Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement
Year: 2021
Type: article
Abstract: Modern bioprocess development employs statistically optimized design of experiments (DOE) and regression modeling to find optimal bioprocess set points. Using modeling software, such as JMP Pro, it is possible to leverage artificial neural networks (ANNs) to improve model accuracy beyond the capabilities of regression models. Herein, we bridge the gap between a DOE skill set and a machine learning skill set by demonstrating a novel use of DOE to systematically create and evaluate ANN architecture using JMP Pro software. Additionally, we run a mammalian cell culture process at historical, one factor at a time, standard least squares regression, and ANN-derived set points. This case study demonstrates the significant differences between one factor at a time bioprocess development, DOE bioprocess development and the relative power of linear regression versus an ANN-DOE hybrid modeling approach.
Authors: Daniel Rodriguez-Granrose, Amanda Jones, Hannah Loftus, Terry Tandeski, Will Heaton, Kevin T. Foley, Lara Silverman
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,280
Funder: National Science Foundation
OpenAlex ID: W3132912743