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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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Advances in metabolic engineering of Corynebacterium glutamicum to produce high-value active ingredients for food, feed, human health, and well-being. Essays Biochem 2021; 65:197-212. [PMID: 34096577 PMCID: PMC8313993 DOI: 10.1042/ebc20200134] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
The soil microbe Corynebacterium glutamicum is a leading workhorse in industrial biotechnology and has become famous for its power to synthetise amino acids and a range of bulk chemicals at high titre and yield. The product portfolio of the microbe is continuously expanding. Moreover, metabolically engineered strains of C. glutamicum produce more than 30 high value active ingredients, including signature molecules of raspberry, savoury, and orange flavours, sun blockers, anti-ageing sugars, and polymers for regenerative medicine. Herein, we highlight recent advances in engineering of the microbe into novel cell factories that overproduce these precious molecules from pioneering proofs-of-concept up to industrial productivity.
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Becker J, Wittmann C. Metabolic Engineering of
Corynebacterium glutamicum. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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4
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Tangyu M, Fritz M, Aragao-Börner R, Ye L, Bogicevic B, Bolten CJ, Wittmann C. Genome-based selection and application of food-grade microbes for chickpea milk fermentation towards increased L-lysine content, elimination of indigestible sugars, and improved flavour. Microb Cell Fact 2021; 20:109. [PMID: 34049541 PMCID: PMC8161961 DOI: 10.1186/s12934-021-01595-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/15/2021] [Indexed: 01/01/2023] Open
Abstract
Background Plant-based milk alternatives are more popular than ever, and chickpea-based milks are among the most commercially relevant products. Unfortunately, limited nutritional value because of low levels of the essential amino acid l-lysine, low digestibility and unpleasant taste are challenges that must be addressed to improve product quality and meet consumer expectations. Results Using in-silico screening and food safety classifications, 31 strains were selected as potential l-lysine producers from approximately 2,500 potential candidates. Beneficially, 30% of the isolates significantly accumulated amino acids (up to 1.4 mM) during chickpea milk fermentation, increasing the natural level by up to 43%. The best-performing strains, B. amyloliquefaciens NCC 156 and L. paracasei subsp. paracasei NCC 2511, were tested further. De novo lysine biosynthesis was demonstrated in both strains by 13C metabolic pathway analysis. Spiking small amounts of citrate into the fermentation significantly activated l-lysine biosynthesis in NCC 156 and stimulated growth. Both microbes revealed additional benefits in eliminating indigestible sugars such as stachyose and raffinose and converting off-flavour aldehydes into the corresponding alcohols and acids with fruity and sweet notes. Conclusions B. amyloliquefaciens NCC 156 and L. paracasei subsp. paracasei NCC 2511 emerged as multi-benefit microbes for chickpea milk fermentation with strong potential for industrial processing of the plant material. Given the high number of l-lysine-producing isolates identified in silico, this concept appears promising to support strain selection for food fermentation. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-021-01595-2.
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Affiliation(s)
- Muzi Tangyu
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | - Michel Fritz
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany
| | | | - Lijuan Ye
- Nestlé Research Center, Lausanne, Switzerland
| | | | | | - Christoph Wittmann
- Institute of Systems Biotechnology, Saarland University, Saarbrücken, Germany.
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Answer Set Programming for Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism. Processes (Basel) 2020. [DOI: 10.3390/pr8121649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Elementary Flux Modes (EFMs) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of EFMs typically grows exponentially with the size of the metabolic network, leading to excessive computational demands, and unfortunately, a large fraction of these EFMs are not biologically feasible due to system constraints. This combinatorial explosion often prevents the complete analysis of genome-scale metabolic models. Traditionally, EFMs are computed by the double description method, an efficient algorithm based on matrix calculation; however, only a few constraints can be integrated into this computation. They must be monotonic with regard to the set inclusion of the supports; otherwise, they must be treated in post-processing and thus do not save computational time. We present aspefm, a hybrid computational tool based on Answer Set Programming (ASP) and Linear Programming (LP) that permits the computation of EFMs while implementing many different types of constraints. We apply our methodology to the Escherichia coli core model, which contains 226×106 EFMs. In considering transcriptional and environmental regulation, thermodynamic constraints, and resource usage considerations, the solution space is reduced to 1118 EFMs that can be computed directly with aspefm. The solution set, for E. coli growth on O2 gradients spanning fully aerobic to anaerobic, can be further reduced to four optimal EFMs using post-processing and Pareto front analysis.
