Systems Biology, Bioinformatics and Livestock Science

Author(s): Prachi P. Parvatikar*, Shrilaxmi Bagali, Pallavi S. Kanthe, Aravind V. Patil and Kusal K. Das

DOI: 10.2174/9789815165616123010008

The Perspective of Physiome Modelling in Systems Biology: New Horizon

Pp: 51-75 (25)

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Abstract

SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.

Abstract

Scientific understanding has rapidly expanded in the new biological age, with the rapid advancement of genomic science and molecular biology, It is a challenge to reintegrate the enormous quantity of information and data that was generated from works related to genomics, transcriptomics, proteomics, and metabolomics in order to effectively explain the organism and connect molecular processes with higher-level biological phenomena. Scientific understanding has expanded quickly in the new biological age due to the rapid advancement of genomic science and molecular biology. This inspired contemporary interest in systems biology, which investigates organisms as integrated systems made up of dynamic and interconnected genetic, protein, metabolic, and cellular components using biology, mathematics, biophysics, biochemistry, bioinformatics, and computer science. Systems biology is the key concept underlying Physiome, a mathematical measure of how an organism functions in normal and pathologic states which is based on morphome. The simulation models based on mathematical expressions and physics can aid in the interpretation and encapsulation of biological phenomena in a computable and repeatable manner. Researchers have created tools and standards to allow the reproducibility and reuse of mathematical models of biological systems, as well as tools and guidelines to promote semantic representation of computational models and repositories where models can be archived, shared, and discovered. 

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