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Rodriguez-Granrose D, Jones A, Loftus H, et al. Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess Biosyst Eng. 2021;44(6):1301-1308doi: 10.1007/s00449-021-02529-3.
Rodriguez-Granrose, D., Jones, A., Loftus, H., Tandeski, T., Heaton, W., Foley, K. T., & Silverman, L. (2021). Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess and biosystems engineering, 44(6), 1301-1308. https://doi.org/10.1007/s00449-021-02529-3
Rodriguez-Granrose, Daniel, et al. "Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement." Bioprocess and biosystems engineering vol. 44,6 (2021): 1301-1308. doi: https://doi.org/10.1007/s00449-021-02529-3
Rodriguez-Granrose D, Jones A, Loftus H, Tandeski T, Heaton W, Foley KT, Silverman L. Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement. Bioprocess Biosyst Eng. 2021 Jun;44(6):1301-1308. doi: 10.1007/s00449-021-02529-3. Epub 2021 Feb 27. PMID: 33638725; PMCID: PMC8144078.
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Bioprocess Biosyst Eng. 2021 Jun;44(6):1301-1308. doi: 10.1007/s00449-021-02529-3. Epub 2021 Feb 27.

Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement.

Bioprocess and biosystems engineering

Daniel Rodriguez-Granrose, Amanda Jones, Hannah Loftus, Terry Tandeski, Will Heaton, Kevin T Foley, Lara Silverman

Affiliations

  1. DiscGenics Inc, Salt Lake City, Utah, USA. [email protected].
  2. Department of Biochemistry and Molecular Biology, University of Miami, Miami, FL, USA. [email protected].
  3. DiscGenics Inc, Salt Lake City, Utah, USA.
  4. Department of Neurosurgery, University of Tennessee Health Science Center, Memphis, TN, USA.
  5. Semmes-Murphey Clinic, Memphis, TN, USA.

PMID: 33638725 PMCID: PMC8144078 DOI: 10.1007/s00449-021-02529-3

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.

Keywords: Artificial neural network; Bioprocess; Design of experiments (DOE); MachineLearning; Process modeling

References

  1. Psychol Rev. 1958 Nov;65(6):386-408 - PubMed
  2. Biotechnol Bioeng. 2001 Apr 5;73(1):55-68 - PubMed
  3. Biotechnol Prog. 2008 Nov-Dec;24(6):1191-203 - PubMed
  4. Bioprocess Biosyst Eng. 2004 Dec;27(1):9-15 - PubMed
  5. Spine J. 2020 Jan;20(1):138-149 - PubMed

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