B. Alberts, Biologie moléculaire de la cellule. Flammarion, 2004.

N. Alon, R. Yuster, and U. Zwick, Color-coding, Proceedings of the twenty-sixth annual ACM symposium on Theory of computing , STOC '94, pp.326-335, 1994.
DOI : 10.1145/195058.195179

S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, Basic local alignment search tool, Journal of Molecular Biology, vol.215, issue.3, pp.403-410, 1990.
DOI : 10.1016/S0022-2836(05)80360-2

S. Angibaud, Comparaisons de génomes avec gènes dupliqués : étude théorique et algorithmes, Thèse de doctorat, 2009.

S. Angibaud, P. Bordron, D. Eveillard, and G. Fertin, RUSU : Integration of omics data to investigate common intervals, Proceedings of the 1st International Conference on Bioscience, pp.101-105, 2011.

S. Angibaud, D. Eveillard, G. Fertin, and I. Rusu, Comparing Bacterial Genomes by Searching Their Common Intervals, Proceedings of the 1st Bioinformatics and Computational Biology conference, pp.102-113, 2009.
DOI : 10.1073/pnas.95.12.6578

URL : https://hal.archives-ouvertes.fr/hal-00416466

S. Angibaud, G. Fertin, I. Rusu, A. Thévenin, and S. Vialette, Efficient Tools for Computing the Number of Breakpoints and the Number of Adjacencies between Two Genomes with Duplicate Genes, Journal of Computational Biology, vol.15, issue.8, pp.1093-1115, 2008.
DOI : 10.1089/cmb.2008.0061

URL : https://hal.archives-ouvertes.fr/hal-00416446

S. Angibaud, G. Fertin, and I. Rusu, A Pseudo-Boolean Framework for Computing Rearrangement Distances between Genomes with Duplicates, Journal of Computational Biology, vol.14, issue.4, pp.379-393, 2007.
DOI : 10.1089/cmb.2007.A001

M. Arita, The metabolic world of Escherichia coli is not small, Proceedings of the National Academy of Sciences, vol.101, issue.6, pp.1543-1547, 2004.
DOI : 10.1073/pnas.0306458101

T. Baba, T. Ara, M. Hasegawa, Y. Takai, Y. Okumura et al., Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection, Molecular Systems Biology, vol.170, issue.1, pp.2006-2014, 2006.
DOI : 10.1038/msb4100050

T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev et al., NCBI GEO: archive for high-throughput functional genomic data, Nucleic Acids Research, vol.37, issue.Database, pp.885-890, 2009.
DOI : 10.1093/nar/gkn764

URL : http://doi.org/10.1093/nar/gkn764

Z. Barutcuoglu, R. E. Schapire, and O. G. , Hierarchical multi-label prediction of gene function, Bioinformatics, vol.22, issue.7, pp.830-836, 2006.
DOI : 10.1093/bioinformatics/btk048

O. Bénichou, C. Loverdo, and R. Voituriez, How gene colocalization can be optimized by tuning the diffusion constant of transcription factors, EPL (Europhysics Letters), vol.84, issue.3, pp.1-3, 2008.
DOI : 10.1209/0295-5075/84/38003

A. Bergeron and J. Stoye, On the Similarity of Sets of Permutations and Its Applications to Genome Comparison, Journal of Computational Biology, vol.13, issue.7, pp.1340-1354, 2006.
DOI : 10.1089/cmb.2006.13.1340

F. R. Blattner, G. Plunkett, C. A. Bloch, N. T. Perna, V. Burland et al., The Complete Genome Sequence of Escherichia coli K-12, Science, vol.277, issue.5331, pp.2771453-1462, 1997.
DOI : 10.1126/science.277.5331.1453

P. Bordron, D. Eveillard, and I. Rusu, Integrated analysis of the gene neighbouring impact on bacterial metabolic networks, IET Systems Biology, vol.5, issue.4, pp.261-268, 2011.
DOI : 10.1049/iet-syb.2010.0070

URL : https://hal.archives-ouvertes.fr/hal-00626709

P. Bordron, D. Eveillard, and I. Rusu, SIPPER: A flexible method to integrate heterogeneous data into a metabolic network, 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp.40-45, 2011.
DOI : 10.1109/ICCABS.2011.5729937

