Metabolic Flux Analysis (MFA) PDF

Metabolic Flux Anaysis (MFA) Type of Metabolic Pathways Branched pathway A primary module of metabolic networks is the branched pathway which, together with the linear pathway, constitutes the major building blocks of most metabolic networks. The splitting ratios of branching fluxes at key nodes essentially determine the overall distribution of metabolic fluxes in most biological systems. Fig. Branched pathway models. Four types of branched biosynthetic pathways consisting of three metabolites: (a) simple irreversible branched pathway; (b) branched pathway with a reversible reaction (ν2); (c) branched pathway with feedback inhibition; and (d) branched pathway with feedback inhibition and crossover activation. Metabolite Balancing Stoichiometric matrix for Metabolite Balancing in MFA • Stoichiometric modeling framework is mostly characterized by MFA where a set of measured extracellular metabolite concentration rates of change are fitted to relatively simple mass balance models using the stoichiometric mass balance analysis to derive comprehensive metabolic flux maps. • Such quantitative maps have turned out to be extremely useful for comparing the effects of various stressors on metabolism, as they provide a global picture and understanding of the changes in relevant metabolic pathways. The flux distribution is calculated using the basic idea of MFA. The mass balances of all internal metabolites can be written as follows: Where, X is vector of metabolite concentrations, v is the flux distribution vector, and S is the stoichiometric matrix, where rows correspond to the metabolites and columns represent the reaction rates. Stoichiometric matrix for Metabolite Balancing in MFA where rows correspond to the metabolites and columns represent the reaction rates. P Q Fig. A small metabolic network The network includes four internal reactions (v1, v2, v3, and v4) and four external reactions (v5, v6, v7 and v8), which are given as “output” fluxes. The stoichiometric matrix (S) is constructed according to material balance equations. It is generally assumed that the internal metabolites are at a pseudo-steady state, because metabolic transients are rapid compared to environmental changes. Therefore, the mass balance is rewritten as follows: Metabolite Balancing Formulation of an FBA (a) A metabolic network reconstruction consists of a list of stoichiometrically balanced biochemical reactions. (b) This reconstruction is converted into a mathematical model by forming a matrix (labeled S), in which each row represents a metabolite and each column represents a reaction. Growth is incorporated into the reconstruction with a biomass reaction (yellow column), which simulates metabolites consumed during biomass production. Exchange reactions (green columns) are used to represent the flow of metabolites, such as glucose and oxygen, in and out of the cell. (c) At steady state, the flux through each reaction is given by Sv = 0, which defines a system of linear equations. As large models contain more reactions than metabolites, there is more than one possible solution to these equations. (d) Solving the equations to predict the maximum growth rate requires defining an objective function Z = cTv (c is a vector of weights indicating how much each reaction (v) contributes to the objective). In practice, when only one reaction, such as biomass production, is desired for maximization or minimization, c is a vector of zeros with a value of 1 at the position of the reaction of interest. In the growth example, the objective function is Z = vbiomass (that is, c has a value of 1 at the position of the biomass reaction). (e) Linear programming is used to identify a flux distribution that maximizes or minimizes the objective function within the space of allowable fluxes (blue region) defined by the constraints imposed by the mass balance equations and reaction bounds. The thick red arrow indicates the direction of increasing Z. As the optimal solution point lies as far in this direction as possible, the thin red arrows depict the process of linear programming, which identifies an optimal point at an edge or corner of the solution space Figure: Formulation of an FBA problem (Orth et al., 2010, Nature Biotechnology) Tracer Experiments, MS and NMR in labelling measurement • Metabolic flux can be inferred through the use of isotopic tracers along with Mass Spectrometry (MS) or Nuclear Magnetic Resonance (NMR). Stable-isotope-assisted methods like 13C-MFA (Metabolic Flux Analysis) is a gold standard for accurate and precise flux information. • The workflow for 13C-MFA first involves designing an optimal tracer experiment or metabolite labeling that is measured by MS or NMR. • To quantify fluxes at the systems levels i.e., at very large scales the tracer data is synthesized using computational models. That means labeling measurements are fit to a metabolic network model constructed based on large network databases like KEGG or BioCyc. • There are some computational programs that help reach the best global fit for flux estimation and this is followed by statistical analysis to evaluate the fit. This is an iterative process. Based on the result, if the solution is not optimal, the network could be modified or further labeling experiments could be proposed. • Other methods for quantifying metabolic fluxes in biological systems is the Classic MFA without tracer analysis, Flux Balance Analysis (FBA) and a broad range of constraint-based reconstruction and analysis (COBRA) methods. These techniques require no 13C-labeling experiments, so involves fewer experimental data. But, to constrain metabolic fluxes multiple modeling assumptions and simplifications must be applied. There are some established tools that help with FBA interactivity and visualization such as Escher-FBA and Fluxer. Bibliography de Falco B, Giannino F, Carteni F, et al (2022) Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 12:25528–25548. https://doi.org/10.1039/d2ra03326g Emwas AH, Szczepski K, Al-Younis I, et al (2022) Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Front Pharmacol 13:1–13. https://doi.org/10.3389/fphar.2022.805782 Kwilas AR, Donahue RN, Tsang KY, Hodge JW (2015) HHS Public Access. Cancer Cell 2:1–17. https://doi.org/10.1016/j.cell.2018.03.055.Metabolomics Nielsen J (2014) Bioreaction Engineering Principles Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614 https://biochem.web.utah.edu/iwasa/metabolism/chapter5.html Exercise 1. Write the stoichiometric matrix for the given reaction networks. (A) (B) Assignment-I Exercise-1 1. Write the stoichiometric matrix for the given reaction networks. (A) (B) primer What is flux balance analysis? Jeffrey D Orth, Ines Thiele & Bernhard Ø Palsson © 2010 Nature America, Inc. All rights reserved. Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations. F lux balance analysis (FBA) is a widely used approach for studying biochemical networks, in particular the genome-scale metabolic network reconstructions that have been built in the past decade1-4. These network reconstructions contain all of the known metabolic reactions in an organism and the genes that encode each enzyme. FBA calculates the flow of metabolites through this metabolic network, thereby making it possible to predict the growth rate of an organism or the rate of production of a biotechnologically important metabolite. With metabolic models for 35 organisms already available (http://systemsbiology.ucsd.edu/ In_Silico_Organisms/Other_Organisms) and high-throughput technologies enabling the construction of many more each year5-7, FBA is an important tool for harnessing the knowledge encoded in these models. In this primer, we illustrate the principles behind FBA by applying it to predict the maximum growth rate of Escherichia coli in the presence and absence of oxygen. The principles outlined can be applied in many other contexts to analyze the phenotypes and capabilities of organisms with different environmental and genetic perturbations (a Supplementary Tutorial provides ten additional worked examples with figures and computer code). Flux balance analysis is based on constraints The first step in FBA is to mathematically represent metabolic reactions (Box 1 and Fig. 1). Jeffrey D. Orth and Bernhard Ø. Palsson are at the University of California San Diego, La Jolla, California, USA. Ines Thiele is at the University of Iceland, Reykjavik, Iceland. e-mail: [email protected] The core feature of this representation is a tabulation, in the form of a numerical matrix, of the stoichiometric coefficients of each reaction (Fig. 2a,b). These stoichiometries impose constraints on the flow of metabolites through the network. Constraints such as these lie at the heart of FBA, differentiating the approach from theory-based models dependent on biophysical equations that require many difficultto-measure kinetic parameters8,9. Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system. The matrix of stoichiometries imposes flux (that is, mass) balance constraints on the system, ensuring that the total amount of any compound being produced must be equal to the total amount being consumed at steady state (Fig. 2c). Every reaction can also be given upper and lower bounds, which define the maximum and minimum allowable fluxes of the reactions. These balances and bounds define the space of allowable flux distributions of a system—that is, the rates at which every metabolite is consumed or produced by each reaction. Other constraints can also be added10. From constraints to optimizing a phenotype The next step in FBA is to define a phenotype in the form of a biological objective that is relevant to the problem being studied (Fig. 2d). In the case of predicting growth, the objective is biomass production—that is, the rate at which metabolic compounds are converted into biomass constituents such as nucleic acids, proteins and lipids. Biomass production is mathematically represented by adding an artificial ‘biomass reaction’— that is, an extra column of coefficients in the nature biotechnology volume 28 number 3 march 2010 matrix of stoichiometries—that consumes precursor metabolites at stoichiometries that simulate biomass production. The biomass reaction is based on experimental measurements of biomass components. This reaction is scaled so that the flux through it is equal to the exponential growth rate (µ) of the organism. Now that biomass is represented in the model, predicting the maximum growth rate can be accomplished by calculating the conditions that result in the maximum flux through the biomass reaction. In other cases, more than one reaction may contribute to the phenotype of interest. Mathematically, an ‘objective function’ is used to quantitatively define how much each reaction contributes to the phenotype. Taken together, the mathematical representations of the metabolic reactions and of the objective define a system of linear equations. In flux balance analysis, these equations are solved using linear programming (Fig. 2e). Many computational linear programming algorithms exist, and they can very quickly identify optimal solutions to large systems of equations. The COBRA Toolbox11 is a freely available Matlab toolbox for performing these calculations (Box 2). Suppose we want to calculate the maximum aerobic growth of E. coli under the assumption that uptake of glucose, and not oxygen, is the limiting constraint on growth. This calculation can be performed using a published model of E. coli metabolism12. In addition to metabolic reactions and the biomass reaction discussed above, this model also includes reactions that represent glucose and oxygen uptake into the cell. The assumptions are mathematically represented by setting the maximum rate of glucose uptake to a physiologically realistic level (18.5 mmol 245 p r ime r © 2010 Nature America, Inc. All rights reserved. Box 1 Mathematical representation of metabolism Metabolic reactions are represented as a stoichiometric matrix (S) of size m × n. Every row of this matrix represents one unique compound (for a system with m compounds) and every column represents one reaction (n reactions). The entries in each column are the stoichiometric coefficients of the metabolites participating in a reaction. There is a negative coefficient for every metabolite consumed and a positive coefficient for every metabolite that is produced. A stoichiometric coefficient of zero is used for every metabolite that does not participate in a particular reaction. S is a sparse matrix because most biochemical reactions involve only a few different metabolites. The flux through all of the reactions in a network is represented by the vector v, which has a length of n. The concentrations of all metabolites are represented by the vector x, with length m. The system of mass balance equations at steady state (dx/dt = 0) is given in Fig. 2c26: Sv = 0 Any v that satisfies this equation is said to be in the null space of S. In any realistic large-scale metabolic model, there are more reactions than there are compounds (n > m). In other words, there are more unknown variables than equations, so there is no unique solution to this system of equations. Although constraints define a range of solutions, it is still possible to identify and glucose gDW–1 h–1; DW, dry weight) and setting the maximum rate of oxygen uptake to an arbitrarily high level, so that it does not limit growth. Then, linear programming is used to determine the maximum possible flux through the biomass reaction, resulting in a predicted exponential growth rate of 1.65 h–1. Anerobic growth of E. coli can be calculated by constraining the maximum rate of uptake of oxygen to zero and solving the system of equations, resulting in a predicted growth rate of 0.47 h–1 (see Supplementary Tutorial for computer code). As these two examples show, FBA can be used to perform simulations under different conditions by altering the constraints on a model. To change the environmental conditions (such as substrate availability), we change the bounds on exchange reactions (that is, reactions representing metabolites flowing into and out of the system). Substrates that are not available are 246 v3 v3 v3 Optimization maximize Z Constraints 1) Sv = 0 2) a i < v i < b i v1 v1 Unconstrained solution space v2 v1 Allowable solution space v2 Optimal solution