Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains.Metabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including It was observed that four selected commercial wine yeast strains  produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation . Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production.Overall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations.The online version contains supplementary material available at 10.1186/s12934-021-01694-0. Saccharomyces cerevisiae (S. cerevisiae)) strains to complete alcoholic fermentation and produce desirable aroma compounds, so-called volatile organic compounds (VOCs). It has been assessed that wines contain more than 1000 different VOCs of which more than 400 are directly attributed to yeasts [The ability to produce wines with specific sensory profiles would be immensely beneficial to the global wine industry. In addition to characteristics coming from the grape juice, this optimal production is heavily contingent upon commercial yeast  was used to analyze fluxes relative to the stage of fermentation. Fig. . From thIn order to reveal the important fluxes that drive the variation in different time points , a variable factor map was plotted, and these variables are shown with a color scale based on their cos2 values ). The top 20 reactions based on absolute differences are listed in Table Flux sampling was applied using Yeast 8.4.2 and constrained using experimental flux data at several times during yeast cell growth  . The clustergrams were constructed from the median values of the flux sampling analysis of the top 20 absolute different reactions Fig. . This waIn this work, a CBM of random flux sampling was used to examine the differences in intracellular metabolic flux states of commercial wine yeasts under typical enological fermentation conditions. The fluxes were derived from experimental measurements of numerous nutrients, including amino acids and sugars, as well as primary and secondary products, including key VOCs at different phases of cell growth. They were used to constrain the model for flux sampling analysis. The intracellular metabolic flux states were successfully characterized without the need of designating an objective function for optimal states as was necessary in earlier works , 38, 40.While several earlier studies have focused on integrating extracellular metabolite concentration or flux measurements with yeast GSMMs, those studies pertained to nutrient-rich media or aerobic processes , 43, 44.Opale yeast was associated with isobutanol, isoamylol, and isoamyl acetate, therefore it makes sense that it was higher in median flux of the pyruvate decarboxylase reaction (r_0959) because this reaction leads to isoamylaldehyde which is a precursor for isoamylol and isoamyl acetate. On the other hand, R2 which was lower in all VOCs, has higher median fluxes going toward central metabolism  and a lower median flux in the reaction associated with glycerol-3-phosphate dehydrogenase (NAD) (r_0491) which is related to the synthesis of secondary metabolites (VOCs). The other two strains, Elixir and Uvaferm, were associated with 2-phenylethanol, propanol, and ethyl hexanoate. Therefore, it is reasonable that the median fluxes for reactions r_0216, r_0219, r_0064, and r_0491 were higher as these reactions lead to the precursors for these VOCs. Since these two strains were lower in isoamylol and isobutanol, it makes sense that the median flux for the hexokinase associated reaction (r_0534) was higher than that for Opale.In this work, it was observed that Uvaferm and Elixir strains behaved similarly while Opale and R2 were the most distinct. The results point to amino acid and pyruvate metabolism being more active in Opale. Therefore, Opale is associated with isoamylol, isobutanol, and isoamyl acetate. R2 was shown to have lower activity in amino acid and pyruvate metabolism, and hence has lower levels of VOCs than the other strains. Uvaferm and Elixir are similar to each other when examining central carbon, pyruvate, amino acid, and fatty acid degradation metabolism where we predicted higher median fluxes. That would explain why Uvaferm and Elixir produce higher amounts of 2-phenylethyl acetate and ethyl hexanoate, and lower amounts of ethyl acetate. Overall, the phenotypic differences among the strains are predicted to stem from major differences in pyruvate, tyrosine, glycine, serine, threonine and central carbon metabolism. Random flux sampling also predicted substantial differences in metabolic pathways responsible for the generation of secondary metabolites such as Ehrlich pathway. Using Yeast 8.4.2 coupled with our flux sampling approach allowed us to compare predicted fluxes of relevant pathways at different growth phases among the commercial yeast strains. Moreover, by using genome-scale CBM, some insight could be gathered into the global, interconnected pathways responsible for the variation in metabolism and, thus, aroma-producing capabilities among the strains. This work indicates central carbon, amino acid, sulfur, and lipid metabolism play varying