URL : https://hal.archives-ouvertes.fr/hal-00585102

P. E. Bourne, The Gene Ontology's Reference Genome Project: A Unified Framework for Functional Annotation across Species, PLoS Computational Biology, vol.25, issue.7, p.1000431, 2009.
DOI : 10.1371/journal.pcbi.1000431.t001

F. Boyer, A. Morgat, L. Labarre, and J. Pothier, Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data, Bioinformatics, vol.21, issue.23, pp.4209-4215, 2005.
DOI : 10.1093/bioinformatics/bti711

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/21/23/4209

E. I. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein et al., GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes, Bioinformatics, vol.20, issue.18, pp.203710-3715, 2004.
DOI : 10.1093/bioinformatics/bth456

R. W. Brouwer, O. P. Kuipers, S. A. Van, and H. , The relative value of operon predictions, Briefings in Bioinformatics, vol.9, issue.5, pp.367-375, 2008.
DOI : 10.1093/bib/bbn019

M. R. Carlson, B. Zhang, Z. Fang, P. S. Mischel, S. Horvath et al., Gene connectivity, function, and sequence conservation : predictions from modular yeast co-expression networks, BMC Genomics, vol.7, issue.1, p.40, 2006.
DOI : 10.1186/1471-2164-7-40

R. Caspi, H. Foerster, C. A. Fulcher, P. Kaipa, M. Krummenacker et al., The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway, Genome Databases. Nucleic Acids Research, pp.36-623, 2008.

C. Chauve, G. Fertin, R. Rizzi, and S. Vialette, Genomes Containing Duplicates Are Hard to Compare, Proceedings of the International Workshop on Bioinformatics Research and Applications, 2006.
DOI : 10.1007/11758525_105

URL : https://hal.archives-ouvertes.fr/hal-00418260

D. Che, G. Li, F. Mao, H. Wu, and Y. Xu, Detecting uber-operons in prokaryotic genomes, Nucleic Acids Research, vol.34, issue.8, pp.2418-2427, 2006.
DOI : 10.1093/nar/gkl294

L. Chuang, J. Tsai, and C. Yang, Binary particle swarm optimization for operon prediction, Nucleic Acids Research, vol.38, issue.12, p.128, 2010.
DOI : 10.1093/nar/gkq204

URL : http://doi.org/10.1093/nar/gkq204

R. A. Clayton, O. White, K. A. Ketchum, and J. C. , VENTER : The first genome from the third domain of life, Nature, issue.6632, pp.387459-462, 1997.

A. Cornish-bowden and M. L. Cárdenas, Information transfer in metabolic pathways, European Journal of Biochemistry, vol.51, issue.24, pp.6616-6624, 2001.
DOI : 10.1046/j.0014-2956.2001.02616.x

D. Croes, F. Couche, S. J. Wodak, J. Van, H. Croes et al., Metabolic PathFinding: inferring relevant pathways in biochemical networks, Nucleic Acids Research, vol.33, issue.Web Server, pp.326-330222, 2005.
DOI : 10.1093/nar/gki437

URL : http://doi.org/10.1093/nar/gki437

G. Del, R. , D. Koschützki, and G. Coello, How to identify essential genes from molecular networks?, BMC Systems Biology, vol.3, issue.1, p.102, 2009.
DOI : 10.1186/1752-0509-3-102

M. Demerec and P. E. Hartman, Complex Loci in Microorganisms, Annual Review of Microbiology, vol.13, issue.1, pp.377-406, 1959.
DOI : 10.1146/annurev.mi.13.100159.002113

C. Duin, A. Volgenant, and S. Vofl, Solving group Steiner problems as Steiner problems, European Journal of Operational Research, vol.154, issue.1, pp.323-329, 2004.
DOI : 10.1016/S0377-2217(02)00707-5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. Edgar, M. Domrachev, and A. E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository, Nucleic Acids Research, vol.30, issue.1, pp.207-210, 2002.
DOI : 10.1093/nar/30.1.207

URL : http://doi.org/10.1093/nar/30.1.207

J. S. Edwards and B. Ø. Palsson, Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions, BMC Bioinformatics, vol.1, issue.11, 2000.