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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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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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8
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Röhl A, Riou T, Bockmayr A. Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection. Bioinformatics 2020; 35:2618-2625. [PMID: 30590390 DOI: 10.1093/bioinformatics/bty1027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 12/06/2018] [Accepted: 12/14/2018] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks. RESULTS Here we introduce irreversible minimal cut sets (iMCSs). These are MCSs that consist of irreversible reactions only. The advantage of iMCSs is that they can be computed by projecting the flux cone of the metabolic network on the set of irreversible reactions, which usually leads to a smaller cone. Using oriented matroid theory, we show how the projected cone can be computed efficiently and how this can be applied to find iMCSs even in large genome-scale networks. AVAILABILITY AND IMPLEMENTATION Software is freely available at https://sourceforge.net/projects/irreversibleminimalcutsets/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Annika Röhl
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
| | - Tanguy Riou
- Department FRANCE, Ecole Centrale de Nantes, Nantes, France
| | - Alexander Bockmayr
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
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9
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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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10
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Röhl A, Bockmayr A. Finding MEMo: minimum sets of elementary flux modes. J Math Biol 2019; 79:1749-1777. [PMID: 31388689 DOI: 10.1007/s00285-019-01409-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 07/15/2019] [Indexed: 10/26/2022]
Abstract
Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.
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Affiliation(s)
- Annika Röhl
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
| | - Alexander Bockmayr
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany
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11
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H. Identification of metabolic pathways using pathfinding approaches: a systematic review. Brief Funct Genomics 2017; 16:87-98. [PMID: 26969656 DOI: 10.1093/bfgp/elw002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
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Affiliation(s)
- Zeyad Abd Algfoor
- MaGIC-X (Media and Games Innovation Centre of Excellence), UTM-IRDA Digital Media Centre, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Mohd Shahrizal Sunar
- MaGIC-X (Media and Games Innovation Centre of Excellence), UTM-IRDA Digital Media Centre, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Afnizanfaizal Abdullah
- Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.,Duke Global Health Institute, Duke University, Durham, NC, USA.,Global Health Program, Duke Kunshan University, Jiangsu, China
| | - Hoshang Kolivand
- Department of Computer Science, Liverpool John Moores University, Liverpool, UK
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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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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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15
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Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 2015; 5:16009. [PMID: 26530953 PMCID: PMC4632160 DOI: 10.1038/srep16009] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 10/07/2015] [Indexed: 02/07/2023] Open
Abstract
Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.
