for additional data file.S2 TableThe 128 compounds being labeled as significant for at least one of the nine analyses used are shown. For each analysis is indicated if the respective compound is significant (1) or not (0). These values are added for the final votes. Compound with a majority of votes (5 or more) are selected as relevant , for a total of 26 compounds.(XLSX)Click here for additional data file.S3 TableThe 66 compounds being labeled as significant for at least one of the nine analyses used are shown. For each analysis is indicated if the respective compound is significant (1) or not (0). These values are added for the final votes. Compound with the majority of votes (5 or more) are selected as relevant , for a total of 23 compounds.(XLSX)Click here for additional data file.S4 TableThe fold change and adjusted p values are provided, as well as a functional annotation, when available.(XLSX)Click here for additional data file.