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

TL;DR

This paper introduces a population-based optimization method to identify kinetic parameters in biological pathway models, specifically applied to the glycolytic pathway in yeast, using only input-output metabolite data.

Contribution

The paper presents a novel optimization framework capable of identifying non-measurable parameters and detecting deviations in biological pathway models.

Findings

Successfully identified kinetic parameters in yeast glycolysis

Can detect parameter deviations and perturbations

Applicable to models with limited data

Abstract

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.