Cell Fate Reprogramming by Control of Intracellular Network Dynamics

Abstract

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 67

1. 1.

   Takahashi K and Yamanaka S (2006 ) Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell 126 (4), 652–655.

   Not in our library10.1016/j.cell.2006.07.024

2. 2.

   Pera MF and Tam PPL (2010 ) Extrinsic regulation of pluripotent stem cells. Nature 465 (7299), 713–720.

   Not in our library10.1038/nature09228

3. 3.

   Young RA (2011 ) Control of the Embryonic Stem Cell State. Cell 144 (6), 940–954.

   Not in our library10.1016/j.cell.2011.01.032

4. 4.

   Auffray C, Chen Z, and Hood L (2009 ) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2

   Not in our library10.1186/gm2

5. 5.

   Barabási AL, Gulbahce N, and Loscalzo J (2011 ) Network medicine: a network-based approach to human disease. Nature Reviews Genetics 12, 56–68.

   Not in our library10.1038/nrg2918

6. 6.

   Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, and Dammann O (2013 ) The road from systems biology to systems medicine. Pediatric Research 73 (4-2), 502–507.

   Not in our library10.1038/pr.2013.4

7. 7.

   Liu Y, Slotine J, and Barabási AL (2011 ) Controllability of complex networks. Nature 473, 167–173.

   Not in our library10.1038/nature10011

8. 8.

   Müller FJ and Schuppert A (2011 ) Few inputs can reprogram biological networks. Nature 478, E4

   Not in our library10.1038/nature10543

9. 9.

   Liu Y, Slotine J, and Barabási AL (2013 ) Observability of complex systems. Proc Natl Acad Sci USA 110 (7), 2460–2465.

   Not in our library10.1073/pnas.1215508110

10. 10.

    Cowan NJ, Chastain EJ, Vilhena DA, Freudenberg JS, and Bergstrom CT (2012 ) Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks. PLoS ONE 7(6), e38398

    Not in our library10.1371/journal.pone.0038398

11. 11.

    Cornelius SP, Kath WL, and Motter AE (2013 ) Realistic control of network dynamics. Nature Communications 4, 1942

    Not in our library10.1038/ncomms2939

12. 12.

    Fiedler B, Mochizuki A, Kurosawa G, Saito D (2013 ) Dynamics and control at feedback vertex sets I: Informative and determining nodes in regulatory networks. J. Dyn. Differential Equations 2.

    Not in our library10.1016/j.jtbi.2013.06.009

13. 13.

    Mochizuki A, Fiedler B, Kurosawa G, Saito D (2013 ) Dynamics and control at feedback vertex sets. II: A faithful monitor to determine the diversity of molecular activities in regulatory networks. J. Theor. Biol. 335, 130–146.

    Not in our library10.1016/j.jtbi.2013.06.009

14. 14.

    Kalman RE (1963 ) Mathematical description of linear dynamical systems. J. Soc. Indust. Appl. Math. Ser. A 1, 152–192.

    Not in our library10.1137/0301010

15. 15.

    Luenberger DG (1979 ) Introduction to Dynamic Systems: Theory, Models, and Applications. Wiley.

    Not in our library

16. 16.

    Slotine JJ, Li W (1991 ) Applied Nonlinear Control. Prentice-Hall.

    Not in our library

17. 17.

    Lin CT (1974 ) Structural controllability. IEEE Trans. Automat. Contr. 19, 201–208.

    Not in our library10.1109/tac.1974.1100557

18. 18.

    Tyson JJ, Chen KC, and Novak B (2001 ) Network dynamics and cell physiology. Nature Rev. Mol. Cell Biol. 2 (12), 908–916.

    Not in our library10.1038/35103078

19. 19.

    Tyson JJ, Chen KC, and Novak B (2003 ) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Op. Cell Biol. 15, 221–231.

    Not in our library10.1016/s0955-0674(03)00017-6

20. 20.

