  Models in systems biology are mathematical descriptions of biological
processes that are used to answer questions and gain a better understanding of
biological phenomena. Dynamic models represent the network through rates of the
production and consumption for the individual species. The ordinary
differential equations that describe rates of the reactions in the model
include a set of parameters. The parameters are important quantities to
understand and analyze biological systems. Moreover, the perturbation of the
kinetic parameters are correlated with upregulation of the system by
cell-intrinsic and cell-extrinsic factors, including mutations and the
environment changes. Here, we aim at using well-established models of
biological pathways to identify parameter values and point their potential
perturbation/deviation. We present our population-based optimization framework
that is able to identify kinetic parameters in the dynamic model based on only
input and output data (i.e., timecourses of selected metabolites). Our approach
can deal with the identification of the non-measurable parameters as well as
with discovering deviation of the parameters. We present our proposed
optimization framework on the example of the well-studied glycolytic pathway in
Saccharomyces cerevisiae.
