Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Abstract

Due to the complexity of biological processes, developing model-based strategies for monitoring, optimization and control is nontrivial. Hybrid neural models, combining mechanistic modeling with artificial neural networks, have been reported as powerful tools for bioprocess applications. In this paper, a systematic literature review is presented focused on the application of hybrid neural models to bioprocesses by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) over the last 30 years. This analysis showed that hybrid neural modeling has covered a wide range of microbial processes, animal cells, mixed microbial cultures, and enzyme biocatalysis. Hybrid neural models have been mainly applied for predictive modeling/process analysis, process monitoring/software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, process analytical technology, quality-by-design, and more recently, digital twins. Hybrid modeling experienced a decline in the number of publications after a peak in 2004 and is now surging again. A “model scale” research gap was identified, which will likely narrow by a better integration with deep learning and systems biology in the near future. The biopharma sector is currently a major driver but applications to biologics quality attributes (e.g. glycosylation), new modalities and downstream unit operations are significant research gaps.

Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction

“…Hybrid modeling naturally pops up as a digitalization framework as it allows for integrating prior mechanistic knowledge with large volumes of process data in a straightforward way. Hybrid modeling is a well-established framework in process system engineering [6] and in bioprocessing [7]. It has covered a wide range of biological system applications for process…”

A General SBML Compatible Hybrid Modeling Framework: Combining Biochemical Mechanisms with Deep Neural Networks for Systems Biology Applications

“…Many studies followed covering a wide array of microbial, animal cells, mixed microbial and enzyme biocatalysis problems in different industries such as wastewater treatment, clean energy, biopolymers and biopharmaceutical manufacturing (e.g. review by Agharafeie et al [6]). Hybrid models have been mainly applied for predictive modeling/process analysis, process monitor-Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

“…2 of 13 ing/software sensors, open-and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, process analytical technology, qualityby-design and more recently digital twins mostly for upstream processing [2,6].…”

A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard

“…Since the early 1990s, hybrid model structure definition, parameter identification and model-based process control have been extensively covered (e.g., [5][6][7][8][9][10]). Hybrid models were applied to a wide array of microbial, animal cells, mixed microbial and enzyme processes in different industries, such as wastewater treatment, clean energy, biopolymers and biopharmaceutical manufacturing (Agharafeie et al [11]). The potential advantages of hybrid modeling may be summarized as a more rational usage of prior knowledge (mechanistic, heuristic AI 2023, 4 304 and empirical) eventually translating into more accurate, transparent and robust process models [7,10].…”