The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Context-Specific Metabolic Networks Are Consistent with Experiments
Open accessAbstract
Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.
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Cites
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Self-citationEscherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
Ibarra, R. U., Edwards, J. S., & Palsson, B. O. (2002) · Nature · doi:10.1038/nature01149 · Cited by 857 · cited 2×
“However, after selective pressure is applied to maximize growth, the actual growth has been shown to improve to match the prediction, typically after a month or two of serial passage of cells [16].”
BackgroundSelf-citationIntegrating high-throughput and computational data elucidates bacterial networks
Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., & Palsson, B. O. (2004) · Nature · doi:10.1038/nature02456 · Cited by 792
Self-citationTXNIP regulates peripheral glucose metabolism in humans
Parikh, H., Carlsson, E., Chutkow, W. A., Johansson, L. E., Storgaard, H., Poulsen, P., Saxena, R., Ladd, C., Schulze, P. C., Mazzini, M. J., et al. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040158 · Cited by 495
Fong, S. S., Joyce, A. R., & Palsson, B. Ø. (2005) · Genome Research · doi:10.1101/gr.3832305 · Cited by 265 · cited 2×
“Because the evolved strains nearly always grow better than wild-type strains on a variety of carbon sources [19], metabolic networks for optimal growth on nine carbon sources were constructed.”
BackgroundSelf-citation
Cited by
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Self-citationConstraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods
Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2737 · Cited by 779
Self-citationMetabolic modeling of single Th17 cells reveals regulators of autoimmunity
Wagner, A., Wang, C., Fessler, J., DeTomaso, D., Avila-Pacheco, J., Kaminski, J., Zaghouani, S., Christian, E., Thakore, P., Schellhaass, B., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.045 · Cited by 524
Robinson, J. L., Kocabaş, P., Wang, H., Cholley, P.-E., Cook, D., Nilsson, A., Anton, M., Ferreira, R., Domenzain, I., Billa, V., et al. (2020) · Science Signaling · doi:10.1126/scisignal.aaz1482 · Cited by 442
Self-citationIn silico method for modelling metabolism and gene product expression at genome scale
Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., et al. (2012) · Nature Communications · doi:10.1038/ncomms1928 · Cited by 266
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Metadata
- Authors
- Becker, S. A., & Palsson, B. O.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-05-16
- Volume
- 4
- Issue
- 5
- Pages
- e1000082
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2162666840
- Cited by
- 592
- References
- 5
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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