Modeling ncRNA-Mediated Circuits in Cell Fate Decision

Part of the Methods in Molecular Biology book series (MIMB, volume 1912)


Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions on elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework on real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA), as well as the role of mathematical modeling of ncRNA in synthetic biology. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decision.

Key words

Ultrasensitivity Competing endogenous mRNA Posttranscriptional Mathematical modeling Bistability Cell fate decision 


  1. 1.
    Carninci P et al (2005) The transcriptional landscape of the mammalian genome. Science 309:1559–1563CrossRefGoogle Scholar
  2. 2.
    Birney E et al (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816CrossRefGoogle Scholar
  3. 3.
    Inui M, Martello G, Piccolo S (2010) MicroRNA control of signal transduction. Nat Rev Mol Cell Biol 11:252–263CrossRefGoogle Scholar
  4. 4.
    Pauli A, Rinn JL, Schier AF (2011) Non-coding RNAs as regulators of embryogenesis. Nat Rev Genet 12:136–149CrossRefGoogle Scholar
  5. 5.
    Davis GM, Haas MA, Pocock R (2015) MicroRNAs: not “fine-tuners” but key regulators of neuronal development and function. Front Neurol 6:245. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Zhang J, Ma L (2012) MicroRNA control of epithelial-mesenchymal transition and metastasis. Cancer Metastasis Rev 31:653–662CrossRefGoogle Scholar
  7. 7.
    Gregory PA, Bracken CP, Bert AG, Goodall GJ (2008) MicroRNAs as regulators of epithelial-mesenchymal transition. Cell Cycle 7:3112–3117CrossRefGoogle Scholar
  8. 8.
    Guo F, Kerrigan BCP, Yang D, Hu L, Shmulevich I, Sood AK, Xue F, Zhang W (2014) Post-transcriptional regulatory network of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions. J Hematol Oncol 7:19CrossRefGoogle Scholar
  9. 9.
    Jovanovic M, Hengartner MO (2006) miRNAs and apoptosis: RNAs to die for. Oncogene 25:6176–6187CrossRefGoogle Scholar
  10. 10.
    Shurin MR (2010) MicroRNAs are invading the tumor microenvironment: fibroblast microRNAs regulate tumor cell motility and invasiveness. Cell Cycle 9:4430–4430CrossRefGoogle Scholar
  11. 11.
    Bao X, Zhu X, Liao B, Benda C, Zhuang Q, Pei D, Qin B, Esteban MA (2013) MicroRNAs in somatic cell reprogramming. Curr Opin Cell Biol 25:208–214CrossRefGoogle Scholar
  12. 12.
    Lüningschrör P, Hauser S, Kaltschmidt B, Kaltschmidt C (2013) MicroRNAs in pluripotency reprogramming and cell fate induction. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1833:1894–1903CrossRefGoogle Scholar
  13. 13.
    Flynn RA, Chang HY (2014) Long noncoding RNAs in cell-fate programming and reprogramming. Cell Stem Cell 14:752–761CrossRefGoogle Scholar
  14. 14.
    Iorio MV, Croce CM (2012) MicroRNA dysregulation in cancer: diagnostics monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4:143–159CrossRefGoogle Scholar
  15. 15.
    Bracken CP, Scott HS, Goodall GJ (2016) A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet 17:719–732CrossRefGoogle Scholar
  16. 16.
    Tan L, Yu J-T, Tan L (2014) Causes and consequences of MicroRNA dysregulation in neurodegenerative diseases. Mol Neurobiol 51:1249–1262CrossRefGoogle Scholar
  17. 17.
    Tian X-J, Zhang H, Xing J (2013) Coupled reversible and irreversible bistable switches underlying TGFβ-induced epithelial to mesenchymal transition. Biophys J 105:1079–1089CrossRefGoogle Scholar
  18. 18.
    Zhang J, Tian X-J, Zhang H, Teng Y, Li R, Bai F, Elankumaran S, Xing J (2014) TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci Signal 7:ra91CrossRefGoogle Scholar
  19. 19.
