The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network
Abstract
Cells in multicellular organisms switch between distinct cell fates, such as proliferation or differentiation into specialized cell types. Genome-wide gene regulatory networks govern this behavior. Theoretical studies of complex networks suggest that they can exhibit ordered (stable) dynamics, raising the possibility that cell fates may represent high-dimensional attractor states. We used gene expression profiling to show that trajectories of neutrophil differentiation converge to a common state from different directions of a 2773-dimensional gene expression state space, providing the first experimental evidence for a high-dimensional stable attractor that represents a distinct cellular phenotype.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Theoretically, this model can be represented by “attractor states” within a genome-wide gene regulatory network (47).”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [47]
In the citing paper
“In this context, differentiating cells descend toward stable regions of the network (attractor states), characterized by restricted gene expression, whereas metastable cells broadly sample the network, maintaining higher differentiation potential (47).”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [47]
Citations
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
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
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
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 4,871
Classes of small-world networks
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200327197 · Cited by 3,051
Specificity and Stability in Topology of Protein Networks
Maslov, S., & Sneppen, K. (2002) · Science · doi:10.1126/science.1065103 · Cited by 3,051
Will a Large Complex System be Stable?
MAY, R. M. (1972) · Nature · doi:10.1038/238413a0 · Cited by 2,869
Induction of differentiation of the human promyelocytic leukemia cell line (HL-60) by retinoic acid
Breitman, T. R., Selonick, S. E., & Collins, S. J. (1980) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.77.5.2936 · Cited by 1,966
A Protein Interaction Map of Drosophila melanogaster
Giot, L., Bader, J. S., Brouwer, C., Chaudhuri, A., Kuang, B., Li, Y., Hao, Y. L., Ooi, C. E., Godwin, B., Vitols, E., et al. (2003) · Science · doi:10.1126/science.1090289 · Cited by 1,863
Flagged first, then by citations
Research works printed but not linked: 2
- S. Huang, 2002, Gene Regulation and Metabolism: Post-Genomic Computational Approach
- M. Delbrück, 1949, Unités Biologiques Douées de Continuité Génétique Colloques Internationaux du Centre National de la Recherche Scientifique
Cited by
Sorted by citations
Items 701–763 of 763
Etcheverry, M., Moulin-Frier, C., Oudeyer, P., & Levin, M. G. (2025) · eLife · doi:10.7554/elife.92683.4.sa3
Systems Toxicology and Virtual Tissue Models
Knudsenknudsen, T. B., & Daston, G. P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.13023-2
The road to cancer and back: A thermodynamic point of view
Barua, A., & Hatzikirou, H. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780192867636.003.0008
Abi-Rizk, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5734734
Vastola, J. J., Ramdas, T., & Gershman, S. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688304 · Cited by 1
Evolution of robust cell differentiation under epigenetic feedback
Plugers, D., & Kaneko, K. (2025) · Physical Review Research · doi:10.1103/9vhh-qjj1
Park, K. H., & Albert, R. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00717-z
Derivation of Large-Scale Cellular Regulatory Networks from Biological Time Series Data
de Bivort, B. L. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-800-3_7
Additional file 12 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011036
Additional file 13 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011039
Additional file 10 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011027
Additional file 11 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011033
Additional file 6 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011078
Additional file 9 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011087
Additional file 1 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011060
Additional file 7 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011081
Additional file 8 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011084
Additional file 2 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011066
Additional file 4 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011072
Additional file 5 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011075
Additional file 3 of A single-cell survey of cellular hierarchy in acute myeloid leukemia
Wu, J., Xiao, Y., Sun, J., Sun, H., Chen, H., Zhu, Y., Fu, H., Yu, C., Weigao, E., Lai, S., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13011069
Schittler, D., Jouini, T. K., Allgöwer, F., & Waldherr, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.7250
AI-Guided Inference of Morphodynamic Attractor-like States in Glioblastoma
Volovăț, S. R., Panaite, D. I., Ostafe, M. R., Buzea, C. G., Iancu, D. T., Agop, M., Ochiuz, L., Rusu, D. I., & Volovăț, C. C. (2026) · Diagnostics · doi:10.3390/diagnostics16010139
Copes, N., & Canfield, C.-A. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.01.703159
Tsuchiya, M., Yoshikawa, K., & Наймарк, О. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.12.703632
Directing stem cell differentiation by chromatin state approximation
