title: Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.
abstract: The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Austrian Centre of Industrial Biotechnology, 8010 Graz, Austria.|Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria.
authors.forename: Michael
authors.initials: M
authors.lastname: Melcher
authors.affiliation: Austrian Centre of Industrial Biotechnology, 8010 Graz, Austria.|Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria.
authors.forename: Theresa
authors.initials: T
authors.lastname: Scharl
authors.affiliation: Austrian Centre of Industrial Biotechnology, 8010 Graz, Austria.|Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
authors.forename: Markus
authors.initials: M
authors.lastname: Luchner
authors.affiliation: Austrian Centre of Industrial Biotechnology, 8010 Graz, Austria.|Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
authors.forename: Gerald
authors.initials: G
authors.lastname: Striedner
authors.affiliation: Austrian Centre of Industrial Biotechnology, 8010 Graz, Austria.|Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria.
authors.forename: Friedrich
authors.initials: F
authors.lastname: Leisch
mesh_terms: D000465:Algorithms; D061249:Batch Cell Culture Techniques / Q000379:methods*; D019149:Bioreactors / Q000382:microbiology; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D005285:Fermentation; D000069550:Machine Learning; D008954:Models, Biological; D011994:Recombinant Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D012044:Regression Analysis; D012995:Solubility
chemical_list: D011994:Recombinant Proteins
keywords: Escherichia coli; boosting; machine learning; modeling; recombinant protein production; structured additive regression model