Computational Analysis of Altering Cell Fate

  • Hussein M. AbdallahEmail author
  • Domitilla Del Vecchio
Part of the Methods in Molecular Biology book series (MIMB, volume 1975)


The notion of reprogramming cell fate is a direct challenge to the traditional view in developmental biology that a cell’s phenotypic identity is sealed after undergoing differentiation. Direct experimental evidence, beginning with the somatic cell nuclear transfer experiments of the twentieth century and culminating in the more recent breakthroughs in transdifferentiation and induced pluripotent stem cell (iPSC) reprogramming, have rewritten the rules for what is possible with cell fate transformation. Research is ongoing in the manipulation of cell fate for basic research in disease modeling, drug discovery, and clinical therapeutics. In many of these cell fate reprogramming experiments, there is often little known about the genetic and molecular changes accompanying the reprogramming process. However, gene regulatory networks (GRNs) can in some cases be implicated in the switching of phenotypes, providing a starting point for understanding the dynamic changes that accompany a given cell fate reprogramming process. In this chapter, we present a framework for computationally analyzing cell fate changes by mathematically modeling these GRNs. We provide a user guide with several tutorials of a set of techniques from dynamical systems theory that can be used to probe the intrinsic properties of GRNs as well as study their responses to external perturbations.

Key words

iPSC Transdifferentiation Cell fate Dynamical systems Gene regulatory network 


  1. 1.
    Waddington CH (1957) The strategy of genes. Routledge, New YorkGoogle Scholar
  2. 2.
    Mitalipov S, Wolf D (2009) Totipotency, pluripotency and nuclear reprogramming. In: Martin U (ed) Engineering of stem cells. Advances in biochemical engineering/biotechnology, vol vol. 114. Springer, Berlin, HeidelbergGoogle Scholar
  3. 3.
    Xie H, Ye M, Feng R, Graf T (2004) Stepwise reprogramming of B cells into macrophages. Cell 117(5):663–676PubMedCrossRefGoogle Scholar
  4. 4.
    Briggs R, King TJ (1952) Transplantation of living nuclei from blastula cells into enucleated frogs’ eggs. Proc Natl Acad Sci U S A 38:455–463PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Gurdon JB, Elsdale TR et al (1958) Sexually mature individuals of Xenopus laevis from the transplantation of single somatic nuclei. Nature 182:64–65. Scholar
  6. 6.
    Campbell KH, McWhir J, Ritchie WA, Wilmut I (1996) Sheep cloned by nuclear transfer from a cultured cell line. Nature 380:64–66. Scholar
  7. 7.
    Wakayama T, Perry AC et al (1998) Full-term development of mice from enucleated oocytes injected with cumulus cell nuclei. Nature 394:369–374. Scholar
  8. 8.
    Tapscott SJ, Davis RL et al (1988) MyoD1: a nuclear phosphoprotein requiring a Myc homology region to convert fibroblasts to myoblasts. Science 21:405–411CrossRefGoogle Scholar
  9. 9.
    Davis RL, Weintraub H, Lassar AB (1987) Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell 51:987–1000PubMedCrossRefGoogle Scholar
  10. 10.
    Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676PubMedCrossRefGoogle Scholar
  11. 11.
    Radzisheuskaya A, Chia Gle B et al (2013) A defined Oct4 level governs cell state transitions of pluripotency entry and differentiation into all embryonic lineages. Nat Cell Biol 15(6):579–590PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    González F, Boué S, Belmonte JC (2011) Methods for making induced pluripotent stem cells: reprogramming àla carte. Nat Rev Genet 12(4):231–242PubMedCrossRefGoogle Scholar
  13. 13.
    Buganim Y, Faddah DA, Jaenisch R (2013) Mechanisms and models of somatic cell reprogramming. Nat Rev Genet 14(6):427–439. Scholar
  14. 14.
    David L, Polo JM (2014) Review: phases of reprogramming. Stem Cell Res 12(3):754–761PubMedCrossRefGoogle Scholar
  15. 15.
    de BW, Zimm R, Brusch L (2013) Transdifferentiation of pancreatic cells by loss of contact-mediated signaling. BMC Syst Biol 7:77. Scholar
  16. 16.
    Yao E, Lin C et al (2017) Notch signaling controls transdifferentiation of pulmonary neuroendocrine cells in response to lung injury. Stem Cells 36(3):377–391PubMedCrossRefGoogle Scholar
  17. 17.
    Malik N, Rao MS (2013) A review of the methods for human ipsc derivation. Methods Mol Biol 997:23–33PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Schlaeger TM, Daheron L et al (2015) A comparison of non-integrating reprogramming methods. Nat Biotechnol 33:58–63PubMedCrossRefGoogle Scholar
  19. 19.
    Goh PA, Caxaria S et al (2013) A systematic evaluation of integration free reprogramming methods for deriving clinically relevant patient specific induced pluripotent stem (ips) cells. PLoS One 8(11):e81622. Scholar
  20. 20.
    iPS cells 10 years later. Cell 2016;166(6): 1356–1359.
  21. 21.
    Zhou Q, Brown J, Kanarek A, Rajagopal J, Melton DA (2008) In vivo reprogramming of adult pancreatic exocrine cells to b-cells. Nature 455:627–632PubMedCrossRefGoogle Scholar
