Integrating bioinformatics and computational biology: Perspectives and possibilities for in silico network reconstruction in molecular systems biology

Current Bioinformatics

Volumn 3, Issue 2, 2008, Pages 98-129

  Alves, Rui ^a,b   Vilaprinyo, Ester ^a,b,c   Sorribas, Albert ^a,b  

^a Departament Ciències Mèdiques Bàsiques   (Spain)

^b UNIVERSITY OF LLEIDA   (Spain)

^c BELLVITGE BIOMEDICAL RESEARCH INSTITUTE IDIBELL   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; COMPUTATIONAL METHODS; DATA INTEGRATION;

BIOLOGICAL HYPOTHESIS; BIOLOGICAL INFORMATION; CELLULARS; COMPUTATIONAL BIOLOGY; DATA RANGES; IN-SILICO; MOLECULAR DATA; MOLECULAR SYSTEMS BIOLOGIES; NETWORK RECONSTRUCTION; SEQUENCE DATA;

AUTOMATION;

EID: 70449623601     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489308784340694     Document Type: Review

References (411)

1. Richards DE. Medline at thirty-five. Evid Based Dent 2006; 7: 89.
2. Reid T, Dunikowski LG. PubMed Central--at last. Can Fam Physi cian 2006; 52: 159-160, 165.
3. Abulaish M, Dey L. Knowledge enhancement through ontologyguided text mining. Pattern Recognition and Machine Intelligence, Proceedings 2005; 3776: 601-604.
4. Sevinc A. Web of science: a unique method of cited reference searching. J Natl Med Assoc 2004; 96: 980-983.
5. Giglia E. Beyond PubMed. Other free-access biomedical databases. Eura Medicophys 2007; 43: 563-569.
6. De Groote SL. PubMed, Internet Grateful Med, and Ovid: a comparison of three MEDLINE Internet interfaces. Med Ref Serv Q 2000; 19: 1-13.
7. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J 2008; 22: 338-342.
8. Gall C, Brahmi FA. Retrieval comparison of EndNote to search MEDLINE (Ovid and PubMed) versus searching them directly. Med Ref Serv Q 2004; 23: 25-32.
9. Janke RG. Current Contents Connect and PubMed--a comparison of content and currency. Health Info Libr J 2002; 19: 230-232.
10. Sutton VR, Hauser SE. Preliminary comparison of the Essie and PubMed search engines for answering clinical questions using MD on Tap, a PDA-based program for accessing biomedical literature. AMIA Annu Symp Proc 2005: 1128.
11. Vanhecke TE, Barnes MA, Zimmerman J, Shoichet S. PubMed vs. HighWire Press: a head-to-head comparison of two medical litera ture search engines. Comput Biol Med 2007; 37: 1252-1258.
12. Peng Y, Zhang X. Integrative data mining in systems biology: from text to network mining. Artif Intell Med 2007; 41: 83-86.
13. Ananiadou S, Kell DB, Tsujii J. Text mining and its potential applications in systems biology. Trends Biotechnol 2006; 24: 571-579.
14. Liu Y, Navathe SB, Civera J, Dasigi V, Ram A, Ciliax BJ, Dingle-dine R. Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms. IEEE/ACM Trans Comput Biol Bioinform 2005; 2: 62-76.
15. Liu Y, Brandon M, Navathe S, Dingledine R, Ciliax BJ. Text mining functional keywords associated with genes. Medinfo 2004; 11: 292-296.
16. Natarajan J, Berrar D, Dubitzky W, et al. Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line. BMC Bioinformatics 2006; 7: 373.
17. Barthelmes J, Ebeling C, Chang A, Schomburg I, Schomburg D. BRENDA, AMENDA and FRENDA. the enzyme information system in 2007. Nucleic Acids Res 2007; 35: D511-D514.
18. Pharkya P, Nikolaev EV, Maranas CD. Review of the BRENDA Database. Metab Eng 2003; 5: 71-73.
19. Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res 2004; 32: D431-433.
20. Adeva JJG, Calvo R. Mining text with Pimiento. IEEE Internet Computing 2006; 10: 27-35.
21. Antonakaki D, Kanterakis A, Potamias G. Biomedical literature mining for text classification and construction of gene networks. Advances in Artificial Intelligence, Proceedings 2006; 3955: 469-473.
22. Choi YJ, Park SS. Efficient classification method for complex biological literature using text and data mining combination. Intelligent Data Engineering and Automated Learning - Ideal 2006; Proceedings 2006; 4224: 688-696.
23. Davis N, Demetriou G, Gaizauskas R, Guo YK, Roberts I. Web service architectures for text mining: An exploration of the issues via an e-science demonstrator. Int J Web Ser Res 2006; 3: 95-112.
24. Denicia-Carral C, Montes-y-Gomez M, Villasenor-Pineda L, Hernandez RG. A text mining approach for definition question answering. Adv Natl Lang Proc Proc 2006; 4139: 76-86.
25. Doms A, Jakoniene V, Lambrix P, Schroeder M, Wachter T. Ontologies and text mining as a basis for a semantic web for the life sciences. Reasoning Web 2006; 4126: 164-183.
26. Erhardt RAA, Schneider R, Blaschke C. Status of text-mining techniques applied to biomedical text. Drug Discov Today 2006; 11: 315-325.
27. Fan WG, Wallace L, Rich S, Zhang ZJ. Tapping the power of text mining. Commun Acm 2006; 49: 77-82.
28. Hong K, Park J, Yang J, Park S. Biological text mining for extraction of proteins and their interactions. Pattern Recognition and Machine Intelligence, Proceedings 2005; 3776: 645-648.
29. Hu XH, Zhang XD, Wu D, Zhou XH, Rumm P. Integration of instance-based learning and text mining for identification of potential virus/bacterium as bio-terrorism weapons. Intelligence and Security Informatics, Proceedings 2006; 3975: 548-553.
30. Jang H, Song SK, Myaeng SH. Text mining for medical documents using a Hidden Markov Model. Info Retri Technol Proc 2006; 4182: 553-559.
31. Kim HJ. On text mining algorithms for automated maintenance of hierarchical knowledge directory. Knowledge Science, Engineering and Management 2006; 4092: 202-214.
32. Kuhn T, Royer L, Fuchs NE, Schroder M. Improving text mining with controlled natural language: A case study for protein interactions. Data Integration in the Life Sciences, Proceedings 2006; 4075: 66-81.
33. Lai KK, Yu L, Wang SY. Multi-agent web text mining on the grid for enterprise decision support. Advanced Web and Network Technologies, and Applications, Proceedings 2006; 3842: 540-544.
34. Liu Y, Navathe SB, Civera J, Dasigi V, Ram A, Ciliax BJ, Dingle-dine R. Text mining biomedical literature for discovering gene-to-gene relationships: A comparative study of algorithms. Ieee-Acm Trans Comput Biol Bioinfo 2005; 2: 62-76.
35. Malik R, Franke L, Siebes A. Combination of text-mining algorithms increases the performance. Bioinformatics 2006; 22: 2151-2157.
36. Rice SB, Nenadic G, Stapley BJ. Mining protein function from text using term-based support vector machines. BMC Bioinformatics 2005; 6 Suppl 1: S22.
37. SanJuan E, Ibekwe-SanJuan F. Text mining without document context. Info Proc Manag 2006; 42: 1532-1552.
38. Shaban K, Basir O, Kamel M. Document mining based on semantic understanding of text. Progress in Pattern Recognition, Image Analysis and Applications, Proceedings 2006; 4225: 834-843.
39. Karamanis N, Lewin I, Seal R, Drysdale R, Briscoe E. Integrating natural language processing with FlyBase curation. Pac Symp Bio-comput 2007; 245-256.
40. Hoffmann R, Valencia A. Implementing the iHOP concept for navigation of biomedical literature. Bioinformatics 2005; 2: ii252-ii258.