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Horvat P, Koller M, Braunegg G. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World J Microbiol Biotechnol 2015; 31:1315-28. [DOI: 10.1007/s11274-015-1887-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 06/05/2015] [Indexed: 11/25/2022]
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17
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Becker J, Wittmann C. Advanced Biotechnology: Metabolically Engineered Cells for the Bio-Based Production of Chemicals and Fuels, Materials, and Health-Care Products. Angew Chem Int Ed Engl 2015; 54:3328-50. [DOI: 10.1002/anie.201409033] [Citation(s) in RCA: 223] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Indexed: 12/16/2022]
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18
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Biotechnologie von Morgen: metabolisch optimierte Zellen für die bio-basierte Produktion von Chemikalien und Treibstoffen, Materialien und Gesundheitsprodukten. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201409033] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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19
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Pey J, Villar JA, Tobalina L, Rezola A, García JM, Beasley JE, Planes FJ. TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm. ACTA ACUST UNITED AC 2014; 31:897-904. [PMID: 25380956 DOI: 10.1093/bioinformatics/btu733] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Elementary flux modes (EFMs) analysis constitutes a fundamental tool in systems biology. However, the efficient calculation of EFMs in genome-scale metabolic networks (GSMNs) is still a challenge. We present a novel algorithm that uses a linear programming-based tree search and efficiently enumerates a subset of EFMs in GSMNs. RESULTS Our approach is compared with the EFMEvolver approach, demonstrating a significant improvement in computation time. We also validate the usefulness of our new approach by studying the acetate overflow metabolism in the Escherichia coli bacteria. To do so, we computed 1 million EFMs for each energetic amino acid and then analysed the relevance of each energetic amino acid based on gene/protein expression data and the obtained EFMs. We found good agreement between previous experiments and the conclusions reached using EFMs. Finally, we also analysed the performance of our approach when applied to large GSMNs. AVAILABILITY AND IMPLEMENTATION The stand-alone software TreeEFM is implemented in C++ and interacts with the open-source linear solver COIN-OR Linear program Solver (CLP).
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Juan A Villar
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Luis Tobalina
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Alberto Rezola
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - José Manuel García
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - John E Beasley
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
| | - Francisco J Planes
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain, Computer Engineering Department, School of Computer Science, POB 30100 University of Murcia, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH Uxbridge, UK
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Oddsdóttir HÆ, Hagrot E, Chotteau V, Forsgren A. On dynamically generating relevant elementary flux modes in a metabolic network using optimization. J Math Biol 2014; 71:903-20. [PMID: 25323319 DOI: 10.1007/s00285-014-0844-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 10/06/2014] [Indexed: 11/24/2022]
Abstract
Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution.
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Affiliation(s)
- Hildur Æsa Oddsdóttir
- Department of Mathematics, Optimization and Systems Theory, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden,
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Chan SHJ, Solem C, Jensen PR, Ji P. Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property. ACTA ACUST UNITED AC 2014; 30:3232-9. [PMID: 25100687 DOI: 10.1093/bioinformatics/btu529] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
MOTIVATION Elementary flux mode (EFM) is a useful tool in constraint-based modeling of metabolic networks. The property that every flux distribution can be decomposed as a weighted sum of EFMs allows certain applications of EFMs to studying flux distributions. The existence of biologically infeasible EFMs and the non-uniqueness of the decomposition, however, undermine the applicability of such methods. Efforts have been made to find biologically feasible EFMs by incorporating information from transcriptional regulation and thermodynamics. Yet, no attempt has been made to distinguish biologically feasible EFMs by considering their graphical properties. A previous study on the transcriptional regulation of metabolic genes found that distinct branches at a branch point metabolite usually belong to distinct metabolic pathways. This suggests an intuitive property of biologically feasible EFMs, i.e. minimal branching. RESULTS We developed the concept of minimal branching EFM and derived the minimal branching decomposition (MBD) to decompose flux distributions. Testing in the core Escherichia coli metabolic network indicated that MBD can distinguish branches at branch points and greatly reduced the solution space in which the decomposition is often unique. An experimental flux distribution from a previous study on mouse cardiomyocyte was decomposed using MBD. Comparison with decomposition by a minimum number of EFMs showed that MBD found EFMs more consistent with established biological knowledge, which facilitates interpretation. Comparison of the methods applied to a complex flux distribution in Lactococcus lactis similarly showed the advantages of MBD. The minimal branching EFM concept underlying MBD should be useful in other applications. CONTACT sinhu@bio.dtu.dk or p.ji@polyu.edu.hk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Siu Hung Joshua Chan
