참고문헌 (64)

1. 1 MacNeil LT Walhout AJ Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression Genome Res 2011 21 5 645 57 21324878 

   상세보기

2. 2 Greene JA Loscalzo J Putting the patient back together - social medicine, network medicine, and the limits of reductionism N Engl J Med 2017 377 25 2493 9 29262277 

3. 3 Sugimura Ryohichi Jha Deepak Kumar Han Areum Soria-Valles Clara da Rocha Edroaldo Lummertz Lu Yi-Fen Goettel Jeremy A. Serrao Erik Rowe R. Grant Malleshaiah Mohan Wong Irene Sousa Patricia Zhu Ted N. Ditadi Andrea Keller Gordon Engelman Alan N. Snapper Scott B. Doulatov Sergei Daley George Q. Haematopoietic stem and progenitor cells from human pluripotent stem cells Nature 2017 545 7655 432 438 28514439 

4. 4 Lis Raphael Karrasch Charles C. Poulos Michael G. Kunar Balvir Redmond David Duran Jose G. Barcia Badwe Chaitanya R. Schachterle William Ginsberg Michael Xiang Jenny Tabrizi Arash Rafii Shido Koji Rosenwaks Zev Elemento Olivier Speck Nancy A. Butler Jason M. Scandura Joseph M. Rafii Shahin Conversion of adult endothelium to immunocompetent haematopoietic stem cells Nature 2017 545 7655 439 445 28514438 

5. 5 Ieda M Fu J-D Delgado-Olguin P Vedantham V Hayashi Y Bruneau BG Srivastava D Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors Cell 2010 142 3 375 86 20691899 

   상세보기

6. 6 Madhamshettiwar PB Maetschke SR Davis MJ Reverter A Ragan MA Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets Genome Med 2012 4 5 41 22548828 

7. 7 Creixell P Schoof EM Erler JT Linding R Navigating cancer network attractors for tumor-specific therapy Nat Biotechnol 2012 30 9 842 22965061 

   상세보기

8. 8 Chai Lian En Loh Swee Kuan Low Swee Thing Mohamad Mohd Saberi Deris Safaai Zakaria Zalmiyah A review on the computational approaches for gene regulatory network construction Computers in Biology and Medicine 2014 48 55 65 24637147 

   상세보기

9. 9 Dojer N Gambin A Mizera A Wilczyński B Tiuryn J Applying dynamic bayesian networks to perturbed gene expression data BMC Bioinformatics 2006 7 1 249 16681847 

10. 10 Vinh NX Chetty M Coppel R Wangikar PP Gene regulatory network modeling via global optimization of high order dynamic bayesian networks BMC Bioinf 2012 27 2765 6 

11. 11 Akutsu T Miyano S Kuhara S Identification of genetic networks from a small number of gene expression pattern under the boolean model Pac Symp Biocomput 1999 4 17 28 

12. 12 Saadatpour A Albert R Boolean modeling of biological regulatory networks: A methodology tutorial Methods 2013 62 1 3 12 23142247 

    상세보기

13. 13 Zhao W Serpedin E Dougherty ER Inferring gene regulatory networks from time series data using the minimum description length principle Bioinformatics 2006 22 17 2129 35 16845143 

    상세보기

14. 14 Polynikins A Hogan SJ Bernardo M Comparing different ode modelling approaches forgene regulatory networks J Theor Biol 2009 261 511 30 19665034 

    상세보기

15. 15 Bansal M Belcastro V Ambesi-Impiombato A Di Bernardo D How to infer gene networks from protein profiles Mol Syst Biol 2007 3 1 10 

16. 16 Svensson V Vento-Tormo R Teichmann S Exponential scaling of single-cell rnaseq in the last decade Nat Protoc 2018 13 599 604 29494575 

17. 17 Fiers Mark W E J Minnoye Liesbeth Aibar Sara Bravo González-Blas Carmen Kalender Atak Zeynep Aerts Stein Mapping gene regulatory networks from single-cell omics data Briefings in Functional Genomics 2018 17 4 246 254 29342231 

    상세보기

18. 18 Babtie A Chan TE Stumpf MPH Learning regulatory models for cell development from single cell transcriptomic data Curr Opin Syst Biol 2017 5 72 81 

19. 19 Yvert G ‘particle genetics’: treating every cell as unique Trends Genet 2014 30 2 49 56 24315431 

    상세보기

20. 20 Dueck H Eberwine J Kim J Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function Bioessays 2016 38 2 172 80 26625861 

21. 21 Symmons O Raj A What’s luck got to do with it: Single cells, multiple fates, and biological nondeterminism Mol Cell 2016 62 5 788 802 27259209 

