Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions , and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites. Multi-tissue models based on these reconstructions, or from scratch9, are used to investigate the interplay between different tissues. And while there are profiles for uptake, and release of substances from individual cell lines , this information is often lacking for human tissues, and has to be inferred from other species. Thus, it is difficult to properly constrain a multi-tissue model, as literature sources vary, and bounds might be too stringent. Recently a study by Hyötyläinen et al.11 suggested to relax these hard constraints by using a quadratic programming approach in which hard bounds are replaced by penalties for data violation. This approach enables the direct use of data in a model, while contradicting bounds are relaxed in an automatic fashion, with many smaller violations preferred to large individual violations. However, this approach still does not address another issue with multi-tissue models of higher organisms. What is the metabolic objective of the organism? For microbes, biomass accumulation has been extensively used as objective yielding good results12, and this has also shown promising results for plants13, and even infant growth14. This objective is also commonly used for cancerous tissues which grow rapidly15. However, for a mature organism that no longer grows, this objective is questionable. Other suggested options include enzymatic optimization, or energetic optimization, the former trying to minimize the overall fluxes, the latter trying to minimize the use of ATP16. But while efficiency is a reasonable target, it is likely not to be the major driving force in an organism. In human, one observable factor is that blood metabolite levels are tightly regulated17, and metabolism is likely to play an important role in keeping these levels steady. Abnormal levels of metabolites commonly indicate diseases, e.g., Diabetes Mellitus and Inborn Errors of Metabolism (IEMs). Temporary perturbations of metabolite levels occur primarily due to nutrient uptake or food scarcity. This would imply that a good objective drives metabolite levels towards the healthy range after perturbations by nutrient uptake and simultaneously tries to keep metabolite levels stable while fasting.Genome scale reconstructions of the human metabolism have become ever more sophisticated in the recent years , each one was evaluated independently to determine how many known tissue-specific functions each model was able to perform, as defined in Gille et al.18. Two examples of tested functions are: (1) the ability to produce ATP from glucose or a fatty acid, in presence or absence of oxygen; (2) the ability to degrade an amino acid to ammonium. In total, the liver model could perform 139 out of the 142 tested functions, the muscle model 88 out of the 98 functions, and the adipose tissue model 91 out of the 98 tested functions. To conclude, each model was able to perform a minimum of 90% of the known tissue specific functions , protein and amino acids stores, if applicable, and with the blood compartment. The latter contains a storage for the different blood metabolites, thus allowing for the description of the dynamics of these metabolites. Each of these stores were initialized with the average amounts generally found in healthy individuals, as described in the Methods section. To mimic food absorption from the gut to the blood, a previously established differential equation20 based approach was employed to simulate the final coupled model. In contrast to the general FBA approach, where the metabolic flux can be calculated for a given specific time point and condition, the dFBA approach enables to account for the dynamics of metabolites by integrating the individual FBA solution over time. In both settings, constraints on reaction fluxes, and the optimization of an objective function are usually employed to narrow down the solution space and to obtain biological relevant flux solutions, respectively. The model in this paper represents the dynamics of metabolism of three interconnected tissue models, and to properly simulate it, an objective function, integrating multiple sub-objectives, has been developed as follows: (1) blood homeostasis should be maintained; (2) energy ingested in excess may be stored in the tissue energy stores, and may be used in periods of food scarcity; and (3) metabolic transitions are smooth over time, and between conditions. For a more detailed description of the model simulation workflow, and the objective function, please refer to the Methods section.In this work, a dynamic Flux Balance Analysis (dFBA)A second evaluation on the coupled multi-tissue model was performed. The second set of tests consisted of simulating different physiological conditions and a set of IEMs, and validating the model predictions using published data. In the next sections, the model predictions will be presented and discussed thoroughly.After setting up the model, we validated it through the simulation of different metabolic conditions occurring throughout the day. I.e. in the morning when we wake up we are in a fasting state, and after having breakfast the metabolism needs to adapt to a fed condition. Thus, these states were simulated and the ability of metabolism adaption between conditions was verified.starch and sucrose metabolism flux was at the maximum in the liver, but once the glycogen stores became depleted it became inactive. This pathway contains all the reactions involved in either the production or hydrolysis of glycogen. The effect was further confirmed by the flux through the glycolysis/gluconeogenesis pathway, and through the tissue storage degradation pathway. The latter one, in the liver, contains the reaction which leads to the degradation of the glycogen stores. These two pathways followed a similar pattern as the starch and sucrose metabolism. They became fully inactive after the glycogen stores were depleted. To compensate for this effect, and to fulfill the energy demands of the multi-tissue model, there was an increase in the flux through the tissue storage degradation, and the triacylglycerol synthesis pathways in the adipose tissue. The triacylglycerol synthesis pathway contains reactions producing and degrading TAG. In the liver, the flux through the glycolysis/gluconeogenesis pathway was decreased, while the flux through the fatty acid oxidation increased. To conclude, the model predicted that after 2 days of prolonged fasting, the liver glycogen stores were completely depleted, which led to an increase of TAG hydrolysis rate in the adipose tissue.We started by simulating a fasting condition for 3 days, which corresponds to 4320 min, to determine if the model was able to capture known effects occurring during the fasting state, such as liver glycogen depletion. As can be seen in Fig. 21. These two meals, representing a healthy and a high fat breakfast, were compared to each other and to the fasting condition  on the energy-associated metabolic pathways, the fluxes were normalized to the maximum flux per pathway, and per tissue Fig. .glycolysis/gluconeogenesis), or inactivation  of metabolic pathways, which as the post-absorptive state progressed, slowly returned to the fasting flux values. The effect of the meals on the majority of the energy-associated pathways was still highly pronounced 6 h after the meal ingestion.When compared to the fasting condition, the fed state led to the activation  were used and the surplus was stored in the internal energy stores. The activation of these metabolic pathways, following a meal, are in good agreement with known human metabolism22. In addition, the ingestion of a high fat meal led to an increase of ROS detoxification in the liver, and to a higher degree in the muscle. It has already been shown that meals rich in carbohydrates and lipids lead to the increased production of ROS27, which has been associated with the development of insulin resistance28. These results confirm the potential detrimental effect that high fat diets might have on muscle and liver metabolism, and show the value of the availability of such a modeling approach.The results correctly suggest that the liver was the main metabolizer of amino acidsAs shown in Supplementary Fig. To conclude, these results confirm that during fasting the internal energy stores were used for energy production. In the fed state, during the analyzed time-course, the internal energy stores were not depleted. Instead meal metabolites were used for energy production. The surplus of energy obtained from the meal was stored in the internal energy stores as glycogen or TAG. As expected, the high fat meal caused an important increase of TAG storage, which was minor after the consumption of a low fat meal, showing the potential detrimental effect of high fat meals consumption in the development of obesity.22. Thus, to determine if the model was able to capture key metabolic changes occurring during exercise, exercise was simulated for various intensities and durations. Exercise was mimicked by increasing the ATP consumption in the muscle model. I.e. an intense exercise load of 90% of the maximum oxygen consumption rate  or exercise (M → E), and exercise followed by a low fat meal (E → M). Specific tissue fluxes and tissue stores amounts were compared among these three conditions , the whole-model fatty acid oxidation was