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Computational Analysis of Altering Cell Fate

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Computational Stem Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1975))

Abstract

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.

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References

  1. Waddington CH (1957) The strategy of genes. Routledge, New York

    Google Scholar 

  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, Heidelberg

    Google Scholar 

  3. Xie H, Ye M, Feng R, Graf T (2004) Stepwise reprogramming of B cells into macrophages. Cell 117(5):663–676

    CAS  PubMed  Google Scholar 

  4. Briggs R, King TJ (1952) Transplantation of living nuclei from blastula cells into enucleated frogs’ eggs. Proc Natl Acad Sci U S A 38:455–463

    CAS  PubMed Central  PubMed  Google Scholar 

  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. https://doi.org/10.1038/182064a0

    Article  CAS  PubMed  Google Scholar 

  6. Campbell KH, McWhir J, Ritchie WA, Wilmut I (1996) Sheep cloned by nuclear transfer from a cultured cell line. Nature 380:64–66. https://doi.org/10.1038/380064a0

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1038/28615

    Article  CAS  PubMed  Google Scholar 

  8. Tapscott SJ, Davis RL et al (1988) MyoD1: a nuclear phosphoprotein requiring a Myc homology region to convert fibroblasts to myoblasts. Science 21:405–411

    Google Scholar 

  9. Davis RL, Weintraub H, Lassar AB (1987) Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell 51:987–1000

    CAS  PubMed  Google Scholar 

  10. Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676

    CAS  PubMed  Google Scholar 

  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):579–590

    CAS  PubMed Central  PubMed  Google Scholar 

  12. González F, Boué S, Belmonte JC (2011) Methods for making induced pluripotent stem cells: reprogramming àla carte. Nat Rev Genet 12(4):231–242

    PubMed  Google Scholar 

  13. Buganim Y, Faddah DA, Jaenisch R (2013) Mechanisms and models of somatic cell reprogramming. Nat Rev Genet 14(6):427–439. https://doi.org/10.1038/nrg3473

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. David L, Polo JM (2014) Review: phases of reprogramming. Stem Cell Res 12(3):754–761

    PubMed  Google Scholar 

  15. de BW, Zimm R, Brusch L (2013) Transdifferentiation of pancreatic cells by loss of contact-mediated signaling. BMC Syst Biol 7:77. https://doi.org/10.1186/1752-0509-7-77

    Article  Google Scholar 

  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):377–391

    PubMed  Google Scholar 

  17. Malik N, Rao MS (2013) A review of the methods for human ipsc derivation. Methods Mol Biol 997:23–33

    CAS  PubMed Central  PubMed  Google Scholar 

  18. Schlaeger TM, Daheron L et al (2015) A comparison of non-integrating reprogramming methods. Nat Biotechnol 33:58–63

    CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1371/journal.pone.0081622

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. iPS cells 10 years later. Cell 2016;166(6): 1356–1359. https://doi.org/10.1016/j.cell.2016.08.043

  21. Zhou Q, Brown J, Kanarek A, Rajagopal J, Melton DA (2008) In vivo reprogramming of adult pancreatic exocrine cells to b-cells. Nature 455:627–632

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Pang ZP, Yang N et al (2011) Induction of human neuronal cells by defined transcription factors. Nature 476(7359):220–223. https://doi.org/10.1038/nature10202

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Bussmann LH, Schubert A et al (2009) A robust and highly efficient immune cell reprogramming system. Cell Stem Cell 5:554–566

    CAS  PubMed  Google Scholar 

  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):731–744

    CAS  PubMed  Google Scholar 

  25. Vierbuchen T, Ostermeier A et al (2010) Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463:1035–1041

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Yu U, Lee SH et al (2004) Review: bioinformatics in the post-genome era. J Biochem Mol Biol 37(1):75–82

    CAS  PubMed  Google Scholar 

  27. Boyer LA, Lee TI et al (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122(6):947–956

    CAS  PubMed Central  PubMed  Google Scholar 

  28. Zhang B, Wolynes P (2014) Stem cell differentiation as a many body problem. Proc Natl Acad Sci 111:10185–10190

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Orkin SH, Wang J et al (2008) The transcriptional network controlling pluripotency in ES cells. Cold Spring Harb Symp Quant Biol 73:195–202