22. 22 Cannoodt R Saelens W Saeys Y Computational methods for trajectory inference from single-cell transcriptomics Eur J Immunol 2016 46 11 2496 506 27682842 

    상세보기

23. 23 Chen H Guo J Mishra SK Robson P Niranjan M Zheng J Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development Bioinformatics 2015 31 7 1060 6 25416748 

    상세보기

24. 24 Lim CY Wang H Woodhouse S Piterman N Wernisch L Fisher J Gottgens B Btr: training asynchronous boolean models using single-cell expression data BMC Bioinformatics 2016 17 1 355 27600248 

25. 25 Moignard Victoria Woodhouse Steven Haghverdi Laleh Lilly Andrew J Tanaka Yosuke Wilkinson Adam C Buettner Florian Macaulay Iain C Jawaid Wajid Diamanti Evangelia Nishikawa Shin-Ichi Piterman Nir Kouskoff Valerie Theis Fabian J Fisher Jasmin Göttgens Berthold Decoding the regulatory network of early blood development from single-cell gene expression measurements Nature Biotechnology 2015 33 3 269 276 

    상세보기

26. 26 Matsumoto H Kiryu H Scoup: a probabilistic model based on the ornstein-uhlenbeck process to analyze single-cell expression data during differentiation BMC Bioinformatics 2016 17 1 232 27277014 

27. 27 Cordero P, Stuart JM. Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories: World scientific; 2016, pp. 576–87. 10.1142/9789813207813-0053. 

28. 28 Sanchez-Castillo M Blanco D Tienda-Luna I M Carrion M C Huang Yufei A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data Bioinformatics 2017 34 6 964 970 

29. 29 Matsumoto H Kiryu H Furusawa C Ko MSH Ko SBH Gouda N Hayashi T Nikaido I Scode: an efficient regulatory network inference algorithm from single-cell rna-seq during differentiation Bioinformatics 2017 33 15 2314 21 28379368 

    상세보기

30. 30 Ocone A Haghverdi L Mueller NS Theis FJ Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data Bioinformatics 2015 31 12 89 96 

31. 31 Huang S Non-genetic heterogeneity of cells in development: more than just noise Development 2009 136 23 3853 62 19906852 

32. 32 Sokolik C Liu Y Bauer D McPherson J Broeker M Heimberg G Qi LS Sivak DA Thomson M Transcription factor competition allows embryonic stem cells to distinguish authentic signals from noise Cell Syst 2015 1 2 117 29 26405695 

    상세보기

33. 33 Munsky B Trinh B Khammash M Listening to the noise: random fluctuations reveal gene network parameters Mol Syst Biol 2009 5 318 19888213 

34. 34 Moris N Pina C Arias AM Transition states and cell fate decisions in epigenetic landscapes Nat Rev Genet 2016 17 11 693 703 27616569 

    상세보기

35. 35 Papili Gao N, Ud-Dean MSM, Gandrillon O, Gunawan R. Sincerities: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. 2016. 10.1101/089110. 

36. 36 Herbach U Bonnaffoux A Espinasse T Gandrillon O Inferring gene regulatory networks from single-cell data: a mechanistic approach BMC Syst Biol 2017 11 105 29157246 

37. 37 Richard A Boullu L Herbach U Bonnaffoux A Morin V Vallin E Guillemin A Papili Gao N Gunawan R Cosette J Arnaud O Kupiec JJ Espinasse T Gonin-Giraud S Gandrillon O Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process PLoS Biol 2016 14 12 1002585 

38. 38 Gandrillon O Schmidt U Beug H Samarut J Tgf-beta cooperates with tgf-alpha to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism Embo J 1999 18 10 2764 81 10329623 

    상세보기

39. 39 Leduc M, Gautier E-F, Guillemin A, Broussard C, Salnot V, Lacombe C, Gandrillon O, Guillonneau F, Mayeux P. Deep proteomic analysis of chicken erythropoiesis. bioRxiv. 2018. 10.1101/289728. https://www.biorxiv.org/content/early/2018/03/27/289728.full.pdf . 

