Markov Chain Process (Theory and Cases)

Computational Biology Issues

Author(s): Carlos Polanco * .

Pp: 71-77 (7)

DOI: 10.2174/9789815080476123010013

* (Excluding Mailing and Handling)

Abstract

This chapter defines Discrete and Continuous-Time Markov Chain Process aimed to identify the preponderant function of a protein from the analysis of its sequence, adapting the matrix of transition probabilities so that the elements of the latter are occupied by the relative frequencies of the interactions of the pairs of amino acids located there. The chapter illustrates in detail this methodology and robustness to rescue the preponderant activity among other possible functions that the protein could offer, if minimal changes were made in its primary structure. The present approach is presumed to be used for the construction of synthetic proteins. This chapter defines Discrete and Continuous-Time Markov Chain Process aimed to identify proteins from the specific regularities found in their sequences.

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