Bayer et al., 2021 - Google Patents
Model transferability and reduced experimental burden in cell culture process development facilitated by hybrid modeling and intensified design of experimentsBayer et al., 2021
View HTML- Document ID
- 3081125721984924213
- Author
- Bayer B
- Duerkop M
- Striedner G
- Sissolak B
- Publication year
- Publication venue
- Frontiers in Bioengineering and Biotechnology
External Links
Snippet
Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE)(intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development …
- 238000002474 experimental method 0 title abstract description 52
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
- C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
- C12M41/48—Automatic or computerized control
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bayer et al. | Model transferability and reduced experimental burden in cell culture process development facilitated by hybrid modeling and intensified design of experiments | |
Haringa et al. | From industrial fermentor to CFD-guided downscaling: what have we learned? | |
von Stosch et al. | Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study | |
Huang et al. | Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures | |
CN115151869A (en) | Computer-implemented method, program, and hybrid system for observing cell metabolic state | |
US10872680B2 (en) | Computer-implemented method for creating a fermentation model | |
US20230323275A1 (en) | Monitoring and control of bioprocesses | |
CN111615674B (en) | Scaling tool | |
US20240304284A1 (en) | Monitoring, simulation and control of bioprocesses | |
Shah et al. | Multi‐rate observer design and optimal control to maximize productivity of an industry‐scale fermentation process | |
Spann et al. | A compartment model for risk-based monitoring of lactic acid bacteria cultivations | |
Smiatek et al. | Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes | |
Hagrot et al. | Novel column generation-based optimization approach for poly-pathway kinetic model applied to CHO cell culture | |
Hartmann et al. | Digital models in biotechnology: towards multi-scale integration and implementation | |
Suarez-Zuluaga et al. | Accelerating bioprocess development by analysis of all available data: A USP case study | |
Bogaerts et al. | From MFA to FBA: Defining linear constraints accounting for overflow metabolism in a macroscopic FBA-based dynamical model of cell cultures in bioreactor | |
Pinto et al. | Hybrid deep modeling of a CHO-K1 fed-batch process: combining first-principles with deep neural networks | |
Abbate et al. | Inference of dynamic macroscopic models of cell metabolism based on elementary flux modes analysis | |
Hernández Rodríguez et al. | Design, optimization, and adaptive control of cell culture seed trains | |
Hebing et al. | Efficient generation of models of fed-batch fermentations for process design and control | |
Komives et al. | Bioprocessing technology for production of biopharmaceuticals and bioproducts | |
Narayanan et al. | Consistent value creation from bioprocess data with customized algorithms: Opportunities beyond multivariate analysis | |
Vorlet et al. | Digitalization in Processes: FH-HES Universities of Applied Sciences | |
Agarwal et al. | Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process | |
Mueller et al. | Self-driving development of perfusion processes for monoclonal antibody production |