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Models in systems biology are mathematical descriptions of biologicalprocesses that are used to answer questions and gain a better understanding ofbiological phenomena. Dynamic models represent the network through rates of theproduction and consumption for the individual species. The ordinarydifferential equations that describe rates of the reactions in the modelinclude a set of parameters. The parameters are important quantities tounderstand and analyze biological systems. Moreover, the perturbation of thekinetic parameters are correlated with upregulation of the system bycell-intrinsic and cell-extrinsic factors, including mutations and theenvironment changes. Here, we aim at using well-established models ofbiological pathways to identify parameter values and point their potentialperturbation/deviation. We present our population-based optimization frameworkthat is able to identify kinetic parameters in the dynamic model based on onlyinput and output data (i.e., timecourses of selected metabolites). Our approachcan deal with the identification of the non-measurable parameters as well aswith discovering deviation of the parameters. We present our proposedoptimization framework on the example of the well-studied glycolytic pathway inSaccharomyces cerevisiae.

Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae