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52. Balaban NQ, Merrin J, Chait R et al (2004)
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Science 305:1622–1625. https://doi.org/10.
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Sci U S A 108:15004–15009. https://doi.
org/10.1073/pnas.1013171108
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division. Nat Genet 43:95–100. https://doi.
org/10.1038/ng.729
55. Boyer LA, Lee TI, Cole MF et al (2005) Core
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56. Moignard V, Macaulay IC, Swiers G et al
(2013) Characterization of transcriptional networks in blood stem and progenitor cells using
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57. Yan J, Wang H, Liu Y, Shao C (2008) Analysis
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pcbi.1000193
58. Zhang R, Lahens NF, Ballance HI et al (2014)
A circadian gene expression atlas in mammals:
implications for biology and medicine. Proc
Natl Acad Sci U S A 111:16219–16224.
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59. Milo R, Shen-Orr S, Itzkovitz S et al (2002)
Network motifs: simple building blocks of
complex networks. Science 298:824–827.
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824
60. Huang B, Lu M, Jia D et al (2017) Interrogating the topological robustness of gene regulatory circuits by randomization. PLoS
Comput Biol 13:e1005456. https://doi.org/
10.1371/journal.pcbi.1005456
61. Lamouille S, Xu J, Derynck R (2014) Molecular mechanisms of epithelial–mesenchymal
transition. Nat Rev Mol Cell Biol
15:178–196.
https://doi.org/10.1038/
nrm3758
62. Jia D, George JT, Tripathi SC et al (2019)
Testing the gene expression classification of
the EMT spectrum. Phys Biol 16:025002.
https://doi.org/10.1088/1478-3975/
aaf8d4
63. Losick R, Desplan C (2008) Stochasticity and
cell fate. Science 320:65–68. https://doi.org/
10.1126/science.1147888
64. Soltani M, Vargas-Garcia CA, Antunes D,
Singh A (2016) Intercellular variability in protein levels from stochastic expression and noisy
cell cycle processes. PLoS Comput Biol 12:
e1004972. https://doi.org/10.1371/journal.
pcbi.1004972
65. Tripathi S, Chakraborty P, Levine H, Jolly MK
(2020) A mechanism for epithelial-mesenchymal heterogeneity in a population of cancer
cells. PLoS Comput Biol 16(2):e1007619.
66. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys
Chem 81:2340–2361. https://doi.org/10.
1021/j100540a008
67. Lovisa S, LeBleu VS, Tampe B et al (2015)
Epithelial-to-mesenchymal transition induces
cell cycle arrest and parenchymal damage in
renal fibrosis. Nat Med 21:998–1009.
https://doi.org/10.1038/nm.3902
68. Vega S, Morales AV, Ocan ˜a OH et al (2004)
Snail blocks the cell cycle and confers resistance
to cell death. Genes Dev 18:1131–1143.
https://doi.org/10.1101/gad.294104
69. Handler J, Cullis J, Avanzi A et al (2018)
Pre-neoplastic pancreas cells enter a partially
mesenchymal state following transient TGF-β
exposure. Oncogene 37:4334. https://doi.
org/10.1038/s41388-018-0264-6
70. Bray SJ (2016) Notch signalling in context.
Nat Rev Mol Cell Biol 17:722–735. https://
doi.org/10.1038/nrm.2016.94
71. Siebel C, Lendahl U (2017) Notch signaling in
development, tissue homeostasis, and disease.
Physiol Rev 97:1235–1294. https://doi.org/
10.1152/physrev.00005.2017
72. Shaya O, Sprinzak D (2011) From notch signaling to fine-grained patterning: modeling
meets experiments. Curr Opin Genet Dev
21:732–739. https://doi.org/10.1016/j.gde.
2011.07.007
73. Boareto M, Jolly MK, Lu M et al (2015) Jagged–Delta asymmetry in notch signaling can
412
Shubham Tripathi et al.
clinical outcome. Oncogene 38:913–934.
https://doi.org/10.1038/s41388-018-04885
50. Creighton CJ, Li X, Landis M et al (2009)
Residual breast cancers after conventional therapy display mesenchymal as well as tumorinitiating features. Proc Natl Acad Sci
106:13820–13825.
https://doi.org/10.
1073/pnas.0905718106
51. Tie `che CC, Gao Y, Bu ¨hrer ED et al (2018)
Tumor initiation capacity and therapy resistance are differential features of EMT-related
subpopulations in the NSCLC cell line A549.
Neoplasia 21:185–196. https://doi.org/10.
1016/j.neo.2018.09.008
52. Balaban NQ, Merrin J, Chait R et al (2004)
Bacterial persistence as a phenotypic switch.
