Modeling bioprocess scale-up utilizing regularized linear and logistic regression

Muhammad Farhan, Antti Larjo, Olli Yli-Harja, Tommi Aho

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    Bioprocess scale-up from optimized flask cultivations to large industrial fermentations carries technical challenges and economical risks. Essentially, the prediction of optimal process conditions in large fermentations based on small scale experiments is non-trivial. For example, common statistical methods encounter problems with the high-dimensional, small sample size and, on the other hand, the use of various scale-up criteria requires a priori knowledge that may be difficult to obtain. We propose a novel computational scale-up approach applicable to various bioprocesses. The method bases on regularized linear and logistic regression. With embedded feature selection, it automatically identifies the most influential parameters and predicts their values in large scale. In addition, the method predicts the resulting large scale yield. As a case study, we examined the production of a cytotoxic compound. We predicted scale-up from flask and 2L to 30L fermentations and found that, in both cases, the product yield predictions are close to experimentally observed yields.

    Original languageEnglish
    Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Workshop on Machine Learning for Signal Processing - Southampton, United Kingdom
    Duration: 22 Sep 201325 Sep 2013
    Conference number: 16

    Workshop

    WorkshopIEEE International Workshop on Machine Learning for Signal Processing
    Abbreviated titleMLSP
    CountryUnited Kingdom
    CitySouthampton
    Period22/09/201325/09/2013

    Keywords

    • Bioprocesses modeling
    • regression model
    • regularized logistic regression
    • scale-up
    • yield prediction

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