Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework,
is increasingly being adopted in bioprocess modeling, model‐based experimental design …

The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …

Introduction: Hybrid modeling combining First-Principles with machine learning is becoming
a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells …

Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular
metabolism with computers for real-time dynamic metabolic control. The focus is on control …

Modeling is a crucial tool in the biomanufacturing industry, namely in fermentation
processes. This work discusses both mechanistic and data-driven models, each with unique …

Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a
novel approach to maximize bioprocess efficiency by leveraging enforced adenosine …

Mammalian cell cultures make a significant contribution to the pharmaceutical industry. They
produce many of the biopharmaceuticals obtaining FDA-approval each year. Motivated by …

The measurement of batch quality indicators in real time operation is plagued with many
challenges, hence soft sensing has become a promising solution within industrial research …

Fed-batch cultures are the preferred operation mode for industrial bioprocesses requiring
high cellular densities. Avoids accumulation of major fermentation by-products due to …

Background Operation designs on rapid developed advanced chemical processes require
proper models and parameter identification of these models needs input-output data sets …