Netwerk-gebaseerde analyse van reactiewegen betrokken in Salmonella biofilmvorming

Nele Cosemans
De digitale strijd tegen bacteriënBacteriën hebben een zeer sterk aanpassingsvermogen waardoor ze steeds nieuwe manieren vinden om te overleven. De mens moet daardoor steeds nieuwe geneesmiddelen zoeken om ze te bestrijden, maar de bronnen raken stilaan uitgeput.

Netwerk-gebaseerde analyse van reactiewegen betrokken in Salmonella biofilmvorming

De digitale strijd tegen bacteriën

Bacteriën hebben een zeer sterk aanpassingsvermogen waardoor ze steeds nieuwe manieren vinden om te overleven. De mens moet daardoor steeds nieuwe geneesmiddelen zoeken om ze te bestrijden, maar de bronnen raken stilaan uitgeput. Zijn onze huidige verdedigingsstrategieën  nog wel opgewassen tegen deze ‘die hards’ of is het tijd om de aanval in te zetten?

Alles of niets: wat zijn de kansen?

De bestrijding van een bepaalde bacterie is een alles of niets actie: als één bacterie overleeft, kan deze zich vermenigvuldigen zodat zijn nakomelingen weer in grote aantallen aanwezig zijn. Geen probleem zou je kunnen denken, we moeten ze alleen om de zoveel tijd eens aanpakken. Dat is inderdaad een mogelijke strategie, tenzij de overleving van deze ene bacterie gebaseerd is op een bepaalde aanpassing die het gebruikte geneesmiddel inactiveert. Alle nakomelingen zullen deze aanpassing ook hebben en het geneesmiddel zal geen van deze bacteriën meer kunnen doden.

In tegenstelling tot wat je zou vermoeden, zijn deze aanpassingen willekeurige processen. De bacterie kan een geneesmiddel enkel overleven als hij toevallig vanaf zijn ontstaan al de juiste aanpassing heeft. De kans hierop is zeer klein, maar één bacterie met de aanpassing is voldoende om het geneesmiddel voor eens en altijd te inactiveren. En de bacteriën zijn met heel veel, zodat dit fenomeen mogelijk is en ook effectief voorkomt.

De aanvalsstrategie

In het begin werd niet zoveel aandacht besteed aan de manier waarop geneesmiddelen bacteriën doden. Dat het werkte was het enige dat van belang was. De mens selecteerde dus eigenlijk net zoals de bacteriën zijn wapens per toeval. De stapel van geneesmiddelen die niet meer werken wordt echter steeds groter en die van nieuwe mogelijke geneesmiddelen is beperkt. Als we deze strijd willen winnen, zullen we  het over een andere boeg moeten gooien. Daarom zijn wetenschappers tegenwoordig meer en meer op zoek naar de exacte werking van bepaalde geneesmiddelen, zodat ze bacteriën met verschillende geneesmiddelen op verschillende plaatsen tegelijk kunnen aanvallen.

De zoektocht naar de exacte werking van een bepaald geneesmiddel is echter vergelijkbaar met het zoeken naar een speld in een hooiberg. Eén bacterie bestaat al uit duizenden onderdelen en het kan zijn dat de uitschakeling van ééntje voldoende is om de bacterie te doden. Waar moeten we dus beginnen met zoeken?

Huidige technieken in het laboratorium laten toe om het effect van een bepaald geneesmiddel op de meerderheid van deze onderdelen apart te bestuderen. Hierdoor kunnen onderzoekers al een eerste idee krijgen van de werking ervan. Meestal zijn er meerdere onderdelen die beïnvloed worden door een bepaald geneesmiddel, wat aangeeft dat deze samenwerken in de bacterie. Het geneesmiddel werkt dan niet op elk van deze onderdelen apart, maar op één gemeenschappelijk onderdeel dat al de andere beïnvloedt. De werking van een bepaald geneesmiddel bepalen is het vinden van dit gemeenschappelijke onderdeel.

Het geheime wapen van de mens

Tegenwoordig weet men al veel over de samenwerkingen die tussen de onderdelen van een bacterie optreden, zodat het moeilijk wordt al deze informatie handmatig te doorzoeken zonder dingen over het hoofd te zien. Maar waarom zouden we in deze tijd nog dingen handmatig doorzoeken? We maken dagelijks gebruik van Google om informatie op het internet te doorzoeken! Waarom zouden we deze digitale technieken niet toepassen in de strijd tegen bacteriën?

In de biologie is er een specifiek domein, de bio-informatica, dat zich bezighoudt met dit soort toepassingen. Men kan al de beschikbare informatie over de onderdelen in een bepaalde bacterie bundelen en vervolgens doorzoeken met specifieke computerprogramma’s. Om deze zoektocht te vereenvoudigen worden de onderdelen in een bacterie voorgesteld als punten in een netwerk. De verbindingen in dit netwerk geven dan de samenwerking tussen verschillende onderdelen aan. Nu kunnen we zien hoe de onderdelen, die beïnvloed worden door een bepaald geneesmiddel, met elkaar verbonden zijn in dit netwerk en hopelijk een gemeenschappelijk onderdeel vinden.

