Basic concepts of metabolic flux analysis
Main Article Content
Metabolism represents the biological level that is most related to the phenotypes of the cell and, alterations or reprogramming of this can (i) affect the production of primary or secondary metabolites in microorganisms of biotechnological interest, (ii) favor or not the inhibition of growth in pathogenic organisms and (iii) developing metabolic disorders such as obesity or diabetes, among others. The study of metabolism, redesign, and redirection of metabolic fluxes has become an important area of research (also known as Metabolic Engineering), as it has allowed the development and design of improved biological processes, the identification of therapeutic targets, the design of therapeutic strategies to cure metabolic disorders and the identification of biomarkers in cancer, among others. Currently, the development of computational methodologies is allowing to study the cell metabolism under different environmental conditions and directing experiments with the model's predictions. The purpose of this review is to highlight the importance of metabolic flux analysis as a general methodology to study metabolic reprogramming in different organisms of biotechnological, medical, and therapeutic interest. This work condenses the theoretical bases and key concepts to understand the analysis of metabolic fluxes, which will be a fundamental input for those who are entering to the world of systems biology or related areas.
(1) Gianchandani EP, Chavali AK, Papin JA. The application of flux balance analysis in systems biology. Wiley Interdiscip. Rev Syst Biol Med. 2010;2(3):372–82. https://doi.org/10.1002/wsbm.60.
(2) Woolston BM, Edgar S, Stephanopoulos G. Metabolic Engineering: Past and Future. Annu Rev Chem Biomol Eng. 2013;4(1):259–88. https://doi.org/10.1146/annurev-chembioeng-061312-103312.
(3) Bordbar A, Monk JM, King ZA, Palsson BO. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet. 2014;15(2):107–20. https://doi.org/10.1038/nrg3643.
(4) Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012 Apr;10(4):291–305. https://doi.org/10.1038/nrmicro2737.
(5) Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Nat Protoc. 2019;14(3):639. https://doi.org/10.1038/s41596-018-0098-2.
(6) Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, et al. RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol. 2018;14(10):e1006541. https://doi.org/10.1371/journal.pcbi.1006541.
(7) Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, et al. BiGG Models 2020: Multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 2020;48(D1):D402–6. https://doi.org/10.1093/nar/gkz1054.
(8) King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 2015;44(D1):D515–22. https://doi.org/10.1093/nar/gkv1049.
(9) Arnold A, Nikoloski Z. Bottom-up metabolic reconstruction of arabidopsis and its application to determining the metabolic costs of enzyme production. Plant Physiol. 2014;165(3):1380–91. https://doi.org/10.1104/pp.114.235358.
(10) Shimizu H. Metabolic engineering - Integrating methodologies of molecular breeding and bioprocess systems engineering. J Biosci Bioeng. 2002;94(6):563–73. https://doi.org/10.1016/S1389-1723(02)80196-7.
(11) Orth JD, Thiele I, Palsson BO. What is flux balance analysis? Nat Biotechnol. 2010;28(3):245–8. https://doi.org/10.1038/nbt.1614.
(12) Rios-Estepa R, Lange BM. Experimental and mathematical approaches to modeling plant metabolic networks. Phytochemistry. 2007;68(16–18):2351–74. https://doi.org/10.1016/j.phytochem.2007.04.021.
(13) Reimers AM, Reimers AC. The steady-state assumption in oscillating and growing systems. J Theor Biol. 2016;406:176–86. https://doi.org/10.1016/j.jtbi.2016.06.031.
(14) Erdrich P, Steuer R, Klamt S. An algorithm for the reduction of genome-scale metabolic network models to meaningful core models. BMC Syst Biol. 2015;9(1):48. https://doi.org/10.1186/s12918-015-0191-x.
(15) Schuetz R, Kuepfer L, Sauer U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol. 2007 Jan;3(119):119. https://doi.org/10.1038/msb4100162.
(16) López-Agudelo VA, Baena A, Ramirez-Malule H, Ochoa S, Barrera LF, Ríos-Estepa R. Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection. BMC Syst Biol. 2017;11(1). https://doi.org/10.1186/s12918-017-0496-z.