roles throughout fermentation to lead to strain-specific characteristics. Results highlighted here reveal the need for more studies to comprehensively investigate nitrogen and lipid metabolism as well as central carbon metabolism to understand their impact on yeast aroma formation. For instance, studies have confirmed the essential role acetaldehyde has within core carbon metabolism and have linked the enzyme ADH2 to oxidizing ethanol to form acetaldehyde . SubsequFrom the flux sampling analysis, it was observed that not only ADH2 associated reactions are accounted for the strain variation, but also PDC1, PDC5, and PDC6 pyruvate decarboxylase as well as 3-methyl-2-oxopentanoate decarboxylase related reactions. This result highlights the interdependent relationship of carbon and nitrogen metabolism and how the expression of intricate pathways can lead to aroma differences. PDC1, PDC5, and PDC6 are involved in the Ehrlich pathway, leading to the irreversible decarboxylation of the α-keto acid to an aldehyde . This paThe CBM approach utilized in this work analyzed and compared the various predicted intracellular metabolic flux states of commercial yeast strains during enological fermentation, including examining the metabolic shifts within the production of VOCs and the consumption of nutrients . The intracellular flux distribution predictions show qualitative agreement with the specific variations found from performing principal component analysis on extracellular flux values. Furthermore, these results indicate elaborate fluctuations and distinctness in nitrogen, carbon, and lipid metabolism that lead to strain-specific differences in VOC formation. From the changing metabolic flux distributions among the strains, the differences in GPR activity were compared and highlighted. Therefore, probable genetic differences among the strains could be inferred and targets for genetic modification could be explored. Although the results in this study identified nitrogen metabolism which is supported by other works as causing VOC specific strain behavior, the results also show carbon and lipid metabolism play a role in VOC formation. This revelation points to the need for additional studies to explore impact of other parts of metabolism on VOC formation in yeasts. Overall, the approach and insight gained here were in good agreement with experimental observations and other studies, making this a promising approach for future use in studies related to individual fluxes of important metabolites in enological conditions and comparing metabolic differences between commercial wine yeast strains. In addition, this work help spur new quests in creating more precise aroma producing wine yeast strains.The experimental data used in this study is from Scott et al. . In thisTM(Uvaferm), Lalvin R2TM (R2), Lalvin ICV OpaleTM , and VitilevureTM Elixir YSEO (Elixir). All strains were Lallemand  commercial yeast strains. In addition, all yeast strains were obtained from the UC Davis Enology Culture Collection containing the following culture collection numbers: Uvaferm (UCD4004), R2 (UCD2033), Opale (UCD2797), and Elixir (UCD4008). These yeast strains were selected based on the different fermentation and aroma-producing performance attributes reported by the manufacturer.The yeast strains used in experiments were Uvaferm 43Yeast 8.4.2 [S. cerevisiae, S288C, a laboratory yeast strain not typically used in industrial settings. However, since this study was applied to fermentations under enological conditions, the GSMM was modified to reflect the anaerobic state of metabolism appropriately. Here, a strategy was applied as described by Heavner et al. [Yeast 8.4.2 includes expanded coverage of aroma-associated pathways such as an extended Ehrlich pathway, more ester formation reactions, and enhanced sulfur reduction pathways as previously performed and described in the literature [The GSMM employed in this study was st 8.4.2 , which ir et al. , constraz et al. , 53 . Principal component analysis (PCA) was conducted using the FactoMineR package [Data analysis was performed using R . ) and many were considered irrelevant because they are reactions describing transport between compartments. Finally, histograms were generated to characterize the solution space of the 20 key reactions, which contributed the metabolic difference among the strains related to experimental criteria. These histograms illustrated respective reaction fluxes along with solution frequencies. Random sampling was performed using Cobra Toolbox 3.0 [https://github.com/opencobra/COBRA.tutorials/tree/master/analysis/uniformSampling).Random flux sampling is an adept approach used to characterize the solution space within a GSMM network. This method involves obtaining a statistically significant number of solutions that have been uniformly distributed throughout the entire solution space . By usinlbox 3.0  functionModeling was performed in MATLAB® 2018b  using Cobra Toolbox 3.0  and implAdditional file 1: Table S1. The top 20 reactions that hat represented the greatest percent flux variation among the distributions among yeast strains for every condition.


