English

G Maria

doi:10.15255/cabeq.2014.2002

Abstract

Modelling bacteria glycolysis is a classical subject but still of high interest. Glycolysis, together with the phosphotransferase (PTS)-system for glucose transport into the cell, the pentose-phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) characterize the central carbon metabolism. Such a model usually serves as the foundation for developing modular simulation platforms used for consistent analysis of the control / regulation of target metabolite synthesis. The present study is focused on analyzing the advantage and limitations of using a simplified but versatile ‘core’ model of mTRM) of glycolysis when incomplete experimental information is available. Exemplification is made for a reduced glycolysis model from literature for Escherichia coli cells, by performing a few modifications (17 identifiable parameters) to increase its agreement with simulated data generated by using an extended model (127 parameters) over a large operating domain of an experimental bioreactor. With the expense of ca. 8–13 % increase in the relative model error vs. extended simulation models, derivation of reduced kinetic structures to describe some parts of the core metabolism is worth the associated identification effort, due to the considerable reduction in model parameterization (e.g. 17 parameters in mTRM vs. 127 in the extendedChassM model of Chassagnole et al.), while preserving a fair adequacy over a wide experimental domain generated in-silico by using the valuable extended ChassM. The reduced model flexibility is tested by reproducing stationary or oscillatory glycolysis conditions. The reduced mTRM model includes enough information to reproduce not only the cell energy-related potential through the total A(MDT)P level, but also the role played by ATP/ADP ratio as a glycolysis driving force. The model can also reproduce the oscillatory behaviour occurrence conditions, as well as situations when homeostatic conditions are not fulfilled.

Highlights

Modelling the central carbon metabolism, and the glycolysis pathway in bacteria is one of the essential bioengineering / bioinformatics topics as long as these models, completed by the –omics data, are considered as a ‘core’ part of any systematic and structured analysis of the cell metabolism with immediate practical applications
The aim of this paper is to extend a comparative analysis of these two valuable models (ChassM and TRM) used for representing the glycolysis in Escherichia coli cells, by proposing a few completions of the reduced TRM to better fit the extended ChassM model predictions over a broader bioprocess operating domain than those experimentally investigated, under both stationary and dynamic conditions
Being part of modular constructions, reduced glycolytic models deserve various objectives, e.g. analysis of genetic regulatory circuits controlling synthesis of target metabolites, flux distribution and in silico reprogramming of some metabolic pathways, whole cell model studies, etc.[7,8,10,25,43,46] iii) The mTRM simplicity is an advantage for easier characterization of the cell system in terms of Metabolic Control Analysis (MCA) definitions.[57,58,43,25,46] iv) Being less parameterized, the required structured and unstructured information from experiments and –omics data banks by the mTRM identification step is considerably smaller than that necessary for extended models

Summary

Introduction

Modelling the central carbon metabolism, and the glycolysis pathway in bacteria is one of the essential bioengineering / bioinformatics topics as long as these models, completed by the –omics data, are considered as a ‘core’ part of any systematic and structured analysis of the cell metabolism with immediate practical applications (such as target metabolite synthesis optimization, in silico reprogramming of the cell metabolism and design of new micro-organisms, bioreactor / bioprocess optim iza tion[1,2,3]) Such representations are able to simulate, in a consistent and accurate way and at a certain degree of detail, the kinetics of a large number of cell biosyntheses and genetic circuits controlling the cell adaptation to environmental changes. Model quality tests, sensitivity analysis of model outputs vs. parameters and species concentrations, principal component, and other algorithms to find the redundant part of the model should be applied.[13,14,15]

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