(17) Sánchez C, Quintero JC, Ochoa S. Flux Balance Analysis in the Production of Clavulanic Acid by Streptomyces clavuligerus. Biotechnol Prog. 2015;31(5):1226–36. https://doi.org/10.1002/btpr.2132.
(18) Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010;13(3):344–9. https://doi.org/10.1016/j.mib.2010.03.003.
(19) Acevedo A, Conejeros R, Aroca G. Ethanol production improvement driven by genome-scale metabolic modeling and sensitivity analysis in Scheffersomyces stipitis. PLoS One. 2017;12(6):1–26. https://doi.org/10.1371/journal.pone.0180074.
(20) Price ND, Reed JL, Palsson B. Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nat Rev Microbiol. 2004;2(11):886–97. https://doi.org/10.1038/nrmicro1023.
(21) Palsson BØ. Systems Biology Properties of Reconstructed Networks. 1st ed. New York, NY: Cambridge University Press; 2006. 336 p.
(22) Palsson B, Price ND, Famili I, Beard DA. Extreme pathways and Kirchhoff’s second law. Biophys J. 2002;83(5):2879–82. https://doi.org/10.1016/S0006-3495(02)75297-1.
(23) Marinos G, Kaleta C, Waschina S. Defining the nutritional input for genome-scale metabolic models: A roadmap. PLoS One. 2020;15(8):e0236890. https://doi.org/10.1371/journal.pone.0236890.
(24) Krömer J, Quek L, Nielsen L. 13C-FLUXOMICS : A TOOL FOR MEASURING METABOLIC PHENOTYPES. Aust Biochem. 2009;40(3):17–20.
(25) Wiechert W. 13C metabolic flux analysis. Metab Eng. 2001 Jul;3(3):195–206. https://doi.org/10.1006/mben.2001.0187.
(26) Wu H, Von Kamp A, Leoncikas V, Mori W, Sahin N, Gevorgyan A, et al. MUFINS: Multi-formalism interaction network simulator. npj Syst Biol Appl. 2016;2:16032. https://doi.org/10.1038/npjsba.2016.32.
(27) Maranas CD, Zomorrodi AR. Optimization Methods in Metabolic Networks. 1st ed. New Jersey: John Wiley & Sons; 2016.
(28) Beard DA, Liang S, Qian H. Energy balance for analysis of complex metabolic networks. Biophys J. 2002;83(1):79–86. https://doi.org/10.1016/S0006-3495(02)75150-3.
(29) Qian H, Beard DA, Liang SD. Stoichiometric network theory for nonequilibrium biochemical systems. Eur J Biochem. 2003;270(3):415–21. https://doi.org/10.1046/j.1432-1033.2003.03357.x.
(30) Henry CS, Broadbelt LJ, Hatzimanikatis V. Thermodynamics-based metabolic flux analysis. Biophys J. 2007;92(5):1792–805. https://doi.org/10.1529/biophysj.106.093138.
(31) Covert MW, Palsson BO. Constraints-based models: Regulation of gene expression reduces the steady-state solution space. J Theor Biol. 2003;221(3):309–25. https://doi.org/10.1006/jtbi.2003.3071.
(32) Burgard AP, Pharkya P, Maranas CD. OptKnock: A Bilevel Programming Framework for Identifying Gene Knockout Strategies for Microbial Strain Optimization. Biotechnol Bioeng. 2003;84(6):647–57. https://doi.org/10.1002/bit.10803.
(33) Mahadevan R, Schilling CH. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003;5(4):264–76. https://doi.org/10.1016/j.ymben.2003.09.002.
(34) Gudmundsson S, Thiele I. Computationally efficient flux variability analysis. BMC Bioinformatics. 2010;11(2):2–4. https://doi.org/10.1186/1471-2105-11-489.
(35) Noor E. Removing both Internal and Unrealistic Energy-Generating Cycles in Flux Balance Analysis. arXiv Prepr arXiv180304999. 2018; Available from: http://arxiv.org/abs/1803.04999.
(36) Fritzemeier CJ, Hartleb D, Szappanos B, Papp B, Lercher MJ. Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. PLoS Comput Biol. 2017;13(4):e1005494. https://doi.org/10.1371/journal.pcbi.1005494.
