Digital Twins and Mechanistic Modeling for Mammalian Cell Bioprocessing

The biopharmaceutical industry is rapidly moving from empirical, trial and error process development toward digitalized and model driven paradigms. Mammalian cell systems such as CHO, HEK293, and hybridoma lines remain central to producing complex biotherapeutics, yet optimizing their cultivation requires managing highly nonlinear metabolic processes sensitive to environmental conditions. Traditional Process Analytical Technology (PAT) and Quality by Design (QbD) strategies often rely on empirical statistical models that provide limited biological insight and perform inconsistently under new operating conditions. In contrast, mechanistic models, such as dynamic kinetic and genome scale metabolic models, offer deep process understanding but face challenges in calibration, computational efficiency, and real time application.

The emergence of digital twin technology presents a transformative path forward. A digital twin operates as a dynamic virtual replica of a bioprocess, continuously updated through real time data streams. By integrating mechanistic modeling with data driven and machine learning methods, hybrid digital twins can predict culture performance, optimize feeding strategies, and automate control while retaining biological interpretability. This Research Topic aims to unify expertise in systems biology, bioprocess engineering, and data science to advance the practical deployment of digital twins for mammalian cell biomanufacturing. The goal is to develop robust, scalable, and adaptive modeling frameworks that accelerate process design, monitoring, and control from laboratory to industrial scales.

To gather further insights into advanced modeling and control of mammalian cell bioprocesses, we welcome articles addressing, but not limited to, the following themes:

• Dynamic kinetic and metabolic modeling of mammalian cell growth and protein production

• Hybrid frameworks integrating machine learning with mechanistic models

• Model based soft sensors and real time bioprocess monitoring

• Model predictive control and automated feedback optimization of critical quality attributes

• Spatiotemporal and scale up modeling through coupling with computational fluid dynamics

• Digital twins for continuous and perfusion bioprocessing

• Algorithms for model calibration, uncertainty quantification, and sensitivity analysis

We welcome Original Research, Review, Methods, Perspective, Technology and Code, and Brief Research Report articles.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: digital twin, mechanistic modeling, mammalian cell culture, hybrid modeling, bioprocess optimization, model predictive control, process analytical technology, CHO cells, soft sensors, uncertainty quantification

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.