De ultieme test

De scriptie “Netwerk-gebaseerde analyse van reactiewegen betrokken in Salmonella biofilmvorming” bestudeert of we via een netwerkanalyse, zoals in de vorige paragraaf beschreven, de werking van geneesmiddelen op de Salmonella bacterie kunnen bestuderen. Dit is de bacterie die voedselvergiftiging veroorzaakt als je niet doorbakken of slecht bewaard vlees eet. Bovendien verzamelen deze bacteriën zich in kleine kolonies, biofilmen genaamd, wat een extra verdedigingsmechanisme vormt tegen bestrijdingsmiddelen. Maar… door deze scriptie hebben we nu meer inzicht in dit verdedigingsmechanisme zodat we ons in de toekomst er beter tegen kunnen wapenen. En dat allemaal dankzij de toepassing van computertechnieken op biologische gegevens!

 

Bibliografie

Akramifar, S. & Ghassem-Sani, G. (2010). Fast forward planning by guided enforced hill climbing. Engineering Applications of Artificial Intelligence, 23(8): 1327-1339.

 

Albayrak, M. & Allahverdi, N. (2011). Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms. Expert Systems with Applications, 38(3): 1313-1320.

 

Alm, E. & Arkin, A. P. (2003). Biological networks. Current Opinion in Structural Biology, 13: 193-202.

 

Alon, U. (2003). Biological networks: the tinkerer as an engineer. Science, 301(5641): 1866-1867.

 

Babu, M. M., Luscombe, N. M., Aravind, L., Gerstein, M. & Teichmann, S. A. (2004). Structure and evolution of

transcriptional regulatory networks. Current opinion in structural biology, 14(3): 283-291.

 

Bader, D. & Madduri, K. (2006). Designing multithreaded algorithms for breadth-first search and st-connectivity on

the Cray MTA-2. Proceedings of the 35th International Conference on Parallel Processing (ICPP-2006), Washington DC, Washington, USA: 523-530.

 

Bailly-Bechet, M., Braunstein, A. & Zecchina, R. (2009). Computational methods in systems biology. chapter A Prize-Collecting Steiner Tree Approach for Transduction Network Inference, pp. 83-95. Springer, Berlin, Germany.

 

Barabasi, A. L. & Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2): 101-113.

 

Basha, O., Tirman, S., Eluk, A. & Yeger-Lotem, E. (2013). ResponseNet2.0: revealing signaling and regulatory

pathways connecting your proteins and genes - now with human data. Nucleic Acids Research, 41(W1): W198-

W203.

 

Beisel, C. L. & Storz, G. (2010). Base pairing small RNAs and their roles in global regulatory networks. Federation

of European Microbiological Societies Microbiology Reviews, 34(5): 866-882.

 

Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to

multiple testing. Journal of the Royal Statistical Society Series B (Methodological), 57(1): 289-300.

 

Benson, N. R., Wong, R. M. Y. & McClelland, M. (2000). Analysis of the SOS response in Salmonella enterica serovar Typhimurium using RNA fingerprinting by arbitrarily primed PCR. Journal of bacteriology, 182(12): 3490-3497.

 

Bhalla, U. S. & Iyengar, R. (1999). Emergent properties of networks of biological signaling pathways. Science,

283(5400): 381-387.

 

Bonetta, L. (2010). Interactome under construction. Nature, 468(7325): 851-854.

 

Boyan, J. & Moore, A. W. (2001). Learning evaluation functions to improve optimization by local search. Journal

of Machine Learning Research, 1: 77-112.

 

Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J., Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P., Lara, G. G., Oezcimen, A., Rocca-Serra, P. & Sansone, S. A. (2003). ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic acids research, 31(1): 68-71.

 

Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. (2002). Genetic dissection of transcriptional regulation in budding yeast. Science, 296(5568): 752-755.

 

Brodsky, I. E., Ernst, R. K., Miller, S. I. & Falkow, S. (2002). mig-14 is a Salmonella gene that plays a role in

bacterial resistance to antimicrobial peptides. Journal of bacteriology, 184(12): 3203-3213.

 

Burke, E. K. & Kendall, G. (2014). Search methodologies. chapter Introduction, pp. 1-17. Springer, New York City,

New York, USA.

 

Cadoli, M. & Donini, F. M. (1997). A survey on knowledge compilation. Artificial Intelligence Communications, 10:

137-150.

 

Caspi, R., Altman, T., Dreher, K., Fulcher, C. A., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M.,

Latendresse, M., Mueller, L. A., Ong, Q., Paley, S., Pujar, A., Shearer, A. G., Travers, M., Weerasinghe, D.,

Zhang, P. & Karp, P. D. (2012). The MetaCyc database of metabolic pathways and enzymes and the BioCyc

collection of pathway/genome databases. Nucleic acids research, 40(D1): D742-D753.

 

Cherry, J. M., Hong, E. L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E. T., Christie, K. R., Costanzo,

M. C., Dwight, S. S., Engel, S. R., Fisk, D. G., Hirschman, J. E., Hitz, B. C., Karra, K., Krieger, C. J., Miyasato,

S. R., Nash, R. S., Park, J., Skrzypek, M. S., Simison, M., Weng, S. & Wong, E. D. (2012). Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Research, 40(D1): D700-D705.