(37) López-Agudelo VA, Mendum TA, Laing E, Wu HH, Baena A, Barrera LF, et al. A systematic evaluation of mycobacterium tuberculosis genome-scale metabolic networks. PLoS Comput Biol. 2020;16(6):e1007533. https://doi.org/10.1371/journal.pcbi.1007533.
(38) Schellenberger J, Lewis NE, Palsson B. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys J. 2011;100(3):544–53. https://doi.org/10.1016/j.bpj.2010.12.3707.
(39) Martínez VS, Nielsen LK. NExT : Integration of Thermodynamic Constraints and Metabolomics Data into a Metabolic Network. In: Krömer J, Nielsen L, Blank L, editors. Metabolic Flux Analysis Methods in Molecular Biology (Methods and Protocols). New York, NY: Humana Press; 2012. p. 65–78.
(40) Desouki AA, Jarre F, Gelius-Dietrich G, Lercher MJ. CycleFreeFlux: Efficient removal of thermodynamically infeasible loops from flux distributions. Bioinformatics. 2015;31(13):2159–65. https://doi.org/10.1093/bioinformatics/btv096.
(41) Schellenberger J, Palsson B. Use of randomized sampling for analysis of metabolic networks. J Biol Chem. 2009;284(9):5457–61. https://doi.org/10.1074/jbc.R800048200.
(42) Haraldsdóttir HS, Cousins B, Thiele I, Fleming RMT, Vempala S. CHRR: Coordinate hit-and-run with rounding for uniform sampling of constraint-based models. Bioinformatics. 2017;33(11):1741–3. https://doi.org/10.1093/bioinformatics/btx052.
(43) Megchelenbrink W, Huynen M, Marchiori E. optGpSampler: An improved tool for uniformly sampling the solution-space of genome-scale metabolic networks. PLoS One. 2014;9(2). https://doi.org/10.1371/journal.pone.0086587.
(44) Saa PA, Nielsen LK. Ll-ACHRB: A scalable algorithm for sampling the feasible solution space of metabolic networks. Bioinformatics. 2016;32(15):2330–7. https://doi.org/10.1093/bioinformatics/btw132.
(45) Kaufman DE, Smith RL. Direction choice for accelerated convergence in hit-and-run sampling. Oper Res. 1998;46(1):84–95. https://doi.org/10.1287/opre.46.1.84.
(46) Price ND, Schellenberger J, Palsson BO. Uniform sampling of steady-state flux spaces: Means to design experiments and to interpret enzymopathies. Biophys J. 2004;87(4):2172–86. https://doi.org/10.1529/biophysj.104.043000.
(47) Bordbar A, Lewis NE, Schellenberger J, Palsson B, Jamshidi N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol. 2010;6(1):422. https://doi.org/10.1038/msb.2010.68.
(48) Herrmann HA, Dyson BC, Vass L, Johnson GN, Schwartz J-M. Flux sampling is a powerful tool to study metabolism under changing environmental conditions. NPJ Syst Biol Appl. 2019;5(32):1-8. https://doi.org/10.1038/s41540-019-0109-0.
(49) Piubeli F, Salvador M, Argandoña M, Nieto JJ, Bernal V, Pastor JM, et al. Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model. Microb Cell Fact. 2018;17(1):2. https://doi.org/10.1186/s12934-017-0852-0.
(50) Meyer C. Matrix analysis and applied linear algebra. 1st ed. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM); 2000. 718 p.
(51) Moreno-Sánchez R, Saavedra E, Rodríguez-Enríquez S, Olín-Sandoval V. Metabolic Control Analysis: A tool for designing strategies to manipulate metabolic pathways. J Biomed Biotechnol. 2008;2008(1). https://doi.org/10.1155/2008/597913.
(52) Villadsen J, Nielsen J, Lidén G. Bioreaction Engineering Principles. 3rd ed. Springer; 2011.
(53) Kacser H, and Burns J. The control of flux. In: Symp Soc Exp Biol. 1973. p. 65–104.
(54) Kacser H, Burns JA, Fell DA. The control of flux. Biochem Soc Trans. 1995;23(2):341–66. https://doi.org/10.1042/bst0230341.