increased and the glucose one was decreased when compared to the M → E condition.The model predicted a different effect, if exercise was performed before or after a meal. The muscle glucose uptake was larger in the E → M condition, the degradation rate of TAG in adipose tissue and glycogen in muscle was larger than in the M → E condition.During exercise, the liver glycogen usage was similar in both conditions. However, in the On the other hand, the ingestion of a low fat meal led to a slight storage of energy in the different tissues  and predicted their biomarkers. To validate the results, the set of predicted biomarkers for 17 IEMs was compared to biomarkers previously32 manually extracted from OMIM.IEMs are inherited metabolic diseases characterized by an impairment in the metabolic activity of an enzyme or a transporter. If people affected by one IEM have unrestricted diets, toxic metabolites might build up in the body fluids  and tissues. Therefore, it is important to identify potential biomarkers for all IEMs to get a quicker diagnosis and to avoid health complications at long termFigure 4, which is toxic for the human body. In the healthy fasting condition, we observed a basal urea production  biomarker. Thus, we investigated the dynamics of tyrosine at the different storage and excretion levels to determine if it decreased in this condition  maintenance of blood homeostasis. During fasting conditions, blood levels should remain stable, and in the fed conditions they should return to the basal levels within a few hours after the meal. This sub-objective was supplemented by two others: (2) all energy ingested in excess will be stored, i.e., as triacylglycerol in the adipose tissue, and will be used, to a higher extent, when the body goes through periods of food scarcity, i.e., long periods of fasting. Most of the reactions’ flux in constraint-based metabolic models are not constrained, and during the optimization of the model, multiple flux solutions might be equally valid. Thus, a third sub-objective was added, (3) which aim was to prevent sharp changes over time, in the metabolic fluxes. The choice of this multi-objective function allowed the model to mimic quite well the metabolism and the metabolic flow between the three tissues and the blood. Each of the sub-objectives are optimized simultaneously, and can be weighted equally, or not. As an example, in this model, the same weight was selected for each of the blood metabolites. The effect of this assumption led to each metabolite being penalized similarly, as will be further described during the discussion of the IEMs results. In the future, the penalization coefficients could be further tuned to further improve the prediction capacity of the model.The proposed model was simulated using a dynamic Flux Balance Analysis approach11 can be applied on the reactions’ bounds. During model optimization, the flux solution will try to remain inside the expected flux range, and it will only deviate from the expected range, if really necessary. Because of the large nature of the model, and the complexity of the objective function, and to avoid infeasible solutions, we decided to apply flexible constraints on the reaction bounds.Commonly, reactions flux are constrained with hard bounds. However, due to model incompleteness, experimental errors, and noise, imposing hard constraints on reaction fluxes can lead to infeasible solutions. In order to prevent it, “flexible” constraintsOne advantage of constraint-based metabolic models is that they necessitate much less parameters, when compared to those models based on ordinary differential equations (ODEs). As described, constraint-based metabolic models rely on the optimization of one objective function, and the application of specific constraints in order to obtain condition specific flux solutions. Due to the small number of parameters, these models can be easily scaled to describe the complete metabolism of one, or more tissues. As demonstrated, the model was able to describe the complete metabolism of three tissues, showing the enormous potential of this approach. The proposed model was validated by simulating different physiological , and unhealthy conditions (different IEMs).34, therefore the predicted time of the complete glycogen depletion is in the same range as the reported one. When glycogen stores deplete, the body relies mainly on fatty acids issued from the degradation of adipose tissue TAG22, to avoid the depletion of blood glucose. The model was able to capture this metabolic change by predicting an increase in the triacylglycerol synthesis and fatty acid oxidation fluxes. Concomitantly, the glycolysis/gluconeogenesis fluxes decreased, confirming the usage of fatty acids as main energy source.During the first hours of fasting, the model showed little dynamics, and the metabolic pathways usage was stable. At around 2 days of fasting, the model showed a dynamical switch, when the liver glycogen stores were completely depleted, forcing the system to adapt and choose other energy sources. It is known that liver glycogen depletion occurs after 48 to 60 h of fasting22. However, the aromatic amino acids, phenylalanine, tyrosine, and tryptophan were predicted to be mainly metabolized in the muscle. This was unexpected, as in reality the degradation of these amino acids occurs primary in the liver22. This occurred because prior to the model reconstruction, we collected the complete canonical pathway of the aromatic amino acids degradation, and used these reactions as part of the set of core reactions for the contextualization of the tissue models. Thus, this will be addressed in future iterations of this model.It has been reported, that the liver is the primary metabolizer of amino acids, with the exception of branched chain ones 24, and the internal energy stores are no longer used. If energy is ingested in excess, it is stored either as glycogen or TAG22. The model predicted that the energy storage pattern after a low, or a high fat meal was different. The results show that a high fat meal elicited a larger energy storage than the low fat meal. This is expected as the high fat meal contained a larger amount of TAG and of glucose.The predicted metabolic pathways activities during the fasting, and the fed condition are in agreement with the literature. It is known that in the fed state, fatty acid oxidation rates decrease25, which was predicted to be increased in the three tissues. In addition, the increase of the glycolysis rate was proportional to the amount of glucose contained in the meal.Furthermore, it has been shown that high levels of blood glucose lead to the increase of the glycolysis rate27. Previously, ROS has been associated with the development of insulin resistance28, which is the first detectable defect in type 2 diabetic patients and it can occur 10 years before the apparent disease35.The activity of the ROS detoxification pathway was affected by the fed condition, mostly in the liver and in the muscle, and this effect was more pronounced after the high fat meal. Increased levels of blood glucose, and fatty acids lead to the production of increased amounts of ROS2 uptake. All the other exercise intensities were calculated linearly. The approach used to calculate the different exercise intensities relies on two main assumptions. The first assumption is that an average individual was used in this work. In the future, the model could be further adapted to represent the metabolism of athletes, by i.e., adapting the maximal O2 uptake. The second assumption, which was used due to the lack of data in this field, assumes that the linear increase of ATP consumption in the muscle, leads to the linear increase of O2 uptake in humans.During a normal day, individuals go from a state of fasting to feeding, and also do some kind of physical activity, which can be done either in the fasting state or after eating. Therefore, to further validate the model, we predicted and partially validated the effect of the exercise intensity and the meal composition on metabolism. This was achieved by modifying the ATP consumption in the muscle, to reflect the exercise intensities of interest. First, we determined by how much the ATP consumption in the muscle would need to be increased to reach 90% of the maximal O37, at high intensity glycogen becomes the main one38. The developed model was able to correctly reflect this switch in energy source depending on the intensity level. It correctly predicted that the muscle glycogen usage was proportional to the exercise intensity38. The fatty acids uptake in the muscle reached the maximum flux at 60% OIt has been previously established that fat, and carbohydrates are the main energy sources during exercise, and depending on the intensity of the exercise, one source is favored over the other. While at lower to moderate exercise intensities, fatty acids are the main source40, which is close to the maximal predicted fatty acid oxidation rate , rather then after a meal (M → E)23. We demonstrated that performing exercise during fasting promotes fatty acid oxidation, through the increase the TAG degradation rates. If a meal was ingested after the exercise, the TAG level in the adipose tissue remained lower when compared to the level where a meal was ingested prior to the exercise. This effect was similar for both meals. Carbohydrates ingestion after exercise allows the glycogen stores to replenish41. In the