41. Fernandez JM, Hoffmann R, Valencia A. Ihop web services. Nucleic Acids Res 2007; 35: W21-W26.
42. Hoffmann R, Krallinger M, Andres E, Tamames J, Blaschke C, Valencia A. Text mining for metabolic pathways, signaling cascades, and protein networks. Sci STKE 2005; 2005: pe21.
43. Hoffmann R, Valencia A. Life cycles of successful genes. Trends Genet 2003; 19: 79-81.
44. Hoffmann R, Valencia A. A gene network for navigating the literature. Nat Genet 2004; 36: 664.
45. Scherf M, Epple A, Werner T. The next generation of literature analysis: integration of genomic analysis into text mining. Brief Bioinform 2005; 6: 287-297.
46. Skusa A, Ruegg A, Kohler J. Extraction of biological interaction networks from scientific literature. Brief Bioinform 2005; 6: 263-276.
47. Stapley BJ, Benoit G. Biobibliometrics: information retrieval and visualization from co-occurrences of gene names in Medline abstracts. Pac Symp Biocomput 2000; 529-540.
48. Stapley BJ, Kelley LA, Sternberg MJ. Predicting the sub-cellular location of proteins from text using support vector machines. Pac Symp Biocomput 2002; 374-385.
49. Rogers A, Antoshechkin I, Bieri T, Blasiar D, et al. WormBase 2007. Nucleic Acids Res 2008; 36: D612-617.
50. Crosby MA, Goodman JL, Strelets VB, Zhang P, Gelbart WM. FlyBase: genomes by the dozen. Nucleic Acids Res 2007; 35: D486-491.
51. Wilson RJ, Goodman JL, Strelets VB. FlyBase: integration and improvements to query tools. Nucleic Acids Res 2008, 36: D588-D593.
52. Chiromatzo AO, Oliveira TY, Pereira G, et al. miRNApath: a database of miRNAs, target genes and metabolic pathways. Genet Mol Res 2007; 6: 859-865.
53. Putz J, Dupuis B, Sissler M, Florentz C. Mamit-tRNA, a database of mammalian mitochondrial tRNA primary and secondary structures. RNA 2007; 13: 1184-1190.
54. Yin Y, Zhao Y, Wang J, et al. antiCODE. a natural sense-antisense transcripts database. BMC Bioinformatics 2007; 8: 319.
55. Griffiths-Jones S. miRBase: the microRNA sequence database. Methods Mol Biol 2006; 342: 129-138.
56. Kazakov AE, Cipriano MJ, Novichkov PS, et al. RegTransBase--a database of regulatory sequences and interactions in a wide range of prokaryotic genomes. Nucleic Acids Res 2007; 35: D407-412.
57. Ankeny RA. Sequencing the genome from nematode to human: changing methods, changing science. Endeavour 2003; 27: 87-92.
58. Gustafson AM, Snitkin ES, Parker SC, DeLisi C, Kasif S. Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genomics 2006; 7: 265.
59. Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005; 437: 376-380.
60. Service RF. Gene sequencing. The race for the $1000 genome. Science 2006; 311: 1544-1546.
61. Venter JC, Smith HO, Hood L. A new strategy for genome sequencing. Nature 1996; 381: 364-366.
62. McGinnis S, Madden TL. BLAST. at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 2004; 32: W20-25.
63. Kuroda M, Hiramatsu K. Genome sequencing and annotation: an overview. Methods Mol Biol 2004; 266: 29-45.
64. Besemer J, Borodovsky M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res 2005; 33: W451-W454.
65. Cawley SL, Pachter L. HMM sampling and applications to gene finding and alternative splicing. Bioinformatics 2003; 19 Suppl 2: II36-II41.
66. Chatterji S, Pachter L. Large multiple organism gene finding by collapsed Gibbs sampling. J Comput Biol 2005; 12: 599-608.
67. Eyras E, Reymond A, Castelo R, Bye JM, et al. Gene finding in the chicken genome. BMC Bioinformatics 2005; 6: 131.
68. Korf I. Gene finding in novel genomes. BMC Bioinformatics 2004; 5: 59.
69. Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 2006; 34: 5623-5630.
70. Wang J, Kraemer E. GFPE. gene-finding program evaluation. Bioinformatics 2003; 19: 1712-1713.
71. Cuff JA, Coates GM, Cutts TJ, Rae M. The Ensembl computing architecture. Genome Res 2004; 14: 971-975.
72. Cowan D, Meyer Q, Stafford W, Muyanga S, Cameron R, Wittwer P. Metagenomic gene discovery: past, present and future. Trends Biotechnol 2005; 23: 321-329.
73. Meyer QC, Burton SG, Cowan DA. Subtractive hybridization magnetic bead capture: a new technique for the recovery of full-length ORFs from the metagenome. Biotechnol J 2007; 2: 36-40.
74. Remington KA, Heidelberg K, Venter JC. Taking metagenomic studies in context. Trends Microbiol 2005; 13: 404.
75. Rusch DB, Halpern AL, Sutton GK, et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol 2007; 5: e77.
76. Yooseph S, Sutton G, Rusch DB, et al. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol 2007; 5: e16.
77. Hulo N, Bairoch A, Bulliard V. The PROSITE database. Nucleic Acids Res 2006; 34: D227-230.
78. Raes J, Korbel JO, Lercher MJ, von Mering C, Bork P. Prediction of effective genome size in metagenomic samples. Genome Biol 2007; 8: R10.
79. Schneider M, Tognolli M, Bairoch A. The Swiss-Prot protein knowledgebase and ExPASy: providing the plant community with high quality proteomic data and tools. Plant Physiol Biochem 2004; 42: 1013-1021.
80. Kanehisa M. The KEGG database. Novartis Found Symp 2002; 247: 91-101; discussion 101-103, 119-128, 244-152.
81. Kanehisa M, Goto S, Hattori M, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006; 34: D354-D357.
82. Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res 2002; 30: 42-46.
83. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res 2004; 32: D277-280.
84. Ostell JM, Wheelan SJ, Kans JA. The NCBI data model. Methods Biochem Anal 2001; 43: 19-43.
85. Guda C. pTARGET. a web server for predicting protein subcellular localization. Nucleic Acids Res 2006; 34: W210-W213.
86. Hoglund A, Donnes P, Blum T, Adolph HW, Kohlbacher O. Mul-tiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 2006; 22: 1158-1165.
87. Lee K, Kim DW, Na D, Lee KH, Lee D. PLPD. reliable protein localization prediction from imbalanced and overlapped datasets. Nucleic Acids Res 2006; 34: 4655-4666.
88. Petsalaki EI, Bagos PG, Litou ZI, Hamodrakas SJ. PredSL. a tool for the N-terminal sequence-based prediction of protein subcellular localization. Genom Prot Bioinfo 2006; 4: 48-55.
89. Altermann E, Klaenhammer TR. PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics 2005; 6: 60.
90. Arakawa K, Kono N, Yamada Y, Mori H, Tomita M. KEGG-based pathway visualization tool for complex omics data. In Silico Biol 2005; 5: 419-423.
91. Cornwell P. Designing tools for pathway implementation and evaluation. J Burn Care Rehabil 1995; 16: 202-208.
92. Ekins S, Nikolsky Y, Bugrim A, Kirillov E, Nikolskaya T. Pathway mapping tools for analysis of high content data. Methods Mol Biol 2007; 356: 319-350.
93. Holford M, Li N, Nadkarni P, Zhao H. VitaPad: visualization tools for the analysis of pathway data. Bioinformatics 2005; 21: 1596-1602.
94. Kacser H. Kinetic models of development and heredity. Symp Soc Exp Biol 1960; 14: 13-27.
95. Karp PD, Ouzounis CA, Moore-Kochlacs C, et al. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 2005; 33: 6083-6089.
