Convergence dynamics of biochemical pathway steady state stochastic global optimization

Mozga, Ivars; Stalidzāns, Egils

doi:10.1109/cinti.2011.6108504

Cited by 5 publications

(5 citation statements)

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“…Thus the screening of all the combinations with optimization runs is very demanding in terms of time and computational resources. complicated or almost impossible due to the huge number of combinations and unpredictable optimization time to reach the global optimum close objective function value (Mozga and Stalidzans, 2011a). The main advantage is that checking all the possible combinations it is guaranteed that the best one is found.…”

Section: Full Searchmentioning

confidence: 99%

“…Global stochastic optimization methods are often used to solve both kind of tasks (Banga, 2008;Rodriguez-Fernandez et al, 2006). Some disadvantages of global stochastic optimization methods are 1) the hardly predictable duration of optimization (Mozga and Stalidzans, 2011a;Nikolaev, 2010) and 2) possible stagnation in local optima (Mozga and Stalidzans, 2011b;Sulins and Mednis, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Determinition of best set of adjustable parameters with full search and limited search methods

Mozga

2013

Bit-Journal

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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 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.

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Section: Full Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determinition of best set of adjustable parameters with full search and limited search methods

Mozga

2013

Bit-Journal

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“…The model contains 2 compartments, 24 reactions and 25 metabolites. Objective function in all optimization runs was The sets of adjustable parameters and the optimization method were chosen on purpose to observe the stagnation behaviour (Mozga and Stalidzans, 2011b). Concentrations of enzymes catalysing five reactions (Hexokinase, Alcohol dehydrogenase, ATP consumption, Glycerol synthesis, Phosphofructokinase) were chosen as adjustable parameters.…”

Section: Methodsmentioning

confidence: 99%

“…It is suggested to test another optimization method or settings of the method to improve the performance. In case if several methods perform similar way it might indicate the peculiarity of the model or particular set of adjustable parameters (Mozga and Stalidzans, 2011c).…”

Section: Stagnationmentioning

confidence: 99%

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Automatic termination of parallel optimization runs of stochastic global optimization methods in consensus or stagnation cases

Sulins¹,

Mednis²

2012

BIT-journal

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Two stage optimization of biochemical pathways using parallel runs of global stochastic optimization methods

Stalidzāns

Kostromins

Sulins

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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Convergence dynamics of biochemical pathway steady state stochastic global optimization

Cited by 5 publications

References 24 publications

Determinition of best set of adjustable parameters with full search and limited search methods

Determinition of best set of adjustable parameters with full search and limited search methods

Automatic termination of parallel optimization runs of stochastic global optimization methods in consensus or stagnation cases

Two stage optimization of biochemical pathways using parallel runs of global stochastic optimization methods

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