Abstract
The epithelial-mesenchymal transition (EMT) and the corresponding reverse process, mesenchymal-epithelial transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at both biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: (a) multi-stability—how many phenotypes can cells attain during an EMT/MET?, (b) reversibility/irreversibility—what time and/or concentration of an EMT inducer marks the “tipping point” when cells induced to undergo EMT cannot revert?, (c) symmetry in EMT/MET—do cells take the same path when reverting as they took during the induction of EMT?, and (d) non-cell autonomous mechanisms—how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT? These dynamical traits may facilitate a heterogenous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jolly MK, Ware KE, Gilja S et al (2017) EMT and MET: necessary or permissive for metastasis? Mol Oncol 11:755–769. https://doi.org/10.1002/1878-0261.12083
Aiello NM, Kang Y (2019) Context-dependent EMT programs in cancer metastasis. J Exp Med 216:1016–1026. https://doi.org/10.1084/jem.20181827
Kumar S, Das A, Sen S (2014) Extracellular matrix density promotes EMT by weakening cell–cell adhesions. Mol BioSyst 10:838–850. https://doi.org/10.1039/C3MB70431A
Boareto M, Jolly MK, Goldman A et al (2016) Notch-Jagged signalling can give rise to clusters of cells exhibiting a hybrid epithelial/mesenchymal phenotype. J R Soc Interface 13. https://doi.org/10.1098/rsif.2015.1106
Li X, Jolly MK, George JT et al (2019) Computational modeling of the crosstalk between macrophage polarization and tumor cell plasticity in the tumor microenvironment. Front Oncol 9:10. https://doi.org/10.3389/fonc.2019.00010
Grosse-Wilde A, Kuestner RE, Skelton SM et al (2018) Loss of inter-cellular cooperation by complete epithelial-mesenchymal transition supports favorable outcomes in basal breast cancer patients. Oncotarget 9:20018–20033. https://doi.org/10.18632/oncotarget.25034
Neelakantan D, Zhou H, Oliphant MUJ et al (2017) EMT cells increase breast cancer metastasis via paracrine GLI activation in neighbouring tumour cells. Nat Commun 8:15773. https://doi.org/10.1038/ncomms15773
Nieto MA, Huang RY-J, Jackson RA, Thiery JP (2016) EMT: 2016. Cell 166:21–45. https://doi.org/10.1016/j.cell.2016.06.028
Lu M, Jolly MK, Levine H et al (2013) MicroRNA-based regulation of epithelial–hybrid–mesenchymal fate determination. Proc Natl Acad Sci 110:18144–18149. https://doi.org/10.1073/pnas.1318192110
Font-Clos F, Zapperi S, La Porta CAM (2018) Topography of epithelial–mesenchymal plasticity. Proc Natl Acad Sci 115:5902–5907. https://doi.org/10.1073/pnas.1722609115
Steinway SN, Zanudo JGT, Ding W et al (2014) Network modeling of TGF signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation. Cancer Res 74:5963–5977. https://doi.org/10.1158/0008-5472.CAN-14-0225
Celià-Terrassa T, Bastian C, Liu D et al (2018) Hysteresis control of epithelial-mesenchymal transition dynamics conveys a distinct program with enhanced metastatic ability. Nat Commun 9:5005. https://doi.org/10.1038/s41467-018-07538-7
Tian X-J, Zhang H, Xing J (2013) Coupled reversible and irreversible bistable switches underlying TGFβ-induced epithelial to mesenchymal transition. Biophys J 105:1079–1089. https://doi.org/10.1016/j.bpj.2013.07.011
Hong T, Watanabe K, Ta CH et al (2015) An Ovol2-Zeb1 mutual inhibitory circuit governs bidirectional and multi-step transition between epithelial and mesenchymal states. PLoS Comput Biol 11:e1004569. https://doi.org/10.1371/journal.pcbi.1004569
