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Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine

  • Protocol
Book cover Systems Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1386))

Abstract

Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.

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References

  1. Robinton DA, Daley GQ (2012) The promise of induced pluripotent stem cells in research and therapy. Nature 481:295–305. doi:10.1038/nature10761

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Kolios G, Moodley Y (2013) Introduction to stem cells and regenerative medicine. Respiration 85:3–10. doi:10.1159/000345615

    Article  PubMed  Google Scholar 

  3. Nichols J, Smith A (2009) Naive and primed pluripotent states. Cell Stem Cell 4:487–492. doi:10.1016/j.stem.2009.05.015

    Article  CAS  PubMed  Google Scholar 

  4. Rezza A, Sennett R, Rendl M (2014) Adult stem cell niches: cellular and molecular components. Curr Top Dev Biol. doi:10.1016/B978-0-12-416022-4.00012-3

    PubMed  Google Scholar 

  5. Takahashi K, Yamanaka S (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:663–676. doi:10.1016/j.cell.2006.07.024

    Article  CAS  PubMed  Google Scholar 

  6. Wu SM, Hochedlinger K (2011) Harnessing the potential of induced pluripotent stem cells for regenerative medicine. Nat Cell Biol 13:497–505. doi:10.1038/ncb0511-497

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Nishikawa S, Goldstein RA, Nierras CR (2008) The promise of human induced pluripotent stem cells for research and therapy. Nat Rev Mol Cell Biol 9:725–729

    Article  CAS  PubMed  Google Scholar 

  8. Theunissen TW, Powell BE, Wang H et al (2014) Systematic identification of defined conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 1–17. doi: 10.1016/j.stem.2014.07.002

    Google Scholar 

  9. Takashima Y, Guo G, Loos R et al (2014) Resetting transcription factor control circuitry toward ground-state pluripotency in human. Cell 158:1254–1269. doi:10.1016/j.cell.2014.08.029

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Sheridan C (2014) Stem cell therapy clears first hurdle in AMD. Nat Biotechnol. doi:10.1016/S0140-6736(14)61376-3

    Google Scholar 

  11. Dunn S-J, Martello G, Yordanov B et al (2014) Defining an essential transcription factor program for naïve pluripotency. Science 344:1156–1160. doi:10.1126/science.1248882

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Xu H, Ang Y-S, Sevilla A et al (2014) Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells. PLoS Comput Biol 10:e1003777. doi:10.1371/journal.pcbi.1003777

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  13. Papp B, Plath K (2011) Reprogramming to pluripotency: stepwise resetting of the epigenetic landscape. Cell Res 21:486–501. doi:10.1038/cr.2011.28

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Buzi G, Lander AD, Khammash M (2015) Cell lineage branching as a strategy for proliferative control. BMC Biol. doi:10.1186/s12915-015-0122-8

    PubMed Central  PubMed  Google Scholar 

  15. Le Novère N (2015) Quantitative and logic modelling of molecular and gene networks. Nat Publ Gr 16:146–158. doi:10.1038/nrg3885

    Google Scholar 

  16. Mazo IB, Massberg S, von Andrian UH (2011) Hematopoietic stem and progenitor cell trafficking. Trends Immunol 32:493–503. doi:10.1016/j.it.2011.06.011

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Moignard V, Woodhouse S, Fisher J, Göttgens B (2013) Transcriptional hierarchies regulating early blood cell development. Blood Cells Mol Dis 51:239–247. doi:10.1016/j.bcmd.2013.07.007

    Article  CAS  PubMed  Google Scholar 

  18. Sive JI, Göttgens B (2014) Transcriptional network control of normal and leukaemic haematopoiesis. Exp Cell Res 329:255–264. doi:10.1016/j.yexcr.2014.06.021

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Thomas ED, Lochte HL, Cannon JH et al (1959) Supralethal whole body irradiation and isologous marrow transplantation in man. J Clin Invest 38:1709–1716. doi:10.1172/JCI103949

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Till JE, Mcculloch EA, Siminovitch L (1963) A stochastic model of stem cell proliferation, based on the growth of spleen colony-forming cells. PNAS 51:29–36

    Article  Google Scholar 

  21. Loeffler M, Wichmann HE (1980) A comprehensive mathematical model of stem cell proliferation which reproduces most of the published experimental results. Cell Tissue Kinet 13:543–561

