The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.The online version contains supplementary material available at 10.1186/s12859-021-04281-7. Biochemists have amassed a large amount of knowledge about the topology of the chemical reaction network that cells use to transform nutrients into energy and the building blocks for more cells, collectively known as “metabolism”. The substrates, products, and cofactors for hundreds of reactions have been elucidated, from the most central pathways like glycolysis to more distant pathways for the biosynthesis of uncommon metabolites. Many of these pathways are extremely well-conserved across the tree of life , with thOne critical form of direct regulation of metabolic reactions (and arguably the most common) is allosteric regulation, where a regulator and a protein (in this case an enzyme) interact at a location other than the active site . In thisEscherichia coli is vital for predicting flux dynamics and can reveal “metabolic hubs,” where a metabolite is connected to many reactions instead of only the few found in the stoichiometric topology [Metabolic models that use only the known stoichiometry of the system and exclude metabolite-dependent regulation often have extremely limited accuracy. Machado et al. showed that including allosteric regulation in a model of topology . Despitetopology , 8), thetopology .In contrast, the comparatively more mature fields of genomics and transcriptomics have yielded a variety of experimental approaches to explore transcriptional regulation, providing sufficient data to motivate the development of computational approaches to map out gene regulatory networks , 11. SomThere have been far fewer computational approaches to identify metabolite-dependent regulatory interactions of enzymes in metabolic systems. Link et al.  used dynSaccharomyces cerevisiae and E. coli metabolism, to show that SCOUR can be used on a variety of systems. Applying SCOUR to poorly-studied organisms has the potential to enable discovery of previously unknown regulatory interactions that are key to developing accurate and predictive metabolic models.Here, we present a new machine learning approach for stepwise classification of unknown regulation (SCOUR) that leverages metabolomics and fluxomics data to predict likely metabolite-dependent regulatory interactions. SCOUR uses established machine learning algorithms in a stepwise process that focuses on identifying reactions controlled by one, two, or three metabolites. While SCOUR benefits from stepwise, serial inference of these increasingly complex interactions, each step is independent, uses different classification features, and can be performed without the others. Importantly, the classification task that SCOUR looks to address typically has insufficient training data available to generate useful models, so we devise a strategy we refer to as “autogeneration” that we use to create sufficient data to train the models. We test our framework on two synthetic model networks, as well as on models of In this work, we examined four metabolic networks of varying size and complexity: two synthetic model networks and two biological systems. We simulated each metabolic network with fifteen sets of randomly generated metabolite concentration initial conditions  to produce fifteen sets of metabolite concentration and flux data used in the testing sets of SCOUR. Each metabolic network is described in detail below.1) is a constant flux that is not controlled by any metabolites and is not considered when using SCOUR. Both models contain reactions controlled by one, two, or three metabolites, including both positive and negative regulatory interactions. Table To initially test and evaluate SCOUR, we created two small synthetic model networks. The smaller synthetic model Fig. a containEscherichia coli and a model of glycolysis in Saccharomyces cerevisiae [E. coli model contains 18 metabolites and 48 reactions, while the S. cerevisiae model contains 22 metabolites and 24 reactions. We used the previously published kinetic equations and parameters for these systems; in both cases, the mathematical forms of the rate expressions include Michaelis–Menten, Hill, and mass action kinetics. In the E. coli model, the fluxes for glucose kinetics, murein synthesis, tryptophan synthesis, and methionine synthesis are constant and not controlled by any metabolites. Likewise, in the S. cerevisiae model, the fluxes for glucose mixed flow to extracellular medium and cyanide flow are constant and not controlled by any metabolites. As in the synthetic models, both the S. cerevisiae and E. coli models include reactions controlled by one, two or three metabolites, although they also have reactions controlled by four metabolites. Table To test SCOUR on more biologically relevant systems, we examined a model of central carbon metabolism in revisiae , 24. TheMachine learning models must be trained using data that are broadly representative of the input data they are likely to encounter, which often entails using datasets that are as large as possible. In metabolism, there is a wide variety of metabolic reactions with disparate mechanisms and functional behaviors   or that Damped sine waves with random parameters were generated to mimic common metabolite concentration profiles; these profiles were then used to calculate flux profiles using BST equations with random parameters . FeatureNi,k ~ (yi(tk),CoV·yi(tk)), where yi(tk) is the value of species (metabolite or flux) i at time point k. For each timepoint, three noisy data values were generated to mimic triplicate samples, which is a common practice in metabolomics and fluxomics experiments.To generate noisy data that are more representative of what is expected to be acquired experimentally, we used two different sampling frequencies and two coefficients of variation (CoV) for randomly-added noise, for a total of four conditions. Sampling frequencies of 50 and 15 timepoints (nT) and CoVs of 0.05 and 0.15 were used, where a higher CoV represents more noise . The smaller number of timepoints and the two levels of added noise are reasonable values for what one might expect from experimental mass spectrometry data for metabolomics or fluxomics. Starting with noiseless data, each metabolite and flux value in each time course was replaced with a random value drawn from For noisy data, we applied two different pre-processing steps to the data. For the one-controller metabolite interaction inference step of the framework, we used the median sample of the triplicate noisy data to calculate the features in the training and testing sets. For the two- and three-controller metabolite interaction inference steps, instead of using the medians, a moving Gaussian filter was applied to smooth the triplicate noisy data before calculating their features. The window size of the filter was chosen to be ¼ of the total simulation time, which was found to smooth the data without overfitting to the noise itself. While smoothing the noisy data for two- and three-controller metabolite interactions led to an increase in SCOUR’s performance, it was detrimental for one-controller metabolite interactions. We found that a few of the one-controller metabolite interaction features were more sensitive to the smoothed data than the noisy median data and would cause greater variability in SCOUR’s performance across repetitions.Each step of the framework contains different “features” used to predict whether a particular interaction is likely to be correct. These “features” are scalar-valued outputs of functions that quantify characteristics of concentration and flux profiles and the relationships between different profiles, which may thus indicate whether a given metabolite or set of metabolites regulates a given flux. Different features were used for the prediction of interactions controlled by different numbers of metabolites . This allows the features to be customized to specific interaction types , which is expected to increase SCOUR’s overall accuracy compared to using the same features for all steps. Features were designed using biochemical insight into how metabolites are known to interact with enzymes and how these interactions would manifest in concentration and flux profile data. For example, for the one-controller metabolite interaction step, the Spearman correlation between fluxes and metabolites was used as a feature, as reaction fluxes are expected to be highly (though not necessarily linearly) correlated with the metabolites that control them. Additionally, features were created based on the expectation that for every set of metabolites that completely defines an output flux, each possible set of metabolite input concentrations can only yield one single output of flux reaction rate. A list and description of all features used in SCOUR can be found in Additional File Stacking is a technique used in machine learning that consolidates predictions from multiple machine learning algorithms . Due to SCOUR uses a stepwise approach to identify different types of regulatory interactions at each step, beginning with the identification of one-controller metabolite interactions. First, two training datasets that consist of true positive (controlled by a single metabolite) and true negative (controlled by multiple metabolites) interactions are autogenerated for the two levels of the stacking model. The features described in Additional File This stepwise approach has two key advantages. First, it allows for completely independent classification models and features that can be crafted for specifically identifying reactions that are controlled by one, two, or three metabolites. We found (data not shown) that developing a one-step platform for simultaneous multiclass prediction (i.e. reactions controlled by different numbers of metabolites) led to worse performance even when using machine learning algorithms such as random forest and neural networks that can be tailored toward multiclass classification. Because SCOUR is trying to predict not just the number of metabolites controlling a reaction but also the exact controller metabolites, this additional layer of complexity is easier to address with multiple binary classification models.The second advantage of using a stepwise approach is that after each step, fluxes whose regulatory status has already been identified are removed from consideration in the next step so that there are fewer interactions to be tested by the machine learning algorithms. This reduces the computation time of the entire stepwise process, reduces the chances of false positives, and allows subsequent steps and features to be more simply designed under the assumption that lower-order regulatory interactions will not be present in later steps. However, these advantages are at the risk of removing fluxes at a step earlier than when their true regulatory status could be identified. A comparison of the number of interactions that need to be tested whether or not the stepwise framework is used for each of the evaluated models can be found in Table To assess the performance of our framework in identifying different types of regulatory interactions, we evaluated four different metrics: accuracy, sensitivity, specificity, and positive predictive value (PPV). Accuracy is the percent of candidate regulatory interactions that are identified correctly as existing or not existing in a model. While accuracy can be a good metric if the classes (i.e. candidate interactions that are truly in the model vs. candidate interactions that are not in the model) are well-balanced, this is not the case for combinatorial consideration of potential metabolic regulatory interactions: there are many more candidate metabolite and reaction flux combinations than there are actual regulatory interactions in a given biological system. Sensitivity and specificity separate accuracy into two metrics that measure, respectively, the percent of positives (i.e. true regulatory interactions) that are identified correctly and percent of negatives  that are identified correctly. PPV is the percentage of interactions predicted by the model that are true positives, an important metric to consider when one plans on experimental validation of predictions because it indicates how much effort is typically required for the validation of every newly discovered interaction. Exceedingly low PPVs are undesirable for predictions that are difficult to experimentally test, including metabolite-dependent regulation of reaction rates, because they signify that a large number of predicted interactions must be tested with these difficult experimental methods in order to find any true, validated interactions.When evaluating SCOUR on noiseless data, we found good overall predictive