(55) Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, et al. Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology. Front Plant Sci. 2020;11. https://doi.org/10.3389/fpls.2020.00944.
(56) Crown SB, Antoniewicz MR. Publishing 13C metabolic flux analysis studies: A review and future perspectives. Metab Eng. 2013;20:42–8. https://doi.org/10.1016/j.ymben.2013.08.005.
(57) Antoniewicz MR. A guide to 13C metabolic flux analysis for the cancer biologist. Exp Mol Med. 2018;50(4):1–13. https://doi.org/10.1038/s12276-018-0060-y.
(58) Sauer U, Hatzimanikatis V, Bailey JE, Hochuli M, Szyperski T, Wüthrich K. Metabolic Fluxes In Riboflavin-Producing Bacillus Subtilis. Nat Biotechnol. 1997;15(5):448–52. https://doi.org/10.1038/nbt0597-448.
(59) Heer D, Heine D, Sauer U. Resistance of Saccharomyces cerevisiae to high concentrations of furfural is based on NADPH-dependent reduction by at least two oxireductases. Appl Environ Microbiol. 2009;75(24):7631–8. https://doi.org/10.1128/AEM.01649-09.
(60) Bommareddy RR, Chen Z, Rappert S, Zeng AP. A de novo NADPH generation pathway for improving lysine production of Corynebacterium glutamicum by rational design of the coenzyme specificity of glyceraldehyde 3-phosphate dehydrogenase. Metab Eng. 2014;25:30–7. https://doi.org/10.1016/j.ymben.2014.06.005.
(61) Bartek T, Blombach B, Lang S, Eikmanns BJ, Wiechert W, Oldiges M, et al. Comparative 13C metabolic flux analysis of pyruvate dehydrogenase complex-deficient, L-valine-producing Corynebacterium glutamicum. Appl Environ Microbiol. 2011;77(18):6644–52. https://doi.org/10.1128%2FAEM.00575-11.
(62) Jordà J, Jouhten P, Cámara E, Maaheimo H, Albiol J, Ferrer P. Metabolic flux profiling of recombinant protein secreting Pichia pastoris growing on glucose:methanol mixtures. Microb Cell Fact. 2012;11(1):57. https://doi.org/10.1186/1475-2859-11-57.
(63) Van Gulik WM, De Laat WTAM, Vinke JL, Heijnen JJ. Application of metabolic flux analysis for the identification of metabolic bottlenecks in the biosynthesis of penicillin-G. Biotechnol Bioeng. 2000;68(6):602–18. https://doi.org/10.1002/(SICI)1097-0290(20000620)68:6%3C602::AID-BIT3%3E3.0.CO;2-2.
(64) Pedersen H, Christensen B, Hjort C, Nielsen J. Construction and characterization of an oxalic acid nonproducing strain of Aspergillus niger. Metab Eng. 2000;2(1):34–41. https://doi.org/10.1006/mben.1999.0136.
(65) Becker J, Reinefeld J, Stellmacher R, Schäfer R, Lange A, Meyer H, et al. Systems-wide analysis and engineering of metabolic pathway fluxes in bio-succinate producing basfia succiniciproducens. Biotechnol Bioeng. 2013;110(11):3013–23. https://doi.org/10.1002/bit.24963.
(66) McKinlay JB, Oda Y, Ruhl M, Posto AL, Sauer U, Harwood CS. Non-growing rhodopseudomonas palustris increases the hydrogen gas yield from acetate by shifting from the glyoxylate shunt to the tricarboxylic acid cycle. J Biol Chem. 2014;289(4):1960–70. https://doi.org/10.1074/jbc.m113.527515.
(67) Gómez-Ríos D, López-Agudelo VA, Ramírez-Malule H, Neubauer P, Junne S, Ochoa S, et al. A genome-scale insight into the effect of shear stress during the fed-batch production of clavulanic acid by streptomyces clavuligerus. Microorganisms. 2020;8(9):1–19. https://doi.org/10.3390/microorganisms8091255.
(68) Ramirez-Malule H, Junne S, Nicolás Cruz-Bournazou M, Neubauer P, Ríos-Estepa R. Streptomyces clavuligerus shows a strong association between TCA cycle intermediate accumulation and clavulanic acid biosynthesis. Appl Microbiol Biotechnol. 2018 May 9;102(9):4009–23. https://doi.org/10.1007/s00253-018-8841-8.