96. Karp PD, Paley S, Romero P. The Pathway Tools software. Bioin-formatics 2002; 18 Suppl 1: S225-S232.
97. Klukas C, Schreiber F. Dynamic exploration and editing of KEGG pathway diagrams. Bioinformatics 2006.
98. Krummenacker M, Paley S, Mueller L, Yan T, Karp PD. Querying and computing with BioCyc databases. Bioinformatics 2005; 21: 3454-3455.
99. Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 2005; 21: 3787-3793.
100. Paley SM, Karp PD. The Pathway Tools cellular overview diagram and Omics Viewer. Nucleic Acids Res 2006; 34: 3771-3778.
101. Meier-Kolthoff JP, Auch AF, Huson DH, Goker M. COPYCAT. cophylogenetic analysis tool. Bioinformatics 2007; 23: 898-900.
102. Berezikov E, Guryev V, Cuppen E. Exploring conservation of transcription factor binding sites with CONREAL. Methods Mol Biol 2007; 395: 437-448.
103. Brown DP, Krishnamurthy N, Sjolander K. Automated protein subfamily identification and classification. PLoS Comput Biol 2007; 3: e160.
104. Glanville JG, Kirshner D, Krishnamurthy N, Sjolander K. Berkeley Phylogenomics Group web servers: resources for structural phylo-genomic analysis. Nucleic Acids Res 2007; 35: W27-32.
105. Guindon S, Lethiec F, Duroux P, Gascuel O. PHYML Online--a web server for fast maximum likelihood-based phylogenetic inference. Nucleic Acids Res 2005; 33: W557-559.
106. Loots GG, Ovcharenko I. Mulan: multiple-sequence alignment to predict functional elements in genomic sequences. Methods Mol Biol 2007; 395: 237-254.
107. Meinel T, Krause A, Luz H, Vingron M, Staub E. The SYSTERS Protein Family Database in 2005. Nucleic Acids Res 2005; 33: D226-229.
108. Tarraga J, Medina I, Arbiza L, et al. Phylemon: a suite of web tools for molecular evolution, phylogenetics and phylogenomics. Nucleic Acids Res 2007; 35: W38-42.
109. Shah JK, Garner HR, White MA, Shames DS, Minna JD. sIR. siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database. BMC Bioinformatics 2007; 8: 178.
110. Abreu-Goodger C, Merino E. RibEx: a web server for locating riboswitches and other conserved bacterial regulatory elements. Nucleic Acids Res 2005; 33: W690-692.
111. Bengert P, Dandekar T. Riboswitch finder--a tool for identification of riboswitch RNAs. Nucleic Acids Res 2004; 32: W154-W159.
112. Huang HY, Chien CH, Jen KH, et al. An integrated web server for identifying regulatory RNA motifs and elements. Nucleic Acids Res 2006; 34: W429-W434.
113. Liu CC, Lin CC, Chen WS, Chen HY, Chang PC, Chen JJ, Yang PC. CRSD. a comprehensive web server for composite regulatory signature discovery. Nucleic Acids Res 2006; 34: W571-W577.
114. Tong J, Jiang P, Lu ZH. RISP. A web-based server for prediction of RNA-binding sites in proteins. Comput Methods Programs Bio-med 2008.
115. Zhang Y. miRU. an automated plant miRNA target prediction server. Nucleic Acids Res 2005; 33: W701-704.
116. Burley SK, Bonanno JB. Structural genomics. Methods Biochem Anal 2003; 44: 591-612.
117. Goldsmith-Fischman S, Honig B. Structural genomics: computational methods for structure analysis. Protein Sci 2003; 12: 1813-1821.
118. Berman HM, Westbrook JD. The impact of structural genomics on the protein data bank. Am J Pharmacogenomics 2004; 4: 247-252.
119. Berman H, Henrick K, Nakamura H, Markley JL. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res 2007; 35: D301-303.
120. Bourgeois D, Royant A. Advances in kinetic protein crystallography. Curr Opin Struct Biol 2005; 15: 538-547.
121. Esposito L, Vitagliano L, Mazzarella L. Recent advances in atomic resolution protein crystallography. Protein Pept Lett 2002; 9: 95-106.
122. Helliwell JR. Overview and new developments in softer X-ray (2A < lambda < 5A) protein crystallography. J Synchrotron Radiat 2004; 11: 1-3.
123. Massover WH. Radiation damage to protein specimens from electron beam imaging and diffraction: a mini-review of anti-damage approaches, with special reference to synchrotron X-ray crystallography. J Synchrotron Radiat 2007; 14: 116-127.
124. Myles DA. Neutron protein crystallography: current status and a brighter future. Curr Opin Struct Biol 2006; 16: 630-637.
125. Riekel C, Burghammer M, Schertler G. Protein crystallography microdiffraction. Curr Opin Struct Biol 2005; 15: 556-562.
126. Grishaev A, Llinas M. Protein structure elucidation from minimal NMR data: the CLOUDS approach. Methods Enzymol 2005; 394: 261-295.
127. Kay LE. NMR studies of protein structure and dynamics. J Magn Reson 2005; 173: 193-207.
128. Snyder DA, Bhattacharya A, Huang YJ, Montelione GT. Assessing precision and accuracy of protein structures derived from NMR data. Proteins 2005; 59: 655-661.
129. Takeuchi K, Wagner G. NMR studies of protein interactions. Curr Opin Struct Biol 2006; 16: 109-117.
130. Wishart D. NMR spectroscopy and protein structure determination: applications to drug discovery and development. Curr Pharm Bio-technol 2005; 6: 105-120.
131. Burkhardt K, Schneider B, Ory J. A biocurator perspective: annotation at the Research Collaboratory for Structural Bioinformatics Protein Data Bank. PLoS Comput Biol 2006; 2: e99.
132. Sussman JL, Abola EE, Lin D, Jiang J, Manning NO, Prilusky J. The protein data bank. Bridging the gap between the sequence and 3D structure world. Genetica 1999; 106: 149-158.
133. Szabadka Z, Grolmusz V. High throughput processing of the structural information in the protein data bank. J Mol Graph Model 2006.
134. Bernstein HJ. Recent changes to RasMol, recombining the variants. Trends Biochem Sci 2000; 25: 453-455.
135. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL. An automated protein homology-modeling server. Nucleic Acids Res 2003; 31: 3381-3385.
136. Dobson PD, Cai YD, Stapley BJ, Doig AJ. Prediction of protein function in the absence of significant sequence similarity. Curr Med Chem 2004; 11: 2135-2142.
137. Godzik A. Fold recognition methods. Methods Biochem Anal 2003; 44: 525-546.
138. Bates PA, Kelley LA, MacCallum RM, Sternberg MJ. Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins 2001; Suppl 5: 39-46.
139. Bourne PE. CASP and CAFASP experiments and their findings. Methods Biochem Anal 2003; 44: 501-507.
140. Falcon H, Crosse A, Donaghy J, et al. CASP and CONSORT. Br Dent J 2006; 201: 130; author reply 130-131.
141. Kryshtafovych A, Venclovas C, Fidelis K, Moult J. Progress over the first decade of CASP experiments. Proteins 2005; 61 Suppl 7: 225-236.
142. Moult J. A decade of CASP. progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol 2005; 15: 285-289.
143. Moult J, Fidelis K, Rost B, Hubbard T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)--round 6. Proteins 2005; 61 Suppl 7: 3-7.
144. Valencia A. Protein refinement: a new challenge for CASP in its 10th anniversary. Bioinformatics 2005; 21: 277.
145. Venclovas C, Zemla A, Fidelis K, Moult J. Assessment of progress over the CASP experiments. Proteins 2003; 53 Suppl 6: 585-595.
146. Chivian D, Kim DE, Malmstrom L, Schonbrun J, Rohl CA, Baker D. Prediction of CASP6 structures using automated Robetta protocols. Proteins 2005; 61 Suppl 7: 157-166.