Data of muscle pH and metabolite accumulation were based on published research for intense exercise to contractile failure within ~3 min, from which capacities and time profiles were modeled. Data were obtained from prior research for multiple competitive cation dissociation constants of metabolites and the chemical reactions of non‐mitochondrial energy catabolism, and pH dependent calculations of ~H+e from specific chemical reactions. Data revealed that the 3 min of intense exercise incurred a total ATP turnover of 142.5 mmol L−1, with a total intramuscular ~H+ exchange (‐‘ve = release) of −187.9 mmol L−1. Total ~H+ metabolic consumption was 130.6 mmol L−1, revealing a net total ~H+e (~H+te) of −57.3 mmol L−1. Lactate production had a ~H+te of 44.2 mmol L−1 (for a peak accumulation = 45 mmol L−1). The net ~H+te for the sum of the CK, AK, and AMPD reactions was 36.33 mmol L−1. The ~H+te from ATP turnover equaled −47.5 mmol L−1. The total ~H+ release to lactate ratio was 4.3 (187.9/44). Muscle ~H+ release during intense exercise is up to ~4‐fold larger than previously assumed based on the lactic acid construct.The purpose of this investigation was to present calculations of fractional H + exchange (~H+e) from the chemical reactions of non‐mitochondrial energy catabolism were performed to quantify the extent of ~H+ release during 3 min of intense muscle contractions. The total ~H+ release was −187.9 mmol L−1 and total ~H+ consumption was 130.6 mmol L−1. The largest sources of ~H+ release were from glycolysis and ATP hydrolysis and the largest contributor to ~H+ consumption was lactate production (44.2 mmol L−1).Calculations of fractional H For example, the Nobel Prize work of Hill and Meyerhoff in 1922  consumer and as such opposes not contributes to metabolic ~H+ release. For example, Robergs et al.  for binding with ionizable groups of metabolites  and many reactions presented in biochemical textbooks do not present ~H+ balanced chemical reactions, thereby misleading readers of the actual involvement of ~H+ release or consumption in many reactions of cytosolic energy catabolism. Even reactions that do not directly involve covalent H+e still have ~H+e due to differences in weak ~H+ dissociation or association between the substrates and/or products of the reaction across the physiological pH range  as proposed by Stewart Stewart, , 1983. T+ from muscle to blood , though each method has its own limitations as explained in the Discussion.Regardless of the mechanism(s) accounting for cellular metabolic acidosis, the last 80 years of research inquiry of muscle metabolic acidosis during intense exercise documents a clear cellular acidosis that directly alters systemic  acidosis by multiple transport mechanisms of H+e of the creatine kinase (CK) reaction prior to the meaningful activation of and substrate flux through the glycolytic pathway , which was further compared to the data for ATPto from Bangsbo et al.  were used as an external validation of the computed data for ATPto from this research.The list of metabolites and reactions included in this computational evaluation of skeletal muscle ~Hs et al.  and are s et al. , 1987b wo et al.  and Medbo et al. . The ATP−1 dry wt. or mmol kg−1 wet wt. to mmol L−1 muscle water based on the assumption of the cellular mass being 76% water   Table . For lacn Figure . Thereaft et al. .+ exchange coefficients (~H+) of each reaction from Robergs et al. (2017) were imported into the software. Metabolite data were then corrected based on the subtraction of resting values (ΔM) (Equation incr) at 1 Hz . The polynomial coefficients for the accumulation of each metabolite and also the pH dependent fractional HEquation  and thenEquation .a), which was assumed to represent production in this anoxic model, the increment metabolite data were adjusted based on the equations of Table a was equal to the accumulation (or breakdown) of pertinent substrates or products based on the integral of the increment data . Noted examples of this approach are for the LDH and CK reactions. For reactions involved in sequential multiple reaction pathways, Mf was based on adjustment to the final accumulating product of the pathway. For glycolysis during the modeled ATP demand of this study, this was lactate. As such, the muscle lactate accumulation data were used to derive all other metabolite flux calculations from glycogenolysis and glycolysis, as presented in Table f was quantified by adding the profiled Ma of muscle pyruvate accumulation to the Mf of the LDH reaction . This pyruvate kinase Mf was then added to the Ma of phosphoenolpyruvate to compute the enolase reaction Mf. The enolase Mf was then used as the core component of the preceding reaction, as shown in the equations of Table To compute metabolite accumulation (MEquation . For reatf) was the integral of the Mf data corrected for carbon numbers, the multiple reactions of aldolase and TPI at the start of phase‐II of glycolysis, as well as for the summation of the AK and AMPD reactions to account for flux through the AK