accuracy for both synthetic models Fig. a, b. We E. coli and S. cerevisiae systems .We found similar results when testing SCOUR on noiseless data simulated from the ems Fig. c, d. As ems Fig. , only a While using noiseless data gives a sense for the framework’s performance under ideal conditions, the realities of metabolomic and fluxomic experimental limitations can lead to significant deviation from these idealized assumptions. To assess SCOUR’s performance under more biologically relevant conditions, we examined two factors that need to be considered when using real metabolomics and fluxomics data: decreased experimental sampling frequency (and thus less information content to enable identification of true regulatory interactions) and increased experimental measurement noise. To give SCOUR a baseline performance level to compare to, we also created a classifier that randomly predicted whether a metabolite–flux interaction was a true positive or true negative interaction and used this to calculate a PPV at each step. Each interaction had a 50% chance of being classified as either a true positive or true negative in each step of the framework. For this random predictor, we assumed that the correct reaction fluxes were removed at each step, giving this classifier an advantage over our framework by greatly reducing the number of possible false positive interactions.Assessment of SCOUR’s performance on noisy data from the synthetic models Fig. a–f yieldS. cerevisiae and E. coli models, the PPV was fairly consistent (with slight decreases) for any given interaction type across the increasingly challenging noisy conditions, while accuracy, sensitivity, and specificity sometimes exhibited slightly more variability across those conditions. For the S. cerevisiae model, the PPV remained high (> 69% on average) in all conditions for identification of one-controller metabolite interactions and was above 25% for identification of two-controller metabolite interactions. These PPVs for one- and two-controller metabolite interactions are sufficiently high to allow for feasible experimental validation of these predictions to identify previously unknown interactions. For three-controller metabolite interactions, the PPV was below 12% for all conditions, which is not ideal from the standpoint of experimental practicality. In the E. coli model, the accuracy, sensitivity, specificity, and PPV were high for one-controller metabolite interactions in all conditions (> 92%), but the PPV dropped to less than 13% for two-controller metabolite interactions and was essentially 0% for three-controller metabolites  PPV for the synthetic models and the E. coli model between the one- and two-controller interaction predictions is largely attributable to two reasons. First, the E. coli model contains three two-controller metabolite interactions where the two controller metabolites are highly correlated with each other , where one class (e.g. true negative interactions) significantly outnumbers the other class (e.g. true positive interactions) [We believe the unusually sharp decrease in PPV for the actions) .Throughout the evaluation of SCOUR, we have relied on PPV as a performance metric because it is a valuable indicator of whether the predictions by SCOUR are worth experimentally validating. F1 score is another performance metric that is calculated from PPV and sensitivity and is often used for imbalanced datasets such as those found in this work. When using F1 scores, we found that SCOUR still outperformed random classification of one- and two-controller metabolite interactions in all models under all noisy conditions evaluated (Additional File Perhaps the most striking feature of SCOUR is its use of the autogeneration of synthetic interactions for training data. Because machine learning models generally require large amounts of data for training, and because this scale of data is typically not available for metabolomics and fluxomics data, we created a method to automatically generate training data that are in some way representative of a wide variety of real biological interactions. While these autogenerated “interactions” may not perfectly recapitulate the data that result from real reactions, SCOUR’s success shows that this autogeneration method can sufficiently train machine learning algorithms to identify regulatory interactions in many different systems. Because dynamic metabolomics and especially fluxomics data are so expensive and difficult to acquire with current analytical tools, this autogeneration method may prove useful for other tasks that require large amounts of these types of data.Although this proof-of-principle framework has demonstrated significant potential for identification of many different regulatory interactions, there are several potential future avenues to improve overall performance. We note that both training and testing on autogenerated data produces higher PPVs (Additional File Second, as in all machine learning approaches, there is room to improve the features used to help predict true interactions. We designed knowledge-driven features based on how metabolites interact with the reaction fluxes they control. Data-driven features derived from raw metabolomics and fluxomics data could be beneficial if there are sufficient data to drive the derivation of these features, including the underlying “ground truth” about whether a given interaction truly exists in a system. Such features could include graph theoretical characterization of the network topology of how metabolites and fluxes are connected to each other, which has previously been used in metabolic contexts , 38. HowFinally, the preprocessing of noisy experimental data undoubtedly can impact downstream analytical performance. While we settled on the median sample of the triplicate data when calculating features for one-controller metabolite interactions, and a Gaussian moving filter to smooth the data for two- and three-controller metabolite interactions, we also tried an average moving filter as well as an in-house smoothing approach . BecauseNotwithstanding these potential avenues for improvement, SCOUR is already a useful tool. At the very least, SCOUR can determine with high confidence reaction fluxes that are only controlled by a single metabolite, eliminating swaths of the metabolic network where metabolite-dependent regulation is unlikely to occur. However, SCOUR can also identify many more complex interactions, including possibly pointing towards reaction fluxes controlled by four or more metabolites (Additional File SCOUR is a proof-of-principle for how metabolomics and fluxomics data can be leveraged with machine learning to find metabolite-dependent regulatory interactions; to our knowledge, this is the first reported example of such an approach. The identification of metabolite-dependent regulatory interactions has to date been critically hampered by experimental limitations in measuring and validating these interactions, making SCOUR’s predictions and triaging particularly valuable for such labor-intensive endeavors. Enabled by a method for autogenerating training data that reasonably mimic data from real biological systems, SCOUR circumvents the requirement for massive training sets that is typically associated with machine learning approaches. While metabolomics and fluxomics data are often collected at putative steady states, it is quite feasible to collect these data dynamically to leverage SCOUR’s potential for biological discovery. This means that as analytical methods for measuring metabolomics and fluxomics become cheaper and easier, and more data are available for analysis, SCOUR will be ready to take full advantage of these new datasets to discover biochemical regulatory interactions.Additional file 1 . Supplementary methods and figures.


Colorectal cancer (CRC) is one of the most malignant cancers and results in a substantial rate of morbidity and mortality. Diagnosis of this malignancy in early stages increases the chance of effective treatment. High-throughput data analyses reveal omics signatures and also provide the possibility of developing computational models for early detection of this disease. Such models would be able to use as complementary tools for early detection of different types of cancers including CRC. In this study, using gene expression data, the Flux balance analysis (FBA) applied to decode metabolic fluxes in cancer and normal cells. Moreover, transcriptome and genome analyses revealed driver agents of CRC in a biological network scheme. By applying comprehensive publicly available data from TCGA, different aspect of CRC regulome including the regulatory effect of gene expression, methylation, microRNA, copy number aberration and point mutation profile over protein levels investigated and the results provide a regulatory picture underlying CRC. Compiling omics profiles indicated snapshots of changes in different omics levels and flux rate of CRC. In conclusion, considering obtained CRC signatures and their role in biological operating systems of cells, the results suggest reliable driver regulatory modules that could potentially serve as biomarkers and therapeutic targets and furthermore expand our understanding of driving mechanisms of this disease. Cancer is the leading cause of death worldwide . There are several type of cancer, such as lung, stomach, colorectal, liver, and breast . Among the various cancers, gastrointestinal cancers occur mainly in men and women living in developing countries .Colorectal cancer (CRC) is the third most common cancer among men and women in the US and affects all racial groups . The reports indicated 50,260 deaths during 2017 . Similar to other types of cancers, CRC could be prevented and treated with higher probability if detection occurs at the early stage of tumor initiation . There are several common tests to detect this malignancy including colonoscopy and serum Carcinoembryonic Antigen (CEA) test, but their sensitivity is not desirable . There are also some common methods to screen susceptible individuals such as endoscopic, histopathological examination of biopsies, and surgically removed specimens that are undesirable in terms of sensitivity and also are painful which bring complications for patients . Generating biological high-throughput data and optimization of computational methods obviate obstacles to study biological systems using systems biology as a multidisciplinary approach . While omics puzzle is being completed in parallel with evolving data integration and computational technics , numerous studies have tried to find biomarkers and enhanced the chance of early detection to improve diagnosis and prognosis of patients . Since in an up to bottom approach in omics levels, metabolome perturbation is the final change, it could provide a clearer vision of cell abnormalities status during initiation and progression of disease due to alteration in genomic, post-genomic, transcriptomic, and proteomic profiles . Besides, it has been shown that metabolic perturbation is one of the common characteristics of cancerous cells. Numerous studies have tried to develop metabolic models by integrating gene expression data in the metabolic framework of target cells and design predictive cancerous metabolic models which could help to understand differences between tumor and normal samples . Exploring such alterations in genome, transcriptome, and metabolome could pave the way of finding biomarkers and understanding metabolic alterations that may be useful for diagnosis at the early stage of cancer. Study of underlying molecular mechanisms of CRC has conducted in many research by systems biology approach . Accumulating changes in CNA, point mutations, gene expression and flux balance alteration of metabolic drivers indicated to promote the cancer initiation and progression . In this study, we tried to unravel the landscape of metabolic, genomic and transcriptomic alterations and their integrated role in fine-tuning of regulome for initiation and progression of CRC by systems biology approach and using the huge numbers of publicly available data. Since there have been very few studies that simultaneously considered changes in different omics levels for CRC, in this study we applied data from different sources to obtain signatures of each omics levels that could be applied to understand the molecular mechanisms of CRC. Data collectionGene expression microarray data related to colorectal cancer were downloaded from the Gene Expression Omnibus (GEO) database. Microarray data platform was Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) for both datasets. To analyzing copy number aberration and point mutations related to CRC, next generation sequencing data retrieved