147. Buzko OV, Bishop AC, Shokat KM. Modified AutoDock for accurate docking of protein kinase inhibitors. J Comput Aided Mol Des 2002; 16: 113-127.
148. Carter P, Lesk VI, Islam SA, Sternberg MJ. Protein-protein docking using 3D-Dock in rounds 3, 4, and 5 of CAPRI. Proteins 2005; 60: 281-288.
149. Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res 2004; 32: W96-99.
150. Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics 2004; 20: 45-50.
151. Fahmy A, Wagner G. TreeDock: a tool for protein docking based on minimizing van der Waals energies. J Am Chem Soc 2002; 124: 1241-1250.
152. Gao Y, Douguet D, Tovchigrechko A, Vakser IA. DOCK-GROUND system of databases for protein recognition studies: unbound structures for docking. Proteins 2007; 69: 845-851.
153. Goto J, Kataoka R, Hirayama N. Ph4Dock: pharmacophore-based protein-ligand docking. J Med Chem 2004; 47: 6804-6811.
154. Grosdidier A, Zoete V, Michielin O. EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins 2007; 67: 1010-1025.
155. Gupta A, Gandhimathi A, Sharma P, Jayaram B. ParDOCK. an all atom energy based Monte Carlo docking protocol for protein-ligand complexes. Protein Pept Lett 2007; 14: 632-646.
156. Lensink MF, Mendez R, Wodak SJ. Docking and scoring protein complexes: CAPRI 3rd Edition. Proteins 2007; 69: 704-718.
157. Martin L, Catherinot V, Labesse G. kinDOCK. a tool for comparative docking of protein kinase ligands. Nucleic Acids Res 2006; 34: W325-329.
158. May A, Zacharias M. Protein-protein docking in CAPRI using ATTRACT to account for global and local flexibility. Proteins 2007; 69: 774-780.
159. Pierce B, Weng Z. ZRANK. reranking protein docking predictions with an optimized energy function. Proteins 2007; 67: 1078-1086.
160. Seifert MH, Schmitt F, Herz T, Kramer B. ProPose: a docking engine based on a fully configurable protein-ligand interaction model. J Mol Model 2004; 10: 342-357.
161. Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein-protein docking. Nucleic Acids Res 2006; 34: W310-314.
162. van Dijk M, van Dijk AD, Hsu V, Boelens R, Bonvin AM. Information-driven protein-DNA docking using HADDOCK. it is a matter of flexibility. Nucleic Acids Res 2006; 34: 3317-3325.
163. Zacharias M. ATTRACT. protein-protein docking in CAPRI using a reduced protein model. Proteins 2005; 60: 252-256.
164. Ritchie DW. Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2. Proteins 2003; 52: 98-106.
165. Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO. mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res 2007; 35: D760-765.
166. Kasai Y, Hashimoto S, Yamada T, et al. 5' SAGE -end Serial Analysis of Gene Expression database. Nucleic Acids Res 2005; 33: D550-552.
167. Parkinson H, Kapushesky M, Shojatalab M, et al. ArrayExpress--a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 2007; 35: D747-750.
168. Tateno Y, Ikeo K. [International public gene expression database (CIBEX) and data submission]. Tanpakushitsu Kakusan Koso 2004; 49: 2678-2683.
169. Ikeo K, Ishi-i J, Tamura T, Gojobori T, Tateno Y. CIBEX. center for information biology gene expression database. C R Biol 2003; 326: 1079-1082.
170. Rocca-Serra P, Brazma A, Parkinson H, Sarkans U, et al. ArrayEx-press: a public database of gene expression data at EBI. C R Biol 2003; 326: 1075-1078.
171. Kodzius R, Matsumura Y, Kasukawa TS, et al. Absolute expression values for mouse transcripts: re-annotation of the READ expression database by the use of CAGE and EST sequence tags. FEBS Lett 2004; 559: 22-26.
172. Xia Y, Campen A, Rigsby D, et al. DGEM--a microarray gene expression database for primary human disease tissues. Mol Diagn Ther 2007; 11: 145-149.
173. Zhang Y, Luoh SM, Hon LS, Baertsch R, Wood WI, Zhang Z. GeneHub-GEPIS. digital expression profiling for normal and cancer tissues based on an integrated gene database. Nucleic Acids Res 2007; 35: W152-158.
174. Smith CM, Finger JH, Hayamizu TF, et al. The mouse Gene Expression Database (GXD): 2007 update. Nucleic Acids Res 2007; 35: D618-623.
175. Jen CH, Manfield IW, Michalopoulos I, et al. The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for mi-croarray-based gene expression analysis. Plant J 2006; 46: 336-348.
176. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA mi-croarray. Science 1995; 270: 467-470.
177. Shalon D, Smith SJ, Brown PO. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 1996; 6: 639-645.
178. Nguyen DV, Arpat AB, Wang N, Carroll RJ. DNA microarray experiments: biological and technological aspects. Biometrics 2002; 58: 701-717.
179. Dai JL. Serial analyses of gene expression (SAGE). Methods Mol Med 2005; 103: 161-174.
180. Knox DP, Skuce PJ. SAGE and the quantitative analysis of gene expression in parasites. Trends Parasitol 2005; 21: 322-326.
181. Kodzius R, Kojima M, Nishiyori H, et al. CAGE. cap analysis of gene expression. Nat Methods 2006; 3: 211-222.
182. Mockler TC, Chan S, Sundaresan A, Chen H, Jacobsen SE, Ecker JR. Applications of DNA tiling arrays for whole-genome analysis. Genomics 2005; 85: 1-15.
183. Tannenbaum E. An RNA-centered view of eukaryotic cells. Biosys-tems 2006; 84: 217-224.
184. Bueno Filho JS, Gilmour SG, Rosa GJ. Design of microarray experiments for genetical genomics studies. Genetics 2006; 174: 945-957.
185. Imbeaud S, Auffray C. 'The 39 steps' in gene expression profiling: critical issues and proposed best practices for microarray experiments. Drug Discov Today 2005; 10: 1175-1182.
186. Blalock EM, Chen KC, Stromberg AJ, et al. Harnessing the power of gene microarrays for the study of brain aging and Alzheimer's disease: statistical reliability and functional correlation. Ageing Res Rev 2005; 4: 481-512.
187. Leung YF, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet 2003; 19: 649-659.
188. Olson NE. The microarray data analysis process: from raw data to biological significance. NeuroRx 2006; 3: 373-383.
189. Pounds S, Cheng C. Statistical development and evaluation of microarray gene expression data filters. J Comput Biol 2005; 12: 482-495.
190. Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol 2006; 411: 352-369.
191. Saeed AI, Bhagabati NK, Braisted JC, et al. TM4 microarray software suite. Methods Enzymol 2006; 411: 134-193.
192. Troein C, Vallon-Christersson J, Saal LH. An introduction to BioArray Software Environment. Methods Enzymol 2006; 411: 99-119.
193. Reimers M, Carey VJ. Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 2006; 411: 119-134.
194. Beer I, Barnea E, Admon A. Centralized data analysis of a large interlaboratory proteomics project: a feasibility study. Proteomics 2005; 5: 3491-3496.
195. Garwood K, Garwood C, Hedeler C, et al. Model-driven user interfaces for bioinformatics data resources: regenerating the wheel as an alternative to reinventing it. BMC Bioinformatics 2006; 7: 532.
196. Garwood K, McLaughlin T, Garwood C, et al. PEDRo: a database for storing, searching and disseminating experimental proteomics data. BMC Genomics 2004; 5: 68.
197. Hill A, Kim H. The UAB Proteomics Database. Bioinformatics 2003; 19: 2149-2151.
198. Kremer A, Schneider R, Terstappen GC. A bioinformatics perspective on proteomics: data storage, analysis, and integration. Biosci Rep 2005; 25: 95-106.