  22. 22.
    Pang ZP, Yang N et al (2011) Induction of human neuronal cells by defined transcription factors. Nature 476(7359):220–223. Scholar
  23. 23.
    Bussmann LH, Schubert A et al (2009) A robust and highly efficient immune cell reprogramming system. Cell Stem Cell 5:554–566PubMedCrossRefGoogle Scholar
  24. 24.
    Laiosa CV, Stadtfeld M et al (2006) Reprogramming of committed T cell progenitors to macrophages and dendritic cells by C/EBPα and PU.1 transcription factors. Immunity 25(5):731–744PubMedCrossRefGoogle Scholar
  25. 25.
    Vierbuchen T, Ostermeier A et al (2010) Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463:1035–1041PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Yu U, Lee SH et al (2004) Review: bioinformatics in the post-genome era. J Biochem Mol Biol 37(1):75–82PubMedGoogle Scholar
  27. 27.
    Boyer LA, Lee TI et al (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122(6):947–956PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Zhang B, Wolynes P (2014) Stem cell differentiation as a many body problem. Proc Natl Acad Sci 111:10185–10190PubMedCrossRefGoogle Scholar
  29. 29.
    Orkin SH, Wang J et al (2008) The transcriptional network controlling pluripotency in ES cells. Cold Spring Harb Symp Quant Biol 73:195–202PubMedCrossRefGoogle Scholar
  30. 30.
    Chickarmane V, Enver T et al (2009) Computational modeling of the hematopoietic erythroid-myeloid switch reveals insights into cooperativity, priming, and irreversibility. PLoS Comput Biol 5:e1000268PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Goldfarb AN (2007) Transcriptional control of megakaryocyte development. Oncogene 26(47):6795–6802PubMedCrossRefGoogle Scholar
  32. 32.
    Friedman AD (2007) Transcriptional control of granulocyte and monocyte development. Oncogene 26(47):6816–6828PubMedCrossRefGoogle Scholar
  33. 33.
    Gupta P, Gurudutta GU et al (2009) PU.1 and partners: regulation of haematopoietic stem cell fate in normal and malignant haematopoiesis. J Cell Mol Med 13:4349–4363PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Chickarmane V, Troein C et al (2006) Transcriptional dynamics of the embryonic stem cell switch. PLoS Comput Biol 2(9):e123. Scholar
  35. 35.
    Del Vecchio D, Abdallah H et al (2017) A blueprint for a synthetic genetic feedback controller to reprogram cell fate. Cell Syst 4(1):109–120PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Abdallah H, Del Vecchio D, Qian Y, Collins JJ (2016) A dynamical model for the low efficiency of induced pluripotent stem cell reprogramming. Paper presented at American Control Conference, Boston, MA, June 2016Google Scholar
  37. 37.
    Olariu V, Lövkvist C et al (2016) Nanog, Oct4 and Tet1 interplay in establishing pluripotency. Sci Rep 6:25438PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Huang S, Guo YP, May G, Enver T (2007) Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol 305(2):695–713PubMedCrossRefGoogle Scholar
  39. 39.
    Santillán M (2008) On the use of the Hill functions in mathematical models of gene regulatory networks. Math Modell Nat Phenom 3(2):85–97. Scholar
  40. 40.
    Liew CW, Rand KD et al (2006) Molecular analysis of the interaction between the hematopoietic master transcription factors GATA-1 and PU.1. J Biol Chem 281:28296–28306PubMedCrossRefGoogle Scholar
  41. 41.
    Tian T, Smith-Miles K (2014) Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell. BMC Syst Biol 8(Suppl 1):S8PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Zhou JX, Brusch L, Huang S (2011) Predicting pancreas cell fate decisions and reprogramming with a hierarchical multi-attractor model. PLoS One 6(3):e14752PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Strogatz S (2014) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry and engineering (studies in nonlinearity). Westview Press, Boulder, COGoogle Scholar
  44. 44.
    Del Vecchio D, Murray RM (2014) Biomolecular feedback systems. Princeton University Press, Boston, MACrossRefGoogle Scholar
  45. 45.
    Gillespie DT (2009) Deterministic limit of stochastic chemical kinetics. J Phys Chem 113(6):1640–1644. Scholar
  46. 46.
    Gillespie DT (2000) The chemical langevin equation. J Chem Phys 113(1):297–306CrossRefGoogle Scholar
  47. 47.
    Van Kampen NG (1992) Stochastic processes in physics and chemistry, vol 1. North Holland Publishing Co., AmsterdamGoogle Scholar
  48. 48.
    Al-Radhawi MA, Sontag E, Del Vecchio D (2017) Multi-modality in gene regulatory networks with slow gene binding. arXiv:1705.02330Google Scholar
  49. 49.
    Polynikis A, Hogan SJ, di Bernardo M (2009) Comparing different ODE modelling approaches for gene regulatory networks. J Theor Biol 261(4):511–530PubMedCrossRefGoogle Scholar
  50. 50.
    Agrawal N, Dasaradhi PV et al (2003) RNA interference: biology, mechanism, and applications. Microbiol Mol Biol Rev 67(4):657–685PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry, 5th edn. W. H. Freeman, New York. Section 10.4, Covalent modification is a means of regulating enzyme activityGoogle Scholar