 

Clark, M., Kim, Y., Kruschwitz, U., Song, D., Albakour, D., Dignum, S., Beresi, U. C., Fasli, M. & Roeck, A. D.

(2012). Automatically structuring domain knowledge from text: an overview of current research. Information

Processing & Management, 48(3): 552-568.

 

Cloots, L., De Maeyer, D. & Marchal, K. (2014). Springer handbook of bio-/neuroinformatics. chapter Path Finding

in Biological Networks to Interpret Functional Data, pp. 289-309. Springer, Berlin, Germany.

 

Cloots, L. & Marchal, K. (2011). Network-based functional modeling of genomics, transcriptomics and metabolism

in bacteria. Current Opinion in Microbiology, 14: 599-607.

 

Cohen, K. B. & Hunter, L. (2008). Getting started in text mining. Public Library of Science Computational Biology,

4(1): e20.

 

Cormen, T., Leiserson, C., Rivest, R. & Stein, C. (2001). Introduction to algorithms. The Massachusetts Institute

of Technology Press, Cambridge, Massachusetts, USA. 1191 p.

 

Darwiche, A. (2009). Modelling and reasoning with Bayesian networks. chapter Compiling Bayesian Networks, pp. 287-312. Cambridge University Press, Cambridge, New York, USA.

 

Darwiche, A. & Marquis, P. (2002). A knowledge compilation map. Journal of Artificial Intelligence Research, 17:

229-264.

 

Daudin, J. J., Picard, F. & Robin, S. (2008). A mixture model for random graphs. Statistics and Computing, 18(2):

173-183.

 

De Maeyer, D., Renkens, J., Cloots, L., De Raedt, L. & Marchal, K. (2013). PheNetic: network-based interpretation of unstructured gene lists in E. coli. Molecular BioSystems, 9: 1594-1603.

 

De Smet, R. & Marchal, K. (2010). Advantages and limitations of current network inference methods. Nature Reviews Microbiology, 8: 717-729.

 

DeLuca, T. F., Wu, I.-H., Pu, J., Monaghan, T., Peshkin, L., Singh, S. & Wall, D. P. (2006). Roundup: a multigenome repository of orthologs and evolutionary distances. Bioinformatics, 22(16): 2044-2046.

 

Deng, K., Wang, S., Rui, X., Zhang, W. & Tortorello, M. L. (2011). Functional analysis of ycfR and ycfQ in

Escherichia coli O157: H7 linked to outbreaks of illness associated with fresh produce. Applied and environmental

microbiology, 77(12): 3952-3959.

 

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische mathematik, 1(1): 269-271.

 

Domka, J., Lee, J., Bansal, T. & Wood, T. K. (2007). Temporal gene-expression in Escherichia coli K-12 biofilms.

Environmental microbiology, 9(2): 332-346.

 

Donaldson, I., Martin, J., De Bruijn, B., Wolting, C., Lay, V., Tuekam, B., Zhang, S., Baskin, B., Bader, G. D.,

Michalickova, K., Pawson, T. & Hogue, C. W. (2003). PreBIND and Textomy - mining the biomedical literature

for protein-protein interactions using a support vector machine. BioMed Central Bioinformatics, 4(1): 11.

 

Downsland, K. A. (2014). Search methodologies. chapter Classical techniques, pp. 19-65. Springer, New York City, New York, USA.

 

Doyle, P. G. & Snell, J. L. (2000). Random walks and electric networks. Free Software Foundation, Boston, Massachusetts, USA. 120 p.

 

Dupont, P., Callut, J., Dooms, G., Monette, J. & Deville, Y. (2006). Relevant subgraph extraction from random

walks in a graph. Université catholique de Louvain Research reports, 7.

 

Edgar, R., Domrachev, M. & Lash, A. E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1): 207-210.

 

Emmert-Streib, F. & Dehmer, M. (2011). Networks for systems biology: conceptual connection of data and function. Systems Biology, Institution of Engineering and Technology, 5(3): 185-207.

 

Faust, K., Dupont, P., Callut, J. & Van Helden, J. (2010). Pathway discovery in metabolic networks by subgraph

extraction. Bioinformatics, 26(9): 1211-1218.

 

Felner, A., Modenhauer, C., Sturtevant, N. & Schaeffer, J. (2010). Single-frontier bidirectional search. Proceedings of the Third Annual Symposium on Combinatorial Search (SOCS-10), Atlanta, Georgia, USA: 59-64.

 

Fitch, W. M. (1970). Distinguishing homologous from analogous proteins. Systematic Biology, 19(2): 99-113.

 

Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C. & Jensen, L. J. (2013). STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research, 41(D1): D808-D815.

 

Frye, J. G., Porwollik, S., Blackmer, F., Cheng, P. & McClelland, M. (2005). Host gene expression changes and DNA amplification during temperate phage induction. Journal of bacteriology, 187(4): 1485-1492.

