Bioinformatics networks

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the 3D structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70%. 

Cavalieri D, De Filippo C. Bioinformatic methods for integrating whole-genome expression results into cellular networks. Drug Discov Today 2005 10 727-34. 

Cabusora L, Sutton E, Eulmer A, Eorst CV. Differential network expression during drug and stress response. Bioinformatics 2005 21 2898-905. 

Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML) a medium for representation and exchange of biochemical network models. Bioinformatics 2003 19 524-31. 

Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway stndio—the analysis and navigation of molecular networks. Bioinformatics 2003 19 2155-7. 

Chen H, Sharp BM. Content-rich biological network constructed by mining. BMC Bioinformatics 2004 5 147. 

The specificity determinants surrounding the tyrosine phospho-acceptor sites have been determined by various procedures. In PTK assays using various substrates, it was determined that glutamic residues of the N-terminal or C-terminal side of the acceptor are often preferred. The substrate specificity of PTK catalytic domains has been analyzed by peptide library screening for prediction of the optimal peptide substrates. Finally, bioinformatics has been applied to identify phospho-acceptor sites in proteins of PTKs by application of a neural network algorithm. 

Schachter V. Bioinformatics of large-scale protein interaction networks. Biotechniques 2002 32(Suppl.) S16-S27. 

Yang, Z. R., Thomson, R., McNeil, P., and Esnouf, R. M. (2005). RONN The bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21, 3369-3376. 

H. Ma and A. P. Zeng, Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19(2), 270 277 (2003). 

R. Steuer and G. Zamora Lopez, Global network properties. Analysis of Biological Networks, Wiley Series on Bioinformatics Computational Techniques and Engineering. B. H. Junker and F. Schreiber, eds., John Wiley Sons, Inc. 2008. 

R. Schwarz, C. Liang, C. Kaleta, M. Kiihnel, E. Hoffmann, S. Kuznetsov, M. Hecker, G. Griffiths, S. Schuster, and T. Dandekar, Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinformatics 8, 313 (2007). 

Robert Urbanczik, SNA a toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinformatics 7, 129 (2006). 

S. L. Bell and B. 0. Palsson, Expa A program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 21(8), 1739 1740 (2005). 

M. D. Haunschild, B. Freisleben, R. Takors, and W. Wiechert, Investigating the dynamic behavior of biochemical networks using model families. Bioinformatics 21(8), 1617 1625 (2005). 

F. Jourdan, R. Breitling, M. P. Barrett, and D. Gilbert, MetaNetter Inference and visualization of high resolution metabolomic networks. Bioinformatics 24(1), 143 145 (2008). 

Handling such interwoven networks and complex feedback loops is beyond the capability of common laboratory methods, not to mention that just the complexity of scientific literature itself is already beyond measure. Help from computers and bioinformatics has become a must in today s biomedical research. In fact, bioinformatics methods have become indispensable for each step in biomedical research, from high-throughput data collection to clinical decision support. This chapter focuses on the application of bioinformatics methods in the study of pharmacogenomics, drug discovery, and systems biology. 

Finally, it is the choice of bioinformatics software that enables the researcher to quickly assess obtained array data. Clustering using self-organizing maps and literature networks are ideally suited for this task. 

Ball, G., Mian, S., Holding, R, Allibone, R.O., Lowe, J., Ali, S., Li, G., McCardle, S., Ellis, I.O., Creaser, C., and Rees, R.C., An integrated approach utilizing neural networks and SELDI mass spectrometry for classification of human tumours and rapid identification of potential biomarkers. Bioinformatics, 18, 395 04, 2002. 

Proteomics includes a variety of technologies that include differential protein display on gels, protein chips, quantitation of protein amoimts, analysis of post-translational modifications, characterization of protein complexes and networks and bioinformatics. All this information in combination with genome and phenotype studies will ultimately yield a comprehensive picture of a cellular or tissue proteome (Wasinger and Corthals 2002). 

Rahman SA, Schomburg D (2006) Observing local and global properties of metabolic pathways load points and choke points in the metabolic networks. Bioinformatics 22 1767-1774... 

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