v2 Figure 1 The conceptual basis of constraint-based modeling. With no constraints, the flux distribution of a biological network may lie at any point in a solution space. When mass balance constraints imposed by the stoichiometric matrix S (labeled 1) and capacity constraints imposed by the lower and upper bounds (ai and bi) (labeled 2) are applied to a network, it defines an allowable solution space. The network may acquire any flux distribution within this space, but points outside this space are denied by the constraints. Through optimization of an objective function, FBA can identify a single optimal flux distribution that lies on the edge of the allowable solution space. analyze single points within the solution space. For example, we may be interested in identifying which point corresponds to the maximum growth rate or to maximum ATP production of an organism, given its particular set of constraints. FBA is one method for identifying such optimal points within a constrained space (Fig. 1). FBA seeks to maximize or minimize an objective function Z = cTv, which can be any linear combination of fluxes, where c is a vector of weights indicating how much each reaction (such as the biomass reaction when simulating maximum growth) contributes to the objective constrained to an uptake rate of 0 mmol gDW–1 h–1. Constraints can also be tailored to the organism being studied, with lower bounds of 0 mmol gDW–1 h–1 used to simulate reactions that are irreversible in some organisms. Nonzero lower bounds can also force a minimal flux through artificial reactions (like the biomass reaction) such as the ‘ATP maintenance reaction’, which is a balanced ATP hydrolysis reaction used to simulate energy demands not associated with growth13. Constraints can even be used to simulate gene knockouts by limiting reactions to zero flux. FBA does not require kinetic parameters and can be computed very quickly even for large networks. This makes it well suited to studies that characterize many different perturbations such as different substrates or genetic manipulations. An example of such a case is given in example 6 in Supplementary Tutorial, which explores the effects on function. In practice, when only one reaction is desired for maximization or minimization, c is a vector of zeros with a value of 1 at the position of the reaction of interest (Fig. 2d). Optimization of such a system is accomplished by linear programming (Fig. 2e). FBA can thus be defined as the use of linear programming to solve the equation Sv = 0, given a set of upper and lower bounds on v and a linear combination of fluxes as an objective function. The output of FBA is a particular flux distribution, v, which maximizes or minimizes the objective function. growth of deleting every pairwise combination of 136 E. coli genes to find double gene knockouts that are essential for survival of the bacteria. FBA has limitations, however. Because it does not use kinetic parameters, it cannot predict metabolite concentrations. It is also only suitable for determining fluxes at steady state. Except in some modified forms, FBA does not account for regulatory effects such as activation of enzymes by protein kinases or regulation of gene expression. Therefore, its predictions may not always be accurate. The many uses of flux balance analysis Because the fundamentals of flux balance analysis are simple, the method has found diverse uses in physiological studies, gap-filling efforts and genome-scale synthetic biology3. By altering the bounds on certain reactions, growth on different media (example 1 in Supplementary Tutorial) or of bacteria with multiple gene volume 28 number 3 march 2010 nature biotechnology © 2010 Nature America, Inc. All rights reserved. p r ime r knockouts (example 6 in Supplementary Tutorial) can be simulated14. FBA can then be used to predict the yields of important cofactors such as ATP, NADH, or NADPH15 (example 2 in Supplementary Tutorial). Whereas the example described here yielded a single optimal growth phenotype, in large metabolic networks, it is often possible for more than one solution to lead to the same desired optimal growth rate. For example, an organism may have two redundant pathways that both generate the same amount of ATP, so either pathway could be used when maximum ATP production is the desired phenotype. Such alternate optimal solutions can be identified through flux variability analysis, a method that uses FBA to maximize and minimize every reaction in a network16 (example 3 in Supplementary Tutorial), or by using a mixed-integer linear programming–based algorithm17. More detailed phenotypic studies can be performed such as robustness analysis18, in which the effect on the objective function of varying a particular reaction flux can be analyzed (example 4 in Supplementary Tutorial). A B+C B + 2C D Genome-scale metabolic reconstruction Reactions b 1 2 Mathematically represent metabolic reactions and constraints A B C D ... n Bi o Gl mas uc s Ox ose yg en a Met abolit es Figure 2 Formulation of an FBA problem. (a) A metabolic network reconstruction consists of a list of stoichiometrically balanced biochemical reactions. (b) This reconstruction is converted into a mathematical model by forming a matrix (labeled S), in which each row represents a metabolite and each column represents a reaction. Growth is incorporated into the reconstruction with a biomass reaction (yellow column), which simulates metabolites consumed during biomass production. Exchange reactions (green columns) are used to represent the flow of metabolites, such as glucose and oxygen, in and out of the cell. (c) At steady state, the flux through each reaction is given by Sv = 0, which defines a system of linear equations. As large models contain more reactions than metabolites, there is more than one possible solution to these equations. (d) Solving the equations to predict the maximum growth rate requires defining an objective function Z = cTv (c is a vector of weights indicating how much each reaction (v) contributes to the objective). In practice, when only one reaction, such as biomass production, is desired for maximization or minimization, c is a vector of zeros with a value of 1 at the position of the reaction of interest. In the growth example, the objective function is Z = vbiomass (that is, c has a value of 1 at the position of the biomass reaction). (e) Linear programming is used to identify a flux distribution that maximizes or minimizes the objective function within the space of allowable fluxes (blue region) defined by the constraints imposed by the mass balance equations and reaction bounds. The thick red arrow indicates the direction of increasing Z. As the optimal solution point lies as far in this direction as possible, the thin red arrows depict the process of linear programming, which identifies an optimal point at an edge or corner of the solution space. ... * –1 –1 m d Mass balance defines a system of linear equations = 0 vn vbiomass vglucose voxygen –v1 + ... = 0 v1 – v2 + ... = 0 v1 – 2v2 + ... = 0 v2 + ... = 0 etc. Define objective function (Z = c1* v1 + c2* v2 ... ) To predict growth, Z = v biomass v2 e ... Fluxes, v Stoichiometric