It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products  along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics  using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.BioTransformer 3.0 (  Metabolism is typically divided into two broad categories: (i) primary metabolism and (ii) secondary metabolism. Primary metabolism refers to the metabolic processes associated with the production (anabolism) and breakdown (catabolism) of essential metabolites such as nucleic acids, lipids, amino acids and steroids. Primary metabolism is critical to the production of energy and the sustenance of essential cellular processes . Secondary metabolism refers to the metabolic processes associated with the production and breakdown of non-essential, xenobiotic, foreign or exogenous chemicals. For humans, secondary metabolism is mostly limited to those processes associated with the catabolism or breakdown or modification (i.e. biotransformation) of chemicals rather than their biosynthesis. This xenobiotic biotransformation process usually involves the activation, detoxification and eventually the elimination of xenobiotics. Xenobiotics are compounds such as drugs, cosmetics and personal care products, clothing dyes, pesticides and herbicides, plant-derived food compounds, food additives, surfactants, solvents, and other synthetic or biologically foreign substances that can be converted into energy or primary metabolites needed for essential cellular processes.Over the past 20 years, two fields of ‘omics’ science have emerged that are focused on comprehensively characterizing the products of both primary and secondary metabolism: metabolomics and exposomics. Metabolomics uses high-throughput techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy to comprehensively characterize the metabolome (i.e. endogenous molecules) while exposomics uses many of the same methods to characterize the exposome  ,2. WhileFrom the experimental perspective both metabolomics and exposomics continue to face similar challenges. Indeed, both fields struggle with the identification of chemical ‘unknowns’. These are the compounds that defy facile identification or which are not found in any known chemical or spectral (MS or NMR) database. These unknowns are often called chemical dark matter . Estimatin silico strategies to help with this process. The general concept behind these in silico strategies is to have a computer predict the metabolic reactions and reaction products from a given query molecule using various expert-derived rules or pattern recognition techniques  are examples of in silico metabolism predictors that predict only human cytochrome P450  metabolism. Other in silico metabolism predictors, such as Meteor Nexus  biotransformations arising from primary or secondary metabolism. BioTransformer 3.0 also supports the prediction of environmental (i.e. soil/water) biotransformations of man-made chemicals. BioTransformer 3.0 can be used to not only predict the structures of metabolites or metabolic byproducts, but it can also assist with the identification of molecules through SMILES  are provided in Figure BioTransformer 3.0 is very simple to use. Users submit a single molecular structure as input (using SMILES or an SDF file) and the web server outputs an interactively viewable/sortable table of the predicted metabolites or transformation products along with the reaction equation(s), the reaction types and the enzymes or biological processes that are predicted to be responsible for those reactions. BioTransformer 3.0 will accept most types of small molecule structures as input but the molecule must be organic, it should have a molecular weight <1500 Da and it must not be a mixture. In addition to producing an easily navigated output data table, a variety of downloadable files  containing additional or more detailed metabolite prediction information are also available. BioTransformer's calculations typically take less than a minute but for more complex biotransformation predictions with more iterations and/or with much larger molecules, these calculations can take up to 30 minutes. Screenshots of BioTransformer's https://biotransformer.ca). From here, users can press the red ‘Metabolism Prediction’ banner on the home page to start a prediction session. This takes the user to the Metabolism Prediction page. At the top of the page, there are five menu items: Metabolism Prediction, About, Download, Contact and Help. Selecting Metabolism Prediction allows users to easily perform additional predictions without having to go to the home page. Clicking About produces a web page that provides a brief summary of BioTransformer's functions, its overall design, implementation, evaluation/performance and examples of the types of biotransformation predictions it can perform. Selecting Download allows users to go to a page with instructions on how to download and install a command-line version of BioTransformer 3.0 through the (Bitbucket) hyperlink listed on the page. Details about the computer requirements and installation/operation process are also provided. The Help link provides a submenu  with written details and screenshots to help explain each of the different topics or features.The user interface for BioTransformer 3.0 has been completely redesigned. Therefore, a brief summary of its layout and operation is certainly warranted. To begin a BioTransformer session, users must first go to the BioTransformer 3.0 website (Metabolic Transformation from the pull-down menu at the top of the Metabolism Prediction page. BioTransformer supports eight types of metabolic transformation predictions including phase I reactions (cytochrome P450), promiscuous enzymatic (EC) reactions, phase II reactions, human gut microbial reactions, environmental microbial transformations and different combinations of the above, known as AllHuman, SuperBio and MultiBio. More specifically, the ‘Phase I (CYP450) Transformation’ option predicts cytochrome P450 metabolism. In humans, CYP450 metabolism is commonly done by nine major CYP450 enzymes. These enzymes play a very important role in drug or xenobiotic metabolism by performing various oxidation or hydroxylation reactions to increase the water solubility of the substrate. The ‘EC-Based Transformation’ option predicts regular and promiscuous enzymatic metabolism which mostly corresponds to primary metabolism. Predicted metabolites are generated using reaction rules associated with different enzymes specified by EC numbers. The ‘Phase II Transformation’ option predicts major conjugative reactions, including glucuronidation, sulfation, glycination, N-acetylation, and glutathione transfer, among others. These reactions also play important roles in xenobiotic or secondary metabolism, elimination and detoxification. The option ‘Human Gut Microbial Transformation’ predicts metabolism arising from gut microbial enzymes using known reaction types and bacterial enzymes found in human gut microbes. Gut microbes perform a wide range of unusual catabolic reactions on both primary and secondary metabolites. The ‘Environmental Microbial Transformation’ option predicts metabolites arising from environmental microbial degradation of small organic molecules and is intended for predicting the breakdown products of xenobiotics and secondary metabolites left in soil or water or exposed to light.To perform metabolism prediction with BioTransformer, users must first select a specific AllHuman, SuperBio and MultiBio. The ‘AllHuman’ option predicts biotransformations occurring both in human tissues  as well as the gut microbiota. It is intended to mimic general human metabolism without a specific sequence of transformation events. The ‘SuperBio’ option also predicts biotransformations occurring both in human tissues and the gut microbiota. Specifically, it runs the AllHuman metabolism prediction through four iterations or until no novel metabolites are produced. If users wish to run SuperBio metabolism through more than four iterations, they can download and run the executable version of BioTransformer 3.0, which is available on the Download page. Finally, the ‘MultiBio’ option allows users to create a customized biotransformation sequence (a maximum of four types) by choosing any combination or sequence of CYP450, Phase II, EC-Based, Gut Microbial and Environmental reactions for up to three iterations each. Clicking on the red ‘Help?’ hyperlink provides a detailed explanation of these options. Depending on which Metabolic Transformation option is chosen, additional text boxes or pull-downs will appear that must be filled in by the user.Three ‘combination’ transformation options are also offered: Metabolic Transformation has been chosen, users must indicate how (i.e. which format) their input molecule will be entered using Select an Input Type. Users may choose to type or paste a single SMILES string into the text box or they may select to upload a single SDF file containing only one structure. Choosing the SDF option (Upload SDF File) creates a new web form where users must choose a local SDF file on their computer using a standard file browse. As noted earlier, BioTransformer will accept most types of small molecule structures as input, but the molecule must be organic, it must contain carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur (CHNOPS) and halogen atoms only, and it should have a molecular weight <1500 Da. BioTransformer is capable of handling or metabolizing a number of small biopolymers including small peptides, small polynucleotides, polysaccharides, polymerized polyphenols and phenolic glycosides. Once the SMILES string or SDF file has been uploaded, users must choose the Number of Reaction Iterations to Calculate. This is a pull-down option that allows users to choose from one (the default) to three (the maximum) reaction iterations. Reaction iterations refer to the number of times the chosen in silico reaction will be performed. If one chooses two iterations, the reaction products of the first reaction will be fed back into the selected reaction scheme for BioTransformer to predict further transformations.After a Metabolism Prediction submission form, users can press Submit and the calculations will be performed. If an error has been made during the data entry process, users can always press Reset to clear the form. The Metabolism Prediction page also lists eight different biotransformation examples  users may bookmark the page and return after a few minutes. If users wish to perform transformation predictions on many molecules or if they routinely perform complex transformation predictions, we strongly recommend that they download and install a local, command-line version of BioTransformer and run it on their own computer.After filling out the Submit button, they are taken to a new page that indicates the progress of the calculation. This is shown along with summary information about the input structure and selected calculation parameters. After the calculations are complete an output page or Biotransformation Viewer is generated , the chemical formula or the molecular weight ranges. Both the SMILES and chemical formula filters require exact matches. The chemical formula must be in the standard CHNOPS order. These filtering functions are particularly