199. Martens L, Hermjakob H, Jones P, et al. PRIDE. the proteomics identifications database. Proteomics 2005; 5: 3537-3545.
200. Martens L, Nesvizhskii AI, Hermjakob H, et al. Do we want our data raw? Including binary mass spectrometry data in public pro-teomics data repositories. Proteomics 2005; 5: 3501-3505.
201. Jones P, Cote RG, Martens L, et al. PRIDE. a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res 2006; 34: D659-663.
202. Desiere F, Deutsch EW, King NL, et al. The PeptideAtlas project. Nucleic Acids Res 2006; 34: D655-658.
203. Beavis RC. Using the global proteome machine for protein identification. Methods Mol Biol 2006; 328: 217-228.
204. Linding R, Jensen LJ, Pasculescu A, et al. NetworKIN. a resource for exploring cellular phosphorylation networks. Nucleic Acids Res 2008, 36: D695-699.
205. Proteomics. Nat Biotechnol 2000; 18 Suppl: IT45-IT46.
206. Aggarwal K, Choe LH, Lee KH. Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomic Proteomic 2006; 5: 112-120.
207. Beynon RJ. The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale. Brief Funct Ge-nomic Proteomic 2005; 3: 382-390.
208. Beynon RJ, Pratt JM. Strategies for measuring dynamics: the temporal component of proteomics. Methods Biochem Anal 2006; 49: 15-25.
209. Celis JE, Kruhoffer M, Gromova I, et al. Gene expression profiling: monitoring transcription and translation products using DNA microarrays and proteomics. FEBS Lett 2000; 480: 2-16.
210. DeVoe DL, Lee CS. Microfluidic technologies for MALDI-MS in proteomics. Electrophoresis 2006; 27: 3559-3568.
211. Elrick MM, Walgren JL, Mitchell MD, Thompson DC. Proteomics: recent applications and new technologies. Basic Clin Pharmacol Toxicol 2006; 98: 432-441.
212. Gevaert K, Vandekerckhove J. Protein identification methods in proteomics. Electrophoresis 2000; 21: 1145-1154.
213. Haoudi A, Bensmail H. Bioinformatics and data mining in pro-teomics. Expert Rev Proteomics 2006; 3: 333-343.
214. Harry JL, Wilkins MR, Herbert BR, Packer NH, Gooley AA, Williams KL. Proteomics: capacity versus utility. Electrophoresis 2000; 21: 1071-1081.
215. Hayter JR, Robertson DH, Gaskell SJ, Beynon RJ. Proteome analysis of intact proteins in complex mixtures. Mol Cell Proteomics 2003; 2: 85-95.
216. Hogan JM, Higdon R, Kolker E. Experimental standards for high-throughput proteomics. Omics 2006; 10: 152-157.
217. Jensen ON. Interpreting the protein language using proteomics. Nat Rev Mol Cell Biol 2006; 7: 391-403.
218. Kellner R. Proteomics. Concepts and perspectives. Fresenius J Anal Chem 2000; 366: 517-524.
219. Nelson RW, Nedelkov D, Tubbs KA. Biosensor chip mass spec-trometry: a chip-based proteomics approach. Electrophoresis 2000; 21: 1155-1163.
220. Smith JC, Figeys D. Proteomics technology in systems biology. Mol Biosyst 2006; 2: 364-370.
221. Suter B, Auerbach D, Stagljar I. Yeast-based functional genomics and proteomics technologies: the first 15 years and beyond. Bio-techniques 2006; 40: 625-644.
222. Topaloglou T. Informatics solutions for high-throughput pro-teomics. Drug Discov Today 2006; 11: 509-516.
223. Uttamchandani M, Wang J, Yao SQ. Protein and small molecule microarrays: powerful tools for high-throughput proteomics. Mol Biosyst 2006; 2: 58-68.
224. Wasinger V. Holistic biology of microorganisms: genomics, tran-scriptomics, and proteomics. Methods Biochem Anal 2006; 49: 3-14.
225. Wittmann-Liebold B, Graack HR, Pohl T. Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry. Proteomics 2006; 6: 4688-4703.
226. Ram RJ, Verberkmoes NC, Thelen MP, et al. Community pro-teomics of a natural microbial biofilm. Science 2005; 308: 1915-1920.
227. McCarthy FM, Burgess SC, van den Berg BH, Koter MD, Pharr GT. Differential detergent fractionation for non-electrophoretic eu-karyote cell proteomics. J Proteome Res 2005; 4: 316-324.
228. Vener AV, Stralfors P. Vectorial proteomics. IUBMB Life 2005; 57: 433-440.
229. Whitelegge JP. Mass spectrometry for high throughput quantitative proteomics in plant research: lessons from thylakoid membranes. Plant Physiol Biochem 2004; 42: 919-927.
230. Doherty MK, McClean L, Edwards I, et al. Protein turnover in chicken skeletal muscle: understanding protein dynamics on a pro-teome-wide scale. Br Poult Sci 2004; 45 Suppl 1: S27-S28.
231. Doherty MK, Whitehead C, McCormack H, Gaskell SJ, Beynon RJ. Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics 2005; 5: 522-533.
232. Artavanis-Tsakonas K, Misaghi S, Comeaux CA, et al. Identification by functional proteomics of a deubiquitinating/deNeddylating enzyme in Plasmodium falciparum. Mol Microbiol 2006; 61: 1187-1195.
233. Camerini S, Polci ML, Bachi A. Proteomics approaches to study the redox state of cysteine-containing proteins. Ann Ist Super Sanita 2005; 41: 451-457.
234. Gruhler A, Olsen JV, Mohammed S, et al. Quantitative phos-phoproteomics applied to the yeast pheromone signaling pathway. Mol Cell Proteomics 2005; 4: 310-327.
235. Hjerrild M, Gammeltoft S. Phosphoproteomics toolbox: computational biology, protein chemistry and mass spectrometry. FEBS Lett 2006; 580: 4764-4770.
236. Julka S, Regnier F. Quantification in proteomics through stable isotope coding: a review. J Proteome Res 2004; 3: 350-363.
237. Kwon SJ, Choi EY, Choi YJ, Ahn JH, Park OK. Proteomics studies of post-translational modifications in plants. J Exp Bot 2006; 57: 1547-1551.
238. Nielsen ML, Savitski MM, Zubarev RA. Extent of modifications in human proteome samples and their effect on dynamic range of analysis in shotgun proteomics. Mol Cell Proteomics 2006; 5: 2384-2391.
239. Stern DF. Phosphoproteomics. Exp Mol Pathol 2001; 70: 327-331.
240. Tannu NS, Hemby SE. Chapter 3 Methods for proteomics in neuro-science. Prog Brain Res 2006; 158: 41-82.
241. Cui Q, Lewis IA, Hegeman AD, et al. Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 2008, 26: 162-164.
242. Cottingham K. New database for human metabolomics. J Proteome Res 2007; 6: 1243.
243. Kotera M, McDonald AG, Boyce S, Tipton KF. Functional group and substructure searching as a tool in metabolomics. PLoS ONE 2008, 3: e1537.
244. Markley JL, Anderson ME, Cui Q, Eghbalnia HR, et al. New bioin-formatics resources for metabolomics. Pac Symp Biocomput 2007: 157-168.
245. Castle AL, Fiehn O, Kaddurah-Daouk R, Lindon JC. Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results. Brief Bioin-form 2006; 7: 159-165.
246. Fiehn O, Kristal B, van Ommen B, et al. Establishing reporting standards for metabolomic and metabonomic studies: a call for participation. Omics 2006; 10: 158-163.