    Lu M, Jolly MK, Levine H, Onuchic JN, Ben-Jacob E (2013) MicroRNA-based regulation of epithelial-hybrid-mesenchymal fate determination. Proc Natl Acad Sci U S A 110:18144–18149CrossRefGoogle Scholar
  20. 20.
    Aguda BD, Kim Y, Piper-Hunter MG, Friedman A, Marsh CB (2008) MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR-17-92 E2F, and Myc. Proc Natl Acad Sci U S A 105:19678–19683CrossRefGoogle Scholar
  21. 21.
    Sengupta D, Govindaraj V, Kar S (2017) Subtle alteration in microRNA dynamics accounts for differential nature of cellular proliferation.
  22. 22.
    Zhou C-H, Zhang X-P, Liu F, Wang W (2014) Involvement of miR-605 and miR-34a in the DNA damage response promotes apoptosis induction. Biophys J 106:1792–1800CrossRefGoogle Scholar
  23. 23.
    Lai X, Wolkenhauer O, Vera J (2012) Modeling miRNA regulation in cancer signaling systems: miR-34a regulation of the p53/Sirt1 signaling module. Methods Mol Biol 880:87–108CrossRefGoogle Scholar
  24. 24.
    Gérard C, Gonze D, Lemaigre F, Novák B (2014) A model for the epigenetic switch linking inflammation to cell transformation: deterministic and stochastic approaches. PLoS Comput Biol 10:e1003455CrossRefGoogle Scholar
  25. 25.
    Lee J, Lee J, Farquhar KS, Yun J, Frankenberger CA, Bevilacqua E, Yeung K, Kim E-J, Balazsi G, Rosner MR (2014) Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc Natl Acad Sci U S A 111:E364–E373CrossRefGoogle Scholar
  26. 26.
    Milo R (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827CrossRefGoogle Scholar
  27. 27.
    Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461CrossRefGoogle Scholar
  28. 28.
    Ferrell JE, Xiong W (2001) Bistability in cell signaling: how to make continuous processes discontinuous and reversible processes irreversible. Chaos 11:227CrossRefGoogle Scholar
  29. 29.
    Tyson JJ, Chen KC, Novak B (2003) Sniffers buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15:221–231CrossRefGoogle Scholar
  30. 30.
    Novák B, Tyson JJ (2008) Design principles of biochemical oscillators. Nat Rev Mol Cell Biol 9:981–991CrossRefGoogle Scholar
  31. 31.
    Ma W, Trusina A, El-Samad H, Lim WA, Tang C (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138:760–773CrossRefGoogle Scholar
  32. 32.
    Tsai TY-C, Choi YS, Ma W, Pomerening JR, Tang C, Ferrell JE (2008) Robust tunable biological oscillations from interlinked positive and negative feedback loops. Science 321:126–129CrossRefGoogle Scholar
  33. 33.
    Tian X-J, Zhang X-P, Liu F, Wang W (2009) Interlinking positive and negative feedback loops creates a tunable motif in gene regulatory networks. Phys Rev E Stat Nonlin Soft Matter Phys 80(1 Pt 1):011926. CrossRefPubMedGoogle Scholar
  34. 34.
    Suel GM, Kulkarni RP, Dworkin J, Garcia-Ojalvo J, Elowitz MB (2007) Tunability and noise dependence in differentiation dynamics. Science 315:1716–1719CrossRefGoogle Scholar
  35. 35.
    Brandman O (2005) Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310:496–498CrossRefGoogle Scholar
  36. 36.
    Zhang X-P, Cheng Z, Liu F, Wang W (2007) Linking fast and slow positive feedback loops creates an optimal bistable switch in cell signaling. Phys Rev E Stat Nonlin Soft Matter PhysPhys Rev E 76(3 Pt 1):031924. CrossRefGoogle Scholar
  37. 37.
    Siemens H, Jackstadt R, Hünten S, Kaller M, Menssen A, Götz U, Hermeking H (2011) miR-34 and SNAIL form a double-negative feedback loop to regulate epithelial-mesenchymal transitions. Cell Cycle 10:4256–4271CrossRefGoogle Scholar
  38. 38.
