Spice: discovery of phenotype-determining component interplays

BMC Systems Biology

Volumn 6, Issue , 2012, Pages

  Chen, Zhengzhang ^a,b   Padmanabhan, Kanchana ^a,b   Rocha, Andrea M ^c   Shpanskaya, Yekaterina ^d   Mihelcic, James R ^c   Scott, Kathleen ^e   Samatova, Nagiza F ^a,b  

^a North Carolina State University   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c University of South Florida   (United States)

^d Duke University   (United States)

^e UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HYDROGEN; HYDROGENASE; NITROGENASE;

ALGORITHM; ARTICLE; GENE REGULATORY NETWORK; METABOLISM; METHODOLOGY; PHENOTYPE; SYSTEMS BIOLOGY;

ALGORITHMS; GENE REGULATORY NETWORKS; HYDROGEN; HYDROGENASE; NITROGENASE; PHENOTYPE; SYSTEMS BIOLOGY;

EID: 84860826810     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-6-40     Document Type: Article

References (91)

1. Ash C. From simplicity to complexity. Science 2010, 329:1125.
2. Bellman R. Adaptive Control Processes: A Guided Tour. Princeton 1961, Princeton University Press, NJ.
3. Chen W, Schmidt M, Tian W, Samatova N. A fast, accurate algorithm for identifying functional modules through pairwise local alignment of protein interaction networks. Proceedings of the International Conference on Bioinformatics & Computational Biology. 2009, 816-821. Las Vegas, NV, USA.
4. Chen W, Rocha A, Hendrix W, Schmidt M, Samatova N. The multiple alignment algorithm for metabolic pathways without abstraction. Proceedings of IEEE International Conference on Data Mining Workshops 669-678.
5. Koyutürk M, Kim Y, Subramaniam S, Szpankowski W, Grama A. Detecting conserved interaction patterns in biological networks. J Comput Biol 2006, 13(7):1299-1322. 10.1089/cmb.2006.13.1299, 17037960.
6. Nijssen S, Kok J. The gaston tool for frequent subgraph mining. Electron Notes Theor Comput Sci 2005, 127:77-87.
7. Schmidt MC, Samatova NF. An algorithm for the discovery of phenotype related metabolic pathways. BIBM. 2009, 60-65. Washington, DC, USA.
8. Slonim N, Elemento O, Tavazoie S. Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks. Mol Syst Biol 2006, 2:0005. 1681479, 16732191.
9. Levesque M, Shasha D, Kim W, Surette M, Benfey P. Trait-to-Gene: A computational method for predicting the function of uncharacterized genes. Curr Biol 2003, 13:129-133. 10.1016/S0960-9822(03)00009-5, 12546786.
10. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23:2507-2517. 10.1093/bioinformatics/btm344, 17720704.
11. Lei Y, Huan L. Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 2004, 5:1205-1224.
12. Li L, Weinberg C, Darden T, Pedersen L. Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 2001, 17:1131-1142. 10.1093/bioinformatics/17.12.1131, 11751221.
13. Diaz-Uriarte R, Alvarez de Andres S. Gene selection and classification of microarray data using random forest. BMC Bioinf 2006, 7:3.
14. Curtis R, Oresic M, Vidal-Puig A. Pathways to the analysis of microarray data. Trends Biotechnol 2005, 23:429-435. 10.1016/j.tibtech.2005.05.011, 15950303.
15. Johannes M, Brase J, Fröhlich H, Gade S, Gehrmann M, Fälth M, Sültmann H, Beißbarth T. Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 2010, 26:2136-2144. 10.1093/bioinformatics/btq345, 20591905.
16. Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert J. Classification of microarray data using gene networks. BMC Bioinf 2007, 8:35.
17. Yousef M, Ketany M, Manevitz LM, Showe LC, Showe MK. Classification and biomarker identification using gene network modules and support vector machines. BMC Bioinf 2009, 10:337.
18. Chuang H, Lee Y Eand L, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007, 3:140. 2063581, 17940530.
19. Chen L, Xuan J, Riggins R, Clarke R, Wang Y. Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst Biol 2011, 5:161. 10.1186/1752-0509-5-161, 3214162, 21992556.
20. Ma S, Shi M, Li Y, Yi D, Shia B. Incorporating gene co-expression network in identification of cancer prognosis markers. BMC Bioinf 2010, 11:271.
21. Kapdan I, Kargi F. Bio-hydrogen production from waste materials. Enzyme Microb Technol 2006, 38:569-582.