useful if the biotransformation prediction generates dozens to hundreds of possible metabolites where the resulting products are similar in structure or isomeric. They are also useful for metabolite identification (in metabolomics and exposomics studies) as users may use accurate mass matching or derived chemical formula matching (from high resolution mass spectrometry) to identify possible hits.As indicated, once users press the Biotransformation Viewer, users can also download BioTransformer's results in several different formats . All the predicted structures are available in SDF formatted files, which are obviously much larger and more detailed than SMILES or InChI files. Additionally, the JSON and CSV files contain the predicted structures in SMILES, InChI and InChIkey formats. The generated JSON and CSV files also contain additional information that is not displayed in the more simplified Biotransformation Viewer. In particular, these downloadable files include the names and synonyms of the generated compounds (if available), their PubChem IDs (if available), the enzymatic biosystem  associated with the transformation(s), as well as the product and precursor ALogP values. Users can obviously use the JSON or CSV files for further offline processing and manipulation.In addition to the browsable product table presented through the While the frontend of BioTransformer 3.0 is designed to be intuitive and very simple to use, it actually hides a very complex and sophisticated backend. Many of the details of BioTransformer's backend and how the components were implemented, tested and integrated were provided in the initial description of BioTransformer 1.0 . HoweverBioTransformer's reaction knowledge database (BTKB) consists of >900 reaction rules primarily intended for xenobiotic, secondary or exogenous compound metabolism. These rules were derived from extensive literature searches and implemented as carefully validated, hand-crafted SMARTS  and SMIRBiotransformation Viewer) was hard to navigate and provided very limited information about the predicted metabolites and their reactions or provenance. Likewise, its layout differed radically from the layout and design of the home page and query (i.e. the Metabolism Prediction) page. This gave the appearance that different parts of the web server were built completely independently of each other. Furthermore, the information about how to operate the server and explanations about its design, performance, input requirements and capabilities were minimal or largely absent. In addition, the number of examples with which users could test the server was quite minimal and did not span all possible queries or query types.Some of the most significant changes and improvements to BioTransformer 3.0 were made to its user interface. Earlier versions of BioTransformer had query and output viewing pages that many users noted were not particularly intuitive. The output page  was removed. Furthermore, the BioTransformer Metabolite Identification module (BTMI) was eliminated from the query interface and, instead, new functions were added to the Biotransformation Viewer page to allow users to perform metabolite identification through the SMILES/structure, chemical formula, and molecular weight filters. This modification was done to improve usability and simplify the Metabolism Prediction page.Given the many issues related to BioTransformer 1.0’s design and usability, a complete redesign of its graphical user interface (GUI) was undertaken. For BioTransformer 3.0 the home page, the  as HMDB , and NP- as HMDB . These lBiotransformation Viewer. Rather than having multiple tabbed views or displaying hard-to-understand InChI strings (instead of more understandable SMILES or structure images) or having numerous options to reformat the table of predicted metabolites, the layout was redesigned and simplified so that all the most relevant information  could be viewed in a single table. Users can click on the ‘eye’ icon in the first column of the result table to generate a detailed result card for the selected biotransformation. Each card contains details such as the synonyms, InChI string, InChIKey, molecular formula, major isotope mass, AlogP etc. These details are also available to the user as JSON and CSV files in the download section. Furthermore, to facilitate metabolite identification and comparison, new table sorting utilities and filtering functions for structure, chemical formula, and mass range have been added. Similarly, the quality of the structure renderings and chemical reaction renderings was significantly improved. Users can also click on the structure image, which is then rendered as an expanded modal view. This allows users to more closely inspect the structure and to download the image as an SVG file. Additionally, the amount of information available in the JSON and CSV files about each of the predicted metabolites and their provenance was substantially increased. Overall, we believe these user interface improvements should significantly improve the usability, stability and user friendliness of this web server.The most important interface changes for BioTransformer 3.0 were for the Download page). We then manually checked the BioTransformer-predicted metabolites (version 2.0 versus version 3.0) against the known, published metabolites for each substrate-enzyme pair. As shown in Table The focus of most of the backend improvements to BioTransformer 3.0 was on improving performance, functionality, accessibility, and speed. From the performance perspective there are at least four major improvements worth noting. One of the more important enhancements involved the addition of CyProduct to the BioTransformer system. Cytochrome P450 metabolism and metabolites are notoriously difficult to predict. Older versions of BioTransformer used rule-based predictions built as SMIRKS strings into the BTKB. While very fast to calculate, these were not particularly accurate. In particular, we compared the CYP450 metabolism module in BioTransformer 3.0 with the previous version of BioTransformer using a dataset of 68 well studied reactants (as previously described in CyProduct ). Each rAnother backend performance improvement for BioTransformer 3.0 involved the introduction of new software and new transformation rules (in the BTKB) to handle small biological polymers such as small peptides, small polynucleotides, polysaccharides, polymerized polyphenols and phenolic glycosides. In particular, BioTransformer 3.0 is now able to recognize these types of biopolymers and to cleave or depolymerize them appropriately. Prior to the addition of this module, BioTransformer would often metabolize these biopolymers inappropriately and generate unrealistic or biologically infeasible metabolites. In many cases, older versions of BioTransformer would generate a combinatorial explosion of multiple Phase I and Phase II metabolites for simple biopolymers, leading to multi-hour processing times and long, useless lists of thousands of incorrect or unlikely metabolites.Yet another important performance improvement involved the introduction of statistical methods to prevent other kinds of combinatorial metabolic explosions or interminable metabolic reactions from taking place. Specifically, we introduced probabilistic models that are based on HMDB data about human urinary metabolites, fecal metabolites and blood metabolites. This allows BioTransformer to predict if a compound is physico-chemically more similar to known liver/kidney metabolites (which are more abundant in urine) or gut microbial metabolites (which are more abundant in feces). If a predicted metabolite generated by BioTransformer 3.0 is not sufficiently similar, as measured by various physico-chemical criteria, to known liver/kidney or gut microbial metabolites, the biotransformation reaction that produced that metabolite is aborted and the precursor compound will serve as the biotransformation end product. This algorithm appears to substantially reduce excessive metabolism of many compounds and generates results that more closely match known metabolite datasets from well-studied molecules such as nicotine, acetaminophen and epicatechin.2) as well as removing duplicate biotransformations in the BTKB. Through extensive testing, several other bugs were identified and corrected that would occasionally send BioTransformer into an infinite loop. A total of 17 incorrect SMIRKS strings were corrected and another 15 SMIRKS strings were added to accommodate new reactions. Another bug that mishandled SMILES queries with specific stereo specifications was also corrected. Likewise rewrites of the AllHuman and SuperBio modules were done to improve both the efficiency and performance of these modules.Other performance improvements were achieved by correcting various bugs or programming errors in BioTransformer's original reaction databases or algorithms. These included fixing a bug that removed small metabolite byproducts  metabolite structures in the HMDB. If an exact match is found, it uses the reactions, substrates and reaction products in HMDB to generate the product structures and annotate the reactions accordingly.MultiBio or the Custom Human Multi-Step Transformation module. This option allows users to create a customized biotransformation sequence (a maximum of 4) by choosing any combination or sequence of CYP450, Phase II, EC-Based, Gut Microbial and Environmental reactions with up to three iterations for each step and up to four iterations overall. For example, if a user selects CYP450 with 2 iterations for step 1, Gut Microbial with 1 iteration for step 2, Phase II with 1 iteration for step 3 and N/A for step 4, BioTransformer will generate all the metabolites for the given query molecule arising from one round of CYP450 transformation along with the CYP450 metabolic products of all those CYP450 metabolites. Then all those doubly-processed CYP450 metabolites will be processed via human gut microbial metabolism, whereupon all of those metabolites (which may now number in the dozens) will go through a single round of Phase II metabolism. Clearly it is possible to generate dozens to hundreds of metabolites from a single precursor through this custom biotransformation module. Overall, we believe these functional improvements to BioTransformer 3.0 should greatly improve its utility and coverage.The other major functional improvement introduced to BioTransformer 3.0 is the customizable biotransformation module called Help section and the submenu entitled ‘Programmatic Access’. The second enhancement has been directed towards improvements of the command-line version of BioTransformer. The JAR version of this program, which contains essentially all of the predictive tools and options in the web server, can be downloaded from Bitbucket . Significant improvements have been made to simplify the installation, robustness, performance, customizability and compatibility of the command-line version. Likewise, more information has been provided on the website to facilitate installation and operation of the program. The improved accessibility of the command-line version of BioTransformer 3.0 should allow users to perform large-scale biotransformation studies on their local computers and to more easily