    Akutsu T, Hayashida M, Ching WK, and Ng MK (2007 ) Control of Boolean networks: Hardness results and algorithms for tree structured networks. J. Theor. Biol. 244 (4), 670–679.

    Not in our library10.1016/j.jtbi.2006.09.023

21. 21.

    Cheng D and Qi H (2009 ) Controllability and observability of Boolean control networks. Automatica 45 (7), 1659–1667.

    Not in our library10.1016/j.automatica.2009.03.006

22. 22.

    Akutsu T, Yang Z, Hayashida M, and Tamura T (2012 ) Integer Programming-Based Approach to Attractor Detection and Control of Boolean Networks. IEICE TRANS. INF. & SYST. E95-D (12), 2960–2970.

    Not in our library10.1587/transinf.e95.d.2960

23. 23.

    Bornholdt S (2005 ) Systems biology: Less is more in modeling large genetic networks. Science 310 (5747), 449–451.

    Not in our library10.1126/science.1119959

24. 24.

    Miskov-Zivanov N, Turner MS, Kane LP, Morel PA and Faeder JR (2013 ) The Duration of T Cell Stimulation Is a Critical Determinant of Cell Fate and Plasticity. Sci. Signal. 6, ra97

    Not in our library10.1126/scisignal.2004217

25. 25.

    Benitez M, Espinosa-Soto C, Padilla-Longoria P and Alvarez-Buylla ER (2008 ) Interlinked nonlinear subnetworks underlie the formation of robust cellular patterns in Arabidopsis epidermis: a dynamic spatial model. BMC Systems Biology 2:98

    Not in our library10.1186/1752-0509-2-98

26. 26.

    Saez-Rodriguez J, Alexopoulos LG, Zhang M, Morris MK, Lauffenburger DA, and Sorger PK (2011 ). Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 71, 5400–11.

    Not in our library10.1158/0008-5472.can-10-4453

27. 27.

    Orlando DA, Lin CY, Bernard A, Wang JY, Socolar JES, Iversen ES, et al (2008 ). Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 453, 944–947.

    Not in our library10.1038/nature06955

28. 28.

    Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, et al (2008 ) Network Model of Survival Signaling in LGL Leukemia. Proc Natl Acad Sci USA 105, 16308–16313.

    Not in our library10.1073/pnas.0806447105

29. 29.

    Morris MK, Saez-Rodriguez J, Sorger PK, and Lauffenburger DA (2010 ). Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 3216–24.

    Not in our library10.1021/bi902202q

30. 30.

    Wang RS, Saadatpour A, and Albert R (2012 ). Boolean modeling in systems biology: an overview of methodology and applications. Physical Biology 9, 055001

    Not in our library10.1088/1478-3975/9/5/055001

31. 31.

    Kauffman SA (1969 ) Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467.

    Not in our library10.1016/0022-5193(69)90015-0

32. 32.

    Glass L and Kauffman SA (1973 ) The logical analysis of continous, nonlinear biochemical control networks. J. Theor. Biol. 39, 103–129.

    Not in our library10.1016/0022-5193(73)90208-7

33. 33.

    Glass L (1975 ) Classification of biological networks by their qualitative dynamics. J. Theor. Biol. 54 (1), 85–107.

    Not in our library10.1016/s0022-5193(75)80056-7

34. 34.

    Thomas R, Thieffry D, and Kaufman M (1995 ) Dynamical behaviour of biological regulatory networks-I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull. Math. Biol. 57 (2), 247–276.

    Not in our library10.1007/bf02460618

35. 35.

    Chaves M, Sontag ED, and Albert R (2006 ) Methods of robustness analysis for Boolean models of gene control networks. Syst. Biol. (Stevenage) 153 (4), 154–167

    Not in our library10.1049/ip-syb:20050079

36. 36.

    Saadatpour A, Albert I, and Albert R (2010 ) Attractor analysis of asynchronous Boolean models of signal transduction networks. J. Theor. Biol. 266, 641–656.

    Not in our library10.1016/j.jtbi.2010.07.022

37. 37.