22. Nath K, Das D. Improvement of fermentative hydrogen production: Various approaches. Appl Microbiol Biotechnol 2004, 65(5):520-9.
23. Rey FE, Heiniger EK, Harwood CS. Redirection of metabolism for biological hydrogen production. Appl Environ Microbiol 2007, 73(5):1665-1671. 10.1128/AEM.02565-06, 1828789, 17220249.
24. Huang Y, Zong W, Yan X, Wang R, Hemme C, Zhou J, Zhou Z. Succession of the bacterial community and dynamics of hydrogen producers in a hydrogen-producing bioreactor. Appl Environ Microbiol 2010, 76:3387-3390. 10.1128/AEM.02444-09, 2869144, 20305018.
25. Jim K, Parmar K, Singh M, Tavazoie S. A Cross-Genomic approach for systematic mapping of phenotypic traits to genes. Genome Res 2004, 14:109-115. 314287, 14707173.
26. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vectormachines. Machine Learning 2002, 46:389-422.
27. Hawkes F, Dinsdale R, Hawkes D, Hussy I. Sustainable fermentative hydrogen production: Challenges for process optimisation. Int J Hydrogen Energy 2002, 27:1339-1347.
28. Mathews J, Wang G. Metabolic pathway engineering for enhanced biohydrogen production. Int J Hydrogen Energy 2009, 34:7404-7416.
29. Li C, Fang HHP. Fermentative hydrogen production from wastewater and solid wastes by mixed cultures. Crit Rev Environ Sci Technol 2007, 37:1-39.
30. Khanal S. Biohydrogen production: Fundamentals, challenges, and operation strategies for enhanced yield. Anaerobic Biotechnology for Bioenergy Production: Principles and Applications 2008, 180-219. Wiley-Blackwell, USA.
31. Li R, Fang H. Heterotrophic photo fermentative hydrogen production. Crit Rev Environ Sci Technol 2009, 39(12):1081-1108.
32. White D. The Physiology and Biochemistry of Prokaryotes 2007, Oxford University Press, New York.
33. Hallenbeck P, Ghosh D. Improvements in fermentative biological hydrogen production through metabolic engineering. J Environ Manage 2010, 1-5.
34. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA. The COG database: an updated version includes eukaryotes. BMC Bioinf 2003, 4:41+.
35. Vignais PM, Billoud B, . Meyer Classification and phylogeny of hydrogenases. FEMS Microbiol Rev 2001, 25(4):455-501. Meyer.
36. Butland G, Zhang J, Yang W. Interactions of the Escherichia coli hydrogenase biosynthetic proteins: HybG complex formation. FEBS Lett 2006, 580:677-681. 10.1016/j.febslet.2005.12.063, 16412426.
37. McKinlay J, Harwood C. Clostridium ljungdahlii represents a microbial production platform based on syngas. Proc Natl Acad Sci 2010, 107(26):11669-75. 10.1073/pnas.1006175107, 2900684, 20558750.
38. Rey F, Oda Y, Harwood C. Regulation of uptake hydrogenase and effects of hydrogen utilization on gene expression in Rhodopseudomonas palustris. J Bacteriol 2006, 188(17):6143-6152. 10.1128/JB.00381-06, 1595397, 16923881.
39. Zhang R, Andersson C, Savchenko A, Skarina T, Evdokimova E, Beasley S, Arrowsmith CH, Edwards AM, Joachimiak A, Mowbray SL. Structure of Escherichia coli ribose-5-phosphate isomerase: A ubiquitous enzyme of the pentose phosphate pathway and the calvin cycle. Structure 2003, 11:31-42. 10.1016/S0969-2126(02)00933-4, 2792023, 12517338.
40. Das D, Veziroglu N. Hydrogen production by biological processes: A survey of literature. Int J Hydrogen Energy 2001, 26:13-28.
41. Black K, Parsons R, Osborne B. Uptake and metabolism of glucose in the nostoc-gunnera symbiosis. New Phytol 2002, 153:297-305.
42. Steffes C, Ellis J, Wu J, . Rosen The lysP gene encodes the lysine-specific permease. J Bacteriol 1992, 174:3242-3249. 205992, 1315732, Rosen.
43. Veit A, Akhtar M, Mizutani T, Jones P. Constructing and testing the thermodynamic limits of synthetic NAD(P)H:H2 pathways. Microb Biotechnol 2008, 1(5):382-94. 10.1111/j.1751-7915.2008.00033.x, 21261858.
44. Antoni D, Zverlov V, Schwarz W. Biofuels from microbes. Appl Microbiol Biotechnol 2007, 77:23-35. 10.1007/s00253-007-1163-x, 17891391.
45. Hart D. Hydrogen Power: The Commercial Future of 'the Ultimate Fuel' 1997, Financial Times Energy Publishing, London.
