Parameter estimation in batch bioreactor simulation using metabolic models: Sequential solution with direct sensitivities

Juha Leppävuori, M.M. Domach, L.T. Biegler (Corresponding Author)

    Research output: Contribution to journalArticleScientificpeer-review

    13 Citations (Scopus)

    Abstract

    In this study, we propose a parameter estimation method for genome-scale dynamic flux balance (DFBA) models. A bilevel optimization problem is reformulated as a differential-algebraic equation (DAE) optimization problem and solved sequentially, using gradient-based optimization with direct sensitivity equations. The resulting solution method is computationally efficient for today’s largest genome-scale metabolic models. The parameter estimation method combined with parameter selection algorithm is applied on simulated and experimental data. This paper presents the parameter estimation and selection method and numerical results of estimation of kinetic parameters of the DFBA model of anaerobic batch fermentation. The results show improved computational performance over previous approaches, thus making parameter estimation available for genome-scale DFBA models.
    Original languageEnglish
    Pages (from-to)12080-12091
    Number of pages12
    JournalIndustrial & Engineering Chemistry Research
    Volume50
    Issue number21
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Bioreactors
    Parameter estimation
    Genes
    Fluxes
    Kinetic parameters
    Fermentation
    Differential equations

    Cite this

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    title = "Parameter estimation in batch bioreactor simulation using metabolic models: Sequential solution with direct sensitivities",
    abstract = "In this study, we propose a parameter estimation method for genome-scale dynamic flux balance (DFBA) models. A bilevel optimization problem is reformulated as a differential-algebraic equation (DAE) optimization problem and solved sequentially, using gradient-based optimization with direct sensitivity equations. The resulting solution method is computationally efficient for today’s largest genome-scale metabolic models. The parameter estimation method combined with parameter selection algorithm is applied on simulated and experimental data. This paper presents the parameter estimation and selection method and numerical results of estimation of kinetic parameters of the DFBA model of anaerobic batch fermentation. The results show improved computational performance over previous approaches, thus making parameter estimation available for genome-scale DFBA models.",
    author = "Juha Lepp{\"a}vuori and M.M. Domach and L.T. Biegler",
    year = "2011",
    doi = "10.1021/ie201020g",
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    Parameter estimation in batch bioreactor simulation using metabolic models : Sequential solution with direct sensitivities. / Leppävuori, Juha; Domach, M.M.; Biegler, L.T. (Corresponding Author).

    In: Industrial & Engineering Chemistry Research, Vol. 50, No. 21, 2011, p. 12080-12091.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Parameter estimation in batch bioreactor simulation using metabolic models

    T2 - Sequential solution with direct sensitivities

    AU - Leppävuori, Juha

    AU - Domach, M.M.

    AU - Biegler, L.T.

    PY - 2011

    Y1 - 2011

    N2 - In this study, we propose a parameter estimation method for genome-scale dynamic flux balance (DFBA) models. A bilevel optimization problem is reformulated as a differential-algebraic equation (DAE) optimization problem and solved sequentially, using gradient-based optimization with direct sensitivity equations. The resulting solution method is computationally efficient for today’s largest genome-scale metabolic models. The parameter estimation method combined with parameter selection algorithm is applied on simulated and experimental data. This paper presents the parameter estimation and selection method and numerical results of estimation of kinetic parameters of the DFBA model of anaerobic batch fermentation. The results show improved computational performance over previous approaches, thus making parameter estimation available for genome-scale DFBA models.

    AB - In this study, we propose a parameter estimation method for genome-scale dynamic flux balance (DFBA) models. A bilevel optimization problem is reformulated as a differential-algebraic equation (DAE) optimization problem and solved sequentially, using gradient-based optimization with direct sensitivity equations. The resulting solution method is computationally efficient for today’s largest genome-scale metabolic models. The parameter estimation method combined with parameter selection algorithm is applied on simulated and experimental data. This paper presents the parameter estimation and selection method and numerical results of estimation of kinetic parameters of the DFBA model of anaerobic batch fermentation. The results show improved computational performance over previous approaches, thus making parameter estimation available for genome-scale DFBA models.

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    DO - 10.1021/ie201020g

    M3 - Article

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    EP - 12091

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    JF - Industrial & Engineering Chemistry Research

    SN - 0888-5885

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