through Cbioportal . Moreover, TCGA data including gene expression, copy number alteration, point mutation, microRNA, methylation, and protein profiles used to portrait regulome signatures of CRC. Microarray raw data were preprocessed using MATLAB software (version R2015b). First, a preprocessing of gene expression raw data has been performed by transferring all gene expression values to log2 scale, and by doing normalization step (using quantilenorm command in MATLAB based on Median). This procedure was repeated for both data types . Metabolic Model ReconstructionWe have used the corrected human metabolic model  which includes 2766 metabolites, 3748 reactions, and 1905 genes to work with. Gene expression values corresponding to metabolic genes have been mapped into the human metabolic model using E-Flux method . E-Flux approach uses pre-processed gene expression data, finds metabolic genes through them, and considers the corresponding expression values. Next, all values were replaced into the gene-reaction association matrix available in the human metabolic model. The new gene-reaction association matrix now includes some changed elements due to mathematical operators and some unchanged elements because they did not have any metabolic genes. Then, the gene-reaction association matrix values were rescaled to , multiplied by the current upper bound values of the model , and set as the new upper bound values of the model. The lower bound values were negative values of the new upper bound values (for reversible reactions). Therefore, this method constrains the upper and lower bounds of each reaction according to its corresponding gene expression level. We wrote a Mathematica script to apply E-Flux algorithm to the human metabolic model. So, we have reconstructed normal and cancer colorectal metabolic models. The biomass and the medium compositions (RPMI-1640) added to the models are shown in the supplementary file 1. All normal and cancer metabolic models are also available in the supplementary file 2 as MATLAB structure files.Flux Balance Analysis Flux Balance Analysis (FBA) considers appropriate constraints whereas the system is in its steady-state. In FBA, a metabolic network is considered as a stoichiometric set of equations (in a matrix format including the stoichiometric (S) and the flux (V) matrices . Since a higher number of reactions in comparison to metabolites is a common feature of most metabolic networks, this property prevents the system of linear equations to be solved analytically. One approach is using linear programming in which it tries to solve a system of equations in association with minimization/maximization of an objective function as follows:T.vmin/max: cv = 0 a < v < bsubject to: S.T is a transposed vector of stoichiometric coefficients of metabolites incorporating with the objective function, and v is a vector of fluxes which will be determined. Vectors a and b are also lower and upper bounds of all reaction, respectively. The solution of the metabolic fluxes is underdetermined when a system is unconstrained. Applying additional constraints (eg. a < v < b) would decrease the solution space. In such a condition, one could obtain the optimal set of the flux distribution while an objective function is optimized. Therefore, FBA would turn into a linear programming (LP) problem. For this study, we used the COBRA toolbox  for evaluating the metabolic models by maximization of the biomass equation . The glpk solver was used for linear programming problems. For FBA, running the optimizeCbModel function builds four main output structures: f, x, w, and y, where f is the objective value, x includes reaction fluxes, w is a vector of reduced costs, and y is a vector of shadow prices. Every reaction belongs to a known metabolic subsystem. For example, there are 99 subsystems presented in the human metabolic model according to metabolic pathway classification . Subsystems information are available through the vector called subSystems in the MATLAB model structure. All FBA results related to normal and cancer metabolic models are available in the supplementary file3. where cDifferentially expression gene and gene enrichment analysis by GSEATo find differentially expressed genes (DEGs) in CRC cells in comparison with the normal cells, gene expression microarray data sets preprocessed by FRMA package  in R and then limma  was used to develop a linear model for finding DEGs. Obtained DEGs for each dataset combined by fisher method. To find pathways that over and under-expressed in CRC samples mentioned datasets merged by COMBAT method to remove study bias. In the next step, a dataset consisting of DEGs and their expression values and Reactom geneset fed into the GSEA . To run the software, permutation type set to gene-set and the number of permutations set to 1000. After finding enriched pathways, 20 top-ranked pathways selected for leading edge analysis that clusters genes according to the number of genes involved in common gene-sets. Network analysis of genomic and transcriptomic signatures of CRCTo illustrate tissue-specific networks in genomic and transcriptomic levels of CRC, signatures corresponding to DEGs, point mutations and CNAs were constructed by data retrieved from the DifferentialNet database . The DEGs network created by DEGs with signal to noise ratio (SNR) more than 0.5. Also, in case of point mutation and CNA signatures frequency of 5% defined as a cut-off to select the genes for network construction. Construction and enrichment analyses of networks were done using networkanalyst .Regulome analyses of CRCTo this end, we retrieved 621 samples related to CRC from TCGA through Regulome Explorer . To unfold the regulatory effects of different omics levels on protein level as a final manifest of central dogma process, we investigated the correlation between methylation, microRNA, point mutation, copy number alteration and gene expression profiles with protein levels in CRC. The total number of gene expression microarray samples for normal and cancer cells were 56 and 67, respectively . Moreover, 1596 samples with point mutation data, 1354 samples with CNA data and 621 samples that applied in regulome analysis retrieved from cBioportal and TCGA .Flux balance analysis reveals metabolic subsystems in CRC-10 in the results.Impacted subsystems in colorectal cancer showed in According to FBA results the highest flux rate drop was in retinol dehydrogenase reaction in the “Vitamin A Metabolism” subsystem with the value of -1875. The notable flux value of -1398 was calculated for bicarbonate transport which is part of the” Transport Extracellular” subsystem. The results showed that in the “Pyrimidine Catabolism” subsystem, Cytosine deaminase reaction flux decreased in colorectal cancerous cells with the value of -529. Also, Glutathione peroxidase reaction in the “Glutathione Metabolism” subsystem represented a reduction in the colorectal cancer model with the value of -439. There was also a decrement in the “Transport Mitochondrial” subsystem in which the value of ADP/ATP transporter reaction was -382. There was a reduction in the “Fatty acid elongation” subsystem with the value of -147, which was calculated for palmitoyl-CoA desaturase reaction. In the “Glutamate Metabolism” subsystem, there was a reduction in glutamine synthetase reaction with the value of -129. Moreover, two other subsystems  decreased in colorectal cancer model for ATP synthase and UTP-glucose-1-phosphate uridylyl transferase reactions with the values of -95 and -80, respectively.However, FBA results demonstrated some subsystems in cancerous model with an increment in their reactions. For example, in the “Nucleotides” and “Pyruvate Metabolism” subsystems, there were increment with the value of 280 and 142, respectively which were due to nucleoside-diphosphate kinase and L-lactate dehydrogenase pathways. Also, Adenosine deaminase as a reaction in the “Purine Catabolism” subsystem showed increment in the colorectal cancer model with the value of 56. In” Glycolysis/Gluconeogenesis” subsystem, the highest flux value was for glucose-6-phosphate isomerase pathway with the value of about 53. Moreover, our results presented that hyaluronan synthase reaction with the value of 21 was increased in the “Hyaluronan Metabolism” subsystem.Differentially expression genes, point mutations and CNAs Differentially expression gene analysis shows the CRC gene signature including 396 genes with signal/noise ratio more than 0.5 and FDR<0.05. Genomic data of 1,596 samples with point mutation data and 1,354 samples with CNA data retrieved from eight studies through cBioportal to find point mutation and CNA signatures corresponding to colorectal cancer. Analyzing the retrieved data indicated 7 genes with CNA (gain or lose area) and 37 genes with point mutations. Pathway enrichment analysis results Enrichment analysis results revealed pathways that contributed to CRC phenotype. Some of the most impacted pathways were related to cell cycle and involved pathways with cell growth that represent as up-regulated pathways and conversely some pathways related to the immune system, rhodopsin-like receptors (class A/1) that serve as components of hormones, light, and neurotransmitter receptors are examples of down-regulated pathways in CRC cells. Tissue-specific network analysis Analysis of three different networks constructed for DEG, point mutation and CNA signatures show different driver nodes related to CRC. The results show that finding driver nodes should be investigated in different level of omics network to obtain high reliable centrality for hub nodes of CRC. The result of PPIN of DEGs shows although this network illustrates a part of affected driver nodes of CRC, some CRC hallmarks cannot be found in this network. For example, in PPIN constructed by DEG, the P53 is not indicated as a driver node, while in point mutation network it was the most important node in the network. As a result, to find the CRC reliable hub nodes, applying comprehensive information from all of the omics levels is important. Regulome analyses resultsIGFBP2, BCL2L1, INPP4B and CCNE1 gene expression with their corresponding protein levels. Furthermore, COL10A1 gene expression level has a positive correlation with FN1 protein level, . Application of flux balance analysis in cancer is rapidly developing into a considerable scientific field and has been noticed as a helpful method for cancer diagnosis . Indeed, the feasibility of data mining and exploration of subsystem fluxes provided a reliable explanation of metabolism alteration during cancer development . In this study, we proposed a new metabolic model which is useful for cancer diagnosis and treatment. The results showed miscellaneous metabolic pathways in which flux changes were associated with the mechanism of cancer. According to FBA results, the highest reduction in flux levels for the CRC samples was in the “Extracellular Transport” subsystem. Such decrement has been discussed by Netti et al., (2000) which is caused due to cellular transfer reductions in the extracellular matrix (ECM). There is a controlling mechanism for molecular traffics in ECM. Barriers in molecular transport in ECM play an important role in tumor cells viability, for example, to prevent penetration of some therapeutic agents . Moreover, initiation of pro- or anti-apoptotic effects in cells particularly are related to ECM. Some of these functional components in cells promote tumor progression . Also, flux value for the “Vitamin A Metabolism” reduced in colorectal cancer cells. This result was in concordance with a previous study indicated the decrease of retinoic acid production consequently results in tumor immune evasion in colorectal cancer . In addition to the FBA result that shows the reduction in the production of retinoic acid (which leads to tumor immune evasion), enrichment analysis of CRC’s DEG signature shows the “immune response” and “Class_A1_Rhedopsin_like_receptors” as examples of under-expressed pathways that were in concordance with the FBA results. Moreover, FBA results showed the Nucleotides Metabolism as an activated subsystem that is in concordance with the GSEA pathway analysis results (including activation of cell growth and involved pathways), . Major aIn conclusion, considering paradigm shift of cancer studies that focus on the personalized study of cancers which ignore the heterogeneity of tumors and also paucity of multi-omics studies of colorectal cancer, this study directed to provide a reliable population-based perspective from molecular mechanisms underlying CRC. These results obtained from analysis of huge and different types of data to uncover alterations that are difficult to explain by analysis of one omics level in CRC.