- Systems Biotechnology and Biorefining, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Christian Solem
- Systems Biotechnology and Biorefining, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Peter Ruhdal Jensen
- Systems Biotechnology and Biorefining, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Ping Ji
- Systems Biotechnology and Biorefining, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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22
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Rezola A, Pey J, Rubio Á, Planes FJ. In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes. PLoS One 2014; 9:e103998. [PMID: 25093336 PMCID: PMC4122379 DOI: 10.1371/journal.pone.0103998] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 07/09/2014] [Indexed: 01/11/2023] Open
Abstract
Metabolism expresses the phenotype of living cells and understanding it is crucial for different applications in biotechnology and health. With the increasing availability of metabolomic, proteomic and, to a larger extent, transcriptomic data, the elucidation of specific metabolic properties in different scenarios and cell types is a key topic in systems biology. Despite the potential of the elementary flux mode (EFM) concept for this purpose, its use has been limited so far, mainly because their computation has been infeasible for genome-scale metabolic networks. In a recent work, we determined a subset of EFMs in human metabolism and proposed a new protocol to integrate gene expression data, spotting key 'characteristic EFMs' in different scenarios. Our approach was successfully applied to identify metabolic differences among several human healthy tissues. In this article, we evaluated the performance of our approach in clinically interesting situation. In particular, we identified key EFMs and metabolites in adenocarcinoma and squamous-cell carcinoma subtypes of non-small cell lung cancers. Results are consistent with previous knowledge of these major subtypes of lung cancer in the medical literature. Therefore, this work constitutes the starting point to establish a new methodology that could lead to distinguish key metabolic processes among different clinical outcomes.
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Affiliation(s)
- Alberto Rezola
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Jon Pey
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Ángel Rubio
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
| | - Francisco J. Planes
- Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain
- * E-mail:
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Bazzani S. Promise and reality in the expanding field of network interaction analysis: metabolic networks. Bioinform Biol Insights 2014; 8:83-91. [PMID: 24812497 PMCID: PMC3999820 DOI: 10.4137/bbi.s12466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 03/02/2014] [Accepted: 03/03/2014] [Indexed: 12/25/2022] Open
Abstract
In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.
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Affiliation(s)
- Susanna Bazzani
- PhD candidate in Biophysics. Former laboratory: Computational Systems Biochemistry Group, Charitè Universitätsmedizin, Berlin, Germany
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24
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Pey J, Planes FJ. Direct calculation of elementary flux modes satisfying several biological constraints in genome-scale metabolic networks. ACTA ACUST UNITED AC 2014; 30:2197-203. [PMID: 24728852 DOI: 10.1093/bioinformatics/btu193] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy. RESULTS Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs. AVAILABILITY AND IMPLEMENTATION Matlab code is provided in the supplementary online materials CONTACT fplanes@ceit.es. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain
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25
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Rezola A, Pey J, Tobalina L, Rubio A, Beasley JE, Planes FJ. Advances in network-based metabolic pathway analysis and gene expression data integration. Brief Bioinform 2014; 16:265-79. [DOI: 10.1093/bib/bbu009] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Abstract