matrix, S c v1 v2 –1 1 –1 1 –2 1 Reaction 1 Reaction 2 ... Reaction n Z Point of optimal v Calculate fluxes that maximize Z Solution space defined by constraints v1 A more advanced form of robustness analysis involves varying two fluxes simultaneously to form a phenotypic phase plane19 (example 5 in Supplementary Tutorial). All genome-scale metabolic reconstructions are incomplete, as they contain ‘knowledge gaps’ where reactions are missing. FBA is the basis for several algorithms that predict which reactions are missing by comparing in silico growth simulations to experimental results20-22. Constraint-based models can also be used for metabolic engineering where FBA-based algorithms, such as OptKnock23, can predict gene knockouts that allow an organism to produce desirable compounds24,25. Box 2 Tools for FBA FBA computations, which fall into the category of constraint-based reconstruction and analysis (COBRA) methods, can be performed using several available tools 27-29. The COBRA Toolbox11 is a freely available Matlab toolbox (http://systemsbiology.ucsd.edu/ Downloads/Cobra_Toolbox) that can be used to perform a variety of COBRA methods, including many FBA-based methods. Models for the COBRA Toolbox are saved in the Systems Biology Markup Language (SBML) 30 format and can be loaded with the function ‘readCbModel’. The E. coli core model used in this Primer is available at http://systemsbiology.ucsd.edu/Downloads/E_coli_Core/. In Matlab, the models are structures with fields, such as ‘rxns’ (a list of all reaction names), ‘mets’ (a list of all metabolite names) and ‘S’ (the stoichiometric matrix). The function ‘optimizeCbModel’ is used to perform FBA. To change the bounds on reactions, use the function ‘changeRxnBounds’. The Supplementary Tutorial contains examples of COBRA toolbox code for performing FBA, as well as several additional types of constraint-based analysis. nature biotechnology volume 28 number 3 march 2010 247 p r ime r Ultimately, FBA produces predictions that must be verified. Experimental studies are used as part of the model reconstruction process and to validate model predictions. Studies have shown that growth rates of E. coli on several different substrates predicted by FBA agree well with those obtained by experimental measurements14. Model-based predictions of gene essentiality have also been shown to be quite accurate2. This primer and the accompanying tutorials based on the COBRA toolbox (Box 2) should help those interested in harnessing the growing cadre of genome-scale metabolic reconstructions that are becoming available. © 2010 Nature America, Inc. All rights reserved. ACKNOWLEDGMENTS This work was supported by National Institutes of Health grant no. R01 GM057089. 1. Duarte, N.C. et al. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007). 2. Feist, A.M. et al. Mol. Syst. Biol. 3, 121 (2007). 3. Feist, A.M. & Palsson, B.O. Nat. Biotechnol. 26, 248 659–667 (2008). 4. Oberhardt, M.A., Palsson, B.O. & Papin, J.A. Mol. Syst. Biol. 5, 320 (2009). 5. Thiele, I. & Palsson, B.O. Nat. Protoc. 5, 93–121 (2010). 6. Feist, A.M., Herrgard, M.J., Thiele, I., Reed, J.L. & Palsson, B.O. Nat. Rev. Microbiol. 7, 129–143 (2009). 7. Durot, M., Bourguignon, P.Y. & Schachter, V. FEMS Microbiol. Rev. 33, 164–190 (2009). 8. Covert, M.W. et al. Trends Biochem. Sci. 26, 179– 186 (2001). 9. Edwards, J.S., Covert, M. & Palsson, B. Environ. Microbiol. 4, 133–140 (2002). 10. Price, N.D., Reed, J.L. & Palsson, B.O. Nat. Rev. Microbiol. 2, 886–897 (2004). 11. Becker, S.A. et al. Nat. Protoc. 2, 727–738 (2007). 12. Orth, J.D., Fleming, R.M. & Palsson, B.O. in EcoSal —Escherichia coli and Salmonella Cellular and Molecular Biology (ed. Karp, P.D.) (ASM Press, Washington, DC, 2009). 13. Varma, A. & Palsson, B.O. Biotechnol. Bioeng. 45, 69–79 (1995). 14. Edwards, J.S., Ibarra, R.U. & Palsson, B.O. Nat. Biotechnol. 19, 125–130 (2001). 15. Varma, A. & Palsson, B.O. J. Theor. Biol. 165, 477– 502 (1993). 16. Mahadevan, R. & Schilling, C.H. Metab. Eng. 5, 264–276 (2003). 17. Lee, S., Phalakornkule, C., Domach, M.M. & Grossmann, I.E. Comput. Chem. Eng. 24, 711–716 (2000). 18. Edwards, J.S. & Palsson, B.O. Biotechnol. Prog. 16, 927–939 (2000). 19. Edwards, J.S., Ramakrishna, R. & Palsson, B.O. Biotechnol. Bioeng. 77, 27–36 (2002). 20. Reed, J.L. et al. Proc. Natl. Acad. Sci. USA 103, 17480–17484 (2006). 21. Kumar, V.S. & Maranas, C.D. PLoS Comput. Biol. 5, e1000308 (2009). 22. Satish Kumar, V., Dasika, M.S. & Maranas, C.D. BMC Bioinformatics 8, 212 (2007). 23. Burgard, A.P., Pharkya, P. & Maranas, C.D. Biotechnol. Bioeng. 84, 647–657 (2003). 24. Feist, A.M. et al. Metab. Eng. published online, doi:10.1016/j.ymben.2009.10.003 (17 October 2009). 25. Park, J.M., Kim, T.Y. & Lee, S.Y. Biotechnol. Adv. 27, 979–988 (2009). 26. Palsson, B.O. Systems Biology: Properties of Reconstructed Networks (Cambridge University Press, New York, 2006). 27. Jung, T.S., Yeo, H.C., Reddy, S.G., Cho, W.S. & Lee, D.Y. Bioinformatics 25, 2850–2852 (2009). 28. Klamt, S., Saez-Rodriguez, J. & Gilles, E.D. BMC Syst. Biol. 1, 2 (2007). 29. Lee, D.Y., Yun, H., Park, S. & Lee, S.Y. Bioinformatics 19, 2144–2146 (2003). 30. Hucka, M. et al. Bioinformatics 19, 524–531 (2003). volume 28 number 3 march 2010 nature biotechnology REVIEW published: 21 March 2022 doi: 10.3389/fphar.2022.805782 Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways Abdul-Hamid Emwas 1*, Kacper Szczepski 2, Inas Al-Younis 3, Joanna Izabela Lachowicz 4 and Mariusz Jaremko 2 1 King Abdullah University of Science and Technology, Core Labs, Thuwal, Saudi Arabia, 2Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, 3King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences & Engineering Division (BESE), Thuwal, Saudi Arabia, 4Department of Medical Sciences and Public Health, University of Cagliari, Cittadella Universitaria, Monserrato, Italy Edited by: Giuseppe Lucarelli, University of Bari Aldo Moro, Italy Reviewed by: Xinwen Wang, Northeast Ohio Medical University, United States Junzeng Zhang, National Research Council Canada (NRC-CNRC), Canada Fluxomics is an innovative -omics research ﬁeld that measures the rates of all intracellular ﬂuxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics ﬁelds, portraying the whole picture of molecular interactions. Recently, ﬂuxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic ﬂux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and proteinmetabolite interaction networks in actual time. Thus, ﬂuxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main ﬂuxomics approaches and experimental platforms. Moreover, we summarize recent ﬂuxomic results in different biological systems. Keywords: ﬂuxomics, metabolomics, nuclear magnetic resonance (NMR), mass spectrometry (MS), ﬂux, pharmacometabolomics INTRODUCTION *Correspondence: Abdul-Hamid Emwas [email protected] Specialty section: This article was submitted to Translational Pharmacology, a section of the journal Frontiers in Pharmacology Received: 30 October 2021 Accepted: 24 January 2022 Published: 21 March 2022 Citation: Emwas A-H, Szczepski K, Al-Younis I, Lachowicz JI and Jaremko M (2022) Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Front. Pharmacol. 