    CAS  PubMed  Google Scholar 

  22. Viswanathan S, Zandstra PW (2003) Towards predictive models of stem cell fate. Cytotechnology 41(2–3):75–92. doi:10.1023/A:1024866504538

    Google Scholar 

  23. Foster SD, Oram SH, Wilson NK, Göttgens B (2009) From genes to cells to tissues – modelling the haematopoietic system. Mol Biosyst 5:1413–1420. doi:10.1039/B907225j

    Article  CAS  PubMed  Google Scholar 

  24. Pisu M, Concas A, Cao G (2007) A novel simulation model for stem cells differentiation. J Biotechnol 130:171–182. doi:10.1016/j.jbiotec.2007.02.028

    Article  CAS  PubMed  Google Scholar 

  25. Tabatabai MA, Bursac Z, Eby WM, Singh KP (2011) Mathematical modeling of stem cell proliferation. Med Biol Eng Comput 49:253–262. doi:10.1007/s11517-010-0686-y

    Article  PubMed  Google Scholar 

  26. Wu J, Tzanakakis ES (2012) Contribution of stochastic partitioning at human embryonic stem cell division to NANOG heterogeneity. PLoS One 7:e50715. doi:10.1371/journal.pone.0050715

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. White DE, Kinney MA, McDevitt TC, Kemp ML (2013) Spatial pattern dynamics of 3D stem cell loss of pluripotency via rules-based computational modeling. PLoS Comput Biol 9:e1002952. doi:10.1371/journal.pcbi.1002952

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Blagovic K, Kim LY, Voldman J (2011) Microfluidic perfusion for regulating diffusible signaling in stem cells. PLoS One 6:e22892. doi:10.1371/journal.pone.0022892

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Evans MJ, Kaufman MH (1981) Establishment in culture of pluripotential cells from mouse embryos. Nature 292:154–156

    Article  CAS  PubMed  Google Scholar 

  30. Ying Q-L, Wray J, Nichols J et al (2008) The ground state of embryonic stem cell self-renewal. Nature 453:519–523. doi:10.1038/nature06968

    Article  CAS  PubMed  Google Scholar 

  31. Viswanathan S, Benatar T, Zandstra PW et al (2002) Ligand/receptor signaling threshold (LIST) model accounts for gp130-mediated embryonic stem cell self-renewal responses to LIF and HIL-6. Stem Cells 20:119–138

    Article  CAS  PubMed  Google Scholar 

  32. Niwa H, Burdon T, Chambers I, Smith A (1998) Self-renewal of pluripotent embryonic stem cells is mediated via activation of STAT3. Genes Dev 12:2048–2060. doi:10.1101/gad.12.13.2048

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Prudhomme WA, Duggar KH, Lauffenburger DA (2004) Cell population dynamics model for deconvolution of murine embryonic stem cell self-renewal and differentiation responses to cytokines and extracellular matrix. Biotechnol Bioeng 88:264–272. doi:10.1002/bit.20244

    Article  CAS  PubMed  Google Scholar 

  34. Viswanathan S, Davey RE, Cheng D et al (2005) Clonal evolution of stem and differentiated cells can be predicted by integrating cell-intrinsic and -extrinsic parameters. Biotechnol Appl Biochem 42:119–131. doi:10.1042/BA20040207

    Article  CAS  PubMed  Google Scholar 

  35. Woolf PJ, Prudhomme W, Daheron L et al (2005) Bayesian analysis of signaling networks governing embryonic stem cell fate decisions. Bioinformatics 21:741–753. doi:10.1093/bioinformatics/bti056

    Article  CAS  PubMed  Google Scholar 

  36. Davey RE, Onishi K, Mahdavi A, Zandstra PW (2007) LIF-mediated control of embryonic stem cell self-renewal emerges due to an autoregulatory loop. FASEB J 21:2020–2032. doi:10.1096/fj.06-7852com

    Article  CAS  PubMed  Google Scholar 

  37. Mahdavi A, Davey RE, Bhola P et al (2007) Sensitivity analysis of intracellular signaling pathway kinetics predicts targets for stem cell fate control. PLoS Comput Biol 3:e130. doi:10.1371/journal.pcbi.0030130