Science 305:1622–1625. https://doi.org/10.
1126/science.1099390
53. Huh D, Paulsson J (2011) Random partitioning of molecules at cell division. Proc Natl Acad
Sci U S A 108:15004–15009. https://doi.
org/10.1073/pnas.1013171108
54. Huh D, Paulsson J (2011) Non-genetic heterogeneity from stochastic partitioning at cell
division. Nat Genet 43:95–100. https://doi.
org/10.1038/ng.729
55. Boyer LA, Lee TI, Cole MF et al (2005) Core
transcriptional regulatory circuitry in human
embryonic stem cells. Cell 122:947–956.
https://doi.org/10.1016/j.cell.2005.08.020
56. Moignard V, Macaulay IC, Swiers G et al
(2013) Characterization of transcriptional networks in blood stem and progenitor cells using
high-throughput single-cell gene expression
analysis. Nat Cell Biol 15:363–372. https://
doi.org/10.1038/ncb2709
57. Yan J, Wang H, Liu Y, Shao C (2008) Analysis
of gene regulatory networks in the mammalian
circadian rhythm. PLoS Comput Biol 4:
e1000193. https://doi.org/10.1371/journal.
pcbi.1000193
58. Zhang R, Lahens NF, Ballance HI et al (2014)
A circadian gene expression atlas in mammals:
implications for biology and medicine. Proc
Natl Acad Sci U S A 111:16219–16224.
https://doi.org/10.1073/pnas.1408886111
59. Milo R, Shen-Orr S, Itzkovitz S et al (2002)
Network motifs: simple building blocks of
complex networks. Science 298:824–827.
https://doi.org/10.1126/science.298.5594.
824
60. Huang B, Lu M, Jia D et al (2017) Interrogating the topological robustness of gene regulatory circuits by randomization. PLoS
Comput Biol 13:e1005456. https://doi.org/
10.1371/journal.pcbi.1005456
61. Lamouille S, Xu J, Derynck R (2014) Molecular mechanisms of epithelial–mesenchymal
transition. Nat Rev Mol Cell Biol
15:178–196.
https://doi.org/10.1038/
nrm3758
62. Jia D, George JT, Tripathi SC et al (2019)
Testing the gene expression classification of
the EMT spectrum. Phys Biol 16:025002.
https://doi.org/10.1088/1478-3975/
aaf8d4
63. Losick R, Desplan C (2008) Stochasticity and
cell fate. Science 320:65–68. https://doi.org/
10.1126/science.1147888
64. Soltani M, Vargas-Garcia CA, Antunes D,
Singh A (2016) Intercellular variability in protein levels from stochastic expression and noisy
cell cycle processes. PLoS Comput Biol 12:
e1004972. https://doi.org/10.1371/journal.
pcbi.1004972
65. Tripathi S, Chakraborty P, Levine H, Jolly MK
(2020) A mechanism for epithelial-mesenchymal heterogeneity in a population of cancer
cells. PLoS Comput Biol 16(2):e1007619.
66. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys
Chem 81:2340–2361. https://doi.org/10.
1021/j100540a008
67. Lovisa S, LeBleu VS, Tampe B et al (2015)
Epithelial-to-mesenchymal transition induces
cell cycle arrest and parenchymal damage in
renal fibrosis. Nat Med 21:998–1009.
https://doi.org/10.1038/nm.3902
68. Vega S, Morales AV, Ocan ˜a OH et al (2004)
Snail blocks the cell cycle and confers resistance
to cell death. Genes Dev 18:1131–1143.
https://doi.org/10.1101/gad.294104
69. Handler J, Cullis J, Avanzi A et al (2018)
Pre-neoplastic pancreas cells enter a partially
mesenchymal state following transient TGF-β
exposure. Oncogene 37:4334. https://doi.
org/10.1038/s41388-018-0264-6
70. Bray SJ (2016) Notch signalling in context.
Nat Rev Mol Cell Biol 17:722–735. https://
doi.org/10.1038/nrm.2016.94
71. Siebel C, Lendahl U (2017) Notch signaling in
development, tissue homeostasis, and disease.
Physiol Rev 97:1235–1294. https://doi.org/
10.1152/physrev.00005.2017
72. Shaya O, Sprinzak D (2011) From notch signaling to fine-grained patterning: modeling
meets experiments. Curr Opin Genet Dev
21:732–739. https://doi.org/10.1016/j.gde.
2011.07.007
73. Boareto M, Jolly MK, Lu M et al (2015) Jagged–Delta asymmetry in notch signaling can
412
Shubham Tripathi et al.