process data from metabolomic and exposomic experiments.In terms of accessibility improvements, there have been two notable enhancements with this latest release of BioTransformer. One has been through the addition of an improved API (application programming interface) that allows BioTransformer users to programmatically access the BioTransformer 3.0 web server to perform automated queries. Currently the number of post requests is limited to 2 per minute. Details on how to use the API along with screenshots and example code are provided under the SuperBio and AllHuman modules along with modifications to the logic workflow of CyProduct have led to speed-ups of up to 20× for some of these modules. The performance improvement involving CyProduct was particularly important as it is now comparable in speed to the (less accurate) rule-based CYP450 metabolism predictor. Had CyProduct's speed not been significantly optimized, it is unlikely users would have opted to use this module.Finally, BioTransformer has also had some of its modules optimized for speed. For instance, in order to accelerate the prediction process, a new feature has been implemented that allows BioTransformer 3.0 to retrieve previously predicted results submitted from other users. Given the frequency with which the same molecules and the same transformation schemes are submitted to BioTransformer, we expect this feature will be heavily used. The rewrites of the Download link) is compatible with Linux, Mac OS X, and Windows. BioTransformer also uses two other open-source tools, namely the Chemistry Development Kit (CDK) (The backend for BioTransformer 3.0 was implemented in the Java programming language. The command-line version (as a JAR file available via Bitbucket and the it (CDK)  and the it (CDK) ,24. CDK it (CDK) , CypReacit (CDK)  and CyPrit (CDK) .Overall, we believe the enhancements, additions and improvements to both the backend and the frontend of BioTransformer 3.0 should make it easier to use, faster, more user-friendly, much more accessible, far more stable, more broadly usable and significantly more accurate. Likewise, the availability of a much more robust and stable, downloadable version of BioTransformer 3.0 should help address the needs of those users demanding much higher throughput or larger scale biotransformation analyses. While the improvements we have enumerated here are significant, there are still some shortcomings and limitations to BioTransformer 3.0 that we would like to address in the future. Some of these suggestions have come from regular BioTransformer users while others have emerged from our own investigations. These include the following:The incorporation of new rules or the development of new machine learning algorithms to better predict sulfation reactions (a common Phase II biotransformation).The integration of more diverse gut microbial metabolism reactions and enzymes, especially those that have arisen from many recent gut metagenomic and gut microbial metabolomics studies.Improvements to the SMIRKS rules (number and type) in the BTKB to better handle known pesticide and herbicide breakdown reactions—both in mammals and in the environment.The addition of new reaction rules to handle water decontamination byproducts arising from chlorination, ozonation and ultraviolet radiation.Developing better methods  for recognizing when biotransformation reactions should be terminated.Improving the overall accuracy of Phase I and Phase II metabolism prediction.Developing probability scores to indicate the likelihood of a reaction occurring or a product existing.in silico metabolism is still very much in its infancy and so there is still plenty to do to improve it. In this regard, it is also important to remember that the primary role of tools such as BioTransformer is to be an idea generator or a hypothesis generator. The molecules generated by BioTransformer 3.0 are biologically feasible, but they are not biological certainties. Indeed, the accuracy of in silico metabolism prediction has not yet reached a point that a given prediction guarantees the existence of a proposed molecule. Likewise, continuing challenges in experimental metabolite extraction and experimental metabolite identification mean that many biotransformed metabolites that really do exist in nature have not been (and may never be) formally identified. This means that the training and testing data needed for improving in silico metabolism prediction tools will always be less than perfect. Given that we will never have ‘perfect knowledge’ of all the transformed molecules existing in humans (or in nature), or their provenance, it likely means that we will have to live with the fact that some things in life will always remain a mystery.No doubt there are many other ideas that could/should be pursued, and we certainly invite the community to contribute to the discussion. Indeed, the field of


This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate  Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. Fluxes allow investigation of the effect of genetic and environmental conditions/modifications on a huge set of reactions  that define the metabolism and physiology of cells.The term “metabolic flux” was used for the first time in the forties by Greenstein and Chalkley,e.g., METRAN, INCA, OpenFLUX) for both data collection and data analysis to determine and predict metabolic fluxes with much more accuracy and precision. The selection of specific techniques largely depends on what kind of analysis needs to be performed. Overall, there are seven methods to perform flux analysis: flux balance analysis (FBA), MFA, 13C-MFA, isotopic nonstationary MFA (INST-MFA), dynamic MFA (DMFA), 13C-DMFA, and COMPLETE-MFA widely discussed in a recent mini-review published in 2015.3 Briefly, FBA is the oldest mathematical approach that uses a large-scale model (up to thousand reactions relative to the entire metabolism) and assumes the stationary state of the metabolic network.4 This predictive in silico model was then simplified in MFA, an easier and smaller-scale model that, in contrast to FBA, is exclusively focused on the central carbon metabolism such as glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle,5 anaplerotic pathways and gluconeogenesis.6 MFA can be now supported by the use of stable isotopes detectable as tracers by mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy.7 In 13C-MFA one 13C-labelled  or multiple singly labelled substrates (COMPLETE-MFA)8 are used as the carbon source for cell growth. The 13C of the labelled substrate in the medium will be then incorporated into the metabolic network of an organism of interest. This technique aims to identify and quantify flux changes and isotope distribution with the assumption of both metabolic steady state  and isotopic steady state (when isotopes are fully incorporated and static). However, one of the limitations of this method is that certain cells  reach the isotopic steady state relatively late (4 hours or even a day).9 To address this issue, 13C-INST-MFA allows transient 13C-labelling data at the metabolic steady state. In other words, the accumulation of tracers in intracellular metabolites is monitored over time before the system reaches the isotopic steady state but with the assumption of the metabolic steady state.10 From an experimental point of view, this method is faster than 13C-MFA because it does not need to wait the isotopic stationary state but from a computational point of view, it is much more complex because for each time point it requires solution of differential equations rather than algebraic balance equations. However, using the elementary metabolite unit (EMU) modelling approach is possible to dramatically reduce the computational difficulty.11,12 Other options are based on dynamic fluxomics experiments which are highly demanding but provide much more comprehensive information especially if 13C-DMFA is applied. The aim of DMFA is to determine changes in fluxes during a culture that is not at the metabolic steady state, thus the experiment is divided into time intervals and for each time interval it is assumed that flux transients are relatively slow. Carbon labelling measurements in dynamic system (13C-DMFA) is performed by extrapolating data similarly to 13C-MFA technique but splitting the experiment in multiple time points in order to have information of flux transients, which cannot be observed using classical MFA.13,14 One of the major limitations of DMFA is the huge amount of data and the complexity of computational models since this technique calculates fluxes for each time interval (from t1 to tn) and it is based on the assumption that flux transients are relatively slow (on the order of hours instead of seconds or minutes).Several flux analysis techniques have been developed and implemented with very powerful software  to determine new metabolic pathways, (b) to predict toxic effects of new drugs, (c) to identify targets after genetic modifications, (d) to explain mechanism of diseases and (e) to optimise biotechnological processes in metabolic engineering.15–19 Therefore, since this is an emerging area, we will report the recent advancements in the methods of 13C-MFA, various tools available for MFA analysis and its application areas through this systematic review. Mainly we will describe the steps to perform 13C-MFA: (a) pre-culture of cells until metabolic steady state and replacement of the medium with a labelled substrate; (b) cell cultivation until isotopic steady state in which molecules incorporate isotopes; (c) extraction of intra and extracellular metabolites for identification and quantification with respect to labelling state using targeted MS or NMR spectroscopy; (d) data processing and computational modelling to evaluate and predict cell fluxes, respectively.To date, among all techniques briefly introduced above, the isotopic stationary 213C-MFA. Publications were filtered for English language, duplicates, full text availability and document type . We used the same electronic databases to survey data analysis and application areas sections. Based on the results obtained we drafted a typical workflow for 13C-MFA starting from sample preparation (including labelled solutions and cell cultures), sample treatment for different analytical techniques to data analysis and ending with computational modelling to understand and predict metabolic fluxes. The tools that describe the genotype of biological systems are genomics, transcriptomics and proteomics, which produce data for gene sequence, mRNA and protein abundance, respectively . Among them, 13C has been mostly used because of its universal presence in each bioorganic molecule and its relative high abundance compared to the corresponding element 12C. Deuterium (2H or D) has very low natural abundance and it is easily interchangeable as well as 18O whereas 15N is mostly used to follow the pathway of nitrogen in proteins and nucleic acids. Therefore, because of above mentioned limitations of other isotopes, in this review we focused on the stable incorporation of 13C in one or more substrates. One of the first flux measurements using 13C NMR was done by Malloy et al. in the 1988 who investigated the citric acid cycle of the rat heart.26 However, 