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  38. Batsilas L, Berezhkovskii AM, Shvartsman SY (2003) Stochastic model of autocrine and paracrine signals in cell culture assays. Biophys J 85:3659–3665. doi:10.1016/S0006-3495(03)74783-3

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Peerani R, Onishi K, Mahdavi A et al (2009) Manipulation of signaling thresholds in “engineered stem cell niches” identifies design criteria for pluripotent stem cell screens. PLoS One 4:e6438. doi:10.1371/journal.pone.0006438

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  40. Ellison D, Munden A, Levchenko A (2009) Computational model and microfluidic platform for the investigation of paracrine and autocrine signaling in mouse embryonic stem cells. Mol Biosyst 5:1004–1012. doi:10.1039/b905602e

    Article  CAS  PubMed  Google Scholar 

  41. Przybyla LM, Voldman J (2012) Attenuation of extrinsic signaling reveals the importance of matrix remodeling on maintenance of embryonic stem cell self-renewal. Proc Natl Acad Sci U S A 109:835–840. doi:10.1073/pnas.1103100109

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Yeo D, Kiparissides A, Cha JM et al (2013) Improving embryonic stem cell expansion through the combination of perfusion and Bioprocess model design. PLoS One 8:e81728. doi:10.1371/journal.pone.0081728

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  43. Moledina F, Clarke G, Oskooei A et al (2012) Predictive microfluidic control of regulatory ligand trajectories in individual pluripotent cells. Proc Natl Acad Sci U S A 109:3264–3269. doi:10.1073/pnas.1111478109

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Niwa H (2007) How is pluripotency determined and maintained? Development 134:635–646. doi:10.1242/dev.02787

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  46. Chickarmane V, Peterson C (2008) A computational model for understanding stem cell, trophectoderm and endoderm lineage determination. PLoS One 3:e3478. doi:10.1371/journal.pone.0003478

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  47. Krupinski P, Chickarmane V, Peterson C (2011) Simulating the mammalian blastocyst – molecular and mechanical interactions pattern the embryo. PLoS Comput Biol 7:e1001128. doi:10.1371/journal.pcbi.1001128

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Ralston A, Rossant J (2008) Cdx2 acts downstream of cell polarization to cell-autonomously promote trophectoderm fate in the early mouse embryo. Dev Biol 313:614–629. doi:10.1016/j.ydbio.2007.10.054

    Article  CAS  PubMed  Google Scholar 

  49. Bessonnard S, De Mot L, Gonze D et al (2014) Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network. Development 3637–3648. doi: 10.1242/dev.109678

    Google Scholar 

  50. Singh AM, Hamazaki T, Hankowski KE, Terada N (2007) A heterogeneous expression pattern for Nanog in embryonic stem cells. Stem Cells 25:2534–2542. doi:10.1634/stemcells.2007-0126

    Article  CAS  PubMed  Google Scholar 

  51. Canham MA, Sharov AA, Ko MSH, Brickman JM (2010) Functional heterogeneity of embryonic stem cells revealed through translational amplification of an early endodermal transcript. PLoS Biol 8:e1000379. doi:10.1371/journal.pbio.1000379

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  52. Kalmar T, Lim C, Hayward P et al (2009) Regulated fluctuations in nanog expression mediate cell fate decisions in embryonic stem cells. PLoS Biol 7:e1000149. doi:10.1371/journal.pbio.1000149

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  53. Glauche I, Herberg M, Roeder I (2010) Nanog variability and pluripotency regulation of embryonic stem cells – insights from a mathematical model analysis. PLoS One 5:e11238. doi:10.1371/journal.pone.0011238

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  54. Chickarmane V, Olariu V, Peterson C (2012) Probing the role of stochasticity in a model of the embryonic stem cell: heterogeneous gene expression and reprogramming efficiency. BMC Syst Biol 6:98. doi:10.1186/1752-0509-6-98

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. Lakatos D, Travis ED, Pierson KE et al (2014) Autocrine FGF feedback can establish distinct states of Nanog expression in pluripotent stem cells: a computational analysis. BMC Syst Biol 8:112. doi:10.1186/s12918-014-0112-4

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  56. Luo Y, Lim CL, Nichols J, Martinez-Arias A, Wernisch L (2012) Cell signalling regulates dynamics of Nanog distribution in embryonic stem cell populations. J R Soc Interface. [Epub ahead of print].

