Design and development of a model for sequences and structure of biomolecules

Abstract

In the recent past it was observed that a growing interest of computer professionals in the field of molecular biology has resulted into development of computational techniques and models for experimental data generated in laboratories. Finite automata, formal grammar and molecular computing can be viewed as a hope for reducing the huge gap between laboratory experiments and computational models. Molecular biology experiments with bio-molecules such as DNA, RNA and protein sequences. Proteins are produced from DNA. These proteins are molecules made up from twenty different amino acids. Proteins also carry most functional activities like regulation and biosynthesis in cells. The important function of a protein is to interact with other proteins and molecules to keep the cell alive and interacting with nature. Biologists wishes to find the biological meaning of some portions of bio-molecular sequences. The identification and prediction of these molecules includes identifying sequences, structures and behavior. Modeling and prediction of biomolecules is very complicated and it requires all the mathematical, statistical and computational tools that we can create. The scientists and researchers have tried various techniques such as Hidden Markov Model, Artificial Neural Networks, and Natural Language Processing (NLP) etc for modeling and prediction of biomolecules. In this research work an attempt is being made to apply the NLP techniques in the current work for modeling the sequences and structure of biomolecules from the available biological data in biological field and to use the proposed techniques for finding the characteristics of bio-molecules.