 

Gibbs, D., Baratt, A., Baric, R., Kawaoka, Y., Smith, R., Orwoll, E., Katze, M. & McWeeney, S. (2013). Protein

co-expression network analysis (ProCoNA). Journal of Clinical Bioinformatics, 3(1): 11.

 

Gilad, Y., Rifkin, S. A. & Pritchard, J. K. (2008). Revealing the architecture of gene regulation: the promise of

eQTL studies. Trends in genetics, 24(8): 408-415.

 

Glover, F. (1989). Tabu search - part I. Operations Research Society of America's Journal on Computing, 1(3):

190-206.

 

Gnad, F., Gunawardena, J. & Mann, M. (2011). PHOSIDA 2011: the posttranslational modification database.

Nucleic Acids Research, 39(suppl 1): D253-D260.

 

Goldberg, D. E. & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2): 95-99.

 

Golomb, S. W. & Baumert, L. D. (1965). Backtrack programming. Journal of the Association for Computing Machinery, 12(4): 516-524.

 

Granville, V., Krivanek, M. & Rasson, J. P. (1994). Simulated annealing: a proof of convergence. Institute of

Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence, 16(6): 652-656.

 

Guimera, R. & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028):

895-900.

 

Hirschhorn, J. N. & Daly, M. J. (2005). Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics, 6(2): 95-108.

 

Hopcroft, J. & Tarjan, R. (1973). Algorithm 447: efficient algorithms for graph manipulation. Communications

of the Association for Computing Machinery, 16(6): 372-378.

 

Huang, C. Y., Sun, C. T. & Lin, H. C. (2005). Influence of local information on social simulations in small-world

network models. Journal of Artificial Societies and Social Simulation, 8(4): 8.

 

Huang, J., Liu, Y., Zhang, W., Yu, H. & Han, J. D. J. (2011). eResponseNet: a package prioritizing candidate disease genes through cellular pathways. Bioinformatics, 27(16): 2319-2320.

 

Huang, S. C. & Fraenkel, E. (2009). Integrating proteomic, transcriptional, and interactome data reveals hidden

components of signaling and regulatory networks. Science Signaling, 2(81): ra40.

 

Huerta, A. M., Salgado, H., Thieffry, D. & Collado-Vides, J. (1998). RegulonDB: A database on transcriptional

regulation in Escherichia coli. Nucleic Acids Research, 26(1): 55-59.

 

Hwang, F., Richards, D. & Winter, P. (1992). The steiner tree problem. Elsevier Science Publischers B.V., Amsterdam, The Netherlands. 340 p.

 

Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A. F. (2002). Discovering regulatory and signaling circuits in

molecular interaction networks. Bioinformatics, 18(suppl 1): S233-S240.

 

Irwin, J. D. & Nelms, R. M. (2008). Basic engineering circuit analysis. Wiley Publishing, Hoboken, New Jersey,

USA. 864 p.

 

Jensen, L. J., Julien, P., Kuhn, M., von Mering, C., Muller, J., Doerks, T. & Bork, P. (2008). eggNOG: automated

construction and annotation of orthologous groups of genes. Nucleic acids research, 36(suppl 1): D250-D254.

 

Jensen, L. J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., Bork, P. & von Mering, C. (2009). STRING 8 - a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Research, 37(suppl 1): D412-D416.

 

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000). The large-scale organization of metabolic

networks. Nature, 407(6804): 651-654.

 

Jimenez, V. M. & Marzal, A. (1999). Computing the k shortest paths: a new algorithm and an experimental

comparison. Proceedings of the Third International Workshop on Algorithm Engineering (WAE 1999), London,

UK: 15-29.

 

Joyce, A. R. & Palsson, B. O. (2006). The model organism as a system: integrating 'omics' data sets. Nature Reviews Molecular Cell Biology, 7(3): 198-210.

 

Kanehisa, M. & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28(1): 27-30.

 

Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. (2004). The KEGG resource for deciphering the

genome. Nucleic acids research, 32(suppl 1): D277-D280.

 

Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Research, 42(D1): D199-D205.

 

Karp, P. D., Ouzounis, C. A., Moore-Kochlacs, C., Goldovsky, L., Kaipa, P., Ahren, D., Tsoka, S., Darzentas, N.,

Kunin, V. & Lopez-Bigas, N. (2005). Expansion of the BioCyc collection of pathway/genome databases to 160

genomes. Nucleic Acids Research, 33(19): 6083-6089.

 

Karp, P. D., Riley, M., Paley, S. M., Pellegrini-Toole, A. & Krummenacker, M. (1997). EcoCyc: Enyclopedia of

Escherichia coli genes and metabolism. Nucleic Acids Research, 25(1): 43-50.

 

Karp, P. D., Riley, M., Saier, M., Paulsen, I. T., Collado-Vides, J., Paley, S. M., Pellegrini-Toole, A., Bonavides, C.

& Gama-Castro, S. (2002). The EcoCyc database. Nucleic Acids Research, 30(1): 56-58.

 

Khanin, R. & Wit, E. (2006). How scale-free are biological networks. Journal of Computational Biology, 13(3):

810-818.