model, this effect was more pronounced when a high fat meal was ingested before or after exercising. It is important to note again that, not only this meal contained more TAG, but also more glucose.Performing exercise in the fasting state has been suggested to be more favorable regarding the lipid metabolismM → E level.Overall, these results suggest that more TAG is burned in the adipose tissue, if exercise is performed in the fasting state. If a meal is ingested after exercising, it allows the slight replenishment of glycogen, and simultaneously, it keeps the adipose tissue TAG levels below the These results can be relevant for the treatment of human obesity. To maximize fat oxidation and potentially fat loss, exercise should be performed in the fasting state, and at low to medium intensity levels, as for high intensities, fatty acid oxidation was predicted to be almost suppressed.After analyzing the metabolic effect of different physiological conditions, the model was further validated, by predicting biomarkers for a set of IEMs and by comparing them to literature data. The model mainly predicted urine biomarkers, which is explained by the blood homeostasis assumption used to constrain the model. Metabolites could be exported through the urine, if the exporting rate was kept within the healthy range, while their storage in the blood was penalized. Therefore, it was preferable to export biomarkers through the urine, instead of accumulating them in the blood.The model predicted blood biomarkers, that were also predicted as urine biomarkers. This happens when the urine secretion rate is at the maximum allowed flux, or outside the healthy range. When the urine secretion fluxes are saturated, the model minimizes the objective function value by splitting the penalization among different reactions. Thus, the metabolites that accumulate in the blood are also exported through the urine. To summarize, the model only predicted a blood biomarker, if the respective urine metabolite was also predicted as a potential biomarker.4, which is one by-product produced during the degradation of amino acids. Due to the impairment on urea synthesis, NH4 could not be removed through the urea synthesis pathway. Thus, the model tries to remove NH4 from the system by eliminating it through the urine. When this elimination is saturated, NH4 might accumulate in the blood. As the final step of the amino acids degradation is impaired, amino acids are eliminated through the urine, and to some extent they are stored as protein in each tissue, even if protein storage is penalized. One way to avoid this effect would be to have a specific penalization coefficient for each metabolite, which would reflect the preference for the elimination, or the accumulation of the metabolite. However, this kind of data would be very hard to obtain. E.g., the biomarkers data, collected in Supplementary Table Arginase deficiency led to the prediction of almost all urine amino acids as potential biomarkers. During this condition, urea synthesis was completely impaired. The urea synthesis pathway is involved in the detoxification of NHThe model predicted 5 false positive biomarkers at the urine level, of which 3 corresponded to the increase of phenylalanine in 3 IEMs . In these conditions, the tyrosine degradation pathway is impaired and this impairment leads to the known accumulation of tyrosine. Phenylalanine, which is the tyrosine precursor, will also accumulate as tyrosine cannot be degraded, and will be secreted through the urine to avoid any blood concentration changes. Similar to the Arginase deficiency, this occurs because the model penalizes big changes at the blood level, and favors the sum of small changes. Thus, the objective function value is lower if the model accumulates small amounts of the two amino acids, instead of having a high accumulation of tyrosine.DLD gene associated with MSUD, also regulates one reaction belonging to the downstream degradation pathway of lysine. This impairment in the lysine degradation pathway leads to the possible accumulation of oxoadipic acid, which is also the degradation product of tryptophan. Thus, an increase in the levels of lysine and tryptophan was not surprising, because of the impairment in the degradation pathways of these two amino acids. These results might suggest that either these amino acids have not yet been reported as potential biomarkers, or that the degradation pathway of these two amino acids is less affected than the one from the branched chain amino acids, potentially because an unknown enzyme might be able to compensate for this loss.Two other false positive biomarkers were predicted for Maple Syrup Urine disease (MSUD), and corresponded to lysine and tryptophan. After an in depth investigation, we observed that the We have demonstrated that this approach shows good agreement with literature data and allows to prioritize potential biomarkers when performing metabolomics analysis.In conclusion, we developed a dynamic multi-tissue model which not only captures the physiological effects, but is also able to identify potential biomarkers. This framework can be applied in the field of personalized sports nutrition, such as in the improvement of sports performance. This could be done by predicting which diet would elicit the faster muscular glycogen and protein storage repletion. In addition, this approach can be extended to the analysis and biomarker prediction of other metabolic diseases, by performing gene knock-outs for each gene present in the model. In addition, personalized models of any metabolic disease could be contextualized with individual expression data and metabolomics data, which would serve as a basis for the development of personalized treatments and adapted meals for each type of studied disease.7, high quality models of individual tissues are still rare. However, algorithms for automatic reconstruction of draft context-specific models are available and can be used to create a starting point for tissue specific models, with the FASTCORE family of algorithms having shown the ability to generate good quality draft models42. Here, an outline about how these algorithms were adapted to generate draft models for three tissues  is presented. Further details are given about how the quality of the generated models was assessed. Finally, the approach used to connect these models and the concepts applied to interrogate the system are described.While genome-scale reconstructions of human metabolism are readily available45 discretization approach, and integrated in the FASTCORMICS workflow46. In short, the microarrays were read into R version 3.2.0, with the affy package (1.48.0). They were subsequently normalized with the fRMA package (1.22.0), and then discretized with Barcode using the hgu133plus2frmavecs vector (1.5.0).Tissue specific transcriptomics data from the GEO database was extracted for the three tissues of interest. Tissue datasets were selected by filtering for the following keywords: liver, skeletal muscle, and subcutaneous adipose tissue. Only transcriptomics datasets in the healthy condition, generated by the platform U133 Plus 2.0, were selected  were reconstructed from Recon2.04Reactions belonging to canonical pathways which should be present in the tissue models i.e., degradation of amino acids.Reactions not expressed based on the transcriptomic level, but expressed based on proteomics data (data from the Human Protein Atlas database).Reactions involved in the recycling of co-factors.These supplementary core reactions were comprised of:The full list of supplementary reactions is given in Supplementary Data 18  was used, if human data was not available. All included fluxes and their references are listed in Supplementary Data Tissue coupling is important to allow a systematic investigation of the reconstructed model, which was achieved by the following approach:50. For each of these metabolites, Recon2.043, and Recon35 were searched for possible transport reactions. If the reaction was present, then it was added to the model, if not the following transport reaction was added: met[c] ↔ met[e]. These transporters were added because during the reconstruction process many transporters were removed, and some IEMs biomarkers (either in urine or in blood) were metabolites which were only present in the cytosol of the models.In the first step, after each tissue model was reconstructed, transport reactions between the cytosol and the extracellular space were added for metabolites which were present in the model cytosol and have already been detected in either blood or urine, according to the data from the human metabolome database (HMDB)50, together with the respective ranges.In the second step, a blood compartment was added to allow metabolite exchange between the different tissue models. Furthermore, a blood pool, and a virtual gut, mimicking food absorption Fig. , from whThe blood concentrations were then converted to amounts, assuming a total blood volume of 5 L (Supplementary Data The metabolite concentrations in urine were given in μmol per mmol of creatinine51 (9.4 ± 2.68 mmol per 24 h)The healthy creatinine amount secreted over a period of 24 h in urine was gatheredThe 24 h creatinine value was converted from mmol per 24 h to mmol per minThe urine metabolite concentrations were multiplied by the creatinine amount to obtain metabolite secretion fluxes . A general overview of the model structure can be found in