reaction. Note that for several reactions, where limited data exist for their accumulation in skeletal muscle, metabolite accumulation for G1P, F1,6P, DHP, G3P, 3PG, 2PG, and PEP were assumed to fit the same profile.Total metabolite flux  pH dependent calculation of the ~H+ exchange (H+e). For this, the Mf data were multiplied by the pH dependent ~H+ coefficient data (42) for the given pH at each data point, and then each ~H+e metabolite array for the 3 min exercise condition was integrated to yield the total ~H+e (H+te) for the specific reaction .The calculation of MEquation . The onlto, the flux of substrate through the reactions that regenerate ATP were summed to produce total ATPto  pH. This mathematical relationship is expressed in Equation (+) buffering  as this (after baseline correction) equaled the LDH Mf and was the basis for Mf and Mtf estimation of all reactions of glycolysis  and chemical reactions, and the subsequent H+e and H+te  was able to be calculated from the Mf of the ATP regenerating reactions  added to the temporal profile of the decrease in muscle [ATP]  equated to 142.46 mmol L−1, and PFK reaction corrected ATPto equated to 111.35 mmol L−1. The net release of inorganic phosphate (Pi) (19.74 mmol L−1) is the balance of Pi release from the PFK reaction corrected ATPto minus the Pi consumption in the phosphorylase and G3PDH reactions which equaled 91.61 mmol L−1. This simultaneous Pi release and metabolic consumption during cytosolic energy catabolism adds complexity to understanding the role of Pi as a H+ buffer, as will be explained in the Discussion. Also, note that the negative data for Pi release in Figure a.The M] Figure . The diff and H+e did not conform to a standard polynomial function of any order. Consequently, figure data of the temporal profiles of the pH dependent H+e for the main reactions of interest  total buffering capacity  equated to 187.86 Slykes. The total ~H+ release to lactate accumulation ratio was 4.27 (187.86/44). Use of the now outdated and incorrect stoichiometric lactate derivation of H+ release would have revealed an erroneous buffer capacity of 44 Slykes (ΔLa to ΔpH ratio); an error factor equal to the H+ release to [La−] ratio.Total H+ balance and ADP balance revealed large changes of −12.63 and 24.63 mmol L−1 respectively. For NAD+ balance, this means that there was a decrease in NAD+ of this amount by the end of the exercise bout, which then means that the relative redox potential would have had a 25.26‐fold decrease. For ADP balance, this is a remarkably large ADP release which clearly does not result in an appreciable ADP accumulation, and such a result discrepancy is explained in the Discussion.Each of NAD4+e for all chemical reactions of non‐mitochondrial energy catabolism to model cytosolic Mf during 3 min of intense exercise to contractile failure. Data for Mf and ~H+e was used to calculate the H+te for all pertinent reactions. The results revealed an appreciably large ~H+te for each of glycolysis and ATP hydrolysis, and a fourfold larger total ~H+ release to lactate ratio than previously assumed from the lactic acidosis construct. The most ~H+ releasing reaction of glycolysis was the G3PDH reaction, which involves a more complex understanding of cellular inorganic phosphate metabolism than currently acknowledged. It will be argued that this research provides the most accurate and thorough quantification of cellular ~H+e to date and the implications of these results to the biochemistry of cellular energy catabolism and metabolic acidosis will be discussed in detail.This research used prior published data of muscle metabolite accumulation in combination with prior documentation of the pH dependent multiple competitive cation ~Hf and then ~H+e. Discussion will then be directed to the implications of these results to the biochemistry of metabolic acidosis, past theories that have been proposed to cause metabolic acidosis, and finally to the limitations of this study.The Discussion content to follow will be structured by metabolic energy system, with each consisting of M4.131P MRS documents larger concentrations of resting muscle CrP (30–40 mmol L−1 muscle water) than research based on muscle biopsy (15–20 mmol L−1), and this is understandable given the invasiveness and inherent delays in tissue sample excision and freezing for the biopsy procedure  degradation during exercise of different intensities, as well as CrP regeneration during the exercise recovery interval . As such, the resting cellular [ATP] is not an energy store, as it is essential to stabilize the concentrations of all the phosphorylated adenylate molecules  to ensure sufficient free energy release from ATP hydrolysis. This has been repeatedly verified by the relatively meagre reductions (<30%) in cellular [ATP] despite extreme conditions of voluntary and involuntary contraction‐induced ATP demand  + Δ[CrP] + (2 × Δ[ATP]) − Δ[ADP])/time (s) /time (s) )/time (s)  is a form of chemical work, and ATPto should be based on the total amount of ATP regeneration from energy catabolism. The value for ATPto from this study