A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.Michaelis constants (K The Michaelis–Menten equation was derived by Leonor Michaelis and Maud Menten to quantify the velocity of an enzymatic reaction using measurable concentrations of enzyme and substrate even before the exact nature of enzymes was elucidated  1]. Des. Des1]. m) in the equation is a pseudo-equilibrium constant that corresponds to the substrate concentration at which an enzyme operates at half of its maximum catalytic rate  ieactions  3]. For. Form) ibstrates [3]. In tPLOS Biology by Kroll and colleagues uses AI to predict Km purely from protein and substrate information [Artificial intelligence (AI), empowered by the increasing availability of Big Data, is transforming many aspects of our lives and multiple research fields . Rooted ormation . Their gm values from public databases to train AI models with an increasing amount of additional substrate and protein information [m values only deviated from experimental values by 4-fold on average. However, model performance was still increasing with the size of the training dataset, and, therefore, it will be important to continue improving the model as more experimental data become available, particularly regarding extreme values poorly represented in public datasets. Overall, Kroll and colleagues provide a very significant step forward that outperforms previous attempts at predicting Km.The authors used Kormation . First, ormation . The autm predictions for 47 model organisms. We foresee that these invaluable predictions will open new avenues of research in multiple fields. In particular, we think they could be an important step toward dynamic, genome-scale metabolic models (GSMMs). GSMMs emerged in the last decade as powerful constraint-based modeling platforms to achieve quantitative predictions of metabolic fluxes through multiomics data integration [m predictions proteome-wide by Kroll and colleagues could fuel a new generation of GSMMs that accurately predict dynamic metabolic flux maps to uncover new drug targets and boost our ability to quantitatively and accurately model metabolism.Importantly, the authors not only provide the code in a public repository, but they also make available genome-scale Kegration . GSMMs hegration ,10, but


We find that paying attention to redox states illuminates which reactions are endergonic or exergonic. Our results highlight the role of acetate in proto-metabolic cycles, and its connection to other prebiotic molecules such as glyoxalate, glycolaldehyde, and glycolic acid.How did metabolism arise and evolve? What chemical compounds might be suitable to support and sustain a proto-metabolism before the advent of more complex co-factors? We explore these questions by using first-principles quantum chemistry to calculate the free energies of CHO compounds in aqueous solution, allowing us to probe the thermodynamics of core extant cycles and their closely related chemical cousins. By framing our analysis in terms of the simplest feasible cycle and its permutations, we analyze potentially favorable thermodynamic cycles for CO However, at its core , represeAs pointed out by Pross  in this 2 as its carbon source to build biomass. While proto-life on the early Earth may have had a variety of carbon sources, the ability to incorporate C1 molecules into larger structures remains fundamental for a self-sustaining system to emerge  term) in the reaction quotient Q would then result in different ΔG values. These only address equilibrium thermodynamics in a perturbative way. Temperature, pH, and solute activities are also expected to impact kinetics. Our group is working to calculate baseline activation barriers for reaction types in these cycles, although this requires the lengthy process of optimizing transition states.This baseline provides a preliminary step to exploring the effect of temperature, pH, and different effective concentrations (activities) of the various solutes including the reference species. One could recalculate the relative free energy changes using ΔOnce baseline kinetics is established and we have made better estimates of the rate constants, we plan to incorporate both thermodynamic and kinetic parameters into a dynamic network model that tracks the fluxes of chemical species as they cycle through their myriad reactions. Our hope is that this will provide a preliminary model (with its attendant limitations) that represents the establishment of proto-metabolic cycles under non-equilibrium conditions, and we hope to provide a richer story in the future.


Escherichia coli under various conditions . One key finding is that, under all conditions tested, the glucose uptake increases with rising ATP demand, but only to a critical level beyond which it drops markedly, even below wild‐type levels. Focusing on anaerobic growth and using metabolomics and proteomics data in combination with a kinetic model, we show that this biphasic behavior is induced by the dual dependency of the phosphofructokinase on ATP (substrate) and ADP . This mechanism buffers increased ATP demands by a higher glycolytic flux but, as shown herein, it collapses under very low ATP concentrations. Model analysis also revealed two major rate‐controlling steps in the glycolysis under high ATP demand, which could be confirmed experimentally. Our results provide new insights on fundamental mechanisms of bacterial energy metabolism and guide the rational engineering of highly productive cell factories.One long‐standing question in microbiology is how microbes buffer perturbations in energy metabolism. In this study, we systematically analyzed the impact of different levels of ATP demand in  E. coli under various conditions reveals a biphasic response curve of the glucose uptake rate with respect to increasing ATP demand.A systematic analysis of the impact of different levels of ATP demand on  As one key result, we found that the glucose uptake rate shows under all conditions a biphasic response curve with respect to increasing ATPase activity, reaching a maximum at a medium ATPase level but dropping markedly when this level is exceeded. Focusing on anaerobic growth, we combined metabolome and proteome data with a kinetic model of E. coli’s central metabolism to reveal the underlying mechanism of this behavior. Analysis of the model showed that the dual dependency of the phosphofructokinase on ATP as substrate and ADP as activator can explain the biphasic steady‐state response curve of the glycolytic flux. The model also helped to explain unexpected phenomena such as the accumulation of glycolytic metabolites, and it suggested two major rate‐controlling steps under high ATP drain, which were confirmed experimentally by overexpressing the genes of the associated metabolic enzymes.In this study, we systematically analyzed the consequences of varying levels of ATP turnover in et al, et al, et al, atpAGD‐encoded F1‐subunit of the FOF1‐ATP synthase (ATPase) from E. coli, which hydrolyzes ATP to ADP and phosphate. We decided to regulate the expression strength via different origins of replication and thus varying copy numbers of the plasmids harboring the ATPase genes. Three different plasmids were constructed: a low copy plasmid , a medium copy plasmid , and a high copy plasmid   inducible trcP‐promoter, and the same amount of IPTG was used for all strains. Additional controls were the E. coli wild‐type strain MG1655 without plasmid and without IPTG addition (“WT”) and without plasmid but with IPTG addition (“WT + IPTG”).As in previous studies . Importantly, while the ATPase flux increased with higher ATPase abundance from LC to MC ATPase strain, we determined the lowest ATPase flux among all ATPase strains for the HC ATPase strain  in the exponential phase. As expected, the growth rates and biomass yields of the three ATPase strains decreased with increasing expression levels of the ATPase Fig ; Table 1et al, et al, Glc/gDW/h), the MC ATPase strain a 380% increase (10.46 mmolGlc/gDW/h), and the HC ATPase still a 175% increase (8.10 mmolGlc/gDW/h). To our knowledge, the specific glucose uptake rate of the MC ATPase strain is the highest rate ever reported for growth‐arrested E. coli cells. The HC ATPase strain showed again the lowest glucose uptake rate among the three ATPase strains. While the WT and control strains metabolized most of the glucose‐carbon to ethanol, acetate, formate, and succinate, the amount of formed lactate increased with the expression strength of ATPase. The HC ATPase strain converted all glucose almost entirely to lactate (1.74 molLac/molGlc)  Fig ; Table 1E. coli under aerobic conditions , which was more than 10‐fold higher than in the control strain (and even higher than in the WT with growth). Compared to the anaerobic case, we estimated considerably higher ATPase fluxes for the ATPase strains, which are possible due to the high ATP yield under aerobic conditions.We repeated the same experiments under aerobic conditions Figs  and S3. s Wolfe, , increasE. coli not further increase (or at least maintain a high) glycolytic flux under maximal ATPase expression? For an in‐depth analysis of this phenomenon, we focused on the response of the different ATPase strains under anaerobic growth, where the highest specific glucose uptake rates for the LC and MC ATPase strain and the steepest drop of the uptake rate of the HC ATPase strain could be observed.The data of the anaerobic and aerobic cultivations with and without growth consistently show a biphasic curve of the steady‐state glucose uptake rate as response to the increasing overexpression of the ATPase genes. As summarized in Fig For a comprehensive metabolic characterization of the different ATPase strains under anaerobic growth, we quantified the intracellular concentration of metabolites Fig  and deteThe concentrations of several other intracellular metabolites changed also significantly. Generally, we observed that the concentration of metabolites involved in glycolysis , fructose‐1,6‐bisphosphate (FBP), dihydroxyacetone phosphate (DHAP), glyceraldehyde‐3‐phosphate (G3P), PEP, and pyruvate) consistently increased with expression strength