Discrete logic modelling optimization to contextualize prior knowledge networks using PRUNET

PLoS ONE

Volumn 10, Issue 6, 2015, Pages

  Rodriguez, Ana ^a   Crespo, Isaac ^a   Androsova, Ganna ^a   Del Sol, Antonio ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; EMBRYONIC STEM CELL; EPITHELIAL MESENCHYMAL TRANSITION; EVOLUTIONARY ALGORITHM; GENE EXPRESSION PROFILING; HEART MUSCLE CELL; INFORMATION SCIENCE; INTRACELLULAR SIGNALING; MACHINE LEARNING; PLURIPOTENT STEM CELL; PRIOR KNOWLEDGE NETWORK; PROCESS OPTIMIZATION; TH1 CELL; TH2 CELL; VALIDATION STUDY; ALGORITHM; CARDIAC MUSCLE CELL; COMPUTER INTERFACE; COMPUTER SIMULATION; CYTOLOGY; HUMAN; HUMAN EMBRYONIC STEM CELL; INDUCED PLURIPOTENT STEM CELL; KNOWLEDGE; LOGIC; SOFTWARE;

ALGORITHMS; COMPUTER SIMULATION; EPITHELIAL-MESENCHYMAL TRANSITION; HUMAN EMBRYONIC STEM CELLS; HUMANS; INDUCED PLURIPOTENT STEM CELLS; KNOWLEDGE; LOGIC; MYOCYTES, CARDIAC; SOFTWARE; TH1 CELLS; TH2 CELLS; USER-COMPUTER INTERFACE;

EID: 84936805401     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0127216     Document Type: Article

References (54)

1. Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, et al. (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33: D428-432. PMID: 15608231
2. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, et al. (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39: D685-690. doi: 10.1093/nar/gkq1039 PMID: 21071392
3. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27-30. PMID: 10592173
4. Xiong M, Li J, Fang X (2004) Identification of genetic networks. Genetics 166: 1037-1052. PMID: 15020486
5. Dempster AP (1972) Covariance selection. Biometrics: 157-175.
6. Whittaker J (2009) Graphical models in applied multivariate statistics: Wiley Publishing.
7. Kollar D, Friedman N (2009) Probabilistic graphical models: principles and techniques: The MIT Press.
8. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, et al. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC bioinformatics 7: S7. PMID: 17254312
9. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, et al. (2005) Reverse engineering of regulatory networks in human B cells. Nature genetics 37: 382-390. PMID: 15778709
10. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, et al. (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS biology 5: e8. PMID: 17214507
11. De La Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20: 3565-3574. PMID: 15284096
12. Soranzo N, Bianconi G, Altafini C (2007) Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23: 1640-1647. PMID: 17485431
13. Liang K-C, Wang X (2008) Gene Regulatory Network Reconstruction Using Conditional Mutual Information. EURASIP Journal on Bioinformatics and Systems Biology 2008: 253894.
14. Markowetz F, Bloch J, Spang R (2005) Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics 21: 4026-4032. PMID: 16159925
15. Markowetz F, Kostka D, Troyanskaya OG, Spang R (2007) Nested effects models for high-dimensional phenotyping screens. Bioinformatics 23: i305-312. PMID: 17646311
16. Frohlich H, Fellmann M, Sultmann H, Poustka A, Beissbarth T (2008) Estimating large-scale signaling networks through nested effect models with intervention effects from microarray data. Bioinformatics 24: 2650-2656. doi: 10.1093/bioinformatics/btm634 PMID: 18227117
17. Tresch A, Markowetz F (2008) Structure learning in Nested Effects Models. Stat Appl Genet Mol Biol 7: Article9. doi: 10.2202/1544-6115.1359 PMID: 18312223
18. Frohlich H, Tresch A, Beissbarth T (2009) Nested effects models for learning signaling networks from perturbation data. Biom J 51: 304-323. doi: 10.1002/bimj.200800185 PMID: 19358219
19. Frohlich H, Sahin O, Arlt D, Bender C, Beissbarth T (2009) Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions. BMC Bioinformatics 10: 322. doi: 10.1186/1471-2105-10-322 PMID: 19814779
20. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, et al. (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular systems biology 5.
21. Gat-Viks I, Tanay A, Shamir R (2004) Modeling and analysis of heterogeneous regulation in biological networks. Journal of computational biology: a journal of computational molecular cell biology 11: 1034-1049. PMID: 15662196
22. Layek RK, Datta A, Dougherty ER (2011) From biological pathways to regulatory networks. Molecular bioSystems 7: 843-851. doi: 10.1039/c0mb00263a PMID: 21161088
23. Crespo I, Krishna A, Le Bechec A, del Sol A (2013) Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states. Nucleic Acids Research 41: e8. doi: 10.1093/nar/gks785 PMID: 22941654
24. Ulitsky I, Gat-Viks I, Shamir R (2008) MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data. Genome Biol 9: R1. doi: 10.1186/gb-2008-9-1-r1 PMID: 18171474
25. Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, et al. (2012) CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol 6: 133. doi: 10.1186/1752-0509-6-133 PMID: 23079107
26. Ourfali O, Shlomi T, Ideker T, Ruppin E, Sharan R (2007) SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics 23: i359-366. PMID: 17646318
27. Lan A, Smoly IY, Rapaport G, Lindquist S, Fraenkel E, et al. (2011) ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res 39: W424-429. doi: 10.1093/nar/gkr359 PMID: 21576238
28. Hashemikhabir S, Ayaz ES, Kavurucu Y, Can T, Kahveci T (2012) Large-scale signaling network reconstruction. IEEE/ACM Trans Comput Biol Bioinform 9: 1696-1708. doi: 10.1109/TCBB.2012.128 PMID: 23221085
29. Knapp B, Kaderali L (2013) Reconstruction of cellular signal transduction networks using perturbation assays and linear programming. PLoS One 8: e69220. doi: 10.1371/journal.pone.0069220 PMID: 23935958
30. Melas IN, Samaga R, Alexopoulos LG, Klamt S (2013) Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs. PLoS Comput Biol 9: e1003204. doi: 10.1371/journal.pcbi.1003204 PMID: 24039561
31. Morganella S, Zoppoli P, Ceccarelli M (2009) IRIS: a method for reverse engineering of regulatory relations in gene networks. BMC Bioinformatics 10: 444. doi: 10.1186/1471-2105-10-444 PMID: 20030818
32. Kaderali L, Dazert E, Zeuge U, Frese M, Bartenschlager R (2009) Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks. Bioinformatics 25: 2229-2235. doi: 10.1093/bioinformatics/btp375 PMID: 19542154
33. Kim S, Li H, Dougherty ER, Cao N, Chen Y, et al. (2002) Can Markov chain models mimic biological regulation? Journal of Biological Systems 10: 337-357.
34. Pal R, Ivanov I, Datta A, Bittner ML, Dougherty ER (2005) Generating Boolean networks with a prescribed attractor structure. Bioinformatics 21: 4021-4025. PMID: 16150807
35. Crespo I, Del Sol A (2013) A general strategy for cellular reprogramming: the importance of transcription factor cross-repression. STEM CELLS 31: 2127-2135. doi: 10.1002/stem.1473 PMID: 23873656
36. Gu Y, Liu GH, Plongthongkum N, Benner C, Yi F, et al. (2014) Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes. Protein & Cell 5: 59-68.
37. Crespo I, Perumal TM, Jurkowski W, del Sol A (2013) Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks. BMC Systems Biology 7: 140. doi: 10.1186/1752-0509-7-140 PMID: 24350678
38. Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18: 261-274. PMID: 11847074
39. Zhou X, Wang X, Pal R, Ivanov I, Bittner M, et al. (2004) A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks. Bioinformatics 20: 2918-2927. PMID: 15145802
40. Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G (2008) Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics 24: 1917-1925. doi: 10.1093/bioinformatics/btn336 PMID: 18614585
41. Larrañaga P, Lozano JA (2002) Estimation of distribution algorithms: A new tool for evolutionary computation: Springer.
42. Moes M, Le Béchec A, Crespo I, Laurini C, Halavatyi A, et al. (2012) A novel network integrating a miRNA-203/SNAI1 feedback loop which regulates epithelial to mesenchymal transition. PloS one 7: e35440. doi: 10.1371/journal.pone.0035440 PMID: 22514743
43. Mendoza L, Pardo F (2010) A robust model to describe the differentiation of T-helper cells. Theory in Biosciences 129: 283-293. doi: 10.1007/s12064-010-0112-x PMID: 20922578
44. Chang R, Shoemaker R, Wang W (2011) Systematic search for recipes to generate induced pluripotent stem cells. PLoS Comput Biol 7: e1002300. doi: 10.1371/journal.pcbi.1002300 PMID: 22215993
45. Gu Y, Liu G-H, Plongthongkum N, Benner C, Yi F, et al. (2013) Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes. Protein & Cell.
46. Novichkova S, Egorov S, Daraselia N (2003) MedScan, a natural language processing engine for MED-LINE abstracts. Bioinformatics 19: 1699-1706. PMID: 12967967
47. Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, et al. (2004) Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 20: 604-611. PMID: 15033866
48. Lee HJ, Takemoto N, Kurata H, Kamogawa Y, Miyatake S, et al. (2000) GATA-3 induces T helper cell type 2 (Th2) cytokine expression and chromatin remodeling in committed Th1 cells. The Journal of experimental medicine 192: 105-116. PMID: 10880531
49. Chang R, Shoemaker R, Wang W (2011) Systematic search for recipes to generate induced pluripotent stem cells. PLoS computational biology 7: e1002300. doi: 10.1371/journal.pcbi.1002300 PMID: 22215993
50. Schmidt R, Plath K (2012) The roles of the reprogramming factors Oct4, Sox2 and Klf4 in resetting the somatic cell epigenome during induced pluripotent stem cell generation. Genome biology 13: 251. doi: 10.1186/gb-2012-13-10-251 PMID: 23088445
51. Yang L, Soonpaa MH, Adler ED, Roepke TK, Kattman SJ, et al. (2008) Human cardiovascular progenitor cells develop from a KDR+ embryonic-stem-cell-derived population. Nature 453: 524-528. doi: 10.1038/nature06894 PMID: 18432194
52. Lian X, Hsiao C, Wilson G, Zhu K, Hazeltine LB, et al. (2012) Robust cardiomyocyte differentiation from human pluripotent stem cells via temporal modulation of canonical Wnt signaling. Proceedings of the National Academy of Sciences 109: E1848-E1857. doi: 10.1073/pnas.1200250109 PMID: 22645348
53. Willems E, Cabral-Teixeira J, Schade D, Cai W, Reeves P, et al. (2012) Small molecule-mediated TGFβ type II receptor degradation promotes cardiomyogenesis in embryonic stem cells. Cell stem cell 11: 242-252. doi: 10.1016/j.stem.2012.04.025 PMID: 22862949
54. Gu Y, Liu G-H, Plongthongkum N, Benner C, Yi F, et al. (2014) Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes. Protein & Cell 5: 59-68.