Jolly MK, Levine H (2017) Computational systems biology of epithelial-hybrid-mesenchymal transitions. Curr Opin Syst Biol 3:1–6. https://doi.org/10.1016/j.coisb.2017.02.004
Pastushenko I, Brisebarre A, Sifrim A et al (2018) Identification of the tumour transition states occurring during EMT. Nature 556:463–468. https://doi.org/10.1038/s41586-018-0040-3
Ruscetti M, Dadashian EL, Guo W et al (2016) HDAC inhibition impedes epithelial-mesenchymal plasticity and suppresses metastatic, castration-resistant prostate cancer. Oncogene 35:3781–3795. https://doi.org/10.1038/onc.2015.444
Karacosta LG, Anchang B, Ignatiadis N, et al (2019) Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. bioRxiv 570341. https://doi.org/10.1101/570341
Jolly MK, Huang B, Lu M et al (2014) Towards elucidating the connection between epithelial-mesenchymal transitions and stemness. J R Soc Interface 11:20140962. https://doi.org/10.1098/rsif.2014.0962
Grosse-Wilde A, d’Hérouël AF, McIntosh E et al (2015) Stemness of the hybrid epithelial/mesenchymal state in breast cancer and its association with poor survival. PLoS One 10:e0126522. https://doi.org/10.1371/journal.pone.0126522
Kröger C, Afeyan A, Mraz J et al (2019) Acquisition of a hybrid E/M state is essential for tumorigenicity of basal breast cancer cells. Proc Natl Acad Sci 116:7353–7362. https://doi.org/10.1073/pnas.1812876116
Bierie B, Pierce SE, Kroeger C et al (2017) Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells. Proc Natl Acad Sci 114:E2337–E2346. https://doi.org/10.1073/pnas.1618298114
Drubin DG, Oster G (2010) Experimentalist meets theoretician: a tale of two scientific cultures. Mol Biol Cell 21:2099–2101. https://doi.org/10.1091/mbc.e10-02-0143
Igoshin O, Chen J, Xing J et al (2019) Biophysics at the coffee shop: lessons learned working with George Oster. Mol Biol Cell 30(16):1882
Albert R, Thakar J (2014) Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions. Wiley Interdiscip Rev Syst Biol Med 6:353–369. https://doi.org/10.1002/wsbm.1273
Zhang J, Tian X-J, Chen Y-J et al (2018) Pathway crosstalk enables cells to interpret TGF-β duration. NPJ Syst Biol Appl 4:18. https://doi.org/10.1038/s41540-018-0060-5
Blagoev KB, Shukla K, Levine H (2013) We need theoretical physics approaches to study living systems. Phys Biol 10:040201. https://doi.org/10.1088/1478-3975/10/4/040201
Zhang J, Tian X-J, Zhang H et al (2014) TGF-β–induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci Signal 7:ra91. https://doi.org/10.1126/scisignal.2005304
Bustin SA, Huggett JF (2017) Reproducibility of biomedical research – the importance of editorial vigilance. Biomol Detect Quantif 11:1–3. https://doi.org/10.1016/j.bdq.2017.01.002
Fu Y, Glaros T, Zhu M et al (2012) Network topologies and dynamics leading to endotoxin tolerance and priming in innate immune cells. PLoS Comput Biol 8:1–14. https://doi.org/10.1371/journal.pcbi.1002526
Wang P, Song C, Zhang H et al (2014) Epigenetic state network approach for describing cell phenotypic transitions. Interface Focus 4:20130068. https://doi.org/10.1098/rsfs.2013.0068
Harris LA, Hogg JS, Tapia J-J et al (2016) BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32:3366–3368. https://doi.org/10.1093/bioinformatics/btw469
Box GEP (1976) Science and statistics. J Am Stat Assoc 71:791–799. https://doi.org/10.1080/01621459.1976.10480949
Jolly MK, Tripathi SC, Jia D et al (2016) Stability of the hybrid epithelial/mesenchymal phenotype. Oncotarget 7:27067–27084. https://doi.org/10.18632/oncotarget.8166
