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 equations and therefore can not be optimized analytically. Usually they are optimized using computationally demanding global stochastic optimization

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... methods which may have hardly predictable duration of optimization. Optimization of all the possible combinations is necessary to find the best set of adjustable parameters per number of parameters in combination. Due to the combinatorial explosion the full search often is not feasible approach if computational and time resources are limited. In this study the performance of full search is compared with forward selection and three metabolic control based methods which need significantly less combinations to be examined. It is found that forward selection is a good compromise in case of larger number of adjustable parameters where the number of possible combinations exceeds the available resources needed for full search in the adjustable parameter space. It is proposed to combine full search for small number of adjustable parameters with forward selection at larger number of adjustable parameters per combination.