exceeded all prior integrated totals from the pertinent studies presented in Figure to across multiple prior research studies and researchers provides remarkable external validation of the methods and data results of this research.The slight discrepancies between these equations are problematic due to subsequent differences in validity. However, the more important concern pertains to the lactate constant of 1.5 used in these equations, as this is an adjustment of the gross 4 ATP yield from the production of 2 La+e during intense muscle contractions . Clearly, ATP hydrolysis has a meaningful capacity of ~H+ release in transition from neutral and acidic cellular conditions and should not be overlooked as a potential contributor to the development of metabolic acidosis.Researchers who have focused on ATP hydrolysis as a potential major contributor to net cellular ~Hes Kemp, . For thier Table , which w4.43−2]) occur from an imbalance in ATP turnover (excess) and mitochondrial ATP regeneration (limited), where the latter is an important consumer of Pi as a substrate in oxidative phosphorylation. During cytosolic energy catabolism, Pi is also a substrate for the phosphorylase and G3PDH reactions. The data of Table −1  and that Pi consumption during glycogenolysis and glycolysis equated to 91.61 mmol L−1, revealing a net Pi release of 19.74 mmol L−1.Increased intracellular concentrations of inorganic phosphate  occuto. However, before comparisons to past research, it is important to clarify the differences in absolute concentrations of phosphate metabolites obtained from muscle biopsy research versus research from 31‐phosphorous magnetic resonance (31P MRS) methodologies.The relatively small Pi accumulation calculated for this modelling represents just 63% of the decrease in CrP, and only 18% of the PFK adjusted total ATP31P MRS, it is important to acknowledge the influence of magnetic field strength. Early research (pre‐2000) of 31P MRS was confined to magnetic field strengths <2 Tesla, whereas in recent years it is now common to see use of large bore magnets between 3–10 Tesla. For a given tissue sampling volume, the larger the magnetic field strength the greater the resolution of peaks for different phosphate metabolites, as well as the increased signal intensity for each metabolite and more rapid nuclear relaxation time, thereby allowing higher signal to noise, higher acquisition frequencies, reduced averaging of spectra and thereby improved temporal resolution. Whereas early studies required up to 1–2 min of signal averaging at anywhere from 2 to 5 s acquisition intervals, current high field strength studies now produce quality data with approximately 6–15 s of spectra averaging at 1–2 s acquisition intervals . Added to these assumptions is the difficulty in resolving the pure Pi peak for intense exercise conditions, and this is especially true for lower magnetic field strength magnets, as the increasing accumulation of the hexose and triose phosphate intermediates of glycolysis have resonance frequencies close to that of Pi and as such appear as a broad shoulder off the Pi peak, though this resolution is improved in higher magnetic field strength magnets.Research from 31P MRS of muscle metabolism during intense exercise, there are large discrepancies in the extent of muscle Pi accumulation. For example, Taylor et al.  of 119.5%. More recent research from 31P MRS has revealed peak Pi accumulation during intense exercise that is considerably lower than that of Taylor et al.  were acquired every 2 s (each contraction cycle gated to end recovery), and each spectra was an average of 4 FIDs, resulting in an 8 s time average. Similarly, Layec et al. , and this can be seen from the G3PDH reaction H+e curve of Figure 2PO4−1 from 0.3599 at pH 7.0 to 0.849 at pH 6.0. This ~H+ association of Pi increases the ~H+ release for each of the reactions where Pi is a substrate (phosphorylase and G3PDH). For sustained intense exercise to contractile failure and volitional exhaustion, as modeled in this research, the anapleurotic nature of Pi in cellular energy catabolism complicates how to interpret the buffering nature of Pi, as the integral of the ~H+e of the phosphorylase and G3PDH can cause a net ~H+ release that exceeds the perceived ~H+ buffering by Pi accumulation.The metabolic role of Pi becomes even more interesting when you compute the pH dependent ~H4.5+. How do these competing rates of NADH and NAD+ appearance and disappearance influence net cytosolic NAD+/NADH proportions (redox) once the specific substrate flux through these reactions are integrated over time? Based on the observed increase in substrate flux for preceding reactions to the LDH reaction as you backtrack along phase II of glycolysis, more NADH is produced from the G3PDH reaction than NAD+ regenerated from the LDH reaction. As such, it is simple biochemical logic for the NAD+ to NADH redox ratio to gradually fall and remain low as lactate production increases. This deficiency in lactate coupled NAD+ regeneration integrates to a 24‐fold relative change at the end of the exercise