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Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity.[1] While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process.[2] The significance of fluxomics is that metabolic fluxes determine the cellular phenotype.[3] It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.[4]

Fluxomics falls within the field of systems biology which developed with the appearance of high throughput technologies.[5] Systems biology recognizes the complexity of biological systems and has the broader goal of explaining and predicting this complex behavior.[2]

Metabolic flux

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Metabolic flux refers to the rate of metabolite conversion in a metabolic network.[1][6] For a reaction this rate is a function of both enzyme abundance and enzyme activity.[1] Enzyme concentration is itself a function of transcriptional and translational regulation in addition to the stability of the protein.[1] Enzyme activity is affected by the kinetic parameters of the enzyme, the substrate concentrations, the product concentrations, and the effector molecules concentration.[1] The genomic and environmental effects on metabolic flux are what determine healthy or diseased phenotype.[6]

Fluxome

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Similar to genome, transcriptome, proteome, and metabolome, the fluxome is defined as the complete set of metabolic fluxes in a cell.[5] However, unlike the others the fluxome is a dynamic representation of the phenotype.[5] This is due to the fluxome resulting from the interactions of the metabolome, genome, transcriptome, proteome, post-translational modifications and the environment.[5]

Flux analysis technologies

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Two important technologies are flux balance analysis (FBA) and 13C-fluxomics. In FBA metabolic fluxes are estimated by first representing the metabolic reactions of a metabolic network in a numerical matrix containing the stoichiometric coefficients of each reaction.[7] The stoichiometric coefficients constrain the system model and are why FBA is only applicable to steady state conditions.[7] Additional constraints can be imposed.[7] By providing constraints the possible set of solutions to the system are reduced. Following the addition of constraints the system model is optimized.[7] Flux-balance analysis resources include the BIGG database,[8] the COBRA toolbox,[9] and FASIMU.[10]

In 13C-fluxomics, metabolic precursors are enriched with 13C before being introduced to the system.[11] Using an imaging technique such as mass spectrometry or nuclear magnetic resonance spectroscopy the level of incorporation of 13C into metabolites can be measured and with stoichiometry the metabolic fluxes can be estimated.[11]

Stoichiometric and kinetic paradigms

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A number of different methods, broadly divided into stoichiometric and kinetic paradigms.

Within the stoichiometric paradigm, a number of relatively simple linear algebra methods use restricted metabolic networks or genome-scale metabolic network models to perform flux balance analysis and the array of techniques derived from it. These linear equations are useful for steady state conditions. Dynamic methods are not yet usable.[12] On the more experimental side, metabolic flux analysis allows the empirical estimation of reaction rates by stable isotope labelling.

Within the kinetic paradigm, kinetic modelling of metabolic networks can be purely theoretical, exploring the potential space of dynamic metabolic fluxes under perturbations away from steady state using formalisms such as biochemical systems theory. Such explorations are most informative when accompanied by empirical measurements of the system under study following actual perturbations, as is the case in metabolic control analysis.[13]

Constraint based reconstruction and analysis

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Collected methods in fluxomics have been described as "COBRA" methods, for constraint based reconstruction and analysis. A number of software tools and environments have been created for this purpose.[14][15][16][17][18][19][20]

Although it can only be measured indirectly, metabolic flux is the critical link between genes, proteins and the observable phenotype. This is due to the fluxome integrating mass-energy, information, and signaling networks.[21] Fluxomics has the potential to provide a quantifiable representation of the effect the environment has on the phenotype because the fluxome describes the genome environment interaction.[21] In the fields of metabolic engineering[22] and systems biology,[23] fluxomic methods are considered a key enabling technology due to their unique position in the ontology of biological processes, allowing genome scale stoichiometric models to act as a framework for the integration of diverse biological datasets.[24]

Examples of use in research

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One potential application of fluxomic techniques is in drug design. Rama et al.[25] used FBA to study the mycolic acid pathway in Mycobacterium tuberculosis. Mycolic acids are known to be important to M. tuberculosis survival and as such its pathway has been studied extensively.[25] This allowed the construction of a model of the pathway and for FBA to analyze it. The results of this found multiple possible drug targets for future investigation.

FBA was used to analyze the metabolic networks of multidrug-resistant Staphylococcus aureus.[26] By performing in silico single and double gene deletions many enzymes essential to growth were identified.

