Bioinformatics linearization

MENDES, P., KELL, D.B., Non-linear optimization of biochemical pathways applications to metabolic engineering and parameter estimation, Bioinformatics, 1998, 14, 869-883. 

The current situation in bioinformatics is characterized by an avalanche of DNA sequences from the human genome project and similar programs and, consequently, an exponential increase in DNA sequences but only a linear increase in protein 3D structures. While multitudes of putative genes have been annotated, up to 90% of all known DNA sequences have no assigned, i.e., experimentally proven, function. From this situation arise the need for interpretation of DNA sequences by information technology, and moreover, analysis of functional genomics and proteomics (see Chapter 15). 

Sequence analysis is a core area of bioinformatics research. There are four basic levels of biological structure (Table 1), termed primary, secondary, tertiary, and quaternary structure. Primary structure refers to the representation of a linear, hetero-polymeric macromolecule as a string of monomeric units. For example, the primary structure of DNA is represented as a string of nucleotides (G, C, A, T). Secondary structure refers to the local three-dimensional shape in subsections of macromolecules. For example, the alpha- and beta-sheets in protein structures are examples of secondary structure. Tertiary structure refers to the overall three-dimensional shape of a macromolecule, as in the crystal structure of an entire protein. Finally, quaternary structure represents macromolecule interactions, such as the way different peptide chains dimerize into a single functional protein. 

El-Manzalawy Y, Dobbs D, HonavarV (2008) Predicting flexible length linear B-cell epitopes. Proceedings of the 7th international conference on computational systems bioinformatics, Imperial College Press, London, pp 121-131... 

The analysis of a proteome, described as the ensemble of the proteins expressed by a genome in a given tissue, for a given organism at a given time, requires to use and to combine a number of procedures, both experimental (wet-lab experiments) and bioinformatics (dry-lab experiments). Due to the chemical and physical complexity of proteomes, various methodological approaches have to be considered. Nevertheless, a consensus principle of proteome analysis can be described as in Figure 4.1. This linear pathway includes most of the wet- and dry-lab steps required for the complete analysis of a proteome. 

Pey, J., Villar, J.A., Tobalina, L., Rezola, A., Garcia, J.M., Beasley, J.E., and Planes, F.J. (2015) TreeEFM calculating elementary flux modes using linear optimization in a tree-based algorithm. Bioinformatics, 31 (6), 897-904. 

Scholz,M.,Kaplan,E,Guy,C.L.,Kopka,X,Selbig,X(2005) Non-linear PCA a missing data approach. Bioinformatics, 21, 3887-3895. 

Caraux G, Pinloche S. PermutMatrix a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics. 2005 21 1280-1. doi 10.1093/bioinformatics/btil41. 

Hernandez, A., and M. T. Ruiz. 1998. An Excel Template for Calculation of Enzyme Kinetic Parameters by Non-Linear Regression. Bioinformatics 14 (2) 227-228. 

De Hoon, M. J., S. Imoto, et al. (2002). "Statistical analysis of a small set of time-ordered gene expression data using linear splines." Bioinformatics 18(11) 1477-1485. 

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