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
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 501–600 of 763
From Molecular Cell Engineering to Biologically Inspired Engineering
Ingber, D. E. (2008) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-008-0006-x · Cited by 5
Self-citationLineage Specification: Reading the Instructions May Help!
May, G., & Enver, T. (2013) · Current Biology · doi:10.1016/j.cub.2013.06.054 · Cited by 5
Correction: Estimating the Stochastic Bifurcation Structure of Cellular Networks
Song, C., Phenix, H., Abedi, V., Scott, M., Ingalls, B., Kærn, M., & Perkins, T. J. (2010) · PLoS Computational Biology · doi:10.1371/annotation/9a35fa58-f81c-41b4-9fa9-c1aeedbf0fff · Cited by 5
Dávila-Velderrain, J., & Alvarez-Buylla, E. R. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/004218 · Cited by 5
On the Development of the Turtle Scute Pattern and the Origins of its Variation
Zimm, R. (2019) · Työväentutkimus Vuosikirja · Cited by 5
Quantifying the Dynamics of Coupled Networks of Switches and Oscillators
Francis, M. R., & Fertig, E. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029497 · Cited by 5
Fooladi, H., Moradi, P., Sharifi‐Zarchi, A., & Khalaj, B. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz201 · Cited by 4
Giri, R., Brady, S. C., Papadopoulos, D. K., & Carthew, R. W. (2022) · iScience · doi:10.1016/j.isci.2022.105097 · Cited by 4
Zhang, X., Chen, Y.-C., Yao, M.-C., Xiong, R., Liu, B., Zhu, X., & Ao, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-63812-3 · Cited by 4
Medical and scientific evidence for the cancer attractor
Demicheli, R., Hrushesky, W. J. M., Saenger, E., & Biganzoli, E. M. (2024) · Academia oncology. · doi:10.20935/acadonco7445 · Cited by 4
Generative prediction of causal gene sets responsible for complex traits
Kuznets-Speck, B., Ogonor, B. K., Wytock, T. P., & Motter, A. E. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415071122 · Cited by 6
Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems
Zhou, P., Cai, S., Liu, Z., Chen, L., & Wang, R. (2013) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2012.11.011 · Cited by 4
Molecular phenotypic portraits - Exploring the ‘OMES’ with individual resolution
Binder, H., Hopp, L., Çakir, V., Fasold, M., von Bergen�, M., & Loeffler‐Wirth, H. (2011) · International Symposium on Health Informatics and Bioinformatics (HIBIT) · doi:10.1109/hibit.2011.6450817 · Cited by 4
Chen, A., Wang, P., Zhou, T., & Tian, T. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.127879 · Cited by 4
General determinants of aging: The size and geometry of living beings
Barragán, J., & Sebastián, S. (2023) · Archive of Gerontology and Geriatrics Research · doi:10.17352/aggr.000033 · Cited by 4
From Cell States to Cell Fates: Control of Cell State Transitions
Tsuchiya, M., Giuliani, A., & Brazhnik, P. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3577-3_9 · Cited by 4
The Universal Non-Neuronal Nature of Parkinson's Disease: A Theory
Valente, A. X. C. N., Adilbayeva, A., Tokay, T., & Rizvanov, A. A. (2016) · Central Asian Journal of Global Health · doi:10.5195/cajgh.2016.231 · Cited by 4
Properties of Boolean dynamics by node classification using feedback loops in a network
Kwon, Y. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0322-z · Cited by 4
The Hidden Memory of Differentiating Cells
Moris, N., & Arias, A. M. (2017) · Cell Systems · doi:10.1016/j.cels.2017.09.009 · Cited by 4
Process equation as a model for the development of cells
Nazarimehr, F., Hosseini, S. S., Khalaf, A. J. M., Jafari, S., & Sprott, J. C. (2020) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2020-900089-7 · Cited by 4
Tipping the Balance: A Criticality Perspective
Bose, I. (2022) · Entropy · doi:10.3390/e24030405 · Cited by 4
Refining cell subpopulation identification and cell fate mapping using cell energy
Long, C., Li, H., Li, Z., Zhang, J., Xi, Q., & Zuo, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128999 · Cited by 3
Evolution from Composome to RNA Replicase
Deng, S., Lancet, D., & Yaniv, R. (2026) · Life · doi:10.3390/life16020219 · Cited by 4
Cardiac fibroblast heterogeneity in cardiac fibrosis implication for cell-type-specific treatment