Fig. Finally, glycogen stores were added to the liver, and the muscle models, while triacylglycerol (TAG), and protein stores were added to the adipose tissue, liver, and the muscle models. Tissue weights, and tissue stores initial conditions are listed in Supplementary Table That metabolism aims to maintain a blood concentration homeostasis.That energy might be stored if in excess, to be used during scarcity conditions.That metabolic changes are smooth and not sharp.A common issue for metabolic modeling in mature higher organisms is the selection of an appropriate objective function that properly reflects the aim of metabolism. The modeling approach proposed here was based on three main assumptions:11.This objective must then be subjected to limitations and requirements, such as flux bounds or known turnover and maintenance costs. Extensive literature search was performed to collect tissue specific metabolic fluxes to constrain the multi-tissue model , which are defined as:i from reactions in RK, respectively. i ∈ RK. For all supplementary transport fluxes (RCS), and protein degradation reactions (RPD), similar constraints with Constraints on the fluxes of reactions with known flux ranges  The maximum allowed flux for the liver glycogen storage reaction was very high, as this reaction can carry a higher or a lower flux depending on the physiological state of the body. Therefore, a flexible constraint, based on the fasting rates, was set to a very high coefficient value to further penalize the deviation from these bounds. The same procedure was employed for the CS reactions. (2) The flexible bounds for the oxygen uptake in the muscle are bounds in the resting state. However, during the simulation of exercise, the model needs to uptake more oxygen. Thus, the uptake of oxygen was slightly penalized. (3) When going from one metabolic state to another, the model needs to adapt its metabolism. Since it might be impossible for the model to have a smooth transition from the previous to the current time step, a coefficient of 0.001 was given to still penalize the deviation from the previous time step, while still allowing some flexibility during the metabolic adaptation. (4) All the other coefficients for all the other reactions included in the quadratic objective function were set to a value of 1.In addition to the aims detailed above, there are several flexible constraints  are restricted by:vm as defined in Eq.  are incorporated in two different ways. For all blood metabolites which are available in the food (NU), the uptake rates  is retaCplex.Param.timelimit.Cur = 60 ⋅ 10Cplex.Param.barrier.algorithm.Cur = 1Cplex.Param.barrier.convergetol.Cur = qp.Param.barrier.convergetol.Cur ⋅ 10Cplex.Param.emphasis.numerical.Cur = 1A set of Cplex parameters were modified to address the numerical difficulties occurring during the optimization steps.Cplex.Param.preprocessing.presolve.Cur = 0In addition, if the solver was still unable to identify an optimal solution, the presolver was turned off, which in some cases helped to optimize the model.Furthermore, because of the numerical difficulties, occasionally the solution became infeasible during the second optimization step (flux minimization), while it was optimal during the first optimization step. Therefore, for each time step the solution from the first optimization step was used to update all the stores, i.e., the blood concentrations and the internal tissue stores.20, and optimized for the objective function described above, using the COBRA Toolbox for MATLAB and the ILOG Cplex solver. Each simulation was performed for a period of time, i.e., 360 min, using a time step of 1 min.The multi-tissue model was simulated using a dynamic Flux Balance Analysis-based approachInitialize the model (only for time = 1 min)Calculate the food amount that is absorbed from the gut to the blood using Eq. Update the blood amountsvm using Eq.  using the flux solution from step 6Restart from step 2 until the end of the simulation, i.e., time = 360 minThe pipeline for the simulation of each time step is described by the following steps:21. In this dataset, 20 metabolite profiles  were available. These metabolites were measured at baseline, and then every 60 min for 300 min, after ingestion of a low or a high fat meal. The average metabolite profiles of young individuals were used to fit the model.Diet information, and blood metabolite profiles were obtained from Milan et al.55. If they could not be found in this database, the SelfNutrition database56 was used instead.The foods included in the low and in the high fat meal were converted to nutrients using the Frida databaseIf starch values were present in the database, it was assumed that half of the starch was starch1 and the other half was starch2, to match the starch metabolites in Recon2.04. If the starch value was not available from the database, then the following calculation was performed:This value was assumed to be the value of starch missing. Similarly, it was assumed that half of this value corresponded to starch1 and the other half to starch2.Starch1, starch2, disaccharides, fructose, and galactose were converted to glucose with the conversion factor of 11, 3, 2, 1, and 1, respectively. The conversion step was performed as the carbohydrate hydrolysis in the gut was not explicitly modeled.19 employed Eq.  is the absorption rate of a metabolite m from the pool, Dm is the amount of metabolite m in the pool, Am is the bioavailability of m, and m in the blood. For each time step, before optimising the model, the amount determined by Um(t) was added to the current blood metabolite level. All the blood metabolite amounts were then converted back to concentrations by multiplying them by a blood volume of 5 L. Note: in the fasting condition, the gut is empty and Um(t) is equal to 0.Hovorka’s equation:Sm], was used to limit the maximum uptake rate of the food metabolites from the blood stores to the tissues:m]basal is the basal blood metabolite concentration. Due to numerical errors, [Sm] might become negative , dependm considered in the food pool, the parameters 21. The optimization was performed using the lsqnonlin function from the Optimization Toolbox in MATLAB.For each metabolite The upper bounds of the fitting parameters were set in the following way: the 2 consumption in the model. This was achieved by simulating exercise by increasing the ATP demand in the muscle until the expected O2 consumption was observed. Thus, by multiplying the muscle basal ATP demand by a factor of 42, an exercise eliciting the consumption of 90% O2 consumption. I.e. to determine by how much the ATP consumption in the muscle needed to be increased to reach 80% OTo simulate exercise at different intensity levels, it is necessary to represent the exercise in the model. Since exercise mainly consists of energy expenditure, exercise was simulated by varying the ATP demand in the muscle. First, we investigated by how much the ATP demand in the muscle would need to be increased to reach 90% of the maximal O29.Simulation 1: 90 min of 40% O29.Simulation 2: 60 min of 60% OSimulation including steady exercise at different intensity levels during fasting:30 were simulated.Simulation 3: 5 min of 50% OSimulation including steady and incremental exercise at different intensity levels during fasting:23.Simulation 4: 60 min of 55% O23.Simulation 5: the meal was applied. After 60 min resting, 60 min of 55% OThe following simulations were performed for a low fat meal:Simulation 6: the meal was applied, and no exercise was simulated.An additional simulation was performed to compare the effect of exercising, or resting in the fed state:The fatty acid oxidation was calculated in multiple steps. First, the individual fatty acid oxidation reactions were collected. Depending on the reaction, more than one molecule of acetyl-coa could be produced. The acetyl-coa coefficient was extracted, and each flux was multiplied by the respective coefficient. The fluxes were summed up, and the AUC calculated.Multiple energy precursors can be used to provide energy during exercise to the muscle: (1) free fatty acids absorbed from the blood; (2) other fat sources, representing the sum of blood TAG absorption and the degradation of TAG stores in muscle; (3) blood glucose absorbed; and (4) glycogen degraded in muscle.The AUC of the flux of the different energy precursors for the muscle was calculated.TAG and glycogen were broken down into free fatty acids, and glucose molecules, respectively. TAG contains 3 fatty acids, thus the AUC value of "other fat sources" was multiplied by 3. Glycogen degradation produces 8 molecules of glucose and 3 molecules of glucose-1-phosphate, thus the AUC value of muscle glycogen degradation was multiplied by 11.57, respectively. Therefore, to obtain the ATP contribution of each energy source, the AUC values of glucose, and glycogen-derived glucose were multiplied by 31, and those from free fatty acids, and TAG-derived free fatty acids were multiplied by 106.The degradation of one molecule of glucose, and an average free fatty acid leads to the production of around 31, and 106 ATP moleculesThe calculation of the availability of each of these sources for ATP production was performed in several steps:59 to simulate IEMs in the multi-tissue model. These studies were selected, as literature biomarkers were included in the papers. The IEMs were mapped to a list of IEMs impaired genes/enzymes60, and the respective genes extracted. IEMs were simulated only if the impaired genes directly affected a metabolic reaction of the multi-tissue model .A list of IEMs was collected from several published studiesIn total, 65 IEMs were simulated under different conditions . The simulation of IEMs was performed by setting the targeted reaction(s) fluxes to 0 . In addition, 100 "individual" models for each condition were simulated . For each of these models, the flux bounds of all the flexible constraints were randomly decreased, or increased to mimic the metabolism differences between individuals. The random change ranged from 0 to 10%.65 IEMs ⋅ 3 conditions100 “Individual” healthy models ⋅ 3 conditionsAverage healthy model ⋅ 3 conditionsIn total, 498 models were simulated:Metabolic biomarkers are metabolites whose levels are outside the healthy range, either above or below. Thus, one method which might help in their identification is by predicting which metabolite levels might be outliers. This was achieved by performing several steps: (1) preprocessing, (2) “outliers” identification, and (3) filtering.