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Guil F, Hidalgo JF, García JM. On the representativeness and stability of a set of EFMs. BIOINFORMATICS (OXFORD, ENGLAND) 2023; 39:btad356. [PMID: 37252834 PMCID: PMC10264373 DOI: 10.1093/bioinformatics/btad356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/18/2023] [Accepted: 05/30/2023] [Indexed: 06/01/2023]
Abstract
MOTIVATION Elementary flux modes are a well-known tool for analyzing metabolic networks. The whole set of elementary flux modes (EFMs) cannot be computed in most genome-scale networks due to their large cardinality. Therefore, different methods have been proposed to compute a smaller subset of EFMs that can be used for studying the structure of the network. These latter methods pose the problem of studying the representativeness of the calculated subset. In this article, we present a methodology to tackle this problem. RESULTS We have introduced the concept of stability for a particular network parameter and its relation to the representativeness of the EFM extraction method studied. We have also defined several metrics to study and compare the EFM biases. We have applied these techniques to compare the relative behavior of previously proposed methods in two case studies. Furthermore, we have presented a new method for the EFM computation (PiEFM), which is more stable (less biased) than previous ones, has suitable representativeness measures, and exhibits better variability in the extracted EFMs. AVAILABILITY AND IMPLEMENTATION Software and additional material are freely available at https://github.com/biogacop/PiEFM.
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Affiliation(s)
- Francisco Guil
- Grupo de Arquitectura y Computación Paralela, Departamento de Ingeniería y Tecnología de Computadores, Facultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia 30100, Spain
| | - José F Hidalgo
- Grupo de Arquitectura y Computación Paralela, Departamento de Ingeniería y Tecnología de Computadores, Facultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia 30100, Spain
| | - José M García
- Grupo de Arquitectura y Computación Paralela, Departamento de Ingeniería y Tecnología de Computadores, Facultad de Informática, Universidad de Murcia, Campus de Espinardo, Murcia 30100, Spain
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2
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Buchner BA, Zanghellini J. EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search. BMC Bioinformatics 2021; 22:547. [PMID: 34758748 PMCID: PMC8579665 DOI: 10.1186/s12859-021-04417-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Background Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed mplrs—a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487, 2017). Here we test its applicability for EFM enumeration. Results We developed EFMlrs, a Python package that gives users access to the enumeration capabilities of mplrs. EFMlrs uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for mplrs as well as efmtool, providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, EFMlrs also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows. Conclusion We show that due to mplrs’s properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. EFMlrs is an open-source program that comes together with a designated workflow and can be easily installed via pip. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04417-9.
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Affiliation(s)
- Bianca A Buchner
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
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3
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Guil F, Hidalgo JF, García JM. Flux Coupling and the Objective Functions' Length in EFMs. Metabolites 2020; 10:E489. [PMID: 33260526 PMCID: PMC7759806 DOI: 10.3390/metabo10120489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/18/2020] [Accepted: 11/24/2020] [Indexed: 11/24/2022] Open
Abstract
Structural analysis of constraint-based metabolic network models attempts to find the network's properties by searching for subsets of suitable modes or Elementary Flux Modes (EFMs). One useful approach is based on Linear Program (LP) techniques, which introduce an objective function to convert the stoichiometric and thermodynamic constraints into a linear program (LP), using additional constraints to generate different nontrivial modes. This work introduces FLFS-FC (Fixed Length Function Sampling with Flux Coupling), a new approach to increase the efficiency of generation of large sets of different EFMs for the network. FLFS-FC is based on the importance of the length of the objective functions used in the associated LP problem and the imposition of additional negative constraints. Our proposal overrides some of the known drawbacks associated with the EFM extraction, such as the appearance of unfeasible problems or multiple repeated solutions arising from different LP problems.
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Affiliation(s)
| | - José F. Hidalgo
- Grupo de Arquitectura y Computación Paralela, Universidad de Murcia, 30080 Murcia, Spain; (F.G.); (J.M.G.)
