Cell Fate Reprogramming by Control of Intracellular Network Dynamics

PLoS Computational Biology

Volumn 11, Issue 4, 2015, Pages

  Zañudo, Jorge G T ^a   Albert, Réka ^a,b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL SIGNALING; CYTOLOGY; DISEASE CONTROL; DISEASES;

BIOLOGICAL NETWORKS; CELL REPROGRAMMING; CELL-FATE; CONTROL FRAMEWORK; CONTROL STRATEGIES; CONTROL TARGET; INTRACELLULAR NETWORKS; NETWORK DYNAMICS; NETWORK-CONTROL; STEM-CELL;

STEM CELLS;

ARTICLE; CANCER THERAPY; CELL DIFFERENTIATION; CELL FATE; CONCEPTUAL FRAMEWORK; CONTROL STRATEGY; HELPER CELL; HUMAN; INTRACELLULAR NETWORK DYNAMICS; LEUKEMIA; NUCLEAR REPROGRAMMING; PREDICTION; ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; PATHOPHYSIOLOGY; PHYSIOLOGY; SIGNAL TRANSDUCTION;

ALGORITHMS; CELLULAR REPROGRAMMING; COMPUTATIONAL BIOLOGY; HUMANS; LEUKEMIA; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION;

EID: 84929486039     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004193     Document Type: Article

References (67)

