Background: Kinetic models with predictive ability are important to be used in industrial biotechnology.
However, the most challenging task in kinetic modeling is parameter estimation, which
can be addressed using metaheuristic optimization methods. The methods are utilized to minimize scalar
distance between model output and experimental data. Due to highly nonlinear nature of biological systems
and large number of kinetic parameters, parameter estimation becomes difficult and time consuming.
Methods: This paper provides a review on recent development of parameter estimation methods, which
has received increasing attention in the field of systems biology. The development of metaheuristic optimization
methods is mostly focused in this review along with the development of large-scale kinetic
Results: Although a plethora of methods have been applied to the problem of parameter estimation, recent
results show that most of the successful approaches are those based on hybrid methods and parallel
strategies. In addition, the current software used for parameter estimation and the sources of biological
data for kinetic modeling are also described in this review. This review also presents future direction in
parameter estimation to meet current industrial demands, especially in systems biology applications.
Conclusion: The development of numerous optimization methods for parameter estimation in kinetic
models has brought much advancement in the application of systems biology. Currently, it seems that
there are highly demanded for further development of efficient optimization methods to address the expansion
of systems biology applications.