The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The yeast cell-cycle network is robustly designed
Open accessAbstract
The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state, the G 1 state, is a global attractor of the dynamics. The biological pathway, the cell-cycle sequence of protein states, is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.
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
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
The Origins of Order: Self-Organization and Selection in Evolution
Kauffman, S. A. (1993) · doi:10.1093/oso/9780195079517.001.0001 · Cited by 7,855
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,467
Lethality and centrality in protein networks
Jeong, H., Mason, S. P., Barabási, A.-L., & Oltvai, Z. N. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,287
The Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Metabolic stability and epigenesis in randomly constructed genetic nets
Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,819
Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,722
Towards a network theory of the immune system.
Jerne, N. K. (1974) · PubMed · Cited by 3,362
Cited by
Modelling and analysis of gene regulatory networks
Karlebach, G., & Shamir, R. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2503 · Cited by 1,296
Analyzing and modeling real-world phenomena with complex networks: a survey of applications
Costa, L. D. F., Oliveira, O. N., Travieso, G., Rodrigues, F. A., Villas Boas, P. R., Antiqueira, L., Viana, M. P., & Correa Rocha, L. E. (2011) · Advances in Physics · doi:10.1080/00018732.2011.572452 · Cited by 800
Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’
Tamsir, A., Tabor, J. J., & Voigt, C. A. (2011) · Nature · doi:10.1038/nature09565 · Cited by 770
Interactome: gateway into systems biology
Cusick, M. E., Klitgord, N., Vidal, M., & Hill, D. E. (2005) · Human Molecular Genetics · doi:10.1093/hmg/ddi335 · Cited by 414
Robustness can evolve gradually in complex regulatory gene networks with varying topology
Ciliberti, S., Martin, O. C., & Wagner, A. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030015 · Cited by 390
Metadata
- Authors
- Li, F., Long, T., Lu, Y., Ouyang, Q., & Tang, C.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2004-03-22
- Volume
- 101
- Issue
- 14
- Pages
- 4781-4786
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2053961182
- Cited by
- 1,108
- References
- 30
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- green
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: The yeast cell-cycle network is robustly designed. https://app.cochat.ai/papers/10.1073/pnas.0305937101
- The paper
- Li, F., Long, T., Lu, Y., Ouyang, Q., & Tang, C. (2004). The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0305937101
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.