13:805782. doi: 10.3389/fphar.2022.805782 Throughout recent decades discoveries explaining the complex nature of the cell have provided the scientiﬁc community with an immense amount of data. As more information has been revealed, the need for classiﬁcation and quantiﬁcation of this data has resulted in the creation of various–omics ﬁelds. This approach recognizes whole systems, rather than groups of separated processes (VailatiRiboni et al., 2017). Many types of–omics have been created, the most prominent being genomics, transcriptomics, proteomics and metabolomics. All of these ﬁelds are part of systems biology - a strategy used to examine the interactions, relationships and behavior between all system constituents (Ideker et al., 2001). However, even though the fundamental–omics approaches focus only on their system of interest (e.g. the genome for genomics, or the proteome for proteomics), their constituents are connected. For example, the ﬁeld of proteomics exists as the directional effect of transcriptomics that is further inﬂuenced by genomics. Given these factors, a new discipline called ﬂuxomics emerged that connects genomics, transcriptomics, proteomics and metabolomics. Although a new addition to the–omics family, ﬂuxomics studies have been steadily increasing over the past 2 decades (Figure 1). Recent examples of ﬂuxomics studies are shown in Supplementary Table S1. The emerging importance of ﬂuxomics is reﬂected not only by the amount of research articles published every year but also through its potential applications in industrial biotechnology and Frontiers in Pharmacology | www.frontiersin.org 1 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach FIGURE 1 | Number of ﬂuxomic publications. A literature review was conducted on SciFinder (https://sciﬁnder.cas.org/sciﬁnder/view/sciﬁnder/sciﬁnderExplore.jsf) using the keyword ﬂuxomics. pharmacology (Feng et al., 2010; Wojtowicz and Mlynarz, 2016; Hansen et al., 2017; Emwas, 2021). Several recent studies used ﬂuxomics as an alternative approach in the ﬁeld of drug discovery, by targeting bacterial metabolic pathways distinct from human metabolic routes. Viral and bacterial infection depends on the ability of pathogens to convert nutrients into energy (e.g., ATP) (Eisenreich, 2021). Importantly, bacteria have partially distinct metabolic pathways compared to their human host cells (Rohmer et al., 2011). Selective inhibition of differential mechanisms is unlikely to have major side effects in humans. Innovative drug therapies that reprogram the core carbon metabolism of human infections make bacteria more susceptible to antibiotics (Liu et al., 2019a; Stokes et al., 2019). A recent study examined the metabolomic proﬁle of Vibrio alginolyticus, which is resistant to cephalosporin antibiotics, and the role of bacterial metabolism in drug and multidrug resistance. This was achieved by detecting the metabolic differences of acetyl-CoA ﬂuxes into and through the P-cycle and fatty acid biosynthesis (Liu et al., 2019b). These ﬁndings shed light on ceftazidime (CAZ) and other antibiotic resistance pathways, as well as multidrug resistance of Vibrio and other pathogens. A combined metabolomics and ﬂuxomics approach was used in studies of Leishmania infantum promastigotes. The origin of the detected alterations was analyzed with untargeted analysis of metabolic snapshots (of treated and untreated parasites), both resistant and responders, and by using a 13C traceability experiment (Rojo et al., 2015). This showed a signiﬁcant shift in amino acid metabolism, and multi-target metabolic change as a result of treatment, particularly affecting the cell redox Frontiers in Pharmacology | www.frontiersin.org system, which is critical for detoxiﬁcation and biosynthetic activities (Rojo et al., 2015). Although there are costs and current challenges associated with ﬂuxomics approaches, there have been studies supporting its use in models such as Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae or Pichia pastoris (Feng et al., 2010; Zahrl et al., 2017). These studies provided information such as optimal fermentation conditions, improved ethanol and riboﬂavin production and better yield in protein expression (Feng et al., 2010; Zahrl et al., 2017; Choi et al., 2018). The constant development and improvement of analytical tools and methodologies in ﬂuxomics will only increase its future prevalence (Wiechert et al., 2001; Beyß, 2019; Foguet et al., 2019; Giraudeau, 2020). ADVANTAGES AND DISADVANTAGES OF CHROMATOGRAPHY AND NUCLEAR MAGNETIC RESONANCE TOOLS IN FLUXOMICS Similar to other -omics ﬁelds, ﬂuxomics is a technology driven ﬁeld where recent advances in instrumentation, software and databases have signiﬁcantly contributed to development. Different analytical tools and approaches in ﬂuxomics have been reviewed recently (Wiechert et al., 2007; Niittylae et al., 2009; Klein and Heinzle, 2012; Winter and Krömer, 2013; Niedenführ et al., 2015). Even if different analytical tools are utilized in ﬂuxomics/metabolomics research, nuclear magnetic resonance (NMR) spectroscopy (Giraudeau, 2020) and mass spectrometry (MS) (Wiechert et al., 2007; Choi and 2 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach Antoniewicz, 2019; Babele and Young, 2020) are the most commonly used tools in metabolomic studies. Each applied analytical platform, either NMR or MS, has its strength, advantages and limitations. For example, gas chromatography-mass spectrometry (GC-MS) is commonly used in ﬂuxomics analyses but is only applicable for volatile metabolites or ones that can be treated to become volatile compounds through derivatization processes. Liquid chromatography-mass spectrometry (LC-MS) provides potent approaches that offer combined sensitivity and selectivity. MS approaches such as different ionization modes (positive or negative) or mass analyzer technology can be used to increase the number of detected metabolites. Nevertheless, chromatographic experiments require speciﬁc sample pretreatment, have limited experimental time scales, and do not depict the 3D structure or interactions of the molecule. Beside its exceptionally high sensitivity, mass spectrometry is usually combined with other powerful analytical platforms, mainly gas chromatography (GC) or liquid chromatography (LC), bringing powerful advantages that can overcome both peak overlaps and the low sensitivities of NMR approaches (Kvitvang et al., 2014; Kvitvang and Bruheim, 2015; Lien et al., 2015; Sá et al., 2017). NMR is a non-destructive, non-selective and fast method that has been widely used for molecular identiﬁcation and structural elucidation used with minimal sample preparation requirements (Atiqullah et al., 2015; Alahmari et al., 2019; Dhahri et al., 2020). While the sample is placed in a static magnetic ﬁeld, it can be recovered for future analysis using other techniques and it is possible to obtain spectral results regarding how molecules move, ﬂex, react, appear/disappear, or bind with other molecules over several time scales, providing an optimum approach for ﬂuxomics (Blindauer et al., 1997; Wolak et al., 2012; Nargund et al., 2013; Davaasuren et al., 2017). Thanks to the