Montano-Gutierrez, L. F., Müller, S., Kutschat, A. P., Adameyko, I. I., Seruggia, D., & Halbritter, F. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag124
Non-Coding RNA: Architects of Cellular Complexity and Agents of Malignancy
Shah, A. (2026) · Genes · doi:10.3390/genes17030304
Stochastic modeling of epigenetic memory
Bruno, S., & Vecchio, D. D. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00664-9
Blot, G., & Sapieha, P. S. (2026) · Science · doi:10.1126/science.aeg3891
Wu, J., Dong, H., Xu, X., & Wang, M. (2026) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.1080/10428194.2026.2649879
de Freitas, J. A. N. L. F. (2020) · theses.fr (ABES) · doi:10.70675/0e6e3614z3915z4fb8za2cdz24b40b9679b2
Curiosity-driven AI for Science : Automated Discovery of Self-Organized Structures
Etcheverry, M. (2023) · theses.fr (ABES) · doi:10.70675/5041450bz7b8az4fcez966azf89b0a4e7e48
Duan, X., Liu, Y., Tang, X., Jiang, H., & Jin, L. (2026) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2026.118311
Li, R., Li, R., & Pugh, B. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.28.721354
Yamaki, N., & Churiki, T. (2026) · Research Square · doi:10.21203/rs.3.rs-9410970/v1
Yang, S., Tyagi, S., Rosenmund, C., & Herman, M. A. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1819116
Yang, J.-M., Kim, N., & Cho, K. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3692800
Moreno, C. S., Duckworth, R. A., & Badyaev, A. V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.23.727322
Evolution of phenocopying in a dynamical model of developmental trajectories
Matsushita, Y., & Raju, A. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014385
Miao, Y., Du, L., Liu, S., Deng, Z., & Grebogi, C. (2026) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2026.131774
Density-dependent growth emerges from Bayesian adaptation of phenotype
Gupta, M. K., Barua, A., & Hatzikirou, H. (2026) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2026.135437
Shao, S., Jin, C., HUANG, Y., Feng, X., Zhu, R., Zheng, Z., Li, S., Guo, Z., Shi, S., He, Z., et al. (2026) · Plant Communications · doi:10.1016/j.xplc.2026.101985
Angelini, E., Leveille, C. L., Parent, S. E., Zaunbrecher, R. J., Barszczewski, T., Dixon, J. C., Mohammed, F. S., Morris, B., Yu, J. S., Arakaki, J., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7135687
Sigdel, D., & Panday, N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.17.739210
Masumori, A., Sato, H., & Ikegami, T. (2026) · Entropy · doi:10.3390/e28080893
The structural origin of irreversible transitions in biological networks
Kim, J., Jang, S., Lee, J., Hopper, C., & Cho, K. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2600800123
Hamilton-Jacobi theory of quantized biological function
Monroy, F. (2026) · APS Open Science · doi:10.1103/x2fd-pjlb
Higher-order assembly and emergent persistence in biology
Vidiella, B., Anonymous, Fernández, R., Spiridonov, A., Sánchez, Á., Hussain, S. T., & Valverde, S. (2026) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2026.100608
Oriquat, G., Ahmed, H. M., Rizaev, J., Kucharova, M., Polatova, D. S., Mhysn, M. A., & Tailor, N. K. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1979150
Biological Systems as Physical Data Processors: Evolution as a Case of Embodied Computing
de Lorenzo, V. (2026) · BioEssays · doi:10.1002/bies.70196
Regulation of cellular plasticity: A historical development of scientific concept
Vorotelyak, E. A. (2026) · Регенерация органов и тканей. · doi:10.60043/2949-5938-2026-3-7-18
Model-guided design of single-cargo, dual-attractor gene circuitsPreprint
Peterman, E. L., Nolan, N., Naydenkov, P. M., Godavarti, D. R., Vecchio, D. D., & Galloway, K. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.19.760260
The Tri-Lineage Competition Network: A Tristable Model of Neural Stem Cell Fate
Toncheva, D., & Sgurev, V. (2026) · IEEE International Conference on Intelligent Systems · doi:10.1109/is71416.2026.11727376
System-level reorganization of gene networks underlies the nurse-to-forager transition in honeybees
Page, R. E., & BONABEAU, E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2623939123
The p53 Super-Hub Architecture: From Planar to Volumetric Network Pharmacology
Bazanov, D. R. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27219752
The Attractor Type of Complex Networks
Ning, T., Huang, J., Zhang, W., & Fenghong, Y. (2008) · Springer eBooks · doi:10.1007/978-1-4020-8387-7_54
Cellular-Level Gene Regulatory Networks: Their Derivation and Properties
de Bivort, B. L. (2010) · Systems biology · doi:10.1007/978-1-4419-5797-9_17
Metadata
- Authors
- Huang, S., Eichler, G., Bar-Yam, Y., & Ingber, D. E.
- Affiliations
- Boston Children's Hospital (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2005-04-01
- Volume
- 94
- Issue
- 12
- Pages
- 128701-128701
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2141545875
- Cited by
- 775
- References
- 33
- Citations by year
- 2026: 42 · 2025: 33 · 2024: 30 · 2023: 31 · 2022: 49 · 2021: 42 · 2020: 56 · 2019: 30 · 2018: 54 · 2017: 39 · 2016: 34 · 2015: 32 · 2014: 34 · 2013: 42 · 2012: 26
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- cell fate, gene regulatory networks, granulocytic differentiation, cell phenotype, gene expression profiling, network theory
- MeSH terms
- Cell Differentiation, Dimethyl Sulfoxide, Gene Expression Regulation, Humans, Neutrophils, RNA, Messenger, Tretinoin, HL-60 Cells, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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