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Bioinformation network

Various information networks such as gene, intercellular, intracellular, sensory, and brain information networks are implemented in biological systems. The information transduction, conduction, and retreaval functions are integrated in these bioinformation networks. Every bioinformation network is totally consisted of organic molecules including protein. A keen interest has been focused on the molecular mechanisms of the information transduction by the biomolecular assemblies because of their excellent selectivity and sensitivity. This chapter concerns protein molecular... [Pg.334]

Molecular communication is the characteristic information system in the bioinformation networks. The endocrine system, which is one of intermolecular information networks, may represent the feature of molecular communication. The gland is a collection of specialized cells that synthesize, store, and release hormones. A hormone, molecular information, is released into the extracellular fluid and transported via the blood to two types of cells target cells where the hormone acts, and other cells that degrade the hormone as schematically presented in Fig.l. In some systems the target cell and the degradation site are in the same organ or even the same cell. Both activities may even be located on the same plasma membrane. The receptor for the hormone is located on the surface of the plasma membrane. [Pg.335]

On the model of the receptors in the bioinformation networks, several types of molecular assemblies may be designed for molecular information transduction. The molecular assembly should contain at least one receptor molecular component that can recognize selectively a specific molecular information. The receptor component responds to a specific molecular information in changing in conformation and electron transfer, which results in information transduction as schematically shown in Fig.4. [Pg.336]

E. Grafahrend Belau, F. Schreiber, M. Heiner, A. Sackmann, B. H. Junker, S. Grunwald, A. Speer, K. Winder, and I. Koch, Modularization of biochemical networks based on classification of Petri net t invariants. BMC Bioinform. 9, 90 (2008). [Pg.245]

The experimental exploration and confirmation of protein functions are relatively slow processes and always require dedicated experiments. The analysis of protein-protein associations as such improved remarkably in quality and speed. This is accompanied by the creation of new databases that will reflect the network of interacting proteins (the Protein Function and Metabolic Pathway project, http //bioinformer.ebi. ac.uk 80/newsletter/archives/4/pfmp.html, and the Biomolecular Interaction Network Database project http //bioinfo.mshri.on.ca/ BIND/). These activities contribute to the idea that cellular mechanisms can be better understood when they are seen as a multicomponent networked process. [Pg.26]

W. J. Heuett and H. Qian. Combining flux and energy balance analysis to model large-scale biochemical networks../. Bioinform. Comput. Biol., 4 1227-1243,... [Pg.299]

Li M, Zeng T, Liu R, Chen L (2014) Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers study of type 2 diabetes by cross-tissue analysis. Brief Bioinform 15(2) 229-243... [Pg.17]

Richon, A. B. Network Science. Available at http //www.netsci.org/Science/Bioinform/ feature06.html... [Pg.11]

Haseong, K., Jae, L., and Taesung, P. (2007). Boolean networks using the chi-square test for inferring large-scale gene regulatory networks. BMC Bioinform., 8 38. [Pg.280]

Kim, S. Y., Imoto, S., and Miyano, S. (2003). Irtferring gene networks from time series microarray data using dynarrric Bayesian networks. Bri. Bioinform., 4(3) 228—235. [Pg.281]

Meissner, M., Schmuker, M., Schneider, G. Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinform. 7, 125 (2006)... [Pg.14]

Yuryev, A., Mulyukov, Z., Kotelnikova, E., Maslov, S., Egorov, S., Nikitin, A., Daraselia, N., Mazo, I. Automatic pathway building in biological association networks. BMC Bioinform. 7,... [Pg.52]

Pantazis, Y., Katsoulakis, M.A., Vlachos, D.G. Parametric sensitivity analysis for biochcanical reaction networks based on pathwise information theory. BMC Bioinform. 14, 311 (2013) Peng, Z., Dobrijevic, M., Hebrard, E., Carrasco, N., Pemot, P. Photochemical modeling of Titan atmosphere at the 10 percent uncertainty horizon . Faraday Discuss. 147, 137-153 (2010) Perger, T., Kovacs, T., Turmyi, T., Trevino, C. Determination of adsorptimi and desorption parameters from ignition temperature measurements in catalytic combustion systems. J. Phys. Chem. B 107, 2262-2274 (2003)... [Pg.139]


See other pages where Bioinformation network is mentioned: [Pg.388]    [Pg.244]    [Pg.78]    [Pg.56]    [Pg.282]    [Pg.41]   
See also in sourсe #XX -- [ Pg.323 ]




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