−7, and occurred because of numerical errors during the optimization process.For each metabolite, the minimum amount among all simulations was calculated. For some metabolites, the blood amount became negative during the simulation, as well as the urine fluxes. These negative values were in the order of 10For those blood metabolites where a negative amount was found during the simulation, the absolute value of the negative change was added to all the simulations in order to obtain only positive blood amounts.For the urine metabolites, any negative flux was replaced by zero.Data was pre-processed as following:n = 498). The number of clusters represents the following: a cluster number of 1 indicates that the metabolite was inside the healthy range for all the conditions; a cluster number of 2 indicates that at least one metabolite level was outside the healthy range ; finally, a cluster number of 3 indicates i.e., that in some conditions the metabolite level was decreased, while in others it was increased.After the preprocessing, the AUC of the blood level/urine excretion flux over time was calculated for each metabolite in each condition, and hierarchical clustering analysis was performed to identify potential biomarkers. This was achieved by using the MATLAB cluster function from the Statistics and Machine Learning Toolbox. Default parameters were selected, with the exception of the linkage measure which was set to single. The silhouette function, from the same toolbox, was used to identify the best number of clusters (ranging from 1 to 3), for each metabolite among all conditions (n = 303).In the first step, metabolites with AUC larger or smaller than the healthy range were kept, and the others removed from the list. The healthy range is defined as the minimum and maximum AUC values obtained from the simulations of the healthy models  that a biomarker was identified in. The score value ranged from 0 to 3. A score of 0 represents non biomarkers ; a score of 1 represents biomarkers identified in only one condition ; a score of 2 represents biomarkers identified in two conditions; and a score of 3 represents biomarkers which were identified in all 3 conditions. Furthermore, a sign was added to the score value to represent an increase (plus sign), or a decrease in the biomarker (minus sign) level, when compared to the healthy range.After extracting the potential list of biomarkers, two steps of filtering were performed.61. The figures were either created with R version 3.4.0 or MATLAB 2014b 32bit.The micro array data processing was performed with R version 3.2.0. All the simulations were performed with MATLAB 2014b 32bit using ILOG Cplex 32 bit (cplex1261). The model manipulation, and simulation was performed using the COBRA Toolbox (v2)Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.Metabolism underpins the pathogenic strategy of the causative agent of TB,  Mycobacterium tuberculosis (Mtb), is a global killer causing millions of deaths every year and is therefore a major burden to human health. Treatment of tuberculosis requires a cocktail of antibiotics for a minimum of 6 months. Treatment failure is common and is a major driver in the upward trend of antibiotic resistance, recognized by the World Health Organization as one of top ten threats to global health. A key to the success of Mtb as a human pathogen is ascribed to its extraordinary metabolic flexibility. Understanding the metabolism of Mtb is therefore an important goal of TB researchers as metabolic pathways present attractive drug targets. A powerful approach to study metabolism is through the use of genome-scale metabolic networks which enable metabolism to be studied at the whole system level rather than one enzyme at a time. Here, we comprehensively compare available genome scale metabolic networks. Our results identify the best performing networks for a variety of modelling approaches. This work allowed us to refine these models for the TB community to use in future studies to probe the metabolism of this formidable human pathogen.The tuberculosis bacillus,  Mycobacterium tuberculosis (Mtb) is the causative bacterial agent of the global tuberculosis (TB) epidemic, which is now the biggest infectious disease killer worldwide, causing 1.6 million deaths in 2017 alone  + XTP[c] -> H[c] + XDP[c] + Pi[c] and 2) maximization of each EDR flux vd while no substrate uptake is allowed into the model as follows:Energy generating cycles (EGCs) exist in metabolic networks and can charge energy metabolites like ATP, GTP, CDP, and UTP without any input of nutrients; therefore, their elimination is essential for correcting energy metabolism ,94. FritS is the stoichiometric matrix, v the vector of fluxes, d the index of EDRs, E is the set of indices of all exchange reactions of the model. If the optimal value of vd for this optimization is vd>0, there exist in the genome-scale model at least one EGC that is able to generate energy metabolites like ATP, GTP, CTP, or UTP.Here, Two types of modifications were performed on the curated Mtb genome-scale metabolic network in order to eliminate TICs .+- and NADP+-dependent) with the same catalytic activity. In this instance, we forced the use of NADPH/NADP+ in anabolic reactions and NADH/NAD+ for catabolic reactions, as recommended by Pereira and colleague Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this paper, the authors evaluate 8 published genome-scale models (GEMs) of M. tuberculosis H3Rv and refine the best performing GEMs for future use. Specifically, the authors compare the gene content, currency metabolite connectivity, biomass reactions, charge balance, mass balance, blocked reactions, thermodynamics, gene essentiality predictions, and utilization of carbon sources across the different models. The authors use standard metrics to assess the various model performances and also place the model predictions within the context of TB literature to help discern biological relevance. The authors identify iEK1011 and sMtb2018 as the best overall GEM and improve their quality by adding various metabolic reactions to the models with detailed reasons. Given the power of GEMs to enable deep analysis of TB metabolism, as well as the large availability and variability in TB GEMs, we believe that this study adds significant value to the TB research community and thus should be published in PLOS Computational Biology. However, there are more GEM evaluations and details described below in the major comments that we would like to see the authors address before publication. In particular, we would like to see the authors detail differences in pathway utilization strategies between the improved models, if there are any.To the Authors:Major commentshttps://www.biorxiv.org/content/10.1101/350991v1). Rather than dictating whether the models are worthy of being published, the MEMOTE report should help understand what the limitations are and where they should focus their efforts in potentially improving the model. Importantly, the MEMOTE report may help the authors improve improve the refined models and comparisons before publication.1. It is highly recommended by the COBRA community that the authors provide a MEMOTE report of both the compared and improved reconstructions . Currently, only a comparison of the biomass molecular weight is provided at the end of the “Checking mass and charge balances of biochemical reactions” section.3. The authors should provide a description/comparison of the growth-associated maintenance (GAM) or non-growth associated maintenance (NGAM) reactions across the different TB models. One or two sentences is sufficient.4. Both flux variability analysis (FVA) and flux sampling are key FBA tools used to evaluate the solution space of GEMs. We recommend that the authors perform FVA and flux sampling of the improved GEMs (iEK1011_2.0 and sMtb2.0) to both evaluate the flux bottlenecks and distributions between models.5. Continuing from comment 5, the authors should provide detailed comparisons of the flux profile between the improved GEMs (iEK1011_2.0 and sMtb2.0) in standard TB media using either parsimonious FBA (pFBA), FVA, or flux sampling. For example, do the models prefer different nutrients? Do they have similar flux strategies in TCA and oxPPP? We would like to see the authors detail differences in pathway utilization strategies between