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

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.

SUBMITTER: Rodriguez-Granrose D

PROVIDER: S-EPMC8144078 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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

Rodriguez-Granrose Daniel D Jones Amanda A Loftus Hannah H Tandeski Terry T Heaton Will W Foley Kevin T KT Silverman Lara L

Bioprocess and biosystems engineering 20210227 6

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 architectur ...[more]

PMID: 33638725

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Project description:BackgroundLittle is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only approximately 50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms.ResultsWe designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested.ConclusionThe motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts.

Dataset Information

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

Publications

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

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