DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks
The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expr...
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| Published in | PLoS computational biology Vol. 17; no. 2; p. e1008730 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.02.2021
PLOS Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1008730 |
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| Abstract | The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce
DEXOM
, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in
Saccharomyces cerevisiae
using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide
DEXOM
as an open-source library compatible with COBRA Toolbox 3.0, available at
https://github.com/MetExplore/dexom
. |
|---|---|
| AbstractList | More specifically, post-transcriptional control of mRNA, post-translational modifications of enzymes, as well as biochemical constraints —including for example the laws for mass and charge conservation, cell growth requirements, biomass composition and nutrient availability— make the identification of which pathways are altered between conditions very complicated by the mere observation of changes in gene expression or changes in metabolite concentrations.
Several methods were proposed in the literature to automatically reconstruct context-specific metabolic networks from gene or protein expression, mostly based on Linear Programming (LP) or Mixed Integer Linear Programming (MILP) models [7–9, 13–17], as well as benchmarks comparing their capabilities [18, 19].
[...]it cannot recover the whole set of possible optimal metabolic networks, as not all possible combinations of reactions are tested.
[...]there is no guarantee that the solution set is representative and diverse of the full space of possible networks. The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom. Understanding deregulations of metabolism based on isolated measures of gene expression or protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for a given data and reconstruction method, there are usually multiple solutions that satisfy the same constraints and with the same quality, but only one solution is returned by the commonly used reconstruction methods. Here, we formalize this problem and we propose and analyze different methods to obtain diverse samples of metabolic sub-networks. We evaluate them by performing an extensive comparison and we show how the different sets of optimal networks discovered by the different methods are biological meaningful by constructing ensembles of networks to improve the prediction of essential genes in Saccharomyces cerevisiae and to detect enriched metabolic pathways in four different human cancer cell lines. The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom. The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM , an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom . The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom. The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different subnetworks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3. More specifically, post-transcriptional control of mRNA, post-translational modifications of enzymes, as well as biochemical constraints —including for example the laws for mass and charge conservation, cell growth requirements, biomass composition and nutrient availability— make the identification of which pathways are altered between conditions very complicated by the mere observation of changes in gene expression or changes in metabolite concentrations. Several methods were proposed in the literature to automatically reconstruct context-specific metabolic networks from gene or protein expression, mostly based on Linear Programming (LP) or Mixed Integer Linear Programming (MILP) models [7–9, 13–17], as well as benchmarks comparing their capabilities [18, 19]. [...]it cannot recover the whole set of possible optimal metabolic networks, as not all possible combinations of reactions are tested. [...]there is no guarantee that the solution set is representative and diverse of the full space of possible networks. |
| Author | de Blasio, Carlo Rodríguez-Mier, Pablo Poupin, Nathalie Le Cam, Laurent Jourdan, Fabien |
| AuthorAffiliation | 2 IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier, Montpellier, France Christian Albrechts Universitat zu Kiel, GERMANY 3 Equipe Labellisée par la Ligue contre le Cancer, Paris, France 1 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France |