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4
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Guil F, Hidalgo JF, García JM. Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach. Bioinformatics 2020; 36:4163-4170. [PMID: 32348455 PMCID: PMC7390993 DOI: 10.1093/bioinformatics/btaa280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/16/2020] [Accepted: 04/22/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. RESULTS Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. AVAILABILITY AND IMPLEMENTATION Software is freely available at https://github.com/biogacop/Boost_LP_EFM. CONTACT fguil@um.es. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Guil
- Departamento de Ingeniería y Tecnología de Computadores, Universidad de Murcia, Murcia 30080, Spain
| | - José F Hidalgo
- Departamento de Ingeniería y Tecnología de Computadores, Universidad de Murcia, Murcia 30080, Spain
| | - José M García
- Departamento de Ingeniería y Tecnología de Computadores, Universidad de Murcia, Murcia 30080, Spain
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5
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Sarathy C, Kutmon M, Lenz M, Adriaens ME, Evelo CT, Arts IC. EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models. Metabolites 2020; 10:metabo10020066. [PMID: 32059585 PMCID: PMC7074156 DOI: 10.3390/metabo10020066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 12/22/2022] Open
Abstract
Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.
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Affiliation(s)
- Chaitra Sarathy
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Correspondence:
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
- Preventive Cardiology and Preventive Medicine—Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Michiel E. Adriaens
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Chris T. Evelo
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Ilja C.W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, 6229 ER Maastricht, The Netherlands
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6
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Ullah E, Yosafshahi M, Hassoun S. Towards scaling elementary flux mode computation. Brief Bioinform 2019; 21:1875-1885. [PMID: 31745550 DOI: 10.1093/bib/bbz094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 01/05/2023] Open
Abstract
While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.
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Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mona Yosafshahi
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford MA 02155, USA
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7
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Vieira V, Maia P, Rocha M, Rocha I. Comparison of pathway analysis and constraint-based methods for cell factory design. BMC Bioinformatics 2019; 20:350. [PMID: 31221092 PMCID: PMC6585037 DOI: 10.1186/s12859-019-2934-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 06/05/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. RESULTS In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in Saccharomyces cerevisiae. We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering. CONCLUSIONS The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo.
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Affiliation(s)
- Vítor Vieira
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
| | | | - Miguel Rocha
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
| | - Isabel Rocha
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal
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8
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Song HS, Goldberg N, Mahajan A, Ramkrishna D. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics 2018; 33:2345-2353. [PMID: 28369193 DOI: 10.1093/bioinformatics/btx171] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 03/23/2017] [Indexed: 01/22/2023] Open
Abstract
Motivation Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. Availability and Implementation The software is implemented in Matlab, and is provided as supplementary information . Contact hyunseob.song@pnnl.gov. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Noam Goldberg
- Department of Management, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Ashutosh Mahajan
- Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai 400076, India
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9
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Zanghellini J, Gerstl MP, Hanscho M, Nair G, Regensburger G, Müller S, Jungreuthmayer C. Toward Genome-Scale Metabolic Pathway Analysis. Ind Biotechnol (New Rochelle N Y) 2016. [DOI: 10.1002/9783527807796.ch3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Jürgen Zanghellini
- Department of Biotechnology; University of Natural Resources and Life Sciences; Vienna, Muthgasse 18 A1190 Vienna Austria EU
- Austrian Centre of Industrial Biotechnology (ACIB); Muthgasse 11 A1190 Vienna Austria EU
| | - Matthias P. Gerstl
- Austrian Centre of Industrial Biotechnology (ACIB); Muthgasse 11 A1190 Vienna Austria EU