Gu, K., Song, D., & Chen, J. H. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1740147 · Cited by 3
pbn-STAC: Deep reinforcement learning-based framework for cellular reprogramming
Mizera, A., & Zarzycki, J. (2025) · Theoretical Computer Science · doi:10.1016/j.tcs.2025.115382 · Cited by 3
Quantitative Approaches to Model Pluripotency and Differentiation in Stem Cells
Papatsenko, D., Xu, H., Ma’ayan, A., & Lemischka, I. R. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7696-2_4 · Cited by 3
Control of synthetic gene networks and its applications
Menn, D. J., Su, R., & Wang, X. (2017) · Quantitative Biology · doi:10.1007/s40484-017-0106-5 · Cited by 3
DNA Breathers and Cell Dynamics
Nikitiuk, A. S., Korznikova, E. A., Dmitriev, S. V., & Наймарк, О. (2019) · Mathematical Biology and Bioinformatics · doi:10.17537/2019.14.137 · Cited by 3
Analysis of Th1/Th2 response pattern with Treg cell inhibition and stochastic effect
Liang, Q., & Lo, W.-C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111472 · Cited by 3
Gene Regulatory Network Dynamical Logical Models for Plant Development
Dávila-Velderrain, J., Caldú-Primo, J. L., Martínez‐García, J. C., & Alvarez-Buylla, E. R. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1816-5_4 · Cited by 3
A two-galectin network establishes mesenchymal condensation phenotype in limb development
Glimm, T., Kaźmierczak, B., Newman, S. A., & Bhat, R. (2023) · Mathematical Biosciences · doi:10.1016/j.mbs.2023.109054 · Cited by 3
Shifting the landscape: The role of IDH inhibitors in glioma cell fate
Giacobetti, S. A., & Fine, H. A. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.02.008 · Cited by 4
Kim, Y., Han, Y.-H., Hopper, C., Lee, J., Joo, J. I., Gong, J., Lee, C., Jang, S., Kang, J., Kim, T., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100773 · Cited by 3
Genomic-Thermodynamic Phase Synchronization: Maxwell’s Demon-like Regulation of Cell Fate Transition
Tsuchiya, M., Yoshikawa, K., & Giuliani, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26104911 · Cited by 3
Identification of regulatory variables for state transition of biological networks
Yuan, M., Hong, W., & Li, P. (2019) · Biosystems · doi:10.1016/j.biosystems.2019.05.001 · Cited by 3
Between mechanical clocks and emergent flocks: complexities in biology
Groß, F. (2021) · Synthese · doi:10.1007/s11229-021-03324-z · Cited by 3
Wang, M., Wang, J., Zhang, X., & Yuan, R. (2021) · Royal Society Open Science · doi:10.1098/rsos.211289 · Cited by 3
Generic optimization by fast chaotic exploration and slow feedback fixation
Matsushita, Y., & Kaneko, K. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023017 · Cited by 3
Sil, P., Subbaroyan, A., Kulkarni, S., Martin, O., & Samal, A. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae150 · Cited by 3
Trajectory inference for a branching SDE model of cell differentiation via lineage tracing
Ventre, E., Forrow, A., Gadhiwala, N., Chakraborty, P., Angel, O., & Schiebinger, G. R. (2026) · Journal of Mathematical Biology · doi:10.1007/s00285-026-02375-5 · Cited by 3
Tsuchiya, M., Tomita, M., & Hashimoto, M. (2012) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/iccme.2012.6275649 · Cited by 3
Why Systems Biology Can Promote a New Way of Thinking
Giuliani, A. (2014) · Springer eBooks · doi:10.1007/978-94-017-9514-2_2 · Cited by 3
Coherent organization in gene regulation: a study on six networks
Aral, N., & Kabakçıoğlu, A. (2016) · Physical Biology · doi:10.1088/1478-3975/13/2/026006 · Cited by 3
CHAOTIC MEAN FIELD DYNAMICS OF A BOOLEAN NETWORK WITH RANDOM CONNECTIVITY
Joy, M. P., Ingber, D. E., & Huang, S. (2007) · International Journal of Modern Physics C · doi:10.1142/s0129183107011467 · Cited by 3
Self-citationA Publish-Subscribe Model of Genetic Networks
Calcott, B., Balcan, D., & Hohenlohe, P. A. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003245 · Cited by 3
Sgariglia, D., Conforte, A. J., de Carvalho, L. A. V., Carels, N., & da Silva, F. A. B. (2018) · Computational biology · doi:10.1007/978-3-319-74974-7_3 · Cited by 3
Network Analysis to Interpret Complex Phenotypes
Yu, H. J., Huang, J., Zhang, W., & Han, J. J. (2011) · Wiley eBooks · doi:10.1002/9783527638079.ch1 · Cited by 3