MOTIVATION Pathway analysis tools are a powerful strategy to analyze 'omics' data in the field of systems biology. From a metabolic perspective, several pathway definitions can be found in the literature, each one appropriate for a particular study. Recently, a novel pathway concept termed carbon flux paths (CFPs) was introduced and benchmarked against existing approaches, showing a clear advantage for finding linear pathways from a given source to target metabolite. CFPs are simple paths in a metabolite-metabolite graph that satisfy typical constraints in stoichiometric models: mass balancing and thermodynamics (irreversibility). In addition, CFPs guarantee carbon exchange in each of their intermediate steps, but not between the source and the target metabolites and consequently false positive solutions may arise. These pathways often lack biological interest, particularly when studying biosynthetic or degradation routes of a metabolite. To overcome this issue, we amend the formulation in CFP, so as to account for atomic fate information. This approach is termed atomic CFP (aCFP). RESULTS By means of a side-by-side comparison in a medium scale metabolic network in Escherichia Coli, we show that aCFP provides more biologically relevant pathways than CFP, because canonical pathways are more easily recovered, which reflects the benefits of removing false positives. In addition, we demonstrate that aCFP can be successfully applied to genome-scale metabolic networks. As the quality of genome-scale atomic reconstruction is improved, methods such as the one presented here will undoubtedly be of value to interpret 'omics' data.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK
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28
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Ates O, Arga KY, Oner ET. The stimulatory effect of mannitol on levan biosynthesis: Lessons from metabolic systems analysis ofHalomonas smyrnensisAAD6T. Biotechnol Prog 2013; 29:1386-97. [DOI: 10.1002/btpr.1823] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Revised: 10/02/2013] [Indexed: 01/21/2023]
Affiliation(s)
- Ozlem Ates
- Dept. of Bioengineering; Marmara University; Goztepe 34722 Istanbul Turkey
| | - Kazim Y. Arga
- Dept. of Bioengineering; Marmara University; Goztepe 34722 Istanbul Turkey
| | - Ebru Toksoy Oner
- Dept. of Bioengineering; Marmara University; Goztepe 34722 Istanbul Turkey
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29
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Seifert J, Herbst FA, Halkjaer Nielsen P, Planes FJ, Jehmlich N, Ferrer M, von Bergen M. Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics 2013; 13:2786-804. [PMID: 23625762 DOI: 10.1002/pmic.201200566] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Revised: 03/07/2013] [Accepted: 03/28/2013] [Indexed: 11/06/2022]
Abstract
Metaproteomics of microbial communities promises to add functional information to the blueprint of genes derived from metagenomics. Right from its beginning, the achievements and developments in metaproteomics were closely interlinked with metagenomics. In addition, the evaluation, visualization, and interpretation of metaproteome data demanded for the developments in bioinformatics. This review will give an overview about recent strategies to use genomic data either from public databases or organismal specific genomes/metagenomes to increase the number of identified proteins obtained by mass spectrometric measurements. We will review different published metaproteogenomic approaches in respect to the used MS pipeline and to the used protein identification workflow. Furthermore, different approaches of data visualization and strategies for phylogenetic interpretation of metaproteome data are discussed as well as approaches for functional mapping of the results to the investigated biological systems. This information will in the end allow a comprehensive analysis of interactions and interdependencies within microbial communities.
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Affiliation(s)
- Jana Seifert
- Department of Proteomics, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany; Institute of Animal Nutrition, University of Hohenheim, Stuttgart, Germany
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Rezola A, Pey J, de Figueiredo LF, Podhorski A, Schuster S, Rubio A, Planes FJ. Selection of human tissue-specific elementary flux modes using gene expression data. ACTA ACUST UNITED AC 2013; 29:2009-16. [PMID: 23742984 DOI: 10.1093/bioinformatics/btt328] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. RESULTS In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.
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Affiliation(s)
- Alberto Rezola
- Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain
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31
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Jevremović D, Boley D. Finding minimal generating set for metabolic network with reversible pathways. Biosystems 2013; 112:31-6. [PMID: 23474418 DOI: 10.1016/j.biosystems.2013.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2012] [Revised: 10/07/2012] [Accepted: 02/06/2013] [Indexed: 11/17/2022]
Abstract
Elementary flux modes give a mathematical representation of metabolic pathways in metabolic networks satisfying the constraint of non-decomposability. The large cost of their computation shifts attention to computing a minimal generating set which is a conically independent subset of elementary flux modes. When a metabolic network has reversible reactions and also admits a reversible pathway, the minimal generating set is not unique. A theoretical development and computational framework is provided which outline how to compute the minimal generating set in this case. The method is based on combining existing software to compute the minimal generating set for a "pointed cone" together with standard software to compute the Reduced Row Echelon Form.