unique features brieﬂy mentioned above, NMR is one of the main analytical techniques in metabolomics, and as such it is crucial to accurately highlight its advantages and limitations for different metabolomics applications (Emwas FIGURE 2 | The relationships between each of the “-omics”. Each of the arrows shows the direction in which a particular “-omic” inﬂuences another. In the case of ﬂuxomics, it combines all approaches, granting better understanding. Dauner describes observed ﬂux/activity as a two component - capacity-based and kinetics-based - regulation (Figure 3). Created with Biorender.com. Frontiers in Pharmacology | www.frontiersin.org 3 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach et al., 2019). NMR spectroscopy, particularly hydrogen detection NMR (commonly referred to as proton or 1H-NMR spectroscopy) can be inherently quantitative, providing a potent analytical tool for metabolomics studies (Dona et al., 2016; Markley et al., 2017). In comparison to other analytical platforms such as GC-MS and LC-MS (Ciborowski et al., 2012; Guo et al., 2013; Raji et al., 2013; Liu et al., 2014), NMR does not require extra steps for sample preparation or metabolite isolation prior to measurement, such as chromatographic separation and/ or chemical derivatization. On the other hand, spectral overlap and low signal sensitivity are still the main limitations of NMR approaches, and detection of metabolites at very low concentrations is still beyond the capability of even the most sensitive NMR technologies (Emwas and Bjerrum, 2015). Even if NMR spectroscopy offers indisputable advantages, low sensitivity is still its main limitation in ﬂuxomic research (Emwas et al., 2013; Clendinen et al., 2014; Emwas et al., 2016; Giraudeau, 2020). Overlapping of peaks is also a major challenge in peak assignment, limiting the number of metabolites that can be identiﬁed by NMR spectroscopy (Emwas et al., 2018; O’Rourke et al., 2018; Giraudeau, 2020). The sensitivity of NMR spectroscopy has been improved signiﬁcantly by dynamic-nuclear polarization (DNP) (Ardenkjær-Larsen et al., 2003; Emwas et al., 2008; Ludwig et al., 2010), cryo-probes, ultrahigh magnetic ﬁelds (Deborde et al., 2017; Emwas et al., 2019), and the development of new faster methods. However, sensitivity remains a main limitation in the ﬁeld (Emwas et al., 2019; Robertson et al., 2020; Chandra et al., 2021). For instance, secondary metabolites (usually existing at very low concentrations) are beyond the detection limit of NMR spectroscopy, while for volatile molecules can be detected by GC-MS combined with the mass spectrum and retention time (Emwas et al., 2015; Kohlstedt and Wittmann, 2019). Thus, integrating NMR spectroscopy with MS methods is important to give more comprehensive analysis (Fan et al., 2014; Elbaz et al., 2015; Emwas and Bjerrum, 2015; Sá et al., 2017; Bergès et al., 2021). FLUXOMICS Fluxomics is a new metabolomics application, which is focused on actual rates within metabolic networks. Since the reaction rates (ﬂuxes) of metabolic pathways cannot be measured directly due to the intrinsic properties of metabolism such as dynamics, the ﬂuxes can be measured indirectly by the shifts in metabolite levels (Cascante and Marin, 2008; Winter and Krömer, 2013). What distinguishes ﬂuxomics from other–omics is the fact that the ﬂuxome (total set of ﬂuxes in metabolic network of a cell) occur as a resultant of all other “–omes” combined (mainly the proteome and the metabolome). While the genome, transcriptome, proteome and metabolome focus only on their own elements–for example the interactions between proteins in the proteome–the ﬂuxome captures the real and dynamic picture of phenotypes by observing the interactions between all of the “-omes”, therefore granting a unique synergistic insight (Cascante and Marin, 2008; Aon and Cortassa, 2015) (Figure 2). Capacity-based regulation is a function related strictly to gene regulatory processes of the cell. Those processes such as enzyme production and stability within the cell (Ei) will differ, depending on the cell and its function in a multicellular organism. As for kinetic regulation, it is a function of kinetic parameters of FIGURE 3 | Observed ﬂux/activity a of a reaction step I. Adapted with permission from (Dauner, 2010). Frontiers in Pharmacology | www.frontiersin.org 4 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach enzymes catalyzing the reaction (k) (accounting also for enzyme modiﬁcations such as phosphorylation), concentration of substrate (S) and product (P) and effector/signaling molecules (I). (Dauner, 2010). Those variables can be measured by using e.g. quantitative proteomics to calculate enzyme concentration (Ei) (Ong et al., 2003; Mann, 2006; Winter and Krömer, 2013) and quantitative metabolomics for substrate, product and effector concentrations (Winter and Krömer, 2013). The type of approach used to describe the metabolic network will depend on its nature. For example, metabolic ﬂux analysis (MFA) identiﬁes the whole set of ﬂuxes in a part of the metabolic network of a microorganism in vivo (Wiechert et al., 2005). Information about ﬂuxes is obtained by assuming an intracellular pseudo-steady state (a state, where intracellular metabolites do not accumulate in the cell and the balance between the consumption and production ﬂuxes of a metabolite is in equilibrium) and reaction stoichiometry (a ﬁxed conﬁguration of the metabolic network that does not account for cell adaptation to the environmental changes), to estimate the balances around intracellular metabolites, by calculating the uptake rates of substrates and secretion rates of metabolites (Stephanopoulos et al., 1998; Provost and Bastin, 2006; Antoniewicz, 2015). Those rates are measured by monitoring external rate changes such as substrate consumption (glucose uptake rate), biomass synthesis (growth rate), energy consumption and production (CO2 evolution rate), and metabolite production. The ﬁnal result is a metabolic ﬂux map with an estimate of the ﬂux of each reaction (Figure 4). For mathematical explanation of the ﬂux calculation, the reader is referred to (Stephanopoulos et al., 1998; Provost and Bastin, 2006; Shimizu and Shimizu, 2013). A variant of MFA called dynamic metabolic ﬂux analysis (DMFA) focuses on describing metabolic ﬂuxes in a metabolic non-steady state, in which a time-series of extracellular concentration and rate measurements are used. In this approach the experiment is divided into a set of time intervals from which the external rates are calculated for each time interval. Then the results are averaged and combined to obtain a time proﬁle of related ﬂuxes (Antoniewicz, 2013; Antoniewicz, 2015). FIGURE 4 | Example of a ﬂux map, representing a metabolic ﬂux distribution of Chlorella cells in autotrophic cultures. The ﬂux values are expressed in mmol/g/h. Adapted with permission from (Shimizu and Shimizu, 2013). Frontiers in Pharmacology | www.frontiersin.org 5 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach FIGURE 5 | The basics of MFA and FBA approaches. S is the stoichiometric matrix, v is the ﬂux vector, r is the external metabolic rates. In MFA, ﬂuxes are calculated by