247. Jenkins H, Hardy N, Beckmann M, et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 2004; 22: 1601-1606.
248. Lindon JC, Holmes E, Nicholson JK. So what's the deal with me-tabonomics? Anal Chem 2003; 75: 384A-391A.
249. Lindon JC, Nicholson JK, Holmes, et al. Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 2005; 23: 833-838.
250. Kell DB. Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 2004; 7: 296-307.
251. Fiehn O. Metabolomics--the link between genotypes and pheno-types. Plant Mol Biol 2002; 48: 155-171.
252. Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spec-troscopic data. Xenobiotica 1999; 29: 1181-1189.
253. Brown JA, Sherlock G, Myers CL, et al. Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol Syst Biol 2006; 2: 2006 0001.
254. Brown SC, Kruppa G, Dasseux JL. Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrom Rev 2005; 24: 223-231.
255. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 2004; 22: 245-252.
256. Griffin JL. The Cinderella story of metabolic profiling: does me-tabolomics get to go to the functional genomics ball? Philos Trans R Soc Lond B Biol Sci 2006; 361: 147-161.
257. Grivet JP, Delort AM, Portais JC. NMR and microbiology: from physiology to metabolomics. Biochimie 2003; 85: 823-840.
258. Hollywood K, Brison DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics 2006; 6: 4716-4723.
259. Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief Bioinform 2006; 7: 140-150.
260. Reo NV. NMR-based metabolomics. Drug Chem Toxicol 2002; 25: 375-382.
261. Rochfort S. Metabolomics reviewed: a new omics platform technology for systems biology and implications for natural products research. J Nat Prod 2005; 68: 1813-1820.
262. Sanford K, Soucaille P, Whited G, Chotani G. Genomics to fluxomics and physiomics - pathway engineering. Curr Opin Mi-crobiol 2002; 5: 318-322.
263. Shulaev V. Metabolomics technology and bioinformatics. Brief Bioinform 2006; 7: 128-139.
264. van der Werf MJ, Jellema RH, Hankemeier T. Microbial me-tabolomics: replacing trial-and-error by the unbiased selection and ranking of targets. J Ind Microbiol Biotechnol 2005; 32: 234-252.
265. Wang L, Birol I, Hatzimanikatis V. Metabolic control analysis under uncertainty: framework development and case studies. Bio-phys J 2004; 87: 3750-3763.
266. Wang M, Lamers RJ, Korthout HA, et al. Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. Phytother Res 2005; 19: 173-182.
267. Wang QZ, Wu CY, Chen T, Chen X, Zhao XM. Integrating me-tabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms. Appl Microbiol Biotechnol 2006; 70: 151-161.
268. Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol 2003; 54: 669-689.
269. Weckwerth W, Morgenthal K. Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today 2005; 10: 1551-1558.
270. Morgenthal K, Weckwerth W, Steuer R. Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. Biosystems 2006; 83: 108-117.
271. Morgenthal K, Wienkoop S, Wolschin F, Weckwerth W. Integra-tive profiling of metabolites and proteins: improving pattern recognition and biomarker selection for systems level approaches. Methods Mol Biol 2007; 358: 57-75.
272. Holmes E, Antti H. Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst 2002; 127: 1549-1557.
273. Vance W, Arkin A, Ross J. Determination of causal connectivities of species in reaction networks. Proc Natl Acad Sci USA 2002; 99: 5816-5821.
274. Marino S, Voit EO. An automated procedure for the extraction of metabolic network information from time series data. J Bioinform Comput Biol 2006; 4: 665-691.
275. Voit EO, Almeida J, Marino S, et al. Regulation of glycolysis in Lactococcus lactis: an unfinished systems biological case study. Syst Biol (Stevenage) 2006; 153: 286-298.
276. Chou IC, Martens H, Voit EO. Parameter estimation in biochemical systems models with alternating regression. Theor Biol Med Model 2006; 3: 25.
277. Voit EO, Almeida J. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 2004; 20: 1670-1681.
278. Voit EO, Marino S, Lall R. Challenges for the identification of biological systems from in vivo time series data. In Silico Biol 2005; 5: 83-92.
279. Gerdes SY, Scholle MD, Campbell JW, et al. Experimental determination and system level analysis of essential genes in Es-cherichia coli MG1655. J Bacteriol 2003; 185: 5673-5684.
280. Tong AH, Evangelista M, Parsons AB, et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 2001; 294: 2364-2368.
281. Rodriguez-Manzaneque MT, Tamarit J, Belli G, Ros J, Herrero E. Grx5 is a mitochondrial glutaredoxin required for the activity of iron/sulfur enzymes. Mol Biol Cell 2002; 13: 1109-1121.
282. Fernandez-Ricaud L, Warringer J, Ericson E, et al. PROPHECY--a database for high-resolution phenomics. Nucleic Acids Res 2005; 33: D369-373.
283. Buckholz RG, Simmons CA, Stuart JM, Weiner MP. Automation of yeast two-hybrid screening. J Mol Microbiol Biotechnol 1999; 1: 135-140.
284. Ding X, Zhang Y, Song WY. Use of rolling-circle amplification for large-scale yeast two-hybrid analyses. Methods Mol Biol 2006; 354: 85-98.
285. Fields S. High-throughput two-hybrid analysis. The promise and the peril. FEBS J 2005; 272: 5391-5399.
286. Parrish JR, Gulyas KD, Finley RL, Jr. Yeast two-hybrid contributions to interactome mapping. Curr Opin Biotechnol 2006; 17: 387-393.
287. Gavin AC, Bosche M, Krause R, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002; 415: 141-147.
288. Ito T, Chiba T, Yoshida M. Exploring the protein interactome using comprehensive two-hybrid projects. Trends Biotechnol 2001; 19: S23-27.
289. Ito T, Ota K, Kubota H, et al. Roles for the two-hybrid system in exploration of the yeast protein interactome. Mol Cell Proteomics 2002; 1: 561-566.
290. Rain JC, Selig L, De Reuse H, et al. The protein-protein interaction map of Helicobacter pylori. Nature 2001; 409: 211-215.
291. Starling AL, Ortega JM, Gollob KJ, et al. Evaluation of alternative reporter genes for the yeast two-hybrid system. Genet Mol Res 2003; 2: 124-135.
292. Stelzl U, Worm U, Lalowski M, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 2005; 122: 957-968.
293. Uetz P, Hughes RE. Systematic and large-scale two-hybrid screens. Curr Opin Microbiol 2000; 3: 303-308.
294. Walhout AJ, Boulton SJ, Vidal M. Yeast two-hybrid systems and protein interaction mapping projects for yeast and worm. Yeast 2000; 17: 88-94.
295. Walhout AJ, Sordella R, Lu X, et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 2000; 287: 116-122.
296. Walhout AJ, Vidal M. High-throughput yeast two-hybrid assays for large-scale protein interaction mapping. Methods 2001; 24: 297-306.
297. Ho Y, Gruhler A, Heilbut A, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spec-trometry. Nature 2002; 415: 180-183.
298. Santonico E, Castagnoli L, Cesareni G. Methods to reveal domain networks. Drug Discov Today 2005; 10: 1111-1117.
299. Izarzugaza JM, Juan D, Pons C, Ranea JA, Valencia A, Pazos F. TSEMA. interactive prediction of protein pairings between interacting families. Nucleic Acids Res 2006; 34: W315-319.
300. Juan D, Pazos F, Valencia A. High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc Natl Acad Sci USA 2008, 105: 934-939.
301. Pazos F, Rausell A, Valencia A. Phylogeny-independent detection of functional residues. Bioinformatics 2006; 22: 1440-1448.
302. Pazos F, Valencia A. In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins 2002; 47: 219-227.
303. Gilbert D. Biomolecular interaction network database. Brief Bioin-form 2005; 6: 194-198.
304. Koike A, Takagi T. PRIME. automatically extracted PRotein Interactions and Molecular Information databasE. In Silico Biol 2005; 5: 9-20.
305. Pagel P, Kovac S, Oesterheld M, et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 2005; 21: 832-834.
306. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 2002; 30: 303-305.