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| Author_xml | – sequence: 1 givenname: Pablo orcidid: 0000-0002-4938-4418 surname: Rodríguez-Mier fullname: Rodríguez-Mier, Pablo – sequence: 2 givenname: Nathalie orcidid: 0000-0002-3393-1405 surname: Poupin fullname: Poupin, Nathalie – sequence: 3 givenname: Carlo orcidid: 0000-0003-2388-2555 surname: de Blasio fullname: de Blasio, Carlo – sequence: 4 givenname: Laurent surname: Le Cam fullname: Le Cam, Laurent – sequence: 5 givenname: Fabien orcidid: 0000-0001-9401-2894 surname: Jourdan fullname: Jourdan, Fabien |
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| CitedBy_id | crossref_primary_10_1287_ijoc_2022_0164 crossref_primary_10_1186_s12859_024_05845_z crossref_primary_10_1038_s41540_023_00280_x crossref_primary_10_1371_journal_pcbi_1011814 crossref_primary_10_3389_fsysb_2022_896265 crossref_primary_10_3390_a18030140 |
| Cites_doi | 10.1371/journal.pcbi.1005568 10.1371/journal.pcbi.1007764 10.1371/journal.pcbi.1004808 10.1038/msb.2010.56 10.1038/s41596-018-0098-2 10.1016/j.ccr.2012.02.014 10.1093/nar/gkr1029 10.1038/cddis.2013.60 10.1038/msb.2011.51 10.1186/1471-2164-10-461 10.1007/s10732-007-9053-z 10.1371/journal.pcbi.1002518 10.1371/journal.pcbi.1003580 10.1074/jbc.R800048200 10.1007/s10878-007-9123-z 10.1016/j.cels.2017.01.010 10.1186/s12864-015-1984-4 10.1007/11427186_39 10.1126/scisignal.aaz1482 10.1042/BST0381302 10.1038/nbt.1487 10.1371/journal.pcbi.1003424 10.1137/1.9781611972870.15 10.1038/nbt.4072 10.1007/978-3-540-72792-7_22 10.1007/978-3-030-58942-4_26 10.1371/journal.pcbi.1005413 10.1038/nbt.1614 10.1016/j.cell.2016.03.014 10.1002/msb.145122 10.1007/978-3-540-68155-7_22 10.1016/j.ejor.2006.11.024 10.1016/S0925-7721(98)00021-2 10.1073/pnas.0610772104 10.1007/978-3-319-11008-0 10.4155/fmc.14.119 10.1186/1752-0509-6-153 10.1038/nbt0302-243 10.1126/sciadv.1600200 10.1371/journal.pcbi.1002988 10.1093/database/bat059 10.1093/nar/gkq1259 |
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| Copyright | 2021 Rodríguez-Mier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Attribution 2021 Rodríguez-Mier et al 2021 Rodríguez-Mier et al |
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| References | R Agren (pcbi.1008730.ref017) 2014; 10 D Machado (pcbi.1008730.ref018) 2014; 10 PS Ward (pcbi.1008730.ref001) 2012; 21 M Behle (pcbi.1008730.ref036) 2008; 16 pcbi.1008730.ref039 T Shlomi (pcbi.1008730.ref007) 2008; 26 A Schultz (pcbi.1008730.ref009) 2016; 12 pcbi.1008730.ref038 Y Wang (pcbi.1008730.ref008) 2012; 6 P Greistorfer (pcbi.1008730.ref042) 2008; 14 H Nam (pcbi.1008730.ref011) 2014; 10 pcbi.1008730.ref035 pcbi.1008730.ref034 N Vlassis (pcbi.1008730.ref015) 2014; 10 JF Tsai (pcbi.1008730.ref041) 2008; 184 IM De Mas (pcbi.1008730.ref004) 2014; 6 BD Heavner (pcbi.1008730.ref046) 2013; 2013 E Rintala (pcbi.1008730.ref026) 2009; 10 T Serra (pcbi.1008730.ref037) 2019 JM Cherry (pcbi.1008730.ref047) 2012; 40 N Poupin (pcbi.1008730.ref020) 2018; 18 L Heirendt (pcbi.1008730.ref027) 2019; 14 BH Junker (pcbi.1008730.ref028) 2011 JL Robinson (pcbi.1008730.ref030) 2020; 13 O Folger (pcbi.1008730.ref010) 2011; 7 M Cascante (pcbi.1008730.ref005) 2002; 20 L Jerby (pcbi.1008730.ref013) 2010; 6 DD Bremner (pcbi.1008730.ref032) 1997 R Agren (pcbi.1008730.ref014) 2012; 8 NC Duarte (pcbi.1008730.ref048) 2007; 104 S Opdam (pcbi.1008730.ref019) 2017; 4 MB Biggs (pcbi.1008730.ref024) 2017; 13 pcbi.1008730.ref025 Y Zhao (pcbi.1008730.ref003) 2013; 4 M Conforti (pcbi.1008730.ref031) 2014 M Cascante (pcbi.1008730.ref012) 2010; 38 S Rossell (pcbi.1008730.ref021) 2013; 9 J Schellenberger (pcbi.1008730.ref023) 2009; 284 E Brunk (pcbi.1008730.ref029) 2018; 36 JD Orth (pcbi.1008730.ref043) 2010; 28 Y Liu (pcbi.1008730.ref006) 2016; 165 S Robaina-Estévez (pcbi.1008730.ref022) 2017; 13 CJ Joshi (pcbi.1008730.ref044) 2020; 16 RJ DeBerardinis (pcbi.1008730.ref002) 2016; 2 MR Bussieck (pcbi.1008730.ref040) 1998; 11 MP Pacheco (pcbi.1008730.ref016) 2015; 16 MN McCall (pcbi.1008730.ref045) 2011; 39 GM Ziegler (pcbi.1008730.ref033) 2000 |