Glimm, T., Kaźmierczak, B., Cui, C., Newman, S. A., & Bhat, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.15.435301 · Cited by 3
AI-driven automated discovery tools reveal diverse behavioral competencies of biological networksPreprint
Etcheverry, M., Moulin-Frier, C., Oudeyer, P., & Levin, M. G. (2024) · eLife · doi:10.7554/elife.92683 · Cited by 4
Developmental Incongruity as a Dynamical Representation of HeterochronyPreprint
Alicea, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.31.231456 · Cited by 3
immgenT: A Comprehensive Reference of Convergent T-cell States in the MousePreprint
Magill, I., Casey, O., Mallah, D., Panigrahi, S. S., Zhou, L. L., del Río, O. B., Bangs, D. J., Bee, G. C. W., Borys, S. M., Choi, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702892 · Cited by 3
Understanding the role of toggle genes in chronic lymphocytic leukemia proliferation
Sirbu, O., Agarwal, G., Giuliani, A., & Selvarajoo, K. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00575-1 · Cited by 2
Pioneer: Dynamical Systems Biology for Spatiotemporal Omics Data
Chen, L. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169864 · Cited by 2
NAD⁺ circuits in aging, tissue repair and fibrosis
Izadpanah, R., Rappaport, J., & Alt, E. U. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103305 · Cited by 5
Developing a Systems Biology of Aging
Kriete, A., & Cloutier, M. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00017-4 · Cited by 2
Demicheli, R., & Biganzoli, E. M. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3882-8_1 · Cited by 2
Role of frustrations in cell reprogramming
Yao, Y., Zhu, J., Li, W., & Pei, D. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf303 · Cited by 2
A stochastic model of homeostasis: The roles of noise and nuclear positioning in deciding cell fate
Jangid, A., Selvarajan, S., & Ramaswamy, R. (2021) · iScience · doi:10.1016/j.isci.2021.103199 · Cited by 2
Mathematical modeling of cell-fate specification: From simple to complex epigenetics
(2015) · Stem Cell Epigenetics · doi:10.14800/sce.752 · Cited by 2
Modeling and Visualizing Cell Type Switching
Ghaffarizadeh, A., Podgorski, G. J., & Flann, N. S. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/293980 · Cited by 2
Can a system approach help radiobiology?
Baverstock, K. F., & Nikjoo, H. (2010) · Radiation Protection Dosimetry · doi:10.1093/rpd/ncq467 · Cited by 2
Coloured noise induces phenotypic diversity with energy dissipation
Chen, L., Wang, Y., Liu, J., & Wang, H. (2022) · Biosystems · doi:10.1016/j.biosystems.2022.104648 · Cited by 2
Transition Yet Energy Consuming for Tumor Growth Regulated by the Colored Microenvironment
Chen, L., Wang, Y., Li, Q., & Wang, H. (2023) · Complexity · doi:10.1155/2023/5557123 · Cited by 2
Design principles of multi-map variation in biological systems
Poyatos, J. F. (2024) · Physical Biology · doi:10.1088/1478-3975/ad5d6c · Cited by 2
Self-Organization for Fault-Tolerance
Dubrova, E. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-92157-8_13 · Cited by 2
Stochastic and Deterministic Decision in Cell Fate
Menn, D. J., & Wang, X. (2014) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0025319 · Cited by 2
Robustness in Boolean Models of Genetic Regulatory Systems
Willadsen, K. (2006) · The University of Queensland · doi:10.14264/158209 · Cited by 2
Quantifying positional information during early embryonic development
Dubuis, J. (2012) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 2
Hauri, A. (2013) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-009997273 · Cited by 3
The geometry of normal tissue and cancer gene expression manifoldsPreprint
Nieves, J., & González, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.20.457160 · Cited by 2
Boolean composition restricts biological logicsPreprint
Fink, T., & Hannam, R. (2021) · arXiv (Cornell University) · Cited by 2
AI-driven Automated Discovery Tools Reveal Diverse Behavioral Competencies of Biological NetworksPreprint
Etcheverry, M., Moulin-Frier, C., Oudeyer, P., & Levin, M. G. (2024) · eLife · doi:10.7554/elife.92683.2 · Cited by 2
Amendable decisions in living systemsPreprint
Neri, I., & Simone Pigolotti, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657747 · Cited by 2