 

Kirkpatrick, S. (1984). Optimization by simulated annealing: quantitative studies. Journal of Statistical Physics,

34(5-6): 975-986.

 

Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):

671-680.

 

Klein, P. & Ravi, R. (1995). A nearly best-possible approximation algorithm for node-weighted Steiner trees. Journal of Algorithms, 19(1): 104-115.

 

Ko, M. & Park, C. (2000). H-NS-dependent regulation of agellar synthesis is mediated by a LysR family protein.

Journal of Bacteriology, 182(16): 4670-4672.

 

Korf, R. E. (1985). Depth-first iterative-deepening: an optimal admissible tree search. Artificial Intelligence, 27(1):

97-109.

 

Korf, R. E. & Schultze, P. (2005). Large-scale parallel breadth-first search. Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-2005), Pittsburgh, Pennsylvania, USA: 1380-1385.

 

Kou, L., Markowsky, G. & Berman, L. (1981). A fast algorithm for Steiner trees. Acta informatica, 15(2): 141-145.

Kwa, J. B. (1989). BS*: An admissible bidirectional staged heuristic search algorithm. Artificial Intelligence, 38(1):

95-109.

 

Lan, A., Smoly, I. Y., Rapaport, G., Lindquist, S., Fraenkel, E. & Yeger-Lotem, E. (2011). ResponseNet: revealing

signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Research,

39(suppl 2): W424-W429.

 

Latapy, M. & Pons, P. (2005). Computing communities in large networks using random walks. Proceedings of the

20th International Symposium on Computer and Information Sciences (ISCIS'05), Paris, France: 284-293.

Lee, I., Ambaru, B., Thakkar, P., Marcotte, E. M. & Rhee, S. Y. (2010). Rational association of genes with traits

using a genome-scale gene network for Arabidopsis thaliana. Nature Biotechnology, 28: 149-156.

 

Lee, I., Date, S. V., Adai, A. T. & Marcotte, E. M. (2004). A probabilistic functional network of yeast genes. Science, 306(5701): 1555-1558.

 

Lee, J., Bansal, T., Jayaraman, A., Bentley, W. E. & Wood, T. K. (2007). Enterohemorrhagic Escherichia coli

biofilms are inhibited by 7-hydroxyindole and stimulated by isatin. Applied and environmental microbiology,

73(13): 4100-4109.

 

Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T.,

Thompson, C. M., Simon, I., Zeitlinger, J., Jennings, E. G., Murray, H. L., Gordon, D. B., Ren, B., Wyrick,

J. J., Tagne, J.-B., Volkert, T. L., Fraenkel, E., Gifford, D. K. & Young, R. A. (2002). Transcriptional regulatory

networks in Saccharomyces cerevisiae. Science, 298(5594): 799-804.

 

Licatalosi, D. D. & Darnell, R. B. (2010). RNA processing and its regulation: global insights into biological networks. Nature Reviews Genetics, 11(1): 75-87.

 

Lima-Mendez, G. & van Helden, J. (2009). The powerful law of the power law and other myths in network biology.

Molecular BioSystems, 5: 1482-1493.

 

Lipowski, A. & Lipowska, D. (2012). Roulette-wheel selection via stochastic acceptance. Physica A: Statistical

Mechanics and its Applications, 391(6): 2193-2196.

 

Lovasz, L. (1993). Random walks on graphs: a survey. Bolyai Society Mathematical Studies, 2: 353-397.

 

Lowerre, B. (1990). Readings in speech recognition. chapter The Harpy speech understanding system, pp. 576-586. Morgan Kaufmann Publishers Incorporation, San Francisco, California, USA.

 

Macek, B., Gnad, F., Soufi, B., Kumar, C., Olsen, J. V., Mijakovic, I. & Mann, M. (2008). Phosphoproteome analysis of E. coli reveals evolutionary conservation of bacterial Ser/Thr/Tyr phosphorylation. Molecular & Cellular

Proteomics, 7(2): 299-307.

 

Maere, S., Heymans, K. & Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene

ontology categories in biological networks. Bioinformatics, 21(16): 3448-3449.

 

Mason, O. & Verwoerd, M. (2008). Graph theory and networks in biology. Science Foundation, Ireland, UK. 52 p.

 

Meysman, P., Sonego, P., Bianco, L., Fu, Q., Ledezma-Tejeida, D., Gama-Castro, S., Liebens, V., Michiels, J.,

Laukens, K., Marchal, K., Collado-Vides, J. & Engelen, K. (2014). COLOMBOS v2.0: an ever expanding collection

of bacterial expression compendia. Nucleic Acids Research, 42(D1): D649-D653.

 

Mika, F. & Hengge, R. (2014). Small RNAs in the control of RpoS, CsgD, and biofilm architecture of Escherichia

coli. RiboNucleid Acid Biology, 11(5): in press.

 

Miljkovic, D., Stare, T., Mozetic, I., Podpecan, V., Petek, M., Witek, K., Dermastia, M., Lavrac, N. & Gruden,

K. (2012). Signalling network construction for modelling plant defence response. Public Library of Science ONE,

7(12): e51822.