13C-MFA was established in the mid-90s when Zupke and Stephanopoulos presented the first 13C flux analysis based on a general mathematical modelling approach.27 Then in the 21st century the 13C-MFA has widespread also thanks to the development of easy-to-use software such as 13CFLUX.28 Sample preparation is based on two main steps; the first one is the preparation of carbon labelling solutions and the second is the feeding of cell cultures using tracers followed by quenching and extraction of metabolites.The use of stable isotope to study metabolic processes date back to 1935, when Schoenheimer and Rittenberg synthesised deuterated fatty acids to analyse lipid metabolism in animals.2.1.112C, one of its isotopes is 13C which can be incorporated in glucose (Glc) in six different positions giving 6 different isotopomers singly labelled and called [1-13C], [2-13C], [3-13C], [4-13C], [5-13C] and [6-13C] Glc , M+1, M+2, M+3, etc. are different isotopologues of the same molecule. In other words, the isotopomers differ only in the position of an isotope, while the isotopologues differ in the isotopic composition. The numbers of isotopomers and isotopologues dramatically increase when hydrogen and oxygen isotopes are included in the molecule. It is very important to understand that in both unlabelled substrates and labelled substrates there is 1.11% of probability to find the 13C, because of its natural abundance, percentage to take in consideration as correction factor in 13C-MFA experiments. This is why the unlabelled molecules are also identified as “naturally labelled”.In carbon labelling experiments (CLE) one or more substrates are used to feed biological systems. The tracers are used as carbon source for cell growth and isotopes are immediately incorporated in cell metabolism. Because each carbon of the molecule can be labelled at each position, for the same molecule, there are multiple carbon labelling substrates called isotopomers. For example, considering 13C] Glc . Additioe.g., for 1 g of  glucose the price can range between £500 and £800, 1 g of  glucose is commercially available for £2300 (https://www.omicronbio.com/index.html). The most common glucose isotopomers used for CLE are [1-13C] glucose and [U-13C] glucose (uniformly labelled). However, to obtain a comprehensive and accurate flux map of a biological system the selection of appropriate 13C-tracers is an important step in 13C-MFA. There are four ways to select the best tracer in CLE, the first option is based on a comprehensive literature research. The investigator extrapolates and compares data on different labelled substrates used for studying the same metabolic pathway of the same organism (or similar) to select those one that achieved the best precisions. This option based on data obtained from different laboratories is the cheapest and fastest approach. The second option is based only on empirical experiments which might be a long path and many experiments might be run before finding the optimal tracer and so it can be very expensive considering the price of 1 g of a specific tracer such as [1-13C] glucose and [U-13C] glucose (∼£300 per g), [2-13C] glucose (∼£700 per g) and  glucose (∼£900 per g). The third option is based on practical experiments at first followed by simulation experiments, for example Metallo et al. have experimentally determined metabolic fluxes in a tumour cell line and then they have computationally evaluated a wide selection of labelled 13C glucose tracers for their ability to precisely estimate fluxes in central carbon metabolism. In the end they identified as optimal tracer  Glc for glycolysis, PPP, and the overall network.29 The fourth option is to evaluate a priori all possible tracer combinations using simulations and predictive approaches. Hence, Crown and Antoniewicz introduced a new framework for optimal 13C-tracer experiment design based on elementary metabolite units (EMU) decomposition of metabolic network models, in which a measured metabolite is decomposed into a linear combination of so called EMU basis vectors (EMU-BV), where each EMU-BV represents a unique way of assembling substrate's EMUs into measured metabolites .30 With this methodology the same group achieved the identification of two optimal tracers  glucose and  glucose for elucidating the oxidative PPP flux and pyruvate carboxylase flux, respectively in mammalian cells.31,33 Later on, in another work they evaluated 100 random flux maps for commercially available glucose tracers on single, mixed, and parallel CLE having E. coli as object of the study.32 Results showed the best tracers that achieved the highest precision for flux analysis are ,  and  glucose.Isotopologues can be easily obtained by commercial suppliers, but they are very expensive etc.  in wild type brown preadipocytes.34 Metallo et al. experimentally determined metabolic fluxes in A549 lung carcinoma cell line using different 13C-labelled glucose and glutamine tracers.35 Results showed that  glucose provided the most precise estimates for glycolysis and PPP, while [U-13C] glutamine emerged as the preferred isotopic tracer for the analysis of TCA cycle. Kempa et al. used 13CO2 and 13C-acetate to study the uptake in the unicellular green alga Chlamydomonas reinhardtii in both photoautotrophic and mixotrophic growth conditions, respectively.36 Very recently, Tomàs-Gamisans et al. used 20%  glycerol as sole C-source in the steady state CLE to obtain a more reliable estimation of metabolic fluxes through the central carbon pathways of Pichia pastoris culture.37Many other substrates other than glucose can be used in CLE as carbon source, such as glutamine, carbon dioxide, glycerol, acetate, etc. . Since mThe solution of labelled tracers is typically prepared in deionised water and then filtered with 0.22 μm filters. It can be stored at 4 °C until use and in the meanwhile the investigator can proceed with cell pre-cultures.2.1.2E. coli to more complex systems such as human cells and plants.63,64 Whatever is the type of cells chosen for the experiments after the preparation of labelled solution, the second step is the pre-culture of cells in unlabelled medium. For example, regarding microorganisms, continuous cultures in bioreactor  are the preferred method to achieve maximum reproducibility and to maintain the metabolic steady state during the isotope incorporation. Isotope tracer solution is introduced by switching the medium from unlabelled to labelled at the same concentration to minimise cell's perturbation and maintain the stationary state. Alternatively, the exponential growth phase in batch cultures mimics the metabolic steady state. In this case, the procedure requires the inoculum of the species  from unlabelled pre-culture to labelled medium when the optical density measurements indicate the stationary metabolic state  and ferric ammonium citrate was replaced by ferric ammonium sulfate.46 The serum bottles were sealed with rubber septa to prevent atmospheric CO2 intrusion immediately after the inoculum of unlabelled Synechocystis 6803 (at OD730 = 0.9) in 30 mL 13C-labelled medium.Experimental procedure for the cultivation of different cells is well documented in the literature. Researchers all over the world reported data from the well-studied prokaryotic model organism ic state . As ment13C tracer is quickly distributed all over the metabolic pathways until the isotopic steady state is reached in all cells. In this phase, the growth rate, uptake rate, and biomass must be quantified  on cell cultures, typically methanol, became the most common applied quenching method.5,70 Alternatively, to decrease the risk of cell lysis and the subsequent leak of intracellular metabolites in the medium, rapid quenching of cells by immersion of culture flasks in a dry ice–ethanol bath at 0 °C, has been proposed by Robinson et al.71 The application of acidic or alkaline conditions to extract metabolites has also been proposed in the 90s.72 Later on, Maharjan and Ferenci compared the metabolome of E. coli extracted with six different methods: hot ethanol (90 °C), hot methanol (70 °C), cold methanol (−20 °C), perchloric acid, potassium hydroxide, and methanol/chloroform.73 Results showed that higher temperature methods give loss of compounds because of thermal degradation while the use of extreme pH is more suitable for extraction of nucleotides, but compounds like pyruvate, NAD, NADH+ are unstable at these conditions. Thus, they selected the cold methanol approach as the best method for metabolome extraction. More recently, Prasannan et al. presented a new method for metabolites extraction from three strains of cyanobacteria .74 To reduce the risk of metabolite leak in the medium during quenching, the improved method is based on fast filtration to separate cells from supernatant, subsequent immersion of the filter in cold methanol and the use of a water–methanol–chloroform–NH4OH system for metabolite extraction. Data showed that the addition of NH4OH to water/methanol/chloroform solution increased the intensity of metabolite classes such as sugar phosphates, bisphosphates, and nucleotide triphosphates.74 Although, the use of weak base did not improve the extraction of organic acids, they observed an increase of fatty acids and amino acids into the aqueous layer. In 13C-MFA both isotopic labelling and external rates, such as substrate uptake rates, product secretion rates and growth rates, must be quantified to obtain essential constraints for flux determination. Therefore, the separation of the spent growth medium from cell pellet is a necessary step in 13C-MFA to analyse intra and extra-cellular metabolites. There are two main cell collection methods: fast filtration, as previously mentioned, and centrifugation at high speed  typically performed after quenching. Analysis of external rates and product yields  might be challenging due to low concentrations of extracellular metabolites. For an excellent discussion on how to determine external rate and yields see Box 2 of the paper in Nature Protocols by Long and Antoniewicz.43 Regarding the intracellular metabolites, researchers focused their attention on central metabolic intermediates and mostly on proteinogenic amino acids since they reflect the labelling patterns of multiple key metabolites at steady state. To extract intracellular metabolites cell hydrolysis is required and this mostly depends on the type of cells and the biological system studied. Typically, to perform cell hydrolysis the pellet is mixed with a strong acid at high temperature, for example it has been reported for Corynebacterium glutamicum and E. coli that 0.5 mL of 6 M HCl and incubation for 24 h at 110 °C is a good method for intracellular metabolites extraction.41,75 For cyanobacteria different solvent systems used for extraction led to statistically significant changes in the extraction efficiency for a large number of metabolites.74 Thus, cell lysis depends on the membrane structure which can differ from bacteria (Gram negative and Gram positive), yeasts, plants, to eukaryotic and prokaryotic cells. In each case, to minimise degradation after extraction, samples can be either dried under vacuum and stored in a desiccator at room temperature for up to 4 days or directly stored at −80 °C (for a year) until