Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-8597
Main Title: Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design
Author(s): Krausch, Niels
Barz, Tilman
Sawatzki, Annina
Gruber, Mathis
Kamel, Sarah
Neubauer, Peter
Cruz-Bournazou, Mariano Nicolas
Type: Article
Language Code: en
Abstract: Especially in biomanufacturing, methods to design optimal experiments are a valuable technique to fully exploit the potential of the emerging technical possibilities that are driving experimental miniaturization and parallelization. The general objective is to reduce the experimental effort while maximizing the information content of an experiment, speeding up knowledge gain in R&D. The approach of model-based design of experiments (known as MBDoE) utilizes the information of an underlying mathematical model describing the system of interest. A common method to predict the accuracy of the parameter estimates uses the Fisher information matrix to approximate the 90% confidence intervals of the estimates. However, for highly non-linear models, this method might lead to wrong conclusions. In such cases, Monte Carlo sampling gives a more accurate insight into the parameter's estimate probability distribution and should be exploited to assess the reliability of the approximations made through the Fisher information matrix. We first introduce the model-based optimal experimental design for parameter estimation including parameter identification and validation by means of a simple non-linear Michaelis-Menten kinetic and show why Monte Carlo simulations give a more accurate depiction of the parameter uncertainty. Secondly, we propose a very robust and simple method to find optimal experimental designs using Monte Carlo simulations. Although computational expensive, the method is easy to implement and parallelize. This article focuses on practical examples of bioprocess engineering but is generally applicable in other fields.
URI: https://depositonce.tu-berlin.de/handle/11303/9544
http://dx.doi.org/10.14279/depositonce-8597
Issue Date: 24-May-2019
Date Available: 25-Jun-2019
DDC Class: 570 Biowissenschaften; Biologie
Subject(s): Monte Carlo
design of experiments
variance analysis
modeling
dynamic processes
Sponsor/Funder: DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Frontiers in Bioengineering and Biotechnology
Publisher: Frontiers Media
Publisher Place: Lausanne
Volume: 7
Article Number: 122
Publisher DOI: 10.3389/fbioe.2019.00122
EISSN: 2296-4185
Appears in Collections:FG Bioverfahrenstechnik » Publications

Files in This Item:
File Description SizeFormat 
fbioe-07-00122.pdf2.75 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons