%0 Journal Article %1 Klamt2017From %A Klamt, Steffen %A Regensburger, Georg %A Gerstl, Matthias P. %A Jungreuthmayer, Christian %A Schuster, Stefan %A Mahadevan, Radhakrishnan %A Zanghellini, Jürgen %A Müller, Stefan %D 2017 %I Public Library of Science %J PLOS Computational Biology %K elementary-modes flux-analysis %N 4 %P e1005409+ %R 10.1371/journal.pcbi.1005409 %T From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints %U http://dx.doi.org/10.1371/journal.pcbi.1005409 %V 13 %X Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis FBA), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks.

@article{Klamt2017From, abstract = {Elementary flux modes ({EFMs}) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. {EFMs} are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. {EFMs} account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [{FBA}]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of {EFMs} is not directly applicable anymore. For this reason, there has been a conceptual gap between {EFM}-based (pathway) analysis methods and linear optimization ({FBA}) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors ({EFVs}). Only recently has the community started to recognize the potential of {EFVs} for metabolic network analysis. In fact, {EFVs} exactly represent the conceptual development required to generalize the idea of {EFMs} from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to {EFVs} that is accessible to a broad audience. We highlight the close relationship between {EFMs} and {EFVs} and demonstrate that almost all applications of {EFMs} (in flux cones) are possible for {EFVs} (in flux polyhedra) as well. In fact, certain properties can only be studied with {EFVs}. Thus, we conclude that {EFVs} provide a powerful and unifying framework for constraint-based modeling of metabolic networks.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Klamt, Steffen and Regensburger, Georg and Gerstl, Matthias P. and Jungreuthmayer, Christian and Schuster, Stefan and Mahadevan, Radhakrishnan and Zanghellini, J\"{u}rgen and M\"{u}ller, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2048f68b731ec0dfbc2c9f4b631d83c04/karthikraman}, citeulike-article-id = {14347398}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1005409}, day = 13, doi = {10.1371/journal.pcbi.1005409}, interhash = {be6bd86f004ad7127a2ae23fbfbc04bd}, intrahash = {048f68b731ec0dfbc2c9f4b631d83c04}, journal = {PLOS Computational Biology}, keywords = {elementary-modes flux-analysis}, month = apr, number = 4, pages = {e1005409+}, posted-at = {2017-06-22 07:52:24}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints}, url = {http://dx.doi.org/10.1371/journal.pcbi.1005409}, volume = 13, year = 2017 }