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The stochastic nature of convergence of steady state stochastic global optimization methods in design optimization tasks with steady state precondition is a hardly predictable step in development of industrially efficient strains of microorganisms.
Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic… (More)
The search for minimal set of adjustable parameters through optimising a kinetic model of biochemical networks is needed in industrial biotechnology to increase the productivity of industrial organism strains while keeping low the chance of causing unwanted side effects of implemented changes. As the search for minimal set of adjustable parameters is of… (More)
Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important to for reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience- and intuition-based subsets of… (More)
In case of optimization of biochemical networks the best combination of adjustable parameters has to be found keeping low the costs of modification of biochemical network and reducing the risk of unpredicted side effects of processes outside the scope of the model. Dynamic models of biochemical networks usually are in form of system of differential… (More)