A Data Mining Approach To Gen Dynamic Behavioral Process

Abstract

Biology systems theory and cell biology have enjoyed a long relationship, which, in the context of systems biology, has received renewed interest in recent years by computer science as bioinformatics models. It has often been noted that computer simulation, by providing explicit hypotheses for a particular system and bridging across different levels of organization, can provide an organizational focus which can be leveraged to form substantive hypotheses. Simulations lend meaning to data and can be updated and adapted as further data comes in. Systems biology is concerned with the dynamic behavior of biochemical reaction networks within cells and in cell populations. The biologist’s conceptual frameworks, in which to identify the variables of a biochemical reaction network and to describe their relationships, are pathway maps. A principal goal of systems biology is therefore to turn these static maps into dynamical models. In this paper introduces a data mining approach can be use to process of dynamic behavior of a biochemical network from different perspectives. Most bioinformatics tools require specialized input formats for sequence comparison and analysis. This is particularly true for molecular phylogeny programs, which accept only certain formats. In addition, it is often necessary to eliminate highly similar sequences among the input, especially when the dataset is large. Moreover, most programs have restrictions upon the sequence name