of the ATPase genes, reaching highest values in the HC ATPase strain Fig . ContrarE. coli were not as clearly up‐ or downregulated , +549% (MC ATPase), and +708% (HC ATPase) in comparison to the WT strain  and PFK2 (pfkB)); however, more than 90% of the PFK activity in E. coli can be attributed to PFK1 , reaction converting F6P to FBP under consumption of (Mg)ATP, might cause the biphasic behavior. This reaction is considered as the committing step of glycolysis and is a major point of regulation in E. coli for anaerobic growth on glucose  in all four strains  in the kinetic model simulations. We were able to find a parametrization that gave a reasonable fit with the experimental data  continuously from 0 (WT) to a maximal value of 85 mmol/gDW/h . Available ATP is rapidly consumed by the large amounts of ATPase in the HC ATPase strain, which keeps the concentration of ATP at a very low level limiting in turn both the PFK as well as the ATPase flux. To further test our hypothesis regarding the PFK mechanism and its effect on the glucose uptake under increasing ATP demand, we utilized the model to evaluate how the steady‐state response curves in Fig Next, we used the model to simulate the steady‐state response curves of glucose uptake rate, energy charge, and ATPase flux when increasing the ATPase level  by an instantaneous adjustment of the glycolytic flux, but it collapses under very high ATP demand as in the HC ATPase strain.vmax) under low ATP concentrations. As a possible mechanistic explanation, we hypothesized that, at low energy charges, the large and costly PFL (consisting of 759 amino acids) is replaced by cheaper pathways , although this may partially reduce the ATP yield . However, even with introduction of a downregulation of the PFL under low ADP concentrations, the model was still not able to reflect the high concentration levels of PEP and FBP in the HC ATPase strain. In fact, with (i) the observed high concentrations of PEP and ADP (the substrates for the pyruvate kinase (PYK) reaction), (ii) the high FBP level , and (iii) the highly negative standard Gibbs free energy change of this reaction (ΔrG°’ = −21.78 kJ/mol)  reproduced most experimental data in the different strains reasonably well, especially the exchange rates Fig , some deWith these two changes, the resulting model version 2 was now able to reproduce the qualitative trends in the fluxes and metabolite concentrations in all three ATPase strains Fig . With thE coli's central metabolism constructed in the previous section is relatively large and comprises more than 100 unknown parameters. To assess the robustness of model predictions with respect to kinetic parameters, we employed Monte Carlo analysis as previously described in  of s Sauro, . The resFor the wild‐type metabolic steady state, we found predominantly narrow distributions of FCCs, indicating a low sensitivity of these FCCs against parameter variations  as reference metabolic (steady) state, most FCCs still show narrow distributions, but the fraction of enzymes with a broader distribution of FCCs increased  encoding gene pgk . Overexpression of pgk was chosen as a control since the model predicted an FCC close to zero for the PGK reaction. Thus, in contrast to the other three strains, PGK overexpression should not have a major influence on the glycolytic flux of the HC ATPase strain. The four variants were grown under anaerobic conditions and compared to the HC ATPase strain.Motivated by the Monte Carlo analysis from the previous section, in a final step, we aimed to use the kinetic model (version 2) to make experimentally testable predictions and, in this way, to further verify our reasoning of the low glycolytic flux in the HC ATPase strain. We hypothesized that we could enhance the glycolytic flux by overexpressing genes of enzymes catalyzing reactions with the highest metabolic control. We computed the FCCs of all glycolytic reactions in the kinetic model. Since FCCs are valid only for small changes in the enzyme level, we also computed the resulting steady‐state glycolytic flux in the model when doubling the enzyme level, corresponding to doubled pfkA and pflB indeed had significant effects on the specific glucose uptake rate as well as on the composition of the fermentation products, while we observed no significant changes for overexpression of pgk compared to the HC ATPase strain. In particular, the glucose uptake rate increased by 46.4% (pfkA), 48.0% (pflB), and 53.4% (pfkA+pflB) compared to the HC ATPase strain and remained almost constant for the pgk overexpressing strain. As expected and qualitatively predicted by the model, due to the enhancement of the PFL flux, the lactate yield is largely reduced in the pflB and pfkA+pflB overexpressing strains. The lactate yield dropped also in the pfkA overexpressing strain, although to a lesser extent. Here, it is likely that the higher overall glycolytic flux, enabled by higher PFK activity, increases ATP supply and thus leads to less downregulation of PFL. Therefore, more carbon is redirected to acetate, ethanol, and formate and less to lactate also in the pfkA overexpressing strain. We also observed that the growth rate was increased, at most in the two pfkA overexpressing strains and less in the strain overproducing (only) PFL. This might again be related to the relative high protein costs of PFL.As shown in Fig pfkA and pflB overexpression strains were still below WT level, the data in Table Although the glucose uptake rates of the E. coli wild‐type strain MG1655 under various conditions . On the one hand, this study was curiosity‐driven to explore maximal physiological capabilities of E. coli and to investigate how this bacterium responds to situations of high ATP demand . On the other hand, this study aimed to deliver new insights toward the use and potential of enforced ATP wasting as a metabolic engineering strategy. We collected a comprehensive dataset on metabolic fluxes, metabolite concentrations, and protein abundances. In order to integrate these data with our current knowledge of the complex metabolism and its regulation in E. coli, we constructed a kinetic model that enabled us to modulate the ATP maintenance reaction and thus simulating different levels of ATP wasting in the cells.In this study, we systematically analyzed the consequences of increasing ATP demand on the physiology of the The key findings of this study can be summarized as follows. First, in all conditions tested, there is a biphasic steady‐state response curve of the glucose uptake rate with respect to increasing ATPase activity. There is a maximum uptake rate at a medium ATPase level, and the glucose uptake rate drops markedly beyond this level. Second, the model indicates that the PFK reaction with its dual dependency on ADP/ATP causes this biphasic behavior. The PFK mechanism is known to buffer increased ATP demands by a higher glycolytic flux (due to elevated ADP levels) but, as shown herein, it collapses under high ATP demands with low ATP concentrations. Third, the metabolomics data under anaerobic conditions show an increasing accumulation of glycolytic metabolites reaching highest values at maximal ATPase level. This behavior cannot be explained with current knowledge, and we postulate that there are unknown regulatory mechanisms for PYK  and PFL (enzyme‐level regulation). Finally, we validated the model predictions that PFK and PFL are rate‐limiting in the HC ATPase strain and found that overexpressing the genes of these enzymes indeed restores some of the glycolytic capacity.E. coli that led to the observed phenotypes under high ATP demand. It has to be noted that the model is relatively large and comprises more than 100 unknown parameters, many of which will not be uniquely identifiable, despite fitting the model against a considerable set of data. However, the model is based on established biological knowledge of E. coli’s central metabolism, it is able to reproduce measurements of the different strains reasonably well and it gave predictions that could be successfully verified. Moreover, the results of the Monte Carlo sampling of kinetic parameters showed that key properties of the kinetic model and its predictions are robust over a wide range of parameter variations. Hence, despite potential parameter identifiability issues, the model could demonstrate its predictive power and thus represents a solid and plausible basis that supports our hypotheses and explains major findings of this study. However, as is true for every model, we can neither prove its correctness nor that other models with alternative mechanisms may exist that reproduce the observed phenomena equally well.The kinetic model played an important role in this study to identify and analyze potential mechanisms in the metabolism of E. coli but, at the same time, indicate gaps in our understanding. For example, the increasing levels of FBP in the LC and MC ATPase strains are consistent with the linear correlation between glycolytic flux and FBP concentration that has been shown for E. coli under various conditions and carbon sources  toward overflow metabolism. This consequently results in lower ATP yields but enables higher glycolytic fluxes , due to reduced protein costs  to use the lactate pathway. The lactate dehydrogenase appears to be constitutively available under anaerobic conditions, and the pathway becomes active with rising pyruvate concentrations. Under growth‐arrested conditions with a limited nitrogen source, which further limits the proteome pool, the effect is even more drastic. Here, almost the entire carbon is converted into lactate in the HC ATPase strain  seems disadvantageous compared to the PFL reaction in combination with the formation of acetate, ethanol, and formate resulting in an ATP yield of 2.5 mol ATP per mol of glucose. However, since PFL is a rather large enzyme , the proteomic data indicated a moderate averaged 8.1‐fold increase of the three F1‐ATPase components in the HC ATPase strain, which is still a comparably small fraction of the overall proteome pool. Moreover, we see a 6.5‐fold increase of ATPase abundance already in the MC ATPase strain; hence, there