Application of genetic algorithms to parameter estimation of bioprocesses

Park, L J; Park, C H; Park, C; Lee, Taewon

doi:10.1007/bf02510391

Cited by 42 publications

(28 citation statements)

References 3 publications

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“…This validation step was necessary due to differences between the current algorithm and that applied previously (Park et al, 1997). A model for denitrification by Pseudomonas denitrificans (ATCC 13867) under carbon-and/or oxidizednitrogen-limited conditions was used for this purpose (Kornaros et al, 1996).…”

Section: Resultsmentioning

confidence: 99%

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Modeling of biological processes using self-cycling fermentation and genetic algorithms

Pinchuk

Brown

Hughes

et al. 2000

Biotechnol. Bioeng.

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Self-cycling fermentation (SCF) was coupled with a genetic algorithm (GA) to provide a simple system for evaluating biological models. The SCF provided the necessary system excitation and data "richness" required to completely define the fitted biological models. The solution scheme based on the GA avoided the computational difficulties often associated with calculusbased nonlinear regression techniques, resulting in rapid and accurate convergence. After validating the mathematical approach, data from the SCF obtained under denitrifying conditions were fitted successfully to an established model using the GA. Finally, data obtained in the SCF for the removal of phenol were used to compare multiple models. This work suggests that the SCF, in conjunction with the GA, provides a coherent system that can facilitate the characterization of biological systems.

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Section: Resultsmentioning

confidence: 99%

“…Unlike other nonlinear regression techniques, the GA does not require information about the gradient of the function, as differentiation is not required (Park et al, 1997). In fact, it is completely indifferent to the model being fit.…”

Section: Introductionmentioning

confidence: 99%

Modeling of biological processes using self-cycling fermentation and genetic algorithms

Pinchuk

Brown

Hughes

et al. 2000

Biotechnol. Bioeng.

View full text Add to dashboard Cite

show abstract

“…Thus the model is monotonic with respect to all of the parameters if the discretization window, [t 0 , t 2 ] lies within one of the intervals [0, 6]. Determining whether a specific discretization time window yields a model that is monotonic with respect to the parameters over that window is simply a function of the parameter ranges and the ranges of the variables over that window.…”

Section: Exploitation Of Monotonicitymentioning

confidence: 99%

“…Sometimes this purpose is also achieved by allowing the updating step to not only go "downhill" but also occasionally explore far-off regions of parameter space. Various strategies used in global minimization may be applied to this problem [6,7], but regardless of the specific form of the above algorithm, the overall computational effort is typically large. The final result of such a procedure is a point estimate in parameter space that (hopefully) is a best fit of the model to these particular data.…”

Section: Introductionmentioning

confidence: 99%

Parameter range reduction for ODE models using monotonic discretizations

Willms

Szusz

2013

Journal of Computational and Applied Mathematics

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This paper analyzes the effectiveness of various monotonic discretizations of an ODE in a parameter range reduction algorithm. Several properties of discretizations are given, and five classes of discretizations are defined for various step numbers s. The range reduction algorithm that employs these discretizations is described. Using both analytical results based on the prototypical model x ′ = λx, and empirical results based on two more complicated models, it is shown that one particular class of discretizations (the A1OUT class) results in the tightest bounds on the parameters. This result is shown to be attributed to a certain characteristic value, A 0 , of the discretization. Accumulation of these discretizations is also defined, and its usefulness in the range reduction algorithm is described.

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“…Parameter estimation is a recurrent issue in the model building process. It deals with the finding of the numerical values characterizing the mathematical representation of a given system from experimental data [9]. A key feature of the experimental measurements is that they must come from variables representing their main features both at a given particular time as well as along its evolution over time [10].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a Modified Genetic Algorithm for Estimating Parameters in GMA Models

Hormiga¹,

González-Alcón²,

Torres³

2014

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In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathematical model of the amplification and responsiveness of the JAK2/STAT5 pathway representing an actual, experimentally studied system). The two case studies serve to illustrate the utility and potentialities of the MGA method for concerning parameter estimation in complex models of biological significance. The analysis of the results obtained from the application of the MGA algorithm allows an evaluation of the potentialities and shortcomings of the proposed algorithm when compared with other parameter estimation algorithm such as the simple genetic algorithm (SGA) and the simulated annealing (SA). MGA shows better performance in both studied cases than SGA and SA, either in the presence or absence of noise. It is suggested that these advantages are due to the fact that the objective function definition in the MGA could include the experimental error as a weight factor, thus minimizing the distance between the data and the predicted value. Actually, MGA is slightly slower that the SGA and the SA, but this limitation is compensated by its greater efficiency in finding objective values closer to the global optimum. Finally, MGA can lead to an early local optimum, but this shortcoming may be prevented by providing a great population diversity through the insertion of different selection processes.

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Application of genetic algorithms to parameter estimation of bioprocesses

Cited by 42 publications

References 3 publications

Modeling of biological processes using self-cycling fermentation and genetic algorithms

Modeling of biological processes using self-cycling fermentation and genetic algorithms

Parameter range reduction for ODE models using monotonic discretizations

Development and Application of a Modified Genetic Algorithm for Estimating Parameters in GMA Models

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