Jolly MK, Preca B-T, Tripathi SC et al (2018) Interconnected feedback loops among ESRP1, HAS2, and CD44 regulate epithelial-mesenchymal plasticity in cancer. APL Bioeng 2:031908. https://doi.org/10.1063/1.5024874
Meacham CE, Morrison SJ (2013) Tumour heterogeneity and cancer cell plasticity. Nature 501:328–337. https://doi.org/10.1038/nature12624
Lapidot T, Sirard C, Vormoor J et al (1994) A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367:645–648. https://doi.org/10.1038/367645a0
Al-Hajj M, Wicha MS, Benito-Hernandez A et al (2003) Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci 100:3983–3988. https://doi.org/10.1073/pnas.0530291100
O’Brien CA, Pollett A, Gallinger S, Dick JE (2007) A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature 445:106–110. https://doi.org/10.1038/nature05372
Ricci-Vitiani L, Lombardi DG, Pilozzi E et al (2007) Identification and expansion of human colon-cancer-initiating cells. Nature 445:111–115. https://doi.org/10.1038/nature05384
Singh SK, Hawkins C, Clarke ID et al (2004) Identification of human brain tumour initiating cells. Nature 432:396–401. https://doi.org/10.1038/nature03128
Collins AT, Berry PA, Hyde C et al (2005) Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res 65:10946–10951. https://doi.org/10.1158/0008-5472.CAN-05-2018
Lehmann BD, Bauer JA, Chen X et al (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121:2750–2767. https://doi.org/10.1172/JCI45014
Calbo J, van Montfort E, Proost N et al (2011) A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer. Cancer Cell 19:244–256. https://doi.org/10.1016/j.ccr.2010.12.021
Stanta G, Bonin S (2018) Overview on clinical relevance of intra-tumor heterogeneity. Front Med 5:85. https://doi.org/10.3389/fmed.2018.00085
Hong D, Fritz AJ, Zaidi SK et al (2018) Epithelial-to-mesenchymal transition and cancer stem cells contribute to breast cancer heterogeneity. J Cell Physiol 233:9136–9144. https://doi.org/10.1002/jcp.26847
Kim JE, Leung EY, Baguley BC, Finlay GJ (2013) Heterogeneity of expression of epithelial–mesenchymal transition markers in melanocytes and melanoma cell lines. Front Genet 4:97. https://doi.org/10.3389/fgene.2013.00097
Pereira L, Mariadason JM, Hannan RD, Dhillon AS (2015) Implications of epithelial–mesenchymal plasticity for heterogeneity in colorectal cancer. Front Oncol 5:13. https://doi.org/10.3389/fonc.2015.00013
Stylianou N, Lehman ML, Wang C et al (2018) A molecular portrait of epithelial–mesenchymal plasticity in prostate cancer associated with clinical outcome. Oncogene 38:913–934. https://doi.org/10.1038/s41388-018-0488-5
Creighton CJ, Li X, Landis M et al (2009) Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proc Natl Acad Sci 106:13820–13825. https://doi.org/10.1073/pnas.0905718106
Tièche CC, Gao Y, Bührer 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
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
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
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
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
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
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
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
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
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
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
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
Losick R, Desplan C (2008) Stochasticity and cell fate. Science 320:65–68. https://doi.org/10.1126/science.1147888
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
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.