condition. This relative decrease in cytosolic NAD+ is likely to be a significant contributor to a gradual decrease in substrate flux through glycolysis during intense exercise, which in‐turn would gradually compromise ATPto necessitating the gradual decrease in muscle [ATP] with simultaneous increases in [Pi], [ADP], and contractile failure.Glycolysis produces NADH, while lactate production oxidizes NADH and reduces pyruvate to lactate, regenerating cytosolic NAD−1. This is remarkably large and no research of muscle metabolism during intense exercise has calculated intramuscular ADP accumulation anywhere near close to this extent. As explained for muscle ATP metabolism, a cell would not tolerate such increases in [ADP] based purely on drastic alterations of the free energy transfer of ATP hydrolysis, or indeed any reaction where ADP is a product. Clearly, the cellular handling of ADP is complex, and it is assumed that cellular mitochondria must act as a sink for ADP during intense muscle contractions. To some extent, this is logical as there would then be a readily available supply of ADP substrate, and presumably also Pi and H+ for oxidative phosphorylation to respond to the improved oxygenation from increased blood flow and perfusion during the recovery from intense muscle contractions. Such a rapid response ATP supply is also important for the rapid regeneration of CrP by the CK reaction in the immediate post‐exercise recovery period. Perhaps this is further evidence for the importance of the creatine kinase shuttle in connecting phosphate transfer between the mitochondria and the cytosol  regulate the pH of biological fluids.Given that cells do not produce lactic acid, the only remaining theory of the cause of metabolic acidosis is Peter Stewart's physico‐chemical theory Corey, , 1983. S3O+] of biological solutions being a dependent variable. In other words, there was no cellular or systemic source of H+ separate to this electro‐chemical correction (see Introduction)  pre pre16) pEquation . Based o Robergs , the neg+e and therefore the pH of body fluids. A new model is needed to improve the construct validity of understanding metabolic acidosis and perhaps the broader acid‐base regulation of biological systems. This approach would need to account for cellular determinants to intracellular pH and the cell to extracellular transport of H+. In this context, there needs to be experimental verification or refutation of many of the assumptions of the Stewart approach, and based on this evidence, a new computation model can be developed.Peter Stewart has to be acknowledged for his attempt at explaining body fluid pH based on physico‐chemical principles, but is it clear that despite some clinical acceptance of this approach Corey, , the mod4.7tf, and H+te, and such preliminary evidence across a shortened exercise duration to 1.5 min has already been published  and H+ (~H+e) in inverse proportion to the exercise duration, with minimal influence on the integral of these measures. The minimal changes that did occur were because of how changes in the temporal profile of Mf for each metabolite and cellular pH differed to their relative profiles of this study.The duration of intense exercise was chosen to be 3 min due to repeated studies documenting the validity of this duration to the maximal accumulated oxygen deficit and muscle anaerobic capacity  of this ~H+ release. New insights were also provided on the capacity of ~H+ release and consumption for specific reactions of the phosphagen, glycogenolytic, and glycolytic energy systems. We now know that the ~H+e from chemical reactions during times of high metabolic demand are remarkably large, and that despite considerable ~H+ consumption, cellular energy catabolism during intense exercise exceeding mitochondrial capacities is net ~H+ releasing, with the exact numerics of the release being dependent on cellular pH and the extent of mitochondrial deficiency.The lactic acidosis construct has been an accepted component within acid‐base chemistry and physiology, and related topics, for more than 100 years. Such acceptance had prevented the need to research alternative causes of ~H+e of chemical reactions may or may not be involved in the determination of the cellular and systemic acid‐base balance of biological solutions.The data may have important implications to understanding the cause of metabolic acidosis coincident with increased cellular metabolic demand. Nevertheless, considerably more research is required to establish how the ~HThe author has no competing interests or conflicts of interest in the completion of this research and publication of the manuscript.


Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of  In adin silico analogues of these experiments . As such, the objective of the GA was to minimize this quantity. We found that, by iteratively searching this large combinatorial space, the GA framework successfully identified environments that allowed each organism to reach a high relative abundance while allowing the remaining two to reach low, but nonzero abundances (a–c). Notably, the algorithm converged on multiple such environmental compositions, indicating a type of metabolic flexibility with regard to specific final taxonomic compositions.To search this larger combinatorial space, we carried out dFBA simulations of our community in the selected environments as they were produced by the GA, instead of generating a full environment–phenotype mapping undances a–c. Notad–f). For example, in one of the environments that was selected to have B. subtilis dominate the community, our dFBA simulation revealed that it was the exclusive consumer of two out of three primary nutrients, while the third nutrient was shared between the three organisms (d). A similar structure was also observed for the environments that optimized dominance of E. coli and S. coelicolor , as well as in the other compositions selected by the GA , suggesting that nutrient specificity was a major driving force of organism dominance in these communities. We also observed dense networks of metabolic byproduct exchange, with molecules such as acetate, formate, glycine and succinate being frequently transferred between organisms, paralleling previous experimental observations of organic acid transfer [We examined the highest scoring environmental compositions in greater detail, identifying common interaction network structures that conferred the desired community phenotypes d–f. For rganisms d. A simielicolor e,f, as wtransfer –75. Give. 3in silico microbial communities to show how their ecological properties can be modulated via environmental modification and presented a search algorithm to identify specific nutrient combinations that would result in desired phenotypes. We showed how this algorithm was quickly able to identify high-quality solutions for a variety of ecological objectives: from overall taxonomic balance to specific organism abundances and patterns of metabolic secretion and exchange. Given these capabilities, this method represents a computationally inexpensive way to rapidly screen very large combinatorial spaces to produce desired community properties. Therefore, in addition to optimizing the various objectives tested here, our dFBA–GA framework can be extended to encompass a greater number of important environmental attributes and experimental designs such as varying nutrient concentrations, continuous culture platforms, spatio-temporal nutrient variation and periodic changes in species abundances [The rational design of multispecies communities toward defined phenotypes is an enticing, yet challenging, goal of synthetic ecology. As the phenotypic traits of microbiomes in complex settings remain difficult to predict ,76,77, fundances ,48,81. Iundances ,82, thisin vitro will inevitably require experimental validation of modelling predictions. Our approach can be applied to this goal in two ways. First, in silico analogues of the desired community may be iteratively screened as we have performed here, and the final environments generated by the GA may then be explicitly tested experimentally. In this way, our method serves as a way to generate an accessible number of testable hypotheses pertaining to specific ecological systems. The pairing of flux balance models and confirmatory experiments in this way has been used extensively to obtain a greater understanding of organism function, as well as to explore previously unknown phenotypes [Despite the flexibility and mechanistic insight afforded by a dFBA approach, engineering synthetic ecosystems enotypes ,83–86. Henotypes ,88, (ii)enotypes  and  and formulations (e.g. population size and scoring) that can aid its applicability to experimental systems. We therefore propose that, given the increasing accessibility of high-throughput platforms for microbial ecology  [A second strategy can thus forgo the dFBA component altogether and use the evolutionary algorithm as a way to search through experimentally derived community phenotypic data. As we showed how the GA was able to reach high-quality solutions with relatively few experimental data points, one could envision implementing a similar framework alongside the eriments ,42. It is, etc.) –45, a se. 4. 4.1in silico minimal medium with limiting quantities of a set of carbon sources. This minimal medium, modelled after the composition of M9, contained nonlimiting concentrations of molecules necessary for growth such as water and ions, as well as sources of nitrogen, phosphorus and sulfur. Limiting amounts of 20 carbon sources were then added on an environment-by-environment basis. These nutrients, an assortment of sugars, organic acids and amino acids , were added in all combinations of up to 4 at equimolar ratios such that the total concentration of carbon in each environment was 50 mM C in 400 µl. This scheme resulted in 6196 unique environmental compositions. (ii) Genome-scale reconstructions [in silico media compositions. These organism-specific models span a wide range of taxa and metabolic strategies and were selected to maximize variation in endpoint community composition and interactions across our combinatorial environments . Based