| | - Michael Hanscho
- Austrian Centre of Industrial Biotechnology (ACIB); Muthgasse 11 A1190 Vienna Austria EU
| | - Govind Nair
- Department of Biotechnology; University of Natural Resources and Life Sciences; Vienna, Muthgasse 18 A1190 Vienna Austria EU
- Austrian Centre of Industrial Biotechnology (ACIB); Muthgasse 11 A1190 Vienna Austria EU
| | - Georg Regensburger
- Institute for Algebra; Johannes Kepler University Linz; Altenberger Straβe 69 A-4040 Linz Austria EU
| | - Stefan Müller
- Johann Radon Institute for Computational and Applied Mathematics; Austrian Academy of Sciences; Altenberger Straβe 69 A-4040 Linz Austria EU
| | - Christian Jungreuthmayer
- Austrian Centre of Industrial Biotechnology (ACIB); Muthgasse 11 A1190 Vienna Austria EU
- TGM - Technologisches Gewerbemuseum; Wexstraβe 19-23 A1200 Vienna Austria EU
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10
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Gerstl MP, Jungreuthmayer C, Müller S, Zanghellini J. Which sets of elementary flux modes form thermodynamically feasible flux distributions? FEBS J 2016; 283:1782-94. [PMID: 26940826 PMCID: PMC4949704 DOI: 10.1111/febs.13702] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2015] [Revised: 12/24/2015] [Accepted: 02/29/2016] [Indexed: 01/10/2023]
Abstract
Elementary flux modes (EFMs) are non-decomposable steady-state fluxes through metabolic networks. Every possible flux through a network can be described as a superposition of EFMs. The definition of EFMs is based on the stoichiometry of the network, and it has been shown previously that not all EFMs are thermodynamically feasible. These infeasible EFMs cannot contribute to a biologically meaningful flux distribution. In this work, we show that a set of thermodynamically feasible EFMs need not be thermodynamically consistent. We use first principles of thermodynamics to define the feasibility of a flux distribution and present a method to compute the largest thermodynamically consistent sets (LTCSs) of EFMs. An LTCS contains the maximum number of EFMs that can be combined to form a thermodynamically feasible flux distribution. As a case study we analyze all LTCSs found in Escherichia coli when grown on glucose and show that only one LTCS shows the required phenotypical properties. Using our method, we find that in our E. coli model < 10% of all EFMs are thermodynamically relevant.
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Affiliation(s)
- Matthias P Gerstl
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Christian Jungreuthmayer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Stefan Müller
- Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
| | - Jürgen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, Vienna, Austria
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11
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Dersch LM, Beckers V, Wittmann C. Green pathways: Metabolic network analysis of plant systems. Metab Eng 2016; 34:1-24. [DOI: 10.1016/j.ymben.2015.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/30/2015] [Accepted: 12/01/2015] [Indexed: 12/18/2022]
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12
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Tobalina L, Pey J, Planes FJ. Direct calculation of minimal cut sets involving a specific reaction knock-out. Bioinformatics 2016; 32:2001-7. [PMID: 27153694 DOI: 10.1093/bioinformatics/btw072] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 02/02/2016] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION The concept of Minimal Cut Sets (MCSs) is used in metabolic network modeling to describe minimal groups of reactions or genes whose simultaneous deletion eliminates the capability of the network to perform a specific task. Previous work showed that MCSs where closely related to Elementary Flux Modes (EFMs) in a particular dual problem, opening up the possibility to use the tools developed for computing EFMs to compute MCSs. Until recently, however, there existed no method to compute an EFM with some specific characteristic, meaning that, in the case of MCSs, the only strategy to obtain them was to enumerate them using, for example, the standard K-shortest EFMs algorithm. RESULTS In this work, we adapt the recently developed theory to compute EFMs satisfying several constraints to the calculation of MCSs involving a specific reaction knock-out. Importantly, we emphasize that not all the EFMs in the dual problem correspond to real MCSs, and propose a new formulation capable of correctly identifying the MCS wanted. Furthermore, this formulation brings interesting insights about the relationship between the primal and the dual problem of the MCS computation. AVAILABILITY AND IMPLEMENTATION A Matlab-Cplex implementation of the proposed algorithm is available as a supplementary material CONTACT fplanes@ceit.es SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain
| | - Jon Pey
- CEIT and Tecnun, University of Navarra, San Sebastián 20018, Spain
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