(69) Toro L, Pinilla L, Avignone-Rossa C, Ríos-Estepa R. An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies. Bioprocess Biosyst Eng. 2018;41(5):657–69. https://doi.org/10.1007/s00449-018-1900-9.
(70) Sánchez C, Gómez N, Quintero JC, Ochoa S, Rios R. A Combined Sensitivity and Metabolic Flux Analysis Unravel the Importance of Amino Acid Feeding Strategies in Clavulanic Acid Biosynthesis. In: Castillo LF, Cristancho M, Isaza G, Pinzón A, Rodríguez JMC, editors. Advances in Computational Biology. Cham: Springer International Publishing; 2014. p. 169–75.
(71) He L, Xiao Y, Gebreselassie N, Zhang F, Antoniewicz MR, Tang YJ, et al. Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis. Biotechnol Bioeng. 2014;111(3):575–85. https://doi.org/10.1002/bit.25124.
(72) Ranganathan S, Tee TW, Chowdhury A, Zomorrodi AR, Yoon JM, Fu Y, et al. An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Metab Eng. 2012;14(6):687–704. https://doi.org/10.1016/j.ymben.2012.08.008.
(73) Wang Y, San KY, Bennett GN. Improvement of NADPH bioavailability in Escherichia coli through the use of phosphofructokinase deficient strains. Appl Microbiol Biotechnol. 2013;97(15):6883–93. https://doi.org/10.1007/s00253-013-4859-0.
(74) Fu Y, Yoon JM, Royce LA, Rodriguez-Moya M, Gonzalez R, Jarboe L, et al. Metabolic flux analysis of Escherichia coli MG1655 under octanoic acid stress. Sustain Eng Forum Core Program Top 2011 AIChE Annu Meet. 2011;1(10):626–7.
(75) Feng X, Zhao H. Investigating xylose metabolism in recombinant Saccharomyces cerevisiae via 13C metabolic flux analysis. Microb Cell Fact. 2013;12(1):114. https://doi.org/10.1186/1475-2859-12-114.
(76) Lian J, Si T, Nair NU, Zhao H. Design and construction of acetyl-CoA overproducing Saccharomyces cerevisiae strains. Food, Pharm Bioeng Div 2014 - Core Program Area 2014 AIChE Annu Meet. 2014;2:750–60.
(77) Shiba Y, Paradise EM, Kirby J, Ro DK, Keasling JD. Engineering of the pyruvate dehydrogenase bypass in Saccharomyces cerevisiae for high-level production of isoprenoids. Metab Eng. 2007;9(2):160–8. https://doi.org/10.1016/j.ymben.2006.10.005.
(78) Papini M, Nookaew I, Siewers V, Nielsen J. Physiological characterization of recombinant Saccharomyces cerevisiae expressing the Aspergillus nidulans phosphoketolase pathway: Validation of activity through 13C-based metabolic flux analysis. Appl Microbiol Biotechnol. 2012;95(4):1001–10. https://doi.org/10.1007/s00253-012-3936-0.
(79) Curran KA, Leavitt JM, Karim AS, Alper HS. Metabolic engineering of muconic acid production in Saccharomyces cerevisiae. Metab Eng. 2013;15(1):55–66. https://doi.org/10.1016/j.ymben.2012.10.003.
(80) Hayakawa K, Kajihata S, Matsuda F, Shimizu H. 13C-metabolic flux analysis in S-adenosyl-l-methionine production by Saccharomyces cerevisiae. J Biosci Bioeng. 2015;120(5):532–8. https://doi.org/10.1016/j.jbiosc.2015.03.010.
(81). Ghaffari P, Mardinoglu A, Asplund A, Shoaie S, Kampf C, Uhlen M, et al. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling. Sci Rep. 2015;5(1):1–10. https://doi.org/10.1038/srep08183.
(82) Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. Maranas CD, editor. PLoS Comput Biol. 2012 May;8(5):e1002518. https://doi.org/10.1371/journal.pcbi.1002518.