ﬁtting extracellular rates measured experimentally. In FBA, a ﬂux solution space is determined by assuming a biological objective, for example, maximization of growth rate, and solving a linear optimization problem. Adapted with permission from (Antoniewicz, 2015). Another approach to describe a metabolic network is called ﬂux balance analysis (FBA). When compared to MFA, FBA works on a broader scale, and it enables reconstruction of a metabolic Frontiers in Pharmacology | www.frontiersin.org network on the genome-scale level. These reconstructions utilize all information about metabolic reactions in an organism and the genes that encode each enzyme. However, this approach does not 6 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach FIGURE 6 | Summary of the most used techniques within ﬂuxomic studies. FIGURE 7 | Summary of the most used organisms within ﬂuxomic studies. count for regulatory interactions and detailed kinetics, giving only partial biological information of the situation of systems at steady state. To obtain ﬂuxes from FBA, ﬁrst a reconstructed metabolic network must be converted to a mathematical matrix. Within this Frontiers in Pharmacology | www.frontiersin.org matrix, a set of constraints is imposed by mass balance equations and reaction bounds. Then, based on the biological objective (e.g., biomass production), linear programming is used to determine the sought ﬂuxes by either maximizing or minimizing objective 7 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach FIGURE 8 | Summary of the commonly described pathways within ﬂuxomic studies. function while considering given constrains (Orth et al., 2010; Antoniewicz, 2015; Aon and Cortassa, 2015). The differences between the MFA and FBA approaches are shown in Figure 5. Extracellular ﬂuxes between different cells and their environment can also be determined by using 13C-isotope substrate followed by NMR monitoring of 13C-labeled metabolite propagation through the metabolic intermediates in certain metabolic pathways. 13C-labeled ﬂuxomics is an extension of FBA in which all the precursors (substrates) used by the cells are 13C-enriched. Consumed substrates are later incorporated into the metabolic pathways connected to the used substrate. The level of incorporation will depend on intracellular ﬂuxes that could be measured using NMR and/or MS. The information obtained from those experiments can be utilized to discriminate metabolic variants (isotopic proﬁling), measure speciﬁc ﬂuxes (targeted ﬂux analysis–TFA) and investigate the whole ﬂuxome (global ﬂuxomics) (Wiechert et al., 2001; Krömer et al., 2008; Heux et al., 2017). For example, GC-MS and NMR were employed to monitor metabolic ﬂux in neural stem cells (NSCs) using labeled carbon -13C glucose. By following 13C labeling pattern and monitoring an isotopic non-stationary metabolic ﬂux analysis, it was demonstrated that pyruvate entered the tricarboxylic acid (TCA) cycle mostly through Frontiers in Pharmacology | www.frontiersin.org pyruvate carboxylase (81%) (Sá et al., 2017). Another practical example of isotope labelling is to identify isotopomers (one of the different labeling states in which a particular metabolite can be encountered). The isotopomer redistribution of a metabolite is calculated based on the percentage value of each isotopomer within the metabolite pool. The information obtained from such an approach describes how the various isotopomers react with each other (Wiechert et al., 2001). Isotopic labelling is not limited only to 13C. Other elements such as 15N, 18O or 31P can also be used to study, e.g. nitrogen metabolism and muscle energetics (Klein and Heinzle, 2012; Nemutlu, 2015). Isotope labelling was recently used to determine whether pyruvate or glutamine are anaplerotic sources requiring pyruvate carboxylase (PC) and glutaminase 1 (GLS1) activity. Sellers et al. (Sellers et al., 2015) utilized NMR-based metabolomics approaches to monitor the Krebs cycle of patients with early-stage non–small-cell lung cancer (NSCLC) infused with uniformly 13C-labeled glucose followed by tissue resection. NMR analysis of patient cancerous tissues showed enhancement of pyruvate carboxylase (PC) activity. Furthermore, results from patient cancer tissues cultured in 13C6-glucose or 13C5,15N2glutamine tracers provided clear evidence of selective activation of PC over glutaminase (GLS) in NSCLC (Sellers et al., 2015). 8 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach Another prominent example of isotope labelling used for ﬂuxomics is work by Cocuron et. al. (Cocuron et al., 2019), comparing the metabolism of two different maize lines - Alex and LH59. The goal of this work was to test if a change in carbon metabolism may increase oil content in maize kernels to help sustain the demand for vegetable oil. Cocuron et al. labeled Alex embryos with 13C labeled glucose and utilized NMR, GC-MS and LC-MS/MS to measure carbon ﬂow through the metabolic network (13C-MFA). Alex line embryos (which accumulate more oil when compared to LH59) increased the amount of Glucose 6-phosphate (G6P) entering into the plastid, the aldolase in the plastid, the export of TPs (Glyceraldehyde 3-phosphate) to the cytosol, the glycolytic ﬂux in the cytosol, Phosphoenolpyruvate carboxylase (PEPC), and plastidic malic enzyme. It was concluded that increasing the levels of plastidic malic enzyme should enhance the fatty acid content of seeds (Cocuron et al., 2019). In the recent studies, Bergès et al. utilized both NMR and MS approaches to obtain high resolution ﬂuxotypes for huge numbers of a strains in a library. They deﬁne ﬂuxotype as “the particular distribution of metabolic ﬂuxes measured for a given strain under given physiological conditions” (Bergès et al., 2021). The authors studied the ﬂuxotype of 180 different E. coli strains with deleted y-genes. Bacteria were grown in 13C labeled glucose as a single source of carbon while monitoring metabolic ﬂuxes. Deletion of two y-genes led to a signiﬁcant modiﬁcation of metabolic ﬂuxes indicating the role of the studied genes in metabolic regulation (Bergès et al., 2021). Both NMR and MS have been frequently used to investigate the impact of ﬂuxomics in drug delivery and pharmacology. For instance, the production of artemisinic acid in an engineered E. coli strain that encodes S. cerevisiae enzymes allows the cell to enter the mevalonate pathway and supplement endogenous isopentenyl pyrophosphate (IPP) biosynthesis. This then enhances the production of the antimalarial drug artemisinin. This shift in pathways relies on the ﬂux rate and metabolites concentration (Ro et al., 2006). In addition, the emergence of multi-drug resistant strains of tuberculosis provides a need to develop additional medications for disease treatment. The application of ﬂuxomics to target metabolic enzymes and genome-scale models can be used for analysis, discovery, and as hypothesis-generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. A study in 2007 reconstructed the metabolic network of Mycobacterium tuberculosis H37Rv (Jamshidi and Palsson, 2007). This strain can produce many of the complex compound’s characteristic to tuberculosis, such as mycolic