    Brabletz S, Brabletz T (2010) The ZEB/miR-200 feedback loopa motor of cellular plasticity in development and cancer? EMBO Rep 11:670–677CrossRefGoogle Scholar
  39. 39.
    Yamakuchi M, Lowenstein CJ (2009) MiR-34 SIRT1, and p53: The feedback loop. Cell Cycle 8:712–715CrossRefGoogle Scholar
  40. 40.
    Rokavec M, Ö-ner MG, Li H et al (2014) IL-6R/STAT3/miR-34a feedback loop promotes EMT-mediated colorectal cancer invasion and metastasis. J Clin Investig 124:1853–1867CrossRefGoogle Scholar
  41. 41.
    Wu H, Wang G, Wang Z, An S, Ye P, Luo S (2016) A negative feedback loop between miR-200b and the nuclear factor-κB pathway via IKBKB/IKK-β in breast cancer cells. FEBS J 283:2259–2271CrossRefGoogle Scholar
  42. 42.
    Lu Y-X, Yuan L, Xue X-L, Zhou M, Liu Y, Zhang C, Li J-P, Zheng L, Hong M, Li X-N (2014) Regulation of colorectal carcinoma stemness growth, and metastasis by an miR-200c-Sox2-negative feedback loop mechanism. Clin Cancer Res 20:2631–2642CrossRefGoogle Scholar
  43. 43.
    Kundu ST, Byers LA, Peng DH, Roybal JD, Diao L, Wang J, Tong P, Creighton CJ, Gibbons DL (2015) The miR-200 family and the miR-183~96~182 cluster target Foxf2 to inhibit invasion and metastasis in lung cancers. Oncogene 35:173–186CrossRefGoogle Scholar
  44. 44.
    Ding X, Park SI, McCauley LK, Wang C-Y (2013) Signaling between Transforming Growth Factor β (TGF-β) and Transcription Factor SNAI2 Represses Expression of MicroRNA miR-203 to Promote Epithelial-Mesenchymal Transition and Tumor Metastasis. J Biol Chem 288:10241–10253CrossRefGoogle Scholar
  45. 45.
    Yang X, Lin X, Zhong X et al (2010) Double-negative feedback loop between reprogramming factor LIN28 and microRNA let-7 regulates aldehyde dehydrogenase 1-positive cancer stem cells. Cancer Res 70:9463–9472CrossRefGoogle Scholar
  46. 46.
    Iliopoulos D, Hirsch HA, Struhl K (2009) An epigenetic switch involving NF-κB Lin28, Let-7 MicroRNA and IL6 links inflammation to cell transformation. Cell 139:693–706CrossRefGoogle Scholar
  47. 47.
    Pasquinelli AE (2012) MicroRNAs and their targets: recognition regulation and an emerging reciprocal relationship. Nat Rev Genet 13:271–282CrossRefGoogle Scholar
  48. 48.
    Mukherji S, Ebert MS, Zheng GXY, Tsang JS, Sharp PA, van Oudenaarden A (2011) MicroRNAs can generate thresholds in target gene expression. Nat Genet 43:854–859CrossRefGoogle Scholar
  49. 49.
    Tian X-J, Zhang H, Zhang J, Xing J (2016) Reciprocal regulation between mRNA and microRNA enables a bistable switch that directs cell fate decisions. FEBS Lett 590:3443–3455CrossRefGoogle Scholar
  50. 50.
    Markevich NI, Hoek JB, Kholodenko BN (2004) Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J Cell Biol 164:353–359CrossRefGoogle Scholar
  51. 51.
    Ortega F, Garcés JL, Mas F, Kholodenko BN, Cascante M (2006) Bistability from double phosphorylation in signal transduction. FEBS J 273:3915–3926CrossRefGoogle Scholar
  52. 52.
    Grande MT, Sánchez-Laorden B, López-Blau C, Frutos CAD, Boutet A, Arévalo M, Rowe RG, Weiss SJ, López-Novoa JM, Nieto MA (2015) Snail1-induced partial epithelial-to-mesenchymal transition drives renal fibrosis in mice and can be targeted to reverse established disease. Nat Med 21:989–997CrossRefGoogle Scholar
  53. 53.