the improved models, if there are any.Minor comments1. Line 100: The description of red in Fig 2 is written the same as blue.Reviewer #2: In their manuscript titled “A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks” Lope-agudelo et al. have systematically examined 8 of the most recent Mtb GEM models and have identified a significant number of problems with them. As I note below, I have questions about some of their choices but overall, I congratulate and thank the authors for a detailed examination of models for an organism of significant importance.Despite this, I don’t find the work novel and suited for Plos Computational biology. I expect Plos Comp bio papers to either introduce a novel computational biology method or report a significant biological find. Neither of these apply to this paper. The manuscript is better suited for more general journals like Plos One or Scientific reports.Perhaps if the authors had generated a single unified improved model of Tb metabolism, then it would have been appropriate for Plos Comp Bio. As it stands, by their own admission, one of their models is better suited for interacting with Recon model while the other is better suited for simulating growth under a variety of different conditions.Major concerns• The paragraph starting on line 38 is not sufficient to describe the examined models and how they differ. The order of publication and combination of models is included in figure 1 and further detail is provided in S1 but there is no mention of it in the main text.• The model with the greatest number of reactions and genes is iAB-AMF-1410-Mt-661 updated published on 2017. Why was this model ignored? There is no mention of this model. How can the purported updated models be considered most complete if they still have fewer reactions and genes than the noted model?• Examining figure 2 it is obvious that some models are just the old models plus some new reactions e.g. sMtb and sMtb2018. Why include the former when instead iAB-AMF-1410-Mt-661 could have been examined?• Line 63, what do you mean by multi-scale simulation platform? What scales are you talking about?• Line 92, I’m confused about the statement “has poor annotation of genes”? Does that mean the genes identified were incorrectly assigned a function or that the coverage of the pathways was lacking?• How does iCG760 operate as a system-level model without water as one of its metabolites? To me the sorry biochemical states of three of the published models is the most shocking find of the paper.• When highlighting the results of analyses for blocked reactions and dead-end metabolites it is important to note if the cause is due to gaps in genome or incomplete GPRs. The former could be viewed as the modeler trying not to add a new phenotype without experimental validation whereas the latter is a poorly curated model.• Why not combine sMtb2.0 and iEK1011_2.0 into one unified model that would work well with Recon for host pathogen interactions as well as correctly predicts growth on a variety of different compounds? That would be the perfect outcome from your extensive efforts.Minor concerns• Do not use acronyms in the abstract prior to defining them, i.e. TB• Some figures are labeled Fig. others Figure. Please choose one format.• The sentence starting on line 152 is incomplete.Reviewer #3: The article entitled, 'A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks', Lopez-Agudelo et al present a comparative analysis of Mtb genome scale metabolic reconstructions.This is a welcome analysis in the literature, given the growing number of models and derivations of models and the need for reconciliation and assessment of the current state. While the most recent update iEK1011 presented an update, the authors identify aspects of the model that area missing.+ Figure 1: Provides a nice summary of an otherwise confusing development of multiple models using genome-scale models of Mtb.+ p7: How did the authors arrive at the cutoff of 10% for the deviation of 1 g/mmol? Why is iEK1011 so much higher than some of the other models?+ Fig 5: It is understandable to revise reaction directionality based on thermodynamic feasibility assessment, however to remove a particular reaction(s) (DFRA1 and DFRA3) should require a little more justification, including any experimental support/arguments and/or explanations of why the arguments made for a different genus (yeast) are also relevant for Mtb.+ p13: the identification of a need for a menaquinone-dependent succinate dehydrogenase reaction is an interesting modification that appeared to improve the predictive capability of the model. However the authors state that they had to add this reaction to iEK1011, whereas in reality all that was required was to change this from an irreversible to a reversible reaction. It would be helpful to note whether there is experimental evidence to suggest that concentrations are present to enable thermodynamic reversibility of the reaction. Additionally, will the reversible form of the reaction result in any infeasible reaction cycles?+ p16: similar to the question raised on p13, which one of these involved addition of new reactions vs making reactions reversible+ p18: 'thermodynamically unstable' -- perhaps the authors meant thermodynamically infeasible, since there does not appear to be any discussion of stability of particular flux states+ p17: what specifically is meant by "unblocking B12 synthesis"? The authors themselves cite specific articles that highlight the incomplete knowledge about B12 handling in Mtb.+ p17: it is not clear what addition is made by addition of biotin?+ p17: again, it is not clear what specifically the authors mean by "including ... pyridoxal-5-phosphate", although from the referenced article, in this case they presumably are referring to the addition of pyridoxal 5' synthase. For biotin, it is not clear what revisions they made or the rational/justification for it.+ Table 5: it is not clear where the cited values for O2, NADH/NAD, and NADPH/NADP are obtained from the referenced citations, since the paper and textbook (non-specific reference) do not appear to cite measurements for Mtb. Additionally it is not clear what is meant by "Other metabolites".+ p26: What was the scientific rationale/calculation to identify the 5% cutoff for+ minor comments:there are a number of small grammatical errors, e.g.p13, l255: "is as a result"p16, l312: "exempler", did you mean exemplar?p 29: supplemental file legends: S10, S11, S12, S13, "for classify essential"etc.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Anders WallqvistAssociate EditorPLOS Computational BiologyWilliam NobleDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: We are happy with the authors revisions and have no further comments.Reviewer #2: The authors have answered all my concerns.Reviewer #3: The revised version provides clarifications for many of the points that were raised. Further the incorporation of MEMOTE report, as suggested by a different reviewer, is a nice addition.A few follow up points are worth addressing,+ Regarding the question of Vitamin B12 synthesis, regardless of whether another paper has included the pathway based on ‘physiological’ argument is generally not considered sufficient criteria for incorporating content into genome-scale metabolic networks . Generally even if ‘physiological’ evidence arguments are made, this typically involves inclusion of a single enzyme or transporter, thus the inclusion of multiple enzymatic steps introduces the potential for significant error into the model.The manuscript by Minas et al poses a possible pathway, but lacks any strong biochemical evidence for the actual enzymes and pathway. The authors should clearly identify which enzymes/reactions were added. Microbial vitamin B12 synthesis is not a trivial or singular pathway, as recently summarized by Fang et al, Microbial production of vitamin B12: a review and future perspectives, DOI 10.1186/s12934-017-0631-y. It should clearly be stated which enzymes/reactions were added and ideally some type of justification , this issue is adequately addressed.+ There are still a few grammatical/spelling errors, e.g. Mathews Correlation Coefficient should read Matthews Correlation Coefficient, the authors likely mean ‘bona fide’ and not ‘bone-fide’ on page 10, etc. Recommend one more read through by authors to minimize misspellings.