further treatments.43 An extremely rapid sampling technique for high throughput fluxome profiling has been developed by Heux et al.54 They presented an automated cultivation and sampling platform for CLE composed by a bioreactor and an automatic workstation.76,77 The bioreactor has a capacity of 48 stirred tank (from 8 to 15 mL) equipped with solid state sensors for oxygen and pH. The robot workstation, activated via software, provides sterile conditions and is composed by three robotic arms to handle liquids, transport tubes to/from centrifuge and filter station, microtiter plates, a plat reader, a barcode reader, a cooling and heating module. This platform has been applied to discriminate a set of E. coli mutants with varying level of glucose-6-phosphate dehydrogenase by NMR-based isotopic profiling. With this automated system, they generated 80 fluxes of E. coli mutants (10 strains) in only 4 days, including all steps from cultivation to calculated fluxes. It is important to mention that the work published by Shaikh et al. in 2008 provided the foundation to extend isotopomer-based flux analysis to study metabolism of individual strains in microbial communities.78 Then in 2011 Rühl et al. developed a method for 13C flux analysis of subpopulations within mixed cultures of E. coli (wild type with two metabolic mutants) using a plasmid-based reporter protein with glutathione S-transferase (GST) as the high-affinity purification tag and green fluorescent protein (GFP).79 These methods required physical separation of either proteins or cells, that could lead to inaccurate flux results. To overcome this issue, a novel approach for performing 13C-MFA of co-culture systems was presented by Gebreselassie and Antoniewicz in 2015.47 This approach did not require any physical separation to measure species-specific fluxes, but a careful selection of isotopic tracers was needed. The methodology was validated using as tracer  Glc and a co-culture of two E. coli knockout strains Δpgi (knockout of upper glycolysis) and Δzwf (knockout of oxidative PPP).47 This method was lately employed to investigate for the first time the metabolism of E. coli grown on agar that formed two distinct cell populations engaged in acetate cross-feeding.80 Given the great importance of the synergistic functions of microbial communities, these works have extended the scope of 13C-MFA to a large number of multi-cellular systems.The final samples are collected when metabolic and isotopic steady state are reached. The collection of samples is a critical step because all metabolic reactions need to be stopped as quickly as possible and any leak of metabolites must be avoided during the extraction. Ideally, the selected method should extract the higher number of metabolites without compromise their chemical or physical properties. Quenching and extraction methods can slightly change, depending on cell types and metabolites of interest.2.281 The range for the sensitivity depends on the type of instruments, usually accurate NMR measurements are possible in the concentration range 10−1 to 10−3 M, with structural elucidation still being possible at concentrations of approximately 10−4 to 10−5 M. Electron impact (EI)-MS is more sensitive and can operate at concentrations of 10−6 M, while commonly available electrospray ionisation (ESI)-MS can be efficient up to a concentration of 10−12 M.82 The possibility to choose between a full scan, selective ion monitoring, single reaction monitoring (SRM), multiple reaction monitoring (MRM) and MS/MS fragmentation allow to obtain more information about isotope patterns. In addition, with these techniques, the molecular formula can be directly determined without previous purification because of the high mass resolution of the instrument . On the other hand, the major advantage of NMR is that this technique is not destructive; samples can be used again after NMR analysis. NMR spectroscopy and MS are often complementary to each other and provide different information, for example, NMR spectroscopy can provide detailed isotopic labelling position for each specific carbon in a metabolite of interest and MS can provide the mass isotopomer distribution (MID). Therefore, the combined approach is highly suggested to obtain more precision and accuracy of results.55,56Multiple analytical techniques can be applied in MFA. After the extraction of metabolites, samples are treated differently based on which analytical technique has been selected for flux analysis. The most common used techniques are MS and NMR spectroscopy. For each technique, there are advantages and disadvantages to take in consideration before starting the analysis. In general, it is difficult to apply NMR to extracellular metabolites because of their relatively low amount, while MS provides more information because of its higher sensitivity.2.2.152 Within NMR analytical techniques, proton and carbon NMR are the most common one , making 3D experiments suitable for 13C-MFA.60 Data showed that this new strategy is linear and quantitative. The authors were able to provide valuable site-specific 13C enrichment information. This is one of the major advantages of NMR as specific positional information for each carbon atom can be obtained without chemical degradation of metabolites allowing to quantify the abundance of positional isotopomers. Moreover, because of its non-invasive approach, NMR can be also used to evaluate metabolism in vivo study. Des Rosiers et al. determined cardiac metabolism in ex vivo rat heart perfusion with 13C-substrates.84 A more recent in vivo study was reported by Jeffrey et al. who measured metabolic fluxes in the rat brain using the additional information provided by 13C multiplets.62 However, for NMR analysis metabolites must be at least in μM quantities, thus a relatively large amount of sample is needed compared to MS analysis.Nuclear magnetic resonance spectroscopy is a non-destructive analytical technique and one of the first method applied in CLE.mmon one . Mono-die.g., potassium dihydrogen phosphate, sodium phosphate). A typical sample for proton spectroscopy in 5 mm NMR tube ranges between 500–700 μL and it is composed by 0.02% sodium azide in 2H2O with 0.5 mM internal standard like 4,4-dimethyl-4-silapentane-1-sulfonic acid.85–87 Sodium azide limits the growth of microorganism in samples if longer experiments are needed, phosphate buffer helps to control the pH of solutions to reduce possible shifts in the spectra between samples and DSS is used as reference standard to set its singlet at δ = 0.00 for proton experiments. A centrifuge step is required to remove salt precipitations in the tube. Experiments are normally acquired at 40 °C on 400, 500, 600 and 700 MHz spectrometer depending on the availability of laboratories.53,88 Then, after data acquisition, sample can be dried again and used for further analysis, for example MS-based approach.Sample treatment for NMR analysis does not require long time as for GC-MS analysis because there is no need of sample derivatisation for spectroscopy. On the other hand, NMR analysis can take much longer than MS analysis . Once the sample has been dried (see “cell cultures” section), it is dissolved in a deuterated solvent with buffering agent  by biosynthetic retro analysis using NMR spectroscopy.89 They used the mevalonate pathway and the deoxy xylulose pathway of terpenoid biosynthesis as examples.Bacher et al. studied the fungus Ashbya gossypii to improve its production of riboflavin (vitamin B2), a very important precursor for the cofactors FAD and FMN, from vegetable oil as raw material.57 They used a highly sophisticated MFA approach based on parallel CLE (with glycine and serine as tracers) using GC-MS, LC-MS, 1D, and 2D NMR to resolve carbon fluxes. Results explained a complex industrial process showing that glycine was exclusively used as carbon-two donor of the vitamin's pyrimidine ring, while formate and serine do not play an important role in riboflavin biosynthesis.Schwechheimer et al. compared fluxes estimates from the metabolite balancing and isotope tracer methods for Chinese hamster ovary (CHO) cells using 13C glucose and 2D-NMR spectroscopy.58 They observed good agreement between the 2 methods in the glycolytic, TCA cycle and oxidative phosphorylation fluxes with less than 8% difference for most fluxes . This data suggests that relatively little labelling information is needed to get a complete picture of metabolic network. Thus, their study can be used for routine monitoring and for bioprocess development experiments without the need for frequent isotope labelling experiments reducing the analytical investment.Goudar et al. determined metabolic fluxes from he Actinobacillus succinogenes  by using a combined approach based on GC-MS and NMR spectroscopy to understand carbon flux distribution to succinate and alternative products.59 They described the labelled experiment based on [1-13C] glucose to obtain metabolic pathways of A. succinogenes by analyses of its cell composition, extracellular fluxes, and isotopomers of amino acids, organic acids, and glycogen monomers. In particular, for NMR analysis all compounds were analysed by 1H-NMR and 1H decoupled 13C-NMR using a Varian VXR 500 MHz spectrometer. They found that the partitioning of flux between succinate and alternative fermentation products can occur at multiple nodes in A. succinogenes and metabolic engineering strategies can be easily designed to increase succinate production.McKinlay 2.2.2Mass spectrometry-based methods are a great alternative to spectroscopy techniques, ensuring efficiency, precisions, accuracy, and high sensitivity . HoweverN-methyl-N-t-butyl dimethyl silyl-trifluoroacetamide (MBDSTFA) or N-tert-butyl dimethyl silyl-N-methyl trifluoro acetamide with 1% (wt/wt) tert-butyl dimethyl chloro silane (MTBSTFA + 1% TBDMSClS) and leave 30 min at 60 °C for reaction to be completed. Time and temperature should be monitored over time to verify the completion reaction, for example sample injection after 30 min and 60 min at the same temperature (60 °C) should be compared between them and another high temperature  should be verified to choose the best solution for the samples. 