Mathematical Modeling Of Systems Biology

Abstract

ABSTRACTA modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions by which a real world problem can be described by a mathematical formulation. It has become indispensable tools for integrating and interpreting heterogeneous biological data, validating hypothesis and identifying potential diagnostic markers. The modern molecular biology that is characterized by experiments that reveal the behaviours of entire molecular systems is called systems biology. A fundamental step in synthetic biology and systems biology is to derive appropriate mathematical model for the purposes of analysis and design. This manuscript has been engaged in the use of mathematical modeling in the Gene Regulatory System (GRN). Different mathematical models that are inspired in gene regulatory network such as Central dogma, Hill function, Gillespie algorithm, Oscillating gene network and Deterministic vs Stochastic modelings are discussed along with their codes that are programmed in Python using different modules. Here, we underlined that the model should describes the continuous nature of the biochemical processes and reflect the non-linearity. It is also found that the stochastic model is far better than deterministic model to calculate future event exactly with low chance of error.