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Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Bioinformatics, 34, 2018, 22, S. 3882-3888 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Tian, Mingyuan Reed, Jennifer L Tian, Mingyuan Reed, Jennifer L |
|---|---|
| author |
Tian, Mingyuan Reed, Jennifer L |
| spellingShingle |
Tian, Mingyuan Reed, Jennifer L Bioinformatics Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
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tian, mingyuan |
| spelling |
Tian, Mingyuan Reed, Jennifer L 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty445 <jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Code is available in the Supplementary data available at Bioinformatics online.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec> Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis Bioinformatics |
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Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
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Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_full |
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_fullStr |
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_full_unstemmed |
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_short |
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_sort |
integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/bty445 |
| publishDate |
2018 |
| physical |
3882-3888 |
| description |
<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Code is available in the Supplementary data available at Bioinformatics online.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec> |
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| author | Tian, Mingyuan, Reed, Jennifer L |
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| description | <jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Code is available in the Supplementary data available at Bioinformatics online.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec> |
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| imprint_str_mv | Oxford University Press (OUP), 2018 |
| institution | DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15 |
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| spelling | Tian, Mingyuan Reed, Jennifer L 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty445 <jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Code is available in the Supplementary data available at Bioinformatics online.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec> Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis Bioinformatics |
| spellingShingle | Tian, Mingyuan, Reed, Jennifer L, Bioinformatics, Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_full | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_fullStr | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_full_unstemmed | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_short | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_sort | integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| title_unstemmed | Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/bty445 |