is effectively only a 25% increase of the ATPase level in the HC strain compared to the MC strain. With this relatively small change, it appears unlikely that the drastic change in the glycolytic flux and ATP levels between these two strains is mainly caused by a reduction of available resources for protein synthesis. Furthermore, except for the mentioned major changes, other enzymes of central metabolic steps, especially in the glycolysis, show a relatively constant level in the proteomic data and appear thus to be affected to a minor extent only. Another evidence in this direction is the fact that the additional overexpression of the pfkA or/and of the pflB gene (but not of the pgk gene) in the HC ATPase strain increased the glycolytic flux, as predicted, although this will even further reduce the available proteome pool for other enzymes.When analyzing proteome (re)allocation, we also need to consider the effect of the ATPase overproduction on the proteome pool. Especially in the HC ATPase strain, the expression of the ATPase genes consumes cellular resources such as amino acids and may thereby reduce the overall capacity to synthesize other proteins, including metabolic enzymes. Hence, in addition to the discussed low ATP concentrations, changes in the proteome composition (such as the reduced PFL abundance) may also be induced by the synthesis costs of the ATPase subunits. However, there are several evidences that the To further demonstrate that the lack of ATP and not proteome burden causes the low glycolytic flux in the HC ATPase strain, we cultivated the latter and its control strain anaerobically as before (with glucose as main substrate), but this time with addition of fumarate enabling the strains to gain more ATP (via fumarate respiration). As also suggested by our model, the glycolytic flux in the HC ATPase strain should then increase due to the higher ATP levels fueling the PFK reaction while we do not expect significant changes in the HC control as it is not ATP‐limited. In fact, with addition of fumarate, the glucose uptake rate even decreased slightly in the HC control strain, but increased markedly in the HC ATPase strain by more than 100% almost reaching the level of the control strain Fig . The extE. coli, as could be expected for the extreme perturbation in the HC ATPase strain, is not for all conditions primed to adjust an optimal expression pattern maximizing its growth rate  were used for all cloning techniques and plasmid propagation. Standard molecular cloning techniques followed protocols described earlier , yielding plasmids pSB58.6 and pSB62.6, respectively. To construct plasmid pSB66.1, the pMB1 replicon was cut out from pSB62.6 using restriction enzymes AscI  and SpeI‐HF . The p15A replicon was amplified by PCR from plasmid pZA31‐luc  and the blunt‐ended DNA fragments were self‐ligated. The E. coli wild‐type strain MG1655  from the natural ATPase operon of E. coli MG1655 (between atpD and atpC) was inserted into each forward primer to allow polycistronic expression of atpAGD and the respective gene. The linearized plasmid pSB62.6 and the DNA fragments harboring the amplified genes were ligated by Gibson assembly, yielding plasmids pSB84.3, pSB85.3, and pSB88.11 . A single colony was picked and used to inoculate 5 ml of LB0 medium. The medium was incubated at 37°C and 150 rpm for 5 h.All liquid and solid media used for cultivation of the ATPase and control strains contained kanamycin (except for “WT” and “WT + IPTG”) with a final concentration of 50 µg/ml. For growth assays, cells were freshly transformed with the corresponding plasmid and plated on LB2PO4, 64 mM K2HPO4, 20 mM (NH4)2SO4, 1 μM Fe(SO4)4, 300 μM MgSO4, 1 μM ZnCl2, 10 μM CaCl2, adapted from  to an optical density at 420 nm (OD420) of 0.2 (0.4 for the HC ATPase strain). The cells were cultivated in 250‐ml shake flasks with three baffles at 37°C and 250 rpm.For aerobic cultivation, cells were diluted 1:500 into chemically defined medium  and cultivated at 37°C without shaking overnight. The cells were centrifuged at 5,000 g, washed, and used to inoculate fresh MM  to an OD420 of 0.2 . The medium was filled into 5‐ml screw‐cap glass vials (completely filled to the top), and the vials were incubated at 37°C without shaking. For every time point, new vials were opened to guarantee anaerobic conditions.For anaerobic cultivation, cells from the LB4)2SO4 and an initial OD420 of 2.0 were used for cultivation.For cultivation of growth‐arrested cells, the same procedures for aerobic and anaerobic cultivation were followed as described above, but MM without added . All cultivations were performed in biological triplicates, if not stated otherwise.Cell growth was monitored measuring the ODet al, Extracellular glucose, ethanol, acetate, formate, succinate, lactate, pyruvate, and fumarate in the medium were quantified as described earlier  and normalized to OD at the time point of sampling.For quantification of intracellular metabolites (except pyruvate), cells  were applied to filter disks  under constant nitrogen flow to keep anaerobic conditions. The medium was removed by suction filtration, and the filter disks were immediately transferred to 1 ml of a −20°C cold acetonitrile/methanol/water (40:40:20) quenching solution. After incubation at −20°C for at least 30 min, the samples were shaken vigorously, and 500 μl of the mixture was centrifuged at 17,000 9 cells were harvested by centrifugation , the supernatant discarded and the cells resuspended in 2 ml of ice‐cold PBS buffer. The cells were centrifuged again  and the PBS‐washing step was repeated twice. Cell pellets were stored at −80°C until further analysis.For proteomics analysis, cells were cultivated as described above. ~1 × 10g, the protein concentration in the supernatant was determined with a bicinchoninic acid (BCA)‐based protein assay kit . 7.5 µl of 0.2 M tris(carboxylethyl)phosphine in 100 mM ammonium carbonate were added to 300 µl of the supernatant. The solution was incubated for 15 min at 90°C. 7.5 µl of 74 mg/ml iodoacetamide were added to the cooled‐off samples and incubated for 30 min at 25°C under shaking of 500 rpm. Using 2% SLS in 100 mM ammonium bicarbonate, 200 µl aliquots of the samples were prepared containing a total protein mass of 50 µg. 600 µl of 100 mM ammonium bicarbonate and 8.5 µl of 0.1 µg/µl porcine trypsin were added to the samples for incubation overnight at 30°C.The pelletized cells were lysed in 400 µl of 2% sodium lauroyl sarcosinate (SLS) in 100 mM ammonium bicarbonate by heat  and sonication. After 10 min of centrifugation at 17,000 g for 10 min at 4°C. The supernatant was used for solid phase extraction of the peptides using C18‐columns (Macherey‐Nagel).5% trifluoroacetic acid was added to the samples to a final concentration of 1.5%. After incubation for 10 min at room temperature, the samples were centrifuged at 17,000 et al, Peptides were analyzed using a Q‐Exactive Plus mass spectrometer connected to an Ultimate 3000 RSLC nano and a nanospray flex ion source (Thermo Scientific). The analytical setting was reported in detail previously  of the data was performed using Progenesis QIP (Waters) and MASCOT  for spectrum/peptide identification. Progenesis outputs were further processed with SafeQuant (Glatter µ) for the exponential phase was determined by plotting the natural logarithm of the biomass concentrations of each sampled time point  against the cultivation time. The slope of the linear regression equals µ.For experiments with growth, the growth rate , and cX,e and cX,s represent the end and start concentrations of the biomass (gDW/l). In experiments with growth arrest, where the biomass concentration remained nearly constant, the specific rates are calculated as:XAv is the average biomass concentration (gDW/l), and Δt = te − ts the length of the time period (difference of end and start time). This procedure was used for each of the three replicates from which then the mean and the standard deviation was calculated for each rate.Specific uptake and excretion rates for the exponential phase in growth‐coupled experiments were determined with the formula:cM (mmol/l) against ΔcGlc (mmol/l) for every sampled time point of the exponential growth period (for growth‐coupled cultivation) or of the indicated time period (of growth‐arrested cultivation). The slope of the linear regression equals the yield of the respective metabolite. Biomass yields were determined by plotting ΔcX (gDW/l) against ΔcGlc (g/l) for every sampled time point of the exponential growth period (for growth‐coupled cultivation). The slope of the linear regression equals the biomass yield.Metabolite yields were determined by plotting ΔP‐values for comparisons between different strains were calculated using an unpaired two‐sample t‐test with the software OriginPro . Statistical details of the individual experiments can be found in the captions of the respective tables and figures.Unless stated otherwise, E. coli  before the ATPM flux is maximized. The stoichiometric model together with a detailed description of the calculations is provided on GitHub (see Data availability).Using a stoichiometric model of the central metabolism of et al, The two versions for the kinetic model were implemented and simulated with COPASI (Hoops SK conceived and supervised the study. SB performed plasmid and strain construction, cultivations, external metabolite quantification, sampling for proteomics and metabolomics analysis, and determination of metabolites fluxes. GS constructed the kinetic model and carried out the simulations. RS helped in model construction and the Monte Carlo‐based sampling of kinetic parameters. TS and HL generated the metabolomics data. TS and WS generated the proteomic data. SB, GS, and SK analyzed the data and wrote the manuscript. SB and GS generated the figures. All authors discussed and approved the content of the manuscript.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Source Data for Expanded View and AppendixClick here for additional data file.Source Data for Figure 1Click here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 7Click here for additional data file.