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes  in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin–diadinoxanthin pathway over violaxanthin–neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions , validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds. They plompounds ,4,5. Diaompounds , a polygompounds  and syntompounds ,8 and fuompounds ,9 makes Thalassiosira pseudonana is a model diatom. It was the first diatom chosen for genome sequencing due to its utility in physiological and silica biomineralization [T. pseudonana for enhanced production/accumulation of lipids, TAGs, and omega-3 fatty acids [lization  studies.lization . The abity acids ,13,14.Chlamydomonas [Chlorella [Nannochloropsis [Phaeodactylum tricornutum [P. tricornutum, a GEM for T. pseudonana is still not available. P. tricornutum and T. pseudonana have been segregated under taxonomically distinct subgroups (order and genus) [P. tricornutum and T. pseudonana, respectively [+ was found to be an essential nutritional component for the growth of T. pseudonana [P. tricornutum [PEPCK (phosphoenolpyruvate carboxykinase) show differential regulation in T. pseudonana and P. tricornutum [P. tricornutum cannot be used to investigate the metabolism and biotechnological potential of a significantly different organism, T. pseudonana, as suggested by a recent work where eight different strains of the same organism (E. coli) were found to be physiologically different [Genome-scale metabolic models (GEMs) make it possible to simulate metabolic behavior of the organism and predict internal metabolism under different conditions ,16,17. Tydomonas ,25, Chlohlorella , and Nanloropsis ,27) and cornutum ,29) haved genus) ,31. Theyectively , based oeudonana  while itcornutum ,35. Morecornutum . Thus, aifferent . TherefoiThaps987) of T. pseudonana, to overcome this gap and perform its systems level investigation. The model iThaps987 includes 987 genes, 2477 reactions, and 2456 metabolites. The FBA analysis revealed the CO2 fixation by C3/Calvin–Benson–Bassham (CBB) cycle. The different pathway routes for biosynthesis of fucoxanthin were explored for their activity and the results indicate a preference for the violaxanthin–diadinoxanthin pathway over the violaxanthin–neoxanthin pathway. The features of this model were compared to the previously published P. tricornutum model (iLB1025) [T. pseudonana to photosynthetically produce some industrially useful compounds. In this work, we present the first high-quality manually-curated GEM (iLB1025) . The worThalassiosira pseudonana, iThaps987, was reconstructed and analysed using established protocols and an approach described for a number other organisms such as Geobacillus thermoglucosidasius [Salmonella typhimurium [Arabidopsis thaliana [Thalassiosira pseudonana biomass was measured to construct the biomass reaction which provides molar fractions of metabolites required to make a gram of biomass. Information from various biological database were used as per requirement. For example, reaction directionalities were taken from BioCyc [The genome-scale metabolic model of sidasius , Salmonehimurium , and Arathaliana . The modthaliana . The modthaliana  databasem BioCyc  and Metam BioCyc  databasem BioCyc  databasem BioCyc ,20,41.Thalassiosira pseudonana  was downloaded from BioCyc and extracted to obtain a flat-files database. The PGDB of Thalassiosira pseudonana has information related to reactions, their genes and sequences. ScrumPy tool was used to fetch these flat-file databases using the inbuilt module ‘PyoCyc’. Finally, a draft model was created by obtaining all the reactions of the database.The Pathway Genome Database (PGDB) for The initial draft model included several non-specific metabolites, e.g., ‘Aldoses’, ‘Fatty aldehydes’, and reactions—e.g., ‘DNA-LIGASE’—that would create uncertainties in the model. The draft model was refined iteratively using literature, biological database(s), and previously published models until a final, robust model is obtained. In the first round of refinement, the non-specific metabolites and reactions were removed from the model. In the subsequent step, each reaction was checked and corrected, wherever required, for mass balance in terms of carbon, nitrogen, phosphorus, and sulfur. Further refinements were carried out as explained below.Thalassiosira pseudonana genome sequence.The draft model obtained after the initial refining was unable to synthesise some essential biomass precursors, indicating an absence of at least one key reaction that participates in the synthesis of the corresponding precursor. The absence of such key reactions results in gaps in the network. These gaps in the draft model were manually identified by performing optimisations for production of all the biomass precursors one by one. Those precursors which were unable to be produced by the draft model were termed as gapped metabolites. The presence of gapped metabolites in a model indicates existence of some missing links (reactions/pathways), also known as gapped reactions/pathways, in the network. These gaps were filled manually by searching for biosynthetic pathways and reactions that will ensure biosynthesis of these biomass precursors, thereby filling those gaps in the network. The gene sequences of these added reactions were subjected to BLAST searches against the Thalassiosira pseudonana is a eukaryotic microbe and has different compartments for different functions. All reactions of the model were localized into three different compartments: cytosol, chloroplast, and mitochondrion. The sub-cellular localisation of reactions was obtained using ngLoc web-server [b-server . A reactPhaeodactylum tricornutum [Synechococcus sp. BDU130192 had utilized ATP maintenance values from previously published models of Synechococcus sp. PCC 7002 [Thermus thermophilus HB27 [Solanum lycopersicum L.) [E.coli and Arabidopsis thaliana models, respectively. We have also evaluated the effect of variation of NGAM on intracellular flux distributions.A characteristic feature of all organisms is to grow and maintain their structural integrity. They require energy to perform these two basic functions of life. This energy is required in the form of the cellular currency ‘ATP’. The energy (ATP) required for growth is termed as growth-associated ATP maintenance (GAM). The GAM accounts for energy required to synthesize precursors, polymerize purine and pyrimidine bases and amino acids to form DNA and proteins, respectively, and polymerize cell wall precursors, etc. The other type of energy (ATP) which accounts to maintain cellular structure and integrity is termed Non growth-associated ATP maintenance (NGAM). As the values of GAM and NGAM are not available for this strain, the values from the GEM of cornutum  were usePCC 7002 , while tlus HB27 , and tomicum L.)  have utiThe biomass equation was formulated by experimental measurement of important biomass components like proteins, carbohydrates, lipids, DNA, and RNA while the minor components—e.g., pigments—were taken from previously published models . FurtheriThaps987, representing the metabolic reactions of T. pseudonana was reconstructed as a ScrumPy-readable text file , a constraint-based modeling (CBM) approach based on linear programming technique using ScrumPy. FBA determines the internal flux distribution which satisfies the steady state assumption besides some other constraints. According to the steady state assumption, the concentrations of intracellular metabolites remain steady . This implies that the compounds taken in by the cell should either go towards biomass production or towards the formation some byproducts. Overall, the carbon, nitrogen, etc. should be balanced in the determined flux distributions. The FBA approach optimizes any metabolic property, e.g., biomass formation reaction, or other reaction(s) within the constraints provided. In this study, minimization of total cellular fluxes was used as the cellular objective function . For sim−1 see  while th2 intake rate was fixed to 2.05 mmol/(gDCW.h) while the silica uptake rate was varied from 0 mmol/(gDCW.h) to 0.05 mmol/(gDCW.h) and minimization of total flux was used as the objective function.In order to analyse the effect of silica limitation on chrysolaminarin production, a virtual transport reaction was added to the model for transport of chrysolaminarin from cytosol to an ‘extracellular’ compartment. That is, the accumulation of chrysolaminarin is assumed as ‘export’. The COS is the stoichiometry matrix, th value in flux vector.The FBA approach  was used to perform the reaction essentiality analysis. The flux through each reaction was fixed to zero at a time while the flux through biomass reaction (growth rate) was fixed to the experimentally observed value at the time of simulations. A reaction was defined as essential if fixing the flux through the reaction to zero results in an infeasible optimization.Diatoms can be exploited as cell factories . TherefoiThaps987, and the missing reactions were ‘temporarily’ added to the model. The maximum theoretical yields for the heterologous products were predicted by adding the required external reactions and constraining the model as done for indigenous products. The MetaCyc database contains pathways and their respective reactions as mini modules, e.g., pathway for ‘styrene’ biosynthesis contains specific reactions that synthesises ‘styrene’. Using an in-house script, the presence of these specific reactions was checked in S is the stoichiometry matrix, c is the objective function. In this case, the objective function is the exchange reaction(s) for product formation. vBiomass was kept as 80% of the measured growth rate. The maximum yields (x) were obtained as a ratio of product formation to carbon intake rates. The linear programming formulation to perform FBA for the production of the native and heterologous products isT. pseudonana genome) for biomass-precursor biosynthesis. Out of these 62 enzymes, 21 were already present in the PGDB of T. pseudonana while the 14 enzymes were found in P. tricornutum or Chlamydomonas reinhardtii genomes. Also, there are 19 orphan metabolic reactions present in the model that do not have any associated Enzyme Commission (EC) number. A list of gap-filling reactions is summarised in The draft model was subjected to manual gap finding analysis, which identified only 62 orphan enzymes additionally required  numbers. The comparison revealed 483 enzymes are common in both models , followed by lipid metabolism pathways with 29 enzymes. Pathways related to vitamins and co-factor metabolism, pigments, and nucleotides have relatively fewer unique enzymes (The dels see  and 183 ectively a. Furthe enzymes b. L-lactate dehydrogenase (cytochrome), and phosphoenolpyruvate carboxykinase (diphosphate). Sub-pathways of lipids metabolism- glycerophospholipid (8) and fatty acid (8) metabolism have equal number of unique enzymes while arachidonic acid (3) and glycerolipid (1) pathways have lesser number of enzymes. Among the vitamins and co-factor metabolism pathways, almost all sub-pathways have approximately the same number of unique enzymes. Porphyrin and chlorophyll (6) and carotenoids (3) metabolism pathways have more unique enzymes than other sub-pathways of the pigment metabolism pathway. The T. pseudonana.