G. Fang, E. P. Rocha, and A. , How Essential Are Nonessential Genes?, Molecular Biology and Evolution, vol.22, issue.11, pp.2147-2156, 2005.
DOI : 10.1093/molbev/msi211

K. Faust, P. Dupont, J. Callut, J. Van, and H. , Pathway discovery in metabolic networks by subgraph extraction, Bioinformatics, vol.26, issue.9, pp.1211-1218, 2010.
DOI : 10.1093/bioinformatics/btq105

G. Fertin, A. Labarre, I. Rusu, E. Tannier, and S. Vialette, Combinatorics of genome rearrangements, 2009.
DOI : 10.7551/mitpress/9780262062824.001.0001

URL : https://hal.archives-ouvertes.fr/hal-00416453

J. Figueira, S. Greco, and M. Ehrgott, Multiple Criteria Decision Analysis : State of the Art Surveys, 2005.
DOI : 10.1007/b100605

R. D. Fleischmann, M. D. Adams, O. White, R. A. Clayton, E. F. Kirkness et al., Whole-genome random sequencing and assembly of Haemophilus influenzae Rd, Science, vol.269, issue.5223, pp.269496-512, 1995.
DOI : 10.1126/science.7542800

J. Gagneur and D. B. Jackson, Hierarchical analysis of dependency in metabolic networks, Bioinformatics, vol.19, issue.8, pp.1027-1034, 2003.
DOI : 10.1093/bioinformatics/btg115

M. Y. Galperin and E. V. Koonin, Who's your neighbor? New computational approaches for functional genomics, Nature Biotechnology, vol.14, issue.6, pp.609-613, 2000.
DOI : 10.1038/76443

A. L. Guedes, MARKENZON : Directed Hypergraph Planarity, Pesquisa Operacional, vol.25, issue.3, pp.383-390, 2005.

M. Hashimoto, T. Ichimura, H. Mizoguchi, K. Tanaka, K. Fujimitsu et al., Cell size and nucleoid organization of engineered Escherichia coli cells with a reduced genome, Molecular Microbiology, vol.145, issue.1, pp.137-149, 2005.
DOI : 10.1111/j.1365-2958.2004.04386.x

R. Hassin, Approximation Schemes for the Restricted Shortest Path Problem, Mathematics of Operations Research, vol.17, issue.1, pp.36-42, 1992.
DOI : 10.1287/moor.17.1.36

M. Hattori, Y. Okuno, S. Goto, and M. Kanehisa, Development of a Chemical Structure Comparison Method for Integrated Analysis of Chemical and Genomic Information in the Metabolic Pathways, Journal of the American Chemical Society, vol.125, issue.39, pp.11853-11865, 2003.
DOI : 10.1021/ja036030u

A. B. Horne, T. C. Hodgman, H. D. Spence, and A. R. , Constructing an enzyme-centric view of metabolism, Bioinformatics, vol.20, issue.13, pp.2050-2055, 2004.
DOI : 10.1093/bioinformatics/bth199

F. Jacob, D. Perrin, and C. Sánchez, MONOD : L'opéron : groupe de gènes à expression coordonnée par un opérateur, Comptes rendus hebdomadaires des séances de l'Académie des sciences, pp.1727-1729, 1960.

H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A. Barabási, The large-scale organization of metabolic networks, Nature, issue.6804, pp.407651-407655, 2000.

A. R. Joyce, J. L. Reed, A. White, R. Edwards, A. L. Osterman et al., AGARWALLA : Experimental and computational assessment of conditionally essential genes in escherichia coli, Journal of Bacteriology, issue.23, pp.1888259-8271, 2006.

M. Kanehisa, M. Araki, S. Goto, M. Hattori, M. Hirakawa et al., KEGG for linking genomes to life and the environment, Nucleic Acids Research, vol.36, issue.Database, pp.480-484, 2008.
DOI : 10.1093/nar/gkm882

M. Kanehisa and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, vol.28, issue.1, pp.27-30, 2000.
DOI : 10.1093/nar/28.1.27

M. Kanehisa, S. Goto, S. Kawashima, and A. Nakaya, The KEGG databases at GenomeNet, Nucleic Acids Research, vol.30, issue.1, pp.42-46, 2002.
DOI : 10.1093/nar/30.1.42

J. Kato and M. Hashimoto, Construction of consecutive deletions of the Escherichia coli chromosome, Molecular Systems Biology, vol.20, issue.1, 2007.
DOI : 10.1038/msb4100174