 

Moore, E. (1959). The shortest path through a maze. Proceedings of an International Symposium on the Theory of Switching, Part II, Cambridge, Massachusetts, USA: 285-292.

 

Muise, C., McIlraith, S. A., Beck, J. C. & Hsu, E. I. (2012). Advances in artificial intelligence. chapter DSHARP:

Fast d-DNNF compilation with sharpSAT, pp. 356-361. Springer, Berlin, Germany.

 

Nelson, P. C. & Toptsis, A. A. (1992). Unidirectional and bidirectional search algorithms. Software, Institute of

Electrical and Electronics Engineers, 9(2): 77-83.

 

Nicholson, T. A. J. (1966). Finding the shortest route between two points in a network. The Computer Journal, 9(3): 275-280.

 

Oliveira, C. A. & Pardalos, P. M. (2011). Mathematical aspects of network routing optimization. chapter Steiner

Trees and Multicast, pp. 29-45. Springer, Berlin, Germany.

 

Oliver, S. (2000). Proteomics: guilt-by-association goes global. Nature, 403(6770): 601-603.

 

Olsen, J. V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P. & Mann, M. (2006). Global, in vivo, and

site-specific phosphorylation dynamics in signaling networks. Cell, 127(3): 635-648.

 

Ourfali, O., Shlomi, T., Ideker, T., Ruppin, E. & Sharan, R. (2007). SPINE: a framework for signaling-regulatory

pathway inference from cause-effect experiments. Bioinformatics, 23(13): i359-i366.

 

Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., Schneider, R. & Bagos, P. G. (2011). Using graph theory to analyze biological networks. BioData Mining, 4(1): 10.

 

Pearl, J. (1984). Heuristics: Intelligent search strategies for computer problem solving. Addison-Wesley Reading,

Boston, Massachusetts, USA.

 

Peregrin-Alvarez, J. M., Xuejian, X., Chong, S. & Parkinson, J. (2009). The modular organization of protein

interactions in Escherichia coli. Public Library of Science Computational Biology, 5(10): 1-16.

 

Pesavento, C. & Hengge, R. (2012). The global repressor FliZ antagonizes gene expression by fis-containing rna

polymerase due to overlapping DNA binding specificity. Nucleic acids research, 40(11): 4783-4793.

 

Pijls, W. & Post, H. (2009). A new bidirectional search algorithm with shortened postprocessing. European Journal of Operational Research, 198(2): 363-369.

 

Pohl, I. (1970). Bi-directional search. Machine Intelligence, 6: 124-140.

 

Prigent-Combaret, C., Prensier, G., Le Thi, T. T., Vidal, O., Lejeune, P. & Dorel, C. (2000). Developmental

pathway for biofilm formation in curli-producing Escherichia coli strains: role of agella, curli and colanic acid.

Environmental microbiology, 2(4): 450-464.

 

Rao, V. & Kumar, V. (1987). Parallel depth first search. part I. Implementation. International Journal of Parallel

Programming, 16(6): 479-499.

 

Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. (2002). Hierarchical organization of

modularity in metabolic networks. Science, 297(5586): 1551-1555.

 

Raza, S., McDerment, N., Lacaze, P., Robertson, K., Watterson, S., Chen, Y., Chisholm, M., Eleftheriadis, G., Monk, S., O'Sullivan, M., Turnbull, A., Roy, D., Theocharidis, A., Ghazal, P. & Freeman, T. (2010). Construction of

a large scale integrated map of macrophage pathogen recognition and effector systems. BioMed Central Systems

Biology, 4(1): 63.

 

Ren, D., Zuo, R., Barrios, A. F. G., Bedzyk, L. A., Eldridge, G. R., Pasmore, M. E. &Wood, T. K. (2005). Differential gene expression for investigation of Escherichia coli biofilm inhibition by plant extract ursolic acid. Applied and environmental microbiology, 71(7): 4022-4034.

 

Rockman, M. V. & Kruglyak, L. (2006). Genetics of global gene expression. Nature Reviews Genetics, 7(11): 862-872.

 

Rosete-Suarez, A. & Ochao-Rodriguez, A. (1999). Automatic graph drawing and stochastic hill climbing. Proceedings of the Genetic and Evolutionary Computation Conference (Gecco'99), Orlando, Florida, USA: 1699-1706.

 

Russel, S. & Norvig, P. (2003). Artificial intellegence: a modern approach. Pearson Education International, Upper

Saddle River, New Jersey, USA. 1081 p.

 

Salgado, H., Peralta-Gil, M., Gama-Castro, S., Santos-Zavaleta, A., Muniz Rascado, L., Garcia-Sotelo, J. S.,

Weiss, V., Solano-Lira, H., Martinez-Flores, I., Medina-Rivera, A., Salgado-Osorio, G., Alquicira-Hernandez, S., Alquicira-Hernandez, K., Lopez-Fuentes, A., Porron-Sotelo, L., Huerta, A. M., Bonavides-Martinez, C., Balderas- Martinez, Y. I., Pannier, L., Olvera, M., Labastida, A., Jimenez-Jacinto, V., Vega-Alvarado, L., del Moral-Chavez,

V., Hernandez-Alvarez, A., Morett, E. & Collado-Vides, J. (2013). RegulonDB v8.0: omics data sets, evolutionary

conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Research, 41(D1):

D203-D213.