    Sevim V, Gong X, and Socolar JE (2010 ) Reliability of Transcriptional Cycles and the Yeast Cell-Cycle Oscillator. PLoS Comput. Biol. 6 (7), e1000842

    Not in our library10.1371/journal.pcbi.1000842

38. 38.

    Murrugarra D, Veliz-Cuba A, Aguilar B, Arat S, and Laubenbacher R (2012 ) Modeling stochasticity and variability in gene regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology 2012: 5

    Not in our library10.1186/1687-4153-2012-5

39. 39.

    Huang S and Ingber DE (2000 ). Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks. Exp Cell Res. 261(1), 91–103.

    Not in our library10.1006/excr.2000.5044

40. 40.

    Huang S, Ernberg I, Kauffman S (2009 ). Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin Cell Dev Biol. 7, 869–76.

    Not in our library10.1016/j.semcdb.2009.07.003

41. 41.

    Zañudo JGT and Albert R (2013 ) An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks. Chaos 23 (2), 025111 Focus Issue: Quantitative Approaches to Genetic Networks

    Not in our library10.1063/1.4809777

42. 42.

    Bilke S and Sjunnesson F (2001 ) Stability of the Kauffman model. Phys. Rev. E 65, 016129.

    Not in our library10.1103/physreve.65.016129

43. 43.

    Naldi A, Remy É Thieffry D, and Chaouiya C (2011 ) Dynamically consistent reduction of logical regulatory graphs. Theor Comput Sci 412, 2207–2218.

    Not in our library10.1016/j.tcs.2010.10.021

44. 44.

    Veliz-Cuba A (2011 ) Reduction of Boolean network models. J. Theor. Biol. 289, 167–172.

    Not in our library10.1016/j.jtbi.2011.08.042

45. 45.

    Steinway SN, Zañudo JGT, Ding W, Rountree CB, Feith DJ, Loughran TP, et al (2014 ) Network Modeling of TGFβ Signaling in Hepatocellular Carcinoma Epithelial-to-Mesenchymal Transition RevealsJoint Sonic Hedgehog and Wnt Pathway Activation. Cancer Research 74 (21), 5963–77.

    Not in our library10.1158/0008-5472.can-14-0225

46. 46.
    Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia · 2011

    No notice
47. 47.

    Shah MV, Zhang R, Irby R, Kothapalli R, Liu X, Arrington T, et al (2008 ) Molecular profiling of LGL leukemia reveals role of sphingolipid signaling in survival of cytotoxic lymphocytes. Blood 112, 770–781.

    Not in our library10.1182/blood-2007-11-121871

48. 48.

    Naldi A, Carneiro J, Chaouiya C, and Thieffry D (2010 ) Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling. PLoS Computational Biology 6(9): e1000912

    Not in our library10.1371/journal.pcbi.1000912

49. 49.
    Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling · 2009

    No notice
50. 50.

    Kharas MG, Janes MR, Scarfone VM, Lilly MB, Knight ZA, Shokat KM, et al (2008 ) Ablation of PI3K blocks BCR-ABL leukemogenesis in mice, and a dual PI3K/mTOR inhibitor prevents expansion of human BCR-ABL+ leukemia cells. J Clin Invest. 118(9), 3038–50.

    Not in our library10.1172/jci33337

51. 51.
    Evolutionary dynamics of cancer in response to targeted combination therapy · 2013

    No notice
52. 52.

    Al-Lazikani B, Banerji U, and Workman P (2012 ) Combinatorial drug therapy for cancer in the post-genomic era. Nature biotechnology 30, 679–92.

    Not in our library10.1038/nbt.2284

53. 53.

    Campbell C, Yang S, Albert R, and Shea K (2011 ) A network model for plant-pollinator community assembly. Proc Natl Acad Sci USA 108 (1), 197–202.

    Not in our library10.1073/pnas.1008204108

54. 54.

    Castellano C, Fortunato S, and Loreto V (2009 ) Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591

    Not in our library10.1103/revmodphys.81.591

55. 55.