    Google Scholar 

  57. Muñoz Descalzo S, Rué P, Faunes F et al (2013) A competitive protein interaction network buffers Oct4-mediated differentiation to promote pluripotency in embryonic stem cells. Mol Syst Biol 9:694. doi:10.1038/msb.2013.49

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  58. Herberg M, Kalkan T, Glauche I et al (2014) A model-based analysis of culture-dependent phenotypes of mESCs. PLoS One 9:e92496. doi:10.1371/journal.pone.0092496

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  59. Faunes F, Hayward P, Descalzo SM et al (2013) A membrane-associated β-catenin/Oct4 complex correlates with ground-state pluripotency in mouse embryonic stem cells. Development 140:1171–1183. doi:10.1242/dev.085654

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  60. Marucci L, Pedone E, Di Vicino U et al (2014) β-catenin fluctuates in mouse ESCs and is essential for Nanog-mediated reprogramming of somatic cells to pluripotency. Cell Rep 8:1686–1696. doi:10.1016/j.celrep.2014.08.011

    Article  CAS  PubMed  Google Scholar 

  61. Waddington CH (1956) Principles of embryology. G. Allen, London

    Google Scholar 

  62. Lee HJ, Hore TA, Reik W (2014) Reprogramming the methylome: erasing memory and creating diversity. Cell Stem Cell 14:710–719. doi:10.1016/j.stem.2014.05.008

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  63. Tomizawa S, Shirakawa T, Ohbo K (2014) Stem cell epigenetics: insights from studies on embryonic, induced pluripotent, and germline stem cells. Curr Pathobiol Rep 2:1–9. doi:10.1007/s40139-013-0038-3

    Article  Google Scholar 

  64. Fagan MB (2011) Waddington redux: models and explanation in stem cell and systems biology. Biol Philos 27:179–213. doi:10.1007/s10539-011-9294-y

    Article  Google Scholar 

  65. Boland MJ, Nazor KL, Loring JF (2014) Epigenetic regulation of pluripotency and differentiation. Circ Res 115:311–324. doi:10.1161/CIRCRESAHA.115.301517

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  66. Griffiths DS, Li J, Dawson MA et al (2011) LIF-independent JAK signalling to chromatin in embryonic stem cells uncovered from an adult stem cell disease. Nat Cell Biol 13:13–21. doi:10.1038/ncb2135

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  67. Gurdon JB, Melton DA (2008) Nuclear reprogramming in cells. Science 322:1811–1815

    Article  CAS  PubMed  Google Scholar 

  68. MacArthur BD, Please CP, Oreffo ROC (2008) Stochasticity and the molecular mechanisms of induced pluripotency. PLoS One 3:e3086. doi:10.1371/journal.pone.0003086

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  69. Hanna J, Saha K, Pando B et al (2009) Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462:595–601. doi:10.1038/nature08592

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  70. Artyomov MN, Meissner A, Chakraborty AK (2010) A model for genetic and epigenetic regulatory networks identifies rare pathways for transcription factor induced pluripotency. PLoS Comput Biol 6:e1000785. doi:10.1371/journal.pcbi.1000785

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  71. Hu Z, Qian M, Zhang MQ (2011) Novel Markov model of induced pluripotency predicts gene expression changes in reprogramming. BMC Syst Biol 5(Suppl 2):S8. doi:10.1186/1752-0509-5-S2-S8

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  72. Flöttmann M, Scharp T, Klipp E (2012) A stochastic model of epigenetic dynamics in somatic cell reprogramming. Front Physiol 3:216. doi:10.3389/fphys.2012.00216

    Article  PubMed Central  PubMed  Google Scholar 

  73. Grácio F, Cabral J, Tidor B (2013) Modeling stem cell induction processes. PLoS One 8:e60240. doi:10.1371/journal.pone.0060240

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  74. Miyanari Y, Torres-Padilla ME (2012) Control of ground-state pluripotency by allelic regulation of Nanog. Nature 483:470–473. doi:10.1038/nature10807

    Article  CAS  PubMed  Google Scholar 

  75. Sasai M, Kawabata Y, Makishi K et al (2013) Time scales in epigenetic dynamics and phenotypic heterogeneity of embryonic stem cells. PLoS Comput Biol 9:e1003380. doi:10.1371/journal.pcbi.1003380