  52. 52.
    Gyorgy A, Del Vecchio D (2014) Modular composition of gene transcription networks. PLoS Comput Biol 10(3):e1003486PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Geertz M, Maerkl SJ (2010) Experimental strategies for studying transcription factor—DNA binding specificities. Brief Funct Genomics 9(5–6):362–373. Scholar
  54. 54.
    Horak CE, Snyder M (2002) ChIP-chip: a genomic approach for identifying transcription factor binding sites. Methods Enzymol 350:469–483PubMedCrossRefGoogle Scholar
  55. 55.
    Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:669–680PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. Scholar
  57. 57.
    Alberts B, Johnson A, Lewis J et al (2002) Molecular biology of the cell. Garland Science, New York. Available from: Scholar
  58. 58.
    Zhou P (2004) Determining protein half-lives. Methods Mol Biol 284:67–77PubMedGoogle Scholar
  59. 59.
    Kuhar MJ (2010) Measuring levels of proteins by various technologies: can we learn more by measuring turnover? Biochem Pharmacol 79(5):665–668. Scholar
  60. 60.
    Sezonov G, Joseleau-Petit D et al (2007) Escherichia coli physiology in Luria-Bertani broth. J Bacteriol 189(23):8746–8749PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Cooper GM (2000) The cell: a molecular approach, The eukaryotic cell cycle, 2nd edn. Sinauer Associates, Sunderland, MA. Available from: Scholar
  62. 62.
    Takahashi K, Yamanaka S (2016) A decade of transcription factor-mediated reprogramming to pluripotency. Nat Rev Mol Cell Biol 17(3):183–193. Scholar
  63. 63.
    Milo R, Jorgensen P et al (2010) BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res 38:D750–D753PubMedCrossRefGoogle Scholar
  64. 64.
    Schwanhäusser B, Busse D et al (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342. Scholar
  65. 65.
    Alon U (2006) An introduction to systems biology: design principles of biological circuits. CRC Press, Boca Raton, FLGoogle Scholar
  66. 66.
    Yuan L, Chan GC et al (2016) A role of stochastic phenotype switching in generating mosaic endothelial cell heterogeneity. Nat Commun 7:10160PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    To T-L, Maheshri N (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327(5969):1142–1145PubMedCrossRefGoogle Scholar
  68. 68.
    Kauffman S (1973) Control circuits for determination and transdetermination. Science 181:310–318PubMedCrossRefGoogle Scholar
  69. 69.
    Huang S, Eichler G et al (2005) Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett 94(12):128701PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Huang S (2009) Reprogramming cell fates: reconciling rarity with robustness. BioEssays 31:546–560PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Strang G (2009) Introduction to linear algebra. Wellesley-Cambridge Press, Wellesley, MAGoogle Scholar
  72. 72.
    Khalil H (2014) Nonlinear control. Pearson, LondonGoogle Scholar
  73. 73.
    Slotine JJ, Li W (1991) Applied nonlinear control. Pearson, LondonGoogle Scholar
  74. 74.
    Wiggins S (2003) Introduction to applied nonlinear dynamical systems and chaos. Springer, New York CityGoogle Scholar
  75. 75.
    Carr J (1981) Applications of centre manifold theory. Springer, New York CityCrossRefGoogle Scholar
  76. 76.
    Saltelli A, Ratto M et al (2008) Global sensitivity analysis: the primer. Wiley-Interscience, Hoboken, NJGoogle Scholar
  77. 77.
    McKay M, Beckman RJ et al (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245. Scholar
  78. 78.
    Stein M (1987) Large sample properties of simulations using latin hypercube sampling. Technometrics 29(2):143–151. Correction, Vol. 32, p. 367CrossRefGoogle Scholar
  79. 79.
    Astrom KJ, Murray RM (2008) Feedback systems: an introduction for scientists and engineers. Princeton University Press, Boston, MAGoogle Scholar
  80. 80.
    Swain PS, Elowitz MB et al (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci 99(20):12795–12800PubMedCrossRefGoogle Scholar
  81. 81.
    Elowitz MB, Levine AJ et al (2002) Stochastic gene expression in a single cell. Science 297:1183–1186PubMedCrossRefGoogle Scholar
  82. 82.
    Allis DC, Caparros M-L et al (2015) Epigenetics. Cold Spring Harbor, New YorkGoogle Scholar
  83. 83.
    Bagci H, Fisher AG (2013) DNA demethylation in pluripotency and reprogramming: the role of tet proteins and cell division. Cell Stem Cell 13:265–269PubMedCrossRefGoogle Scholar
  84. 84.
    De Carvalho DD, You JS et al (2010) DNA methylation and cellular reprogramming. Trends Cell Biol 20:609–617PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Huang K, Fan G (2010) DNA methylation in cell differentiation and reprogramming: an emerging systematic view. Regen Med 5:531–544PubMedCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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