| References_xml | – volume: 13 start-page: e1005568 issue: 5 year: 2017 ident: pcbi.1008730.ref022 article-title: On the effects of alternative optima in context-specific metabolic model predictions publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1005568 – volume: 16 start-page: e1007764 issue: 5 year: 2020 ident: pcbi.1008730.ref044 article-title: StanDep: capturing transcriptomic variability improves context-specific metabolic models publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1007764 – volume: 12 issue: 3 year: 2016 ident: pcbi.1008730.ref009 article-title: Reconstruction of tissue-specific metabolic networks using CORDA publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1004808 – volume: 6 issue: 1 year: 2010 ident: pcbi.1008730.ref013 article-title: Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism publication-title: Molecular systems biology doi: 10.1038/msb.2010.56 – volume: 14 start-page: 639 issue: 3 year: 2019 ident: pcbi.1008730.ref027 article-title: Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0 publication-title: Nature protocols doi: 10.1038/s41596-018-0098-2 – volume: 21 start-page: 297 issue: 3 year: 2012 ident: pcbi.1008730.ref001 article-title: Metabolic reprogramming: a cancer hallmark even warburg did not anticipate publication-title: Cancer cell doi: 10.1016/j.ccr.2012.02.014 – volume: 40 start-page: D700 issue: D1 year: 2012 ident: pcbi.1008730.ref047 article-title: Saccharomyces Genome Database: the genomics resource of budding yeast publication-title: Nucleic acids research doi: 10.1093/nar/gkr1029 – volume: 4 start-page: e532 issue: 3 year: 2013 ident: pcbi.1008730.ref003 article-title: Targeting cellular metabolism to improve cancer therapeutics publication-title: Cell death & disease doi: 10.1038/cddis.2013.60 – volume: 7 issue: 1 year: 2011 ident: pcbi.1008730.ref010 article-title: Predicting selective drug targets in cancer through metabolic networks publication-title: Molecular systems biology doi: 10.1038/msb.2011.51 – volume: 10 start-page: 461 issue: 1 year: 2009 ident: pcbi.1008730.ref026 article-title: Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae publication-title: BMC genomics doi: 10.1186/1471-2164-10-461 – volume-title: Analysis of biological networks year: 2011 ident: pcbi.1008730.ref028 – volume: 14 start-page: 613 issue: 6 year: 2008 ident: pcbi.1008730.ref042 article-title: Experiments concerning sequential versus simultaneous maximization of objective function and distance publication-title: Journal of Heuristics doi: 10.1007/s10732-007-9053-z – volume: 10 issue: 9 year: 2014 ident: pcbi.1008730.ref011 article-title: A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks publication-title: PLoS computational biology – volume: 8 issue: 5 year: 2012 ident: pcbi.1008730.ref014 article-title: Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1002518 – volume: 10 start-page: e1003580 issue: 4 year: 2014 ident: pcbi.1008730.ref018 article-title: Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1003580 – volume: 284 start-page: 5457 issue: 9 year: 2009 ident: pcbi.1008730.ref023 article-title: Use of randomized sampling for analysis of metabolic networks publication-title: Journal of biological chemistry doi: 10.1074/jbc.R800048200 – volume: 16 start-page: 107 issue: 2 year: 2008 ident: pcbi.1008730.ref036 article-title: On threshold BDDs and the optimal variable ordering problem publication-title: Journal of Combinatorial Optimization doi: 10.1007/s10878-007-9123-z – volume: 4 start-page: 318 issue: 3 year: 2017 ident: pcbi.1008730.ref019 article-title: A systematic evaluation of methods for tailoring genome-scale metabolic models publication-title: Cell systems doi: 10.1016/j.cels.2017.01.010 – volume: 16 start-page: 809 issue: 1 year: 2015 ident: pcbi.1008730.ref016 article-title: Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network publication-title: BMC genomics doi: 10.1186/s12864-015-1984-4 – ident: pcbi.1008730.ref034 doi: 10.1007/11427186_39 – volume: 13 issue: 624 year: 2020 ident: pcbi.1008730.ref030 article-title: An atlas of human metabolism publication-title: Science Signaling doi: 10.1126/scisignal.aaz1482 – volume: 38 start-page: 1302 issue: 5 year: 2010 ident: pcbi.1008730.ref012 article-title: Metabolic network adaptations in cancer as targets for novel therapies publication-title: Biochemical Society Transactions doi: 10.1042/BST0381302 – volume: 18 start-page: 204 issue: 1 year: 2018 ident: pcbi.1008730.ref020 article-title: Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation publication-title: Journal of proteome research – volume: 26 start-page: 1003 issue: 9 year: 2008 ident: pcbi.1008730.ref007 article-title: Network-based