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  76. Zhang B, Wolynes PG (2014) Stem cell differentiation as a many-body problem. Proc Natl Acad Sci U S A 111:10185–10190. doi:10.1073/pnas.1408561111

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Muraro MJ, Kempe H, Verschure PJ (2013) Concise review: the dynamics of induced pluripotency and its behavior captured in gene network motifs. Stem Cells 31:838–848. doi:10.1002/stem.1340

    Article  CAS  PubMed  Google Scholar 

  78. Selekman JA, Das A, Grundl NJ, Palecek SP (2013) Improving efficiency of human pluripotent stem cell differentiation platforms using an integrated experimental and computational approach. Biotechnol Bioeng 110:3024–3037. doi:10.1002/bit.24968

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Prudhomme W, Daley GQ, Zandstra P, Lauffenburger DA (2004) Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling. Proc Natl Acad Sci U S A 101:2900–2905. doi:10.1073/pnas.0308768101

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  80. Sun Y, Li H, Liu Y et al (2008) Evolutionarily conserved transcriptional co-expression guiding embryonic stem cell differentiation. PLoS One 3:e3406. doi:10.1371/journal.pone.0003406

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  81. Chavez L, Bais AS, Vingron M et al (2009) In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach. BMC Genomics 10:314. doi:10.1186/1471-2164-10-314

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  82. Trott J, Hayashi K, Surani A et al (2012) Dissecting ensemble networks in ES cell populations reveals micro-heterogeneity underlying pluripotency. Mol Biosyst 8:744–752. doi:10.1039/c1mb05398a

    Article  CAS  PubMed  Google Scholar 

  83. Tan MH, Au KF, Leong DE et al (2013) An Oct4-Sall4-Nanog network controls developmental progression in the pre-implantation mouse embryo. Mol Syst Biol 9:632. doi:10.1038/msb.2012.65

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  84. Walker E, Ohishi M, Davey RE et al (2007) Prediction and testing of novel transcriptional networks regulating embryonic stem cell self-renewal and commitment. Cell Stem Cell 1:71–86. doi:10.1016/j.stem.2007.04.002

    Article  CAS  PubMed  Google Scholar 

  85. Gu P, Reid JG, Gao X et al (2008) Novel microRNA candidates and miRNA-mRNA pairs in embryonic stem (ES) cells. PLoS One 3:e2548. doi:10.1371/journal.pone.0002548

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  86. Markowetz F, Mulder KW, Airoldi EM et al (2010) Mapping dynamic histone acetylation patterns to gene expression in nanog-depleted murine embryonic stem cells. PLoS Comput Biol 6:e1001034. doi:10.1371/journal.pcbi.1001034

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  87. Teif VB, Vainshtein Y, Caudron-Herger M et al (2012) Genome-wide nucleosome positioning during embryonic stem cell development. Nat Struct Mol Biol 19:1185–1192. doi:10.1038/nsmb.2419

    Article  CAS  PubMed  Google Scholar 

  88. Mah N, Wang Y, Liao MC et al (2011) Molecular insights into reprogramming-initiation events mediated by the OSKM gene regulatory network. PLoS One 6:e24351. doi:10.1371/journal.pone.0024351

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  89. Qin H, Diaz A, Blouin L et al (2014) Systematic identification of barriers to human iPSC generation. Cell 158:449–461. doi:10.1016/j.cell.2014.05.040

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  90. Hassani SN, Totonchi M, Gourabi H et al (2014) Signaling roadmap modulating naive and primed pluripotency. Stem Cells Dev 23:193–208. doi:10.1089/scd.2013.0368

    Article  CAS  PubMed  Google Scholar 

  91. Peterson H, Abu Dawud R, Garg A et al (2013) Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells. Front Physiol 4:303. doi:10.3389/fphys.2013.00303

    Article  PubMed Central  PubMed  Google Scholar 

  92. Mathew S, Sundararaj S, Mamiya H, Banerjee I (2014) Regulatory interactions maintaining self-renewal of human embryonic stem cells as revealed through a systems analysis of PI3K/AKT pathway. Bioinformatics 30:2334–2342. doi:10.1093/bioinformatics/btu209

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  93. Lutter D, Bruns P, Theis FJ (2012) An ensemble approach for inferring semi-quantitative regulatory dynamics for the differentiation of mouse embryonic stem cells using prior knowledge. Adv Exp Med Biol 736:247–260. doi:10.1007/978-1-4419-7210-1_14

    Article  CAS  PubMed  Google Scholar 

  94. Cahan P, Li H, Morris SA et al (2014) Cell net: network biology applied to stem cell engineering. Cell 158:903–915. doi:10.1016/j.cell.2014.07.020

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  95. Warsow G, Greber B, Falk SSI et al (2010) ExprEssence – revealing the essence of differential experimental data in the context of an interaction/regulation net-work. BMC Syst Biol 4:164. doi:10.1186/1752-0509-4-164

    Article  PubMed Central  PubMed  Google Scholar 

  96. Cline MS, Smoot M, Cerami E et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2:2366–2382. doi:10.1038/nprot.2007.324

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  97. Sarda S, Hannenhalli S (2014) Next-generation sequencing and epigenomics research: a hammer in search of nails. Genomics Inform 12:2–11. doi:10.5808/GI.2014.12.1.2

    Article  PubMed Central  PubMed  Google Scholar 

  98. Dowell KG, Simons AK, Wang ZZ et al (2013) Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate. PLoS One 8:e56810. doi:10.1371/journal.pone.0056810

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  99. Dowell KG, Simons AK, Bai H et al (2014) Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks. Stem Cells 32:1161–1172

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  100. Xu H, Baroukh C, Dannenfelser R et al (2013) ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database (Oxford) 2013:bat045. doi:10.1093/database/bat045

    Google Scholar 

  101. Franceschini A, Szklarczyk D, Frankild S et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–D815. doi:10.1093/nar/gks1094

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  102. Guan Y, Myers CL, Lu R et al (2008) A genomewide functional network for the laboratory mouse. PLoS Comput Biol 4:e1000165. doi:10.1371/journal.pcbi.1000165

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  103. Hackett JA, Surani MA (2014) Regulatory principles of pluripotency: from the ground state up. Cell Stem Cell 15:416–430. doi:10.1016/j.stem.2014.09.015

    Article  CAS  PubMed  Google Scholar 

  104. Kondoh H, Lleonart ME, Nakashima Y et al (2007) A high glycolytic flux supports the proliferative potential of murine embryonic stem cells. Antioxid Redox Signal 9:293–299. doi:10.1089/ars.2006.1467

    Article  CAS  PubMed  Google Scholar 

  105. Xu X, Duan S, Yi F et al (2013) Mitochondrial regulation in pluripotent stem cells. Cell Metab 18:325–332. doi:10.1016/j.cmet.2013.06.005

    Article  CAS  PubMed  Google Scholar 

  106. Varum S, Rodrigues AS, Moura MB et al (2011) Energy metabolism in human pluripotent stem cells and their differentiated counterparts. PLoS One 6:e20914. doi:10.1371/journal.pone.0020914

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  107. Li C, Donizelli M, Rodriguez N et al (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92. doi:10.1186/1752-0509-4-92

    Article  PubMed Central  PubMed  Google Scholar 

  108. Lloyd CM, Halstead MDB, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85:433–450. doi:10.1016/j.pbiomolbio.2004.01.004

    Article  CAS  PubMed  Google Scholar 

  109. Snoep JL, Olivier BG (2002) Java Web Simulation (JWS); a web based database of kinetic models. Mol Biol Rep 29:259–263

    Article  CAS  PubMed  Google Scholar 

  110. Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H (2009) The systems biology graphical notation. Nat Biotechnol 27(8):735–741. doi:10.1038/nbt.1558

    Google Scholar 

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Acknowledgements

We would like to thank editors for the critical reading of the manuscript and the constructive comments. PP and NL are funded by the BBSRC Institute Strategic Programme BBS/E/B/000C0419.

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Authors and Affiliations

  1. Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK

    Pınar Pir & Nicolas Le Novère

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  1. Pınar Pir
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  2. Nicolas Le Novère
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Correspondence to Pınar Pir .

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Editors and Affiliations

  1. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Ulf Schmitz

  2. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Olaf Wolkenhauer

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Pir, P., Le Novère, N. (2016). Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_15

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  • DOI: https://doi.org/10.1007/978-1-4939-3283-2_15

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3282-5

  • Online ISBN: 978-1-4939-3283-2

  • eBook Packages: Springer Protocols

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