I. M. Keseler, C. Bonavides-martínez, J. Collado-vides, S. Gama-castro, R. P. Gunsalus et al., EcoCyc: A comprehensive view of Escherichia coli biology, Nucleic Acids Research, vol.37, issue.Database, pp.464-470, 2009.
DOI : 10.1093/nar/gkn751

H. Kitano, Biological robustness, Nature Reviews Genetics, vol.37, issue.11, pp.826-863, 2004.
DOI : 10.1038/nrg1471

S. Klamt and E. D. Gilles, Minimal cut sets in biochemical reaction networks, Bioinformatics, vol.20, issue.2, pp.226-234, 2004.
DOI : 10.1093/bioinformatics/btg395

P. Kolman, O. K. Pangrac, L. D. Kovács, B. Hurst, J. G. Papp et al., On the complexity of paths avoiding forbidden pairs, Discrete Applied Mathematics, vol.157, issue.13, pp.2871-2876, 2009.
DOI : 10.1016/j.dam.2009.03.018

N. C. Kyrpides, Genomes OnLine Database (GOLD 1.0): a monitor of complete and ongoing genome projects world-wide, Bioinformatics, vol.15, issue.9, pp.773-774, 1999.
DOI : 10.1093/bioinformatics/15.9.773

V. Lacroix, L. Cottret, P. Thébault, and M. , An Introduction to Metabolic Networks and Their Structural Analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, issue.4, pp.594-617, 2008.
DOI : 10.1109/TCBB.2008.79

URL : https://hal.archives-ouvertes.fr/hal-00355497

A. Larhlimi, A. M. Berthold, R. Glen, and I. Fischer, A New Approach to Flux Coupling Analysis of Metabolic Networks, Proceedings of the Computational Life Sciences II, pp.205-215, 2006.
DOI : 10.1007/11875741_20

N. Lemke, F. Herédia, C. K. Barcellos, A. N. Reis, and J. C. Mombach, Essentiality and damage in metabolic networks, Bioinformatics, vol.20, issue.1, pp.115-119, 2004.
DOI : 10.1093/bioinformatics/btg386

K. Liolios, I. A. Chen, K. Mavromatis, N. Tavernarakis, P. Hugenholtz et al., The Genomes On Line Database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata, Nucleic Acids Research, vol.38, issue.Database, pp.38-346, 2010.
DOI : 10.1093/nar/gkp848

K. Liolios, K. Mavromatis, N. Tavernarakis, and N. C. Kyrpides, The Genomes On Line Database (GOLD) in 2007: status of genomic and metagenomic projects and their associated metadata, Nucleic Acids Research, vol.36, issue.Database, pp.475-479, 2008.
DOI : 10.1093/nar/gkm884

K. Liolios, N. Tavernarakis, P. Hugenholtz, and N. C. Kyrpides, The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide, Nucleic Acids Research, vol.34, issue.90001, pp.332-334, 2006.
DOI : 10.1093/nar/gkj145

H. Ma, X. Zhao, Y. Yuan, and A. Zeng, Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph, Bioinformatics, vol.20, issue.12, pp.1870-1876, 2004.
DOI : 10.1093/bioinformatics/bth167

S. Maslov and K. Sneppen, Specificity and Stability in Topology of Protein Networks, Science, vol.296, issue.5569, pp.910-913, 2002.
DOI : 10.1126/science.1065103

P. Michalak, Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes, Genomics, vol.91, issue.3, pp.243-248, 2008.
DOI : 10.1016/j.ygeno.2007.11.002

H. J. Motulsky, Biostatistique : une approche intuitive, 2002.

A. Nakaya, S. Goto, and M. Kanehisa, Extraction of correlated gene clusters by multiple graph comparison, Genome Informatics, vol.12, pp.44-53, 2001.