40. 40 Liu Zhe Tjian Robert Visualizing transcription factor dynamics in living cells The Journal of Cell Biology 2018 217 4 1181 1191 29378780 

    상세보기

41. 41 Lambert SA Jolma A Campitelli LF Das PK Yin Y Albu M Chen X Taipale J Hughes TR Weirauch MT The human transcription factors Cell 2018 172 650 65 29425488 

    상세보기

42. 42 Baba A Komatsuzaki T Construction of effective free energy landscape from single-molecule time series Proc Natl Acad Sci U S A 2007 104 49 19297 302 18048341 

    상세보기

43. 43 Chai LE Loh SK Low ST Mohamad MS Deris S Zakaria Z A review on the computational approaches for gene regulatory network construction Comput Biol Med 2014 48 55 65 24637147 

    상세보기

44. 44 Hecker M Lambeck S Toepfer S Van Someren E Guthke R Gene regulatory network inference: data integration in dynamic models—a review Biosystems 2009 96 1 86 103 19150482 

45. 45 Chen S Mar JC Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data BMC Bioinformatics 2018 19 1 232 29914350 

    상세보기

46. 46 Stolovitzky G Monroe D Califano A Dialogue on reverse engineering assessment and methods Ann N Y Acad Sci 2007 1115 1 1 22 17925349 

47. 47 Schaffter T Marbach D Floreano D Genenetweaver: in silico benchmark generation and performance profiling of network inference methods Bioinformatics 2011 27 16 2263 70 21697125 

    상세보기

48. 48 Fisher J Henzinger TA Executable cell biology Nat Biotechnol 2007 25 11 1239 49 17989686 

    상세보기

49. 49 Woodhouse S Piterman N Wintersteiger CM Gottgens B Fisher J Scns: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data BMC Syst Biol 2018 12 1 59 29801503 

    상세보기

50. 50 Bonnaffoux A, Caron E, Croubois H, Gandrillon O. A cloud-aware autonomous workflow engine and its application to gene regulatory networks inference. Presented at CLOSER 2018-8th International conference on Cloud computing and Service Science. Funchal: 2018. p. 1–8. 

51. 51 Olsen JV Mann M Status of large-scale analysis of post-translational modifications by mass spectrometry Mol Cell Proteomics 2013 12 12 3444 52 24187339 

    상세보기

52. 52 Manning KS Cooper TA The roles of rna processing in translating genotype to phenotype Nat Rev Mol Cell Biol 2017 18 2 102 14 27847391 

    상세보기

53. 53 Mandic A Strebinger D Regali C Phillips NE Suter DM A novel method for quantitative measurements of gene expression in single living cells Methods 2017 120 65 75 28456689 

    상세보기

54. 54 Lin YT Hufton PG Lee EJ Potoyan DA A stochastic and dynamical view of pluripotency in mouse embryonic stem cells PLoS Comput Biol 2018 14 2 1006000 

55. 55 Zheng GXY Terry JM Belgrader P Ryvkin P Bent ZW Wilson R Ziraldo SB Wheeler TD McDermott GP Zhu J Gregory MT Shuga J Montesclaros L Underwood JG Masquelier DA Nishimura SY Schnall-Levin M Wyatt PW Hindson CM Bharadwaj R Wong A Ness KD Beppu LW Deeg HJ McFarland C Loeb KR Valente WJ Ericson NG Stevens EA Radich JP Mikkelsen TS Hindson BJ Bielas JH Massively parallel digital transcriptional profiling of single cells Nat Commun 2017 8 14049 28091601 

    상세보기

56. 56 Ud-Dean SM Gunawan R Optimal design of gene knockout experiments for gene regulatory network inference Bioinformatics 2016 32 6 875 83 26568633 

    상세보기

57. 57 Kreutz C Timmer J Systems biology: experimental design FEBS J 2009 276 4 923 42 19215298 

    상세보기

58. 58 Semrau S, Goldmann J, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A. Lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. 2016. 10.1101/068288. 

59. 59 Jang S, Choubey S, Furchtgott L, Zou LN, Doyle A, Menon V, Loew EB, Krostag AR, Martinez RA, Madisen L, Levi BP, Ramanathan S. Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states. Elife. 2017; 6. 10.7554/eLife.20487. 

60. 60 Barabasi AL Oltvai ZN Network biology: understanding the cell’s functional organization Nat Rev Genet 2004 5 2 101 13 14735121 

    상세보기

61. 61 Hart Y Alon U The utility of paradoxical components in biological circuits Mol Cell 2013 49 2 213 21 23352242 

62. 62 Peccoud J Ycart B Markovian modelling of gene product synthesis Theor Popul Biol 1995 48 222 34 

63. 63 Houska T Kraft P Chamorro-Chavez A Breuer L Spotting model parameters using a ready-made python package PLoS ONE 2015 10 12 0145180 

64. 64 Schwanhausser B Busse D Li N Dittmar G Schuchhardt J Wolf J Chen W Selbach M Corrigendum: Global quantification of mammalian gene expression control Nature 2013 495 7439 126 7 23407496 

    상세보기