−1 (see 2 and photon uptake rates were predicted to be 1.05 and 13.02 mmol/(gDCW.h), respectively. These uptake rates are within the physiological ranges suggested by previous reports [2 evolution of 1.51 mmol/(gDCW.h). Thus, the O2/CO2 ratio was predicted to be 1.43 which is very close to the value 1.37 obtained with the Phaeodactylum tricornutum model iLB1025 [For the photoautotrophic simulations, the growth rate was fixed to the experimentally determined value of 0.024 h reports ,28. The 2 fixation by the C3 cycle by utilising ATP and NADPH produced during light reactions in chloroplast. The photon directs the photolysis of water and electron flow from water to NADPH involving both photosystems PSII and PSI.The light-driven growth simulations (photoautotrophic growth) showed CODuring the electron flow, protons are pumped in thylakoid lumen which generates an electrochemical gradient across the thylakoid membrane. This gradient provides the force (proton motive force) for plastidial ATP synthesis by ATP synthase complex. The flux distribution reveals that under normal photoautotrophic condition, the cyclic electron flow (CEF), which only allows production of ATP, is inactive. Flux through the linear electron flow (LEF), that facilitates simultaneous and coupled biosynthesis of ATP and NADPH, was found to be 3.4 mmol/(gDCW.h).2 is fixed by the enzyme Rubisco that converts three molecules of CO2 and three molecules of ribulose 1,5-bisphosphate (RUBP) into six molecules of 3-phosphoglycerate (3PGA) which is finally reduced to glyceraldehyde-3-phosphate (GAP). The carbon flux is mainly distributed through GAP to three different routes—i.e., the CBB cycle for the regeneration of RUBP—the pentose phosphate pathway and the upper part of glycolysis in chloroplast. About 0.3% of the carbon goes to lower part of glycolysis to form PEP, PYR, and acetyl CoA while 6.7% forms hydroxypyruvate. The rest 92.7% of the carbon flux goes to form GAP . The flux through pyruvate to alpha-ketoglutarate is 0.03 mmol/(gDCW.h) . Althougl-ornithine to form l-citrulline which is then transported to cytoplasm. In cytoplasm, l-citrulline reacts with l-aspartate to form arginosuccinate which, in turn, dissociates into arginine and fumarate. The flux through all above reactions is same (0.004 mmol/(gDCW.h)). The last reaction of the urea cycle, which converts arginine to urea and l-ornithine, is not active under normal conditions. Only upon increasing the nitrate uptake rate without increasing the growth rate is this reaction activated.Urea cycle was found to be partially active under normal growth conditions. The flux through carbamoyl phosphate synthase, which converts ammonium and carbonate ions (inside mitochondrion) to carbamoyl phosphate, was found to be 0.004 mmol/(gDCW.h). Carbamoyl-phosphate then reacts with T. pseudonana accumulates a significant (20–25%) amount of chrysolaminarin [+ transport was varied and the effect on the flux through chrysolaminarin production was calculated . This range was chosen because the lowest ATPM value of 0.36 mmol/(gDCW.h) has been reported for netarium  while thnetarium , has beeynthesis a and finynthesis a.The effect of ATPM values on various reactions reveal that as ATPM increases, more photons are absorbed and the flux through linear electron flow via the PSII and PSI increases. The cyclic electron flow (CEF) remains inactive. The flux through oxaloacetate–malate valve was also increased b. Simula2 is consumed in mitochondria and more O2 is produced in the chloroplast. The O2 production increases with O2 consumption linearly  of these products were predicted by the model using the FBA approach by fixing the growth rate to 80% of the wild type growth rate and maximizing for product formation. Among the native metabolites, malate, and succinate have same yield of 0.22 while citrate has a yield of 0.14. Among non-native compounds, isobutanol and PHB (polyhydroxybutyrate), have a maximum yield of 0.22 followed by 2-methylsuccinate with 0.17 and styrene with 0.17. Additionally, isobutanol production should require the least metabolic engineering, i.e., introduction of only a single heterologous gene.The model ompounds . The theT. pseudonana across the metabolic pathways suggests the presence of alternative pathways/routes for the biosynthesis of biomass precursors.The reaction-deletion analysis revealed a total of 327 essential reactions under photoautotrophic condition. T. pseudonana was used to interrogate different aspects of metabolism and biotechnological potential of the T. pseudonana in a systems approach. The model iThaps987 is mass balanced and does not contain any thermodynamically infeasible cycles.Diatoms are a major constituent of phytoplanktons and play a central role in marine primary productivity. They can also be exploited as potential feedstock for biotechnological processes. Although a considerable progress has been made in understanding their basic and molecular biology, much remains to be done in the domain of systems biology. In this report, a first genome-scale metabolic model of T. pseudonana. Therefore, the model can be used for improvement of genome annotation. The draft model lacked the “PHOSACETYLTRANS-RXN” reaction but BLAST search revealed that locus “XP_002287976.1” codes for the same enzyme. The reactions “ISOCITRATE-DEHYDROGENASE-NAD+-RXN” is coded by the gene locus “XP_002290637.1”, “MALATE-DEHYDROGENASE-NADP+-RXN” by the gene locus “XP_002296311.1” and “MALATE--COA-LIGASE-RXN” by the gene locus “XP_002286405.1”. All of these gene loci were labelled as “hypothetical protein” or “predicted protein”.During the course of model reconstruction, we identified a number of previously unannotated genes in iLB1025) of another diatom P. tricornutum revealed common and unique features of the model, iThaps987  or by the cyclic electron flow (CEF). The ATP synthesized by LEF is tightly coupled with NADPH production whereas the ATP synthesized by the CEF does not generate any NADPH. The model simulation indicates that the plastidial ATP was synthesized exclusively by LEF. This prediction is also in agreement with the experimental observation by  that theiThaps987 has ‘2-oxoglutarate-malate shuttle’ besides ‘malate–oxaloacetate shuttle’ for exchange of metabolites between mitochondrion and chloroplast. Amongst these, the ‘malate–oxaloacetate shuttle’ plays a major role in translocation of photosynthate from chloroplast to mitochondrion. ‘Malate–aspartate shuttle’ was found to be absent in the iThaps987 model as it was in iLB1025.The model was further utilized to explore the effect of different ATPM values on metabolism b. The in3−) intake does the last reaction of urea cycle get activated to flush out the excess nitrogen in the form of urea.Unlike other unicellular organisms such as bacteria, cyanobacteria, yeast, and green algae, diatoms have a complete urea cycle . HoweverThe simulations for Si-limited condition revealed a trade-off between formation of storage product and biomass production under nutrient-limiting condition. The carbon was partitioning between biomass and chrysolaminarin at various levels of silica. Under stressed conditions (very low silica), most of the carbon goes to chrysolaminarin while at higher silica intake flux, the carbon flux shifts towards biomass formation.iThaps987 with respect to its biotechnological potential revealed iso-butanol as the non-native product that can be produced with the introduction of only a single foreign gene “kivD’. On the contrary, two genes ‘kivD and ADH2′ are required in the P. tricornutum model.The systems analyses of T. pesudonana under different conditions [13C-MFA can be performed in different conditions to validate the predicted FBA solutions.The reconstructed GEM can also be utilized to reveal new systems-level insights about the organism. For example, integration of expression data to the available GEM could provide novel insights into metabolic regulation under different conditions. The model is provided in ScrumPy as well as in mat format that can be used for further studies. Some studies have performed transcriptomic and proteomic analysis of nditions ,54,55. TThalassiosira pseudonana. Several results obtained by the model were found to be in agreement with previous reports. The model was used to probe the metabolic engineering needed to produce various native and heterologous industrially relevant compounds that can be produced by the organism. The model will be a useful tool for systems-level investigation of Thalassiosira pseudonana.This report presents the first publicly available, comprehensive, and manually curated genome scale metabolic model of