13C-MFA approaches, including different derivatisation methods.Thus, for proteinogenic amino acids analysis, after cell hydrolysis, the lyophilised sample needs to be further solubilised in a derivatising agent such as  PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dbd" format="PBM" resolution="300" x-size="8" y-size="10" xml:space="preserve">00000000000000000000000000000000111111110000000011111111000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>N bond, resulting only in the formation of syn and anti forms.90 Also the oxymation reaction needs to be performed with dried sample and it is generally carried out by adding 2% (wt/vol) hydroxylamine hydrochloride or methoxyamine hydrochloride in anhydrous pyridine to lyophilised sample and leaving for 1 h at 90 °C  of the reducing sugars, before silylation another reaction called oxymation is required. The high number of tautomers that silylation would form, would cause major problems for the identification and quantification of complex mixtures as in the case of MFA. However, by converting the aldehyde and keto groups of saccharides into oximes, the number of tautomeric forms can be reduced, due to the Cn (where n is the number of 13C atoms). MS methods enable to measure the relative amount of each mass isotopomer for each detected metabolite. Another advantage for applying GC-MS methods on MFA is the wide spectral database of metabolites that strongly support their identification by GC-MS analysis.MS approach allows the measurement of the mass isotopomer distributions (MID). The MID is the number of the relative amount of each mass isotopomer for each measured metabolite. As previously described, isotopomers are molecules that differ only in the number of isotopes incorporated, defined as M+0, M+1, M+2, M+et al. introduced a new strategy based on GC-combustion-isotope ratio MS (GC-C-IRMS) to determine in vivo flux central metabolism of C. glutamicum involving 4 labelling degrees from 0.5% to 10% [1-13C] glucose mixed with naturally labelled glucose.41 Authors demonstrated that using GC-C-IRMS technique is possible to develop the experiments with extremely low amount of the expensive labelled substrates, such as 1% which is enough to measure the 13C enrichment in proteinogenic amino acid hydrolysates. This work was of great interest for industry because the laboratory scale fermentation of C. glutamicum (very important for amino acids production) has been translated into a large scale of bioreactors used in biotechnological processes that otherwise would have been much more expensive considering the cost of using 99% [1-13C] glucose. They also proved that conventional GC-MS methods of the experiment in the same conditions are not reliable due to the poor sensitivity of the instrument and to low amount of 13C tracers' enrichments in amino acids (0.001–0.05 atom percent excess-APE).91Yuan Chlamydomonas reinhardtii, widely studied as model system in photo-synthesis.36 In addition to high resolution and high sensitivity, other advantages of this method are better separation of co-eluted peaks and the presence of second retention time/index for detected metabolites. Experiments were performed using 13C sodium hydrogen carbonate and 13C sodium acetate for carbon flow under photoautotrophic and mixotrophic growth. Authors identified several metabolites such as alanine, aspartic acid, leucine, lysine, anthranilic acid, fructose, glucose, fumaric acid, malonic acid, and glycerol. According to different pathways of 13C incorporation, data showed that the assimilation of carbon in photoautotrophic conditions proceeds via Calvin–Benson cycle while in mixotrophic conditions mainly by glyoxylic acid cycle and subsequent TCA cycle.Another promising technique to overcome limits of MFA-based GC-MS is the two-dimensional GC coupled to time-of-flight MS (GCxGCTOF-MS) applied on a unicellular alga 13C-MFA using E. coli as case of study.43 They explained clearly how to measure isotopic labelling of amino acids from hydrolysed biomass proteins, glucose from glycogen and ribose from RNA by GC-MS. They used GC system to measure mass isotopomer distributions with DB-5 ms capillary column, connected to a single quadrupole MS operating at electron impact (EI) ionisation at 70 eV. MID can be easily calculated by the relative abundances of each successive mass isotopomer .Recently, Long and Antoniewicz published a very detailed protocol for 92 Jeffrey et al. tested for the first time in 2002 the feasibility of mass isotopomer analysis using GC-tandem MS to determine pathway flux using 13C-enriched substrates.40 They compared the obtained results to those obtained by 13C NMR spectroscopy. Authors analysed glutamate and citric acid cycles from rat hearts perfused with three substrate mixtures: glucose plus [2-13C] acetate, [1-13C] glucose plus U/L insulin, and glucose plus [3-13C] pyruvate. They showed that although analysis by NMR was simpler, tandem MS could combine higher sensitivity and larger amount of information allowing better metabolic flux analysis, while full-scan MS was the least accurate method. Since tandem MS is based on more fragmentation steps, the key advantage is the detailed positional labelling information obtained by the daughter fragments.96 This was demonstrated for the first time in 2012 by Choi et al. who developed a method for measuring the complete isotopomer distribution of aspartate  using GC tandem MS (MS/MS).93 At this time, the major limitation of tandem MS was the lack of computationally efficient method for simulating isotopic labelling data. Then in 2015 Tepper and Shlomi presented a novel approach called tandemer (from tandem mass-isotopomer) for efficiently simulating MS/MS measurements of metabolites in a metabolic network that showed how all tandemer distributions can be computed by sequentially solving linear balance equations for sets of tandemer distributions with increasing size.94 However, only in 2019 it was demonstrated that tandem MS data can be used for 13C-MFA calculations using the EMU framework, which is currently the most used mathematical approach for modelling isotopic labelling in 13C-MFA and is the core of several main software.95GC-tandem MS has been used as advanced analytical technique for isotopic analyses showing more advantages compared to full scan analysis. Using tandem MS more information can be obtained on positional labelling compounds because individual mass isotopomers (parent ion) can be isolated from the rest of the sample, trapped and then fragmented into smaller compounds (daughter fragments).2.2.3m/z of primary ions  that possess low internal energy to undergo further fragmentation. To achieve further fragmentations triple quadrupole or ion trap instruments are commonly employed. GC-MS has been mostly used to measure MIDs of proteinogenic amino acids, characterised by a slow turnover, good stability, and high abundance. On the other hand, metabolic intermediates, with fast turnover, reflect the instantaneous biochemistry of the cell also after perturbations.97 However, metabolic intermediates in the cell are less stable and less abundant than proteinogenic amino acids. Therefore, intermediate metabolites are often analysed by LC-MS, due to its higher sensitivity and softer ionisation.There are many instances where certain metabolites are incompatible for GC-MS analysis or require derivatisation, making LC-MS the preferred approach in such cases. In contrast to GC-MS, the electrospray ionisation used in LC-MS is a soft ionisation technique which reduces in source fragmentation. This is important because the soft ionisation gives the additional information of et al. presented a 13C-MFA method based on rapid sampling and quenching of microorganisms from a continuous cultivation, followed by extraction and detection by LC-MS of free intracellular metabolites, mainly glycolytic intermediates, such as glucose-6-phosphate and phosphoenolpyruvate.48van Winden 7 As previously mentioned, in MS/MS experiments the signal/noise ratio and the mass transitions between parent and fragment ions are drastically improved. Kiefer et al. developed a straightforward MS/MS approach investigating the correlation between the mass isotopomers of fragment and molecule ions.98 Authors applied this method to phosphorylated metabolites because their fragmentation results in high yields of [PO3]− (m/z = 79) and/or [H2PO4]− (m/z = 97) ions. They determined the mass isotopomer distribution from the molecule ions [M–H]− obtained by multiple ion monitoring and from the carbon free fragment ions obtained by MRM. Using this method, a very small biomass amount is necessary for the analysis, allowing very small cultivation volumes in the microliter range.LC-tandem MS (LC-MS/MS) is a highly sensitive and specific technique that allows detection of molecules in the femtomolar range.et al. determined the metabolic flux changes during fed-batch cultivation from measurements of intracellular amino acids by LC-MS/MS.49 They applied this method to lysine production in fed-batch culture of E. coli and they compared this “new” approach with the conventional approach based on (i) continuous culture; (ii) assumption that MDVfree amino acids (mass distribution vector) is equal to MDVproteinogenic amino acids. For the first time, it was considered the isotopic effect of protein degradation on the intracellular metabolite pools, coming from the incorporation of the naturally labelled carbon from medium components into cellular metabolism. This approach made the basis for investigating cellular behaviour in industrial processes. Until the first decade of “2000s”, all LC-MS-based 13C-MFA studies investigated only 13C-data of intact molecules without positional information obtained by fragmentation. Then, Rühl et al. exploited for the first time collisional fragmentation of central carbon metabolites by LC-MS/MS.50 Authors acquired product ion scans for 20 intermediates of the central carbon metabolism to compile a catalogue of all possible fragments. Results showed more precise estimates of fluxes in pentose phosphate pathway, glycolysis, and tricarboxylic acid cycle.In the same year, Iwatani et al. have used polarity switching selection reaction monitoring (SRM) mode on hybrid triple quadrupole MS coupled with hydrophilic interaction chromatography (HILIC) to monitor the incorporation of heavy atoms such as 15N and 13C.99 This approach has been reported to be robust, fast and reproducible for large-scale research and stable isotope labelled metabolites for clinical studies. There is an added benefit of improved sensitivity and dynamic range by using SRM over untargeted high-resolution LC-MS/MS. This approach is proving popular because method can be customised for specific pathways targeting isotope labelled metabolites using precursor and fragmentation ions by adjusting the mass to account for the labelled species. McCloskey et al. presented a novel LC-MS/MS method for measuring the precursor and product mass isotopomer distributions of metabolic intermediates and cofactors for MFA applications, the MID Max.51 This method has been applied on E. coli and has the advantage to maximize the number of mass isotopomer distributions that can be acquired in a single run. The authors also generated a library of product ion scans and spectral annotation that can be used for further experiments during the process. Recently, Yuan et al. presented a very detailed protocol for analysing incorporation of 13C and 15N into polar metabolites of central carbon metabolism and related pathways.100 This protocol is an extension of the protocol published in 2012 and it focused on targeted LC-MS/MS based approach using selected reaction monitoring (SRM) with polarity switching and amide HILIC to capture transitions for carbon and nitrogen incorporation into selected metabolites.99 The authors specified all details for reagent preparation and equipment setup, including the setting for the QTRAP mass spectrometer and HPLC system. The entire procedure presented by Yuan et al. (2019) takes 6–7 h for a single sample from experimental labelling and metabolite extraction to peak integration.Yuan 2.3Data collection and data analysis are the final steps to determine metabolic networks in organisms. Identification and quantification of isotopomers is a very delicate and laborious phase since