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Affiliation(s)
- Dimitrije Jevremović
- Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
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32
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Jouhten P. Metabolic modelling in the development of cell factories by synthetic biology. Comput Struct Biotechnol J 2012; 3:e201210009. [PMID: 24688669 PMCID: PMC3962133 DOI: 10.5936/csbj.201210009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 11/05/2012] [Accepted: 11/07/2012] [Indexed: 11/22/2022] Open
Abstract
Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.
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Affiliation(s)
- Paula Jouhten
- VTT Technical Research Centre of Finland, Tietotie 2, 02044 VTT, Espoo, Finland
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33
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Dandekar T, Fieselmann A, Majeed S, Ahmed Z. Software applications toward quantitative metabolic flux analysis and modeling. Brief Bioinform 2012; 15:91-107. [PMID: 23142828 DOI: 10.1093/bib/bbs065] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches.
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Affiliation(s)
- Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Wüerzburg, Am Hubland, 97074 Wuerzburg, Germany. Tel.: +49-931-318-4551; Fax: +49-931-318-4552;
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34
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Machado D, Soons Z, Patil KR, Ferreira EC, Rocha I. Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics 2012; 28:i515-i521. [PMID: 22962475 PMCID: PMC3436828 DOI: 10.1093/bioinformatics/bts401] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. RESULTS Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. AVAILABILITY Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. CONTACT dmachado@deb.uminho.pt SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Machado
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
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Gebauer J, Schuster S, de Figueiredo LF, Kaleta C. Detecting and investigating substrate cycles in a genome-scale human metabolic network. FEBS J 2012; 279:3192-202. [PMID: 22776428 DOI: 10.1111/j.1742-4658.2012.08700.x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Substrate cycles, also known as futile cycles, are cyclic metabolic routes that dissipate energy by hydrolysing cofactors such as ATP. They were first described to occur in the muscles of bumblebees and brown adipose tissue in the 1970s. A popular example is the conversion of fructose 6-phosphate to fructose 1,6-bisphosphate and back. In the present study, we analyze a large number of substrate cycles in human metabolism that consume ATP and discuss their statistics. For this purpose, we use two recently published methods (i.e. EFMEvolver and the K-shortest EFM method) to calculate samples of 100,000 and 15,000 substrate cycles, respectively. We find an unexpectedly high number of substrate cycles in human metabolism, with up to 100 reactions per cycle, utilizing reactions from up to six different compartments. An analysis of tissue-specific models of liver and brain metabolism shows that there is selective pressure that acts against the uncontrolled dissipation of energy by avoiding the coexpression of enzymes belonging to the same substrate cycle. This selective force is particularly strong against futile cycles that have a high flux as a result of thermodynamic principles.
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Affiliation(s)
- Juliane Gebauer
- Department of Bioinformatics, School of Biology and Pharmaceutics and JenAge Research Core, Friedrich Schiller University of Jena, Germany
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36
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Wang J, Zhang Y, Marian C, Ressom HW. Identification of aberrant pathways and network activities from high-throughput data. Brief Bioinform 2012; 13:406-19. [PMID: 22287794 PMCID: PMC3404398 DOI: 10.1093/bib/bbs001] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2011] [Revised: 01/03/2012] [Indexed: 02/06/2023] Open
Abstract
Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.
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Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 2012; 10:291-305. [PMID: 22367118 DOI: 10.1038/nrmicro2737] [Citation(s) in RCA: 537] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.
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Affiliation(s)
- Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0412, USA
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38
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Tonon T, Eveillard D, Prigent S, Bourdon J, Potin P, Boyen C, Siegel A. Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2011; 15:883-92. [PMID: 22136637 DOI: 10.1089/omi.2011.0089] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach.
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Affiliation(s)
- Thierry Tonon
- UPMC Univ Paris 6 , UMR 7139 Marine Plants and Biomolecules, Station Biologique, 29680 Roscoff, France.