46. Vignais PM, Colbeau A. Molecular biology of microbial hydrogenases. Curr Issues Mol Biol 2004, 6:159-188.
47. Martins A, Shuman S. An end-healing enzyme from Clostridium thermocellum with 5' kinase, 2',3' phosphatase, and adenylyltransferase activities. RNA 2005, 11:1271-80. 10.1261/rna.2690505, 1370810, 15987807.
48. Paschos A, Bauer A, Zimmermann A, Zehelein E, Böck A. HypF, a carbamoyl phosphate-converting enzyme involved in [NiFe] hydrogenase maturation. J Biol Chem 2002, 277:49945-51. 10.1074/jbc.M204601200, 12377778.
49. Härtel U, Buckel W. Sodium ion-dependent hydrogen production in Acidaminococcus fermentans. Arch Microbiol 1996, 166:350-356. 10.1007/s002030050394, 8929282.
50. Koskinen P, Kaksonen A, Puhakka J. The relationship between instability of H2 production and compositions of bacterial communities within a dark fermentation fluidised-bed bioreactor. Biotechnol Bioeng 2007, 97:742-758. 10.1002/bit.21299, 17163514.
51. Bagramyan K, Trchounian A. Structural and functional features of formate hydrogen Lyase, an enzyme of mixed-acid fermentation from escherichia coli. Biochemistry 2003, 68:1159-1170.
52. Akhtar K, Jones P. Engineering of a synthetic hydF-hydE-hydG-hydA operon for biohydrogen production. Anal Biochem 2008, 373:170-172. 10.1016/j.ab.2007.10.018, 17996187.
53. Shomura Y, Komori H, Miyabe N, Tomiyama M, Shibata N, Higuchi Y. Crystal structures of hydrogenase maturation protein HypE in the apo and ATP-bound forms. J Mol Biol 2007, 372:1045-1054. 10.1016/j.jmb.2007.07.023, 17706667.
54. Blokesch M, Albracht SPJ, Matzanke BF, Drapal NM, Jacobi A, Bock A. The complex between hydrogenase-maturation proteins HypC and HypD is an intermediate in the supply of cyanide to the active site iron of [NiFe]-hydrogenases. J Mol Biol 2004, 344:155-167. 10.1016/j.jmb.2004.09.040, 15504408.
55. Vignais P, Billoud J Band M. Classification and phylogeny of hydrogenases. FEMS Microbiol Rev 2001, 25:455-501.
56. Liu X, Zhu Y, Yang S. Construction and characterization of ack deleted mutant of Clostridium tyrobutyricum for enhanced butyric acid and hydrogen production. Biotechnol Prog 2006, 22:1265-75.
57. Rajagopala SV, Titz B, Goll J, Parrish JR, Wohlbold K, McKevitt MT, Palzkill T, Mori H, Finley RL, Uetz P. The protein network of bacterial motility. Mol Syst Biol 2007, 3:128. 1943423, 17667950.
58. Singh AH, Wolf DM, Wang P, Arkin AP. Modularity of stress response evolution. Proc Nat Acad Sci 2008, 105(21):7500-7505. 10.1073/pnas.0709764105, 2396705, 18495925.
59. Blocker A, Komoriya K, Aizawa SI. Type III secretion systems and bacterial flagella: Insights into their function from structural similarities. Proc Nat Acad Sci USA 2003, 100(6):3027-3030. 10.1073/pnas.0535335100, 152238, 12631703.
60. Tan A, Gilbert D. Ensemble machine learning on gene expression data for cancer classification. Applied Bioinf 2003, 2(3 Suppl):S75-S83.
61. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci 2000, 97(22):12182-12186. 10.1073/pnas.220392197, 17315, 11027309.
62. Yanagi Y, Yoshikai Y, Leggett K, Clark S, Aleksander I, Mak T. A human T cell-specific cDNA clone encodes a protein having extensive homology to immunoglobulin chains. Nature 1984, 308(5955):145-149. 10.1038/308145a0, 6336315.
63. Motz C, Martin H, Krimmer T, Rassow J. Bcl-2 and porin follow different pathways of TOM-dependent insertion into the mitochondrial outer membrane. J Mol Biol 2002, 323(4):729-738. 10.1016/S0022-2836(02)00995-6, 12419260.
64. Karakas T, Maurer U, Weidmann E, Miething C, Hoelzer D. Bergmann: High expression of bcl-2 mRNA as a determinant of poor prognosis in acute myeloid leukemia. Ann Oncol 1998, 9(2):159-65. 10.1023/A:1008255511404, 9553660.
65. Hirota T, Morisaki T, Nishiyama Y, Marumoto T, Tada K, Hara T, Masuko N, Inagaki M, Hatakeyama K, Saya H. Zyxin, a regulator of actin filament assembly, targets the mitotic apparatus by interacting with h-warts/LATS1 tumor suppressor. J Cell Biol 2000, 149(5):1073-86. 10.1083/jcb.149.5.1073, 2174824, 10831611.
