The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Predicting perturbation patterns from the topology of biological networks
Open accessAbstract
Significance: The development of high-throughput technologies has allowed mapping a significant proportion of interactions between biochemical entities in the cell. However, it is unclear how much information is lost given the lack of measurements on the kinetic parameters governing the dynamics of these interactions. Using biochemical networks with experimentally measured kinetic parameters, we show that a knowledge of the network topology offers 65–80% accuracy in predicting the impact of perturbation patterns. In other words, we can use the increasingly accurate topological models to approximate perturbation patterns, bypassing expensive kinetic constant measurement. These results could open new avenues in modeling drug action and in identifying drug targets relying on the human interactome only.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Self-citationNetwork medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Self-citationThe Gene Expression Omnibus Database
Clough, E., & Barrett, T. (2016) · Methods in Molecular Biology · doi:10.1007/978-1-4939-3578-9_5 · Cited by 2,459
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Self-citationUncovering disease-disease relationships through the incomplete interactome
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546
Self-citationA Proteome-Scale Map of the Human Interactome Network
Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S., Lemmens, I., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,360
Self-citationUniversally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261 · cited 1×
“Indeed, only a small subset of these model parameters affect the overall dynamics, a property known as “sloppiness” (25).”
BackgroundRobustness in bacterial chemotaxis
Alon, U., Surette, M. G., Barkai, N., & Leibler, S. (1999) · Nature · doi:10.1038/16483 · Cited by 1,129
An empirical framework for binary interactome mapping
Venkatesan, K., Rual, J.-F., Vazquez, A., Stelzl, U., Lemmens, I., Hirozane-Kishikawa, T., Hao, T., Zenkner, M., Xin, X., Goh, K.-I., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1280 · Cited by 904
Self-citationAssociating Genes and Protein Complexes with Disease via Network Propagation
Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., & Sharan, R. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000641 · Cited by 872
Cited by
Banerjee, S., Walder, F., Büchi, L., Meyer, M., Held, A. Y., Gattinger, A., Keller, T., Charles, R., & van der Heijden, M. G. A. (2019) · The ISME Journal · doi:10.1038/s41396-019-0383-2 · Cited by 1,356
Plant developmental stage drives the differentiation in ecological role of the maize microbiome
Xiong, C., Singh, B. K., He, J.-Z., Han, Y.-L., Li, P.-P., Wan, L.-H., Meng, G.-Z., Liu, S.-Y., Wang, J.-T., Wu, C.-F., et al. (2021) · Microbiome · doi:10.1186/s40168-021-01118-6 · Cited by 438
A Guide to Conquer the Biological Network Era Using Graph Theory
Koutrouli, M., Karatzas, E., Paez-Espino, D., & Pavlopoulos, G. A. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00034 · Cited by 264
Network Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95
Towards perturbation prediction of biological networks using deep learning
Li, D., & Gao, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-48391-y · Cited by 16
Metadata
- Authors
- Santolini, M., & Barabási, A.-L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2018-06-20
- Volume
- 115
- Issue
- 27
- Pages
- E6375-E6383
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2807869290
- Cited by
- 323
- References
- 39
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Predicting perturbation patterns from the topology of biological networks. https://app.cochat.ai/papers/10.1073/pnas.1720589115
- The paper
- Santolini, M., & Barabási, A.-L. (2018). Predicting perturbation patterns from the topology of biological networks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1720589115
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