    CAS  PubMed  Google Scholar 

  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:e1000268

    PubMed Central  PubMed  Google Scholar 

  31. Goldfarb AN (2007) Transcriptional control of megakaryocyte development. Oncogene 26(47):6795–6802

    CAS  PubMed  Google Scholar 

  32. Friedman AD (2007) Transcriptional control of granulocyte and monocyte development. Oncogene 26(47):6816–6828

    CAS  PubMed  Google Scholar 

  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 13:4349–4363

    CAS  PubMed Central  PubMed  Google Scholar 

  34. Chickarmane V, Troein C et al (2006) Transcriptional dynamics of the embryonic stem cell switch. PLoS Comput Biol 2(9):e123. https://doi.org/10.1371/journal.pcbi.0020123

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):109–120

    PubMed Central  PubMed  Google Scholar 

  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 2016

    Google Scholar 

  37. Olariu V, Lövkvist C et al (2016) Nanog, Oct4 and Tet1 interplay in establishing pluripotency. Sci Rep 6:25438

    CAS  PubMed Central  PubMed  Google Scholar 

  38. Huang S, Guo YP, May G, Enver T (2007) Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol 305(2):695–713

    CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1051/mmnp:2008056

    Article  Google Scholar 

  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 281:28296–28306

    CAS  PubMed  Google Scholar 

  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):S8

    PubMed Central  PubMed  Google Scholar 

  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):e14752

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Strogatz S (2014) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry and engineering (studies in nonlinearity). Westview Press, Boulder, CO

    Google Scholar 

  44. Del Vecchio D, Murray RM (2014) Biomolecular feedback systems. Princeton University Press, Boston, MA

    Google Scholar 

  45. Gillespie DT (2009) Deterministic limit of stochastic chemical kinetics. J Phys Chem 113(6):1640–1644. https://doi.org/10.1021/jp806431b

    Article  CAS  Google Scholar 

  46. Gillespie DT (2000) The chemical langevin equation. J Chem Phys 113(1):297–306

    CAS  Google Scholar 

  47. Van Kampen NG (1992) Stochastic processes in physics and chemistry, vol 1. North Holland Publishing Co., Amsterdam

    Google Scholar 

  48. Al-Radhawi MA, Sontag E, Del Vecchio D (2017) Multi-modality in gene regulatory networks with slow gene binding. arXiv:1705.02330

    Google Scholar 

  49. Polynikis A, Hogan SJ, di Bernardo M (2009) Comparing different ODE modelling approaches for gene regulatory networks. J Theor Biol 261(4):511–530

    CAS  PubMed  Google Scholar 

  50. Agrawal N, Dasaradhi PV et al (2003) RNA interference: biology, mechanism, and applications. Microbiol Mol Biol Rev 67(4):657–685

    CAS  PubMed Central  PubMed  Google Scholar 

  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 activity

    Google Scholar 

  52. Gyorgy A, Del Vecchio D (2014) Modular composition of gene transcription networks. PLoS Comput Biol 10(3):e1003486

    PubMed Central  PubMed  Google Scholar 

  53. Geertz M, Maerkl SJ (2010) Experimental strategies for studying transcription factor—DNA binding specificities. Brief Funct Genomics 9(5–6):362–373. https://doi.org/10.1093/bfgp/elq023

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Horak CE, Snyder M (2002) ChIP-chip: a genomic approach for identifying transcription factor binding sites. Methods Enzymol 350:469–483

    CAS  PubMed  Google Scholar 

  55. Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:669–680

    CAS  PubMed Central  PubMed  Google Scholar 

  56. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. https://doi.org/10.1038/nrg2484

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Alberts B, Johnson A, Lewis J et al (2002) Molecular biology of the cell. Garland Science, New York. Available from: https://www.ncbi.nlm.nih.gov/books/NBK26887/

    Google Scholar 

  58. Zhou P (2004) Determining protein half-lives. Methods Mol Biol 284:67–77

    CAS  PubMed  Google Scholar 

  59. Kuhar MJ (2010) Measuring levels of proteins by various technologies: can we learn more by measuring turnover? Biochem Pharmacol 79(5):665–668. https://doi.org/10.1016/j.bcp.2009.09.029

    Article  CAS  PubMed  Google Scholar 

  60. Sezonov G, Joseleau-Petit D et al (2007) Escherichia coli physiology in Luria-Bertani broth. J Bacteriol 189(23):8746–8749