    Lovisa S, LeBleu VS, Tampe BÃ et al (2015) Epithelial-to-mesenchymal transition induces cell cycle arrest and parenchymal damage in renal fibrosis. Nat Med 21:998–1009CrossRefGoogle Scholar
  54. 54.
    Voon DC, Huang RY, Jackson RA, Thiery JP (2017) The EMT spectrum and therapeutic opportunities. Mol Oncol 11:878–891CrossRefGoogle Scholar
  55. 55.
    Huang RY-J, Wong MK, Tan TZ et al (2013) An EMT spectrum defines an anoikis-resistant and spheroidogenic intermediate mesenchymal state that is sensitive to e-cadherin restoration by a src-kinase inhibitor saracatinib (AZD0530). Cell Death Dis 4:e915CrossRefGoogle Scholar
  56. 56.
    Tan TZ, Miow QH, Miki Y, Noda T, Mori S, Huang RY-J, Thiery JP (2014) Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients. EMBO Mol Med 6:1279–1293CrossRefGoogle Scholar
  57. 57.
    Figliuzzi M, Marinari E, Martino AD (2013) MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory. Biophys J 104:1203–1213CrossRefGoogle Scholar
  58. 58.
    Figliuzzi M, De Martino A, Marinari E (2014) RNA-based regulation: dynamics and response to perturbations of competing RNAs. Biophys J 107:1011–1022CrossRefGoogle Scholar
  59. 59.
    Yuan Y, Liu B, Xie P, Zhang MQ, Li Y, Xie Z, Wang X (2015) Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc Natl Acad Sci U S A 112:3158–3163CrossRefGoogle Scholar
  60. 60.
    Yuan Y, Ren X, Xie Z, Wang X (2016) A quantitative understanding of microRNA-mediated competing endogenous RNA regulation. Quant Biol 4:47–57CrossRefGoogle Scholar
  61. 61.
    Bloom RJ, Winkler SM, Smolke CD (2015) Synthetic feedback control using an RNAi-based gene-regulatory device. J Biol Eng 9:5. CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Wroblewska L, Kitada T, Endo K, Siciliano V, Stillo B, Saito H, Weiss R (2015) Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery. Nat Biotechnol 33:839–841CrossRefGoogle Scholar
  63. 63.
    Miki K, Endo K, Takahashi S et al (2015) Efficient detection and purification of cell populations using synthetic MicroRNA switches. Cell Stem Cell 16:699–711CrossRefGoogle Scholar
  64. 64.
    Yu M, Bardia A, Wittner BS et al (2013) Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339:580–584CrossRefGoogle Scholar
  65. 65.
    Ilina O, Friedl P (2009) Mechanisms of collective cell migration at a glance. J Cell Sci 122:3203–3208CrossRefGoogle Scholar
  66. 66.
    Morel M, Shtrahman R, Rotter V, Nissim L, Bar-Ziv RH (2016) Cellular heterogeneity mediates inherent sensitivityspecificity tradeoff in cancer targeting by synthetic circuits. Proc Natl Acad Sci U S A 113:8133–8138CrossRefGoogle Scholar
  67. 67.
    Mitchell PS, Parkin RK, Kroh EM et al (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105:10513–10518CrossRefGoogle Scholar
  68. 68.
    Fischer KR, Durrans A, Lee S et al (2015) Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527:472–476CrossRefGoogle Scholar
  69. 69.
    Zheng X, Carstens JL, Kim J, Scheible M, Kaye J, Sugimoto H, Wu C-C, LeBleu VS, Kalluri R (2015) Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 527:525–530CrossRefGoogle Scholar
  70. 70.
    Yoon J-H, Abdelmohsen K, Gorospe M (2014) Functional interactions among microRNAs and long noncoding RNAs. Semin Cell Dev Biol 34:9–14CrossRefGoogle Scholar
  71. 71.
    Tay Y, Rinn J, Pandolfi PP (2014) The multilayered complexity of ceRNA crosstalk and competition. Nature 505:344–352CrossRefGoogle Scholar
  72. 72.
    Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK, Kjems J (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495:384–388CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Biological and Health Systems EngineeringArizona State UniversityTempeUSA

Personalised recommendations