References

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  1. ^ a b c d e Winter, Gal; Krömer, Jens O. (2013-07-01). "Fluxomics – connecting 'omics analysis and phenotypes". Environmental Microbiology. 15 (7): 1901–1916. doi:10.1111/1462-2920.12064. ISSN 1462-2920. PMID 23279205.
  2. ^ a b Cascante, Marta; Marin, Silvia (2008-09-30). "Metabolomics and fluxomics approaches". Essays in Biochemistry. 45: 67–82. doi:10.1042/bse0450067. ISSN 0071-1365. PMID 18793124.
  3. ^ Cascante, Marta; Benito, Adrián; Mas, Igor Marín de; Centelles, Josep J.; Miranda, Anibal; Atauri, Pedro de (2014-01-01). "Fluxomics". In Orešič, Matej; Vidal-Puig, Antonio (eds.). A Systems Biology Approach to Study Metabolic Syndrome. Springer International Publishing. pp. 237–250. doi:10.1007/978-3-319-01008-3_12. ISBN 9783319010076.
  4. ^ Raamsdonk, Léonie M.; Teusink, Bas; Broadhurst, David; Zhang, Nianshu; Hayes, Andrew; Walsh, Michael C.; Berden, Jan A.; Brindle, Kevin M.; Kell, Douglas B. (2001-01-01). "A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations". Nature Biotechnology. 19 (1): 45–50. doi:10.1038/83496. ISSN 1087-0156. PMID 11135551. S2CID 15491882.
  5. ^ a b c d Aon, Miguel A.; Cortassa, Sonia (2015-07-22). "Systems Biology of the Fluxome". Processes. 3 (3): 607–618. doi:10.3390/pr3030607.
  6. ^ a b Cortassa, S; Caceres, V; Bell, LN; O'Rourke, B; Paolocci, N; Aon, MA (2015). "From Metabolomics to Fluxomics: A Computational Procedure to Translate Metabolite Profiles into Metabolic Fluxes". Biophysical Journal. 108 (1): 163–172. Bibcode:2015BpJ...108..163C. doi:10.1016/j.bpj.2014.11.1857. PMC 4286601. PMID 25564863.
  7. ^ a b c d Orth, Jeffrey D.; Thiele, Ines; Palsson, Bernhard Ø (2010-03-01). "What is flux balance analysis?". Nature Biotechnology. 28 (3): 245–248. doi:10.1038/nbt.1614. ISSN 1087-0156. PMC 3108565. PMID 20212490.
  8. ^ King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E. (2016). "BiGG Models: A platform for integrating, standardizing and sharing genome-scale models". Nucleic Acids Research. 44 (D1): D515–D522. doi:10.1093/nar/gkv1049. ISSN 0305-1048. PMC 4702785. PMID 26476456.
  9. ^ Schellenberger, Jan; Que, Richard; Fleming, Ronan M T; Thiele, Ines; Orth, Jeffrey D; Feist, Adam M; Zielinski, Daniel C; Bordbar, Aarash; Lewis, Nathan E; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R; Palsson, Bernhard Ø (2011). "Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0". Nature Protocols. 6 (9): 1290–1307. doi:10.1038/nprot.2011.308. ISSN 1754-2189. PMC 3319681. PMID 21886097.
  10. ^ Hoppe, Andreas; Hoffmann, Sabrina; Gerasch, Andreas; Gille, Christoph; Holzhütter, Hermann-Georg (2011). "FASIMU: flexible software for flux-balance computation series in large metabolic networks". BMC Bioinformatics. 12 (1): 28. doi:10.1186/1471-2105-12-28. ISSN 1471-2105. PMC 3038154. PMID 21255455.
  11. ^ a b Krömer, J.; Quek, L. E.; Nielsen, L. (2009). "13C-Fluxomics: a tool for measuring metabolic phenotypes". Aust Biochem. 40 (3): 17–20.
  12. ^ Winter, Gal; Krömer, Jens O. (2013-07-01). "Fluxomics – connecting 'omics analysis and phenotypes". Environmental Microbiology. 15 (7): 1901–1916. doi:10.1111/1462-2920.12064. ISSN 1462-2920. PMID 23279205.
  13. ^ Demin, O.; Goryanin, I. (2010). Kinetic Modelling in Systems Biology. Taylor and Francis. ISBN 9781420011661.
  14. ^ Klamt, S.; Saez-Rodriguez, J.; Gilles, E. D. (2007). "Structural and functional analysis of cellular networks with CellNetAnalyzer". BMC Systems Biology. 1: 2. doi:10.1186/1752-0509-1-2. PMC 1847467. PMID 17408509.