acids and mycocerosates. Researchers in this study grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and ﬁnally they analyzed the network in an ‘unbiased’ manner by calculating Hard Coupled Reaction (HCR) sets and FBA. The results showed growth rates comparable to experimental observations in different media, and by considering HCR sets in the context of known drug targets for tuberculosis treatment they proposed new alternative, but equivalent drug targets (Jamshidi and Palsson, 2007). Recent articles proving the constant increase in popularity of the ﬂuxomic ﬁeld have been collected in Supplementary Table S1. The summary of most popular techniques, organisms and pathways described within the studies are shown in Figures 6–8. TABLE 1 | Examples of databases useful for ﬂuxomic-related studies. Database Link Brief description Ref Central Carbon Metabolic Flux database (CeCaFDB) www.cecafdb.org Contains 581 cases of quantitative ﬂux results among 36 organisms. CeCaFDB can be used for comparison and alignment of different ﬂuxes and to understand how they are changed by other factors Zhang et al. (2015) Datanator www.datanator.info Multisource database containing information about metabolites, RNA, proteins and reactions. Datanor will include information about ﬂuxes in near future, in which case it could be used for comparative analyses of relationships between variable systems and their constituents Roth, (2021) BiGG Models www.bigg.ucsd.edu Contains more than 100 genome-scale metabolic network reconstructions that provide information about biochemical reactions, metabolites and genes related to metabolism for a speciﬁc organism King, (2015) The Human Metabolome database (HMDB) www.hmdb.ca Contains 220,945 metabolite entries (both water-soluble and lipid soluble) with 8,610 protein sequences (enzymes/transporters) linked to them including pathways and reactions related to the metabolite. Provides users with data obtained by MS and NMR analyses performed on urine, blood, and cerebrospinal ﬂuid samples (Wishart et al., 2007; Wishart et al., 2018) SABIO-RK www.sabio.h-its.org Contains information about biochemical reactions and their kinetics. Provides the user with information about the involvement of reaction in various pathways, modiﬁers of reaction enzymes involved in reactions and measured kinetic data (including kinetic rate equations) Wittig, (2011) Braunschweig Enzyme database (BRENDA) www.brendaenzymes.org The largest depository of all classiﬁed enzymes, including biochemical and molecular information. The database includes information such as enzyme class, reaction in which the enzyme is involved, speciﬁcity of reaction, functional parameters of the reaction, localization of enzyme, the application of enzymes, and ligand-related data Chang et al. (2009) Frontiers in Pharmacology | www.frontiersin.org 9 March 2022 | Volume 13 | Article 805782 Emwas et al. Fluxomics as New Metabolomics Approach that can be useful for studying the ﬂuxes. Some of them are listed in Table 1. FLUXOMICS DATABASES Rising interest in–omics ﬁelds (i.e., proteomics, genomics, and metabolomics) has resulted in an increased number of recent studies. The massive amount of data produced from these studies must be properly managed to increase its accessibility. This has given rise to various -omics databases such as PeptideAtlas (http://www. peptideatlas.org/) (Deutsch et al., 2008), PRIDE (https://www.ebi.ac. uk/pride/) (Martens et al., 2005) (proteomics related databases), Human Metabolome database (HMDB) (www.hmdb.ca) (Wishart et al., 2007) and METLIN (https://metlin.scripps.edu/) (Smith et al., 2005) (metabolomics related databases). Fluxomics still has not reached its true potential, partly due to a lack of uniform data standards in the reconstruction of metabolic networks (Crown and Antoniewicz, 2013) (Thiele and Palsson, 2010). Therefore, there is an emerging need to construct new and userfriendly databases, which not only store, but also match ﬂux results and create metabolic network models. Recently, novel solutions to reach these ambitious goals have been developed and are brieﬂy described here. The Central Carbon Metabolic Flux database (CeCaFDB, available at http://www.cecafdb.org) is a novel database published in 2014 that focus on central carbon metabolic systems of microbes and animal cells. The database contains 581 cases of quantitative ﬂux results among 36 organisms including: Homo sapiens, Escherichia coli, Saccharomyces cerevisiae and Pichia pastoris. Based on user input it can utilize four modules (vector-based similarity, a stoichiometrybased comparison, a topology-based similarity, and enzymetopology based similarity) for comparison and alignment of different ﬂux distributions. Additionally, this database provides the opportunity to perform similarity calculations by utilizing deposited data and altering genetic and environmental factors (Zhang et al., 2015). Datanator (https://datanator.info) is an integrated multisource database that contains quantitative molecular data of several types including metabolite concentrations, RNA modiﬁcations and half-lives, protein abundances and modiﬁcations, and reaction rate parameters. Developed in 2020, Datanator includes various data for 1,030 organisms integrated from over 8,000 articles. Although it does not contain ﬂux related data yet, the authors are planning to include it in the near future, as well as information on RNA/protein localizations and protein half-lives. In such case, Datanator would be a valuable source for comparative analyses of relationships between variable networks and systems (Roth, 2021). BiGG Models (http://bigg.ucsd.edu) is a large-scale database containing genome-scale metabolic network reconstructions. It contains more than 100 genome-scale metabolic models. Those models contain information about biochemical reactions, metabolites and genes related to the metabolism of speciﬁc organisms. The information provided in BIGG Models is standardized across different models, which allows users to browse, share and visualize the networks in a structured manner (King, 2015). Besides those three databases, various other databases used in different–omics ﬁelds can be used to obtain partial information Frontiers in Pharmacology | www.frontiersin.org CONCLUSION Matching genomic, transcriptomic, proteomic, and metabolomic data is essential for global understanding of biological systems. Fluxomics provides insight into actual rates within metabolic networks, both because of both cellular activity and environmental changes. Such knowledge can be obtained using different approaches including metabolic ﬂux analysis (MFA), dynamic metabolic ﬂux analysis (DMFA), ﬂux balance analysis (FBA) or 13 C-labeled metabolite monitoring. In addition to this wide variety of approaches in ﬂuxomics, the signiﬁcant advances in instrumentation methods such as NMR and MS, along with new databases and software, increase the prevalence of ﬂuxomics studies. Nowadays, ﬂuxomics gives rewarding data of complexed multi-molecular interactions in biological systems, which has never been observed before. AUTHOR CONTRIBUTION

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