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Modeling and Simulation of Metabolic Networks for Estimation of Biomass Accumulation Parameters

Ugur Kaplan(ukaplan***at***ku.edu.tr)
Metin Turkay(mturkay***at***ku.edu.tr)
Lorenz T. Biegler(biegler***at***cmu.edu)
Bulent Karasozen(bulent***at***metu.edu.tr)

Abstract: Metabolic networks are defined as the collection of biochemical reactions within a cell that define the functions of that cell. Due to the growing need to understand the functions of biological organisms for industrial and medical purposes, modeling and simulation of metabolic networks has attracted a lot of attention recently. Traditionally, metabolic networks are modeled such as flux-balance analysis that considers steady-state nature of the cell. But it is important to consider the dynamic behavior of a cell since the environmental conditions continuously change. Sometimes due to the critical changes in the environment some of the reactions exhibit completely different behavior leading to discrete changes in the metabolic network. Therefore, a cell exhibits discrete-continuous behavior in continuous time. Since hybrid systems exhibit the same characteristics, modeling a cell as a hybrid system gives an accurate representation. The aim of this paper is to develop a simulation framework to model the evolving structure of the cell metabolism under changes in the environment. The metabolic responses that cell gives, against multiple changes in the environment are not fully understood. Therefore, in this study, a cell is modeled as a hybrid system that is composed of a system of differential and algebraic equations. The changes in the concentration of metabolites in the environment are represented by Ordinary Differential Equations and intracellular cell metabolism is represented by a set of algebraic equations. To understand the feedback relationship between intracellular and extracellular changes, the system is solved considering the effects of extra cellular stresses on the metabolic responses.

Keywords: metabolic networks, hybrid systems, parameter estimation,

Category 1: Applications -- Science and Engineering (Biomedical Applications )

Category 2: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Citation: in print, Discrete Applied Mathematics

Download: [PDF]

Entry Submitted: 04/13/2007
Entry Accepted: 04/13/2007
Entry Last Modified: 04/13/2007

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