    Fernández-Gracia J, Suchecki K, Ramasco JJ, San Miguel M, and Eguíluz VM (2014 ) Is the Voter Model a Model for Voters? Phys. Rev. Lett. 112, 158701

    Not in our library10.1103/physrevlett.112.158701

56. 56.

    Pastor-Satorras R and Vespignani A (2001 ) Epidemic Spreading in Scale-Free Networks Phys. Rev. Lett. 86, 3200

    Not in our library10.1103/physrevlett.86.3200

57. 57.

    Víctor M. Eguíluz and Klemm Konstantin (2002 ) Epidemic Threshold in Structured Scale-Free Networks Phys. Rev. Lett. 89, 108701

    Not in our library10.1103/physrevlett.89.108701

58. 58.

    Krumsiek J, Pölsterl S, Wittmann DM, and Theis FJ (2010 ) Odefy-from discrete to continuous models. BMC Bioinformatics 11, 233

    Not in our library10.1186/1471-2105-11-233

59. 59.

    Yang J, Liu X, Nyland SB, Zhang R, Ryland LK, Broeg K, et al (2010 ) Platelet-derived growth factor mediates survival of leukemic large granular lymphocytes via an autocrine regulatory pathway. Blood 115, 51–60.

    Not in our library10.1182/blood-2009-06-223719

60. 60.

    Lamy T, Liu JH, Landowski TH, Dalton WS, and Loughran TP Jr (1998 ) Dysregulation of CD95/CD95 ligand-apoptotic pathway in CD3+ large granular lymphocyte leukemia. Blood 92, 4771–4777.

    Not in our library

61. 61.

    Epling-Burnette PK, Bai F, Wei S, Chaurasia P, Painter JS, Olashaw N, et al (2004 ) ERK couples chronic survival of NK cells to constitutively activated Ras in lymphoproliferative disease of granular lymphocytes (LDGL). Oncogene 23, 9220–9229.

    Not in our library10.1038/sj.onc.1208122

62. 62.

    Schade AE, Powers JJ, Wlodarski MW, and Maciejewski JP (2006 ) Phosphatidylinositol-3-phosphate kinase pathway activation protects leukemic large granular lymphocytes from undergoing homeostatic apoptosis. Blood 107, 4834–4840.

    Not in our library10.1182/blood-2005-08-3076

63. 63.

    Glimcher LH and Murphy KM (2000 ) Lineage commitment in the immune system: the T helper lymphocyte grows up. Genes Dev 14, 1693–711.

    Not in our library

64. 64.

    Szabo SJ, Kim ST, Costa GL, Zhang X, Fathman CG, and Glimcher LH (2000 ) A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100 (6), 655–69.

    Not in our library10.1016/s0092-8674(00)80702-3

65. 65.

    Zheng W and Flavell RA (1997 ) The transcription factor GATA-3 is necessary and sufficient for Th2 cytokine gene expression in CD4 T cells. Cell 89 (4), 587–96.

    Not in our library10.1016/s0092-8674(00)80240-8

66. 66.

    Zhou L, Ivanov II, Spolski R, Min R, Shenderov K, Egawa T, Levy DE, Leonard WJ, and Littman DR (2007 ) IL-6 programs T(H)-17 cell differentiation by promoting sequential engagement of the IL-21 and IL-23 pathways. Nat Immunol. 8 (9), 967–74.

    Not in our library10.1038/ni1488

67. 67.

    Hori S, Nomura T, Sakaguchi S (2003 ) Control of regulatory T cell development by the transcription factor Foxp3. Science 299, 1057–61.

    Not in our library10.1126/science.1079490

Cited by papers we hold 4

• The basis of easy controllability in Boolean networks · 2021
  Not checked
• Unsupervised logic-based mechanism inference for network-driven biological processes. · 2021
  Not checked
• Identifying (un)controllable dynamical behavior in complex networks. · 2018
  Not checked
• Global Stabilization of Boolean Networks to Control the Heterogeneity of Cellular Responses · 2018
  Not checked

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.