  15. ^ Boele, J.; Olivier, B. G.; Teusink, B. (2012). "FAME, the Flux Analysis and Modeling Environment". BMC Systems Biology. 6: 8. doi:10.1186/1752-0509-6-8. PMC 3317868. PMID 22289213.
  16. ^ Rocha, I.; Maia, P.; Evangelista, P.; Vilaça, P.; Soares, S. O.; Pinto, J. P.; Nielsen, J.; Patil, K. R.; Ferreira, E. N. C.; Rocha, M. (2010). "OptFlux: An open-source software platform for in silico metabolic engineering". BMC Systems Biology. 4: 45. doi:10.1186/1752-0509-4-45. PMC 2864236. PMID 20403172.
  17. ^ Grafahrend-Belau, E.; Klukas, C.; Junker, B. H.; Schreiber, F. (2009). "FBA-SimVis: Interactive visualization of constraint-based metabolic models". Bioinformatics. 25 (20): 2755–2757. doi:10.1093/bioinformatics/btp408. PMC 2759546. PMID 19578041.
  18. ^ Schellenberger, J.; Que, R.; Fleming, R. M. T.; Thiele, I.; Orth, J. D.; Feist, A. M.; Zielinski, D. C.; Bordbar, A.; Lewis, N. E.; Rahmanian, S.; Kang, J.; Hyduke, D. R.; Palsson, B. Ø. (2011). "Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0". Nature Protocols. 6 (9): 1290–1307. doi:10.1038/nprot.2011.308. PMC 3319681. PMID 21886097.
  19. ^ Agren, R.; Liu, L.; Shoaie, S.; Vongsangnak, W.; Nookaew, I.; Nielsen, J. (2013). Maranas, Costas D (ed.). "The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum". PLOS Computational Biology. 9 (3): e1002980. Bibcode:2013PLSCB...9E2980A. doi:10.1371/journal.pcbi.1002980. PMC 3605104. PMID 23555215.
  20. ^ Mendes, P.; Hoops, S.; Sahle, S.; Gauges, R.; Dada, J.; Kummer, U. (2009). "Computational Modeling of Biochemical Networks Using COPASI". Systems Biology. Methods in Molecular Biology. Vol. 500. pp. 17–59. doi:10.1007/978-1-59745-525-1_2. ISBN 978-1-934115-64-0. PMID 19399433.
  21. ^ a b Aon, Miguel A.; Cortassa, Sonia (2015-07-22). "Systems Biology of the Fluxome". Processes. 3 (3): 607–618. doi:10.3390/pr3030607.
  22. ^ Kildegaard, HF.; Baycin-Hizal, D.; Lewis, NE.; Betenbaugh, MJ. (Mar 2013). "The emerging CHO systems biology era: harnessing the 'omics revolution for biotechnology". Curr Opin Biotechnol. 24 (6): 1102–7. doi:10.1016/j.copbio.2013.02.007. PMID 23523260.
  23. ^ Carlson, RP.; Oshota, OJ.; Taffs, RL. (2012). "Systems Analysis of Microbial Adaptations to Simultaneous Stresses". Reprogramming Microbial Metabolic Pathways. Subcellular Biochemistry. Vol. 64. pp. 139–57. doi:10.1007/978-94-007-5055-5_7. ISBN 978-94-007-5054-8. PMID 23080249. {{cite book}}: |journal= ignored (help)
  24. ^ Duarte, NC.; Becker, SA.; Jamshidi, N.; Thiele, I.; Mo, ML.; Vo, TD.; Srivas, R.; Palsson, BØ. (Feb 2007). "Global reconstruction of the human metabolic network based on genomic and bibliomic data". Proc Natl Acad Sci U S A. 104 (6): 1777–82. Bibcode:2007PNAS..104.1777D. doi:10.1073/pnas.0610772104. PMC 1794290. PMID 17267599.
  25. ^ a b Raman, Karthik; Rajagopalan, Preethi; Chandra, Nagasuma (2005). "Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-Tubercular Drugs". PLOS Computational Biology. 1 (5): e46. Bibcode:2005PLSCB...1...46R. doi:10.1371/journal.pcbi.0010046. PMC 1246807. PMID 16261191.
  26. ^ Lee, Deok-Sun; Burd, Henry; Liu, Jiangxia; Almaas, Eivind; Wiest, Olaf; Barabási, Albert-László; Oltvai, Zoltán N.; Kapatral, Vinayak (2009-06-15). "Comparative Genome-Scale Metabolic Reconstruction and Flux Balance Analysis of Multiple Staphylococcus aureus Genomes Identify Novel Antimicrobial Drug Targets". Journal of Bacteriology. 191 (12): 4015–4024. doi:10.1128/JB.01743-08. ISSN 0021-9193. PMC 2698402. PMID 19376871.
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