PFA toolbox: a MATLAB tool for Metabolic Flux Analysis

Background: Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. Results: Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User s Guide with a thorough description of its functions and several examples. Conclusions: The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.This research has been partially supported by the Spanish Government (FEDER-CICYT: DPI 2014-55276-C5-1-R). Yeimy Morales is grateful for the BR Grants of the University of Girona (BR2012/26). Gabriel Bosque Chacon is recipient of a doctoral fellowship from the Spanish Government (BES-2012-053772).Morales, Y.; Bosque Chacón, G.; Vehi, J.; Picó Marco, JA.; Llaneras, F. (2016). PFA toolbox: a MATLAB tool for Metabolic Flux Analysis. BMC Systems Biology. 10(46):1-10. https://doi.org/10.1186/s12918-016-0284-1S1101046Sauer U, Hatzimanikatis V, Bailey J, Hochuli M, Szyperski T, Wuethrich K. Metabolic fluxes in riboflavin-producing Bacillus subtilis. Nature biotechnology. 1997;15(5):448–52.Wittmann C. Metabolic flux analysis using mass spectrometry. In: Tools and Applications of Biochemical Engineering Science. Berlin: Springer; 2002. p. 39–64.Antoniewicz M. Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biot. 2015;42(3):317–25.Araúzo-Bravo MR, Shimizu JK. An improved method for statistical analysis of metabolic flux analysis using isotopomer-mapping matrices with analytical expressions. J Biotech. 2003;05:117–33.Klamt S, Schuster S, Gilles D. Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnol Bioeng. 2002;77(7):734–51.Llaneras F. Interval and possibilistic methods for constraint-based metabolic models, PhD Thesis. Universidad Politécnica de Valencia: Departamento de Ingeniería de Sistemas y Automática; 2011.Llaneras F, Picó J. An interval approach for dealing with flux distributions and elementary modes activity patterns. J Theor Biol. 2007;246(2):290–308.Llaneras F, Sala A, Picó J. A possibilistic framework for constraint-based metabolic flux analysis. BMC Syst Biol. 2009;3(1):79.Tortajada M, Llaneras F, Picó J. Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity. BMC Syst Biol. 2010;4(1):115.Llaneras F, Picó J. A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient. BMC Bioinformatics. 2007;8(1):421.Iyer VV, Ovacik MA, Androulakis IP, Roth CM, Ierapetritou MG. Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes. Toxicology and applied pharmacology. 2010;248(3):165–77.Zamorano F, Wouwer A, Bastin G. Detailed metabolic flux analysis of an underdetermined network of CHO cells. J Biotechnol. 2010;150(4):497–508.Iyer V, Yang H, Ierapetritou M, Roth C. Effects of glucose and insulin on HepG2‐C3A cell metabolism. Biotechnol Bioeng. 2010;107(2):347–56.Iyer V, Androulakis I, Roth C, Ierapetritou M. Effects of Triadimefon on the Metabolism of Cultured Hepatocytes. In: BioInformatics and BioEngineering (BIBE), IEEE International Conference on. 2010. p. 118–23.Orman MA, Arai K, Yarmush ML, Androulakis IP, Berthiaume F, Ierapetritou MG. Metabolic flux determination in perfused livers by mass balance analysis: effect of fasting. Biotechnology and bioengineering. 2010;107(5):825–35.Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter H. FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC bioinformatics. 2011;12(1):28.González J, Folch-Fortuny A, Llaneras F, Tortajada M, Picó J, Ferrer A. Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media. Chemometr Intell Lab. 2014;134:89–99.Morales Y, Tortajada M, Picó J, Vehí J, Llaneras F. Validation of an FBA model for Pichia pastoris in chemostat cultures. BMC System Biol. 2014;8(1):142.Stephanopoulos GN, Aristidou AA, Nielsen J. Metabolic Engineering: Principles and Methodologies. San Diego, USA: Academic; 1998.Heijden R, Romein B, Heijnen J, Hellinga C, Luyben K. Linear constraint relations in biochemical reaction systems: I & II. Biotech Bioeng. 1994;43(1):3–10.Lofberg J. YALMIP: A toolbox for modeling and optimization in MATLAB. In: IEEE International Symposium on Computer Aided Control Systems Design. 2004. p. 284–9.YALMIP Home Page [ http://users.isy.liu.se/johanl/yalmip/ ]. Accessed 11 May 2016.IBM ILOG CPLEX- High-performance mathematical programming engine. [ http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/ ]. Accessed 11 May 2016.GLPK (GNU Linear programming kit) [ http://www.gnu.org/software/glpk/ ]. Accessed 11 May 2016.Orth D, Fleming M, Palsson B. Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal Plus. 2010;4:1.Emmerling M, Dauner M, Ponti A, Fiaux J, Hochuli M, Szyperski T, Wüthrich K, Bailey J, Sauer U. Metabolic flux responses to pyruvate kinase knockout in Escherichia coli. Journal of bacteriology. 2002;184(1):152–64.Orth J, Conrad T, Na J, Lerman J, Nam H, Feist A, Palsson B. A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011. Molecular systems biology. 2011;7(1):535.Bonarius H, Schmid G, Tramper J. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends in Biotechnology. 1997;15(8):308–14.Palsson BØ. Systems biology: properties of reconstructed networks. New York: Cambridge University Press; 2006.Schilling C, Covert M, Famili I, Church G, Edwards J, Palsson B. Genome-scale metabolic model of Helicobacter pylori 26695. Journal of Bacteriology. 2002;184(16):4582–93.Solà A, Jouhten P, Maaheimo H, Sánchez-Ferrando F, Szyperski T, Ferrer P. Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates. Microbiol. 2007;153:281–90.Solà A. Estudi del metabolisme central del carboni de Pichia pastoris, PhD Thesis. Universitat Autònoma de Barceloana: Escola Tècnica Superior d’Enginyeria; 2004.Jungo C, Rerat C, Marison IW, von Stockar U. Quantitative characterization of the regulation of the synthesis of alcohol oxidase and of the expression of recombinant avidin in a Pichia pastoris Mut + strain. Enzyme Microb Technol. 2006;39:936–44.Tortajada M. Process development for the obtention and use of recombinant glycosidases: expression, modelling and immobilization, PhD Thesis. Universidad Politécnica de Valencia: Departamento de Ingeniería de Sistemas y Automática; 2012.Jordà J, de Jesus SS, Peltier S, Ferrer P, Albiol J. Metabolic flux analysis of recombinant Pichia pastoris growing on different glycerol/methanol mixtures by iterative fitting of NMR-derived 13C-labelling data from proteinogenic amino acids. New Biotecnol. 2014;31(1):120–32