Every system naturally has nonlinear dynamics. We classify nonlinearity based on whether it's inherent or intentionally added. Inherent nonlinearity which is unavoidable, appears as dead zones, saturation, and friction. Intentional nonlinearity purposely added, alters system performance. Accurate system parameter estimation is crucial for modeling and control design. It allows us to develop models that closely mimic actual system behavior and design control strategies. Initial conditions, system dynamics, and input and output data are vital in identifying system parameters. The accuracy of identified models relies on high-quality data and an understanding of system dynamics. The proposed survey aims to highlight the importance of parameter estimation in nonlinear biological control systems. It introduces a symbolic method to characterize system inputs. Nonlinear observability and extended Lie theory are used to analyze and determine structural identifiability. A suitable methodology integrates these methods to assess structural identifiability. It involves deriving mathematical expressions and designing input profiles to enhance identifiability. MATLAB and SIMULINK are used for simulation and analysis.