this step requires the use of mathematical models to describe the relationship between isotopomer abundances and metabolic fluxes. Over the past decades various tools and approaches have been developed for mathematical modelling and computer simulation for metabolic system studies .era. The so called “model-driven” approach has been the most used method in the early stage of the systems biology when the sensitivity of mass spectrometry and NMR spectroscopy was not able to measure absolute concentrations of molecules with so much precision and accuracy as today.82 Usually, the first step of the “model-driven” approach is the generation of computational networks from existing knowledge of kinetics data and transcription rates (from scientific literature and/or lab-based), the second step is the integrative use of predictive models and the last step is the reduction of the known processes by selection techniques.127 Previously, most of the used methods required kinetic and concentration data of enzymes and cofactors but in the last two decades the most successful techniques such as constrain-based, genome-scale and FBA overcame this issue.128 For example, to deal with the lack of kinetic information, FBA based on optimality principles describes steady-state flux distributions in metabolic network using linear programming.4,129,130 In more detail, the first step in FBA is to mathematically represent metabolic reactions. The metabolic reactions are expressed as stoichiometric matrix (S) of size [m × n]. Every row of this matrix represents a metabolite (m), and every column represents a chemical or transport reaction (n). For each metabolite consumed there is a negative coefficient and vice versa, for each metabolite produced there is a positive coefficient, while the zero coefficient is used for those metabolites that do not participate in that reaction. The metabolites concentration is described by the following differential equation (c):where v(t) is the vector of reaction rates, including exchange fluxes that account for metabolites transport through the membrane. FBA assumes that metabolic dynamics have reached quasi- or pseudo-steady state, where metabolite concentrations do not change in time. In other word, the assumption is that each metabolite is consumed in the same quantity as it is produced, and this is expressed by the following equation:The ability to quantitatively describe metabolic fluxes through metabolic networks has been one of the major challenges of the early fluxomics ex nihilo formation of mass and even its vanishing is not possible. Additional constraints can be introduced in the model, such as the reversibility of reactions and compartments in which reactions take place. For example, upper and lower limits can be applied to individual fluxes (vmin ≤ v ≤ vmax), for irreversible reactions vmin = 0. Because there are more reactions than metabolites (n > m), FBA does not have a unique solution for flux distribution but provides a “solution space” that contains all the possible steady-state flux distributions.131 To identify a single optimal flux distribution, it is necessary to state an objective function, which, in microbial cells, has generally been defined as the maximal cellular growth. Thus, in FBA the growth rate is maximised to predict metabolic fluxes. However, the assumption of optimality and steady state, and the application of a large set of constraints do not always reflect the actual cellular metabolism, thus the prediction is not always accurate. Moreover, the size and complexity of the huge dataset, composed by thousands of reactions 132 requires a huge effort in computational modelling for their analysis. Therefore, a reduction procedure to obtain smaller models can sometimes be the best choice to study basic principle of organisms. Recently, Erdrich et al. presented the “NetworkReducer” which is a new algorithm to obtain smaller models using an automated reduction of metabolic reconstructions.133 The algorithm consists of two main steps: “pruning” and “compression”. Network pruning delivers a subnetwork of the system composed by a list of protected elements and functions (phenotypes) removing the inessential ones. This operation decreased the genome-scale network of E. coli from 2384 reactions to 455 and from 1669 metabolites to 438. Then, the compression further reduces the network size by collapsing some sequential reactions. The procedure has been applied to the iAF1260 genome-scale model of E. coli and in the end, they obtained 88 reactions and 69 metabolites.133This is also called “mass balance constraint” which explains that the 13C-MFA the metabolic reaction network can be reconstructed as a linear system where v =  is the flux vector and S is the stoichiometric matrix (m × n)As for FBA also in 13C-MFA the metabolic fluxes are estimated from isotopic labelling data  and extracellular rates measurements , such as substrate uptake, oxygen uptake, growth and product secretion rates.134 Fluxes are estimated by iteratively minimising the sum of squared residuals (SSR) between the measured and simulated isotopic labeling patterns and external rate measurements135In this case, the “mass balance constraint” is valid for each isotopomer belonging to a metabolite. Another difference is that in 134 Several mathematical approaches have been developed to solve these non-linear isotope-labeling balances.13,101,102,136 The computational methods used for 13C-MFA have become progressively more advanced, especially by speeding up the calculations of metabolite labelling from an assumed set of fluxes. Schmidt et al. (1997) proposed an elegant method to reduce the model complexity of isotopomer models to that of classical metabolic models by expressing the 2n isotopomer mass balances of a metabolite pool in a single matrix equation.101 The authors showed how the isotopomer model calculates the steady state label distribution in all metabolite pools as a function of the steady state fluxes.101 Wiechert et al. (1999) introduced the concept of cumomers, from “cumulated isotopomer fraction” referring to a certain sum of isotopomer fractions of a metabolite and provided an efficient procedure for solving isotopomer models.102 Then in 2007 Antoniewicz et al. developed a novel approach to simplify isotopomer calculations called the elementary metabolite units (EMUs) framework which is based on a highly efficient decomposition algorithm that identifies the minimal amount of labelling information required to simulate isotopomers of metabolites.13 The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and isotopomer abundances. The authors showed that the isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods but the EMU models are orders-of-magnitude smaller than the isotopomer and cumomer models and require significantly less computation time.13 Many and different software tools are available to estimate metabolic fluxes, some of them are non-open source but available for academic research and some other are free for use .Most of the software use the EMU framework to calculate isotopomers distribution. Among them, the most popular packages are OpenFLUX and 13C-Flux.28 Recently, it has been updated to 13C-Flux2 with advanced features such as highly efficient implementation of EMU simulation algorithms, specification of arbitrary analytical technique  and the FluxML, a standardized XML-based document format to specify all types of measurements.105 It consists of several steps: network modelling and measurement specification, simulation of isotope labelling states, parameter estimation, statistical quality analysis and experimental design (https://www.13cflux.net/13cflux2/). Results can be easily post-processed with MATLAB, moreover the software Omix, which is a network drawing tool, may be used for graphical and visualization purposes.116 This software is available with a commercial license and it has been developed and tested to run on Linux/Unix system, however, it is free of charge for universities and non-profit research institutes.13C-Flux is another option available for the detailed quantification of intracellular (quasi) steady-state fluxes.et al. described a user-friendly software named FiatFlux for non-expert users who open their research window to metabolic flux analysis.115 FiatFlux was developed on a MATLAB basis, with programming language MATLAB R14 (The Mathworks) and allows to use the MATLAB Optimisation Toolbox. For non-expert users this software is relatively easy and more intuitive because it allows to calculate the flux ratios directly from MS data. In particular, it consists of two modules, the first calculates fluxes obtained from 13C tracers detected at GC-MS from amino acids, then automatically identifies fragmentation pattern and operates statistical analysis in the RATIO module, while the second module (NETTO) allows to determine fluxes in and out of the cell and therefore absolute intracellular fluxes. Nowadays, FiatFlux is almost entirely replaced by SUMOFLUX a new method based on a machine learning predictor trained using in silico13C dataset comprising thousands of data between flux ratios and corresponding labelled patterns.45 This in silico dataset is practically used for training and for prediction of metabolic network. They validated the method estimating five key flux ratios of the central metabolism of E. coli and Bacillus subtilis by GC-MS of proteinogenic amino acids upon silylation. In addition, SUMOFLUX allows measurements data also from parallel experiments where different 13C tracers have been used.Zamboni 13C NMR spectroscopy. The tcaSIM program simulates flow of carbon isotopic tracer atoms through the TCA cycle and connected networks and when used in combination with the tcaCALC  performs isotopomer analysis to estimate pathway fluxes in biological systems at metabolic and isotopic steady state.117The tcaSIM program is another predictive software primarily applied to MS and https://tlo.mit.edu/technologies/metran-software-13c-metabolic-flux-analysis). A request should be sent through email to obtain a copy (mranton@udel.edu). METRAN allows to calculate MID for 13C-MFA and works in correlation with MATLAB language. Yoo et al. used this software for quantifying the reductive carboxylation flux from glutamine to lipid in adipocyte cells and they compared the result to that obtained from another software, ISA.34 The authors found that both software ISA and METRAN provided similar results, but ISA model requires only the MID of a single molecule as input, while METRAN models can use MID of all metabolic intermediates and fragments of selected pathways. Therefore, METRAN provides much more information about metabolic flux analysis. To estimate fluxes, METRAN minimises the variance between the observed and predicted measurements using non-linear least-squares regression approaches. The user needs to communicate to software how many and which tracers are using and with which ratios they should be mixed. An example of metabolic network for E. coli and its data set is distributed with METRAN software and analysed following a detailed protocol based on MID.43Recently, a new software called METRAN has been developed as a freely available tool for academic and educational use  to generate a single