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)


    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
    Issue number21
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed


    Cite this