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.
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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
A Map of the Interactome Network of the Metazoan C. elegans
Li, S., Armstrong, C. M., Bertin, N., Ge, H., Milstein, S., Boxem, M., Vidalain, P., Han, J.-D. J., Chesneau, A., Hao, T., et al. (2004) · Science · doi:10.1126/science.1091403 · Cited by 1,628
Collins, S. J., Ruscetti, F. W., Gallagher, R. E., & Gallo, R. C. (1978) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.75.5.2458 · Cited by 1,594
MIPS: a database for genomes and protein sequences
Mewes, H. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.37 · Cited by 1,268
Collins, S. (1987) · Blood · doi:10.1182/blood.v70.5.1233.1233 · Cited by 1,189
General Conclusions: Teleonomic Mechanisms in Cellular Metabolism, Growth, and DifferentiationPreprint
Monod, J., & Jacob, F. (1961) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.1961.026.01.048 · Cited by 1,072
Multistability in the lactose utilization network of Escherichia coli
Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I., & van Oudenaarden, A. (2004) · Nature · doi:10.1038/nature02298 · Cited by 1,030
ENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON
Novick, A., & Weiner, M. (1957) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.43.7.553 · Cited by 891
MIPS: a database for genomes and protein sequences
Mewes, H. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.31 · Cited by 790
A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision.
Xiong, W., & Ferrell, J. E. (2003) · Nature · doi:10.1038/nature02089 · Cited by 741
Huang, S., & Ingber, D. E. (2000) · Experimental Cell Research · doi:10.1006/excr.2000.5044 · Cited by 602
Self-citationWaddington, C. H. (1956) · Routledge eBooks · doi:10.4324/9781315665405 · Cited by 468
Are randomly grown graphs really random?
Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., & Strogatz, S. H. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.041902 · Cited by 451
A model of transcriptional regulatory networks based on biases in the observed regulation rules
Harris, S. E., Sawhill, B. K., Wuensche, A., & Kauffman, S. A. (2002) · Complexity · doi:10.1002/cplx.10022 · Cited by 277
Options available—from start to finish—for obtaining data from DNA microarrays II
Holloway, A. J., van Laar, R. K., Tothill, R. W., & Bowtell, D. D. L. (2002) · Nature Genetics · doi:10.1038/ng1030 · Cited by 251
A natural class of robust networks
Aldana, M., & Cluzel, P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1536783100 · Cited by 250
Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles
Eichler, G. S., Huang, S., & Ingber, D. E. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg307 · Cited by 176
Self-citationThe role of certain Post classes in Boolean network models of genetic networks
Ilya Shmulevich, C., Lähdesmäki, H. J., Dougherty, E. R., Astola, J. T., & Zhang, W. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1534782100 · Cited by 141
From topology to dynamics in biochemical networks
Fox, J. J., & Hill, C. C. (2001) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1414882 · Cited by 102
Response of complex networks to stimuli
Bar-Yam, Y., & Epstein, I. R. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400673101 · Cited by 101
Marcotte, E. M. (2001) · Nature Biotechnology · doi:10.1038/90222 · Cited by 67
Statistical Mechanics of Voting
MEYER, D. A., & Brown, T. A. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.1718 · Cited by 48
Mesostructure of polymer collapse and fractal smoothing
Crooks, G. E., Ostrovsky, B., & Bar‐Yam, Y. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.60.4559 · Cited by 24
Self-citationStructural information content and Lyapunov exponent calculation in protein unfolding
Gerstman, B. S., & Garbourg, Y. (1998) · Journal of Polymer Science Part B Polymer Physics · doi:10.1002/(sici)1099-0488(19981115)36:15 · Cited by 20
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
Single-cell transcriptional diversity is a hallmark of developmental potential
Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571
Transcriptome-wide noise controls lineage choice in mammalian progenitor cells
Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E., & Huang, S. (2008) · Nature · doi:10.1038/nature06965 · Cited by 1,067
Self-citationPisarchik, A. N., & Feudel, U. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.02.007 · Cited by 795
Bifurcation dynamics in lineage-commitment in bipotent progenitor cells
Huang, S., Guo, Y.-P., May, G., & Enver, T. (2007) · Developmental Biology · doi:10.1016/j.ydbio.2007.02.036 · Cited by 560
Self-citationHuang, S., Ernberg, I. T., & Kauffman, S. A. (2009) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2009.07.003 · Cited by 572
Self-citation
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
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