66. Fang J, Grzymala-Busse J. Leukemia prediction from gene expression data-a rough set approach. Artificial Intelligence and Soft Computing-ICAISC 2006, Volume 4029 of Lecture Notes in Computer Science 2006, 899-908. Zakopane, Poland.
67. Pansombut T, Hendrix W, Gao ZJ, Harrison BE, Samatova NF. Biclustering-driven ensemble of bayesian belief network Classifiers for Underdetermined Problems 2011, Barcelona, Spain.
68. Dettling M. BagBoosting for tumor classification with gene expression data. Bioinformatics 2004, 20(18):3583+. 10.1093/bioinformatics/bth447, 15466910.
69. Zhou Q, Hong W, Luo L, Yang F. Gene selection using random forest and proximity differences criterion on DNA microarray data. JCIT 2010, 5(6):161-170.
70. Weston J, Elisseeff A, Schölkopf B, Tipping M. Use of the zero-norm with linear models and kernel methods. J Machine Learning Res 2003, 3:1439-1461.
71. Wang L, Chu F, Xie W. Accurate cancer classification using expressions of very few genes. IEEE/ACM Trans Comput Biol Bioinfo 2007, 4:40-53.
72. Hwang T, Sun CH, Yun T, Yi GS. FiGS: a filter-based gene selection workbench for microarray data. BMC Bioinf 2010, 11:50.
73. Tajunisha N, Saravanan V. An improved method of unsupervised sample clustering based on information genes for microarray cancer data sets. IJCB 2011, 2:24-31.
74. Czajkowski M, Krȩtowski M. Top scoring pair decision tree for gene expression data analysis. Advances in experimental medicine and biology 2011, 696:27-35. http://dx.doi.org/10.1007/978-1-4419-7046-6{set minus}_3, 10.1007/978-1-4419-7046-6_3, 21431543.
75. Dagliyan O, Uney-Yuksektepe F, Kavakli IH, Turkay M. Optimization based tumor classification from microarray gene expression data. PLoS ONE 2011, 6(2):e14579. http://dx.doi.org/10.1371/Fjournal.pone.0014579, 10.1371/journal.pone.0014579, 3033885, 21326602.
76. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record 2002, 31:76-77.
77. Efron B, Tibshirani RJ. An Introduction to the Bootstrap 1993, Chapman & Hall, New York.
78. Breiman L. Bagging predictors. Machine Learning 1996, 24:123-140.
79. Freund Y, Schapire R. Experiments with a new boosting algorithm. International Conference on Machine Learning 1996, 148-156. Bari, Italy.
80. Breiman L. Random Forests. Machine Learning 2001, 45:5-32.
81. Wang L, Chu F, Xie W. Accurate cancer classification using expressions of very few genes. IEEE/ACM Trans Comput Biol Bioinf 2007, 4:40-53. http://dx.doi.org/10.1109/TCBB.2007.1006.
82. Díaz-Uriarte R. Variable selection from random forests: application to gene expression data. Spanish Bioinformatics Conference 2004, 47-52. Barcelona, Spain.
83. Jolliffe IT, Stephenson DB. Forecast Verification: A Practitioner's Guide in Atmospheric Science 2003, Wiley and Sons, Oxford.
84. Melville P, Mooney RJ. Diverse ensembles for active learning. Proceedings of 21st International Conference on Machine Learning (ICML-2004) 2004, 584-591.
85. He X, Yan S, Hu Y, Niyogi P, Zhang HJ. Face recognition using laplacianfaces. IEEE Transact Pattern Anal Machine Int 2005, 27:328-340.
86. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. From molecular to modular cell biology. Nature 1999, 402(6761):47-52.
87. Breiman L, Friedman J, Olshen R, Stone C. Classification and Regression Trees. Monterey 1984, Wadsworth and Brooks, CA.
88. Zhang B, Park B, Karpinets T, Samatova NF. From pull-down data to protein interaction networks and complexes with biological relevance. Bioinformatics 2008, 24:979-986. 10.1093/bioinformatics/btn036, 18304937.
89. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, Bork P, . von Mering STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009, 37(Database issue):D412-416. http://dx.doi.org/10.1093/nar/gkn760, 2686466, 18940858, von Mering.
90. Hendrix W, Rocha A, Padmanabhan K, Choudhary A, Scott K, Mihelcic J, Samatova N. DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules. BMC Syst Biol 2011, 5:172. 10.1186/1752-0509-5-172, 3231954, 22024446.
91. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G. GO: TermFinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 2004, 20(18):3710-3715. 10.1093/bioinformatics/bth456, 3037731, 15297299.