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Leveraging Historical Process Data for Recombinant P. pastoris Fermentation Hybrid Deep Modeling and Model Predictive Control Development
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A hybrid neural network‐first principles approach to process modeling
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Optimal fed‐batch control of induced foreign protein production by recombinant bacteria
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Process analytical technology (PAT) for biopharmaceuticals
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Hybrid modeling for quality by design and PAT‐benefits and challenges of applications in biopharmaceutical industry
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A transfer learning approach for predictive modeling of bioprocesses using small data
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Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges
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Deep hybrid modeling of a HEK293 process: Combining long short‐term memory networks with first principles equations
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Control strategy for biopharmaceutical production by model predictive control
inferredcites →https://doi.org/10.1007/s00449-013-1029-9openalex-snapshot
Fast development of Pichia pastoris GS115 Mut+ cultures employing batch-to-batch control and hybrid semi-parametric modeling
inferredcites →https://doi.org/10.1007/s00449-022-02754-4openalex-snapshot
High-level production of recombinant HBcAg virus-like particles in a mathematically modelled P. pastoris GS115 Mut+ bioreactor process under controlled residual methanol concentration
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When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development
inferredcites →https://doi.org/10.1016/j.cherd.2022.01.041openalex-snapshot
Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae
inferredcites →https://doi.org/10.1016/j.coche.2021.100691openalex-snapshot
Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses
inferredcites →https://doi.org/10.1016/j.compchemeng.2013.08.008openalex-snapshot
Hybrid semi-parametric modeling in process systems engineering: Past, present and future
inferredcites →https://doi.org/10.1016/j.compchemeng.2022.107952openalex-snapshot
A general deep hybrid model for bioreactor systems: Combining first principles with deep neural networks
inferredcites →https://doi.org/10.1016/j.csbj.2020.10.018openalex-snapshot
History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance
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A review and perspective on hybrid modeling methodologies
inferredcites →https://doi.org/10.1016/j.ifacol.2022.07.420openalex-snapshot
Model Predictive Controller Design For Bioprocesses Based On Machine Learning Algorithms
inferredcites →https://doi.org/10.1016/j.jbiotec.2005.11.013openalex-snapshot
Assembly of bacteriophage Qβ virus-like particles in yeast Saccharomyces cerevisiae and Pichia pastoris
inferredcites →https://doi.org/10.1016/j.jbiotec.2017.01.008openalex-snapshot
A review of control strategies for manipulating the feed rate in fed-batch fermentation processes
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Machine learning in bioprocess development: from promise to practice
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Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes
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Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation
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Mathematical Modeling and Analysis in Biochemical Engineering: Past Accomplishments and Future Opportunities
inferredcites →https://doi.org/10.1093/bioinformatics/btl469openalex-snapshot
Synthetic biology—putting engineering into biology
inferredcites →https://doi.org/10.1098/rsif.2017.0502openalex-snapshot
Mathematical modelling of microbes: metabolism, gene expression and growth
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Deep vs. shallow networks: An approximation theory perspective
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Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
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A survey of transfer learning
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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Hybrid Modeling in Process Industries
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Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics
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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
inferredcites →https://doi.org/10.3389/fceng.2023.1157889openalex-snapshot
Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation
inferredcites →https://doi.org/10.3390/fermentation9030206openalex-snapshot
Model Predictive Control—A Stand Out among Competitors for Fed-Batch Fermentation Improvement
inferredcites →https://doi.org/10.3390/fermentation9070643openalex-snapshot
Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction
inferredcites →https://doi.org/10.3390/fermentation9100922openalex-snapshot
From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
inferredcites →https://doi.org/10.3390/life11060557openalex-snapshot
Bioprocess Control: Current Progress and Future Perspectives
inferredcites →https://doi.org/10.3390/math12142205openalex-snapshot
Model-Based Adaptive Control of Bioreactors—A Brief Review
inferredcites →https://doi.org/10.3390/pr11113215openalex-snapshot
Production and Purification of Soy Leghemoglobin from Pichia pastoris Cultivated in Different Expression Media
inferredcites →https://doi.org/10.3390/pr12081635openalex-snapshot
Hybrid Modeling for On-Line Fermentation Optimization and Scale-Up: A Review
inferredcites →https://doi.org/10.3390/s21041268openalex-snapshot
Application of In-Situ and Soft-Sensors for Estimation of Recombinant P. pastoris GS115 Biomass Concentration: A Case Analysis of HBcAg (Mut+) and HBsAg (MutS) Production Processes under Varying Conditions
inferredcites →https://doi.org/10.3390/s21248435openalex-snapshot
Hyperparameter Optimization Techniques for Designing Software Sensors Based on Artificial Neural Networks
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Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon
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A Review of Deep Transfer Learning and Recent Advancements
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Application of Artificial Intelligence–Based Methods for Foam Monitoring and Control in Fermentative Bioprocesses
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inferred← citeshttps://doi.org/10.1016/j.jafr.2025.102409openalex-snapshot
Leveraging IoT and machine learning for smart fermentation of amasi: A predictive framework for acidity control
inferred← citeshttps://doi.org/10.1016/j.jfutfo.2026.08.012openalex-snapshot
Digital Twin Applications in Food Processing and Supply Chains: A Systematic Review
inferred← citeshttps://doi.org/10.1016/j.ymben.2026.102555openalex-snapshot
Transcriptional reprogramming with endogenous cis- and trans-elements for efficient food protein expression from safe substrates in Komagataella phaffii
inferred← citeshttps://doi.org/10.1111/1541-4337.70618openalex-snapshot
Toward Rational Design of Precision‐Fermented Milk Proteins: Integrating Cross‐Species Selection, Post‐Translational Modification, and AI Optimization
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