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Title: Studies on the control of time-dependent metabolic processes
Author: Acerenza, Luis
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1991
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Sensitivity analysis studies how changes in the parameters affect the system's variables. Its application to metabolic systems (Metabolic Control Analysis, MCA) was traditionally developed under certain assumptions:i) the steady state is stable (the effect on the steady state values only is studied).ii) each reaction is catalyzed by one enzyme, the rates being proportional to the corresponding enzyme concentration.iii) the parameters are changed by a small (strictly speaking infinitesimal) amount. In the present work MCA is extended to deal with the instantaneous values of time-dependent metabolite concentrations and fluxes. Their summation and connectivity relationships are derived. In some cases it is more convenient to characterize the time courses by time-invariant variables (such as period and amplitude in oscillating systems). Summation relationships for time-invariant variables are also derived. Stability analysis shows that a linear chain of four enzyme-catalized reactions, where the third metabolite is a negative effector of the first enzyme constitutes a 'minimal' oscillator. The model is used to gain insight into the control of oscillations. The control exerted by enzyme concentrations and other parameters that are not proportional to the rate is appropriately described by parameter-unspecified coefficients (Cv). A proof of the theorems of steady-state MCA in terms of Cv is given. By a similar procedure an attempt is made to derive the theorems in terms of Cv for time-dependent systems, which is only successful for the particular case of constant π-matrix. The effect that a simultaneous change in all the enzyme concentrations by the same factor α (Coordinate-Control Operation. CCO) has on the variables of time-dependent metabolic systems is investigated. This factor α can have any arbitrary large value. The metabolic variables are classified according to the relationships they fulfil when the CCO is applied. A method is given to test these relationships in experimental systems and quantify deviations from the predicted behaviour.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available