    CAS  PubMed Central  PubMed  Google Scholar 

  61. Cooper GM (2000) The cell: a molecular approach, The eukaryotic cell cycle, 2nd edn. Sinauer Associates, Sunderland, MA. Available from: https://www.ncbi.nlm.nih.gov/books/NBK9876/

    Google Scholar 

  62. Takahashi K, Yamanaka S (2016) A decade of transcription factor-mediated reprogramming to pluripotency. Nat Rev Mol Cell Biol 17(3):183–193. https://doi.org/10.1038/nrm.2016.8

    Article  CAS  PubMed  Google Scholar 

  63. Milo R, Jorgensen P et al (2010) BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res 38:D750–D753

    CAS  PubMed  Google Scholar 

  64. Schwanhäusser B, Busse D et al (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342. https://doi.org/10.1038/nature10098

    Article  CAS  PubMed  Google Scholar 

  65. Alon U (2006) An introduction to systems biology: design principles of biological circuits. CRC Press, Boca Raton, FL

    Google Scholar 

  66. Yuan L, Chan GC et al (2016) A role of stochastic phenotype switching in generating mosaic endothelial cell heterogeneity. Nat Commun 7:10160

    CAS  PubMed Central  PubMed  Google Scholar 

  67. To T-L, Maheshri N (2010) Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327(5969):1142–1145

    CAS  PubMed  Google Scholar 

  68. Kauffman S (1973) Control circuits for determination and transdetermination. Science 181:310–318

    CAS  PubMed  Google Scholar 

  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):128701

    PubMed  Google Scholar 

  70. Huang S (2009) Reprogramming cell fates: reconciling rarity with robustness. BioEssays 31:546–560

    CAS  PubMed  Google Scholar 

  71. Strang G (2009) Introduction to linear algebra. Wellesley-Cambridge Press, Wellesley, MA

    Google Scholar 

  72. Khalil H (2014) Nonlinear control. Pearson, London

    Google Scholar 

  73. Slotine JJ, Li W (1991) Applied nonlinear control. Pearson, London

    Google Scholar 

  74. Wiggins S (2003) Introduction to applied nonlinear dynamical systems and chaos. Springer, New York City

    Google Scholar 

  75. Carr J (1981) Applications of centre manifold theory. Springer, New York City

    Google Scholar 

  76. Saltelli A, Ratto M et al (2008) Global sensitivity analysis: the primer. Wiley-Interscience, Hoboken, NJ

    Google Scholar 

  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. https://doi.org/10.2307/1268522

    Article  Google Scholar 

  78. Stein M (1987) Large sample properties of simulations using latin hypercube sampling. Technometrics 29(2):143–151. Correction, Vol. 32, p. 367

    Google Scholar 

  79. Astrom KJ, Murray RM (2008) Feedback systems: an introduction for scientists and engineers. Princeton University Press, Boston, MA

    Google Scholar 

  80. Swain PS, Elowitz MB et al (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci 99(20):12795–12800

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Elowitz MB, Levine AJ et al (2002) Stochastic gene expression in a single cell. Science 297:1183–1186

    CAS  PubMed  Google Scholar 

  82. Allis DC, Caparros M-L et al (2015) Epigenetics. Cold Spring Harbor, New York

    Google Scholar 

  83. Bagci H, Fisher AG (2013) DNA demethylation in pluripotency and reprogramming: the role of tet proteins and cell division. Cell Stem Cell 13:265–269

    CAS  PubMed  Google Scholar 

  84. De Carvalho DD, You JS et al (2010) DNA methylation and cellular reprogramming. Trends Cell Biol 20:609–617

    PubMed Central  PubMed  Google Scholar 

  85. Huang K, Fan G (2010) DNA methylation in cell differentiation and reprogramming: an emerging systematic view. Regen Med 5:531–544

    CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Hussein M. Abdallah

  2. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Domitilla Del Vecchio

Authors
  1. Hussein M. Abdallah
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  2. Domitilla Del Vecchio
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Corresponding author

Correspondence to Hussein M. Abdallah .

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Editors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick Cahan

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Abdallah, H.M., Del Vecchio, D. (2019). Computational Analysis of Altering Cell Fate. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_17

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  • DOI: https://doi.org/10.1007/978-1-4939-9224-9_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9223-2

  • Online ISBN: 978-1-4939-9224-9

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