307. Chatr-aryamontri A, Ceol A, Palazzi LM, et al. MINT. the Molecular INTeraction database. Nucleic Acids Res 2007; 35: D572-574.
308. Misra RV, Horler RS, Reindl W, Goryanin, II, Thomas GH. Echo-BASE. an integrated post-genomic database for Escherichia coli. Nucleic Acids Res 2005; 33: D329-333.
309. Buck MJ, Lieb JD. ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipita-tion experiments. Genomics 2004; 83: 349-360.
310. Wu J, Smith LT, Plass C, Huang TH. ChIP-chip comes of age for genome-wide functional analysis. Cancer Res 2006; 66: 6899-6902.
311. Fujii S. Database and structural characterization of intermolecular interactions in nucleic acid and protein complex. Nucleic Acids Symp Ser (Oxf) 2004: 127-128.
312. Gong S, Yoon G, Jang I, Bolser D, et al. PSIbase: a database of Protein Structural Interactome map (PSIMAP). Bioinformatics 2005; 21: 2541-2543.
313. Hoffman MM, Khrapov MA, Cox JC, Yao J, Tong L, Ellington AD. AANT. the Amino Acid-Nucleotide Interaction Database. Nucleic Acids Res 2004; 32: D174-181.
314. Wu T, Wang J, Liu C, Zhang Y, et al. NPInter: the noncoding RNAs and protein related biomacromolecules interaction database. Nucleic Acids Res 2006; 34: D150-152.
315. Mak HC, Daly M, Gruebel B, Ideker T. CellCircuits: a database of protein network models. Nucleic Acids Res 2007; 35: D538-545.
316. [IUPAC-IUB Commission on Biochemical Nomenclature (CBN). Nomenclature of multiple enzyme types. Recommendations 1971]. Hoppe Seylers Z Physiol Chem 1972, 353: 852-854.
317. Sabatti C, Rohlin L, Lange K, Liao JC. Vocabulon: a dictionary model approach for reconstruction and localization of transcription factor binding sites. Bioinformatics 2005; 21: 922-931.
318. Schomburg I, Chang A, Hofmann O, Ebeling C, Ehrentreich F, Schomburg D. BRENDA. a resource for enzyme data and metabolic information. Trends Biochem Sci 2002; 27: 54-56.
319. Schomburg I, Chang A, Schomburg D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res 2002; 30: 47-49.
320. Ji ZL, Chen X, Zhen CJ, et al. KDBI. Kinetic Data of Bio-molecular Interactions database. Nucleic Acids Res 2003; 31: 255-257.
321. Nagano N. EzCatDB. the Enzyme Catalytic-mechanism Database. Nucleic Acids Res 2005; 33: D407-412.
322. Nagano N, Noguchi T, Akiyama Y. Systematic comparison of catalytic mechanisms of hydrolysis and transfer reactions classified in the EzCatDB database. Proteins 2007; 66: 147-159.
323. Holliday GL, Almonacid DE, Bartlett GJ, O'Boyle N M, Torrance JW, Murray-Rust P, Mitchell JB, Thornton JM. MACiE (Mechanism, Annotation and Classification in Enzymes): novel tools for searching catalytic mechanisms. Nucleic Acids Res 2006.
324. Holliday GL, Bartlett GJ, Almonacid DE, et al. MACiE. a database of enzyme reaction mechanisms. Bioinformatics 2005; 21: 4315-4316.
325. Kumar MD, Bava KA, Gromiha MM, et al. ProTherm and ProNIT. thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res 2006; 34: D204-D206.
326. Rigali S, Schlicht M, Hoskisson P, et al. Extending the classification of bacterial transcription factors beyond the helix-turn-helix motif as an alternative approach to discover new cis/trans relationships. Nucleic Acids Res 2004; 32: 3418-3426.
327. Stegmaier P, Kel AE, Wingender E. Systematic DNA-binding domain classification of transcription factors. Genome Inform 2004; 15: 276-286.
328. Chen M, Lin S, Hofestaedt R. STCDB. Signal Transduction Classification Database. Nucleic Acids Res 2004; 32: D456-458.
329. Saier MH, Jr.: A functional-phylogenetic system for the classification of transport proteins. J Cell Biochem 1999; Suppl 32-33: 84-94.
330. Tamura M, Hendrix DK, Klosterman PS, Schimmelman NR, Brenner SE, Holbrook SR. SCOR. Structural Classification of RNA, version 2.0. Nucleic Acids Res 2004; 32: D182-184.
331. von Grotthuss M, Koczyk G, Pas J, Wyrwicz LS, Rychlewski L. Ligand.Info small-molecule Meta-Database. Comb Chem High Throughput Screen 2004; 7: 757-761.
332. Ceroni A, Costa F, Frasconi P. Classification of small molecules by two- and three-dimensional decomposition kernels. Bioinformatics 2007; 23: 2038-2045.
333. Selkov E, Basmanova S, Gaasterland T, et al. The metabolic pathway collection from EMP. the enzymes and metabolic pathways database. Nucleic Acids Res 1996; 24: 26-28.
334. Campagne F, Neves S, Chang CW, Skrabanek L, Ram PT, Iyengar R, Weinstein H. Quantitative information management for the biochemical computation of cellular networks. Sci STKE 2004; pl11.
335. Keseler IM, Collado-Vides J, Gama-Castro S, et al. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res 2005; 33: D334-337.
336. Le Novere N. Model storage, exchange and integration. BMC Neu-rosci 2006; 7 Suppl 1: S11.
337. Le Novere N, Bornstein B, Broicher A, et al. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 2006; 34: D689-691.
338. Le Novere N, Finney A, Hucka M, et al. Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 2005; 23: 1509-1515.
339. Olivier BG, Snoep JL. Web-based kinetic modelling using JWS Online. Bioinformatics 2004; 20: 2143-2144.
340. Salgado H, Gama-Castro S, Martinez-Antonio A, et al. RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12. Nucleic Acids Res 2004; 32: D303-D306.
341. Sivakumaran S, Hariharaputran S, Mishra J, Bhalla US. The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks. Bioinformatics 2003; 19: 408-415.
342. Hucka M, Finney A, Bornstein BJ, et al. Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Syst Biol (Stevenage) 2004; 1: 41-53.
343. Hucka M, Finney A, Sauro HM, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003; 19: 524-531.
344. Lloyd CM, Halstead MD, Nielsen PF. CellML. its future, present and past. Prog Biophys Mol Biol 2004; 85: 433-450.
345. Stromback L, Lambrix P. Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics 2005; 21: 4401-4407.
346. Cao Y, Gillespie D, Petzold L. Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems. J Comput Physics 2005; 206: 395-411.
347. Cao Y, Gillespie DT, Petzold LR. Accelerated stochastic simulation of the stiff enzyme-substrate reaction. J Chem Phys 2005; 123: 144917.
348. Cao Y, Gillespie DT, Petzold LR. The slow-scale stochastic simulation algorithm. J Chem Phys 2005; 122: 14116.
349. Cao Y, Li H, Petzold L. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J Chem Phys 2004; 121: 4059-4067.
350. Machne R, Finney A, Muller S, Lu J, Widder S, Flamm C. The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks. Bioinformatics 2006; 22: 1406-1407.
351. Schmidt H, Jirstrand M. Systems Biology Toolbox for MATLAB. a computational platform for research in systems biology. Bioinfor-matics 2006; 22: 514-515.
352. Zi Z, Klipp E. SBML-PET. a Systems Biology Markup Language-based parameter estimation tool. Bioinformatics 2006; 22: 2704-2705.
353. Mulholland AJ. Modelling enzyme reaction mechanisms, specificity and catalysis. Drug Discov Today 2005; 10: 1393-1402.
354. Urbanc B, Borreguero JM, Cruz L, Stanley HE. Ab initio discrete molecular dynamics approach to protein folding and aggregation. Methods Enzymol 2006; 412: 314-338.