In this Commentary, we will discuss some of the current trends and challenges in modeling microbiome metabolism. A focus will be the state of the art in the integration of metabolic networks, ecological and evolutionary principles, and spatiotemporal considerations, followed by envisioning integrated frameworks incorporating different principles and data to generate predictive models in the future. The past 2 decades have seen significant increases in the technical ability of metabolic models to more accurately represent the underlying metabolic dynamics in biological systems. What started as models of simple organisms, representing only a subset of their genes , has nowA hallmark approach in metabolic modeling is genome-scale metabolic modeling (GEM). The goal of a GEM is to depict the metabolic structure of a cell using the genome sequence as a blueprint, allowing for a complete mapping of reactions in a cell. On top of this blueprint is layered the known metabolic reactions, gene data, and biochemical data to generate a curated reaction network . Within The GEM approach described above has shown significant success in human systems  or a dynamically more accurate community model (microbiome as multiple-assembled organism genomes). A second important consideration is whether static versus dynamic simulations will be used (discussed below) . A thirdSingle-level approaches optimize across the entire community . Evolutionary biologists could be critical of this approach because it bypasses selection at the individual level. Indeed, FBA omits certain stable interactions within communities and can be prone to unrealistic altruistic predictions . CommuniIn contrast, multilevel approaches try to find solutions in which each individual member of a community maximizes its fitness function. This approach is closer to the accepted theory of natural selection/adaptation but elevates the computational demand. OptCom was the first bilevel optimization paradigm to consider both individual and community fitness . BuildinEscherichia coli pairs, because each can act only upon metabolites present in their environments  juxtaposes the steady-state assumptions of FBA . dFBA moronments . That isThis dFBA deficiency is partially addressed by a recent algorithm based also on NE by Zomorrodi and Segrè  in whichdFBA has achieved success in ground truthing models to experimental data and allowed for introducing spatiotemporal dynamics to microbial ecosystems. This is possible through the addition of differential equations for spatial dynamics and/or compartmentalization based on spatial structures to produce a realistic picture. We were able to capture the observed differential relative abundance profiles of microbiota radially, from the intestinal lumen to the mucosal layer, and longitudinally, along the intestinal tract, by proposing a dynamic model of the mucosal cell layer shedding into the lumen directing flow into the succeeding intestinal section and combining SteadyCom and dFBA . ExpliciClostridia causing observed gut microbial metabolomic change after drug treatment  principles in a systematic modeling framework with parameters learned through rigorous model inference techniques will connect theory and data to enhance the usefulness of GEMs for studying complex microbiomes. This process of integration includes gathering heterogenous data sets where myriad supervised and unsupervised methods can be used to concatenate, transform, or model to create algorithm-based models . For exaWithin the set of experimental techniques to generate these data sets, there are two flavors: bottom-up versus top-down. As an alternative to the more commonly used bottom-up synthetic community approach, top-down approaches to study smaller communities of a complex microbiome are emerging. A recent study deconstructed the soil microbiome into functional modules through targeted enrichment of desired metabolic traits. It allows for investigating a community with significantly less complexity and drawing mechanistic conclusions . Another


Litopenaeus vannamei is the largest and most productive shrimp species. Feed improvement is one of the important methods to improve the yield of L. vannamei and control water pollution caused by the inadequate absorption of feed. In this work, the first L. vannamei GSMN named iGH3005 was reconstructed and applied to the optimization of feed. iGH3005 was reconstructed based on the genomic data. The model includes 2,292 reactions and 3,005 genes. iGH3005 was used to analyze the nutritional requirements of five different L. vannamei commercial varieties and the genes influencing the metabolism of the nutrients. Based on the simulation, we found that tyrosine-protein kinase src64b like may catalyze different reactions in different commercial varieties. The preference of carbohydrate utilization is different in various commercial varieties, which may due to the different expressions of some genes. In addition, this investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate. The feed for different commercial varieties should be adjusted accordingly, and possible adjustment schemes were provided. The results of this work provided important information for physiological research and optimization of the components in feed of L. vannamei.As an important tool for systematic analysis, genome-scale metabolic network (GSMN) model has been widely used in various organisms. However, there are few reports on the GSMNs of aquatic crustaceans.  Litopenaeus vannamei, also known as South American white-leg shrimp, is naturally distributed in the pacific coastal waters from northern Peru to Mexico. It has become the world’s largest and most productive shrimp species  model was proposed in 1999 , GSMN haEscherichia coli culture medium with E. coli iAF1260, including 174 carbon sources, 78 nitrogen sources, 49 phosphorus sources, and 11 sulfur sources. Similarly, Bacillus subtilis culture medium with a B. subtilis GSMN. However, the GSMN for L. vannamei has still not been reconstructed due to the lack of genomic data. Fortunately, the research team of Jianhai Xiang and Fuhua Li completed the whole-genome sequencing of L. vannamei in 2019  database, including the information of genes, metabolic reactions , enzymes, metabolites , pathways, and subsystems. The GSMN was draft reconstructed after all the data were sorted out and mapped.The missing information in the network mainly includes balance of reaction, main reaction, pathway, and subsystem for some reactions. For the unbalanced reactions, their equations were balanced according to element conservation and charge conservation principles. The lacking main reaction was added according to the pathway map and “rclass” in KEGG database. The pathway of the reaction is determined by the pathway of metabolites involved in the reaction. After pathway information has been added, the subsystem information can be paired to the corresponding reaction through the pathway–subsystem relationship downloaded from KEGG database to make the reaction information more comprehensive.D-glucose), C00267 , and C00221 (beta-D-glucose). D-glucose is a molecule with uncertain chiral properties. In order to prevent the identification confusion caused by different IDs of the same compound in the metabolic network, the D-glucose in the reaction equation was changed to alpha-D-glucose because glucose in animal cells tends to exist in the form of alpha chirality.In the KEGG database, different molecular conformations of the same metabolite were assigned different IDs. Taking glucose as an example, there are three IDs for glucose in KEGG, namely, C00031  from the environment or transporting extracellular metabolites (secretory products) into the environment, while transport reactions are in charge of transporting metabolites between intracellular and extracellular parts. Considering the nutritional requirement of L. vannamei, we added the exchange and transport reactions for amino acids, carbohydrates, lipids, vitamins, mineral elements, and cofactors.The addition of transport and exchange reactions is based on the nutritional requirements of The cellular location in the model was divided into two compartments, cytoplasmic  and extracellular . Cellular compartment information was added for all the reactions and metabolites in the network. The same metabolite in different locations is considered as different metabolites.The relationships between gene–enzyme and enzyme–reaction were all derived from the KEGG database. Using enzyme as a bridge, the relationships between gene–enzyme–reaction were connected. If multiple genes correspond to one reaction, intergenic relationships need to be determined. When the genes of a reaction correspond to different enzymes, their relationships are considered “or,” and if several genes encode the same enzyme, their relationships are considered “and” .L. vannamei were taken from the Haitong lake culture farm. As we all know, muscle is the main edible part of L. vannamei. Meanwhile, metabolic processes mostly occur in the hepatopancreas. Therefore, muscle and hepatopancreas were sampled for detection. Here, 150–200-g samples from each variety were collected and sent to Beijing Institute of Nutritional Sources for nutritional composition detection using the standard method specified in “GB28050-2011 national food safety standard general principles for nutrition labeling of prepackaged food”  mmolgDW–1h–1. The flux of transport reactions was set to be  mmolgDW–1h–1. For the nutrients that can be synthesized by the model, the flux of related exchange reactions was set to be  mmolgDW–1h–1. The flux of the nutrients that need to be absorbed from the feeds was set to be  mmolgDW–1h–1, except for trace elements set to be  mmolgDW–1h–1. The transformation and simulation of the model were performed under MATLAB platform using COBRA toolbox  mmolgDW toolbox .The model was evaluated and revised by simulating the synthesis of 10 non-essential amino acids, 25 fatty acids, five carbohydrates, five nucleic acids, 10 mineral elements, and biomass to make it capable of synthesizing the substances that were mentioned above.Taking glutamate as an example, the exchange reaction of glutamate was firstly set to be the objective function, and the maximum value of the objective function  was calculated with the model. If it was 0, it indicates that glutamate cannot be synthesized by the model, which means the model is flawed and needs to be revised. To find the defect of the model, the completeness of synthesis pathway for glutamate was checked. The lacking reaction(s) of the pathway was supplemented to the model and followed by a second simulation until the maximum value of the objective function is not 0, indicating that the model can synthesize glutamate.In the biomass synthesis simulation, the biomass equation was set to be the objective function and the maximum value of the objective function  was calculated with the model. The detailed calculation process was supplied in –1h–1. The flux of exchange reactions for each nutrient was simulated by FBA, which is the optimal nutrient requirement of these nutrients for 1 g dry weight increase of L. vannamei. The nutritional requirements of the five commercial varieties of L. vannamei were calculated separately. For the purpose of giving constructive suggestions for fodder improvement, we compared the calculated results with literature and further analyzed the differences in the nutritional requirements among the five varieties. The detailed calculation process was supplied in The biomass equation was set to be the objective function, and its synthesis rate was fixed to be 1 gDWThe reaction, main reaction, enzyme, pathway files were downloaded from the KEGG database. Information including reaction ID, name, equation, enzyme number, gene, pathway ID and name, subsystem name was extracted and collated. The results are shown in L. vannamei genome and related information were included in the KEGG database. To reconstruct the GSMN of L. vannamei, we downloaded the catalytic enzyme of L. vannamei, and 3,005 enzymes were obtained. Using enzyme number as a bridge, the genes of L. vannamei were matched to the reactions, and then the information of pathway and subsystem was integrated to each reaction, which composed the draft reconstruction of L. vannamei GSMN. The draft network contains 3,005 genes, 3,005 enzymes, 1,572 reactions, 108 pathways, and 12 subsystems.In 2019, the 2O, H+, electron (e–), CH4, and ethylene. The main reactions, pathways, and subsystems of 441 reactions were supplemented with purine metabolism subsystem supplemented the most main reactions. After the standardization of chiral metabolites, 23 reaction equations were modified. Here, 100 redundant reactions were removed from the network, of which 23 were step reactions, 46 were uncompleted reactions, 13 were duplicated reactions due to the replacement of chiral metabolite, and 18 are reactions with coefficient “m” or “n.” After the manual revision, 1,472 reactions were included in the network.The draft reconstruction was manually refined as described in the “Materials and Methods” section. Based on the conservation of elements and charges, 54 reactions were artificially balanced. Most of the reactions were balanced with the addition of HThe network was subsequently partitioned into WCCs, and gap filling was performed. There were 301 WCCs initially in the network. The gaps were filled first on the pathway scale and then on the global scale. In total, 207 and 415 reactions were added on the pathway and global scale, respectively. After gap filling, the number of WCCs in the network decreased from 301 to 162, indicating that the network gap filling enhanced the global connection of the network.L. vannamei, transport and exchange reactions were added for 20 amino acids, 28 fatty acids, five saccharides, 15 vitamins, 10 mineral elements, and 12 cofactors. In total, 91 transport reactions and 91 exchange reactions were added. The subcellular localizations of metabolic reactions were all considered as cytoplasm, while the location of transport reactions spans both cytoplasm and extracellular part. The exchange reactions were all located at the extracellular part.Based on the nutrient requirements of The refined network is composed of 3,005 genes, 2,261 reactions including 2,079 metabolic reactions, 91 transport reactions and 91 exchange reactions, 2,149 metabolites, including 2,056 cytosol metabolites and 93 extracellular metabolites, 128 pathways, 162 WCCs, and 12 subsystems.L. vannamei were constructed  for the five ectively . RiyekuaFor aquatic animals, protein in feed is almost the only nitrogen source used for cellular protein synthesis. Protein is not only the primary material to produce new cells and make up for old cells but also an essential component of various tissues, enzymes, and hormones. Lack of protein in the feed will cause a series of physiological and biochemical reaction obstacles, which will affect the growth and survival of shrimp. However, the high protein levels in feed will not only affect the development of shrimp, pollute the environment, but also increase the cost of feed and cause waste .L. vannamei itself or the amount of synthesis is quite small, which cannot meet the needs of growth of L. vannamei and must be provided by feed. The appropriate content and proportion of essential amino acids in feed are conducive to make proteins and feeds work at full capacity, which is helpful for the growth of shrimps , linolenic acid (LNA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA)  . Previouid (DHA) . EPA andid (DHA) . Gonzale dosages . We compL. vannamei commercial varieties are higher than that in literature. It indicates that the addition of EPA and DHA in feed is conducive to the growth of shrimp, which is consistent with the research results of L. vannamei, we found that the flux of reaction R08181 -Octadecatrienoic acid) reached 4.88 mmol/gDW–1h–1. The metabolite flow analysis of DHA accumulation revealed that the fluxes of the reaction related to DHA synthesis R08180: [-docosahexaenoyl COA + H2O <=> COA + -docosahexaenoic acid] were 4.88 and 4.876 mmolgDW–1h–1 in Lutai and Kehai, respectively.The theoretical demands for EPA and DHA of the five L. vannamei has not only different expression levels but also catalyzes different reactions. The enzyme catalyzes reaction R08181 to rapidly synthesize γ-LNA in Riyekuai, while in Lutai and Kehai, the enzyme catalyzes R08180 to accumulate large amounts of DHA. According to the sampling results in The corresponding genes of R08180 and R08181 were both pvm:113803399 and pvm:113804504. By searching the KEGG database, we found that their corresponding enzyme was tyrosine-protein kinase src64b like, which means that this enzyme in different commercial varieties of L. vannamei. We compared the simulation results of iGH3005 with this literature  reaches 4.878 mmolgDW–1h–1 and that of other commercial varieties is 0. The corresponding genes of R00503 were pvm:113828354 (LOC113828354), pvm:113828357 (LOC113828357), pvm:113828366 (LOC113828366), pvm:113828370 (LOC113828370), pvm:113828374 (LOC113828374), and pvm:113828381 (loc113828381). We speculated that the high expression level of these genes in Lutai resulted in the accumulation of lactose. In addition, in the simulation results, the glucose demand of Lutai is greater than that of the other four commercial varieties. By studying the metabolic process of glucose, we found that the flux of alpha-D-glucose producing reaction R01602  in Lutai network was lower than that of the other four varieties. In comparison, the flux of alpha-D-glucose consuming reaction R01788  was higher than that of the other four varieties, which resulted in the much more glucose demand in Lutai than the other four varieties. Based on the simulation, the gene PVM:113819971 corresponding to R01602 has a low expression level, and the gene PVM:113819863 corresponding to R01788 has a high expression level in Lutai, which points out the direction for the further wet experiment.fructose . The simL. vannamei all have a small demand for sucrose and maltose. Therefore, we suggest that sucrose and maltose can be added appropriately in feed. We can also see that the demand for glucose of Lutai is larger than that of the other four varieties, and the demand for lactose in Riyekuai, Kehai, Guangtai, and Puiying is larger than that in Lutai. Hence, we advise that carbohydrate content in the feed for different commercial varieties of L. vannamei should be adjusted. The appropriate amount of glucose can be supplied to Lutai, and some lactose can be provided to the other four varieties. It should be noted that at the experimental level, glucose can be directly added to shrimp feed as a carbohydrate source (L. vannamei is abnormally large, which may be due to the lack of key speed limiting reaction in the model.In addition, in the simulation results, the five commercial varieties of e source , while aL. vannamei and KEGG database, the first GSMN of L. vannamei iGH3005 was reconstructed. The biomass equations of five L. vannamei commercial varieties were further constructed based on the nutritional composition detection and added to the model. The optimal nutrient requirements of the five L. vannamei commercial varieties were simulated. Based on the simulation results, we discussed the different expressions of some important genes and put forward some suggestions on the improvement of L. vannamei feed. We found that tyrosine-protein kinase src64b like has not only different expression levels but also catalyzed different reactions in different commercial varieties of L. vannamei. In addition, each shrimp race has a different carbohydrate usage probably due to differences in gene expression of glycolytic genes. We suggest that EPA, DHA, potassium, iron, sucrose, and maltose should be added to the feed of all five varieties. Reducing the amount of manganese could also promote the growth of L. vannamei. This investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate.Genome-scale metabolic network has become an indispensable tool for systematic research on metabolism and applied to many microorganisms and model organisms. Based on the genomic data of The original contributions presented in the study are included in the article/The study was approved by the College of Life Sciences, Tianjin Normal University.CG analyzed the data and wrote the manuscript. JY and JL organized the data. JS and TH designed the experiment and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.