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361. https://doi.org/10.1021/j100540a008
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
Vega S, Morales AV, Ocaña 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
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
Bray SJ (2016) Notch signalling in context. Nat Rev Mol Cell Biol 17:722–735. https://doi.org/10.1038/nrm.2016.94
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
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
Boareto M, Jolly MK, Lu M et al (2015) Jagged–Delta asymmetry in notch signaling can give rise to a sender/receiver hybrid phenotype. Proc Natl Acad Sci 112:E402–E409. https://doi.org/10.1073/pnas.1416287112
Yuan Y (2016) Spatial heterogeneity in the tumor microenvironment. Cold Spring Harb Perspect Med 6:a026583. https://doi.org/10.1101/cshperspect.a026583
Liu S, Cong Y, Wang D et al (2014) Breast cancer stem cells transition between epithelial and mesenchymal states reflective of their normal counterparts. Stem Cell Rep 2:78–91. https://doi.org/10.1016/j.stemcr.2013.11.009
Bocci F, Gearhart-Serna L, Boareto M et al (2019) Toward understanding cancer stem cell heterogeneity in the tumor microenvironment. Proc Natl Acad Sci 116:148–157. https://doi.org/10.1073/pnas.1815345116
Nassar D, Blanpain C (2016) Cancer stem cells: basic concepts and therapeutic implications. Annu Rev Pathol Mech Dis 11:47–76. https://doi.org/10.1146/annurev-pathol-012615-044438
Mani SA, Guo W, Liao M-J et al (2008) The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133:704–715. https://doi.org/10.1016/j.cell.2008.03.027
Morel A-P, Lièvre M, Thomas C et al (2008) Generation of breast cancer stem cells through epithelial-mesenchymal transition. PLoS One 3:e2888. https://doi.org/10.1371/journal.pone.0002888
Iliopoulos D, Hirsch HA, Wang G, Struhl K (2011) Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion. Proc Natl Acad Sci 108:1397–1402. https://doi.org/10.1073/pnas.1018898108
Yang G, Quan Y, Wang W et al (2012) Dynamic equilibrium between cancer stem cells and non-stem cancer cells in human SW620 and MCF-7 cancer cell populations. Br J Cancer 106:1512–1519. https://doi.org/10.1038/bjc.2012.126
Wang W, Quan Y, Fu Q et al (2014) Dynamics between cancer cell subpopulations reveals a model coordinating with both hierarchical and stochastic concepts. PLoS One 9:e84654. https://doi.org/10.1371/journal.pone.0084654
Gupta PB, Fillmore CM, Jiang G et al (2011) Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146:633–644. https://doi.org/10.1016/j.cell.2011.07.026
Qiu C, Ma Y, Wang J et al (2010) Lin28-mediated post-transcriptional regulation of Oct4 expression in human embryonic stem cells. Nucleic Acids Res 38:1240–1248. https://doi.org/10.1093/nar/gkp1071
Niwa H, Miyazaki J, Smith AG (2000) Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells. Nat Genet 24:372–376. https://doi.org/10.1038/74199
Karwacki-Neisius V, Göke J, Osorno R et al (2013) Reduced Oct4 expression directs a robust pluripotent state with distinct signaling activity and increased enhancer occupancy by Oct4 and Nanog. Cell Stem Cell 12:531–545. https://doi.org/10.1016/j.stem.2013.04.023
Shu J, Wu C, Wu Y et al (2013) Induction of pluripotency in mouse somatic cells with lineage specifiers. Cell 153:963–975. https://doi.org/10.1016/j.cell.2013.05.001
Theunissen TW, van Oosten AL, Castelo-Branco G et al (2011) Nanog overcomes reprogramming barriers and induces pluripotency in minimal conditions. Curr Biol 21:65–71. https://doi.org/10.1016/j.cub.2010.11.074
Jolly MK, Jia D, Boareto M et al (2015) Coupling the modules of EMT and stemness: a tunable “stemness window” model. Oncotarget 6:25161–25174. https://doi.org/10.18632/oncotarget.4629
Acknowledgements
This work was supported by the National Science Foundation grant PHY- 1427654 and by the Ramanujan Fellowship awarded to M.K.J. by SERB, DST, Government of India (SB/S2/RJN-049/2018).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Tripathi, S., Xing, J., Levine, H., Jolly, M.K. (2021). Mathematical Modeling of Plasticity and Heterogeneity in EMT. In: Campbell, K., Theveneau, E. (eds) The Epithelial-to Mesenchymal Transition. Methods in Molecular Biology, vol 2179. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0779-4_28
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0779-4_28
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0778-7
Online ISBN: 978-1-0716-0779-4
eBook Packages: Springer Protocols