The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT
Open accessAbstract
Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment.
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Cites
A gene atlas of the mouse and human protein-encoding transcriptomes
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Otto Warburg's contributions to current concepts of cancer metabolism
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A protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789
Induction of autophagy by spermidine promotes longevity
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The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli
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“Understanding the function of metabolism therefore requires analysis of the complete metabolic network, and this is best done through the use of so-called genome-scale metabolic models (GEMs) [4,5,6].”
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Cited by
A community-driven global reconstruction of human metabolism
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019
Self-citationRobinson, 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-citationThe gut microbiota modulates host amino acid and glutathione metabolism in mice
Mardinoglu, A., Shoaie, S., Bergentall, M., Ghaffari, P., Zhang, C., Larsson, E., Bäckhed, F., & Nielsen, J. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156487 · Cited by 383
Self-citationWang, H., Marcišauskas, S., Sánchez, B. J., Domenzain, I., Hermansson, D., Agren, R., Nielsen, J., & Kerkhoven, E. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006541 · Cited by 381
Self-citationNielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252
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Metadata
- Authors
- Agren, R., Bordel, S., Mardinoglu, A., Pornputtapong, N., Nookaew, I., & Nielsen, J.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-05-17
- Volume
- 8
- Issue
- 5
- Pages
- e1002518
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2144589197
- Cited by
- 430
- References
- 6
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- gold
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- Agren, R., Bordel, S., Mardinoglu, A., Pornputtapong, N., Nookaew, I., & Nielsen, J. (2012). Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1002518
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