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Combining Metabolic Pathway Analysis with Evolutionary Game Theory. Explaining the occurrence of low-yield pathways by an analytic optimization approach. Biosystems 2011; 105:147-53. [DOI: 10.1016/j.biosystems.2011.05.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Revised: 05/12/2011] [Accepted: 05/12/2011] [Indexed: 01/22/2023]
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Kaleta C, de Figueiredo LF, Werner S, Guthke R, Ristow M, Schuster S. In silico evidence for gluconeogenesis from fatty acids in humans. PLoS Comput Biol 2011; 7:e1002116. [PMID: 21814506 PMCID: PMC3140964 DOI: 10.1371/journal.pcbi.1002116] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2011] [Accepted: 05/24/2011] [Indexed: 11/25/2022] Open
Abstract
The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is “No”. Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO2. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet. That sugar can be converted into fatty acids in humans is a well-known fact. The question whether the reverse direction, i.e., gluconeogenesis from fatty acids, is also feasible has been a topic of intense debate since the end of the 19th century. With the discovery of the glyoxylate shunt that allows this conversion in some bacteria, plants, fungi and nematodes it has been considered infeasible in humans since the corresponding enzymes could not be detected. However, by this finding only a single route for gluconeogenesis from fatty acids has been ruled out. To address the question whether there might exist alternative routes in humans we searched for gluconeogenic routes from fatty acids in a metabolic network comprising all reactions known to take place in humans. Thus, we were able to identify several pathways showing that this conversion is indeed feasible. Analyzing evidence concerning the detected pathways lends support to their importance during times of starvation, fasting, carbohydrate reduced and ketogenic diets and other situations in which the nutrition is low on carbohydrates. Moreover, the energetic investment required for this pathway can help to explain the particular efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet.
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Affiliation(s)
- Christoph Kaleta
- Department of Bioinformatics, School of Biology and Pharmaceutics, Friedrich Schiller University of Jena, Jena, Germany.
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Chan SHJ, Ji P. Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks. ACTA ACUST UNITED AC 2011; 27:2256-62. [PMID: 21685054 DOI: 10.1093/bioinformatics/btr367] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
MOTIVATION Elementary flux mode (EFM) is a fundamental concept as well as a useful tool in metabolic pathway analysis. One important role of EFMs is that every flux distribution can be decomposed into a set of EFMs and a number of methods to study flux distributions originated from it. Yet finding such decompositions requires the complete set of EFMs, which is intractable in genome-scale metabolic networks due to combinatorial explosion. RESULTS In this article, we proposed an algorithm to decompose flux distributions into EFMs in genome-scale networks. It is an iterative scheme of a mixed integer linear program. Unlike previous optimization models to find pathways, any feasible solutions can become EFMs in our algorithm. This advantage enables the algorithm to approximate the EFM of largest contribution to an objective reaction in a flux distribution. Our algorithm is able to find EFMs of flux distributions with complex structures, closer to the realistic case in which a cell is subject to various constraints. A case of Escherichia coli growth in the Lysogeny broth (LB) medium containing various carbon sources was studied. Essential metabolites and their syntheses were located. Information on the contribution of each carbon source not obvious from the apparent flux distribution was also revealed. Our work further confirms the utility of finding EFMs by optimization models in genome-scale metabolic networks. CONTACT joshua.chan@connect.polyu.hk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Siu Hung Joshua Chan
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
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Pey J, Prada J, Beasley JE, Planes FJ. Path finding methods accounting for stoichiometry in metabolic networks. Genome Biol 2011; 12:R49. [PMID: 21619601 PMCID: PMC3219972 DOI: 10.1186/gb-2011-12-5-r49] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2011] [Revised: 05/14/2011] [Accepted: 05/27/2011] [Indexed: 01/30/2023] Open
Abstract
Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain
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