1. Takahashi K, Yamanaka S, (2006) Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell 126 (4), 652–655. doi: 10.1016/j.cell.2006.07.024
2. Pera MF, Tam PPL, (2010) Extrinsic regulation of pluripotent stem cells. Nature 465 (7299), 713–720. doi: 10.1038/nature09228 20535200
3. Young RA, (2011) Control of the Embryonic Stem Cell State. Cell 144 (6), 940–954. doi: 10.1016/j.cell.2011.01.032 21414485
4. Auffray C, Chen Z, Hood L, (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2. doi: 10.1186/gm2 19348689
5. Barabási AL, Gulbahce N, Loscalzo J, (2011) Network medicine: a network-based approach to human disease. Nature Reviews Genetics 12, 56–68. doi: 10.1038/nrg2918 21164525
6. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O, (2013) The road from systems biology to systems medicine. Pediatric Research 73 (4-2), 502–507. doi: 10.1038/pr.2013.4 23314297
7. Liu Y, Slotine J, Barabási AL, (2011) Controllability of complex networks. Nature 473, 167–173. doi: 10.1038/nature10011 21562557
8. Müller FJ, Schuppert A, (2011) Few inputs can reprogram biological networks. Nature 478, E4. doi: 10.1038/nature10543 22012402
9. Liu Y, Slotine J, Barabási AL, (2013) Observability of complex systems. Proc Natl Acad Sci USA 110 (7), 2460–2465. doi: 10.1073/pnas.1215508110 23359701
10. Cowan NJ, Chastain EJ, Vilhena DA, Freudenberg JS, Bergstrom CT, (2012) Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks. PLoS ONE 7(6), e38398. doi: 10.1371/journal.pone.0038398 22761682
11. Cornelius SP, Kath WL, Motter AE, (2013) Realistic control of network dynamics. Nature Communications 4, 1942 doi: 10.1038/ncomms2939 23803966
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.
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. doi: 10.1016/j.jtbi.2013.06.009 23774067
14. Kalman RE, (1963) Mathematical description of linear dynamical systems. J. Soc. Indust. Appl. Math. Ser. A 1, 152–192. doi: 10.1137/0301010
15. Luenberger DG, (1979) Introduction to Dynamic Systems: Theory, Models, and Applications. Wiley.
16. Slotine JJ, Li W, (1991) Applied Nonlinear Control. Prentice-Hall.
17. Lin CT, (1974) Structural controllability. IEEE Trans. Automat. Contr. 19, 201–208. doi: 10.1109/TAC.1974.1100557
18. Tyson JJ, Chen KC, Novak B, (2001) Network dynamics and cell physiology. Nature Rev. Mol. Cell Biol. 2 (12), 908–916. doi: 10.1038/35103078
19. Tyson JJ, Chen KC, Novak B, (2003) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Op. Cell Biol. 15, 221–231. doi: 10.1016/S0955-0674(03)00017-6 12648679
20. Akutsu T, Hayashida M, Ching WK, Ng MK, (2007) Control of Boolean networks: Hardness results and algorithms for tree structured networks. J. Theor. Biol. 244 (4), 670–679. doi: 10.1016/j.jtbi.2006.09.023 17069859
21. Cheng D, Qi H, (2009) Controllability and observability of Boolean control networks. Automatica 45 (7), 1659–1667. doi: 10.1016/j.automatica.2009.03.006
22. Akutsu T, Yang Z, Hayashida M, Tamura T, (2012) Integer Programming-Based Approach to Attractor Detection and Control of Boolean Networks. IEICE TRANS. INF. & SYST. E95-D (12), 2960–2970. doi: 10.1587/transinf.E95.D.2960
23. Bornholdt S, (2005) Systems biology: Less is more in modeling large genetic networks. Science 310 (5747), 449–451. doi: 10.1126/science.1119959 16239464
24. Miskov-Zivanov N, Turner MS, Kane LP, Morel PA, Faeder JR, (2013) The Duration of T Cell Stimulation Is a Critical Determinant of Cell Fate and Plasticity. Sci. Signal. 6, ra97. doi: 10.1126/scisignal.2004217 24194584
25. Benitez M, Espinosa-Soto C, Padilla-Longoria P, 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. doi: 10.1186/1752-0509-2-98 19014692
26. Saez-Rodriguez J, Alexopoulos LG, Zhang M, Morris MK, Lauffenburger DA, Sorger PK, (2011). Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 71, 5400–11. doi: 10.1158/0008-5472.CAN-10-4453 21742771
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. doi: 10.1038/nature06955 18463633
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. doi: 10.1073/pnas.0806447105 18852469
29. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA, (2010). Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 3216–24. doi: 10.1021/bi902202q 20225868
30. Wang RS, Saadatpour A, Albert R, (2012). Boolean modeling in systems biology: an overview of methodology and applications. Physical Biology 9, 055001. doi: 10.1088/1478-3975/9/5/055001 23011283
31. Kauffman SA, (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467. doi: 10.1016/0022-5193(69)90015-0 5803332
32. Glass L, Kauffman SA, (1973) The logical analysis of continous, nonlinear biochemical control networks. J. Theor. Biol. 39, 103–129. doi: 10.1016/0022-5193(73)90208-7 4741704
33. Glass L, (1975) Classification of biological networks by their qualitative dynamics. J. Theor. Biol. 54 (1), 85–107. doi: 10.1016/S0022-5193(75)80056-7 1202295
34. Thomas R, Thieffry D, 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. doi: 10.1007/BF02460618 7703920
35. Chaves M, Sontag ED, Albert R, (2006) Methods of robustness analysis for Boolean models of gene control networks. Syst. Biol. (Stevenage) 153 (4), 154–167 doi: 10.1049/ip-syb:20050079