(83) Pacheco MP, Bintener T, Ternes D, Kulms D, Haan S, Letellier E, et al. Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine. 2019;43:98–106. https://doi.org/10.1016/j.ebiom.2019.04.046.
(84) Aden K, Rehman A, Waschina S, Pan WH, Walker A, Lucio M, et al. Metabolic Functions of Gut Microbes Associate With Efficacy of Tumor Necrosis Factor Antagonists in Patients With Inflammatory Bowel Diseases. Gastroenterology. 2019;157(5):1279-1292.e11. https://doi.org/10.1053/j.gastro.2019.07.025.
(85) Greenhalgh K, Ramiro-Garcia J, Heinken A, Ullmann P, Bintener T, Pacheco MP, et al. Integrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells. Cell Rep. 2019;27(5):1621-1632.e9. https://doi.org/10.1016/j.celrep.2019.04.001.
(86) Beste DJ V, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, et al. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol. 2007;8(5):R89. https://doi.org/10.1186/gb-2007-8-5-r89.
(87) Beste DJV, Bonde B, Hawkins N, Ward JL, Beale MH, Noack S, et al. 13C Metabolic Flux Analysis Identifies an Unusual Route for Pyruvate Dissimilation in Mycobacteria Which Requires Isocitrate Lyase and Carbon Dioxide Fixation. PLoS Pathog. 2011;7(7). https://doi.org/10.1371/journal.ppat.1002091.
(88) Borah K, Beyß M, Theorell A, Wu H, Basu P, Mendum TA, et al. Intracellular Mycobacterium tuberculosis Exploits Multiple Host Nitrogen Sources during Growth in Human Macrophages. Cell Rep. 2019;29(11):3580-3591.e4. https://doi.org/10.1016/j.celrep.2019.11.037.
(89) Beste DJV, Mendum TA, McFadden J, Nöh K, Niedenführ S, Wiechert W, et al. 13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for intracellular mycobacterium tuberculosis. Chem Biol. 2013;20(8):1012–21. https://doi.org/10.1016/j.chembiol.2013.06.012.
(90) Cordes H, Thiel C, Baier V, Blank LM, Kuepfer L. Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation. npj Syst Biol Appl. 2018;4(1):10. https://doi.org/10.1038/s41540-018-0048-1.
(91) Sier JH, Thumser AE, Plant NJ. Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology. BMC Syst Biol. 2017;11(1):1–16. https://doi.org/10.1186/s12918-017-0520-3.
(92) Maldonado EM, Leoncikas V, Fisher CP, Moore JB, Plant NJ, Kierzek AM. Integration of genome scale metabolic networks and gene regulation of metabolic enzymes with physiologically based pharmacokinetics. CPT pharmacometrics Syst Pharmacol. 2017;6(11):732–46. https://doi.org/10.1002/psp4.12230.
(93) Pienaar E, Sarathy J, Prideaux B, Dietzold J, Dartois V, Kirschner DE, et al. Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLoS Comput Biol. 2017;13(8):e1005650. https://doi.org/10.1371/journal.pcbi.1005650.
(94) Kirschner D, Pienaar E, Marino S, Linderman JJ. A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment. Curr Opin Syst Biol. 2017;3:170–85. https://doi.org/10.1016/j.coisb.2017.05.014.
(95) Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE. Multiscale model of Mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions. Infect Immun. 2016;84(5):1650–69. https://doi.org/10.1128/IAI.01438-15.
- David Andrés Gómez Ríos, Víctor A. López-Agudelo, Juan Camilo Urrego-Sepúlveda, Howard Diego Ramirez-Malule, Research on repurposed antivirals currently available in Colombia as treatment alternatives for COVID-19 , Ingeniería y Competitividad: Vol. 23 No. 1 (2021): Engineering and Competitiveness
Accepted 2020-09-27
Published 2021-01-15
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors grant the journal and Universidad del Valle the economic rights over accepted manuscripts, but may make any reuse they deem appropriate for professional, educational, academic or scientific reasons, in accordance with the terms of the license granted by the journal to all its articles.
Articles will be published under the Creative Commons 4.0 BY-NC-SA licence (Attribution-NonCommercial-ShareAlike).