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Detailed Biochemical Modelling and Analysis Methodologies for Industrial Biotechnology.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Detailed Biochemical Modelling and Analysis Methodologies for Industrial Biotechnology./
Author:	Angeles Martinez, Liliana.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	215 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-12C.
Contained By:	Dissertation Abstracts International74-12C.
Subject:	Chemical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10174896

Detailed Biochemical Modelling and Analysis Methodologies for Industrial Biotechnology.
Angeles Martinez, Liliana.

Detailed Biochemical Modelling and Analysis Methodologies for Industrial Biotechnology. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 215 p.

Source: Dissertation Abstracts International, Volume: 74-12C.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2015.

Many industrial processes use biological agents as catalysts. In this context, the study of the cellular metabolism becomes relevant for planning the best strategies (environmental and/or genetic modifications) to manipulate the cell in order to maximise the production of a metabolite of interest and minimise the by-products one. This increases the yield of the fermentation and reduces the cost of product recovery; thereby the profitability of the process is improved.The intracellular reactions are carried out in a complex, crowded and heterogeneous medium composed by solid components (macromolecules, ions, enzymes, small solutes, etc.) in a fluid phase called cytoplasm, all of them enclosed within the cellular membrane. The interactions among the intracellular components (as well as with the extracellular environment) determine the behaviour of the organism. The modelling and simulations of these interactions help the understanding of the metabolism. The aim of this thesis is to provide generic tools for the analysis and simulation of metabolic systems under the intracellular environmental conditions. In particular, this research focuses on the estimation of metabolic fluxes and the simulation of the diffusion process.The stoichiometric models have been widely used for the calculation of unmeasured fluxes in a metabolic network, assuming the system is at steady state. The addition of thermodynamic constraints allows only the prediction of fluxes that go in the direction of the Gibbs free energy drop. The Gibbs free energy change ( ) depends on the (intracellular) environmental conditions and determine the direction, feasibility and reversibility of the reactions involved in the pathways. The thermodynamically constrained stoichiometric model proposed here allows the estimation of the range of fluxes of a metabolic network, where the information about the presence of the enzymes that catalyse the reactions can be incorporated (if available). The effect of considering a zero flux reaction as blocked or at equilibrium on the flux predictions was investigated, as well as the environmental conditions ionic strength, temperature and pH. Additionally, since the solid components within the cell occupy about 40% of its total volume, these crowding conditions could alter the thermodynamic feasibility of the pathways. For this reason, the thermodynamically constrained stoichiometric model is extended to incorporate the crowding effect. The case study used in this work is the central carbon metabolic network of Actinobacillus succinogenes for the production of succinic acid from glycerol, a by-product in the biodiesel manufacture.Moreover, the crowding conditions also affect the diffusion of the molecules. The prokaryotic cells have been widely used in fermentation processes for the production of metabolites of interest. In this type of cells the diffusion is the primary mean of the particles' motion, so that the diffusion reduction due to the crowding conditions could affect the possibility of encounter among the reactants, decreasing the reactions' rate and therefore the yield of the process. A methodology based on the Lattice Boltzmann Method (LBM) and the Scaled Particle Theory (SPT) is presented in this thesis for fast simulations of the diffusion of hard-disk molecules in 2D crowded systems, which also allows evaluating the effect of the molecules' size on their diffusion.Subjects--Topical Terms:

555952
Chemical engineering.

Detailed Biochemical Modelling and Analysis Methodologies for Industrial Biotechnology.
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500 $a Source: Dissertation Abstracts International, Volume: 74-12C.
500 $a Adviser: Konstantinos Theodoropoulos.
502 $a Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2015.
520 $a Many industrial processes use biological agents as catalysts. In this context, the study of the cellular metabolism becomes relevant for planning the best strategies (environmental and/or genetic modifications) to manipulate the cell in order to maximise the production of a metabolite of interest and minimise the by-products one. This increases the yield of the fermentation and reduces the cost of product recovery; thereby the profitability of the process is improved.The intracellular reactions are carried out in a complex, crowded and heterogeneous medium composed by solid components (macromolecules, ions, enzymes, small solutes, etc.) in a fluid phase called cytoplasm, all of them enclosed within the cellular membrane. The interactions among the intracellular components (as well as with the extracellular environment) determine the behaviour of the organism. The modelling and simulations of these interactions help the understanding of the metabolism. The aim of this thesis is to provide generic tools for the analysis and simulation of metabolic systems under the intracellular environmental conditions. In particular, this research focuses on the estimation of metabolic fluxes and the simulation of the diffusion process.The stoichiometric models have been widely used for the calculation of unmeasured fluxes in a metabolic network, assuming the system is at steady state. The addition of thermodynamic constraints allows only the prediction of fluxes that go in the direction of the Gibbs free energy drop. The Gibbs free energy change ( ) depends on the (intracellular) environmental conditions and determine the direction, feasibility and reversibility of the reactions involved in the pathways. The thermodynamically constrained stoichiometric model proposed here allows the estimation of the range of fluxes of a metabolic network, where the information about the presence of the enzymes that catalyse the reactions can be incorporated (if available). The effect of considering a zero flux reaction as blocked or at equilibrium on the flux predictions was investigated, as well as the environmental conditions ionic strength, temperature and pH. Additionally, since the solid components within the cell occupy about 40% of its total volume, these crowding conditions could alter the thermodynamic feasibility of the pathways. For this reason, the thermodynamically constrained stoichiometric model is extended to incorporate the crowding effect. The case study used in this work is the central carbon metabolic network of Actinobacillus succinogenes for the production of succinic acid from glycerol, a by-product in the biodiesel manufacture.Moreover, the crowding conditions also affect the diffusion of the molecules. The prokaryotic cells have been widely used in fermentation processes for the production of metabolites of interest. In this type of cells the diffusion is the primary mean of the particles' motion, so that the diffusion reduction due to the crowding conditions could affect the possibility of encounter among the reactants, decreasing the reactions' rate and therefore the yield of the process. A methodology based on the Lattice Boltzmann Method (LBM) and the Scaled Particle Theory (SPT) is presented in this thesis for fast simulations of the diffusion of hard-disk molecules in 2D crowded systems, which also allows evaluating the effect of the molecules' size on their diffusion.
590 $a School code: 1543.
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710 2 $a The University of Manchester (United Kingdom). $3 1213791
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