 

Sanchez, C., Lachaize, C., Janody, F., Bellon, B., Roder, L., Euzenat, J., Rechenmann, F. & Jacq, B. (1999). Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an internet database. Nucleic Acids Research, 27(1): 89-94.

 

Sardiu, M. E. & Washburn, M. P. (2011). Building protein-protein interaction networks with proteomics and informatics tools. Journal of Biological Chemistry, 286(27): 23645-23651.

 

Sastry, K., Goldberg, D. E. & Kendall, G. (2014). Search methodologies. chapter Genetic Algorithms, pp. 93-117. Springer, New York City, New York, USA.

 

Schadt, E. E. & Lum, P. Y. (2006). Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. Journal of lipid research, 47(12): 2601-2613.

 

Schneider, A., Dessimoz, C. & Gonnet, G. H. (2007). OMA Browser-Exploring orthologous relations across 352

complete genomes. Bioinformatics, 23(16): 2180-2182.

 

Schomburg, I., Chang, A. & Schomburg, D. (2002). BRENDA, enzyme data and metabolic information. Nucleic

acids research, 30(1): 47-49.

 

Schuster, S., Fell, D. A. & Dandekar, T. (2000). A general definition of metabolic pathways useful for systematic

organization and analysis of complex metabolic networks. Nature biotechnology, 18(3): 326-332.

 

Scott, M. S., Perkins, T., Bunnell, S., Pepin, F., Thomas, D. Y. & Hallett, M. (2005). Identifying regulatory

subnetworks for a set of genes. Molecular & Cellular Proteomics, 4(5): 683-692.

 

Sint, L. & de Champeaux, D. (1977). An improved bidirectional heuristic search algorithm. Journal of the Association for Computing Machinery, 24(2): 177-191.

 

Sivaraj, R. & Ravichandran, T. (2011). A review of selection methods in genetic algorithm. International Journal of

Engineering Science & Technology, 3(5).

 

Smith, C., Arany, Z., Orrego, C. & Eisenstadt, E. (1991). DNA damage-inducible loci in Salmonella typhimurium.

Journal of bacteriology, 173(11): 3587-3590.

 

Snel, B., Lehmann, G., Bork, P. & Huynen, M. A. (2000). STRING: a web-server to retrieve and display the

repeatedly occurring neighbourhood of a gene. Nucleic Acids Research, 28(18): 3442-3444.

 

Snel, B., Van Noort, V. & Huynen, M. A. (2004). Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes. Nucleic acids research, 32(16): 4725-4731.

 

Spory, A., Bosserhoff, A., von Rhein, C., Goebel, W. & Ludwig, A. (2002). Differential regulation of multiple proteinsof Escherichia coli and Salmonella enterica serovar Typhimurium by the transcriptional regulator SlyA. Journal of bacteriology, 184(13): 3549-3559.

 

Stark, C., Breitkreutz, B. J., Reguly, T., Boucher, L., Breitkreutz, A. & Tyers, M. (2006). BioGRID: a general

repository for interaction datasets. Nucleic Acids Research, 34(1): D535-D539.

 

Steenackers, H., Hermans, K., Vanderleyden, J. & De Keersmaecker, S. C. (2012). Salmonella biofilms: an overview on occurrence, structure, regulation and eradication. Food Research International, 45(2): 502-531.

 

Suthram, S., Beyer, A., Karp, R. M., Eldar, Y. & Ideker, T. (2008). eQED: an efficient method for interpreting eQTL

associations using protein networks. Molecular Systems Biology, 4(162).

 

Takahashi, H. & Matsuyama, A. (1980). An approximate solution for the Steiner problem in graphs. Mathematica

Japonica, 24(6): 573-577.

 

Tarjan, R. (1972). Depth-first search and linear graph algorithms. Society for Industrial and Applied Mathematics,

Journal on Computing, 1(2): 146-160.

 

Tatusov, R. L., Koonin, E. V. & Lipman, D. J. (1997). A genomic perspective on protein families. Science, 278(5338): 631-637.

 

Thede, S. M. (2004). An introduction to genetic algorithms. Journal of computing sciences in colleges, 20(1): 115-123.

 

Tian, F., Shah, P. K., Liu, X., Negre, N., Chen, J., Karpenko, O., White, K. P. & Grossman, R. L. (2009). Flynet: a

genomic resource for Drosophila melanogaster transcriptional regulatory networks. Bioinformatics, 25(22): 3001-

3004.

 

Tirosh, I., Bilu, Y. & Barkai, N. (2007). Comparative biology: beyond sequence analysis. Current opinion in

biotechnology, 18(4): 371-377.

 

Tomasz, M. (1995). Mitomycin C: small, fast and deadly (but very selective). Chemistry & Biology, 2(9): 575-579.

Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Page, N., Robinson, M., Raghibizadeh,

S., Hogue, C. W. V., Bussey, H., Andrews, B., Tyers, M. & Boone, C. (2001). Systematic genetic analysis with

ordered arrays of yeast deletion mutants. Science, 294(5550): 2364-2368.