flux map. The INCA application is free of charge for academic users. However, the software only supports MID data and proton NMR fractional enrichment data while the ability to specify for 13C-NMR fine spectra and tandem MS/MS data will be update in the future versions. For LC-MS analysis there are other software available, for example VistaFlux allows flux visualization and pathway integration for Agilent MassHunter LC-MS data. This software can create target lists, extract batch isotopologues, and visualize stable isotope label flux results on pathways for MS-only data from Agilent TOF-based high resolution LC/MS systems.118 Although some studies have highlighted limited performance in quantitative predictions of stoichiometry-based approaches,137 recently it was developed a two-step computational pipeline incorporating raw 13C-isotopic labelling data under multiple genetic and environmental perturbations and the computationally inexpensive parameterisation algorithm K-FIT138 demonstrating the potential use of a single metabolic network for both flux elucidation and kinetic parameterisation.139In addition to previous software, the Isotopomer Network Compartmental Analysis (INCA) is the first software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis, meaning that this software simulates transients CLE also if 140In case no known algebraic relationship exists between isotopic labelling data and physiologically relevant variables, multivariate regression models, such as multiple linear regression (MLR), partial least-square regression (PLS) and regression using artificial neural networks (ANN), have been proposed for the analysis of isotopomer flux data to estimate physiological parameters for metabolism of mammalian gluconeogenesis.141–143 Among these, Mazzoleni et al.144 following the principles of System Dynamics,145 presented a first model simulating the growth of two strains of Saccharomyces cerevisiae (CEN.PK113-7D and CEN.PK2-1C) in batch and fed-batch cultures. More recently, the same model was extended to two prokaryotic species, E. coli and B. subtilis.146 In this paper, the authors explicitly stated that this modelling procedure directly aims at the identification of the minimal number of processes sufficient to simulate the emergent properties of a complex system based on logical reasoning and existing knowledge of the same system. Instead of considering the whole range of reactions and then reduce them, this modelling approach starts with the most basic mathematical representation of a very limited number of processes and increase the complexity by adding only those processes that are necessary to reach a representation of the system behaviour consistent with the aim of the specific model. In other words, the peculiarity of this different approach is that rather than reducing the number of reactions from thousands to hundreds, it starts selecting and fixing the main variables in order to obtain a minimal number of equations able to reach a significant fitting with observed data.146,147 In particular, this model was based only on 7 ordinary differential equations representing the main steps of glucose metabolism, i.e., glucose uptake from the medium, glycolysis (from glucose-6-phosphate to pyruvate), respiration and/or ethanol/acetate production by fermentation, reserves accumulation and production of growth-associated inhibitory by-products.146,147 Taking into account only these selected (and compressed) processes, without explicit representation of any secondary pathway, the model is able to provide a robust representation of cell growth under all tested feeding conditions.Interestingly, a completely different approach from the abovementioned computational models is based on highly simplified macro-kinetic representations of metabolism aiming to study emergent properties such as biomass growth and the production of specific metabolites. This type of approach has been mainly used in the field of industrial chemistry and applied microbiology.2.413C-MFA played a crucial role in furthering the basic understanding of metabolism in general of different species.38,148 For example, 13C-MFA was applied to evaluate CHO cell metabolism, experiments suggest that promoting oxidative TCA cycle and PPP flux may provide a possible strategy to increase specific antibody production and reduce lactate accumulation during the production phase of industrial fed-batch CHO cell cultures.149 The use of 13C labelling experiments also helped to detect a significant transketolase-like protein 1 flux in CHO cells at the stationary phase and in 2017 has been published the first direct evidence that circulating lactate contributes to central metabolism in human non-small-cell lung cancers in vivo.150,151 Considering E. coli metabolism important contributions were given using a metabolic flux ratio (METAFoR) analysis and CLE that revealed how the central carbon metabolism of Escherichia coli responds to genetic and environmental manipulations.39,152,153154 This economically and environmentally friendly technology appeals many industrial processes, which have as main goal the increase of specific metabolites production.155 For example, the demand for the essential amino acids, such as l-lysine, is rapidly growing in the global market, for their wide use as feed additive, composition of pharmaceuticals, cosmetics, and polymer materials. Xu et al. engineered the carbohydrate metabolism systems in Corynebacterium glutamicum to enhance the efficiency of l-lysine production from mixed sugar.156 Firstly, they engineered a functional metabolic pathway of sucrose and fructose through introduction of fructokinase from Clostridium acetobutylicum, then they further increase the l-lysine production by replacing the phosphoenolpyruvate-dependent glucose and fructose uptake system by inositol permeases and ATP-dependent glucokinase. Results showed that the engineered C. glutamicum produces 221.3 ± 17.6 g L−1l-lysine with productivity of 5.53 g L−1 h−1 and carbon yield of 0.71 g g−1 glucose in fed-batch fermentation.In the last three decades the “omics” sciences have seen an exponential growth in different fields, including agriculture, industrial processes, and medicine. Nowadays, metabolic engineering is one of the main areas for fluxomics application. The modification of specific biochemical reactions results in improvement of cellular properties under different environmental conditions.2. These characteristics make them the target organisms of metabolic engineering for photosynthetic production of biofuels. Knoot et al. recently presented an overview about MFA applied on the metabolic pathways that have been engineered in cyanobacteria to increase the production of several chemicals such as ethanol, isobutanol, isopropanol, isoprene and isoprenoids, succinate, and glycerol.157 Authors listed in their study most of the bioproduction strategies from carbon dioxide that have been adopted in different cyanobacterial strains.Cyanobacteria are prokaryotic organisms that grow very fast and they can perform oxygenic photosynthesis starting from water, sunlight, and CO13C-MFA was applied in metabolic engineering also to maximise the overall production yield and productivity of acetol, which is mainly used as an organic intermediate to produce polyols and acrolein and as a reducing agent in the textile industry. Nowadays, the chemical processes for acetol production are petroleum based. However, Yao et al. applied 13C-MFA to identify bottlenecks in the bioconversion of glycerol into acetol by E. coli.15 The overexpression of nadK encoding NAD kinase or pntAB encoding membrane-bound transhydrogenase resulted in increasing the acetol titer from 0.91 g L−1 to 2.81 g L−1. In another study on metabolic engineering of E. coli strains with 13C-metabolic flux analysis, authors increased the isopropyl alcohol yield of 55% mol mol−1 glucose by combining nitrogen-starved culture conditions with metabolic redirection from glycolytic flux to the glucose 6-phosphate dehydrogenase (G6PDH) and Entner–Doudoroff (ED) pathway.158159 Schwechheimer et al. resolved carbon fluxes of a complex industrial process of riboflavin biosynthesis, using 13C isotope experiments, vegetable oil as raw material and the fungus Ashbya gossypii as main producer.160 The rational process optimisation resulted in a final riboflavin titer increase of 45%.Another important example of the MFA application in the metabolic engineering field is represented by the bioproduction of riboflavin, mainly used as food and feed additive but also as food colorant and pharmaceutical. The constant increase demand of this vitamin from the world market driven the research community to move its production from chemical synthesis to biotechnology strategies.13C-MFA plays an important role to elucidate the complex mechanism of toxicity161 and diseases,16 but also to better understand the effect of drugs and to unravel novel drug targets.162,163 Dong et al. proved that isotopic labelling and network analysis are very powerful tools for identifying the main metabolic pathways  in cancer cells.164 It has been demonstrated that metabolic disfunction is an essential hallmark of tumorigenesis, and MFA provides suitable framework to study human disease.165 Interestingly, the study of metabolic fluxes in human lung cells has already seen application for inhibiting the reproduction of SARS-CoV-2 through perturbations of metabolic network.166 Although the authors developed a computational approach, the predicted model can offer a platform for rapid formulation of drugs experimentally testable (through MFA) against this emerging virus responsible of the world pandemic started in 2019.Regarding the biomedical field, 313C-MFA can be applied to quantify actual cellular fluxes in prokaryotic organisms. New and non-invasive methods allow quantification of in vivo fluxes. The use of stable isotopes as 13C in MFA is becoming extremely important in the study of metabolic network having as main applications areas metabolic engineering, biomedical and pharmaceutical research. The development of analytical techniques such as mass spectrometry and NMR spectroscopy and the use of dedicated software tools highly increased the precision and the accuracy of flux analysis. New and increasingly sophisticated computational methods have found application in systems biology. Moreover, the use of a modelling procedure inspired by macro-kinetic models developed in the field of applied microbiology and based on a minimal number of processes to simulate the emergent properties of a biological system, should be explored in 13C-MFA. Indeed, further progress in metabolic flux analysis greatly depends on the improvement and co-application of analytical methods, software packages and optimised computational modelling.Metabolic flux analysis has emerged as a powerful technique for providing important quantitative information of biological systems otherwise unobtainable from other “omics” approaches. Based on well-known stoichiometry from scientific literature and experimental data, metabolic pathways of biological systems can be predicted in advanced and This work was supported by the Green Chemicals Beacon of Excellence, University of Nottingham.There are no conflicts to declare.