36. Saadatpour A, Albert I, Albert R, (2010) Attractor analysis of asynchronous Boolean models of signal transduction networks. J. Theor. Biol. 266, 641–656. doi: 10.1016/j.jtbi.2010.07.022 20659480
37. Sevim V, Gong X, Socolar JE, (2010) Reliability of Transcriptional Cycles and the Yeast Cell-Cycle Oscillator. PLoS Comput. Biol. 6 (7), e1000842. doi: 10.1371/journal.pcbi.1000842 20628620
38. Murrugarra D, Veliz-Cuba A, Aguilar B, Arat S, Laubenbacher R, (2012) Modeling stochasticity and variability in gene regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology 2012: 5. doi: 10.1186/1687-4153-2012-5 22673395
39. Huang S, 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. doi: 10.1006/excr.2000.5044 11082279
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. doi: 10.1016/j.semcdb.2009.07.003
41. Zañudo JGT, 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 doi: 10.1063/1.4809777 23822509
42. Bilke S, Sjunnesson F, (2001) Stability of the Kauffman model. Phys. Rev. E 65, 016129.
43. Naldi A, Remy É Thieffry D, Chaouiya C, (2011) Dynamically consistent reduction of logical regulatory graphs. Theor Comput Sci 412, 2207–2218. doi: 10.1016/j.tcs.2010.10.021
44. Veliz-Cuba A, (2011) Reduction of Boolean network models. J. Theor. Biol. 289, 167–172. doi: 10.1016/j.jtbi.2011.08.042 21907211
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. doi: 10.1158/0008-5472.CAN-14-0225 25189528
46. Saadatpour A, Wang RS, Liao A, Liu X, Loughran TP, Albert I, et al. (2011) Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia. PLoS Computational Biology 7(11): e1002267. doi: 10.1371/journal.pcbi.1002267 22102804
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. doi: 10.1182/blood-2007-11-121871 18477771
48. Naldi A, Carneiro J, Chaouiya C, Thieffry D, (2010) Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling. PLoS Computational Biology 6(9): e1000912. doi: 10.1371/journal.pcbi.1000912 20824124
49. Wittmann DM, Krumsiek J, Saez-Rodriguez J, Lauffenburger DA, Klamt S, Theis FJ, (2009) Transforming Boolean models to continuous models: Methodology and application to T-cell receptor signaling. BMC Systems Biology 3, 98. doi: 10.1186/1752-0509-3-98 19785753
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. doi: 10.1172/JCI33337 18704194
51. Bozic I, Reiter JG, Allen B, Antal T, Chatterjee K, Shah P, et al. (2013) Evolutionary dynamics of cancer in response to targeted combination therapy. Elife 2:e00747. doi: 10.7554/eLife.00747 23805382
52. Al-Lazikani B, Banerji U, Workman P, (2012) Combinatorial drug therapy for cancer in the post-genomic era. Nature biotechnology 30, 679–92. doi: 10.1038/nbt.2284 22781697
53. Campbell C, Yang S, Albert R, Shea K, (2011) A network model for plant-pollinator community assembly. Proc Natl Acad Sci USA 108 (1), 197–202. doi: 10.1073/pnas.1008204108 21173234
54. Castellano C, Fortunato S, Loreto V, (2009) Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591. doi: 10.1103/RevModPhys.81.591
55. Fernández-Gracia J, Suchecki K, Ramasco JJ, San Miguel M, Eguíluz VM, (2014) Is the Voter Model a Model for Voters? Phys. Rev. Lett. 112, 158701. doi: 10.1103/PhysRevLett.112.158701 24785078
56. Pastor-Satorras R, Vespignani A, (2001) Epidemic Spreading in Scale-Free Networks Phys. Rev. Lett. 86, 3200. doi: 10.1103/PhysRevLett.86.3200
57. Víctor M, Eguíluz Klemm Konstantin, (2002) Epidemic Threshold in Structured Scale-Free Networks Phys. Rev. Lett. 89, 108701. doi: 10.1103/PhysRevLett.89.108701
58. Krumsiek J, Pölsterl S, Wittmann DM, Theis FJ, (2010) Odefy-from discrete to continuous models. BMC Bioinformatics 11, 233. doi: 10.1186/1471-2105-11-233 20459647
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. doi: 10.1182/blood-2009-06-223719 19880494
60. Lamy T, Liu JH, Landowski TH, Dalton WS, Loughran TP, Jr (1998) Dysregulation of CD95/CD95 ligand-apoptotic pathway in CD3+ large granular lymphocyte leukemia. Blood 92, 4771–4777. 9845544
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. 15516985
62. Schade AE, Powers JJ, Wlodarski MW, Maciejewski JP, (2006) Phosphatidylinositol-3-phosphate kinase pathway activation protects leukemic large granular lymphocytes from undergoing homeostatic apoptosis. Blood 107, 4834–4840. doi: 10.1182/blood-2005-08-3076 16484592
63. Glimcher LH, Murphy KM, (2000) Lineage commitment in the immune system: the T helper lymphocyte grows up. Genes Dev 14, 1693–711. 10898785
64. Szabo SJ, Kim ST, Costa GL, Zhang X, Fathman CG, Glimcher LH, (2000) A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100 (6), 655–69. doi: 10.1016/S0092-8674(00)80702-3 10761931
65. Zheng W, 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. doi: 10.1016/S0092-8674(00)80240-8 9160750
66. Zhou L, Ivanov II, Spolski R, Min R, Shenderov K, Egawa T, Levy DE, Leonard WJ, 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. doi: 10.1038/ni1488 17581537
67. Hori S, Nomura T, Sakaguchi S, (2003) Control of regulatory T cell development by the transcription factor Foxp3. Science 299, 1057–61. doi: 10.1126/science.1079490 12522256