Workflow to set up substantial target-oriented mechanistic process models in bioprocess engineering. (November 2017)

• Record Type: Journal Article
• Authors: Kroll, Paul; Hofer, Alexandra; Stelzer, Ines V.; Herwig, Christoph
• Is Part Of: Process biochemistry. Volume 62(2017)
• Page Start: 24
• Page End: 36
• Publication Date: 2017-11
• Subjects: CHO Chinese hamster ovary -- DCC dead cell count -- DoE design of experiment -- NRMSE normalized root-mean-square error -- VCC viable cell count -- ANN artificial neural network -- Glu glutamine -- Glc glucose -- Lac lactate -- NH4 ammonia -- LS limiting substrate -- ODE ordinary differential equations -- QbD quality by design; Modeling workflow -- Substantial target-oriented mechanistic models -- Mechanistic links -- Practical identifiability -- Bioprocess engineering,; Biochemical engineering -- Periodicals; Biotechnology -- Periodicals; Biochemistry -- periodicals; Biotechnology -- periodicals; Chemical Engineering -- periodicals; Génie biochimique -- Périodiques; Biotechnologie -- Périodiques; Biochemical engineering; Biotechnology; Periodicals; 660.63
• DOI: 10.1016/j.procbio.2017.07.017
• Languages: English
• ISSNs: 1359-5113

Abstract

Graphical abstract: Highlights: Simplified model construction with mechanistic links. Practical identifiability analysis to assess model validity. Powerful tool for bioprocess development. Abstract: A multitude of new applications in bioprocess technology strongly depend on model-based methods as they feature prediction and control capabilities. The critical path is usually the availability of suitable models. In this work a workflow for the generation of substantial target-oriented mechanistic process models is presented. This workflow is based on backpropagation starting from a material balance for a certain target variable. Iteratively, necessary states as well as mechanistic links are included in the model using a model library reducing the computational effort. The parameters of these links are estimated using a simplex algorithm whose objective function depends on the target variable only. Practical identifiability analysis is used for the assessment of the need of further iterations and for validating the mechanistic model. To demonstrate the workflow, a model describing a mammalian cell culture process aiming at modeling viable cell count is used as an example. The generated model satisfies the predefined requirements and is very simple, consisting of three states and seven model parameters only. The presented workflow is simple, generic, transparent, so that also applications in a regulatory environment should be possible. It also provides additional process knowledge that can be used in bioprocess development and optimization.