355. Alvarez-Vasquez F, Sims KJ, Cowart LA, Okamoto Y, Voit EO, Hannun YA. Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae. Nature 2005; 433: 425-430.
356. Veflingstad SR, Almeida J, Voit EO. Priming nonlinear searches for pathway identification. Theor Biol Med Model 2004; 1: 8.
357. Kaufman M, Thomas R. Model analysis of the bases of multista-tionarity in the humoral immune response. J Theor Biol 1987, 129: 141-162.
358. Kaufman M, Thomas R. Emergence of complex behaviour from simple circuit structures. C R Biol 2003; 326: 205-214.
359. Thieffry D, Thomas R. Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda. Bull Math Biol 1995; 57: 277-297.
360. Schilling CH, Covert MW, Famili I, Church GM, Edwards JS, Palsson BO. Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol 2002; 184: 4582-4593.
361. Schilling CH, Edwards JS, Letscher D, Palsson BO. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol Bioeng 2000; 71: 286-306.
362. Schilling CH, Palsson BO. The underlying pathway structure of biochemical reaction networks. Proc Natl Acad Sci USA 1998; 95: 4193-4198.
363. Thieffry D, Thomas R. Qualitative analysis of gene networks. Pac Symp Biocomput 1998: 77-88.
364. Sorribas A, Hernandez-Bermejo B, Vilaprinyo E, Alves R. Coop-erativity and saturation in biochemical networks: A saturable formalism using Taylor series approximations. Biotechnol Bioeng 2006.
365. Frasch HF, Barbero AM. Steady-state flux and lag time in the stratum corneum lipid pathway: results from finite element models. J Pharm Sci 2003; 92: 2196-2207.
366. Barrett CL, Kim TY, Kim HU, Palsson BO, Lee SY. Systems biology as a foundation for genome-scale synthetic biology. Curr Opin Biotechnol 2006; 17: 488-492.
367. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2007; 2: 727-738.
368. Becker SA, Palsson BO. Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol 2005; 5: 8.
369. Becker SA, Price ND, Palsson BO. Metabolite coupling in genome-scale metabolic networks. BMC Bioinformatics 2006; 7: 111.
370. Duarte NC, Becker SA, Jamshidi N, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007; 104: 1777-1782.
371. Duarte NC, Herrgard MJ, Palsson BO. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 2004; 14: 1298-1309.
372. Duarte NC, Palsson BO, Fu P. Integrated analysis of metabolic phenotypes in Saccharomyces cerevisiae. BMC Genomics 2004; 5: 63.
373. Herrgard MJ, Covert MW, Palsson BO. Reconciling gene expression data with known genome-scale regulatory network structures. Genome Res 2003; 13: 2423-2434.
374. Herrgard MJ, Fong SS, Palsson BO. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol 2006; 2: e72.
375. Herrgard MJ, Lee BS, Portnoy V, Palsson BO. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res 2006; 16: 627-635.
376. Joyce AR, Palsson BO. The model organism as a system: integrating 'omics' data sets. Nat Rev Mol Cell Biol 2006; 7: 198-210.
377. Joyce AR, Palsson BO. Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach. Prog Drug Res 2007; 64: 265, 267-309.
378. Joyce AR, Reed JL, White A, Edwards R, et al. Experimental and computational assessment of conditionally essential genes in Es-cherichia coli. J Bacteriol 2006; 188: 8259-8271.
379. Price ND, Papin JA, Schilling CH, Palsson BO. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 2003; 21: 162-169.
380. Reed JL, Palsson BO. Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 2004; 14: 1797-1805.
381. Reed JL, Patel TR, Chen KH, et al. Systems approach to refining genome annotation. Proc Natl Acad Sci USA 2006; 103: 17480-17484.
382. Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physico-chemical modelling of cell signalling pathways. Nat Cell Biol 2006; 8: 1195-1203.
383. Jahnke T, Huisinga W. Solving the chemical master equation for monomolecular reaction systems analytically. J Mathematical Biol 2007; 54: 1-26.
384. Munsky B, Khammash M. The finite state projection algorithm for the solution of the chemical master equation. J Chem Phys 2006; 124.
385. Qian H. Open-system nonequilibrium steady state: Statistical thermodynamics, fluctuations, and chemical oscillations. J Phys Chem B 2006; 110: 15063-15074.
386. Rao CV, Arkin AP. Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm. Journal of Chemical Physics 2003; 118: 4999-5010.
387. Chance B, Greenstein DS, Higgins J, Yang CC. The mechanism of catalase action. II. Electric analog computer studies. Arch Biochem 1952, 37: 322-339.
388. Chance B, Greenstein DS, Roughton FJ. The mechanism of cata-lase action. I. Steady-state analysis. Arch Biochem 1952, 37: 301-321.
389. Michaelis L, Menten M. Die Kinetik der Invertinwirkung. Biochem Z 1913, 49: 333-369.
390. Savageau MA. Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol 1969, 25: 370-379.
391. Savageau MA. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol 1969, 25: 365-369.
392. Savageau MA. Biochemical systems analysis. 3. Dynamic solutions using a power-law approximation. J Theor Biol 1970, 26: 215-226.
393. Hlavacek WS, Savageau MA. Subunit structure of regulator proteins influences the design of gene circuitry: analysis of perfectly coupled and completely uncoupled circuits. J Mol Biol 1995; 248: 739-755.
394. Hlavacek WS, Savageau MA. Rules for coupled expression of regulator and effector genes in inducible circuits. J Mol Biol 1996; 255: 121-139.
395. Irvine DH, Savageau MA. Network regulation of the immune response: alternative control points for suppressor modulation of effector lymphocytes. J Immunol 1985, 134: 2100-2116.
396. Irvine DH, Savageau MA. Network regulation of the immune response: modulation of suppressor lymphocytes by alternative signals including contrasuppression. J Immunol 1985, 134: 2117-2130.
397. Savageau MA. Comparison of classical and autogenous systems of regulation in inducible operons. Nature 1974, 252: 546-549.
398. Savageau MA. Optimal design of feedback control by inhibition. J Mol Evol 1974, 4: 139-156.
399. Savageau MA. Optimal design of feedback control by inhibition: dynamic considerations. J Mol Evol 1975, 5: 199-222.
400. Savageau MA. Mathematics of organizationally complex systems. Biomed Biochim Acta 1985, 44: 839-844.
401. Savageau MA. A theory of alternative designs for biochemical control systems. Biomed Biochim Acta 1985, 44: 875-880.
402. Savageau MA. The challenge of reconstruction. New Biol 1991; 3: 101-102.
403. Savageau MA. Reconstructionist molecular biology. New Biol 1991; 3: 190-197.
404. Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics 1998; 149: 1665-1676.
405. Savageau MA. Demand theory of gene regulation. II. Quantitative application to the lactose and maltose operons of Escherichia coli. Genetics 1998; 149: 1677-1691.
406. Savageau MA. Design principles for elementary gene circuits: Elements, methods, and examples. Chaos 2001; 11: 142-159.
407. Visser D, Heijnen JJ. The mathematics of metabolic control analysis revisited. Metab Eng 2002; 4: 114-123.
408. Visser D, Heijnen JJ. Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. Metab Eng 2003; 5: 164-176.
409. Heijnen JJ. Approximative kinetic formats used in metabolic network modeling. Biotechnol Bioeng 2005; 91: 534-545.
410. Alves R, Vilaprinyo E, Hernandez-Bermejo B, Sorribas A. Mathematical formalisms based on approximated kinetic representations for modeling genetic and metabolic pathways. Biotechnol Genet Eng Rev 2008, 25: submitted.
411. Liebermeister W, Klipp E. Bringing metabolic networks to life: convenience rate law and thermodynamic constraints. Theor Biol Med Model 2006; 3: 41.