R. A. Notebaart, B. Teusink, R. J. Siezen, and B. Papp, Co-Regulation of Metabolic Genes Is Better Explained by Flux Coupling Than by Network Distance, PLoS Computational Biology, vol.34, issue.1, p.26, 2008.
DOI : 10.1371/journal.pcbi.0040026.st007

H. Ogata, W. Fujibuchi, and S. Goto, A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters, Nucleic Acids Research, vol.28, issue.20, pp.4021-4028, 2000.
DOI : 10.1093/nar/28.20.4021

C. S. Osborne, L. Chakalova, K. E. Brown, D. Carter, A. Horton et al., Active genes dynamically colocalize to shared sites of ongoing transcription, Nature Genetics, vol.375, issue.10, pp.361065-1071, 2004.
DOI : 10.1016/S1097-2765(00)80432-3

J. A. Papin, J. Stelling, N. D. Price, S. Klamt, S. Schuster et al., Comparison of network-based pathway analysis methods, Trends in Biotechnology, vol.22, issue.8, pp.400-405, 2004.
DOI : 10.1016/j.tibtech.2004.06.010

C. A. Phillips, The network inhibition problem, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing , STOC '93, pp.776-785, 1993.
DOI : 10.1145/167088.167286

S. A. Rahman and D. Schomburg, Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks, Bioinformatics, vol.22, issue.14, pp.221767-1774, 2006.
DOI : 10.1093/bioinformatics/btl181

E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A. Barabási, Hierarchical Organization of Modularity in Metabolic Networks, Science, vol.297, issue.5586, pp.2971551-1555, 2002.
DOI : 10.1126/science.1073374

M. Remm, C. Strom, and E. Sonnhammer, Automatic clustering of orthologs and in-paralogs from pairwise species comparisons, Journal of Molecular Biology, vol.314, issue.5, pp.1041-1052, 2001.
DOI : 10.1006/jmbi.2000.5197

S. C. Rison, S. A. Teichmann, and J. M. Thornton, Homology, Pathway Distance and Chromosomal Localization of the Small Molecule Metabolism Enzymes in Escherichia coli, Journal of Molecular Biology, vol.318, issue.3, pp.318911-932, 2002.
DOI : 10.1016/S0022-2836(02)00140-7

I. Rivals, L. Personnaz, L. Taing, and M. C. , Enrichment or depletion of a GO category within a class of genes: which test?, Bioinformatics, vol.23, issue.4, pp.401-407, 2007.
DOI : 10.1093/bioinformatics/btl633

URL : https://hal.archives-ouvertes.fr/hal-00801557

E. P. Rocha, The Organization of the Bacterial Genome, Annual Review of Genetics, vol.42, issue.1, pp.211-233, 2008.
DOI : 10.1146/annurev.genet.42.110807.091653

J. Ruan, A. K. Dean, and W. Zhang, A general co-expression network-based approach to gene expression analysis: comparison and applications, BMC Systems Biology, vol.4, issue.1, p.8, 2010.
DOI : 10.1186/1752-0509-4-8

H. Salgado, S. Gama-castro, M. Peralta-gil, E. Díaz-peredo, F. Sánchez-solano et al., RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions, Nucleic Acids Research, vol.34, issue.90001, pp.34-394, 2006.
DOI : 10.1093/nar/gkj156

H. Salgado, G. Moreno-hagelsieb, T. Smith, and J. Collado-vides, Operons in Escherichia coli: Genomic analyses and predictions, Proceedings of the National Academy of Sciences of the United States of America, pp.6652-6657, 2000.
DOI : 10.1073/pnas.110147297

D. P. Sangurdekar, F. Srienc, and A. B. , KHODURSKY : A classification based framework for quantitative description of large-scale microarray data, Genome Biology, vol.7, issue.4, p.32, 2006.
DOI : 10.1186/gb-2006-7-4-r32

D. Sankoff, Genome rearrangement with gene families, Bioinformatics, vol.15, issue.11, pp.909-917, 1999.
DOI : 10.1093/bioinformatics/15.11.909

C. H. Schilling, S. Schuster, B. Ø. Palsson, R. S. Heinrich, T. Schuster et al., Metabolic Pathway Analysis: Basic Concepts and Scientific Applications in the Post-genomic Era, Biotechnology Progress, vol.15, issue.3, pp.296-30353, 1999.
DOI : 10.1021/bp990048k

S. Schuster, D. A. Fell, and T. Dandekar, A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks, Nature Biotechnology, vol.18, issue.3, pp.326-358, 2000.
DOI : 10.1038/73786

J. Schwartz, C. Gaugain, J. C. Nacher, A. D. Daruvar, and M. Kanehisa, Observing metabolic functions at the genome scale, Genome Biology, vol.8, issue.6, p.123, 2007.
DOI : 10.1186/gb-2007-8-6-r123