on an approximate total inoculum of OD600 0.05 corresponding to in silico media at equal ratios of We employed a dFBA method  to test ructions ,32 of 13ht (gDW) , all 13 f 0.01 h . A more a), we first computed Spearman correlation coefficients between the species relative abundance vectors under each environment. We then performed hierarchical clustering on these coefficients using the ‘clustergram' function in MATLAB, which calculated distances between clusters using the UPGMA method based on Euclidean distance.To determine whether our communities had stopped growing by the end of the 24 h timespan, we analysed the growth curves of each organism in each environment. If the derivative of the organism's growth curve was greater than zero at least once during the simulation (i.e. the organism grew), and was less than or equal to zero at the end of the simulation, we determined that organism to have stopped growing. For our visualization of the clustered relative abundances of our communities a, we firH asi in a community. Organisms in a community with a larger H have more equal relative abundances, while those in one with a smaller H are less equal, due for example to a single organism outcompeting the rest.We calculated the Shannon entropy for each community in order to quantify their degrees of taxonomic balance. We define Shannon entropy B. subtilis, E. coli and S. coelicolor ) from our list of 13 were used and inoculated into our environments at initial amounts of For our second, exploratory application of the GA, a larger pool of 154 carbon sources was used from which a maximum of three nutrients were selected per environment, resulting in 596 904 unique environmental compositions. Here, we did not explicitly simulate the community phenotypes in all combinatorial environments. Instead, only the environmental compositions selected by the GA in each generation were tested and their performance recorded as above. For these simulations, three organism genome-scale reconstructions  the total number of metabolic exchanges, (iv) the total metabolic flux directed at each of the 13 in silico organisms, (v) the total secretion flux of 24 different metabolic byproducts and (vi) the approximation of target relative abundances. The modifications of different solutions take place over the course of multiple ‘generations', in which each solution is scored according to the phenotype being optimized, and the best solutions are used to seed a new generation of candidate solutions. This process continues with the intent of converging on a set of optimal solutions.A GA is a search heuristic based on the principle of evolution by natural selection, which optimizes a particular objective function via the modification of a population of individual solutions . Our selP environmental compositions. In order to demonstrate its extensibility to be used in parallel to an in vitro experimental system, we sought to minimize the number of environmental compositions P tested in each generation. Therefore, we limited the number of compositions to an experimentally tractable  1.P environments are initialized with random assortments of up to N nutrients (The  2.in vitro system) are recorded and used to assign fitness values to each environment.The community phenotypes resulting from each environment in the population  and mutation (existing nutrients are replaced with new randomly sampled ones). In our implementation, the parent nutrient vectors are linearized, and the remaining Having selected a set of  5.Steps 2–4 are repeated for the new environmental compositions until convergence criteria are met, or for a predetermined number of generations.Our implementation of the GA begins with a randomly generated population made up of B. subtilis and (iii) maximization of the total number of metabolic exchanges. We then varied the values of S. This score is based on a combination of two metrics: (i) the number of generations required for a set of solutions to surpass the 99th percentile of a given objective S is defined as follows:S can range from 0 to 2. We found that the best S to changing mutation probabilities We determined optimal values for the crossover and mutation probabilities r values , our cal 1.Populations are internally consistent: the difference between the best fitness and the average fitness within a generation is less than 10% of the average fitness of that generation. 2.Solutions have reached a maximum: the scaled difference in fitness between the best individual in the current generation and the best individual ever discovered is less than 0.01. 3.t-test.No further improvement: the fitness scores of the individuals in a generation have not shown a statistically significant increase from those of the preceding generation for at least 10 generations, as determined using a one-tailed To determine whether the algorithm has converged to an optimum, we implemented a set of three criteria based on the fitness values of each tested environmental composition. All three of these criteria, based on those previously implemented in evolutionary algorithms ,94, must