Dichotomous noise-induced switching in the synergistic toggle switch
Tang, L., Zhang, J., Yin, H., & Wang, H. (2025) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-025-06521-8 · Cited by 1
The Primary Role of Noncoding RNA in the Pathogenesis of Cancer
Shah, A. (2025) · Genes · doi:10.3390/genes16070771 · Cited by 1
Structural-Scaling Transitions and Criticality Cascade in DNA with Open States
Nikitiuk, A. S., Bayandin, Y. V., & Наймарк, О. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26178428 · Cited by 1
Role of heterogeneity in dictating tumorigenesis in epithelial tissues
Muthukrishnan, S., & Vishwakarma, M. (2023) · Computational and Systems Oncology · doi:10.1002/cso2.1045 · Cited by 1
Recursive Self-Composite Approach Toward Structural Understanding of Boolean Networks
Kim, J., Lee, W., & Cho, K. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3415352 · Cited by 1
Feng, Z., Chen, X. X., Duren, Z., Xin, J., Hao, M., Yuan, Q., Wang, Y., & Wong, W. H. (2025) · Genome biology · doi:10.1186/s13059-025-03680-w · Cited by 2
Biological object as real patterns: reconciling processualism and scientific realism
Jiang, Y. (2025) · Synthese · doi:10.1007/s11229-025-05285-z · Cited by 1
Uthamacumaran, A. (2025) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416526500481 · Cited by 1
Constructing a holistic map of cell fate decision by hyper solution landscape
Zhang, X., Li, Z., & Zhang, L. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101562 · Cited by 1
Bavisetty, V. S. N., Wheeler, M., & Kadelka, C. (2026) · PRX Life · doi:10.1103/v7kd-n9xh · Cited by 1
Wang, C. (2026) · Biosystems · doi:10.1016/j.biosystems.2026.105850 · Cited by 1
Kim, S. J., Salib, S., Foster, B. A., Woloszynska, A., & Mager, D. E. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1869953 · Cited by 1
Computational Modeling and Stem Cell Engineering
Zandstra, P. W., & Clarke, G. D. (2014) · Science policy reports · doi:10.1007/978-3-319-05074-4_4 · Cited by 1
Theoretical Considerations for Reprogramming Multicellular Systems
Zhou, J., & Huang, S. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-394430-6.00005-4 · Cited by 1
Self-citationThomas, G. M. H. (2010) · Biochemical Society Transactions · doi:10.1042/bst0381314 · Cited by 1
Computational Modeling of Biochemical Processes and Cell Differentiation
Flöttmann, M., Scharp, T., & Klipp, E. (2011) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814317931_0001 · Cited by 1
“Big Data and Dynamics”—The Mathematical Toolkit Towards Personalized Medicine
Turnhoff, L., Kusch, N., & Schuppert, A. (2017) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-64173-7_19 · Cited by 1
Selvarajoo, K. (2013) · Systems biology · doi:10.1007/978-1-4614-7690-0_12 · Cited by 1
Evolutionary acquisition of complex traits in artificial epigenetic networks
Turner, A. P., Tyrrell, A. M., Trefzer, M. A., & Lones, M. A. (2018) · Biosystems · doi:10.1016/j.biosystems.2018.12.001 · Cited by 1
Ridden, S. J., & MacArthur, B. D. (2012) · Springer eBooks · doi:10.1007/978-94-007-4330-4_2 · Cited by 1
An Investigation of Graceful Degradation in Boolean Network Robots Subject to Online Adaptation
Braccini, M., Baldini, P., & Roli, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-57430-6_16 · Cited by 1
Yousefian, M., Frank, A.-S., Weber, M., & Röblitz, S. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06174-5 · Cited by 1
Pechenick, D. A., Payne, J. L., & Moore, J. H. (2013) · European Conference on Artificial Life · doi:10.7551/978-0-262-31709-2-ch053 · Cited by 1
Principal component analysis of gene expression data: The case of atrial fibrillation
Censi, F., Calcagnini, G., Bartolini, P., & Giuliani, A. (2010) · International Symposium on Applied Sciences in Biomedical and Communication Technologies · doi:10.1109/isabel.2010.5702916 · Cited by 1
Mathematical Models in Stem Cell Differentiation and Fate Predictability
Eby, W. M., & Coleman, N. (2016) · Springer eBooks · doi:10.1007/978-3-319-27583-3_6 · Cited by 1
Systems Toxicology and Virtual Tissue Models
Knudsen, T. B., & Daston, G. P. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.95684-8 · Cited by 1
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