URL : https://hal.archives-ouvertes.fr/hal-00306566

E. Simeonidis, S. C. Rison, J. M. Thornton, I. D. Bogle, and L. G. Papageorgiou, Analysis of metabolic networks using a pathway distance metric through linear programming, Metabolic Engineering, vol.5, issue.3, pp.211-219, 2003.
DOI : 10.1016/S1096-7176(03)00043-0

J. Stelling, S. Klamt, K. Bettenbrock, S. Schuster, and E. D. Gilles, Metabolic network structure determines key aspects of functionality and regulation, Nature, vol.292, issue.6912, pp.420190-420193, 2002.
DOI : 10.1103/PhysRevE.64.036106

J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules, Science, vol.302, issue.5643, pp.302249-255, 2003.
DOI : 10.1126/science.1087447

J. Tang and B. M. Moret, Phylogenetic Reconstruction from Gene-Rearrangement Data with Unequal Gene Content, Proceedings of the Workshop on Software Architectures for Dependable Systems, pp.37-46, 2003.
DOI : 10.1007/978-3-540-45078-8_4

T. Uno, Fast Algorithms to Enumerate All Common Intervals of Two Permutations, Algorithmica, vol.26, issue.2, pp.290-309, 2000.
DOI : 10.1007/s004539910014

A. Wagner and D. A. , The small world inside large metabolic networks, Proceedings of the Royal Society B: Biological Sciences, vol.268, issue.1478, pp.1803-1810, 1478.
DOI : 10.1098/rspb.2001.1711

S. Wimer, I. Koren, and I. Cederbaum, On paths with the shortest average arc length in weighted graphs, Discrete Applied Mathematics, vol.45, issue.2, pp.169-179, 1993.
DOI : 10.1016/0166-218X(93)90059-W

T. Yamada, M. Kanehisa, and S. Goto, Extraction of phylogenetic network modules from the metabolic network, BMC Bioinformatics, vol.7, issue.1, p.130, 2006.
DOI : 10.1186/1471-2105-7-130

C. Yang, A pseudo-polynomial algorithm for detecting minimum weighted length paths in a network, European Journal of Operational Research, vol.57, issue.1, pp.123-131, 1992.
DOI : 10.1016/0377-2217(92)90311-V

Q. Yang and S. , Path Matching and Graph Matching in Biological Networks, Journal of Computational Biology, vol.14, issue.1, pp.56-67, 2007.
DOI : 10.1089/cmb.2006.0076

B. Zhang and S. Horvath, A General Framework for Weighted Gene Co-Expression Network Analysis, Statistical Applications in Genetics and Molecular Biology, vol.4, issue.1, p.17, 2005.
DOI : 10.2202/1544-6115.1128

Y. Zheng, J. D. Szustakowski, L. Fortnow, R. J. Roberts, and S. Kasif, Computational Identification of Operons in Microbial Genomes, Genome Research, vol.12, issue.8, pp.1221-1230, 2002.
DOI : 10.1101/gr.200602

.. Exemple-de-niveau-d, expression de gènes montrant que la distance de coexpression n'est pas une métrique, p.46

G. De and .. , Test statistique sur les fréquences d'apparition de gènes essentiels et non-essentiels qui interviennent dans les k-SIPs, p.136

K. Modules-de and E. , coli qui correspondent exactement à au moins un 1-SIP chacun145 C.5 Couples et triplets de modules de KEGG qui correspondent exactement à au moins un k-SIP, pour k compris entre 1 et 10, p.145

S. De, Construction de G int dans la version " gène, p.26

.. De-jaccard, Taux de correspondance entre les k-SIPs regroupés par densité génomique et les opérons d'E. coli en utilisant la mesure, p.63

E. Coli......., Intérêt modulaire de tous les k-SIPs pour des k distincts, p.78

A. De and D. .. , Algorithme de recherche du plus court chemin, p.36

Y. Algorithme-de, Algorithme de recherche des k plus courts chemins sans circuit, p.39

A. Computekspatp and .. , Algorithme de recherche des k plus courts chemins sans jumeaux, p.41

.. La-recherche-automatique-de-sous-graphes-biologiquement-significatifs, 27 3.3.1 Notion de k-SIPs, p.28