prediction of human tissue-specific metabolism publication-title: Nature biotechnology doi: 10.1038/nbt.1487 – volume: 10 issue: 1 year: 2014 ident: pcbi.1008730.ref015 article-title: Fast reconstruction of compact context-specific metabolic network models publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1003424 – ident: pcbi.1008730.ref035 doi: 10.1137/1.9781611972870.15 – volume: 36 start-page: 272 issue: 3 year: 2018 ident: pcbi.1008730.ref029 article-title: Recon3D enables a three-dimensional view of gene variation in human metabolism publication-title: Nature biotechnology doi: 10.1038/nbt.4072 – start-page: 1 year: 2019 ident: pcbi.1008730.ref037 article-title: Compact representation of near-optimal integer programming solutions publication-title: Mathematical Programming – ident: pcbi.1008730.ref025 doi: 10.1007/978-3-540-72792-7_22 – ident: pcbi.1008730.ref038 doi: 10.1007/978-3-030-58942-4_26 – volume: 13 start-page: e1005413 issue: 3 year: 2017 ident: pcbi.1008730.ref024 article-title: Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1005413 – volume: 28 start-page: 245 issue: 3 year: 2010 ident: pcbi.1008730.ref043 article-title: What is flux balance analysis? publication-title: Nature biotechnology doi: 10.1038/nbt.1614 – volume: 165 start-page: 535 issue: 3 year: 2016 ident: pcbi.1008730.ref006 article-title: On the dependency of cellular protein levels on mRNA abundance publication-title: Cell doi: 10.1016/j.cell.2016.03.014 – volume: 10 issue: 3 year: 2014 ident: pcbi.1008730.ref017 article-title: Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling publication-title: Molecular systems biology doi: 10.1002/msb.145122 – volume-title: On the complexity of vertex and facet enumeration for convex polytopes year: 1997 ident: pcbi.1008730.ref032 – ident: pcbi.1008730.ref039 doi: 10.1007/978-3-540-68155-7_22 – volume: 184 start-page: 802 issue: 2 year: 2008 ident: pcbi.1008730.ref041 article-title: Finding multiple solutions to general integer linear programs publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.11.024 – volume: 11 start-page: 103 issue: 2 year: 1998 ident: pcbi.1008730.ref040 article-title: The vertex set of a 01-polytope is strongly P-enumerable publication-title: Computational Geometry doi: 10.1016/S0925-7721(98)00021-2 – volume: 104 start-page: 1777 issue: 6 year: 2007 ident: pcbi.1008730.ref048 article-title: Global reconstruction of the human metabolic network based on genomic and bibliomic data publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.0610772104 – volume-title: Integer programming year: 2014 ident: pcbi.1008730.ref031 doi: 10.1007/978-3-319-11008-0 – volume: 6 start-page: 1791 issue: 16 year: 2014 ident: pcbi.1008730.ref004 article-title: Cancer cell metabolism as new targets for novel designed therapies publication-title: Future medicinal chemistry doi: 10.4155/fmc.14.119 – volume: 6 start-page: 153 issue: 1 year: 2012 ident: pcbi.1008730.ref008 article-title: Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE publication-title: BMC systems biology doi: 10.1186/1752-0509-6-153 – volume: 20 start-page: 243 issue: 3 year: 2002 ident: pcbi.1008730.ref005 article-title: Metabolic control analysis in drug discovery and disease publication-title: Nature biotechnology doi: 10.1038/nbt0302-243 – volume: 2 start-page: e1600200 issue: 5 year: 2016 ident: pcbi.1008730.ref002 article-title: Fundamentals of cancer metabolism publication-title: Science advances doi: 10.1126/sciadv.1600200 – start-page: 1 volume-title: Polytopes—combinatorics and computation year: 2000 ident: pcbi.1008730.ref033 – volume: 9 issue: 3 year: 2013 ident: pcbi.1008730.ref021 article-title: Inferring metabolic states in uncharacterized environments using gene-expression measurements publication-title: PLoS computational biology doi: 10.1371/journal.pcbi.1002988 – volume: 2013 year: 2013 ident: pcbi.1008730.ref046 article-title: Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance publication-title: Database doi: 10.1093/database/bat059 – volume: 39 start-page: D1011 issue: suppl_1 year: 2011 ident: pcbi.1008730.ref045 article-title: The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes publication-title: Nucleic acids research doi: 10.1093/nar/gkq1259 |
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| Title | DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks |
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