 

Tu, Z., Wang, L., Arbeitman, M. N., Chen, T. & Sun, F. (2006). An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics, 22(14): e489-e496.

 

Tuncbag, N., McCallum, S., Huang, S. C. & Fraenkel, E. (2012). SteinerNet: a web server for integrating omic data to discover hidden components of response pathways. Nucleic Acids Research, 40(W1): W505-W509.

 

Van Helden, J., Naim, A., Mancuso, R., Eldridge, M., Wernisch, L., Gilbert, D. & Wodak, S. J. (2000). Representing and analysing molecular and cellular function using the computer. Biological chemistry, 381: 921-935.

 

Van Landeghem, S., Ginter, F., Van de Peer, Y. & Salakoski, T. (2011). EVEX: a pubmed-scale resource for

homology-based generalization of text mining predictions. Proceedings of Biomedical Natural Language Processing 2011 Workshop Association for Computational Linguistics (BioNLP11), Portland, Oregon, USA: 28-37.

 

von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P. & Snel, B. (2003). STRING: a database of predicted

functional associations between proteins. Nucleic Acids Research, 31(1): 258-261.

 

von Mering, C., Jensen, L. J., Kuhn, M., Chaffron, S., Doerks, T., Kruger, B., Snel, B. & Bork, P. (2007). STRING 7 - recent developments in the integration and prediction of protein interactions. Nucleic acids research, 35(suppl 1): D358-D362.

 

von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S. G., Fields, S. & Bork, P. (2002). Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417: 399-403.

 

Wang, F. & Lim, A. (2007). A stochastic beam search for the berth allocation problem. Decision Support Systems,

42(4): 2186-2196.

 

Wessels, L., van Someren, E. & Reinders, M. (2001). A comparison of genetic network models. Pacific Symposium on Biocomputing, 6: 508-519.

 

Wilson, N. (2004). Human protein reference database. Nature Reviews Genetics, 5(1): 8.

 

Xenarios, I., Rice, D. W., Salwinski, L., Baron, M. K., Marcotte, E. M. & Eisenberg, D. (2000). DIP: the database

of interacting proteins. Nucleic Acids Research, 28(1): 289-291.

 

Yeang, C. H., Ideker, T. & Jaakkola, T. (2004). Physical network models. Journal of computational biology, 11(2-3): 243-262.

 

Yeang, C. H. & Jaakkola, T. (2003). Physical network models and multi-source data integration. Proceedings of

the Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB '03),

Berlin, Germany: 312-321.

 

Yeger-Lotem, E. & Margalit, H. (2003). Detection of regulatory circuits by integrating the cellular networks of

protein-protein interactions and transcription regulation. Nucleic acids research, 31(20): 6053-6061.

 

Yeger-Lotem, E., Riva, L., Su, L. J., Gitler, A. D., Cashikar, A. G., King, O. D., Auluck, P. K., Geddie, M. L.,

Valastyan, J. S., Karger, D. R., Lindquist, S. & Fraenkel, E. (2009). Bridging high-throughput genetic and

transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nature Genetics, 41(3): 316-323.

 

Yeger-Lotem, E., Sattath, S., Kashtan, N., Itzkovitz, S., Milo, R., Pinter, R. Y., Alon, U. & Margalit, H. (2004). Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America, 101(16): 5934-5939.

 

Yoo, A., Chow, E., Henderson, K., McLendon, W., Hendrickson, B. & Catalyurek, U. (2005). A scalable distributed

parallel breadth-first search algorithm on BlueGene/L. Proceedings of the Association for Computing Machine-

ry/Institute of Electrical and Electronics Engineers Super Computing 2005 Conference (SC'05), Washington DC,

Washington, USA: 19.

 

Yvert, G., Brem, R. B., Whittle, J., Akey, J. M., Foss, E., Smith, E. N., Mackelprang, R. & Kruglyak, L. (2003).

Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature genetics, 35(1): 57-64.

 

Zanzoni, A., Montecchi-Palazzi, L., Quondam, M., Ausiello, G., Helmer-Citterich, M. & Cesareni, G. (2002). MINT:

a Molecular INTeraction database. Federation of European Biochemical Societies Letters, 513(1): 135-140.

 

Zhang, B. & Horvarth, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1): Artikel 17.

 

Zhang, J., Lu, K., Xiang, Y., Islam, M., Kotian, S., Kais, Z., Lee, C., Arora, M., Liu, H., Parvin, J. D. & Huang,

K. (2012). Weighted frequent gene co-expression network mining to identify genes involved in genome stability.

Public Library of Science Computational Biology, 8(8): 1-15.

 

Zhang, X.-S., Garcia-Contreras, R. & Wood, T. K. (2007). YcfR (BhsA) influences Escherichia coli biofilm formation through stress response and surface hydrophobicity. Journal of Bacteriology, 189(8): 3051-3062.

 

Universiteit of Hogeschool
Bio-ingenieurswetenschappen
Publicatiejaar
2014
Kernwoorden
Share this on: